Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Differential Association Analysis

Identifying how dependence relationships vary across different conditions plays a significant role in many scientific investigations. For example, it is important for the comparison of biological systems to see if relationships between genomic features differ between cases and controls. In this paper, we seek to evaluate whether the relationships between two sets of variables is different across two conditions. Specifically, we assess: do two sets of high-dimensional variables have similar dependence relationships across two conditions? We propose a new kernel-based test to capture the differential dependence. Specifically, the new test determines whether two measures that detect dependence relationships are similar or not under two conditions. We introduce the asymptotic permutation null distribution of the test statistic and it is shown to work well under finite samples such that the test is computationally efficient, making it easily applicable to analyze large data sets. We demonstrate through numerical studies that our proposed test has high power for detecting differential linear and non-linear relationships. The proposed method is implemented in an R package kerDAA.
Shadow Implications: What Does Measuring the Photon Ring Imply for Gravity?

With the imaging and characterization of the horizon-scale images of M87* and Sgr A* by the Event Horizon Telescope (EHT), it has become possible to resolve the near-horizon region of astrophysical black holes. As a result, there has been considerable interest in the implications of the measurement of the shadow size, i.e., the asymptotic photon ring. We explore the general implications of such a measurement, identifying what is and, more importantly, is not constrained by such measurements, with applications to EHT and future instruments. We consider a general spherically symmetric metric, which effectively applies for a polar observer (appropriate for M87*) in the slow rotation limit. We propose a nonperturbative, nonparametric spacetime-domain characterization of shadow size and related measurements that makes explicit the nature and power (or lack thereof) of shadow-size-based constraints, and facilitates comparisons among observations and targets.
Poisson Multi-Bernoulli Radar Mapping Using Gibbs Sampling

This paper addresses the radar mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multi-object posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multi-object posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform an state-of-the-art method.
Isotropic and numerical equivalence for Chow groups and Morava K-theories

In this paper we prove the conjecture claiming that, over a flexible field, isotropic Chow groups coincide with numerical Chow groups (with ${\Bbb{F}}_p$-coefficients). This shows that Isotropic Chow motives coincide with Numerical Chow motives. In particular, homs between such objects are finite groups and $\otimes$ has no zero-divisors. It provides a large supply of new points for the Balmer spectrum of the Voevodsky motivic category. We also prove the Morava K-theory version of the above result, which permits to construct plenty of new points for the Balmer spectrum of the Morel-Voevodsky ${\Bbb{A}}^1$-stable homotopic category. This substantially improves our understanding of the mentioned spectra whose description is a major open problem.
A quantitative phase-field model for void evolution in defect supersaturated environments: a novel introduction of defect reaction asymmetry

Voids develop in crystalline materials under energetic particle irradiation, as in nuclear reactors. Understanding the underlying mechanisms of void nucleation and growth is of utmost importance as it leads to dimensional instability of the metallic materials. In the past two decades, researchers have adopted the phase-field approach to study the phenomena of void evolution under irradiation. The approach involves modeling the boundary between the void and matrix with a diffused interface. However, none of the existing models are quantitative in nature. This work introduces a thermodynamically consistent, quantitative diffuse interface model based on KKS formalism to describe the void evolution under irradiation. The model concurrently considers both vacancies and self-interstitials in the description of void evolution. Unique to our model is the presence of two mobility parameters in the equation of motion of the phase-field variable. The two mobility parameters relate the driving force for vacancy and self-interstitial interaction to the interface motion, analogous to dislocation motion through climb and glide processes. The asymptotic matching of the phase-field model with the sharp-interface theory fixes the two mobility parameters in terms of the material parameters in the sharp-interface model. The Landau coefficient, which controls the height of the double-well function in the phase field variable, and the gradient coefficient of the phase field variable are fixed based on the interfacial energy and interface width of the boundary. With all the parameters in the model determined in terms of the material parameters, we thus have a new phase field model for void evolution. Simple test cases will show the void evolution under various defect supersaturation to validate our new phase-field model.
SimDETR: Simplifying self-supervised pretraining for DETR

DETR-based object detectors have achieved remarkable performance but are sample-inefficient and exhibit slow convergence. Unsupervised pretraining has been found to be helpful to alleviate these impediments, allowing training with large amounts of unlabeled data to improve the detector's performance. However, existing methods have their own limitations, like keeping the detector's backbone frozen in order to avoid performance degradation and utilizing pretraining objectives misaligned with the downstream task. To overcome these limitations, we propose a simple pretraining framework for DETR-based detectors that consists of three simple yet key ingredients: (i) richer, semantics-based initial proposals derived from high-level feature maps, (ii) discriminative training using object pseudo-labels produced via clustering, (iii) self-training to take advantage of the improved object proposals learned by the detector. We report two main findings: (1) Our pretraining outperforms prior DETR pretraining works on both the full and low data regimes by significant margins. (2) We show we can pretrain DETR from scratch (including the backbone) directly on complex image datasets like COCO, paving the path for unsupervised representation learning directly using DETR.
Studying the variability of the X-ray spectral parameters of high-redshift GRBs' afterglows

