Miracle-dz/UniFAR-FTUs huggingface dataset:
Social-Psychological Factors Associated with Bullying Behaviour among Secondary School Students

This study investigated the association between social-psychological factors (misconduct, impulsivity, school sense of belonging, intention to use non-violent strategies, beliefs supportive of violence) and bullying behaviour. A total of two-hundred and fifteen (n=215) secondary school students randomly selected from five secondary schools in Ago-Iwoye, Ogun State participated in the study (boys =106, or 49.30%) and girls n=109 or 50.70%; age ranges = 16 to 22 years, mean = 19 years, SD= 3.19 years). The participants completed measures of misconduct, impulsivity, school sense of belonging, intention to use non-violent strategies, beliefs supportive of violence and bullying behaviour. Demographics data of the participants were also obtained. Hierarchical multiple regression analysis revealed that misconduct is the most important correlate of bullying behaviour. Pro-aggression behaviours explained bullying behaviour, intention to use non-violent strategies, beliefs supportive of violence, impulsivity and school sense of belonging also significantly explained lower bullying behaviour.
sAXI: A High-Efficient Hardware Inter-Node Link in ARM Server for Remote Memory Access

The ever-growing need for fast big-data operations has made in-memory processing increasingly important in modern datacenters. To mitigate the capacity limitation of a single server node, techniques of inner-rack cross-node memory access have drawn attention recently. However, existing proposals exhibit inefficiency in remote memory access among server nodes due to inter-protocol conversions and non-transparent coarse-grained accesses. In this study, we propose the high-performance and efficient serialized AXI (sAXI) link and its associated cross-node memory access mechanism for emerging ARM-based servers. The key idea behind sAXI is directly extending the on-chip AMBA AXI-4.0 interconnection of the SoC in a local server node to the outside, and then bringing into remote server nodes via high-speed serial lanes. As a result, natively accessing remote memory in adjacent nodes in the same manner of local assets is supported by purely using existing software. Experimental results show that, using the sAXI data-path, performance of remote memory access in the user-level micro-benchmark is very promising (min. latency: 1.16μs, max. bandwidth: 1.52GB/s on our in-house FPGA prototype). In addition, through this efficient hardware inter-node link, performance of an in-memory key-value framework, Redis, can be improved up to 1.72x and large latency overhead of database query can be effectively hidden.
Decentralized charging control for large populations of plug-in electric vehicles

The paper develops a novel decentralized charging control strategy for large populations of plug-in electric vehicles (PEVs). We consider the situation where PEV agents are rational and weakly coupled via their operation costs. At an established Nash equilibrium, each of the PEV agents reacts optimally with respect to the average charging strategy of all the PEV agents. Each of the average charging strategies can be approximated by an infinite population limit which is the solution of a fixed point problem. The control objective is to minimize electricity generation costs by establishing a PEV charging schedule that fills the overnight demand valley. The paper shows that under certain mild conditions, there exists a unique Nash equilibrium that almost satisfies that goal. Moreover, the paper establishes a sufficient condition under which the system converges to the unique Nash equilibrium. The theoretical results are illustrated through various numerical examples.
Cognitive performance and engagement in physical, social and intellectual activities in older adults: The FIBRA study

Cognitive decline in aging can negatively impact quality of life in the elderly. However, studies have shown that elderly engaged in advanced activities of daily living (AADLs) can maintain or enhance global cognitive function or specific domains. Objective To investigate the relationship between engagement in AADLs and domains of cognition in elderly from seven different locations in Brazil. Methods A cross-sectional study involving 2,549 elderly without cognitive deficits suggestive of dementia was conducted. Data were collected on sociodemographic characteristics, health status, the Mini-Mental State Exam (MMSE) by subdomain (orientation, memory, attention/calculus, language and constructional praxis), and engagement in AADL grouped under physical, social and intellectual activities. Results Multivariate linear regression analysis revealed an association, albeit modest, between intellectual AADLs and the domains orientation, attention/calculus, language and constructional praxis (R2=0.005, 0.008, 0.021, and 0.021 respectively). Social AADLs were correlated with memory (R2=0.002) and language (R2=0.004) domains. No association was found between physical AADLs and MMSE domains. Schooling and family income were the sociodemographic variables exhibiting the strongest relationship with cognitive domains. Conclusion The study found associations between intellectual and social AADLs with higher cognitive performance, suggesting that active aging can provide opportunities to attenuate cognitive decline in aging.
Control flow analysis: a functional languages compilation paradigm

