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ebc88a17fcd7760552020a7cc83284dd28fc7833136558d15fc7e6c6640a4671
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2026-01-01T00:00:00-05:00
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Hedonic Prices and Quality Adjusted Price Indices Powered by AI
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arXiv:2305.00044v3 Announce Type: replace-cross Abstract: We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon's data for first-party apparel sales, and estimate hedonic prices. The resulting models have a very high out-of-sample predictive accuracy, with $R^2$ ranging from $80\%$ to $90\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices.
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https://arxiv.org/abs/2305.00044
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Academic Papers
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b92ed826d9dd3f8a3fc71cdd0c2c12c03006f959da416981b9d150527410d10b
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2026-01-01T00:00:00-05:00
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Machine learning for option pricing: an empirical investigation of network architectures
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arXiv:2307.07657v2 Announce Type: replace-cross Abstract: We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of articles in this literature considers a (plain) feed forward neural network architecture in order to connect the neurons used for learning the function mapping inputs to outputs. In this article, motivated by methods in image classification and recent advances in machine learning methods for PDEs, we investigate empirically whether and how the choice of network architecture affects the accuracy and training time of a machine learning algorithm. We find that the generalized highway network architecture achieves the best performance, when considering the mean squared error and the training time as criteria, within the considered parameter budgets for the Black-Scholes and Heston option pricing problems. Considering the transformed implied volatility problem, a simplified DGM variant achieves the lowest error among the tested architectures. We also carry out a capacity-normalised comparison for completeness, where all architectures are evaluated with an equal number of parameters. Finally, for the implied volatility problem, we additionally include experiments using real market data.
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https://arxiv.org/abs/2307.07657
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Academic Papers
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060c8db1aad1c5c5558fb4ff7e2d60333cb2e10bd86e260e2480b802f6ad0591
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2026-01-01T00:00:00-05:00
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Generative Modelling of L\'evy Area for High Order SDE Simulation
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arXiv:2308.02452v2 Announce Type: replace-cross Abstract: It is well understood that, when numerically simulating SDEs with general noise, achieving a strong convergence rate better than $O(\sqrt{h})$ (where h is the step size) requires the use of certain iterated integrals of Brownian motion, commonly referred to as its "L\'evy areas". However, these stochastic integrals are difficult to simulate due to their non-Gaussian nature and for a $d$-dimensional Brownian motion with $d > 2$, no fast almost-exact sampling algorithm is known. In this paper, we propose L\'evyGAN, a deep-learning-based model for generating approximate samples of L\'evy area conditional on a Brownian increment. Due to our "Bridge-flipping" operation, the output samples match all joint and conditional odd moments exactly. Our generator employs a tailored GNN-inspired architecture, which enforces the correct dependency structure between the output distribution and the conditioning variable. Furthermore, we incorporate a mathematically principled characteristic-function based discriminator. Lastly, we introduce a novel training mechanism termed "Chen-training", which circumvents the need for expensive-to-generate training data-sets. This new training procedure is underpinned by our two main theoretical results. For 4-dimensional Brownian motion, we show that L\'evyGAN exhibits state-of-the-art performance across several metrics which measure both the joint and marginal distributions. We conclude with a numerical experiment on the log-Heston model, a popular SDE in mathematical finance, demonstrating that high-quality synthetic L\'evy area can lead to high order weak convergence and variance reduction when using multilevel Monte Carlo (MLMC).
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https://arxiv.org/abs/2308.02452
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Academic Papers
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2ff3b00c841368871d268badece849fbea0e01eb9a1235a4bac3cdb82d643acc
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2026-01-01T00:00:00-05:00
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Are Ensembles Getting Better all the Time?
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arXiv:2311.17885v3 Announce Type: replace-cross Abstract: Ensemble methods combine the predictions of several base models. We study whether or not including more models always improves their average performance. This question depends on the kind of ensemble considered, as well as the predictive metric chosen. We focus on situations where all members of the ensemble are a priori expected to perform equally well, which is the case of several popular methods such as random forests or deep ensembles. In this setting, we show that ensembles are getting better all the time if, and only if, the considered loss function is convex. More precisely, in that case, the loss of the ensemble is a decreasing function of the number of models. When the loss function is nonconvex, we show a series of results that can be summarised as: ensembles of good models keep getting better, and ensembles of bad models keep getting worse. To this end, we prove a new result on the monotonicity of tail probabilities that may be of independent interest. We illustrate our results on a medical problem (diagnosing melanomas using neural nets) and a "wisdom of crowds" experiment (guessing the ratings of upcoming movies).
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https://arxiv.org/abs/2311.17885
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Academic Papers
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b17ea44d90e58a81ed7329b054694e9719f2429e728def97e104bd3997c41dec
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2026-01-01T00:00:00-05:00
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Distribution-Dependent Rates for Multi-Distribution Learning
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arXiv:2312.13130v2 Announce Type: replace-cross Abstract: To address the needs of modeling uncertainty in sensitive machine learning applications, the setup of distributionally robust optimization (DRO) seeks good performance uniformly across a variety of tasks. The recent multi-distribution learning (MDL) framework tackles this objective in a dynamic interaction with the environment, where the learner has sampling access to each target distribution. Drawing inspiration from the field of pure-exploration multi-armed bandits, we provide distribution-dependent guarantees in the MDL regime, that scale with suboptimality gaps and result in superior dependence on the sample size when compared to the existing distribution-independent analyses. We investigate two non-adaptive strategies, uniform and non-uniform exploration, and present non-asymptotic regret bounds using novel tools from empirical process theory. Furthermore, we devise an adaptive optimistic algorithm, LCB-DR, that showcases enhanced dependence on the gaps, mirroring the contrast between uniform and optimistic allocation in the multi-armed bandit literature. We also conduct a small synthetic experiment illustrating the comparative strengths of each strategy.
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https://arxiv.org/abs/2312.13130
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Academic Papers
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495a4c7ea1d9a25e34f768ff57575f56ea6c4f01119163619972753d2d0a7cc8
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2026-01-01T00:00:00-05:00
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Stochastic Gradient Descent for Nonparametric Additive Regression
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arXiv:2401.00691v5 Announce Type: replace-cross Abstract: This paper introduces an iterative algorithm for training nonparametric additive models that enjoys favorable memory storage and computational requirements. The algorithm can be viewed as the functional counterpart of stochastic gradient descent, applied to the coefficients of a truncated basis expansion of the component functions. We show that the resulting estimator satisfies an oracle inequality that allows for model mis-specification. In the well-specified setting, by choosing the learning rate carefully across three distinct stages of training, we demonstrate that its risk is minimax optimal in terms of the dependence on both the dimensionality of the data and the size of the training sample. Unlike past work, we also provide polynomial convergence rates even when the covariates do not have full support on their domain.
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https://arxiv.org/abs/2401.00691
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Academic Papers
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b4be19a45aea85969f75222807e62fe2aef452cc94c17f9672978d104dd18b43
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2026-01-01T00:00:00-05:00
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Conditions for eigenvalue configurations of two real symmetric matrices (signature approach)
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arXiv:2401.00866v4 Announce Type: replace-cross Abstract: For two real symmetric matrices, their eigenvalue configuration is therelative arrangement of their eigenvalues on the real line. We consider the following problem: given two parametric real symmetric matrices and an eigenvalue configuration, find a simple condition on the parameters such that the two matrices have the given eigenvalue configuration. In this paper, we develop theory and give an algorithm for this problem. The output of the algorithm is a condition written in terms of the signatures of certain related symmetric matrices.
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https://arxiv.org/abs/2401.00866
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Academic Papers
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3de17706b1bc80a87acfbb05df838bb18811f8318b42f942cdbbf3dc4cc9dfce
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2026-01-01T00:00:00-05:00
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Shill-Proof Auctions
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arXiv:2404.00475v3 Announce Type: replace-cross Abstract: We characterize single-item auction formats that are shill-proof in the sense that a profit-maximizing seller has no incentive to submit shill bids. We distinguish between strong shill-proofness, in which a seller with full knowledge of bidders' valuations can never profit from shilling, and weak shill-proofness, which requires only that the expected equilibrium profit from shilling is non-positive. The Dutch auction (with a suitable reserve) is the unique (revenue-)optimal and strongly shill-proof auction. Any deterministic auction can satisfy only two properties in the set {static, strategy-proof, weakly shill-proof}. Our main results extend to settings with affiliated and interdependent values.
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https://arxiv.org/abs/2404.00475
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Academic Papers
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109dd8127f1fab8a2f57ec5c429751e609f3e330680e423bbe77409e6e3731ad
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2026-01-01T00:00:00-05:00
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Symmetric Linear Bandits with Hidden Symmetry
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arXiv:2405.13899v3 Announce Type: replace-cross Abstract: High-dimensional linear bandits with low-dimensional structure have received considerable attention in recent studies due to their practical significance. The most common structure in the literature is sparsity. However, it may not be available in practice. Symmetry, where the reward is invariant under certain groups of transformations on the set of arms, is another important inductive bias in the high-dimensional case that covers many standard structures, including sparsity. In this work, we study high-dimensional symmetric linear bandits where the symmetry is hidden from the learner, and the correct symmetry needs to be learned in an online setting. We examine the structure of a collection of hidden symmetry and provide a method based on model selection within the collection of low-dimensional subspaces. Our algorithm achieves a regret bound of $ O(d_0^{2/3} T^{2/3} \log(d))$, where $d$ is the ambient dimension which is potentially very large, and $d_0$ is the dimension of the true low-dimensional subspace such that $d_0 \ll d$. With an extra assumption on well-separated models, we can further improve the regret to $ O(d_0\sqrt{T\log(d)} )$.
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https://arxiv.org/abs/2405.13899
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Academic Papers
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aaf0fade3b658e2c24d2b9f80a98074cf71ff1991f84d6e33a4eddd112e5248b
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2026-01-01T00:00:00-05:00
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Lattice operations for the stable set in many-to-many markets via re-equilibration dynamics
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arXiv:2407.21198v2 Announce Type: replace-cross Abstract: We compute the lattice operations for the (pairwise) stable set in many-to-many matching markets where only path-independence on agents' choice functions is imposed. To do this, we construct Tarski operators defined on the lattices of worker-quasi-stable and firm-quasi-stable matchings. These operators resemble lay-off and vacancy chain dynamics, respectively.
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https://arxiv.org/abs/2407.21198
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Academic Papers
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c58bb9ba89a6a85c4656a93e0ca96d9af01c400d31aab7768b2d286b38dc8c5c
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2026-01-01T00:00:00-05:00
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The Z-Gromov-Wasserstein Distance
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arXiv:2408.08233v4 Announce Type: replace-cross Abstract: The Gromov-Wasserstein (GW) distance is a powerful tool for comparing metric measure spaces which has found broad applications in data science and machine learning. Driven by the need to analyze datasets whose objects have increasingly complex structure (such as node and edge-attributed graphs), several variants of GW distance have been introduced in the recent literature. With a view toward establishing a general framework for the theory of GW-like distances, this paper considers a vast generalization of the notion of a metric measure space: for an arbitrary metric space $Z$, we define a $Z$-network to be a measure space endowed with a kernel valued in $Z$. We introduce a method for comparing $Z$-networks by defining a generalization of GW distance, which we refer to as $Z$-Gromov-Wasserstein ($Z$-GW) distance. This construction subsumes many previously known metrics and offers a unified approach to understanding their shared properties. This paper demonstrates that the $Z$-GW distance defines a metric on the space of $Z$-networks which retains desirable properties of $Z$, such as separability, completeness, and geodesicity. Many of these properties were unknown for existing variants of GW distance that fall under our framework. Our focus is on foundational theory, but our results also include computable lower bounds and approximations of the distance which will be useful for practical applications.
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https://arxiv.org/abs/2408.08233
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Academic Papers
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4c872de4e9557b4e5530416b138b3a9cfe6a304821319a9466077147b4211a35
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2026-01-01T00:00:00-05:00
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Regularized autoregressive modeling and its application to audio signal reconstruction
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arXiv:2410.17790v3 Announce Type: replace-cross Abstract: Autoregressive (AR) modeling is invaluable in signal processing, in particular in speech and audio fields. Attempts in the literature can be found that regularize or constrain either the time-domain signal values or the AR coefficients, which is done for various reasons, including the incorporation of prior information or numerical stabilization. Although these attempts are appealing, an encompassing and generic modeling framework is still missing. We propose such a framework and the related optimization problem and algorithm. We discuss the computational demands of the algorithm and explore the effects of various improvements on its convergence speed. In the experimental part, we demonstrate the usefulness of our approach on the audio declipping and dequantization problems. We compare its performance against state-of-the-art methods and demonstrate the competitiveness of the proposed method in declipping musical signals, and its superiority in declipping speech. The evaluation includes a heuristic algorithm of generalized linear prediction (GLP), a strong competitor which has only been presented as a patent and is new in the scientific community.
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https://arxiv.org/abs/2410.17790
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Academic Papers
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49ecb473cda35e40949a79c5a62f9251e145ee25c43277b4c061ead4038c033a
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2026-01-01T00:00:00-05:00
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NeuroPMD: Neural Fields for Density Estimation on Product Manifolds
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arXiv:2501.02994v2 Announce Type: replace-cross Abstract: We propose a novel deep neural network methodology for density estimation on product Riemannian manifold domains. In our approach, the network directly parameterizes the unknown density function and is trained using a penalized maximum likelihood framework, with a penalty term formed using manifold differential operators. The network architecture and estimation algorithm are carefully designed to handle the challenges of high-dimensional product manifold domains, effectively mitigating the curse of dimensionality that limits traditional kernel and basis expansion estimators, as well as overcoming the convergence issues encountered by non-specialized neural network methods. Extensive simulations and a real-world application to brain structural connectivity data highlight the clear advantages of our method over the competing alternatives.
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https://arxiv.org/abs/2501.02994
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Academic Papers
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0777b341b36c90d822bd9dd5f8d6e8c22057aea282e093b8f5c3b98a48f7860d
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2026-01-01T00:00:00-05:00
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coverforest: Conformal Predictions with Random Forest in Python
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arXiv:2501.14570v3 Announce Type: replace-cross Abstract: Conformal prediction provides a framework for uncertainty quantification, specifically in the forms of prediction intervals and sets with distribution-free guaranteed coverage. While recent cross-conformal techniques such as CV+ and Jackknife+-after-bootstrap achieve better data efficiency than traditional split conformal methods, they incur substantial computational costs due to required pairwise comparisons between training and test samples' out-of-bag scores. Observing that these methods naturally extend from ensemble models, particularly random forests, we leverage existing optimized random forest implementations to enable efficient cross-conformal predictions. We present coverforest, a Python package that implements efficient conformal prediction methods specifically optimized for random forests. coverforest supports both regression and classification tasks through various conformal prediction methods, including split conformal, CV+, Jackknife+-after-bootstrap, and adaptive prediction sets. Our package leverages parallel computing and Cython optimizations to speed up out-of-bag calculations. Our experiments demonstrate that coverforest's predictions achieve the desired level of coverage. In addition, its training and prediction times can be faster than an existing implementation by 2--9 times. The source code for the coverforest is hosted on GitHub at https://github.com/donlap/coverforest.
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https://arxiv.org/abs/2501.14570
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Academic Papers
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3e32c2dac8d8b3619e2cb14645dddc6a0abe1c05005bfef099a1ec3215aae820
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2026-01-01T00:00:00-05:00
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Concentration Inequalities for Stochastic Optimization of Unbounded Objective Functions with Application to Denoising Score Matching
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arXiv:2502.08628v2 Announce Type: replace-cross Abstract: We derive novel concentration inequalities that bound the statistical error for a large class of stochastic optimization problems, focusing on the case of unbounded objective functions. Our derivations utilize the following key tools: 1) A new form of McDiarmid's inequality that is based on sample-dependent one-component mean-difference bounds and which leads to a novel uniform law of large numbers result for unbounded functions. 2) A new Rademacher complexity bound for families of functions that satisfy an appropriate sample-dependent Lipschitz property, which allows for application to a large class of distributions with unbounded support. As an application of these results, we derive statistical error bounds for denoising score matching (DSM), an application that inherently requires one to consider unbounded objective functions and distributions with unbounded support, even in cases where the data distribution has bounded support. In addition, our results quantify the benefit of sample-reuse in algorithms that employ easily-sampled auxiliary random variables in addition to the training data, e.g., as in DSM, which uses auxiliary Gaussian random variables.
