Determining the number of change-points is a first-step and fundamental task in change-point detection problems, as it lays the groundwork for subsequent change-point position estimation. While the ex...
Human genetics offers a promising route to therapeutic discovery, yet practical frameworks translating genotype-derived signal into ranked target and drug hypotheses remain limited, particularly when ...
Understanding the interplay between high-dimensional data from different views is essential in biomedical research, particularly in fields such as genomics, neuroimaging and biobank-scale studies invo...
To address the critical challenge of excessive junction temperature caused by ultra-high heat flux densities (>100 W/cm2) in deep-sea LED Fish-Attracting Lamp (FAL) arrays, this study proposes a hybri...
Artificial intelligence depends on a stack of inputs, models on compute, compute on chips, and chips on electricity and refined minerals. This paper measures the concentration of each layer on one sca...
Five previously undescribed polyhydroxylated spirostanols, fusaspironols A-E (1-5), along with eight known compounds (6-13), were isolated from the deep-sea-derived fungus Fusarium inflexum ZEN8. Thei...
Seafloor litter is an applied indicator for the assessment of marine pollution. Harmonized seafloor litter categorization is essential for quality assured assessments. This study aimed to assess the q...
This paper evaluates five sensor network architectures for coastal marine pollution monitoring using a fuzzy multi-criteria decision-making framework. Thirty domain experts assessed the alternatives a...
The pursuit of reducing the memory footprint of the self-attention mechanism in multi-headed self attention (MHA) spawned a rich portfolio of methods, e.g., group-query attention (GQA) and multi-head ...
Deep-sea imagery is a non-destructive tool for monitoring seafloor litter, but manual inspection limits its scalability. DeepLitterAI, a YOLOv11x-based detector combined with BoT-SORT tracking, was de...
We introduce PACE, a backpropagation-free continual test-time adaptation system that directly optimizes the affine parameters of normalization layers. Existing derivative-free approaches struggle to b...
This paper introduces a new hybrid framework that combines Reinforcement Learning (RL) and Large Language Models (LLMs) to improve robotic manipulation tasks. By utilizing RL for accurate low-level co...
This study investigates the effectiveness of synthetic data for sim-to-real transfer in object detection under constrained data conditions and embedded deployment requirements. Synthetic datasets were...
AI-assisted coding has rapidly reshaped software practice and research workflows, yet today's models still struggle to produce correct code for complex 3D geometric vision. If models could reliably wr...
Psychological scale development has traditionally required extensive expert involvement, iterative revision, and large-scale pilot testing before psychometric evaluation can begin. The `AIGENIE` R pac...
Macroecological patterns of life history traits, such as larval size, are fundamental to understanding the biogeography and diversity of marine invertebrates. The long-established Thorson's Rule hypot...
This study quantifies survivorship bias in India's NIFTY Smallcap 250 index using a dataset of 1,437 stocks over nine years (2016-2025). By reconstructing historical index composition through market c...
Many modern products are highly reliable, often exhibiting long lifetimes. As a result, conducting experiments under normal operating conditions can be prohibitively time-consuming to collect sufficie...
Video diffusion models exhibit emergent reasoning capabilities like solving mazes and puzzles, yet little is understood about how they reason during generation. We take a first step towards understand...
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