Miracle-dz/UniFAR-FTUs huggingface dataset:
Antiminority Riots in Unified Germany: Cultural Conflicts and Mischanneled Political Participation

Two large antiforeigner riots in eastern Germany, at Hoyerswerda in September 1991 and at Rostock in August 1992, created a political sensation. They attracted far more attention, outcry, and veiled approval from political leaders than two thousand smaller assaults on foreigners that took place across unified Germany during those two years. Explanations of the riots in unified Germany and also much of the theoretical work on antiminority riots in other democratic systems have focused on three sets of causes: ethnonationalism, insecure ethnic identities, and racism; poverty, inequality, and competition between ethnic groups for scarce resources; and political elites' agitation of enmity to mobilize groups along ethnic lines.1 These analyses are flawed in two ways. First, they often treat riots as simply the most extreme manifestation of ethnic conflict and violence rather than as a qualitatitively different phenomenon. Because of their nature and their political significance, antiminority riots should be analyzed separately from other forms of ethnic conflict.2 Of course, violence is distinct from other forms of conflict in that it requires risky behavior, usually involves some confrontation with police, and has terrible consequences for its targets. Moreover, antiminority riots are a quite distinct form of ethnic violence. By antiminority riots, I mean sustained physical attacks on members of a subordinate ethnic minority group by large numbers of people who belong to the dominant ethnic group, are motivated by ethnic or racial animosity, and are not agents of the state.3 Hence antiminority riots differ from commodity riots by minority group members against property and police (often called race riots) and from hitand-run assaults by small groups against minority group members (racist violence). Moreover, antiminority riots in democratic settings have unusual political significance. Such riots often attract large numbers of approving adult spectators who are not evidently members of extreme nationalist or racist organizations. Hence politicians and news media may interpret the riots as indicators of widespread and salient ethnic antagonisms. Therefore, political reactions to antiminority riots can lead to major policy shifts, such as immigration restrictions and limitations on minority rights. Furthermore, because of the scale of the mobilization other perpetrators may try to imitate the attacks. At Hoyerswerda and Rostock crowds of many hundreds or several thousands of adults
Towards a Theory of Enterprise Training: Propositions for a Future Research Agenda

Research into the training provided by enterprises in recent years has been characterised by broad conclusions drawn from analysis of statistical data on the quantity of enterprise training in the economy. This research, whilst providing general indications of overall trends in training, has failed to illuminate the nature and operation of training at the enterprise level. This paper presents findings from a qualitative study of enterprise training in a number of manufacturing enterprises in Australia. The paper examines the type of training provided in the enterprises and relates the occurrence of training to a variety of independent variables including business strategy, technology, work organisation, industrial relations, management attitudes and the competitive performance of the enterprises. The discussion of the relationship of enterprise training to each of these variables leads to the development of a series of propositions describing the role and nature of training within enterprises which could form the basis for a future research agenda aimed at developing a more comprehensive theory of enterprise training.
Metabolomics Analysis Uncovers That Dietary Restriction Buffers Metabolic Changes Associated with Aging in Caenorhabditis elegans

Dietary restriction (DR) is one of the most universal means of extending lifespan. Yet, whether and how DR specifically affects the metabolic changes associated with aging is essentially unknown. Here, we present a comprehensive and unbiased picture of the metabolic variations that take place with age at the whole organism level in Caenorhabditis elegans by using 1H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) analysis of intact worms. We investigate metabolic variations potentially important for lifespan regulation by comparing the metabolic fingerprint of two previously described genetic models of DR, the long-lived eat-2(ad465) and slcf-1(tm2258) worms, as single mutants or in combination with a genetic suppressor of their lifespan phenotype. Our analysis shows that significant changes in metabolite profiles precede the major physiological decline that accompanies aging and that DR protects from some of those metabolic changes. More specifically, low phosphocholine (PCho) correlates with high life expectancy. A mutation in the tumor suppressor gene PTEN/DAF-18, which suppresses the beneficial effects of DR in both C. elegans and mammals, increases both PCho level and choline kinase expression. Furthermore, we show that choline kinase function in the intestine can regulate lifespan. This study highlights the relevance of NMR metabolomic approaches for identifying potential biomarkers of aging.
A weighted sum validity function for clustering with a hybrid niching genetic algorithm

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.
Automatic Detection and Classification of Plant Disease through Image Processing

We propose and experimentally evaluate a software solution for automatic detection and classification of plant diseases through Image Processing. Farmers in rural India have minimal access to agricultural experts, who can inspect crop images and render advice. Delayed expert responses to queries often reach farmers too late. This paper addresses this problem with the objective of developing image processing algorithms that can recognize problems in crops from images, based on colour, texture and shape to automatically detect diseases or other conditions that might affect crops and give the fast and accurate solutions to the farmer with the help of SMS. The design and implementation of these technologies will greatly aid in selective chemical application, reducing costs and thus leading to improved productivity, as well as improved produce.
Efficient manipulation of large datasets on heterogeneous storage systems