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https://arxiv.org/abs/2502.08628
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Academic Papers
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ac1e683d56e497e4b41750a4627b876c40230c56b4e82386b3ad351a1e6da9e0
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2026-01-01T00:00:00-05:00
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A mathematical model for a universal digital quantum computer with an application to the Grover-Rudolph algorithm
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arXiv:2503.13388v5 Announce Type: replace-cross Abstract: In this work, we develop a novel mathematical framework for universal digital quantum computation using algebraic probability theory. We rigorously define quantum circuits as finite sequences of elementary quantum gates and establish their role in implementing unitary transformations. A key result demonstrates that every unitary matrix in \(\mathrm{U}(N)\) can be expressed as a product of elementary quantum gates, leading to the concept of a universal dictionary for quantum computation. We apply this framework to the construction of quantum circuits that encode probability distributions, focusing on the Grover-Rudolph algorithm. By leveraging controlled quantum gates and rotation matrices, we design a quantum circuit that approximates a given probability density function. Numerical simulations, conducted using Qiskit, confirm the theoretical predictions and validate the effectiveness of our approach. These results provide a rigorous foundation for quantum circuit synthesis within an algebraic probability framework and offer new insights into the encoding of probability distributions in quantum algorithms. Potential applications include quantum machine learning, circuit optimization, and experimental implementations on real quantum hardware.
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https://arxiv.org/abs/2503.13388
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Academic Papers
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dd3fedf8f696d7bc31e32b74300261078e48da93da67f95889ec5905b953f458
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2026-01-01T00:00:00-05:00
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Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
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arXiv:2504.02382v2 Announce Type: replace-cross Abstract: The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and efficiently delineating the bone fragments remains a significant challenge due to complex anatomy and imaging limitations. The PENGWIN challenge, organized as a MICCAI 2024 satellite event, aimed to advance automated fracture segmentation by benchmarking state-of-the-art algorithms on these complex tasks. A diverse dataset of 150 CT scans was collected from multiple clinical centers, and a large set of simulated X-ray images was generated using the DeepDRR method. Final submissions from 16 teams worldwide were evaluated under a rigorous multi-metric testing scheme. The top-performing CT algorithm achieved an average fragment-wise intersection over union (IoU) of 0.930, demonstrating satisfactory accuracy. However, in the X-ray task, the best algorithm achieved an IoU of 0.774, which is promising but not yet sufficient for intra-operative decision-making, reflecting the inherent challenges of fragment overlap in projection imaging. Beyond the quantitative evaluation, the challenge revealed methodological diversity in algorithm design. Variations in instance representation, such as primary-secondary classification versus boundary-core separation, led to differing segmentation strategies. Despite promising results, the challenge also exposed inherent uncertainties in fragment definition, particularly in cases of incomplete fractures. These findings suggest that interactive segmentation approaches, integrating human decision-making with task-relevant information, may be essential for improving model reliability and clinical applicability.
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https://arxiv.org/abs/2504.02382
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Academic Papers
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83ca0bcf9e765758126adae1fb7668f6618a98e205629c5221a1e53bc04cfc57
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2026-01-01T00:00:00-05:00
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Discovery and inference beyond linearity by integrating Bayesian regression, tree ensembles and Shapley values
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arXiv:2505.00571v2 Announce Type: replace-cross Abstract: Machine Learning (ML) is gaining popularity for hypothesis-free discovery of risk and protective factors in healthcare studies. ML is strong at discovering nonlinearities and interactions, but this power is compromised by a lack of reliable inference. Although Shapley values provide local measures of features' effects, valid uncertainty quantification for these effects is typically lacking, thus precluding statistical inference. We propose RuleSHAP, a framework that addresses this limitation by combining a dedicated Bayesian sparse regression model with a new tree-based rule generator and Shapley value attribution. RuleSHAP provides detection of nonlinear and interaction effects with uncertainty quantification at the individual level. We derive an efficient formula for computing marginal Shapley values within this framework. We demonstrate the validity of our framework on simulated data. Finally, we apply RuleSHAP to data from an epidemiological cohort to detect and infer several effects for high cholesterol and blood pressure, such as nonlinear interaction effects between features like age, sex, ethnicity, BMI and glucose level.
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https://arxiv.org/abs/2505.00571
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Academic Papers
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25057a19f5f5cef3c3ad3878912a0bf4f5c03617237146338df7e21e22d8088d
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2026-01-01T00:00:00-05:00
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Decomposing graphs into stable and ordered parts
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arXiv:2505.00594v2 Announce Type: replace-cross Abstract: Connections between structural graph theory and finite model theory recently gained a lot of attention. In this setting, many interesting questions remain on the properties of dependent (NIP) hereditary classes of graphs, in particular related to first-order transductions. In this paper, we study modelizations (which are strong forms of transduction pairings) of classes of graphs by classes of structures. In particular, we consider models obtained by coupling a partial order and a colored graph (thus forming a partially ordered colored graph). Motivated by Simon's decomposition theorem of dependent types into a stable part and a distal (order-like) part, we conjecture that every dependent hereditary class of graphs admits a modelization in a monadically dependent coupling of a class of posets with bounded treewidth cover graphs and a monadically stable class of colored graphs. In this paper, we consider the first non-trivial case (classes with bounded linear cliquewidth) and prove that the conjecture holds in a strong form, the model class being a monadically dependent coupling of a class of disjoint unions of chains and a class of colored graphs with bounded pathwidth. We extend our study to classes that admit bounded-size bounded linear cliquewidth decompositions and prove that they have a modelization in a monadically dependent coupling of a class of disjoint unions of chains and a class of colored graphs with bounded expansion, the model class also admitting bounded-size bounded linear cliquewidth decompositions.
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https://arxiv.org/abs/2505.00594
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Academic Papers
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30467a5f1a09aa25c5e5173f8aa4e56f2f939a999540dbe04871c841679dceaa
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2026-01-01T00:00:00-05:00
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Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
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arXiv:2505.01985v2 Announce Type: replace-cross Abstract: In constraint learning, we use a neural network as a surrogate for part of the constraints or of the objective function of an optimization model. However, the tractability of the resulting model is heavily influenced by the size of the neural network used as a surrogate. One way to obtain a more tractable surrogate is by pruning the neural network first. In this work, we consider how to approach the setting in which the neural network is actually a given: how can we solve an optimization model embedding a large and predetermined neural network? We propose surrogating the neural network itself by pruning it, which leads to a sparse and more tractable optimization model, for which we hope to still obtain good solutions with respect to the original neural network. For network verification and function maximization models, that indeed leads to better solutions within a time limit, especially -- and surprisingly -- if we skip the standard retraining step known as finetuning. Hence, a pruned network with worse inference for lack of finetuning can be a better surrogate.
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https://arxiv.org/abs/2505.01985
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Academic Papers
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38b123d6e98d04ab83f63ccb3cbe74cb07abcf91e5020ba94db5c39f65ae6bb9
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2026-01-01T00:00:00-05:00
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New affine invariant ensemble samplers and their dimensional scaling
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arXiv:2505.02987v3 Announce Type: replace-cross Abstract: We introduce new affine invariant ensemble Markov chain Monte Carlo (MCMC) samplers that are easy to construct and improve upon existing methods, especially for high-dimensional problems. We first propose a simple derivative-free side move sampler that improves upon popular samplers in the \texttt{emcee} package by generating more effective proposal directions. We then develop a class of derivative-based affine invariant ensemble Hamiltonian Monte Carlo (HMC) samplers based on antisymmetric preconditioning using complementary ensembles, which outperform standard, non-affine-invariant HMC when sampling highly anisotropic distributions. We provide asymptotic scaling analysis for high-dimensional Gaussian targets to further elucidate the properties of these affine invariant ensemble samplers. In particular, with derivative information, the affine invariant ensemble HMC can scale much better with dimension compared to derivative-free ensemble samplers.
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https://arxiv.org/abs/2505.02987
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Academic Papers
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2615febbbade08994cc94d197ab29de1a5732b008f2438e719c3bf75f885835d
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2026-01-01T00:00:00-05:00
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Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for Joint MRI Reconstruction and Denoising in Low-Field MRI
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arXiv:2505.05703v2 Announce Type: replace-cross Abstract: Deep learning has demonstrated strong potential for MRI reconstruction. However, conventional supervised learning requires high-quality, high-SNR references for network training, which are often difficult or impossible to obtain in different scenarios, particularly in low-field MRI. Self-supervised learning provides an alternative by removing the need for training references, but its reconstruction performance can degrade when the baseline SNR is low. To address these limitations, we propose hybrid learning, a two-stage training framework that integrates self-supervised and supervised learning for joint MRI reconstruction and denoising when only low-SNR training references are available. Hybrid learning is implemented in two sequential stages. In the first stage, self-supervised learning is applied to fully sampled low-SNR data to generate higher-quality pseudo-references. In the second stage, these pseudo-references are used as targets for supervised learning to reconstruct and denoise undersampled noisy data. The proposed technique was evaluated in multiple experiments involving simulated and real low-field MRI in the lung and brain at different field strengths. Hybrid learning consistently improved image quality over both standard self-supervised learning and supervised learning with noisy training references at different acceleration rates, noise levels, and field strengths, achieving higher SSIM and lower NMSE. The hybrid learning approach is effective for both Cartesian and non-Cartesian acquisitions. Hybrid learning provides an effective solution for training deep MRI reconstruction models in the absence of high-SNR references. By improving image quality in low-SNR settings, particularly for low-field MRI, it holds promise for broader clinical adoption of deep learning-based reconstruction methods.
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https://arxiv.org/abs/2505.05703
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Academic Papers
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5913273a9319c735186b745b1b5e6ed68ca4ee25d99e536dad7b84248313d947
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2026-01-01T00:00:00-05:00
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Value of Information-based assessment of strain-based thickness loss monitoring in ship hull structures
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arXiv:2505.07427v2 Announce Type: replace-cross Abstract: Recent advances in Structural Health Monitoring (SHM) have attracted industry interest, yet real-world applications, such as in ship structures remain scarce. Despite SHM's potential to optimise maintenance, its adoption in ships is limited due to the lack of clearly quantifiable benefits for hull maintenance. This study employs a Bayesian pre-posterior decision analysis to quantify the value of information (VoI) from SHM systems monitoring corrosion-induced thickness loss (CITL) in ship hulls, in a first-of-its-kind analysis for ship structures. We define decision-making consequence cost functions based on exceedance probabilities relative to a target CITL threshold, which can be set by the decision-maker. This introduces a practical aspect to our framework, that enables implicitly modelling the decision-maker's risk perception. We apply this framework to a large-scale, high-fidelity numerical model of a commercial vessel and examine the relative benefits of different CITL monitoring strategies, including strain-based SHM and traditional on-site inspections.
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https://arxiv.org/abs/2505.07427
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Academic Papers
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818c9d2ab25f8fedb97895c97a0af42c6eed73ebf019697836bfa271725bc892
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2026-01-01T00:00:00-05:00
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Algorithms for Nonlinear Mixed-Integer Location Estimation
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arXiv:2505.12980v2 Announce Type: replace-cross Abstract: For three decades, carrier-phase observations have been used to obtain the most accurate location estimates using global navigation satellite systems (GNSS). These estimates are computed by minimizing a nonlinear mixed-integer least-squares problem. Existing algorithms linearize the problem, orthogonally project it to eliminate real variables, and then solve the integer least-square problem. There is now considerable interest in developing similar localization techniques for terrestrial and indoor settings. We show that algorithms that linearize first fail in these settings and we propose several algorithms for computing the estimates. Some of our algorithms are elimination algorithms that start by eliminating the non-linear terms in the constraints; others construct a geometric arrangement that allows us to efficiently enumerate integer solutions (in polynomial time). We focus on simplified localization problems in which the measurements are range (distance) measurements and carrier phase range measurements, with no nuisance parameters. The simplified problem allows us to focus on the core question of untangling the nonlinearity and the integer nature of some parameters. We show using simulations that the new algorithms are effective at close ranges at which the linearize-first approach fails.
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https://arxiv.org/abs/2505.12980
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Academic Papers
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1fff28f292e1239de149e01cc3d6158e9ae142f7c001eddea32997f6b10ca089
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2026-01-01T00:00:00-05:00
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Adversarial quantum channel discrimination
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arXiv:2506.03060v2 Announce Type: replace-cross Abstract: We introduce a new framework for quantum channel discrimination in an adversarial setting, where the tester plays against an adversary. We show that in asymmetric hypothesis testing, the optimal type-II error exponent is precisely characterized by a new notion of quantum channel divergence (termed the minimum output channel divergence). This serves as a direct analog of the quantum Stein's lemma in this new framework, and complements previous studies on ``best-case'' channel discrimination, thereby providing a complete understanding of the ultimate limits of quantum channel discrimination. Notably, the optimal error exponent can be achieved by simple non-adaptive adversarial strategies, and despite the need for regularization, it remains efficiently computable and satisfies the strong converse property in general. Furthermore, we show that entropy accumulation, a powerful tool in quantum cryptography, can be reframed as an adversarial channel discrimination problem, establishing a new connection between quantum information theory and quantum cryptography.
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https://arxiv.org/abs/2506.03060
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5f9f9e115e9902bfd1ed63a7466880dcd0f809918714b32f983450d46cebf6fa
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2026-01-01T00:00:00-05:00
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Some remarks on stochastic converse Lyapunov theorems
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arXiv:2506.06053v2 Announce Type: replace-cross Abstract: In this brief note, we investigate some constructions of Lyapunov functions for stochastic discrete-time stabilizable dynamical systems, in other words, controlled Markov chains. The main question here is whether a Lyapunov function in some statistical sense exists if the respective controlled Markov chain admits a stabilizing policy. We demonstrate some constructions extending on the classical results for deterministic systems. Some limitations of the constructed Lyapunov functions for stabilization are discussed, particularly for stabilization in mean. Although results for deterministic systems are well known, the stochastic case was addressed in less detail, which the current paper remarks on. A distinguishable feature of this work is the study of stabilizers that possess computationally tractable convergence certificates.
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https://arxiv.org/abs/2506.06053
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52ef29eb08794cacbf06b532b78217a9e14cbbee3b42e8af43b9e3397b1651c1
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2026-01-01T00:00:00-05:00
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Learning quadratic neural networks in high dimensions: SGD dynamics and scaling laws
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arXiv:2508.03688v3 Announce Type: replace-cross Abstract: We study the optimization and sample complexity of gradient-based training of a two-layer neural network with quadratic activation function in the high-dimensional regime, where the data is generated as $f_*(\boldsymbol{x}) \propto \sum_{j=1}^{r}\lambda_j \sigma\left(\langle \boldsymbol{\theta_j}, \boldsymbol{x}\rangle\right), \boldsymbol{x} \sim N(0,\boldsymbol{I}_d)$, $\sigma$ is the 2nd Hermite polynomial, and $\lbrace\boldsymbol{\theta}_j \rbrace_{j=1}^{r} \subset \mathbb{R}^d$ are orthonormal signal directions. We consider the extensive-width regime $r \asymp d^\beta$ for $\beta \in [0, 1)$, and assume a power-law decay on the (non-negative) second-layer coefficients $\lambda_j\asymp j^{-\alpha}$ for $\alpha \geq 0$. We present a sharp analysis of the SGD dynamics in the feature learning regime, for both the population limit and the finite-sample (online) discretization, and derive scaling laws for the prediction risk that highlight the power-law dependencies on the optimization time, sample size, and model width. Our analysis combines a precise characterization of the associated matrix Riccati differential equation with novel matrix monotonicity arguments to establish convergence guarantees for the infinite-dimensional effective dynamics.
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https://arxiv.org/abs/2508.03688
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e259ff75812e102509e2a0853c01749bae9db7ff5eb5111aa9495a43660ac494
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2026-01-01T00:00:00-05:00
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Rapid Variable Resolution Particle Initialization for Complex Geometries
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arXiv:2508.12835v2 Announce Type: replace-cross Abstract: The accuracy of meshless methods like Smoothed Particle Hydrodynamics (SPH) is highly dependent on the quality of the particle distribution. Existing particle initialization techniques often struggle to simultaneously achieve adaptive resolution, handle intricate boundaries, and efficiently generate well-packed distributions inside and outside a boundary. This work presents a fast and robust particle initialization method that achieves these goals using standard SPH building blocks. Our approach enables simultaneous initialization of fluid and solid regions, supports arbitrary geometries, and achieves high-quality, quasi-uniform particle arrangements without complex procedures like surface bonding. Extensive results in both 2D and 3D demonstrate that the obtained particle distributions exhibit good boundary conformity, low spatial disorder, and minimal density variation, all with significantly reduced computational cost compared to existing approaches. This work paves the way for automated particle initialization to accurately model flow in and around bodies with meshless methods, particularly with SPH.
