Miracle-dz/UniFAR-FTUs huggingface dataset:
Women Empowerment and Domestic Abuse : Theory and Experimental Evidence from Vietnam

Intimate partner violence is an important global health problem that policy makers seek to address by a variety of interventions, including efforts to promote “women empowerment. ” We use data from an RCT in Vietnam and find that this strategy may backfire: women who participated in a gender and entrepreneurship training suffer more frequent abuse than women in the control group. Guided by theory, we conjecture increased female income is the mechanism linking the training to domestic violence. We also make a methodological contribution, and show that the outcomes of our impact analysis depend on how we measure intimate partner violence.
ABSTRACTS OF TALKS OF 15th WORKSHOP: NON-COMMUTATIVE HARMONIC ANALYSIS, 23-29.09.2012, Będlewo, Poland

S OF TALKS OF 15th WORKSHOP: NON-COMMUTATIVE HARMONIC ANALYSIS, 23-29.09. 2012, Będlewo, Poland 1. Helene Airault (Université de Picardie Jules Verne, Insset & LAMFA) Vector fields and Laplacians related to the infinitesimal representation of the symplectic group Abstract: For the infinitesimal holomorphic representation of Sp(2n), the symplectic group of order n, into the set Dn of symmetric complex n×n matrices, let ρ(v) = L(v)+β(v) I, the first order differential operator on Dn associated to the element v in the Lie algebra G of Sp(2n). We show the existence of a basis (ek) in the Lie algebra G and of constants (ak) such that the operator ∑ k akρ(ek) 2 is equal to the multiplication by a constant. We discuss the operators on Dn of the form ∑ k akρ(ek)L(ek) where L(ek) is the complex conjugate of L(ek) and show that the Kählerian Laplacian on Dn is expressed as ∑ k akL(ek)L(ek). The imaginary part of the vector field ∑ k akβ(ek)L(ek) is divergence free for the measure of the holomorphic representation. This extends for the symplectic group, identities obtained for the Poincaré disk in [1], [2]. For the infinitesimal holomorphic representation of Sp(2n), the symplectic group of order n, into the set Dn of symmetric complex n×n matrices, let ρ(v) = L(v)+β(v) I, the first order differential operator on Dn associated to the element v in the Lie algebra G of Sp(2n). We show the existence of a basis (ek) in the Lie algebra G and of constants (ak) such that the operator ∑ k akρ(ek) 2 is equal to the multiplication by a constant. We discuss the operators on Dn of the form ∑ k akρ(ek)L(ek) where L(ek) is the complex conjugate of L(ek) and show that the Kählerian Laplacian on Dn is expressed as ∑ k akL(ek)L(ek). The imaginary part of the vector field ∑ k akβ(ek)L(ek) is divergence free for the measure of the holomorphic representation. This extends for the symplectic group, identities obtained for the Poincaré disk in [1], [2].
Studies on evaluation of resistance in common ixodid tick : A preliminary study

Rhipicephalus (Boophilus) microplus, commonest tick species in India, cause significant economic losses to dairy and leather industries by adversely affecting the milk production and quality of hides. Tick control strategy involves mainly the use of synthetic acaricides which results into development of acaricide resistance. In the present study, the resistance status of Rhipicephalus (Boophilus) microplus against commonly used acaricides was evaluated by standardized method of FAO. The tick samples were collected from cattle owned by livestock holders to determine the status of development of acaricide resistance to Deltamethrin and cypermethrin using larval packet test. The results indicated that isolates of R (B) microplus has developed resistance to deltamethrin and cypermethrin. The data on field status of acaricide resistance will be helpful to adopt suitable strategy to overcome the process of development of resistance in ticks.
Readability Analysis of Bengali Literary Texts

ABSTRACT In this paper we propose a set of novel regression models for readability scoring in Bengali language, which can also be used for Hindi, making use of several lexical, surface-level, syntactic and semantic features. We perform 5-fold and leave-one-out cross-validation on a human-annotated gold standard dataset of 30 passages, written by 4 eminent Bengali litterateurs. On this dataset, our best model achieves a mean squared error (MSE) of 57%, which is better than state-of-the-art results (73% MSE). We further perform feature analysis to identify potentially useful features in learning a regression model for Bengali readability. Ablation studies indicate the importance of compound characters (Juktakkhors) in readability assessment.
Waste Reduction Applying Lean Thinking and Six Sigma Tools in Steel Industry

