nils-herrmann/clean_scirepeval huggingface dataset:
Active localization with dynamic obstacles
Venue: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

This paper addresses the problem of robot global localization in a known environment, in the presence of many dynamic obstacles. Deploying a robot in crowded spaces such as museums, shopping malls, department stores, or university campuses is especially challenging because the moving people occlude the static parts of the environment, such as walls and doorways, making the robot essentially blind. A new weighting function is proposed for a particle filter state estimation algorithm that accounts for the presence of dynamic obstacles and avoids population depletion. An active localization strategy is employed which guides the robot to locations that resolve ambiguities and eliminate hypotheses in a systematic manner. Experimental results from multiple simulations and from real robot deployments validate the localization improvements achieved by the proposed method.
Associations among sleep disturbances, nocturnal sleep duration, daytime napping, and incident prediabetes and type 2 diabetes: the Heinz Nixdorf Recall Study.
Venue: Sleep medicine

Poor sleep quality as well as short and long sleep duration has been linked to type 2 diabetes. In addition to confirmational analyses, we examined the impact of daytime napping on incident diabetes, and we assessed associations between sleep characteristics and incident prediabetes.  In a subgroup of the Heinz Nixdorf Recall Study, a population based cohort study in Germany (N 2962; age 45 75 years, without history of cancer, stroke, or cardiovascular diseases) diabetes at baseline and at 5 year follow up was assessed by self report and measurement of serum glucose levels. Prediabetes was defined as impaired fasting glucose (6.1 6.9 mmol/L) A sleep questionnaire was used to assess difficulties falling asleep, difficulties maintaining sleep, early morning arousal, and duration of nocturnal and daytime sleep.  In adjusted regression models, short =5 hours) and long =7.5 hours) sleepers were at greater risk for diabetes (relative risk [RR] 1.56, 95% confidence interval [CI] 1.02 2.39, and 1.40, 95% CI 1.01 1.96, respectively  Moreover, the prevalence of any regular sleep disorder was associated with incident diabetes (RR 1.30, 95% CI 1.01 1.68) and with incident prediabetes (RR 1.31, 95% CI 1.00 1.72) Regular daytime nappers had no increased risk of incident diabetes (RR 1.00, 95% CI 0.70 1.41)  This study shows that people with regular sleep disorders, people with short and long sleep duration, but not regular daytime nappers are at increased risk of diabetes. Furthermore, regular sleep disorders are associated with an increased risk of prediabetes.
Non Muslim Customers' Purchase Intention on Halal Food Products in Malaysia

Halal market has been growing tremendously recently. The food products occupies the most share in the Halal market category. The phenomenon could be explained by the increased number of consumer among Halal products. Apart from Muslims, who consumes Halal product due to religious obligations, it is assumed that non Muslims have also started consuming Halal products, especially food items. Halal food products have been perceived as safer, animal friendly and environmental sustainable. Hence, the awareness of Halal principles, Islamic Brand, moral obligation animal welfare and food safety have been studied in this research in order to investigate the influence of Halal food product purchase intention among non Muslim consumer using quantitative research method. Food safety has been identified to be the most significant in predicting the purchase intention of Halal food product. Furthermore, future studies are suggested to include additional variables such as habit and self awareness.
Improving chemical similarity ensemble approach in target prediction
Venue: Journal of Cheminformatics

BackgroundIn silico target prediction of compounds plays an important role in drug discovery. The chemical similarity ensemble approach (SEA) is a promising method, which has been successfully applied in many drug related studies. There are various models available analogous to SEA, because this approach is based on different types of molecular fingerprints. To investigate the influence of training data selection and the complementarity of different models, several SEA models were constructed and tested.ResultsWhen we used a test set of 37,138 positive and 42,928 negative ligand target interactions, among the five tested molecular fingerprint methods, at significance level 0.05, Topological based model yielded the best precision rate (83.7 and {F_{0.25}\text{ }Measure}$F0.25 Measure (0.784) while Atom pair based model yielded the best $F_{0.5}\text{ }Measure$$F0.5 Measure (0.694) By employing an election system to combine the five models, a flexible prediction scheme was achieved with precision range from 71 to 90.6 $F_{0.5}\text{ }Measure$$F0.5 Measure range from 0.663 to 0.684 and $F_{0.25}\text{ }Measure$$F0.25 Measure range from 0.696 to 0.817.ConclusionsThe overall effectiveness of all of the five models could be ranked in decreasing order as follows: Atom pair \approx$ Topological Morgan MACCS Pharmacophore. Combining multiple SEA models, which takes advantages of different models, could be used to improve the success rates of the models. Another possibility of improving the model could be using target specific classes or more active compounds.
DrugE Rank: improving drug target interaction prediction of new candidate drugs or targets by ensemble learning to rank
Venue: Bioinform.

