Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Negative Matrix Factorization with Sinkhorn Distance

Non-negative Matrix Factorization (NMF) has received considerable attentions in various areas for its psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in the human brain. Despite its good practical performance, one shortcoming of original NMF is that it ignores intrinsic structure of data set. On one hand, samples might be on a manifold and thus one may hope that geometric information can be exploited to improve NMF's performance. On the other hand, features might correlate with each other, thus conventional L2 distance can not well measure the distance between samples. Although some works have been proposed to solve these problems, rare connects them together. In this paper, we propose a novel method that exploits knowledge in both data manifold and features correlation. We adopt an approximation of Earth Mover's Distance (EMD) as metric and add a graph regularized term based on EMD to NMF. Furthermore, we propose an efficient multiplicative iteration algorithm to solve it. Our empirical study shows the encouraging results of the proposed algorithm comparing with other NMF methods.
Quantifying sensing coverage and data delivery delay in mobile sensor networks

In mobile wireless sensor networks where sensor nodes are general mobile terminals equipped by vehicles and pedestrians, it is difficult for applications to estimate coverages of sensor nodes due to their mobility. In addition, when a network is sparse, it is also difficult to estimate delivery delays of sensor readings to the sink node(s) because delays depend on both the distribution of nodes and the change of the network topology. These two factors, sensing coverages and data delivery delays, are very significant when designing applications and underlying protocols. In this paper, we aim to quantify sensing coverages and data delivery delays in mobile sensor networks. We propose several general metrics to quantify these two factors for a given mobility pattern. We also report results of some simulation experiments that measure the proposed metrics assuming several typical mobility models.
EXILE: Experience based Interactive Learning Environment

In a hyper-aged society, health promotive activities for senior people became important social issues. In this paper, we propose an interactive virtual learning environment for health promotive exercises. It is designed for self-training at a standard home environment and to provide training effects without an informed training instructor. Proposed system consists of 3 items; a PC, a Kinect camera, and a display. In exercises, trainee posturesand ideal postures which trainees should imitate are visualized simultaneously in a 3D virtual environment. A trainee observes the differences in postures which are emphasized by real time feedback system and corrects own training postures.
Association between diabetes and perinatal depression among low-income mothers.

CONTEXT Perinatal depression affects at least 10% to 12% of new mothers, and diabetes complicates up to 9% of pregnancies. Prior research shows a higher rate of major depression among individuals with diabetes. OBJECTIVE To examine the association between diabetes and depression during pregnancy and the postpartum period among a sample of low-income women. DESIGN, SETTING, AND PATIENTS Retrospective cohort study using data from New Jersey's Medicaid administrative claims database of 11,024 women who gave birth between July 1, 2004, and September 30, 2006, and who were continuously enrolled in Medicaid for 6 months prior to delivery and 1 year after giving birth. MAIN OUTCOME MEASURES Multivariate logistic regression was used to assess the association between prepregnancy diabetes or gestational diabetes and perinatal depression. Depression was defined as an International Classification of Diseases, Ninth Revision, diagnosis for depression or a prescription drug claim for an antidepressant medication, and diabetes was defined as having a diabetes diagnosis or filling a prescription for a diabetes medication. Both measures were assessed during the 6 months prior to and up to 1 year following delivery. RESULTS In the sample of women who gave birth, 15.2% (n = 100) with prepregnancy or gestational diabetes and 8.5% (n = 886) without diabetes were depressed during pregnancy or postpartum. After adjusting for age, race, year of delivery, and gestational age at birth, women with diabetes compared with those without diabetes had nearly double the odds of experiencing depression during the perinatal period (odds ratio, 1.85; 95% confidence interval, 1.45-2.36). Women with diabetes and no prenatal indication of depression (n = 62, 9.6%) had higher odds than their counterparts without diabetes (n = 604, 5.9%) of receiving a postpartum depression diagnosis or taking an antidepressant medication in the year following delivery (odds ratio, 1.69; 95% confidence interval, 1.27-2.23). CONCLUSION Prepregnancy or gestational diabetes was independently associated with perinatal depression, including new onset of postpartum depression, in our sample of low-income new mothers.
Integration of mechanotransduction concepts in bone tissue engineering

