Miracle-dz/UniFAR-FTUs huggingface dataset:
One-dimensional Stein's method with bespoke derivatives

We introduce a version of Stein's method of comparison of operators specifically tailored to the problem of bounding the Wasserstein-1 distance between continuous and discrete distributions on the real line. Our approach rests on a new family of weighted discrete derivative operators, which we call bespoke derivatives. We also propose new bounds on the derivatives of the solutions of Stein equations for Integrated Pearson random variables; this is a crucial step in Stein's method. We apply our result to several examples, including the Central Limit Theorem, Polya-Eggenberger urn models, the empirical distribution of the ground state of a many-interacting-worlds harmonic oscillator, the stationary distribution for the number of genes in the Moran model, and the stationary distribution of the Erlang-C system. Whenever our bounds can be compared with bounds from the literature, our constants are sharper.
Sensitivity analysis for causality in observational studies for regulatory science

Abstract Objective: The United States Congress passed the 21st Century Cures Act mandating the development of Food and Drug Administration guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data conducted a meeting with various stakeholder groups to build consensus around best practices for the use of real-world data (RWD) to support regulatory science. Our companion paper describes in detail the context and discussion of the meeting, which includes a recommendation to use a causal roadmap for study designs using RWD. This article discusses one step of the roadmap: the specification of a sensitivity analysis for testing robustness to violations of causal model assumptions. Methods: We present an example of a sensitivity analysis from a RWD study on the effectiveness of Nifurtimox in treating Chagas disease, and an overview of various methods, emphasizing practical considerations on their use for regulatory purposes. Results: Sensitivity analyses must be accompanied by careful design of other aspects of the causal roadmap. Their prespecification is crucial to avoid wrong conclusions due to researcher degrees of freedom. Sensitivity analysis methods require auxiliary information to produce meaningful conclusions; it is important that they have at least two properties: the validity of the conclusions does not rely on unverifiable assumptions, and the auxiliary information required by the method is learnable from the corpus of current scientific knowledge. Conclusions: Prespecified and assumption-lean sensitivity analyses are a crucial tool that can strengthen the validity and trustworthiness of effectiveness conclusions for regulatory science.
Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations

Various data imbalances that naturally arise in a multi-territory personalized recommender system can lead to a significant item bias for globally prevalent items. A locally popular item can be overshadowed by a globally prevalent item. Moreover, users' viewership patterns/statistics can drastically change from one geographic location to another which may suggest to learn specific user embeddings. In this paper, we propose a multi-task learning (MTL) technique, along with an adaptive upsampling method to reduce popularity bias in multi-territory recommendations. Our proposed framework is designed to enrich training examples with active users representation through upsampling, and capable of learning geographic-based user embeddings by leveraging MTL. Through experiments, we demonstrate the effectiveness of our framework in multiple territories compared to a baseline not incorporating our proposed techniques. ~Noticeably, we show improved relative gain of up to $65.27\%$ in PR-AUC metric. A case study is presented to demonstrate the advantages of our methods in attenuating the popularity bias of global items.
Entanglement transitions in quantum-enhanced experiments

A quantum-enhanced experiment, in which information is transduced from a system of interest and processed on a quantum computer, has the possibility of exponential advantage in sampling tasks over a traditional classical experiment, where only the measurement outcomes of projective or weak measurements are stored on a classical computer. In this work, we demonstrate that, similar to the measurement induced phase transition~(MIPT) occurring in classical experiments, quantum-enhanced experiments can also show entanglement phase transitions. We introduce a noisy transduction operation which replaces projective measurements and acts independently on two qubits, recording the quantum state of one qubit in the measurement apparatus, while erasing the quantum state of the other qubit with the environment. We then construct a random brickwork circuit which shows an entanglement transition tuned by the rate of noisy transduction operations. Below the critical rate of noisy transduction, an extensive amount of entanglement is generated between regions of the system and apparatus, while above the critical rate, entanglement satisfies area law scaling. Our work delineates the prerequisites for a broad spectrum of quantum-enhanced experiments to manifest entanglement transitions, thereby establishing a foundational framework that connects quantum-enhanced sensing with emergent entanglement phenomena.
Unbiased estimation of sampling variance for Simpson's diversity index

