Miracle-dz/UniFAR-FTUs huggingface dataset:
Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications

Discrepancy measures between probability distributions, often termed statistical distances, are ubiquitous in probability theory, statistics and machine learning. To combat the curse of dimensionality when estimating these distances from data, recent work has proposed smoothing out local irregularities in the measured distributions via convolution with a Gaussian kernel. Motivated by the scalability of this framework to high dimensions, we investigate the structural and statistical behavior of the Gaussian-smoothed p-Wasserstein distance W p , for arbitrary p ≥ 1. After establishing basic metric and topological properties of W (σ) p , we explore the asymptotic statistical behavior of W p (μ̂n, μ), where μ̂n is the empirical distribution of n independent observations from μ. We prove that W p enjoys a parametric empirical convergence rate of n−1/2, which contrasts the n−1/d rate for unsmoothed Wp when d ≥ 3. Our proof relies on controlling W p by a pth-order smooth Sobolev distance d p and deriving the limit distribution of √ n d (σ) p (μ̂n, μ), for all dimensions d. As applications, we provide asymptotic guarantees for two-sample testing and minimum distance estimation using W p , with experiments for p = 2 using a maximum mean discrepancy formulation of d 2 .
Compressed Maximum Likelihood

Maximum likelihood (ML) is one of the most fundamental and general statistical estimation techniques. Inspired by recent advances in estimating distribution functionals, we propose compressed maximum likelihood (CML) that applies ML to compressed samples. We show that CML is sample-efﬁcient for several fundamental learning tasks over both discrete and continuous domains, including learning structural densities, estimating probability multisets, and inferring symmetric distribution functionals.
KNAS: Green Neural Architecture Search

Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations. Considering that these computations bring a large carbon footprint, this paper aims to explore a green (namely environmental-friendly) NAS solution that evaluates architectures without training. Intuitively, gradients, induced by the architecture itself, directly decide the convergence and generalization results. It motivates us to propose the gradient kernel hypothesis: Gradients can be used as a coarse-grained proxy of downstream training to evaluate random-initialized networks. To support the hypothesis, we conduct a theoretical analysis and find a practical gradient kernel that has good correlations with training loss and validation performance. According to this hypothesis, we propose a new kernel based architecture search approach KNAS. Experiments show that KNAS achieves competitive results with orders of magnitude faster than"train-then-test"paradigms on image classification tasks. Furthermore, the extremely low search cost enables its wide applications. The searched network also outperforms strong baseline RoBERTA-large on two text classification tasks. Codes are available at \url{https://github.com/Jingjing-NLP/KNAS} .
Implicit rate-constrained optimization of non-decomposable objectives

We consider a popular family of constrained optimization problems arising in machine learning that involve optimizing a non-decomposable evaluation metric with a certain thresholded form, while constraining another metric of interest. Examples of such problems include optimizing the false negative rate at a fixed false positive rate, optimizing precision at a fixed recall, optimizing the area under the precision-recall or ROC curves, etc. Our key idea is to formulate a rate-constrained optimization that expresses the threshold parameter as a function of the model parameters via the Implicit Function theorem. We show how the resulting optimization problem can be solved using standard gradient based methods. Experiments on benchmark datasets demonstrate the effectiveness of our proposed method over existing state-of-the art approaches for these problems. The code for the proposed method is available at https://github.com/google-research/google-research/tree/master/implicit_constrained_optimization .
An Identifiable Double VAE For Disentangled Representations

A large part of the literature on learning disen-tangled representations focuses on variational au-toencoders (VAEs). Recent developments demonstrate that disentanglement cannot be obtained in a fully unsupervised setting without inductive biases on models and data. However, Khemakhem et al., AISTATS, 2020 suggest that employing a particular form of factorized prior, conditionally dependent on auxiliary variables complementing input observations, can be one such bias, resulting in an identiﬁable model with guarantees on disen-tanglement. Working along this line, we propose a novel VAE-based generative model with theoretical guarantees on identiﬁability. We obtain our conditional prior over the latents by learning an optimal representation, which imposes an additional strength on their regularization. We also extend our method to semi-supervised settings. Experimental results indicate superior performance with respect to state-of-the-art approaches, according to several established metrics proposed in the literature on disentanglement.
CFM-RFM: A Cascading Failure Model for Inter-Domain Routing Systems with the Recovery Feedback Mechanism