This study presents an operational interruption analysis in bars and profiles rolling mills, developed with the support of a steel company. The study was performed by analyzing the operational interruption records related to guidance equipment in order to identify the fundamental causes of the interruptions, helping in their subsequent minimization or even elimination. The analysis included the equipment assembly and maintenance parameter, physical conditions and performance during operation. The equipment assembly and maintenance cell has undergone a layout change aiming higher functionality, the equipment was standardized, and the employees were trained by the manufacturers. The applied solutions have been proven to be effective with superior results than 35% of reduction and tending to stabilize the process.
Dimension Reduction Supervised Feature Selection Feature Transformation Unsupervised Feature Selection Feature Transformation

Large textual corpora are often represented by the document-term frequency matrix whose elements are the frequency of terms; however, this matrix has two problems: sparsity and high dimensionality. Four dimension reduction strategies are used to address these problems. Of the four strategies, unsupervised feature transformation (UFT) is a popular and efficient strategy to map the terms to a new basis in the document-term frequency matrix. Although several UFTbased methods have been developed, fuzzy clustering has not been considered for dimensionality reduction. This research explores fuzzy clustering as a new UFT-based approach to create a lower-dimensional representation of documents. Performance of fuzzy clustering with and without using global term weighting methods is shown to exceed principal component analysis and singular value decomposition. This study also explores the effect of applying different fuzzifier values on fuzzy clustering for dimensionality reduction purpose.
a ) PaleoDeepDive Dataflow ( b ) Quality Assessment ( c ) Scientific Application : Biodiversity

The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.
DRAFT 0 Unsupervised Rare Pattern Mining : A Survey

Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare patterns in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution we give a general framework for categorizing algorithms: Apriori and Tree based. We highlight the differences between these methods. Finally we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.
Legend of Wrong Mountain : Full Generation of Traditional Chinese Opera Using Multiple Machine Learning Algorithms

As one of the oldest forms of Chinese Opera since the 16th century, Kunqu features literary virtuosity in its scripts, sophisticated vocal techniques in its singing, emotional and elegant yet rigorous bodily motions and facial expressions in its performance. Over its history, Kunqu has developed established modes and patterns, which makes it especially suitable for neural networks to learn. In order to generate Kunqu literature and performance computationally, we applied machine learning techniques such as LSTM [4], pix2pix [5], pix2pixHD [9], RNNs [2], Markov Chain, OpenPose [7] and Detectron [3], as well as tools such as Canny Edge [1], PIL and SketchUp. Our result, Legend of Wrong Mountain, is novel twofold: it is the first and only generated Opera at the time of this writing, and it is a machine’s attempt at Gesamtkunstwerk [8], “the Total Artwork”. It explores the marriage between contemporary technologies and traditional art form. By studying historical scripts, musical notations and traditional methods for creating Kunqu, we tweaked existing algorithms and devised new ones to conform to the traditional rules and norms as closely as possible. We present this project as a video accompanied by audio.
Uncertainty in Robotics - Tractable Inference and Policy Optimization

English The goal in robotics is to bring the robot and the world from some initial state into a desired state. For instance, we may want the robot to move to a particular place, or we may want it to arrange a set of external objects in a specific way. In order to achieve this, the robot needs to physically interact with the world. However, our models describing this interaction are an imperfect representation of reality and their predictions are hence subject to a large degree of uncertainty. In this thesis we investigate two key problems which arise as a consequence of this uncertainty. First, we consider the filtering problem, i.e. estimating the underlying state of our robot and the world given sensory information. We analyze the limitations of the two most widely used filtering methods in robotics, the particle filter (PF) and the Gaussian filter (GF), and we propose according extensions. The key problem of the PF is the exponential growth of the computational cost with the state dimension. We identify certain independence structures among the state and observation variables which occur in robotics, and we show how PFs can be extended to exploit those. These extensions allow us to apply PFs to such systems even if they have a high-dimensional state-space. In contrast, the main drawback of the GF is that its parametric form can be too restrictive to accurately capture the dependences in systems with nonlinear observation models. We show how the GF can be seen as the optimal approximation to the exact posterior distribution, subject to some constraints. This new perspective then motivates extensions to relax this constraint, which can greatly improve the filtering accuracy in nonlinear systems. As a concrete application for the proposed PF and GF extensions, we consider the problem of 3D object and manipulator tracking using depth cameras. We evaluate the proposed tracking methods extensively to show their accuracy under difficult conditions, such as heavy occlusions and fast motion. A second consequence of the uncertainty in our physics model is that we are not able to accurately predict how well a certain controller will perform on the real robot. Unfortunately, in robotics problems the number of experiments required to fully explore the parameter space of a controller is often prohibitively large. Hence, we may not be able to identify the global
Dataset Generation Using Stochasic Optimization Liquid Outflow Location Mean Trajectory Prior Optimization-based Feedback Motion Planner

