Miracle-dz/UniFAR-FTUs huggingface dataset:
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets

The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery. This lack of understanding is linked to a lack of learning algorithms for recurrent networks of spiking neurons (RSNNs) that are both functionally powerful and can be implemented by known biological mechanisms. Since RSNNs are simultaneously a primary target for implementations of brain-inspired circuits in neuromorphic hardware, this lack of algorithmic insight also hinders technological progress in that area. The gold standard for learning in recurrent neural networks in machine learning is back-propagation through time (BPTT), which implements stochastic gradient descent with regard to a given loss function. But BPTT is unrealistic from a biological perspective, since it requires a transmission of error signals backwards in time and in space, i.e., from post- to presynaptic neurons. We show that an online merging of locally available information during a computation with suitable top-down learning signals in real-time provides highly capable approximations to BPTT. For tasks where information on errors arises only late during a network computation, we enrich locally available information through feedforward eligibility traces of synapses that can easily be computed in an online manner. The resulting new generation of learning algorithms for recurrent neural networks provides a new understanding of network learning in the brain that can be tested experimentally. In addition, these algorithms provide efficient methods for on-chip training of RSNNs in neuromorphic hardware.
Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums

In this work, we study the problem of minimizing the sum of strongly convex functions split over a network of $n$ nodes. We propose the decentralized and asynchronous algorithm ADFS to tackle the case when local functions are themselves finite sums with $m$ components. ADFS converges linearly when local functions are smooth, and matches the rates of the best known finite sum algorithms when executed on a single machine. On several machines, ADFS enjoys a $O (\sqrt{n})$ or $O(n)$ speed-up depending on the leading complexity term as long as the diameter of the network is not too big with respect to $m$. This also leads to a $\sqrt{m}$ speed-up over state-of-the-art distributed batch methods, which is the expected speed-up for finite sum algorithms. In terms of communication times and network parameters, ADFS scales as well as optimal distributed batch algorithms. As a side contribution, we give a generalized version of the accelerated proximal coordinate gradient algorithm using arbitrary sampling that we apply to a well-chosen dual problem to derive ADFS. Yet, ADFS uses primal proximal updates that only require solving one-dimensional problems for many standard machine learning applications. Finally, ADFS can be formulated for non-smooth objectives with equally good scaling properties. We illustrate the improvement of ADFS over state-of-the-art approaches with simulations.
Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees

Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources. In this paper, we strongly argue for the models and methods that are simple in machine learning characteristics, efficient in computing resource usage, and powerful to achieve very high accuracy levels. To demonstrate this, we develop a single task-based chemical toxicity prediction framework using only 2D features that are less compute intensive. We effectively use a decision tree to obtain an optimum number of features from a collection of thousands of them. We use a shallow neural network and jointly optimize it with decision tree taking both network parameters and input features into account. Our model needs only a minute on a single CPU for its training while existing methods using deep neural networks need about 10 min on NVidia Tesla K40 GPU. However, we obtain similar or better performance on several toxicity benchmark tasks. We also develop a cumulative feature ranking method which enables us to identify features that can help chemists perform prescreening of toxic compounds effectively.
Representation Degeneration Problem in Training Natural Language Generation Models

We study an interesting problem in training neural network-based models for natural language generation tasks, which we call the \emph{representation degeneration problem}. We observe that when training a model for natural language generation tasks through likelihood maximization with the weight tying trick, especially with big training datasets, most of the learnt word embeddings tend to degenerate and be distributed into a narrow cone, which largely limits the representation power of word embeddings. We analyze the conditions and causes of this problem and propose a novel regularization method to address it. Experiments on language modeling and machine translation show that our method can largely mitigate the representation degeneration problem and achieve better performance than baseline algorithms.
Assessment and Quality in Higher Education: A Model with Best Practice.

A study of a continuous quality imprcdment effort at Auburn University (Alabama) is reported. The study investigated variables predicting which students are at risk of failure. Data were drawn from the records of pre-engineering students enrolling at the university in fall terms of 1991 and 1993-95 (n=2,505). Data sources included achievement tests, high school transcripts, several standardized psychometric tests, freshman survey results, college grades, and exit questionnaire responses. Results are presented concerning the proportion of white and black pre-engineering students making a successful transition to the engineering program, correlation between college entrance examination scores and college grades/program completion, the predicted impact of raising entry standards, racial differences in achievement in pre-engineering, sex differences in achievement, correlation of personality factors and achievement, relationship of persistence to student use of time, program elements that students liked or disliked, and reasons offered for leaving engineering. Results of a survey of students (undergraduate and graduate) and faculty in the college of engineering, designed to identify factors important in improving the program (curriculum, accreditation, financial support, post-graduation employment, group working experiences, course attributes, cooperative experience, instruction) are then summarized.
Epidemiology of Bordetella pertussis in a children's hospital.

