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Nejen pod rukama designerů světových automobilek mohou vznikat krásná auta. Důkazem může být koncept vědců z Vysoké školy báňské – Technické univerzity v Ostravě (VŠB-TUO), kteří uspěli na mezinárodní přehlídce designu Berlin Design Week 2026 s prototypem unikátního elektrického supersportovního vozu StudentCar Titan. Sami si ho navrhli i vyrobili.
Jih Itálie krátce po pondělní půlnoci zasáhlo silnější zemětřesení. Podle systému EMSC mělo sílu 6,2 stupně. Otřes byl cítit i daleko od epicentra, píší agentury. Zatím však není jasné, zda způsobil škody či nějaké oběti.
Čínský gigant BYD v květnu prodal téměř 199 tisíc elektromobilů, nejvíce od loňského listopadu. Zlepšení vykázaly také celkové prodeje, které letos poprvé v některém z měsíců meziročně vzrostly. Významnou roli sehrála poptávka mimo Čínu.
Lékaři hlásí slibný průlom v léčbě některých druhů rakoviny. Nová injekce s názvem amivantamab během mezinárodní studie výrazně zmenšila nádory u desítek pacientů, a u části z nich je dokonce zcela odstranila. Odborníci mluví o mimořádných výsledcích u lidí, kterým už nezabírala chemoterapie ani imunoterapie. Výsledky představí na největší světové onkologické konferenci v Chicagu.
Senátní a komunální volby se blíží a s nimi i potřeba politických stran vést a platit kampaň. Zatímco na transparentních účtech hnutí ANO, občanských demokratů, Starostů, Pirátů, lidovců a TOP 09 napršely od sponzorů miliony korun, SPD a Motoristům dárci scházejí. Nejmenší vládní strana se navíc potýká s nedostatkem kandidátů do komunálních voleb v Praze.
arXiv:2606.00128v1 Announce Type: new Abstract: Each year the American Statistical Association (ASA) hosts the Annual Data Challenge Expo, which tasks participants with analyzing a given dataset and presenting their work at the Joint Statistical Meeting (JSM). The 2025 Data Challenge Expo tasked participants with analyzing over 35 years of commercial flight data from the United States Bureau of Transportation Statistics (BTS). These data p....
arXiv:2606.00157v1 Announce Type: new Abstract: We consider establishing the interpretability theory of deep learning through constructing a corresponding relationship between the renormalization group (RG) method in statistical physics and the training process of deep neural networks (DNNs). We have proved the constructed relationship using the one-dimensional Ising model as the input data. In this paper we generalize our results to the c....
arXiv:2606.00181v1 Announce Type: new Abstract: We introduce a novel regression framework designed to model non-linear responses situated on a sphere $\mathbb{S}$ of finite or infinite dimension. Unlike traditional tangent-space regressions, which lift responses to a tangent space $T_o \mathbb{S}$ and thereby violate intrinsic spherical distances, our proposed method employs an intrinsic approach. We model the conditional mean through an i....
arXiv:2606.00231v1 Announce Type: new Abstract: Bayesian models are claimed to be fully robust against outliers if, asymptotically, observations infinitely far from the other data do not influence the posterior. Early works in robust Bayesian inference concentrated on continuous distributions and i.i.d. observations. Robustness results were then extended to linear regression in the presence of infinite residuals, either through an outlying....
arXiv:2606.00233v1 Announce Type: new Abstract: Density estimation is often presented as a choice among parametric summaries, finite mixtures, and nonparametric smoothers. This review argues for a complementary view: a data set can be studied through a path of densities indexed by smoothing scale, diffusion time, model complexity, density level, or noise level. We call this perspective density evolution. Under this lens, Gaussian kernel de....
arXiv:2606.00265v1 Announce Type: new Abstract: We study quantile regression in an extrapolation regime where the covariate takes unusually large values. Under regular variation assumptions, extreme observations can be effectively characterized through their angular components, enabling learning strategies that focus on the angle of the most extreme observations. This approach is formalized through the minimization of an asymptotic conditi....
arXiv:2606.00296v1 Announce Type: new Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grids without additional retraining. Despite strong empirical evidence, the theoretical foundations of this phenomenon remain unclear. In this work, we provide a systematic theoretical study of zero-shot super-resolutio..
arXiv:2606.00302v1 Announce Type: new Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework. We present an analysis called evaluating replicability via iterative clustering assignments (ERICA) that is applied to a dataset to determine whether clusters are identified in a replicable manner. The pipeline computes a statistic that describes whether struct..
arXiv:2606.00327v1 Announce Type: new Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries. However, clustering results are highly sensitive to the choice of algorithm, preprocessing, and the number of clusters $k$, producing scientific claims that are often not reproducible. The current state of the art for validating clustering solutions consists of clustering validat....
