nils-herrmann/clean_scirepeval huggingface dataset:
Glass Blowing on a Wafer Level

A fabrication process for the simultaneous shaping of arrays of glass shells on a wafer level is introduced in this paper. The process is based on etching cavities in silicon, followed by anodic bonding of a thin glass wafer to the etched silicon wafer. The bonded wafers are then heated inside a furnace at a temperature above the softening point of the glass, and due to the expansion of the trapped gas in the silicon cavities the glass is blown into three-dimensional spherical shells. An analytical model which can be used to predict the shape of the glass shells is described and demonstrated to match the experimental data. The ability to blow glass on a wafer level may enable novel capabilities including mass-production of microscopic spherical gas confinement chambers, microlenses, and complex microfluidic networks
Using Matched Samples to Estimate the Effects of Exercise on Mental Health via Twitter

Recent work has demonstrated the value of social media monitoring for health surveillance (e.g., tracking influenza or depression rates). It is an open question whether such data can be used to make causal inferences (e.g., determining which activities lead to increased depression rates). Even in traditional, restricted domains, estimating causal effects from observational data is highly susceptible to confounding bias. In this work, we estimate the effect of exercise on mental health from Twitter, relying on statistical matching methods to reduce confounding bias. We train a text classifier to estimate the volume of a user’s tweets expressing anxiety, depression, or anger, then compare two groups: those who exercise regularly (identified by their use of physical activity trackers like Nike+), and a matched control group. We find that those who exercise regularly have significantly fewer tweets expressing depression or anxiety; there is no significant difference in rates of tweets expressing anger. We additionally perform a sensitivity analysis to investigate how the many experimental design choices in such a study impact the final conclusions, including the quality of the classifier and the construction of the control group.
A Lazy CGI Namespace in Scheme

@inproceedings{Hudak2007, author = {Hudak, Paul and Hughes, John and Peyton Jones, Simon and Wadler, Philip}, title = {A History of Haskell: Being Lazy with Class}, booktitle = {Proceedings of the Third ACM SIGPLAN Conference on History of Programming Languages}, series = {HOPL III}, year = {2007}, isbn = {978-1-59593-766-7}, location = {San Diego, California}, pages = {12-1--12-55}, url = {http://doi.acm.org/10.1145/1238844.1238856}, doi = {10.1145/1238844.1238856}, acmid = {1238856}, publisher = {ACM}, address = {New York, NY, USA}, }
Evaluation of mitochondrial respiratory chain activity in muscle healing by low-level laser therapy.

Recent studies demonstrate that low-level laser therapy (LLLT) modulates many biochemical processes, especially the decrease of muscle injures, the increase in mitochondrial respiration and ATP synthesis for accelerating the healing process.



In this work, we evaluated mitochondrial respiratory chain complexes I, II, III and IV and succinate dehydrogenase activities after traumatic muscular injury.



Male Wistar rats were randomly divided into three groups (n=6): sham (uninjured muscle), muscle injury without treatment, muscle injury with LLLT (AsGa) 5J/cm(2). Gastrocnemius injury was induced by a single blunt-impact trauma. LLLT was used 2, 12, 24, 48, 72, 96, and 120 hours after muscle-trauma.



Our results showed that the activities of complex II and succinate dehydrogenase after 5days of muscular lesion were significantly increased when compared to the control group. Moreover, our results showed that LLLT significantly increased the activities of complexes I, II, III, IV and succinate dehydrogenase, when compared to the group of injured muscle without treatment.



These results suggest that the treatment with low-level laser may induce an increase in ATP synthesis, and that this may accelerate the muscle healing process.
Green Tea and Its Extracts in Cancer Prevention and Treatment

Green tea (GT) and green tea extracts (GTE) have been postulated to decrease cancer incidence. In vitro results indicate a possible effect; however, epidemiological data do not support cancer chemoprevention. We have performed a PubMED literature search for green tea consumption and the correlation to the common tumor types lung, colorectal, breast, prostate, esophageal and gastric cancer, with cohorts from both Western and Asian countries. We additionally included selected mechanistical studies for a possible mode of action. The comparability between studies was limited due to major differences in study outlines; a meta analysis was thus not possible and studies were evaluated individually. Only for breast cancer could a possible small protective effect be seen in Asian and Western cohorts, whereas for esophagus and stomach cancer, green tea increased the cancer incidence, possibly due to heat stress. No effect was found for colonic/colorectal and prostatic cancer in any country, for lung cancer Chinese studies found a protective effect, but not studies from outside China. Epidemiological studies thus do not support a cancer protective effect. GT as an indicator of as yet undefined parameters in lifestyle, environment and/or ethnicity may explain some of the observed differences between China and other countries.
Orofacial pain

