Miracle-dz/UniFAR-FTUs huggingface dataset:
SNMFCA: Supervised NMF-Based Image Classification and Annotation

In this paper, we propose a novel supervised nonnegative matrix factorization-based framework for both image classification and annotation. The framework consists of two phases: training and prediction. In the training phase, two supervised nonnegative matrix factorizations for image descriptors and annotation terms are combined to identify the latent image bases, and to represent the training images in the bases space. These latent bases can capture the representation of the images in terms of both descriptors and annotation terms. Based on the new representation of training images, classifiers can be learnt and built. In the prediction phase, a test image is first represented by the latent bases via solving a linear least squares problem, and then its class label and annotation can be predicted via the trained classifiers and the proposed annotation mapping model. In the algorithm, we develop a three-block proximal alternating nonnegative least squares algorithm to determine the latent image bases, and show its convergent property. Extensive experiments on real-world image data sets suggest that the proposed framework is able to predict the label and annotation for testing images successfully. Experimental results have also shown that our algorithm is computationally efficient and effective for image classification and annotation.
B in Rats κ Persistent but Not Transient Activation of Nuclear Factor-Drug-Induced Neuroprotection From Global Ischemia Is Associated With Prevention of Print

James A. Clemens, Diane T. Stephenson, Tinggui Yin, E. Barry Smalstig, Jill A. Panetta and B in Rats κ Persistent but Not Transient Activation of Nuclear FactorDrug-Induced Neuroprotection From Global Ischemia Is Associated With Prevention of Print ISSN: 0039-2499. Online ISSN: 1524-4628 Copyright © 1998 American Heart Association, Inc. All rights reserved. is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Stroke doi: 10.1161/01.STR. 29.3. 677 1998;29:677-682 Stroke. http://stroke.ahajournals. org/content/29/3/677 World Wide Web at: The online version of this article, along with updated information and services, is located on the
Adjuvant Cardioprotection in Cardiac Surgery: Update

Cardiac surgery patients are now more risky in terms of age, comorbidities, and the need for complex procedures. It brings about reperfusion injury, which leads to dysfunction and/or loss of part of the myocardium. These groups of patients have a higher incidence of postoperative complications and mortality. One way of augmenting intraoperative myocardial protection is the phenomenon of myocardial conditioning, elicited with brief nonlethal episodes of ischaemia-reperfusion. In addition, drugs are being tested that mimic ischaemic conditioning. Such cardioprotective techniques are mainly focused on reperfusion injury, a complex response of the organism to the restoration of coronary blood flow in ischaemic tissue, which can lead to cell death. Extensive research over the last three decades has revealed the basic mechanisms of reperfusion injury and myocardial conditioning, suggesting its therapeutic potential. But despite the enormous efforts that have been expended in preclinical studies, almost all cardioprotective therapies have failed in the third phase of clinical trials. One reason is that evolutionary young cellular mechanisms of protection against oxygen handling are not very robust. Ischaemic conditioning, which is among these, is also limited by this. At present, the prevailing belief is that such options of treatment exist, but their full employment will not occur until subquestions and methodological issues with the transfer into clinical practice have been resolved.
Myosin Va mediates Rab8A-regulated GLUT4 vesicle exocytosis in insulin-stimulated muscle cells

Skeletal muscle is the main site of insulin-dependent glucose uptake. Insulin inactivation of the Akt substrate Rab-GAP AS160 leads to Rab8A activation in myocytes. The molecular motor myosin Va is a Rab8A effector in this pathway, leading to GLUT4 translocation to the myocyte surface, linking signal transduction to vesicle traffic.
Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Topfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method.
Computation of modal coupling effects in meandered long-slot leaky-wave antennas

This paper shows how the coupling between different leaky modes can be efficiently computed, to accurately obtain its effect on the radiation pattern of tapered leaky-wave antennas. The particular case of meander-long-slot rectangular waveguide antenna is analyzed, illustrating the coupling between the main perturbed waveguide leaky mode and the undesired channel-guide leaky mode. Dispersion analysis of both modes must be carried out as a function of frequency and also as a function of the tapered geometry, to efficiently compute the effect of this coupling in the performance of this type of antennas.
Multiple-Draft/Multiple-Party Feedback Practices in an EFL Tertiary Writing Course: Teachers’ and Students’ Perspectives

