Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Trainable Self-Attentive Shallow Network for Text-Independent Speaker Verification

Generalized end-to-end (GE2E) model is widely used in speaker verification (SV) fields due to its expandability and generality regardless of specific languages. However, the long-short term memory (LSTM) based on GE2E has two limitations: First, the embedding of GE2E suffers from vanishing gradient, which leads to performance degradation for very long input sequences. Secondly, utterances are not represented as a properly fixed dimensional vector. In this paper, to overcome issues mentioned above, we propose a novel framework for SV, end-to-end trainable self-attentive shallow network (SASN), incorporating a time-delay neural network (TDNN) and a self-attentive pooling mechanism based on the self-attentive x-vector system during an utterance embedding phase. We demonstrate that the proposed model is highly efficient, and provides more accurate speaker verification than GE2E. For VCTK dataset, with just less than half the size of GE2E, the proposed model showed significant performance improvement over GE2E of about 63%, 67%, and 85% in EER (Equal error rate), DCF (Detection cost function), and AUC (Area under the curve), respectively. Notably, when the input length becomes longer, the DCF score improvement of the proposed model is about 17 times greater than that of GE2E.
Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans

Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled alternative plans. For successful application, sampling methods must scale to large maps with many districts, incorporate realistic legal constraints, and accurately sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these three areas. We present a new Sequential Monte Carlo (SMC) algorithm that draws representative redistricting plans from a realistic target distribution of choice. Because it yields nearly independent samples, the SMC algorithm can efficiently explore the relevant space of redistricting plans than the existing Markov chain Monte Carlo algorithms that yield dependent samples. Our algorithm can simultaneously incorporate several constraints commonly imposed in real-world redistricting problems, including equal population, compactness, and preservation of administrative boundaries. We validate the accuracy of the proposed algorithm by using a small map where all redistricting plans can be enumerated. We then apply the SMC algorithm to evaluate the partisan implications of several maps submitted by relevant parties in a recent high-profile redistricting case in the state of Pennsylvania. Open-source software is available for implementing the proposed methodology.
Positional Voting and Doubly Stochastic Matrices

Abstract We provide elementary proofs of several results concerning the possible outcomes arising from a fixed profile within the class of positional voting systems. Our arguments enable a simple and explicit construction of paradoxical profiles, and we also demonstrate how to choose weights that realize desirable results from a given profile. The analysis ultimately boils down to thinking about positional voting systems in terms of doubly stochastic matrices.
A Spatial Stochastic SIR Model for Transmission Networks with Application to COVID-19 Epidemic in China

Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time Markov model to analyze the propagation of COVID-19 across 33 provincial regions in China. This approach enables us to evaluate the effect of mobility restriction policies on the spread of the disease. We use data on daily human mobility across regions and apply the Bayesian framework to estimate the proposed model. The results show that the spread of the disease in China was predominately driven by community transmission within regions and the lockdown policy introduced by local governments curbed the spread of the pandemic. Further, we document that Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020, and the disease spread out to connected regions. The transmission from these epicenters substantially declined following the introduction of human mobility restrictions across regions.
SPINN: synergistic progressive inference of neural networks over device and cloud

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper, we propose SPINN, a distributed inference system that employs synergistic device-cloud computation together with a progressive inference method to deliver fast and robust CNN inference across diverse settings. The proposed system introduces a novel scheduler that co-optimises the early-exit policy and the CNN splitting at run time, in order to adapt to dynamic conditions and meet user-defined service-level requirements. Quantitative evaluation illustrates that SPINN outperforms its state-of-the-art collaborative inference counterparts by up to 2× in achieved throughput under varying network conditions, reduces the server cost by up to 6.8× and improves accuracy by 20.7% under latency constraints, while providing robust operation under uncertain connectivity conditions and significant energy savings compared to cloud-centric execution.
Wavelet Sets and Generalized Scaling Sets on Vilenkin Group

For Vilenkin group only the existence of multiwavelets associated with multiresolution analysis (MRA) is known. In this paper, we have shown that by using wavelet sets we can also construct single wavelet in case of Vilenkin group which are not associated with MRA.We have given characterization of single and multi-wavelet sets on Vilenkin group. Further, we have studied generalized scaling sets, some of their properties and relation between wavelet sets and generalized scaling sets.
Semi-supervised learning using teacher-student models for vocal melody extraction