Identifying drug target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug target interactions of new candidate drugs or targets.  Approaches based on machine learning for this problem can be divided into two types: feature based and similarity based methods. Learning to rank is the most powerful technique in the feature based methods. Similarity based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE Rank uses LTR, for which multiple well known similarity based methods can be used as components of ensemble learning.  The performance of DrugE Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs.
Predicting cancer relevant proteins using an improved molecular similarity ensemble approach
Venue: Oncotarget

In this study, we proposed an improved algorithm for identifying proteins relevant to cancer. The algorithm was named two layer molecular similarity ensemble approach (TL SEA) We applied TL SEA to analyzing the correlation between anticancer compounds (against cell lines K562, MCF7 and A549) and active compounds against separate target proteins listed in BindingDB. Several associations between cancer types and related proteins were revealed using this chemoinformatics approach. An analysis of the literature showed that 26 of 35 predicted proteins were correlated with cancer cell proliferation, apoptosis or differentiation. Additionally, interactions between proteins in BindingDB and anticancer chemicals were also predicted. We discuss the roles of the most important predicted proteins in cancer biology and conclude that TL SEA could be a useful tool for inferring novel proteins involved in cancer and revealing underlying molecular mechanisms.
Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion
Venue: ArXiv

Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately. In this work, we propose a novel framework to embed words, entities and relations into the same continuous vector space. In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration. In experiments, we evaluate the joint learning model on three tasks including entity prediction, relation prediction and relation classification from text. The experiment results show that our model can significantly and consistently improve the performance on the three tasks as compared with other baselines.
Loyal to Whom? The Effect of Relational Embeddedness and Managers' Mobility on Market Tie Dissolution*

In this study we use a social embeddedness perspective to investigate the paradoxical role that individual level embedded relationships have on the dissolution of interorganizational ties. Prior studies have found that managers who form close interpersonal relationships with clients can stabilize market ties, but these relationships can also be a source of increased market tie dissolution in the event of an exchange manager's departure from the firm. Using data on all state level lobbyists and clients in the Texas lobbying industry from 2001 to 2009, we confirm that a client is more likely to remain with a firm than to follow a departing manager to a new firm, but the depth, focus, and shared ownership of a manager's client relationship moderate the impact of his or her mobility on market tie dissolution. We find that the tenure of the relationship with a manager, the number of clients on a manager's roster, the level of attention received from a manager, and the presence of an additional manager relationship at the same firm all influence where a client places its business when a manager departs. Our results suggest that embeddedness explanations strongly predict interpersonal market tie dissolution when managers are stationary but lose their predictive power when managers move to new firms. We propose that an alternative logic of interorganizational market ties, based on power and resource dependence, may be at play in such situations.
Fast vector quantization using a Bat algorithm for image compression

Linde Buzo Gray (LBG) a traditional method of vector quantization (VQ) generates a local optimal codebook which results in lower PSNR value. The performance of vector quantization (VQ) depends on the appropriate codebook, so researchers proposed optimization techniques for global codebook generation. Particle swarm optimization (PSO) and Firefly algorithm (FA) generate an efficient codebook, but undergoes instability in convergence when particle velocity is high and non availability of brighter fireflies in the search space respectively. In this paper, we propose a new algorithm called BA LBG which uses Bat Algorithm on initial solution of LBG. It produces an efficient codebook with less computational time and results very good PSNR due to its automatic zooming feature using adjustable pulse emission rate and loudness of bats. From the results, we observed that BA LBG has high PSNR compared to LBG, PSO LBG, Quantum PSO LBG, HBMO LBG and FA LBG, and its average convergence speed is 1.841 times faster than HBMO LBG and FA LBG but no significance difference with PSO.
Landau quantization, Aharonov Bohm effect and two dimensional pseudoharmonic quantum dot around a screw dislocation