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https://arxiv.org/abs/2508.12835
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1b1ffe0b2a20a4367cee5d9b3c1729b7a1ef1f894849f7615227d6778d844677
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2026-01-01T00:00:00-05:00
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Reconstructing graphs and their connectivity using graphlets
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arXiv:2508.19189v2 Announce Type: replace-cross Abstract: Graphlets are subgraphs rooted at a fixed vertex. The number of occurrences of graphlets aligned to a particular vertex, called graphlet degree sequence (gds), gives a topological description of the surrounding of the analyzed vertex. Graphlet degree distribution (gdd) of a graph is a matrix containing graphlet degree sequence for all vertices in the given graph. A long standing open problem called reconstruction conjecture (RC) asks whether the structure of a graph is uniquely determined by the multiset of its vertex-deleted subgraphs. Graphlet degree distribution up to size (n - 1), (<= n - 1)-gdd, gives more information to reconstruct the graph and we use it to reconstruct any graph having a unique almost-asymmetric vertex-deleted subgraph, where almost-asymmetric means that at most one automorphism orbit has size larger than one. Moreover, we prove that any graph containing a vertex-cut of size 1 or any graph of order n having a vertex with degree at most 2 or at least n-2 is reconstructible from its (<= n - 1)-gdd, which expands results shown in the standard RC. We also discuss the relation between gdd and graph connectivity and the conditions on (<= 3)-gdd, whose breaking means that no graph with such gdd exists.
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https://arxiv.org/abs/2508.19189
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d96f73217b2798742a3d09b6e3ce89d999c503e4cd953b25fc79daa4fcc36bec
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2026-01-01T00:00:00-05:00
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Lipschitz-Guided Design of Interpolation Schedules in Generative Models
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arXiv:2509.01629v2 Announce Type: replace-cross Abstract: We study the design of interpolation schedules in the stochastic interpolants framework for flow and diffusion-based generative models. We show that while all scalar interpolation schedules achieve identical statistical efficiency under Kullback-Leibler divergence in path space after optimal diffusion coefficient tuning, their numerical efficiency can differ substantially. This motivates focusing on numerical properties of the resulting drift fields rather than purely statistical criteria for schedule design. We propose averaged squared Lipschitzness minimization as a principled criterion for numerical optimization, providing an alternative to kinetic energy minimization used in optimal transport approaches. A transfer formula is derived that enables conversion between different schedules at inference time without retraining neural networks. For Gaussian distributions, the optimized schedules achieve exponential improvements in Lipschitz constants over standard linear schedules, while for Gaussian mixtures, they reduce mode collapse in few-step sampling. We also validate our approach on high-dimensional invariant distributions from stochastic Allen-Cahn equations and Navier-Stokes equations, demonstrating robust performance improvements across resolutions.
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https://arxiv.org/abs/2509.01629
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0f240bc2503e9b0dab9ceded908c288166757e542c838fd2c5089fa8fd3f8a0b
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2026-01-01T00:00:00-05:00
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Dynamical Learning in Deep Asymmetric Recurrent Neural Networks
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arXiv:2509.05041v2 Announce Type: replace-cross Abstract: We investigate recurrent neural networks with asymmetric interactions and demonstrate that the inclusion of self-couplings or sparse excitatory inter-module connections leads to the emergence of a densely connected manifold of dynamically accessible stable configurations. This representation manifold is exponentially large in system size and is reachable through simple local dynamics, despite constituting a subdominant subset of the global configuration space. We further show that learning can be implemented directly on this structure via a fully local, gradient-free mechanism that selectively stabilizes a single task-relevant network configuration. Unlike error-driven or contrastive learning schemes, this approach does not require explicit comparisons between network states obtained with and without output supervision. Instead, transient supervisory signals bias the dynamics toward the representation manifold, after which local plasticity consolidates the attained configuration, effectively shaping the latent representation space. Numerical evaluations on standard image classification benchmarks indicate performance comparable to that of multilayer perceptrons trained using backpropagation. More generally, these results suggest that the dynamical accessibility of fixed points and the stabilization of internal network dynamics constitute viable alternative principles for learning in recurrent systems, with conceptual links to statistical physics and potential implications for biologically motivated and neuromorphic computing architectures.
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https://arxiv.org/abs/2509.05041
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36584b79a3e9d9754076a3af37d87ceabf50776bdd28448b396ed989318d275b
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2026-01-01T00:00:00-05:00
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Cubic Incompleteness: Hilbert's Tenth Problem at Degree Three
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arXiv:2510.00759v4 Announce Type: replace-cross Abstract: We analyze the cubic fragment $\mathcal D_3$ over $\mathbb N$ by isolating the uniform closure principle any total correct cubic solver would have to realize. In $\mathsf{HA}$ we give a fully constructive, additive and degree-controlled encoding of bounded verification: for each externally fixed bound, we effectively produce a finite system of degree-3 Diophantine equations whose solutions represent the existence of the corresponding finite proof or computation trace. The encoding is purely syntactic, using "gadgets" and "Carryless Pairing". In a classical metatheory (e.g. $\mathsf{PA}$) we show that the global solver hypothesis implies a uniform operator eliminating the bound inside $\mathcal D_3$, which is incompatible with standard non-uniformity/realizability constraints. Hence no uniform cubic can exist clasically.
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https://arxiv.org/abs/2510.00759
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a831164fca8930df203ba400e31f87529d107939ae85b03eae789769b6126a84
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2026-01-01T00:00:00-05:00
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Enhancing Diffusion-Based Sampling with Molecular Collective Variables
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arXiv:2510.11923v2 Announce Type: replace-cross Abstract: Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we encourage exploration by introducing a sequential bias along bespoke, information-rich, low-dimensional projections of atomic coordinates known as collective variables (CVs). We introduce a repulsive potential centered on the CVs from recent samples, which pushes future samples towards novel CV regions and effectively increases the temperature in the projected space. Our resulting method improves efficiency, mode discovery, enables the estimation of free energy differences, and retains independent sampling from the approximate Boltzmann distribution via reweighting by the bias. On standard peptide conformational sampling benchmarks, the method recovers diverse conformational states and accurate free energy profiles. We are the first to demonstrate reactive sampling using a diffusion-based sampler, capturing bond breaking and formation with universal interatomic potentials at near-first-principles accuracy. The approach resolves reactive energy landscapes at a fraction of the wall-clock time of standard sampling methods, advancing diffusion-based sampling towards practical use in molecular sciences.
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https://arxiv.org/abs/2510.11923
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e04256e3509a0e38f9c69d6c34720ac1777681d4b95509514f381afef0ed0b50
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2026-01-01T00:00:00-05:00
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Fast, Differentiable, GPU-Accelerated Ray Tracing for Multiple Diffraction and Reflection Paths
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arXiv:2510.16172v2 Announce Type: replace-cross Abstract: We present a fast, differentiable, GPU-accelerated optimization method for ray path tracing in environments containing planar reflectors and straight diffraction edges. Based on Fermat's principle, our approach reformulates the path-finding problem as the minimization of total path length, enabling efficient parallel execution on modern GPU architectures. Unlike existing methods that require separate algorithms for reflections and diffractions, our unified formulation maintains consistent problem dimensions across all interaction sequences, making it particularly suitable for vectorized computation. Through implicit differentiation, we achieve efficient gradient computation without differentiating through solver iterations, significantly outperforming traditional automatic differentiation approaches. Numerical simulations demonstrate convergence rates comparable to specialized Newton methods while providing superior scalability for large-scale applications. The method integrates seamlessly with differentiable programming libraries such as JAX and DrJIT, enabling new possibilities in inverse design and optimization for wireless propagation modeling. The source code is openly available at https://github.com/jeertmans/fpt-jax.
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https://arxiv.org/abs/2510.16172
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0f16f0c4f0d69c286ecf0ae3b95cf55834cfaca23c24efff1e629f2db979a1aa
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2026-01-01T00:00:00-05:00
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Value of Multi-pursuer Single-evader Pursuit-evasion Game with Terminal Cost of Evader's Position: Relaxation of Convexity Condition
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arXiv:2510.27271v2 Announce Type: replace-cross Abstract: In this study, we consider a multi-pursuer single-evader quantitative pursuit-evasion game with payoff function that includes only the terminal cost. The terminal cost is a function related only to the terminal position of the evader. This problem has been extensively studied in target defense games. Here, we prove that a candidate for the value function generated by geometric method is the viscosity solution of the corresponding Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) Dirichlet problem. Therefore, the value function of the game at each point can be computed by a mathematical program. In our work, the convexity of the terminal cost or the target is not required. The terminal cost only needs to be locally Lipschitz continuous. The cases in which the terminal costs or the targets are not convex are covered. Therefore, our result is more universal than those of previous studies, and the complexity of the proof is improved. We also discuss the optimal strategies in this game and present an intuitive explanation of this value function.
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https://arxiv.org/abs/2510.27271
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b4137281cf9eb917f9f605f1bbba3b8f4c3287151b4f0c4ce5724002e849f9ca
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2026-01-01T00:00:00-05:00
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Orchestrating Rewards in the Era of Intelligence-Driven Commerce
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arXiv:2512.00738v2 Announce Type: replace-cross Abstract: Despite their evolution from early copper-token schemes to sophisticated digital solutions, loyalty programs remain predominantly closed ecosystems, with brands retaining full control over all components. Coalition loyalty programs emerged to enable cross-brand interoperability, but approximately 60\% fail within 10 years in spite of theoretical advantages rooted in network economics. This paper demonstrates that coalition failures stem from fundamental architectural limitations in centralized operator models rather than operational deficiencies, and argues further that neither closed nor coalition systems can scale in intelligence-driven paradigms where AI agents mediate commerce and demand trustless, protocol-based coordination that existing architectures cannot provide. We propose a hybrid framework where brands maintain sovereign control over their programs while enabling cross-brand interoperability through trustless exchange mechanisms. Our framework preserves closed system advantages while enabling open system benefits without the structural problems that doom traditional coalitions. We derive a mathematical pricing model accounting for empirically-validated market factors while enabling fair value exchange across interoperable reward systems.
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https://arxiv.org/abs/2512.00738
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432c16e6a1147eb759a73423170465556bb9abdea3aab4a0000fe888b2393401
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2026-01-01T00:00:00-05:00
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GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics
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arXiv:2512.19936v2 Announce Type: replace-cross Abstract: We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are measured, our approach infers unknown closure terms in the governing equations as neural networks. The target to be discovered is the constitutive relations only, while the temporal derivative, convective transport terms, and pressure-gradient term in the governing equations are prescribed. The formulation is rooted in a variational principle from non-equilibrium thermodynamics, where the dynamics is defined by a free-energy functional and a dissipation functional. The unknown constitutive terms arise as functional derivatives of these functionals with respect to the state variables. To enable a flexible and structured model discovery, the free-energy and dissipation functionals are parameterized using neural networks, while their functional derivatives are obtained via automatic differentiation. This construction enforces thermodynamic consistency by design, guaranteeing monotonic decay of the total free energy and non-negative entropy production. The resulting method, termed GIMLET (Generalizable and Interpretable Model Learning through Embedded Thermodynamics), avoids reliance on a predefined library of candidate functions, unlike sparse regression or symbolic identification approaches. The learned models are generalizable in that functionals identified from one dataset can be transferred to distinct datasets governed by the same underlying equations. Moreover, the inferred free-energy and dissipation functions provide direct physical interpretability of the learned dynamics. The framework is demonstrated on several benchmark systems, including the viscous Burgers equation, the Kuramoto--Sivashinsky equation, and the incompressible Navier--Stokes equations for both Newtonian and non-Newtonian fluids.
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https://arxiv.org/abs/2512.19936
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92c7237802f408140ce1be8f7ea37d4992974bd5d59d0f63bfc5204bfc9fab13
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2026-01-01T00:00:00-05:00
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Contingency Model-based Control (CMC) for Communicationless Cooperative Collision Avoidance in Robot Swarms
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arXiv:2512.20391v3 Announce Type: replace-cross Abstract: Cooperative collision avoidance between robots, or `agents,' in swarm operations remains an open challenge. Assuming a decentralized architecture, each agent is responsible for making its own decisions and choosing its control actions. Most existing approaches rely on a (wireless) communication network between (some of) the agents. In reality, however, communication is brittle. It may be affected by latency, further delays and packet losses, and transmission faults. Moreover, it is subject to adversarial attacks, such as jamming or spoofing. This paper proposes Contingency Model-based Control (CMC), a decentralized cooperative approach that does not rely on communication. Instead, the control algorithm is based on consensual rules that are designed for all agents offline, similar to traffic rules. For CMC, this includes the definition of a contingency trajectory for each robot, and perpendicular bisecting planes as collision avoidance constraints. The setup permits a full guarantee of recursive feasibility and collision avoidance between all swarm members in closed-loop operation. CMC naturally satisfies the plug & play paradigm, i.e., new robots may enter the swarm dynamically. The effectiveness of the CMC regime is demonstrated in two numerical examples, showing that the collision avoidance guarantee is intact and the robot swarm operates smoothly in a constrained environment.
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https://arxiv.org/abs/2512.20391
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6aff86192395cc74cc330b9e88298b58c3db54942003f1cc3a659a0b1be6023d
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2026-01-01T00:00:00-05:00
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Poincar\'e Duality and Multiplicative Structures on Quantum Codes
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arXiv:2512.21922v2 Announce Type: replace-cross Abstract: Quantum LDPC codes have attracted intense interest due to their advantageous properties for realizing efficient fault-tolerant quantum computing. In particular, sheaf codes represent a novel framework that encompasses all well-known good qLDPC codes with profound underlying mathematics. In this work, we generalize Poincar\'e duality from manifolds to both classical and quantum codes defined via sheaf theory on $t$-dimensional cell complexes. Viewing important code properties including the encoding rate, code distance, local testability soundness, and efficient decoders as parameters of the underlying (co)chain complexes, we rigorously prove a duality relationship between the $i$-th chain and the $(t-i)$-th cochain of sheaf codes. We further build multiplicative structures such as cup and cap products on sheaved chain complexes, inspired by the standard notions of multiplicative structures and Poincar\'e duality on manifolds. This immediately leads to an explicit isomorphism between (co)homology groups of sheaf codes via a cap product. As an application, we obtain transversal disjoint logical $\mathrm{C}Z$ gates with $k_{\mathrm{C}Z}=\Theta(n)$ on families of good qLDPC and almost-good quantum locally testable codes. Moreover, we provide multiple new methods to construct transversal circuits composed of $\mathrm{C}\mathrm{C}Z$ gates as well as for higher order controlled-$Z$ that are provably logical operations on the code space. We conjecture that they generate nontrivial logical actions, pointing towards fault-tolerant non-Clifford gates on nearly optimal qLDPC sheaf codes. Mathematically, our results are built on establishing the equivalence between sheaf cohomology in the derived-functor sense, \v{C}ech cohomology, and the cohomology of sheaf codes, thereby introducing new mathematical tools into quantum coding theory.
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https://arxiv.org/abs/2512.21922
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784ba869239a7d1d6747601d487e7ace5578701df82d8cc72168502aaf58568a
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2026-01-01T00:00:00-05:00
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LogosQ: A High-Performance and Type-Safe Quantum Computing Library in Rust
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arXiv:2512.23183v2 Announce Type: replace-cross Abstract: Developing robust and high performance quantum software is challenging due to the dynamic nature of existing Python-based frameworks, which often suffer from runtime errors and scalability bottlenecks. In this work, we present LogosQ, a high performance backend agnostic quantum computing library implemented in Rust that enforces correctness through compile time type safety. Unlike existing tools, LogosQ leverages Rust static analysis to eliminate entire classes of runtime errors, particularly in parameter-shift rule gradient computations for variational algorithms. We introduce novel optimization techniques, including direct state-vector manipulation, adaptive parallel processing, and an FFT optimized Quantum Fourier Transform, which collectively deliver speedups of up to 900 times for state preparation (QFT) and 2 to 5 times for variational workloads over Python frameworks (PennyLane, Qiskit), 6 to 22 times over Julia implementations (Yao), and competitive performance with Q sharp. Beyond performance, we validate numerical stability through variational quantum eigensolver (VQE) experiments on molecular hydrogen and XYZ Heisenberg models, achieving chemical accuracy even in edge cases where other libraries fail. By combining the safety of systems programming with advanced circuit optimization, LogosQ establishes a new standard for reliable and efficient quantum simulation.
