Miracle-dz/UniFAR-FTUs huggingface dataset:
Experience-Dependent Modulation of Feedback Integration during Singing: Role of the Right Anterior Insula

Somatosensation plays an important role in the motor control of vocal functions, yet its neural correlate and relation to vocal learning is not well understood. We used fMRI in 17 trained singers and 12 nonsingers to study the effects of vocal-fold anesthesia on the vocal-motor singing network as a function of singing expertise. Tasks required participants to sing musical target intervals under normal conditions and after anesthesia. At the behavioral level, anesthesia altered pitch accuracy in both groups, but singers were less affected than nonsingers, indicating an experience-dependent effect of the intervention. At the neural level, this difference was accompanied by distinct patterns of decreased activation in singers (cortical and subcortical sensory and motor areas) and nonsingers (subcortical motor areas only) respectively, suggesting that anesthesia affected the higher-level voluntary (explicit) motor and sensorimotor integration network more in experienced singers, and the lower-level (implicit) subcortical motor loops in nonsingers. The right anterior insular cortex (AIC) was identified as the principal area dissociating the effect of expertise as a function of anesthesia by three separate sources of evidence. First, it responded differently to anesthesia in singers (decreased activation) and nonsingers (increased activation). Second, functional connectivity between AIC and bilateral A1, M1, and S1 was reduced in singers but augmented in nonsingers. Third, increased BOLD activity in right AIC in singers was correlated with larger pitch deviation under anesthesia. We conclude that the right AIC and sensory-motor areas play a role in experience-dependent modulation of feedback integration for vocal motor control during singing.
Bandwidth Reduction through Multithreaded Compression of Seismic Images

One of the main challenges of modern computer systems is to overcome the ever more prominent limitations of disk I/O and memory bandwidth, which today are thousands-fold slower than computational speeds. In this paper, we investigate reducing memory bandwidth and overall I/O and memory access times by using multithreaded compression and decompression of large datasets. Since the goal is to achieve a significant overall speedup of I/O, both level of compression achieved and efficiency of the compression and decompression algorithms, are of importance. Several compression methods for efficient disk access for large seismic datasets are implemented and empirically tested on on several modern CPUs and GPUs, including the Intel i7 and NVIDIA c2050 GPU. To reduce I/O time, both lossless and lossy symmetrical compression algorithms as well as hardware alternatives, are tested. Results show that I/O speedup may double by using an SSD vs. HDD disk on larger seismic datasets. Lossy methods investigated include variations of DCT-based methods in several dimensions, and combining these with lossless compression methods such as RLE (Run-Length Encoding) and Huffman encoding. Our best compression rate (0.16%) and speedups (6 for HDD and 3.2 for SSD) are achieved by using DCT in 3D and combining this with a modified RLE for lossy methods. It has an average error of 0.46% which is very acceptable for seismic applications. A simple predictive model for the execution time is also developed and shows an error of maximum 5% vs. our obtained results. It should thus be a good tool for predicting when to take advantage of multithreaded compression. This model and other techniques developed in this paper should also be applicable to several other data intensive applications.
Fractional order measures of anomalous diffusion in healthy aging of neural tissue

In this paper we use fractional calculus to characterize diffusion in brain tissue by generalizing Fick's 2nd Law. This approach, rooted in the physics of the Continuous Time Random Walk (CTRW) Theory expresses separate measures of tissue complexity through the fractional order of the time derivative, α, and the space derivative, ß. We also calculate the entropy of the characteristic function of the probability distribution function (pdf) as a measure of the heterogeneity of the tissue. We applied this theory to the analysis of high-field (17.6 Tesla) diffusion-weighted MRI data to characterize the structural complexity of neural tissue. When interpreted in the context of anomalous diffusion, healthy aging in the normal rat brain is observed in both white matter (WM) and gray matter (GM).
Features of the Organized Youth Activity Setting that Protect Against Exposure to Community Violence

