Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of precise and less precise arguments in the wake of happy mood

This study aims to investigate how people agree to precise and less precise arguments and if happy mood affects the agreement to precise and less precise arguments. Four hundred and forty three participants were recruited through Facebook and were randomly divided into the two conditions happy mood condition and neutral mood condition. Participants in both conditions read a scenario where an eyewitness saw a man selling drugs in a club. Eyewitness’ credit card showed that she had consumed X (2 or 5) glasses of wine. Later the participants read either one of the arguments from the defense lawyer. A precise argument that “the witness is not trustworthy because she has consumed X (2 or 5) glasses of wine”. A less precise argument that “the witness is not trustworthy because she was under the influence of alcohol”. Happy and neutral mood was induced with text. People in the happy mood condition read a story about a professor and his students and people in the neutral mood condition read a story about the universe. The results show that there is no difference in how much people agree to precise or less precise arguments. The results also show that happy mood condition did not differ from the neutral mood condition in terms of the participant’s agreement to the two types of arguments. The reason for these results can be that the mood induction was not successful.
Error exponents for bayesian detection with randomly spaced sensors

We study the detection of Gauss-Markov signals using randomly spaced sensors. We derive a lower bound on the Bayesian detection error based on the Kullback-Leibler divergence, and from this, define an error exponent. We then evaluate the error exponent for stationary and non-stationary Gauss-Markov models where the sensor spacings, di, di,. , are drawn independently from a common distribution Fd. In both models, error exponents take on simple forms involving the parameters of the Markov process and expectations over Fd of certain functions of di. These expressions are evaluated explicitly when Fd corresponds to (i) exponentially distributed sensors with placement density lambda (ii) equally spaced sensors, and (iii) the proceeding cases when sensors also fail with probability Q. Many insights follow. For example, in the non-stationary case, we determine the optimal lambda as a function of q. Numerical simulations show that the error exponent, based on an asymptotic analysis of the lower bound, predicts trends of the actual error rate accurately, even for small data sizes.
What is the Role of Theory in Research on Social Work Practice?

The social work profession suffers from a dearth of empirically based studies of practice outcomes. One reason for this is the message conveyed in research texts, doctoral program standards, and disciplinary guidelines that research should build and test theory, rather than solve applied problems. Since many legitimate forms of empirical, scientific research do not involve theory testing, social work should consider itself less an academic discipline that advances social and behavioral science knowledge and more an applied profession that finds solutions to psychosocial problems. Research inquiries along these lines may or may not involve direct theory testing or development.
Credit scoring model: A combination of genetic programming and deep learning

In recent years, the market of customer lending grows rapidly, that is a reason why credit scoring becomes a core task of financial institutes. Many models based on machine learning have been widely using and providing robust performance. Because most machine learning based models are black-box, it is hard to see the relations between input data and scoring results. Therefore, this paper focuses on improving both the accuracy and the reliability of machine learning based model. Thus, we propose a hybrid idea to combine the power of deep learning network and the comprehensive genetic programming which is extracted rules to build a robust credit model. Our empirical experiment on Australian/German customer credit data sets shows that our model provides the best accuracy, highly reduce credit risk, and reliable IF-THEN rules.
The Impact of Violence on the Voting Behavior: A Critical View

Voting is the main form of political participation in democratic societies and the study of voting behavior is a highly specialized sub-field in political and social science. The study was concerned with the reasons why people vote the way they do and what is the role of violence on the voting behavior. The main objectives of the present study were to find out the factors like the violence which affect the voting behavior & to explore the factors that constitute political affiliation. A sample of 100 respondents (students) was selected by simple random technique from Gomal University, Dera Ismail Khan, Khyber Pakhtunkhwa, Pakistan. Specifically, this study explored the relationship of voting behavior with violence other factors that had strong effect on voting decisions. The results of the present study showed a diverse response of the student regarding the impact of violence on the voting behavior.
Voltage unbalance treatment for distribution network with massively connected distributed generators