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https://arxiv.org/abs/2512.23183
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78eee755f1780fd2a13fe91b255d17e21342ceb1b9064891ed0e4321a178d935
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2026-01-01T00:00:00-05:00
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A space-time extension of a conservative two-fluid cut-cell method for moving diffusion problems
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arXiv:2512.23358v2 Announce Type: replace-cross Abstract: We present a space-time extension of a conservative Cartesian cut-cell finite-volume method for two-phase diffusion problems with prescribed interface motion. The formulation follows a two-fluid approach: one scalar field is solved in each phase with discontinuous material properties, coupled by sharp interface conditions enforcing flux continuity and jump laws. To handle moving boundaries on a fixed Cartesian grid, the discrete balance is written over phase-restricted space-time control volumes, whose geometric moments (swept volumes and apertures) are used as weights in the finite-volume operators. This construction naturally accounts for the creation and destruction of cut cells (fresh/dead-cell events) and yields strict discrete conservation. The resulting scheme retains the algebraic structure of the static cut-cell formulation while incorporating motion through local geometric weights and interface coupling operators. A series of verification and validation tests in two and three dimensions demonstrate super-linear accuracy in space, robust behavior under repeated topology changes and conservation across strong coefficient jumps and moving interfaces. The proposed space-time cut-cell framework provides a conservative building block for multiphase transport in evolving geometries and a foundation for future free-boundary extensions such as Stefan-type phase change.
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https://arxiv.org/abs/2512.23358
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4e895a2ed7794ec0fffd701493f6d29ded97c9cbf144e8f2aafd42d97e0df34d
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2026-01-01T00:00:00-05:00
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Notes on Crowther and the "Interpretation" of Quantum Mechanics (arXiv:2512.14315)
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arXiv:2512.23721v1 Announce Type: new Abstract: We read Karen Crowther's \emph{Another 100 Years of Quantum Interpretation?} with two practical goals. First, we spell out what she means by interpretation'': an attempt to provide understanding (not just predictions), which may be representationalist or non-representationalist, and which she contrasts with deeper \emph{reductive} (inter-theoretic) explanation -- especially in the quantum-gravity setting. Second, we list twelve points where the paper's physics-facing wording could be sharpened. In our view, several claims are directionally well-motivated but stated more strongly than the underlying physics supports, or they run together distinct notions (e.g.\ degrees of freedom,'' singularity,'' and different senses of locality'') that need careful separation. We end by suggesting that the philosophical question is genuinely worthwhile, but the physics should be phrased more cautiously so that heuristic motivation is not mistaken for strict implication.
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https://arxiv.org/abs/2512.23721
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928d877e42c5c6bc23e1f1a995ab2dbca39ce7ac4389bb1ee40fab128855ea72
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2026-01-01T00:00:00-05:00
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Lasing at a Stationary Inflection Point: erratum
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arXiv:2512.23723v1 Announce Type: new Abstract: This erratum provides an updated fitting function for the lasing threshold of finite-length cavities operating at a stationary inflection point (SIP) or regular band edge (RBE) resonance, clarifying their asymptotic scaling with the number of unit cells of the periodic cavity.
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https://arxiv.org/abs/2512.23723
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01dc30bcb6d42bad343970e995559806b44d854ce9839ce6907021a965411bf9
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2026-01-01T00:00:00-05:00
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When the Earth and Sky Dance: Seismic Shakes Meet Weather Patterns
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arXiv:2512.23731v1 Announce Type: new Abstract: A new modelling approach shows how the Earth's hidden vibrations may drive global weather dynamics and atmospheric pressure variations, hinting that the planet's own beat could be imprinted on our climate. The atmospheric rotational patterns of the mean sea level pressure, in connection to the development of powerful storms, are shown to be caused by Earth's rotational elastic dynamics and earthquake-induced oscillations. These seismic excitations are discussed in relation to storm formation and the global atmospheric patterns of high-pressure regions.
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https://arxiv.org/abs/2512.23731
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4fe797b21dba05f76fb783d674645f6f55d354504125a6aa45345955efc802b7
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2026-01-01T00:00:00-05:00
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Learning Density Functionals to Bridge Particle and Continuum Scales
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arXiv:2512.23840v1 Announce Type: new Abstract: Predicting interfacial thermodynamics across molecular and continuum scales remains a central challenge in computational science. Classical density functional theory (cDFT) provides a first-principles route to connect microscopic interactions with macroscopic observables, but its predictive accuracy depends on approximate free-energy functionals that are difficult to generalize. Here we introduce a physics-informed learning framework that augments cDFT with neural corrections trained directly against molecular-dynamics data through adjoint optimization. Rather than replacing the theory with a black-box surrogate, we embed compact neural networks within the Helmholtz free-energy functional, learning local and nonlocal corrections that preserve thermodynamic consistency while capturing missing correlations. Applied to Lennard-Jones fluids, the resulting augmented excess free-energy functional quantitatively reproduces equilibrium density profiles, coexistence curves, and surface tensions across a broad temperature range, and accurately predicts contact angles and droplet shapes far beyond the training regime. This approach combines the interpretability of statistical mechanics with the adaptability of modern machine learning, establishing a general route to learned thermodynamic functionals that bridge molecular simulations and continuum-scale models.
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https://arxiv.org/abs/2512.23840
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7fdd79069203d4d13dd411f77eb953686f75f5cd6dde5939a02b1c706c1ce6ca
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2026-01-01T00:00:00-05:00
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A Relative Liutex Method for Vortex Identification
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arXiv:2512.23857v1 Announce Type: new Abstract: A relative Liutex vortex identification method is proposed in this study, together with its explicit mathematical formulation. The method is designed to identify vortical structures based solely on local flow-field information and is inherently Galilean invariant, ensuring robustness under different reference frames. To validate the proposed approach, a three-dimensional flat-plate boundary-layer transition case is investigated, in which the relative Liutex is systematically compared with conventional vortex identification methods, including the Q-criterion and the original Liutex method. The results show that the relative Liutex is capable of simultaneously capturing both strong and weak vortical structures. Importantly, its behavior cannot be interpreted as a simple superposition of Liutex iso-surfaces obtained using different threshold values. Instead, the relative Liutex provides a more selective and physically coherent identification of weak vortices, particularly in regions above the $\Lambda$-vortex and in the downstream hairpin-vortex structures, while effectively suppressing spurious and noise-induced features. These advantages arise from its formulation based on local velocity-gradient strength rather than a global vortical-intensity measure. Owing to its ability to consistently identify vortices across a wide range of intensities, the relative Liutex demonstrates strong potential for revealing complex vortex structures and underlying flow mechanisms in vortex-dominated flows.
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https://arxiv.org/abs/2512.23857
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Academic Papers
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ae6a42f3a96c7b97c594ac143e0ed61c38ce0c0c388072191dd804c5b8f27a30
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2026-01-01T00:00:00-05:00
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Investigation of the benefits and disadvantages of using double-pair anti-Helmholtz coils in BEC-producing MOT setups and optimizing their design
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arXiv:2512.23874v1 Announce Type: new Abstract: This work has investigated the Magneto-Optical Trap (MOT) system used to produce Bose-Einstein Condensate (BEC). A primary challenge addressed in this study concerns the geometric limitations of traditional single-pair anti-Helmholtz coil configurations, where the magnetic field peaks occur outside the accessible inter-coil region. To overcome this limitation, we have explored the use of double-pair anti-Helmholtz coil configurations that create well-shaped magnetic field potentials centered at the experimentally accessible $z=0$ location. This investigation encompasses the three sequential processes of atom cooling: cooling in a linear external magnetic field through Doppler cooling, cooling in a well-shaped magnetic field through trapping, and evaporative cooling of atoms to achieve sub-microkelvin temperatures. Through theoretical analysis and numerical simulation, we have determined optimal geometric parameters for the coil configuration and operational parameters including laser detuning, saturation intensity, and initial atom populations for ${}^{87}\text{Rb}$ BEC production. The results indicate that with the optimized configuration, the system can achieve final temperatures of approximately $T_f \sim 60\,\mathrm{nK}$ and produce condensate populations of $\sim 10^5$ atoms with a mean density of $n_0 = 4.9 \times 10^{15}\,\mathrm{m}^{-3}$, providing systematic design guidance for experimental BEC systems
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https://arxiv.org/abs/2512.23874
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Academic Papers
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9408f15d6942949b54a509f88ffa276e3e6af0e237d3544cd67a68b9304ec20d
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2026-01-01T00:00:00-05:00
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Surface adsorption at the thermodynamic limit using periodic DLPNO-MP2 theory: A study of CO on MgO at dilute and dense coverages
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arXiv:2512.23879v1 Announce Type: new Abstract: We apply periodic domain-based local pair natural orbital second-order M{\o}ller--Plesset perturbation theory (DLPNO-MP2) to probe the adsorption energy of CO on MgO(001), the consensus toy model system for surface adsorption. A number of robust correlated wavefunction methods now achieve excellent agreement with experiment for the adsorption of a single CO molecule onto the MgO surface. However, studies probing denser coverage ratios are scarce because of the increased computational expense and the larger configuration space to optimize. We leverage the computational efficiency of periodic DLPNO-MP2 to perform simulations beyond a single unit cell. By using large supercells, we highlight the importance of accurately representing the thermodynamic limit of the surface, and demonstrate in turn that different coverage ratios can be consistently probed. In the dilute regime, we show that adsorption energies obtained from periodic DLPNO-MP2 agree with existing benchmarks. We then obtain adsorption energies at increasing densities approaching full monolayer coverage. Our results show a reduction in binding strength at full coverage, agreeing with experimental observations, which is explained by the increasing lateral repulsions between the COs. This study demonstrates the efficacy of periodic DLPNO-MP2 for probing increasingly sophisticated adsorption systems at the thermodynamic limit.
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https://arxiv.org/abs/2512.23879
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781cf98e72ca44899ecb3324fd289b81eeb3249cb7d1a9b9e0e683784dc6cfc3
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2026-01-01T00:00:00-05:00
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Control and read-out of the HEPD-02 tracking system onboard CSES-02 satellite
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arXiv:2512.23885v1 Announce Type: new Abstract: The High Energy Particle Detector (HEPD-02) is a payload of the second China Seismo-Electromagnetic Satellite (CSES-02), designed and built by the Italian Limadou collaboration. Its purpose is to detect cosmic rays and trapped particles of radiation belts, in the kinetic energy range 3-100 MeV for electrons, 30-200 MeV for protons. HEPD-02 is the first space detector to use a tracking detector based on Monolithic Active Pixel Sensors (MAPS). The MAPS provides high spatial resolution, low noise, increased robustness, and low production costs. Operating MAPS in space presents a significant challenge due to strict power consumption requirements. To meet such constraints, a custom Tracker Data Acquisition (TDAQ) board and firmware have been designed and implemented, by using a commercial low-power Field Programmable Gate Array (FPGA). This paper addresses the design features of the TDAQ unit, enabling the tracking detector to be operated efficiently, with particular focus on the power consumption performance.
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https://arxiv.org/abs/2512.23885
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35655da47456ffc3f806221994255924eeb88a24b5cc658b51453556a1fbb4d9
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2026-01-01T00:00:00-05:00
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Turbulence Kinetic Energy Distribution and Heat Transfer in a Porous Layer Induced by Bluff Body Vortex Shedding
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arXiv:2512.23893v1 Announce Type: new Abstract: When a turbulent vortex impinges on a porous layer, it creates a complex multiscale interaction: the wake structures that form in the free fluid engage with the intricate geometry of the pores, and this interplay governs both the turbulent energy budget and the rate of heat transfer. Here we use interface-resolved two-dimensional direct numerical simulations (DNS) to examine how a bluff-body wake impinges on an in-line porous array heated to maintain a constant wall temperature. The Reynolds number is fixed at Re = 10000, and the porosity is varied between $\phi$ = 0.80 and $\phi$ = 0.95. In all cases, the incoming von Karman vortices undergo rapid breakdown at the porous/fluid interface and do not persist as coherent macroscale structures within the porous layer. The interface instead acts as a spectral filter: large-scale wake energy is strongly attenuated, while turbulence is regenerated locally within the matrix via shear layers and microscale vortex shedding around individual obstacles. Thermal statistics show that the lower-porosity medium produces higher local and surface-averaged Nusselt numbers across representative interface and interior locations. This is consistent with the stronger shear and enhanced fluid/solid thermal interaction associated with the larger surface-area-to-volume ratio. These results clarify the mechanisms by which wake-driven turbulence is converted into pore-scale motions and how porosity tunes the balance between turbulence attenuation and convective heat transfer in porous coatings and inserts.
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https://arxiv.org/abs/2512.23893
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92fc81ead7a4c90631d32c3dfd72a0909e5b91a8f4f92def1c26c26b92f62080
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2026-01-01T00:00:00-05:00
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Exact analysis of potential flow past bodies of irregular shapes
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arXiv:2512.23895v1 Announce Type: new Abstract: Fluid flow past one or more solid bodies is a fundamental problem of much practical importance. Standard solutions of simplified problems involving incompressible inviscid irrotational flow past common geometries such as circular cylinders and airfoils are commonly available. This work presents exact analysis of a potential flow problem involving fluid flow past one or more bodies of irregular shapes. The problem is solved by expressing the shape of each body using Heaviside functions, and writing the potential function as an eigenfunction-based series. Using the properties of Heaviside functions, the series coefficients are determined by deriving a set of linear algebraic equations that govern the coefficients. Benchmarking of the analytical technique against well-known solutions of standard problems is carried out, showing excellent agreement. Good agreement with past work on the specific problem of potential flow past multiple circular cylinders further establishes the accuracy of the analytical technique. Illustrative problems of flow past complicated geometries are solved. Implementation aspects and limitations of the analytical technique are discussed.
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https://arxiv.org/abs/2512.23895
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0d2fd2253757dd88baa6b2b2a824ca9764955dace24bcd454e49388268defbb0
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2026-01-01T00:00:00-05:00
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Edge emission from 265~nm UV-C LEDs grown by MBE on bulk AlN
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arXiv:2512.23896v1 Announce Type: new Abstract: UV-C LEDs pseudomorphically grown by MBE on bulk AlN substrates emitting at 265 nm are demonstrated. High current density up to 800 A/cm$^2$, 5 orders of on/off ratio, and low differential on-resistance of 2.6 m$\Omega\cdot$cm$^2$ at the highest current density is achieved. The LED heterostructure has a high refractive index waveguide core surrounded by n- and p-cladding layers similar to a laser diode designed for mode confinement at 270 nm to facilitate edge emission and collection of photons. Edge-emitting devices are made by cleaving the fabricated LEDs along the $m$-plane of the wurtzite crystal. Electrical injection results in emission of high energy 4.7 eV photons that are collected from the cleaved edge of the LEDs corresponding to the optical bandgap of the AlGaN active region. The contribution of power dissipation across the n- and p-regions of the diode is discussed. The n-contact resistance to n-AlGaN is identified as the largest contributor to the series resistance of the LED in the present generation of devices.
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https://arxiv.org/abs/2512.23896
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f8cc445bfa3a55ceb96518b56ee229650244192e5c8803e5ed5cc97cb4a27019
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2026-01-01T00:00:00-05:00
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Inelastic dilatancy as a mechanism for coseismic fluid depressurization of a shallow fault zone
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arXiv:2512.23912v1 Announce Type: new Abstract: Hydrologic observations and experimental studies indicate that inelastic dilation from coseismic fault damage can cause substantial pore pressure reduction, yet most near-fault hydromechanical models ignore such inelastic effects. Here, we present a 3-D groundwater flow model incorporating the effects of inelastic dilation based on an earthquake dynamic rupture model with inelastic off fault deformation, both on pore pressure and permeability enhancement. Our results show that inelastic dilation causes mostly notable depressurization within 1 to 2 km off the fault at shallow depths (< 3 km). We found agreement between our model predictions and recent field observations, namely that both sides of the fault can experience large magnitude (~tens of meters) water level drawdowns. For comparison, simulations considering only elastic strain produced smaller water level changes (~several meters) and contrasting signs of water level change on either side of the fault. The models show that inelastic dilation is a mechanism for coseismic fault depressurization at shallow depths. While the inelastic dilation is a localized phenomenon which is most pronounced in the fault zone, the pressure gradients produced in the coseismic phase have a broader effect, increasing fluid migration back into the fault zone in the postseismic phase. We suggest field hydrologic measurements in the very near field (1 to 2 km) of active faults could capture damage-related pore pressure signals produced by inelastic dilation, helping improve our understanding of fault mechanics and groundwater management near active faults.