Quantification of measurement uncertainty is crucial for robust scientific inference, yet accurate estimates of this uncertainty remain elusive for ecological measures of diversity. Here, we address this longstanding challenge by deriving a closed-form unbiased estimator for the sampling variance of Simpson's diversity index. In numerical tests the estimator consistently outperforms existing approaches, particularly for applications in which species richness exceeds sample size. We apply the estimator to quantify biodiversity loss in marine ecosystems and to demonstrate ligand-dependent contributions of T cell receptor chains to specificity, illustrating its versatility across fields. The novel estimator provides researchers with a reliable method for comparing diversity between samples, essential for quantifying biodiversity trends and making informed conservation decisions.
ResolverFuzz: Automated Discovery of DNS Resolver Vulnerabilities with Query-Response Fuzzing

Domain Name System (DNS) is a critical component of the Internet. DNS resolvers, which act as the cache between DNS clients and DNS nameservers, are the central piece of the DNS infrastructure, essential to the scalability of DNS. However, finding the resolver vulnerabilities is non-trivial, and this problem is not well addressed by the existing tools. To list a few reasons, first, most of the known resolver vulnerabilities are non-crash bugs that cannot be directly detected by the existing oracles (or sanitizers). Second, there lacks rigorous specifications to be used as references to classify a test case as a resolver bug. Third, DNS resolvers are stateful, and stateful fuzzing is still challenging due to the large input space. In this paper, we present a new fuzzing system termed ResolverFuzz to address the aforementioned challenges related to DNS resolvers, with a suite of new techniques being developed. First, ResolverFuzz performs constrained stateful fuzzing by focusing on the short query-response sequence, which has been demonstrated as the most effective way to find resolver bugs, based on our study of the published DNS CVEs. Second, to generate test cases that are more likely to trigger resolver bugs, we combine probabilistic context-free grammar (PCFG) based input generation with byte-level mutation for both queries and responses. Third, we leverage differential testing and clustering to identify non-crash bugs like cache poisoning bugs. We evaluated ResolverFuzz against 6 mainstream DNS software under 4 resolver modes. Overall, we identify 23 vulnerabilities that can result in cache poisoning, resource consumption, and crash attacks. After responsible disclosure, 19 of them have been confirmed or fixed, and 15 CVE numbers have been assigned.
Solving a Class of Non-Convex Minimax Optimization in Federated Learning

The minimax problems arise throughout machine learning applications, ranging from adversarial training and policy evaluation in reinforcement learning to AUROC maximization. To address the large-scale data challenges across multiple clients with communication-efficient distributed training, federated learning (FL) is gaining popularity. Many optimization algorithms for minimax problems have been developed in the centralized setting (\emph{i.e.} single-machine). Nonetheless, the algorithm for minimax problems under FL is still underexplored. In this paper, we study a class of federated nonconvex minimax optimization problems. We propose FL algorithms (FedSGDA+ and FedSGDA-M) and reduce existing complexity results for the most common minimax problems. For nonconvex-concave problems, we propose FedSGDA+ and reduce the communication complexity to $O(\varepsilon^{-6})$. Under nonconvex-strongly-concave and nonconvex-PL minimax settings, we prove that FedSGDA-M has the best-known sample complexity of $O(\kappa^{3} N^{-1}\varepsilon^{-3})$ and the best-known communication complexity of $O(\kappa^{2}\varepsilon^{-2})$. FedSGDA-M is the first algorithm to match the best sample complexity $O(\varepsilon^{-3})$ achieved by the single-machine method under the nonconvex-strongly-concave setting. Extensive experimental results on fair classification and AUROC maximization show the efficiency of our algorithms.
Machine learning the interaction network in coupled dynamical systems

