Miracle-dz/UniFAR-FTUs huggingface dataset:
Training Continuous Space Language Models: Some Practical Issues

Using multi-layer neural networks to estimate the probabilities of word sequences is a promising research area in statistical language modeling, with applications in speech recognition and statistical machine translation. However, training such models for large vocabulary tasks is computationally challenging which does not scale easily to the huge corpora that are nowadays available. In this work, we study the performance and behavior of two neural statistical language models so as to highlight some important caveats of the classical training algorithms. The induced word embeddings for extreme cases are also analysed, thus providing insight into the convergence issues. A new initialization scheme and new training techniques are then introduced. These methods are shown to greatly reduce the training time and to significantly improve performance, both in terms of perplexity and on a large-scale translation task.
Optimization of Hidden Markov Models and Neural Networks

In this diploma thesis, combination methods for hidden markov models (HMMs) and artificial neural networks (ANNs) are examined and evaluated on several offline handwriting recognition tasks. ANN based features have been applied to automatic speech recognition and are an important component of state-of-the-art systems. The main focus of this work is the integration of recurrent neural networks (RNNs), specifically the Long-Short-Term-Memory (LSTM) which recently lead to significant improvements in several handwriting recognition tasks. A model combination of HMMs and ANNs following two different approaches is proposed, where a previously trained HMM is used to create a labeling of the input features in order to train an ANN. The choice of the input features as well as the initial labeling play an important role and the effect is analyzed in an experimental setup. Further, the model complexity of different ANN types is considered and evaluated on real world handwriting recognition tasks. Moreover, this work investigates the use of probabilistic labelings and extends the ANN based systems by an alternating optimization procedure referred to as realignment. This procedure replaces the Connectionist Temporal Classification (CTC) output layer, which enables an RNN to perform sequence learning without HMMs. The results show large improvements on two different feature sets for each of three different databases with Arabic, English and French handwriting. The combination methods with ANN based features show a superior performance compared to conventional HMMs and outperform the best published results on the English and Arabic database.
Telomere Biology and Cardiovascular Disease

Accumulation of cellular damage with advancing age leads to atherothrombosis and associated cardiovascular disease. Ageing is also characterized by shortening of the DNA component of telomeres, the specialized genetic segments located at the end of eukaryotic chromosomes that protect them from end-to-end fusions. By inducing genomic instability, replicative senescence and apoptosis, shortening of the telomeric DNA is thought to contribute to organismal ageing. In this Review, we discuss experimental and human studies that have linked telomeres and associated proteins to several factors which influence cardiovascular risk (eg, estrogens, oxidative stress, hypertension, diabetes, and psychological stress), as well as to neovascularization and the pathogenesis of atherosclerosis and heart disease. Two chief questions that remain unanswered are whether telomere shortening is cause or consequence of cardiovascular disease, and whether therapies targeting the telomere may find application in treating these disorders (eg, cell “telomerization” to engineer blood vessels of clinical value for bypass surgery, and to facilitate cell-based myocardial regeneration strategies). Given that most research to date has focused on the role of telomerase, it is also of up most importance to investigate whether alterations in additional telomere-associated proteins may contribute to the pathogenesis of cardiovascular disease.
Signs of Dehydration in Severe Childhood Malaria

percentage of women who attended ANC delivered in medical establishments ( x 2 = 26.4764; df = 1, P<O.OOl). The two variables in combination conferred an enhanced risk for NNT (OR 1.38; 95% confidence interval, 0.586-3.330). The other factors shown in Table 1 are largely dependent on the place of delivery. The incidence rate of lSjl000 live births is high by any standards. Several publications in the past have identified some risk factors for NNT. A closer look at Table 1 however, will show that the factors could be ranked into three major subgroups: low literacy level for the mothers; non attendance for ANC in the current pregnancy; and delivery in a non-medical establishment. Of the three, the first group has a dominant influence over the others. The mother who is educated beyond the primary school level is more likely to understand the rationale for ANC during pregnancy and will therefore avail herself of the benefits including tetanus toxoid vaccination. She is also less likely to delivery in a non-medical establishment. In this survey, the vast majority of deliveries among the cases occurred in homes of traditional birth attendants. Again the linkage is this; the environment in these homes is less likely to be clean, the attending personnel are less likely to have any academic training and are even less likely to observe the rules of aseptics and antiseptics. The authors believe that health intervention strategies should concentrate on activities that will promote health seeking behaviour in women, generally, and health care personnel, particularly, at the primary care level. However, eradication of mass illiteracy, particularly among women, should be given top priority.
The Tyrosine Kinase Receptor ROR1 Is Constitutively Phosphorylated in Chronic Lymphocytic Leukemia (CLL) Cells

