arXiv:2510.01339v2 Announce Type: replace-cross Abstract: Computational imaging methods increasingly rely on powerful generative diffusion models to tackle challenging image restoration tasks. In particular, state-of-the-art zero-shot image inverse solvers leverage distilled text-to-image latent diffusion models (LDMs) to achieve unprecedented accuracy and perceptual quality with high computational efficiency. However, extending these advances to high-definition video restoration remains a significant challenge, due to the need to recover fine spatial detail while capturing subtle temporal dependencies. Consequently, methods that naively apply image-based […]

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arXiv:2601.15297v1 Announce Type: new Abstract: We introduce AfriEconQA, a specialized benchmark dataset for African economic analysis grounded in a comprehensive corpus of 236 World Bank reports. The task of AfriEconQA is to answer complex economic queries that require high-precision numerical reasoning and temporal disambiguation from specialized institutional documents. The dataset consists of 8,937 curated QA instances, rigorously filtered from a pool of 10018 synthetic questions to ensure high-quality evidence-answer alignment. Each instance is composed of: (1) a question […]

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I have been trying to solve classification problem on a low resource language. I am doing comparative analysis, LinearSVC and Logistic regression performed the best and the only models with 80+ accuracy and no overfitting. I have to classify it using deep learning model as well. I applied BERT on the dataset, model is ‘bert-base-multilingual-cased’, and I am fine tuning it, but issue is overfitting. Training logs: Epoch 6/10 | Train Loss: 0.4135 | Train Acc: 0.8772 | […]

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arXiv:2601.16315v1 Announce Type: new Abstract: Data centers are growing rapidly, creating the pressing need for the development of critical infrastructure build out to support these resource-intensive large loads. Their immense consumption of electricity and, often, freshwater, continues to stress an already constrained and aging power grid and water resources. This paper presents a comprehensive modeling approach to determine the optimal locations to construct such facilities by quantifying their resource use and minimizing associated costs. The interdisciplinary modeling approach […]

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A practical guide to identifying, restoring, and transforming elements within your images The post Detecting and Editing Visual Objects with Gemini appeared first on Towards Data Science.

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In my previous analysis of Power Stranding, I explored the multi-billion-dollar art of ensuring that massive power infrastructure doesn’t sit idle. But for a data center financial analyst, solving the power equation isn’t just about building better substations. It’s about what sits inside the racks. Looking at a modern data center’s balance sheet in early 2026, the “Hardware” line item is undergoing a silent coup. For decades, the industry operated as a one-stop shop: cloud providers bought their […]

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arXiv:2601.08447v1 Announce Type: cross Abstract: Spike-timing-dependent plasticity (STDP) provides a biologically-plausible learning mechanism for spiking neural networks (SNNs); however, Hebbian weight updates in architectures with recurrent connections suffer from pathological weight dynamics: unbounded growth, catastrophic forgetting, and loss of representational diversity. We propose a neuromorphic regularization scheme inspired by the synaptic homeostasis hypothesis: periodic offline phases during which external inputs are suppressed, synaptic weights undergo stochastic decay toward a homeostatic baseline, and spontaneous activity enables memory consolidation. We […]

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arXiv:2602.12680v1 Announce Type: new Abstract: The rule of thumb regarding the relationship between the bias-variance tradeoff and model size plays a key role in classical machine learning, but is now well-known to break down in the overparameterized setting as per the double descent curve. In particular, minimum-norm interpolating estimators can perform well, suggesting the need for new tradeoff in these settings. Accordingly, we propose a regularization-sharpness tradeoff for overparameterized linear regression with an $ell^p$ penalty. Inspired by the […]

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