Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Multi-Class Selective Sampling on Graphs

A graph-based multi-class classification problem is typically converted into a collection of binary classification tasks via the one-vs.-all strategy, and then tackled by applying proper binary classification algorithms. Unlike the one-vs.-all strategy, we suggest a unified framework which operates directly on the multi-class problem without reducing it to a collection of binary tasks. Moreover, this framework makes active learning practically feasible for multi-class problems, while the one-vs.-all strategy cannot. Specifically, we employ a novel randomized query technique to prioritize the informative instances. This query technique based on the hybrid criterion of "margin" and "uncertainty" can achieve a comparable mistake bound with its fully supervised counterpart. To take full advantage of correctly predicted labels discarded in traditional conservative algorithms, we propose an aggressive selective sampling algorithm that can update the model even if no error occurs. Thanks to the aggressive updating strategy, the aggressive algorithm attains a lower mistake bound than its conservative competitors in expectation. Encouraging experimental results on real-world graph databases show that the proposed technique by querying an extremely small ratio of labels is able to accomplish better classification accuracy.
A comparative study of bankruptcy prediction models of Fulmer and Toffler in firms accepted in Tehran Stock Exchange

The recent bankruptcies of the mega-corporations worldwide and the fluctuations in Stock Exchange in Iran have created the need for instruments to measure the financial capabilities of the firms. Financial ratios are considered to be one of the tools to measure financial capabilities of the companies. Also several models are used to predict bankruptcy. The environmental changes and the exceeding competition between the entities have limited achieving the expected profit for them. Thus, the financial decision making has become more important compared with the past and has forced the mangers to use the advanced techniques in order to make benefit of the new controlling methods. This research is carried out to present the theoretical fundamentals of the research and compare the results of utilizing Falmer and Toffler models in order to predict the companies' bankruptcy. Thus, the data related to 90 firms accepted in Tehran Stock Exchange for the years between 2005 and 2010 were tested. To analyze the data, we used the non-parametric binomial statistical methods. The results showed that in predicting the firms' status with Wilcockson's statistical method, there is a meaningful difference between the results of the two models. Also, in forthcoming studied it was found out that Falmer's model acts more conservatively in bankruptcy prediction than Toffler model.
Assessing hot and cool executive functions in preschoolers: affective flexibility predicts emotion regulation

ABSTRACT Affective flexibility (AF) is the ability to alternate between processing emotional and non-emotional information. This hot executive function has been understudied during early development. The first aim of our investigation was to generate preliminary construct validity evidence for a new measure of AF: the Emotional Flexible Item Selection Task (EM-FIST). Second, to investigate if AF represented a better predictor of preschoolers’ emotion regulation (ER) compared to, cognitive flexibility (CF). Preschoolers (N = 56; 48.2% girls) completed AF and CF measures (also working memory and inhibitory control). ER was measured through maternal report. We found evidence of EM-FIST’s validity, as an appropriate measure of AF for preschoolers, by showing that it is related to cool executive functions’ measures and to children’s ER. Both AF and maternal level of education predicted children’s ER while CF did not. Our investigation highlights a stronger relation between ER and AF in preschoolers than with CF.
Optimizing Back-and-Forth Live Migration

Back-and-forth live migration, which means a running VM migrates between two physical machines back and forth, has several important applications. Traditional methods treat each migration as a single event, so the VM releases its system resources on the source site after migration. However, many resources can be kept to mitigate the cost of the next migration back to the machine. This paper presents performance optimization methods for back-and-forth live migration. Different from previous work, our approach can keep the data center resiliency. We leverage the technique of snapshot and the bitmap model, which are available in most existing VM management systems. Using the snapshot, a VM can be immediately restarted from the saved state. The bitmaps model is used to avoid redundant data transmission to decrease the costs of migration. We implemented the bank-and-forth live migration optimization methods in QEMU-KVM 2.0. The experiments show that the proposed methods can significantly reduce the overhead of migrations. The total migration time can be saved up to 99% for some applications.
Usage patterns and social circles on Facebook among elderly people with diverse personality traits

ABSTRACT Elderly people are exposed to information technologies to keep them in touch with younger generations. Among various technologies, social network sites (SNSs) are seldom used by the majority of elderly people. To bridge the digital divide, it is necessary to dig deeply into the minority elderly users of SNSs. This study explores usage patterns of elderly Facebook users and further investigates how their diverse characteristics influence their usage and social circles. Three findings were derived. First, a sharp digital divide on SNSs does exist, and existing elderly Facebook users are mainly the dabblers. Second, personality profiling could predict elderly people’s SNS usage behaviors. Elderly users with a high level of openness and extroversion have more friends, status updates, and group memberships, and those with a high level of conscientiousness have fewer likes and status updates. Elderly users are likely to seek insightful content on SNSs. Moreover, they may have a higher degree of emotional stability than young users when using SNSs. Third, elderly people tend to build small social circles on SNSs that “copy” the family relationship in the real world; whereas, they build large social circles with interests and hobbies that are difficult to share in the real world. Based on these results, detailed suggestions are presented for better targeting elderly people in ways that move them toward SNSs.
Adrenocorticotropic hormone elicits gonadotropin secretion in premenopausal women.

