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Claude Skills by hiyenwong
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- Arxiv 2609 02344v1 Subcellularly Resolved Single Cell Embedding Learn**arXiv ID:** 2609.02344v1 **Authors:** Zhen Zhou, Jiachen Li, Yuan Liu, Xiaoyong Pan, Hong-Bin Shen **URL:** http://arxiv.org/abs/2609.02344v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 03377v1 Simpledesign A Joint Model For Protein Sequence An**arXiv ID:** 2609.03377v1 **Authors:** Jiarui Lu, Yuyang Wang, Yizhe Zhang, Jiatao Gu, Navdeep Jaitly, Joshua M. Susskind, Miguel Ángel Bautista **URL:** http://arxiv.org/abs/2609.03377v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08566v1 Bio Memart Biometric Aware Kv Cache Memory For Mul**arXiv ID:** 2609.08566v1 **Authors:** Yanhong Qian, Xuanying He, Qingguo Meng, Shihao Ding, Xingbo Dong, Zhe Jin **URL:** http://arxiv.org/abs/2609.08566v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08582v1 Leveraging Cardiac Imaging To Improve Ecg Based De**arXiv ID:** 2609.08582v1 **Authors:** Laura Alvarez-Florez, Daniel Uyterlinde, Samuel Ruipérez-Campillo, Lukas P. A. Arts, Folkert W. Asselbergs, Fleur V. Y. Tjong **URL:** http://arxiv.org/abs/2609.08582v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 08593v1 Fedgensc Federated Generative Semantic Communicati**arXiv ID:** 2609.08593v1 **Authors:** Rita Abou Fares, Razan Al Kakoun, Maher Nouiehed, Hadi Sarieddeen **URL:** http://arxiv.org/abs/2609.08593v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09070v1 Performance Of Clinical Ai System And Physicians A**arXiv ID:** 2609.09070v1 **Authors:** Andy Nkansah, Hanna Plotnitskaya, Stanislau Salavei, Anna Kozlova, Piotr Gibas, Julian Milek, Viktar Harbachou, Aleksey Ropan, Pavel Satalkin **URL:** http://arxiv.org/abs/2609.09070v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09140v1 Noah Learning The Full Patient Journey A Longitudi**arXiv ID:** 2609.09140v1 **Authors:** Tobias Susetzky, Raphael Rehms, Dmitrii Seletkov, Özgün Turgut, Michelle Espranita Liman, Lisa Steinhelfer, Rickmer Braren, Daniel Rueckert **URL:** http://arxiv.org/abs/2609.09140v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 09735v1 Can Artificial Intelligence Support Healthcare And**arXiv ID:** 2609.09735v1 **Authors:** Hamed Jelodar, Amir Firouzi, Yen-Wu Lo, Maryam Tanha, Sajjad Dadkhah **URL:** http://arxiv.org/abs/2609.09735v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2609 10049v1 Meddeid Enables Locally Governed Clinical Text De**arXiv ID:** 2609.10049v1 **Authors:** Stig Hellemans, Tom Stroobants, Elyne Scheurwegs, Pieter Meysman, Philippe G. Jorens, Kris Laukens **URL:** http://arxiv.org/abs/2609.10049v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Automated Pulmonary Nodule Detection Using 3d Deep Convolutional Neural Networks**arXiv ID:** 1903.09876 **Authors:** Hao Tang, Daniel R. Kim, Xiaohui Xie **Published:** 2019-03-23T20:20:15Z **Abstract:** Early detection of pulmonary nodules in computed tomography (CT) images is essential for successful outcomes among lung cancer patients. Much attention has been given to deep convolutional neural network (DCNN)-based approaches to this task, but models have relied at least partly on 2D or 2.5D components for inherently 3D data. In this paper, we introduce a novel DCNN a...Votes: 0GitHub stars: 3
- Clinical Reasoning Llm Hepatocellular Carcinoma Risk StratificationHCC-STAR: clinically aligned LLM for hepatocellular carcinoma staging, treatment, and prognosis. Reads EMR narratives, outputs risk stratification, guideline-consistent treatments with rationales, and survival estimates. Outperforms GPT-5 and Gemini-2.5 Pro. Activation: clinical-reasoning LLM, hepatocellular carcinoma, risk stratification, treatment guidance, EMR.Votes: 0GitHub stars: 3
