Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient semismooth Newton method for adaptive sparse signal recovery problems

We know that compressive sensing can establish stable sparse recovery results from highly undersampled data under a restricted isometry property condition. In reality, however, numerous problems are coherent, and vast majority conventional methods might work not so well. Recently, it was shown that using the difference between - and -norm as a regularization always has superior performance. In this paper, we consider an adaptive - model where the -norm with measures the data fidelity and the -term measures the sparsity. This proposed model has the ability to deal with different types of noises and extract the sparse property even under high coherent condition. We use a proximal majorization-minimization technique to handle the non-convex regularization term and then employ a semismooth Newton method to solve the corresponding convex relaxation subproblem. We prove that the sequence generated by the semismooth Newton method admits fast local convergence rate to the subproblem under some technical assumptions. Finally, we do some numerical experiments to demonstrate the superiority of the proposed model and the progressiveness of the proposed algorithm.
Maximum relative distance between symmetric rank-two and rank-one tensors

It is shown that the relative distance in Frobenius norm of a real symmetric order-d tensor of rank two to its best rank-one approximation is upper bounded by √ 1 − (1 − 1/d)d−1. This is achieved by determining the minimal possible ratio between spectral and Frobenius norm for symmetric tensors of border rank two, which equals (1 − 1/d). These bounds are also verified for nonsymmetric tensors of order d = 3.
Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications

In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, Markov Chain Generative Adversarial Neural Networks (MCGANs), to alleviate the computational costs associated with solving the Bayesian inference problem. GANs pose a very suitable framework to aid in the solution of Bayesian inference problems, as they are designed to generate samples from complicated high-dimensional distributions. By training a GAN to sample from a low-dimensional latent space and then embedding it in a Markov Chain Monte Carlo method, we can highly efficiently sample from the posterior, by replacing both the high-dimensional prior and the expensive forward map. We prove that the proposed methodology converges to the true posterior in the Wasserstein-1 distance and that sampling from the latent space is equivalent to sampling in the high-dimensional space in a weak sense. The method is showcased on two test cases where we perform both state and parameter estimation simultaneously. The approach is shown to be up to two orders of magnitude more accurate than alternative approaches while also being up to two orders of magnitude computationally faster, in multiple test cases, including the important engineering setting of detecting leaks in pipelines.
The Influence of Viscous Coefficients on the Lifespan of 3-D Anisotropic Navier–Stokes System

The anisotropic Navier-Stokes system arises in geophysical fluid dynamics, which is derived by changing $-\nu\D$ in the classical Navier-Stokes system to $-(\nu_1\partial^2_1+\nu_2\partial^2_2+\nu_3\partial^2_3)$. Here $\nu_1,\,\nu_2\,,\nu_3$ are the viscous coefficients, which can be different from each other. This reflects that the fluid can behave differently in each direction. The purpose of this paper is to derive some lower bound estimates on the lifespan to such anisotropic Navier-Stokes system. We not only investigate the case when $\nu_1,\,\nu_2\,,\nu_3$ are all positive, but also the more sophisticated cases when one or two of them vanish. We find that in these lower bound estimates, the weights of $\nu_1,\,\nu_2\,,\nu_3$ are not equal. A detailed study of this problem can also help us to have a better understanding of the nonlinear structure in the classical Navier-Stokes system.
Automatic Mapping with Obstacle Identification for Indoor Human Mobility Assessment

We propose a framework that allows a mobile robot to build a map of an indoor scenario, identifying and highlighting objects that may be considered a hindrance to people with limited mobility. The map is built by combining recent developments in monocular SLAM with information from inertial sensors of the robot platform, resulting in a metric point cloud that can be further processed to obtain a mesh. The images from the monocular camera are simultaneously analyzed with an object recognition neural network, tuned to detect a particular class of targets. This information is then processed and incorporated on the metric map, resulting in a detailed survey of the locations and bounding volumes of the objects of interest. The result can be used to inform policy makers and users with limited mobility of the hazards present in a particular indoor location. Our initial tests were performed using a micro-UAV and will be extended to other robotic platforms.
Imaginary-time evolution using forward and backward real-time evolution with a single ancilla: ﬁrst-quantized eigensolver of quantum chemistry