With the increase and diversification of network users, the scale of the inter-domain routing system is becoming larger and larger. Cascading failure analysis and modeling are of great significance to improve the security of inter-domain routing networks. To solve the problem that the propagation principle of cascading failure does not conform to reality, a Cascading Failure Model for inter-domain routing systems with the Recovery Feedback Mechanism (CFM-RFM) is proposed in this paper. CFM-RFM comprehensively considers the main factors that cause cascading failure. Based on two types of update message propagation mechanism and traffic redistribution, it simulates the cascading failure process. We found that under the action of the recovery feedback mechanism, the cascading failure process was accelerated, and the network did not quickly return to normal, but was rather quickly and extensively paralyzed. The average attack cost can be reduced by 38.1% when the network suffers the same damage.
Watermarking Deep Neural Networks with Greedy Residuals

Deep neural networks (DNNs) are considered as intellectual property of their corresponding owners and thus are in urgent need of ownership protection, due to the massive amount of time and resources invested in designing, tuning and training them. In this paper, we propose a novel watermark-based ownership protection method by using the residuals of important parameters. Different from other watermark-based ownership protection methods that rely on some speciﬁc neural network architectures and during veriﬁcation require external data source, namely ownership indicators, our method does not explicitly use ownership indicators for veriﬁcation to defeat various attacks against DNN watermarks. Speciﬁcally, we greedily select a few and important model parameters for embedding so that the impairment caused by the changed parameters can be reduced and the robustness against different attacks can be improved as the selected parameters can well preserve the model information. Also, without the ex-ternal data sources for veriﬁcation, the adversary can hardly cast doubts on ownership veriﬁcation by forging counterfeit watermarks. The extensive experiments show that our method outperforms previous state-of-the-art methods in ﬁve tasks.
Feature Clustering for Support Identification in Extreme Regions

Understanding the complex structure of multivariate extremes is a major challenge in various fields from portfolio monitoring and environmental risk management to insurance. In the framework of multivariate Extreme Value Theory, a common characterization of extremes' dependence structure is the angular measure. It is a suitable measure to work in extreme regions as it provides meaningful insights concerning the subregions where extremes tend to concentrate their mass. The present paper develops a novel optimization-based approach to assess the dependence structure of extremes. This support identification scheme rewrites as estimating clusters of features which best capture the support of extremes. The dimension reduction technique we provide is applied to statistical learning tasks such as feature clustering and anomaly detection. Numerical experiments provide strong empirical evidence of the relevance of our approach.
Design and Development of an Efficient Network Intrusion Detection System Using Machine Learning Techniques

Today’s internets are made up of nearly half a million different networks. In any network connection, identifying the attacks by their types is a difficult task as different attacks may have various connections, and their number may vary from a few to hundreds of network connections. To solve this problem, a novel hybrid network IDS called NID-Shield is proposed in the manuscript that classifies the dataset according to different attack types. Furthermore, the attack names found in attack types are classified individually helping considerably in predicting the vulnerability of individual attacks in various networks. The hybrid NID-Shield NIDS applies the efficient feature subset selection technique called CAPPER and distinct machine learning methods. The UNSW-NB15 and NSL-KDD datasets are utilized for the evaluation of metrics. Machine learning algorithms are applied for training the reduced accurate and highly merit feature subsets obtained from CAPPER and then assessed by the cross-validation method for the reduced attributes. Various performance metrics show that the hybrid NID-Shield NIDS applied with the CAPPER approach achieves a good accuracy rate and low FPR on the UNSW-NB15 and NSL-KDD datasets and shows good performance results when analyzed with various approaches found in existing literature studies.
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity

As humans, our goals and our environment are persistently changing throughout our lifetime based on our experiences, actions, and internal and external drives. In contrast, typical reinforcement learning problem set-ups consider decision processes that are stationary across episodes. Can we develop reinforcement learning algorithms that can cope with the persistent change in the former, more realistic problem settings? While on-policy algorithms such as policy gradients in principle can be extended to non-stationary settings, the same cannot be said for more efﬁcient off-policy algorithms that replay past experiences when learning. In this work, we formalize this problem setting, and draw upon ideas from the online learning and probabilistic inference literature to derive an off-policy RL algorithm that can reason about and tackle such lifelong non-stationarity. Our method leverages latent variable models to learn a representation of the environment from current and past experiences, and performs off-policy RL with this representation. We further introduce several simulation environments that exhibit lifelong non-stationarity, and empirically ﬁnd that our approach substantially outperforms approaches that do not reason about environment shift.
MMGuard: Automatically Protecting On-Device Deep Learning Models in Android Apps

