digitado

Strategically locating a sawmill is vital for enhancing the efficiency, profitability, and sustainability of timber supply chains. Our study proposes a Learning-Based Multi-Criteria Decision-Making (LB-MCDM) framework that integrates machine learning (ML) with GIS-based spatial location analysis via MCDM. The proposed framework provides a data-driven, unbiased, and replicable approach to assessing site suitability. We demonstrate the utility of the proposed model through a case study in Mississippi (MS). We apply five ML algorithms (Random Forest Classifier, Support Vector Classifier, […]

Ver mais

Despite the sustained popularity of Q-learning as a practical tool for policy determination, a majority of relevant theoretical literature deals with either constant ($η_{t}equiv η$) or polynomially decaying ($η_{t} = ηt^{-α}$) learning schedules. However, it is well known that these choices suffer from either persistent bias or prohibitively slow convergence. In contrast, the recently proposed linear decay to zero (texttt{LD2Z}: $η_{t,n}=η(1-t/n)$) schedule has shown appreciable empirical performance, but its theoretical and statistical properties remain largely unexplored, especially in […]

Ver mais

How are you, hacker? 🪐 What’s happening in tech today, April 5, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From When Supply Is Just Code: Why Traditional Supply Demand Shouldnt Apply to Digital Money to The Ultimate C# QR Code Library Comparison for 2026, let’s dive right in. What is Agentic Testing? By @qatech [ 6 Min read ] If your test […]

Ver mais

The Markup, now a part of CalMatters, uses investigative reporting, data analysis, and software engineering to challenge technology to serve the public good. Sign up for Klaxon, a newsletter that delivers our stories and tools directly to your inbox. The Markup’s series on the impact of misinformation on the Vietnamese immigrant community, “Languages of Misinformation,” has won the Asian American Journalism Association’s excellence in online/digital journalism engagement award. Judges said the series “brilliantly tackled misinformation on YouTube, hitting home for […]

Ver mais

In the early days of distributed systems, we lived in a world of Request-Response. One service asked, another answered. It was synchronous, predictable, and easy to trace. But as our systems scaled from a handful of servers to hundreds of microservices, this “web of calls” began to tangle. Today, architects face a fundamental choice when designing how these services talk to one another: Do we use Orchestration (the Conductor approach) or Choreography (the Dance approach)? This choice isn’t […]

Ver mais

Continual learning in artificial neural networks is fundamentally limited by the stability–plasticity dilemma: systems that retain prior knowledge tend to resist acquiring new knowledge, and vice versa. Existing approaches, most notably elastic weight consolidation~(EWC), address this empirically without a physical account of why plasticity eventually collapses as tasks accumulate. Separately, the distinction between sudden insight and gradual skill acquisition through repetitive practice has lacked a unified theoretical description. Here, we show that both problems admit a common resolution […]

Ver mais

Performance benchmarks are converging. The real moat now is collective decision-making – and most networks are nowhere near ready. There is a script that every new Layer-1 follows. Launch. Announce throughput numbers. Position yourself as the fix for whatever the previous generation broke. Watch the roadmap grow. Watch the marketing get louder. We have been running this loop for nearly a decade. And it has quietly produced something nobody planned for: convergence. Sub-second finality is no longer a […]

Ver mais

This paper investigates the Restless Multi-Armed Bandit (RMAB) framework under individual penalty constraints to address resource allocation challenges in dynamic wireless networked environments. Unlike conventional RMAB models, our model allows each user (arm) to have distinct and stringent performance constraints, such as energy limits, activation limits, or age of information minimums, enabling the capture of diverse objectives including fairness and efficiency. To find the optimal resource allocation policy, we propose a new Penalty-Optimal Whittle (POW) index policy. The […]

Ver mais

Classical approximation bases such as Chebyshev polynomials provide principled and interpretable representations, but their multivariate tensor-product constructions scale exponentially with dimension and impose axis-aligned structure that is poorly matched to real tabular data. We address this by replacing tensorised oscillations with directional harmonic modes of the form $cos(mathbf{m}^{top}arccos(mathbf{x}))$, which organise multivariate structure by direction in angular space rather than by coordinate index. This representation yields a discrete spectral regression model in which complexity is controlled by selecting a […]

Ver mais