Imaginary-time evolution (ITE) on a quantum computer is a promising formalism for obtaining the ground state of a quantum system. The probabilistic ITE (PITE) exploits measurements to implement nonunitary operations and it can avoid the restriction of dynamics to a low-dimensional subspace imposed by variational parameters unlike other types of ITE. In this study, we propose a new PITE approach that uses only one ancillary qubit. Unlike the existing PITE approaches, the new one under a practical approximation constructs the circuit from forward and backward real-time evolution (RTE) gates as black boxes for the original Hamiltonian. Thus, all eﬃcient unitary algorithms for RTE can be transferred to the ITE without any modiﬁcations. Our approach can be used to obtain the Gibbs state at a ﬁnite temperature and partition function. We validate the approach via several illustrative systems where the trial states are found to converge rapidly to the ground states. In addition, we discuss its applicability to quantum chemistry by focusing on the scaling of computational cost; this leads to the development of a novel framework referred to as a ﬁrst-quantized eigensolver. The nonvariational generic approach will expand the scope of practical quantum computation for versatile objectives.
Event-level variables for semivisible jets using anomalous jet tagging

Semivisible jets are a characteristic signature of many confining dark sectors and consist of jets of visible hadrons intermixed with invisible stable particles. Since their initial proposal, considerable progress has been made in developing techniques for tagging anomalous jets. In this paper, we show that the ability to tag semivisible jets can be used to define new event-level variables which use generally ignored kinematic information and can considerably increase our ability to discover dark confining sectors. In practice, our best results are obtained by using the coefficients of the decomposition of the missing transverse momentum in terms of the transverse momenta of the jets tagged as anomalous. A benchmark scenario is introduced and the increase in significance due to these new variables is studied over some of its parameter space.
A Review on Analysis and Visualization Methods for Biclustering

Recently, biclustering is one of the hot topics in bioinformatics and takes the attention of authors from several different disciplines. Hence, many different methodologies from a variety of disciplines are proposed as a solution to the biclustering problem. As a consequence of this issue, a variety of solutions makes it harder to evaluate the proposed methods. With this review paper, we are aimed to discuss both analysis and visualization of biclustering as a guide for the comparisons between brand new and existing biclustering algorithms. Additionally, we concentrate on the tools that provide visualizations with accompanied analysis techniques. Through the paper, we give several references that are also a short review of the state of the art for the ones who will pursue research on biclustering. The Paper outline is as follows; we first give the visualization and analysis methods, then we evaluate each proposed tool with the visualization contribution and analysis options, finally, we discuss future directions for biclustering and we propose standards for future work.
Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion

Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. However, CD is usually insensitive to mismatched local density, and EMD is usually dominated by global distribution while overlooks the fidelity of detailed structures. Besides, their unbounded value range induces a heavy influence from the outliers. These defects prevent them from providing a consistent evaluation. To tackle these problems, we propose a new similarity measure named Density-aware Chamfer Distance (DCD). It is derived from CD and benefits from several desirable properties: 1) it can detect disparity of density distributions and is thus a more intensive measure of similarity compared to CD; 2) it is stricter with detailed structures and significantly more computationally efficient than EMD; 3) the bounded value range encourages a more stable and reasonable evaluation over the whole test set. We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other. We can also use DCD as the training loss, which outperforms the same model trained with CD loss on all three metrics. In addition, we propose a novel point discriminator module that estimates the priority for another guided down-sampling step, and it achieves noticeable improvements under DCD together with competitive results for both CD and EMD. We hope our work could pave the way for a more comprehensive and practical point cloud similarity evaluation. Our code will be available at: https://github.com/wutong16/Density_aware_Chamfer_Distance .
Causal versions of maximum entropy and principle of insufficient reason

Abstract The principle of insufficient reason (PIR) assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The maximum entropy principle (MaxEnt) generalizes PIR to the case where statistical information like expectations are given. It is known that both principles result in paradoxical probability updates for joint distributions of cause and effect. This is because constraints on the conditional P ( effect ∣ cause ) P\left({\rm{effect}}| {\rm{cause}}) result in changes of P ( cause ) P\left({\rm{cause}}) that assign higher probability to those values of the cause that offer more options for the effect, suggesting “intentional behavior. ” Earlier work therefore suggested sequentially maximizing (conditional) entropy according to the causal order, but without further justification apart from plausibility on toy examples. We justify causal modifications of PIR and MaxEnt by separating constraints into restrictions for the cause and restrictions for the mechanism that generates the effect from the cause. We further sketch why causal PIR also entails “Information Geometric Causal Inference. ” We briefly discuss problems of generalizing the causal version of MaxEnt to arbitrary causal DAGs.
TIC 5724661: A Long-period Binary with a Pulsating sdB Star and δ Scuti Variable

