snu-aidas/HVMap-dataset huggingface dataset:
Yoga as an adjunctive treatment for posttraumatic stress disorder: a randomized controlled trial.

BACKGROUND
More than a third of the approximately 10 million women with histories of interpersonal violence in the United States develop posttraumatic stress disorder (PTSD). Currently available treatments for this population have a high rate of incomplete response, in part because problems in affect and impulse regulation are major obstacles to resolving PTSD. This study explored the efficacy of yoga to increase affect tolerance and to decrease PTSD symptomatology.


METHOD
Sixty-four women with chronic, treatment-resistant PTSD were randomly assigned to either trauma-informed yoga or supportive women's health education, each as a weekly 1-hour class for 10 weeks. Assessments were conducted at pretreatment, midtreatment, and posttreatment and included measures of DSM-IV PTSD, affect regulation, and depression. The study ran from 2008 through 2011.


RESULTS
The primary outcome measure was the Clinician-Administered PTSD Scale (CAPS). At the end of the study, 16 of 31 participants (52%) in the yoga group no longer met criteria for PTSD compared to 6 of 29 (21%) in the control group (n = 60, χ²₁ = 6.17, P = .013). Both groups exhibited significant decreases on the CAPS, with the decrease falling in the large effect size range for the yoga group (d = 1.07) and the medium to large effect size decrease for the control group (d = 0.66). Both the yoga (b = -9.21, t = -2.34, P = .02, d = -0.37) and control (b = -22.12, t = -3.39, P = .001, d = -0.54) groups exhibited significant decreases from pretreatment to the midtreatment assessment. However, a significant group × quadratic trend interaction (d = -0.34) showed that the pattern of change in Davidson Trauma Scale significantly differed across groups. The yoga group exhibited a significant medium effect size linear (d = -0.52) trend. In contrast, the control group exhibited only a significant medium effect size quadratic trend (d = 0.46) but did not exhibit a significant linear trend (d = -0.29). Thus, both groups exhibited significant decreases in PTSD symptoms during the first half of treatment, but these improvements were maintained in the yoga group, while the control group relapsed after its initial improvement.


DISCUSSION
Yoga significantly reduced PTSD symptomatology, with effect sizes comparable to well-researched psychotherapeutic and psychopharmacologic approaches. Yoga may improve the functioning of traumatized individuals by helping them to tolerate physical and sensory experiences associated with fear and helplessness and to increase emotional awareness and affect tolerance.


TRIAL REGISTRATION
ClinicalTrials.gov identifier: NCT00839813.
Predicting football match results with logistic regression

Many efforts has been made in order to predict football matches result and selecting significant variables in football. Prediction is very useful in helping managers and clubs make the right decision to win leagues and tournaments. In this paper a logistic regression model is built to predict matches results of Barclays' Premier League season 2015/2016 for home win or away win and to determine what are the significant variable to win matches. Our work is different from others as we use only significant variables gathered from researches in the same field rather than making our own guess on what are the significant variables. We also used data gathered from video game FIFA, as Shin and Gasparyan [8] showed us that including data from the video game could improve prediction quality. The model was built using variations of training data from 2010/2011 season until 2015/2016. Logistic regression is a classification method which can be used to predict sports results and it can gives additional knowledge through regression coefficients. The variables used are “Home Offense”, “Home Defense”, “Away Offense”, and “Away Defense”. We conducted experiments by altering seasons of training data used. Prediction accuracy of built model is 69.5%. We found our work improved significantly compared to the one Snyder [9] did. We concluded that the significant variables are “Home Defense”, and “Away Defense”.
Action recognition with trajectory-pooled deep-convolutional descriptors

Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features [31] and deep-learned features [24]. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMD-B51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features [31] and deep-learned features [24]. Our method also achieves superior performance to the state of the art on these datasets.
Tonsillectomy and risk of Parkinson's disease: A danish nationwide population-based cohort study.

BACKGROUND
We hypothesized that tonsillectomy modifies the risk of PD.


OBJECTIVES
To test the hypothesis in a nationwide population-based cohort study.