Large scale interconnection of single-phase distributed generation (DG) with utility distribution grid can cause current unbalances resulting in voltage unbalances. The methods of Newton-Raphson and Gauss-Seidel are generally used for power flow's calculation. However, these methods are sometimes complex and their convergences may be long [1]. The iterative process of Newton-Raphson method, using the admittances matrix is practically independent of bus bars number. The calculation's time of Gauss-Seidel method increases almost proportionally with the bus bars. In addition, the calculation's time of the Jacobien's matrix and each iterative process are considerably long for the Newton-Raphson method. In this paper, a method based on the impedance order reduction and identification of the power flow's direction is presented. Analytical and simulation studies were performed in order to validate the accuracy of the proposed method. For an application network model, the voltage unbalances are calculated. The results are compared with those of two commercial softwares.
Sparse SAR patch classification using complex valued approach

This paper presents change detection using very high resolution SAR data. Small patches of SAR data were used for graphical Lasso based algorithm. The graphical Lasso for time series is defined as solution of an l1-regularized maximum likelihood problem. The optimization problem was solved using alternating direction method of multipliers (ADMM). The time series of patches was observed. The efficiency of change detection of the ADMM algorithm was observed using different window sizes. The time series of 10 interferometric SAR Very high resolution Spotlight data was used, which were acquired over period of 3 years. Experimental results showed that ADMM can be successfully applied to the time series in order to detect changes using complex valued SAR data.
Boolean Matrix Factorization and Completion via Message Passing

Boolean factor analysis is the task of decomposing a Binary matrix to the Boolean product of two binary factors. This unsupervised data-analysis approach is desirable due to its interpretability, but hard to perform due its NP-hardness. A closely related problem is low-rank Boolean matrix completion from noisy observations. We treat these problems as maximum a posteriori inference problems, and present message passing solutions that scale linearly with the number of observations and factors. Our empirical study demonstrates that message passing is able to recover low-rank Boolean matrices, in the boundaries of theoretically possible recovery and outperform existing techniques in real-world applications, such as large-scale binary valued collaborative filtering tasks. A large body of problems in machine learning, communication theory and combinatorial optimization involve the product form Z = X Y where Z = {Zm,n},X = {Xm,k},Y = {Yk,n} where one or two components (out of three) are (partially) known and the task is to recover the unknown component(s). Here, operation corresponds to a type of matrix multiplication. A subset of these problems, which are most closely related to Boolean matrix factorization and matrix completion, can be expressed over the Boolean domain – i.e., Zm,n, Xm,k, Yk,n ∈ {false, true} ∼= {0, 1}. The two most common Boolean matrix products used in such applications are Z = X •Y ⇒ Zm,n = K ∨
Managing Quality of Context in Pervasive Computing

Context-awareness plays a key role in a paradigm shift from traditional desktop styled computing to emerging pervasive computing. Many context-aware systems have been built to achieve the vision of pervasive computing and alleviate the human attention bottleneck; however, these systems are far from real world applications. Quality of context is critical in reducing the gap between existing systems and real-life applications. Aiming to provide the support of quality of context, in this paper, we propose a novel quality model for context information and a context management mechanism for inconsistency resolution. We also build a prototype system to validate our proposed model and mechanism, and to assist the development of context-aware applications. Through our evaluations and case study, context-aware applications can be built with the support of quality of context
Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

BACKGROUND The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. METHODS We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. RESULTS The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003-2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. CONCLUSION We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors.
Approaches To The Evolution Of Language: Social And Cognitive Bases

