Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear mountain drag and averaged pseudo‐momentum flux profiles in the presence of trapped lee waves

The linear mountain drag in the presence of trapped lee waves is calculated using a twodimensionallinear anelastic model. In many cases, it is shown that the drag is affected by thewave refraction index aloft, but remains well predicted by the drag due to hydrostatic freelypropagating mountain waves. In contrary, the vertical profile of the waves’ Reynolds stress isvery sensitive to the mean flow variation, and often decays with altitude in the steady case andin the absence of dissipation. This apparent contradiction with the conventional Eliassen-Palmrelation is simply related to the non-zonal, non-periodic geometry of the domain in which themomentum budget is calculated and to the presence of trapped lee-waves. In this context, thespatial average of the pseudo-momentum conservation equation shows that the wave drag atthe ground is equal to the wave pseudo-momentum entering in the domain through its upperand leeward boundaries. In the presence of trapped lee-waves, the amount of pseudo-momentumentering through the leeward boundary represents a significant part of the drag, and explainsthe difference between the Reynolds stress and the surface drag. In this case, the large-scaleflow does not need to be modified inside the physical domain, because the entering pseudomomentumequals the entering momentum transported by the waves across the domain boundaries.It is suggested that conventional gravity wave drag schemes can easily represent thetrapped waves by altering the large scale momentum at low level, when the waves are dissipated. DOI: 10.1034/j.1600-0870.1998. 00002.x
Constructing an Annotated Corpus of Verbal MWEs for English

This paper describes the construction and annotation of a corpus of verbal MWEs for English, as part of the PARSEME Shared Task 1.1 on automatic identification of verbal MWEs. The criteria for corpus selection, the categories of MWEs used, and the training process are discussed, along with the particular issues that led to revisions in edition 1.1 of the annotation guidelines. Finally, an overview of the characteristics of the final annotated corpus is presented, as well as some discussion on inter-annotator agreement.
Material and method Design : Descriptive longitudinal study with two years follow-up

Background and Objectives: The greatest predictor of eventual suicide is parasuicide. The aim of this research was to identify the clinical features that can identify people with high parasuicide purpose among a parasuicidal population. Methods: A total of 48 persons who had commited a parasuicide episode 2-6 years after, were interviewed and classified according Research Diagnostic Criteria. During the interviews, socio-demographic data and psychopathological features were picked up through several scales. Scores on Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Beck Hopelessness Scale (BHS) and The Plutchick Impulsivity Scale (PI) were measured. These persons were then followed-up for two years. Results: At follow-up, eight persons (16,7%) had repeated at least one parasuicide act. On comparison of repeaters and no repeaters, a higher significant differences in the final values reported in BHS was found but not significative differences were founded in the BAI, BHS or PI scores or in the proportion of the different mental illness (according RDC). An interesting finding is that in the repeaters group the average value in the IP scale tended to be lower than in the non-repeaters, although not significant. A high percentage (72.8%) related the existence of negative vital events with their suicide attempt, more often, relationship problems with the spouse (42.8%), with their parents (17.1%) or the breakdown of an interpersonal relationship (14.2%).
ReB : Balancing Resource Allocation for Iterative Data-Parallel Jobs

The under-allocation of computation resource would slow down the completion of data-parallel jobs like Hadoop and Spark applications. Hence, users are inclined to speed jobs by requiring more computation resource. In this paper, we reveal an unexpected fact that the over-allocation of computation resource would also lengthen the job completion time (JCT). We attribute the over-allocation problem to the underlying system overheads and utilize machine learning techniques to predict the optimal resource allocation for a given job. Via the prediction model, the resource manager prevents jobs from demanding excessive computation resource. Furthermore, we discuss the optimal multi-job resource allocation if the JCT of one job is known in advance as a function in terms of the resource allocation. CCS CONCEPTS • Computer systems organization Distributed architectures;
INFORMATION TECHNOLOGY TOOLS FOR BUSINESS INTELLIGENCE DEVELOPMENT IN ORGANIZATIONS