Pain in the oral and craniofacial system represents a major medical and social problem. Indeed, a U.S. Surgeon General’s report on orofacial health concludes that, ‘‘. . .oral health means much more than healthy teeth. It means being free of chronic oral-facial pain conditions. . .’’ [172]. Community-based surveys indicate that many subjects commonly report pain in the orofacial region, with estimates of >39 million, or 22% of Americans older than 18 years of age, in the United States alone [108]. Other population-based surveys conducted in the United Kingdom [111,112], Germany [91], or regional pain care centers in the United States  report similar occurrence rates [135]. Importantly, chronic widespread body pain, patient sex and age, and psychosocial factors appear to serve as risk factors for chronic orofacial pain [1,2,92,99,138]. In addition to its high degree of prevalence, the reported intensities of various orofacial pain conditions are similar to that observed with many spinal pain disorders . Moreover, orofacial pain is derived from many unique target tissues, such as the meninges, cornea, tooth pulp, oral/ nasal mucosa, and temporomandibular joint , and thus has several unique physiologic characteristics compared with the spinal nociceptive system [23]. Given these considerations, it is not surprising that accurate diagnosis and effective management of orofacial pain conditions represents a significant health care problem. Publications in the field of orofacial pain demonstrate a steady increase over the last several decades . This is a complex literature; a recent bibliometric analysis of orofacial pain articles published in 2004–2005 indicated that 975 articles on orofacial pain were published in 275 journals from authors representing 54 countries [142]. Thus, orofacial pain disorders represent a complex constellation of conditions with an equally diverse literature base. Accordingly, this review will focus on a summary of major research foci on orofacial pain without attempting to provide a comprehensive review of the entire literature.
Basic pharmacokinetics

Pharmacokinetics is a fundamental scientific discipline that underpins applied therapeutics. Patients need to be prescribed appropriate medicines for a clinical condition. The medicine is chosen on the basis of an evidencebased approach to clinical practice and assured to be compatible with any other medicines or alternative therapies the patient may be taking. The design of a dosage regimen is dependent on a basic understanding of the drug use process (DUP). When faced with a patient who shows specific clinical signs and symptoms, pharmacists must always ask a fundamental question: ‘Is this patient suffering from a drug-related problem?’ Once this issue is evaluated and a clinical diagnosis is available, the pharmacist can apply the DUP to ensure that the patient is prescribed an appropriate medication regimen, that the patient understands the therapy prescribed, and that an agreed concordance plan is achieved. Pharmacists using the DUP consider:
Material Handling Equipment Selection using Hybrid Monte Carlo Simulation and Analytic Hierarchy Process

The many feasible alternatives and conflicting objectives make equipment selection in materials handling a complicated task. This paper presents utilizing Monte Carlo (MC) simulation combined with the Analytic Hierarchy Process (AHP) to evaluate and select the most appropriate Material Handling Equipment (MHE). The proposed hybrid model was built on the base of material handling equation to identify main and sub criteria critical to MHE selection. The criteria illustrate the properties of the material to be moved, characteristics of the move, and the means by which the materials will be moved. The use of MC simulation beside the AHP is very powerful where it allows the decision maker to represent his/her possible preference judgments as random variables. This will reduce the uncertainty of single point judgment at conventional AHP, and provide more confidence in the decision problem results. A small business pharmaceutical company is used as an example to illustrate the development and application of the proposed model. Analytic Hierarchy Process (AHP), Material handling equipment selection, Monte Carlo simulation, Multi-criteria decision making
Secure Data Analytics for Cloud-Integrated Internet of Things Applications

Cloud-integrated Internet of Things (IoT) is emerging as the next-generation service platform that enables smart functionality worldwide. IoT applications such as smart grid and power systems, e-health, and body monitoring applications along with large-scale environmental and industrial monitoring are increasingly generating large amounts of data that can conveniently be analyzed through cloud service provisioning. However, the nature of these applications mandates the use of secure and privacy-preserving implementation of services that ensures the integrity of data without any unwarranted exposure. This article explores the unique challenges and issues within this context of enabling secure cloud-based data analytics for the IoT. Three main applications are discussed in detail, with solutions outlined based on the use of fully homomorphic encryption systems to achieve data security and privacy over cloud-based analytical phases. The limitations of existing technologies are discussed and models proposed with regard to achieving high efficiency and accuracy in the provisioning of analytic services for encrypted data over a cloud platform.
Modified transpose Jacobian control of robotic systems

