Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast logarithmic Fourier-Laplace transform of nonintegrable functions.

We present an efficient and very flexible numerical fast Fourier-Laplace transform that extends the logarithmic Fourier transform introduced by Haines and Jones [Geophys. J. Int. 92, 171 (1988)GJINEA0956-540X10.1111/j.1365-246X.1988. tb01131.x] for functions varying over many scales to nonintegrable functions. In particular, these include cases of the asymptotic form f(ν→0)∼ν^{a} and f(|ν|→∞)∼ν^{b} with arbitrary real a>b. Furthermore, we prove that the numerical transform converges exponentially fast in the number of data points, provided that the function is analytic in a cone |Imν|<θ|Reν| with a finite opening angle θ around the real axis and satisfies |f(ν)f(1/ν)|<ν^{c} as ν→0 with a positive constant c, which is the case for the class of functions with power-law tails. Based on these properties we derive ideal transformation parameters and discuss how the logarithmic Fourier transform can be applied to convolutions. The ability of the logarithmic Fourier transform to perform these operations on multiscale (nonintegrable) functions with power-law tails with exponentially small errors makes it the method of choice for many physical applications, which we demonstrate on typical examples. These include benchmarks against known analytical results inaccessible to other numerical methods, as well as physical models near criticality.
A Keck/DEIMOS spectroscopic survey of faint Galactic satellites: searching for the least massive dwarf galaxies

We present the results of a spectroscopic survey of the recently discovered faint Milky Way satellites Bootes, Ursa Major I, Ursa Major II and Willman 1 (Will). Using the DEep Imaging Multi-Object Spectrograph mounted on the Keck II telescope, we have obtained samples that contain from ∼ 15 to ∼85 probable members of these satellites for which we derive radial velocities precise to a few km s -1 down to i ∼ 21-22. About half of these stars are observed with a high enough signal-to-noise ratio to estimate their metallicity to within ±0.2 dex. The characteristics of all the observed stars are made available, along with those of the Canes Venatici I dwarf galaxy that have been analysed in a companion paper. From this data set, we show that Ursa Major II is the only object that does not show a clear radial velocity peak. However, the measured systemic radial velocity (υ r = 115 ± 5kms -1 ) is in good agreement with simulations in which this object is the progenitor of the recently discovered Orphan Stream. The three other satellites show velocity dispersions that make them highly dark matter dominated systems (under the usual assumptions of symmetry and virial equilibrium). In particular, we show that despite its small size and faintness, the Will object is not a globular cluster given its metallicity scatter over -2.0 ≤ [Fe/H] ≤ -1.0 and is therefore almost certainly a dwarf galaxy or dwarf galaxy remnant. We measure a radial velocity dispersion of only 4.3 +2.3-1.3 km s -1 around a systemic velocity of -12.3 ± 2.3 km s -1 which implies a mass-to-light ratio of ∼700 and a total mass of ∼5 x 10 5 M O for this satellite, making it the least massive satellite galaxy known to date. Such a low mass could mean that the 10 7 M O limit that had until now never been crossed for Milky Way and Andromeda satellite galaxies may only be an observational limit and that fainter, less massive systems exist within the Local Group. However, more modelling and an extended search for potential extratidal stars are required to rule out the possibility that these systems have not been significantly heated by tidal interaction.
A systematic review of extended reality (XR) for understanding and augmenting vision loss

Over the past decade, extended reality (XR) has emerged as an assistive technology not only to augment residual vision of people losing their sight but also to study the rudimentary vision restored to blind people by a visual neuroprosthesis. A defining quality of these XR technologies is their ability to update the stimulus based on the user’s eye, head, or body movements. To make the best use of these emerging technologies, it is valuable and timely to understand the state of this research and identify any shortcomings that are present. Here we present a systematic literature review of 227 publications from 106 different venues assessing the potential of XR technology to further visual accessibility. In contrast to other reviews, we sample studies from multiple scientific disciplines, focus on technology that augments a person’s residual vision, and require studies to feature a quantitative evaluation with appropriate end users. We summarize prominent findings from different XR research areas, show how the landscape has changed over the past decade, and identify scientific gaps in the literature. Specifically, we highlight the need for real-world validation, the broadening of end-user participation, and a more nuanced understanding of the usability of different XR-based accessibility aids.
Uniform asymptotic formulas for the Fourier coefficients of certain inverse theta functions

