nils-herrmann/clean_scirepeval huggingface dataset:
Food insufficiency and American school-aged children's cognitive, academic, and psychosocial development.

This study investigates associations between food insufficiency and cognitive, academic, and psychosocial outcomes for US children and teenagers ages 6 to 11 and 12 to 16 years.



Data from the Third National Health and Nutrition Examination Survey (NHANES III) were analyzed. Children were classified as food-insufficient if the family respondent reported that his or her family sometimes or often did not get enough food to eat. Regression analyses were conducted to test for associations between food insufficiency and cognitive, academic, and psychosocial measures in general and then within lower-risk and higher-risk groups. Regression coefficients and odds ratios for food insufficiency are reported, adjusted for poverty status and other potential confounding factors.



After adjusting for confounding variables, 6- to 11-year-old food-insufficient children had significantly lower arithmetic scores and were more likely to have repeated a grade, have seen a psychologist, and have had difficulty getting along with other children. Food-insufficient teenagers were more likely to have seen a psychologist, have been suspended from school, and have had difficulty getting along with other children. Further analyses divided children into lower-risk and higher-risk groups. The associations between food insufficiency and children's outcomes varied by level of risk.



The results demonstrate that negative academic and psychosocial outcomes are associated with family-level food insufficiency and provide support for public health efforts to increase the food security of American families.
F-DES: Fast and Deep Event Summarization

In the multimedia era a large volume of video data can be recorded during a certain period of time by multiple cameras. Such a rapid growth of video data requires both effective and efficient multiview video summarization techniques. The users can quickly browse and comprehend a large amount of audiovisual data. It is very difficult in real-time to manage and access the huge amount of video-content-handling issues of interview dependencies significant variations in illumination and presence of many unimportant frames with low activity. In this paper we propose a local-alignment-based FASTA approach to summarize the events in multiview videos as a solution of the aforementioned problems. A deep learning framework is used to extract the features to resolve the problem of variations in illumination and to remove fine texture details and detect the objects in a frame. Interview dependencies among multiple views of video are then captured via the FASTA algorithm through local alignment. Finally object tracking is applied to extract the frames with low activity. Subjective as well as objective evaluations clearly indicate the effectiveness of the proposed approach. Experiments show that the proposed summarization method successfully reduces the video content while keeping momentous information in the form of events. A computing analysis of the system also shows that it meets the requirement of real-time applications.
Leveraging microservices architecture by using Docker technology

Microservices architecture is not a hype and for awhile, started getting attention from organizations who want to shorten time to market of a software product by improving productivity effect through maximizing the automation in all life circle of the product. However, microservices architecture approach also introduces a lot of new complexity and requires application developers a certain level of maturity in order to confidently apply the architectural style. Docker has been a disruptive technology which changes the way applications are being developed and distributed. With a lot of advantages, Docker is a very good fit to implementing microservices architecture. In this paper we will discuss about how Docker can effectively help in leveraging mircoservices architecture with a real working model as a case study.
Area-efficient cross-coupled charge pump for on-chip solar cell

In this paper, an area-efficient cross-coupled charge pump for standalone chip using on-chip solar cell is proposed. The proposed cross-coupled charge pump outputs higher voltage than conventional cross-coupled charge pump on the same area by adding capacitors to drive MOS transistors. The proposed circuit is fabricated in 0.18um standard CMOS process, and outputs 80mV higher than the conventional circuit with 500mV input and 100kHz clock frequency.
Pelvic Arterial Anatomy Relevant to Prostatic Artery Embolisation and Proposal for Angiographic Classification