Control flow analysis (cfa) is now well known but is not widely used in real compilers since optimizations that can be achieved via cfa are not so clear. This paper aims at showing that control flow analysis is very valuable in practice by presenting a fundamental optimization based on cfa: the closure representation algorithm, the essential optimizing phase of a X-language compiler. Since naive and regular schemes to represent functions as heap allocated structures is far too inefficient, the main effort of modern functional languages compilers is devoted to minimize the amount of memory allocated for functions. In particular, compilers try to discover when a procedure can safely be handled without any allocation at all. Previously described methods to do so are ad hoc, depending on the language compiled, and not very precise. Using cfa, we present a general approach which produces better results. This refined closure analysis subsumes previously known ones and optimizes more than 90 % of closure on the average. This optimization is fully integrated into the Bigloo compiler, so that we can report reliable measures obtained for real world programs. Time figures show that analyses based on cfa can be very efficient: when the compiler uses the improved closure allocation scheme the resulting executable programs run more than two time faster.
Classical dependence analysis techniques: sufficiently accurate in practice

Data dependence analysis is the foundation of any parallelizing compiler. The GCD (greatest common divisor) test and the Banerjee-Wolfe test (U. Banerjee 1988, M. Wolfe 1989) are the two tests traditionally used to determine statement data dependence in automatic vectorization/parallelization of loops. These tests are approximate in the sense that they are necessary but not sufficient conditions for data dependence. In an earlier work (Proc. 6th ACM Int. Conf. Supercomputing, Washington, DC, USA, July 1992), we extended the Banerjee-Wolfe test and a combination of the GCD and Banerjee-Wolfe tests with a set of conditions to derive exact data dependence information. In this paper, we perform an empirical study on the Perfect benchmarks to demonstrate the effectiveness and practical importance of our conditions. We show that the Banerjee-Wolfe test extended with our conditions becomes an exact test for data dependence in actual practice. <<ETX>>
White racial attitudes in the age of Obama

LeCount, Ryan Jerome. Ph.D., Purdue University, December 2014. White Racial Attitudes in the Age of Obama. Major Professor: Kevin Stainback. What is the nature of White racial attitudes in the age of Obama? This dissertation project seeks to answer this question in three distinct ways. The first empirical chapter examines the role of economic insecurity and education on White racial attitudes. The second empirical chapter evaluates the relative importance of individual vs. contextual factors in shaping Whites’ attitudes about race. The third empirical chapter seeks to evaluate the extent to which racial color-blindness (as opposed to other racial attitudes) motivates White opposition to race-targeted programs. Findings in empirical chapters one and two are conditional, while clear evidence is demonstrated that color-blindness does not predict white opposition to race-targeted programs in empirical chapter three. Implications for the future study of white racial attitudes are discussed.
Current Practices in Measuring Ontological Concept Similarity

Ontologies are widely used and play important roles in applications related to knowledge management, artificial intelligence, natural language processing, etc. Measuring the semantic similarity between ontological concepts is necessary in applications that use ontologies. This paper presents a survey of approaches to compute ontological concept similarity. A taxonomy showing the classification of approaches is introduced. The advantages and disadvantages of each approach are discussed.
The Ability to Form Primary Tumor Xenografts Is Predictive of Increased Risk of Disease Recurrence in Early-Stage Non–Small Cell Lung Cancer

