Miracle-dz/UniFAR-FTUs huggingface dataset:
Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay

Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4-5 log10 decline in virus within one day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the infected cell clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and gives further insight into the regulation of viral control.
Development and research of the autonomous system for providing security and quality of service for multi-cloud platform

In this research we present the results of approbation of the prototype of the autonomous system for ensuring cyber security and the quality of maintenance of the software-defined infrastructure of the multi-cloud platform. A hybrid neural network based on the multi-layer perceptron and self-organizing Kohonen network was used as an approach to detecting harmful traffic. The study showed that the use of the proposed approach makes it possible to increase the effectiveness of detecting attacks such as denial of service and preserving the quality of service in the corporate network.
Pulmonary Arterial Hypertension and Endothelial Dysfunction Is Linked to NADPH Oxidase-Derived Superoxide Formation in Venous Thrombosis and Pulmonary Embolism in Mice

Pulmonary embolism (PE) results from deep vein thrombosis (DVT) and can lead to chronic thromboembolic pulmonary hypertension (CTEPH) involving vascular dysfunction. Mechanisms are incompletely understood, in part due to lack of mouse models. We induced PE in C57BL/6 mice by intravenous injection of thrombin (166 U/kg BW), confirmed by a sudden bradycardia, bradypnea, and an increase in pulmonary artery (PA) pressure observed by high-frequency ultrasound. While symptoms resolved rapidly after single thrombin application, repeated PEs resulted in sustained PA-pressure increase, increased PA superoxide formation assessed by oxidative fluorescent microtopography, increased PA gp91phox expression, and endothelial dysfunction assessed by isometric tension studies of isolated PA segments after 24 hours. DVT was modeled in C57BL/6 mice by ligation of the inferior vena cava (IVC). Importantly, small pulmonary emboli could be detected along with a mild phenotype of PA endothelial dysfunction and oxidative stress in the absence of PA-pressure elevation. mRNA expression of plasminogen activator inhibitor-1 was increased in PAs of mice with recurrent PE after repetitive thrombin injections and to a lesser extent in DVT mice. In summary, our data suggest that PA endothelial dysfunction, induced by gp91phox-derived ROS, is an early event upon repetitive PE. This phenomenon might help to elucidate the mechanisms of PA dysfunction in the pathogenesis of CTEPH.
Zero-shot Domain Adaptation without Domain Semantic Descriptors

We propose a method to infer domain-specific models such as classifiers for unseen domains, from which no data are given in the training phase, without domain semantic descriptors. When training and test distributions are different, standard supervised learning methods perform poorly. Zero-shot domain adaptation attempts to alleviate this problem by inferring models that generalize well to unseen domains by using training data in multiple source domains. Existing methods use observed semantic descriptors characterizing domains such as time information to infer the domain-specific models for the unseen domains. However, it cannot always be assumed that such metadata can be used in real-world applications. The proposed method can infer appropriate domain-specific models without any semantic descriptors by introducing the concept of latent domain vectors, which are latent representations for the domains and are used for inferring the models. The latent domain vector for the unseen domain is inferred from the set of the feature vectors in the corresponding domain, which is given in the testing phase. The domain-specific models consist of two components: the first is for extracting a representation of a feature vector to be predicted, and the second is for inferring model parameters given the latent domain vector. The posterior distributions of the latent domain vectors and the domain-specific models are parametrized by neural networks, and are optimized by maximizing the variational lower bound using stochastic gradient descent. The effectiveness of the proposed method was demonstrated through experiments using one regression and two classification tasks.
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders

We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities. We introduce {\sigma}-connection graphs ({\sigma}-CG), a new class of mixed graphs (containing undirected, bidirected and directed edges) with additional structure, and extend the concept of {\sigma}-separation, the appropriate generalization of the well-known notion of d-separation in this setting, to apply to {\sigma}-CGs. We prove the closedness of {\sigma}-separation under marginalisation and conditioning and exploit this to implement a test of {\sigma}-separation on a {\sigma}-CG. This then leads us to the first causal discovery algorithm that can handle non-linear functional relations, latent confounders, cyclic causal relationships, and data from different (stochastic) perfect interventions. As a proof of concept, we show on synthetic data how well the algorithm recovers features of the causal graph of modular structural causal models.
Effects of CYP2D6 genetic polymorphisms on the efficacy and safety of fluvoxamine in patients with depressive disorder and comorbid alcohol use disorder