To describe and categorize the angiographic findings regarding prostatic vascularization, propose an anatomic classification, and discuss its implications for the PAE procedure. Angiographic findings from 143 PAE procedures were reviewed retrospectively, and the origin of the inferior vesical artery (IVA) was classified into five subtypes as follows: type I: IVA originating from the anterior division of the internal iliac artery (IIA), from a common trunk with the superior vesical artery (SVA); type II: IVA originating from the anterior division of the IIA, inferior to the SVA origin; type III: IVA originating from the obturator artery; type IV: IVA originating from the internal pudendal artery; and type V: less common origins of the IVA. Incidences were calculated by percentage. Two hundred eighty-six pelvic sides (n = 286) were analyzed, and 267 (93.3 %) were classified into I–IV types. Among them, the most common origin was type IV (n = 89, 31.1 %), followed by type I (n = 82, 28.7 %), type III (n = 54, 18.9 %), and type II (n = 42, 14.7 %). Type V anatomy was seen in 16 cases (5.6 %). Double vascularization, defined as two independent prostatic branches in one pelvic side, was seen in 23 cases (8.0 %). Despite the large number of possible anatomical variations of male pelvis, four main patterns corresponded to almost 95 % of the cases. Evaluation of anatomy in a systematic fashion, following a standard classification, will make PAE a faster, safer, and more effective procedure.
Interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system

An interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system is proposed. In a conventional single flyback inverter for the photovoltaic AC module system, this inverter has drawbacks of a conduction losses of each switch, transformer copper and high ripple current of capacitor. To overcome these problems, two flyback converter modules with interleaved pulse-width modulation (PWM) are connected in parallel at the input and output sides reducing the ripple current on the input and output capacitors and decrease the current stress on the transformer windings. Thus, the transformer copper losses and the conduction losses on the switches are reduced. Also, reliability and life of capacitor are increased due to reducing the ripple current of input and output capacitors. In order to verify these, theoretical analysis and simulation are performed.
ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition

This paper describes the ASU system submitted in the COLING W-NUT 2016 Twitter Named Entity Recognition (NER) task. We present an experimental study on applying deep learning to extracting named entities (NEs) from tweets. We built two Long Short-Term Memory (LSTM) models for the task. The first model was built to extract named entities without types while the second model was built to extract and then classify them into 10 fine-grained entity classes. In this effort, we show detailed experimentation results on the effectiveness of word embeddings, brown clusters, part-of-speech (POS) tags, shape features, gazetteers, and local context for the tweet input vector representation to the LSTM model. Also, we present a set of experiments, to better design the network parameters for the Twitter NER task. Our system was ranked the fifth out of ten participants with a final f1-score for the typed classes of 39% and 55% for the non typed ones.
Propagating Uncertainty through the tanh Function with Application to Reservoir Computing

Many neural networks use the tanh activation function, however when given a probability distribution as input, the problem of computing the output distribution in neural networks with tanh activation has not yet been addressed. One important example is the initialization of the echo state network in reservoir computing, where random initialization of the reservoir requires time to wash out the initial conditions, thereby wasting precious data and computational resources. Motivated by this problem, we propose a novel solution utilizing a moment based approach to propagate uncertainty through an Echo State Network to reduce the washout time. In this work, we contribute two new methods to propagate uncertainty through the tanh activation function and propose the Probabilistic Echo State Network (PESN), a method that is shown to have better average performance than deterministic Echo State Networks given the random initialization of reservoir states. Additionally we test single and multi-step uncertainty propagation of our method on two regression tasks and show that we are able to recover similar means and variances as computed by Monte-Carlo simulations.
Bandwidth Extension Techniques for CMOS Amplifiers

Inductive-peaking-based bandwidth extension techniques for CMOS amplifiers in wireless and wireline applications are presented. To overcome the conventional limits on bandwidth extension ratios, these techniques augment inductive peaking using capacitive splitting and magnetic coupling. It is shown that a critical design constraint for optimum bandwidth extension is the ratio of the drain capacitance of the driver transistor to the load capacitance. This, in turn, recommends the use of different techniques for different capacitance ratios. Prototype wideband amplifiers in 0.18-mum CMOS are presented that achieve a measured bandwidth extension ratio up to 4.1 and simultaneously maintain high gain (>12 dB) in a single stage. Even higher enhancement ratios are shown through the introduction of a modified series-peaking technique combined with staggering techniques. Ultra-wideband low-noise amplifiers in 0.18-mum CMOS are presented that exhibit bandwidth extension ratios up to 4.9
The Impact of E-Commerce Announcements on the Market Value of Firms