Using TESS 20 s cadence data, we have discovered an unusual combination of pulsating stars in what we infer to be a binary system. The binary consists of a standard δ Scuti star with pulsations over the range 32–41 day−1; this is in a likely wide orbit with a hot subdwarf-B (sdB) star, which itself has a large-amplitude p-mode pulsation at 524 day−1. We establish constraints on the period of the putative binary by using radial velocity measurements of the δ Scuti star and show that any sdB companion star must orbit with a period greater than approximately thirty days. Our identification of this sdB binary serves as an important addition to the relatively small number of sdB binaries known to have orbital periods longer than a few days. We model such a binary using MESA and find that this system could be formed through stable, nonconservative mass transfer from either a low-mass or an intermediate-mass progenitor, without undergoing a common-envelope phase.
Rhythm is a Dancer: Music-Driven Motion Synthesis With Global Structure

Synthesizing human motion with a global structure, such as a choreography, is a challenging task. Existing methods tend to concentrate on local smooth pose transitions and neglect the global context or the theme of the motion. In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre. In addition, our framework enables generation of diverse motions that are controlled by the content of the music, and not only by the beat. Our music-driven dance synthesis framework is a hierarchical system that consists of three levels: pose, motif, and choreography. The pose level consists of an LSTM component that generates temporally coherent sequences of poses. The motif level guides sets of consecutive poses to form a movement that belongs to a specific distribution using a novel motion perceptual-loss. And the choreography level selects the order of the performed movements and drives the system to follow the global structure of a dance genre. Our results demonstrate the effectiveness of our music-driven framework to generate natural and consistent movements on various dance types, having control over the content of the synthesized motions, and respecting the overall structure of the dance.
TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation

While various deep learning-based approaches have been developed for stereo vision, binocular setups with ﬁxed baselines present limited input data and face occlusion is-sues when covering a large depth range. We address this problem by using both narrow and wide baselines. Also, with increased evidence coming from the wide baseline, the recovered depth range can be extended. This scheme is ben-eﬁcial for autonomous urban and highway driving applications. Thus, we present a model for processing the data from a trinocular setup with a narrow and a wide stereo pair. In this design, two pairs of binocular data with a common reference image are treated with one network in an end-to-end manner. We explore different methods and levels of fusion, and propose a Guided Addition module for a mid-level fusion of the two baseline data. In addition, a method is presented to iteratively optimize the network with sequential self-supervised and supervised learning on real and synthetic datasets. With this method, we can train the model on real-world data without needing the ground-truth disparity maps. Quantitative and qualitative results demonstrate that the multi-baseline network surpasses the model with a similar architecture but trained with individual baseline data. In particular, the trinocular setup outperforms the narrow and wide baselines by 13% and 24% in D1 error, respectively.
Identifying Terms and Conditions Important to Consumers using Crowdsourcing

Terms and conditions (T&Cs) are pervasive on the web and often contain important information for consumers, but are rarely read. Previous research has explored methods to surface alarming privacy policies using manual labelers, natural language processing, and deep learning techniques. However, this prior work used predetermined categories for annotations, and did not investigate what consumers really deem as important from their perspective. In this paper, we instead combine crowdsourcing with an open definition of “what is important” in T&Cs. We present a workflow consisting of pairwise comparisons, agreement validation, and Bradley-Terry rank modeling, to effectively establish rankings of T&C statements from non-expert crowd workers on this open definition, and further analyzed consumers’ preferences. We applied this workflow to 1,551 T&C statements from 27 e-commerce websites, contributed by 3,462 unique crowd workers doing 203,068 pairwise comparisons, and conducted thematic and readability analysis on the statements considered as important/unimportant. We found that consumers especially cared about policies related to after-sales and money, and tended to regard harder-to-understand statements as more important. We also present machine learning models to identify T&C clauses that consumers considered important, achieving at best a 92.7% balanced accuracy, 91.6% recall, and 89.2% precision. We foresee using our workflow and model to efficiently and reliably highlight important T&Cs on websites at a large scale, improving consumers’ awareness.
Compressed Sensing Channel Estimation for OTFS Modulation in Non-Integer Delay-Doppler Domain

This paper introduces a Compressed Sensing (CS) estimation scheme for Orthogonal Time Frequency Space (OTFS) channels with sparse multipath. The OTFS waveform represents signals in a two dimensional Delay-Doppler (DD) orthonormal basis. The proposed model does not require the assumption that the delays are integer multiples of the sampling period. The analysis shows that non-integer delay and Doppler shifts in the channel cannot be accurately modelled by integer approximations. An Orthogonal Matching Pursuit with Binary-division Refinement (OMPBR) estimation algorithm is proposed. The proposed estimator finds the best channel approximation over a continuous DD dictionary without integer approximations. This results in a significant reduction of the estimation normalized mean squared error with reasonable computational complexity.
An MAP Estimation for Between-Class Variance