The simplicity of Transpose Jacobian (TJ) control is a significant characteristic of this algorithm for controlling robotic manipulators. Nevertheless, a poor performance may result in tracking of fast trajectories, since it is not dynamics-based. Use of high gains can deteriorate performance seriously in the presence of feedback measurement noise. Another drawback is that there is no prescribed method of selecting its control gains. In this paper, based on feedback linearization approach a Modified TJ (MTJ) algorithm is presented which employs stored data of the control command in the previous time step, as a learning tool to yield improved performance. The gains of this new algorithm can be selected systematically, and do not need to be large, hence the noise rejection characteristics of the algorithm are improved. Based on Lyapunov’s theorems, it is shown that both the standard and the MTJ algorithms are asymptotically stable. Analysis of the required computational effort reveals the efficiency of the proposed MTJ law compared to the Model-based algorithms. Simulation results are presented which compare tracking performance of the MTJ algorithm to that of the TJ and Model-Based algorithms in various tasks. Results of these simulations show that performance of the new MTJ algorithm is comparable to that of Computed Torque algorithms, without requiring a priori knowledge of plant dynamics, and with reduced computational burden. Therefore, the proposed algorithm is well suited to most industrial applications where simple efficient algorithms are more appropriate than complicated theoretical ones with massive computational burden.
Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.
Datenkompetenzen für die Massen - Muss Self-Service-Data-Mining scheitern?

Mining ist ein Prozess, bei dem mittels statistischer Verfahren komplexe Muster in meist großen Mengen von Daten gesucht werden. Damit dieser von Organisationen verstärkt zur Entscheidungsunterstützung eingesetzt werden kann, wäre es hilfreich, wenn Domänenexperten durch Self-Service-Anwendungen in die Lage versetzt würden, diese Form der Analysen eigenständig durchzuführen, damit sie nicht mehr auf Datenwissenschaftler und IT-Fachkräfte angewiesen sind. In diesem Artikel soll eine Versuchsreihe vorgestellt werden, die eine Bewertung darüber ermöglicht, wie geeignet etablierte Data-MiningSoftwareplattformen (IBM SPSS Modeler, KNIME, RapidMiner und WEKA) sind, um sie Gelegenheitsanwendern zur Verfügung zu stellen. In den vorgestellten Versuchen sollen Entscheidungsbäume im Fokus stehen, eine besonders einfache Form von Algorithmen, die der Literatur und unserer Erfahrung nach am ehesten für die Nutzung in Self-Service-Data-Mining-Anwendungen geeignet sind. Dabei werden mithilfe eines einheitlichen Datensets auf den verschiedenen Plattformen Entscheidungsbäume für identische Zielvariablen konstruiert. Die Ergebnisse sind im Hinblick auf die Klassifikationsgenauigkeit zwar relativ ähnlich, die Komplexität der Modelle variiert jedoch. Aktuelle grafische Benutzeroberflächen lassen sich zwar auch ohne tiefgehende Kompetenzen in den Bereichen Informatik und Statistik bedienen, sie ersetzen aber nicht den Bedarf an datenwissenschaftlichen Kompetenzen, die besonders beim Schritt der Datenvorbereitung zum Einsatz kommen, welcher den größten Teil des Data-Mining-Prozesses ausmacht.
Learning at a distance I. Statistical learning of non-adjacent dependencies

In earlier work we have shown that adults, young children, and infants are capable of computing transitional probabilities among adjacent syllables in rapidly presented streams of speech, and of using these statistics to group adjacent syllables into word-like units. In the present experiments we ask whether adult learners are also capable of such computations when the only available patterns occur in non-adjacent elements. In the first experiment, we present streams of speech in which precisely the same kinds of syllable regularities occur as in our previous studies, except that the patterned relations among syllables occur between non-adjacent syllables (with an intervening syllable that is unrelated). Under these circumstances we do not obtain our previous results: learners are quite poor at acquiring regular relations among non-adjacent syllables, even when the patterns are objectively quite simple. In subsequent experiments we show that learners are, in contrast, quite capable of acquiring patterned relations among non-adjacent segments-both non-adjacent consonants (with an intervening vocalic segment that is unrelated) and non-adjacent vowels (with an intervening consonantal segment that is unrelated). Finally, we discuss why human learners display these strong differences in learning differing types of non-adjacent regularities, and we conclude by suggesting that these contrasts in learnability may account for why human languages display non-adjacent regularities of one type much more widely than non-adjacent regularities of the other type.
Mycosis Fungoides Variants.