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https://arxiv.org/abs/2512.23912
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8558ce021f738aada9bdb2a8d4db3ece9498ddff5265ad010304709c7ecd0fd4
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2026-01-01T00:00:00-05:00
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Ab Initio Melting Properties of Water and Ice from Machine Learning Potentials
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arXiv:2512.23939v1 Announce Type: new Abstract: Liquid water exhibits several important anomalous properties in the vicinity of the melting temperature ($T_{\mathrm{m}}$) of ice Ih, including a higher density than ice and a density maximum at 4~$^{\circ}$C. Experimentally, an isotope effect on $T_{\mathrm{m}}$ is observed: the melting temperature of H$_2$O is approximately 4~K lower than that of D$_2$O. This difference can only be explained by nuclear quantum effects (NQEs), which can be accurately captured using path integral molecular dynamics (PIMD). Here we run PIMD simulations driven by Deep Potential (DP) models trained on data from density functional theory (DFT) based on SCAN, revPBE0-D3, SCAN0, and revPBE-D3 and a DP model trained on the MB-pol potential. We calculate the \tm of ice, the density discontinuity at melting, and the temperature of density maximum ($T_{\mathrm{dm}}$) of the liquid. We find that the model based on MB-pol agrees well with experiment. The models based on DFT incorrectly predict that NQEs lower $T_{\mathrm{m}}$. For the density discontinuity, SCAN and SCAN0 predict values close to the experimental result, while revPBE-D3 and revPBE0-D3 significantly underestimate it. Additionally, the models based on SCAN and SCAN0 correctly predict that the $T_{\mathrm{dm}}$ is higher than $T_{\mathrm{m}}$, while those based on revPBE-D3 and revPBE0-D3 predict the opposite. We attribute the deviations of the DFT-based models from experiment to the overestimation of hydrogen bond strength. Our results set the stage for more accurate simulations of aqueous systems grounded on DFT.
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https://arxiv.org/abs/2512.23939
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Academic Papers
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a758ff007f434168cc6ad28e3b412b8d5d6d1e94796da15d4c30cb098269ae9c
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2026-01-01T00:00:00-05:00
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Assessment of First-Principles Methods in Modeling the Melting Properties of Water
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arXiv:2512.23940v1 Announce Type: new Abstract: First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on these first-principles data widen the range of accessible properties. However, the capabilities of different first-principles methods are not yet fully understood due to the lack of systematic benchmarks, the underestimation of the uncertainties introduced by MLPs, and the neglect of nuclear quantum effects (NQEs). Here, we systematically assess first-principles methods by calculating key melting properties using path integral molecular dynamics (PIMD) driven by Deep Potential (DP) models trained on data from density functional theory (DFT) with SCAN, revPBE0-D3, SCAN0 and revPBE-D3 functionals, as well as from the MB-pol potential. We find that MB-pol is in qualitatively good agreement with the experiment in all properties tested, whereas the four DFT functionals incorrectly predict that NQEs increase the melting temperature. SCAN and SCAN0 slightly underestimate the density change between water and ice upon melting, but revPBE-D3 and revPBE0-D3 severely underestimate it. Moreover, SCAN and SCAN0 correctly predict that the maximum liquid density occurs at a temperature higher than the melting point, while revPBE-D3 and revPBE0-D3 predict the opposite behavior. Our results highlight limitations in widely used first-principles methods and call for a reassessment of their predictive power in aqueous systems.
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https://arxiv.org/abs/2512.23940
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aa4f456f124382f106d52fb9a1e7f4eebb4a7747e34751e47ab436c886e8878e
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2026-01-01T00:00:00-05:00
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Design, construction, and testing of the PandaX-xT cryogenics system
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arXiv:2512.23974v1 Announce Type: new Abstract: The PandaX-xT is a next-generation experiment with broad scientific goals, including the search for dark matter, Neutrinoless Double Beta Decay, and astrophysical neutrinos, using a dual-phase time projection chamber with about 43 tons of liquid xenon. A new cryogenics system of the PandaX-xT is described in this paper. It is developed to handle large mass of liquid xenon efficiently and safely, including two cooling towers for normal operation and one liquid-nitrogen coil for emergency case. Each cooling tower equipped with an AL600 Gifford-McMahon cryocooler features a 1300 W heater, specifically designed to maintain the cold finger's temperature at the desired setpoint. The performance of the cooling tower and the coil has been tested. The cryogenics system with two cooling towers has achieved about 1900~W cooling power at 178~K. The liquid nitrogen coil provides emergency cooling power of more than 1500~W at liquid xenon temperature. For the prototype of a 1-tonne liquid xenon detector, the fluctuation of xenon saturated vapor pressure remains below 1 kPa over one month, while the pressure is around 210~kPa.
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https://arxiv.org/abs/2512.23974
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bc669b98e18104de8dc0b8d9a5f7c803a401889de30669a08b0fe22580b1b0ac
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2026-01-01T00:00:00-05:00
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The role of particle feedback on particle acceleration in magnetic reconnection
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arXiv:2512.24054v1 Announce Type: new Abstract: Magnetic reconnection is a ubiquitous process in astrophysical plasmas and an efficient mechanism for particle acceleration. Using 2.5D magnetohydrodynamic (MHD) simulations with a co-evolving fluid-particle framework, we investigate how particle feedback affects reconnection and acceleration. Our simulations demonstrate that particle feedback to the fluid amplifies shear flows within magnetic islands, which strengthens the convective electric field and thereby boosts particle acceleration. This mechanism results in a higher maximum particle energy and a harder non-thermal energy spectrum. The guide field suppresses both the increase in gas internal energy and particle acceleration. These findings highlight the complex interplay between feedback, guide fields, and reconnection dynamics.
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https://arxiv.org/abs/2512.24054
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64b0b90502ac986c27f7e6cb85387d0e45866863a50acc47282c89d4c09b2f91
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2026-01-01T00:00:00-05:00
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A two-dimensional terahertz smart wristband for integrated sensing and communication
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arXiv:2512.24060v1 Announce Type: new Abstract: The development of wearable devices for terahertz (THz) integrated sensing and communication (ISAC) is pivotal for forthcoming 6G Internet of Things (IoT) and wearable optoelectronics. However, existing THz system suffers from bulkiness, narrow spectral response and limited flexibility constrained by their dependence on external antennas, complex coupling architectures and rigid components. Here, we present a 2D THz smart wristband based on a graphene plasmon polariton atomic cavity (PPAC) array, which integrates sensing and communication within a monolithic microdetector. Operating without any external antenna, the compact and flexible device enables self-powered, polarization-sensitive and frequency-selective THz detection across a broad response spectrum from 0.25 to 4.24 THz, with a responsivity of 6 V/W, a response time of 62 ms, and mechanical robustness maintained over 2000 bending cycles. Notably, we further exploit its multi-parameter THz responses for dual-purpose ISAC functionality. For sensing, the polarization- and strain-dependent THz responses are utilized as high-dimensional features for a convolutional neural network (CNN), enabling circuit fault diagnosis with 97% accuracy. For communication, the device implements secure encrypted communication under simulated on-body wearing condition through dual-channel encoding of THz polarization and on-off signals. This 2D ISAC platform paves the way for miniaturized, intelligent wearable systems for advanced human-machine interaction.
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https://arxiv.org/abs/2512.24060
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e9beb131c355a81717bc393c18ae800c2cc6b7ade4e4dc8fc27414150ac0bd40
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2026-01-01T00:00:00-05:00
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Intrinsic Meron Spin Textures in Generic Focused Fields
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arXiv:2512.24084v1 Announce Type: new Abstract: Optical spin textures with nontrivial topology hold promise for structured light and photonic information processing, yet their generation typically relies heavily on externally structured light with care. This raises questions about their universal existence and true robustness. Here, we uncover and experimentally verify a meron-like spin texture that emerges intrinsically in focused fields, without any wavefront engineering. This intrinsic meron spin texture, unlike their externally engineered counterparts, exhibits exceptional robustness against a wide range of inputs, including partially polarized and spatially disordered pupils corrupted by decoherence and depolarization. We attribute its resilience to topological protection from phase vortices in the focal field. Our findings reveal a naturally occurring spin structure that is intrinsic to the focused field with exceptional robustness against noise, which complements the existing externally engineered ones. It offers new ingredients into topological spin textures in optics and enriches their potentials for disorder-resilient photonic applications.
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https://arxiv.org/abs/2512.24084
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9f14044c1086cdd9325decf09a42d2e9268aa1794609242469630c44f37d8e6e
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2026-01-01T00:00:00-05:00
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Protocellular energetics: Free energy estimates for all metabolic, self-assembly and vesicle fission processes
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arXiv:2512.24095v1 Announce Type: new Abstract: As minimal cells or protocells are dramatically simpler than modern unicells it is possible to quantitatively estimate free energy changes for every process in the lifecycle of a protocell and compare these with estimates of the free energy changes for lifecycles in modern unicells. We present quantitative estimates of all metabolic changes in part by new density function theory (DFT) estimations, in part by compiling previously measured or estimated free energy changes, and in part by new thermodynamic calculations for all self-assembly, vesicle bending, and fission energies.
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https://arxiv.org/abs/2512.24095
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Academic Papers
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36dd044c8bfe46d1925d46ef49e3eca6eefa3eb4fd9065d6db1beab158c21887
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2026-01-01T00:00:00-05:00
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Exceptional Points in the Scattering Resonances of a Sphere Dimer
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arXiv:2512.24104v1 Announce Type: new Abstract: We investigate exceptional points of degeneracy (EPDs) in electromagnetic scattering of a sphere dimer from the electroquasistatic limit to the fully retarded regime. In the quasistatic limit, we prove that $\parity\trev$-symmetric configurations, realized by spheres with complex-conjugate susceptibilities, host EPDs. Beyond this limit, retardation breaks $\parity\trev$-symmetry; nevertheless, by jointly tuning the material dispersion of the two spheres, we derive analytic conditions for the existence of EPDs at \textit{real-frequencies}. Near an EPD, we show that single-parameter perturbations yield the characteristic square-root splitting of the eigenfrequencies, and we quantify its impact on scattering, extinction, and absorption, clarifying sensing implications.
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https://arxiv.org/abs/2512.24104
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bc602dd8331b4f353ec9dac42aa0f096392b5a4091b363e51b8aad4789e798b2
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2026-01-01T00:00:00-05:00
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Entanglement dynamics driven by topology and non-Hermiticity
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arXiv:2512.24107v1 Announce Type: new Abstract: The interplay between topology and non-Hermiticity gives rise to exotic dynamic phenomena that challenge conventional wave-packet propagation and entanglement dynamics. While recent studies have established the non-Hermitian skin effect (NHSE) as a key mechanism for anomalous wave dynamics, a unified framework for characterizing and controlling entanglement evolution in non-Hermitian topological systems remains underdeveloped. Here, by combining theory and experiments, we demonstrate that entanglement entropy (EE) and transport currents serve as robust dynamic probes to distinguish various non-Hermitian topological regimes. Using a generalized non-Hermitian Su-Schrieffer-Heeger model implemented in an acoustic analog platform, we identify three dynamic phases, bulk-like, edge-like, and skin-like regimes, each exhibiting unique EE signatures and transport characteristics. In particular, skin-like dynamics exhibit periodic information shuttling with finite, oscillatory EE, while edge-like dynamics lead to complete EE suppression. We further map the dynamic phase diagram and show that EE scaling and temporal profiles directly reflect the competition between coherent delocalization and NHSE-driven localization. Our results establish a programmable approach to steering entanglement and transport via tailored non-Hermitian couplings, offering a pathway for engineering quantum information dynamics in synthetic phononic, photonic, and quantum simulators.
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https://arxiv.org/abs/2512.24107
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5b364f6cc2cb21a915b6cfdad40ffd9d4df16b63b72c629dfc5c4816ae3a357a
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2026-01-01T00:00:00-05:00
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The effect of HVDC lines in power-grids via Kuramoto modelling
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arXiv:2512.24122v1 Announce Type: new Abstract: We present a numerical study on the synchronization and cascade failure behaviour by solving the adaptive second-order Kuramoto model on a large high voltage (HV) European power-grid. This non-perturbative analysis takes into account non-linear effects, which occur even when phase differences are large, when the system is away from the steady state, and even during a blackout cascade. Our dynamical simulations show that improvements in the phase synchronziation stabilization as well as the in the cascade sizes can be related to the finite size scaling behaviour of the second order Kuramoto on graphs with $d_s<4$ spectral dimensions. On the other hand drawbacks in the frequency spread and Braess effects also occur by varying the total transmitted power at large and small global couplings, presumably when the fluctuations are small, causing a freezing in the dynamics. We compare simulations of the fully AC model with those of static or adaptive High Voltage Direct Current (HVDC) line replacements. The adaptive (local frequency difference-based) HVDC lines are more efficient in the steady state, at the expense of very long relaxation times.
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https://arxiv.org/abs/2512.24122
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c2bbafdd91029474179aa4d22c92145e39d779c0cb2e259c3a03a9ddb9cf9049
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2026-01-01T00:00:00-05:00
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Unidirectional reflection lasing based on destructive interference and Bragg scattering modulation in defective atomic lattice
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arXiv:2512.24130v1 Announce Type: new Abstract: The novel and ingenious scheme we propose for achieving unidirectional reflection lasing (URL) involves integrating a one-dimensional (1D) defective atomic lattice with a coherent gain atomic system. Its physical essence lies in the fact that the right-side reflectivity is drastically reduced due to the destructive interference between primary and secondary reflections, whereas on the left-side primary reflection is effectively suppressed and the secondary reflection is efficiently enhanced, ultimately reaching the lasing threshold. Through numerical results and further analyses, we have elucidated how to precisely tailor the lattice parameters and coupling fields to control destructive interference point (DIP), thereby realizing URL and enabling its active modulation. Our scheme is experimentally feasible and not only effectively circumvents the stringent conditions faced in directly realizing URL, providing a new pathway, but also beneficial for integrating active photonic devices into compact quantum networks and may improve the efficiency of optical information transmission.
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https://arxiv.org/abs/2512.24130
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0da53f1a192b64fd3295df7ade0249dab05fdd3f1b9b49f2e413a1e3c85c9963
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2026-01-01T00:00:00-05:00
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Bridging Visual Intuition and Chemical Expertise: An Autonomous Analysis Framework for Nonadiabatic Dynamics Simulations via Mentor-Engineer-Student Collaboration
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arXiv:2512.24133v1 Announce Type: new Abstract: Analyzing nonadiabatic molecular dynamics trajectories traditionally heavily relies on expert intuition and visual pattern recognition, a process that is difficult to formalize. We present VisU, a vision-driven framework that leverages the complementary strengths of two state-of-the-art large language models to establish a "virtual research collective." This collective operates through a "Mentor-Engineer-Student" paradigm that mimics the collaborative intelligence of a professional chemistry laboratory. Within this ecosystem, the Mentor provides physical intuition through visual reasoning, while the Engineer adaptively constructs analysis scripts, and the Student executes the pipeline and manages the data and results. VisU autonomously orchestrates a four-stage workflow comprising Preprocessing, Recursive Channel Discovery, Important-Motion Identification, and Validation/Summary. This systematic approach identifies reaction channels and key nuclear motions while generating professional academic reports. By bridging visual insight with chemical expertise, VisU establishes a new paradigm for human-AI collaboration in the analysis of excited-state dynamics simulation results, significantly reducing dependence on manual interpretation and enabling more intuitive, scalable mechanistic discovery.
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https://arxiv.org/abs/2512.24133
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2dd1de37c0c76e3052046ed25663d94e26271b41b32f61a56b429e72575fa15e
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2026-01-01T00:00:00-05:00
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Photon Echo in Uniaxially Stressed Germanium with Antimony Donors
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arXiv:2512.24142v1 Announce Type: new Abstract: Photon echo is observed in n-type Ge uniaxially stressed along the [111] crystallographic direction, with a coherence relaxation time of 300 ps. The nonlinear polarization responsible for the effect originates from antimony donors. Uniaxial stress induces valley splitting of the donor states, substantially enhancing the coherence time and enabling the observation of photon echo.