METHODS
We used Danish medical registries to construct a cohort of all patients in Denmark with an operation code of tonsillectomy 1980-2010 (n = 195,169) and a matched age and sex general population comparison cohort (n = 975,845). Patients were followed until PD diagnosis, death, censoring, or end of follow-up 30 November 2013. Using Cox regression, we computed hazard ratios for PD and corresponding 95% confidence intervals, adjusting for age and sex by study design, and potential confounders.


RESULTS
We identified 100 and 568 patients diagnosed with PD among the tonsillectomy and general population comparison cohort, respectively, finding similar risks of PD (adjusted hazard ratio = 0.95 [95% confidence interval: 0.76-1.19]; for > 20 years' follow-up (adjusted hazard ratio = 0.96 [95% confidence interval: 0.64-1.41]).


CONCLUSION
Tonsillectomy is not associated with risk of PD, especially early-onset PD. © 2017 International Parkinson and Movement Disorder Society.
Local optima smoothing for global optimization

It is widely believed that in order to solve large scale global optimization problems an appropriate mixture of local approximation and global exploration is necessary. Local approximation, if first order information on the objective function is available, is efficiently performed by means of local optimization methods. Unfortunately, global exploration, in absence of some kind of global information on the problem, is a “blind” procedure, aimed at placing observations as evenly as possible in the search domain. Often this procedure reduces to uniform random sampling (like in Multistart algorithms, or in techniques based on clustering). In this paper we propose a new framework for global exploration which tries to guide random exploration towards the region of attraction of low-level local optima. The main idea originated by the use of smoothing techniques (based on gaussian convolutions): the possibility of applying a smoothing transformation not to the objective function but to the result of local searches seems to have never been explored yet. Although an exact smoothing of the results of local searches is impossible to implement, in this paper we propose a computational approximation scheme which has proven to be very efficient and (maybe more important) extremely robust in solving large scale global optimization problems with huge numbers of local optima. ∗Email: b.addis@ing.unifi.it Dip. Sistemi e Informatica Università di Firenze †Email: locatell@di.unito.it Dip. Informatica Università di Torino ‡Email: schoen@ing.unifi.it Dip. Sistemi e Informatica Università di Firenze
TCS-TRA-1260 TCS Technical Report ZDD-Based Computation of the Number of Paths in a Graph by

(Abstract) Counting the number of paths in a graph, for example the number of nonintersecting (or self-avoiding) rook paths joining opposite corners of an n × n grid, is not an easy problem since no mathematical formula is found and the number becomes too large to enumerate one by one except for small graphs. We have implemented software based on the ZDD technique in Knuth's " The Art of Computer Programming " carefully so as to achieve better memory efficiency, and have succeeded in computing the exact numbers for some graphs that were not known until now.
Resting state networks and consciousness Alterations of multiple resting state network connectivity in physiological , pharmacological , and pathological consciousness states

*Correspondence: Athena Demertzi , Coma Science Group, Cyclotron Research Center, Allée du 6 août no 8, Sart Tilman B30, University of Liège, 4000 Liège, Belgium. e-mail: a.demertzi@ulg.ac.be In order to better understand the functional contribution of resting state activity to conscious cognition, we aimed to review increases and decreases in functional magnetic resonance imaging (fMRI) functional connectivity under physiological (sleep), pharmacological (anesthesia), and pathological altered states of consciousness, such as brain death, coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state.The reviewed resting state networks were the DMN, left and right executive control, salience, sensorimotor, auditory, and visual networks. We highlight some methodological issues concerning resting state analyses in severely injured brains mainly in terms of hypothesis-driven seed-based correlation analysis and data-driven independent components analysis approaches. Finally, we attempt to contextualize our discussion within theoretical frameworks of conscious processes.We think that this “lesion” approach allows us to better determine the necessary conditions under which normal conscious cognition takes place. At the clinical level, we acknowledge the technical merits of the resting state paradigm. Indeed, fast and easy acquisitions are preferable to activation paradigms in clinical populations. Finally, we emphasize the need to validate the diagnostic and prognostic value of fMRI resting state measurements in non-communicating brain damaged patients.
A Secure and Efficient Uniqueness-and-Anonymity-Preserving Remote User Authentication Scheme for Connected Health Care

