Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning the Optimal Use of Dependency-parsing Information for Finding Translations with Comparable Corpora

Using comparable corpora to find new word translations is a promising approach for extending bilingual dictionaries (semi-) automatically. The basic idea is based on the assumption that similar words have similar contexts across languages. The context of a word is often summarized by using the bag-of-words in the sentence, or by using the words which are in a certain dependency position, e.g. the predecessors and successors. These different context positions are then combined into one context vector and compared across languages. However, previous research makes the (implicit) assumption that these different context positions should be weighted as equally important. Furthermore, only the same context positions are compared with each other, for example the successor position in Spanish is compared with the successor position in English. However, this is not necessarily always appropriate for languages like Japanese and English. To overcome these limitations, we suggest to perform a linear transformation of the context vectors, which is defined by a matrix. We define the optimal transformation matrix by using a Bayesian probabilistic model, and show that it is feasible to find an approximate solution using Markov chain Monte Carlo methods. Our experiments demonstrate that our proposed method constantly improves translation accuracy.
Applying Ontologies and Intelligent Text Processing in Requirements Reuse

Requirements reuse plays a decisive role in order to get benefits in the software industry. The reuse process implies a retrieval of stored requirements, but these requirements are generally expressed in natural language. In order to obtain an effective but flexible system, we need a natural language retrieval system supported by a knowledge model. The knowledge of the requirements engineering area is modeled by the Requirements Engineering Ontology (REOntology), which includes lexical-syntactic patterns in order to capture the semantic of the relationships in the domain. The approach takes advantage of both techniques: domain models and natural language processing. Furthermore, it supports an incremental reuse methodology, as new requirements are semi-automatically processed in order to improve the REOntology by means of adding specific application domain knowledge step by step.
Doubly-fed induction machine models for stability assessment of wind farms

The increasing size of wind farms requires power system stability analysis including dynamic models of the wind power generation. Nowadays, the most widely used generator type for units above 1 MW is the doubly-fed induction machine. Doubly-fed induction machines allow active and reactive power control through a rotor-side converter, while the stator is directly connected to the grid. Detailed models for doubly-fed induction machines are well known but the efficient simulation of entire power systems with hundreds of generators requires reduced order models. This paper presents a fundamental frequency doubly-fed induction machine model including a typical control system and discusses the accuracy of reduced order models under various operating conditions.
Optimizing acoustic models for commercial speech recognition using foreground scores and data weighting

This paper describes a data-driven technique for optimizing the acoustic models for speech recognition systems that target commercial applications over telephones. Frame-averaged foreground log-likelihoods (foreground scores) correlate to recognition errors. These scores are used together with gender to optimize data weighting for the acoustic model. This process is interpreted as increasing the priors and associated parameters for poorly modeled data. The score-based optimization leads to about 7% fewer semantic errors on a live evaluation set collected after the last data used to estimate the acoustic model.
Millimeter-Wave Near-Field Probe Designed for High-Resolution Skin Cancer Diagnosis

This paper presents a detailed technical characterization of a micromachined millimeter-wave near-field probe developed for skin cancer diagnosis. The broadband probe is optimized for frequencies from 90 to 104 GHz and consists of a dielectric-rod waveguide, which is metallized and tapered towards the tip to achieve high resolution by concentrating the electric field in a small sample area. Several probes with different tip sizes were fabricated from high-resistivity silicon by micromachining and were successfully characterized using silicon test samples with geometry-defined tailor-made permittivity. The probes show a high responsivity for samples with permittivities in the range of healthy and cancerous skin tissue at 100 GHz (from 3.2-j2.3 to 7.2-j8.0, loss tangent of approximately 1.26). The sensing depth was determined by simulations and measurements from 0.3 to 0.4 mm, which is adapted for detecting early-stage skin tumors before they metastasize. The lateral resolution was determined to 0.2 mm for a tip size of 0.6 × 0.3 mm, which allows for resolving small skin tumors and inhomogeneities within a tumor.
Multiview Clustering Based on Tensor Solutions

Integrating multiview cluster is an crucial issue in heterogeneous environment. Spectral clustering is used for integrating cluster in heterogeneous environment. In this paper, we used tensor decomposition for identifying hidden pattern in the context of spectral clustering. This gives the good result when compared to other methods. Here synthetic datasets are used for evaluating the results. In most of the tensor solution two dimensional or three dimensional data are used. The higher order datasets are used in multiview clustering
Towards Spike-Based Speech Processing: A Biologically Plausible Approach to Simple Acoustic Classification