Mechanical stimulus has been identified for a long time as a key player in the adaptation of the musculo-skeletal tissues to their function. Mechanical loading is then an intrinsic variable to be considered when new developments are proposed in bone tissue engineering. By combining structural biomechanics and mechanotransduction aspects, a new paradigm is presented for bone tissue engineering. It is proposed that in vivo mechanical loading be used to increased bone formation in the scaffold instead of pre-seeding the scaffold with cells or delivering growth factors. In this article, we demonstrated the feasibility of this approach and compared it to the classical tissue engineering strategy. In particular, we showed that bone formation could be increased in the scaffold that underwent mechanical loading during an in vivo study in rats. A model of bone formation was then proposed to translate the in vivo results into a possible clinical application where the loading of the scaffold would be transmitted by the sharing of the load between an implant and the bone scaffold.
Optimal Power Flow in Islanded Microgrids Using a Simple Distributed Algorithm

In this paper, the problem of distributed power losses minimization in islanded distribution systems is dealt with. The problem is formulated in a very simple manner and a solution is reached after a few iterations. The considered distribution system, a microgrid, will not need large bandwidth communication channels, since only closeby nodes will exchange information. The correction of generated active powers is possible by means of the active power losses partition concept that attributes a portion of the overall power losses in each branch to each generator. The experimental part shows the first results of the proposed method on an islanded microgrid. Simulation results of the distributed algorithm are compared to a centralized Optimal Power Flow approach and very small errors can be observed.
A formal experiment comparing extreme programming with traditional software construction

This paper describes an experiment carried out during the Spring/2002 academic semester with computer science students at the University of Sheffield. The aim of the experiment was to assess extreme programming and compare it with a traditional approach. With this purpose the students constructed software for real clients. We observed 20 teams working for 4 clients. Ten teams worked with extreme programming and ten with the traditional approach. In terms of quality and size teams working with extreme programming produced similar final products to traditional teams. The major implication for the current practice of traditional software engineering is that in spite of the absence of design and the presence of testing before coding the product obtained still has similar quality and size. The implication for extreme programming is the possibility of growth and maturation given the fact that it provided results that were as good as those from the traditional approach.
Identifying Medical Terms Related to Specific Diseases

We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The results illustrate that our model for disease term classification finds all disease term classes with an average F-score of 0.966.
The impact of security on cooperative awareness in VANET

Vehicular networking enables new safety applications that aim at improving roads safety. Because of their direct relation to driver's safety, this goal can only be achieved if vehicular networking is based on a technology that is robust against malicious attackers. Therefore, security mechanisms such as authentication are proposed. However, security comes at a cost in terms of computational and communication overhead. For example, a signature and certificate are appended to every beacon sent, which generates an extra load on the network. Moreover, most of the safety applications require a perfect awareness of the vehicle's surroundings to perform adequately. To represent such awareness, the Awareness Quality is used to indicate the current level of awareness of the vehicle. This metric was previously used by the Decentralized Congestion Control community to improve channel usage. In this paper, we use the Awareness Quality to investigate the impact of security on cooperative awareness in VANET. Then, we apply this metric to the mechanism of certificate omission, and provide extensive simulation results. The attributes of Awareness Quality metrics enable us to investigate the behavior of certificate omission schemes with a precision that was not provided by aggregate metrics. This enables us to show that congestion-based certificate omission with a quadratic adaption function is the most effective scheme among existing certificate omission schemes.
The Strain From Procedural Injustice on Parolees: Bridging Procedural Justice Theory and General Strain Theory

The procedural justice literature explains why people obey the law. However, prior research has largely neglected the implication of procedural justice in the correctional context in general and in parole efficacy in particular. In an attempt to bridge the propositions of procedural justice and general strain theory, this study assesses the effect of parolees’ perceived procedural injustice on their success in reentry. Using data from a longitudinal study of prisoner reentry, we investigate the nexus of procedural injustice, negative emotions, family bonds, and postrelease criminal propensity. Findings indicate that procedural injustice increases criminal propensity, and the negative emotion of depression partially mediates this relationship. We also find parolees’ family bonds totally mediate the effect of procedural injustice on criminal propensity.
Exploiting a Map-Based Interface in Conversational Recommender Systems for Mobile Travelers