Probabilistic linear discriminant analysis (PLDA) has been widely used in open-set verification tasks, such as speaker verification. A potential issue of this model is that the training set often contains limited number of classes, which makes the estimation for the between-class variance unreliable. This unreliable estimation often leads to degraded generalization. In this paper, we present an MAP estimation for the between-class variance, by employing an Inverse-Wishart prior. A key problem is that with hierarchical models such as PLDA, the prior is placed on the variance of class means while the likelihood is based on class members, which makes the posterior inference intractable. We derive a simple MAP estimation for such a model, and test it in both PLDA scoring and length normalization. In both cases, the MAP-based estimation delivers interesting performance improvement.
Acoustical Analysis of Speech Under Physical Stress in Relation to Physical Activities and Physical Literacy

Human speech production encompasses physiological processes that naturally react to physic stress. Stress caused by physical activity (PA), e.g., running, may lead to significant changes in a person's speech. The major changes are related to the aspects of pitch level, speaking rate, pause pattern, and breathiness. The extent of change depends presumably on physical fitness and well-being of the person, as well as intensity of PA. The general wellness of a person is further related to his/her physical literacy (PL), which refers to a holistic description of engagement in PA. This paper presents the development of a Cantonese speech database that contains audio recordings of speech before and after physical exercises of different intensity levels. The corpus design and data collection process are described. Preliminary results of acoustical analysis are presented to illustrate the impact of PA on pitch level, pitch range, speaking and articulation rate, and time duration of pauses. It is also noted that the effect of PA is correlated to some of the PA and PL measures.
Multi-scale deep learning for estimating horizontal velocity fields on the solar surface

The dynamics in the photosphere is governed by the multi-scale turbulent convection termed as granulation and supergranulation. It is important to derive 3-dimensional velocity vectors to understand the nature of the turbulent convection. However, it is difficult to obtain the velocity component perpendicular to the line-of-sight, which corresponds to the horizontal velocity in disk center observations. We developed a convolutional neural network model with a multi-scale deep learning architecture. The method consists of multiple convolutional kernels with various sizes of the receptive fields, and it performs convolution for spatial and temporal axes. The network is trained with data from three different numerical simulations of turbulent convection, and we introduced a coherence spectrum to assess the horizontal velocity fields that were derived at each spatial scale. The multi-scale deep learning method successfully predicts the horizontal velocities for each convection simulation in terms of the global-correlation-coefficient, which is often used for evaluating the prediction accuracy of the methods. The coherence spectrum reveals the strong dependence of the correlation coefficients on the spatial scales. Although coherence spectra are higher than 0.9 for large-scale structures, they drastically decrease to less than 0.3 for small-scale structures wherein the global-correlation-coefficient indicates a high value of approximately 0.95. We determined that this decrease in the coherence spectrum occurs around the energy injection scales. The accuracy for the small-scale structures is not guaranteed solely by the global-correlation-coefficient. To improve the accuracy in small-scales, it is important to improve the loss function for enhancing the small-scale structures and to utilize other physical quantities related to the non-linear cascade of convective eddies as input data.
Measurement of the branching fraction and 
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We measure the branching fractions and $CP$ asymmetries for the decays $B^{0}\to \bar{D}^{0}\pi^{0}$ and $B^{+}\to \bar{D}^{0}\pi^{+}$, using a data sample of $772\times 10^{6}$ $B\bar{B}$ pairs collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB $e^{+}e^{-}$ collider. The branching fractions obtained and direct $CP$ asymmetries are $\mathcal{B}(B^{0}\to \bar{D}^{0}\pi^{0}) = [2.70 \pm 0.06~ \text{(stat.)} \pm 0.10~ \text{(syst.)}] \times 10^{-4}$, $\mathcal{B}(B^{+}\to \bar{D}^{0}\pi^{+}) = [4.53 \pm 0.02~ \text{(stat.)} \pm 0.15~ \text{(syst.)}] \times 10^{-3}$, $ {\cal A}_{CP}(B^{0}\to \bar{D}^{0}\pi^{0}) = [+0.42 \pm 2.05~ \text{(stat.)} \pm 1.22~ \text{(syst.)}]\%$, and $ {\cal A}_{CP}(B^{+}\to \bar{D}^{0}\pi^{+}) = [+0.19 \pm 0.36~ \text{(stat.)} \pm 0.57~ \text{(syst.)}]\%$. The measurements of $\mathcal{B}$ are the most precise to date and are in good agreement with previous results, as is the measurement of ${\cal A}_{CP}(B^{+}\to \bar{D}^{0}\pi^{+})$. The measurement of ${\cal A}_{CP}$ for $B^{0}\to \bar{D}^{0}\pi^{0}$ is the first for this mode, and the value is consistent with Standard Model expectations.