STUDY QUESTION Does adrenocorticotropic hormone (ACTH) induce gonadotropin release in premenopausal women? SUMMARY ANSWER Administration of ACTH stimulates gonadotropin release, most likely by stimulation of the production of cortisol, in premenopausal women. WHAT IS KNOWN ALREADY In animal models, acute activation of the hypothalamic-pituitary-adrenal (HPA) axis has been shown to induce gonadotropin release in the presence of sufficiently high estrogen levels. However, it is unknown whether the HPA axis has a similar influence on gonadotropin release in humans. STUDY DESIGN, SIZE, DURATION This study had a mixed factorial design. A total of 60 healthy female participants participated in the experimental study. PARTICIPANTS/MATERIALS, SETTING, METHODS The study sample comprised three distinct hormonal-based populations according to their levels of progesterone (PROG) and estradiol (E2): (i) low-PROG-low-E2, (ii) low-PROG-high-E2 and (iii) high-PROG-high-E2 women. A low dose (1 µg) of ACTH was administered to all study participants. Serum steroid and gonadotropin concentrations were measured prior to, and at 30 and 90 minutes after, intravenous ACTH administration. MAIN RESULTS AND THE ROLE OF CHANCE Mean serum cortisol levels increased significantly following ACTH administration in all groups (P < 0.001). Similarly, the serum levels of 17-OH-PROG, androstenedione, dehydroepiandrosterone and testosterone increased significantly in all groups (P < 0.01). The low-PROG-high-E2 and high-PROG-high-E2 groups exhibited a significant increase in LH and FSH levels (P < 0.001), whereas the low-PROG-low-E2 group demonstrated blunted LH and FSH responses to ACTH administration (P < 0.05). LIMITATIONS, REASONS FOR CAUTION Testing was performed during the luteal phase of the natural menstrual cycle. Testing during the follicular phase might have elicited premature, or more pronounced, LH surges in response to ACTH administration. WIDER IMPLICATIONS OF THE FINDINGS Our findings suggest a novel mechanism by which the adrenal cortex functions as a mediator of gonadotropin release. These findings contribute to a greater understanding of the influence of acute stress on reproductive endocrinology. STUDY FUNDING/COMPETING INTERESTS Funding was received from the Erasmus University Medical Center. The authors have no conflicts of interest. TRIAL REGISTRATION NUMBER EudraCT Number 2012-005640-14.
A neural network hybrid model for an optical braille recognitor

: - Braille is the most widely used system for written communication using tactile means. Automation of the Braille reading process will effectively enable the transcription and duplication of existing documents as well as their preservation. By converting a Braille document into electronic form, all the benefits of developments for the electronic form of printed documents can be enjoyed: easier transmission, efficient storage and manipulation etc. The proposed procedures use approaches commonly applied in standard OCR systems. It uses a commercially available flatbed scanner to acquire a grayscale image of a Braille document from which the characters are recognized. This prototype system has been developed by using methods from image processing and analysis to AI. These methods achieve high speed and accuracy by using hybrid neural network approaches. The system dynamically adapts to factors such as the quality of the input pattern, its intrinsic similarities and differences between patterns.
A Sequential Testing Framework for Identifying a Transmission Line Outage in a Power System

The topology of a power system changes when a line outage is encountered. Identifying which line has failed in the shortest possible time is of importance due to the cascading nature of such failures. In this work, we propose a state estimation based sequential hypothesis testing procedure to locate the failed line. We focus on single line outages as these are the most frequently occurring failures. Earlier work on state estimation based sequential testing procedure used a DC approximation model assuming that the sensors provided angle and voltage information. This is known to be a coarse model but results in a simpler linear estimation problem. In this work, we look at a finer nonlinear model of power measurements and treat phase angles and voltages as hidden states. After a local linearization, we propose a Kalman filter based state estimation followed by a generalized likelihood ratio testing procedure to determine which of the lines has failed. We consider both centralized and decentralized approaches. In the centralized case, measurements from every installed meter is made available to the system operator. In the decentralized case, only limited aggregated information is made available because of, for example, communication capacity constraints. We test our algorithms on the IEEE 14 and 118 bus systems and show that all high risk link failures are quickly identified.
(−)-Epigallocatechin Gallate Targets Notch to Attenuate the Inflammatory Response in the Immediate Early Stage in Human Macrophages