Firms are undertaking growing numbers of e-commerce initiatives and increasinglymaking significant investments required to participate in the growing online market. However, empirical support for the benefits to firms from e-commerce is weaker than glowing accounts in the popular press, based on anecdotal evidence, would lead us to believe. In this paper, we explore the following questions: What are the returns to shareholders in firms engaging in e-commerce? How do the returns to conventional, brick and mortar firms from e-commerce initiatives compare with returns to the new breed of net firms? How do returns from businessto-business e-commerce compare with returns from business-to-consumer e-commerce? How do the returns to e-commerce initiatives involving digital goods compare to initiatives involving tangible goods? We examine these issues using event study methodology and assess the cumulative abnormal returns to shareholders (CARs) for 251 e-commerce initiatives announced by firms between October and December 1998. The results suggest that e-commerce initiatives do indeed lead to significant positive CARs for firms’ shareholders. While the CARs for conventional firms are not significantly different from those for net firms, the CARs for businessto-consumer (B2C) announcements are higher than those for business-to-business (B2B) announcements. Also, the CARs with respect to e-commerce initiatives involving tangible goods are higher than for those involving digital goods. Our data were collected in the last quarter of 1998 during a unique bull market period and the magnitudes of CARs (between 4.9 and 23.4% for different subsamples) in response to e-commerce announcements are larger than those reported for a variety of other firm actions in prior event studies. This paper presents the first empirical test of the dot com effect, validating popular anticipations of significant future benefits to firms entering into e-commerce arrangements.
Churn Prediction in MMORPGs: A Social Influence Based Approach

Massively Multiplayer Online Role Playing Games(MMORPGs) are computer based games in which players interactwith one another in the virtual world. Worldwide revenuesfor MMORPGs have seen amazing growth in last few years and itis more than a 2 billion dollars industry as per current estimates.Huge amount of revenue potential has attracted several gamingcompanies to launch online role playing games. One of the majorproblems these companies suffer apart from fierce competitionis erosion of their customer base. Churn is a big problem for thegaming companies as churners impact negatively in the wordof-mouth reports for potential and existing customers leading tofurther erosion of user base.We study the problem of player churn in the popularMMORPG EverQuest II. The problem of churn predictionhas been studied extensively in the past in various domainsand social network analysis has recently been applied to theproblem to understand the effects of the strength of social tiesand the structure and dynamics of a social network in churn.In this paper, we propose a churn prediction model based onexamining social influence among players and their personalengagement in the game. We hypothesize that social influence is avector quantity, with components negative influence and positiveinfluence. We propose a modified diffusion model to propagatethe influence vector in the players network which represents thesocial influence on the player from his network. We measure aplayers personal engagement based on his activity patterns anduse it in the modified diffusion model and churn prediction. Ourmethod for churn prediction which combines social influenceand player engagement factors has shown to improve predictionaccuracy significantly for our dataset as compared to predictionusing the conventional diffusion model or the player engagementfactor, thus validating our hypothesis that combination of boththese factors could lead to a more accurate churn prediction.
Selective Transfer Learning for Cross Domain Recommendation

Collaborative filtering (CF) aims to predict users’ ratings on items according to historical user-item preference data. In many realworld applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring knowledge from some manually selected source domains, the data sparseness problem could be mitigated. However for most cases, parts of source domain data are not consistent with the observations in the target domain, which may misguide the target domain model building. In this paper, we propose a novel criterion based on empirical prediction error and its variance to better capture the consistency across domains in CF settings. Consequently, we embed this criterion into a boosting framework to perform selective knowledge transfer. Comparing to several state-of-the-art methods, we show that our proposed selective transfer learning framework can significantly improve the accuracy of rating prediction tasks on several real-world recommendation tasks.
Accurate and robust localization of duplicated region in copy–move image forgery

Copy–move image forgery detection has recently become a very active research topic in blind image forensics. In copy–move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy–move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy–move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using “dense” MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy–move image forgery with higher accuracy, especially when the size of the duplicated region is small.
An Ontology Based Formalism for the Arabic Language Using Verbs and their Derivatives