arXiv:2606.00343v1 Announce Type: new Abstract: Motivated by the analysis of the behaviour of extremes from multivariate heavy-tailed distributions, we introduce a novel notion of statistical depth, referred to as Polar Depth. The polar depth function is naturally expressed in polar coordinates, as is the limiting distribution of a regularly varying random variable, beyond asymptotically large thresholds, once its marginals have been appro....
arXiv:2606.00346v1 Announce Type: new Abstract: Phenomena such as epidemiological processes, hydrologic systems, social platforms, utility services, and supply chains can be represented as topological networks. A central question about these networks concerns connectivity and the permeability of edges. Dyadic regression and related approaches have been proposed to identify network features associated with pairwise node-level differences. I....
arXiv:2606.00402v1 Announce Type: new Abstract: We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees without retraining. Our key observation is that rewrite-based detection implicitly constructs knockoff samples, enabling LLM-generated text detection to be formulated as a multiple hypothesis testing problem with knockoff structure. This perspec..
arXiv:2606.00413v1 Announce Type: new Abstract: Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that preserves the conditional mean of the response. Existing gradient-based estimators either operate in the ambient space and suffer from the curse of dimensionality, or localize in the reduced space at a per-outer-iteration cost at least quadratic in....
arXiv:2606.00419v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.e., data may not be exchangeable). Online conformal prediction (OCP) methods address this issue at the expense of either (i) group-wise error control or (ii) learning-rate independent implementation. Group-conditional coverage....
arXiv:2606.00425v1 Announce Type: new Abstract: Moment conditions are widely used to identify parameters in models where the full likelihood is either unknown or intentionally left unspecified. Empirical likelihood methods address this problem by assigning probability weights to the observed data so that the sample moment conditions hold exactly. Building on this idea, we propose a nonparametric Bayesian framework based on exponentially ti....
arXiv:2606.00436v1 Announce Type: new Abstract: Clustering is a central tool for discovering latent structure in unlabeled data; yet modern clustering pipelines often end with a hard assignment of each observation to a cluster without rigorous measures of assignment uncertainty. We propose a novel weighted conformal approach for constructing valid confidence sets for cluster labels. The key difficulty is that the labels available for calib....
arXiv:2606.00465v1 Announce Type: new Abstract: One investigates the extrinsic statistical analysis on the space of Billera- Holmes-Vogtmann tree space with four leaves (T4 or BHV4) based on its recently proposed novel representation (see [1])- the Spiky Projective ExcavatedDodecahedron (SPED). Due to the symmetry of the SPED, the Veronese- Whitney (VW) embeddingwe consider here produces a natural extrinsicmetric for a statistical analysis..
arXiv:2606.00478v1 Announce Type: new Abstract: Online high-dimensional regression has gained increasing attention in recent years, yet existing methods typically assume that all candidate features, including important ones, are observed from the outset of data collection. This assumption is often violated in real-world scenarios, where new variables become available gradually as data accumulate. To address this gap, we introduce a novel f....
arXiv:2606.00578v1 Announce Type: new Abstract: We study generalized Monte Carlo permutation tests under a non-uniform distribution on permutations. Focusing on the difference-in-means statistic, we introduce two scalar dispersion measures that quantify departures from complete randomization at the individual and pairwise levels. We show that if both dispersions vanish asymptotically, then the conditional permutation distribution converges..
arXiv:2606.00584v1 Announce Type: new Abstract: This paper proposes Spectra-Guided Neural Tucker Factorization (SG-NTF) for High-Dimensional and Incomplete (HDI) tensor completion. Circumventing discrete representational limits, SG-NTF maps scalar timestamps into a continuous spectral space to abstract temporal periodicities. Concurrently, a Spatio-Temporal Co-Gating (STCG) mechanism explicitly filters latent interactions via multiplicativ..
arXiv:2606.00643v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) often train slowly or fail to converge on challenging partial differential equations (PDEs), a behavior recently linked to severely ill-conditioned loss landscapes inherited from the underlying differential operator. We study PINNs augmented with a pointwise data-fidelity term, added at a few points in the domain to the standard residual and boundary l....
arXiv:2606.00661v1 Announce Type: new Abstract: We establish the finite-sample concentration rate for the Median-of-Incomplete-U-Statistics (MIU), an efficient robust estimator for the expectation of symmetric kernels.
arXiv:2606.00715v1 Announce Type: new Abstract: We study boundary detection for unlabeled noisy images from a statistical perspective. The aim is to recover an unknown object region from raw intensity observations without pixel-wise annotating labels or a parametric model for the intensity distributions. Motivated by robust Gibbs posterior approaches based on thresholded misclassification losses, we propose a continuous hinge-type surrogat....
arXiv:2606.00754v1 Announce Type: new Abstract: We introduce causal density functions: Radon-Nikodym derivatives that compare interventional laws to observational laws and therefore act as local density ratios for causal effects. Whereas many causal-strength measures compare whole distributions after graph surgery, causal density functions provide a pointwise change-of-measure object that can be estimated, calibrated, and used to score dir..
