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EQT acquires energy and data center developer Copia Power from Carlyle
Investment firm EQT has acquired Copia Power, a developer of renewable energy and data centers, to expand its digital infrastructure portfolio.
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China’s drug industry pivots to AI-powered candidates to drive next wave of deals
The Chinese pharmaceutical industry is increasingly integrating artificial intelligence into drug discovery processes to drive future development and investment deals.
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Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?
A study explores whether inducing emotional context in large language models influences their decision-making processes in sequential tasks.
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Deep Learning-based Surrogate Modelling of the LOD Method for Multiscale Problems
Researchers proposed a deep learning-based surrogate model to address the computational challenges of multiscale problems in scientific and engineering applications.
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Text-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings
A new multimodal approach has been introduced to identify and categorize symbols within CAD floor plan drawings by leveraging both graphical and textual information.
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Traceback Translators Against Forgetting in Continual Fake Speech Detection
A new method using domain traceback translators is proposed to mitigate catastrophic forgetting in continual learning systems for fake speech detection.
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ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning
The authors introduce ARDepth, an autoregressive model for monocular depth estimation that processes geometric structure progressively across spatial scales.
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A Longitudinal Analysis of Public Discourse on AI Ethics in Education Using Twitter Data
This longitudinal analysis examines public discourse on Twitter regarding the ethical implications of integrating generative AI into educational environments.
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Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure
This study addresses the challenges of class imbalance in hierarchical cybersecurity vulnerability classification tasks.
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Evaluating Nonuniform Dependability Across Response Conditions: A Conditional Generalizability Framework Illustrated in Automated Essay Scoring
This study presents a conditional generalizability framework to better evaluate the reliability of automated essay scoring systems across different response conditions.
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HPC-Enabled Video-based Coastal Wave Parameter Estimation Using V-JEPA and Deep Spatiotemporal Learning
A deep learning framework utilizing V-JEPA is developed to estimate coastal wave parameters from video data using high-performance computing.
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Beyond Coordinate Gauge: An Audited Protocol for Detecting Donor-Specific Functional Fingerprints after Neural Collapse
This paper introduces a protocol to identify donor-specific functional fingerprints in neural networks that have undergone neural collapse.
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BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification
Researchers developed a hybrid physics-probabilistic model to predict lithium-ion battery degradation across various operating conditions.
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Burst Spiking Neural Networks
This study proposes Burst Spiking Neural Networks to enhance the accuracy and robustness of low-power neuromorphic computing architectures.
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OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes
OmniPMNet is a new neural network model designed to integrate station-based and gridded data for more accurate particulate matter forecasting.
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Higher Embedding Dimension Creates a Stronger World Model for a Simple Sorting Task
An investigation into transformer training reveals that increasing embedding dimensions leads to more robust and consistent internal world models for simple algorithmic tasks.
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Prime Fourier Embeddings: A Principled Basis for Modular Arithmetic
Researchers have developed Prime Fourier Embeddings to help neural networks better represent and process the algebraic structure of integers.
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Cross-Receiver Open-Set Radio Frequency Fingerprinting via Structure-First Adaptation
Researchers developed a structure-first adaptation method to improve radio frequency fingerprint identification across heterogeneous receivers in IoT networks.
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Action-Aware Generative Sequence Modeling for Short Video Recommendation
A new generative sequence modeling approach improves short video recommendations by analyzing specific actions within videos rather than treating them as single entities.
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恒指午间休盘涨1.46%，恒生科技指数涨1.39%
The Hang Seng Index and Hang Seng Tech Index both rose during midday trading, with AI-related concept stocks like MiniMax and Zhipu seeing notable gains.
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OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
A new benchmark framework, OOD-RL-Bench, is introduced to evaluate out-of-distribution detection capabilities in reinforcement learning agents.
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The TIME Machine: On The Power of Motion for Efficient Perception
The TIME Machine framework aims to improve video representation learning efficiency by prioritizing motion dynamics over language-based supervision.
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NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing
The authors introduce a neuro-symbolic benchmark designed to evaluate the route planning capabilities of multimodal large language models in remote sensing contexts.
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Partial Identification with Multiple Nonlinear Measurements of a Latent Regressor
This study examines the statistical challenges of linear regression when using multiple noisy, nonlinear measurements to estimate latent variables like AI occupational exposure.