Mycosis fungoides (MF) is a cutaneous T-cell lymphoma that usually manifests as patches and plaques with a propensity for nonphotoexposed areas. MF is a common mimicker of inflammatory and infectious skin diseases, because it can be manifested with a wide variety of clinical and pathologic presentations. These atypical presentations of MF may be difficult to diagnose, requiring a high level of suspicion and careful clinicopathologic correlation. Within this array of clinical presentations, the World Health Organization classification recognizes 3 MF variants: folliculotropic MF, pagetoid reticulosis, and granulomatous slack skin. These 3 variants, as well as hypopigmented MF, are addressed in this article.
Transferability of Object-Oriented Image Analysis Methods for Slum Identification

Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed.
Applying WebTables in Practice

We started investigating the collection of HTML tables on the Web and developed the WebTables system a few years ago . Since then, our work has been motivated by applying WebTables in a broad set of applications at Google, resulting in several product launches. In this paper, we describe the challenges faced, lessons learned, and new insights that we gained from our efforts. The main challenges we faced in our efforts were (1) identifying tables that are likely to contain high-quality data (as opposed to tables used for navigation, layout, or formatting), and (2) recovering the semantics of these tables or signals that hint at their semantics. The result is a semantically enriched table corpus that we used to develop several services. First, we created a search engine for structured data whose index includes over a hundred million HTML tables. Second, we enabled users of Google Docs (through its Research Panel) to find relevant data tables and to insert such data into their documents as needed. Most recently, we brought WebTables to a much broader audience by using the table corpus to provide richer tabular snippets for fact-seeking web search queries on Google.com.
Debiasing Decisions : Improved Decision Making With a Single Training Intervention

From failures of intelligence analysis to misguided beliefs about vaccinations, biased judgment and decision making contributes to problems in policy, business, medicine, law, education, and private life. Early attempts to reduce decision biases with training met with little success, leading scientists and policy makers to focus on debiasing by using incentives and changes in the presentation and elicitation of decisions. We report the results of two longitudinal experiments that found medium to large effects of one-shot debiasing training interventions. Participants received a single training intervention, played a computer game or watched an instructional video, which addressed biases critical to intelligence analysis (in Experiment 1: bias blind spot, confirmation bias, and fundamental attribution error; in Experiment 2: anchoring, representativeness, and social projection). Both kinds of interventions produced medium to large debiasing effects immediately (games ≥ −31.94% and videos ≥ −18.60%) that persisted at least 2 months later (games ≥ −23.57% and videos ≥ −19.20%). Games that provided personalized feedback and practice produced larger effects than did videos. Debiasing effects were domain general: bias reduction occurred across problems in different contexts, and problem formats that were taught and not taught in the interventions. The results suggest that a single training intervention can improve decision making. We suggest its use alongside improved incentives, information presentation, and nudges to reduce costly errors associated with biased judgments and decisions.
EVALUATING EFFECT OF SOCIAL FACTORS AFFECTING CONSUMER BEHAVIOR IN PURCHASING HOME FURNISHING PRODUCTS IN JORDAN

The present study examined the impact of social factors on consumer behavior in evaluative criteria of the purchased home furnishing in Amman (Jordan). In the literature, there are a few previous studies which have explored the topics on consumer behavior and home furniture industry in Jordan. Furthermore, the objective of this study is to investigate of purchasing behavior of home furniture consumers in Jordan. This study then will evaluate the factors that have influences on furniture purchasing decision process. The findings will allow the researcher to be able to recommend to Jordan furniture manufacturers and retailers. Also, questionnaires were distributed and self-administered to 400 respondents. Descriptive analysis, factors analysis, test of reliability, correlation test, and regression analysis were used in this study. The study results demonstrated that there is a positive and significant relationship between reference group, family, price, quality, color, and purchasing decision. In addition, implications of this work and directions for future research are discussed.