Miracle-dz/UniFAR-FTUs huggingface dataset:
Technical report: supervised training of convolutional spiking neural networks with PyTorch

Recently, it has been shown that spiking neural networks (SNNs) can be trained efficiently, in a supervised manner, using backpropagation through time. Indeed, the most commonly used spiking neuron model, the leaky integrate-and-fire neuron, obeys a differential equation which can be approximated using discrete time steps, leading to a recurrent relation for the potential. The firing threshold causes optimization issues, but they can be overcome using a surrogate gradient. Here, we extend previous approaches in two ways. Firstly, we show that the approach can be used to train convolutional layers. Convolutions can be done in space, time (which simulates conduction delays), or both. Secondly, we include fast horizontal connections a la Deneve: when a neuron N fires, we subtract to the potentials of all the neurons with the same receptive the dot product between their weight vectors and the one of neuron N. As Deneve et al. showed, this is useful to represent a dynamic multidimensional analog signal in a population of spiking neurons. Here we demonstrate that, in addition, such connections also allow implementing a multidimensional send-on-delta coding scheme. We validate our approach on one speech classification benchmarks: the Google speech command dataset. We managed to reach nearly state-of-the-art accuracy (94%) while maintaining low firing rates (about 5Hz). Our code is based on PyTorch and is available in open source at this http URL
Representations and Deformations of Hom-Lie-Yamaguti Superalgebras

Let L,α be a Hom-Lie-Yamaguti superalgebra. We first introduce the representation and cohomology theory of Hom-Lie-Yamaguti superalgebras. Also, we introduce the notions of generalized derivations and representations of L,α and present some properties. Finally, we investigate the deformations of L,α by choosing some suitable cohomology.
Parallelising MCMC via Random Forests

For Bayesian computation in big data contexts, the divide-and-conquer MCMC concept splits the whole data set into batches, runs MCMC algorithms separately over each batch to produce samples of parameters, and combines them to produce an approximation of the target distribution. In this article, we embed random forests into this framework and use each subposterior/partial-posterior as a proposal distribution to implement importance sampling. Unlike the existing divide-and-conquer MCMC, our methods are based on scaled subposteriors, whose scale factors are not necessarily restricted to being equal to one or to the number of subsets. Through several experiments, we show that our methods work well with models ranging from Gaussian cases to strongly non-Gaussian cases, and include model misspecification.
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines

The diversity of intrinsic qualities of multimedia entities tends to impede their effective retrieval. In a SelfLearning Search Engine architecture, the subtle nuances of human perceptions and deep knowledge are taught and captured through unsupervised reinforcement learning, where the degree of reinforcement may be suitably calibrated. Such architectural paradigm enables indexes to evolve naturally while accommodating the dynamic changes of user interests. It operates by continuously constructing indexes over time, while injecting progressive improvement in search performance. For search operations to be effective, convergence of index learning is of crucial importance to ensure efficiency and robustness. In this paper, we develop a Self-Learning Search Engine architecture based on reinforcement learning using a Markov Decision Process framework. The balance between exploration and exploitation is achieved through evolutionary exploration Strategies. The evolutionary index learning behavior is then studied and formulated using stochastic analysis. Experimental results are presented which corroborate the steady convergence of the index evolution mechanism.
Universal adversarial examples in speech command classification

Adversarial examples are inputs intentionally perturbed with the aim of forcing a machine learning model to produce a wrong prediction, while the changes are not easily detectable by a human. Although this topic has been intensively studied in the image domain, classification tasks in the audio domain have received less attention. In this paper we address the existence of universal perturbations for speech command classification. We provide evidence that universal attacks can be generated for speech command classification tasks, which are able to generalize across different models to a significant extent. Additionally, a novel analytical framework is proposed for the evaluation of universal perturbations under different levels of universality, demonstrating that the feasibility of generating effective perturbations decreases as the universality level increases. Finally, we propose a more detailed and rigorous framework to measure the amount of distortion introduced by the perturbations, demonstrating that the methods employed by convention are not realistic in audio-based problems.
High Quality ELMo Embeddings for Seven Less-Resourced Languages