Introduction. Pertussis or whooping cough continues to be a major cause of morbidity and mortality in infants younger than 1 year old. Objectives. To describe the clinical and epidemiological profile of Bordetella pertussis and to analyze the factors associated with confirmation by PCR and case fatality rate. Material and Methods. Prospective, cohort study conducted between December 2003 and December 2011. The study included children seen at the Hospital de Ninos Ricardo Gutierrez suspected of pertussis. The factors associated with confirmation by PCR and the case fatality rate by relative risk (RR) with a 95% confidence interval were studied. Results. Six hundred and twenty patients with a 38% of positive cases (236/620) were included, 3 cases were confirmed by epidemiological link. Confirmed cases (239) showed a seasonal pattern from September through February, a median age of 3 months old, and 89% had received less than three vaccine doses. Eighty six percent of patients were hospitalized: their median length of stay was 7 days. A total of 99% of patients were eutrophic, 98% were immunocompetent and 17.5% required intensive care. The clinical presentation was analyzed in 480 patients. Of them, 38% (184) had a positive PCR result and their symptoms were: 96.2%, cough; 76.5%, paroxysmal cough; 57.9% cyanosis; 55.7%, respiratory distress; 29%, fever; 22.4%, apnea; 21.9%, vomiting after coughing. A multivariate analysis identified the following as independent predictors associated with confirmation of pertussis by PCR: paroxysmal cough (OR 2.52: 1.50-4.22; p= 0.000) and leukocytosis upon admission >20 000 white blood
Simplex FastICA: An Accelerated and Low Complex Architecture Design Methodology for $n$ D FastICA

This paper proposes an <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional Simplex FastICA (FICA), an accelerated and low complex architectural design methodology for FICA to attain high computation speed targeted for resource-constrained applications. This is achieved by exploiting the algorithmic redundancies of FICA that make the proposed Simplex FICA faster than the conventional FICA without adding any extra architectural complexities. The proposed methodology has been verified and validated by applying it for separating 6D EEG signals. Subsequently, the corresponding hardware has been designed using Verilog HDL. It is synthesized using UMC 90-nm technology resulting in 0.5703-mW power and 0.495 mm<sup>2</sup> area at 1.08 V. The computation time saving for 4D–12D FICA was computed by varying the number of iterations of convergence for the <inline-formula> <tex-math notation="LaTeX">$n\text {th}$ </tex-math></inline-formula> stage from 2 to 4 for 4096 samples. The average percentage of computation time saved for <inline-formula> <tex-math notation="LaTeX">$n\text {th}$ </tex-math></inline-formula> stage achieved with the proposed methodology in comparison to the state of the art varies from 99.783% to 99.891%. The overall average percentage of computation time saving for the proposed design with the aforementioned specifications varies from 9.14% to 16.6%.
Automatic syntactic analysis of learner English

Automatic syntactic analysis is essential for extracting useful information from large-scale learner data for linguistic research and natural language processing (NLP). Currently, researchers use standard POS taggers and parsers developed on native language to analyze learner language. Investigation of how such systems perform on learner data is needed to develop strategies for minimizing the cross-domain effects. Furthermore, POS taggers and parsers are developed for generic NLP purposes and may not be useful for identifying specific syntactic constructs such as subcategorization frames (SCFs). SCFs have attracted much research attention as they provide unique insight into the interplay between lexical and structural information. An automatic SCF identification system adapted for learner language is needed to facilitate research on L2 SCFs. In this thesis, we first provide a comprehensive evaluation of standard POS taggers and parsers on learner and native English. We show that the common practice of constructing a gold standard by manually correcting the output of a system can introduce bias to the evaluation, and we suggest a method to control for the bias. We also quantitatively evaluate the impact of fine-grained learner errors on POS tagging and parsing, identifying the most influential learner errors. Furthermore, we show that the performance of probabilistic POS taggers and parsers on native English can predict their performance on learner English. Secondly, we develop an SCF identification system for learner English. We train a machine learning model on both native and learner English data. The system can label individual verb occurrences in learner data for a set of 49 distinct SCFs. Our evaluation shows that the system reaches an accuracy of 84% F1 score. We then demonstrate that the level of accuracy is adequate for linguistic research. We design the first multidimensional SCF diversity metrics and investigate how SCF diversity changes with L2 proficiency on a large learner corpus. Our results show that as L2 proficiency develops, learners tend to use more diverse SCF types with greater taxonomic distance; more advanced learners also use different SCF types more evenly and locate the verb tokens of the same SCF type further away from each other. Furthermore, we demonstrate that the proposed SCF diversity metrics contribute a unique perspective to the prediction of L2 proficiency beyond existing syntactic complexity metrics.
Clinical and etiological profile of an exudative pleural effusion in a tertiary care center