The research objective for this study is to investigate different Information Technology (IT) tools for Business Intelligence (BI) development. Firstly, the issue of BI was identified and the various ages in BI development were presented. Then, the most important IT tools used in BI development were discussed. Finally, some strengths and weaknesses of described tools were demonstrated. The study was based mainly on a critical analysis of literature, creative thinking and an interpretive philosophy. The results of this research can be used by IT and business leaders as they plan and develop BI applications in their organizations.
Assessing quality of life of idiopathic pulmonary fibrosis patients: the INSTAGE study

Idiopathic pulmonary fibrosis (IPF) is a chronic disease characterised by progressive lung interstitial fibrosis of unknown cause [1]. IPF incidence increases with older age and clinical manifestations include dry cough, exertional dyspnoea and overall progressive deterioration of patient quality of life (QOL) [1]. In the past decade, new treatment options have become available to treat IPF, such as nintedanib, an intracellular tyrosine kinase inhibitor. The INPULSIS trials evaluated the efficacy and safety of nintedanib versus placebo in 1066 IPF patients with a diffusing capacity of the lung for carbon monoxide (DLCO) of 30–79% of the predicted value [2]. These trials showed that treatment with nintedanib slowed the rate of forced vital capacity (FVC) decline but no significant difference was observed in the St George's Respiratory Questionnaire (SGRQ) score [2]. The addition of sildenafil to nintedanib treatment does not improve outcomes to a greater extent than monotherapy with nintedanib, and it is not recommended for IPF subjects http://ow.ly/Gaq530o9kEg
SOWING THE SEEDS OF MASSIVE BLACK HOLES IN SMALL GALAXIES: YOUNG CLUSTERS AS THE BUILDING BLOCKS OF ULTRACOMPACT DWARF GALAXIES

Interacting galaxies often have complexes of hundreds of young stellar clusters of individual masses ∼104–106 M☉ in regions that are a few hundred parsecs across. These cluster complexes interact dynamically, and their coalescence is a candidate for the origin of some ultracompact dwarf galaxies. Individual clusters with short relaxation times are candidates for the production of intermediate-mass black holes of a few hundred solar masses, via runaway stellar collisions prior to the first supernovae in a cluster. It is therefore possible that a cluster complex hosts multiple intermediate-mass black holes that may be ejected from their individual clusters due to mergers or binary processes, but bound to the complex as a whole. Here we explore the dynamical interaction between initially free-flying massive black holes and clusters in an evolving cluster complex. We find that, after hitting some clusters, it is plausible that the massive black hole will be captured in an ultracompact dwarf forming near the center of the complex. In the process, the hole typically triggers electromagnetic flares via stellar disruptions, and is also likely to be a prominent source of gravitational radiation for the advanced ground-based detectors LIGO and VIRGO. We also discuss other implications of this scenario, notably that the central black hole could be considerably larger than expected in other formation scenarios for ultracompact dwarfs.
Alpha-glucosidase activity of novel pyrazolobenzothiazine 5,5-dioxide derivatives for the treatment of diabetes mellitus. Invitro combined with molecular docking approach