Connected health care has several applications including telecare medicine information system, personally controlled health records system, and patient monitoring. In such applications, user authentication can ensure the legality of patients. In user authentication for such applications, only the legal user/patient himself/herself is allowed to access the remote server, and no one can trace him/her according to transmitted data. Chang et al. proposed a uniqueness-and-anonymity-preserving remote user authentication scheme for connected health care (Chang et al., J Med Syst 37:9902, 2013). Their scheme uses the user’s personal biometrics along with his/her password with the help of the smart card. The user’s biometrics is verified using BioHashing. Their scheme is efficient due to usage of one-way hash function and exclusive-or (XOR) operations. In this paper, we show that though their scheme is very efficient, their scheme has several security weaknesses such as (1) it has design flaws in login and authentication phases, (2) it has design flaws in password change phase, (3) it fails to protect privileged insider attack, (4) it fails to protect the man-in-the middle attack, and (5) it fails to provide proper authentication. In order to remedy these security weaknesses in Chang et al.’s scheme, we propose an improvement of their scheme while retaining the original merit of their scheme. We show that our scheme is efficient as compared to Chang et al.’s scheme. Through the security analysis, we show that our scheme is secure against possible attacks. Further, we simulate our scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. In addition, after successful authentication between the user and the server, they establish a secret session key shared between them for future secure communication.
Evaluation of Consumer Understanding of Different Front-of-Package Nutrition Labels, 2010–2011

INTRODUCTION
Governments throughout the world are using or considering various front-of-package (FOP) food labeling systems to provide nutrition information to consumers. Our web-based study tested consumer understanding of different FOP labeling systems.


METHODS
Adult participants (N = 480) were randomized to 1 of 5 groups to evaluate FOP labels: 1) no label; 2) multiple traffic light (MTL); 3) MTL plus daily caloric requirement icon (MTL+caloric intake); 4) traffic light with specific nutrients to limit based on food category (TL+SNL); or 5) the Choices logo. Total percentage correct quiz scores were created reflecting participants' ability to select the healthier of 2 foods and estimate amounts of saturated fat, sugar, and sodium in foods. Participants also rated products on taste, healthfulness, and how likely they were to purchase the product. Quiz scores and product perceptions were compared with 1-way analysis of variance followed by post-hoc Tukey tests.


RESULTS
The MTL+caloric intake group (mean [standard deviation], 73.3% [6.9%]) and Choices group (72.5% [13.2%]) significantly outperformed the no label group (67.8% [10.3%]) and the TL+SNL group (65.8% [7.3%]) in selecting the more healthful product on the healthier product quiz. The MTL and MTL+caloric intake groups achieved average scores of more than 90% on the saturated fat, sugar, and sodium quizzes, which were significantly better than the no label and Choices group average scores, which were between 34% and 47%.


CONCLUSION
An MTL+caloric intake label and the Choices symbol hold promise as FOP labeling systems and require further testing in different environments and population subgroups.
Low Data Drug Discovery with One-Shot Learning

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf.


MODEL
2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).
A survey on nature inspired metaheuristic algorithms for partitional clustering

The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed. & 2014 Published by Elsevier B.V.
A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset

Intrusion detection systems aim to detect malicious viruses from computer and network traffic, which is not possible using common firewall. Most intrusion detection systems are developed based on machine learning techniques. Since datasets which used in intrusion detection are imbalanced, in the previous methods, the accuracy of detecting two attack classes, R2L and U2R, is lower than that of the normal and other attack classes. In order to overcome this issue, this study employs a hybrid approach. This hybrid approach is a combination of synthetic minority oversampling technique (SMOTE) and cluster center and nearest neighbor (CANN). Important features are selected using leave one out method (LOO). Moreover, this study employs NSL KDD dataset. Results indicate that the proposed method improves the accuracy of detecting U2R and R2L attacks in comparison to the baseline paper by 94% and 50%, respectively. Keywords—intrusion detection system; feature selection; imbalanced dataset; SMOTE; NSL KDD
Karma: A System for Mapping Structured Sources into the Semantic Web