The Swift satellite has observed more than a thousand GRBs with X-ray data. Almost a third of them have redshift measurement, too. Here we start to investigate the X-ray spectral fitting of the data considering the low energy part where the N(H) absorption happens. Based on the available more accurate input data we examined the robustness of previous fittings and tested how sensitive the changes of the starting parameters are. We studied the change of the intrinsic hydrogen column density during the outburst for a few events. No significant variability of N(H) column density was identified.
Notes concerning K\"ahler and anti-K\"ahler structures on quasi-statistical manifolds

Let $(\acute{N},g,\nabla )$\ be a $2n$-dimensional quasi-statistical manifold that admits a pseudo-Riemannian metric $g$ (or $h)$ and a linear connection $\nabla $ with torsion. This paper aims to study an almost Hermitian structure $(g,L)$ and an almost anti-Hermitian structure $(h,L)$ on a quasi-statistical manifold that admit an almost complex structure $L$. Firstly, under certain conditions, we present the integrability of the almost complex structure $L$. We show that when $d^\nabla L =0$ and the condition of torsion-compatibility are satisfied, $(\acute{N},g,\nabla ,$ $L)$ turns into a K\"{a}hler manifold. Secondly, we give necessary and sufficient conditions under which $(\acute{N},h,\nabla ,L)$ is an anti-K\"{a}% hler manifold, where $h$ is an anti-Hermitian metric. Moreover, we search the necessary conditions for $(\acute{N},h,\nabla ,L)$ to be a quasi-K\"{a}hler-Norden manifold.
Introduction to Stochastic Processes

Preface to Second Edition Preface to First Edition PRELIMINARIES Introduction Linear Differential Equations Linear Difference Equations Exercises FINITE MARKOV CHAINS Definitions and Examples Large-Time Behavior and Invariant Probability Classification of States Return Times Transient States Examples Exercises COUNTABLE MARKOV CHAINS Introduction Recurrence and Transience Positive Recurrence and Null Recurrence Branching Process Exercises CONTINUOUS-TIME MARKOV CHAINS Poisson Process Finite State Space Birth-and-Death Processes General Case Exercises OPTIMAL STOPPING Optimal Stopping of Markov Chains Optimal Stopping with Cost Optimal Stopping with Discounting Exercises MARTINGALES Conditional Expectation Definition and Examples Optional Sampling Theorem Uniform Integrability Martingale Convergence Theorem Maximal Inequalities Exercises RENEWAL PROCESSES Introduction Renewal Equation Discrete Renewal Processes M/G/1 and G/M/1 Queues Exercises REVERSIBLE MARKOV CHAINS Reversible Processes Convergence to Equilibrium Markov Chain Algorithms A Criterion for Recurrence Exercises BROWNIAN MOTION Introduction Markov Property Zero Set of Brownian Motion Brownian Motion in Several Dimensions Recurrence and Transience Fractal Nature of Brownian Motion Scaling Rules Brownian Motion with Drift Exercises STOCHASTIC INTEGRATION Integration with Respect to Random Walk Integration with Respect to Brownian Motion Ito's Formula Extensions if Ito's Formula Continuous Martingales Girsanov Transformation Feynman-Kac Formula Black-Scholes Formula Simulation Exercises Suggestions for Further Reading Index
Spectral theory of regular sequences: parametrisation and spectral characterisation

We extend the existence of ghost measures beyond nonnegative primitive regular sequences to a large class of nonnegative real-valued regular sequences. In the general case, where the ghost measure is not unique, we show that they can be parametrised by a compact abelian group. For a subclass of these measures, by replacing primitivity with a commutativity condition, we show that these measures have an infinite convolution structure similar to Bernoulli convolutions. Using this structure, we show that these ghost measures have pure spectral type. Further, we provide results towards a classification of the spectral type based on inequalities involving the spectral radius, joint spectral radius, and Lyapunov exponent of the underlying set of matrices. In the case that the underlying measure is pure point, we show that the support of the measure must be a subset of the rational numbers, a result that resolves a new case of the finiteness conjecture.
Fast but multi-partisan: Bursts of communication increase opinion diversity in the temporal Deffuant model