Based on various sources of data collection for a qualitative research project, the study reported in this paper set out to examine four teachers’ and sixteen students’ perceptions of a multiple-draft/multiple-party feedback approach to English as a Foreign Language (EFL) student writing. This approach had been implemented as a trial in a tertiary setting in Vietnam. Three sources of feedback at three phases were provided. These included (1) peer/group written and oral feedback on the students’ first drafts, (2) a teaching assistant’s written and oral feedback on their second drafts and (3) the lecturer’s written feedback on their final drafts. Content analysis of the data revealed that all participants valued this multiple feedback approach because of its practicality and the quality of the feedback which participants believed contributed to writing improvement. Based on the participants’ reactions, the study highlights the potential of multiple-draft/multiple-party feedback practices for improving English language writing in a tertiary context.
Stable core and chaos control in random Boolean networks

A variant of a simple method for chaos control is applied to achieve control in random Boolean networks (RBNs). It is shown that a RBN in the chaotic phase can be forced to behave periodically if a certain quenched fraction of the automata is given a fixed state (the system variables) every T time-steps. An analytic relationship between and T is derived using the size of the stable core as an order parameter.
A novel fast otsu digital image segmentation method

Steganography is the art of hiding user information in various file types including image, audio and video. Security of steganography lies in imperceptibility of secret information in the cover image. Human Visual System (HVS) is not able to detect the changes in low color values of an image. To the best of our knowledge, none of the available steganographic techniques have exploited this weakness of HVS. In this paper, a new LSB technique is presented which hides information in the cover image taking into account the pixel value of color or grey level of every pixel. Our experiments show that the proposed technique has a high payload and low perceptibility of secret information hidden in the cover image as compared to the existing Least Significant Bit (LSB) based algorithms. We have used MATLAB for the implementation of proposed algorithm.
The story of Samantha: the teaching performances and inauthenticities of an early career human geography lecturer

ABSTRACT This paper offers an autoethnographic account of my first academic year as a Human Geography lecturer at a ‘new’ public university in the North West of England. This research is timely and much needed, since teaching at universities in England has recently come under increasing scrutiny. The Teaching Excellence Framework is a new scheme, which aims to recognise and reward excellence in teaching, learning, and outcomes, and helps to inform student choice. This paper is theoretically framed by drawing on notions of theatrical performance, and performativity. This paper offers insight into the coping strategies, in respect of teaching, that I deployed as a new university lecturer. Findings are discussed around the themes of: performing teaching identities, and inauthenticity. With regard to performing teaching identities, this paper discusses the need for identity to be multiple and shifting, and how, as a young female, I undertook identity work, in order to perform competence. I also bring to the fore feelings of inauthenticity; that is, how I did not feel as if I was a genuine academic, and how I fabricated / falsified aspects of my academic identity in order to ‘fit in’ with the expectations of both students and staff. As the voice of a new lecturer in her first year of teaching, this paper makes a useful contribution to the scholarship on early career academics and teaching development. This paper concludes with recommendations for change in practice-based settings, in order to assist new lecturers to settle into the job role, and enhance and enrich teaching practice.
Flow mosaicking: Real-time pedestrian counting without scene-specific learning

In this paper, we present a novel algorithm based on flow velocity field estimation to count the number of pedestrians across a detection line or inside a specified region. We regard pedestrians across the line as fluid flow, and design a novel model to estimate the flow velocity field. By integrating over time, the dynamic mosaics are constructed to count the number of pixels and edges passed through the line. Consequentially, the number of pedestrians can be estimated by quadratic regression, with the number of weighted pixels and edges as input. The regressors are learned off line from several camera tilt angles, and have taken the calibration information into account. We use tilt-angle-specific learning to ensure direct deployment and avoid overfitting while the commonly used scene-specific learning scheme needs on-site annotation and always trends to overfitting. Experiments on a variety of videos verified that the proposed method can give accurate estimation under different camera setup in real-time.
Fusion of algorithms for multiple measurement vectors

We consider the recovery of sparse signals that share a common support from multiple measurement vectors. The performance of several algorithms developed for this task depends on parameters like dimension of the sparse signal, dimension of measurement vector, sparsity level, measurement noise. We propose a fusion framework, where several multiple measurement vector reconstruction algorithms participate and the final signal estimate is obtained by combining the signal estimates of the participating algorithms. We present the conditions for achieving a better reconstruction performance than the participating algorithms. Numerical simulations demonstrate that our fusion algorithm often performs better than the participating algorithms.
Dhat syndrome – A culture-bound sex neurosis of the Indian subcontinent: A prospective randomized study

