Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovative approach to design of corporate clothing for the staff of SPIRIT Slovenia

The main aim of this article is to present the complex challenges of design and development of the corporate clothing which represented the image of Slovenia at the international trade fair Expo Milano 2015. Details of uniforms are defi ned in various aspects: superior protection of the body, functionality, design, aesthetics, quality, communications power, cost and other areas. To achieve those aspects we needed to use different methods for making the concept fi rst, then getting from concept to design and later on to the production of corporate clothing outfi t. Designing corporate clothing is a challenge from many points of view, thus innovative approaches are always needed. In the example given here we needed to design a clothing image that corresponds to all requirements and needs of the subscriber and prepare a convincing and unique concept. In the fi rst phase we needed to observe the situation to gain an understanding of what the subscriber needed, research the possibilities of implementation of design, and experiment with different materials. It was a challenge to manage the collaboration between subscriber, designer, suggested manufacturer and faculty. As a result, we developed and made corporate clothing in the company Moda Mi&Lan d.o. o. , which is equipped with the necessary accessories for the technique of air lace in the shape of Slovenia, made by students. This project shows a good example of story and products, created by successful collaboration of experts in different areas of working. Comfortable corporate clothing satisfi ed the end user for different weather conditions, dependent on place and time, whilst refl ecting many stories about Slovenia.
Computational Models of Interactional

People express their opinions on blogs and other social media platforms. Automated ways to categorize views of people in such user-generated corpora is of immense value. My thesis aims to develop computational models to learn to predict the stance of users who are active on social media platforms like Twitter. In particular, I explore machine learning approaches for stance learning, which involves learning people’s opinion about a topic of interest. Most existing studies on stance learning take a simplistic view that assumes a ‘sentence’ (like a Tweet) holds a perspective that is independent of the context and the author. This approach to stance learning ignores the complex activities and interactions of social media users. According to one definition of stance, it is a public act of evaluating objects, positioning subjects, and aligning with other subjects. In the same spirit, I approach stance learning in the broader context of social action wherein authors take a stance to position themselves on topics of interests, thereby aligning with other stance takers. This approach, therefore, brings a new direction to the stance learning problem which is grounded in social theory and is more amenable to analyzing conversations on social media. In this thesis, I plan to develop models for different interactions, and then find ways to combine them to improve the stance prediction accuracy. I would like to apply these models to quantify polarization in online communities.
No 8 ALGEBRAIC PROPERTIES OF TRANSLATIONAL SHAPE INVARIANT POTENTIALS IN ARBITRARY STEPS

Within the framework of supersymmetric quantum mechanics, we investigate the general algebraic properties of translational shape invariant potentials in arbitrary k steps, in which the k remainders Rs(am) are analytic functions of the parameter am that is related to others by translation: am = am−1 + δ. The present study is based on the fact that the simplified potential algebra of shape invariance condition in k steps is equivalent to that of generalized deformed oscillators with a built-in Zk-grading structure. We shall show that, despite the complexity in the study, the general algebraic properties still can be systematically determined.
Predictors of Maternal DepressionManagement among Primary Care Physicians

Purpose. The present surveillance study examined predictors of the management of maternal depression in primary care settings. Methods. A total of 217 physicians completed a 60-item survey assessing demographics, physicians’ attitudes, beliefs, efficacy, current practices, and perceived barriers regarding the management of maternal depression. Structural equation modeling was used to estimate a model that examined the relationships among physicians’ knowledge, beliefs, self-efficacy, perceived barriers, past training toward and current management practices for maternal depression. Results. In a model predicting physician depression management practices, a good overall fit was observed (χ2 = 136.63, CFI = .97, TLI = .95, RMSEA = .05), with physician comfort with, confidence in, and perceived responsibility for managing maternal depression all having prominent positive associations. Conclusions. These findings will guide the development of future multifaceted intervention strategies to enhance physician skills in managing maternal depression in primary care settings.
Evidence from the Finnish market

This study examines the ex-dividend day trading behavior of all investors in the Finnish stock market. Consistent with dynamic dividend clientele theories, investors with a preference for dividend income buy shares cum-dividend and sell ex-dividend; the reverse is true for investors with the opposite preference. Investors also engage in overnight arbitrage, earning on average a 2% overnight return on their invested capital. Trades at the investor-level reveal that idiosyncratic risk is an important determinant in the choice of stock for short-term ex-day trading. Furthermore, transaction costs and dividend yield jointly determine whether the volume of short-term trading activity is nonzero. JEL Classifications: G35
A MULTI-ROBOT SYSTEM FOR ASSEMBLY TASKS IN AUTOMOTIVE INDUSTRY