Introduction Michael Studdert-Kennedy, Chris Knight, and James R. Hurford Part I. Grounding Language Function in Social Cognition: 1. Introduction: Grounding language function in social cognition Chris Knight 2. On discontinuing the continuity-discontinuity debate Jean Aitchison 3. The origin of language and cognition Ib Ulbaek 4. Mimesis and the executive suite: missing links in language evolution Merlin Donald 5. Ritual/speech co-evolution: a 'selfish gene' solution to the problem of deception Chris Knight 6. Theory of mind and the evolution of language Robin Dunbar 7. Old wives' tales: the gossip hypothesis and the reliability of cheap signals Camilla Power 8. Altruism, status, and the origin of relevance Jean-Louis Dessalles 9. The evolution of language from social intelligence Robert Worden Part II. The Emergence of Phonology: 10. Introduction: the emergence of phonology Michael Studdert-Kennedy 11. Long call structure in apes as a possible precursor for language Maria Ujhelyi 12. Social sound-making as a precursor to spoken language John F. Locke 13. The particulate origins of language generativity: from syllable to gesture Michael Studdert-Kennedy 14. Evolution of the mechanisms of language output: comparative neurobiology of vocal and manual communication Peter MacNeilage 15. Systemic constraints and adaptive change in the formation of sound structure Bjoern Lindblom 16. The development of sound systems in human language Klaus J. Kohler 17. Synonymy avoidance, phonology and the origin of syntax Andrew Carstairs-McCarthy Part III. The Emergence of Syntax: 18. Introduction: the emergence of syntax James R. Hurford 19. On the supposed 'counterfunctionality' of universal grammar: some evolutionary implications Frederick J. Newmeyer 20. Language evolution and the minimalist program: the origins of syntax Robert C. Berwick 21. Catastrophic evolution: the case for a single step from protolanguage to full human language Derek Bickerton 22. Fitness and the selective adaptation of language Simon Kirby 23. Synthesizing the origins of language and meaning using co-evolution, self-organization and level formation Luc Steels 24. Computational simulations of the emergence of grammar John Batali.
Hot Flashes and Subclinical Cardiovascular Disease: Findings From the Study of Women’s Health Across the Nation Heart Study

Background— Although evidence suggests adverse vascular changes among women with hot flashes, it is unknown whether hot flashes are associated with subclinical cardiovascular disease. The aim of this study was to examine relations between menopausal hot flashes and indices of subclinical cardiovascular disease. We hypothesized that women with hot flashes would show reduced flow-mediated dilation and greater coronary artery and aortic calcification compared with women without hot flashes. Methods and Results— The Study of Women’s Health Across the Nation Heart Study (2001 to 2003) is an ancillary study to the Study of Women’s Health Across the Nation, a community-based cohort study. Participants were 492 women (35% black, 65% white) 45 to 58 years of age who were free of clinical cardiovascular disease and had a uterus and at least 1 ovary. Measures included a brachial artery ultrasound to assess flow-mediated dilation, electron beam tomography to assess coronary artery and aortic calcification, reported hot flashes (any/none, previous 2 weeks), and a blood sample for measurement of estradiol concentrations. Cross-sectional associations were evaluated with linear regression and partial proportional odds models. Hot flashes were associated with significantly lower flow-mediated dilation (&bgr;=−1.01; SE, 0.41; P=0.01) and greater coronary artery (odds ratio, 1.48; 95% confidence interval, 1.04 to 2.12) and aortic (odds ratio, 1.55; 95% confidence interval, 1.10 to 2.19) calcification in age- and race-adjusted models. Significant associations between hot flashes and flow-mediated dilation (&bgr;=−0.97; SE, 0.44; P=0.03) and aortic calcification (odds ratio, 1.63; 95% confidence interval, 1.07 to 2.49) remained in models adjusted for cardiovascular disease risk factors and estradiol. Conclusions— Women with hot flashes had reduced flow-mediated dilation and greater aortic calcification. Hot flashes may mark adverse underlying vascular changes among midlife women.
Effective population size dynamics reveal impacts of historic climatic events and recent anthropogenic pressure in African elephants

Two hundred years of elephant hunting for ivory, peaking in 1970–1980s, caused local extirpations and massive population declines across Africa. The resulting genetic impacts on surviving populations have not been studied, despite the importance of understanding the evolutionary repercussions of such human‐mediated events on this keystone species. Using Bayesian coalescent‐based genetic methods to evaluate time‐specific changes in effective population size, we analysed genetic variation in 20 highly polymorphic microsatellite loci from 400 elephants inhabiting the greater Samburu‐Laikipia region of northern Kenya. This area experienced a decline of between 80% and 90% in the last few decades when ivory harvesting was rampant. The most significant change in effective population size, however, occurred approximately 2500 years ago during a mid–Holocene period of climatic drying in tropical Africa. Contrary to expectations, detailed analyses of four contemporary age‐based cohorts showed that the peak poaching epidemic in the 1970s caused detectable temporary genetic impacts, with genetic diversity rebounding as juveniles surviving the poaching era became reproductively mature. This study demonstrates the importance of climatic history in shaping the distribution and genetic history of a keystone species and highlights the utility of coalescent‐based demographic approaches in unravelling ancestral demographic events despite a lack of ancient samples. Unique insights into the genetic signature of mid‐Holocene climatic change in Africa and effects of recent poaching pressure on elephants are discussed.
Magnetic resonance imaging of lung parenchyma

