Proteins, the essential molecular machinery of life, play a central role in numerous biological processes. Decoding their intricate sequence, structure, and function (SSF) is a fundamental pursuit in biochemistry, molecular biology, and drug development. Understanding the interplay between these three aspects is crucial for uncovering the principles of life at a molecular level. Computational tools… →

Large language models (LLMs) have brought significant progress to AI applications, including code generation. However, evaluating their true capabilities is not straightforward. Existing benchmarks, such as LiveCodeBench and USACO, have limitations. They lack robust private test cases, do not support specialized judgment systems, and often work with inconsistent execution environments. These gaps make it challenging… →

Inspired by the brain, neural networks are essential for recognizing images and processing language. These networks rely on activation functions, which enable them to learn complex patterns. However, many activation functions face challenges. Some struggle with vanishing gradients, which slows learning in deep networks, while others suffer from “dead neurons,” where certain parts of the… →

The self-attention mechanism is a building block of transformer architectures that faces huge challenges both in the theoretical foundations and practical implementation. Despite such successes in natural language processing, computer vision, and other areas, their development often relies on heuristic approaches, limiting interpretability and scalability. Self-attention mechanisms are also vulnerable to data corruption and adversarial… →