There is consistent evidence that at least one in every pair of youth in inner-city areas has previous exposure to community violence (Stein et al., 2003). Affected communities have responded by developing indigenous programs that successfully engage vulnerable youth, to diminish negative outcomes. Yet few studies have researched these activities in the wake of high prevalence rates. Using a cross-sectional design, the current study investigated setting features of an indigenous, community-based drill team, differentiating behavioral and mental health outcomes among African-American youth exposed to community violence. Specifically, positive perceptions of supportive relationships with adult staff, sense of community and connectedness, and norms for behavior were expected to protect against community violence exposure. Sixty-five participants age 13-20 responded to surveys indicating their level of exposure to community violence and violence-related crime, perceptions of activity setting features, and behavior and mental health. Multiple linear regression revealed a nullified relationship between exposure and delinquency and exposure and drug use for youth perceiving more supportive relationships or a greater sense of community (protective-stabilizing effect). In addition, psychological well-being, which was unaffected by exposure, was better for those reporting a greater sense of community or perceived peer-acceptance of prosocial behavior. These findings emerged regardless of the amount of time spent in the drill team and the level of youths’ parental monitoring and involvement. Additional studies are needed to investigate indigenous youth activities as promotive settings for youth exposed to community violence, and program setting features as mechanisms by which programs may impact youth outcomes.
Dampak Inkuiri Berjenjang terhadap Dimensi Literasi Sains Calon Guru Biologi

This study examined how students’ scientific literacy changed over as they participated in hierarchy of inquiry learning. The hierarchy of inquiry is a comprehensive approach in which students working for an extended period of time that integrated several ways of inquiry systematically to investigate and respond to a complex question, problem, or challenge. This action research was conducted in Biology Teacher Education Program Faculty of Teacher Training and Education Sebelas Maret University. The students, who were at the 4rd semester and who studied the class of Plant Embriology, were purposely selected. The data were collected from interviews with researchers, classroom observations, and collection of student portfolios. Student learning gains were measured using academic writing assessment. which measured students’ ability in nominal literacy, functional literacy, conceptual literacy, and multi-dimensional literacy. The results showed that the scientific literacy level increased on the last inquiry activities. It was suggested that hierarchy of inquiry could have a positive impact on students’ scientific literacy which drawn in their writing projects. Students who experienced the hierarchy of inquiry emphasized the sentences reflecting the application and used high cognitive level sentences. The findings can be helpful in the process of designing the best new curricula for teacher candidates in order to foster scientific literacy. Keywords: scientific literacy, hierarchy of inquiry, cognitive level
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition

In this paper we address the problem of human action recognition from video sequences. Inspired by the exemplary results obtained via automatic feature learning and deep learning approaches in computer vision, we focus our attention towards learning salient spatial features via a convolutional neural network (CNN) and then map their temporal relationship with the aid of Long-Short-Term-Memory (LSTM) networks. Our contribution in this paper is a deep fusion framework that more effectively exploits spatial features from CNNs with temporal features from LSTM models. We also extensively evaluate their strengths and weaknesses. We find that by combining both the sets of features, the fully connected features effectively act as an attention mechanism to direct the LSTM to interesting parts of the convolutional feature sequence. The significance of our fusion method is its simplicity and effectiveness compared to other state-of-the-art methods. The evaluation results demonstrate that this hierarchical multi stream fusion method has higher performance compared to single stream mapping methods allowing it to achieve high accuracy outperforming current state-of-the-art methods in three widely used databases: UCF11, UCFSports, jHMDB.
Breast cancer and serum organochlorine residues