Human interactions create social networks forming the backbone of societies. Individuals adjust their opinions by exchanging information through social interactions. Two recurrent questions are whether social structures promote opinion polarisation or consensus in societies and whether polarisation can be avoided, particularly on social media. In this paper, we hypothesise that not only network structure but also the timings of social interactions regulate the emergence of opinion clusters. We devise a temporal version of the Deffuant opinion model where pairwise interactions follow temporal patterns and show that burstiness alone is sufficient to refrain from consensus and polarisation by promoting the reinforcement of local opinions. Individuals self-organise into a multi-partisan society due to network clustering, but the diversity of opinion clusters further increases with burstiness, particularly when individuals have low tolerance and prefer to adjust to similar peers. The emergent opinion landscape is well-balanced regarding clusters' size, with a small fraction of individuals converging to extreme opinions. We thus argue that polarisation is more likely to emerge in social media than offline social networks because of the relatively low social clustering observed online. Counter-intuitively, strengthening online social networks by increasing social redundancy may be a venue to reduce polarisation and promote opinion diversity.
Assessing the Representation of Phonological Rules by a Production Study of Non-Words in Coratino

Abstract Phonological regularities in a given language can be described as a set of formal rules applied to logical expressions (e.g., the value of a distinctive feature) or alternatively as distributional properties emerging from the phonetic substance. An indirect way to assess how phonology is represented in a speaker’s mind consists in testing how phonological regularities are transferred to non-words. This is the objective of this study, focusing on Coratino, a dialect from southern Italy spoken in the Apulia region. In Coratino, a complex process of vowel reduction operates, transforming the /i e ɛ u o ɔ a/ system for stressed vowels into a system with a smaller number of vowels for unstressed configurations, characterized by four major properties: (1) all word-initial vowels are maintained, even unstressed; (2) /a/ is never reduced, even unstressed; (3) unstressed vowels /i e ɛ u o ɔ/ are protected against reduction when they are adjacent to a consonant that shares articulation (labiality and velarity for /u o ɔ/ and palatality for /i e ɛ/); (4) when they are reduced, high vowels are reduced to /ɨ/ and mid vowels to /ə/. A production experiment was carried out on 19 speakers of Coratino to test whether these properties were displayed with non-words. The production data display a complex pattern which seems to imply both explicit/formal rules and distributional properties transferred statistically to non-words. Furthermore, the speakers appear to vary considerably in how they perform this task. Altogether, this suggests that both formal rules and distributional principles contribute to the encoding of Coratino phonology in the speaker’s mind.
Type Learning for Binaries and Its Applications

Binary type inference is a challenging problem due partly to the fact that during the compilation much type-related information has been lost. Most existing research work resorts to program analysis techniques, which can be either too heavyweight to be viable in practice or too conservative to be able to infer types with high accuracy. In this paper, we propose a new approach to learning types for binary code. Motivated by “duck typing,” our approach learn types for recovered variables from their features and properties (e.g., related representative instructions). We first use machine learning to train a classifier with basic types as its levels from binaries with debugging information. The classifier is then used to learn types for new and unseen binaries. While for composite types, such as <inline-formula><tex-math notation="LaTeX">${pointer}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${struct}$</tex-math></inline-formula>, a points-to analysis is performed. Finally, several experiments are conducted to evaluate our approach. The results demonstrate that our approach is more precise, both in terms of correct types and compatible types, than the commercial tool Hex-Rays, the open source tool Snowman, and a recent tool EKLAVYA using machine learning. We also show that the type information our proposed system learns is capable of helping detect malware.
Complex hyperbolic Gromov-Thurston metrics and almost $\frac{1}{4}$-pinched K\"{a}hler manifolds

In this paper we construct an almost negatively $\frac{1}{4}$-pinched Riemannian metric on a class of compact manifolds that, via previous work, was already known to be K\"{a}hler and not locally symmetric. This is the first known example of such manifolds and, via the result of Hernandez [7] and Yau and Zheng [14], these manifolds cannot admit a negatively quarter-pinched Riemannian metric. This metric is also interesting because it is the complex hyperbolic analogue to the famous pinched metric constructed by Gromov and Thurston in [6].
Predicate Transfer: Efficient Pre-Filtering on Multi-Join Queries

This paper presents predicate transfer, a novel method that optimizes join performance by pre-filtering tables to reduce the join input sizes. Predicate transfer generalizes Bloom join, which conducts pre-filtering within a single join operation, to multi-table joins such that the filtering benefits can be significantly increased. Predicate transfer is inspired by the seminal theoretical results by Yannakakis, which uses semi-joins to pre-filter acyclic queries. Predicate transfer generalizes the theoretical results to any join graphs and use Bloom filters to replace semi-joins leading to significant speedup. Evaluation shows predicate transfer can outperform Bloom join by 3.1x on average on TPC-H benchmark.
Observation of Three‐Dimensional Magnetic Reconnection in the Terrestrial Magnetotail

