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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Ntt Accelerator PqcHigh-performance NTT accelerator design for post-quantum cryptography (PQC). Novel redundant number representation eliminates conditional corrections for Montgomery modulo multiplication and combined subtract-multiply operations.Votes: 0GitHub stars: 3
- Null Dynamical State Models Of Human Cognitive Dysfunction**arXiv ID:** 1712.09014 **Authors:** M. J. Gagen **Published:** 2017-12-25T05:46:19Z **Abstract:** The hard problem in artificial intelligence asks how the shuffling of syntactical symbols in a program can lead to systems which experience semantics and qualia. We address this question in three stages. First, we introduce a new class of human semantic symbols which appears when unexpected and drastic environmental change causes humans to become surprised, confused, uncertain, and in extreme c...Votes: 0GitHub stars: 3
- On Convergence And Stability Of Gans**arXiv ID:** 1705.07215 **Authors:** Naveen Kodali, Jacob Abernethy, James Hays, Zsolt Kira **Published:** 2017-05-19T22:41:56Z **Abstract:** We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hypothesize the existence of undesir...Votes: 0GitHub stars: 3
- On Nonlinear Operators For Geometric Deep Learning**arXiv ID:** 2207.03485 **Authors:** Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon **Published:** 2022-07-06T06:45:33Z **Abstract:** This work studies operators mapping vector and scalar fields defined over a manifold $\mathcal{M}$, and which commute with its group of diffeomorphisms $\text{Diff}(\mathcal{M})$. We prove that in the case of scalar fields $L^p_ω(\mathcal{M,\mathbb{R}})$, those operators correspond to point-wise non-linearities, recovering and extending k...Votes: 0GitHub stars: 3
- On The Limitations Of Representing Functions On Sets**arXiv ID:** 1901.09006 **Authors:** Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, Michael Osborne **Published:** 2019-01-25T18:11:52Z **Abstract:** Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance. In particular, it has been conjectured that the dimension of this latent space may remain fixed as the cardinality of the sets under consideration increases. However, we demonstrate t...Votes: 0GitHub stars: 3
- On The Markov Property Of Neural Algorithmic Reasoning Analyses And Methods**arXiv ID:** 2403.04929 **Authors:** Montgomery Bohde, Meng Liu, Alexandra Saxton, Shuiwang Ji **Published:** 2024-03-07T22:35:22Z **Abstract:** Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in existing designs involves the use of historical embeddings in predicting the results of future execution steps. Our observation in this work is that such historical dependence ...Votes: 0GitHub stars: 3
- On The Performance Of Generative Adversarial Network Gan Variants A Clinical Data Study**arXiv ID:** 2009.09579 **Authors:** Jaesung Yoo, Jeman Park, An Wang, David Mohaisen, Joongheon Kim **Published:** 2020-09-21T02:18:58Z **Abstract:** Generative Adversarial Network (GAN) is a useful type of Neural Networks in various types of applications including generative models and feature extraction. Various types of GANs are being researched with different insights, resulting in a diverse family of GANs with a better performance in each generation. This review focuses on various GANs...Votes: 0GitHub stars: 3
- On The Principles Of Relu Networks With One Hidden Layer**arXiv ID:** 2411.06728 **Authors:** Changcun Huang **Published:** 2024-11-11T05:51:11Z **Abstract:** A neural network with one hidden layer or a two-layer network (regardless of the input layer) is the simplest feedforward neural network, whose mechanism may be the basis of more general network architectures. However, even to this type of simple architecture, it is also a ``black box''; that is, it remains unclear how to interpret the mechanism of its solutions obtained by the back-propagat...Votes: 0GitHub stars: 3
- Ontoextend A Framework For Requirement Driven AndDerived from arXiv:2607.17963 - OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMsVotes: 0GitHub stars: 3
- Operator Kirigami Symmetry ConservationOperator Kirigami methodology for symmetry conservation in quantum algorithms — cut-and-fold technique for preserving non-Abelian symmetries in Trotterized quantum circuits by orthogonal projection and unitary rotation folding.Votes: 0GitHub stars: 3
- Optimal Parametric Quantum EstimationOptimal control-based strategy for enhancing impulse estimation in Gaussian quantum systems via parametric modulation. Use when designing quantum sensing protocols, estimating transient disturbances in quantum systems, optimizing state preparation for impulse detection, or comparing parametric driving vs squeezing protocols. Bridges quantum control theory, optimal estimation, and non-equilibrium quantum dynamics.Votes: 0GitHub stars: 3