On-device deep learning models have shown growing popularity in mobile apps, which allows offline model inference while preserving user privacy. However, on-device deep learning models also introduce security challenges, i.e., the trained models can be easily stolen or even be tampered by attackers. Recent studies suggested that most of the on-device models are lacking of sufficient protection, i.e., can be stolen by decompiling the apps directly. In this work, we present MMGUARD, an automated framework for building mutual authentication between Android apps and deep neural network models, which can thus protect on-device models from being easily attacked (piracy and tampering). Unlike existing model protect methods, our approach does not require model re-training or any prior knowledge of the training data. The key idea of MMGUARD is to verify the deep learning model in the app before inference, i.e., feeding owner- and apprelated information to it, which can greatly increase the effort of model hacking. We evaluate MMGUARD on 5 popular image classification DNNs and 43 real world Android apps from Google Play. Experiment results suggest that MMGUARD introduces negligible latency on models and can be automatically applied to real world apps.
Impulse Buying Behaviors in Live Streaming Commerce Based on the Stimulus-Organism-Response Framework

Live streaming commerce, which evolved from social commerce, has continued to flourish rapidly over the past few years in China. It is a new business model that allows vendors to directly face and interact with consumers. This study focuses on the impulsive buying behavior on consumers in live streaming commerce. We proposed a research model based on the stimulus organism response (S-O-R) framework to explore the reaction and behavior of consumers after certain stimuli factors. A total of 433 valid sample questionnaires with the shopping experience in the live streaming platform were taken. This research adopted PLS-SEM statistical analysis as an empirical research evaluation. After the empirical investigation, we found that perceived enjoyment positively affects the urge to buy impulsively. Perceived usefulness positively affects perceived enjoyment. However, perceived usefulness does not positively affect the urge to buy impulsively. Attractiveness and expertise positively affect perceived enjoyment. Product usefulness and purchase convenience positively affect perceived usefulness. We found that consumers in live streaming commerce are easier to have impulsive buying through the presentation and urging of the live streamer in a short period. In this paper, we build a model for impulsive buying in live streaming commerce. We verify this model under the Chinese context. The findings of this paper provide concrete suggestions to vendors.
Cardiomyopathy in Genetic Aortic Diseases

Genetic aortic diseases are a group of illnesses characterized by aortic aneurysms or dissection in the presence of an underlying genetic defect. They are part of the broader spectrum of heritable thoracic aortic disease, which also includes those cases of aortic aneurysm or dissection with a positive family history but in whom no genetic cause is identified. Aortic disease in these conditions is a major cause of mortality, justifying clinical and scientific emphasis on the aorta. Aortic valve disease and atrioventricular valve abnormalities are known as important additional manifestations that require careful follow-up and management. The archetype of genetic aortic disease is Marfan syndrome, caused by pathogenic variants in the Fibrillin-1 gene. Given the presence of fibrillin-1 microfibers in the myocardium, myocardial dysfunction and associated arrhythmia are conceivable and have been shown to contribute to morbidity and mortality in patients with Marfan syndrome. In this review, we will discuss data on myocardial disease from human studies as well as insights obtained from the study of mouse models of Marfan syndrome. We will elaborate on the various phenotypic presentations in childhood and in adults and on the topic of arrhythmia. We will also briefly discuss the limited data available on other genetic forms of aortic disease.
Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review

Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability of individuals with profound motor impairments to interact and communicate. While much literature points to BCIs' promise as an alternative access pathway, there have historically been few applications involving children and young adults with severe physical disabilities. As research is emerging in this sphere, this article aims to evaluate the current state of translating BCIs to the pediatric population. A systematic review was conducted using the Scopus, PubMed, and Ovid Medline databases. Studies of children and adolescents that reported BCI performance published in English in peer-reviewed journals between 2008 and May 2020 were included. Twelve publications were identified, providing strong evidence for continued research in pediatric BCIs. Research evidence was generally at multiple case study or exploratory study level, with modest sample sizes. Seven studies focused on BCIs for communication and five on mobility. Articles were categorized and grouped based on type of measurement (i.e., non-invasive and invasive), and the type of brain signal (i.e., sensory evoked potentials or movement-related potentials). Strengths and limitations of studies were identified and used to provide requirements for clinical translation of pediatric BCIs. This systematic review presents the state-of-the-art of pediatric BCIs focused on developing advanced technology to support children and youth with communication disabilities or limited manual ability. Despite a few research studies addressing the application of BCIs for communication and mobility in children, results are encouraging and future works should focus on customizable pediatric access technologies based on brain activity.
The Development and Validation of a Short Form of the Forbearance Scale