Background Fluvoxamine therapy is used for treatment of patients with depressive disorder, but it is often ineffective, and some patients suffer from dose-dependent undesirable side effects such as vertigo, headache, indigestion, xerostomia, increased anxiety, etc. CYP2D6 is involved in the biotransformation of fluvoxamine. Meanwhile, the genes encoding these isoenzymes have a high level of polymorphism, which may affect the protein synthesis. Objective The primary objective of our study was to investigate the effects of CYP2D6 genetic polymorphisms on the efficacy and safety of fluvoxamine in patients with depressive disorder and comorbid alcohol use disorder, in order to develop the algorithms of optimization of fluvoxamine therapy for reducing the risk of dose-dependent undesirable side effects and pharmacoresistance. Methods The study involved 45 male patients (average age: 36.44±9.96 years) with depressive disorder and comorbid alcohol use disorder. A series of psychometric scales was used in the research. Genotyping of CYP2D6 (1846G>A) was performed using real-time polymerase chain reaction. Results According to results of Mann–Whitney U-test, statistically significant differences between the efficacy and safety of fluvoxamine were obtained on 9th and 16th days of therapy in patients with GG and GA genotypes (The Hamilton Rating Scale for Depression: 10.0 [10.0; 23.0] vs 25.0 [24.0; 16.0] (P<0.001) on the 9th day and 4.0 [2.0; 5.0] vs 6.0 [6.0; 7.0] on the 16th day; The UKU Side Effect Rating Scale: 6.0 [4.0; 6.0] vs 9.0 [9.0; 10.0] (P<0.001) on the 9th day and 5.0 [1.0; 9.0] vs 19.0 [18.0; 22.0] on the 16th day). Conclusion This study demonstrated the lower efficacy and safety of fluvoxamine in patients with depressive disorder and comorbid alcohol use disorders with GA genotype in CYP2D6 1846G>A polymorphic marker.
Genetic Diversity, Multidrug Resistance, and Virulence of Citrobacter freundii From Diarrheal Patients and Healthy Individuals

Objectives: Citrobacter freundii is a frequent cause of nosocomial infections and a known cause of diarrheal infections, and has increasingly become multidrug resistant (MDR). In this study, we aimed to determine the genetic diversity, the antimicrobial resistance profiles and in vitro virulence properties of C. freundii from diarrheal patients and healthy individuals. Methods: 82 C. freundii isolates were obtained from human diarrheal outpatients and healthy individuals. Multilocus Sequence Typing (MLST) of seven housekeeping genes was performed. Antimicrobial susceptibility testing was carried out using the disk diffusion method according to the Clinical and Laboratory Standards Institute (CLSI) recommendations. Adhesion and cytotoxicity to HEp-2 cells were assessed. PCR and sequencing were used to identify blaCTX−M, blaSHV, blaTEM, qnrA, qnrB, qnrS, qnrC, qnrD, aac(6')-Ib-cr, and qepA genes. Results: The 82 C. freundii isolates were divided into 76 sequence types (STs) with 65 STs being novel, displaying high genetic diversity. Phylogenetic analysis divided the 82 isolates into 5 clusters. All 82 isolates were sensitive to imipenem (IPM), but resistant to one or more other 16 antibiotics tested. Twenty-six isolates (31.7%) were multidrug resistant to three or more antibiotic classes out of the 10 distinct antibiotic classes tested. Five MDR isolates, all of which were isolated from 2014, harbored one or more of the resistance genes, blaTEM−1, blaCTX−M−9, aac(6')-Ib-cr, qnrS1, qnrB9, and qnrB13. All 11 qnrB-carrying C. freundii isolates belonged to cluster 1, and one C. freundii isolate carried a new qnrB gene (qnrB92). Six isolates showed strong cytotoxicity to HEp-2 cells, one of which was multidrug resistant. Conclusions: C. freundii isolates from human diarrheal outpatients and healthy individuals were diverse with variation in sequence types, antibiotic resistance profiles and virulence properties.
On the effectiveness of machine and deep learning for cyber security