- Optimal Photostimulation Selection For ConnectomicsOptimal photostimulation selection framework (OPhELIA) for efficient causal connectomics mapping using Bayesian experimental design. Enables reconstruction of exhaustive functional connectomes with minimal trials by combining Bayesian inference, active learning, and compressed sensing.Votes: 0GitHub stars: 3
- Optimal Stellar Rank Photon CatalysisMethodology for provably optimal generation of non-Gaussian quantum states (squeezed cat states) via photon catalysis, characterized using stellar rank formalism — enables systematic comparison of fidelity against theoretical maximum for given non-Gaussian resources.Votes: 0GitHub stars: 3
- Optimising Communication Overhead In Federated Learning Using Nsgaii**arXiv ID:** 2204.02183 **Authors:** José Ángel Morell, Zakaria Abdelmoiz Dahi, Francisco Chicano, Gabriel Luque, Enrique Alba **Published:** 2022-04-01T18:06:20Z **Abstract:** Federated learning is a training paradigm according to which a server-based model is cooperatively trained using local models running on edge devices and ensuring data privacy. These devices exchange information that induces a substantial communication load, which jeopardises the functioning efficiency. The difficulty...Votes: 0GitHub stars: 3
- Optimizing Coordinative Schedules For Tanker Terminals An Intelligent Large Spatialtemporal Datadriven Approach Part 1**arXiv ID:** 2204.03899 **Authors:** Deqing Zhai, Xiuju Fu, Xiao Feng Yin, Haiyan Xu, Wanbing Zhang, Ning Li **Published:** 2022-04-08T07:56:32Z **Abstract:** In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time. The proposed approach consists of enhanced particle swarm optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global optimal search. Two paradigm methods of th...Votes: 0GitHub stars: 3
- Optimizing Coordinative Schedules For Tanker Terminals An Intelligent Large Spatialtemporal Datadriven Approach Part 2**arXiv ID:** 2204.03955 **Authors:** Deqing Zhai, Xiuju Fu, Xiao Feng Yin, Haiyan Xu, Wanbing Zhang, Ning Li **Published:** 2022-04-08T09:30:23Z **Abstract:** In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing weighted average turnaround time. The proposed approach is developed as a heuristic algorithm applied and investigated through different observation windows with weekly rolling horizon paradigm method. The experimenta...Votes: 0GitHub stars: 3
- Optimizing Mario Adventures In A Constrained Environment**arXiv ID:** 2312.14963 **Authors:** Sanyam Jain **Published:** 2023-12-14T08:45:26Z **Abstract:** This project proposes and compares a new way to optimise Super Mario Bros. (SMB) environment where the control is in hand of two approaches, namely, Genetic Algorithm (MarioGA) and NeuroEvolution (MarioNE). Not only we learn playing SMB using these techniques, but also optimise it with constrains of collection of coins and finishing levels. Firstly, we formalise the SMB agent to maximize the to...Votes: 0GitHub stars: 3
- Optimizing Pm25 Forecasting Accuracy With Hybrid Metaheuristic And Machine Learning Models**arXiv ID:** 2407.01647 **Authors:** Parviz Ghafariasl, Masoomeh Zeinalnezhad, Amir Ahmadishokooh **Published:** 2024-07-01T05:24:19Z **Abstract:** Timely alerts about hazardous air pollutants are crucial for public health. However, existing forecasting models often overlook key factors like baseline parameters and missing data, limiting their accuracy. This study introduces a hybrid approach to address these issues, focusing on forecasting hourly PM2.5 concentrations using Support Vector Re...Votes: 0GitHub stars: 3
- Oracle Gap And Signal Fidelity A Fixed Pool DiagnoDerived from arXiv:2607.17531 - Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time CollaborationVotes: 0GitHub stars: 3
- Oracle Gap Signal Fidelity Fixed Pool Diagnostic Test Time CollaborationSkill derived from arXiv:2607.17531 - Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time CollaborationVotes: 0GitHub stars: 3
- Order Sensitive Fast Synapse LimitsAnalyze E/I arrival order effects in threshold-reset SNNs.Votes: 0GitHub stars: 3
- Organic Quantum Reservoir ComputingFramework for designing magnetic-field-free quantum reservoir computing systems using engineered organic materials. Bridges quantum computing and neuroscience through the 3-layer quantum brain hypothesis. Use when: designing organic qubit systems, quantum reservoir computing architectures, neuromorphic quantum computing, or bio-inspired quantum systems.Votes: 0GitHub stars: 3
- Otapstructure Aware Optimal Transport For EvaluatiDerived from arXiv:2607.17082 - Otap:Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent TrajectoriesVotes: 0GitHub stars: 3