Towards Spike-Based Speech Processing: A Biologically Plausible Approach to Simple Acoustic Classification Shortcomings of automatic speech recognition (ASR) applications are becoming more evident as they are more widely used in real life. The inherent non-stationarity associated with the timing of speech signals as well as the dynamical changes in the environment make the ensuing analysis and recognition extremely difficult. Researchers often turn to biology seeking clues to make better engineered systems, and ASR is no exception with the usage of feature sets such as Mel frequency cepstral coefficients, which employ filter banks similar to cochlear filter banks in frequency distribution and bandwidth. In this paper, we delve deeper into the mechanics of the human auditory system to take this biological inspiration to the next level. The main goal of this research is to investigate the computation potential of spike trains produced at the early stages of the auditory system for a simple acoustic classification task. First, various spike coding schemes from temporal to rate coding are explored, together with various spike-based encoders with various simplicity levels such as rank order coding and liquid state machine. Based on these findings, a biologically plausible system architecture is proposed for the recognition of phonetically simple acoustic signals which makes exclusive use of spikes for computation. The performance tests show superior performance on a noisy vowel data set when compared with a conventional ASR system.
Proliferation and Tumorigenesis of Colon Cancer Cells Hath 1 , Down-Regulated in Colon Adenocarcinomas , Inhibits

A striking feature of colon tumors is the significant reduction of goblet cells. Although targeted deletion of Math1 in mice leads to a loss of intestinal secretory cells, including goblet cells, the role of Hath1 in colon tumorigenesis remains unknown. Here we report that Hath1, the human ortholog of Math1, was dramatically down-regulated in colon tumor samples and colon cancer cell lines. Overexpression of Hath1 in HT29, an aggressive colon cancer cell line, resulted in a significant inhibition on cell proliferation, anchorage-independent growth in soft agar and, more importantly, growth of human colon cancer cell xenografts in athymic nude mice. Such inhibition was accompanied by altered expression of a goblet cell differentiation marker, MUC2, and cell cycle regulators cyclin D1 and p27. Hath1 expression also was up-regulated on inhibition of the Wnt pathway, which has been well implicated in colon tumorigenesis. Hence, this study suggests that Hath1 may be a novel factor downstream of the Wnt pathway capable of suppressing anchorage-independent growth of colon cancer cell lines. More importantly, this study is the first to establish a link between down-regulation of Hath1 expression and colon tumorigenesis.
Optimally Efficient Multicast in Structured Peer-to-Peer Networks

The Distributed Tree Construction (DTC) algorithm is designed for optimally efficient multicast tree construction over structured peer-to-peer networks. It achieves this by creating a spanning tree over the peers in the multicast group, using only information available locally on each peer. Furthermore, we show that the tree depth has the same upper bound as a regular DHT lookup which in turn guarantees fast and responsive runtime behavior. Our DTC algorithm is DHT-agnostic and works with most existing DHTs. We evaluate the performance of DTC over several DHTs by comparing the performance to existing application-level multicast solutions, we show that DTC sends 30-250% fewer messages than common solutions.
Oxidative stress as an indicator of the costs of reproduction among free-ranging rhesus macaques

ABSTRACT Sex differences in longevity may reflect sex-specific costs of intra-sexual competition and reproductive effort. As male rhesus macaques experience greater intrasexual competition and die younger, we predicted that males would experience greater oxidative stress than females and that oxidative stress would reflect sex-specific measures of reproductive effort. Males, relative to females, had higher concentrations of 8-OHdG and malondialdehyde, which are markers of DNA oxidative damage and lipid peroxidation, respectively. Older macaques had lower 8-OHdG levels than younger ones, suggesting that oxidative stress decreases in parallel with known age-related declines in reproductive investment. Among males, a recent period of social instability affected oxidative status: males who attacked others at higher rates had higher 8-OHdG levels. Multiparous lactating females with daughters had higher 8-OHdG levels than those with sons. No differences in antioxidant capacity were found. These results lend initial support for the use of oxidative stress markers to assess trade-offs between reproductive effort and somatic maintenance in primates. Summary: Measures of oxidative stress in rhesus macaques reveal more DNA oxidative damage in males than females, in mothers raising daughters rather than sons and show enduring costs of aggression for males.
A component of the mir-17-92 polycistronic oncomir promotes oncogene-dependent apoptosis