- Coalitions Of Aibased Methods Predict 15year Risks Of Breast Cancer Metastasis Using Realworld Clinical Data With Auc Up To 09**arXiv ID:** 2408.16256 **Authors:** Xia Jiang, Yijun Zhou, Alan Wells, Adam Brufsky **Published:** 2024-08-29T04:35:36Z **Abstract:** Breast cancer is one of the two cancers responsible for the most deaths in women, with about 42,000 deaths each year in the US. That there are over 300,000 breast cancers newly diagnosed each year suggests that only a fraction of the cancers result in mortality. Thus, most of the women undergo seemingly curative treatment for localized cancers, but a signific...Votes: 0GitHub stars: 3
- Cold Atom Medical ImagingMedical imaging classification using cold-atom (neutral-atom) reservoir computing with auto-encoders and surrogate-driven training. Use when: medical image classification with reservoir computing, quantum-inspired medical imaging, neutral-atom computing for healthcare, polyp detection with quantum reservoir, surrogate-driven training for medical AI, guided auto-encoder for medical images. Trigger: cold-atom medical imaging, reservoir computing healthcare, quantum reservoir medical classificat...Votes: 0GitHub stars: 3
- Cold Atom Reservoir Computing MedicalMedical imaging classification using cold-atom (neutral-atom) reservoir computing. Combines quantum reservoir computing with auto-encoders and surrogate-driven training for medical image analysis. Use when building quantum-enhanced medical imaging pipelines with reservoir computing.Votes: 0GitHub stars: 3
- Comparative Study Of Clustering Models For Multivariate Time Series From Connected Medical Devices**arXiv ID:** 2312.17286 **Authors:** Violaine Courrier, Christophe Biernacki, Cristian Preda, Benjamin Vittrant **Published:** 2023-12-28T07:37:30Z **Abstract:** In healthcare, patient data is often collected as multivariate time series, providing a comprehensive view of a patient's health status over time. While this data can be sparse, connected devices may enhance its frequency. The goal is to create patient profiles from these time series. In the absence of labels, a predictive model can...Votes: 0GitHub stars: 3
- Detecting And Correcting For Label Shift With Black Box Predictors**arXiv ID:** 1802.03916 **Authors:** Zachary C. Lipton, Yu-Xiang Wang, Alex Smola **Published:** 2018-02-12T07:16:03Z **Abstract:** Faced with distribution shift between training and test set, we wish to detect and quantify the shift, and to correct our classifiers without test set labels. Motivated by medical diagnosis, where diseases (targets) cause symptoms (observations), we focus on label shift, where the label marginal $p(y)$ changes but the conditional $p(x| y)$ does not. We propose B...Votes: 0GitHub stars: 3
- Druggen 2 Disease Aware Language Model Drug DiscoveryDrugGen-2: generative model that designs small molecules conditioned on disease ontology and target protein sequences. Fine-tuned GPT-2 with SFT + GRPO. Outperforms baselines on diabetic nephropathy targets with improved binding affinities. Use when working with drug-discovery, disease-aware, language-model.Votes: 0GitHub stars: 3
- Ecglight Compute Light Framework For Paper Ecg Digitization And MyocardialElectrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG printouts for their analysis due to limited con. Based on arXiv:2607.07683.Votes: 0GitHub stars: 3
- Electroencephalogram Signal Processing With Independent Component Analysis And Cognitive Stress Classification Using Convolutional Neural Networks**arXiv ID:** 2108.09817 **Authors:** Venkatakrishnan Sutharsan, Alagappan Swaminathan, Saisrinivasan Ramachandran, Madan Kumar Lakshmanan, Balaji Mahadevan **Published:** 2021-08-22T18:38:12Z **Abstract:** Electroencephalogram (EEG) is the recording which is the result due to the activity of bio-electrical signals that is acquired from electrodes placed on the scalp. In Electroencephalogram signal(EEG) recordings, the signals obtained are contaminated predominantly by the Electrooculogram(EO...Votes: 0GitHub stars: 3
