Miracle-dz/UniFAR-FTUs huggingface dataset:
Infection of sparrows (Passer domesticus) and different mice strains with Lawsonia intracellularis

The susceptibility of sparrows (Passer domesticus) and strains of mice (Swiss, BALB/c, C-57 and DB-A) to Lawsonia intracellularis infection was studied. Thirty-two sparrows were inoculated with pure culture of L. intracellularis and eleven received sham inoculum. Feces were collected on -1, 7, 14 and 21 days post infection (dpi) for detection of L. intracellularis by PCR. After 21 days, all sparrows were euthanized and the tissues processed for histology and immunohistochemistry (IHC). One hundred sixty mice of four different strains (n=40, per strain) were used. For each mouse strain, 16 animals received mucosa homogenate from a pig infected with L. intracellularis, 16 received pure culture of L. intracellularis and eight animals received sham inoculum. Two control and four inoculated mice from each group were euthanized on 7, 14, 21 and 28 dpi. Sections of intestine were collected for histologic analysis and IHC and pooled feces were collected for L. intracellularis PCR. None of the sparrows had any histologic lesions characteristic of proliferative enteropathy or antigen labeling by IHC. All sparrow fecal samples were negative by PCR. All mice strains studied had histopathological lesions typical of PE and IHC labeling consistent with L. intracellularis infection, especially those animals inoculated with pure culture. The most severe lesions were observed in DB-A and Swiss mice. Fecal shedding was detected in all mice strains, with peak at 14 dpi. We conclude that sparrows do not seem to be relevant in the epidemiology of L. intracellularis. The results showed variations in the lesions among the four mice strains used.
Optimal Design Criteria Standardized by the Coefficient of Variation

Many of the usual criteria for optimal experimental design do not take into account the different scale of the variance of the parameters. Dette (1997) provides a standardization based on the efficiencies for estimating each of the parameters. This leads to designs with similar efficiencies for all of the parameters. In this paper a new way of standardization by the coefficient of variation is given. For this approach even in the linear model the information matrix will depend on the parameters. Locally and Bayesian optimal designs are computed.
Health Sector Shortcomings Associated with Measles Outbreaks in Cape Town, South Africa

Measles is viewed mainly as a public health concern constituting an object of study through epidemiological methods. However, the complexity of the risk factors that drive measles outbreaks suggests the need for wider and more inclusive conceptualisation beyond conventional biomedical methods. This prompted the application of a more integrative research approach to measles outbreaks in Cape Town. The study employed a mixed-methods approach, including an epidemiological analysis of measles cases to identify and characterise measles outbreaks. Qualitative methods were used to identify and differentiate key health sector shortcomings as well as risk governance deficit areas associated with the progression of measles risk. Results underlined the value of applying an integrated epidemiological and risk governance framework. They included multi-scalar institutional shortcomings that revealed systemic issues in the management of measles outbreaks. These were wide-ranging, and indicated system-wide issues related to monitoring and early detection, reflected at national, provincial and health worker levels. The research identified recurrent shortcomings at provincial level. These included a focus/investment on mass rather than routine vaccination; as well as, poor mass immunisation planning due to systemic under-estimation of the exposed population (due to use of live births statistics and exclusion of in-migration data, which artificially inflated vaccination coverage). A further limitation was indicated by difficulties in involving key stakeholders in Outbreak Response Immunisation (ORI) and Supplementary Immunisation Activities (SIAs) (especially the Education Department). At health worker level, poor completion of case investigation forms was a recurrent deficit, constraining accurate characterisation of specific outbreaks. An emergent shortcoming following the 2009-2011 outbreak was the failure/delayed diagnosis of the index measles case, which critically delayed intervention, limiting effectiveness of ORI. These findings underline the value of applying an integrative risk lens to the understanding of epidemic risk in urban settings with high levels of population mobility/migration, where public health services face multiple pressures.
Human Leukocyte Antigen: Class I Allele Frequencies and Haplotype Distribution in a Tertiary Care Hospital in Bangladesh