This paper describes the virtual assembly automation systems as decision support systems, for the better knowledge of assembly operation. The study of this procedure is described as well as the necessity for assembly systems design. The work draws on research into product and manufacturing knowledge models, and uses the cases study based on a simplified virtual assembly lines realized in Delphi programming environment. A virtual prototype has been implemented to testify the feasibility of the presented methodology for the assembly of mechanical objects. The paper describes with enough detail the adopted solutions used to perform those tasks, giving special attention to the software designed to supervise the system. To support robot work simulation, a simulator environment is developed. In this paper, the authors have proposed the virtual prototype models for assembly systems architecture of robotic assembly automation in automotive industry.
Comparisons versus Planning Game Partitioning - Experiments on Requirements Prioritisation Techniques", Empirical Software Engineering: An International Journal, Vol. 12, No. 1, pp. 3-33, 2007. Pair-wise comparisons versus planning game partitioning—experiments on requirements prioritisation techniq

The process of selecting the right set of requirements for a product release is dependent on how well the organisation succeeds in prioritising the requirements candidates. This paper describes two consecutive controlled experiments comparing different requirements prioritisation techniques with the objective of understanding differences in time-consumption, ease of use and accuracy. The first experiment evaluates Pair-wise comparisons and a variation of the Planning game. As the Planning game turned out as superior, the second experiment was designed to compare the Planning game to Tool-supported pair-wise comparisons. The results indicate that the manual pair-wise comparisons is the most time-consuming of the techniques, and also the least easy to use. Tool-supported pair-wise comparisons is the fastest technique and it is as easy to use as the Planning game. The techniques do not differ significantly regarding accuracy.
Decomposition Methods for Solving Two-Stage Distributionally Robust Optimization Problems

Decomposition methods have been well studied for solving two-stage and multi-stage stochastic programming problems, see [30, 33, 34]. In this paper, we propose an algorithmic framework based on the fundamental ideas of the methods for solving two-stage minimax distributionally robust optimization (DRO) problems where the underlying random variables take a finite number of distinct values. This is achieved by introducing nonanticipativity constraint for the first stage decision variables, rearranging the minimax problem through Lagrange decomposition and applying the well-known primal-dual hybrid gradient (PDHG) method to the new minimax problem. The algorithmic framework does not depend on specific structure of the ambiguity set. To extend the algorithm to the case that the underlying random variables are continuously distributed, we propose a discretization scheme and quantify the error arising from the discretization in terms of the optimal value and the optimal solutions when the ambiguity set is constructed through generalized prior moment conditions, the Kantorovich ball and φdivergence centred at an empirical probability distribution. Some preliminary numerical tests show the proposed decomposition algorithm featured with parallel computing performs well.
Hydrogen Solubility in Pd 3 Ag Phases from First-Principles Calculation

First-principles calculation was used to systematically investigate hydrogen solubility in Pd3Ag phases. It was found that the solubility of hydrogen in Pd3Ag phases was much greater than in face-centered cubic (FCC) Pd, suggesting that Ag atoms enhanced hydrogen solubility with respect to FCC Pd. In addition, the present calculation also revealed that the anti-site defect formation enthalpies of Pd3Ag were close to zero, and the values of vacancy were positive and large, which indicated that Pd3Ag distributed compactly. In the process of hydrogen separation, anti-site defects decreased the hydrogen solubility in the Pd3Ag phases, i.e., the ordered Pd3Ag phases bestowed excellent properties of H selectivity. The results presented not only explore the fundamental properties of Pd3Ag phases and their various potential applications, but also agree with experimental observations reported in the literature.
Title Positive observers for positive interval linear discrete-time delaysystems

Linear matrix inequalities (LMIs) provide a powerful analysis and synthesis framework for linear systems. In this paper, we use LMIs to develop positive observers for positive linear discrete-time (PLDT) systems with both parameter uncertainties and time delay. Specifically, we first present some equivalent conditions for the asymptotic stability of positive linear discrete-time delay systems, which will be employed to design the positive observers. Then, necessary and sufficient conditions are proposed to check the existence of positive observers for interval PLDT systems with time delay when the positivity of the error signals is considered, and the observer matrices to be constructed can be easily obtained through the solutions of LMIs. Finally, a numerical example is provided to demonstrate the efficacy of the proposed approach.
Chapter 2 The Dynamics of Productivity Change : A Review of the Bottom-Up Approach

This paper considers the relation between (total factor) productivity measures for lower level production units and aggregates thereof such as industries, sectors, or entire economies. In particular, this paper contains a review of the socalled bottom-up approach, which takes an ensemble of individual production units, be it industries or enterprises, as the fundamental frame of reference. At the level of industries the various forms of shift-share analysis are reviewed. At the level of enterprises the additional features that must be taken into account are entry (birth) and exit (death) of production units.
Online Detection of Anomalous Sub-trajectories : A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor

Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to local sub-trajectory anomalies. Third, they involve tuning of many parameters and may suffer from high false alarm rates. The main contribution of this paper is the proposal and discussion of the Sliding Window Local Outlier Conformal Anomaly Detector (SWLO-CAD), which is an algorithm for online detection of local sub-trajectory anomalies. It is an instance of the previously proposed Conformal anomaly detector and, hence, operates online with well-calibrated false alarm rate. Moreover, SWLO-CAD is based on Local outlier factor, which is a previously proposed outlier measure that is sensitive to local anomalies. Thus, SWLO-CAD has a unique set of properties that address the issues above.
Development of supervised classification Techniques for multivariate chemical data

DEVELOPMENT OF SUPERVISED CLASSIFICATION TECHNIQUES FOR MULTIVARIATE CHEMICAL DATA This dissertation is composed by a theoretical contribution about the development of supervised classification techniques for application using multivariate chemical data. For this, chemometric techniques based on quadratic discriminant analysis (QDA) and support vector machines (SVM) were built combined with principal component analysis (PCA), successive projections algorithm (SPA) and genetic algorithm (GA) for supervised classification using data reduction and feature selection. These techniques were employed in analyzing first-order data, composed by attenuated total reflectance Fourier transform infrared spectroscopy (ATRFTIR) and mass spectra obtained from liquid chromatography time of flight (LC/TOF) and surface-enhanced laser desorption/ionization time of flight (SELDI/TOF). ATR-FTIR data were used to differentiate two classes of fungus of Cryptococcus gene, whereas the mass spectra data was used to identify ovarian and prostate cancer in blood serum. In addition, new twodimensional discriminant analysis techniques based on principal component analysis linear discriminant analysis (2D-PCA-LDA), quadratic discriminant analysis (2D-PCA-QDA) and support vectors machine (2D-PCA-SVM) were developed for applications in second-order chemical data composed by excitation-emission matrices (EEM) molecular fluorescence of simulated and real samples. The results show that the developed techniques had better classification performance for both first and second-order data, with classification rates, sensitivity and specificity reaching values between 90 to 100%. Also, the developed twodimensional techniques had overall performance superior than traditional multivariate classification methods using unfolded data, showing its potential to other future analytical
Anxiety and depression during pregnancy in Central America

Background: Around the world, maternal psychopathology during pregnancy is associated with a range of negative consequences for mother and child. Nevertheless, in Central America the magnitude of this public health problem is still unknown. The objective of this first explorative study was to investigate the prevalence and severity of anxiety and depression during pregnancy in the Central American developing country Nicaragua, as well as the availability of mental health care and to compare with a developed country. Methods: A population-based cohort of pregnant women in Nicaragua (N = 98) was compared with a parallel cohort in the Netherlands (N = 4725) on symptoms of anxiety (Spielberger State Trait Anxiety Inventory) and depression (Edinburgh Postnatal Depression Scale). Associations with the women’s knowledge how to reach professional psychological support were assessed using multivariable linear regression analyses. Results: Of the Nicaraguan women, 41 % had symptoms of anxiety and 57 % symptoms of depression, versus 15 % and 6 % of the Dutch women. Symptom scores of both anxiety and depression were significantly higher in Nicaragua (p < 0.001). However, only 9.6 % of the women indicated that professional psychological help was available for the Nicaraguan pregnant women, which was associated with an increased anxiety score. Conclusions: In Nicaragua, both prevalence and severity of symptoms of antenatal anxiety and depression are substantially higher than in developed countries. However, availability of psychological help is very limited for pregnant Nicaraguan women. These findings indicate that there is need for further research and support for these women, to prevent negative consequences for both mother and child.
Estimating Learnability in the Sublinear Data Regime ∗ Weihao Kong