Inflammation plays important roles at different stages of diabetes mellitus, tumorigenesis, and cardiovascular diseases. (−)-Epigallocatechin gallate (EGCG) can attenuate inflammatory responses effectively. However, the immediate early mechanism of EGCG in inflammation remains unclear. Here, we showed that EGCG attenuated the inflammatory response in the immediate early stage of EGCG treatment by shutting off Notch signaling and that the effect did not involve the 67-kDa laminin receptor, the common receptor for EGCG. EGCG eliminated mature Notch from the cell membrane and the nuclear Notch intercellular domain, the active form of Notch, within 2 min by rapid degradation via the proteasome pathway. Transcription of the Notch target gene was downregulated simultaneously. Knockdown of Notch 1/2 expression by RNA interference impaired the downregulation of the inflammatory response elicited by EGCG. Further study showed that EGCG inhibited lipopolysaccharide-induced inflammation and turned off Notch signaling in human primary macrophages. Taken together, our results show that EGCG targets Notch to regulate the inflammatory response in the immediate early stage.
Evaluation of a Resilience Intervention to Enhance Coping Strategies and Protective Factors and Decrease Symptomatology

Objective: In this pilot study, the authors examined the effectiveness of a 4-week resilience intervention to enhance resilience, coping strategies, and protective factors, as well as decrease symptomatology during a period of increased academic stress. Participants and Methods: College students were randomly assigned to experimental (n = 30) and wait-list control (n = 27) groups. The experimental group received a psychoeducational intervention in 4 two-hour weekly sessions. Measures of resilience, coping strategies, protective factors, and symptomatology were administered pre- and postintervention to both groups. Results: Analyses indicated that the experimental group had significantly higher resilience scores, more effective coping strategies (ie, higher problem solving, lower avoidant), higher scores on protective factors (ie, positive affect, self-esteem, self-leadership), and lower scores on symptomatology (ie, depressive symptoms, negative affect, perceived stress) postintervention than did the wait-list control group. Conclusions: These findings indicate that this resilience program may be useful as a stress-management and stress-prevention intervention for college students.
BAC-End Sequence-Based SNP Mining in Allotetraploid Cotton (Gossypium) Utilizing Resequencing Data, Phylogenetic Inferences, and Perspectives for Genetic Mapping

A bacterial artificial chromosome library and BAC-end sequences for cultivated cotton (Gossypium hirsutum L.) have recently been developed. This report presents genome-wide single nucleotide polymorphism (SNP) mining utilizing resequencing data with BAC-end sequences as a reference by alignment of 12 G. hirsutum L. lines, one G. barbadense L. line, and one G. longicalyx Hutch and Lee line. A total of 132,262 intraspecific SNPs have been developed for G. hirsutum, whereas 223,138 and 470,631 interspecific SNPs have been developed for G. barbadense and G. longicalyx, respectively. Using a set of interspecific SNPs, 11 randomly selected and 77 SNPs that are putatively associated with the homeologous chromosome pair 12 and 26, we mapped 77 SNPs into two linkage groups representing these chromosomes, spanning a total of 236.2 cM in an interspecific F2 population (G. barbadense 3-79 × G. hirsutum TM-1). The mapping results validated the approach for reliably producing large numbers of both intraspecific and interspecific SNPs aligned to BAC-ends. This will allow for future construction of high-density integrated physical and genetic maps for cotton and other complex polyploid genomes. The methods developed will allow for future Gossypium resequencing data to be automatically genotyped for identified SNPs along the BAC-end sequence reference for anchoring sequence assemblies and comparative studies.
Potentiation of Hippocampal Synaptic Transmission by Superoxide Requires the Oxidative Activation of Protein Kinase C

Recent evidence suggests that reactive oxygen species (ROS), including superoxide, are not only neurotoxic but function as small messenger molecules in normal neuronal processes such as synaptic plasticity. Consistent with this idea, we show that brief incubation of hippocampal slices with the superoxide-generating system xanthine/xanthine oxidase (X/XO) produces a long-lasting potentiation of synaptic transmission in area CA1. We found that X/XO-induced potentiation was associated with a persistent superoxide-dependent increase in autonomous PKC activity that could be isolated via DEAE column chromatography. The X/XO-induced potentiation was blocked by the inhibition of PKC, indicating that the superoxide-dependent increase in autonomous PKC activity was necessary for the potentiation. We also found that X/XO-induced potentiation and long-term potentiation (LTP) occluded one another, suggesting that these forms of plasticity share similar cellular mechanisms. In further support of this idea, we found that a persistent, superoxide-dependent increase in autonomous PKC activity isolated via DEAE column chromatography also was associated with LTP. Taken together, our findings indicate that X/XO-induced potentiation and LTP share similar cellular mechanisms, including superoxide-dependent increases in autonomous PKC activity. Finally, our findings suggest that superoxide, in addition to its well known role as a neurotoxin, also can be considered a small messenger molecule critical for normal neuronal signaling.
Non-rigid point set registration via mixture of asymmetric Gaussians with integrated local structures