The study of interacting dynamical systems continues to attract research interest in various fields of science and engineering. In a collection of interacting particles, the interaction network contains information about how various components interact with one another. Inferring the information about the interaction network from the dynamics of agents is a problem of long-standing interest. In this work, we employ a self-supervised neural network model to achieve two outcomes: to recover the interaction network and to predict the dynamics of individual agents. Both these information are inferred solely from the observed trajectory data. This work presents an application of the Neural Relational Inference model to two dynamical systems: coupled particles mediated by Hooke's law interaction and coupled phase (Kuramoto) oscillators.
RouteKG: A knowledge graph-based framework for route prediction on road networks

Short-term route prediction on road networks allows us to anticipate the future trajectories of road users, enabling a plethora of intelligent transportation applications such as dynamic traffic control or personalized route recommendation. Despite recent advances in this area, existing methods focus primarily on learning sequential transition patterns, neglecting the inherent spatial structural relations in road networks that can affect human routing decisions. To fill this gap, this paper introduces RouteKG, a novel Knowledge Graph-based framework for route prediction. Specifically, we construct a Knowledge Graph on the road network, thereby learning and leveraging spatial relations, especially moving directions, which are crucial for human navigation. Moreover, an n-ary tree-based algorithm is introduced to efficiently generate top-K routes in a batch mode, enhancing scalability and computational efficiency. To further optimize the prediction performance, a rank refinement module is incorporated to fine-tune the candidate route rankings. The model performance is evaluated using two real-world vehicle trajectory datasets from two Chinese cities, Chengdu and Shanghai, under various practical scenarios. The results demonstrate a significant improvement in accuracy over baseline methods.We further validate our model through a case study that utilizes the pre-trained model as a simulator for real-time traffic flow estimation at the link level. The proposed RouteKG promises wide-ranging applications in vehicle navigation, traffic management, and other intelligent transportation tasks.
Coupling OpenFOAM(R) with BoSSS, a discontinuous Galerkin solver written in C#

In this article, we present the foam-dg project, which provides a bridge between OpenFOAM(R) and the high-order DG (discontinuous Galerkin) framework BoSSS. Thanks to the flexibility of the coupling approach, mixed calculations where some parts of the equation system are solved in OpenFOAM(R) and others are solved in BoSSS are easily possible. This is showcased using the convective Cahn-Hilliard equation, where the Cahn-Hilliard part is solved in BoSSS and the Navier-Stokes part is solved in OpenFOAM(R). The obtained results appear reasonable, though the main focus of this paper is to present and document the foam-dg project rather than on quantitative results.
Comparison of Evidential Reasoning and Neural Network Approaches in a Multi-source Classification of Alpine Tundra Vegetation

