Miracle-dz/UniFAR-FTUs huggingface dataset:
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling

In this work we define a unified mathematical framework to deepen our understanding of the role of stochastic gradient (SG) noise on the behavior of Markov chain Monte Carlo sampling (SGMCMC) algorithms. Our formulation unlocks the design of a novel, practical approach to posterior sampling, which makes the SG noise isotropic using a fixed learning rate that we determine analytically, and that requires weaker assumptions than existing algorithms. In contrast, the common traits of existing \sgmcmc algorithms is to approximate the isotropy condition either by drowning the gradients in additive noise (annealing the learning rate) or by making restrictive assumptions on the \sg noise covariance and the geometry of the loss landscape. Extensive experimental validations indicate that our proposal is competitive with the state-of-the-art on \sgmcmc, while being much more practical to use.
Evaluation of Quantum Approximate Optimization Algorithm based on the approximation ratio of single samples

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm to solve binary-variable optimization problems. Due to its expected robustness to systematic errors and the short circuit depth, it is one of the promising candidates likely to run on near-term quantum devices. We project the performance of QAOA applied to the Max-Cut problem and compare it with some of the best classical alternatives, both for exact or approximate solution. When comparing approximate solvers, their performance is characterized by the computational time taken to achieve a given quality of solution. Since QAOA is based on sampling, we introduce performance metrics based on the probability of observing a sample above a certain quality. In addition, we show that the QAOA performance varies significantly with the graph type. By selecting a suitable optimizer for the variational parameters and reducing the number of function evaluations, QAOA performance improves by up to 2 orders of magnitude compared to previous estimates. Especially for 3-regular random graphs, this setting decreases the performance gap with classical alternatives.
Strongly parity-mixed superconductivity in the Rashba-Hubbard model

Heterostructures containing strongly correlated electron systems provide a platform to clarify interplay of electron correlation and Rashba spin-orbit coupling in unconventional superconductors. Motivated by recent fabrication of artificially engineered heavy fermion superlattices and high-temperature cuprate superconductors, we conduct a thorough study on superconductivity in the Rashba-Hubbard model. In contrast to previous weak coupling approaches, we employ fluctuation-exchange approximation to describe quantum critical magnetic fluctuations and resulting superconductivity. As a result, robust Fermi surfaces against magnetic fluctuations, incommensurate spin fluctuations, and a strongly parity-mixed superconducting phase are demonstrated in a wide range of electron filling from type-II van Hove singularity to half filling. We also clarify impacts of type-II van Hove singularity on magnetic fluctuations and superconductivity. Whereas the ${d}_{{x}^{2}\ensuremath{-}{y}^{2}}$-wave pairing is always dominant, subdominant spin-triplet pairing with either $p$-wave or $f$-wave symmetry shows a comparable magnitude, especially near the type-II van Hove singularity. Our results resolve unsettled issues on strongly correlated Rashba systems and uncover candidate systems of nonreciprocal transport and topological superconductivity.
Estimating semantic structure for the VQA answer space

Since its appearance, Visual Question Answering (VQA, i.e. answering a question posed over an image), has always been treated as a classification problem over a set of predefined answers. Despite its convenience, this classification approach poorly reflects the semantics of the problem limiting the answering to a choice between independent proposals, without taking into account the similarity between them (e.g. equally penalizing for answering cat or German shepherd instead of dog). We address this issue by proposing (1) two measures of proximity between VQA classes, and (2) a corresponding loss which takes into account the estimated proximity. This significantly improves the generalization of VQA models by reducing their language bias. In particular, we show that our approach is completely model-agnostic since it allows consistent improvements with three different VQA models. Finally, by combining our method with a language bias reduction approach, we report SOTA-level performance on the challenging VQAv2-CP dataset.
Collisionless ion modeling in Hall thrusters: Analytical axial velocity distribution function and heat flux closures

The genesis of the ion axial velocity distribution function (VDF) is analyzed for collisionless Hall thruster discharges. An analytical form for the VDF is obtained from the Vlasov equation, by applying the Tonks-Langmuir theory in the thruster channel, under the simplifying assumptions of monoenergetic creation of ions and steady state. The equivalent set of 1D unsteady anisotropic moment equations is derived from the Vlasov equation, and simple phenomenological closures are formulated, assuming a polynomial shape for the ions VDF. The analytical results and the anisotropic moment equations are compared to collisionless PIC simulations, employing either a zero heat flux (Euler-like equations) or the polynomial-VDF closure for the heat flux. The analytical ion VDF and its moments are then compared to experimental measurements.
UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays

In this work we present a method for the detection of radiological findings, their location and differential diagnoses from chest x-rays. Unlike prior works that focus on the detection of few pathologies, we use a hierarchical taxonomy mapped to the Unified Medical Language System (UMLS) terminology to identify 189 radiological findings, 22 differential diagnosis and 122 anatomic locations, including ground glass opacities, infiltrates, consolidations and other radiological findings compatible with COVID-19. We train the system on one large database of 92,594 frontal chest x-rays (AP or PA, standing, supine or decubitus) and a second database of 2,065 frontal images of COVID-19 patients identified by at least one positive Polymerase Chain Reaction (PCR) test. The reference labels are obtained through natural language processing of the radiological reports. On 23,159 test images, the proposed neural network obtains an AUC of 0.94 for the diagnosis of COVID-19. To our knowledge, this work uses the largest chest x-ray dataset of COVID-19 positive cases to date and is the first one to use a hierarchical labeling schema and to provide interpretability of the results, not only by using network attention methods, but also by indicating the radiological findings that have led to the diagnosis.
Sequence Analysis of 5.8S rDNA and the Internal Transcribed Spacer Region in Dinoflagellate Heterocapsa Species (Dinophyceae) and Development of Selective PCR Primers for the Bivalve Killer Heterocapsa circularisquama

Algal exposure assays revealed that all tested strains of H. circularisquama and H. illdefina were markedly toxic to juvenile pearl oysters. H. triquetra, and Heterocapsa sp. 3 and sp. 5 caused low levels of mortality in juveniles (1.7-7.5%), whereas H. lanceolata, H. horiguchii and Heterocapsa sp. 4 caused no mortality. Three strains of H. circularisquama alone exhibited strong toxicity against bivalves among Heterocapsa species isolated in Japan. The sequencing of internal transcribed spacer (ITS) regions including 5.8S rDNA and phylogenetic analysis were performed for isolates of Heterocapsa species. Alignment of the sequences demonstrated that ITS regions were highly conserved among H. circularisquama strains, while the distance between H. circularisquama and other species ranged from 0.165 to 0.254. Except for H. horiguchii and Heterocapsa sp. 4, the phylogenetic tree obtained was congruent with morphological identification. Heterocapsa species were subjected to PCR amplification with a primer set designed based on a specific signature sequence for H. circularisquama in the ITS 2 region. A specific band was detected for all strains of H. circularisquama but not other Heterocapsa species, indicating that the ITS region is suitable as a species-specific marker for H. circularisquama.
Constructing a multivariate distribution function with a vine copula: towards multivariate luminosity and mass functions

The need for a method to construct multidimensional distribution function is increasing recently, in the era of huge multiwavelength surveys. We have proposed a systematic method to build a bivariate luminosity or mass function of galaxies by using a copula. It allows us to construct a distribution function when only its marginal distributions are known, and we have to estimate the dependence structure from data. A typical example is the situation that we have univariate luminosity functions at some wavelengths for a survey, but the joint distribution is unknown. Main limitation of the copula method is that it is not easy to extend a joint function to higher dimensions (d > 2), except some special cases like multidimensional Gaussian. Even if we find such a multivariate analytic function in some fortunate case, it would often be inflexible and impractical. In this work, we show a systematic method to extend the copula method to unlimitedly higher dimensions by a vine copula. This is based on the pair-copula decomposition of a general multivariate distribution. We show how the vine copula construction is flexible and extendable. We also present an example of the construction of a stellar mass–atomic gas–molecular gas three-dimensional mass function. We demonstrate the maximum likelihood estimation of the best functional form for this function, as well as a proper model selection via vine copula.
Tensor train decompositions on recurrent networks

Recurrent neural networks (RNN) such as long-short-term memory (LSTM) networks are essential in a multitude of daily live tasks such as speech, language, video, and multimodal learning. The shift from cloud to edge computation intensifies the need to contain the growth of RNN parameters. Current research on RNN shows that despite the performance obtained on convolutional neural networks (CNN), keeping a good performance in compressed RNNs is still a challenge. Most of the literature on compression focuses on CNNs using matrix product (MPO) operator tensor trains. However, matrix product state (MPS) tensor trains have more attractive features than MPOs, in terms of storage reduction and computing time at inference. We show that MPS tensor trains should be at the forefront of LSTM network compression through a theoretical analysis and practical experiments on NLP task.
Online PMU-Based Wide-Area Damping Control for Multiple Inter-Area Modes