arXiv:2606.00758v1 Announce Type: new Abstract: In recent years, graph signal processing has emerged as a powerful framework at the intersection of signal processing and graph theory, providing tools for the analysis of signals defined on nodes while accounting for their relationships represented by edges. These tools have been successfully applied to various settings, including statistical hypothesis testing. In particular, non-parametric....
arXiv:2606.00767v1 Announce Type: new Abstract: Resting-state fMRI (rs-fMRI) is widely used to investigate brain functional connectivity, but the reliability of these measurements remains a key concern for ensuring reproducibility. The distance-based intraclass correlation coefficient (dbICC) generalizes classical ICC to more general data types, making it well-suited for assessing the reliability of measures of functional connectivity. In ....
arXiv:2606.00783v1 Announce Type: new Abstract: Reliable quantification of malaria dynamics in sub-Saharan Africa is hindered by short, noisy, and spatially heterogeneous surveillance records. In Ghana, health-facility data from 2014 to 2023 reveal non-linear and age-specific fluctuations in hospital admissions, yet existing approaches struggle to capture stochastic variability or provide credible uncertainty bounds. This study develops a ....
arXiv:2606.00797v1 Announce Type: new Abstract: Population-level heterogeneity is ubiquitous in biomedical data, where differences across demographic or clinical subgroups can substantially alter risk patterns. For example, in intensive care unit (ICU) studies, the mortality risk associated with specific admission diagnoses can vary across ethnic groups. Existing approaches for detecting risk heterogeneity are often sensitive to baseline m....
arXiv:2606.00834v1 Announce Type: new Abstract: Accurate malaria forecasting remains a major challenge in sub-Saharan Africa, where strong seasonality, reporting uncertainty, and non-stationary transmission dynamics reduce the reliability of conventional models. In Ghana, district-level malaria surveillance requires forecasting frameworks that are probabilistically rigorous and robust under limited data. This study proposes a hybrid framew....
arXiv:2606.00839v1 Announce Type: new Abstract: In this work, we study the problem of testing the marginal distributions of multiple independent, sequentially observed data streams, where for each stream there are multiple candidate hypotheses to select from, in the presence of prior information on the unknown hypothesis configuration. The goal is to understand the benefit of such information and to design a sequential testing procedure th....
arXiv:2606.00847v1 Announce Type: new Abstract: Partial identification provides informative causal guarantees when point identification is impossible, but existing approaches based on optimal transport (OT) become computationally and statistically intractable in high-dimensional settings. This limitation is particularly severe when both potential outcomes and confounders are high-dimensional, where classical OT-based bounds suffer from the....
arXiv:2606.00858v1 Announce Type: new Abstract: This article is concerned with change point detection for object-valued data that reside in a metric space, which has attracted some recent interests in statistics and econometrics literature. The existing methods either focus on independent data or can only detect change in the Fr\'echet mean or variance. In this paper, we propose a self-normalization (SN, hereafter) based statistic for dete....
arXiv:2606.00864v1 Announce Type: new Abstract: The bandwidth-free tests/inferences for a multi-dimensional parameter have attracted considerable attention in econometrics and statistics literature. These tests can be conveniently implemented due to their tuning-parameter free nature and possess more accurate size as compared to the traditional HAC-based approaches, where consistent long run variance estimation was involved. However, when ....
arXiv:2606.00867v1 Announce Type: new Abstract: Recent publications have suggested using the Shap- ley value for sensor anomaly/attack localization. We study the performance of such an approach by using mathematically de- fined optimum binary classifiers in the Shapley value calculation. To judge localization performance, we study the ability of the Shapley value of a given sensor observation to determine if that observation is anomalous. ....
arXiv:2606.00878v1 Announce Type: new Abstract: Confirmatory adaptive designs were introduced more than 30 years ago and enable for example sample size re-assessments and the selection of treatments, endpoints as well as subpopulations during the course of a clinical trial. Recently, sequential tests based on e-values for an anytime-valid inference have been developed, promising seemingly similar or even more flexibility and utility. In th....
arXiv:2606.00887v1 Announce Type: new Abstract: Testing simple or composite hypothesis on a functional parameter has attracted considerable attention in time series analysis. To accommodate for the unknown temporal dependence, classical nonparametric approaches such as block bootstrapping and subsampling all involve a bandwidth parameter, the choice of which can substantially affect the finite sample performance. The self normalization (SN....
arXiv:2606.00900v1 Announce Type: new Abstract: Randomized controlled trials (RCTs) and person-level observational studies feature prominently in debates over social media harms. I highlight some under-acknowledged limitations of such evidence. Most important is that published RCTs typically identify effects of a \textit{local}, or small-scale, intervention: a person is assigned to quit social media, but her immediate peers continue using ....
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