- Enhancing Personalized Bladder Cancer Treatment ThDerived from arXiv:2607.16916 - Enhancing Personalized Bladder Cancer Treatment Through Reinforcement Learning: A Recurrent Patient State Transition Decision Support FrameworkVotes: 0GitHub stars: 3
- Federated Ecg WearableFederated Learning methodology for privacy-preserving ECG monitoring on ultra-resource-constrained wearable devices. Family-grouped hierarchical FL for sub-5KB cardiovascular models. Activation: federated learning ECG, wearable cardiac monitoring, privacy-preserving ML, sub-5KB model, arrhythmia detection.Votes: 0GitHub stars: 3
- Genetic Architect Discovering Genomic Structure With Learned Neural Architectures**arXiv ID:** 1605.07156 **Authors:** Laura Deming, Sasha Targ, Nate Sauder, Diogo Almeida, Chun Jimmie Ye **Published:** 2016-05-23T19:43:08Z **Abstract:** Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should be learned not presc...Votes: 0GitHub stars: 3
- Graphpine Drug ResponseGraph Importance Propagation (GraphPINE) methodology for interpretable drug response prediction. Propagates importance scores through biological knowledge graphs to constrain explanations to biologically relevant structures. Activation: drug response prediction, interpretable ML, graph importance propagation, biomedical explainability, pharmacogenomics.Votes: 0GitHub stars: 3
- Haca3 Mri HarmonizationHACA3+ MRI harmonization algorithm validated across 100+ scanners with traveling subjects. Incorporates improved artifact encoder, comprehensive multi-site validation, and real-world protocol robustness testing. Most comprehensive multi-site MRI harmonization validation to date. arXiv:2604.19474.Votes: 0GitHub stars: 3
- Medical Ai DiagnosisPatterns for building AI-based medical diagnosis systems with clinical explainability. Covers foundation models for medical imaging, clinical reasoning trace generation, multi-modal patient data integration, and explainable AI for healthcare. Use when building medical AI diagnosis tools, clinical decision support systems, explainable medical ML models, or medical foundation models. Trigger: medical AI, clinical diagnosis AI, explainable healthcare, medical foundation model, DeepMedix, clinica...Votes: 0GitHub stars: 3
- Mediq Gan Medical Image GenerationQuantum-inspired GAN methodology for high-resolution medical image generation with prototype-guided skip connections and dual-stream generator. Addresses data scarcity, class imbalance, and privacy constraints in medical imaging through variational quantum circuits that preserve full-rank mappings and avoid rank collapse. Use when building quantum-inspired generative models for medical image augmentation, designing GAN architectures that balance expressivity with trainability, or analyzing la...Votes: 0GitHub stars: 3
- Medpmc A Systematic Framework For Scaling High Fidelity Medical Multimodal DataMedicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundation models is constrained by limited access to l. Based on arXiv:2607.07673.Votes: 0GitHub stars: 3
- Mobayes Clinical DecisionModular Bayesian Framework (MoBayes) for separating reasoning from language in clinical decision support. Addresses the architectural limitation of LLMs conflating next-token prediction with probabilistic medical reasoning. Activation: clinical reasoning, medical LLM, Bayesian clinical, decision support, MoBayes, 临床决策.Votes: 0GitHub stars: 3
- Multi Vqc HealthcareMulti-VQC approach for healthcare classification using variational quantum circuits to address class imbalance in medical datasets.Votes: 0GitHub stars: 3