- Painful Intelligence What Ai Can Tell Us About Human Suffering**arXiv ID:** 2205.15409 **Authors:** Aapo Hyvärinen **Published:** 2022-05-27T07:32:33Z **Abstract:** This book uses the modern theory of artificial intelligence (AI) to understand human suffering or mental pain. Both humans and sophisticated AI agents process information about the world in order to achieve goals and obtain rewards, which is why AI can be used as a model of the human brain and mind. This book intends to make the theory accessible to a relatively general audience, requiring o...Votes: 0GitHub stars: 3
- Palmclaw A Native On Device Agent Framework For MoPalmClaw: A Native On-Device Agent Framework for Mobile Phones - Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and it...Votes: 0GitHub stars: 3
- Pan Fm Pan Organ FoundationPan-Organ Foundation Model (Pan-FM) for multimodal biomedical imaging with missing-organ robustness. Pre-trained on seven organs (Brain, Heart, Adipose, Liver, Kidney, Spleen, Pancreas) using Saliency-Guided Masking (SGM) to prevent dominant-organ shortcut learning bias. Introduces masking-based self-distillation for handling realistic missing-organ scenarios. Use when: (1) building multi-organ medical imaging foundation models, (2) handling missing modalities/organs in biomedical data, (3) d...Votes: 0GitHub stars: 3
- Paradoxical Noise Preference In Rnns**arXiv ID:** 2601.04539 **Authors:** Noah Eckstein, Manoj Srinivasan **Published:** 2026-01-08T03:11:51Z **Abstract:** In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability and regularize learning. The expectation is that removing the noise at test time should preserve or improve performance. Contrary to this intuition, we find that continuous-time RNNs (CTRNNs) often perform best at or ne...Votes: 0GitHub stars: 3
- Parallelizing Linear Recurrent Neural Nets Over Sequence Length**arXiv ID:** 1709.04057 **Authors:** Eric Martin, Chris Cundy **Published:** 2017-09-12T20:52:22Z **Abstract:** Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence length. We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel scan algorithm, leading to rapid training on long sequences eve...Votes: 0GitHub stars: 3
- Parameter Efficient Quantum MtlParameter-efficient Quantum Multi-task Learning (QMTL) methodology. Replaces conventional task-specific linear heads with fully quantum prediction heads in hybrid architectures. Quantum head parameters scale linearly with task count vs quadratic for classical heads. Use when designing multi-task learning systems for medical imaging, NLP, or multimodal tasks with constrained parameter budgets.Votes: 0GitHub stars: 3
- Particularity**arXiv ID:** 2306.06812 **Authors:** Lee Spector, Li Ding, Ryan Boldi **Published:** 2023-06-12T01:06:22Z **Abstract:** We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges. We trace the development of this "particularity" approach from the use of lexicase selection in genetic programming to "particularist" approaches ...Votes: 0GitHub stars: 3
- Partner Capability Estimation For Task Agnostic AdPartner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc TeamworkVotes: 0GitHub stars: 3
- Pathwise Metastability Galves Locherbach ModelsRigorous mathematical framework for analyzing metastability in stochastic spiking neural networks using the pathwise approach. Reviews Galves-Löcherbach (GL) models, connecting statistical physics to neural dynamics. Covers metastable state transitions, typical trajectory identification, and probability estimation. Use when studying neural state transitions, brain criticality, or applying statistical physics to computational neuroscience.Votes: 0GitHub stars: 3
- Pathwise Metastability Galves LocherbachPathwise approach to metastability for Galves-Löcherbach (GL) stochastic spiking neural network models. Reviews metastability theory from chemistry to probability theory, provides general definition encompassing GL model variants, surveys established metastability results with self-contained proofs, and identifies open problems. arXiv:2607.05652Votes: 0GitHub stars: 3
- Pauli Detecting Quantum CodesQuantum error detecting codes using Pauli group variance geometry — going beyond stabilizer codes via algebraic structure of Pauli operators. Covers variance-based code construction, Pauli weight distribution analysis, higher-than-stabilizer detection rates, and trade-offs between detection capability and code rate. Use when working on: quantum error detection (not correction), non-stabilizer codes, Pauli group geometry, variance codes, or codes with detection rates exceeding stabilizer bound...Votes: 0GitHub stars: 3
- Pauli Noise Symmetry LindbladianPauli noise symmetry analysis from Lindbladian dynamics methodology. Characterizes gate noise channels by exploiting physical symmetry constraints on Pauli fidelities, enabling efficient noise characterization despite SPAM gauge limitations.Votes: 0GitHub stars: 3
