Compiling quantum circuits into Clifford+$T$ gates is a central task for fault-tolerant quantum computing using stabilizer codes. In the near term, $T$ gates will dominate the cost of fault tolerant implementations, and any reduction in the number of such expensive gates could mean the difference between being able to run a circuit or not. While exact synthesis is exponentially hard in the number of qubits, local synthesis approaches are commonly used to compile large circuits by decomposing them […]

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In our Haskell in Production series, we interview developers and technical leaders from companies that use Haskell for real-world tasks. We cover benefits, downsides, common pitfalls, and tips for building useful Haskell products. Our today’s guest is Syed Jafri, a Senior Software Engineer at Caribou – a company that helps car owners get quotes on refinancing and insurance. He managed to successfully pitch and introduce Haskell at Caribou. They are currently using it for some of the services […]

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arXiv:2601.21217v1 Announce Type: new Abstract: We introduce a flexible empirical Bayes approach for fitting Bayesian generalized linear models. Specifically, we adopt a novel mean-field variational inference (VI) method and the prior is estimated within the VI algorithm, making the method tuning-free. Unlike traditional VI methods that optimize the posterior density function, our approach directly optimizes the posterior mean and prior parameters. This formulation reduces the number of parameters to optimize and enables the use of scalable algorithms such […]

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arXiv:2601.14274v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires representations that effectively integrate signals from multiple modalities. These signals include modality-specific cues, information shared across modalities, and interactions that emerge only when modalities are combined. In information-theoretic terms, these correspond to emph{unique}, emph{redundant}, and emph{synergistic} contributions. An ideal representation should leverage all three, yet achieving such balance remains challenging. Recent advances in contrastive learning and augmentation-based methods have made progress, but they often overlook the […]

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arXiv:2601.21717v1 Announce Type: cross Abstract: We establish sample complexity guarantees for estimating the covariance matrix of strongly log-concave smooth distributions using the unadjusted Langevin algorithm (ULA). We quantitatively compare our complexity estimates on single-chain ULA with embarrassingly parallel ULA and derive that the sample complexity of the single-chain approach is smaller than that of embarrassingly parallel ULA by a logarithmic factor in the dimension and the reciprocal of the prescribed precision, with the difference arising from effective bias […]

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arXiv:2602.00968v1 Announce Type: new Abstract: We address the tracking problem for a class of uncertain non-affine nonlinear systems with high relative degrees, performing non-repetitive tasks. We propose a rigorously proven, robust adaptive learning control scheme that relies on a gradient descent parameter adaptation law to handle the unknown time-varying parameters of the system, along with a state estimator that estimates the unmeasurable state variables. Furthermore, despite the inherently complex nature of the non-affine system, we provide an explicit […]

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I’ve been compiling papers on Physical AI — the intersection of foundation models and robotics. This covers Vision-Language-Action (VLA) models like RT-2 and π₀, world models (DreamerV3, Genie 2, JEPA), diffusion policies, real-world deployment and latency problems, cross-embodiment transfer, scaling laws, and safety/alignment for robots. The field has exploded in the past 18 months. We went from “lets try llms on robotics” to having so many dimensions to optimize for. so felt right to maintain a running list […]

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arXiv:2601.14285v1 Announce Type: new Abstract: Tree structures appear in many fields of the life sciences, including phylogenetics, developmental biology and nucleic acid structures. Trees can be used to represent RNA secondary structures, which directly relate to the function of non-coding RNAs. Recent developments in sequencing technology and artificial intelligence have yielded numerous biological data that can be represented with tree structures. This requires novel methods for tree structure data analytics. Tree polynomials provide a computationally efficient, interpretable and […]

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I’ve tried doing sanity checks and they work great for the most part, but what if there is just a part of the data, or an instance where the model fails? How do you watch out for something like that so that hours of GPU compute just don’t go down the drain. I’ve also heard about saving weights/progress at certain checkpoints, but for other tasks such as model evals how would that work? submitted by /u/Specialist-Pool-6962 [link] [comments]

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Placental abruption is detachment of the placenta before delivery from the implantation site that may have a potential to develop life-threating emergency clinic syptoms. The multifactorial nature of this disorder and no lab testing or procedures that can diagnose placental abruption. makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study purposed on predictive 15 ML models for placental abruption high-lighting input characteristics, […]

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