The Problem: Power Markets Are Moving Faster Than the Tools Designed to Manage Them Day-ahead and intraday power markets have entered a new era of volatility. Renewable intermittency, rapid demand swings driven by electrification and digital infrastructure, and the expanding role of flexible assets like battery energy storage systems (BESS) are compressing price risk into shorter and shorter intervals. Imbalance markets illustrate this most sharply. Deviations between forecasted and actual generation or load can trigger significant pricing dislocations […]

Ver mais

arXiv:2601.16438v1 Announce Type: new Abstract: Twisted generalized Reed-Solomon (TGRS) codes, as a flexible extension of classical generalized Reed-Solomon (GRS) codes, have attracted significant attention in recent years. In this paper, we construct two classes of LCD codes from the $(mathcal{L},mathcal{P})$-TGRS code $mathcal{C}_h$ of length $n$ and dimension $k$, where $mathcal{L}={0,1,ldots,l}$ for $lleq n-k-1$ and $mathcal{P}={h}$ for $1leq hleq k-1$. First, we derive the parity check matrix of $mathcal{C}_h$ and provide a necessary and sufficient condition for $mathcal{C}_h$ to […]

Ver mais

arXiv:2601.15308v1 Announce Type: new Abstract: Extended Reality (XR), including virtual, augmented, and mixed reality, provides immersive and interactive experiences across diverse applications, from VR-based education to AR-based assistance and MR-based training. However, widespread XR adoption remains limited due to two key challenges: 1) the high cost and complexity of authoring 3D content, especially for large-scale environments or complex interactions; and 2) the steep learning curve associated with non-intuitive interaction methods like handheld controllers or scripted gestures. Generative AI […]

Ver mais

arXiv:2601.00864v1 Announce Type: cross Abstract: Graphs are commonly used in machine learning to model relationships between instances. Consider the task of predicting the political preferences of users in a social network; to solve this task one should consider, both, the features of each individual user and the relationships between them. However, oftentimes one is not interested in the label of a single instance but rather in the distribution of labels over a set of instances; e.g., when predicting […]

Ver mais

arXiv:2601.02496v1 Announce Type: new Abstract: We introduce APoW, a novel proof-of-work (PoW) construction inspired by Hashcash-style nonce searching, which enables the auditing of other miners’ work through accountable re-scanning of the nonce space. The proposed scheme allows a miner to probabilistically attest to having searched specified regions of the nonce space in earlier mining rounds, while concurrently earning rewards for performing productive work for a new block or pool share. This capability enables miners belonging to a mining […]

Ver mais

Skyjo is a simple stochastic card game with partial information, local replacement decisions, and score-reducing column removal events. This paper develops a formal mathematical model of the game, derives expected-score rules for turn-level actions, proves several dominance and threshold results, and evaluates a family of heuristic strategies through Monte Carlo simulation. The focus here lies on local optimality under explicit belief assumptions rather than a full equilibrium solution of the multiplayer game. Finally a simulation code is provided […]

Ver mais

Post-harvest quality grading is critical for reducing losses in primary banana production. However, the adoption of artificial intelligence at the farm level is often constrained by limited energy and computational infrastructure. This study evaluates whether reliable banana quality grading can be achieved under such constraints by systematically com-paring lightweight object detection models. Four YOLO-based architectures were bench-marked using a curated dataset of over 11,000 annotated images across six commercial quality classes. Energy consumption and carbon emissions during model […]

Ver mais

Hey Hackers! Welcome to the second-ever edition of HackerNoon Projects of the Week, where we spotlight standout projects from the Proof of Usefulness Hackathon, HackerNoon’s competition designed to measure what actually matters: real utility over hype. This week, we’re excited to share three amazing projects that have proven their necessity and usefulness: Agent Observatory, Formonger, and Olio AI. Introducing the Projects of the Week! Agent Observatory https://hackernoon.com/agent-observatory-earns-a-56-proof-of-usefulness-score-by-making-ai-agents-observable-without-risk?embedable=true If you’re a backend or platform engineer, then Agent Observatory is the […]

Ver mais

Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks. While deep learning (DL) techniques have emerged as an efficient alternative to conventional ray-tracing for radio map estimation (RME), most existing DL approaches are confined to 2D near-ground scenarios. They often fail to capture essential 3D signal propagation characteristics and antenna polarization effects, primarily due to the scarcity of 3D data and training challenges. To address these limitations, we present the RadioGen3D framework. […]

Ver mais

India’s, or specifically Tamil Nadu’s, rapidly escalating residential rental market poses a severe affordability crisis, with house rent constituting over 60% of urban household expenditure in major metropolitan areas. Landlords exercise unchecked discretion in annual rent increases, often exceeding inflation and wage growth by significant margins. This paper proposes a Progressive Rental Taxation (PRT) framework that imposes multiplicative tax penalties on landlords who increase rent beyond a permissible threshold anchored to a novel Rent Hike Index (RHI). We […]

Ver mais