Background: Controversy still exists about the breast carcinogenic properties in humans of environmental xenoestrogens (organochlorines), justifying new investigations. Aims: To compare the blood levels of total dichlorodiphenyltrichloroethane (DDT) and hexachlorobenzene (HCB) in samples collected at the time of breast cancer discovery, in order to avoid the potential consequences of body weight change (after chemotherapy or radiotherapy) on the pesticide residue levels. Methods: Blood levels of HCB and total DDT (we calculated total DDT concentrations by adding all DDT and DDE isomers) were compared in 159 women with breast cancer and 250 presumably healthy controls. Risk of breast cancer associated with organochlorine concentration was evaluated. Results: Mean levels of total DDT and HCB were significantly higher for breast cancer patients than for controls. No differences in serum levels of total DDT or HCB were found between oestrogen receptor positive and oestrogen receptor negative patients with breast cancer. Conclusions: These results add to the growing evidence that certain persistent pollutants may occur in higher concentrations in blood samples from breast cancer patients than controls.
Early detection of Alzheimer's disease using MRI hippocampal texture

Cognitive impairment in patients with Alzheimer's disease (AD) is associated with reduction in hippocampal volume in magnetic resonance imaging (MRI). However, it is unknown whether hippocampal texture changes in persons with mild cognitive impairment (MCI) that does not have a change in hippocampal volume. We tested the hypothesis that hippocampal texture has association to early cognitive loss beyond that of volumetric changes. The texture marker was trained and evaluated using T1‐weighted MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and subsequently applied to score independent data sets from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) and the Metropolit 1953 Danish Male Birth Cohort (Metropolit). Hippocampal texture was superior to volume reduction as predictor of MCI‐to‐AD conversion in ADNI (area under the receiver operating characteristic curve [AUC] 0.74 vs 0.67; DeLong test, p = 0.005), and provided even better prognostic results in AIBL (AUC 0.83). Hippocampal texture, but not volume, correlated with Addenbrooke's cognitive examination score (Pearson correlation, r = −0.25, p < 0.001) in the Metropolit cohort. The hippocampal texture marker correlated with hippocampal glucose metabolism as indicated by fluorodeoxyglucose‐positron emission tomography (Pearson correlation, r = −0.57, p < 0.001). Texture statistics remained significant after adjustment for volume in all cases, and the combination of texture and volume did not improve diagnostic or prognostic AUCs significantly. Our study highlights the presence of hippocampal texture abnormalities in MCI, and the possibility that texture may serve as a prognostic neuroimaging biomarker of early cognitive impairment. Hum Brain Mapp 37:1148–1161, 2016. © 2015 Wiley Periodicals, Inc.
Protective role of Hibiscus sabdariffa calyx extract against streptozotocin induced sperm damage in diabetic rats

Diabetes mellitus contributes to male sexual dysfunction and infertility by modulating oxidative damage. To date, a number of studies have demonstrated antioxidant properties of Hibiscus sabdariffa Linn. This study was designed to investigate the effects of H. sabdariffa UKMR-2 variety on sperm functioning of streptozotocin-induced diabetic rats. Male Sprague-Dawley rats were allotted into four groups, namely control group (C), H. sabdariffa extract (HSE) group, diabetes group (D) and diabetes plus HSE group (D+HSE). HSE (100 mg/ kg/body weight) was administered orally for 28 consecutive days. After 28-days of supplementation, the rats were sacrificed to obtain epididymal sperm. Administration of HSE significantly lowered the level of fasting blood glucose and increased plasma insulin level in D+HSE group as compared to D group (p<0.05). Sperm quality in the D+HSE group was improved with significantly higher sperm concentrations (p<0.05) and sperm motility (p<0.001) as well as lower percentage of sperm abnormality (p<0.05) as compared to the diabetic group. Plasma follicle-stimulating hormone (FSH) level was significantly elevated (p<0.05) in D+HSE group than in D group while no significant alteration in plasma testosterone and luteinizing hormone (LH) level were seen between groups. In conclusion, this study suggested that H. sabdariffa UKMR-2 variety has a potential protective role against diabetes-induced sperm damage.
Constructing Subsumption Hierarchies of Web Queries

