Cyber Ranges (CRs) are complex socio-technical ecosystems, combining infrastructure resources, software services, learning mechanisms, and human-in-the-loop processes for cybersecurity training, education, and experimentation. However, their design and representation are conventionally described by diverse architectural representations and a lack of standardization, making them difficult to compare, integrate, and reason in an automated manner. This paper proposes a novel framework that uniquely integrates the structural, functional, informational, and decisional aspects of CR platforms, formalizing them into a common semantic framework. […]

Ver mais

arXiv:2601.00141v1 Announce Type: new Abstract: The rapid development of generative AI has made AI-generated images increasingly realistic and high-resolution. Most AI-generated image detection architectures typically downsample images before inputting them into models, risking the loss of fine-grained details. This paper presents GLASS (Global-Local Attention with Stratified Sampling), an architecture that combines a globally resized view with multiple randomly sampled local crops. These crops are original-resolution regions efficiently selected through spatially stratified sampling and aggregated using attention-based scoring. GLASS […]

Ver mais

Hi everyone, I’m a Senior Machine Learning Engineer planning a focused transition into Reinforcement Learning research over the next 12–18 months, and I’d really value advice from people who’ve done this alongside full-time work. Background (brief): • B.Tech + M.Tech (strong math/PDEs) • ~2+ years in ML/DS (forecasting, optimization, CNNs) • Currently building LLM-based agents & multi-agent systems in fintech (orchestration, tools, OpenAI/Anthropic, knowledge graphs),via AI automation I’m comfortable with Python, PyTorch, probability, linear algebra, and optimization. Why […]

Ver mais

After completing Andrew Ng’s course, CS229, various math and ML stuff and also CS231n, I struggled to find quality practice problems. So I built Neural Forge: – Currently, 73 questions across all ML topics – Code directly in browser (Python via Pyodide) – Spaced repetition for retention – Instant test case validation – Knowledge graph showing prerequisites – 8 question types (MCQ, debug code, implement algorithms, design architectures, math derivations, case studies, paper implementations) Try it: https://neural-forge-chi.vercel.app/ Built […]

Ver mais

ChatGPT grew from zero to 100 million users in two months. Today it has over 800 million. That makes it one of the fastest-growing applications in history. And their database? PostgreSQL. The same 30-year-old relational database running your side project. Most engineering teams would have panicked at 10 million users and started a multi-year migration to Cassandra, DynamoDB, or some other NoSQL system. OpenAI did not. They scaled PostgreSQL itself. And the techniques they used are not exotic […]

Ver mais

arXiv:2601.22378v2 Announce Type: replace Abstract: Maximum likelihood estimators (MLE) and control variate estimators (CVE) have been used in conjunction with known information across sketching algorithms and applications in machine learning. We prove that under certain conditions in an exponential family, an optimal CVE will achieve the same asymptotic variance as the MLE, giving an Expectation-Maximization (EM) algorithm for the MLE. Experiments show the EM algorithm is faster and numerically stable compared to other root finding algorithms for the […]

Ver mais

Continuous stress forecasting could potentially contribute to lifestyle interventions. This paper presents a novel, explainable, and individualized approach for stress prediction using physiological data from consumer-grade smartwatches. We develop a time series forecasting model that leverages multivariate features, including heart rate variability, activity patterns, and sleep metrics, to predict stress levels across 16 temporal horizons (History window: 3, 5, 7, 9 days; forecasting window: 1, 3, 5, 7 days). Our evaluation involves 16 participants monitored for 10-15 weeks. […]

Ver mais

arXiv:2602.08185v1 Announce Type: new Abstract: Discriminative Random Walks (DRWs) are a simple yet powerful tool for semi-supervised node classification, but their theoretical foundations remain fragmentary. We revisit DRWs through the lens of information geometry, treating the family of class-specific hitting-time laws on an absorbing Markov chain as a statistical manifold. Starting from a log-linear edge-weight model, we derive closed-form expressions for the hitting-time probability mass function, its full moment hierarchy, and the observed Fisher information. The Fisher matrix […]

Ver mais

arXiv:2602.04007v1 Announce Type: new Abstract: Block space on the blockchain is scarce and must be allocated efficiently through block building. However, Ethereum’s current block-building ecosystem, MEV-Boost, has become highly centralized due to integration, which distorts competition, reduces blockspace efficiency, and obscures MEV flow transparency. To guarantee equitability and economic efficiency in block building, we propose $mathrm{Boost+}$, a system that decouples the process into collecting and ordering transactions, and ensures equal access to all collected transactions. The core of […]

Ver mais