nils-herrmann/clean_scirepeval huggingface dataset:
AC Transconductance Dispersion (ACGD): A Method to Profile Oxide Traps in MOSFETs Without Body Contact

We introduce an ac transconductance dispersion method (ACGD) to profile the oxide traps in an MOSFET without needing a body contact. The method extracts the spatial distribution of oxide traps from the frequency dependence of transconductance, which is attributed to charge trapping as modulated by an ac gate voltage. The results from this method have been verified by the use of the multifrequency charge pumping (MFCP) technique. In fact, this method complements the MFCP technique in terms of the trap depth that each method is capable of probing. We will demonstrate the method with InP passivated InGaAs substrates, along with electrically stressed Si N-MOSFETs.
Kinase inhibitors and monoclonal antibodies in oncology: clinical implications

Molecularly targeted cancer therapies, such as small-molecule kinase inhibitors and monoclonal antibodies, constitute a rapidly growing and an important part of the oncology armamentarium. Unlike conventional (cytotoxic) chemotherapeutics, targeted therapies were designed to disrupt cancer cell pathogenesis at specific biological points essential for the development and progression of the tumour. These agents were developed to disrupt specific targets with the aim of minimizing treatment burden compared with conventional chemotherapy. Nevertheless the increasingly common use of targeted therapies has revealed some unanticipated, often clinically significant toxic effects, as well as compromising effective palliative and end-of-life management approaches. Although patients and clinicians welcome improvements in cancer prognosis, these changes can also impact patient quality-of-life. Therefore, as demand for oncology expertise increases, physicians need to apprise themselves of targeted therapies and their clinical implications, including drug-specific side effects, impact on quality of life, and cost issues, especially in relation to end-of-life care. This Review provides a useful summary and guide for professionals treating patients with malignant diseases.
Multi-dimensional visualization of large-scale marine hydrological environmental data

With the constant deepening of research on marine environment simulation and information expression, there are higher and higher requirements for the sense of reality of ocean data visualization results and the real-time interaction in the visualization process. This paper tackle the challenge of key technology of three-dimensional interaction and volume rendering technology based on GPU technology, develops large scale marine hydrological environmental data-oriented visualization software and realizes oceanographic planar graph, contour line rendering, isosurface rendering, factor field volume rendering and dynamic simulation of current field. To express the spatial characteristics and real-time update of massive marine hydrological environmental data better, this study establishes nodes in the scene for the management of geometric objects to realize high-performance dynamic rendering. The system employs CUDA (Computing Unified Device Architecture) parallel computing for the improvement of computation rate, uses NetCDF (Network Common Data Form) file format for data access and applies GPU programming technology to realize fast volume rendering of marine water environmental factors. The visualization software of marine hydrological environment developed can simulate and show properties and change process of marine water environmental factors efficiently and intuitively.
Data augmentation for diffusions

The problem of formal likelihood-based (either classical or Bayesian) inference for discretely observed multi-dimensional diffusions is particularly challenging. In principle this involves data-augmentation of the observation data to give representations of the entire diffusion trajectory. Most currently proposed methodology splits broadly into two classes: either through the discretisation of idealised approaches for the continuous-time diffusion setup; or through the use of standard finite-dimensional methodologies discretisation of the diffusion model. The connections between these approaches have not been well-studied. This paper will provide a unified framework bringing together these approaches, demonstrating connections, and in some cases surprising differences. As a result, we provide, for the first time, theoretical justification for the various methods of imputing missing data. The inference problems are particularly challenging for reducible diffusions, and our framework is correspondingly more complex in that case. Therefore we treat the reducible and irreducible cases differently within the paper.  Among others, their appeal lies in their flexibility to deal with nonlinearity, time-inhomogeneity and heteroscedasticity by specifying two interpretable functionals, their amenability to efficient computations due to their Markov property, and the rich existing mathematical theory about their properties. As a result, they are used as models throughout Science; some book references related with this approach to modeling include Section 5.3 of  for physical systems, Section 8.3.3 (in conjunction with Section 6.3) of  for systems biology and mass action stochastic kinetics, and Chapter 10 of [27] for interest rates. A mathematically precise specification of a d-dimensional diffusion process V is as the solution of a stochastic differential equation (SDE) of the type: dVs = b(s, Vs; θ1) ds+ σ(s, Vs; θ2) dBs, s ∈ [0, T ] ; (1) where B is an m-dimensional standard Brownian motion, b(·, · ; · ) : R+ ×Rd ×Θ1 → R is the drift and σ(·, · ; · ) : R+ × R × Θ2 → R is the diffusion coefficient. These ICREA and Department of Economics, Universitat Pompeu Fabra, omiros.papaspiliopoulos@upf.edu Department of Statistics, University of Warwick Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa
Overview of the 1st Classification of Spanish Election Tweets Task at IberEval 2017