We are concerned with the efficient use of a collection of disk-based storage systems and computing platforms in a heterogeneous setting for retrieving and processing large scientific datasets. We demonstrate, in the context of a data-intensive visualization application, how heterogeneity affects performance and show a set of optimization techniques that can be used to improve performance in a component-based framework. In particular, we examine the application of parallelism via transparent copies of application components in the pipelined processing of data.
Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs

Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.
Face Recognition Based on Modified Modular Principal Component Analysis

The technology of face recognition has been widely applied to many fields such as identity authentication. A New Improvement for Face Recognition Using MMPCA is presented in this paper. The proposed algorithm when compared with conventional modular PCA algorithm is different in the computation of image mean value and the recognition process. Comparison of the two algorithms in different face databases proves that the proposed algorithm is more effective and robust than conventional modular PCA algorithm under the large variations in lighting direction and facial expression. The authors also point out that 2DPCA is a special case of improved algorithm, no matter in the process of dimension reduction or recognition.
Two Steps Forward and One Step Back: An Assessment of How Uneven Economic Development Affects the Number of Civil Wars

The effects of economic development are enormously important in understanding the causes of civil war and the requirements for successful post-conflict reconstruction. In recent decades we have seen an increase in the number of civil wars because of a phenomenon known as the conflict trap. I question why we see an increase in civil wars and what role unstable economic development plays in contributing to the conflict trap. This paper offers evidence to support the hypothesis that uneven economic development increases the risk of multiple civil wars occurring in a short amount of time. Based on the results of testing my hypothesis I suggest that the conflict trap can be broken, and the risk of civil war decreased, when economic growth is stabilized. I suggest participation in the global economic community as a mechanism for stabilizing economic growth.
Isolated right ventricular dysfunction in systemic sclerosis: latent pulmonary hypertension?

Right ventricular function is frequently abnormal in patients with systemic sclerosis, but whether this is related to pulmonary vascular complications of the disease is unclear. Standard echocardiography with tissue Doppler imaging was performed at rest and during exercise for the study of right ventricular function and pulmonary circulation in 25 consecutive systemic sclerosis patients and in 13 age-matched healthy controls. When compared with the controls, the patients had no difference in systolic right ventricular pressure gradient, but a decreased pulmonary flow acceleration time, and increased right ventricular free wall thickness and end-diastolic dimensions. At the tricuspid annulus, the E maximal velocity was decreased (8.9±4 versus 11.7±2.3 cm·s−1) and the isovolumic relaxation time corrected to RR interval was increased (6.5±2.9 versus 4.5±2.5%). The tissue Doppler imaging profile at the mitral annulus was similar in both groups. At exercise, 18 patients had a decreased maximum workload and cardiac output, no change in systolic right ventricular pressure gradient, but an increase in the slope of pulmonary artery pressure/flow relationships. These results suggest that patients with systemic sclerosis may present with latent pulmonary hypertension as a likely cause of right ventricular diastolic dysfunction, as revealed by stress echocardiography and tissue Doppler imaging.
Jovian: a framework for optimizing parallel I/O

There has been a great deal of recent interest in parallel I/O. We discuss the design and implementation of the Jovian library, which is intended to optimize the I/O performance of multiprocessor architectures that include multiple disks or disk arrays. We also present preliminary performance measurements from benchmarking the Jovian I/O library on the IBM SP1 distributed memory parallel machine for two application templates. <<ETX>>
Extra Buffer Resources Improving Competitiveness in Minimizing Energy Consumption

In this paper, we consider energy management algorithms for scheduling jobs in power-scare scenarios such as embedded computer systems and sensor networks. We focus on investigating the impact of buffer resources in minimizing the total energy cost in an online setting. The online algorithms do not have any assumptions on job arrivals; their worst-case performance is measured in term of competitive ratio, when they are compared with the optimal algorithms with clairvoyance. We prove that with appropriate extra buffer space, an online algorithm can beat an weak optimal offline algorithm in terms of the total energy required. Our research result helps to quantitatively estimate the optimal on-chip buffer resources allocated in real-time systems with power constraints. We also present the lower bound of competitive ratio that any deterministic online algorithm cannot achieve.
AMBIVALENCE TOWARD THE INGROUP UNDERLIES INDIVIDUAL IDENTITY MANAGEMENT STRATEGIES