Diabetes mellitus is a metabolic disease caused due to the improper secretion of insulin which leads to hyperglycaemia. According to the International Diabetes Federation (IDF), 371 million people are affected by diabetes worldwide, while 7 million people are suffering from this disease in Pakistan. Pyrazolobenzothiazine 5,5-dioxide derivatives have anti-bacterial, anti-inflammatory, antioxidant activity and have the potential to treat diabetes and other diseases. This study was designed to perform in-silico and in-vitro analysis of pyrazolobenzothiazine 5,5-dioxide derivatives against α- glucosidase for the treatment of diabetes mellitus. For this purpose, pyrazolobenzothiazine 5,5-dioxide derivatives were synthesized in the laboratory. Molecular docking analysis of pyrazolobenzothiazine 5,5-dioxide derivatives against α- glucosidase was achieved through Molecular Operating Environment (MOE) and ranked them based on binding affinity. Compounds with strong binding interaction were selected for invitro anti-diabetic analysis by enzyme inhibition assay against α- glucosidase using p-nitrophenyl-α--D-glucopyranoside (PNPG) as a substrate. Furthermore, the dose-response analysis was performed via Microdilution method. Compounds having strong bonding interactions with the active site and high scores were selected for in-vitro analysis. Compounds 1a, 1f, 1 g, 1 h showed strong bonding interaction with the active site of α- glucosidase and have docking score − 11.303, −10.189, −10.360, −10.160 respectively. Compound 1e, 1f, 1 g and 1 h showed IC50 at the concentration of 4.7 μM, 8.8 μM, 12.2 μM and 11.2 μM respectively in ezyme inhibition assay. The outcome of this study proved helpful to determine new antidiabetic agents to minimize diabetes complication.
Causal Identification under Markov Equivalence

Assessing the magnitude of cause-and-effect relations is one of the central challenges found throughout the empirical sciences. The problem of identification of causal effects is concerned with determining whether a causal effect can be computed from a combination of observational data and substantive knowledge about the domain under investigation, which is formally expressed in the form of a causal graph. In many practical settings, however, the knowledge available for the researcher is not strong enough so as to specify a unique causal graph. Another line of investigation attempts to use observational data to learn a qualitative description of the domain called a Markov equivalence class, which is the collection of causal graphs that share the same set of observed features. In this paper, we marry both approaches and study the problem of causal identification from an equivalence class, represented by a partial ancestral graph (PAG). We start by deriving a set of graphical properties of PAGs that are carried over to its induced subgraphs. We then develop an algorithm to compute the effect of an arbitrary set of variables on an arbitrary outcome set. We show that the algorithm is strictly more powerful than the current state of the art found in the literature.
BAS : Branch-Aware Scheduling for Iterative Data-Parallel Jobs

ABSTRACT A data-parallel job consists of multiple dependent stages and coflows. DAG scheduling like the critical path method aims at scheduling stages according to the duration of stages. Coflow scheduling like Varys and Aalo only focuses on the coflow completion and is agnostic to the stage semantics. The divergence between the DAG scheduling and coflow scheduling brings challenges on decreasing the job completion time (JCT). To address this problem, we propose the branch as the abstraction including both the stage and coflows. For decreasing the JCTs, we utilize the job-level semantics of branches to design the branch scheduling method.
Poster: BugZoo – A Platform for Studying Software Bugs

Proposing a new method for automatically detecting, localising, or repairing software faults requires a fair, reproducible evaluation of the effectiveness of the method relative to existing alternatives. Measuring effectiveness requires both an indicative set of bugs, and a mechanism for reliably reproducing and interacting with those bugs. We present BugZoo: a decentralised platform for distributing, reproducing, and interacting with historical software bugs. connects existing datasets and tools to developers and researchers, and provides a controlled environment for conducting experiments. To ensure reproducibility, extensibility, and usability, BugZoo uses Docker containers to package, deliver, and interact with bugs and tools. Adding BugZoo support to existing datasets and tools is simple and non-invasive, requiring only a small number of supplementary files. BugZoo is open-source and available to download at: https://github.com/squaresLab/BugZoo
A copula-based method for reliability sensitivity analysis of structural system with correlated failure modes

Despite many advances in the field of computational reliability analysis, the efficient estimation of the reliability and reliability sensitivity analysis of structural systems with multiple failure modes remains a persistent challenge. The key to deal with the problem lies in the correlation modelling between failure modes. In this paper, the Archimedean copulas are used as an alternative to solve the high-dimensional dependence modeling problem. The probability characteristics of failure modes are described by stochastic perturbation technique, and the reliability index is estimated with the fourth-moment standardization method. Considering that the number of the potential failure modes of the structural systems are relatively large, the probabilistic network evaluation technique is adopted to reduce the computational complexity. The sensitivity analysis is then conducted using the matrix differential technology. The numerical examples show that the applied procedure is able to efficiently consider various failure modes of structural systems in probabilistic assessment and sensitivity analysis.
Healthcare Monitoring Systems : A WBAN Approach for Patient Monitoring