mir-17-92, a potent polycistronic oncomir, encodes six mature miRNAs with complex modes of interactions. In the Eμ-myc Burkitt’s lymphoma model, mir-17-92 exhibits potent oncogenic activity by repressing c-Myc-induced apoptosis, primarily through its miR-19 components. Surprisingly, mir-17-92 also encodes the miR-92 component that negatively regulates its oncogenic cooperation with c-Myc. This miR-92 effect is, at least in part, mediated by its direct repression of Fbw7, which promotes the proteosomal degradation of c-Myc. Thus, overexpressing miR-92 leads to aberrant c-Myc increase, imposing a strong coupling between excessive proliferation and p53-dependent apoptosis. Interestingly, miR-92 antagonizes the oncogenic miR-19 miRNAs; and such functional interaction coordinates proliferation and apoptosis during c-Myc-induced oncogenesis. This miR-19:miR-92 antagonism is disrupted in B-lymphoma cells that favor a greater increase of miR-19 over miR-92. Altogether, we suggest a new paradigm whereby the unique gene structure of a polycistronic oncomir confers an intricate balance between oncogene and tumor suppressor crosstalk. DOI: http://dx.doi. org/10.7554/eLife.00822. 001
Analysis and design of multi-stage passive filters for 100 MVA industrial power converters

This paper presents a design procedure of multi-stage passive filters for high power industrial power converters. The proposed design procedure is based on the calculation of the different variables related to passive filters compensation performance, the restrictions imposed by the IEEE Std. 519-1992, and the attenuation factor associated with the equivalent impedance of the distribution system. The proposed procedure is aimed to obtain the best distributed harmonic filtering effectiveness, reduced filter losses and the number of spare parts in passive filter components. In addition, this paper presents the compensation effectiveness of the passive filter designed with the proposed procedure in presence of inter and sub-harmonics which are typically injected by cycloconverters used in grinding mill drives.
On low-dimensional projections of high-dimensional distributions

Let P be a probability distribution on q -dimensional space. The so-called Diaconis-Freedman effect means that for a fixed dimension d<<q , most d -dimensional projections of P look like a scale mixture of spherically symmetric Gaussian distributions. The present paper provides necessary and sufficient conditions for this phenomenon in a suitable asymptotic framework with increasing dimension q . It turns out, that the conditions formulated by Diaconis and Freedman (1984) are not only sufficient but necessary as well. Moreover, letting P ^ be the empirical distribution of n independent random vectors with distribution P , we investigate the behavior of the empirical process n √ (P ^ −P) under random projections, conditional on P ^ .
Hierarchical Octree and Sub-Volume Texture Block Projection for GPU Accelerated Ray Casting Volume Rendering

Because of large volume data and slow speed in volume rendering, it proposes a new method for ray casting volume rendering using graphics processing unit (GPU). This algorithm firstly uses sub-volume texture block projection and then subdivides 3D volume texture while coding with hierarchical octree, projection information is used for rendering non-empty blocks from front to back. Sub-volume texture blocks including flag information of empty blocks and full blocks of volume data and skipping rendering and computation of empty blocks are used to accelerate ray casting algorithm along the ray direction with projection information of sub-volume. It decreases further subdivision of empty blocks and increases efficiency of octree coding. The experiments results show that this method can speed up at least 2.59 times during rendering on GPU and achieve good rendering effects.
Planning for Agile Earth Observation Satellites

Agile Earth observation satellites are satellites orbiting Earth with the purpose to gather information of the Earth’s surface by slewing the satellite toward regions of interest. Constraints arise not only from dynamical and kinematic aspects of the satellite and its sensors. Regions of interest change over time and bad weather can conceal important observation targets. This results in a constant need to replan the satellite’s tasks and raises the desire to automatize this planning process. We consider the Earth observation problem with the help of the module extension of the numerical planning system Temporal Fast Downward. Complex satellite slew maneuvers are calculated within modules, while the planner selects and schedules the regions to be scanned. First results encourage deeper research in this area so that forthcoming satellite space missions can draw on automated planning to improve the performance of agile Earth observation tasks.
How Many Bucks in a Bang: On the Estimation of the Economic Costs of Conflict

The estimation of the costs of conflict is currently receiving a lot of attention in the literature. This paper aims to give a thorough overview of the existing literature, first by addressing the history of case studies that address conflict costs and second by looking at the existing body of cross-country analyses for conflict costs. In addition to the existing cross-country literature, a number of studies that only concern themselves with particular elements of conflict costs are included as well. In the end, this paper combines the insights from these previous analyses to explore how much room there is to further improve the existing studies. Specific recommendations are given how to proceed with the development of the field of conflict cost measurement.
Labour mobility and organisational proximity: routines as supporting mechanisms for variety, skill integration and productivity