ABSTRACT Dhat syndrome is a culture-bound syndrome commonly reported in the Indian subcontinent, where sexual competence in man is the trademark of masculinity. Socio-demographic profile, phenomenology and clinical profile of Dhat syndrome, and its related misconceptions were ascertained in the study. Fifty random cases were studied with the tools such as Mini International Neuropsychiatric Interview (MINI-plus) scale, the General Health Questionnaire–28 (GHQ – 28), the Hamilton Depression Rating Scale (HAM-D) and the Hamilton Anxiety Rating Scale (HAM-A). The mean age, with standard deviation (SD), of the sample was 16.67 (± 6.65) years. 38% of patients were diagnosed to have a psychiatric illness among which a majority of cases were diagnosed with a depressive illness (18%). The GHQ Score (Mean ± SD), HAM-D (Mean ± SD) and HAM-A (Mean ± SD) of patients were 11.2 ± 8.2, 16.4 ± 6.1 and 16.1 ± 5.8, respectively. Our study suggests the presence of psychiatric co-morbidity in Dhat syndrome, such cases should be referred to consultation-liaison psychiatrists for proper management, and relevant education should be provided to young adults to remove the myths and misconceptions.
Supporting context — Aware service evolution with a process management requirements model

Context-aware services concern with reasoning about surrounding context and adapting services accordingly have attracted much research attention during recent years, whereas the relation between context-aware service evolution and requirements evolution has not caught many researchers' eye. Conventionally, requirements engineering particularly focuses on users' needs, we argue though that developers' needs must be discovered in the course of services evolution process. In this case, the connection between context-aware service evolution and requirements evolution will be fully discovered, in turn, it will better the guide as to how to evolve exiting context-aware services in order to benefit the best from current service assets. To build this connection, we propose a process management requirements model with requirements elicitation techniques to support context-aware service evolution. A medium sized open source case study is carried out for evaluation.
Jury diversity in the age of mass incarceration: an exploratory mock jury experiment examining felon-jurors’ potential impacts on deliberations

ABSTRACT Today, nineteen million American citizens bear the mark of a felony conviction, far more than in any prior era. With that mark comes a host of record-based restrictions that curtail access to various political, social, and civic institutions. One such restriction impacts convicted felons’ eligibility for jury service. Forty-nine states, the federal government, and the District of Columbia statutorily limit convicted felons’ opportunities to serve as jurors. Justifying these restrictions, lawmakers and courts suggest that convicted felons, if allowed to serve, would diminish the quality of the deliberation process. This exploratory mock jury experiment is the first to assess jury deliberations that include felon-jurors, comparing (1) homogenous juries comprised entirely of non-felon-jurors to diverse juries comprised of both non-felon and felon-jurors and (2) non-felon-jurors to felon-jurors. Results suggest that on theoretically derived measures of deliberation structure, deliberation content, and juror perceptions, diverse juries performed as well as homogenous juries. Data also tend to demonstrate few statistically significant differences between felon-jurors and non-felon-jurors. Notably, on measures of novel case facts covered and time spoken as a proportion of deliberation duration, felon-jurors outperformed their non-felon counterparts, perhaps calling into question the necessity of blanket felon-juror exclusion policies.
A Markovian Framework for Performance Evaluation of IEEE 802.11

A new approach for modeling and performance analysis of the IEEE 802.11 medium access control (MAC) protocol is presented. The approach is based on the so-called system approximation technique, where the protocol service time distribution of the IEEE 802.11 MAC protocol is studied and approximated by an appropriate phase-type distribution, leading to the construction of a versatile queueing model which is amenable to analysis and, at the same time, general enough to allow for bursty arrival process as well as key statistical characteristics of the protocol operations. The versatility of the model is demonstrated by considering Markov modulated and on/off arrival processes as well as various data frame size distributions. The accuracy of the analytical results is verified by simulation.
Extracting Data from the Deep Web with Global-as-View Mediators Using Rule-Enriched Semantic Annotations