Developing a strong and complete lexicon is essential for many Natural Language Processing (NLP) related applications such as Information Retrieval, Question Answering Systems, Natural Language Generation and Machine Translation. Such lexicon should be supported by a strong theoretical framework and implemented using tools that will facilitate its implementation and efficient use both in terms of resources and language preciseness. Recognising the derivative and inflexional nature of the Arabic language, the paper presents a formalism based on mathematics and ontologies of part of the morphological knowledge of the Arabic language based on verbs and their derivatives. In this research, we attempt to use the derivations and their patterns to structure the Arabic language and to strongly link the words’ morphology to their semantics. We first perform a thorough study of part of the Arabic Language and propose a model that is based on set theory and ontologies. We then show how this model can be used for some applications that include NLP applications.
Diplomarbeit Intensity Based Rigid 2 D-3 D Registration Algorithms for Radiation Therapy In collaboration with

One of the main applications for computers in medicine is to digitally merge patient data that originates from different sources. For our 2D-3D Registration problem, this means finding the right spatial alignment of a three-dimensional Computed Tomography (CT) data set and one or two X-Ray images from the same patient. An automatic algorithm has to create a digitally reconstructed radiograph (DRR) for a specific orientation in the CT data set, compare the result with the X-Ray image, alter the position and orientation of the DRR and repeat the process until the optimal alignment is reached. A key issue is to assess the alignment of these two images in every step. I present a comprehensive examination of all intensity-based similarity measures known in literature, and use them with a number of different optimization algorithms. We developed a prototypic application for both manual and automatic registration. The performance is evaluated for different data sets, and validation is done using marker-based ground truth information. The results show that we are able to do very precise and fully automatic registration within a couple of minutes. We propose to use this technique to find the correct patient position on the treatment couch for radiation therapy. This would replace the common, often very inconvenient immobilization methods.
Improving Real-Time Captioning Experiences for Deaf and Hard of Hearing Students

We take a qualitative approach to understanding deaf and hard of hearing (DHH) students' experiences with real-time captioning as an access technology in mainstream university classrooms. We consider both existing human-based captioning as well as new machine-based solutions that use automatic speech recognition (ASR). We employed a variety of qualitative research methods to gather data about students' captioning experiences including in-class observations, interviews, diary studies, and usability evaluations. We also conducted a co-design workshop with 8 stakeholders after our initial research findings. Our results show that accuracy and reliability of the technology are still the most important issues across captioning solutions. However, we additionally found that current captioning solutions tend to limit students' autonomy in the classroom and present a variety of user experience shortcomings, such as complex setups, poor feedback and limited control over caption presentation. Based on these findings, we propose design requirements and recommend features for real-time captioning in mainstream classrooms.
Circular and Elliptical CPW-Fed Slot and Microstrip-Fed Antennas for Ultrawideband Applications

This letter presents novel circular and elliptical coplanar waveguide (CPW)-fed slot and mictrostip-fed antenna designs targeting the 3.1¿10.6 GHz band. The antennas are comprised of elliptical or circular stubs that excite similar-shaped slot apertures. Four prototypes have been examined, fabricated and experimentally tested, the three being fed by a CPW and the fourth by a microstrip line, exhibiting a very satisfactory behavior throughout the 7.5 GHz of the allocated bandwidth in terms of impedance matching $(hbox VSWR¿2)$, radiation efficiency and radiation pattern characteristics. Measured impedance bandwidths of beyond 175% will be presented.
Noninvasive human brain stimulation.

Noninvasive brain stimulation with transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS) is valuable in research and has potential therapeutic applications in cognitive neuroscience, neurophysiology, psychiatry, and neurology. TMS allows neurostimulation and neuromodulation, while tDCS is a purely neuromodulatory application. TMS and tDCS allow diagnostic and interventional neurophysiology applications, and focal neuropharmacology delivery. However, the physics and basic mechanisms of action remain incompletely explored. Following an overview of the history and current applications of noninvasive brain stimulation, we review stimulation device design principles, the electromagnetic and physical foundations of the techniques, and the current knowledge about the electrophysiologic basis of the effects. Finally, we discuss potential biomedical and electrical engineering developments that could lead to more effective stimulation devices, better suited for the specific applications.