Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotic Uncertainty of False Discovery Proportion for Dependent $t$-Tests

Multiple testing is a fundamental problem in high-dimensional statistical inference. Although many methods have been proposed to control false discoveries, it is still a challenging task when the tests are correlated to each other. To overcome this challenge, various methods have been proposed to estimate the false discovery rate (FDR) and/or the false discovery proportion (FDP) under arbitrary covariance among the test statistics. An interesting finding of these works is that the estimation of FDP and FDR under weak dependence is identical to that under independence. However, Mei et al. (2021) pointed out that unlike FDR, the asymptotic variance of FDP can still differ drastically from that under independence, and the difference depends on the covariance structure among the test statistics. In this paper, we further extend this result from $z$-tests to $t$-tests when the marginal variances are unknown and need to be estimated. With weakly dependent $t$-tests, we show that FDP still converges to a fixed quantity unrelated to the dependence structure, and further derive the asymptotic expansion and uncertainty of FDP leading to similar results as in Mei et al. (2021). In addition, we develop an approximation method to efficiently evaluate the asymptotic variance of FDP for dependent $t$-tests. We examine how the asymptotic variance of FDP varies as well as the performance of its estimators under different dependence structures through simulations and a real-data study.
Study of an entropy dissipating finite volume scheme for a nonlocal cross-diffusion system
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Abstract. In this paper we analyse a finite volume scheme for a nonlocal version of the Shigesada-Kawazaki-Teramoto (SKT) cross-diffusion system. We prove the existence of solutions to the scheme, derive qualitative properties of the solutions and prove its convergence. The proofs rely on a discrete entropy-dissipation inequality, discrete compactness arguments, and on the novel adaptation of the so-called duality method at the discrete level. Finally, thanks to numerical experiments, we investigate the influence of the nonlocality in the system: on convergence properties of the scheme, as an approximation of the local system and on the development of diffusive instabilities.
Motzkin numbers and flag codes

Motzkin numbers have been widely studied since they count many dif-ferent combinatorial objects. In this paper we present a new appearance of this remarkable sequence in the network coding setting through a particular case of multishot codes called ﬂag codes . A ﬂag code is a set of sequences of nested subspaces ( ﬂags ) of a vector space over the ﬁnite ﬁeld F q . If the list of dimensions is (1 , . . . , n − 1) , we speak about a full ﬂag code . The ﬂag distance is deﬁned as the sum of the respective subspace distances and can be represented by means of the so-called distance vectors . We show that the number of distance vectors corresponding to the full ﬂag variety on F nq is exactly the n -th Motzkin number. Moreover, we can identify the integer sequence that counts the number of possible distance vectors associated to a full ﬂag code with prescribed minimum distance.
Topic Community Based Temporal Expertise for Question Routing

Question Routing in Community-based Question Answering websites aims at recommending newly posted questions to potential users who are most likely to provide"accepted answers". Most of the existing approaches predict users' expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: 1) sparsity of users' past records results in lack of personalized recommendation that at times does not match users' interest or domain expertise, 2) modeling based on all questions and answers content makes periodic updates computationally expensive, and 3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users' activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models
CEN : Cooperatively Evolving Networks

A ﬁnitely repeated game is a dynamic game in which a simultaneous game is played ﬁnitely many times. GANs contain two competing modules: the generator module is trained to generate new examples, and the discriminator module is trained to discriminate real examples from generated examples. Training procedure of GAN is a ﬁnitely repeated game in which each module tries to optimize it’s error at every instance of simultaneous game in a non-cooperative manner. We observed that we can achieve more accurate training, if at each instance of simultaneous game the stronger module cooperate with weaker module and only weaker module only optimize it’s error.
ACT-NET: Asymmetric Co-Teacher Network for Semi-Supervised Memory-Efficient Medical Image Segmentation

While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especially in clinical practice. On the other hand, high-accuracy deep models usually come in large model sizes, limiting their employment in real scenarios. In this work, we propose a novel asymmetric co-teacher framework, ACT-Net, to alleviate the burden on both expensive annotations and computational costs for semi-supervised knowledge distillation. We advance teacher-student learning with a co-teacher network to facilitate asymmetric knowledge distillation from large models to small ones by alternating student and teacher roles, obtaining tiny but accurate models for clinical employment. To verify the effectiveness of our ACT-Net, we employ the ACDC dataset for cardiac substructure segmentation in our experiments. Extensive experimental results demonstrate that ACT-Net outperforms other knowledge distillation methods and achieves lossless segmentation performance with 250× fewer parameters.
Chiral active matter