- Neurrate Single Cell Semantic NarrationNEURRATOR methodology for generating natural language descriptions of visual scenes from single-neuron spike trains. Uses CLIP embeddings and multimodal LLM for zero-shot decoding without language-side training.Votes: 0GitHub stars: 3
- Neurrator Single Cell Semantic NarrationNEURRATOR - Semantic narration of vision at single-cell resolution. Maps spiking activity to natural-language descriptions via CLIP-LLaVA embedding space, enabling functional probing of cell types and brain regions.Votes: 0GitHub stars: 3
- Openmrf Mri Fingerprinting V2磁共振指纹(MRF)的模块化、厂商无关的开源框架。基于Pulseq标准的定量MRI研究平台，支持多厂商、多场强。Votes: 0GitHub stars: 3
- Openmrf Mri Fingerprinting磁共振指纹(MRF)的模块化、厂商无关的开源框架。基于Pulseq标准的定量MRI研究平台。Votes: 0GitHub stars: 3
- Pa Tcnet Brain Tumor SegMulti-stage brain tumor segmentation using Pathology-Aware Temporal Calibration (PA-TCNet) with physiological consistency constraints across temporal sequences for biologically plausible predictions.Votes: 0GitHub stars: 3
- Pa Tcnet Pathology Aware Stroke BciPA-TCNet: Pathology-Aware Temporal Calibration for cross-subject motor imagery EEG decoding in stroke patients. Clinical BCI with physiological guidance and pathology-aware adaptation. Keywords: stroke, BCI, motor imagery, clinical, cross-subject, pathology-aware.Votes: 0GitHub stars: 3
- Pinns Medical ModelingPhysics-Informed Neural Networks (PINNs) for medical modeling and biomedical simulation. Solve differential equations governing physiological processes (cardiovascular, neural, pharmacokinetic) using neural networks that embed physical laws as constraints. Use when: (1) Modeling patient-specific physiology, (2) Solving inverse problems in biomechanics, (3) Drug pharmacokinetic modeling, (4) Cardiac or neural dynamics simulation with limited data.Votes: 0GitHub stars: 3
- Reliable Mechanistic Operator Recovery With Biologically Informed NeuralMany biological processes are governed by complex dynamical mechanisms that remain incompletely understood despite increasing volumes of experimental data. Biologically-informed neural networks (BINNs. Based on arXiv:2607.07425.Votes: 0GitHub stars: 3
- Robots That Can Adapt Like Animals**arXiv ID:** 1407.3501 **Authors:** Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret **Published:** 2014-07-13T19:06:08Z **Abstract:** As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory beh...Votes: 0GitHub stars: 3
- Simple Recurrent Neural Networks Is All We Need For Clinical Events Predictions Using Ehr Data**arXiv ID:** 2110.00998 **Authors:** Laila Rasmy, Jie Zhu, Zhiheng Li, Xin Hao, Hong Thoai Tran, Yujia Zhou, Firat Tiryaki, Yang Xiang, Hua Xu, Degui Zhi **Published:** 2021-10-03T13:07:23Z **Abstract:** Recently, there is great interest to investigate the application of deep learning models for the prediction of clinical events using electronic health records (EHR) data. In EHR data, a patient's history is often represented as a sequence of visits, and each visit contains multiple events. A...Votes: 0GitHub stars: 3
- Stochastic Deep Learning In Memristive Networks**arXiv ID:** 1711.03640 **Authors:** Anakha V Babu, Bipin Rajendran **Published:** 2017-11-09T23:09:36Z **Abstract:** We study the performance of stochastically trained deep neural networks (DNNs) whose synaptic weights are implemented using emerging memristive devices that exhibit limited dynamic range, resolution, and variability in their programming characteristics. We show that a key device parameter to optimize the learning efficiency of DNNs is the variability in its programming charac...Votes: 0GitHub stars: 3
- Synaptic Plasticity Models And Bioinspired Unsupervised Deep Learning A Survey**arXiv ID:** 2307.16236 **Authors:** Gabriele Lagani, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato **Published:** 2023-07-30T13:58:46Z **Abstract:** Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter several challenges related to robustness to adversarial inputs, ecological impact, and the necessity of huge amounts of training data. In response, researchers are...Votes: 0GitHub stars: 3