In this paper we investigate the asymptitics of the Fourier coefficients of certain inverse theta functions, which play a key role in theory of partitions. We establish some uniform asymptotic formulas for those coefficients, which will follows from a more general result about the asymptotic expansion of alternating sum of a class of functions over certain quadratic polynomials. As corollaries, we derived the asymptotics monotonicity properties for rank and crank statistics for integer partitions.
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment

This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and data while being close to a given model probability. We derive a fixed-point iteration method and prove its convergence to the optimal solution. In contrast to baselines, MIRA combined with pseudo-label prediction enables a simple yet effective clustering-based representation learning without incorporating extra training techniques or artificial constraints such as sampling strategy, equipartition constraints, etc. With relatively small training epochs, representation learned by MIRA achieves state-of-the-art performance on various downstream tasks, including the linear/k-NN evaluation and transfer learning. Especially, with only 400 epochs, our method applied to ImageNet dataset with ResNet-50 architecture achieves 75.6% linear evaluation accuracy.
VAID: Indexing View Designs in Visual Analytics System

Visual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up with an index structure VAID to describe advanced and composited visualization designs with comprehensive labels about their analytical tasks and visual designs. The usefulness of VAID was validated through user studies. Our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs.
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic

Through their transfer learning abilities, highly-parameterized large pre-trained language models have dominated the NLP landscape for a multitude of downstream language tasks. Though linguistically proficient, the inability of these models to incorporate the learning of non-linguistic entities (numerals and arithmetic reasoning) limits their usage for tasks that require numeric comprehension or strict mathematical reasoning. However, as we illustrate in this paper, building a general purpose language model that also happens to be proficient in mathematical reasoning is not as straight-forward as training it on a numeric dataset. In this work, we develop a novel framework that enables language models to be mathematically proficient while retaining their linguistic prowess. Specifically, we offer information-theoretic interventions to overcome the catastrophic forgetting of linguistic skills that occurs while injecting non-linguistic skills into language models.
Stochastic equation and exponential ergodicity in Wasserstein distances for affine processes

This work is devoted to the study of conservative affine processes on the canonical state space $D = $R_+^m \times \R^n$, where $m + n > 0$. We show that each affine process can be obtained as the pathwise unique strong solution to a stochastic equation driven by Brownian motions and Poisson random measures. Then we study the long-time behavior of affine processes, i.e., we show that under first moment condition on the state-dependent and log-moment conditions on the state-independent jump measures, respectively, each subcritical affine process is exponentially ergodic in a suitably chosen Wasserstein distance. Moments of affine processes are studied as well.
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects

We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song. This is achieved with an encoder pre-trained with a contrastive objective to extract only audio effects related information from a reference music recording. All our models are trained in a self-supervised manner from an already-processed wet multitrack dataset with an effective data preprocessing method that alleviates the data scarcity of obtaining unprocessed dry data. We analyze the proposed encoder for the disentanglement capability of audio effects and also validate its performance for mixing style transfer through both objective and subjective evaluations. From the results, we show the proposed system not only converts the mixing style of multitrack audio close to a reference but is also robust with mixture-wise style transfer upon using a music source separation model.
Algebras of one-sided subshifts over arbitrary alphabets

We associate a C*-algebra $\widetilde{\mathcal{O}}_{\textsf{X}}$ with a subshift over an arbitrary, possibly infinite, alphabet. We show that $\widetilde{\mathcal{O}}_{\textsf{X}}$ is a full invariant for topological conjugacy of the subshifts of Ott, Tomforde, and Willis. When the alphabet is countable, we show that $\widetilde{\mathcal{O}}_{\textsf{X}}$ is an invariant for isometric conjugacy of subshifts with the product metric. For a suitable partial action associated with a subshift over a countable alphabet, we show that $\widetilde{\mathcal{O}}_{\textsf{X}}$ is also an invariant for continuous orbit equivalence. Additionally, we give a concrete way to compute the K-theory of $\widetilde{\mathcal{O}}_{\textsf{X}}$ and illustrate it with two examples.
Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments

This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.
Bayesian Sequential Experimental Design for a Partially Linear Model with a Gaussian Process Prior

We study the problem of sequential experimental design to estimate the parametric component of a partially linear model with a Gaussian process prior. We consider an active learning setting where an experimenter adaptively decides which data to collect to achieve their goal efficiently. The experimenter's goals may vary, such as reducing the classification error probability or improving the accuracy of estimating the parameters of the data generating process. This study aims to improve the accuracy of estimating the parametric component of a partially linear model. Under some assumptions, the parametric component of a partially linear model can be regarded as a causal parameter, the average treatment effect (ATE) or the average causal effect (ACE). We propose a Bayesian sequential experimental design algorithm for a partially linear model with a Gaussian process prior, which is also considered as a sequential experimental design tailored to the estimation of ATE or ACE. We show the effectiveness of the proposed method through numerical experiments based on synthetic and semi-synthetic data.
Asymptotic normality in random graphs with given vertex degrees

We consider random graphs with a given degree sequence and show, under weak technical conditions, asymptotic normality of the number of components isomorphic to a given tree, first for the random multigraph given by the configuration model and then, by a conditioning argument, for the simple uniform random graph with the given degree sequence. Such conditioning is standard for convergence in probability, but much less straightforward for convergence in distribution as here. The proof uses the method of moments, and is based on a new estimate of mixed cumulants in a case of weakly dependent variables. The result on small components is applied to give a new proof of a recent result by Barbour and Röllin on asymptotic normality of the size of the giant component in the random multigraph; moreover, we extend this to the random simple graph.
An information theoretic vulnerability metric for data integrity attacks on smart grids

A novel metric that describes the vulnerability of the measurements in power systems to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect on the fundamental limits of the disruption and detection tradeoff. The result of computing the VuIx of the measurements in the system yields an ordering of their vulnerability based on the level of exposure to data integrity attacks. This new framework is used to assess the measurement vulnerability of IEEE 9-bus and 30-bus test systems and it is observed that power injection measurements are overwhelmingly more vulnerable to data integrity attacks than power flow measurements. A detailed numerical evaluation of the VuIx values for IEEE test systems is provided.
A Distance-Based Side-Channel Attack in Non Uniform Cache and Possible Defenses

—For a distributed last level cache (LLC) in a large multicore chip, the access time to one LLC bank can signiﬁcantly differ from that to another. The disparity in access time is due to the different physical distances to the target LLC slices. In this paper, we demonstrate the possibility of exploiting such a distance-based side channel, by timing a vulnerable version of AES decryption and extracting part of the secret keys. We introduce several techniques to overcome the challenges of the attack, including using multiple attack threads to ensure LLC hits of the vulnerable memory locations and to time part of the decryption function. We further propose C AMOUFLAGE , an efﬁcient, architectural defense for the proposed distance-based side-channel attack. At runtime, when a potentially leaking memory instruction is executed by a victim function, C AMOUFLAGE uses a combination of jitter and bypass mechanisms to eliminate any LLC hit time difference due to the distance and thereby, prevent the attack. We evaluate two versions of C AMOUFLAGE - C AMOUFLAG - E JITTER and C AMOUFLAGE BYPASS using the Gem5 simulator with PARSEC and Rodinia benchmarks and show that they incur performance overheads of 14.14% or none over the baseline.
Model-based graph clustering of a collection of networks using an agglomerative algorithm

Graph clustering is the task of partitioning a collection of observed networks into groups of similar networks. Here similarity means networks have a similar structure or graph topology. To this end, a model-based approach is developed, where the networks are modelled by a ﬁnite mixture model of stochastic block models. Moreover, a computationally eﬃcient clustering algorithm is developed. The procedure is an agglomerative hierarchical algorithm that maximizes the so-called integrated classi-ﬁcation likelihood criterion. The bottom-up algorithm consists of successive merges of clusters of networks. Those merges require a means to match block labels of two stochastic block models to overcome the label-switching problem. This problem is addressed with a new distance measure for the comparison of stochastic block models based on their graphons. The algorithm provides a cluster hierarchy in form of a dendrogram and valuable estimates of all model parameters.
Ultracontractivity and Gaussian bounds for evolution families associated with nonautonomous forms