The lack of labeled data is a major obstacle in many music information retrieval tasks such as melody extraction, where labeling is extremely laborious or costly. Semi-supervised learning (SSL) provides a solution to alleviate the issue by leveraging a large amount of unlabeled data. In this paper, we propose an SSL method using teacher-student models for vocal melody extraction. The teacher model is pre-trained with labeled data and guides the student model to make identical predictions given unlabeled input in a self-training setting. We examine three setups of teacher-student models with different data augmentation schemes and loss functions. Also, considering the scarcity of labeled data in the test phase, we artificially generate large-scale testing data with pitch labels from unlabeled data using an analysis-synthesis method. The results show that the SSL method significantly increases the performance against supervised learning only and the improvement depends on the teacher-student models, the size of unlabeled data, the number of self-training iterations, and other training details. We also find that it is essential to ensure that the unlabeled audio has vocal parts. Finally, we show that the proposed SSL method enables a baseline convolutional recurrent neural network model to achieve performance comparable to state-of-the-arts.
Material platforms for defect qubits and single-photon emitters

Quantum technology has grown out of quantum information theory and now provides a valuable tool that researchers from numerous fields can add to their toolbox of research methods. To date, various systems have been exploited to promote the application of quantum information processing. The systems that can be used for quantum technology include superconducting circuits, ultracold atoms, trapped ions, semiconductor quantum dots, and solid-state spins and emitters. In this review, we will discuss the state-of-the-art of material platforms for spin-based quantum technology, with a focus on the progress in solid-state spins and emitters in several leading host materials, including diamond, silicon carbide, boron nitride, silicon, two-dimensional semiconductors, and other materials. We will highlight how first-principles calculations can serve as an exceptionally robust tool for finding novel defect qubits and single-photon emitters in solids, through detailed predictions of electronic, magnetic, and optical properties.
Efficient hyperparameter optimization by way of PAC-Bayes bound minimization

Identifying optimal values for a high-dimensional set of hyperparameters is a problem that has received growing attention given its importance to large-scale machine learning applications such as neural architecture search. Recently developed optimization methods can be used to select thousands or even millions of hyperparameters. Such methods often yield overfit models, however, leading to poor performance on unseen data. We argue that this overfitting results from using the standard hyperparameter optimization objective function. Here we present an alternative objective that is equivalent to a Probably Approximately Correct-Bayes (PAC-Bayes) bound on the expected out-of-sample error. We then devise an efficient gradient-based algorithm to minimize this objective; the proposed method has asymptotic space and time complexity equal to or better than other gradient-based hyperparameter optimization methods. We show that this new method significantly reduces out-of-sample error when applied to hyperparameter optimization problems known to be prone to overfitting.
Loghub: A Large Collection of System Log Datasets towards Automated Log Analytics

Logs have been widely adopted in software system development and maintenance because of the rich system runtime information they contain. In recent years, the increase of software size and complexity leads to the rapid growth of the volume of logs. To handle these large volumes of logs efficiently and effectively, a line of research focuses on intelligent log analytics powered by AI (artificial intelligence) techniques. However, only a small fraction of these techniques have reached successful deployment in industry because of the lack of public log datasets and necessary benchmarking upon them. To fill this significant gap between academia and industry and also facilitate more research on AI-powered log analytics, we have collected and organized loghub, a large collection of log datasets. In particular, loghub provides 17 real-world log datasets collected from a wide range of systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. In this paper, we summarize the statistics of these datasets, introduce some practical log usage scenarios, and present a case study on anomaly detection to demonstrate how loghub facilitates the research and practice in this field. Up to the time of this paper writing, loghub datasets have been downloaded over 15,000 times by more than 380 organizations from both industry and academia.
Limitation of symmetry breaking by gravitational collapse: the revisit of Lin–Mestel–Shu instability

We revisit the topic of shape evolution during the spherical collapse of an $N$-body system. Our main objective is to investigate the critical particle number below which, during a gravitational collapse, the amplification of triaxiality from initial fluctuations is effective, and above which it is ineffective. To this aim, we develop the Lin-Mestel-Shu theory for a system of particles initially with isotropic velocity dispersion and with a simple power-law density profile. We first determine, for an unstable cloud, two radii corresponding to the balance of two opposing forces and their fluctuations: such radii fix the sizes of the non-collapsing region and the triaxial seed from density fluctuations. We hypothesize that the triaxial degree of the final state depends on which radius is dominant prior to the collapse phase leading to a different scheme of the self-consistent shape evolution of the core and the rest of the system. The condition where the two radii are equal therefore identifies the critical particle number, which can be expressed as the function of the parameters of initial state. In numerical work, we can pinpoint such a critical number by comparing the virialized flattening with the initial flattening. The difference between these two quantities agrees with the theoretical predictions only for the power-law density profiles with an exponent in the range $[0,0.25]$. For higher exponents, results suggest that the critical number is above the range of simulated $N$. We speculate that there is an additional mechanism, related to strong density gradients that increases further the flattening, requiring higher $N$ to further weaken the initial fluctuations.
RODEO: Replay for Online Object Detection