Il est demontre depuis peu que le raisonnement d'evidences et les approches par reseau de neurones ont beaucoup de potentiel pour l'analyse d'images multisources et la classification de la couverture du sol. Dans cet article, le raisonnement d'evidences et les classificateurs de reseau de neurones sont compares dans une classification multi-source de la vegetation de toundra alpine dans la region de Niwot Ridge, dans les Colorado Rocky Mountains. La performance des deux classificateurs a ete evaluee relativement a l'approche conventionnelle par maximum de vraisemblance. Dans la toundra alpine, le developpement d'une communaute donnee est correle avec le couvert nival et les conditions d'humidite qui, dans le cas de Niwot Ridge, sont associes a la topographie et plus particulierement, a la direction des vents dominants. Les classifications realisees utilisant l'ensemble de donnees multisources (30-m) ont montre que les meilleurs resultats sont obtenus avec un classificateur de raisonnement d'evidences (79%), suivi des classificateurs de reseau de neurones (71%) et du maximum de vraisemblance (69%). Des experiences realisees avec les reseaux de neurones ont demontre la difficulte d'en arriver a une architecture optimale de reseau pour atteindre des niveaux eleves de precision. En particulier, la performance du reseau de neurone s'avere variable, dependant du nombre de couches cachees, du nombre de noeuds caches dans chaque couche cachee et du nombre d'iterations d'apprentissage utilisees, alors que le classificateur de raisonnement d'evidences requiert peu d'intervention de l'utilisateur et est insensible aux caracteristiques des parametres par rapport aux intrants du reseau de neurones. Le classificateur de raisonnement d'evidences permet aussi une vitesse de calcul significativement plus elevee comparativement au reseau de neurone, a partir de quoi nous avons conclu que la vitesse et la precision du raisonnement d'evidences seraient bien adaptees aux ensembles de donnees plus vastes et pour les etudes a l'echelle regionale. Les planches d'erreurs produites dans le cadre de cette recherche ont permis d'examiner la distribution spatiale des pixels classifies erronement. Les patterns spatiaux identifies sur ces planches suggerent que certaines des variables environnementales cles qui controlent la distribution de la vegetation sur Niwot Ridge seraient mieux caracterisees par l'utilisation de donnees a plus haute resolution (5-15 m) et en incorporant des variables supplementaires telles que la geologie de surface et la duree du couvert nival.
Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data

Traditional deep learning (DL) suffers from two core problems. Firstly, it assumes training samples are independent and identically distributed. However, numerous real-world datasets group samples by shared measurements (e.g., study participants or cells), violating this assumption. In these scenarios, DL can show compromised performance, limited generalization, and interpretability issues, coupled with cluster confounding causing Type 1 and 2 errors. Secondly, models are typically trained for overall accuracy, often neglecting underrepresented groups and introducing biases in crucial areas like loan approvals or determining health insurance rates, such biases can significantly impact one's quality of life. To address both of these challenges simultaneously, we present a mixed effects deep learning (MEDL) framework. MEDL separately quantifies cluster-invariant fixed effects (FE) and cluster-specific random effects (RE) through the introduction of: 1) a cluster adversary which encourages the learning of cluster-invariant FE, 2) a Bayesian neural network which quantifies the RE, and a mixing function combining the FE an RE into a mixed-effect prediction. We marry this MEDL with adversarial debiasing, which promotes equality-of-odds fairness across FE, RE, and ME predictions for fairness-sensitive variables. We evaluated our approach using three datasets: two from census/finance focusing on income classification and one from healthcare predicting hospitalization duration, a regression task. Our framework notably enhances fairness across all sensitive variables-increasing fairness up to 82% for age, 43% for race, 86% for sex, and 27% for marital-status. Besides promoting fairness, our method maintains the robust performance and clarity of MEDL. It's versatile, suitable for various dataset types and tasks, making it broadly applicable. Our GitHub repository houses the implementation.
Varying entropy degrees of freedom effects in low-scale leptogenesis

We analyse in detail the effect of varying entropy degrees of freedom on low-scale leptogenesis models. As an archetypal model, we consider the Tri-Resonant Leptogensis${}$ (TRL) scenario introduced recently by the authors, where the neutrino-Yukawa coupling matrix is dictated by an approximate $\mathbb{Z}_n$ discrete symmetry (with $n=3,6$). TRL models exhibit no preferred direction in the leptonic flavour space and have the remarkable feature that leptogenesis can successfully take place even if all light neutrinos are strictly massless up to one-loop order. Most interestingly, for TRL scenarios with heavy Majorana neutrinos lighter than 100 GeV, temperature varying degrees of freedom associated with the entropy of the plasma have a dramatic impact on the predictions of the Baryon Asymmetry in the Universe (BAU), and may sensitively depend on the freeze-out sphaleron temperature $T_{\rm sph}$. We find that this is a generic feature of most freeze-out low-scale leptogenesis models discussed in the literature. In the same context, we consider heavy-neutrino scenarios realising dynamics related to critical unstable qudits in the thermal plasma and assess their significance in generating the BAU. The phenomenological implications of TRL scenarios at the intensity and high-energy frontiers are analysed.
Sparse Deep Learning for Time Series Data: Theory and Applications