We consider the problem of estimating how well a model class is capable of fitting a distribution of labeled data. We show that it is often possible to accurately estimate this “learnability” even when given an amount of data that is too small to reliably learn any accurate model. Our first result applies to the setting where the data is drawn from a d-dimensional distribution with isotropic covariance, and the label of each datapoint is an arbitrary noisy function of the datapoint. In this setting, we show that with O( √ d) samples, one can accurately estimate the fraction of the variance of the label that can be explained via the best linear function of the data. For example, in setting where the label is a linear function of the datapoint plus independent noise, the variance of the noise can be accurately approximated given O( √ d) samples, which is information theoretically optimal. We extend these techniques to the setting of binary classification, where we show that in an analogous setting, the prediction error of the best linear classifier can be accurately estimated given O( √ d) labeled samples. Note that in both the linear regression and binary classification settings, even if there is no noise in the labels, a sample size linear in the dimension, d, is required to learn any function correlated with the underlying model. We further extend our estimation approach to the setting where the data distribution has an (unknown) arbitrary covariance matrix, allowing these techniques to be applied to settings where the model class consists of a linear function applied to a nonlinear embedding of the data. Finally, we demonstrate the practical viability of these approaches on synthetic and real data. This ability to estimate the explanatory value of a set of features (or dataset), even in the regime in which there is too little data to realize that explanatory value, may be relevant to the scientific and industrial settings for which data collection is expensive and there are many potentially relevant feature sets that could be collected.
Threonine Kinases Staphylococcal Infections Heparin Alkaline Phosphatase Brain Diseases Hypoglycemic Agents Actins Creatinine Intellectual Disability Cross Infection Sepsis Genetic Markers Glutathione Brain Injuries NF-kappa B Antifungal Agents Lymphoma Immunoglobulin M Hydrogen Peroxide Acetylcholi

Representation learning techniques, particularly applied deep architectures, can learn effective representations of data, which is important for the performance of machine learning and is also valuable for us to understand large-scale data. However, few visualization techniques were developed for visualizing patterns, especially temporal patterns, in high-dimensional representation data. We introduce a general approach for visualizing temporal patterns in representation data based on t-SNE. We adapted t-SNE for reducing the dimensionality of temporal representation data and proposed a visualization framework to display the temporal data. We also illustrated a case study of a set representation data produced by word embedding technique to demonstrate our proposed visualization.
Rule 3 : Plan Ahead , Really Ahead

1 Department of Statistics, Machine Learning Department, and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, 2 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America, 3 Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America, 4 Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America, 5 Department of Statistics and Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, United States of America, 6 Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
Accelerator Templates and Runtime Support for Variable Precision CNN

Deep learning algorithms are playing an important role in computer vision and image recognition tasks. At the very outset, one can view neural networks as an function approximator. Recent trends in deep learning involves exploring lower precision operations for increasing performance and energy efficiency. To this end, In this paper we provide variable precision accelerator template in FPGA and its corresponding software and runtime stacks that interfaces with these accelerators. We first implement the prototype CNN template to support FP-32 based weights and activations. We then extend the design to support Int16, Int8 ,Int4 ,1-bit and ternary weights with Fp-32 and Int8 activations in Intel HARPv2 platform. These templates can be extended to any arbitrary precisions. We then design runtime and API for dynamically reconfiguring the precision. In terms of performance and energy efficiency, these variable precision template accelerators provides (24 Tops, 545.5 Gops/W),(6.3 Tops, 141 Gops/W), (3.1 Tops, 70 Gops/W) and (1.6 TOPS, 40 Gops/W) for binary, Int4, Int8 and Int16 precisions respectively. To the best of our knowledge, these measured numbers are the state of the art for Arria 10 class of FPGA devices.
Biochemical and Molecular Roles of Nutrients Dietary Long-Chain ( n-3 ) Fatty Acids Facilitate Immune Cell Activation in Sedentary , but not Exercise-Trained Rats 1 , 2 , 3

Dietary long-chain (n-3) fatty acids from fish oil and low intensity exercise have been reported, independently, to inhibit tumor growth in rats. The mechanism for these effects is not known but may be related to diet and exercise-induced alterations in immune function. To study the individual and combined effects of these interventions on anticancer immune responses, healthy Fischer 344 rats were fed, for 4 wk, one of two semipurified diets (polyunsaturated to saturated fatty acid ratio Å 0.9), which differed only in the composition of fat (200 g/kg) and provided long-chain (n-3) fatty acids at 0 or 33 g/kg of total fat. Rats were randomly assigned to groups in a 2 1 2 experimental design to swim 3 h/d or to remain sedentary. For sedentary rats, dietary (n-3) fatty acids increased (Põ 0.05) splenic natural killer (NK) cell cytotoxicity and the percentage of activated (CD71 ) T and B cells and macrophages in spleen after concanavalin A stimulation. For exercise-trained rats, feeding the high (n-3) diet decreased (P õ 0.05) the percentage of CD71 T helper and B cells after stimulation. NK cell cytotoxicity, and the percentages of CD71 T cells, B cells and macrophages after stimulation in the high (n-3)fed exercise-trained group were not different than those of the low (n-3)-fed sedentary group. Thus individually, but not in combination, long-chain (n-3) fatty acids and low intensity exercise may be advantageous by augmenting cell-mediated immune function and NK cell cytotoxicity in healthy rats. J. Nutr. 128: 498–504, 1998.