Humans can incrementally learn to do new visual detection tasks, which is a huge challenge for today's computer vision systems. Incrementally trained deep learning models lack backwards transfer to previously seen classes and suffer from a phenomenon known as $"catastrophic forgetting. "$ In this paper, we pioneer online streaming learning for object detection, where an agent must learn examples one at a time with severe memory and computational constraints. In object detection, a system must output all bounding boxes for an image with the correct label. Unlike earlier work, the system described in this paper can learn this task in an online manner with new classes being introduced over time. We achieve this capability by using a novel memory replay mechanism that efficiently replays entire scenes. We achieve state-of-the-art results on both the PASCAL VOC 2007 and MS COCO datasets.
On Localized Discrepancy for Domain Adaptation

We propose the discrepancy-based generalization theories for unsupervised domain adaptation. Previous theories introduced distribution discrepancies defined as the supremum over complete hypothesis space. The hypothesis space may contain hypotheses that lead to unnecessary overestimation of the risk bound. This paper studies the localized discrepancies defined on the hypothesis space after localization. First, we show that these discrepancies have desirable properties. They could be significantly smaller than the pervious discrepancies. Their values will be different if we exchange the two domains, thus can reveal asymmetric transfer difficulties. Next, we derive improved generalization bounds with these discrepancies. We show that the discrepancies could influence the rate of the sample complexity. Finally, we further extend the localized discrepancies for achieving super transfer and derive generalization bounds that could be even more sample-efficient on source domain.
Transcription and DNA Methylation Patterns of Blood-Derived CD8+ T Cells Are Associated With Age and Inflammatory Bowel Disease But Do Not Predict Prognosis

Abstract Background & Aims Gene expression patterns of CD8+ T cells have been reported to correlate with clinical outcomes of adults with inflammatory bowel diseases (IBD). We aimed at validating these findings in independent patient cohorts. Methods We obtained peripheral blood samples from 112 children with a new diagnosis of IBD (71 with Crohn’s disease and 41 with ulcerative colitis) and 19 children without IBD (controls) and recorded medical information on disease activity and outcomes. CD8+ T cells were isolated from blood samples by magnetic bead sorting at the point of diagnosis and during the course of disease. Genome-wide transcription (n=192) and DNA methylation (n=66) profiles were generated using Affymetrix and Illumina arrays, respectively. Publicly available transcriptomes and DNA methylomes of CD8+ T cells from three adult patient cohorts with and without IBD were included in data analyses. Results Previously reported CD8+ T cell prognostic expression and exhaustion signatures were only found in the original adult IBD patient cohort. These signatures could not be detected either in a pediatric, or in a second adult IBD cohort. In contrast, an association between CD8+ T cell gene expression with age and sex was detected across all three cohorts. CD8+ gene transcription was clearly associated with IBD in the two cohorts that included non-IBD controls. Lastly, DNA methylation profiles of CD8+ T cells from children with Crohn’s disease correlated with age but not with disease outcome. Conclusions We were unable to validate previously reported findings of an association between CD8+ T cell gene transcription and disease outcome in IBD. Our findings reveal the challenges of developing prognostic biomarkers for patients with IBD and the importance of their validation in large, independent cohorts before clinical application.
Preharvest and Postharvest Application of Garlic and Rosemary Essential Oils for Controlling Anthracnose and Quality Assessment of Strawberry Fruit During Cold Storage

