Miracle-dz/UniFAR-FTUs huggingface dataset:
A general system of differential equations to model first order adaptive algorithms

First order optimization algorithms play a major role in large scale machine learning. A new class of methods, called adaptive algorithms, were recently introduced to adjust iteratively the learning rate for each coordinate. Despite great practical success in deep learning, their behavior and performance on more general loss functions are not well understood. In this paper, we derive a non-autonomous system of differential equations, which is the continuous time limit of adaptive optimization methods. We prove global well-posedness of the system and we investigate the numerical time convergence of its forward Euler approximation. We study, furthermore, the convergence of its trajectories and give conditions under which the differential system, underlying all adaptive algorithms, is suitable for optimization. We discuss convergence to a critical point in the non-convex case and give conditions for the dynamics to avoid saddle points and local maxima. For convex and deterministic loss function, we introduce a suitable Lyapunov functional which allow us to study its rate of convergence. Several other properties of both the continuous and discrete systems are briefly discussed. The differential system studied in the paper is general enough to encompass many other classical algorithms (such as Heavy ball and Nesterov's accelerated method) and allow us to recover several known results for these algorithms.
Introducing SPAIN (SParse Audio INpainter)

A novel sparsity-based algorithm for audio inpainting is proposed. It is an adaptation of the SPADE algorithm by Kitić et al., originally developed for audio declipping, to the task of audio inpainting. The new SPAIN (SParse Audio INpainter) comes in synthesis and analysis variants. Experiments show that both A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms. Moreover, A-SPAIN performs on a par with the state-of-the-art method based on linear prediction in terms of the SNR, and, for larger gaps, SPAIN is even slightly better in terms of the PEMO-Q psychoacoustic criterion.
A Secreted Bacterial Peptidylarginine Deiminase Can Neutralize Human Innate Immune Defenses

Bacterial pathogens do not only succeed in breaking the barriers that protect humans from infection, but they also manage to evade insults from the human immune system. The importance of the present study resides in the fact that protein citrullination is shown to represent a new bacterial mechanism for immune evasion. In particular, the oral pathogen P. gingivalis employs this mechanism to defuse innate immune responses by secreting a protein-citrullinating enzyme. Of note, this finding impacts not only the global health problem of periodontitis, but it also extends to the prevalent autoimmune disease rheumatoid arthritis, which has been strongly associated with periodontitis, PPAD activity, and loss of tolerance against citrullinated proteins, such as the histone H3. ABSTRACT The keystone oral pathogen Porphyromonas gingivalis is associated with severe periodontitis. Intriguingly, this bacterium is known to secrete large amounts of an enzyme that converts peptidylarginine into citrulline residues. The present study was aimed at identifying possible functions of this citrullinating enzyme, named Porphyromonas peptidylarginine deiminase (PPAD), in the periodontal environment. The results show that PPAD is detectable in the gingiva of patients with periodontitis, and that it literally neutralizes human innate immune defenses at three distinct levels, namely bacterial phagocytosis, capture in neutrophil extracellular traps (NETs), and killing by the lysozyme-derived cationic antimicrobial peptide LP9. As shown by mass spectrometry, exposure of neutrophils to PPAD-proficient bacteria reduces the levels of neutrophil proteins involved in phagocytosis and the bactericidal histone H2. Further, PPAD is shown to citrullinate the histone H3, thereby facilitating the bacterial escape from NETs. Last, PPAD is shown to citrullinate LP9, thereby restricting its antimicrobial activity. The importance of PPAD for immune evasion is corroborated in the infection model Galleria mellonella, which only possesses an innate immune system. Together, the present observations show that PPAD-catalyzed protein citrullination defuses innate immune responses in the oral cavity, and that the citrullinating enzyme of P. gingivalis represents a new type of bacterial immune evasion factor. IMPORTANCE Bacterial pathogens do not only succeed in breaking the barriers that protect humans from infection, but they also manage to evade insults from the human immune system. The importance of the present study resides in the fact that protein citrullination is shown to represent a new bacterial mechanism for immune evasion. In particular, the oral pathogen P. gingivalis employs this mechanism to defuse innate immune responses by secreting a protein-citrullinating enzyme. Of note, this finding impacts not only the global health problem of periodontitis, but it also extends to the prevalent autoimmune disease rheumatoid arthritis, which has been strongly associated with periodontitis, PPAD activity, and loss of tolerance against citrullinated proteins, such as the histone H3.
Can Deep Learning Identify Tomato Leaf Disease?