Machine learning is adopted in a wide range of domains where it shows its superiority over traditional rule-based algorithms. These methods are being integrated in cyber detection systems with the goal of supporting or even replacing the first level of security analysts. Although the complete automation of detection and analysis is an enticing goal, the efficacy of machine learning in cyber security must be evaluated with the due diligence. We present an analysis, addressed to security specialists, of machine learning techniques applied to the detection of intrusion, malware, and spam. The goal is twofold: to assess the current maturity of these solutions and to identify their main limitations that prevent an immediate adoption of machine learning cyber detection schemes. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic.
Artificial neural network model to predict affinity for virtual network functions

Network Functions Virtualization (NFV) was proposed to migrate middleboxes that compose network services, such as firewalls and Network Address Translation (NAT) servers, from hardware to software running on Virtual Machines (VMs), commonly known as Virtualized Network Functions (VNFs). In NFV-enabled networks, VNFs can be chained in Forwarding Graphs (FGs) to provide services. These FGs establish the logical order in which network packets must traverse until reaching the end-service. In this scenario, network operators establish affinity and anti-affinity rules, which determine restrictions on the placement and chaining of VNFs according to how well or poorly VNFs operate together. To address the subject of identifying affinity relations in NFV-enabled networks, we previously proposed a mathematical model to measure the affinity between pairs of VNFs. However, that affinity model falls short for identifying affinity of VNFs not yet deployed, as they have no resource usage data to take into account. In this paper, we use artificial neural networks to predict affinity estimation for newly introduced VNFs, which still do not have usage data to be analyzed. This affinity neural network is trained using past affinity measurements, containing the data from VNFs, Physical Machines (PMs), and FGs of each measurement as features. We evaluate our solution by analyzing it over real usage data from a Cloud dataset, and conclude that neural networks can be used to provide affinity values for network operators, or NFV orchestrators, to plan the deployment of new VNFs.
Anytime Neural Prediction via Slicing Networks Vertically

The pioneer deep neural networks (DNNs) have emerged to be deeper or wider for improving their accuracy in various applications of artificial intelligence. However, DNNs are often too heavy to deploy in practice, and it is often required to control their architectures dynamically given computing resource budget, i.e., anytime prediction. While most existing approaches have focused on training multiple shallow sub-networks jointly, we study training thin sub-networks instead. To this end, we first build many inclusive thin sub-networks (of the same depth) under a minor modification of existing multi-branch DNNs, and found that they can significantly outperform the state-of-art dense architecture for anytime prediction. This is remarkable due to their simplicity and effectiveness, but training many thin sub-networks jointly faces a new challenge on training complexity. To address the issue, we also propose a novel DNN architecture by forcing a certain sparsity pattern on multi-branch network parameters, making them train efficiently for the purpose of anytime prediction. In our experiments on the ImageNet dataset, its sub-networks have up to $43.3\%$ smaller sizes (FLOPs) compared to those of the state-of-art anytime model with respect to the same accuracy. Finally, we also propose an alternative task under the proposed architecture using a hierarchical taxonomy, which brings a new angle for anytime prediction.
A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials

Most policy search (PS) algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word “big-data,” we refer to this challenge as “micro-data reinforcement learning. ” In this article, we show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based PS), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots, designing generic priors, and optimizing the computing time.
A Comparison of the Effectiveness of Two Computer-Based Learning Aids