We present a novel motion planning algorithm for pouring a liquid body from a source to a target container. Our approach uses a receding-horizon optimization strategy that takes into account liquid dynamics and various other constraints. In order to handle liquid dynamics without costly fluid simulations, we use a neural network to infer a set of key liquid-related parameters from the observation of current liquid configuration. To train the neural network, we generate a dataset of successful pouring examples using stochastic optimization in a problem-specific search space. These parameters are then used in the objective function for trajectory optimization. Our feedback motion planner achieves real-time performance, and we observe a high success rate in our simulated 2D and 3D liquid pouring benchmarks.
Multistep prediction error decomposition in DSGE models : estimation and forecast performance ∗

DSGE models are of interest because they offer structural interpretations, but are also increasingly used for forecasting. Estimation often proceeds by methods which involve building the likelihood by one-step ahead (h = 1) prediction errors. However in principle this can be done using different horizons where h > 1. Using the well-known model of Smets and Wouters (2007), for h = 1 classical ML parameter estimates are similar to those originally reported. As h extends some estimated parameters change, but not to an economically significant degree. Forecast performance is often improved, in several cases significantly. JEL Code: C5
OO-logic: a Successor of F-logic

Object oriented logic (abbreviated: OO-logic) combines the advantages of conceptual modelling that comes from object-oriented framebased languages with the declarative style, compact and simple syntax, and the well defined semantics of a logic-based language. OO-logic supports typing, meta-reasoning, complex objects, properties, classes, inheritance, rules, queries, modularization, and scoped inference. In this paper we describe the capabilities of knowledge representation systems based on OO-logic and illustrate the use of this logic for ontology specification. OO-logic is a successor of F-logic. It incorporates the experience of a decade of using F-logic in real life applications. OO-logic simplifies the syntax of F-logic and has its focus on rule based reasoning with large sets of data (billions of triples).
Spatio-Temporal Multisensor Calibration based on Gaussian Processes Target Tracking

Perception is one of the key abilities of autonomous mobile robotic systems, which often relies on fusion of heterogeneous sensors. Although this heterogeneity presents a challenge for sensor calibration, it is also the main prospect for reliability and robustness of autonomous systems. In this paper, we propose a method for multisensor calibration based on Gaussian processes estimated target trajectories, resulting with temporal and extrinsic parameters. The advantage of the proposed method is computationally efficient coordinate frame invariant temporal calibration based on the batch estimated velocity magnitudes and theoretically grounded trajectory interpolation, which in turn, thereafter, enables 3D point-to-point extrinsic multisensor calibration. The proposed method is validated both in simulation and real-world experiments on an externally triggered stereo camera, thus having temporal ground truth readily available. The results show that the estimated time delays are accurate up to a fraction of the sampling time of the fastest sensor. Moreover, the extrinsic parameters are also estimated and compared to the Kalibr stereo camera calibration toolbox.
Is neoliberalism to blame for Orbàn and Le Pen ? A statistical analysis of populism and economic freedom

This briefing examines one particular hypothesis: that free-market policies such as low taxes and free trade have spurred populism. I show that these claims have no support in real world data. There is no positive association between economic freedom and populism on the country level – if anything, there appears to be a negative association, although the estimates are not statistically significant in most cases.
Evaluating Systems and Benchmarks for Archiving Evolving Linked Datasets

As dynamicity is an indispensable part of Linked Data, which are constantly evolving at both schema and instance level, there is a clear need for archiving systems that are able to support the efficient storage and querying of such data. The purpose of this paper is to provide a framework for systematically studying the state-of-art RDF archiving systems and the different types of queries that such systems should support. Specifically, we describe the strategies that archiving systems follow for storing multiple versions of a dataset, and detail the characteristics of the archiving benchmarks. Moreover, we evaluate the archiving systems, and present results regarding their performance. Finally, we highlight difficulties and open issues arisen during experimentation in order to serve as a springboard for researchers in the Linked Data community.
Functional cloning of TRS 31 , TRS 120 and TRS 130