Recent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task. We offer precomputed embeddings from popular contextual ELMo model for seven languages: Croatian, Estonian, Finnish, Latvian, Lithuanian, Slovenian, and Swedish. We demonstrate that the quality of embeddings strongly depends on the size of training set and show that existing publicly available ELMo embeddings for listed languages shall be improved. We train new ELMo embeddings on much larger training sets and show their advantage over baseline non-contextual FastText embeddings. In evaluation, we use two benchmarks, the analogy task and the NER task.
Too Quiet in the Library: A Study of Native Third-Party Libraries in Android

Android applications ("apps") make avid use of third-party native libraries to increase performance and to reuse already implemented functionality. Native code can be directly executed from apps through the Java Native Interface or the Android Native Development Kit. Android developers drop precompiled native libraries into their projects, enabling their use. Unfortunately, developers are often not aware that these libraries (or their dependencies) must be updated. This results in the continuous use of outdated native libraries with unpatched security vulnerabilities years after patches are available. To assess the severity of the use of outdated and vulnerable libraries in the Android ecosystem, we study the prevalence of native libraries in the top applications of the Google Play market over time, correlating the time when native libraries are updated with the availability of security patches. A core difficulty we have to solve for this research is the identification of libraries and versions. Developers often rename or modify libraries but we require precise information about each binary. Our binary similarity metric bin2sim uses diverse features extracted from the libraries to identify and map the required information. Leveraging this bin2sim, we create an approach called LibRARIAN (LibRAry veRsion IdentificAtioN) that can accurately identify native libraries and their versions as found in Android apps with a a 92.53% true-positive rate, no false positives, and a 7.46% false-negative rate.
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service

Human conversations are complicated and building a human-like dialogue agent is an extremely challenging task. With the rapid development of deep learning techniques, data-driven models become more and more prevalent which need a huge amount of real conversation data. In this paper, we construct a large-scale real scenario Chinese E-commerce conversation corpus, JDDC, with more than 1 million multi-turn dialogues, 20 million utterances, and 150 million words. The dataset reflects several characteristics of human-human conversations, e.g., goal-driven, and long-term dependency among the context. It also covers various dialogue types including task-oriented, chitchat and question-answering. Extra intent information and three well-annotated challenge sets are also provided. Then, we evaluate several retrieval-based and generative models to provide basic benchmark performance on the JDDC corpus. And we hope JDDC can serve as an effective testbed and benefit the development of fundamental research in dialogue task.
Learning End-To-End Scene Flow by Distilling Single Tasks Knowledge

Scene flow is a challenging task aimed at jointly estimating the 3D structure and motion of the sensed environment. Although deep learning solutions achieve outstanding performance in terms of accuracy, these approaches divide the whole problem into standalone tasks (stereo and optical flow) addressing them with independent networks. Such a strategy dramatically increases the complexity of the training procedure and requires power-hungry GPUs to infer scene flow barely at 1 FPS. Conversely, we propose DWARF, a novel and lightweight architecture able to infer full scene flow jointly reasoning about depth and optical flow easily and elegantly trainable end-to-end from scratch. Moreover, since ground truth images for full scene flow are scarce, we propose to leverage on the knowledge learned by networks specialized in stereo or flow, for which much more data are available, to distill proxy annotations. Exhaustive experiments show that i) DWARF runs at about 10 FPS on a single high-end GPU and about 1 FPS on NVIDIA Jetson TX2 embedded at KITTI resolution, with moderate drop in accuracy compared to 10× deeper models, ii) learning from many distilled samples is more effective than from the few, annotated ones available.
Predictors of response of U.S. veterans to treatment for the hepatitis C virus