Instructors are increasingly using computer-based educational technologies to augment their courses. As answering quizzes has been shown to be one of the most effective learning strategies, a growing number of computer-based learning aids use quizzing. So which of these learning aids should instructors recommend to their students? These learning aids typically either present the student with a number of potential answers and require that they recognize the correct answer (i.e., a multiple-choice quiz) or else they might require the student to recall the answer without assistance (i.e., a free recall quiz). Numerous lab-based studies have shown that recall-based quizzes promote more learning and result in higher performance in a subsequent exam/test than recognition-based quizzes. In the present study, we investigated to what extent this finding holds in an actual university setting with two commercially-available learning aids. We found that while both types of learning aid proved to be effective, we could find no evidence that the recall-based learning aid was more effective than the recognition-based learning aid. In light of this, we discuss possible reasons why the laboratory findings did not readily translate to an actual university setting and make practical recommendations for what sort of computer-based learning aid instructors should incorporate into their university courses.
Extrahippocampal Contributions to Age-Related Changes in Spatial Navigation Ability

Age-related decline in spatial navigation is well-known and the extant literature emphasizes the important contributions of a hippocampus-dependent spatial navigation system in mediating this decline. However, navigation is a multifaceted cognitive domain and some aspects of age-related navigational decline may be mediated by extrahippocampal brain regions and/or systems. The current review presents an overview of some key cognitive domains that contribute to the age-related changes in spatial navigation ability, and elucidates such domains in the context of an increased engagement of navigationally relevant extrahippocampal brain regions with advancing age. Specifically, this review focuses on age-related declines in three main areas: (i) allocentric strategy use and switching between egocentric and allocentric strategies, (ii) associative learning of landmarks/locations and heading directions, and (iii) executive functioning and attention. Thus far, there is accumulating neuroimaging evidence supporting the functional relevance of the striatum for egocentric/response strategy use in older adults, and of the prefrontal cortex for mediating executive functions that contribute to successful navigational performance. Notably, the functional role of the prefrontal cortex was particularly emphasized via the proposed relevance of the fronto-locus coeruleus noradrenergic system for strategy switching and of the fronto-hippocampal circuit for landmark-direction associative learning. In view of these putative prefrontal contributions to navigation-related functions, we recommend future spatial navigation studies to adopt a systems-oriented approach that investigates age-related alterations in the interaction between the prefrontal cortex, the hippocampus, and extrahippocampal regions, as well as an individual differences approach that clarifies the differential engagement of prefrontal executive processes among older adults.
Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation

This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford’s winning system for the CoNLL 2017 shared task and make two effective extensions: 1) incorporating deep contextualized word embeddings into both the part of speech tagger and parser; 2) ensembling parsers trained with different initialization. We also explore different ways of concatenating treebanks for further improvements. Experimental results on the development data show the effectiveness of our methods. In the final evaluation, our system was ranked first according to LAS (75.84%) and outperformed the other systems by a large margin.
Core2Vec: A Core-Preserving Feature Learning Framework for Networks

Recent advances in the field of network representation learning are mostly attributed to the application of the skip-gram model in the context of graphs. State-of-the-art analogues of skip-gram model in graphs define a notion of neighbourhood and aim to find the vector representation for a node, which maximizes the likelihood of preserving this neighborhood. In this paper, we take a drastic departure from the existing notion of neighbourhood of a node by utilizing the idea of coreness. More specifically, we utilize the well-established idea that nodes with similar core numbers play equivalent roles in the network and hence induce a novel and an organic notion of neighbourhood. Based on this idea, we propose core2vec, a new algorithmic framework for learning low dimensional continuous feature mapping for a node. Consequently, the nodes having similar core numbers are relatively closer in the vector space that we learn. We further demonstrate the effectiveness of core2vec by comparing word similarity scores obtained by our method where the node representations are drawn from standard word association graphs 11In linguistics, such networks built from various linguistic units are known to have a core-periphery structure (see [3] and the references therein), against scores computed by other state-of-the-art network representation techniques like node2vec, Deep-Walk and LINE. Our results always outperform these existing methods, in some cases achieving improvements as high as 46 % on certain ground-truth word similarity datasets. We make all codes used in this paper available in the public domain: https://github.com/Sam131112/Core2vec_test.