If you’ve spent much time stargazing, you may have noticed that while most stars look white, some are reddish or bluish. Their colors are more than just pretty – they tell us how hot the stars are. Studying their light in greater detail can tell us even more about what they’re like, including whether they have planets. Two women, Williamina Fleming and Annie Jump Cannon, created the system for classifying stars that we use today, and we’re building on their work to map out the universe.
By splitting starlight into spectra – detailed color patterns that often feature lots of dark lines – using a prism, astronomers can figure out a star’s temperature, how long it will burn, how massive it is, and even how big its habitable zone is. Our Sun’s spectrum looks like this:
Astronomers use spectra to categorize stars. Starting at the hottest and most massive, the star classes are O, B, A, F, G (like our Sun), K, M. Sounds like cosmic alphabet soup! But the letters aren’t just random – they largely stem from the work of two famous female astronomers.
Williamina Fleming, who worked as one of the famous “human computers” at the Harvard College Observatory starting in 1879, came up with a way to classify stars into 17 different types (categorized alphabetically A-Q) based on how strong the dark lines in their spectra were. She eventually classified more than 10,000 stars and discovered hundreds of cosmic objects!
That was back before they knew what caused the dark lines in spectra. Soon astronomers discovered that they’re linked to a star’s temperature. Using this newfound knowledge, Annie Jump Cannon – one of Fleming’s protégés – rearranged and simplified stellar classification to include just seven categories (O, B, A, F, G, K, M), ordered from highest to lowest temperature. She also classified more than 350,000 stars!
Type O stars are both the hottest and most massive in the new classification system. These giants can be a thousand times bigger than the Sun! Their lifespans are also around 1,000 times shorter than our Sun’s. They burn through their fuel so fast that they only live for around 10 million years. That’s part of the reason they only make up a tiny fraction of all the stars in the galaxy – they don’t stick around for very long.
As we move down the list from O to M, stars become progressively smaller, cooler, redder, and more common. Their habitable zones also shrink because the stars aren’t putting out as much energy. The plus side is that the tiniest stars can live for a really long time – around 100 billion years – because they burn through their fuel so slowly.
Astronomers can also learn about exoplanets – worlds that orbit other stars – by studying starlight. When a planet crosses in front of its host star, different kinds of molecules in the planet’s atmosphere absorb certain wavelengths of light.
By spreading the star’s light into a spectrum, astronomers can see which wavelengths have been absorbed to determine the exoplanet atmosphere’s chemical makeup. Our James Webb Space Telescope will use this method to try to find and study atmospheres around Earth-sized exoplanets – something that has never been done before.
Our upcoming Nancy Grace Roman Space Telescope will study the spectra from entire galaxies to build a 3D map of the cosmos. As light travels through our expanding universe, it stretches and its spectral lines shift toward longer, redder wavelengths. The longer light travels before reaching us, the redder it becomes. Roman will be able to see so far back that we could glimpse some of the first stars and galaxies that ever formed.
Learn more about how Roman will study the cosmos in our other posts:
Roman’s Family Portrait of Millions of Galaxies
New Rose-Colored Glasses for Roman
How Gravity Warps Light
Make sure to follow us on Tumblr for your regular dose of space!
Researchers discover universal laws of quantum entanglement across all dimensions
A team of theoretical researchers used thermal effective theory to demonstrate that quantum entanglement follows universal rules across all dimensions. Their study was published online on August 5, in Physical Review Letters as an Editors’ Suggestion.
“This study is the first example of applying thermal effective theory to quantum information. The results of this study demonstrate the usefulness of this approach, and we hope to further develop this approach to gain a deeper understanding of quantum entanglement structures,” said lead author and Kyushu University Institute for Advanced Study Associate Professor Yuya Kusuki.
In classical physics, two particles that are far apart behave independently. However, in quantum physics, two particles can exhibit strong correlations regardless of the distance between them. This quantum correlation is known as quantum entanglement. Quantum entanglement is a fundamental phenomenon underlying quantum technologies such as quantum computation and quantum communication, and understanding its structure is important both theoretically and practically. One of the key measures used to quantify quantum entanglement is the Rényi entropy. Rényi entropy quantifies the complexity of quantum states and the distribution of information, and plays a crucial role in the classification of quantum states and in assessing the feasibility of simulating quantum many-body systems. Moreover, Rényi entropy serves as a powerful tool in theoretical investigations of the black hole information loss problem, and frequently appears in the context of quantum gravity.
But uncovering the structure of quantum entanglement is a challenge for both theoretical physics and quantum information theory. However, most studies to date have been limited to (1+1)-dimensional systems, or 1 spatial dimension plus time dimension. In higher dimensions, analyzing the structure of quantum entanglement becomes significantly more difficult (Figure 1).
A research group led by Kusuki, The University of Tokyo Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU, WPI) and the California Institute of Technology (Caltech) Professor Hirosi Ooguri, and Caltech researcher Sridip Pal, has shown the universal features of quantum entanglement structures in higher dimensions by applying theoretical techniques developed in the field of particle physics to quantum information theory.
The research team focused on the thermal effective theory, which has recently led to major advances in the analysis of higher-dimensional theories in particle physics. This is a theoretical framework designed to extract universal behavior from complex systems, based on the idea that observable quantities can often be characterized by only a small number of parameters. By introducing this framework into quantum information theory, the team analyzed the behavior of Rényi entropy in higher-dimensional quantum systems. Rényi entropy is characterized by a parameter known as the replica number. The team demonstrated that, in the regime of small replica number, the behavior of the Rényi entropy is universally governed by only a few parameters, such as the Casimir energy, a key physical quantity within the theory. Furthermore, by leveraging this result, the team clarified the behavior of the entanglement spectrum in the region where its eigenvalues are large. They also investigated how universal behavior changes depending on the method used to evaluate the Rényi entropy. These findings hold not only in (1+1) dimensions, but in arbitrary spacetime dimensions, marking a significant step forward in the understanding of quantum entanglement structures in higher dimensions.
The next step for the researchers is to further generalize and refine this framework. This work represents the first demonstration that thermal effective theory can be effectively applied to the study of quantum entanglement structures in higher dimensions, and there remains ample room to further develop this approach. By improving the thermal effective theory with quantum information applications in mind, researchers could gain a deeper understanding of quantum entanglement structures in higher-dimensional systems.
On the applied side, the theoretical insights gained from this research may lead to improvements in numerical simulation methods for higher-dimensional quantum systems, propose new principles for classifying quantum many-body states, and contribute to a quantum-information-theoretic understanding of quantum gravity. These developments hold promise for broad and impactful future applications.
TOP IMAGE: Quantum entanglement in 1+1 and 2+1 dimensions Credit : Yuya Kusuki
LOWER IMAGE: Looking a quantum entanglement in a quantum many-body system using thermal effective theory, which uncovers universal features of quantum entanglement Credit Yuya Kusuki
Top 10 Data Science Project Ideas For Beginners - 2021
If you are an aspiring data scientist, then it is mandatory to involve in live projects to hone up your skills. These projects will help you to brush up your knowledge on knowledge and skills and boost up your career path. Now, if you write about those live projects on your resume, then there is a very good chance that you land up with your dream job on data science. But to be a top-notch data science engineer, it is essential to work on various projects. For this, it is important to know the best project ideas which you can leverage further on your CV.
Start Working on Live Projects to Build your Data Science Career
To get a sound idea for data science projects, you should be more concerned about it rather than it’s implementation. Because of this, we have come up with the best ideas for you. Here we have enlisted the top 10 project ideas that can shape your future in the world of data science. But to begin such programs or live projects, you need to have a good understanding of Python and R languages.
1. Credit Card Fraud Detection Mechanism
This project requires knowledge of ML and R programming. This project mainly deals with various algorithms that you can get familiar with once you start doing your applied machine learning course. These algorithms mainly cover Logistic Regression, Artificial Neural Networks, Gradient Boosting Classifiers, etc. From the record of the Credit Card transactions, you can surely be able to differentiate between fraudulent and genuine data. After that, you can draw various models and use the performance curve to understand the behavior.
This project involves the Credit Card transaction datasets that give a pure blend of fraudulent as well as non-fraudulent transactions. It implements the machine learning algorithm using which you can easily detect the fraudulent transaction. Also, you will understand how to utilize the machine learning algorithm for classification.
2. Customer Segmentation :
It is another such intriguing data science project where you need to use your machine learning skills. This is basically an application of unsupervised learning where you need to use clustering to find out the targeted user base. Customers are segregated on the basis of various human traits such as age, gender, interests, and habit. Implementation of K-means clustering will help to visualize gender as well as different age distribution. Also, it helps to analyze annual income and spending ideas.
Here the companies deal with segregating various groups of people on the basis of the behavior. If you work on the project, you will understand K means clustering. It is one of the best methods to know the clustering of the unlabeled datasets. Through this platform, companies get a clear understanding of the customers and what are their basic requirements. In this project, you need to work with the data that correlates with the economic scenario, geographical boundaries, demographics, as well as behavioral aspects.
3. Movie Recommendation System :
This data science project can be rewarding since it uses R language to build a movie recommendation system with machine learning. The Recommendation system will help the user with suggestions and there will be a filtering process using which you can determine the preference of the user and the kind of thing they browse. Suppose there are two persons A and B and they both like C and D movies. This message will automatically get reflected. Also, this will engage the customers to a considerable extent.
It gives the user various suggestions on the basis of the browsing history and various preferences. There are basically two kinds of recommendation available-content based and collaborative recommendation. This project revolves around the collaborative filtering recommendation methodology. It tells you on the basis of the browsing history of various people.
4. Fake News :
It is very difficult to find out how an article might deceive you mostly for social media users. So, is it possible to build a prototype to find out the credibility of particular news? This is a major question but thanks to the data science professionals of some of the major universities to answer the problem. They begin with the major focus of the fake news of clickbait. In order to build a classifier, they extracted data from the news that is published on Opensource. It is used to preprocess articles for the content-based work with the help of national language processing. The team came up with a unique machine learning model to segregate news articles and build a web application to work as the front end.
The main objective is to set up a machine learning model that provides you with the correct news since there is much fake news available on social media. You can use TfidfVectorizer and Passive-Aggressive classifier to prepare a top-notch model. TF frequency tells the number of times a particular word is displayed in the document. Inverse Document Frequency tells you the significance of a word on the basis of which it is available on several contents. Therefore, it is important to know how it works.
A TfidfVectorizer helps in analyzing a gamut of documents.
After analyzing, it makes a TF-IDF matrix.
A passive-aggressive Classifier tells you whether the classification outcome is viable. However, it changes if the outcome swings in the opposite direction.
Now, you can build a machine learning model if you have such good project ideas.
5. Color Detection :
It might have happened that you don’t remember the name of the color even after seeing a particular object. There is an ample number of colors that are totally based on the RGB color values but you can hardly remember any. Therefore, this data science project will deal with the building of an interactive app that will find the chosen color from the available options. In order to enable this, there should be a detailed level of data for all the available colors. This will help you to find out which color will work for the selected range of color values.