Phosphorylation of receptor tyrosine kinases (RTKs) has a key role in cellular functions contributing to the malignant phenotype of tumor cells. We and others have previously demonstrated that RTK ROR1 is overexpressed in chronic lymphocytic leukemia (CLL). Silencing siRNA downregulated ROR1 and induced apoptosis of CLL cells. In the present study we analysed ROR1 isoforms and the phosphorylation pattern in CLL cells (n=38) applying western blot and flow-cytometry using anti-ROR1 antibodies and an anti-phospho-ROR1 antibody against the TK domain. Two major ROR1 bands with the size of 105 and 130 kDa respectively were identified, presumably representing unglycosylated (immature) and glycosylated (mature) ROR1 respectively as well as a 260 kDa band which may represent dimerized ROR1. A ROR1 band of 64 kDa that may correspond to a C-terminal fragment was also noted, present only in the nucleus. The 105 kDa ROR1 isoform was more frequently expressed in non-progressive as compared to progressive CLL patients (p=0.03). The 64, 105, 130 and 260 kDa bands were constitutively phosphorylated both at tyrosine and serine residues. Phosphorylation intensity of the mature (130 kDa) isoform was significantly higher in progressive than in non-progressive disease (p<0.001). Incubation of CLL cells with a mouse anti-ROR1 KNG or an anti-ROR1 CRD mAb respectively induced dephosphorylation of ROR1 before entering apoptosis. In conclusion CLL cells expressed different isoforms of ROR1 which were constitutively phosphorylated. The mature, phosphorylated ROR1 isoform was associated with a progressive disease stage. Targeting ROR1 by mAbs induced specific dephosphorylation and leukemic cell death. ROR1 might be an interesting therapeutic target.
Heart murmurs identification using random forests in assistive environments

The aging population in many countries, in combination with high government deficits and financial resources limitations, necessitates new methods for the home care of the elderly at reasonable costs based on the exploitation of modern information and communication technologies (ICT). This requires the installation of assistive environments at the homes of elderly people, which include various types of sensors, generating biosignals of other types of signals, which are transferred through networks to local health centers or hospitals in order to be monitored. However, scaling up the application of such ICT-based methods of elderly home care is going to increase tremendously the workload of the medical staff of local health centers or hospitals. Therefore it is of critical importance to develop capabilities for an automated first screening of these signals and identification of abnormal elements and diseases. In this direction the present paper proposes a system for the automatic identification of murmurs in heart sound signals, and also for the classification of them as systolic or diastolic, using a new generation of advanced Random Forests classification algorithms, which are aggregating the prediction of multiple classifiers (ensemble classification). The proposed system has been applied and validated in a representative global dataset of 198 heart sound signals, which come both from healthy medical cases and from cases having systolic and diastolic murmurs. Also, some alternative classifiers have been applied to the same data for comparison purposes. It has been concluded that the proposed systems shows a good performance, which is higher than the examined alternative classifiers.
Enriching user profiling with affective features for the improvement of a multimodal recommender system

Recommender systems have been systematically applied in industry and academia to help users cope with information uncertainty. However, given the multiplicity of the preferences and needs it has been shown that no approach is suitable for all users in all situations. Thus, it is believed that an effective recommender system should incorporate a variety of techniques and features to offer valuable recommendations and enhance the search experience. In this paper we propose a novel video search interface that employs a multimodal recommender system, which can predict topical relevance. The multimodal recommender accounts for interaction data, contextual information, as well as users' affective responses, and exploits these information channels to provide meaningful recommendations of unseen videos. Our experiment shows that the multimodal interaction feature is a promising way to improve the performance of recommendation.
TwiSent: a multistage system for analyzing sentiment in twitter

In this paper, we present TwiSent, a sentiment analysis system for Twitter. Based on the topic searched, TwiSent collects tweets pertaining to it and categorizes them into the different polarity classes positive, negative and objective. However, analyzing micro-blog posts have many inherent challenges compared to the other text genres. Through TwiSent, we address the problems of 1) Spams pertaining to sentiment analysis in Twitter, 2) Structural anomalies in the text in the form of incorrect spellings, nonstandard abbreviations, slangs etc., 3) Entity specificity in the context of the topic searched and 4) Pragmatics embedded in text. The system performance is evaluated on manually annotated gold standard data and on an automatically annotated tweet set based on hashtags. It is a common practise to show the efficacy of a supervised system on an automatically annotated dataset. However, we show that such a system achieves lesser classification accurcy when tested on generic twitter dataset. We also show that our system performs much better than an existing system.
Bioinformatics Meets Proteomics - Bridging the Gap between Massspectrometry Data Analysis and Cell Biology