The present research deals with low status group members reacting to a social identity threat. According to previous insights, we proposed and found that expressing an ambivalent evaluation toward the ingroup – an evaluation which contains both positive and negative aspects – may represent a way to manage such a threatening situation, particularly when individuals scantly identify with the ingroup. We found evidence that ambivalence toward the ingroup underpins the willingness to invest in individual identity management strategies. Results confirmed our interpretation of ambivalence as a form of psychological disengagement as well as how this disengagement further determines an individual course of action.
A Sentence Interaction Network for Modeling Dependence between Sentences

Modeling interactions between two sentences is crucial for a number of natu-ral language processing tasks including Answer Selection, Dialogue Act Analysis, etc. While deep learning methods like Recurrent Neural Network or Convolutional Neural Network have been proved to be powerful for sentence modeling, prior studies paid less attention on interactions between sentences. In this work, we propose a Sentence Interaction Network (SIN) for modeling the complex interactions between two sentences. By introducing “interaction states” for word and phrase pairs, SIN is powerful and ﬂexi-ble in capturing sentence interactions for different tasks. We obtain signiﬁcant improvements on Answer Selection and Dialogue Act Analysis without any feature engineering.
On Weighted Bipartite Edge Coloring

We study weighted bipartite edge coloring problem, which is a generalization of two classical problems: bin packing and edge coloring. This problem has been inspired from the study of Clos networks in multirate switching environment in communication networks. In weighted bipartite edge coloring problem, we are given an edge-weighted bipartite multi-graph G=(V,E) with weights w:E\rightarrow [0,1]. The goal is to find a proper weighted coloring of the edges with as few colors as possible. An edge coloring of the weighted graph is called a proper weighted coloring if the sum of the weights of the edges incident to a vertex of any color is at most one. Chung and Ross conjectured 2m-1 colors are sufficient for a proper weighted coloring, where m denotes the minimum number of unit sized bins needed to pack the weights of all edges incident at any vertex. We give an algorithm that returns a coloring with at most \lceil 2.2223m \rceil colors improving on the previous result of \frac{9m}{4} by Feige and Singh. Our algorithm is purely combinatorial and combines the Konig's theorem for edge coloring bipartite graphs and first-fit decreasing heuristic for bin packing. However, our analysis uses configuration linear program for the bin packing problem to give the improved result.
Mitigating routing misbehavior in Dynamic Source Routing protocl using trust-based reputation mechanism for wireless ad-hoc networks

Routing is a key factor in the design of modern communication networks, especially in wireless ad-hoc networks (WANs). In WANs, both selfish and malicious nodes are the misbehaving nodes and are significant routing and security problems. The proposed mechanism presents a trust model to detect selfish and malicious nodes to avoid these nodes becoming routing nodes. In addition, the proposed paper provides a way to detect and prevent false accusation and false recommendation. The second-hand reputation information is calculated from its route cache of the interested nodes to reduce traffic overhead. The proposed mechanism is based on the Dynamic Source Routing (DSR) protocol. We compare the performance of the proposed scheme with that of Cooperation of Nodes: Fairness In Dynamic Ad-Hoc Networks (CONFIDANT) in terms of two performance metrics: throughput and reputation computation overheads under selfish and malicious attacks. By comparison, we notice that the proposed mechanism outperforms CONFIDANT in all two categories. The simulation results also indicate that the proposed mechanism can stimulate nodes to cooperate with each other and improve the performance of WANs.
Towards Automated Performance Bug Identification in Python

Context : Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement / marketing domain. Goal : Find a simple and easy to implement solution, predicting performance bugs. Method : We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Naı̈ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file’s age and size as explanatory variables, can be used to predict performance bugs (recall = 0.73, accuracy = 0.85, and precision = 0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs.
Information retrieval model based on neural networks using neighborhood

The information plays an inevitable role in the handling systems of numerical data existing today. The amount of information handled by various organizations in the world today is high and its management without the computer is no longer imaginable. Information Retrieval (IR) is a research area in computer science whose goal is to facilitate access to a set of documents in electronic form (corpus) and allow a user to find the relevant ones for him, that is to say, those whose content best matches the information needs of the user. We propose in this paper a system called “Neuro-RI” putting into practice our model of information retrieval based on neural networks with neighborhood.
TD-PSOLA versus harmonic plus noise model in diphone based speech synthesis

In an effort to select a speech representation for our next generation concatenative text-to-speech synthesizer, the use of two candidates is investigated; TD-PSOLA and the harmonic plus noise model, HNM. A formal listening test has been conducted and the two candidates have been rated regarding intelligibility, naturalness and pleasantness. Ability for database compression and computational load is also discussed. The results show that HNM consistently outperforms TD-PSOLA in all the above features except for computational load. HNM allows for high-quality speech synthesis without smoothing problems at the segmental boundaries and without buzziness or other oddities observed with TD-PSOLA.