Background: Pleural effusion is common clinical entity in day-to-day clinical practice. There are various etiologies for pleural effusion. Among those tubercular pleural effusion, parapneumonic effusion, malignant effusion, and congestive heart failure were the most common causes of pleural effusion. Here, we have done a retrospective study to see the etiology of pleural effusion in our tertiary care center. Patients and Methods: This retrospective study conducted in a tertiary care center over 1 year period. A total of 63 patients were included in this study after verifying in patient records of all patients who were admitted with exudative pleural effusion. The demographic data collected and complete history was obtained. Investigations such as complete hemogram, random blood sugar; renal function tests, serum proteins, chest x-ray, and pleural fluid analysis and investigations such as ultrasonogram of the chest and abdomen, echocardiogram, computed tomography scan of chest, fine-needle aspiration cytology, and pleural biopsy reports (if done) were collected. Results: Among the study participants, 40 were male and 23 were female patients with male-to-female ratio of 1.7:1. Mean age of the study population was 48.8 ± 18.7 years. The most common presenting symptom was dyspnea (84%) followed by cough (80%), fever (65%), and chest pain (43%). The most frequent cause of pleural effusion was tuberculosis in 38% of patients, followed by parapneumonic effusion (28.5%) and malignant pleural effusion (22.2%). Three patients had chylothorax, two patients had pancreatic pleural effusion and the diagnosis was unknown in two patients. Mean ± standard deviation (SD) adenosine deaminase (ADA) value of the study population was 45.3 ± 28.1. Mean ± SD of ADA values in tuberculous, parapneumonic, and malignant pleural effusion was 54.5 ± 16.8, 65.2 ± 30.7, and 18.2 ± 11.0, respectively. Conclusions: Tuberculosis is one of the common causes of exudative effusions along with parapneumonic effusions and malignancy. Pleural fluid ADA levels are highly sensitive with good specificity for the diagnosis of etiology of tubercular effusions. However in view of high levels of ADA in pleural fluid in parapneumonic effusions also, other measures such as clinical evaluation, lymphocyte to neutrophil ratio, and glucose levels are necessary to separate both these entities.
Características de óbitos dos pacientes internados em uma unidade de terapia intensiva de hospital terciário

Introdução: Estudos comparam taxa de óbitos de Unidade de Terapia Intensiva (UTI) no Brasil que se encontra acima da internacional, não se sabendo se isso é devido ao sistema público brasileiro ou a falhas de registro. Objetivo: Traçar uma relação entre mortalidade prevista e características dos pacientes internados na UTI de hospital terciário de grande porte do município de Fortaleza, Ceará. Métodos: Trata-se de um estudo retrospectivo, analítico e transversal sobre pacientes internados na UTI do Hospital Geral de Fortaleza de janeiro a novembro de 2016. Resultados: No período da pesquisa, foram estudados 137 pacientes; desse total vieram a óbito 31 pacientes (24,48%). De acordo com a prioridade estabelecida pela Society Critical Care Medicine para Unidade de Terapia Intensiva, tivemos 53,3% na prioridade I, 10,0% na prioridade II, 30,0% na prioridade III, 3,3% na prioridade IVA e 3,3% na prioridade IVB, resultados estatisticamente significantes (p=0,012). Constatou-se neste trabalho que 80,6% dos óbitos haviam desenvolvido sepse durante o internamento na UTI (p<0,001). Aplicando-se os escores de mortalidade APACHE-II e SOFA, observa-se que o APACHE-II com escore médio de 22 pontos e com mortalidade prevista média de 55,7 (p<0,001), enquanto, o SOFA teve média no primeiro dia de 7 pontos (p<0,001), no quinto dia de 5 pontos (p=0,004) e no décimo dia de 5 pontos (p=0,004). Conclusão: Observou-se a importância da análise epidemiológica em relação aos óbitos ocorridos em Unidades de Terapia Intensiva, visando demonstrar a necessidade de se conhecer melhor a aplicabilidade dos escores SOFA e APACHE II e as medidas que devem ser adotadas.
Challenges in Dental Imaging – Edge and Texture Analysis in Caries Detection