We develop a variational approach in order to study qualitative properties of nonautonomous parabolic equations. Based on the method of product integrals, we discuss invariance properties and ultracontractivity of evolution families in Hilbert space. Our main results give sufficient conditions for the heat kernel of the evolution family to satisfy Gaussian‐type bounds. Along the way, we study examples of nonautonomous equations on graphs, metric graphs, and domains.
Rethinking the positive role of cluster structure in complex networks for link prediction tasks

—Clustering is a fundamental problem in network analysis that ﬁnds closely connected groups of nodes and sep-arates them from other nodes in the graph, while link prediction is to predict whether two nodes in a network are likely to have a link. The deﬁnition of both naturally determines that clustering must play a positive role in obtaining accurate link prediction tasks. Yet researchers have long ignored or used inappropriate ways to undermine this positive relationship. In this article, We construct a simple but efﬁcient clustering-driven link prediction framework( ClusterLP ), with the goal of directly exploiting the cluster structures to obtain connections between nodes as accu- rately as possible in both undirected graphs and directed graphs. Speciﬁcally, we propose that it is easier to establish links between nodes with similar representation vectors and cluster tendencies in undirected graphs, while nodes in a directed graphs can more easily point to nodes similar to their representation vectors and have greater inﬂuence in their own cluster. We customized the implementation of ClusterLP for undirected and directed graphs, respectively, and the experimental results using multiple real- world networks on the link prediction task showed that our models is highly competitive with existing baseline models. The code implementation of ClusterLP and baselines we use are available at https://github.com/ZINUX1998/ClusterLP.
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler

Active learning with strong and weak labelers considers a practical setting where we have access to both costly but accurate strong labelers and inaccurate but cheap predictions provided by weak labelers. We study this problem in the streaming setting, where decisions must be taken online . We design a novel algorithmic template, Weak Labeler Active Cover (WL-AC), that is able to robustly leverage the lower quality weak labelers to reduce the query complexity while retaining the desired level of accuracy. Prior active learning algorithms with access to weak labelers learn a difference classiﬁer which predicts where the weak labels differ from strong labelers; this requires the strong assumption of realizability of the difference classiﬁer (Zhang and Chaudhuri, 2015). WL-AC by-passes this realizability assumption and thus is applicable to many real-world scenarios such as random corrupted weak labels and high dimen-sional family of difference classiﬁers ( e.g., deep neural nets). Moreover, WL-AC cleverly trades off evaluating the quality with full exploitation of weak labelers, which allows to convert any active learning strategy to one that can leverage weak labelers. We provide an instantiation of this template that achieves the optimal query complexity for any given weak labeler, without knowing its accuracy a-priori. Empirically, we propose an instantiation of the WL-AC template that can be efﬁciently implemented for large-scale models
Improving Semi-supervised Deep Learning by using Automatic Thresholding to Deal with Out of Distribution Data for COVID-19 Detection using Chest X-ray Images

Semi-supervised learning (SSL) leverages both labeled and unlabeled data for training models when the labeled data is limited and the unlabeled data is vast. Frequently, the unlabeled data is more widely available than the labeled data, hence this data is used to improve the level of generalization of a model when the labeled data is scarce. However, in real-world settings unlabeled data might depict a different distribution than the labeled dataset distribution. This is known as distribution mismatch. Such problem generally occurs when the source of unlabeled data is different from the labeled data. For instance, in the medical imaging domain, when training a COVID-19 detector using chest X-ray images, different unlabeled datasets sampled from different hospitals might be used. In this work, we propose an automatic thresholding method to filter out-of-distribution data in the unlabeled dataset. We use the Mahalanobis distance between the labeled and unlabeled datasets using the feature space built by a pre-trained Image-net Feature Extractor (FE) to score each unlabeled observation. We test two simple automatic thresholding methods in the context of training a COVID-19 detector using chest X-ray images. The tested methods provide an automatic manner to define what unlabeled data to preserve when training a semi-supervised deep learning architecture.