The Linked Data cloud contains large amounts of RDF data generated from databases. Much of this RDF data, generated using tools such as D2R, is expressed in terms of vocabularies automatically derived from the schema of the original database. The generated RDF would be significantly more useful if it were expressed in terms of commonly used vocabularies. Using today’s tools, it is labor-intensive to do this. For example, one can first use D2R to automatically generate RDF from a database and then use R2R to translate the automatically generated RDF into RDF expressed in a new vocabulary. The problem is that defining the R2R mappings is difficult and labor intensive because one needs to write the mapping rules in terms of SPARQL graph patterns. In this work, we present a semi-automatic approach for building mappings that translate data in structured sources to RDF expressed in terms of a vocabulary of the user’s choice. Our system, Karma, automatically derives these mappings, and provides an easy to use interface that enables users to control the automated process to guide the system to produce the desired mappings. In our evaluation, users need to interact with the system less than once per column (on average) in order to construct the desired mapping rules. The system then uses these mapping rules to generate semantically rich RDF for the data sources. We demonstrate Karma using a bioinformatics example and contrast it with other approaches used in that community. Bio2RDF [7] and Semantic MediaWiki Linked Data Extension (SMW-LDE) [2] are examples of efforts that integrate bioinformatics datasets by mapping them to a common vocabulary. We applied our approach to a scenario used in the SMW-LDE that integrate ABA, Uniprot, KEGG Pathway, PharmGKB and Linking Open Drug Data datasets using a
Convergence of Stochastic Gradient Descent for PCA

We consider the problem of principal component analysis (PC A) in a streaming stochastic setting, where our goal is to find a direction of approximate maximal va riance, based on a stream of i.i.d. data points inR. A simple and computationally cheap algorithm for this is st ochastic gradient descent (SGD), which incrementally updates its estimate based on ea ch new data point. However, due to the non-convex nature of the problem, analyzing its performanc e has been a challenge. In particular, existing guarantees rely on a non-trivial eigengap assumption on the covariance matrix, which is intuitively unnecessary. In this paper, we provide (to the best of our kno wledge) the first eigengap-free convergence guarantees for SGD in the context of PCA. This also part ially resolves an open problem posed in [10]. Moreover, under an eigengap assumption, we show tha t the same techniques lead to new SGD convergence guarantees with better dependence on the eigen gap.
MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) pose novel challenges compared with traditional networks. To answer such challenges a new communication paradigm, data-centric communication, is emerging. One form of data-centric communication is the publish/subscribe messaging system. Compared with other data-centric variants, publish/subscribe systems are common and wide-spread in distributed computing. Thus, extending publish/subscribe systems intoWSNs will simplify the integration of sensor applications with other distributed applications. This paper describes MQTT-S [1], an extension of the open publish/subscribe protocol message queuing telemetry transport (MQTT) [2] to WSNs. MQTT-S is designed in such a way that it can be run on low-end and battery-operated sensor/actuator devices and operate over bandwidth-constraint WSNs such as ZigBee-based networks. Various protocol design points are discussed and compared. MQTT-S has been implemented and is currently being tested on the IBM wireless sensor networking testbed [3]. Implementation aspects, open challenges and future work are also presented.
Using neural network ensembles for bankruptcy prediction and credit scoring

Bankruptcy prediction and credit scoring have long been regarded as critical topics and have been studied extensively in the accounting and finance literature. Artificial intelligence and machine learning techniques have been used to solve these financial decision-making problems. The multilayer perceptron (MLP) network trained by the back-propagation learning algorithm is the mostly used technique for financial decision-making problems. In addition, it is usually superior to other traditional statistical models. Recent studies suggest combining multiple classifiers (or classifier ensembles) should be better than single classifiers. However, the performance of multiple classifiers in bankruptcy prediction and credit scoring is not fully understood. In this paper, we investigate the performance of a single classifier as the baseline classifier to compare with multiple classifiers and diversified multiple classifiers by using neural networks based on three datasets. By comparing with the single classifier as the benchmark in terms of average prediction accuracy, the multiple classifiers only perform better in one of the three datasets. The diversified multiple classifiers trained by not only different classifier parameters but also different sets of training data perform worse in all datasets. However, for the Type I and Type II errors, there is no exact winner. We suggest that it is better to consider these three classifier architectures to make the optimal financial decision. 2007 Elsevier Ltd. All rights reserved.
A Synopsis of Linguistic Theory 1930-1955" in Studies in Linguistic Analysis