Human leukocyte antigen (HLA) are cell surface glycoproteins encoded by Major Histocompatibility Complex (MHC) gene of human genome. HLA antigen frequency and haplotype distribution are useful for determining disease association, origin, migration and genetic relationship between populations and predicting the outcome of transplantation. Thus, the present study was carried out to identify HLA class I (HLA-A and HLA-B) antigen and haplotype distribution among a selected Bangladeshi population. This retrospective study was conducted among 1070 individuals, who were referred by clinicians for HLA typing at the Tissue Typing Laboratory of the Department of Virology, Bangabandhu Sheikh Mujib Medical University (BSMMU) during the period 2009 to 2011. For HLA typing, blood was collected in heparin containing tube and the laboratory tests were performed by the microlymphocytotoxicity technique according to manufacturer’s instructions. Out of 19 HLA-A and 37 HLA-B antigens tested, a total of 19/19 and 36/37 antigens were detected respectively in this study. The most frequent antigens of HLA-A and HLA-B detected were A11 (25.4%), A24 (16.6%), B75 (18.1%) and B35 (11.3%). The least antigen frequency detected for HLA-A locus were A69 (0.09%), A26 (0.28%), A34 (0.28%), while for HLA-B locus were B81 (0.09%) and B56 (0.09%). Among the HLA-A and HLA-B antigens, some alleles were found to be homozygote such as A11 (4.0%), A2 (2.7%), A24 (2.1%) and B75 (2.4%), B35 (1.8%), and B44 (1.4%) respectively. The most frequent haplotype in the study population were A11: B75 (4.9%). The most frequent antigens of HLA-A and HLA-B detected were A11 (25.4%), B75 (18.1%) respectively. The distribution of HLA haplotypes among the study population indicates that it has the influence of Oriental and Asian populations. Thus, this study will be helpful to provide valuable information for population genetics and HLA disease association analysis.
Knowledge hiding as an obstacle of innovation in organizations a qualitative study of software industry

Transmission and popularization of knowledge among personnel leads to a functional synergy in theinnovation processes of organizations. It has been found that most research on organizations are relatedto knowledge sharing, while the knowledge hiding has seldom been investigated. Firms that operatein a software industry are among the knowledge-based organizations in which employees are requiredto possess specialized knowledge and skills to perform their tasks. The dissemination of knowledgeand information can help organizations to be innovative and to improve their competitive advantage.Current study shows that information does not flow through the employees of sample organizations, andemployees prefer to hide their organizational knowledge from their colleagues in order to maintain theirown portfolios. Using thematic analysis, and interviews as data collection method, some thematic issueswere extracted. These themes include, behavioral characteristics, complexity of knowledge, Powerof requesting person, organizational incentives for knowledge sharing, Lack of clear responsibility forknowledge sharing, Sense of internal competition, level of trust to colleagues, effect of ubiquitous media,learning ability of the knowledge demandant, Level of personal contacts with colleagues, Deceivingcolleagues, Violence, Negative feedback from organizational environment.
Improving Unsupervised Subword Modeling via Disentangled Speech Representation Learning and Transformation

This study tackles unsupervised subword modeling in the zero-resource scenario, learning frame-level speech representation that is phonetically discriminative and speaker-invariant, using only untranscribed speech for target languages. Frame label acquisition is an essential step in solving this problem. High quality frame labels should be in good consistency with golden transcriptions and robust to speaker variation. We propose to improve frame label acquisition in our previously adopted deep neural network-bottleneck feature (DNN-BNF) architecture by applying the factorized hierarchical variational autoencoder (FHVAE). FHVAEs learn to disentangle linguistic content and speaker identity information encoded in speech. By discarding or unifying speaker information, speaker-invariant features are learned and fed as inputs to DPGMM frame clustering and DNN-BNF training. Experiments conducted on ZeroSpeech 2017 show that our proposed approaches achieve $2.4\%$ and $0.6\%$ absolute ABX error rate reductions in across- and within-speaker conditions, comparing to the baseline DNN-BNF system without applying FHVAEs. Our proposed approaches significantly outperform vocal tract length normalization in improving frame labeling and subword modeling.
High Speed and High Dynamic Range Video with an Event Camera

Event cameras are novel sensors that report brightness changes in the form of a stream of asynchronous “events” instead of intensity frames. They offer significant advantages with respect to conventional cameras: high temporal resolution, high dynamic range, and no motion blur. While the stream of events encodes in principle the complete visual signal, the reconstruction of an intensity image from a stream of events is an ill-posed problem in practice. Existing reconstruction approaches are based on hand-crafted priors and strong assumptions about the imaging process as well as the statistics of natural images. In this work we propose to learn to reconstruct intensity images from event streams directly from data instead of relying on any hand-crafted priors. We propose a novel recurrent network to reconstruct videos from a stream of events, and train it on a large amount of simulated event data. During training we propose to use a perceptual loss to encourage reconstructions to follow natural image statistics. We further extend our approach to synthesize color images from color event streams. Our quantitative experiments show that our network surpasses state-of-the-art reconstruction methods by a large margin in terms of image quality (<inline-formula><tex-math notation="LaTeX">$>\! 20\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>></mml:mo><mml:mspace width="-0.166667em"/><mml:mn>20</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="rebecq-ieq1-2963386.gif"/></alternatives></inline-formula>), while comfortably running in real-time. We show that the network is able to synthesize high framerate videos (<inline-formula><tex-math notation="LaTeX">$>5,000$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>5</mml:mn><mml:mo>,</mml:mo><mml:mn>000</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="rebecq-ieq2-2963386.gif"/></alternatives></inline-formula> frames per second) of high-speed phenomena (e.g., a bullet hitting an object) and is able to provide high dynamic range reconstructions in challenging lighting conditions. As an additional contribution, we demonstrate the effectiveness of our reconstructions as an intermediate representation for event data. We show that off-the-shelf computer vision algorithms can be applied to our reconstructions for tasks such as object classification and visual-inertial odometry and that this strategy consistently outperforms algorithms that were specifically designed for event data. We release the reconstruction code, a pre-trained model and the datasets to enable further research.
DAV3E – a MATLAB toolbox for multivariate sensor data evaluation