Proteomics research programs typically comprise the identification of protein content of any given cell, their isoforms, splice variants, post-translational modifications, interacting partners and higher-order complexes under different conditions. These studies present significant analytical challenges owing to the high proteome complexity and the low abundance of the corresponding proteins, which often requires highly sensitive and resolving techniques. Mass spectrometry plays an important role in proteomics and has become an indispensable tool for molecular and cellular biology. However, the analysis of mass spectrometry data can be a daunting task in view of the complexity of the information to decipher, the accuracy and dynamic range of quantitative analysis, the availability of appropriate bioinformatics software and the overwhelming size of data files. The past ten years have witnessed significant technological advances in mass spectrometry-based proteomics and synergy with bioinformatics is vital to fulfill the expectations of biological discovery programs. We present here the technological capabilities of mass spectrometry and bioinformatics for mining the cellular proteome in the context of discovery programs aimed at trace-level protein identification and expression from microgram amounts of protein extracts from human tissues.
Imaging the Abdominal Manifestations of Cystic Fibrosis

Cystic fibrosis (CF) is a multisystem disease with a range of abdominal manifestations including those involving the liver, pancreas, and kidneys. Recent advances in management of the respiratory complications of the disease has led to a greater life expectancy in patients with CF. Subsequently, there is increasing focus on the impact of abdominal disease on quality of life and survival. Liver cirrhosis is the most important extrapulmonary cause of death in CF, yet significant challenges remain in the diagnosis of CF related liver disease. The capacity to predict those patients at risk of developing cirrhosis remains a significant challenge. We review representative abdominal imaging findings in patients with CF selected from the records of two academic health centres, with a view to increasing familiarity with the abdominal manifestations of the disease. We review their presentation and expected imaging findings, with a focus on the challenges facing diagnosis of the hepatic manifestations of the disease. An increased familiarity with these abdominal manifestations will facilitate timely diagnosis and management, which is paramount to further improving outcomes for patients with cystic fibrosis.
PEYER'S PATCHES: IMMUNOLOGIC STUDIES

The immune capabilities of the Peyer's patches have been investigated by the use of an in vitro system. Despite our failure to stimulate Peyer's patch lymphocytes in vivo it appears that Peyer's patches behave immunologically as peripheral lymphoid tissues. Cultures prepared from the dissociated Peyer's patches of normal rabbits respond to sheep erythrocytes. The response is comparable to that obtained with spleen cultures from the same animals and is not dependent on the presence of the epithelial cells which line the lumen. Similar thymic cultures do not respond. Our experiments with cultures prepared from rabbits which have received one or two injections of SRC show that the Peyer's patches contain both IgM and IgG "memory" cells which have migrated from the spleen. The concentration of these cells in the spleen remains several hundredfold higher.
Reconfiguration of Network Hub Structure after Propofol-induced Unconsciousness

Introduction:General anesthesia induces unconsciousness along with functional changes in brain networks. Considering the essential role of hub structures for efficient information transmission, the authors hypothesized that anesthetics have an effect on the hub structure of functional brain networks. Methods:Graph theoretical network analysis was carried out to study the network properties of 21-channel electroencephalogram data from 10 human volunteers anesthetized on two occasions. The functional brain network was defined by Phase Lag Index, a coherence measure, for three states: wakefulness, loss of consciousness induced by the anesthetic propofol, and recovery of consciousness. The hub nodes were determined by the largest centralities. The correlation between the altered hub organization and the phase relationship between electroencephalographic channels was investigated. Results:Topology rather than connection strength of functional networks correlated with states of consciousness. The average path length, clustering coefficient, and modularity significantly increased after administration of propofol, which disrupted long-range connections. In particular, the strength of hub nodes significantly decreased. The primary hub location shifted from the parietal to frontal region, in association with propofol-induced unconsciousness. The phase lead of frontal to parietal regions in the &agr; frequency band (8–13 Hz) observed during wakefulness reversed direction after propofol and returned during recovery. Conclusions:Propofol reconfigures network hub structure in the brain and reverses the phase relationship between frontal and parietal regions. Changes in network topology are more closely associated with states of consciousness than connectivity and may be the primary mechanism for the observed loss of frontal to parietal feedback during general anesthesia.
Epilepsy and seizure detection using statistical features in the Complete Ensemble Empirical Mode Decomposition domain

In this paper, we introduce Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to devise an effective feature extraction scheme for physiological signal analysis. Unlike its predecessors- Empirical Mode Decomposition and Ensemble Empirical Mode Decomposition, CEEMDAN resolves mode mixing problem and gives better spectral separation of the modes. To demonstrate the effectiveness of CEEMDAN based features, we apply CEEMDAN to propose an automatic epileptic seizure detection algorithm. In this work, various statistical features are extracted from the EEG signal segments decomposed by CEEMDAN and seizure classification is performed using artificial neural network. The efficacy of our feature extraction scheme is validated by statistical and graphical analyses. The overall performance of our seizure detection scheme as compared to the state-of-the-art ones is also promising.
Sélection de variables dans un cadre Bayésien de traitement de données d'IRM fonctionnelle