The currently recommended treatment for hepatitis C virus (HCV) infection is pegylated interferon alfa (PEG‐INF) and ribavirin, which can be difficult to tolerate. More information about predicting sustained virologic response (SVR) may allow more informed treatment decisions to be made. This retrospective observational cohort study identified predictors of SVR to PEG‐INF and ribavirin in routine medical practice at 121 Department of Veterans Affairs facilities. Among 5,944 patients infected with HCV genotypes 1, 2, or 3 who had been treated with PEG‐INF and ribavirin, SVR rates were 20%, 52%, and 43%, respectively, and discontinuation rates were 68% (prior to 48 weeks), 34% (24 weeks), and 41% (24 weeks), respectively. In multivariate analysis, significant predictors of decreased likelihood of genotype 1 patients having an SVR were being African American, clinical liver disease, diabetes, low cholesterol, low hemoglobin, low platelet count, and treatment at a low‐volume facility. Predictors of increased likelihood of genotype 1 patients having an SVR were low‐level HCV viremia, elevated ALT quotient, and receiving PEG‐INF 2A (rather than 2B). For genotype 2 patients, increasing body mass index, prior use of interferon, and low platelet count were negative predictors; only low‐level HCV viremia was a positive predictor. For genotype 3 patients, only receiving PEG‐INF 2A affected the likelihood of an SVR; its effect was positive. Conclusion: Among patients for whom HCV treatment is initiated during routine medical care, multiple factors including form of PEG‐INF received affect the SVR rate for genotype 1 patients. Few of these factors affect the rate for genotype 2 patients, and even fewer do so for genotype 3 patients. (HEPATOLOGY 2007.)
Increased interleukin-8 (IL-8) in rectal dialysate from patients with ulcerative colitis: evidence for a biological role for IL-8 in inflammation of the colon

OBJECTIVE:Infiltration of neutrophils and their release of toxic reactive oxygen species (ROS) in the colonic mucosa are associated with tissue damage in ulcerative colitis (UC). This neutrophil migration may be induced by chemoattractants, such as cytokines, in the colonic milieu. One such chemoattractant is interleukin-8 (IL-8), a neutrophil chemokine that is present at high concentrations in inflamed mucosa. However, the functional significance of IL-8 in neutrophil attraction and activation in UC has not been established. We hypothesized that IL-8 in the colonic lumen of patients with UC primes neutrophils, leading to their attraction and activation.METHODS:The colonic milieu was sampled by rectal dialysis. Using a semi-permeable membrane with a molecular weight cut-off of 12 kDa, dialysis solution was placed in the rectum and allowed to equilibrate over a 4-h period with the colonic milieu of controls or of patients with UC. IL-8 concentrations were measured by ELISA. Two functions of healthy neutrophils (PMN) were measured: expression of CD11-b surface adhesion molecules (by flow cytometry), and production of ROS (by both chemiluminescence and cytochrome C reduction assays). Neutrophil functions after exposure to rectal dialysates or n-formyl-methionyl-leucyl-phenylalanine (fMLP) were assessed before and after adding anti–IL-8 antibody or the fMLP blocker BMLP.RESULTS:IL-8 concentrations in dialysates from patients with active UC were significantly higher than in controls and correlated with disease activity. UC dialysates significantly increased ROS production and CD11-b expression by neutrophils and anti–IL-8 antibody partially (50%) inhibited these stimulatory effects of UC dialysates. Preincubation of neutrophils with UC dialysates significantly potentiated the fMLP-induced rise in ROS and anti–IL-8 antibody completely abolished this priming effect.CONCLUSIONS:The colonic milieu, sampled by rectal dialysis, from patients with active UC can both activate and prime neutrophils in vitro. High concentrations of IL-8 in the colonic lumen of UC patients are partially responsible for the activating effects of rectal dialysates, and account for all of its priming effects. These findings provide direct evidence for a role for IL-8 in inflammatory bowel disease.
The Prophylactic Effects of Folic Acid and Vitamin E against Valproic Acid During Fetal Thymus Development: an Ultrastructural Study