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https://arxiv.org/abs/2512.24142
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f0485c5b37a1e78055260f4d4fb27ddca268c30de595705467dd9edde012218b
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2026-01-01T00:00:00-05:00
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Robust Physical Encryption and Unclonable Object Identification in Classical Optical Networks using Standard Integrated Photonic Components
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arXiv:2512.24150v1 Announce Type: new Abstract: Spectral complexity is a useful resource in physical device identification, disorder-enhanced spectroscopy, and machine learning, but is often achieved in chip-scale devices at the expense of propagation loss, scalability, or reconfigurability. In this work, we demonstrate that device specific spectral complexity can be achieved using completely standardized photonic building blocks. Using a waveguide Mach-Zehnder interferometer internally loaded with two sets of non-concentric dual ring resonators, we demonstrate the generation of unclonable keys for one-time pad encryption which can be reconfigured on the fly by applying small voltages to on-chip thermo-optic elements. With this method, we access a keyspace larger than 12 Tb for a single device with simple, single-mode waveguide input and output coupling. Using two devices at either end of a communication channel, we show that an eavesdropper tapping the channel fibre link would be unable to recover the same spectrum measured at either end of the link, providing physical encryption for key distribution. Furthermore, being purely classical, this form of secure communications does not require quantum photonic sources or detectors, and can therefore be easily integrated into pre-existing telecommunication architectures.
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https://arxiv.org/abs/2512.24150
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49e470ab84becfa59c2e3c09f482242a2ba66dff39e617ee4e09974f133b3c7f
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2026-01-01T00:00:00-05:00
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Mechanical properties of chiral actin filaments
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arXiv:2512.24154v1 Announce Type: new Abstract: The mechanical properties of actin filaments are essential to their biological functions. Here, we introduce a highly coarse-grained model of actin filaments that preserves helicity and chirality while enabling mesoscale simulations. The framework is implemented in Cytosim, an open-source cytoskeleton simulation platform. We can predict and finely control the shape and mechanical properties of this helical filament, that can be matched to experimental values. Using this model, we investigated the role of filament chirality in motor-driven dynamics. We first show that in two different experimental configurations, motor movement along a helical filament results in a chiral motion of the filament. In a bundle of helical filaments, dimeric motors exert torques on each filament, inducing collective behaviors in the bundle such as rotation, coiling, and helical buckling, reminiscent of those observed in filopodia. Together, these results demonstrate the central role of helicity and chirality in actin mechanics and motor-driven dynamics, and establish our framework as a powerful tool for mesoscale simulations. This framework can also be used for other helical filaments beyond actin.
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https://arxiv.org/abs/2512.24154
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Academic Papers
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bf4ff10ee53ae75c002027423d593637257ded5574efba36d1b5a1da116c476d
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2026-01-01T00:00:00-05:00
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Optical pumping and laser slowing of a heavy molecule
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arXiv:2512.24167v1 Announce Type: new Abstract: Precision measurements of the electron's electric dipole moment (eEDM) are critical for testing fundamental symmetries in particle physics, and heavy polar molecules-such as barium monofluoride (BaF)-have emerged as promising candidates for advancing the sensitivity. However, the achievement of a 3D magneto-optical trap (MOT) required slowing BaF molecules to near-zero velocity by scattering over 10^4 photons per molecule, demanding a quasi-cycling transition with minimal leakage. We present a detailed study of the leakage channels, including higher vibrational and rotational states. By combining microwave remixing with optical pumping of rotational and vibrational dark states, we reduced the total leakage fraction to 10^-5. Using frequency-chirped laser slowing, we slowed a subset of buffer-gas-cooled BaF molecules from approximately 80 m/s to near-zero velocity, which is critical for efficient MOT loading. This work establishes the technical foundation for precision eEDM measurements using laser-cooled heavy molecules.
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https://arxiv.org/abs/2512.24167
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Academic Papers
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47d6d4effdc21da2a95211935abec53fe51aefc7666112377d95026040e36370
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2026-01-01T00:00:00-05:00
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Turbulence enhances bird tail aerodynamic performance
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arXiv:2512.24168v1 Announce Type: new Abstract: Turbulence is omnipresent in the atmosphere and a long-standing scientific conundrum that makes flight complex. This complexity is little understood; surprisingly, when turbulence arises, air vehicles struggle while birds seem to thrive. Birds often encounter intense turbulence during takeoff and landing, because of turbulent boundary layer effects. During landing, birds respond by fanning their tail over a wide range of spreads and angles of attack. How their tail functions aerodynamically under these conditions is little understood. Here, we use a bio-hybrid feathered robot model of a pigeon tail in a wind tunnel to compare its aerodynamics in laminar versus turbulent flow. We measured the lift and drag forces generated by the tail as a function of angle of attack, tail spread, and flow condition. We found tail spread scarcely changes tail aerodynamic lift and drag force coefficients, despite large aspect ratio variations. Consequently, tail spread primarily changes force via tail area modulation, simplifying flight control. The effect of laminar versus turbulent flow is pronounced; at the same tail spread and angle of attack, turbulence increases lift and drag by approximately a factor two. Quantitative flow measurement analysis with proper orthogonal decomposition shows force enhancement is linked to modifications in the spatial and temporal structure of the wake. The results suggest a wake instability that arises in laminar flow is suppressed in turbulent flow, which enhances tail efficiency, benefiting flight control. These insights may inspire engineers to design aerial vehicle tails with improved flight control in turbulence.
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https://arxiv.org/abs/2512.24168
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Academic Papers
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e0fdb65d39a041eca1424c0356e37f3aaf9c65081508ad03ee33744cfc6d97b4
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2026-01-01T00:00:00-05:00
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A biological hydraulic accumulator: How the squirting cucumber, Ecballium elaterium, squirts its seeds
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arXiv:2512.24175v1 Announce Type: new Abstract: Seed dispersal is a fundamental process that allows offspring to reach suitable habitats and colonize new environments. While most plants rely on external vectors, some have evolved mechanisms that employ the buildup of liquid pressure in a closed compartment and its explosive release to disperse their seeds. This form of energy storage, reinvented by humans for engineering applications, is termed a hydraulic accumulator. Here we investigated the fluid mechanics involved in dispersal in the squirting cucumber, Ecballium elaterium integrating high-speed videography (up to 10 000 fps), microtomography, and internal pressure sensors. We recorded long-term pressure time series showing that E. elaterium exhibits circadian (24 hour) and ultradian (short-period) rhythms. Remarkably, the measurements revealed a lack of correlation between fruit and stem turgor; while the stem showed strong circadian cycles, the fruit often did not, suggesting isolated physiological processes in different tissues. The fruit's spongy wall tissue stores elastic potential energy as turgor pressure builds to nearly one atmosphere (92-99 kPa). Upon detachment, this energy is rapidly released to expel a turbulent, particle-laden liquid jet. Microtomography revealed that the seeds are packed around a central funiculus, a configuration that optimizes their exit through the basal orifice at velocities of up to 30 m/s. Seeds eventually move faster than the liquid droplets during the later stages of ejection as they shed their liquid coating. This sophisticated mechanism ensures a broad dispersal cone, effectively spreading offspring across space and environmental conditions.
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https://arxiv.org/abs/2512.24175
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Academic Papers
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657b659c4f2026dc7f56fa48f59f862b390e485d821f089080080f36bceaa16e
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2026-01-01T00:00:00-05:00
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High-flux cold lithium-6 and rubidium-87 atoms from compact two-dimensional magneto-optical traps
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arXiv:2512.24177v1 Announce Type: new Abstract: We report a compact setup with in-series two-dimensional magneto-optical traps (2D MOTs) that provides high-flux cold lithium and rubidium atoms. Thanks to the efficient short-distance Zeeman slowing, the maximum 3D MOT loading rate of lithium atoms reaches a record value of $6.6\times 10^{9}$ atoms/s at a moderate lithium-oven temperature of 372 degrees Celsius, which is 44 times higher than that without the Zeeman slowing light. The flux of rubidium is also as high as $2.3\times10^9$ atoms/s with the rubidium oven held at room temperature. Meanwhile, the entire vacuum-chamber system, including an ultra-high-vacuum science cell, is within a small volume of $55\times65\times70~\mathrm{cm}^3$. Our work represents a substantial improvement over traditional bulky and complex dual-species cold-atom setups. It provides a good starting point for the fast production of a double-degenerate lithium-rubidium atomic mixture and large samples of ultracold lithium-rubidium ground-state molecules.
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https://arxiv.org/abs/2512.24177
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Academic Papers
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d967e7ad292ea999eed80c4e7949e0f95b608cb269071b0c09b4d7b899fdb92f
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2026-01-01T00:00:00-05:00
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Transport and orientation of anisotropic particles settling in surface gravity waves
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arXiv:2512.24190v1 Announce Type: new Abstract: We study the translation and orientation dynamics of an anisotropic particle settling in monochromatic linear surface gravity waves. Recent work has shown that a neutrally buoyant spheroid attains a preferred mean orientation in such wave fields, independent of its initial state and determined solely by its aspect ratio. Comparing the settling parameter $\mathrm{Sv}$, the ratio of settling speed to wave speed, with the asymptotically small wave steepness $\epsilon$, we investigate the long time dynamics of a negatively buoyant particle. We examine the transition from aspect ratio-dependent equilibrium orientation in the weak settling regime ($\mathrm{Sv} \ll \epsilon^2$) to initial-condition-dependent alignment in the strong settling limit ($\mathrm{Sv} \gg 1$). Since translation and orientation are coupled for anisotropic particles, we use orientation dynamics to predict net horizontal transport. Fluid inertia induces an inertial torque that breaks the Stokesian degeneracy and drives broadside alignment. We analyze the influence of this torque on drift and alignment rate as functions of settling and wave parameters. Finally, we evaluate finite-size effects through the parameter $\sigma$, showing that a neutrally buoyant finite-size spheroid exhibits $\sigma$-dependent drift, validating the finite-size approximation when the spheroid size approaches the wavelength.
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https://arxiv.org/abs/2512.24190
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Academic Papers
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b0d666462c5a5416b41ff5962b75802a61100bdc9af639fa1a7dd176fc0ea3e4
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2026-01-01T00:00:00-05:00
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Mesoscale soil moisture heterogeneity can locally amplify humid heat
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arXiv:2512.24202v1 Announce Type: new Abstract: Soil moisture is a key ingredient of humid heat through supplying moisture and modifying boundary layer properties. Soil moisture heterogeneity due to e.g., antecedent rainfall, can strongly influence weather patterns; yet, its effect on humid heat is poorly understood. Idealized numerical simulations are performed with a cloud-resolving ($\Delta x$=500 m), coupled land-atmosphere model wherein wet patches on length-scales $\lambda \in$ 25-150 km are prescribed. Compared to experiments with uniform soil moisture, humid heat is locally amplified by 1-4$^\circ$C, with maximum amplification for the critical soil moisture length-scale $\lambda_c=$ 50 km. Subsidence associated with a soil moisture-induced mesoscale circulation concentrates warm, humid air in a shallower boundary layer. The background wind and the magnitude of the wet-dry contrast control the relationship between $\lambda_c$ and the humid heat amplification. Based on observed soil moisture patterns, these results will help to predict extreme humid heat at city and county scales across the Tropics.
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https://arxiv.org/abs/2512.24202
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Academic Papers
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6e02aaaace53cbad56e5637d01ac2bf460e9d4d414d7d143f729139bec806d34
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2026-01-01T00:00:00-05:00
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Performance of an LYSO-Based Active Converter for a Photon Pair-Spectrometer aiming for 52.8 MeV photon detection in Future $\mu^+ \to e^+ \gamma$ Search Experiments
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arXiv:2512.24209v1 Announce Type: new Abstract: For future $\mu^+ \to e^+ \gamma$ search experiments with a branching-ratio sensitivity of $10^{-15}$, we are developing a photon pair-spectrometer employing an active LYSO converter, aiming at target resolutions of 30 ps in timing and 200 keV in energy measurement for detecting 52.8 MeV photons. The converter generates electron-positron pairs from incident photons while simultaneously measuring their energy deposition and timing. On the basis of simulation studies, we optimized the converter thickness and segment dimensions, and accordingly fabricated prototype LYSO segments. Their single-MIP detection performance was evaluated using an electron beam at the KEK PF-AR test beamline. The prototypes exhibited excellent performance, achieving a time resolution of 25 ps and a light yield of $10^4$ photoelectrons, both substantially surpassing the design requirements.
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https://arxiv.org/abs/2512.24209
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Academic Papers
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d2f4d3cc8bfc0c7a5ca6b6ce0e9dbb87dd0ae368323f1c19b2f559b9d620a035
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2026-01-01T00:00:00-05:00
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GPT-like transformer model for silicon tracking detector simulation
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arXiv:2512.24254v1 Announce Type: new Abstract: Simulating physics processes and detector responses is essential in high energy physics and represents significant computing costs. Generative machine learning has been demonstrated to be potentially powerful in accelerating simulations, outperforming traditional fast simulation methods. The efforts have focused primarily on calorimeters. This work presents the very first studies on using neural networks for silicon tracking detectors simulation. The GPT-like transformer architecture is determined to be optimal for this task and applied in a fully generative way, ensuring full correlations between individual hits. Taking parallels from text generation, hits are represented as a flat sequence of feature values. The resulting tracking performance, evaluated on the Open Data Detector, is comparable with the full simulation.
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https://arxiv.org/abs/2512.24254
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Academic Papers
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f005ded0a316f72ab6378f9f27a1454111749b12aa5ed57e78f4f50ac17ef12b
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2026-01-01T00:00:00-05:00
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Hovering efficiency optimization of the cycloidal propeller with end plates
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arXiv:2512.24258v1 Announce Type: new Abstract: Cycloidal propellers are known for their omnidirectional vectored thrust, enabling smooth transitions between hovering and forward flight, making them ideal for unmanned aerial vehicles (UAVs) and electric vertical take-off and landing (eVTOL) aircraft. However, cycloidal propellers tend to have lower hovering efficiency compared to screw propellers. Adding end plates to the blade tips can enhance hovering efficiency by reducing blade tip vortices. But the impact of these end plates and the optimal design for cycloidal propellers incorporating them have not been thoroughly studied. This paper seeks to optimize hovering efficiency and develop design theories for cycloidal propellers with end plates. Extensive force measurement experiments are conducted to identify designs with optimal hovering efficiency. The sliding mesh technique is employed to solve the unsteady Reynolds-averaged Navier-Stokes (URANS) equations for a detailed analysis. Experimental results indicate that the designs with end plates generally achieve significantly better hovering efficiency than those without end plates. End plates help to maintain hovering efficiency, even though the blade aspect ratio is as small as 1.5. The designs with stationary end plates are superior to those with rotating end plates because rotation introduces additional torque caused by the friction force. Designs featuring thick end plates outperform those with thin end plates, as the rounded edges can eliminate end plate vortices. The best design features stationary thick end plates, a chord-to-radius ratio of 0.65, and a large pitching amplitude of 40 degrees. It achieves a hovering efficiency of 0.72 with a blade aspect ratio of 3, which is comparable to that of helicopters. In contrast, for the cases without end plates, the highest hovering efficiency is merely 0.54.