This paper summarises the COSET shared task organised as part of the IberEval workshop. The aim of this task is to classify the topic discussed in a tweet into one of five topics related to the Spanish 2015 electoral cycle. A new dataset was curated for this task and hand-labelled by experts on the task. Moreover, the results of the 17 participants of the task and a review of their proposed systems are presented. In a second phase evaluation, we provided the participants with 15.8 millions tweets in order to test the scalability of their systems.
Trustworthiness Perceptions of Computer Code: A Heuristic-Systematic Processing Model

The popularity of code reuse and the prospect of computer generated code raises questions of how programmers trust in computer code. Psychological understanding of computer code perceptions and comprehension has yet to be explored in regards to the decision making processes involved with software development and reuse practices. A review of current literature on trust, automation, software reuse, and the intersection of the three is presented. The authors propose a theoretical model of this decision making process, building off of a heuristic-systematic model of persuasion. Future research directions and possible applications are discussed.
H2O: A hybrid and hierarchical outlier detection method for large scale data protection

Data protection is the process of backing up data in case of a data loss event. It is one of the most critical routine activities for every organization. Detecting abnormal backup jobs is important to prevent data protection failures and ensure the service quality. Given the large scale backup endpoints and the variety of backup jobs, from a backup-as-a-service provider viewpoint, we need a scalable and flexible outlier detection method that can model a huge number of objects and well capture their diverse patterns. In this paper, we introduce H2O, a novel hybrid and hierarchical method to detect outliers from millions of backup jobs for large scale data protection. Our method automatically selects an ensemble of outlier detection models for each multivariate time series composed by the backup metrics collected for each backup endpoint by learning their exhibited characteristics. Interactions among multiple variables are considered to better detect true outliers and reduce false positives. In particular, a new seasonal-trend decomposition based outlier detection method is developed, considering the interactions among variables in the form of common trends, which is robust to the presence of outliers in the training data. The model selection process is hierarchical, following a global to local fashion. The final outlier is determined through an ensemble learning by multiple models. Built on top of Apache Spark, H2O has been deployed to detect outliers in a large and complex data protection environment with more than 600,000 backup endpoints and 3 million daily backup jobs. To the best of our knowledge, this is the first work that selects and constructs large scale outlier detection models for multivariate time series on big data platforms.
Smart wireless healthcare monitoring for drivers community

Smart healthcare monitoring systems have been given more attention in recent research efforts as they are not only used for patients but also recommended for old age people, sports persons, drivers' community and home makers. For example, smart healthcare support is introduced in some expensive cars like Mercedes-Benz. In developing countries like India, some road accidents may happen due to driver's poor health condition like heart stroke while driving, over stress due to continuous work, etc. Hence, if a smart healthcare monitoring system is available at an affordable cost, it can be used for drivers' community to take some preventive measures, thus road accidents may be reduced. In this work, a healthcare monitoring system is designed to monitor the health status periodically and abnormal values are informed to both transport office and healthcare provider. The primary function of the system is to monitor a driver's physiological parameters such as, pulse rate, body temperature, etc., using sensors and transferred to a Smartphone using Bluetooth communication. Then, the proposed healthcare application in a Smartphone verifies the criticality. If there is any significant difference with normal value details are communicated to both healthcare service providers and transport office via cellular networks. Healthcare professionals take necessary action using ambulance service and Transport Corporation ensures driver's health status or arranges an alternative driver. The proposed system prototype is implemented and tested in our Network Lab.
Frictional adhesion: A new angle on gecko attachment.