We investigate the influence of a screw dislocation on the energy levels and the wavefunctions of an electron confined in a two dimensional pseudoharmonic quantum dot under the influence of an external magnetic field inside a dot and Aharonov Bohm field inside a pseudodot. The exact solutions for energy eigenvalues and wavefunctions are computed as functions of applied uniform magnetic field strength, Aharonov Bohm flux, magnetic quantum number and the parameter characterizing the screw dislocation, the Burgers vector. We investigate the modifications due to the screw dislocation on the light interband absorption coefficient and absorption threshold frequency. Two scenarios are possible, depending on if singular effects either manifest or not. We found that as the Burgers vector increases, the curves of frequency are pushed up towards of the growth of it. One interesting aspect which we have observed is that the Aharonov Bohm flux can be tuned in order to cancel the screw effect of the model.
Effect of Industrialization on Environment (Indian Scenario)

India is a developing nation. India is well thought out as the world's biggest booming economies. Modernization has led to the development in the lifestyle and the basic needs are no more just food, cloth and shelter. The industrialization has led to development in diverse areas like agriculture, manufacturing sector, coal, timber, bottling plants, automobiles, gas and chemicals. This has definitely developed the economy of India and the lifestyle of people living in the country. It had also led to the degradation of environment and the environmental conditions, the flora and fauna in different ecosystems, extinction of the rare species of animals, plants and birds and the depletion of natural resources. The major cause of this is the deforestation of the ecosystems for industrialization. Industrialization has resulted in the increase in the emission of harmful effluents and pollutants both into water, soil and air. These effluents have caused a severe and irreversible destruction to the different species residing in those specific ecosystems. Many life species have become vulnerable and some are extinct due to deforestation. The ground water reserves are no more pure, global warming has caused in the depletion of the ozone layer and has caused deadly diseases in certain areas. Most of this is caused by heavy industrialization. The ill effects of industrialization are very well known but the major question remains whether we want this to continue and suffer in the near future or make a change.
Status of vaccine research and development of vaccines for leishmaniasis.
Venue: Vaccine

A number of leishmaniasis vaccine candidates are at various stages of pre clinical and clinical development. Leishmaniasis is a vector borne neglected tropical disease (NTD) caused by a protozoan parasite of the genus Leishmania and transmitted to humans by the bite of a sand fly. Visceral leishmaniasis (VL, kala azar) is a high mortality NTD found mostly in South Asia and East Africa, while cutaneous leishmaniasis (CL) is a disfiguring NTD highly endemic in the Middle East, Central Asia, North Africa, and the Americas. Estimates attribute 50,000 annual deaths and 3.3 million disability adjusted life years to leishmaniasis. There are only a few approved drug treatments, no prophylactic drug and no vaccine. Ideally, an effective vaccine against leishmaniasis will elicit long lasting immunity and protect broadly against VL and CL. Vaccines such as Leish F1, F2 and F3, developed at IDRI and designed based on selected Leishmania antigen epitopes, have been in clinical trials. Other groups, including the Sabin Vaccine Institute in collaboration with the National Institutes of Health are investigating recombinant Leishmania antigens in combination with selected sand fly salivary gland antigens in order to augment host immunity. To date, both VL and CL vaccines have been shown to be cost effective in economic modeling studies.
Status of vaccine research and development for Shigella.
Venue: Vaccine

Shigella are gram negative bacteria that cause severe diarrhea and dysentery. In 2013, Shigella infections caused an estimated 34,400 deaths in children less than five years old and, in 2010, an estimated 40,000 deaths in persons older than five years globally. New disease burden estimates from newly deployed molecular diagnostic assays with increased sensitivity suggest that Shigella associated morbidity may be much greater than previous disease estimates from culture based methods. Primary prevention of this disease should be based on universal provision of potable water and sanitation methods and improved personal and food hygiene. However, an efficacious and low cost vaccine would complement and accelerate disease reduction while waiting for universal access to water, sanitation, and hygiene improvements. This review article provides a landscape of Shigella vaccine development efforts. No vaccine is yet available, but human and animal challenge rechallenge trials with virulent Shigella as well as observational studies in Shigella endemic areas have shown that the incidence of disease decreases following Shigella infection, pointing to biological feasibility of a vaccine. Immunity to Shigella appears to be strain specific, so a vaccine that covers the most commonly detected strains (i.e. S. flexneri 2a, 3a, 6, and S. sonnei) or a vaccine using cross species conserved antigens would likely be most effective. Vaccine development and testing may be accelerated by use of animal models, such as the guinea pig keratoconjunctivitis or murine pneumonia models. Because there is no correlate of protection, however, human studies will be necessary to evaluate vaccine efficacy prior to deployment. A diversity of Shigella vaccine constructs are under development, including live attenuated, formalin killed whole cell, glycoconjugate, subunit, and novel antigen vaccines (e.g. Type III secretion system and outer membrane proteins)
Natural Syntax Iconicity And Erosion