As a fundamental problem in computer vision community, non-rigid point set registration is a challenging topic since the corresponding transformation model is often unknown and difficult to model. In this paper, we present a robust method for non-rigid point set registration. Firstly, a mixture of asymmetric Gaussian model (MoAG) is employed to capture spatially asymmetric distributions which the Gaussian mixture model (GMM) based methods neglect instinctively. Secondly, local structures among adjacent points are integrated into the MoAG-based point set registration framework to improve the correspondence estimation. Thirdly, Expectation-Maximization (EM) algorithm which provides a numerical method for finding maximum likelihood estimators is utilized to estimate parameters of the latent variable model in our proposed method. The transformation model is solved under regularization theory in the Reproducing Kernel Hilbert Space (RKHS). Conveniently, a fast implementation is achieved by the sparse approximation. Finally, experiments results show that the robustness of our algorithm outperforms the comparative state of art methods under various types of distortions, such as deformation, noise, outliers and occlusion.
LEAP: Learning-Based Smart Edge with Caching and Prefetching for Adaptive Video Streaming

Dynamic Adaptive Streaming over HTTP (DASH) has emerged as a popular approach for video transmission, which brings a potential benefit for the Quality of Experience (QoE) because of its segment-based flexibility. However, the Internet can only provide no guaranteed delivery. The high dynamic of the available bandwidth may cause bitrate switching or video rebuffering, thus inevitably damaging the QoE. Besides, the frequently requested popular videos are transmitted for multiple times and contribute to most of the bandwidth consumption, which causes massive transmission redundancy. Therefore, we propose a Learning-based Edge with cAching and Prefetching (LEAP) to improve the online user QoE of adaptive video streaming. LEAP introduces caching into the edge to reduce the redundant video transmission and employs prefetching to fight against network jitters. Taking the state information of users into account, LEAP intelligently makes the most beneficial decisions of caching and prefetching by a QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we deploy the implemented prototype of LEAP in both the simulated scenario and the real Internet. Compared with all selected schemes, LEAP at least raises average bitrate by 34.4% and reduces video rebuffering by 42.7%, which leads to at least 15.9% improvement in the user QoE in the simulated scenario. The results in the real Internet scenario further confirm the superiority of LEAP.
Using Virtual CPU Migration to Solve the Lock Holder Preemption Problem in a Multicore Processor-Based Virtualization Layer for Embedded Systems

When a real-time OS (RTOS) and a symmetric multiprocessing general purpose OS (SMP GPOS) are executed on the same multicore processor, there is a possibility to cause the lock holder preemption (LHP) problem, which is a well known problem of the sources of the performance degradation on an SMP GPOS. The problem occurs when a kernel mode thread holding a lock in a SMP GPOS is preempted by other OSes if these OSes share the same physical core. The existing solution does not take into account real-time responsiveness so it is not suitable to use in embedded systems. The paper proposes two new techniques to avoid the LHP problem for ensuring both the real-time responsiveness of real-time OS and the high throughput of SMP GPOS. The new proposed techniques have been implemented on our virtualization layer called SPUMONE, and we measured the results showing that the proposed new techniques reduce the LHP problem without degrading the real-time responsiveness.
WAVELET SNAKE FOR CLASSIFICATION OF NODULES ANDFALSE POSITIVES IN DIGITAL CHEST

| We have developed a technique using a wavelet-based snake model for classiication of nodules and false positives reported by our computer-aided diagnosis (CAD) scheme for detection of pulmonary nodules in digital chest radiographs. In this technique, the boundary of a nodule is rst approximated by the multiscale edge representation. These multiscale edges are used to \guide" the snake to estimate the true boundary of the nodule. The deformation of the snake is performed by changing the wavelet coeecients using a gradient descent algorithm. The degree of overlap between the tted snake and the multiscale edges was calculated as a measure for classiication of nodules and false detections. A set of ROIs consisting of 90 nodules and 152 false positives were used for evaluation of our method by means of the receiver operating characteristic (ROC) analysis. Our method yielded an area under the ROC curve of 0.74 in classiication performance, which reduced 15% of the false positives with sacriice of only one nodule.
Understanding buyers' social information needs during purchase decision process

This paper aims at studying the roles of social information (as obtained from social networking sources) in buyers' decision process when they are searching for products to buy. Through both close observation of users' objective behavior and qualitative interview of their reflective comments, we have discovered the importance of different types of social information in supporting users to achieve a confident purchase decision at the end. Improving suggestions are further derived on how to better present the social information and combine them with static product attributes to enhance current e-commerce decision supports so as to optimally adapt to online buyers' actual information needs.