When a software system or service fails, who owns that failure? Is it the developer who introduced a certain misconfiguration or weakness? Is it the 3rd party dependency that went down or had delays at the wrong time? Is it the surge of unexpected users when a product in your store goes viral? Is it the leadership’s fault for pushing for development velocity at all costs? Failure almost never has just one cause and one owner. That’s why […]

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arXiv:2602.15164v1 Announce Type: new Abstract: Data scientists often need to write programs to process predictions of machine learning models, such as object detections and trajectories in video data. However, writing such queries can be challenging due to the fuzzy nature of real-world data; in particular, they often include real-valued parameters that must be tuned by hand. We propose a novel framework called Quivr that synthesizes trajectory queries matching a given set of examples. To efficiently synthesize parameters, we […]

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arXiv:2602.06055v1 Announce Type: new Abstract: Current annotation agreement metrics are not well-suited for inter-group analysis, are sensitive to group size imbalances and restricted to single-annotation settings. These restrictions render them insufficient for many subjective tasks such as toxicity and hate-speech detection. For this reason, we introduce a quantifiable metric, paired with a statistical significance test, that attributes polarization to various annotator groups. Our metric enables direct comparisons between heavily imbalanced sociodemographic and ideological subgroups across different datasets and […]

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Preliminaries Gaussian distribution log-likelihood Calculus partial derivative Lagrange multiplier EM Algorithm for Gaussian Mixture1 Analysis Maximizing likelihood could not be used in the Gaussian mixture model directly, because of its severe defects which we have come across at ‘Maximum Likelihood of Gaussian Mixtures’. With the inspiration of K-means, a two-step algorithm was developed. The objective function is the log-likelihood function: [ begin{aligned} ln Pr(mathbf{x}|mathbf{pi},mathbf{mu},Sigma)&=ln (Pi_{n=1}^Nsum_{j=1}^{K}pi_kmathcal{N}(mathbf{x}|mathbf{mu}_k,Sigma_k))\ &=sum_{n=1}^{N}ln sum_{j=1}^{K}pi_jmathcal{N}(mathbf{x}_n|mathbf{mu}_j,Sigma_j)\ end{aligned}tag{1} ]

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Analog Ising machines (AIMs) have emerged as a promising paradigm for combinatorial optimization, utilizing physical dynamics to solve Ising problems with high energy efficiency. However, the performance of traditional optimization and sampling algorithms on these platforms is often limited by inherent measurement noise. We introduce BRAIN (Boltzmann Reinforcement for Analog Ising Networks), a distribution learning framework that utilizes variational reinforcement learning to approximate the Boltzmann distribution. By shifting from state-by-state sampling to aggregating information across multiple noisy measurements, […]

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arXiv:2504.04528v3 Announce Type: replace-cross Abstract: Machine learning-supported decisions, such as ordering diagnostic tests or determining preventive custody, often require converting probabilistic forecasts into binary classifications. We adopt a consequentialist perspective from decision theory to argue that evaluation methods should prioritize forecast quality across thresholds and base rates. This motivates the use of proper scoring rules such as the Brier score and log loss. However, our empirical review of practices at major ML venues (ICML, FAccT, CHIL) reveals a […]

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arXiv:2601.05408v1 Announce Type: new Abstract: Electromagnetic formation flying (EMFF) is challenging due to the complex coupling between the electromagnetic fields generated by each satellite in the formation. To address this challenge, this article uses alternating magnetic field forces (AMFF) to decouple the electromagnetic forces between each pair of satellites. Each satellite’s electromagnetic actuation system is driven by a sum of amplitude-modulated sinusoids, where amplitudes are controlled to achieve desired forces between each pair of satellites. The main contribution […]

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Hypergraph Neural Networks (HyGNNs) have demonstrated remarkable success in modeling higher-order relationships among entities. However, their performance often degrades on heterophilic hypergraphs, where nodes connected by the same hyperedge tend to have dissimilar semantic representations or belong to different classes. While several HyGNNs, including our prior work BHyGNN, have been proposed to address heterophily, their reliance on labeled data significantly limits their applicability in real-world scenarios where annotations are scarce or costly. To overcome this limitation, we introduce […]

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arXiv:2506.06571v3 Announce Type: replace-cross Abstract: Including intricate topological information (e.g., cycles) provably enhances the expressivity of message-passing graph neural networks (GNNs) beyond the Weisfeiler-Leman (WL) hierarchy. Consequently, Persistent Homology (PH) methods are increasingly employed for graph representation learning. In this context, recent works have proposed decorating classical PH diagrams with vertex and edge features for improved expressivity. However, these methods still fail to capture basic graph structural information. In this paper, we propose SpectRe — a new topological […]

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