This study assessed the feasibility of using essential oils (EOs) against Colletotrichum nymphaeae inciting strawberry anthracnose. Two EOs, extracted from Allium sativum (garlic) and Rosmarinus officinalis (rosemary), were selected because their fungicide efficacy was already well characterized under laboratory and greenhouse conditions. We characterized both EOs in terms of efficacy and impact on qualitative traits and sensory quality of strawberry fruit. The gas chromatography–mass spectrometry analysis confirmed the Diallyl trisulfide (29.08%) and (α)-pinene (15.779%) as the main components of A. sativum and R. officinalis EOs, respectively. Both A. sativum and R. officinalis EOs significantly inhibited the mycelial growth and conidial germination of C. nymphaeae in contact and vapor assays compared with untreated control. However, EC50 assay indicated A. sativum EO was more effective than R. officinalis EO against the pathogen. Malformations of the vegetative structures of the pathogen exposed to both EOs were revealed as shriveled, collapsed, and swelling mycelia in the cultures. Both EOs confirmed their efficacy under in vivo and greenhouse conditions; in fact, they significantly reduced the development of fruit decay and anthracnose disease incidence and severity, compared with untreated controls. Both EOs preserved sensory attributes and quality parameters of strawberry fruit including firmness, total soluble solids, ascorbic acid, antioxidant activity, and anthocyanin, but may leave unwanted smells. These findings suggest that two EOs can potentially represent an alternative to synthetic chemical fungicides against C. nymphaeae preserving fruit quality factors, although their cost and the impact on the fruit odor must be carefully taken into consideration before developing a commercial product.
Power Equation for Predicting the Risk of Central Nervous System Oxygen Toxicity at Rest

Patients undergoing hyperbaric oxygen therapy and divers engaged in underwater activity are at risk of central nervous system oxygen toxicity. An algorithm for predicting CNS oxygen toxicity in active underwater diving has been published previously, but not for humans at rest. Using a procedure similar to that employed for the derivation of our active diving algorithm, we collected data for exposures at rest, in which subjects breathed hyperbaric oxygen while immersed in thermoneutral water at 33°C (n = 219) or in dry conditions (n = 507). The maximal likelihood method was employed to solve for the parameters of the power equation. For immersion, the CNS oxygen toxicity index is KI = t2 × PO210.93, where the calculated risk from the Standard Normal distribution is ZI = [ln(KI0.5) – 8.99)]/0.81. For dry exposures this is KD = t2 × PO212.99, with risk ZD = [ln(KD0.5) – 11.34)]/0.65. We propose a method for interpolating the parameters at metabolic rates between 1 and 4.4 MET. The risk of CNS oxygen toxicity at rest was found to be greater during immersion than in dry conditions. We discuss the prediction properties of the new algorithm in the clinical hyperbaric environment, and suggest it may be adopted for use in planning procedures for hyperbaric oxygen therapy and for rest periods during saturation diving.
Attention-based 3D Object Reconstruction from a Single Image

Recently, learning-based approaches for 3D reconstruction from 2D images have gained popularity due to its modern applications, e.g., 3D printers, autonomous robots, self-driving cars, virtual reality, and augmented reality. The computer vision community has applied a great effort in developing functions to reconstruct the full 3D geometry of objects and scenes. However, to extract image features, they rely on convolutional neural networks, which are ineffective in capturing long-range dependencies. In this paper, we propose to substantially improve Occupancy Networks, a state-of-the-art method for 3D object reconstruction. For such we apply the concept of self-attention within the network’s encoder in order to leverage complementary input features rather than those based on local regions, helping the encoder to extract global information. With our approach, we were capable of improving the original work in 5.05% of mesh IoU, 0.83% of Normal Consistency, and more than 10× the Chamfer-L1 distance. We also perform a qualitative study that shows that our approach was able to generate much more consistent meshes, confirming its increased generalization power over the current state-of-the-art.
SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity

This paper aims to improve video streaming by leveraging a simple observation: users are more sensitive to low quality in certain parts of a video than in others. For instance, rebuffering during key moments of a sports video (e.g., before a goal is scored) is more annoying than rebuffering during normal gameplay. Such dynamic quality sensitivity, however, is rarely captured by current approaches, which predict QoE (quality-of-experience) using one-size-fits-all heuristics that are too simplistic to understand the nuances of video content. Instead of proposing yet another heuristic, we take a different approach: we run a separate crowdsourcing experiment for each video to derive users' quality sensitivity at different parts of the video. Of course, the cost of doing this at scale can be prohibitive, but we show that careful experiment design combined with a suite of pruning techniques can make the cost negligible compared to how much content providers invest in content generation and distribution. Our ability to accurately profile time-varying user sensitivity inspires a new approach: dynamically aligning higher (lower) quality with higher (lower) sensitivity periods. We present a new video streaming system called SENSEI that incorporates dynamic quality sensitivity into existing quality adaptation algorithms. We apply SENSEI to two state-of-the-art adaptation algorithms. SENSEI can take seemingly unusual actions: e.g., lowering bitrate (or initiating a rebuffering event) even when bandwidth is sufficient so that it can maintain a higher bitrate without rebuffering when quality sensitivity becomes higher in the near future. Compared to state-of-the-art approaches, SENSEI improves QoE by 15.1% or achieves the same QoE with 26.8% less bandwidth on average.