Nowadays travel and tourism Web sites store and offer a large volume of travel related information and services. Furthermore, this huge amount of information can be easily accessed using mobile devices, such as a phone with mobile Internet connection capability. However, this information can easily overwhelm a user because of the large number of information items to be shown and the limited screen size in the mobile device. Recommender systems (RSs) are often used in conjunction with Web tools to effectively help users in accessing this overwhelming amount of information. These recommender systems can support the user in making a decision even when specific knowledge necessary to autonomously evaluate the offerings is not available. Recommender systems cope with the information overload problem by providing a user with personalized recommendations (i.e., a well chosen selection of the items contained in the repository), adapting this selection to the user’s needs and preferences in a particular usage context. In this chapter, the authors present a recommendation approach integrating a conversational preference acquisition technology based on “critiquing” with map visualization technologies to build a new mapbased conversational mobile RS that can effectively and intuitively support travelers in finding their desired products and services. The results of the authors’ real-user study show that integrating map-based visualization and critiquing-based interaction in mobile RSs improves the system’s recommendation effectiveness, and increases the user satisfaction.
Quantized F0 Context and Its Applications to Speech Synthesis, Speech Coding, and Voice Conversion

This paper describes a technique for language-independent prosody modeling using unsupervised prosodic labelling in HMM-based speech synthesis and shows its applications to low bit-rate speech coding and speaker-independent voice conversion. In the proposed technique, sequences of prosodic features are roughly quantized at a phone level and the resultant indexes are used as the prosodic context for the model training. The conventional HMM-based speech synthesis requires accurate prosodic labels corresponding to the speech samples where manual modification is necessary to improve the modeling accuracy, which sometimes takes extra costs and limits its application. In contrast, the proposed technique creates the prosodic label from the training data itself and can apply not only to the speech synthesis but also to the speech coding and voice conversion. Subjective experimental results show the effectiveness of the use of the quantized F0 context without manual prosodic labelling.
Environment and Classical Channels in Categorical Quantum Mechanics

We present a both simple and comprehensive graphical calculus for quantum computing. We axiomatize the notion of an environment, which together with the axiomatic notion of classical structure enables us to define classical channels, quantum measurements and classical control. If we moreover adjoin the axiomatic notion of complementarity, we obtain sufficient structural power for constructive representation and correctness derivation of typical quantum informatic protocols.
Net Juggler and SoftGenLock: Running VR Juggler with Active Stereo and Multiple Displays on a Commodity Component Cluster

High performance commodity components are available today to build clusters powerful enough to run high performance virtual reality applications. But cluster programming is time consumming and error prone. Virtual reality platforms that hide the complexity of the cluster architecture and provide automatic parallelization schemes are required. We present in this paper two open source softwares, Net Juggler and SoftGenLock. The association of these libraries makes possible to run a VR Juggler application with active stereo and multi displays on a commodity component PC cluster. Net Juggler turns a cluster running the VR Juggler platform on each node into a single VR Juggler image cluster. Application parallelization is transparent to the user. SoftGenLock provides software genlock and quad buffer page flipped stereo, without requiring specific hardware or graphics card driver source code. The performed tests show these softwares do no introduce a significant overhead.
Demand Response With Communicating Rational Consumers

The performance of an energy system under a real-time pricing mechanism depends on the consumption behavior of its customers, which involves uncertainties. In this paper, we consider a system operator that charges its customers with a real-time price that depends on the total realized consumption. Customers have unknown and heterogeneous consumption preferences. We propose behavior models in which customers act selfishly, altruistically, or as welfare-maximizers. In addition, we consider information models where customers keep their consumption levels private, communicate with a neighboring set of customers, or receive broadcasted demand from the operator. Our analysis focuses on the dispersion of the system performance under different consumption models. To this end, for each pair of behavior and information model we define and characterize optimal rational behavior, and provide a local algorithm that can be implemented by the consumption scheduler devices. Analytical comparisons of the two extreme information models, namely, private and complete information models, show that communication model reduces demand uncertainty while having negligible effect on aggregate consumer utility and welfare. In addition, we show the impact of real-time price policy parameters have on the expected welfare loss due to selfish behavior affording critical policy insights.
Prioritizing Variable-Strength Covering Array

