arXiv:2602.04994v1 Announce Type: new Abstract: With the deep integration of facial recognition into online banking, identity verification, and other networked services, achieving effective decoupling of identity information from visual representations during image storage and transmission has become a critical challenge for privacy protection. To address this issue, we propose SIDeR, a Semantic decoupling-driven framework for unrestricted face privacy protection. SIDeR decomposes a facial image into a machine-recognizable identity feature vector and a visually perceptible semantic appearance component. By […]

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Siloed working environments wreak havoc on team collaboration. If you’re constantly experiencing project delays and putting out data fires, your business could be dealing with an ongoing silo. Caused by teams or systems operating in isolation, silos can make it difficult to share information across the business. This is especially challenging if your business requires field engineers to operate away from back office teams. In 2026, 83% of US business leaders reported that silos exist within their companies, […]

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Traditional machine learning systems are typically designed for static data distributions, which suffer from catastrophic forgetting when learning from evolving data streams. Class-Incremental Learning (CIL) addresses this challenge by enabling learning systems to continuously learn new classes while preserving prior knowledge. With the rise of pre-trained models (PTMs) such as CLIP, leveraging their strong generalization and semantic alignment capabilities has become a promising direction in CIL. However, existing CLIP-based CIL methods are often scattered across disparate codebases, rely […]

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Large Language Models (LLMs) suffer severe catastrophic forgetting when adapted sequentially to new tasks in a continual learning (CL) setting. Existing approaches are fundamentally limited: replay-based methods are impractical and privacy-violating, while strict orthogonality-based methods collapse under scale: each new task is projected onto an orthogonal complement, progressively reducing the residual degrees of freedom and eliminating forward transfer by forbidding overlap in shared representations. In this work, we introduce ELLA, a training framework built on the principle of […]

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arXiv:2601.09867v1 Announce Type: new Abstract: Passive eavesdropping compromises confidentiality in wireless networks, especially in resource-constrained environments where heavyweight cryptography is impractical. Physical layer security (PLS) exploits channel randomness and spatial selectivity to confine information to an intended receiver with modest overhead. However, typical PLS techniques, such as using beamforming, artificial noise, and reconfigurable intelligent surfaces, often involve added active power or specialized deployment, and, in many designs, rely on precise time synchronization and perfect CSI estimation, which limits […]

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arXiv:2603.24333v1 Announce Type: cross Abstract: Recently, Forr’e (arXiv:2104.11547, 2021) introduced transitional conditional independence, a notion of conditional independence that provides a unified framework for both random and non-stochastic variables. The original paper establishes a strong global Markov property connecting transitional conditional independencies with suitable graphical separation criteria for directed mixed graphs with input nodes (iDMGs), together with a version of causal calculus for iDMGs in a general measure-theoretic setting. These notes aim to further illustrate the motivations behind […]

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arXiv:2601.22350v1 Announce Type: new Abstract: Given a Markov decision process (MDP), we seek to learn representations for a range of policies to facilitate behavior steering at test time. As policies of an MDP are uniquely determined by their occupancy measures, we propose modeling policy representations as expectations of state-action feature maps with respect to occupancy measures. We show that these representations can be approximated uniformly for a range of policies using a set-based architecture. Our model encodes a […]

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While everyone argues about which chatbot is marginally smarter, the real edge is in pipelines. Before 6–8 hours per company Bloomberg → Excel → EDGAR → news $24k/year Bloomberg seat Static assumptions, one “price target” After One command, ~2.5 minutes Live data from free public APIs $0 marginal cost per run Bull / Base / Bear + stress tests I realized this after watching a friend spent an entire day updating a single DCF model. Smart work buried […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## How to Break Your PostgreSQL IIoT Database and Learn Something in the Process By @tigerdata [ 6 Min read ] Learn how to stress test your IIoT database using PostgreSQL. Identify bottlenecks, simulate scale, and design systems that won’t fail in production. Read More. […]

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