Chiral active matter comprises particles which can self-propel and self-rotate. Examples range from sperm cells and bacteria near walls to autophoretic L-shaped colloids. In this perspective article we focus on recent developments in chiral active matter. After briefly discussing the motion of single particles, we discuss collective phenomena ranging from vortex arrays and patterns made of rotating micro-flocks to states featuring unusual rheological properties.
Variational Inference of Dynamic Factor Models with Arbitrary Missing Data

Dynamic factor models are often estimated by point-estimation methods, disregarding parameter uncertainty. We propose a method accounting for parameter uncertainty by means of posterior approximation, using variational inference. Our approach allows for any arbitrary pattern of missing data, including different sample sizes and mixed frequencies. It also yields a straight-forward estimation algorithm absent of time-consuming simulation techniques. In empirical examples using both small and large models, we compare our method to full Bayesian estimation from MCMC-simulations. Generally, the approximation captures factor features and parameters well, with vast computational gains. The resulting predictive distributions are approximated to a very high precision, almost indistinguishable from MCMC both in and out of sample, in a tiny fraction of computational time.
PoF: Post-Training of Feature Extractor for Improving Generalization

It has been intensively investigated that the local shape, especially flatness, of the loss landscape near a minimum plays an important role for generalization of deep models. We developed a training algorithm called PoF: Post-Training of Feature Extractor that updates the feature extractor part of an already-trained deep model to search a flatter minimum. The characteristics are two-fold: 1) Feature extractor is trained under parameter perturbations in the higher-layer parameter space, based on observations that suggest flattening higher-layer parameter space, and 2) the perturbation range is determined in a data-driven manner aiming to reduce a part of test loss caused by the positive loss curvature. We provide a theoretical analysis that shows the proposed algorithm implicitly reduces the target Hessian components as well as the loss. Experimental results show that PoF improved model performance against baseline methods on both CIFAR-10 and CIFAR-100 datasets for only 10-epoch post-training, and on SVHN dataset for 50-epoch post-training. Source code is available at: \url{https://github.com/DensoITLab/PoF-v1
Entropy of sharp restart

Restart has the potential of expediting or impeding the completion times of general random processes. Consequently, the issue of mean-performance takes center stage: quantifying how the application of restart on a process of interest impacts its completion-time’s mean. Going beyond the mean, little is known on how restart affects stochasticity measures of the completion time. This paper is the first in a duo of studies that address this knowledge gap via: a comprehensive analysis that quantifies how sharp restart—a keystone restart protocol—impacts the Shannon entropy of the completion time. The analysis establishes closed-form results for sharp restart with general timers, with fast timers (high-frequency resetting), and with slow timers (low-frequency resetting). These results share a common structure: comparing the completion-time’s hazard rate to a flat benchmark—the constant hazard rate of an exponential distribution whose entropy is equal to the completion-time’s entropy. In addition, using an information-geometric approach based on Kullback–Leibler distances, the analysis establishes results that determine the very existence of timers with which the application of sharp restart decreases or increases the completion-time’s entropy. Our work sheds first light on the intricate interplay between restart and randomness—as gauged by the Shannon entropy.
DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images

Photometric stereo, a problem of recovering 3D surface normals using images of an object captured under different lightings, has been of great interest and importance in computer vision research. Despite the success of existing traditional and deep learning-based methods, it is still challenging due to: (i) the requirement of three or more differently illuminated images, (ii) the inability to model unknown general reflectance, and (iii) the requirement of accurate 3D ground truth surface normals and known lighting information for training. In this work, we attempt to address an under-explored problem of photometric stereo using just two differently illuminated images, referred to as the PS2 problem. It is an intermediate case between a single image-based reconstruction method like Shape from Shading (SfS) and the traditional Photometric Stereo (PS), which requires three or more images. We propose an inverse rendering-based deep learning framework, called DeepPS2, that jointly performs surface normal, albedo, lighting estimation, and image relighting in a completely self-supervised manner with no requirement of ground truth data. We demonstrate how image relighting in conjunction with image reconstruction enhances the lighting estimation in a self-supervised setting.
3D Part Assembly Generation With Instance Encoded Transformer