In this project, you will require Python. You will utilize this language in creating an application that will tell you the name of the color. For this, there is a data file that comes with color names and values. Then it will be utilized to evaluate the distance from each color and find out the shortest one. Colors are segregated into red, green, and blue. Now the PC will analyze the range of the colors varying from 0 to 255. There are a plethora of colors available and in the dataset, you need to align each color value with the corresponding names. It requires a dataset that comprises RGB values as per the names.
6. Driver Drowsiness Detection :
In order to perform training and test data, researchers have come up with a Drowsiness Test which uses the Real Life Drowsiness dataset in order to detect the multi-stage drowsiness. The objective is to find out the extreme and discernible cases related to drowsiness using data science Skill. However, it permits the system to find out the softer signals of drowsiness. After that, comes the feature extraction which needs developing a classification model.
Since overnight driving is really a difficult task and leads to varied problems, the driver gets drowsy and feels quite sleepy while driving. This project helps to detect the time when the driver gets lazy and falls asleep. It produces an alarming sound as soon as it detects it. It implements a unique deep learning model to determine whether the driver is awake or not. This comes with a parameter to find out how long we stay awake. If the score is raised above the threshold value, then the alarm rings up. Now, you can easily be able to get the related dataset and Source Code.
7. Gender and Age Detection :
This is basically a computer vision and machine learning project that implements convolutional neural networks or CNN. The main objective is to find out the gender and age of a person using a single image of the face. In this data science project, you can segregate gender as male or female. After that, you can classify the age on the basis of various ranges like 0-2, 4-6, 15-20, and many more. Because of different factors such as makeup, lighting, etc, it is very difficult to recognize gender and age forms a particular image. Due to this, the project implements a classification model instead of regression.
For the purpose of face detection, you will require a .pb file since this is a protobuf file. It is capable of holding the graph definition and the trained weights of the model. A .pb file is used to hold the protobuf in a binary format. However, the .pbtxt extension is used to hold this in the text format. In order to detect the gender, the .prototxt file is used to find out the network configuration. The .caffemodel file is used here to denote the internal states of various parameters.
8. Prediction Of The Forest Fire :
Both forests, as well as the wildfire, ignites a state of emergency and health disasters in modern times. These disasters can hamper the ecosystem and this can cause too much money. Also, a huge infrastructure is required to deal with such issues. Therefore, using the K-means clustering you can easily be able to detect the forest fire hotspots and the disastrous effect of this nature’s fury. With this, it can cause faster resource allocation and the quick response. The meteorological data can be used to determine the seasons during the forest fires that are more frequent. Also, you can determine the weather conditions and climatic change that can reduce them and bring sustainable weather.
9. Effect of Climate Change on Global Food Supply :
Climatic change seems to affect various parts of the world. As a result, people residing in those areas are also under the wrath of such climatic change. The project mainly deals with the impact the climatic change is having and its effect on the entire food production. Main motive of the project is to determine the adverse effect of the climate on the production of crops. The project ideas mainly revolve around the impact of temperature and the rainfall along with the diversified cause of carbon dioxide on the growth of the plants. This project mainly focuses on the various data visualization techniques and different data comparisons will be drawn to find out the yield in various regions.
10. Chatbot-Best After the Data Science Online Training :
This is one of the famous projects done by the most aspiring data science professionals. It plays an important role in the business. They are used to give better services with very little manpower. In this project, you will see the deep learning techniques to talk with customers and can implement those using Python. There are basically two types of chatbots available. One deals with the domain which is used to solve a particular issue and the other one is an open domain chatbot. The second one you can use to ask various types of questions. Due to this, it requires a lot of data to store.
“ Upskill Yourself Through Online Data Science Courses and Become a Professional ”
The projects discussed in this technical article covers all the major Data Science projects which you need to do if you are a budding data science professional. But before that, you need to have a good grasp on various programming languages like Python and R. If you do the data science online tutorials, then these projects will be a cakewalk for you. Remember, one thing these small steps will make the large blocks so that you can rule the world of data science.. So, go ahead and participate in these live projects to gain relevant experience and confidence.
AN: This chapter’s somehow got long so a lot of stuff I wanted to originally place here’s going in the next one.
FFN Link
Terran Date 2015.4.23
Since I currently lack access to my regular equipment, I’m making do with an audio recording program from a Terran computer. I must admit it’s not nearly as efficient as my usual method, but it will have to suffice.
Pinky is an…interesting host. I won’t deny that he’s rather generous, and the delicacy he identified as cream cheese is surprisingly palatable. I’ve also taken up residence in his cage which he also kindly offered for my use as a safe place to sleep. The sponge bed has been moved to the cage per my request.
Objective assessment of Pinky: his species is a lab mouse, his eyes have to be some odd mutation because it cannot be possible for them to be that blue, and he’s an amiable idiot. As I’m recording this, he’s currently scolding two inanimate objects for their failure to keep the cage clean in his absence.
Today’s goal: Pinky is planning for a trip to the local mall to obtain a hat to wear for the Derby. Once again, it’s an illogical custom I am unfamiliar with. I’ve agreed to accompany him for two purposes. The first, clues on Snowball’s whereabouts. And the second, to gather intel on Terran habits for world domination purposes. Snowball and I will be able to put my information to good use when we’re reunited.
Signing off for now, the Brain.
o-o-o-o-o
Getting lost, losing communications, and the unrelenting solitude were the major dangers of setting foot outside of Penumbra. Only the first two conditions applied now.
Pinky leapt through the mail slot and danced along the pavement. He wore a lavender blouse that left his shoulders exposed, his shorts made of a Terran material called denim. Apparently, this excursion was also an opportunity to make a fashion statement. But Brain didn’t see the practicality of Pinky’s clothes. The silly Terran stepped on an odd rock here and there, but his twirls didn’t slow down. Just looking at him made Brain slightly dizzy.
Thin, white clouds drifted lazily in the vast blue sky far above them. Brain looked up, one hand on his brow to shield his eyes from the bright sunlight. New Selene and the stars weren’t visible, though they were somewhere much higher than the sky.
He squinted and lowered his gaze to the ground, dark spots forming in his vision and making everything rather blurry.
Brain had switched his jumpsuit and gloves for a Terran disguise, a simple red shirt and another pair of denim shorts, both items borrowed from Pinky’s large collection of outfits. But since Pinky’s legs were longer, the shorts technically functioned more like pants, and the shirt was knee-length. Though it was comfortable, so he went along with it for now.
Besides, Pinky had been shockingly adamant about the jumpsuit and gloves needing a wash. Brain had protested at first since the material had anti-olfactory functions built in, but Pinky insisted and Brain agreed if only to shut up the Terran.
Procuring formal clothes for conquest would just have to wait.
And there was another issue he hadn’t anticipated.
Everything was so colorful and loud. He was so used to everything being muted and dark. Already he missed the ever present hum of the lab technology, and he’d barely set foot outside the door. Brain stood on the coarse welcome mat, on the border between safety and the unknown.
He was just grateful his accelerated healing kicked in overnight, and the bandages were no longer necessary.
“Come on, Brain!” Pinky shouted as he skipped along the pavement, careful to avoid all the cracks. “The sidewalk is great! Just don’t step on the crack, or you’ll break your mama’s back!”
Brain scowled. “My mother is on a different planet entirely, if she hasn’t already fallen victim to the many dangers of the natural world. Stepping on a cracked rock here on Terra will have no effect on her skeletal structure. The two actions are entirely uncorrelated.”
“The corals are related?” Pinky gasped, hands flying to his mouth in genuine surprise. “I knew they looked similar!”
There was absolutely no reasoning with him, was there?
A large, sleek metal structure roared down the large stretch of pavement in front of them, a cloud of smoke trailing behind it as it rounded a corner and disappeared. It wasn’t his first time seeing one of those vehicles, since they’d been peppered throughout the satellite images he’d viewed back on Penumbra.
A car. One of the forms of land-based transportation on Terra, Brain recalled from the file on Terran technology. Highly practical for traveling long distances.
Cars were much larger in person. The images made them seem so tiny.
And once again, he found himself woefully lacking essential information. Did cars function similarly to a rover? How did it zoom by so quickly? What was the power source?
He looked up at the sky again, but the sunlight had somehow gotten stronger during his pondering, and he quickly averted his eyes.
“Poit. Your eyes are so squinty, Brain!” Pinky lightly tapped Brain’s head, breaking him out of his thoughts. “Don’t look directly into the sun. It’s bad for your eyes and you’d need to eat lots and lots of carrots to fix them and then your fur will turn orange!”
“A side effect of all this light,” Brain replied, making a mental note that carrots were an edible item that caused orange fur. He’d have to avoid them in the future. “I’m fine. Let’s depart for this…mall.”
The word felt strange on his tongue. But his feet wouldn’t leave the safety of the welcome mat.
“I’d love for you to come along, but if you’d rather not, that’s fine too,” Pinky said. There was a slight tinge of disappointment in his voice though, but he still seemed as sunny as the actual star. It was somewhat unsettling.
“Won’t you join my little expedition, Brain?” Snowball wrapped an arm around Brain’s shoulders. Fine mist trailed from the aisam’s claws, surrounding them with an icy chill that traveled up Brain’s spine and settled into his fur. “The road to Eclipse Lab is awfully barren and I could use a little company. Perhaps we could test our skills with star identification along the way.”
Brain shoved him away and Snowball clicked his tongue in disappointment.
“For the last time, I’m n-not interested in visiting that horrible, scrik-ridden m-mess of a lab, Snowball. If you wish to leave New Selene sometime in the next cycle, you will allow me to fine-tune the propulsion system in peace,” Brain retorted, hating the tremor in his voice caused by a brief yet violent case of the shivers. He picked up a wrench and examined it for overuse damage, turning his back on Snowball so he wouldn’t see Brain’s hands tremble.
Whether it was from the cold or the mere thought of setting foot in the place where he’d been prodded and restrained by long, claw-like fingers, he couldn’t say.
“You can’t be an invertebrate, Brain,” Snowball grumbled. His disappointment was palpable, and Brain’s fingers tightened around the wrench. “Our combined intellect is unparalleled and far superior to those imbecilic Terrans. Whatever it takes to rule, whatever it takes to wear the crown, we must seize it by any means possible.”
Then he was gone, and the Conquistador’s silent frame became Brain’s steadfast companion.
“Earth to Brain! Oh sorry, should I say Terra to Brain instead? Come in, Terra to Brain! This is Lieutenant Pinky reporting in! Over!”
Pinky was suddenly in front of his face, and Brain leapt back in surprise. He must’ve been lost in his ponderings again. Pinky held something behind his back, something bright and yellow poking out near his tail.
“Yes, Pinky. I hear you,” Brain sighed. Then Pinky showed him the item behind his back, and it turned out to be the oddest pair of safety goggles Brain had ever seen in his life. The star-shaped frame was yellow and provided little protection for the nose, and the lens were tinted dark instead of clear. “These goggles are highly impractical for technical work.”
“They’re sunglasses actually. Slipped inside and grabbed ‘em while you were pandering. I use these if I’m playing movie star-slash-chiropractor! Try them on!” Pinky said. Deciding it was best to humor him, Brain slid on the glasses, and his vision became a shade darker. The colors were still there, just not as bright. The headache that had threatened to form dissipated into nothingness.
“This is bearable,” Brain said. Pinky was slightly darker as well, though the tinted lens did nothing to diminish his shining blue eyes.