Purpose: Primary tumor xenografts (PTXG) established directly from patients' primary tumors in immunosuppressed animals might represent the spectrum of histologic complexity of lung cancers better than xenografts derived from established cell lines. These models are important in the study of aberrant biological pathways in cancers and as preclinical models for testing new therapeutic agents. However, not all primary tumors engraft when implanted into immunosuppressed mice. We have investigated factors that may influence the ability of primary non–small cell lung cancer (NSCLC) to form xenografts and their association with clinical outcome. Experimental Design: Tumor fragments from patients undergoing curative surgery were implanted into NOD-SCID (nonobese diabetic-severely combined immunodeficient) mice within 24 hours of surgery. Patient characteristics for tumors that engrafted (XG) and did not engraft (no-XG) were compared. Patient tumor DNA was profiled for the presence of 238 known mutations in 19 cancer-associated genes by using the MassARRAY platform. Results: Xenografts were established and passaged successfully from 63 of 157 (40%) implanted NSCLCs. Tumor factors associated with engraftment included squamous histology, poor differentiation, and larger tumor size. Significantly fewer EGFR (epidermal growth factor receptor)-mutated tumors engrafted (P = 0.03); conversely, more K-RAS–mutated tumors engrafted (P = 0.05). In multivariate analysis including age, sex, stage, and mutation, patients with XG tumors had significantly shorter disease-free survival compared with no-XG patients (hazard ratio: 7.0, 95% CI: 3.1–15.81; P < 0.000003). Conclusion: PTXGs closely mirror the histology and molecular profiles of primary tumors and therefore may serve as important preclinical models. Tumors that engraft are biologically more aggressive and may be more representative of cancers with a higher propensity to relapse after surgery. Clin Cancer Res; 17(1); 134–41. ©2010 AACR.
Detecting anomalies in graphs with numeric labels

This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a combination of structural irregularities and numerical outliers.
Microphone array post-filter for diffuse noise field

This paper proposes a novel technique for estimating the signal power spectral density to be used in the transfer function of a microphone array post-filter. The technique is a modification of the existing Zelinski post-filter, which uses the auto- and cross-spectral densities of the array inputs to estimate the signal and noise spectral densities. The Zelinski technique, however, assumes zero cross-correlation between noise on different sensors. This assumption is inaccurate in real conditions, particularly at low frequencies and for arrays with closely spaced sensors. In this paper we replace this with an assumption of a theoretically diffuse noise field, which is more appropriate in a variety of realistic noise environments. In experiments using noise recordings from an office of computer workstations, the modified post-filter results in significant improvement in terms of objective speech quality measures and speech recognition performance.
A refined DRSA model for the financial performance prediction of commercial banks

In this study, an integrated soft computing method is proposed to solve the performance prediction of commercial banks. The proposed model not only shows how to explore the implicit patterns from historical data, but also supports to provide guidance regarding the performance categorization of banks on each attribute. A group of commercial banks in Taiwan is analyzed to construct the model, and three sample banks are tested (ranked) to validateit. The result provides understandable insights and applicable rules regarding the performance prediction of commercial banks in practice.
Recent Applications of Artificial Neural Networks in Forest Resource Management: An Overview

Making good decisions for adaptive forest management has become increasingly difficult. New artificial intelligence (AI) technology allows knowledge processing to be included in decision‐support tool. The application of Artificial Neural Networks (ANN), known as Parallel Distributed Processing (PDP), to predict the behaviours of nonlinear systems has become an attractive alternative to traditional statistical methods. This paper aims to provide an up‐to‐date synthesis of the use of ANN in forest resource management. Current ANN applications include: (1) forest land mapping and classification, (2) forest growth and dynamics modeling (3) spatial data analysis and modeling (4) plant disease dynamics modeling, and (5) climate change research. The advantages and disadvantages of using ANNs are discussed. Although the ANN applications are at an early stage, they have demonstrated potential as a useful tool for forest resource management.
Site-specifically labeled CA19.9-targeted immunoconjugates for the PET, NIRF, and multimodal PET/NIRF imaging of pancreatic cancer

