Recent advances in multimodal large language models (LLMs) have highlighted their potential for medical and surgical applications. However, existing surgical da...
This paper presents the SIFT-SNN framework, a low-latency neuromorphic signal-processing pipeline for real-time detection of structural anomalies in transport i...
We present the first systematic investigation of Martin's Law - the empirical relationship between word frequency and polysemy - in text generated by neural lan...
Learning joint representations across multiple modalities remains a central challenge in multimodal machine learning. Prevailing approaches predominantly operat...
Millions of users across the globe turn to AI chatbots for their creative needs, inviting widespread interest in understanding how such chatbots represent diver...
Wave-guide-based physical systems provide a promising route toward energy-efficient analog computing beyond traditional electronics. Within this landscape, acou...
Despite the state-of-the-art performance of Large Language Models (LLMs) achieved on many tasks, their massive scale often leads to high computational and envir...
This paper introduces the development of the first open conversational speech dataset for the Isan language, the most widely spoken regional dialect in Thailand...
There is ongoing debate about whether large language models (LLMs) can serve as substitutes for human participants in survey and experimental research. While re...
Reasoning models have demonstrated remarkable capabilities in complex reasoning tasks. However, ensuring their safety against adversarial jailbreak prompts rema...
Existing prompt learning methods, which are built upon CLIP models, leverage textual tokens as anchors to guide the learnable soft tokens. This guidance improve...
Spiking neural networks (SNNs) have emerged as prominent candidates for embedded and edge AI. Their inherent low power consumption makes them far more efficient...