Pinky clapped his hands in glee. “Exactly! Also works for grizzlies and honey bears and teddies! And now you’re a movie star too!”
Brain rolled his eyes, sweeping his antennae back so they didn’t get in the way. “That’s not a classification of any star. Despite your questionable logic, and I use that word in a fairly liberal sense, the color spectrum of your planet is no longer a strain on my eyes. So…thanks.”
“Aww! You’re welcome, Brain,” Pinky said. “And really, you can wear them in the lab too. I don’t mind.”
“No, Pinky. I’m coming along. I have goals to accomplish during this trip,” Brain said. Taking a deep breath, he stepped off the welcome mat, then hopped off the step and onto the pavement.
It wasn’t as difficult as his mind made it out to be.
Pinky laughed, and Brain barely got out of the way in time before several ounces of idiosyncrasies could crash into him.
Brain wouldn’t get anything done by sitting around and being too afraid to leave the lab’s safe haven. Somewhere underneath the massive sky, Snowball was likely planning his own day’s activities. And today, they’d be taking the first steps to conquer Terra.
Through any means possible.
o-o-o-o-o
Brain prided himself on his keen observation skills, something that would serve him well when he and Snowball finally exploited the inhabitants’ many weaknesses. Pinky considered it a ‘a blousery, blustery, beautiful day’, whatever that meant, and skipped to and fro in every direction to take in the sights of the city. Brain kept him in view at all times, not wanting to be left alone in this strange world.
He quickly found that the word ‘Terrans’ failed to encapsulate the biodiversity of the planet, in addition to individual differences between members of the same species. Humans varied greatly in size, shape, and appearance, though even the tallest ones weren’t nearly as large as a Selenian. Some had their heads buried in their devices with cords going into their ears and were oblivious to their surroundings, and Brain had to keep an eye out for those dangerous folks since they didn’t seem to care about anyone in their path.
While inconvenient for him, their failure to pay attention could easily be turned into an advantage.
Several humans walked alongside quadrupedal creatures that sniffed the ground and had collars and ropes around their necks that led to a handle in the human’s hand. Pinky called them ‘dogs’ and ‘leashes’. He was more than happy to clarify anything Brain didn’t understand, and while he figured that he would have to research Terra more in-depth later, Pinky’s happy explanations were sufficient for now.
Brain firmly held Pinky’s hand as they passed by a human and a golden-furred dog with large paws and a long, panting tongue. The dog sniffed them curiously and made a ‘groomph’ noise, and though it didn’t seem hostile, Brain dragged Pinky away before the dog had the opportunity to slobber all over them.
But even the ‘goldy’, as Pinky called it, was more preferable to the tiny, yappy thing that Pinky identified as a ‘Chi-wa-wa’. At least it was yanked back by its leash before it could give chase to them.
Pinky called himself a mouse, and his friend Pharfignewton was a horse. Two species down.
The flying creatures were pigeons, crows, and sparrows. They ate whatever they could scavenge on the ground. The tiny things that scurried around his feet were insects, and Pinky yanked him back from stepping on a sidewalk crack filled with red and black ‘ants’.
“Fire ants will make your feet itchy and tingly!” he warned. “And not the pleasant kind either!”
Brain committed his warning to memory.
Cars crawled by slowly on the street, packed closely as far as the eye could see. They made odd screeching noises from time to time, the humans inside grumpily slamming their palms against their steering devices.
Lights on every corner controlled the flow of cars. Everyone became furious with red and brightened when it was green. He wasn’t exactly sure what yellow was supposed to do since some cars sped right past and others came to a stop. Regardless, humans were dependent on those lights in their vehicles. It was an interesting observation.
There were plenty of additional rules too, which Pinky was adamant on teaching. Only cross at the white strips at the lights, and only when the red hand changed to the green human. Look left, right, then left again before crossing. Pat your head and rub your belly if you see an out-of-state license plate…well, Brain was pretty sure that wasn’t a safety rule since none of the humans were doing it. Just a Pinky thing then.
Everything was alive, from the structures that creaked on the highest buildings to the scattered pebbles underfoot. While he’d known the planet’s atmosphere carried sound far better than New Selene’s, experiencing it for himself was nothing short of fascinating. He’d have to research the exact composition that made it all possible later. Energy flowed towards him in all directions, though the daytime thankfully masked his glowing orbs.
Blending in wasn’t difficult either. Humans were more oblivious than he thought.
“Last corner, Brain!” Pinky exclaimed, twirling happily as they waited for the signal to cross the busy intersection. “Then we’re at the mall! You’ll love it! There’s food and clothing and perfume and toys and-“
“Pinky, what exactly is the purpose of a mall?” Brain asked. Pinky had been rather unclear on that. Mostly he’d just been gushing about all the fun things they could do.
“To do fun fun silly-willy things with your friends and look at stuff you can never afford on a lab mouse’s salary, of course!” Pinky replied.
The signal to cross finally appeared, and Pinky skipped merrily across the white strip, nimbly avoiding getting trampled by several humans walking in the opposite direction. Brain walked at a normal pace, keeping his tail close to his body. He didn’t trust the distracted humans to watch where they were going, especially since their handheld devices seemed to hold more importance than avoiding getting run over heavy wheels.
As Brain stepped onto the sidewalk, an odd texture struck him on the head, knocking his sunglasses askew. Several drops of a lukewarm liquid splashing onto his fur. It didn’t hurt, but it was still an unpleasant surprise. The human next to him didn’t notice. He was too busy yelling into his device and gesturing wildly, then stomped off in a huff. He almost trampled Pinky, who barely managed to pull his tail out of the way before the man’s large foot crushed it.
“Well, he was certainly rude. He littered and didn’t say sorry for dropping the cup on your head!” Pinky complained as he helped Brain to his feet, his blue eyes narrowed at the man’s back as he disappeared into the crowd. He cupped his hands to his mouth and shouted in the man’s general direction. “Hey, litterbug! I bet your mom’s older than you! Narf!”
He gave a firm nod, satisfied with his ludicrous and underwhelming insult.
A furious Pinky. That was an interesting concept, yet anger and Pinky somehow remained mutually exclusive in Brain’s mind.
“Not to worry, Pinky,” Brain said, wiping the liquid away from the base of his antennae. He returned his sunglasses to the proper position. “He’s long gone. I’ve suffered worse.”
Pinky took a deep breath, then took a sniff of the cup’s opening and wrinkled his nose. “Maybe he wouldn’t be so grumpy or litterbuggy if he put more sugar in his cappuccino,” he sighed. “Styrofoam too. Can’t recycle that.”
Dragging the cup over to a nearby garbage can, Pinky hoisted it over his head and trying to stick it through the hole on top. The cup was barely over the rim, Pinky clinging to the metal with one hand and scrabbling for a foothold. He wasn’t giving up without a fight, so Brain grabbed Pinky’s ankles to give him the extra boost needed to push the cup in.
Pinky climbed down once he heard the dull thud from inside the can. “Thanks,” he said gratefully, though he still seemed unusually morose.
Brain walked into a section lined with vegetation and dirt that separated the street from the mall. But Pinky didn’t follow. He was looking into the direction they came from. “The cup’s in the proper place now. Let’s go, Pinky.”
Instead of following Brain, Pinky moved to the curbside, looking down at his feet. Really. Pinky came to the mall for a purpose, however inane it was. He needed to commit to that goal.
Brain growled in frustration, grasping his wayward companion’s wrist and pulling him in the mall’s direction. Pinky stumbled, but hardly budged otherwise. “Quit being stubborn, Pinky. The sun will burn out before you twitch a finger at this rate.”
“But the rest of it…“ Pinky whimpered, pointing to the street.
The road was filled with cups like the one Pinky had just thrown away. Filthy, damp, and unreadable papers lined the curb. A plastic bag tumbled in the wind. There were even a few objects that might’ve been clothing at one point.
Some people passed them by without a care in the world, others clicked their tongue at the mess but hurried on their way. Two people on the other side of the intersection were clothed in white from head to toe, picking away at the garbage with long sticks and depositing them into large bags.
From the sheer amount of garbage that lined the streets, Brain thought it was a futile effort on their part.
This was one of Terra’s downsides. Its inhabitants were destroying the very planet they lived on. It was one of the few observations the Selenian scientists were accurate about.
Pinky reached for a mass of papers, a revolting yellowish-green grime covering its surface, but Brain pulled him back before he could touch it.
“Don’t touch that with your bare hands, Pinky,” Brain scolded. “It’s unsanitary.”
Pinky pouted. Now obstinance. He shifted moods rather quickly, didn’t he? It was baffling.
“We gotta take care of Mother Earth, Brain!” Pinky protested as Brain dragged him into the vegetation. “Or there won’t be any pretty flowers to sniff and the acorn and pinecone elves won’t ever set aside their differences to sign that peace treaty!”
“The databank contained many details regarding the pollution of Terra, Pinky,” Brain admitted. “So I’m aware of the issue. But cleaning this one street would take time we can’t spare. You’re being sidetracked from your goal, and I can’t achieve my own objectives either.”
“Wait…” Pinky murmured. “You’re gonna rule soon, aren’t you? So you can definitely protect the world! That’s wonderful, Brain! I know you can do it!”
The sudden shift in mood caught Brain off-guard.
I can? Brain almost said, but the hope shining in Pinky’s eyes quelled that uncertain response. There was nothing but sincere admiration in that pool of blue, a massive surge of electrons flowing from Pinky’s chest into Brain’s antennae.
He would dare describe the electrons as a positive charge. How? Electrons were supposed to be negative! What kind of anomaly did he have the terrifying pleasure of knowing?
Brain cleared his throat, focusing on the enormous sprawling complex in front of them. Pinky’s blind faith was off-putting, and it was much easier to disregard it. “Of course. I will have unquestionable power in the near-future. Solving these issues will be easier than calibrating an auto-navigation interface.”
Pinky blinked.
“And…I’ll oversee those peace treaty negotiations between the elves.”
Pinky brightened immediately. “Thank you, Brain! Thank you, thank you, thank you!” Long arms snagged Brain and lifted him off the ground in an enormous hug. Brain’s feet kicked out, but the warmth Pinky emitted had the strangest subduing effect. Brain’s antennae weren’t obstructed either, just swept back. Apparently, Pinky learned from last time.
Brain’s chest was oddly warm. Or maybe it was Pinky’s. It was hard to know for certain.
“Your orbs are so glowy,” Pinky said in awe.
And they weren’t achieving anything from this display of sentimentality! With some difficulty, Brain reclaimed his right arm and bopped Pinky on top of his empty noggin.
Pinky immediately let go, stumbling around dizzily and startling a nearby sparrow with his loud giggles. Brain landed on the base of his tail, a brief painful twinge travelling up his spine. In hindsight, he didn’t plan that well. At least there wasn’t another kink.
“That was jolly fun, Brain!” Pinky exclaimed upon recovery.
If he ever had the spare time, he was definitely researching the differences between actual Terran phrases and Pinky-isms.
“I’m sure,” Brain sighed, though he wasn’t sure and never would be, but Pinky didn’t need to know that.
They walked into a large, multi-level structure that Pinky called a ‘parking garage’, which housed a large amount of dormant vehicles. It was similar to the traffic they’d passed earlier, but the drivers were elsewhere. They were packed close, almost touching, and Brain wondered how anyone could possibly get in or out in these tight quarters.
