arXiv:2603.17098v1 Announce Type: new Abstract: The convolutional neural networks (CNNs) are not inherently shift invariant or equivariant. The downsampling operation, used in CNNs, is one of the key reasons which breaks the shift invariant property of a CNN. Conversely, downsampling operation is important to improve computational efficiency and increase the area of the receptive field for more contextual information. In this work, we propose Gaussian-Hermite Sampling (GHS), a novel downsampling strategy designed to achieve accurate shift invariance. GHS […]

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Victoria, Seychelles, May 6, 2026 MEXC, the world leader in 0‑fee digital asset trading, has received four accolades at the 2026 MENA (Middle East & North Africa) Stevie Awards, an international business competition recognizing innovation and achievement across 18 nations in the region. The awards span advancements in AI, corporate social responsibility, and individual leadership, marking significant recognition of MEXC’s progress on multiple fronts. MEXC received two Silver Awards: the Award for Excellence in Innovation in AI Products […]

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Serverless computing is often marketed as a way to eliminate infrastructure management and accelerate software delivery. While this promise is largely true, it also creates a dangerous misconception that removing servers removes security risks. In reality, serverless architectures shift security responsibilities rather than eliminate them. Short-lived functions, event-driven execution models, and managed runtime environments introduce new attack surfaces that many organizations often overlook. Misconfigured permissions, untrusted event data, and reuse of runtime context can silently expose sensitive systems. […]

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arXiv:2602.05379v1 Announce Type: new Abstract: Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data collected in previous iterations to accelerate policy optimization. Classical experience replay treats all past observations uniformly and fails to account for their varying contributions to learning. To overcome this limitation, we propose Variance Reduction Experience Replay (VRER), a principled framework that selectively reuses informative samples to reduce variance in policy gradient estimation. VRER is algorithm-agnostic and integrates seamlessly with existing policy […]

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Service tags in Symfony are often misunderstood as merely a mechanism for Event Listeners or Twig Extensions. While they excel at those tasks, their true power lies in decoupling architecture. When wielded correctly, tags allow you to build systems that are open for extension but closed for modification (Open-Closed Principle) without touching a single line of configuration files. In this article, we will move beyond standard usage. We won’t just “tag a service”; we will build a robust, modular Document […]

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arXiv:2601.22218v1 Announce Type: new Abstract: Visual Question Answering (VQA) has become an important benchmark for assessing how large multimodal models (LMMs) interpret images. However, most VQA datasets focus on real-world images or simple diagrammatic analysis, with few focused on interpreting complex scientific charts. Indeed, many VQA datasets that analyze charts do not contain the underlying data behind those charts or assume a 1-to-1 correspondence between chart marks and underlying data. In reality, charts are transformations (i.e. analysis, simplification, […]

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Autonomous agents operating in continuous environments must decide not only what to do, but when to act. We introduce a lightweight adaptive temporal control system that learns the optimal interval between cognitive ticks from experience, replacing ad hoc biologically inspired timers with a principled learned policy. The policy state is augmented with a predictive hyperbolic spread signal (a "curvature signal" shorthand) derived from hyperbolic geometry: the mean pairwise Poincare distance among n sampled futures embedded in the Poincare […]

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Existing Graphical User Interface (GUI) agents operate through step-by-step calls to vision language models–taking a screenshot, reasoning about the next action, executing it, then repeating on the new page–resulting in high costs and latency that scale with the number of reasoning steps, and limited accuracy due to no persistent memory of previously visited pages. We propose ActionEngine, a training-free framework that transitions from reactive execution to programmatic planning through a novel two-agent architecture: a Crawling Agent that constructs […]

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