TRAPP (transport protein particle), a multiprotein complex containing ten subunits, plays a key role in the late stages of endoplasmic reticulum to Golgi traffic in the yeast Saccharomyces cerevisiae. We previously described the identification of five TRAPP subunits (Bet5p, Trs20p, Bet3p, Trs23p and Trs33p). Now we report the identification of the remaining five subunits (Trs31p, Trs65p, Trs85p, Trs120p and Trs130p) as well as an initial characterization of the yeast complex and its human homologue. We find that three of the subunits are dispensable for growth and a novel sequence motif is found in Bet3p, Trs31p and Trs33p. Furthermore, biochemical characterization of both yeast and human TRAPP suggests that this complex is anchored to a Triton X-100 resistant fraction of the Golgi. Differences between yeast and human TRAPP as well as the relationship of TRAPP subunits to other docking/ tethering factors are discussed.
Adaptive Loudness Compensation in Music Listening

The need for loudness compensation is a well known fact arising from the nonlinear behavior of human sound perception. Music and other sounds are mixed and mastered at a certain loudness level, usually louder than the level at which they are commonly played. This implies a change in the perceived spectral balance of the sound, which is largest in the low-frequency range. As the volume setting in music playing is decreased, a loudness compensation ﬁlter can be used to boost the bass appropriately, so that the low frequencies are still heard well and the perceived spectral balance is preserved. The present paper proposes a loudness compensation function derived from the standard equal-loudness-level contours and its implementation via a digital ﬁrst-order shelving ﬁlter. Results of a formal listening test validate the accuracy of the proposed method.
Wide-Area Spark Streaming: Automated Routing and Batch Sizing

Modern stream processing frameworks, such as Spark Streaming, are designed to support a wide variety of stream processing applications, such as real-time data analytics in social networks. As the volume of data to be processed increases rapidly, there is a pressing need for processing them across multiple geo-distributed datacenters. However, these frameworks are not designed to take limited and varying inter-datacenter bandwidth into account, leading to longer query latencies. In this paper, we present the design and implementation of an extended Spark Streaming framework to automatically and optimally schedule tasks, select data flow routes and determine micro-batch sizes across geo-distributed datacenters in wide-area networks. To make these decisions, we propose a sparsity-regularized ADMM algorithm to efficiently solve a nonconvex optimization problem, based on readily measurable operating traces. Toward incremental real-world deployment, we take a non-intrusive approach to support flexible routing of micro-batches by adding a new DStream transformation we have developed to the existing Spark Streaming framework. As a result, our implementation can enforce scheduling decisions by modifying application workflows only. We have deployed our implementation on Amazon EC2 with emulated bandwidth constraints, and our experimental results on various types of queries have demonstrated the effectiveness of our proposed framework, as compared to the existing Spark Streaming scheduler and other data-locality-based heuristics.
ENTREPRENEURIAL BEHAVIOR IN MALAYSIAN COMMERCIAL BANKS : THE ROLE OF EMOTIONAL INTELLIGENCE , JOB AUTONOMY , PERCEIVED ORGANIZATIONAL SUPPORT , AND ORGANIZATIONAL COMMITMENT

Entrepreneurial behavior within organization (or corporate entrepreneurship) is a key mechanism of organizational development and as an important backbone to the economic growth. To become globally competitive, an effective entrepreneurial behavior is seen as an important path for established organization in achieving competitive advantage and performance improvements toward successful future opportunities. Although a substantial body of research has recommended that entrepreneurial behavior has positive outcomes for individual employees, studies on the predictors of entrepreneurial behavior, particularly among middle-level managers within the banking sector are relatively limited. Since managers act as role models for employees to emulated, entrepreneurial behavior displayed by managers is deemed important. Since, personal characteristics of managers (in the form of emotional intelligence), job characteristics (in the form of job autonomy), and their beliefs regarding the organization‟s support (in the form of perceived organizational support) are bound to affect their inclination to behave entrepreneurially, these three variables will be viewed as potential predictors. In addition, organizational commitment will be explored as a credible moderator in strengthening or weakening the relationships between the predictor variables and entrepreneurial behavior. Therefore, the objective of this paper is to provide a review of the literature on the effects of emotional intelligence, job autonomy, and perceived organizational support on middle-level managers‟ entrepreneurial behavior and how these relationships may differ as a result of changes in their level of organizational commitment. JEL Classification: G2, M1, L2, L8
0 Implementing high-performance complex matrix multiplication via the 3 m and 4 m methods

of these so-called “induced” methods, and observe that the assembly-level method actually resides along the 4M spectrum of algorithmic variants. Implementations are developed within the BLIS framework, and testing on modern hardware confirms that while the less numerically stable 3M method yields the fastest runtimes, the more stable (and thus widely applicable) 4M method’s performance is somewhat limited due