Another few inches closer and the drivers would be completely trapped. That idea had potential.
Pinky hopped onto each yellow marking on the ground, arms flailing as he tried to avoid the gray areas in between. Brain followed at a more sedate pace. Then Pinky gasped and straightened up just as he landed on the last yellow marking before the mall entrance, Brain nearly bumping into him.
“Look, Brain! Somebody’s dropped their wallet!” Pinky gasped, hurrying over to a black object lying against the curb. He undid the zipper and glanced inside. “Egad, that’s a lot of money!”
Brain peeked inside. A wad of folded green paper was tucked inside one of the pockets. “A currency-based economy? Selene and its colonies utilized barter systems,” he said.
Which could be an issue. Brain had originally planned to trade the Conquistador’s spare parts for useful items.
“Oh no, Brain. Currants would get squished in your pants. Then you’d need a really strong stain remover,” Pinky replied. “Besides, this man’s very lucky he can buy so many hats! That’s what I’d do if I had any money!”
He must’ve misheard that. Surely.
“Pinky, tell me you brought the monetary value required for your hat.”
Pinky dug his hand into a fur pocket, but only came out with a piece of fluff. “Hmmm, well, I have some dryer lint! Only money I have is Nicholas the Nickel, and he’s cleaning the cage with—oh.”
His ears and tail fell limp under Brain’s glare.
Brain kicked a loose pebble, and it ricocheted harshly off the base of a metal sign. Of all the native species he could’ve chosen for a guide, it just had to be the one individual whose head was denser than a neutron star.
“Sorry, Brain,” Pinky murmured. “I’m not very good at this goal-setting thing, am I?”
He said ‘sorry’ a lot for placation’s sake. But no matter the context, he always sounded sincere. Brain pushed his sunglasses up to his forehead and rubbed the bridge of his nose. Somehow, he couldn’t find it in himself to be irate with Pinky anymore.
“You require more practice,” Brain replied. He glanced at the strange, valuable green papers in the wallet. Funny how they came across the commodity needed at this moment. “However, it’s most fortunate that we should stumble on the item required in trade for your hat.”
The money was all in 20s and 50s, and while Brain was unfamiliar with this currency, he figured there would be enough to spare. He took the money out of the pocket and tucked it under his arm. Then he flipped his sunglasses down, but Pinky tugged the money out of his grip before he could walk off.
“No, Brain! That’s stealing!” Pinky protested, slipping the money back into the wallet. “This rightfully belongs to a Mr. Joe Lamont! We have to take this wallet to Lost and Found now!”
Pinky’s stubborn side came out randomly, it seemed.
“The money is here at your convenience, Pinky. You have to use every asset possible to achieve your goal,” Brain said.
“What if Mr. Lamont needs this?” Pinky tapped a card that displayed a human’s photo along with other identifying information. Then he pointed to a small picture of a man and woman. “What if he needs this for anniversary or birthday presents, or else his wife won’t be happy and he’ll be sad cause he left his wallet somewhere and what if someone picks it up and won’t give it back? Cause that’s just mean!”
“Then he should’ve been more careful with such a valuable item,” Brain snapped. Pinky made a noise of disbelief and turned his back to Brain. “So take one or two of the papers for yourself and give the rest back.”
While he’d prefer to keep the entire wallet for future use, it seemed he would just have to compromise with Pinky.
“He won’t notice.”
“NARF!” Pinky retorted.
His assumption was wrong. Pinky wouldn’t accept a compromise either. It was a losing battle, and as much as hated conceding defeat, no other options presented themselves.
“Fine! Do what makes you happy! See if I care!” Brain shouted at Pinky’s back.
He was only presenting the most logical solution. It wasn’t his fault this idiot wasn’t taking the opportunity! And none of this was helping him find Snowball or conquer Terra either!
“Returning the wallet would make me happy, Brain,” Pinky said with conviction.
“Why?” Brain asked. This wasn’t the type of goal-setting he’d pictured at all.
“It feels right.”
Tasks should be performed with efficiency in mind, not for emotion’s sake. But it seemed that keeping Pinky in his normal euphoric state would be in Brain’s best interest for now.
“Alright, let’s return that wallet. Neither you nor I shall use any of the money for personal reasons. We’re heading to the…Lost and Found?” Brain said reluctantly. He took a deep breath, reminding himself to keep Pinky in a good mood. “You lead the way. I’m not familiar with this locale.”
Pinky faced Brain, and the bright smile was back. Brain looked away. He wasn’t doing this out of altruism, and Pinky needed to learn that.
“Yup, it’s like the Island of Misfit Toys, but for car keys, jackets, and other things too!” Pinky exclaimed, hoisting the wallet above his head. “And now it’s for Mr. Lamont’s wallet!”
The satellite images never pinpointed a geographical location named the Island of Misfit Toys. Probably situated next to a more prominent landmass then.
“Welcome to Macy’s, Brain!” Pinky cheered as they entered a pristine white building. “For all your expensive brand clothing and Thanksgiving Day needs!”
The store was brightly lit, so Brain kept his sunglasses down. Numerous bottles of varying colors were on display. Women shouted from behind their counters, urging passersby to purchase their products. Most people walked by quickly, looking rather uncomfortable and twitchy until they were far from the display area. Only two women seemed interested at all, spraying misty clouds on tiny strips of paper and sniffing them curiously.
“What are they doing?” Brain whispered as he shuffled closer to Pinky for protection’s sake. There was a predatory gleam in those workers’ eyes, and he didn’t like it one bit.
Even Pinky with his near-perpetual cheer seemed uncomfortable, his fingers anxiously drumming against the wallet. “Poit. Selling perfume. All sorts of lovely scents, but this is definitely why online shopping is more popular these days.”
Before Brain could respond, one of the workers suddenly rushed towards them with a manic smile that showed way too many teeth.
“Hi, you wanna buy some perfume buy one and ya get another half price ‘til May!” she shrieked. Without giving them a chance to respond, she sprayed perfume directly in their faces.
Pink mist engulfed them and obstructed their vision. A pungent scent clogged Brain’s nose, trickling its way down his throat, and he let out a hacking cough to expel it. Pinky’s wheeze suddenly turned into a yelp, and by the time the mist cleared, the woman was walking away with the wallet in hand.
Pinky clung to the wallet desperately, his legs kicking out as he was hoisted into the air. “Please, miss! Brain and I—ehem—Brain and I need to give this wallet to Lost and Found so Mr. Lamont can buy his wife nice presents!”
“Oh, it’s a sizeable wallet you’ve got there too!” the woman exclaimed. Brain found her pitch highly grating. “Let’s see, with money like that you can get lilac, honeysuckle, eau de escargot, a perfume that smells like wet goat hair sponsored by Gwenyth Paltrow-“
“I’m sure they smell lovely, but-“
“Very lovely indeed!” the woman spoke over Pinky, who could only dangle helplessly.
Brain gritted his teeth and hurried after them, shaking off his earlier disorientation. When she stopped to jabber about perfume again, he slammed his tail onto her bare ankle and administered a quick shock. Startled, she dropped Pinky the wallet. Brain darted between her sandals just in time to catch Pinky, who clutched the wallet to his chest, slightly dizzy from his sudden fall.
The perfume bottle was aimed in their direction again.
Brain took off with Pinky in his arms, running as fast as he could when those dreaded sandals got too close for comfort. He allowed Pinky to safekeep the wallet, since he was already so protective of it.
“Relentless scrik!” Brain panted as the woman hurled various sales pitches behind them. Pinky wasn’t heavy, but the wallet was a different story. And Pinky made it look so simple!
Well, Pinky was simple in general. Perhaps it was a distributive effect.
“Brain, go into the carpeted area!” Pinky shouted. “She can’t follow us out of her department!”
Deciding to trust Pinky’s word, Brain ran straight onto the carpet, barely dodging someone’s shoe in time, and his foot caught on the raised border between the carpet and tile. He fell onto his face, one of the sunglasses’ handles digging into his fur on impact. Pinky and the wallet tumbled across the floor, coming to a stop a short distance away.
As Pinky predicted, the woman stopped chasing them.
“Annnnd there goes my bonus,” she muttered dejectedly. She slammed the perfume bottle onto a nearby counter, startling a sleepy coworker who toppled off her chair in surprise and plastered on a fake smile for a passing customer. He glanced at her briefly and walked away with a grimace.
“Sooo…welcome to Macy’s?” Pinky laughed nervously. “On the bright side, we smell like radish roses now!”
Brain threw a button at him.
o-o-o-o-o
They kept to the corners after that fiasco, hoping to avoid drawing attention to a moving wallet. Pinky marveled at the various styles advertised by a human-like object he called a ‘Manny Kin’. He prattled on about the models and clothing, and Brain tuned him out to better observe the humans.
The younger ones appeared restless and bored out of their minds. The adults often stopped to admire an article of clothing, checked the price, and shook their heads before moving onto the next item. Everyone was dressed in a far more casual style than the clothing on sale.
“Oh, here’s the mall center! It’s where all the real fun happens, Brain!” Pinky said, his tail wagging in excitement. “Plus, the Lost and Found is just beyond this store. We’ll make Mr. Lamont happy in no time!”
Instead of a back wall, there was a large, doorless opening that led out of the store. Pinky danced his way across the boundary with a cheerful goodbye to the Macy’s sign. As Brain stepped into the wide open space, he was astounded by the sheer scale of the mall center.
He’d expected a plain corridor that connected different sections, not a massive space with a roof that appeared to touch the sky. The population density was much higher than in Macy’s, humans loudly chatting among themselves, shouting at consumers to purchase wares, and swinging large bags from their arms.
There were two floors above their heads, connected to the ground by staircases and escalators. The escalators seemed by far the popular choice for people moving between floors. Brain felt dizzy just looking at that open space above them, and he decided to focus only straight ahead for now.
Dozens of smaller stores lined the walls. Most of them sold clothes like Macy’s, and Brain couldn’t fathom why humans needed so many stores just to sell clothes. A fresh, rich scent wafted through the air, and though it was much more pleasant than the perfume, it made him somewhat famished as well.
“Look, Brain! The cookie shop! Don’t they smell divine?” Pinky asked with a dreamy sigh. “They taste delicious too!”
“Another one of your foods?” Brain asked, though it fell on deaf ears. Pinky had gone over to the display case, practically drooling on it as he admired the cookies inside, the wallet leaning against his side.
Brain stood on the other side of the wallet, just in case anyone had any ideas about stealing it.
At first, Brain thought the cookies were classified by ingredient, but one of the groups was labelled ‘snickerdoodle’ and Brain was of the opinion that no sane planet in the universe would ever call anything by that strange moniker.
“Let’s be on our way, Pinky,” Brain said, because there wasn’t anything productive he could do while his Terran guide was staring longingly at cookies. “That wallet won’t return itself.”
“Okay, Brain…” Pinky said forlornly. His hands squeaked sadly against the glass, but before he could pick up the wallet, a woman came out from behind the counter, her dark hair tied back in a bun. She approached them with a napkin in one hand.
Brain grabbed Pinky’s hand and the wallet, tensing up in case he had to yank them away at a moment’s notice.