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The Enlightened Self: Identity and Aspiration in Two Communities of Practice

Existing research on religious identity, especially from a narrative perspective, has tended to focus either on accounts of the past (especially occasions of religious change) or on conceptions of religious identity in the present. Religious communities, however, not only provide a sense of identity and belonging in the present as a "Catholic" or "Buddhist," for example they also promote a particular vision of the religious ideal: The way of being in the world that all adherents are (or ought to be) striving to achieve. Drawing on fieldwork and interviews, this paper describes and analyzes the identity and lifestyle goals of participants in two communities of practice: An Integral Yoga studio and a Catholic prayer house. I find that the ideal spiritual self in both communities is defined by three key characteristics: A sacred gaze, a simultaneous sense of presence and detachment, and a holistic style of identity management. I suggest that in constructing and transmitting a shared vision of the "enlightened self," these organizations offer practitioners a highly desirable but ever elusive aspirational identity. This study calls attention to religious organizations as important suppliers of possible identities the identities, either desired and feared, we think we could or might become in the future and reveals the situated and contextual nature of adherents' religious aspirations.
The Enlightened Self Identity and Aspiration in Two Communities of Practice

Existing research on religious identity, especially from a narrative perspective, has tended to focus either on accounts of the past (especially occasions of religious change) or on conceptions of religious identity in the present. Religious communities, however, not only provide a sense of identity and belonging in the present as a "Catholic" or "Buddhist," for example they also promote a particular vision of the religious ideal: The way of being in the world that all adherents are (or ought to be) striving to achieve. Drawing on fieldwork and interviews, this paper describes and analyzes the identity and lifestyle goals of participants in two communities of practice: An Integral Yoga studio and a Catholic prayer house. I find that the ideal spiritual self in both communities is defined by three key characteristics: A sacred gaze, a simultaneous sense of presence and detachment, and a holistic style of identity management. I suggest that in constructing and transmitting a shared vision of the "enlightened self," these organizations offer practitioners a highly desirable but ever elusive aspirational identity. This study calls attention to religious organizations as important suppliers of possible identities the identities, either desired and feared, we think we could or might become in the future and reveals the situated and contextual nature of adherents' religious aspirations.
Malware Detection with Deep Neural Network Using Process Behavior
Venue: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)

Increase of malware and advanced cyber attacks are now becoming a serious problem. Unknown malware which has not determined by security vendors is often used in these attacks, and it is becoming difficult to protect terminals from their infection. Therefore, a countermeasure for after infection is required. There are some malware infection detection methods which focus on the traffic data comes from malware. However, it is difficult to perfectly detect infection only using traffic data because it imitates benign traffic. In this paper, we propose malware process detection method based on process behavior in possible infected terminals. In proposal, we investigated stepwise application of Deep Neural Networks to classify malware process. First, we train the Recurrent Neural Network (RNN) to extract features of process behavior. Second, we train the Convolutional Neural Network (CNN) to classify feature images which are generated by the extracted features from the trained RNN. The evaluation result in several image size by comparing the AUC of obtained ROC curves and we obtained AUC= 0:96 in best case.
Action Video Games Improve Direction Discrimination of Parafoveal Translational Global Motion but Not Reaction Times
Venue: Perception

Playing action video games enhances visual motion perception. However, there is psychophysical evidence that action video games do not improve motion sensitivity for translational global moving patterns presented in fovea. This study investigates global motion perception in action video game players and compares their performance to that of non action video game players and non video game players. Stimuli were random dot kinematograms presented in the parafovea. Observers discriminated the motion direction of a target random dot kinematogram presented in one of the four visual quadrants. Action video game players showed lower motion coherence thresholds than the other groups. However, when the task was performed at threshold, we did not find differences between groups in terms of distributions of reaction times. These results suggest that action video games improve visual motion sensitivity in the near periphery of the visual field, rather than speed response.