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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These are often on the “what you are not supposed to do” list, so why are they so commonplace in ML? Bare pip / requirements.txt is quite bad at managing conflicts / build environments and is very difficult to integrate into an existing project. On the other hand, if you are already using conda, why not actually use conda? pip inside a conda environment is just making both package managers’ jobs harder. There seem to be so many […]

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Levy processes play an important role in the stochastic process theory. However, since samples are non-i.i.d., statistical learning results based on the i.i.d. scenarios cannot be utilized to study the risk bounds for Levy processes. In this paper, we present risk bounds for non-i.i.d. samples drawn from Levy processes in the PAC-learning framework. In particular, by using a concentration inequality for infinitely divisible distributions, we first prove that the function of risk error is Lipschitz continuous with a […]

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You must have heard of, or have suspected first-handedly, the famous conspiracy theory that the Facebook app listens to your phone’s microphone in order to better target ads that match your current interests. I’ve had the funniest experience with that myself: a friend in the cosmetics business has told me about this conspiracy, and in the same conversation she mentioned that an advertising agent has called her to offer advertising her business. Later that day, I got a Facebook […]

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Artificial intelligence now outperforms humans in several scientific and engineering tasks, yet its internal representations often remain opaque. In this Perspective, we argue that explainable artificial intelligence (XAI), combined with causal reasoning, enables {it learning from the learners}. Focusing on discovery, optimization and certification, we show how the combination of foundation models and explainability methods allows the extraction of causal mechanisms, guides robust design and control, and supports trust and accountability in high-stakes applications. We discuss challenges in […]

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Image-to-PointCloud place recognition is vital for autonomous systems, yet faces challenges from the inherent modality gap and drastic environmental variations. We propose Cross-Modal Invariant Representation Learning (CMIRL) to learn highly invariant cross-modal global descriptors. CMIRL introduces an Adaptive Cross-Modal Alignment (ACMA) module, which dynamically projects point clouds based on image semantics to generate view-optimized dense depth maps. A Dual-Stream Invariant Feature Encoder, featuring a Transformer-based Cross-Modal Attention Fusion (CMAF) module, then explicitly learns and emphasizes features shared across […]

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arXiv:2601.13851v1 Announce Type: cross Abstract: Self-Organizing Maps provide topology-preserving projections of high-dimensional data and have been widely used for visualization, clustering, and vector quantization. In this work, we show that the activation pattern of a SOM – the squared distances to its prototypes – can be inverted to recover the exact input under mild geometric conditions. This follows from a classical fact in Euclidean distance geometry: a point in $D$ dimensions is uniquely determined by its distances to […]

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