Le modele generatif bilineaire est l'un des cadres utilises dans l'analyse bayesienne des donnees d'Imagerie par Resonance Magnetique fonctionnelle (IRMf). De maniere standard, cette analyse suppose que le cerveau entier peut etre active par tous les types de stimuli (visuel, auditif, etc.). Cependant la specialisation fonctionnelle des regions cer ebrales montre que l'activite d'une r egion n'est due qu' a certains types de stimuli. La prise en compte de stimuli non pertinents dans l'analyse, peut degrader les resultats. Afin de faire face a ce probleme nous proposons, dans un cadre de Detection-Estimation Conjointe (DEC), une procedure de selection de variables permettant de prendre en compte cette pertinence (en terme d'activation evoquee) et de selectionner automatiquement les types de stimuli activant significativement la region cerebrale en question. Les performances de cette approche sont illustrees sur donnees reelles.
Improved Coinfection with Amphotropic Pseudotyped Retroviral Vectors

Amphotropic pseudotyped retroviral vectors have typically been used to infect target cells without prior concentration. Although this can yield high rates of infection, higher rates may be needed where highly efficient coinfection of two or more vectors is needed. In this investigation we used amphotropic retroviral vectors produced by the Plat-A cell line and studied coinfection rates using green and red fluorescent proteins (EGFP and dsRed2). Target cells were primary human fibroblasts (PHF) and 3T3 cells. Unconcentrated vector preparations produced a coinfection rate of ∼4% (defined as cells that are both red and green as a percentage of all cells infected). Optimized spinoculation, comprising centrifugation at 1200 g for 2 hours at 15°C, increased the coinfection rate to ∼10%. Concentration by centrifugation at 10,000 g or by flocculation using Polybrene increased the coinfection rate to ∼25%. Combining the two processes, concentration by Polybrene flocculation and optimized spinoculation, increased the coinfection rate to 35% (3T3) or >50% (PHF). Improved coinfection should be valuable in protocols that require high transduction by combinations of two or more retroviral vectors.
Bacterial Endophyte Colonization and Distribution within Plants

The plant endosphere contains a diverse group of microbial communities. There is general consensus that these microbial communities make significant contributions to plant health. Both recently adopted genomic approaches and classical microbiology techniques continue to develop the science of plant-microbe interactions. Endophytes are microbial symbionts residing within the plant for the majority of their life cycle without any detrimental impact to the host plant. The use of these natural symbionts offers an opportunity to maximize crop productivity while reducing the environmental impacts of agriculture. Endophytes promote plant growth through nitrogen fixation, phytohormone production, nutrient acquisition, and by conferring tolerance to abiotic and biotic stresses. Colonization by endophytes is crucial for providing these benefits to the host plant. Endophytic colonization refers to the entry, growth and multiplication of endophyte populations within the host plant. Lately, plant microbiome research has gained considerable attention but the mechanism allowing plants to recruit endophytes is largely unknown. This review summarizes currently available knowledge about endophytic colonization by bacteria in various plant species, and specifically discusses the colonization of maize plants by Populus endophytes.
No SQL, No Injection? Examining NoSQL Security

NoSQL data storage systems have become very popular due to their scalability and ease of use. This paper examines the maturity of security measures for NoSQL databases, addressing their new query and access mechanisms. For example the emergence of new query formats makes the old SQL injection techniques irrelevant, but are NoSQL databases immune to injection in general? The answer is NO. Here we present a few techniques for attacking NoSQL databases such as injections and CSRF. We analyze the source of these vulnerabilities and present methodologies to mitigate the attacks. We show that this new vibrant technological area lacks the security measures and awareness which have developed over the years in traditional RDBMS SQL systems.
The Economics of Endogenous , Climate-Driven Extreme Events and Insurance

In this article we extend the literature on climate change and economic growth in two ways. Firstly, we allow for endogenous extreme events both in strength and frequency. Secondly, we introduce insurance in this framework. We build a theoretical growth model and discuss the role of precautionary decisions, which we find to have ambiguous effects on consumption and abatement growth. We observe that the theoretical model analytically corresponds to the DICE model if we allow for a fair insurance premium which is fully internalized. We then develop a fully calibrated integrated assessment model. Our main results are that transparency of the insurance sector is the most important determinant for the evolution of the main economic and environmental variables; different Poisson paths may lead to vastly different outcomes; the expected Poisson path nearly coincides with an internalized full insurance scenario; false beliefs on the frequency of extreme events play a minor role; technical change does not lead to a qualitative change in the ordering of the paths but will be more in line with sustainability criteria. JEL Classification: Q5; O1.