This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as “fc”). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases.
Social conversation skill improvements associated with the Social Tools And Rules for Teens program for adolescents with autism spectrum disorder: Results of a randomized controlled trial

There has been a significant increase in the development of interventions to improve the social competence and success of adolescents with autism spectrum disorder. The current investigation used direct observation and coding of social conversations as a rigorous method to further assess the efficacy of the Social Tools And Rules for Teens socialization intervention for adolescents with autism spectrum disorder in the context of a randomized controlled trial. A total of 35 adolescents with high-functioning autism spectrum disorder were randomized to either a treatment or waitlist control group. The 20-week group intervention took place once a week for 90 min per session. Brief video-recorded conversations between participants and unfamiliar, untrained peers were recorded at pre- and post-time points and coded for selected social behaviors (i.e. questions asked, positive facial expressions, and mutual engagement). Results revealed a significant Group × Time treatment effect for both questions asked and positive facial expressions. The findings support that the Social Tools And Rules for Teens intervention can positively impact specific, observable social behaviors through systematic coding of live social conversations within the context of a randomized controlled trial. This investigation is one of the first randomized controlled trials of a group socialization intervention to use systematic coding of live social conversations to assess social competence improvements.
Attentive Filtering Networks for Audio Replay Attack Detection

An attacker may use a variety of techniques to fool an automatic speaker verification system into accepting them as a genuine user. Anti-spoofing methods meanwhile aim to make the system robust against such attacks. The ASVspoof 2017 Challenge focused specifically on replay attacks, with the intention of measuring the limits of replay attack detection as well as developing countermeasures against them. In this work, we propose our replay attacks detection system - Attentive Filtering Network, which is composed of an attention-based filtering mechanism that enhances feature representations in both the frequency and time domains, and a ResNet-based classifier. We show that the network enables us to visualize the automatically acquired feature representations that are helpful for spoofing detection. Attentive Filtering Network attains an evaluation EER of 8.99% on the ASVspoof 2017 Version 2.0 dataset. With system fusion, our best system further obtains a 30% relative improvement over the ASVspoof 2017 enhanced baseline system.
Effect of Theophylline as Adjunct to Inhaled Corticosteroids on Exacerbations in Patients With COPD: A Randomized Clinical Trial

Importance Chronic obstructive pulmonary disease (COPD) is a major global health issue and theophylline is used extensively. Preclinical investigations have demonstrated that low plasma concentrations (1-5 mg/L) of theophylline enhance antiinflammatory effects of corticosteroids in COPD. Objective To investigate the effectiveness of adding low-dose theophylline to inhaled corticosteroids in COPD. Design, Setting, and Participants The TWICS (theophylline with inhaled corticosteroids) trial was a pragmatic, double-blind, placebo-controlled, randomized clinical trial that enrolled patients with COPD between February 6, 2014, and August 31, 2016. Final follow-up ended on August 31, 2017. Participants had a ratio of forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) of less than 0.7 with at least 2 exacerbations (treated with antibiotics, oral corticosteroids, or both) in the previous year and were using an inhaled corticosteroid. This study included 1578 participants in 121 UK primary and secondary care sites. Interventions Participants were randomized to receive low-dose theophylline (200 mg once or twice per day) to provide plasma concentrations of 1 to 5 mg/L (determined by ideal body weight and smoking status) (n = 791) or placebo (n = 787). Main Outcomes and Measures The number of participant-reported moderate or severe exacerbations treated with antibiotics, oral corticosteroids, or both over the 1-year treatment period. Results Of the 1567 participants analyzed, mean (SD) age was 68.4 (8.4) years and 54% (843) were men. Data for evaluation of the primary outcome were available for 1536 participants (98%) (772 in the theophylline group; 764 in the placebo group). In total, there were 3430 exacerbations: 1727 in the theophylline group (mean, 2.24 [95% CI, 2.10-2.38] exacerbations per year) vs 1703 in the placebo group (mean, 2.23 [95% CI, 2.09-2.37] exacerbations per year); unadjusted mean difference, 0.01 (95% CI, −0.19 to 0.21) and adjusted incidence rate ratio, 0.99 (95% CI, 0.91-1.08). Serious adverse events in the theophylline and placebo groups included cardiac, 2.4% vs 3.4%; gastrointestinal, 2.7% vs 1.3%; and adverse reactions such as nausea (10.9% vs 7.9%) and headaches (9.0% vs 7.9%). Conclusions and Relevance Among adults with COPD at high risk of exacerbation treated with inhaled corticosteroids, the addition of low-dose theophylline, compared with placebo, did not reduce the number COPD exacerbations over a 1-year period. The findings do not support the use of low-dose theophylline as adjunctive therapy to inhaled corticosteroids for the prevention of COPD exacerbations. Trial Registration isrctn.org Identifier: ISRCTN27066620
Tumor‐derived exosomal microRNAs and proteins as modulators of macrophage function

Tumor cells are able to modify their surrounding microenvironment by transmitting bioactive molecules via exosomes. In exosomes, proteins and nucleic acids that can be taken up by surrounding cells have been identified and modulate their functions. Tumor microenvironment consists of different cells such as macrophages. Tumors‐associated macrophages (TAMs) express M2 phenotype and affect many processes including tumor initiation, angiogenesis, and metastasis. It has been demonstrated that a high number of TAMs is associated with poor prognosis of cancers. The contents of tumor‐derived exosomes such as microRNAs and proteins induce macrophages to M2‐like polarization to support tumor growth. Herein, we review the most recent studies on the effect of tumor‐derived exosomes on macrophage polarization and function in different types of cancers.
Autonomy Support in Toddlerhood: Similarities and Contrasts Between Mothers and Fathers