Combinatorial interaction testing is a well-studied testing strategy, and has been widely applied in practice. Combinatorial interaction test suite, such as fixed-strength and variable-strength interaction test suite, is widely used for combinatorial interaction testing. Due to constrained testing resources in some applications, for example in combinatorial interaction regression testing, prioritization of combinatorial interaction test suite has been proposed to improve the efficiency of testing. However, nearly all prioritization techniques may only support fixed-strength interaction test suite rather than variable-strength interaction test suite. In this paper, we propose two heuristic methods in order to prioritize variable-strength interaction test suite by taking advantage of its special characteristics. The experimental results show that our methods are more effective for variable-strength interaction test suite by comparing with the technique of prioritizing combinatorial interaction test suites according to test case generation order, the random test prioritization technique, and the fixed-strength interaction test suite prioritization technique. Besides, our methods have additional advantages compared with the prioritization techniques for fixed-strength interaction test suite.
Generative Incremental Dependency Parsing with Neural Networks

We propose a neural network model for scalable generative transition-based dependency parsing. A probability distribution over both sentences and transition sequences is parameterised by a feedforward neural network. The model surpasses the accuracy and speed of previous generative dependency parsers, reaching 91.1% UAS. Perplexity results show a strong improvement over n-gram language models, opening the way to the efficient integration of syntax into neural models for language generation.
Selectively using linguistic resources throughout the question answering pipeline

It is generally believed that question answering can benefit from natural language processing methods. So far, however, there have been few systematic studies of this conjecture. We report on ongoing work that is aimed at understanding the contribution of linguisticallyinformed modules and resources to the overall performance of a generic question answering system. Specifically, we describe the ways in which currently we use linguistically motivated techniques, and demonstrate the impact of integrating, or not integrating, these techniques on the overall performance of our question answering system. Evaluation results are based on the TREC 2002 and TREC 2003 question sets.
TOX3 protein expression is correlated with pathological characteristics in breast cancer

TOX3 is a newly identified gene that has been observed to correlate with breast cancer by genome-wide association studies (GWAS) in recent years. In addition, it has been noted that single-nucleotide polymorphisms (SNPs) in the TOX3 gene have a strong correlation with estrogen receptor (ER)-positive tumors. However, the role of TOX3 in breast carcinoma development is still unclear. There are limited studies on the subject of TOX3 mRNA expression in breast tumors and little information on the variation of TOX3 protein expression in relation to the clinical pathological features in breast cancer and healthy tissues. In this study, we characterize the protein expression of TOX3 in breast tumors with respect to various clinical and pathological characteristics and explore the correlation between TOX3 protein expression and ER-positive tumors. A breast cancer tissue microarray containing 267 human breast tumors and 25 healthy controls, breast cancer cell lines (ZR-75-1, MDA-MB-231, MCF-7 and Bcap-37) with positive or negative ER expression, tumor tissues and matched controls were used to analyze the protein expression levels of TOX3 by immunohistochemistry, western blot analysis and quantitative polymerase chain reaction. Among the 267 breast tumor specimens, ER expression was detected in 66 tumor tissues. The expression levels of TOX3 increased in breast carcinoma tissue compared with controls, and were higher in advanced carcinoma (T3 and T4), lymph node metastases tissues (N2) and stage III tissues. Furthermore, TOX3 protein expression was more intense in ER-positive tumors, but did not demonstrate a statistical significance. However, it was significantly increased in ER-positive breast cancer cell lines (ZR-75-1, MCF-7 and Bcap-37) compared with the MDA-MB-231 cell line, which had ER-negative expression. Our findings provide support to the hypothesis that TOX3 has a strong correlation with the development of breast cancer. The current study is likely to assist in investigating the mechanisms involved in breast cancer development.
A Pattern Growth Method Based on Memory Indexing for Frequent Patterns Mining

In this paper, we present an algorithm based on memory indexing for frequent patterns mining (called MIndexing), which requires scanning database only one time and does not generate any candidates. The MIndexing algorithm is memory-based and can utilize memory and CPU resources sufficiently to extend the capability in high effectiveness and efficiency. Our experiment results show that the MIndexing algorithm performs better than a priori and FP-growth method for processing sparse data datasets containing long patterns. Furthermore, with MIndexing algorithm, we adopt a partitioning-based strategy to decompose the mining task into a set of smaller tasks for mining frequent patterns for processing very large datasets