The Deep Web offers approximately 500 times more information than the Open Web, but is “hidden” behind search-forms intended for human users, and typically requires interaction, which makes it difficult to index by Web crawlers. We argue that traditional data extraction is therefore not suitable for the Deep Web and suffers from coverage problems similar to those search engines face when trying to index its content. Instead, it is proposed to transform and forward queries on demand using Global-as-View Mediators. To allow automated interaction with databases on the Deep Web, we use rules that exploit features (e.g. HTML attribute values) to identify elements on a Web page and infer semantic annotations that link these elements to known concepts (e.g. query parameters or result values). Using a prototypical implementation, Deep Web Mediator, the performance of this approach is demonstrated in a classified-advertising use case. Our system is able to answer complex queries by transforming and forwarding them to multiple sites as well as integrating the
The Use of Commercially Available Alpha-Amylase Compounds to Inhibit and Remove Staphylococcus aureus Biofilms

Staphylococcus aureus, a versatile human pathogen, is commonly associated with medical device infections. Its capacity to establish and maintain these infections is thought to be related to its ability to form adherent biofilms. In this study, commercially available α-amylase compounds from various biological sources were evaluated for their ability to reduce and prevent biofilm formation of several S. aureus isolates. Our data demonstrates that α-amylase compounds can rapidly detach biofilms of S. aureus, as well as inhibit biofilm formation. Our data also demonstrates that α-amylase compounds have an ability to reduce and disassociate S. aureus cell-aggregates grown in liquid suspension. These findings suggest that commercially available α-amylase compounds could be used in the future to control S. aureus biofilm-related infections.
Poisson models for extended target and group tracking

It is common practice to represent a target group (or an extended target) as set of point sources and attempt to formulate a tracking filter by constructing possible assignments between measurements and the sources. We suggest an alternative approach that produces a measurement model (likelihood) in terms of the spatial density of measurements over the sensor observation region. In particular, the measurements are modelled as a Poisson process with a spatially dependent rate parameter. This representation allows us to model extended targets as an intensity distribution rather than a set of points and, for a target formation, it gives the option of modelling part of the group as a spatial distribution of target density. Furthermore, as a direct consequence of the Poisson model, the measurement likelihood may be evaluated without constructing explicit association hypotheses. This considerably simplifies the filter and gives a substantial computational saving in a particle filter implementation. The Poisson target-measurement model will be described and its relationship to other filters will be discussed. Illustrative simulation examples will be presented.
Quermass-interaction processes: conditions for stability

We consider a class of random point and germ-grain processes, obtained using a rather natural weighting procedure. Given a Poisson point process, on each point one places a grain, a (possibly random) compact convex set. Let Ξ be the union of all grains. One can now construct new processes whose density is derived from an exponential of a linear combination of quermass functionals of Ξ. If only the area functional is used, then the area-interaction point process is recovered. New point processes arise if we include the perimeter length functional, or the Euler functional (number of components minus number of holes). The main question addressed by the paper is that of when the resulting point process is well-defined: geometric arguments are used to establish conditions for the point process to be stable in the sense of Ruelle.
Additive Update Algorithm for Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in data analysis. Mathematically, NMF can be formulated as a minimization problem with nonnegative constraints. This problem is currently attracting much attention from researchers for theoretical reasons and for potential applications. Currently, the most popular approach to solve NMF is the multiplicative update algorithm proposed by D.D. Lee and H.S. Seung. In this paper, we propose an additive update algorithm, that has faster computational speed than the algorithm of D.D. Lee and H.S. Seung.
The Effects of Gratitude on Cortisol Reactivity

Cortisol, a hormone released by the adrenal glands during periods of psychological or physiological stress, has been linked to many of the harmful effects of stress. If cortisol levels are not managed, individuals may be at higher risk of developing certain mental disorders, heart disease, and more. Research examining the effects of gratitude shows promising results in reducing psychological stress, but it is not known if this translates into the physiological effect of reduced cortisol production. This study tests the effect of a gratitude intervention on stress and cortisol levels in college students. In this experiment, a set of 38 participants listed three things they were grateful daily for 28 days and wrote a weekly paragraph about what they were grateful for to test if the intervention could improve cortisol reactivity, perceived stress, satisfaction with life, and gratitude. Cortisol reactivity was measured by salivary cortisol samples taken before and after a stressful social computer game. The hypothesis was not supported. Limitations and future research are discussed as to how gratitude interventions may be effective only under certain conditions.