It is desirable to enable robots capable of automatic assembly. Structural understanding of object parts plays a crucial role in this task yet remains relatively unexplored. In this letter, we focus on the setting of furniture assembly from a complete set of part geometries, which is essentially a 6-DoF part pose estimation problem. We propose a multi-layer transformer-based framework that involves geometric and relational reasoning between parts to update the part poses iteratively. We carefully design a unique instance encoding to solve the ambiguity between geometrically-similar parts so that all parts can be distinguished. In addition to assembling from scratch, we extend our framework to a new task called in-process part assembly. Analogous to furniture maintenance, it requires robots to continue with unfinished products and assemble the remaining parts into appropriate positions. Our method achieves far more than 10% improvements over the current state-of-the-art in multiple metrics on the public PartNet dataset. Extensive experiments and quantitative comparisons demonstrate the effectiveness of the proposed framework.
Walter: A Tool for Predicting Resolved Stellar Population Observations with Applications to the Roman Space Telescope

Studies of resolved stellar populations in the Milky Way and nearby galaxies reveal an amazingly detailed and clear picture of galaxy evolution. Within the Local Group, the ability to probe the stellar populations of small and large galaxies opens up the possibility of exploring key questions such as the nature of dark matter, the detailed formation history of different galaxy components, and the role of accretion in galactic formation. Upcoming wide-field surveys promise to extend this ability to all galaxies within 10 Mpc, drastically increasing our capability to decipher galaxy evolution and enabling statistical studies of galaxies’ stellar populations. To facilitate the optimum use of these upcoming capabilities, we develop a simple formalism to predict the density of resolved stars for an observation of a stellar population at fixed surface brightness and population parameters. We provide an interface to calculate all quantities of interest to this formalism via a public release of the code walter. This code enables calculation of (i) the expected number density of detected stars; (ii) the exposure time needed to reach certain population features, such as the horizontal branch; and (iii) an estimate of the crowding limit, among other features. We provide a limited test of the code and formalism of the paper against existing resolved star observations. These calculations will be very useful for planning surveys with NASA’s upcoming Nancy Grace Roman Space Telescope (Roman, formerly WFIRST), which we use for example calculations throughout this work.
Testing the Limits of AGN Feedback and the Onset of Thermal Instability in the Most Rapidly Star-forming Brightest Cluster Galaxies

We present new, deep, narrow- and broadband Hubble Space Telescope observations of seven of the most star-forming brightest cluster galaxies (BCGs). Continuum-subtracted [OII] maps reveal the detailed, complex structure of warm (T ∼ 104 K) ionized gas filaments in these BCGs, allowing us to measure spatially resolved star formation rates (SFRs) of ∼60–600 M ⊙yr−1. We compare the SFRs in these systems and others from the literature to their intracluster medium cooling rates ( Ṁcool ), measured from archival Chandra X-ray data, finding a best-fit relation of log(SFR)=(1.66±0.17)log(Ṁcool) + (−3.22 ± 0.38) with an intrinsic scatter of 0.39 ± 0.09 dex. This steeper-than-unity slope implies an increasingly efficient conversion of hot (T ∼ 107 K) gas into young stars with increasing Ṁcool , or conversely a gradual decrease in the effectiveness of AGN feedback in the strongest cool cores. We also seek to understand the physical extent of these multiphase filaments that we observe in cluster cores. We show, for the first time, that the average extent of the multiphase gas is always smaller than the radii at which the cooling time reaches 1 Gyr, the t cool/t ff profile flattens, and that X-ray cavities are observed. This implies a close connection between the multiphase filaments, the thermodynamics of the cooling core, and the dynamics of X-ray bubbles. Interestingly, we find a one-to-one correlation between the average extent of cool multiphase filaments and the radius at which the cooling time reaches 0.5 Gyr, which may be indicative of a universal condensation timescale in cluster cores.
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions

In this paper, we propose a novel benchmark called the StarCraft Multi-Agent Challenges+, where agents learn to perform multi-stage tasks and to use environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly concerned with ensuring that all agents cooperatively eliminate approaching adversaries only through fine manipulation with obvious reward functions. This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control. This study covers both offensive and defensive scenarios. In the offensive scenarios, agents must learn to first find opponents and then eliminate them. The defensive scenarios require agents to use topographic features. For example, agents need to position themselves behind protective structures to make it harder for enemies to attack. We investigate MARL algorithms under SMAC+ and observe that recent approaches work well in similar settings to the previous challenges, but misbehave in offensive scenarios. Additionally, we observe that an enhanced exploration approach has a positive effect on performance but is not able to completely solve all scenarios. This study proposes new directions for future research.
Adversarial Multi-Task Deep Learning for Noise-Robust Voice Activity Detection with Low Algorithmic Delay