Healthcare Monitoring System, which is expected to reduce healthcare expenses by enabling the continuous monitoring of patient health remotely during their daily activities in healthcare environment. Healthcare applications based on Wireless Sensor Networks are gaining high popularity in all over the world due to their features like flexibility, mobility and ease of constant monitoring of the patient in both outside and inside the body sensed as more useful. The main focus of such system is remote monitoring of patient, inside and outside the hospital room and in ICU in the sense of implantable feature for analysing the patient data. Recent developments in combining sensors, communication systems, and other fields such as cloud computing and Big Data analysis have provided the perfect tools to develop cutting edge systems for improving energy efficiency and consumption with the datasets. Smart homes, smart sensors, and Internet of Things are just a few examples of these application based technologies that will lead to more sustainable and more resilient energy systems. This research work will focus on the Wireless Sensor Networks in terms of emerging wireless technologies which means supporting infrastructure and technology and challenge design issues and as well as security, mobility and energy consumption.
An Empirical Study of Active Learning for Text Classification

Text categorization or better text classification has recently attracted the interest of several researchers, since the amount of generated documents on daily basis is vast and on many situations their manipulation is infeasible without using any appropriate Machine Learning tools. Several aspects of real-life belong to this field and much research has been made the last two decades over them. However, default learning methods do not exploit uncategorized files which are in abundance on several fields. Thus, new learning schemes are exploited for boosting learning performance of supervised algorithms. Active Learning is such a representative example, incorporating both labeled and unlabeled data and integrating human’s expertise knowledge with the obtained predictions by supervised learners. In this work, four learners are compared under two different Active Learning approaches against Random sampling, examining the efficacy of annotating unlabeled documents that verify specific queries. Classification error has been recorded for two public provided datasets highlighting the improved learning behavior of the first case. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of KES International.
Characteristics and Pathogenicity of a Virulent Aeromonas hydrophila Associated with Ulcerative Syndrome in Farmed Rainbow Trout, Oncorhynchus mykiss (Walbaum)

Raceway-cultured rainbow trout, Oncorhynchus mykiss (Walbaum), suffered skin hemorrhagic ulcers in the caudal regions of the body in March-June 2010 in Uttarakhand State, India. The advanced stage of the disease was characterized by deep ulcers and hemorrhagic frayed fins. The causative agent was identified as Aeromonas hydrophila (designated RTMCX1), a highlypathogenic fast-growing bacterium. Amplification and sequencing of the 16S rRNA gene (1304 base pair) revealed that the sequence was 99% similar to A. hydrophila subsp. hydrophila. Our strain exhibited strong s-hemolytic activity against sheep blood and rainbow trout erythrocytes, and was capable of growing in rainbow trout serum and 0.5-10% NaCl at 4.0-35°C. Mortality was 100% in experimentally-infected fish, with a median lethal dose (LD50) of 1.9 × 10 4 colony forming units/g body weight. Infiltration of inflammatory cells in the kidney and gall bladder, rhabdomyolysis in the muscle, and interstitial nephritis were the dominant histopathological changes. RTMCX1 was sensitive to a majority of 27 tested antibiotics, indicating the possibility of controlling this bacterium by antimicrobial compounds. The IJA appears exclusively as a peer-reviewed online open-access journal at http://www.siamb. org.il. To read papers free of charge, please register online at registration form. Sale of IJA papers is strictly forbidden. Sale of IJA papers is strictly forbidden.
Brown bears possess anal sacs and secretions may code for sex