Significance Pancreatic cancer will soon be the second leading cause of cancer deaths annually, yet no adequate molecular imaging tools exist to aid in the staging, monitoring, and treatment of the disease. Here we describe the development and preclinical evaluation of three unique immunoconjugates for positron emission tomography, near-infrared fluorescent optical imaging, and multimodal imaging of pancreatic ductal adenocarcinoma (PDAC). The probes were developed using a site-specific, chemoenzymatic methodology that is robust, reproducible, and modular. By targeting CA19.9, the most abundant antigen in >90% of PDAC tumors, we were able to obtain high-quality images in multiple murine models of PDAC, suggesting these constructs could be the core of a molecular imaging toolkit aimed at improving outcomes for patients with PDAC. Molecular imaging agents for preoperative positron emission tomography (PET) and near-infrared fluorescent (NIRF)-guided delineation of surgical margins could greatly enhance the diagnosis, staging, and resection of pancreatic cancer. PET and NIRF optical imaging offer complementary clinical applications, enabling the noninvasive whole-body imaging to localize disease and identification of tumor margins during surgery, respectively. We report the development of PET, NIRF, and dual-modal (PET/NIRF) imaging agents, using 5B1, a fully human monoclonal antibody that targets CA19.9, a well-established pancreatic cancer biomarker. Desferrioxamine (DFO) and/or a NIRF dye (FL) were conjugated to the heavy-chain glycans of 5B1, using a robust and reproducible site-specific (ss) labeling methodology to generate three constructs (ssDFO-5B1, ssFL-5B1, and ssdual-5B1) in which the immunoreactivity was not affected by the conjugation of either label. Each construct was evaluated in a s.c. xenograft model, using CA19.9-positive (BxPC3) and -negative (MIAPaCa-2) human pancreatic cancer cell lines. Each construct showed exceptional uptake and contrast in antigen-positive tumors with negligible nonspecific uptake in antigen-negative tumors. Additionally, the dual-modal construct was evaluated in an orthotopic murine pancreatic cancer model, using the human pancreatic cancer cell line, Suit-2. The ssdual-5B1 demonstrated a remarkable capacity to delineate metastases and to map the sentinel lymph nodes via tandem PET-computed tomography (PET/CT) and NIRF imaging. Fluorescence microscopy, histopathology, and autoradiography were performed on representative sections of excised tumors to visualize the distribution of the constructs within the tumors. These imaging tools have tremendous potential for further preclinical research and for clinical translation.
4 First Principles Study of Si / Ge Core-Shell Nanowires-Structural and Electronic Properties

One-dimensional semiconductor nanostructures, such as Si and Ge nanowires, have attracted extensive research efforts over the past decades (B. Y-K Hu & Das Sarma, 1992; Hirschman et al., 1996; Morales & Lieber, 1998; Holmes et al., 2000; Cui & Lieber, 2001; Cui et al., 2001, 2003; Koo et al., 2004; Audoit et al., 2005; X. Y. Wu et al., 2008; Dailey & Drucker, 2009). They are expected to play important roles as both interconnects and functional components in future nanoscale electronic and optical devices, such as lightemitting diodes (LEDs) (Hirschman et al., 1996; Cui & Lieber, 2001), ballistic field-effect transistors (FETs) (Cui et al., 2003; Koo et al., 2004), inverters (Cui & Lieber, 2001), and nanoscale sensors (Cui et al., 2001; Hahm & Lieber, 2004). Experimental and theoretical investigations showed that in these nanoscale structures charge carriers are confined in the lateral direction of the wires, thus quantum confinement effect is expected to play an important role on the electronic properties. This confinement effect has been observed, for example, in photoluminescence studies, by exhibiting substantial blue-shift of emission with reduction of the nanowire diameter (Z. Wu et al., 2008; Beckman et al., 2006; Bruno et al., 2005). Researchers also found that the band gap of Si and Ge nanowires depends on several factors, such as size (Beckman et al., 2006; Bruno et al., 2005; Arantes & Fazzio, 2007), crystalline orientation (Bruno et al., 2005; Arantes & Fazzio, 2007; Medaboina et al., 2007), surface chemistry (Medaboina et al., 2007; Kagimura et al., 2007), and doping (Medaboina et al., 2007; Peelaers et al., 2007). Recently, particular attention has been given to Si/Ge core-shell nanowires, in which factors, such as heterostructure composition and interface geometry, can be further manipulated to tune the electronic properties of nanowires (Kagimura et al., 2007; Lauhon et al., 2002; Y. Y. Wu et al., 2002; Lu et al., 2005; Trammell et al., 2008; J. Q. Hu et al., 2009; Peng & Logan, 2010; Peng et al., 2011b). Compared to pure Si and Ge wires, the core-shell structure has some greater properties. For instance, a better conductance and higher mobility of charge carries can be obtained, due to the conduction and valence band offsets in the core-shell nanowires (Kagimura et al., 2007; Goldthorpe et al., 2008; L. Yang et al., 2008). The band offsets also offer the heterostructure a wide range of applications in solar cells (Tian et al., 2007) and high-switching speed transistors (Y. J. Hu et al., 2008).
Viral homologs of BCL-2: role of apoptosis in the regulation of virus infection.