But the woman made no move to snatch the wallet. She only squatted next to them and held out the napkin, revealing two small pieces of cookies. “Free sample?” she asked. “They’re fresh out of the oven.”
“Thanks so much...Laura!” Pinky read the name tag pinned to her shirt, then snatched up one of the pieces and shoved it into his mouth. Crumbs stained his muzzle. “Narrrrf! That was dee-lish!”
Cautiously, Brain took the second piece and bit into it. Sweetness flooded his taste buds, and he quickly finished his portion, the cookie melting in his mouth. If anything, Pinky had understated how delicious it tasted.
“It’s exquisite,” he said to Laura, who beamed right back.
“Glad you enjoyed it!” Laura said. She provided them with wet napkins so they could rid themselves of the remaining crumbs, and they left the cookie shop behind.
“She was so nice, Brain!” Pinky said, safeguarding the wallet once again. “Sugar cookies are my favorites! Well, after chocolate chip and macadamia and snickerdoodle-“
Brain nodded. “She didn’t steal anything while our guard was down. Count that in your definition of ‘nice’.”
Thankfully, they didn’t have to walk far to get to the Lost and Found. Brain hoped to put this wallet nonsense behind them in the next half hour. They had objectives to fulfill.
The Lost and Found was in a hallway that led to an exit from the mall, and Brain made a mental note of its location. He refused to set foot in that Macy’s ever again.
A podium was situated in front of the doors, and the worker behind it nervously held out a box to an irate man in a formal suit similar to the merchandise at Macy’s. He snatched the box and threw several articles of clothing and various lost items to the ground.
Pinky lifted the wallet above his head, his feet tapping in excitement. “That’s the man! He looks exactly like his pictures!”
Mr. Lamont was practically tearing the box apart without any regard for the other lost belongings, and the worker’s eyes were wide with fear. That didn’t bode well. Brain grabbed Pinky’s tail, but it slipped out of his grasp. The idiot had no sense of impending danger and walked right up to the belligerent man.
“You’re hiding it, aren’t you?” Mr. Lamont snarled, slamming his hand against the podium. The worker cowered behind his chair. “Hand over my wallet this instant, or you’ll be out of a job.”
The worker paled.
Brain rushed over to try and pull Pinky back. Mr. Lamont hadn’t noticed them yet. There was still a chance they could slip the wallet among the other items and leave without detection.
“Hi, Mr. Lamont! You dropped your wallet in the parking garage!” Pinky greeted. “Me and my friend here were just taking it to Lost and Found, and what a coinkydink we’d find you here too! Isn’t that great?”
Pinky held the wallet up expectantly, that silly smile never leaving his face.
Mr. Lamont snatched the wallet out of Pinky’s hands, wrinkling his nose haughtily.
“You’re welcome!” Pinky chirped, then happily turned to Brain. “We did it!”
Pinky had done most of the work, but if he wanted to share credit, Brain chose not to correct him. “Yes. Now we may return to what we originally-“
Mr. Lamont’s foot slammed into Pinky’s side, too fast for Brain to shout a warning. Pinky yelped as he was thrown into a wall. There he laid in a crumpled heap, hands wrapped around his abdomen for protection.
“How much did you take, thief?” Mr. Lamont spat. He cast a looming shadow over Pinky, who whimpered in pain, tears forming in pitiful blue eyes.
It was such a foreign appearance for the idiotic but kindhearted mouse.
A strange fury overtook Brain, one that was much different from dealing with troublesome ships, arguing with Snowball, or frustration with his current predicament. It brewed in the depth of his stomach and spread through the rest of his body.
Brain whipped off his sunglasses, placing himself firmly between Pinky and the ungrateful reprobate.
“He stole nothing from you,” Brain growled. “Count the money yourself, you repugnant excuse of an organism, unless your mind has degraded far beyond the ability to perform simple arithmetic.”
“And just who do you think you are?” Mr. Lamont sneered.
Brain crossed his arms proudly. He refused to cower before the Terran. “A genetically enhanced Selenian mos seeking dominion over your world.”
And when all was said and done, Mr. Lamont would be bowing down to him.
But that glorious fantasy was cut short. Brain saw the black sole of a shoe, there was a forceful pressure against his body. His limbs refused to cooperate. He couldn’t reach his tail for self-defense, his heart pumping faster and faster until it couldn’t compensate for the lack of electrons anymore-
The crushing pressure vanished.
Faraway voices blended together, one angry, one meek, and one familiar.
Someone lifted his head, a gentle hand moving his antennae aside, then slowly pushed his head down until he rested against soft fabric. Brain’s fingers twitched. His full mobility would take several minutes to return, but this wasn’t a terrible position to wait it out.
A drop of moisture fell on his face, followed by several more.
Rain?
He’d heard of that particular climate pattern, but had never seen it in action before.
Brain opened his eyes, craning his neck to see this curious phenomenon. But he was met with Pinky’s tearful gaze instead.
He’d learned much of Terran culture during this expedition, but was it really worth all these ridiculous emotions?
“Stop dampening my fur with your lacrimal ducts, Pinky,” Brain said, his voice hoarse.
Pinky managed a giggle, inanity that was far more preferable to all this crying. “Sorry, Brain. I don’t have any milk. But are you okay? P-p-poit.”
“I’ll need several minutes to recuperate. Then I’ll be ready.” Brain felt his cheeks heat up from the proximity. Mobility returned to his right leg, and he couldn’t wait for this mortifying close contact to be over. “Where’s Mr. Lamont?”
Pinky scowled at the name, an expression that looked odd on him, but not wholly unwelcome. “Mr. Lameany called you vermin and left with his wallet. But you’re not vermin, Brain! You’re my best friend!”
A childish insult. He’d have to teach Pinky about using more sophisticated language.
“And you…are Pinky,” he sighed, patting Pinky’s arm.
Pinky smiled brightly. At least Brain could strive towards one of his objectives. They weren’t quite through with business at the mall though. He’d have to tough it out.
But for now, he settled back against Pinky, who happily taught him the age-old Terran method of settling arguments known as rock-paper-scissors.
AN: FINISHED AT LAST.
I am not making stuff up as I write I totally had a plan for this fic y’all can’t prove nothing.
Brain gets to learn good and bad stuff about Terra, poor Pinky gets hurt. These mice can’t even go the mall without something happening, can they?
Meet IC 1101: The Biggest Known Galaxy In The Universe.
(Pic 2: IC 1101)
Galaxies are cool. Everybody knows that. It is the place that keeps around billions of stars. It’s the place where stars like our Sun live, and where planets like Earth with people like us think and study the world. There are billions of galaxies in our Universe of different shapes and sizes. Everybody should know our 13.5 billion years-old Milky Way. Also, you may have heard of Andromeda or M31. It is the galaxy closest to us, our neighbor.
Actually, Andromeda likes the Milky Way so much it’s coming closer to it every day. They are really going to meet each other, in 4.5 billion years or so. So these are the most known galaxies, right? But what about the biggest? The “biggest of the big”? The name of that beast is IC 1101. As I said, galaxies come in all sizes. But to understand what sizes we are talking about, take some time and imagine it.
If something is located 1 light-year away from us, it means that the light from it will reach us in one year. That means that if looking for an object one light-years away, we will always see it as it was one year ago. Light travels pretty fast. Somewhere around 300,000,000 m/s. The average speed of a car is 25 m/s. It means light travels 12 million times faster than a car.
Now think about our galaxy, whose diameter is 105,000 light-years. Sounds big. But the biggest we know about is estimated to be around 6 million light-years in diameter. That means that if you would look from one corner to the star on the opposite, it would take 6 million years for its light to reach you.
Actually, saying that IC 1101 is the largest galaxy in the Universe is not accurate. Considering how important accuracy is in the world of science, I am impressed by how much I see this mistake made. IC 1101 is the biggest galaxy discovered. It doesn’t mean there isn’t something bigger. It’s just that we didn’t see it yet.
IC 1101 is an elliptical galaxy. There are 3 major types of galaxies, subdivided then in some smaller ones. The author of this classification is the brilliant astronomer of the 1920s, Edwin Hubble. The 3 major types are elliptical, spiral, and irregular. Spiral galaxies are the most common in the Universe. The Milky Way or Andromeda are spiral galaxies. You can read more about the galaxies and their interactions in this article of our Basics of Astrophysics series.
These are also the galaxies with the youngest stars and which give birth to most of the stars. On the other hand, elliptical galaxies are much older and are home to old stars too. These elliptical galaxies are found largely in galaxy clusters and dust clouds. Now irregular galaxies, such as the Small Magellanic Cloud, are as their name suggests, irregular in shape. These are even older galaxies.
So how was this 6 million light-years long elliptical galaxy discovered? Astronomers can calculate short distances easily, using a method called parallax. This works well for a lot of stars. But when talking about distances such as 6 million light-years, we use something different: we calculate their distance based on their brightness. But in such a calculation we would need something to compare it to. That is why we use “candles”, galaxies or stars that have become standard to us, we know their distance, and hence we compute the distance to other objects in the Universe.
IC 1101 was first discovered by William Herschel, in 1870 or so. It was first thought to be just a nebula. Everybody believed that until Hubble came in the 1920s and proved, with his new classification of galaxies, that IC 1101 is a galaxy: a big one actually.
So in a Universe with galaxies like IC 1101, it’s easy to feel small. In fact, some psychologists found out that the biggest feelings of insignificance and loneliness come from the people who came in contact with the Universe in some way.
Mostly, you can find these feelings at the observatory, that being the place where psychologists found their people. But instead of feeling small, and lonely, or insignificant, you may want to feel proud that you are part of such a beautiful species, that you live on such a nice planet, and most of all, that you can contemplate at who you are and why are you here.
Author: Bogdan TeodorescuAuthor at ‘The Secrets Of The Universe’, I am a science student from Romania. I am also the founder of Astronomy Hub, an organization for popularising astronomy and astrophysics.
Computational Aesthetics: shall We Let Computers Measure Beauty?
As we all know, tastes differ and change over time. However, each epoch tried to define its own criteria for beauty and aesthetics. As science was developing, so was the urge to measure beauty quantitatively. Not surprisingly, the recent advancements in Artificial Intelligence pushed forward the question of whether intelligent models can overcome what seems to be human subjectivity.
A separate subfield of artificial intelligence (AI), called ‘computational aesthetics’, was created to assess beauty in domains of human creative expression such as music, visual art, poetry, and chess problems. Typically, it uses mathematical formulas that represent aesthetic features or principles in conjunction with specialized algorithms and statistical techniques to provide numerical aesthetic assessments. Computational aesthetics merges the study of art appreciation with analytic and synthetic properties to bring into view the computational thinking artistic outcome.
Brief History of Computational Aesthetics
Though we are used to thinking about Artificial Intelligence as a recent development, computational aesthetics can be traced back as far as 1933, when American mathematician George David Birkhoff in “Aesthetic Measure” proposed the formula M = O/C where M is the “aesthetic measure,” O is order, and C is complexity. This implies that orderly and simple objects appear to be more beautiful than chaotic and/or complex objects. Order and complexity are often regarded as two opposite aspects, thus, order plays a positive role in aesthetics while complexity often plays a negative role. Birkhoff applied that formula to polygons and artworks as different as vases and poetry, and is considered to be the forefather of modern computational aesthetics.