This paper presents a new phasor measurement unit (PMU)-based wide-area damping control (WADC) method to suppress the critical inter-area modes of large-scale power systems. Modal participation factors, estimated by a practically model-free system identification approach, are used to select the most suitable synchronous generators for control through the proposed WADC algorithm. It is shown that multiple inter-area modes can be sufficiently damped by the proposed approach without affecting the rest of the modes, while only a few machines are needed to perform the control. The proposed technique is applied to the IEEE 68-bus and the IEEE 145-bus systems, including the test cases with PMU measurement noise and with missing PMUs. The simulation results clearly demonstrate the good adaptivity of the control strategy subjected to network model changes, its effective damping performance comparing to power system stabilizers (PSSs), and its great potential for near real-time implementation.
Pruning neural networks without any data by iteratively conserving synaptic flow

Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time. Recent works have identified, through an expensive sequence of training and pruning cycles, the existence of winning lottery tickets or sparse trainable subnetworks at initialization. This raises a foundational question: can we identify highly sparse trainable subnetworks at initialization, without ever training, or indeed without ever looking at the data? We provide an affirmative answer to this question through theory driven algorithm design. We first mathematically formulate and experimentally verify a conservation law that explains why existing gradient-based pruning algorithms at initialization suffer from layer-collapse, the premature pruning of an entire layer rendering a network untrainable. This theory also elucidates how layer-collapse can be entirely avoided, motivating a novel pruning algorithm Iterative Synaptic Flow Pruning (SynFlow). This algorithm can be interpreted as preserving the total flow of synaptic strengths through the network at initialization subject to a sparsity constraint. Notably, this algorithm makes no reference to the training data and consistently competes with or outperforms existing state-of-the-art pruning algorithms at initialization over a range of models (VGG and ResNet), datasets (CIFAR-10/100 and Tiny ImageNet), and sparsity constraints (up to 99.99 percent). Thus our data-agnostic pruning algorithm challenges the existing paradigm that, at initialization, data must be used to quantify which synapses are important.
Band Representations and Topological Quantum Chemistry

In this article, we provide a pedagogical review of the theory of topological quantum chemistry and topological crystalline insulators. We begin with an overview of the properties of crystal symmetry groups in position and momentum space. Next, we introduce the concept of a band representation, which quantifies the symmetry of topologically trivial band structures. By combining band representations with symmetry constraints on the connectivity of bands in momentum space, we show how topologically nontrivial bands can be cataloged and classified. We present several examples of new topological phases discovered using this paradigm and conclude with an outlook toward future developments.
Song structure and variation in yellowhammers Emberiza citrinella from Western Poland

Bird species are characterised by amazing diversity of acoustic communication systems. Their properties are shaped by several ecological factors, which still are weakly known. This paper describes song structure, song types and within-song-type repertoire variation in yellowhammers Emberiza citrinella from a stable Polish population, analysed by using the minimal unit of production approach. We also present data on temporal organisation of song output in two singing contexts (solo vs. counter-singing). Males from the studied population sang strophes from only one dialect, characterised by specific composition of terminal whistles. The dialect from the study area seems to be identical to dialects described in geographically distant populations from eastern Germany, Hungary and Greece. Song type repertoire varied between 1 and 5 (exceptionally 8), but over 75% of extensively recorded males had only 2 or 3 song types in their repertoires, which is typical for the species. Within-song-type variation was analysed on two levels: (1) version level, describing the number of terminal whistles; and (2) variant level, describing any minor variation in syllable presentation. The pattern of song version usage was inconsistent. It could be connected both with singing context or song type characteristics and surely requires detailed research. Song variants were used by birds in a way suggesting that they represent a song production error. We found that males switched between different song types significantly faster when counter-singing, in comparison to solo singing, which could be related to elevated aggressive motivation or to some coordination of counter-singers’ output.
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels

To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted labels. Prior methods attempt to achieve such transition knowledge by pre-assuming strongly confident anchor points with 1-probability belonging to a specific class, generally infeasible in practice, or directly jointly estimating the transition matrix and learning the classifier from the noisy samples, always leading to inaccurate estimation misguided by wrong annotation information especially in large noise cases. To alleviate these issues, this study proposes a new meta-transition-learning strategy for the task. Specifically, through the sound guidance of a small set of meta data with clean labels, the noise transition matrix and the classifier parameters can be mutually ameliorated to avoid being trapped by noisy training samples, and without need of any anchor point assumptions. Besides, we prove our method is with statistical consistency guarantee on correctly estimating the desired transition matrix. Extensive synthetic and real experiments validate that our method can more accurately extract the transition matrix, naturally following its more robust performance than prior arts. Its essential relationship with label distribution learning is also discussed, which explains its fine performance even under no-noise scenarios.
Escherichia coli Producing CTX-M β-Lactamases in Food Animals in Hong Kong