Infant exploration often hinges on parental autonomy support (i.e., parental behaviors that support children’s goals, interests, and choices), a construct that is widely applied in family studies of school-age children and adolescents but less studied in infants and toddlers. Notable gaps concern the equivalence, similarities, and contrasts between mothers’ and fathers’ autonomy support and the correlates of individual differences in autonomy support. To address these underresearched topics, we conducted parallel home-based structured play observations of 195 infants (Mage = 14.42 months, SD = .59) in dyadic interaction with mothers and fathers. Confirmatory factor analyses demonstrated measurement invariance across parent gender, enabling comparisons that revealed significantly moderately higher levels of autonomy support in mothers than in fathers. Individual differences in autonomy support were unrelated to either parental personality or child temperament, highlighting the potential importance of dyadic characteristics. Consistent with this view, whereas maternal autonomy support did not differ by child gender, fathers with sons displayed less autonomy support than did fathers with daughters.
Facebook language predicts depression in medical records

Significance Depression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference. This study suggests that an analysis of social media data could be used to screen consenting individuals for depression. Further, social media content may point clinicians to specific symptoms of depression. Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy [area under the curve (AUC) = 0.69], approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures.
Structure Learning of Deep Neural Networks with Q-Learning

Recently, with convolutional neural networks gaining significant achievements in many challenging machine learning fields, hand-crafted neural networks no longer satisfy our requirements as designing a network will cost a lot, and automatically generating architectures has attracted increasingly more attention and focus. Some research on auto-generated networks has achieved promising results. However, they mainly aim at picking a series of single layers such as convolution or pooling layers one by one. There are many elegant and creative designs in the carefully hand-crafted neural networks, such as Inception-block in GoogLeNet, residual block in residual network and dense block in dense convolutional network. Based on reinforcement learning and taking advantages of the superiority of these networks, we propose a novel automatic process to design a multi-block neural network, whose architecture contains multiple types of blocks mentioned above, with the purpose to do structure learning of deep neural networks and explore the possibility whether different blocks can be composed together to form a well-behaved neural network. The optimal network is created by the Q-learning agent who is trained to sequentially pick different types of blocks. To verify the validity of our proposed method, we use the auto-generated multi-block neural network to conduct experiments on image benchmark datasets MNIST, SVHN and CIFAR-10 image classification task with restricted computational resources. The results demonstrate that our method is very effective, achieving comparable or better performance than hand-crafted networks and advanced auto-generated neural networks.
[Use of selected recombinant proteins in serodiagnosis of infections caused by verotoxigenic Escherichia coli (VTEC) in humans].

INTRODUCTION Verocytotoxin-producing E. coli (VTEC) are a significant cause of haemor- rhagic colitis (HC) and haemolytic uremic syndrome (HUS) in humans. Because VTEC isolates are usually present in patients' feces for only a limited period of time serodiagnosis based on the purified antigens have become the useful tool for laboratory diagnosis and monitoring of prevalence of VTEC infections. The aim of the present study was to evaluate the usefulness of in-house obtained recombinant proteins Tir, intymin and verocytotoxin 2b of E. coli as highly specific antigens in ELISA performed in the serodiagnosis of infec- tions caused by VTEC in humans. MATERIALS AND METHODS The study population, used for characterization of the humoral immune response to the recombinant proteins, consisted of37 patients suspected for VTEC infection, mainly with clinical manifestation of HUS. Additionally serum samples from 78 clinically healthy persons and 96 patients with different bacterial infections (control group) were tested. Recombinant proteins were expressing in E. coli BL21 (DE3) using the pET-30 Ek/LIC expression vector (Novagen). Purification was accomplished by immobilized metal (Ni(2+)) affinity column chromatography (His-trap). RESULTS The antibodies against recombinant proteins were detected using the ELISA in about half of the tested patients suspected in clinical investigation for VTEC infection. Most of the antibodies belong to the IgG and IgA class of immunoglobulins. Statistical analysis of the results showed that the frequency of detecting antibodies among patients with HUS was significantly higher in relation to the clinically healthy persons. However, the percentage of positive results in the control group were also much higher than in healthy persons what may indicate for presence of non-specific reactions. The least non-specific response was detected by ELISA with the protein Tir as antigen. CONCLUSIONS The study showed that recombinant proteins Tir, intimin and verocytotoxin 2b of E. coli may be used as antigens in routine diagnosis of VTEC infections. The most specific antigen is a recombinant protein Tir.
Convolutional Self-Attention Network

Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies. It can be further enhanced with multi-headed attention mechanism by allowing the model to jointly attend to information from different representation subspaces at different positions (Vaswani et al., 2017). In this work, we propose a novel convolutional self-attention network (CSAN), which offers SAN the abilities to 1) capture neighboring dependencies, and 2) model the interaction between multiple attention heads. Experimental results on WMT14 English-to-German translation task demonstrate that the proposed approach outperforms both the strong Transformer baseline and other existing works on enhancing the locality of SAN. Comparing with previous work, our model does not introduce any new parameters.
Predicting T cell recognition of MHC class I restricted neoepitopes

ABSTRACT Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used. Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4