Imaging of the lungs presents a unique challenge for MR technology. Signal from lung parenchyma is limited by low proton density, the effects of motion and susceptibility, and a multiexponential T2 relaxation time. We have illustrated the use of spin echo and projection reconstruction MR sequences in imaging lung parenchyma, and the use of a GRE sequence in imaging the pulmonary vascular tree.
El envejecimiento humano como catalizador de la administración y su enseñanza

This article proposes several original ways through which acknowledgement of and awareness towards human ageing can be used to promote positive changes in management and its teaching, by means of sensibilization and recognition. The introduction and justification of this issue, is followed by the examination of relevant definitions and meaningful trends. Afterwards, some critics to management are discussed alongside the corresponding contributions derived from human ageing. Particularly,ideas are proposed to neutralize administrative fads, to humanize organizations, and to introduce more freedom. Finally, some specific applications are directed to enrich the field of management education with the potential offered by the elders.
Responses of staff towards people with mild to moderate intellectual disability who behave aggressively: a cognitive emotional analysis.

BACKGROUND Traditionally, research into interactions between staff and adults with an intellectual disability who engage in challenging behaviour has been carried out within a behavioural paradigm. Recent work has begun to examine cognitive factors. The majority of this research has drawn upon Weiner's attributional model of helping behaviour. Moreover, staff responses have usually been investigated in relation to client vignettes of challenging behaviour. METHOD The present study involved 38 staff who worked with frequently aggressive clients and its aims were twofold: (1) to compare different methods of examining the cognitive and emotional responses of staff to aggression; namely, descriptive vignettes and real incidents of aggression which staff could recall; and (2) to attempt to replicate previous findings supportive of Weiner's model with a new carer group (day centre staff). RESULTS When staff responses were compared across the hypothetical and real scenarios, using a series of one-way analyses of variance, it was revealed that staff experienced more negative emotions in response to the real incidents of aggression. The present study produced little evidence in support of Weiner's model. However, it was found that staff perceptions of the aggressive clients were linked to their cognitive and emotional responses to the aggression. CONCLUSION The theoretical and clinical implications of these findings are considered, and the possible role played by interpersonal attributions in mediating staff responses to aggression is discussed.
A value-based cache replacement approach for Information-Centric Networks

Information-Centric Networks (ICNs) represent a content-based model which addresses user's requests regardless of the content's location or the nature of its original publisher. The performance of an ICN is highly dependent on replicating the content across the caches of a multitude of nodes in the network. Given the high data turnover rates of contemporary applications and the finite nature of caching space, efficient caching algorithms play a crucial role in determining which data item can be safely dropped in order to accommodate for more important items. In this paper, we present a value-based cache replacement approach that executes a Least Valuable First (LVF) policy. Our approach employs a utility function that uses delay, popularity and age parameters to determine which item to drop from the cache. We present simulation results comparing our approach to other dominant cache replacement policies under varying conditions such as data popularity, in-network cache ratio and connectivity degree. Results show that our approach outperforms in terms of time-to-hit, hit rate, in-network delay and data publisher load.
S100A4 in cancer progression and metastasis: A systematic review

Metastasis is the leading cause of cancer-related death and directly associates with cancer progression, resistance to anticancer therapy, and poor patient survival. Current efforts focusing on the underlying molecular mechanisms of cancer metastasis attract a special attention to cancer researchers. The epithelial-mesenchymal transition is a complex of molecular program during embryogenesis, inflammation, tissue fibrosis, and cancer progression and metastasis. S100A4, an important member of S100 family proteins, functions to increase the tumor progression and metastasis. The molecular mechanisms of S100A4 involving in the progression and metastasis are diverse in various malignant tumors. Detection of S100A4 expression becomes a promising candidate biomarker in cancer early diagnosis and prediction of cancer metastasis and therefore, S100A4 may be a therapeutic target. This review summarized up to date advancement on the role of S100A4 in human cancer development, progression, and metastasis and the underlying molecular events and then strategies to target S100A4 expression experimentally.