Abstract. We present DAV3E, a MATLAB toolbox for feature extraction from, and evaluation of, cyclic sensor data. These kind of data arise from many real-world applications like gas sensors in temperature cycled operation or condition monitoring of hydraulic machines. DAV3E enables interactive shape-describing feature extraction from such datasets, which is lacking in current machine learning tools, with subsequent methods to build validated statistical models for the prediction of unknown data. It also provides more sophisticated methods like model hierarchies, exhaustive parameter search, and automatic data fusion, which can all be accessed in the same graphical user interface for a streamlined and efficient workflow, or via command line for more advanced users. New features and visualization methods can be added with minimal MATLAB knowledge through the plug-in system. We describe ideas and concepts implemented in the software, as well as the currently existing modules, and demonstrate its capabilities for one synthetic and two real datasets. An executable version of DAV3E can be found at http://www.lmt. uni-saarland.de/dave (last access: 14 September 2018). The source code is available on request.
Spectrum of the Laplacian on simplicial complexes by the Ricci curvature

We define the Ricci curvature on simplicial complexes modifying the definition of the Ricci curvature on graphs, and prove some properties. One of our main results is an estimate of the eigenvalues of the Laplacian on simplicial complexes by the Ricci curvature. Another of our main results is obtaining the relation between the Ricci curvature on a simplicial complex and the Ricci curvature on a dual graph of the simplicial complex.
A performance perspective on web optimized protocol stacks: TCP+TLS+HTTP/2 vs. QUIC

Existing performance comparisons of QUIC and TCP compared an optimized QUIC to an unoptimized TCP stack. By neglecting available TCP improvements inherently included in QUIC, comparisons do not shed light on the performance of current web stacks. In this paper, we can show that tuning TCP parameters is not negligible and directly yields significant improvements. Nevertheless, QUIC still outperforms even our tuned variant of TCP. This performance advantage is mostly caused by QUIC's reduced RTT design during connection establishment, and, in case of lossy networks due to its ability to circumvent head-of-line blocking.
IN VITRO CULTURE OF MOSS BRYUM CORONATUM SCHWAEGR.(BRYACEAE) AND IT’S PHYTOCHEMICAL ANALYSIS

Objective : The purpose of the present investigation was to establish in vitro conditions for moss Bryum coronatum Schwaegr. And to carry out preliminary phytochemical screening of B. coronatum leaf extracts in different solvents. Methods: Fresh unopened, mature capsules were used as explant and surface sterilization of spores bearing capsule with different concentration of sodium hypochlorite (0.5, 1, 2, 4 and 8%) with different time duration. The Mu­rashige and Skoog (MS) medium that contains different concentration(full,1/2, 1/4 th , 1/8 th strength) with different concentration of sucrose were used for culture this moss. Phytochemical screening were carried out using ethanol, methanol and ethyl acetate leaf extract of B. coronatum to identify various constitutes using the standard procedures. Results: Surface sterilization of spores of this moss was most effective in 4% commercial bleach for 1 min sterilization. The optimum condition for germination of spores and for proper growth of gametophytes B. coronatum on MS/4 medium strength with sucrose (1.5%), at pH 5.8 and temperature 22±2oC with 16/8h: light/dark condition. Phytochemical analyses revealed the presence of alkaloids, terpenoids and saponins in all extracts. Conclusion: Four percentage NaOCl aqueous solutions are better for surface sterilization of moss sporophytes. MS/4 medium with 1.5% sucrose found the best medium for spore germination. Solvents extracts showed presence of alkaloids, terpenoids and saponins in all extracts.
Linguistic challenges in automatic summarization technology

Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse or paraphrase sections of the source document. As of today, most summarization systems are extractive. Automatic document summarization technology presents interesting challenges for Natural Language Processing. It works on the basis of coreference resolution, discourse analysis, named entity recognition (NER), information extraction (IE), natural language understanding, topic segmentation and recognition, word segmentation and part-of-speech tagging. This study will overview some current approaches to the implementation of auto summarization technology and discuss the state of the art of the most important NLP tasks involved in them. We will pay particular attention to current methods of sentence extraction and compression for single and multi-document summarization, as these applications are based on theories of syntax and discourse and their implementation therefore requires a solid background in linguistics. Summarization technologies are also used for image collection summarization and video summarization, but the scope of this paper will be limited to document summarization.
Bayesian Optimization with Binary Auxiliary Information