Study of magnetic reconnection has been focused on two‐dimensional geometry in the past decades, whereas three‐dimensional structures and dynamics of reconnection X line are poorly understood. In this paper, we report Cluster multispacecraft observations of a three‐dimensional magnetic reconnection X line with a weak guide field (~25% of the upstream magnetic field) in the Earth's magnetotail. We find that the X line not only retreated tailward but also expanded across the tail following the electron flow direction with a maximum average speed of (0.04–0.15) VA,up, where VA,up is the upstream Alfvén speed, or (0.14–0.57) Vde, where Vde is the electron flow speed in the out‐of‐plane direction. An ion diffusion region was observed by two spacecraft that were separated about 10 ion inertial lengths along the out‐of‐plane direction; however, these two spacecraft observed distinct magnetic structures associated with reconnection: one spacecraft observed dipolarization fronts, while the other one observed flux ropes. This indicates that reconnection proceeds in drastically different ways in different segments along the X line only a few ion inertial lengths apart.
Does Unpredictability Influence Driving Behavior?

In this paper we investigate the effect of the unpredictability of surrounding cars on an ego-car performing a driving maneuver. We use Maximum Entropy Inverse reinforcement Learning to model reward functions for an ego-car conducting a lane change in a highway setting. We define a new feature based on the unpredictability of surrounding cars and use it in the reward function. We learn two reward functions from human data: a baseline and one that incorporates our defined unpredictability feature, then compare their performance with a quantitative and qualitative evaluation. Our evaluation demonstrates that incorporating the unpredictability feature leads to a better fit of human-generated test data. These results encourage further investigation of the effect of unpredictability on driving behavior.
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions

Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role. However, real-world drug discovery tasks are often characterized by a scarcity of labeled data and significant covariate shift$\unicode{x2013}\unicode{x2013}$a setting that poses a challenge to standard deep learning methods. In this paper, we present Q-SAVI, a probabilistic model able to address these challenges by encoding explicit prior knowledge of the data-generating process into a prior distribution over functions, presenting researchers with a transparent and probabilistically principled way to encode data-driven modeling preferences. Building on a novel, gold-standard bioactivity dataset that facilitates a meaningful comparison of models in an extrapolative regime, we explore different approaches to induce data shift and construct a challenging evaluation setup. We then demonstrate that using Q-SAVI to integrate contextualized prior knowledge of drug-like chemical space into the modeling process affords substantial gains in predictive accuracy and calibration, outperforming a broad range of state-of-the-art self-supervised pre-training and domain adaptation techniques.
Trends and Topics: Characterizing Echo Chambers' Topological Stability and In-group Attitudes

Social Network sites are fertile ground for several polluting phenomena affecting online and offline spaces. Among these phenomena are included echo chambers, closed systems in which the opinions expressed by the people inside are exacerbated for the effect of the repetition, while opposite views are actively excluded. This paper offers a framework to explore, in a platform-independent manner, the topological changes through time of echo chambers, while considering the content posted by users and the attitude conveyed in discussing specific controversial issues. The proposed framework consists of four steps: (i) data collection and annotation of users' ideology regarding a controversial topic, (ii) construction of a dynamic network of interactions, (iii) ECs extraction and analysis of their dynamics, and (iv) topic extraction and valence analysis. The paper then enhances the formalization of the framework by conducting a case study on Reddit threads about sociopolitical issues (gun control, American politics, and minorities discrimination) during the first two years and a half of Donald Trump's presidency. The results unveil that users often stay inside echo chambers over time. Furthermore, in the analyzed discussions, the focus is on controversies related to right-wing parties and specific events in American and Canadian politics. The analysis of the attitude conveyed in the discussions shows a slight inclination toward a more negative or neutral attitude when discussing particularly sensitive issues, such as fascism, school shootings, or police violence.
AffineGlue: Joint Matching and Robust Estimation

We propose AffineGlue, a method for joint two-view feature matching and robust estimation that reduces the combinatorial complexity of the problem by employing single-point minimal solvers. AffineGlue selects potential matches from one-to-many correspondences to estimate minimal models. Guided matching is then used to find matches consistent with the model, suffering less from the ambiguities of one-to-one matches. Moreover, we derive a new minimal solver for homography estimation, requiring only a single affine correspondence (AC) and a gravity prior. Furthermore, we train a neural network to reject ACs that are unlikely to lead to a good model. AffineGlue is superior to the SOTA on real-world datasets, even when assuming that the gravity direction points downwards. On PhotoTourism, the AUC@10{\deg} score is improved by 6.6 points compared to the SOTA. On ScanNet, AffineGlue makes SuperPoint and SuperGlue achieve similar accuracy as the detector-free LoFTR.