Olfactory communication occurs in carnivores and many scent-mark with anal gland secretions (AGS), which contain a variety of information including sex-related cues. Currently, there is disagreement about whether bear species, other than the giant panda Ailuropoda melanoleuca, possess anal glands or anal sacs. We documented anal sacs in brown bears Ursus arctos and analyzed AGS from 17 free-ranging, sexually mature individuals using gas chromatography–mass spectrometry. We hypothesized that brown bear AGS codes for sex, as it does in giant pandas, and predicted that AGS shows sex differences in gas chromatogram (GC) profiles, number of compounds, the digital and analog coding of chemical compounds, and color. We found 90 different compounds. Our results support the predictions that male and female AGS differs in GC, analog coding and possibly color. However, we found no significant difference between sexes in number of detected compounds or in the digital coding. Our results confirm that brown bears possess anal sacs, that secretions likely relay information about sex, and suggest other chemical information critical to the bears' social system is encoded in the AGS.
Causality Analysis of Twitter Sentiments and Stock Market Returns

Sentiment analysis is the process of identifying the opinion expressed in text. Recently, it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. In this paper, we use a public dataset of labeled tweets that has been labeled by Amazon Mechanical Turk and then we propose a baseline classification model. Then, by using Granger causality of both sentiment datasets with the different stocks, we shows that there is causality between social media and stock market returns (in both directions) for many stocks. Finally, We evaluate this causality analysis by showing that in the event of a specific news on certain dates, there are evidences of trending the same news on Twitter for that stock.
Institutionalism and Culture in Strategies of Multinational Firms: The Case of Johnson Controls Inc.

The aim of this paper is to analyze both internal and external aspects that may pose a risk to the operations of the subsidiaries of the multinational Johnson Controls Inc. in U.S. and Mexico from the point of view based on institutions; the main challengeto initiate the investigation is do have some impact in the culmination of the goals both of workers and of the same organization? , how to influence the institutions in the organizational environment, for the decommissioning of some branch/subsidiary. The method used was Analytics to evaluate all the information obtained from the company and thus achieve the objectives required for this research, applied the theory based on the vision of institutions in multinational companies. For results of the analysis, we first had to get to know the real development of multinational enterprises, and then focus on the enterprise objective Johnson Controls Inc.
A Riemannian Conjugate Gradient Algorithm with Implicit Vector Transport for Optimization on the Stiefel Manifold

In this paper, a reliable curvilinear search algorithm for solving optimization problems over the Stiefel manifold is presented. This method is inspired by the conjugate gradient method, with the purpose of obtain a new direction search that guarantees descent of the objective function in each iteration. The merit of this algorithm lies in the fact that is not necessary extra calculations associated to vector transport. To guarantee the feasibility of each iteration, a retraction based on the QR factorization is considered. In addition, this algorithm enjoys global convergence. Finally, two numerical experiments are given to confirm the effectiveness and efficiency of presented method with respect to some other state of the art algorithms.
Visualization Tool for Sensor Data Fusion

In recent years researchers has focused on the development of techniques for multi-sensor data fusion systems. Data fusion systems process data from multiple sensors to develop improved estimate of the position, velocity, attributes and identity of entities such as the targets or entities of interest. Visualizing sensor data from fused data to raw data from each sensor help analysts to interpret the data and assess sensor data fusion platform, an evolving situation or threats. Immersive visualization has emerged as an ideal solution for exploration of sensor data and provides opportunities for improvement in multi sensor data fusion. The thesis aims to investigate possibilities of applying information visualization to sensor data fusion platform in Volvo. A visualization prototype is also developed to enables multiple users to interactively visualize Sensor Data Fusion platform in real-time, mainly in order to demonstrates, evaluate and analyze the platform functionality. In this industrial study two research methodologies were used; a case study and an experiment for evaluating the results. First a case study was conducted in order to find the best visualization technique for visualizing sensor data fusion platform. Second an experiment was conducted to evaluate the usability of the prototype that has been developed and make sure the user requirement were met. The visualization tool enabled us to study the effectiveness and efficiency of the visualization techniques used. The results confirm that the visualization method used is effective, efficient for visualizing sensor data fusion platform.