This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting correlated auxiliary information of binary type that can be more cheaply obtained, such as in policy search for reinforcement learning and hyperparameter tuning of machine learning models with early stopping. To achieve this, we first propose a mixed-type multi-output Gaussian process (MOGP) to jointly model the continuous target function and binary auxiliary functions. Then, we propose information-based acquisition functions such as mixed-type entropy search (MT-ES) and mixed-type predictive ES (MT-PES) for mixed-type BO based on the MOGP predictive belief of the target and auxiliary functions. The exact acquisition functions of MT-ES and MT-PES cannot be computed in closed form and need to be approximated. We derive an efficient approximation of MT-PES via a novel mixed-type random features approximation of the MOGP model whose cross-correlation structure between the target and auxiliary functions can be exploited for improving the belief of the global target maximizer using observations from evaluating these functions. We propose new practical constraints to relate the global target maximizer to the binary auxiliary functions. We empirically evaluate the performance of MT-ES and MT-PES with synthetic and real-world experiments.
Multi-Lineage Evolution in Viral Populations Driven by Host Immune Systems

Viruses evolve in the background of host immune systems that exert selective pressure and drive viral evolutionary trajectories. This interaction leads to different evolutionary patterns in antigenic space. Examples observed in nature include the effectively one-dimensional escape characteristic of influenza A and the prolonged coexistence of lineages in influenza B. Here we use an evolutionary model for viruses in the presence of immune host systems with finite memory to delineate parameter regimes of these patterns in a in two-dimensional antigenic space. We find that for small effective mutation rates and mutation jump ranges, a single lineage is the only stable solution. Large effective mutation rates combined with large mutational jumps in antigenic space lead to multiple stably co-existing lineages over prolonged evolutionary periods. These results combined with observations from data constrain the parameter regimes for the adaptation of viruses, including influenza.
On the Tractability of Public Persuasion with No Externalities

Persuasion studies how a principal can influence agents' decisions via strategic information revelation --- often described as a signaling scheme --- in order to yield the most desirable equilibrium outcome. Recently, there has been a large body of algorithmic study of designing optimal public signaling schemes, a.k. a. , public persuasion, which however is rifle with computational intractability results. In this paper, we design efficient and tight algorithms for public persuasion, and focus on a fundamental multi-agent persuasion model with no inter-agent externalities and binary actions introduced by Arieli and Babichenko. En route, we develop new algorithmic techniques which may be of independent interests. First, we prove that optimal public persuasion is fixed parameter tractable. Our main result here relies on an interesting connection to a basic question in combinatorial geometry: how many cells can $n$ hyperplanes divide $R^d$ into? We use this connection to show a new characterization of public persuasion, which then enables efficient algorithm design. Second, we relax agent incentives and show that optimal public persuasion admits a bi-criteria PTAS for monotone submodular objectives and this approximation is tight. To prove this result, we establish an intriguing "noise stability" property of submodular functions which strictly generalizes the key result of Cheraghchi et al., originally motivated by applications of learning submodular functions and differential privacy. Finally, motivated by automated persuasion implemented as software, we consider relaxing the equilibrium concept of the model to coarse correlated equilibrium. Here we use a sophisticated primal-dual analysis to establish the polynomial-time equivalence between optimal public persuasion and the combinatorial problem of directly maximizing the sender's objective minus any linear function.
Quantitative changes in protein, glycogen and fat content in the eggs of the locusts, Locusta migratoria migratorioides and Schistocerca gregaria (Orthoptera), during embryogenesis

Changes in the content of protein, glycogen, neutral lipids and sterols were investigated in locust eggs from oviposition until larval hatching. The content of all of these nutritional reserves was higher in the eggs of S. gregaria because of their larger size, although relative changes in utilization or synthesis of these materials during embryogenesis showed a more or less parallel course. The amount of protein increased progressively during embryogenesis, while glycogen and neutral lipids were successively metabolized or utilized for development of the embryo. There appeared significant relative differences in the way of utilizing energetic reserves during embryogenesis between the two species. This was especially manifested by a larger relative decrease in the content of neutral lipids (mainly triglyceride) in the eggs of S. gregaria. Conversely, the eggs of L. migratoria showed a larger relative utilization of glycogen reserves. The content of steroids was higher in the eggs of S. gregaria during the initial 6 days of embryonic development. Later on, during advanced stages of pharate larval development, the steroids were rapidly utilized and decreased in both species. The described changes in utilization of the main energetic resources were correlated with periods of tissue growth and differentiation and with the cuticulogenesis of the growing embryo.