#multipleheads

[fauxeevee]

Ref for my new chimera character rorschach! the wolf normally has control of the legs Artfight page: https://artfight.net/character/4446699.rorschach

[thomasdonaldsonart]

Large scale piece 180x150cm from the end of last year and sold to a collector in Hong Kong. #fineart #visualart #contemporaryart #oilpainting #statementart #multipleheads #portraitpainting #beauty #expressive #inspiration #artcollectors #artforinteriordesigners #artcurators #artoftheday #picoftheday #instaart #artistoninstagram #artgallery #kunst www.thomasdonaldson.biz https://www.instagram.com/p/B71MxWeFtg5/?igshid=17cvnh3diyq3v

[teadoodlesandoddends-blog]

You've probably met a "nice guy" by now.

And you have my deepest condolences.

Work in progress.

[amatersensei-blog]

Series 1 Character 6 - Cherry Duo Saku & Nii-Saku

Shy and nervous girl, and a bold and honest boy, this duo is quite a show.

I like exploring character ides that involve multiple body parts, or rather extra body parts, and one of them was a character with multiple heads and this is a first successful try. :D

[catswilleatyou]

sneaks and stripes

[thomasdonaldsonart]

Another large scale multiple head piece recently sold to a collector in the USA. Thank you and thank you to the lovely model salou. #fineart #contemporaryart #visualart #paintings #artcollectors #artcurators #portraitpainting #multipleheads #expressive #emotive #beauty #artoftheday #picoftheday #instaart #artistoninstagram www.thomasdonaldson.biz https://www.instagram.com/p/B3fFsEalY5i/?igshid=1k2zgte26mtyg

[govindhtech]

Single-Trunk Multi-Head Networks For Materials Research

ST MH single-trunk multi-head

One Neural Network Revolutionises Quantum State Simulation

Quantum computing and complex material research have advanced because to a novel way for simulating numerous quantum states. Single-trunk multi-head (ST-MH) neural networks are useful for understanding complex quantum systems since they need less computational power.

Estimating quantum systems' many, often identical states has been a computational bottleneck. Understanding these degenerate states is necessary to model complex materials and processes, but previous methods were computationally expensive and prone to duplicate or non-orthogonal solutions. Waleed Sherif and his colleagues at Friedrich-Alexander-Universität Erlangen-Nürnberg's Institute for Quantum Gravity devised a single-trunk multi-head ST-MH architecture that shares core calculations across all states to solve this.

The Clever Design: Shared Trunk, Multiple Heads

A simplified focus point of the ST-MH ensemble is its feature-extracting network “trunk”. Common attributes for all target states are extracted by this central stem after processing the original data. This shared trunk holds several lightweight, linearly parametrised "heads," each representing a quantum system eigenstate. Quantum chemistry's shared-orbital approaches and classical machine learning's multi-task learning influence this unique design.

This architecture contrasts with the “multi-trunk multi-head” (MT-MH) approach, which approximates each degenerate state with a distinct neural network with its own trunk and parameters. A K-fold duplication of networks like this increases parameters and computational footprint.

Unmatched Efficiency and Accuracy

The single-trunk multi-head ST-MH method's top benefit is efficiency. Sharing the trunk reduces the number of parameters and computational cost of the ST-MH ensemble, saving memory and runtime. A qualitative cost estimate shows that MT-MH may require K times more parameters and computation if the latent trunk width is the same for both Single-trunk multi-head ST-MH and MT-MH.

Empirical data supports these predictions. Simulations demonstrate that the Single-trunk multi-head ST-MH ensemble's compute time keeps essentially constant as the number of target states (K) increases, while the MT-MH method's calculation time increases linearly due to trunk replication. ST-MH outperforms MT-MH in larger systems.

Importantly, efficiency increase does not impair accuracy. The ST-MH ensemble matches or outperforms multi-trunk systems in fidelity and energy accuracy, especially when trunk widths are higher. The approach also maintains orthogonality across heads, ensuring discrete and physically significant estimated states. This is done by adding an orthogonality penalty element to variational Monte Carlo (VMC) optimisation.

Science: A Firm Basis

This study relies on rigorous proof that the ST-MH ensemble may accurately represent the full degenerate manifold under certain conditions. The representability theorem allows for accurate representation when the common trunk's latent width (h) matches h + 1 ≥ r_both. When all states are non-vanishing, r_both is the linear rank of the log-moduli and phases on a common support.

If this requirement is not met, a single trunk of that width cannot represent the full degenerate manifold. The study shows the minimum width for precise portrayal. This theoretical underpinning ensures that the Single-trunk multi-head ST-MH design approximates quantum states as a principled way rather than a heuristic compression.

Complex System Validation

The researchers tested their method by applying the ST-MH ensemble to the frustrated Heisenberg chain, a complex magnetic model around the Majumdar-Ghosh point. The degenerate momentum eigenstates were appropriately acquired using this model.

The experiments demonstrated that the ST-MH ensemble has high energy accuracy and fidelity on degenerate ground state manifolds. The ensemble generated mutually orthogonal states and converged to the right ground energy for systems with up to eight sites, resolving the whole degenerate ground space. Ablation investigations showed that the Single-trunk multi-head ST-MH ensemble could resolve the complete degenerate ground manifold for a system with N=4 sites, even with a trunk width of 2. This supported the representability theorem.

Way to Larger, More Complex Simulations

ST-MH sets a new standard for expressing and resolving degenerate quantum states. Its robust structure and reduced processing requirements enable simulations of larger and more complex quantum systems.

Nuclear, condensed matter, and quantum chemistry are among the many applications. This method can help calculate ground-state energy and other important properties of molecules, materials, and nuclei. Beyond single-system degenerate eigenspaces, Single-trunk multi-head ST-MH's architectural separation of shared features and linear heads may be useful for transfer learning with foundation neural networks, where a single trunk could learn general representations for several quantum systems.

This discovery provides a powerful and scalable tool for future quantum research and advances our ability to study the quantum environment.

[boorat]

#oldartwork #dragon #multipleheads #hydra #monstergirls #snakes #cobra #anime #sculptthisaceofclay #rattlesnake #redbubble #animegirl #monstermusume https://www.instagram.com/p/B1yrM_Bnfrv/?igshid=6ou98z4vu5