Voice Activity Detection (VAD) is an important pre-processing step in a wide variety of speech processing systems. VAD should in a practical application be able to detect speech in both noisy and noise-free environments, while not introducing significant latency. In this work we propose using an adversarial multi-task learning method when training a supervised VAD. The method has been applied to the state-of-the-art VAD Waveform-based Voice Activity Detection. Additionally the performance of the VADis investigated under different algorithmic delays, which is an important factor in latency. Introducing adversarial multi-task learning to the model is observed to increase performance in terms of Area Under Curve (AUC), particularly in noisy environments, while the performance is not degraded at higher SNR levels. The adversarial multi-task learning is only applied in the training phase and thus introduces no additional cost in testing. Furthermore the correlation between performance and algorithmic delays is investigated, and it is observed that the VAD performance degradation is only moderate when lowering the algorithmic delay from 398 ms to 23 ms.
Variational tensor network operator

We propose a simple and generic construction of the variational tensor network operators to study the quantum spin systems by the synergy of ideas from the imaginary-time evolution and variational optimization of trial wave functions. By applying these operators to simple initial states, accurate variational ground state wave functions with extremely few parameters can be obtained. Furthermore, the framework can be applied to study spontaneously symmetry breaking, symmetry protected topological, and intrinsic topologically ordered phases, and we show that symmetries of the local tensors associated with these phases can emerge directly after the optimization without any gauge fixing. This provides a universal way to identify quantum phase transitions without prior knowledge of the system.
Best Subset Selection with Efficient Primal-Dual Algorithm

Best subset selection is considered the `gold standard' for many sparse learning problems. A variety of optimization techniques have been proposed to attack this non-convex and NP-hard problem. In this paper, we investigate the dual forms of a family of $\ell_0$-regularized problems. An efficient primal-dual method has been developed based on the primal and dual problem structures. By leveraging the dual range estimation along with the incremental strategy, our algorithm potentially reduces redundant computation and improves the solutions of best subset selection. Theoretical analysis and experiments on synthetic and real-world datasets validate the efficiency and statistical properties of the proposed solutions.
A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks

Social media networks have become a significant aspect of people's lives, serving as a platform for their ideas, opinions and emotions. Consequently, automated sentiment analysis (SA) is critical for recognising people's feelings in ways that other information sources cannot. The analysis of these feelings revealed various applications, including brand evaluations, YouTube film reviews and healthcare applications. As social media continues to develop, people post a massive amount of information in different forms, including text, photos, audio and video. Thus, traditional SA algorithms have become limited, as they do not consider the expressiveness of other modalities. By including such characteristics from various material sources, these multimodal data streams provide new opportunities for optimising the expected results beyond text-based SA. Our study focuses on the forefront field of multimodal SA, which examines visual and textual data posted on social media networks. Many people are more likely to utilise this information to express themselves on these platforms. To serve as a resource for academics in this rapidly growing field, we introduce a comprehensive overview of textual and visual SA, including data pre-processing, feature extraction techniques, sentiment benchmark datasets, and the efficacy of multiple classification methodologies suited to each field. We also provide a brief introduction of the most frequently utilised data fusion strategies and a summary of existing research on visual-textual SA. Finally, we highlight the most significant challenges and investigate several important sentiment applications.
Multi-task unscented Kalman inversion (MUKI): a derivative-free joint inversion framework and its application to joint inversion of geophysical data

In the geophysical joint inversion, the gradient and Bayesian Markov Chain Monte Carlo (MCMC) sampling-based methods are widely used owing to their fast convergences or global optimality. However, these methods either require the computation of gradients and easily fall into local optimal solutions, or cost much time to carry out the millions of forward calculations in a huge sampling space. Diﬀerent from these two methods, taking advantage of the recently developed unscented Kalman method in computational math-ematics, we extend an iterative gradient-free Bayesian joint inversion framework, i.e., Multi-task unscented Kalman inversion (MUKI). In this new framework, information from var-ious observations is incorporated, the model is iteratively updated in a derivative-free way, and a Gaussian approximation to the posterior distribution of the model parameters is obtained. We apply the MUKI to the joint inversion of receiver functions and surface wave dispersion, which is well-established and widely used to construct the crustal and upper mantle structure of the earth. Based on synthesized and real data, the tests demonstrate that MUKI can recover the model more eﬃciently than the gradient-based method and the Markov Chain Monte Carlo method, and it would be a promising approach to resolve the geophysical joint inversion problems.