In this work, we present an approach for automatically identifying subsumption relations between web queries, a difficult (due to feature sparseness and ambiguity), but extremely useful task for many applications, ranging from user profiling and semantic enhancement of query logs, to traffic minimisation in distributed search environments (e.g., federations of digital libraries or cloud-based systems). We start by matching each query to the topics of a comprehensive web directory, and use these topics to apply query expansion in an iterative fashion. Subsequently, all expanded queries are mapped onto the DMOZ hierarchy, and the resulting subsumption relations are directly inferred from the directory structure once conflicts in the hierarchy are resolved. We evaluate our technique on real-world queries, and show that our approach is effective under all settings.
Improving Manufacturing System Performance through Rescheduling

Many production facilities generate and update production schedules. Rescheduling is necessary because the manufacturing system is dynamic and unexpected events occur. This paper discusses rescheduling policies and how they affect the performance of manufacturing systems. The benefits of proper rescheduling include improved manufacturing system performance with reduced cost due to computational effort (human or computer) and disruptions to existing plans (nervousness). The paper summarizes previous work in this area and also discusses how rescheduling requires methods to solve production scheduling problems. The goal is to help production planners, engineers, and researchers understand the importance of rescheduling and the proper role of production scheduling in dynamic manufacturing systems.
Exactly solvable quantum state reduction models with time-dependent coupling

A closed-form solution to the energy-based stochastic Schrodinger equation with a time-dependent coupling is obtained. The solution is algebraic in character, and is expressed directly in terms of independent random data. The data consist of (i) a random variable H which has the distribution , where πi is the transition probability |ψ0|i|2 from the initial state |ψ0 to the Luders state |i with energy Ei, and (ii) an independent -Brownian motion, where is the physical probability measure associated with the dynamics of the reduction process. When the coupling is time independent, it is known that state reduction occurs asymptotically—that is to say, over an infinite time horizon. In the case of a time-dependent coupling, we show that if the magnitude of the coupling decreases sufficiently rapidly, then the energy variance will be reduced under the dynamics, but the state need not reach an energy eigenstate. This situation corresponds to the case of a 'partial' or 'incomplete' measurement of the energy. We also construct an example of a model where the opposite situation prevails, in which complete state reduction is achieved after the passage of a finite period of time.
Evaluación del bullying como victimización: Estructura, fiabilidad y validez del Cuestionario de Acoso entre Iguales (CAI)

El objetivo del presente estudio consistio en validar el Cuestionario de Acoso entre Iguales (CAI) (Magaz, Chorot, Sandin, Santed, y Valiente, 2011). Examinamos la estructura factorial, la fiabilidad y la validez convergente y discriminante de la escala de Conductas de Acoso (CAI-CA) en una muestra de ninos y adolescentes. Los analisis factoriales exploratorios y confirmatorios constataron una estructura de siete factores no correlacionados que se corresponden con los siguientes tipos de bullying: maltrato fisico, maltrato verbal, exclusion social directa, exclusion social indirecta, amenazas, ciberbullying, y agresion basada en objetos. Los resultados apoyan la validez factorial, la consistencia interna, y la validez convergente y discriminante del cuestionario. Los datos sugieren que el CAI-CA puede ser una herramienta apropiada para evaluar el bullying de victimizacion tanto en contextos de investigacion como clinicos.
Unnecessary Workup of Asymptomatic Neonates in the Era of Group B Streptococcus Prophylaxis