In the 1950s, German philosopher Max Bense and French engineer Abraham Moles independently combined Birkhoff’s work with Claude Shannon’s information theory to develop a scientific means of grasping aesthetics. These ideas found their niche in the first computer-generated art but did not feel close to human perception.
In the early 1990s, the International Society for Mathematical and Computational Aesthetics (IS-MCA) was founded. This organization is specialized in design with an emphasis on functionality and aesthetics and attempts to be a bridge between science and art.
In the 21st century, computational aesthetics is an established field with its own specialized conferences, workshops, and special issues of journals uniting researchers from diverse backgrounds, particularly AI and computer graphics.
Objectives of Computational Aesthetics
The ultimate goal of computational aesthetics is to develop fully independent systems that have or exceed the same aesthetic “sensitivity” and objectivity as human experts. Ideally, machine assessments should correlate with human experts’ assessment and even go beyond it, overcoming human biases and personal preferences.
Additionally, those systems should be able to explain their evaluations, inspire humans with new ideas, and generate new art that could lie beyond typical human imagination.
Finally, computing aesthetics can also provide a deeper understanding of our aesthetic perception.
In practical terms, computational aesthetics can be applied in various fields and for various purposes. To name a few, aesthetics can be used in the following applications:
as one of the ranking criteria for image retrieval systems;
in image enhancement systems;
managing image or music collections;
improving the quality of amateur art;
distinguishing between videos shot by professionals and by amateurs;
aiding human judges to avoid controversies, etc.
Features
The backbone of all classifiers is a robust selection of features that can be associated with the perception of a certain form of art. In the search for correlation with human perception, aesthetic systems apply specific sets of features for visual art and music that are developed by theorists in arts and domain experts.
Visual Art
Image aesthetic features could be categorized as low-level or high-level plus composition-based. However, some research is based on features related to saliency (Zhang and Sclaroff, 2013), object (Roy et al., 2018), and information theory (Rigau,1998). The selection of features largely depends on the type of art and the level of abstraction, as well as the algorithm applied. For instance, photography assessment relies heavily on the compositional aspects, while measurement of the beauty of abstract art requires another approach assessing color harmony or symmetry (Nishiyama et al.,2011).
Low-level features try to describe an image objectively and intuitively with relatively low time and space complexity. They include color, luminance and exposure, contrast, intensity, edges, and sharpness.
High-level features include regions and contents as aspects that make great contributions to overall human aesthetic judgment and try to establish the regions of an image that seem to be more important for human judgment and find the correlation between the content and human reaction.
Composition-based features differ for photography and artwork and may include depending on the form of art a range of features, such as Rules of Thirds, Golden Ratio (Visual Weight Balance), focus and focal length, ISO speed rating, geometric composition and shutter speed (Aber et al., 2010).
Music
Similarly to image analysis, music aesthetics assessments try to combine research in human perception and cognition of basic dimensions of sound, such as loudness or pitch and in higher-level concepts related to music, including the perception of its emotive content (Juslin and Laukka, 2004), as well as performance specific traits (Palmer, 1997) to develop a comprehensive set of features that would be able to assess a piece of music.
In 2008, Gouyon et al. offered a hierarchy organized in three levels of abstraction starting from the most fundamental acoustic features, to be extracted directly from the signal, and progressively building on top of them to get to model more complex concepts derived from music theory and even from cognitive and social phenomena:
Low-level features are related to the physical aspect of the signal and include loudness, pitch, timbre, onsets, and rhythm (e.g., see Justus and Bharucha, 2002).
Mid-level features move to a higher level of abstraction within the music theory and cover tempo, tonality, modality, etc.
High-level features try to establish a correlation between abstract music descriptors like genre, mood, and instrumentation and human perception.
Methods and Algorithms
At its broadest, we can speak of computational aesthetics as a tool to assess aesthetics in visual art or music and as a means to generate new art.
For aesthetics assessment, various algorithms have been proposed over the past few years based either on classification or clusterization.
Classification approach
There are a number of algorithms that are extensively used to assess image aesthetics by means of classification. Among the most popular are AdaBoost, Naive Bayes, and Support Vector Machine, and substantial work is also conducted using Random Forests and Artificial Neural Networks (ANNs).
AdaBoost in computational aesthetics is a widely used method that is believed to render the best results. It was first offered in 2008 by Luo and Tang who conducted a study on photo quality evaluation, with the unique characteristic of focusing on the subject. They utilized Gentle AdaBoost (Torralba et al., 2004), a variant of AdaBoost that uses a specific way of weighting its data, applying less weight to outliers. The success rate obtained was 96%. However, when Khan and Vogel (2012) utilized their proposed set of features for photographic portraiture aesthetic classification, the accuracy rate with the multiboosting variant (multi-class version) of AdaBoost fell to 59.14% (Benbouzid et al., 2012).
Naïve Bayes is another popular method that was used in the same study by Luo and Tang (2008). In 2009, Li and Chen utilized the Naïve Bayes classifier to aesthetically classify paintings in which the results were described as robust. The success rate achieved utilizing a Bayesian classifier was 94%.
Support Vector Machine is probably the most wide-spread algorithm for binary classification in computational aesthetics. It has been used since 2006 when Datta et al. studied the correlation between a defined set of features and their aesthetic value, by using a previously rated set of photographs and showed up to 76% of accuracy. Other studies that rested on the same classifier include Li and Chen (2009) who aesthetically classified paintings; Wong and Low (2009) who built a classification system of professional photos and snapshots, Nishiyama et al. (2011) who conducted a research on the aesthetic classification of photographs based on color harmony, and others, with an average accuracy rate of about 75% and higher.
Random Forest, though usually showing lower results as compared to Bayesian classifiers or AdaBoost, were used in a number of studies of photograph aesthetics. For instance, Ciesielski et al. (2013) achieved a 73% accuracy to assess photograph aesthetics. Khan and Vogel (2012) utilizing their proposed set of features for photographic portraiture aesthetic classification, achieved an accuracy of 59.79% by making use of random forests (Breiman, 2001).
Artificial Neural Networks (ANNs) rendered extremely good results when used with compression-based features by Machado et al. (2007) and Romero et al. (2012). The former research aimed at the identification of the author of a set of paintings and reported a success rate from 90.9% to 96.7%. The latter work used an ANN classifier to predict the aesthetic merit of photographs at a success rate of 73.27%.
Convolutional Neural Networks (CNNs) are state-of-the-art deep learning models for rating image aesthetics that have been extensively used in the past few years. CNNs learn a hierarchy of filters, which are applied to an input image in order to extract meaningful information from the input. For example, Denzler et al. (2016) applied the AlexNet model (Krizhevsky et al., 2012) on different datasets to experimentally evaluate how well pre-learned features of different layers are suited to distinguish art from non-art images using an SVM classifier. They report the highest discriminatory power with a Network trained on the ImageNet dataset, which outperforms a network solely trained on natural scenes.
Clustering
Image clustering is a very popular unsupervised learning technique. By grouping sets of image data in a particular way, it maximizes the similarity within a cluster, simultaneously minimizing the similarity between clusters. In computational aesthetics, researchers use K-Means, Fuzzy Clustering, and Spectral Clustering in image analysis.
K-Means Clustering is widely used to analyze the color scheme of an image. For instance, Datta et al. (2006) used k-means to compute two features to measure the number of distinct color blobs and disconnected large regions in a photograph. Lo et al. (2012) utilized this method to find dominant colors in an image.
Fuzzy Clustering is a form of clustering in which each data point can belong to more than one cluster, therefore it is used in multi-class classification (see, for example, Felci Rajam and Valli (2011)). Celia and Felci Rajam (2012) utilized FCM clustering for effective image categorization and retrieval.
Spectral Clustering is used to identify communities of nodes in a graph based on the edges connecting them. In computational aesthetics, a spectral clustering technique named normalized cuts (Ncut) was used to organize images with similar feature values (Zakariya et al., 2010).
Generative models
A separate task of computational aesthetics is to generate artwork independently from human experts. At present, the algorithm that is best known for directly learning the transformations between images from the training data is Generative Adversarial Network(GAN). GANs automatically learn the appropriate operations from the training data and, therefore, have been widely adopted for many image-enhancement applications, such as image super-resolution and image denoising. Machado et al. (2015) also used GANs for automatically enhancing image aesthetics by performing mainly tone adjustment.
Example that combines the content of a photo with a well-known artwork
Conclusion: Restrictions and Limitations
Aspiring to reach objectivity, research in computational aesthetics tries to reduce the focus to form, rather than to content and its associations to a person’s mind and memories. However, from a psychophysiological viewpoint, it is not clear whether we can have a dichotomy here or whether aesthetics is intrinsically subjective.
Besides, it is difficult to ascertain whether a system that performs on the same level as a human expert is actually using similar mechanisms as the human brain and, therefore, whether it reveals something about human intelligence.
It might be that in the future we will rely on machines in our artistic preferences, but for now, human experts will dictate their opinions and try to get machines simulate their choices.
Full disclosure: I hated doing this post. Not because the writing was difficult or the topic was boring—far from it. No, the reason I hated doing this was because I got sucked into a wikihole. I started out researching climate zones, and ten hours later I was reading an article about Icelandic hot spring rye bread (which is called hverabrauð by the way and you should absolutely check it out). I only realized what time it was when I looked out my window and saw the sun starting to rise. Try to picture what my sleep schedule has looked like for the last few days, and you can see why I might be just a smidge upset.
Sorry. Where was I?
Ah, yes: geography. The bane of cartographers everywhere. If you’ve ever dabbled in writing stories with a non-Earth setting, you’ll know that one of the most fundamental aspects of worldbuilding is the lay of the land. Even before you’ve started working on the cultures of your fictional people (or hell, even the plot), you need to develop the locations. Any writer worth their salt will correctly tell you that geography dictates who the characters are, what the story’s about, when major actions occur, where the major story beats take place, why things progress the way they do, and how certain steps are achieved.
Want an example of this? Take a look at the geography of Avatar: The Last Airbender and how it influenced the Fire Nation’s culture and resulting imperialistic conquest: [1]
A geographic map of Avatar: The Last Airbender depicting the four major countries: the Fire Nation, Earth Kingdom, Water Tribes, and Air Temples. | Source: Imgur.
The Fire Nation, being located on a volcanic archipelago, was able to jumpstart its industrial revolution decades before anyone else, courtesy of access to natural resources such as coal and metal ore deposits (which were disproportionately scarcer in the other countries). This abundance of minerals was reflected in gold being commonly incorporated into Fire Nation royal attire, and the Fire Nation boasting some of the most proficient blacksmiths and swordfighters in the world (like Piandao).
Being an island nation, their culinary staples included aquatic and marine species such as waterfowl, fish, cephalopods, crustaceans, bivalves, and seaweed.
The mountainous regions of the Fire Nation made the land ill-suited for agriculture, which likely influenced the development of an oceanic trade route. This allowed for the import of otherwise-unavailable resources from the Earth Kingdom.
The trade route helped to reinforce a unified state by connecting all seaports, trading outposts, and settlements in the archipelago to the major urban capital. This interconnectivity created economic advantages, and solidified a sense of cultural unity and loyalty to the nation by making communication (via ship and messenger hawk) direct and expedient.