- Tensor Network Medical ImagingQuantum-inspired tensor network feature engineering for medical image classification. Use PARAFAC/CP tensor decompositions to extract discriminative features from medical imaging data (MRI, CT, X-ray) for multi-class neurological disorder prediction and clinical diagnosis. Applicable to any high-dimensional medical imaging classification task where tensor decompositions can capture latent structure.Votes: 0GitHub stars: 3
- Triple Phase Multimodal Medical DiagnosisTriple-phase multimodal framework for medical image classification — combines cross-modality contrastive learning, modality-specific fine-tuning, and feature-level multimodal ensemble learning for patient-level prediction. Validated on microbial keratitis subtype diagnosis with 85.84% accuracy across 1645 patients.Votes: 0GitHub stars: 3
- Variants Of Rmsprop And Adagrad With Logarithmic Regret Bounds**arXiv ID:** 1706.05507 **Authors:** Mahesh Chandra Mukkamala, Matthias Hein **Published:** 2017-06-17T09:48:55Z **Abstract:** Adaptive gradient methods have become recently very popular, in particular as they have been shown to be useful in the training of deep neural networks. In this paper we have analyzed RMSProp, originally proposed for the training of deep neural networks, in the context of online convex optimization and show $\sqrt{T}$-type regret bounds. Moreover, we propose two vari...Votes: 0GitHub stars: 3
- Zeta Law Biomedical ScalingZeta Law framework for predicting data scaling in biomedical discovery. Uses spectral covariance structure and Riemann zeta function to model cross-modal discoverability, predicting when models transition from underparameterized to overparameterized regimes. Activation: zeta law, biomedical data scaling, cross-modal discoverability, Riemann zeta function, scaling laws, data efficiency.Votes: 0GitHub stars: 3
- Zeta Law Discoverability BiomedicalResearch skill for the paper "How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data" (arXiv:2604.17581) by Paul M. Thompson. Covers the Zeta Law framework derived from Riemann zeta function properties that characterizes how discovery probability scales with sample size in biomedical data. Applicable to sample size estimation, power analysis, discoverability modeling, brain connectomics, data collection planning, zeta function applications, and resource allocation in neur...Votes: 0GitHub stars: 3
- Helmholtz Sde Simulation Free Latent SdesHelmholtz-SDE仿真自由潜在随机微分方程变分推断方法。通过优化与规定边缘分布兼容的路径定律，闭合近似差距，在高后验不确定性下更忠实恢复动力学。Votes: 0GitHub stars: 3
- Hermes Brain ConnectivityHERMES脑连接分析工具箱。整合功能和有效连接分析方法，包括互相关、相干性、Granger因果、相位同步、互信息等。适用于EEG/MEG脑网络分析、神经生理信号处理。触发词：HERMES、脑连接、功能连接、有效连接、Granger因果、相位同步、brain connectivity、effective connectivity。Votes: 0GitHub stars: 3
- Heteroclinic Cognitive State ModelingModeling sequential cognitive states via population-level cortical dynamics using Universal Approximation Theorem to approximate heteroclinic cycles with neural field systems. Activation: heteroclinic cognitive states, sequential brain dynamics, Lotka-Volterra neural model, Amari neural field approximation, meditation state transitions.Votes: 0GitHub stars: 3
- Heteroclinic Neural Field CognitionHeteroclinic dynamics with discrete neural-field equations for modeling sequential cognitive states. Uses Universal Approximation Theorem to approximate target heteroclinic dynamics by Amari-type neural-field systems. Activates: heteroclinic cycle, sequential cognitive states, neural field dynamics, Lotka-Volterra neural, focused attention meditation modeling, cyclic brain activity.Votes: 0GitHub stars: 3