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https://arxiv.org/abs/2512.24258
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Academic Papers
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62e2c501c6b1bbaa5ba8b679b447c75ec863389ea6d18c85070ef4bac3ed1dcf
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2026-01-01T00:00:00-05:00
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First Positronium Lifetime Imaging using $^{52}$Mn and $^{55}$Co with a plastic-based PET scanner
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arXiv:2512.24261v1 Announce Type: new Abstract: Positronium Lifetime Imaging (PLI) extends positron emission tomography by using the lifetime of positronium atoms as a probe of tissue molecular architecture. In this work, we report the first PLI measurements performed with $^{52}$Mn and $^{55}$Co using the modular J-PET. Four samples were studied in each experiment: two Certified Reference Materials (polycarbonate and fused silica) and two human tissues (cardiac myxoma and adipose). The selection of PLI events was based on the registration of two 511~keV annihilation photons and one prompt gamma in triple coincidence. From the resulting lifetime spectra we extracted the mean ortho-positronium lifetime $\tau_{\text{oPs}}$ and the mean positron lifetime $\Delta T_{\text{mean}}$ for each sample. The measured values of $\tau_{\text{oPs}}$ in polycarbonate using both isotopes matches well with the certified reference values. Furthermore, $^{55}$Co reproduced identical results for fused-silica measurements at their respective uncertainty levels. In contrast, measurements with $^{52}$Mn in fused silica show a minor deviation, which could be caused by the Parafilm spacer. In myxoma and adipose tissue, the reduced $\tau_{\text{oPs}}$ values are mainly linked to the long storage history of the samples rather than to the choice of isotope. Comparing peak-to-background ratios and spectral purity, $^{55}$Co provides cleaner PLI data under the same experimental conditions. Although $^{52}$Mn offers a longer half-life and a multi gamma cascade enhancing $\beta^{+}$ + $\gamma$ coincidences, but at the expense of higher background. In this study, we demonstrate that the applied selection criteria on the data measured with the modular J-PET can be used for PLI studies even with radionuclides with complex decay patterns.
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https://arxiv.org/abs/2512.24261
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Academic Papers
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99f69d97e15c9778e43033b73bb0263aa4772e1b231dc0d63935493880611346
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2026-01-01T00:00:00-05:00
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Strategic Network Abandonment
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arXiv:2512.24270v1 Announce Type: new Abstract: Socio-economic networks, from cities and firms to collaborative projects, often appear resilient for long periods before experiencing rapid, cascading decline as participation erodes. We explain such dynamics through a framework of strategic network abandonment, in which interconnected agents choose activity levels in a network game and remain active only if participation yields higher utility than an improving outside option. As outside opportunities rise, agents exit endogenously, triggering equilibrium readjustments that may either dissipate locally or propagate through the network. The resulting decay dynamics are governed by the strength of strategic complementarities, measuring how strongly an agent's incentives depend on the actions of others. When complementarities are weak, decay follows a heterogeneous threshold process analogous to bootstrap percolation: failures are driven by local neighborhoods, vulnerable clusters can be identified ex ante, and large cascades emerge only through bottom-up accumulation of fragility. When complementarities are strong, departures propagate globally, producing rupture-like dynamics characterized by metastable plateaus, abrupt system-wide collapse, and limited predictive power of standard spectral or structural indicators. The comparative effective of intervention depends on the strength of complementarity as well: Supporting central agents is most effective under strong complementarities, whereas targeting marginal agents is essential when complementarities are weak. Together, our results reveal how outside options, network structure, and strategic interdependence jointly determine both the fragility of socio-economic networks and the policies required to sustain them.
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https://arxiv.org/abs/2512.24270
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Academic Papers
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5a97a2aef791bbce88055c4af7f17c68a2eb1d08a3310bb12ee499b70b2bd643
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2026-01-01T00:00:00-05:00
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Coordinates based on a magnetic mirror field
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arXiv:2512.24305v1 Announce Type: new Abstract: We construct a coordinate system fitting the geometry of a given, cylindrically symmetric, magnetic field.
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https://arxiv.org/abs/2512.24305
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Academic Papers
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cdc67b63ae969ad74fd70e271a21115d605c7dd77c32a9ce33aac6360a0667be
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2026-01-01T00:00:00-05:00
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Iterative projected gradient descent for dynamic PET kernel reconstruction
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arXiv:2512.24322v1 Announce Type: new Abstract: Dynamic positron emission tomography (PET) reconstruction often presents high noise due to the use of short duration frames to describe the kinetics of the radiotracer. Here we introduce a new method to calculate a kernel matrix to be used in the kernel reconstruction for noise reduction in dynamic PET. We first show that the kernel matrix originally calculated using a U-net neural network (DeepKernel) can be calculated more efficiently using projected gradient descent (PGDK), with several orders of magnitude faster calculation time for 3D images. Then, using the PGDK formulation, we developed an iterative method (itePGDK) to calculate the kernel matrix without the need of high quality composite priors, instead using the noisy dynamic PET image for calculation of the kernel matrix. In itePGDK, both the kernel matrix and the high quality reference image are iteratively calculated using PGDK. We performed 2D simulations and real 3D mouse whole body scans to compare itePGDK with DeepKernel and PGDK. Brain parametric maps of cerebral blood flow and non-displaceable binding potential were also calculated in 3D images. Performance in terms of bias-variance tradeoff, mean squared error, and parametric maps standard error, was similar between PGDK and DeepKernel, while itePGDK outperformed these methods in these metrics. Particularly in short duration frames, itePGDK presents less bias and less artifacts in fast kinetics organs uptake compared with DeepKernel. itePGDK eliminates the need to define composite frames in the kernel method, producing images and parametric maps with improved quality compared with deep learning methods.
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https://arxiv.org/abs/2512.24322
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Academic Papers
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dbc39da9f7e78252ba0d3e05c40bbc019aa16cdc749be3b32e2177ceb322929b
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2026-01-01T00:00:00-05:00
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Configurational-entropy-driven structural and optical stability in high-entropy halide perovskites for broadband NIR photonics
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arXiv:2512.24328v1 Announce Type: new Abstract: By injecting configurational entropy into soft ionic lattices, high-entropy halide perovskites offer a compelling route toward photonic materials that are both functionally rich and operationally robust; however, converting compositional complexity into predictable optical function remains challenging. Here we demonstrate device-relevant ultrabroadband near-infrared (NIR) photonics by integrating element-specific roles within an entropy-stabilized lattice. We establish high-entropy rare-earth halide double-perovskite single crystals, Cs2Na(Sb,Re)Cl6 (Re3+ = Sc3+, Er3+, Yb3+, Tm3+), where near-equiatomic B(III)-site alloying yields a single-phase cubic solid solution (S_config about 1.6R) with homogeneous multication incorporation. Sb3+ acts as a broadband sensitizer that unifies excitation and cooperatively activates multiple lanthanide emitters, transforming single-mode emission into wide-coverage NIR output (850-1600 nm) with three fingerprint bands at 996, 1220, and 1540 nm. This tri-peak, self-referenced signature enables redundancy-based ratiometric readout with reduced sensitivity to intensity drift, supporting reliable solvent identification and quantitative mixture sensing. Beyond functional expansion, accelerated aging tests show markedly improved tolerance to humidity and oxygen versus single-component analogues. The robustness is experimentally attributed to octahedral contraction-strengthened metal-halide bonding that increases the kinetic barrier for moisture-triggered bond cleavage, together with entropy-induced lattice distortion that impedes long-range halide migration and suppresses defect/impurity-phase formation. Finally, a UV-pumped phosphor-converted LED delivers spectrally stable, wide-coverage NIR illumination, highlighting configurational-entropy engineering as a practical strategy to couple ultrabroadband photonic function with environmental stability.
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https://arxiv.org/abs/2512.24328
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Academic Papers
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6b0f9c8f7aa7669e8e01aa271f4e718b14b58c0dd40bfd5fd9d829a10a5d6c60
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2026-01-01T00:00:00-05:00
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Decarbonizing China's private passenger vehicles: A dynamic material flow assessment of metal demands and embodied emissions
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arXiv:2512.24332v1 Announce Type: new Abstract: The continuous growth of China's private passenger vehicle fleet has intensified material demand and embodied carbon emissions, underscoring the need for effective decarbonization pathways. This study develops a transferable, dynamic material flow analysis framework to assess vehicle stocks, metal flows (steel, aluminum, and copper), and embodied emissions from 2000 to 2070, and to quantify the contributions of demand-side and technology-side efficiency measures. The results reveal that: (1) The vehicle fleet is projected to peak at 327-507 million vehicles by mid-century, with new energy vehicles dominating both in-use stocks and end-of-life flows by the 2040s. (2) Cumulative metal demand is projected to reach 1914-2990 million tonnes over the upcoming five decades, with 879-1320 million tonnes supplied from secondary sources under baseline conditions. Technology-oriented measures substantially enhance recycling performance, enabling secondary steel to fully meet manufacturing demand and allowing aluminum and copper cycles to approach near closure by 2070. (3) Correspondingly, cumulative embodied carbon emissions from vehicle metals by 2070 range from 4958 to 9218 megatonnes of carbon dioxide, with technological upgrading reducing emissions by 1051-1619 megatonnes. In collaborative scenarios, demand management accounts for 64.3% of total emission reductions, while technology-oriented measures become increasingly important over the medium to long term. Overall, the findings demonstrate that unmanaged demand growth can substantially offset technological mitigation gains, highlighting the necessity of integrated demand- and technology-oriented strategies. This study provides a systemic and transferable framework to guide circular economy development and deep decarbonization transitions in vehicle fleets in China and other emerging economies.
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https://arxiv.org/abs/2512.24332
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Academic Papers
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544f5e1b974613cab7c5ceb671eb64b8bbd58068456c74e3a8557463ad9c0cf3
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2026-01-01T00:00:00-05:00
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Impact of Angle Misalignment on the Performance of a combined Optical and millimeter-wave Transceiver enabled by a pair of Optical Harmonically Locked Lasers
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arXiv:2512.24360v1 Announce Type: new Abstract: We demonstrated combined free-space optics (FSO) and D-band(110-170GHz) millimeter(mm-wave) transceiver enabled by precisely locked lasers with low phase noise. Combined capacity and tolerance to angle misalignment are studied using 100Gb/s optical and mm-wave signals.
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https://arxiv.org/abs/2512.24360
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Academic Papers
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baa8d3ca7d59ffbd020eea4aa233c52bcef120526e0d3663818e468a7b17a131
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2026-01-01T00:00:00-05:00
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The sun as colliding beam, betatron cosmic ray factory
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arXiv:2512.24363v1 Announce Type: new Abstract: A theory of cosmic ray production within the solar system (not extra-galactic) is presented. The sun's time variable magnetic flux linkage makes the sun (as well, perhaps, as Jupiter) a natural, all-purpose, betatron storage ring, with semi-infinite acceptance aperture, capable of storing and accelerating counter-circulating, opposite-sign, colliding beams. The puzzle of how positrons and anti-protons can be well represented at all energies, is explained, initially, by the low energy capture of particles of either sign by the sun's magnetic dipole field. Later, as the magnetic field bending has become negligible compared to the gravitational bending, both positive and negative beams will have survived the gradual transition from predominantly magnetic to predominantly gravitational bending. Later, anti-particles produced in QED beam-beam collisions of sufficiently high energy, are also accelerated. The high quality of cosmic ray data collected over recent decades, at steadily increasing energies, especially by the International Space Station (ISS), make the study of cosmic ray production mechanisms both timely and essential. The paper describes how longitudinal electric fields, explained by the Parker solar wind theory can enable the sun to serve as a ``booster'' accelerator of cosmic rays, increasing the maximum cosmic ray energies enough to produce the observed 13 orders of magnitude maximum particle energy and the energy flux needed to maintain the observed cosmic ray atmosphere equilibrium within the solar system. A steady state mechanism is described, based on semi-quantitative discussion of a relativistic Hamilton-Jacobi formalism, according to which the highest energy cosmic rays observed can have been produced by the Parker longitudinal electric field component, during fractionally brief, but periodic, circular or semi-circular turns centered on the sun.
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https://arxiv.org/abs/2512.24363
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Academic Papers
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bfd330fa5b1cc865ababad041231e4cd0d7333eebfb9b5234478892f5362a269
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2026-01-01T00:00:00-05:00
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Mid-Infrared Photothermal Relaxation Intensity Diffraction Tomography for Video-rate Volumetric Chemical Imaging
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arXiv:2512.24375v1 Announce Type: new Abstract: Three-dimensional molecular imaging of living cells is essential for unraveling cellular metabolism and response to therapies. However, existing volumetric methods, including fluorescence microscopy and quantitative phase imaging, either require fluorescent labels or lack chemical specificity. Mid-infrared (mid-IR) photothermal microscopy provides label-free spectroscopic contrast with sub-micrometer resolution but is limited by slow acquisition rates, precluding 3D live-cell studies. Here, we present a photothermal relaxation intensity diffraction tomography (PRIDT) system that encodes mid-IR absorption induced refractive index change via a photothermal relaxation scheme and recovers it through intensity diffraction tomography. PRIDT achieves video-rate volumetric chemical imaging with up to 15 Hz per wavelength and offers lateral and axial resolutions of 264 nm and 1.12 um over a volumetric field of view of 50x50x10 um3. We showcase high-speed PRIDT imaging of protein and lipid metabolism in ovarian cancer cells and lipid-droplet dynamics in live cells. PRIDT opens new avenues for rapid, quantitative, three-dimensional molecular imaging in living systems.
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https://arxiv.org/abs/2512.24375
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Academic Papers
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e1dae3799d319ddff2a8f2906a3e2ce2a2c8042c069f90307abf179b1a73d948
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2026-01-01T00:00:00-05:00
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Analytical phase kurtosis of the constant gradient spin echo
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arXiv:2512.24397v1 Announce Type: new Abstract: The Gaussian phase approximation (GPA) underlies many standard diffusion magnetic resonance (MR) signal models, yet its validity is rarely scrutinized. Here, we assess the validity of the GPA by analytically deriving the excess phase kurtosis $\kappa_4/\kappa_2^2$, where $\kappa_n$ is the $n^{\text{th}}$ cumulant of the accumulated phase distribution due to motion. We consider the signal behavior of the spin echo with constant gradient amplitude $g$ and echo time $T$ in several one-dimensional model systems: (1) a stationary Poisson pore-hopping model with uniform pore spacing $\Delta x$ and mean inter-hop time $\tau_{\text{hop}}$; (2) a trapped-release model in which spin isochromats are initially immobilized and then released with diffusivity $D$ following an exponentially-distributed release time, $\tau_{\text{rel}}$; and (3) restricted diffusion in a domain of length $L$. To our knowledge, this is among the first systematic analytical treatments of spin echo phase kurtosis without assuming Gaussian compartments or infinitesimally short gradient pulses. In the pore-hopping system, $\kappa_4/\kappa^2_2 = (9/5)\tau_{\text{hop}}/T$, inversely proportional to the mean hop number, $T/\tau_{\text{hop}}$. In the trapped-release system, $\kappa_4/\kappa_2^2$ is positive and decreases roughly log-linearly with $T/\langle\tau_{\text{rel}}\rangle$, where $\langle\tau_{\text{rel}}\rangle$ is the average release time. For restriction, $\kappa_4/\kappa_2^2$ vanishes at small and large $L/\sqrt{DT}$, but has complicated intermediate behavior. There is a negative peak at $L/\sqrt{DT}\approx 1.2$ and a positive peak at $L/\sqrt{DT}\approx 4.4$. Monte Carlo simulations are included to validate the analytical findings. Overall, we find that the GPA does not generally hold for these systems under moderate experimental conditions, i.e., $T=10\;\mathrm{ms}$, $g\approx 0.2-0.6\;\mathrm{T/m}$.
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https://arxiv.org/abs/2512.24397
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Academic Papers
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36b7accf0ae8712722b3e638a3669635383f3f950b98f30a90db053fdf74307d
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2026-01-01T00:00:00-05:00
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Achieving high-performance polarization-independent nonreciprocal thermal radiation with pattern-free heterostructures
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arXiv:2512.24398v1 Announce Type: new Abstract: Many advanced energy harvesting technologies rely on advanced control of thermal emission. Recently, it has been shown that the emissivity and absorptivity of thermal emitters can be controlled independently in nonreciprocal emitters. While significant progress has been made in engineering these nonreciprocal thermal emitters, realizing a highly efficient, pattern-free emitter capable of supporting dual-polarization nonreciprocal emission remains a challenging task. Existing solutions are largely based on metamaterials and exhibit polarization-dependent behavior. This work proposes pattern-free multilayer heterostructures combining magneto-optical and magnetic Weyl semimetal materials and systematically evaluates their nonreciprocal emission performance for p- and s-polarized waves. The findings show that omnidirectional polarization-independent nonreciprocity can be achieved utilizing multilayer structures with different magnetization directions that do not follow simple vector summation. To further enhance the performance, Pareto optimization is employed to tune the key design parameters, enabling the maximization of nonreciprocal thermal emission in a given wavelength range. This approach offers a versatile strategy for designing high-performance thermal emitters tailored for multi-objective optical functionalities.