The navy emerged as a natural outgrowth of the oceanic trade route. Martial vessels would have been necessary for protecting merchant ships from pirates, collecting taxes from provincial settlements (because navies have steep operating costs), and enforcing the laws of the central authority. Similarly, as an island country, the only way the Fire Nation could have feasibly been harmed is through a naval attack, which would have given it the incentive to cultivate a naval defense.
At the beginning of the Hundred Year War, the Fire Nation seized control of the northwestern Earth Kingdom because the region was rich in resources that they would need to sustain themselves if they were going to survive without international trade.
Their technologically-advanced navy and control of the major oceanic trade routes allowed the Fire Nation to orchestrate blockades, quickly transport troops and equipment between places, and limit the tactical movements of the other countries.
To say that geography dictates the story is an understatement—without it, the story wouldn’t exist. Good writing and likeable characters can only do so much to save a story that lacks this crucial component of worldbuilding.
So, how does this apply to RWBY?
In order to talk about that, first we have to address the unusual way that Remnant’s map was designed.
Back in 2012, while out at an IHOP with Shane Newville, Monty Oum had the idea of squirting a ketchup bottle into a napkin, crumpling it up, and then unfolding it to reveal the blotchy proto-topography of Remnant. His reason for doing so, as he explains:
“The philosophy behind [making the map that way] is that, I feel like, as a 3D animator […] utilizing all this technology, our process—all these computers, all these numbers and stuff—our process is so artificial, it’s riddled with so much artifice, that not only for that, but for everything else I do, I try to imbue kind of like an anarchy, an anarchic-like chaos, just to give it some sense of, like, randomness. Like, you need to preserve that sense of chaos because the process we do is so robotic. […] But the important thing was, like especially with everything that we just raise in our production value, that you have to preserve that anarchic energy that influences everything you do.” [2]
The original terrain map created by Monty Oum. | Source: RWBY Wiki contributor user:Sgt D Grif.
You’d be hard-pressed to disagree with the artistic merit of this design approach. There’s a simplistic elegance to be found in a creator forfeiting a degree of their control over a project, in order to watch how it might organically evolve.
But here’s the thing: this isn’t like leaving slime molds in a petri dish and letting them network until they resemble Japanese subway lines. While anarchic chaos can work for some disciplines of art, it creates glaring issues when applied to worldbuilding. Nature, although it appears outwardly random, is actually rather ordered. The reason why we don’t leave our houses every day carrying umbrellas is because we don’t have to—we have meteorologists that can anticipate the forecast days or weeks ahead. Plenty of natural phenomena can be predicted: weather systems over vast areas, environmental selection pressures converging on similar traits…
And, of course, plate tectonics.
You see, the problem with Monty’s method is that it didn’t account for the movement of Remnant’s continents. Because the planet’s continents were born from artistic randomness rather than methodical and deliberate forethought, we have no reliable access to certain information, like atmospheric circulation, ocean currents, or plate boundaries. All three of these planetary subsystems—the atmosphere, hydrosphere, and lithosphere—and their dynamics shape the geography of a planet.
Without this information, we can’t answer certain questions.
Was Lake Matsu formed by glacial retreat?
Are Vale’s mountains sitting on a convergent plate boundary? Or are they more like the Appalachians, which are the remains of the Central Pangean Mountains?
If Vale’s mountains were formed by convergent plate boundaries, then why don’t we see evidence of it in the forms of volcanism and earthquakes?
Is Vacuo’s interior desert formed by a rain shadow?
If Solitas’ geography is based on a polar ice cap, then how did early settlers survive long enough to excavate the Dust? How would they have dug through the ice and permafrost?
Has climate change ever resulted in changes in sea level that submerged or exposed the continents? Did early humans and Faunus move between continents by land bridges? Have rises in sea level ever hidden continents (like Earth’s Zealandia)?
Does Mistral’s capital rely on meltwater from the surrounding mountains for irrigating crops?
When the Younger Brother shattered the moon, did the lunar debris alter the landscape when it fell to Remnant? Was it like the Chicxulub asteroid that caused the K-Pg extinction? Did the lunar debris leave craters on the planet’s surface, or cause phenomena like impact winters and ocean acidification?
It bears mentioning that these questions pertain to real-life geographic concepts. This isn’t even touching upon fictional geographic concepts that RWBY introduced, like largescale Dust deposits altering the local environment in such a way that it functionally becomes its own ecosystem (like Lake Matsu’s floating islands). We’re also assuming that RWBY’s continental plates are capable of drift, and weren’t magically glued in place by the gods during the formation of the planet.
Given the scale of these problems, I think it’s safe to say that—while I can appreciate the artistry behind Monty’s design philosophy—the way he designed Remnant ultimately did more harm than good.
While I could spend all afternoon debating the pros and cons of condiment cartography, there are more productive things I could be doing with my time. Instead, I want to discuss Remnant’s geography as it currently is. Specifically, there are three questions I want to test:
How well does the geography hold up?
Does the geography have a realistic influence on society?
How well does the show integrate foreign geographic features into its worldbuilding?
As a quick disclaimer, I’m not an expert on any of the aforementioned subsystems. And because I don’t have any canonical information on Remnant’s atmospheric circulation, ocean currents, or plate boundaries, it becomes impossible to prove or disprove the realism of its geography. For now, we’re going to err on the side of caution and assume that Remnant is a planet with a functionally-analogous lithosphere to Earth’s, and that Remnant’s features are byproducts of such a system.
The current geographic map of Remnant. It boasts five major continents (of which only four have been named) and multiple islands. | Source: World of Remnant, Volume 4, Episode 1: “Vale.”
How Well Does the Geography Hold Up?
To answer this, I used the Köppen-Geiger climate classification to categorize Remnant’s main landmasses (with the exception of the unnamed continent). This model organizes areas into distinct climatic regions based on seasonal precipitation and temperature patterns. The results I cobbled together are based on approximate latitude, ecosystems that we’ve seen in the show, canon maps, and comparisons between the continents and their real-world sources of inspiration (Asia for Anima, North America for eastern Sanus, Australia for Menagerie, etc). Here’s what I came up with:
This isn’t perfect by any means, but I think it satisfies some lingering doubt about the credibility of the geography. Sanus’ interior desert, for example, could easily be a cold desert climate. The exterior band of foliage on the northern and western sides appears to be indicative of a rain shadow effect caused by a mountain belt (the conditions necessary for creating this climate type). We have evidence of there being nearby western mountains courtesy of the earthquakes in Vacuo, [3] as earthquakes often occur near mountain ranges created by subduction boundaries. Similarly, oases (like the one once found in Vacuo) tend to form in cold desert climates as the result of rain shadows (similar to the el-Djerid oases near the Atlas Mountains).
All things considered, I’m inclined to give the climate regions a tentative pass. Like I said, they’re not perfect, but they seem to be holding up so far.
Does the Geography Have a Realistic Influence on Society?
Ehhh. It depends. With Vale it’s hard to say, given how little we’ve seen of the areas outside the capital (like the Emerald Forest and Forever Fall), and the fact that we haven’t visited any other cities in the kingdom. We know that Vale makes use of a massive port for trade and travel due to the nearby body of water. But there doesn’t seem to be anything particularly unique about the capital’s culture that can be directly attributed to its geography. Despite being a coastal city, it doesn’t have any signature delicacies derived from the abundant seafood. The architecture is largely generic urban-Western, and doesn’t incorporate the mountains in any way. Vale’s geography is little more than a convenient buffer against the Grimm.
Mistral, on the other hand, is heavily influenced by the geography. All of its houses and shops are directly integrated into the mountains, with an emphasis on vertical building to accommodate the limited space on the cliffs. Stairs, bridges, and electronic lifts are used for getting around the city. Unlike Vale, Atlas, and Mantle, which use motor vehicles, Mistral doesn’t have the space to accommodate modern roads, and instead relies on railroad transport (like the Argus Limited) to move around the continent. Compared to Vale, Mistral is a vast improvement on how well the writers used geography to influence the culture of a city. However, I still think the show could’ve done more to strengthen this connection. For instance, we see evidence of cave systems in Mistral, which briefly appear on-screen and are never brought up again. [4] Talk about wasted potential. Additionally, the show never addresses how the Council keeps its citizens from falling to death. No joke, the only place in the city that has railings is the safehouse where Qrow and the kids stay. What the hell do people do in the winter when the stairs and paths ice over? How do they not slip and fall and plummet to their deaths? And while I’m thinking about it, why doesn’t the city have a system of ziplines or ski lifts for getting around? Are native-born Mistrali people adapted to the lower oxygen found at higher elevations? And what about Mistral’s agriculture? Do farmers live outside the capital? How do they protect themselves?
Like I said, Mistral is better than Vale in this department, but it could still do with more worldbuilding.
Atlas and Mantle are more akin to Vale when it comes to noticeable geographic influence—or rather, a lack thereof. While the technology accommodates its residents via the heating grid, there doesn’t seem to be any evidence of how the geography shaped the people of this continent. You’d expect a circumpolar indigenous group to have very distinct cultural traits, but there’s none of that. It’s just rampant technological growth. Now, you could argue that any aspect of geographic influence on culture was wiped out around the time of the Great War. But if the show wants me to believe that, it needs to show me proof. Whether it’s a conversation between two characters, or a political movement spearheading cultural revitalization. Something—anything—that might hint at how geography influenced pre-industrial Mantle.
And forgive me if I don’t feel like speculating about Vacuo, given that it’s only appeared in After the Fall and Before the Dawn. When the show decides to unveil it, then I’ll have more to say.
As for Menagerie? Another resounding meh. The inherent intrigue of a settlement that shelters aquatic Faunus is never fully explored. We get to briefly visit the Shallow Sea district of Kuo Kuana, but the scene is too focused on Blake’s and Sun’s conversation to let us fully explore the area. Which is a shame, because a concept like that could easily be taken to some really cool extremes. Like, what about entirely underwater settlements that are built on coral reefs? How cool would that be for Faunus that have gills, webbed appendages, or caudal fins? I’m not expecting Zootopia or anything like that, but it’d be neat if settlers had gone to creative extremes to accommodate the wide variety of Faunus traits.
How Well Does the Show Integrate Foreign Geographic Features into Its Worldbuilding?
In Volume 5 we’re introduced to Lake Matsu, an area rich with naturally-occurring superterranean Gravity Dust. What makes this place so intriguing is the fact that the Dust is in a constant active state, causing the islands to float in the air. Given that Dust is usually inert unless activated by an Aura, the existence of this place is frankly astonishing, and for the life of me I don’t get why the show treated it as little more than set dressing.
This phenomenon—which I’ve taken to calling a Dust vortex—has so much worldbuilding potential. What if Remnant had pseudo-ephemeral lakes created by concentrations of Water Dust? Or how about a cave system with an abundance of Electricity Dust that causes magnetic charges in the surrounding minerals, creating a place similar to Unova’s Chargestone Cave? Maybe Sanus’ southeastern desert has large pockets of Steam Dust that enshroud the area in permanent fog?
Dust vortices wouldn’t just be aesthetically cool, either; they’d have important implications for the lore. Let’s use Lake Matsu as an example.