Abstract The aim of this paper is to introduce an organisational dimension to the discussion of knowledge flows and relatedness. We hypothesise that not only the degree of technological relatedness influence the extent of skill integration in a firm but also that familiarity with firm routines (intra-organisational proximity) should smoothen absorption. Longitudinal micro-data are used in pooled ordinary least square- and fixed effect models to estimate the impact on plant productivity growth of 18,051 labour flows within, and to, four large Swedish firms between 2003 and 2006. Our findings suggest that intra-regional related flows are economically beneficial. Their link to localised capabilities and community creates a weaker but more productive link between individuals than do organisational proximity, which generate too much similarity to allow for cognitively related inflows to impact productivity growth. Also, we find a positive relationship between unrelated flows and plant performance.
Effects of Adolescent Intermittent Alcohol Exposure on the Expression of Endocannabinoid Signaling-Related Proteins in the Spleen of Young Adult Rats

Intermittent alcohol exposure is a common pattern of alcohol consumption among adolescents and alcohol is known to modulate the expression of the endocannabinoid system (ECS), which is involved in metabolism and inflammation. However, it is unknown whether this pattern may have short-term consequences on the ECS in the spleen. To address this question, we examined the plasma concentrations of metabolic and inflammatory signals and the splenic ECS in early adult rats exposed to alcohol during adolescence. A 4-day drinking in the dark (DID) procedure for 4 weeks was used as a model of intermittent forced-alcohol administration (20%, v/v) in female and male Wistar rats, which were sacrificed 2 weeks after the last DID session. First, there was no liver damage or alterations in plasma metabolic parameters. However, certain plasma inflammatory signals were altered according to sex and alcohol exposition. Whereas fractalkine [chemokine (C-X3-C motif) ligand 1] was only affected by sex with lower concentration in male rats, there was an interaction between sex and alcohol exposure in the TNF-α and interleukin-6 concentrations and only female rats displayed changes. Regarding the mRNA and protein expression of the ECS, the receptors and endocannabinoid-synthesizing enzymes were found to be altered with area-specific expression patterns in the spleen. Overall, whereas the expression of the cannabinoid receptor CB1 and the nuclear peroxisome proliferator-activated receptor PPARα were lower in alcohol-exposed rats compared to control rats, the CB2 expression was higher. Additionally, the N-acyl-phosphatidylethanolamine-specific phospholipase D expression was high in female alcohol-exposed rats and low in male alcohol-exposed rats. In conclusion, intermittent alcohol consumption during adolescence may be sufficient to induce short-term changes in the expression of splenic endocannabinoid signaling-related proteins and plasma pro-inflammatory cytokines in young adult rats with a strong sexual dimorphism. The potential impact of these alterations in early adulthood remains to be elucidated.
A Model of the Initial Stages of Drug Abuse: From Reinforcement Learning to Social Contagion

A great deal has been written about the antecedents of drug use and abuse. The importance of the issue is of course incontestable: drug use and abuse carry severe consequences for the individuals themselves as well as society as a whole. For individuals, drug use affects mood and cognitive processes, increases risk of accidental injury or death, is a factor in contracting various diseases such as lung cancer, coronary heart disease and AIDS, and is related to various negative situations such as child abuse, violent crime, unemployment Finch & Cohen, 1996). For society in general, drug abuse has consequences for the cost of health care and mental health services, crime rates, educational systems and more. The issue has been studied from many different perspectives and approaches, and an array of risk factors-biological predisposition, societal and cultural variables and personality variables (such as impulsiveness or sensation seeking)-has been suggested. It is important to note that various definitions of substance or drug use and abuse have been utilized in the literature. In the current chapter, we will rely on the definition of substance abuse adopted by Hawkins, Catalano & Miller (1992), and that is the frequent use of alcohol, drugs or other substances in a manner that is associated with problems and dysfunctions. Taking this perspective, drug abuse is a habit which certain individuals acquire despite its detrimental consequences. In the current chapter we suggest that drug use acquisition is adequately
Graph-constrained supervised dictionary learning for multi-label classification

In this work, we tackle the problem of multi-label classification using a sparsity-based approach. Multi-label classification problems, in which each instance is associated with a set of multiple labels, have received significant attention over the past few years due to the ongoing growth of data dimensions and availability. However, the dependency between labels poses new challenges to existing classification techniques. We propose a supervised dictionary learning algorithm suited for the multi-label setting. The suggested scheme introduces a novel graph Laplacian regularization that encapsulates the training set labels. This regularization explicitly takes into account the local manifold structure of the data, thus promoting the discriminative power of the learned sparse representations. Experiments on two different real-world multi-label learning problems, i.e. natural scene classification and yeast gene functional analysis, demonstrate that our proposed algorithm achieves superior performance to other dictionary based approaches as well as some established multi-label learning algorithms.