Se realizo este estudio para evaluar las diferencias histopatologicas en el timo de fetos de ratas Wistar Albinas expuestas prenatalmente a acido valproico (VPA), acido folico (AF) y vitamina E (Vit-E). VPA (400 mg/kg), FA (400 mcg/kg) y vitamina E (250mg/kg) administradas a ratas en los dias 8, 9 y 10 de gestacion. Los fetos (n=24) fueron divididos en cuatro grupos: control, APV, APV + vitamina E y VPA + FA. En el dia 20 de gestacion, todas las ratas prenadas fueron sacrificadas y los fetos fueron extraidos. Se obtuvieron secciones delgadas del timo de los fetos y se tineron con citrato de uranilo - acetato de plomo, siendo examinados al microscopio electronico de transmision. Los hallazgos histopatologicos del grupo control fueron normales. En el grupo VPA, se observaron cambios degenerativos en todos los compartimentos de tejido en comparacion con los controles. En el grupo VPA+FA, las vacuolas, cristalisis mitocondrial e inflamacion se redujeron en el citoplasma. En grupo VPA + Vitamina E, se observo el almacenamiento de lipidos y vacuolizacion. La cristalisis mitocondrial disminuyo. El estudio permitio analizar los cambios histopatologicos que pueden ocurrir en un modelo experimental de alto riesgo despues de la administracion de VPA, ademas, las funciones de proteccion por la administracion de AF y vitamina E.
Ethics committee consultation due to conflict over life-sustaining treatment: A sociodemographic investigation

ABSTRACT Background: The bioethics literature contains speculation but little data about sociodemographic differences between patients for whom ethics committees (EC) are consulted for conflict about life-sustaining treatment (LST) and the broader hospital population that these committees serve. To provide an empirical context for this discussion, we examined differences in five sociodemographic factors between patients for whom an EC was consulted for conflict over LST and the general inpatient population, hypothesizing that nonwhite patients were most likely to be disproportionately represented. Methods: This was a retrospective cohort study comparing self-reported race/ethnicity, household income, insurance status, primary language, and religious affiliation between patients involved in EC consultations for conflict over LST and the general adult inpatient population at a large academic hospital from January 1, 2007, through December 31, 2013. Results: There were 169 EC consultations involving conflict over LST and 243,197 adult inpatient admissions over the study period. On bivariate analysis, patients for whom the EC was consulted for conflict over LST were more likely to be nonwhite, low-income, and to speak a primary language other than English. They were not more likely to be underinsured or to report a religious affiliation. On multivariate analysis, patients for whom the EC was consulted for conflict over LST were more likely to be nonwhite (odds ratio [OR], 2.5; 95% confidence interval [CI], 1.7–3.7; p < .001). They were also more likely to be low income (OR, 1.8; 95% CI, 1.1–3.0; p = .02). They were not more likely to speak a primary language other than English (OR, 1.4; 95% CI, 0.9–2.1; p = .17). None of the assessed variables were associated with conflict over LST requiring additional meetings beyond the first EC consultation. Conclusions: Nonwhite and low-income status patients are disproportionately represented in EC committee consultation for conflict over LST compared to the general inpatient population.
A expressão neurológica e o diagnóstico genético nas síndromes de Angelman, de Rett e do X-Frágil

Objetivo: discutir os aspectos clinicos, eletroencefalograficos e os mecanismos geneticos de tres sindromes neurogeneticas, que se identificam como entidades nosologicas no grupo heterogeneo de patologias que cursam com retardo mental e autismo. Fontes dos dados: os autores realizaram revisao literaria sobre cada sindrome do estudo, atualizando as informacoes, correlacionando e caracterizando as manifestacoes neurologicas, assim como a descricao dos mecanismos geneticos e a identificacao dos marcadores biologicos. Sintese dos dados: houve a confirmacao de que a sindrome de Rett e uma doenca genetica, consequente a mutacao no gene MECP2, com variacoes clinicas que podem ser explicadas por diferentes mutacoes nesse gene. A sindrome de Angelman tem quatro mecanismos geneticos responsaveis pela variacao fenotipica e pelos diferentes riscos de recorrencia. Na sindrome do X-Fragil, o grau de comprometimento cognitivo esta relacionado com o numero de repeticoes dos trinucleotideos. Conclusoes: os diferentes mecanismos geneticos das tres sindromes sao responsaveis pela variabilidade clinica. Com a identificacao de marcadores biologicos, o diagnostico sera mais precoce, ademais, poderao ser identificadas novas e mais sutis formas de expressao.
Perception of 4 th year Medical students about Problem Based Learning