Directional arrays of branched microscopic setae constitute a dry adhesive on the toes of pad-bearing geckos, nature's supreme climbers. Geckos are easily and rapidly able to detach their toes as they climb. There are two known mechanisms of detachment: (1) on the microscale, the seta detaches when the shaft reaches a critical angle with the substrate, and (2) on the macroscale, geckos hyperextend their toes, apparently peeling like tape. This raises the question of how geckos prevent detachment while inverted on the ceiling, where body weight should cause toes to peel and setal angles to increase. Geckos use opposing feet and toes while inverted, possibly to maintain shear forces that prevent detachment of setae or peeling of toes. If detachment occurs by macroscale peeling of toes, the peel angle should monotonically decrease with applied force. In contrast, if adhesive force is limited by microscale detachment of setae at a critical angle, the toe detachment angle should be independent of applied force. We tested the hypothesis that adhesion is increased by shear force in isolated setal arrays and live gecko toes. We also tested the corollary hypotheses that (1) adhesion in toes and arrays is limited as on the microscale by a critical angle, or (2) on the macroscale by adhesive strength as predicted for adhesive tapes. We found that adhesion depended directly on shear force, and was independent of detachment angle. Therefore we reject the hypothesis that gecko toes peel like tape. The linear relation between adhesion and shear force is consistent with a critical angle of release in live gecko toes and isolated setal arrays, and also with our prior observations of single setae. We introduced a new model, frictional adhesion, for gecko pad attachment and compared it to existing models of adhesive contacts. In an analysis of clinging stability of a gecko on an inclined plane each adhesive model predicted a different force control strategy. The frictional adhesion model provides an explanation for the very low detachment forces observed in climbing geckos that does not depend on toe peeling.
Validation of HPLC-UV method for determination of amoxicillin Trihydrate in capsule

The intention of the present work is to validate an easy, better and reasonable approach for estimation of amoxicillin trihydrate in tablet formulation by opposite segment(reverse phase) HPLC –UV with advanced conditions and parameters for habitual use in Rwanda well known board in pharmaceutical laboratory in order to check if no substandard or counterfeit amoxicillin has entered in our country that can result in antimicrobial resistance, treatment failure which can be a chief diffi culty on public health. an easy, selective, precise, speedy, specifi c, and correct reverse phase HPLC UV-seen technique has been verifi ed for the dedication of amoxicillin, in addition that is a cost-effective technique for the established method, monobasic potassium phosphate (KH2PO4) used as buffer and methanol and had been used as a mobile section in the ratio 95:5 respectively. The elution turned into fi nished in an isocratic mode at a go with the fl ow rate of 1.5ml/minute proposed method became demonstrated as according to ICH guiding principle refereeing additionally to USP necessities for amoxicillin capsule. linearity range of amoxicillin and was evaluated inside the variety of 20–160 g/ml. the correlation coeffi cient r2 changed into 0.9998 and the relative well known deviation between six replicates injection was always much less than 2%. The retention time was found 3.5±0.02. the high percentage of healing of amoxicillin is 100.6±4% indicates that the proposed method is exceptionally correct and precise trueness of with the trueness of 100.06±1.2% .the statistical evaluation proved that the demonstrated method is appropriate for analysis of amoxicillin as the majority drug and pharmaceutical formula with none interference from excipients .with the aid of considering the effi ciency of the drug samples, all analyzed pattern were within the variety of 90-120 % of percentage of labeled amount, but the effi ciency had been distinctive amongst samples. The have a look at located that no counterfeit, no substandard product turned into amongst all batches of amoxicillin samples throughout the c programming language of the look at. Review Article Validation of HPLC-UV method for determination of amoxicillin Trihydrate in capsule Sendanyoye Marcel*, Uwambajineza Tito, Ineza Ines and Nahimana Jean Pierre University of Rwanda, College of Medicine and Health Science, Department of Pharmacy, Kigali, Rwanda *Address for Correspondence: Sendanyoye Marcel, University of Rwanda, College of Medicine and Health Science, Department of Pharmacy, Kigali, Rwanda, Tel: +250783493980;  03 October 2018
Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model

In this paper we study generative modeling via autoencoders while using the elegant geometric properties of the optimal transport (OT) problem and the Wasserstein distances. We introduce Sliced-Wasserstein Autoencoders (SWAE), which are generative models that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or defining a closed-form for the distribution. In short, we regularize the autoencoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a predefined samplable distribution. We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Autoencoders (WAE) and Variational Autoencoders (VAE), while benefiting from an embarrassingly simple implementation.
Ethics of brain emulations