If the Dust vortex has been there for a long time (upwards of thousands of years), then the organisms in this ecosystem would’ve adapted to it. You would have endemic wildlife—agamid-like gliding lizards, plants with wind-dispersed fruit, lianas and mosses draped from the underside of the islands, diving birds that nest on the outcrops, microbial detritivores found exclusively in the islands’ soil. Maybe Lake Matsu is an important stopover for migrating birds. Maybe the shadows from the overhead islands are important for predatory fish, which hide in the shade to ambush flying insects. Because the wildlife would be endemic to this ecosystem, perhaps the Mistrali government would designate it a protected area and prevent Dust companies from excavating the site. What if there were fishing towns on the shore that depended on tourism to sustain the local economy? Would they ever come into conflict with Dust companies that lobby the government to open up the area to selective mining?
I’m sure I must sound like a broken record at this point, but the worldbuilding possibilities on display here are nothing short of incredible. And the failure of RWBY to explore even a single one feels like getting repeatedly kicked in the stomach by a feral horse.
We’re now 3,000 words in and I didn’t even get to include ideas for tautological place names. It sucks, but sometimes you have to compromise and go with the idea that make sense to include, rather than the idea that exists just to be novel.
Sound familiar?
-
[1] Hello Future Me. “Avatar: A Study in Worldbuilding — the Fire Nation [ The Last Airbender ]” YouTube video. October 26, 2019. [https://www.youtube.com/watch?v=Pa2BD13VzxY&t=3s]
[3] Myers, E. C. RWBY: Before the Dawn (Book 2). Scholastic Inc, 2020. Online preview. “The city of Vacuo had some order to it, with different districts for residences and businesses, and a wide street down the center for the market. But the outer edges of it were periodically wiped out, because of sandstorms or sinkholes or earthquakes.”
Data = Sample_Data.rename(columns={“BIO_SEX”:“MALE”})
Data.head()
# Examining Datatype and summary statistics
Data_Clean = Data.dropna()
print("Display Data Type:\n",Data_Clean.dtypes)
print("===============================================")
print("===============================================")
Data_Clean.describe()
# # Set Response and target Variables
predictors = Data_Clean[['MALE','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN',"AGE" ,'ALCEVR1','ALCPROBS1','TREG1','DEP1','ESTEEM1','VIOL1','PASSIST','DEVIANT1','SCHCONN1','GPA1',EXPEL1','FAMCONCT','PARACTV']]
# We will base our analysis on violence as our sample data
targets = Data_Clean.VIOL1
# Splitting Data
To Understand model performance, Dividing the dataset into a training set and test set is a good strategy.
Now , let's split the dataset by using function train_test_split()we need to pass 3 parameters predictors,target , test_set size
split dataset in features and target variable by setting the size ratio to 60% for the training sample and 40% for test sample
# Request the shape predictors o training and test samples.
print("pred_train:",pred_train.shape)
print("pred_test:",pred_test.shape)
print ("tar_train:", tar_train.shape)
print ("tar_test:",tar_test.shape)
Building Decision Tree Model
Let's Create a Decision Tree Model using Scikit-learn
Python Code :
# Create a Decision Tree
clf = DecisionTreeClassifier()
# Train Decision Tree
clf = clf.fit(pred_train, tar_train)
# Predict the response for the test dataset this is my y_pred
predictions = clf.predict(pred_test)
Evaluating Model
Let's estimate , how accurately the classifier or model can predict the type of cultivas Accuracy can be predicted by comparing actual test set values and predicted values.
Python code:
# Model Accuracy , how often is the classifier correct?
# our accuracy came down to .9284 which can be considered to be a very great prediction score.
Visualizing Decision Trees
we are going to use scikit-learn's export_graphiz function for display the tree
with jupyter notebook . For Plotting tree , we also need to install graphviz and pydotplus export_graphviz function converts decision tree classifier into dot file and pydotplus convert this file to png or displayable forn on jupyter.
!pip install graphviz
!pip install pydotplus
# There is a limitation of working with trees in the context of python
Specifically with Sklearn Library does not currently support the pruning of the tree .we are left with overfitting decision tree where many branches of leaves do not likely adds substantially to our predictions and accuracy.
#For Exploratory purpose it can be helpful to test a small number of variables in order to first get the feel of the decision tree output.
# First import all needed dependencies
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO
from sklearn.tree import DecisionTreeClassifier
from IPython.display import Image
from sklearn import tree
import pydotplus
Python Code:
out = StringIO()
# Image(graph.create_png)
tree.export_graphviz(clf, out_file=out)
# request the picture of our decision tree
import pydotplus
graph=pydotplus.graph_from_dot_data(out.getvalue())
Image(graph.create_png())
we can optimize decision tree classifier with With scikit-learn . perform pre-pruning . Maximum depth of the tree can be used as control variable for pre-pruning. In the following example . we will plot a decision tree on the same data with max_depth=3. other than pre-pruning parameters , we can also try to attribute selection measures with entropy.
# Create Decision Tree Classifier object
clf= DecisionTreeClassifier(criterion="entropy", max_depth=4)
clf = clf.fit(pred_train, tar_train)
# Predict the response for the test dataset this is my y_pred
predictions = clf.predict(pred_test)
# Model Accuracy , How often is the classifier correct ?
print ("Accuracy:",metrics.accuracy_score(tar_test ,predictions))
Even though we had a very great prediction score for .9284 before we can see that there a lot that can be done in terms of improving model accuracy now we have .9967 which is better that the previous one.
# let let’s perform a sample test by pruning since this small tree can help us understand more about our classification model .
Decision Tree Strength approach are the following :
1. Can select from among a large number of variables those and their interactions that are most important in determining the target response variable to be explained
2.Decision Trees are able to generate understandable rules
3.Decision Trees perform classification without requiring much computation.
4.Can handle large dataset and can predict both binary and categorical target variables (shown in this example) and also quantitative target variables (known as regression trees)
Weakness of Decision tree methods are :
1. Small changes in the data can lead to different splits and this can undermine the interpretability of the model. Also decision trees are not very reproducible on future data.
2.Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute
3.Decision trees are prone to errors in classification problems with many class and relatively small number of training examples.
4.Decision tree can be computationally expensive to train. The process of growing a decision tree is computationally expensive. At each node, each candidate splitting field must be sorted before its best split can be found. In some algorithms, combinations of fields are used and a search must be made for optimal combining weights. Pruning algorithms can also be expensive since many candidate sub-trees must be formed and compared.
References :
Machine Learning, Tom Mitchell, McGraw Hill, 1997.
https://www.geeksforgeeks.org/decision-tree/
WEEK THREE
Running Lasso Regression Analysis
# from pandas Series , DataFrame
#from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LassoLarsCV
# loading Dataset
Data = pd.read_csv('Tree.csv')
Data
# upper-case all DataFrame column names
Data.columns = map(str.upper, Data.columns)
#select predictor variables and target variable as separate data sets
predvar= Data_Clean[['MALE','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN',
'AGE','ALCEVR1','ALCPROBS1','MAREVER1','COCEVER1','INHEVER1','CIGAVAIL','DEP1',
'ESTEEM1','VIOL1','PASSIST','DEVIANT1','GPA1','EXPEL1','FAMCONCT','PARACTV',
'PARPRES']]
target = Data_Clean.SCHCONN1
#DATA MANAGEMENT
# standardize predictors to have mean=0 and sd=1
predictors=predvar.copy()
from sklearn import preprocessing
predictors['MALE']=preprocessing.scale(predictors['MALE'].astype('float64'))
predictors['HISPANIC']=preprocessing.scale(predictors['HISPANIC'].astype('float64'))
predictors['WHITE']=preprocessing.scale(predictors['WHITE'].astype('float64'))
predictors['NAMERICAN']=preprocessing.scale(predictors['NAMERICAN'].astype('float64'))
predictors['ASIAN']=preprocessing.scale(predictors['ASIAN'].astype('float64'))
predictors['AGE']=preprocessing.scale(predictors['AGE'].astype('float64'))
predictors['ALCEVR1']=preprocessing.scale(predictors['ALCEVR1'].astype('float64'))
predictors['ALCPROBS1']=preprocessing.scale(predictors['ALCPROBS1'].astype('float64'))
predictors['MAREVER1']=preprocessing.scale(predictors['MAREVER1'].astype('float64'))
predictors['COCEVER1']=preprocessing.scale(predictors['COCEVER1'].astype('float64'))
predictors['INHEVER1']=preprocessing.scale(predictors['INHEVER1'].astype('float64'))
predictors['CIGAVAIL']=preprocessing.scale(predictors['CIGAVAIL'].astype('float64'))
predictors['DEP1']=preprocessing.scale(predictors['DEP1'].astype('float64'))
predictors['ESTEEM1']=preprocessing.scale(predictors['ESTEEM1'].astype('float64'))
predictors['VIOL1']=preprocessing.scale(predictors['VIOL1'].astype('float64'))
predictors['PASSIST']=preprocessing.scale(predictors['PASSIST'].astype('float64'))
predictors['DEVIANT1']=preprocessing.scale(predictors['DEVIANT1'].astype('float64'))
predictors['GPA1']=preprocessing.scale(predictors['GPA1'].astype('float64'))
predictors['EXPEL1']=preprocessing.scale(predictors['EXPEL1'].astype('float64'))
predictors['FAMCONCT']=preprocessing.scale(predictors['FAMCONCT'].astype('float64'))
predictors['PARACTV']=preprocessing.scale(predictors['PARACTV'].astype('float64'))
predictors['PARPRES']=preprocessing.scale(predictors['PARPRES'].astype('float64'))
# TESTING A LASSO REGRESSION MODEL IN PYTHON
# split data into train and test sets
pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, target,
test_size=.3, random_state=123
# Specify the Lasso Regression
model= LassoLarsCV(cv=10, precompute=False).fit(pred_train , tar_train)
# Print Variable names and regression coefficients
dict(zip(predictors.columns, model.coef_))
# # MSE FROM TRAINING AND TEST DATA
from sklearn.metrics import mean_squared_error
train_error = mean_squared_error(tar_train , model.predict(pred_train))
test_error = mean_squared_error(tar_test, model.predict(pred_test))
print('training data MSE')
print(train_error)
print('test data MSE')
print (test_error)
# # R-squared from training and test data
rsquared_train = model.score(pred_train, tar_train)
rsquared_test=model.score(pred_test, tar_test)
print ('Training data R-squared')
print (rsquared_train)
print ('Test data R-squared')
print (rsquared_test)
The plot above shows the value of selecting predictors at any given stage.
The selection process can therefore take the regression coefficients and slightly change by adding a new predictor at each stage and the stage at which each variable entered the mode. With that in mind there was a list of regression coefficients which was the largest like self_esteem it was entered into the model first then others followed from the largest to the smallest coefficient and so on.
# Plot mean square error for each fold
m_log_alphascv = -np.log10(model.cv_alphas_)
plt.figure()
plt.plot(m_log_alphascv, model.cv_mse_path_,':')
plt.plot(m_log_alphascv, model.cv_mse_path_.mean(axis=-1),'k',
label='Average across the folds', linewidth=2)
plt.axvline(-np.log10(model.alpha_), linestyle='--',color='k', label = 'alpha CV')
plt.legend()
plt.xlabel('-log(alpha)')
plt.ylabel('Mean squared error')
plt.title('Means squared error on each fold')
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