A study was conducted to evaluate the occurrence of extended-spectrum β-lactamases (ESBLs)-producing Escherichia coli from fecal samples of healthy food animals in Hong Kong. Rectal or cloacal swabs were obtained from cattle, pigs, chicken, ducks, geese, and pigeons in slaughterhouses or wholesale markets over a 5- month period in 2002. Antibiotic-containing medium was used for selective isolation of potentially ESBL-producing E. coli. Of 734 samples analyzed, six (2%) from pigs, three (3.1%) from cattle, and one (3%) from pigeons had E. coli strains with the ESBL phenotype. The ESBL content for the 10 isolates include CTXM- 3 (n = 4), CTX-M-13 (n = 3), CTX-M-14 (n = 2), and CTX-M-24 (n = 1). In five isolates, the bla CTX-M gene was encoded on transferable plasmids (60 or 90 kb), and the gene was found to transfer to E. coli (J53 or JP995) with frequencies of 10−7 to 10−3 per donor cells. The ten isolates had five distinct pulsotypes with some clonal spread. However, the isolates from the different kinds .
Adaptive staggered DG method for Darcy flows in fractured porous media

Modeling flows in fractured porous media is important in applications. One main challenge in numerical simulation is that the flow is strongly influenced by the fractures, so that the solutions typically contain complex features, which require high computational grid resolutions. Instead of using uniformly fine mesh, a more computationally efficient adaptively refined mesh is desirable. In this paper we design and analyze a novel residual-type a posteriori error estimator for staggered DG methods on general polygonal meshes for Darcy flows in fractured porous media. The method can handle fairly general meshes and hanging nodes can be simply incorporated into the construction of the method, which is highly appreciated for adaptive mesh refinement. The reliability and efficiency of the error estmator are proved. The derivation of the reliability hinges on the stability of the continuous setting in the primal formulation. A conforming counterpart that is continuous within each bulk domain for the discrete bulk pressure is defined to facilitate the derivation of the reliability. Finally, several numerical experiments including multiple non-intersecting fractures are carried out to confirm the proposed theories.
Revised Classification Scheme of Phytoplasmas based on RFLP Analyses of 16S rRNA and Ribosomal Protein Gene Sequences

RFLP analyses of 16S rDNA nested PCR products from 34 phytoplasma strains with 17 restriction enzymes delineated distinct pattern types. Based on similarity coefficients derived from RFLP analyses, the 34 representative phytoplasma strains were differentiated into 14 major groups (termed 16Sr groups) and 32 sub-groups. The similarity coefficients of RFLP patterns between distinct groups were 90% or below. By including additional groups and sub-groups from which RFLP analyses were not performed but for which 16S rDNA sequence data were available to predict restriction sites, a total of 14 groups and 41 sub-groups were proposed. By combined RFLP analyses of 16S rRNA and ribosomal protein gene sequences, thus far, a total of 46 subgroups have been recognized. The phytoplasma 16Sr groups were consistent with the phylogenetic groups (subclades) defined by phylogenetic analysis of near-full-length 16S rRNA gene sequences, indicating that the RFLP-based groups are phylogenetically valid. The approach using RFLP analyses of PCR-amplified 16S rDNA (and ribosomal protein gene sequences) provides a simple, reliable and rapid means for differentiation and classification of unknown phytoplasmas.
Social media and COVID‐19: Can social distancing be quantified without measuring human movements?

The COVID‐19 outbreak has posed significant threats to international health and the economy. In the absence of treatment for this virus, public health officials asked the public to practice social distancing to reduce the number of physical contacts. However, quantifying social distancing is a challenging task and current methods are based on human movements. We propose a time and cost‐effective approach to measure how people practice social distancing. This study proposes a new method based on utilizing the frequency of hashtags supporting and encouraging social distancing for measuring social distancing. We have identified 18 related hashtags and tracked their trends between Jan and May 2020. Our evaluation results show that there is a strong correlation (p < .05) between our findings and the Google social distancing report.
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to understand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.