Ethics of brain emulations Whole brain emulation attempts to achieve software intelligence by copying the function of biological nervous systems into software. This paper aims at giving an overview of the ethical issues of the brain emulation approach, and analyse how they should affect responsible policy for developing the field. Animal emulations have uncertain moral status, and a principle of analogy is proposed for judging treatment of virtual animals. Various considerations of developing and using human brain emulations are discussed.  Whole brain emulation (WBE) is an approach to achieve software intelligence by copying the functional structure of biological nervous systems into software. Rather than attempting to understand the high-level processes underlying perception, action, emotions and intelligence, the approach assumes that they would emerge from a sufficiently close imitation of the low-level neural functions, even if this is done through a software process. (Sandberg 2013) of brain emulations have been discussed, little analysis of the ethics of the project so far has been done. The main questions of this paper are to what extent brain emulations are moral patients, and what new ethical concerns are introduced as a result of brain emulation technology. The basic idea is to take a particular brain, scan its structure in detail at some resolution, construct a software model of the physiology that is so faithful to the original that, when run on appropriate hardware, it will have an internal causal structure that is essentially the same as the original brain. All relevant functions on some level of description are present, and higher level functions supervene from these. While at present an unfeasibly ambitious challenge, the necessary computing power and various scanning methods are rapidly developing. Large scale computational brain models are a very active research area, at present reaching the size of mammalian nervous systems. al. 2012) WBE can be viewed as the logical endpoint of current trends in computational neuroscience and systems biology. Obviously the eventual feasibility depends on a number of philosophical issues (physicalism, functionalism, non-organicism) and empirical facts (computability, scale separation, detectability, scanning and simulation tractability) that cannot be predicted beforehand; WBE can be viewed as a program trying to test them empirically. (Sandberg 2013) Early projects are likely to merge data from multiple brains and studies, attempting to show that this can produce a sufficiently rich model to produce nontrivial behaviour but not attempting to emulate any particular individual. However, …
MEASURING TEST ANXIETY IN CHILDREN : SCALE DEVELOPMENT AND INTERNAL CONSTRUCT VALIDATION

Given the increased testing of school-aged children in the United States there is a need for a current and valid scale to measure the effects of test anxiety in children. The domain of children’s test anxiety was theorized to be comprised of three dimensions: thoughts, autonomic reactions, and off-task behaviors. Four stages are described in the evolution of the Children’s Test Anxiety Scale (CTAS): planning, construction, quantitative evaluation, and validation. A 50-item scale was administered to a development sample (N /230) of children in grades 3 /6 to obtain item analysis and reliability estimates which resulted in a refined 30-item scale. The reduced scale was administered to a validation sample (N /261) to obtain construct validity evidence. A three-factor structure fit the data reasonably well. Recommendations for future research with the scale are described.
Enriching Knowledge in Business Process Modelling: A Storytelling Approach

The main goal of Business Process Management (BPM) is conceptualising, operationalizing and controlling workflows in organisations based on process models. In this paper we discuss several limitations of the workflow paradigm and suggest that process models can also play an important role in analysing how organisations think about themselves through storytelling. We contrast the workflow paradigm with storytelling through a comparative analysis. We also report a case study where storytelling has been used to elicit and document the practices of an IT maintenance team. This research contributes towards the development of better process modelling languages and tools.
Idiom Token Classification using Sentential Distributed Semantics

Idiom token classification is the task of deciding for a set of potentially idiomatic phrases whether each occurrence of a phrase is a literal or idiomatic usage of the phrase. In this work we explore the use of Skip-Thought Vectors to create distributed representations that encode features that are predictive with respect to idiom token classification. We show that classifiers using these representations have competitive performance compared with the state of the art in idiom token classification. Importantly, however, our models use only the sentence containing the target phrase as input and are thus less dependent on a potentially inaccurate or incomplete model of discourse context. We further demonstrate the feasibility of using these representations to train a competitive general idiom token classifier.
Time Complexity of Knuth-Morris-Pratt String Matching Algorithm Course Project Report for COMP-160 , Fall 2010

This project centers on the evaluation for the time complexity of Knuth-Morris-Pratt(KMP) string matching algorithm. String matching problem is to locate a pattern string within a larger string. The best performance in terms of asymptotic time complexity is currently linear, given by the KMP algorithm. In this algorithm, firstly a prefix for the pattern string is computed and then based on this prefix, only linear time is needed to find the pattern string in the larger string. In details of both steps, the prefix computation and matcher, two loops are needed, where one goes over the whole string and the other loop travels back to some point of the prefix array. I test these two steps with a large number of random input strings, of which the lengths are different, and for evaluation, ranges of the strings are also under control to simulate the various cases. As much as possible, I expect to get the tight analysis of the KMP algorithm’s time complexity. During these tests, the numbers of basic operations are recorded and then linear analysis is conducted, giving the coefficients of linear regression and the correlation coefficient. Finally, together with the analysis, it is concluded that the linear time complexity is validated based on the experiments.