The Forbearance Scale (FS) is a 16-item self-report measure of forbearance. In this study, we examined the psychometric properties of the FS subscale and composite scores and developed a 9-item short form of the measure (FS-SF 9). A sample of 1,137 participants was drawn from community, NGO, and college settings. The sample was split into a derivation sample (n = 567) and a validation sample (n = 570). Exploratory factor analyses of the derivation sample data were used to select short-form items. Using the validation sample, confirmatory factor analyses were used to assess fit for proposed item-to-factor assignments. The results of the confirmatory factor analyses supported that the FS-SF 9 had a theoretically congruent factor structure and that all the subscale and composite scores displayed high internal consistency. Correlations with scores from established measures of a lack of forgiveness and emotion regulation also supported the validity of the FS-SF 9. Our data suggest that the FS-SF 9 subscales and composite score retained the psychometric strengths of their longer FS counterparts. Overall, the short form of the FS provides a brief assessment of the construct measured by the full form. Theoretical and practical applications are discussed.
Fine-Tuning of Alkaline Residues on the Hydrophilic Face Provides a Non-toxic Cationic α-Helical Antimicrobial Peptide Against Antibiotic-Resistant ESKAPE Pathogens

Antibiotic-resistant ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) has become a serious threat to public health worldwide. Cationic α-helical antimicrobial peptides (CαAMPs) have attracted much attention as promising solutions in post-antibiotic era. However, strong hemolytic activity and in vivo inefficacy have hindered their pharmaceutical development. Here, we attempt to address these obstacles by investigating BmKn2 and BmKn2-7, two scorpion-derived CαAMPs with the same hydrophobic face and a distinct hydrophilic face. Through structural comparison, mutant design and functional analyses, we found that while keeping the hydrophobic face unchanged, increasing the number of alkaline residues (i.e., Lys + Arg residues) on the hydrophilic face of BmKn2 reduces the hemolytic activity and broadens the antimicrobial spectrum. Strikingly, when keeping the total number of alkaline residues constant, increasing the number of Lys residues on the hydrophilic face of BmKn2-7 significantly reduces the hemolytic activity but does not influence the antimicrobial activity. BmKn2-7K, a mutant of BmKn2-7 in which all of the Arg residues on the hydrophilic face were replaced with Lys, showed the lowest hemolytic activity and potent antimicrobial activity against antibiotic-resistant ESKAPE pathogens. Moreover, in vivo experiments indicate that BmKn2-7K displays potent antimicrobial efficacy against both the penicillin-resistant S. aureus and the carbapenem- and multidrug-resistant A. baumannii, and is non-toxic at the antimicrobial dosages. Taken together, our work highlights the significant functional disparity of Lys vs Arg in the scorpion-derived antimicrobial peptide BmKn2-7, and provides a promising lead molecule for drug development against ESKAPE pathogens.
Neuromodulation and Behavioral Flexibility in Larval Zebrafish: From Neurotransmitters to Circuits

Animals adapt their behaviors to their ever-changing needs. Internal states, such as hunger, fear, stress, and arousal are important behavioral modulators controlling the way an organism perceives sensory stimuli and reacts to them. The translucent zebrafish larva is an ideal model organism for studying neuronal circuits regulating brain states, owning to the possibility of easy imaging and manipulating activity of genetically identified neurons while the animal performs stereotyped and well-characterized behaviors. The main neuromodulatory circuits present in mammals can also be found in the larval zebrafish brain, with the advantage that they contain small numbers of neurons. Importantly, imaging and behavioral techniques can be combined with methods for generating targeted genetic modifications to reveal the molecular underpinnings mediating the functions of such circuits. In this review we discuss how studying the larval zebrafish brain has contributed to advance our understanding of circuits and molecular mechanisms regulating neuromodulation and behavioral flexibility.
Biological Therapy in Primary Sjögren's Syndrome: Effect on Salivary Gland Function and Inflammation

Primary Sjögren's syndrome (pSS) is a chronic, systemic autoimmune disease. It is the second most common rheumatic autoimmune disorder, affecting 0.7% of European Americans and up to 1% of people globally. pSS is characterized by the impaired secretory function of exocrine glands, including salivary and lachrymal glands. A lymphocytic infiltration of these organs leads to the common and debilitating symptoms of oral and ocular dryness, majorly affecting the quality of life of these patients. Currently, no disease-modifying drug has been approved for the treatment of pSS, with therapies largely aimed at relieving symptoms of dry mouth and dry eyes. In particular, management of oral dryness still represents a major unmet clinical need in pSS and a significant burden for patients with this condition. Recently, several randomized clinical trials in pSS with biological therapies targeting specific mechanistic pathways implicated in the disease pathogenesis, including B-cell hyperactivity, T-cell co-stimulation and the aberrant role of cytokines, have been completed with mixed results. In this review, we summarize evidence from recent clinical trials investigating biological therapy in pSS, specifically highlighting efficacy, or lack thereof, in modulating local inflammation and improving salivary gland function.