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https://arxiv.org/abs/2512.24398
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Academic Papers
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41098f2325f504f4ed64063463c8a417e4b253e994234f4d516dce2dcebd64b4
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2026-01-01T00:00:00-05:00
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Complexity and dynamics of partially symmetric random neural networks
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arXiv:2512.24439v1 Announce Type: new Abstract: Neural circuits exhibit structured connectivity, including an overrepresentation of reciprocal connections between neuron pairs. Despite important advances, a full understanding of how such partial symmetry in connectivity shapes neural dynamics remains elusive. Here we ask how correlations between reciprocal connections in a random, recurrent neural network affect phase-space complexity, defined as the exponential proliferation rate (with network size) of the number of fixed points that accompanies the transition to chaotic dynamics. We find a striking pattern: partial anti-symmetry strongly amplifies complexity, while partial symmetry suppresses it. These opposing trends closely track changes in other measures of dynamical behavior, such as dimensionality, Lyapunov exponents, and transient path length, supporting the view that fixed-point structure is a key determinant of network dynamics. Thus, positive reciprocal correlations favor low-dimensional, slowly varying activity, whereas negative correlations promote high-dimensional, rapidly fluctuating chaotic activity. These results yield testable predictions about the link between connection reciprocity, neural dynamics and function.
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https://arxiv.org/abs/2512.24439
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Academic Papers
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99a7d572b0ec03f58229e537e15504eb6cdaa4edf273779a5566a4710b153e52
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2026-01-01T00:00:00-05:00
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Sub-Ensemble Correlations as a Covariance Geometry
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arXiv:2512.24451v1 Announce Type: new Abstract: Conventional practice of spatially resolved detection in diffusion-coupled thermal atomic vapors implicitly treat localized responses as mutually independent. However, in this study, it is shown that observable correlations are governed by the intrinsic spatiotemporal covariance of a global spin-fluctuation field, such that spatial separation specifies only overlapping statistical projections rather than independent physical components. A unified field-theoretic description is established in which sub-ensembles are defined as measurement-induced statistical projections of a single stochastic field. Within this formulation, sub-ensemble correlations are determined by the covariance operator, inducing a natural geometry in which statistical independence corresponds to orthogonality of the measurement functionals. For collective spin fluctuations described by a diffusion-relaxation Ornstein-Uhlenbeck stochastic field, the covariance spectrum admits only a finite set of fluctuation modes in a bounded domain, imposing an intrinsic, field-level limit on the number of statistically distinguishable sub-ensembles. The loss of sub-ensemble independence is formalized through the notion of spatial sampling overlap, which quantifies the unavoidable statistical coupling arising from shared access to common low-order fluctuation modes. While multi-channel atomic magnetometry provides a concrete physical setting in which these constraints become explicit, the framework applies generically to diffusion-coupled stochastic fields.
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https://arxiv.org/abs/2512.24451
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Academic Papers
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4bf8bae4bef1357d27845daab5e11d6564f64c45f8efbdd490f53f5bf67739ec
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2026-01-01T00:00:00-05:00
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Beyond chaos: fluctuations, anomalies and spontaneous stochasticity in fluid turbulence
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arXiv:2512.24469v1 Announce Type: new Abstract: In this perspective, we consider the development of statistical hydrodynamics, focusing on the way in which the intrinsic stochasticity of turbulent phenomena was identified and is being explored. A major purpose of our discussion is to bring out the role of anomalies in turbulent phenomena, in ways that are not usually done, and to emphasize how the description of turbulent phenomena requires delicate considerations of asymptotic limits. The scope of our narrative includes selected historical aspects that are not usually emphasized, primarily due to G.I. Taylor, as well as discussions of certain aspects of the laminar-turbulent transition, the behaviour of turbulent drag at intermediate Reynolds numbers, and the statistics of fully-developed turbulence that exhibit spontaneous stochasticity.
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https://arxiv.org/abs/2512.24469
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Academic Papers
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svg
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a99f8eb726c68fa94adeb97119f94116e4fa6300cc763461d7516ecf7183dbd3
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2026-01-01T00:00:00-05:00
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Mathematical Theory for Photonic Hall Effect in Honeycomb Photonic Crystals
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arXiv:2512.24477v1 Announce Type: new Abstract: In this work, we develop a mathematical theory for the photonic Hall effect and prove the existence of guided electromagnetic waves at the interface of two honeycomb photonic crystals. The guided wave resembles the edge states in electronic systems: it is induced by the topological Hall effect, and the wave propagates along the interface but not in the bulk media. Starting from a symmetric honeycomb photonic crystal that attains Dirac points at the high-symmetry points of the Brillouin zone, $K$ and $K'$, we introduce two classes of perturbations for the periodic medium. The perturbations lift the Dirac degeneracy, forming a spectral band valley at the points $K$ and $K'$ with well-defined topological phase that depends on the sign of the perturbation parameters. By employing the layer potential techniques and spectral analysis, we investigate the existence of guided wave along an interface when two honeycomb photonic crystals are glued together. In particular, we elucidate the relationship between the existence of the interface mode and the nature of perturbations imposed on the two periodic media separated by the interface.
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https://arxiv.org/abs/2512.24477
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Academic Papers
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f1e63f8d9b12449d7f4f3b58c6f325b7850e037e8f4245e9545eb609be9977b0
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2026-01-01T00:00:00-05:00
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Local shear signals propagate to suppress local cellular motion in stiff epithelia
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arXiv:2512.24486v1 Announce Type: new Abstract: As small particles skim our airways during breathing, or our intestines during digestion, the surface epithelium is subjected to local exogenous shear that deforms hundreds to thousands of tightly interacting cells. Unlike shear deformations applied at the macro-tissue scale or the micro-cell scale, the effects of such perturbations at the meso-scale remain largely unexplored. To address this, we developed a mesoscopic probe that adheres to the apical surface of an epithelial monolayer and applies magnetically derived local shear. We find that localized shear propagated way beyond immediate neighbors and suppressed cellular migratory dynamics in stiffer layers, yet dissipated locally and left dynamics unchanged in softer layers. This mechano-transductive view is reinforced by the observation that stiffening of a soft layer promotes responsiveness to shear. Interpreted within the epithelial jamming framework, shear-induced migratory suppression in stiff layers was accompanied by reduced MSD scaling exponents and changes in cell shape. These changes suggested a localized shift of the tissue toward a lower-energetic state. Together, these observations provide a new perspective on how a local mechanical perturbation traverses the epithelial monolayer to influence both nearby and distant cellular environments.
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https://arxiv.org/abs/2512.24486
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Academic Papers
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0af26e0e3b221ef50bb275803b2024325406a1ccbab4208ffe0feb77be06ea04
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2026-01-01T00:00:00-05:00
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High Space-bandwidth Product Label-free Examination of iPSC-derived Brain Organoids via Fourier Ptychographic Microscopy
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arXiv:2512.24489v1 Announce Type: new Abstract: Fourier ptychographic microscopy (FPM) is a promising quantitative phase imaging technique that enables high-resolution, label-free imaging over a large field-of-view. Here, we present the first application of FPM for the quantitative analysis of human brain organoid slices, providing a powerful, cost-effective, and label-free enhancement to the current gold-standard fluorescence microscopy. Brain organoids, prepared as thin (5 micrometer) slices, were imaged with a custom-built FPM system consisting of a standard light microscope (4x, 0.2 NA objective) and a 7x7 LED array. This configuration achieved a synthetic numerical aperture of 0.54 and a spatial resolution of approximately 488 nm across an area of 2.077 x 3.65 mm. Fluorescence microscopy was used in parallel for neurons, astrocytes, and nuclei labeling, providing rich fluorescence imaging. Moreover, we designed an automated method to merge classical resolution fluorescence images to visualize the whole brain organoid and align it with the numerically increased space-bandwidth product FPM image. The provided alignment method enables rich phase-fluorescence correlative imaging. Based on the segmentation performed on the stitched fluorescence images, we devised a quantitative phase analysis revealing a higher mean optical thickness of the nuclei versus astrocytes and neurons. Notably, nuclei located in neurogenic regions consistently exhibited significantly higher phase values (optical path difference) compared to nuclei elsewhere, suggesting cell-type-specific biophysical signatures. The label-free, quantitative, and high-throughput capabilities of the FPM approach demonstrated here make it a powerful and accessible tool for future structural and functional studies of whole-section brain organoid development and disease modeling studies.
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https://arxiv.org/abs/2512.24489
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Academic Papers
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svg
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7b4f3df9f3da421a63cea0ce147ef6e9b59e50161d53e0bd4350b6c5e34277c7
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2026-01-01T00:00:00-05:00
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Adiabatic approach for high harmonic generation in solids induced by intense low-frequency pulses
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arXiv:2512.24496v1 Announce Type: new Abstract: An analytic description of high harmonic generation (HHG) in solids induced by intense low-frequency pulses is presented within an adiabatic approach, which treats laser-matter interactions nonperturbatively. We derive the analytical expression for the laser-dressed state of an electron in an arbitrary spatially periodic potential, taking into account multiband structure of the solid target. Closed-form formulas for electron current and HHG spectra are presented. Based on the developed theory, we provide an analytic explanation for key features of HHG yield and show that the interband mechanism of HHG prevails over the intraband one.
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https://arxiv.org/abs/2512.24496
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Academic Papers
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2ac391eb0ae8049e75e26dc44a737f6c54ada6759065298a0b1361561753a01d
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2026-01-01T00:00:00-05:00
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Electrostatic enhancement of particle collision rates in atmospheric flows
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arXiv:2512.24512v1 Announce Type: new Abstract: Collisional growth of tiny particles is a fundamental process governing the growth of cloud droplets and the aggregation of ash particles in volcanic plumes, with direct implications for precipitation formation, cloud lifetime, and ash plume dynamics. The particles in these scenarios often carry electric charges. In this study, we investigate the collision dynamics of a pair of like charged dielectric spheres subjected to a uniaxial compressional flow, an important linear flow that captures key features of atmospheric straining motions. Finite particle size leads to electrostatic interactions that deviate from the point charge approximation, resulting in far field repulsion and near-field attraction, which in turn generate nontrivial particle trajectories and critical collision thresholds. For certain combinations of charge and size, the interplay between hydrodynamic and electrostatic forces creates strong radially inward particle relative velocities that substantially alter particle pair dynamics and modify the conditions required for contact. For uncharged particles, collision efficiency increases monotonically with particle size ratio. However, in the presence of electrostatic forces with high charge ratio values, the collision efficiency exhibits a nonmonotonic dependence, attaining a maximum at small size ratios and decreasing as the ratio increases, with a crossover beyond which larger particles become less favorable for collision. These results demonstrate that the same polarity charges on finite sized atmospheric particles do not necessarily inhibit collisions. Instead, they can enhance collisional growth for specific charge and size ratio combinations, revealing counterintuitive pathways relevant to cloud microphysical processes and volcanic ash aggregation in electrified atmospheric environments.
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https://arxiv.org/abs/2512.24512
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Academic Papers
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svg
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475b29698bd377cce8f8485ae3ce807d270849d5a2fb2277ecc3ec8cabfe198f
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2026-01-01T00:00:00-05:00
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A novel Boltzmann equation solver for calculation of dose and fluence spectra distributions for proton beam therapy
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arXiv:2512.24514v1 Announce Type: new Abstract: Approach. We solve the Boltzmann transport equation using an iterative procedure. Our algorithm accounts for Coulomb scattering and nuclear reactions. It uses the same physical models, as do the most rigorous Monte Carlo systems. Thereby it achieves the same low level of systematic errors. Our solver does not involve random sampling. The solution is not contaminated by statistical noise. This means that the overall uncertainties of our solver are lower than those realistically achievable with Monte Carlo. Furthermore, our solver is orders of magnitude faster. Its another advantage is that it calculates fluence spectra. They are needed for calculation of relative biological effectiveness, especially when advanced radiobiological models are used that may present a challenge for other algorithms. Main results. We have developed a novel Boltzmann equation solver, have written prototype software, and completed its testing for calculations in water. For 40-220 MeV protons we calculated fluence spectra, depth doses, three-dimensional dose distributions for narrow Gaussian beams. The CPU time was 5-11 ms for depth doses and fluence spectra at multiple depths. Gaussian beam calculations took 31-78 ms. All the calculations were run on a single Intel i7 2.9 GHz CPU. Comparison of our solver with Geant4 showed good agreement for all energies and depths. For the 1\%/1 mm $\gamma$-test the pass rate was 0.95-0.99. In this test, 1\% was the difference between our and Geant4 doses at the same point. The test included low dose regions down to 1\% of the maximum dose.
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https://arxiv.org/abs/2512.24514
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Academic Papers
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svg
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d729ae7345b70d1b04d93ac81bbaccd69b9fdc6e0481e468a8e232248bdd1d78
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2026-01-01T00:00:00-05:00
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Polarization-Differential Loss Enabled High Polarization Extinction in Hollow-Core Fibers
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arXiv:2512.24516v1 Announce Type: new Abstract: Delivering a well defined state of polarization over hollow core fibres (HCFs) is pivotal for next generation ultra stable photonic systems. Yet in all existing HCFs, whether birefringent or not, their polarization extinction ratio (PER) rapidly deteriorates during propagation or under mechanical disturbance, leaving no practical high and stable PER solution. Here, we break this impasse by embedding a polarization differential loss (PDL) mechanism directly into the cladding architecture.
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https://arxiv.org/abs/2512.24516
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Academic Papers
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b326455097269952a8b2b579ab61a57655198108a6c54df8cae7ade9e18be00e
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2026-01-01T00:00:00-05:00
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Rainfall forecasts in daily use over East Africa improved by machine learning
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arXiv:2512.24525v1 Announce Type: new Abstract: Ensemble forecasting has proven over the years to be a vital tool for predicting extreme or only partially predictable weather events. In particular life-threatening weather events. Many National Meteorological Services in East Africa do not have the computing resources to enable them to run their local area models in full ensemble mode over the full period of the 2 week medium range. As a result, weather users in these countries are not being given sufficient information about weather risk that is needed to make reliable decisions about taking preventative action. Consequently, society in many parts of the world is not as resilient to weather events as they could be. In this paper we test the performance of our forecast system, cGAN, which is the only high-resolution (10 km) ensemble rainfall product that does real-time, probabilistic correction of global forecasts for East Africa. Compared to existing state-of-the-art AI models, our system offers higher spatial resolution. It is cheap to train/run and requires no additional post-processing. It is run on laptops and can generate many thousands of ensemble members at little computational cost (compared with physical local area models). It is ideally suited to Meteorological Services with limited computational facilities.
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https://arxiv.org/abs/2512.24525
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Academic Papers
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svg
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e06a1a49348ec1f47b8bce095b9dbfc5754fa550ee19300a78084376a816ba89
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2026-01-01T00:00:00-05:00
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Towards Interpretable AI in Personalized Medicine: A Radiological-Biological Radiomics Dictionary Connecting Semantic Lung-RADS and imaging Radiomics Features; Dictionary LC 1.0
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arXiv:2512.24529v1 Announce Type: new Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival strongly dependent on early detection. Standard-dose computed tomography (CT) screening using the Lung Imaging Reporting and Data System (Lung-RADS) standardizes pulmonary nodule assessment but is limited by inter-reader variability and reliance on qualitative descriptors, while radiomics offers quantitative biomarkers that often lack clinical interpretability. To bridge this gap, we propose a radiological-biological dictionary that aligns radiomic features (RFs) with Lung-RADS semantic categories. A clinically informed dictionary translating ten Lung-RADS descriptors into radiomic proxies was developed through literature curation and validated by eight expert reviewers. As a proof of concept, imaging and clinical data from 977 patients across 12 collections in The Cancer Imaging Archive (TCIA) were analyzed; following preprocessing and manual segmentation, 110 RFs per nodule were extracted using PyRadiomics in compliance with the Image Biomarker Standardization Initiative (IBSI). A semi-supervised learning framework incorporating 499 labeled and 478 unlabeled cases was applied to improve generalizability, evaluating seven feature selection methods and ten interpretable classifiers. The optimal pipeline (ANOVA feature selection with a support vector machine) achieved a mean validation accuracy of 0.79. SHapley Additive exPlanations (SHAP) analysis identified key RFs corresponding to Lung-RADS semantics such as attenuation, margin irregularity, and spiculation, supporting the validity of the proposed mapping. Overall, this dictionary provides an interpretable framework linking radiomics and Lung-RADS semantics, advancing explainable artificial intelligence for CT-based lung cancer screening.
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https://arxiv.org/abs/2512.24529
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Academic Papers
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