One of the most frequent diseases all over the world is represented by dental caries, hence it is of utmost importance to detect them as early as possible, in order to prevent massive tooth decay. Next to clinical examination, radiographic images are essential in identifying caries, especially for lesions located on the contact surface between the posterior teeth. Incipient caries are difficult to identify, sometimes even for specialists, so segmentation and detection through various image processing techniques may be useful. This paper presents an analysis of edge and texture parameters in X-rays containing caries, based on Sobel and Canny operators, Gabor filters and local binary pattern (LBP) operator. Our study set consisted in 80 X-rays from Bitewing Radiography Caries Detection Challenge 2015. Final results align with literature results and confirm the fact that caries segmentation in X-rays is a difficult task, with an average of less than 35% edge pixels correctly identified and poor results for texture segmentation; they also motivate us to refine our work in search of new algorithms and methods for caries segmentation and detection.
Use of the design and self-confidence scales in the assessment of maternal-child realistic simulation

Background: The maternal-child simulation replaces or amplifies a real experience with supervision, being able to be applied in several levels of attention to health. Objective: To evaluate the realistic maternal-child simulation through the Design and Self-confidence scales. Methodology: Descriptive cross-sectional study with a quantitative approach performed with 47 students of the Nursing course submitted to realistic simulation. After the simulation and debriefing, they answered two validated instruments that evaluated student satisfaction and self-confidence and the design of the scenario. Data were analyzed through descriptive statistics and inferential statistics. Results: The factorial analysis and the internal consistency analysis revealed good values (0.94 for the Satisfaction Scale, 0.92 for Design). The mean subscale scores for the elements of satisfaction and self-confidence ranged from 3.51 to 4.12 (from a possible score of 5); and the elements of the simulation design ranged from 3.91 to 4.51 (from a possible score of 5). Conclusion: The Scales of Design and Self-confidence revealed good indexes both of the scenario design and student self-confidence and the results were similar to other studies
Long-Term Effect of IQ Up Cognitive Development Method on the Development of Cognitive Process in Children

Present study explores whether IQ Up Cognitive Development Method has any long-term effect on the cognitive development process of healthy children. IQ Up Cognitive Development Method is a cognitive training program. Current research stands as the continuity of a previous empirical study. The research includes 74 healthy participants between 10-12 age group. In present study participants have received 8 neuropsychological tests 3 rd times measuring cognitive processes such as attention, problem solving, and general competency. Participants’ pretest neuropsychological test scores before receiving IQ Up Cognitive Development Method training and 3 rd posttest neuropsychological test scores received 11months later by the participants after the training have been compared. Statistical analysis reports revealed that there is a significant differentiation between pretest and 3 rd posttest score averages. As a cognitive training program, IQ Up Cognitive Development Method has affected cognitive process developments of the participants in a positive and long-term manner.
Initial movement of grains on a stream bed: the effect of relative protrusion

Shields (1936) found that the dimensionless shear stress necessary to move a cohesionless grain on a stream bed depended only on the grain Reynolds number. He ignored the degree of exposure of individual grains as a separate parameter. This report describes experiments to measure the dimensionless threshold stress and its dependence on grain protrusion, which was found to be very marked. The threshold stress for grains resting on the top of an otherwise flat bed in a turbulent stream was measured and found to be 0.01 –considerably less than previously-reported values of 0.03–0.06 for beds where all grains were at the same level. It is suggested that the new lower value be used in all turbulent flow situations where the bed is of natural sediments or unlevelled material. An hypothesis is proposed that the conventional Shields diagram implicitly contains variation with protrusion between the two extremes of (i) large grains and large Reynolds numbers, with small relative protrusion, and (ii) small grains, low Reynolds numbers, and protrusion of almost a complete grain diameter. In view of this, the extent of the dip in the Shields plot is explicable in that it represents a transition between two different standards of levelling as well as the transition between laminar and turbulent flow past the grains, the range of which it overlaps considerably.
Study of two-phase concurrent flow of steam and water in an unconsolidated porous medium

A combined experimental and numerical study of two-phase steam/water flow in porous media was carried out. An experiment consisting of steady state, two-phase concurrent flow of steam and water was conducted to obtain a set of relative permeability and capillary pressure curves. The experimental relative permeability and the capillary pressure curves were included in a numerical code, MULKOM, and the experiment was simulated. A good agreement between the numerical results and the experimental data was found, thus confirming the results. Upon comparing these results with gas/oil and oil/water relative permeabilities obtained by other investigators, it was found that while the wetting phase relative permeabilities in all cases are about the same, the vapor phase relative permeability is larger than the non-wetting phase relative permeabilities in gas/oil or oil/water flows. It is suggested that the phenomenon of enhanced vapor phase relative permeability is caused by channeling and/or phase transformation effects.