arXiv:2602.09246v1 Announce Type: new Abstract: The integration of Artificial Intelligence (AI) into Higher Education Institutions (HEIs) in Ecuador is not a technological option but a strategic imperative to prevent institutional obsolescence and academic irrelevance in Latin America. This paper presents the IA593 Framework, a governance, ethics, and operational model designed for the Universidad Nacional de Loja (UNL) and scalable as a reference for the Ecuadorian higher education system. The current context reveals a critical urgency: the Latin American […]

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arXiv:2512.25017v2 Announce Type: replace-cross Abstract: The aim of this article is to provide a firm mathematical foundation for the application of deep gradient flow methods (DGFMs) for the solution of (high-dimensional) partial differential equations (PDEs). We decompose the generalization error of DGFMs into an approximation and a training error. We first show that the solution of PDEs that satisfy reasonable and verifiable assumptions can be approximated by neural networks, thus the approximation error tends to zero as the […]

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:::info Welcome to the Proof of Usefulness Hackathon spotlight, curated by HackerNoon’s editors to showcase noteworthy tech solutions to real-world problems. Whether you’re a solopreneur, part of an early-stage startup, or a developer building something that truly matters, the Proof of Usefulness Hackathon is your chance to test your product’s utility, get featured on HackerNoon, and compete for $150k+ in prizes. Submit your project to get started! ::: By Florita Bell Griffin, Ph.D. In this interview, we speak […]

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arXiv:2603.20234v1 Announce Type: new Abstract: In contemporary autonomous driving testing, virtual simulation has become an important approach due to its efficiency and cost effectiveness. However, existing methods usually rely on reinforcement learning to generate risky scenarios, making it difficult to efficiently learn realistic emergency behaviors. To address this issue, we propose a behavior guided method for generating high risk lane change scenarios. First, a behavior learning module based on an optimized sequence generative adversarial network is developed to […]

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The media companies of the world are set adrift in seas of video, audio, pictures, and words. We are still being fed content every day by production studios, broadcast feeds, streaming services, and digital archives. All this content is not valuable because of the raw footage, but because of the metadata used to describe it. The most salient content often lies concealed within an overcrowded repository lacking high-quality metadata. The issue is that outdated procedures of metadata control […]

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SSH has no Host header exe.dev is a new hosting service that, for $20/month, gives you up to 25 VMs “that share 2 CPUs and 8GB RAM”. Everything happens over SSH, including creating new VMs. Once configured you can sign into your exe.dev VMs like this: ssh simon.exe.dev Here’s the clever bit: when you run the above command exe.dev signs you into your VM of that name… but they don’t assign every VM its own IP address and […]

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Digital Trust Is Not a Feature – It’s a Philosophical Problem For most of human history, trust was something we negotiated face to face. We trusted people we knew, institutions we could see, and systems that evolved slowly enough for social norms to keep pace. Today, that foundation is gone. Trust now flows through code, algorithms, platforms, and automated decisions – often invisible, often opaque, and rarely negotiable. This is not merely a technological shift. It is ==a […]

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arXiv:2601.18830v1 Announce Type: new Abstract: Accurate multi-label classification of electrocardiogram (ECG) signals remains challenging due to the coexistence of multiple cardiac conditions, pronounced class imbalance, and long-range temporal dependencies in multi-lead recordings. Although recent studies increasingly rely on deep and stacked recurrent architectures, the necessity and clinical justification of such architectural complexity have not been rigorously examined. In this work, we perform a systematic comparative evaluation of convolutional neural networks (CNNs) combined with multiple recurrent configurations, including LSTM, […]

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arXiv:2604.07390v1 Announce Type: new Abstract: The aggressive densification of modern wireless networks necessitates judicious resource allocation to mitigate severe mutual interference. However, classical iterative algorithms remain computationally prohibitive for real-time applications requiring rapid responsiveness. While recent deep learning-based methods show promise, they typically function as task-specific solvers lacking the flexibility to adapt to different objectives and scenarios without expensive retraining. To address these limitations, we propose a graph foundation model for resource allocation (GFM-RA) based on a pre-training […]

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