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Automated depression estimation is highly vulnerable to signal corruption and ambient noise in real-world deployment. Prevailing deterministic methods produce uncalibrated point estimates, exposing safety-critical clinical systems to the severe risk of overconfident misdiagnoses. To establish a highly resilient and trustworthy assessment paradigm, we propose EviDep, an evidential learning framework that jointly quantifies depression severity alongside aleatoric and epistemic uncertainties via a Normal-Inverse-Gamma distribution. A fundamental vulnerability in multimodal evidential fusion is the uncontrolled accumulation of cross-modal redundancies. This […]

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Extracting Alpha in extreme low signal-to-noise ratio (SNR) environments, such as the Chinese A share market, remains a notoriously unsolved challenge for deep learning. Traditional heavily parameterized models, including Transformers, inevitably fall into the ”dimensionality disaster,” memorizing market noise rather than fundamental mechanics. To break this overfitting curse, we propose a novel, ultra-lightweight architecture inspired by thermody namics and Neural Turing Machines (NTMs): the Physics-Informed Ghost Operator. By mapping the cross-sectional stock market into a high-dimensional physical manifold, […]

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Low-light video enhancement aims to restore clear, color-faithful, and temporally consistent visual content from video sequences captured under extremely low signal-to-noise ratios and high dynamic range constraints. Existing multi-frame enhancement methods typically adopt uniform spatio-temporal sampling and feature extraction strategies for all frames, making it challenging to simultaneously achieve long-range temporal denoising and accurate fast-motion modeling. To address this trade-off, we propose a low-light video enhancement framework based on a Fast–Slow dual-branch architecture. The video signal is decomposed […]

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Parallel reasoning enhances Large Reasoning Models (LRMs) but incurs prohibitive costs due to futile paths caused by early errors. To mitigate this, path pruning at the prefix level is essential, yet existing research remains fragmented without a standardized framework. In this work, we propose the first systematic taxonomy of path pruning, categorizing methods by their signal source (internal vs. external) and learnability (learnable vs. non-learnable). This classification reveals the unexplored potential of learnable internal methods, motivating our proposal […]

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At Amazon’s 2025 re:Invent conference, Amazon Web Services (AWS) announced the Nitro Isolation Engine (NIE), a software module tasked with providing resources to AWS clients while ensuring the security of customer data. AWS also announced the formal verification of the isolation engine’s correctness and security guarantees, using a proof assistant called Isabelle/HOL. As the first formally verified cloud hypervisor, NIE sets a new standard for cloud security. A proof assistant is an automated tool that can help human […]

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The gap between AI insiders and everyone else is widening, and the spending, suspicion, and even new vocabulary are starting to show it. While OpenAI is busy buying up everything from finance apps to talk shows, a certain shoe company just rebranded as an AI infrastructure play, and Anthropic unveiled a model it says is too powerful to release publicly …but apparently not too […]

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Hi! I’m currently working on a robot that recognizes trash and sends it to a server. It’s a basic robot with four wheels, motors, and several sensors (ultrasonic sensors in four directions, a gyroscope, accelerometers, etc.). It also has a camera and a Raspberry Pi on top. To recognize trash, I use YOLO, and when it detects trash, it sends a picture to the server. Right now, I’m using a simple algorithm to explore the area with the […]

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Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent advances in variational autoencoders to demonstrate that such models can learn physically meaningful and interpretable representations from a broad class of unlabeled quantum datasets. From raw measurement data alone, the learned representation reveals rich information about the underlying structure of quantum phase spaces. We further augment the learning pipeline with symbolic methods, enabling the discovery of compact analytical […]

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