Objective: To describe perception of 4 th year medical students about comparison of problem based learning with traditional method of teaching. Methodology: The study was conducted on March 2006 at college of medicine in Al Ahsa, King Faisal University (KFU). Students filled a self administered structured questionnaire containing 13 items, on a five point Likert scale where five equaled PBL generally better and one equaled traditional generally better. Results: Fifty two (52) out of 54 students submitted their forms as two students were absent on the day of survey. Majority of the students rated PBL better than traditional method in all 13 aspects. Improvement in team work, ability to communicate, ease of remembering a topic, interest- enthusiasm and self directed life long learning were outcomes with highest perceived benefit of PBL compared to traditional method. Conclusion: PBL was perceived as better learning method, especially in enhancing team work and communication skills.
Transdermal drug delivery system: Formulation aspects and evaluation

The conventional oral dosage forms have significant setbacks of poor bioavailability due to hepatic first pass metabolism. To improve characters of transdermal drug delivery system (TDDS) was emerged, which will improve the therapeutic efficacy and safety of drugs by specific sites within the body, thereby reducing both the size and number of doses. Skin is an effective medium from which absorption of the drug takes place and enters into systematic circulation over a period of time. The present article reviews the selection of drug candidates and polymers suitable to be formulated as transdermal system, advantages, disadvantages of formulation design and the methods of evaluation.
EFFECTS OF RADIATION AND HORMONAL THERAPIES IN FEMALE BREAST CANCER PATIENTS

Breast cancer is reported having highest prevalence in Pakistan in Asia. It is treated with adjuvant radiotherapy after surgery with or without chemotherapy and later with endocrine therapy to get better overall survival rate. This study was conducted to perceive the effects of radiation and hormonal therapies in female breast cancer patients in Karachi, Pakistan. The cross sectional study was conducted from April 2011 to January 2012, n=811 patients were registered in KIRAN hospital, Karachi, Pakistan. The data collected through patients’ interview and their medical profiles were assessed statistically using the version 16.0 of SPSS. Out of n=811 female patients, majority were suffering from stage I or II of breast cancer. Most of the patients (97.16%) received radiation therapy whereas hormonal therapy was given to 62.63% patients, majority belonging to age group of 41-50 years. The patients may have previously received adjuvant chemotherapy and/or radiation therapy. Overall response for radiation therapy was good in 90.86% and partial in 8.88% breast cancer patients. The most common adverse reactions observed after radiation therapies were fatigue (75.88%) and skin hyperpigmentation (72.08%). The overall response observed during the follow ups for hormonal therapy was good in all patients. The most common adverse reactions observed due to hormonal therapy within 3-6 months were heat intolerance (50.98%) and myalgia (40.55%). 55.70% patients were at risk for developing cardiovascular diseases due to their existing comorbidities while having hormonal therapy. The treatment modalities should be selected according to patients’ forbearance and disease stage. Adverse effects arising from these treatments must be managed timely so as to have patients’ compliance and improvement in their quality of life. WO
Adaptação do Perfil de Saúde de Nottingham: um instrumento simples de avaliação da qualidade de vida

O objetivo deste estudo foi avaliar as propriedades psicometricas da versao brasileira do Perfil de Saude de Nottingham (PSN), um instrumento generico de avaliacao da qualidade de vida. O PSN foi traduzido para o portugues, segundo metodologia recomendada, e aplicado em 215 individuos, sendo 170 idosos da comunidade (66,3 ± 8,5 anos), 30 hemiplegicos cronicos (56,4 ± 10,9 anos) e 15 parkinsonianos (59,6 ± 10,2 anos). Os dados foram submetidos a analise Rasch. Dos 38 itens do PSN, apenas dois itens (5%) nao se enquadraram no modelo estatistico, indicando que os itens colaboram para medir um constructo unidimensional. Alguns itens do PSN, no entanto, foram muito faceis para os idosos, hemiplegicos e parkinsonianos, caracterizando um efeito teto, o que pode limitar o uso da escala para individuos com niveis mais altos de habilidade. Apesar dos resultados indicarem que o instrumento se enquadra no modelo estatistico de Rash, ha necessidade de revisao dos itens, de forma a tornar o instrumento mais util clinicamente.