- Pce Qubo Dense Constraint DecodingPauli Correlation Encoding (PCE) methodology for solving densely constrained QUBO problems using qubit compression and problem-aware guided decoding. Combines quantum computing with bioinformatics for mRNA secondary structure prediction.Votes: 0GitHub stars: 3
- Penalty Free Qaoa Protein FoldingPenalty-free QAOA methodology for lattice protein folding using conflict graph independent set formulation. Use when: (1) quantum optimization for protein folding or molecular structure prediction, (2) QAOA without penalty terms, (3) conflict graph maximum independent set (MIS) mixer design, (4) quantum annealing approaches to biological optimization problems. Activation: penalty-free QAOA, protein folding, lattice protein, conflict graph, independent set mixer, QAOA bio-physics, quantum prot...Votes: 0GitHub stars: 3
- Penalty Free Quantum Annealing PortfolioPenalty-free quantum annealer pipeline for portfolio optimization. Removes cardinality penalty from QUBO to reduce chain-break fractions from 92% to 0.04%, enforces feasibility classically post-sampling. Based on arXiv:2605.17628.Votes: 0GitHub stars: 3
- Penalty Free Quantum Protein FoldingPenalty-free quantum optimization methodology for lattice protein folding using QAOA and quantum annealing. Avoids constraint penalty terms that cause energy landscape distortion. Maps protein conformations to binary optimization without penalty parameters. Use when: quantum protein folding, lattice protein models, QAOA protein structure, quantum annealing biology, penalty-free optimization, quantum bio-physics.Votes: 0GitHub stars: 3
- Performance Evaluation Results Of Evolutionary Clustering Algorithm Star For Clustering Heterogeneous Datasets**arXiv ID:** 2105.02810 **Authors:** Bryar A. Hassan, TarikA. Rashid, Seyedali Mirjalili **Published:** 2021-04-30T08:17:19Z **Abstract:** This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ) , expectation ma...Votes: 0GitHub stars: 3
- Persistent State Ai SecuritySecurity framework for analyzing distributed attacks in persistent-state AI control. Addresses attack surfaces created by AI coding agents that ship code iteratively across PRs, including gradual attack distribution, monitor evasion strategies, and stateful link-tracker defense patterns.Votes: 0GitHub stars: 3
- Personalized Recommendation Tool Learning Via AutoSkill generated from arXiv paper 2607.19739: Personalized Recommendation Tool Learning via Autonomous Language AgentsVotes: 0GitHub stars: 3
- Perspective Latents As An Architectural Condition For CausalPerspective Latents as an Architectural Condition for Causal Emergence in Active Inference AgentsVotes: 0GitHub stars: 3
- Perspective Purposeful Failure In Artificial Life And Artificial Intelligence**arXiv ID:** 2102.12076 **Authors:** Lana Sinapayen **Published:** 2021-02-24T05:43:44Z **Abstract:** Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards unders...Votes: 0GitHub stars: 3
- Pfaffian Quantum Hall EngineeringBottom-up engineering methodology for non-Abelian topological order using Floquet-engineered synthetic magnetic fields and Bayesian-optimized adiabatic state preparation.Votes: 0GitHub stars: 3
- Phase Based Spatial Ordinal Patterns Oscillatory DynamicsPhase-based spatial ordinal patterns analysis.Votes: 0GitHub stars: 3
- Phase Model M Current Hippocampal SynchronyPhase model analysis methodology for M-current effects on neural synchronization in hippocampal networks - theoretical framework for understanding neuromodulatory control of synchronyVotes: 0GitHub stars: 3
- Phase Reference Entanglement ControlPhase-reference control methodology for generating steady-state entanglement in open quantum systems using phase-sensitive reservoirs and local dissipation. Activation: steady-state entanglement, phase-sensitive reservoir, open quantum systems, Gaussian entanglement, local dissipation entanglement, covariance matrix quantum dynamics.Votes: 0GitHub stars: 3
- Photon Heralded Error CharacterizationAnalytic perturbative framework for characterizing small Markovian errors in photon-heralded quantum operations between non-interacting quantum emitters. Bridges physical imperfections to abstract Pauli noise models via closed-form perturbative solutions.Votes: 0GitHub stars: 3
- Photonic Qnn Algorithmic AdvantageAlgorithmic advantage of gate-based photonic quantum neural networks over classical ANNs. Use when comparing QNN vs ANN performance, evaluating quantum neural network expressivity via effective dimension, designing photonic quantum classifiers, or analyzing parameter efficiency of variational quantum circuits. Covers effective dimension analysis, photonic qubit implementation, and benchmarking QNN convergence. Activation: photonic QNN, quantum neural network advantage, effective dimension QNN...Votes: 0GitHub stars: 3