Semiconductors are essential in powering various electronic devices and driving development across telecommunications, automotive, healthcare, renewable energy, and IoT industries. In semiconductor manufacturing and design, the two main phases, FEOL and BEOL, present unique challenges. LLMs are trained on vast amounts of text data using self-supervised learning techniques that can capture rich domain knowledge.LLMs can… →

Predicting RNA 3D structures is critical for understanding its biological functions, advancing RNA-targeted drug discovery, and designing synthetic biology applications. However, RNA’s structural flexibility and the limited availability of experimentally resolved data pose challenges. Despite RNA’s importance in gene regulation, RNA-only structures represent less than 1% of the Data Bank, and traditional methods like X-ray… →

Reinforcement Learning (RL) represents a robust computational approach to decision-making formulated through the Markov Decision Processes (MDPs) framework. RL has gained prominence for its ability to address complex tasks in games, robotics, and computational language processing. RL systems are designed to learn through iterative feedback mechanisms by optimizing policies to achieve cumulative rewards. However, despite… →

The capability of multimodal large language models (MLLMs) to enable complex long-chain reasoning that incorporates text and vision raises an even greater barrier in the realm of artificial intelligence. While text-centric reasoning tasks are being gradually advanced, multimodal tasks add additional challenges rooted in the lack of rich, comprehensive reasoning datasets and efficient training strategies.… →

Filtering, scanning, and updating data are important operations in databases, and many data structures are used to perform these operations. In real-world situations, it’s important to manage multidimensional data, and the Kd-tree and its variations are popular structures used for this purpose. Various research studies have focused on improving data structures by learning the distribution… →

Phase-field models serve as a crucial mesoscale simulation method, bridging atomic-scale models and macroscopic phenomena by describing microstructural evolution and phase transformations. These models extract local free energy density information from lower-scale simulations and use it to predict larger-scale material behavior. Phase-field methods are widely applied in processes such as grain growth, crack propagation, dendrite… →

Transformer architectures have revolutionized Natural Language Processing (NLP), enabling significant language understanding and generation progress. Large Language Models (LLMs), which rely on these architectures, have achieved remarkable performance across various applications such as conversational systems, content creation, and summarization. However, the efficiency of LLMs in real-world deployment remains a challenge due to their substantial resource… →

Speech recognition technology has made significant progress, with advancements in AI improving accessibility and accuracy. However, it still faces challenges, particularly in understanding spoken entities like names, places, and specific terminology. The issue is not only about converting speech to text accurately but also about extracting meaningful context in real-time. Current systems often require separate… →