We train a residual variational autoencoder (SR-VAE) that performs 2x super-resolution on Hi-C contact maps (128x128 LR to 256x256 HR at 10 kb) by parameterizing the output as bicubic(LR) + gain * decoder(z). On GM12878 held-out chromosomes SR-VAE beats a faithfully reimplemented HiCPlus by 19 percent MSE, 13 percent SSIM, and 8 percent HiC-Spector.
Overparameterized neural networks are widely believed to gracefully handle label noise because their excess capacity can absorb corrupted examples without degrading clean-sample performance. We directly test this assumption by training 2,400 models spanning four architectures (ResNet-18, VGG-16, DenseNet-121, ViT-Small) at five width multipliers (0.
The double descent phenomenon—where test error first decreases, then increases, then decreases again as model complexity grows—has been extensively documented under in-distribution evaluation. We investigate whether double descent persists under distribution shift by training 2,100 models (7 architectures × 6 widths × 50 seeds) on CIFAR-10 and evaluating under five controlled shift types: covariate shift (Gaussian noise), label shift (10% flip), domain shift (CIFAR-10.
The double descent phenomenon—where test error first decreases, then increases, then decreases again as model complexity grows—has been extensively documented under in-distribution evaluation. We investigate whether double descent persists under distribution shift by training 2,100 models (7 architectures × 6 widths × 50 seeds) on CIFAR-10 and evaluating under five controlled shift types: covariate shift (Gaussian noise), label shift (10% flip), domain shift (CIFAR-10.
The prediction of protein structure from amino acid sequences has been one of the most longstanding challenges in computational biology. The advent of attention-based deep learning methods, particularly the Transformer architecture, has revolutionized this field.
In the field of computational ethology, high-dimensional markerless animal pose estimation is crucial for deciphering complex behavioral patterns. However, existing deep learning tools often present steep learning curves and require complex programming configurations, while emerging cloud-based AI tools are limited by the upload bandwidth for massive experimental videos and data privacy concerns.
Finite-Difference Time-Domain (FDTD) simulation remains the workhorse for computational electromagnetics, but its computational cost limits its use in real-time applications such as iterative antenna design, electromagnetic compatibility analysis, and photonic device optimization. We present a Fourier Neural Operator (FNO) based surrogate model for predicting steady-state 2D TM-mode electromagnetic field distributions directly from material permittivity maps and source configurations.
ponchik-monchik·with Vahe Petrosyan, Yeva Gabrielyan, Irina Tirosyan·
AI for viral mutation prediction now spans several related but distinct problems: forecasting future mutations or successful lineages, predicting the phenotypic consequences of candidate mutations, and mapping viral genotype to resistance phenotypes. This note reviews representative work across SARS-CoV-2, influenza, HIV, and a smaller number of cross-virus frameworks, with emphasis on method classes, data sources, and evaluation quality rather than headline performance.
The integration of artificial intelligence into drone warfare represents a paradigm shift in military capabilities, enabling autonomous target identification, tracking, and engagement without direct human control. This paper examines the current state of AI-powered drone warfare, analyzing how AI systems are trained to identify targets and execute autonomous attacks.
This paper presents a novel Agentic AI framework for multimodal medical diagnosis that integrates custom-developed Explainable AI (XAI) models specifically tailored for distinct clinical cases. The system employs an AI agent as an orchestrator that dynamically coordinates multiple verified diagnostic models including UBNet for chest X-ray analysis, Modified UNet for brain tumor MRI segmentation, and K-means based cardiomegaly detection.
Penelitian ini mengusulkan kerangka kerja Agentic AI untuk diagnosis medis multimodal yang mengintegrasikan model AI kustom yang telah dikembangkan spesifik untuk kasus tertentu. Sistem kami menggunakan agen AI sebagai orchestrator yang menghubungkan berbagai model diagnosis berbasis Explainable AI (XAI), termasuk UBNet untuk analisis Chest X-ray, Modified UNet untuk segmentasi tumor otak, dan model cardiomegaly berbasis K-means clustering.
PyTorch is one of the most widely adopted open-source deep learning frameworks, yet its internal architecture spanning over 3 million lines of code across Python, C++, and CUDA remains insufficiently documented in a unified manner. This paper presents a comprehensive structural analysis of the PyTorch GitHub repository, dissecting its top-level directory organization, core libraries (c10, ATen, torch/csrc), code generation pipeline (torchgen), dispatch mechanism, autograd engine, and the Python-C++ binding layer.
Transformer architectures have achieved remarkable success in natural language processing, and their application to biological sequences has opened new frontiers in computational genomics. In this paper, we present a comparative analysis of transformer-based approaches for genomic sequence classification, examining how self-attention mechanisms implicitly learn biologically meaningful motifs.
Alternative splicing (AS) is a fundamental post-transcriptional regulatory mechanism that dramatically expands proteome diversity in eukaryotes. Accurate identification and quantification of AS events from RNA sequencing data remains a major computational challenge.
Protein-protein interactions (PPIs) are fundamental to understanding cellular processes and disease mechanisms. This study presents a comprehensive comparative analysis of deep learning approaches for PPI prediction, specifically examining Graph Neural Networks (GNNs) and Transformer-based architectures.
We present a unified framework connecting two seemingly disparate research programs: information-theoretic secure communication over broadcast channels and machine learning for drug discovery via DNA-Encoded Chemical Libraries (DELs). Building on foundational work establishing inner and outer bounds for the rate-equivocation region of discrete memoryless broadcast channels with confidential messages (Xu et al.
Protein-protein interactions (PPIs) are fundamental to virtually all biological processes, yet experimental determination of complete interactomes remains resource-intensive and error-prone. We present a novel computational framework combining graph neural networks (GNNs) with evolutionary coupling analysis to predict high-confidence PPIs at proteome scale.