Sparse deep learning has become a popular technique for improving the performance of deep neural networks in areas such as uncertainty quantification, variable selection, and large-scale network compression. However, most existing research has focused on problems where the observations are independent and identically distributed (i.i.d.), and there has been little work on the problems where the observations are dependent, such as time series data and sequential data in natural language processing. This paper aims to address this gap by studying the theory for sparse deep learning with dependent data. We show that sparse recurrent neural networks (RNNs) can be consistently estimated, and their predictions are asymptotically normally distributed under appropriate assumptions, enabling the prediction uncertainty to be correctly quantified. Our numerical results show that sparse deep learning outperforms state-of-the-art methods, such as conformal predictions, in prediction uncertainty quantification for time series data. Furthermore, our results indicate that the proposed method can consistently identify the autoregressive order for time series data and outperform existing methods in large-scale model compression. Our proposed method has important practical implications in fields such as finance, healthcare, and energy, where both accurate point estimates and prediction uncertainty quantification are of concern.
Basin and range crustal and upper mantle structure, northwest to central Nevada

We present an interpretation of the crustal and uppermost mantle structure of the Basin and Range of northwestern Nevada based on seismic refraction/wide-angle reflection, near-vertical reflection, and gravity data. In comparison to most previous estimates, we find that the crust is somewhat thicker (32–36 km versus 22–30 km), and the uppermost mantle velocity is somewhat higher (8.0 km/s versus 7.3–7.9 km/s). Along our transects, the crust is thinnest (32 km) in the Carson Sink-Buena Vista Valley region and increases by 2–4 km to the west and east, respectively. There is considerable complexity throughout the crust where velocities range from of 2.5 km/s at the surface to 7.4 km/s in the lowermost crust. Variations in velocity and structure of the upper crustal layers reveal apparent basement velocity depressions (areas of lower velocities extending up to 10 km in depth) that underlie some surface ranges as well as the basins. The middle crust rises from about 20 km beneath central Nevada to within 12 km of the surface beneath the area of thinnest crust and is characterized by a modest (∼0.1 km/s) change in velocity and low-velocity gradients. These midcrustal layers mark the onset of high crustal reflectivity and the apparent limiting depth to which Basin and Range faults can be traced in near-vertical reflection profiles, suggesting that these midcrustal layers represent the transition between the brittle and ductile zones of the crust. The lower crust is more structurally complex, with layers thickening and thinning in a systematic manner with the upper crustal layers; generally, where there are velocity depressions in the upper crust, the lower crust is thickest and shallowest. The geometry of these lower crustal layers (derived from refraction modeling) coincides with changes in the crustal reflectivity, determined from the Consortium of Continental Reflection Profiling reflection data. The lower crustal layer is unusually high in velocity (7.4 km/s) and is likely the layer identified as mantle in some previous studies. We do not identify the 7.4 km/s layer as mantle because (1) there is an underlying layer with a velocity (8.0 km/s) that is more consistent with the worldwide average velocity for the upper mantle, and (2) the 7.4 km/s layer does not correspond to the “reflection” Moho. Gravity modeling and comparison to existing seismic models show a general consensus in many aspects with respect to crustal structure. This new model forms the basis for speculation on some of the processes associated with rifting of the Basin and Range Province. One such process, lithospheric magmatism, is inferred from the strong attenuation of transmitted seismic waves, which occurs at the same interface at which high-amplitude, bright spot reflections originate. Unlike previous models, the overall structure and velocity of the crust and uppermost mantle of our new model are similar to other regions worldwide which have undergone high degrees of extension.