Asymptomatic term neonates born to mothers who are Group B Streptococcus (GBS) unknown or GBS positive but “inadequately” treated prior to delivery do not require invasive laboratory evaluation. We conducted a retrospective cohort study of mother/baby dyads born from January 1, 2005 until September 30, 2007 at the Medical College of Georgia. Their current protocol is to obtain a Complete Blood Count with Differential (CBC with D), Blood Culture (BC), and C-reactive protein (CRP) after birth. Mother/baby dyads (n = 242) that met inclusion criteria were reviewed. Of these 242 babies 25 (10%) were started on antibiotics after the initial lab values were known. None of the blood cultures were positive and the CRP's were normal. The 2002 GBS guidelines call for laboratory evaluation of “at-risk” neonates, but the workup of these babies is not only costly, it does not provide any advantage over old fashioned clinical observation for the evaluation and treatment of early onset GBS sepsis.
Omnidirectional Sensing and Its Applications

The goal of this paper is to present a critical survey of existing literature on an omnidirectional sensing. The area of vision application such as autonomous robot navigation, telepresence and virtual reality is expanding by use of a camera with a wide angle of view. In particular, a real-time omnidirectional camera with a single center of projection is suitable for analyzing and monitoring, because we can easily generate any desired image projected on any designated image plane, such as a pure perspective image or a panoramic image, from the omnidirectional input image. In this paper, I review designs and principles of existing omnidirectional cameras, which can acquire an omnidirectional (360 degrees) field of view, and their applications in fields of autonomous robot navigation, telepresence, remote surveillance and virtual reality. key words: omnidirectional camera, multiple sensing camera, panoramic view, omnidirectional view, computer vision
Attitudes of preservice teachers towards the inclusion of children with Autism Spectrum Disorders

Thirty-nine preservice teachers‟ registered in an introductory course into Educational Psychology completed a Modified Autism Inclusion Questionnaire to (1) examine their attitudes towards ASD and inclusion; (2) investigate their knowledge about ASD; and (3) investigate the extent to which preservice teacher variables (e.g. experience) influence their attitudes towards ASD and inclusion. Fourteen participants were also interviewed to examine the consistency of their quantitative responses and ascertain their attitudes and rationales. The results indicated that preservice teachers in this sample generally had a positive attitude towards the inclusion of students with ASD in a regular classroom setting; they had limited general knowledge of ASD and the strategies for children with ASD. Furthermore, variables such as years in college, experience, intended level to teach, and teaching area were found to have no statistically significant influence on their knowledge. The implications and future research directions are discussed below.
Bug Reports Prioritization: Which Features and Classifier to Use?

Large open source bug tracking systems receives large number of bug reports daily. Managing these huge numbers of incoming bug reports is a challenging task. Dealing with these reports manually consumes time and resources which leads to delaying the resolution of important bugs which are crucial and need to be identified and resolved earlier. Bug triaging is an important process in software maintenance. Some bugs are important and need to be fixed right away, whereas others are minor and their fixes could be postponed until resources are available. Most automatic bug assignment approaches do not take the priority of bug reports in their consideration. Assigning bug reports based on their priority may play an important role in enhancing the bug triaging process. In this paper, we present an approach to predict the priority of a reported bug using different machine learning algorithms namely Naive Bayes, Decision Trees, and Random Forest. We also investigate the effect of using two feature sets on the classification accuracy. We conduct experimental evaluation using open-source projects namely Eclipse and Fire fox. The experimental evaluation shows that the proposed approach is feasible in predicting the priority of bug reports. It also shows that feature-set-2 outperformsfeature-set-1. Moreover, both Random Forests and Decision Trees outperform Naive Bayes.
School Climate for Academic Success: A Multilevel Analysis of School Climate and Student Outcomes.

This multilevel study examined the relationship between school climate and academic achievement. Using the Educational Longitudinal Survey (ELS, 2002), and a sample of 16,258 students and 1954 schools nationwide, we found that student-level perception of school climate—especially the student learning environment—was highly predictive of academic success in math and reading standardized test scores. This study confirmed that, among school climate variables at the school level, worse institutional facilities have a negative impact on student achievement and higher levels of institutional surveillance negatively affected the positive effects that student perceptions of safety and their learning environments had on student success. Finally, reducing high levels of institutional surveillance was found to mitigate socioeconomic inequalities.