Cellular BCL-2-related proteins (cBCL-2s) function as regulators of programmed cell death (apoptosis), in part by modulating the release of proapoptotic signaling molecules from mitochondria. These factors act to promote activation of cysteine proteases of the caspase family and thereby propagate signaling of apoptotic cell death. DNA viruses are known to encode homologs of cellular antiapoptotic BCL-2 proteins (vBCL-2s), and the role of vBCL-2s in various aspects of viral infection and the mechanism by which they function have been gradually emerging. It is now apparent that inhibition of apoptosis by vBCL-2s in infected cells can prevent premature death of the host cell, which would impair virus production; can enable efficient emergence from latency; can facilitate persistent infection; and contributes to the avoidance of immune surveillance by the host. Thus, apoptosis is clearly a mechanism used by the host immune system and the infected host cell itself as part of the antiviral response. Deregulation of this delicate host– pathogen interaction can alter the course of virus replication, which may explain aspects of viral disease. Recent evidence suggests that vBCL-2s may target the core cellular proapoptotic machinery for inhibition, perhaps to secure a broad spectrum of apoptosis inhibition to the infected cell. Furthermore, because vBCL-2 family members and some of their cellular counterparts are oncogenic, deciphering their mode of action may be useful in understanding and thwarting human cancer. In this review we outline the role and the mechanism of action of vBCL-2 proteins in infected cells and how and why their function may be distinct from some of their cellular homologs.
Why do phase III trials of promising heart failure drugs often fail? The contribution of "regression to the truth".

There has been considerable recent disappointment with the failure of a number of major new pharmacological strategies for the treatment of chronic heart failure. In turn, there has been much speculation as to why trials of these therapies have not shown benefit. Among a number of plausible and scientifically valid reasons, consideration should be afforded to the potential contribution of "regression to the truth. " Regression to the truth derives from the biological concept of regression to the mean, whereby random fluctuations in a biological variable occur over time, such that the true value of the variable is approached with repeated measurements. This same concept can be applied to clinical trial programs for new drugs for heart failure. Because only strongly positive trials generally go on to phase III testing, and some of these early phase studies are positive by chance alone, on retesting in phase III the results are very likely not be as strongly positive. Numerous examples of regression to the truth apply for trials of heart failure therapies, as well as in other areas. A major concern is how to minimize negative outcomes in phase III trials. One approach is to perform major rigorous phase II testing. Alternatively, avoidance of phase II testing will minimize "regression to the truth" because there are no data in phase II from which regression might occur. However, this approach does not obviate the need for an evaluation process in the selection of candidate agents (and their appropriate dose) in order to proceed to definitive testing.