nils-herrmann/clean_scirepeval huggingface dataset:
Nicotinic acid, nicotinamide, and nicotinamide riboside: a molecular evaluation of NAD+ precursor vitamins in human nutrition.

Although baseline requirements for nicotinamide adenine dinucleotide (NAD+) synthesis can be met either with dietary tryptophan or with less than 20 mg of daily niacin, which consists of nicotinic acid and/or nicotinamide, there is growing evidence that substantially greater rates of NAD+ synthesis may be beneficial to protect against neurological degeneration, Candida glabrata infection, and possibly to enhance reverse cholesterol transport. The distinct and tissue-specific biosynthetic and/or ligand activities of tryptophan, nicotinic acid, nicotinamide, and the newly identified NAD+ precursor, nicotinamide riboside, reviewed herein, are responsible for vitamin-specific effects and side effects. Because current data suggest that nicotinamide riboside may be the only vitamin precursor that supports neuronal NAD+ synthesis, we present prospects for human nicotinamide riboside supplementation and propose areas for future research.
Reliability and Validity of a Submaximal Warm-up Test for Monitoring Training Status in Professional Soccer Players.

Rabbani, A, Kargarfard, M, and Twist, C. Reliability and validity of a submaximal warm-up test for monitoring training status in professional soccer players.  2018-Two studies were conducted to assess the reliability and validity of a submaximal warm-up test (SWT) in professional soccer players. For the reliability study, 12 male players performed an SWT over 3 trials, with 1 week between trials. For the validity study, 14 players of the same team performed an SWT and a 30-15 intermittent fitness test (30-15IFT) 7 days apart. Week-to-week reliability in selected heart rate (HR) responses (exercise heart rate [HRex], heart rate recovery [HRR] expressed as the number of beats recovered within 1 minute [HRR60s], and HRR expressed as the mean HR during 1 minute [HRpost1]) was determined using the intraclass correlation coefficient (ICC) and typical error of measurement expressed as coefficient of variation (CV). The relationships between HR measures derived from the SWT and the maximal speed reached at the 30-15IFT (VIFT) were used to assess validity. The range for ICC and CV values was 0.83-0.95 and 1.4-7.0% in all HR measures, respectively, with the HRex as the most reliable HR measure of the SWT. Inverse large (r = -0.50 and 90% confidence limits [CLs] [-0.78 to -0.06]) and very large (r = -0.76 and CL, -0.90 to -0.45) relationships were observed between HRex and HRpost1 with VIFT in relative (expressed as the % of maximal HR) measures, respectively. The SWT is a reliable and valid submaximal test to monitor high-intensity intermittent running fitness in professional soccer players. In addition, the test's short duration (5 minutes) and simplicity mean that it can be used regularly to assess training status in high-level soccer players.
Weakly-supervised Contextualization of Knowledge Graph Facts

Knowledge graphs (KGs) model facts about the world; they consist of nodes (entities such as companies and people) that are connected by edges (relations such as founderOf ). Facts encoded in KGs are frequently used by search applications to augment result pages. When presenting a KG fact to the user, providing other facts that are pertinent to that main fact can enrich the user experience and support exploratory information needs. \em KG fact contextualization is the task of augmenting a given KG fact with additional and useful KG facts. The task is challenging because of the large size of KGs; discovering other relevant facts even in a small neighborhood of the given fact results in an enormous amount of candidates. We introduce a neural fact contextualization method (\em NFCM ) to address the KG fact contextualization task. NFCM first generates a set of candidate facts in the neighborhood of a given fact and then ranks the candidate facts using a supervised learning to rank model. The ranking model combines features that we automatically learn from data and that represent the query-candidate facts with a set of hand-crafted features we devised or adjusted for this task. In order to obtain the annotations required to train the learning to rank model at scale, we generate training data automatically using distant supervision on a large entity-tagged text corpus. We show that ranking functions learned on this data are effective at contextualizing KG facts. Evaluation using human assessors shows that it significantly outperforms several competitive baselines.
Large Margin Deep Networks for Classification

We present a formulation of deep learning that aims at producing a large margin classifier. The notion of margin, minimum distance to a decision boundary, has served as the foundation of several theoretically profound and empirically successful results for both classification and regression tasks. However, most large margin algorithms are applicable only to shallow models with a preset feature representation; and conventional margin methods for neural networks only enforce margin at the output layer. Such methods are therefore not well suited for deep networks. In this work, we propose a novel loss function to impose a margin on any chosen set of layers of a deep network (including input and hidden layers). Our formulation allows choosing any lp norm (p ≥ 1) on the metric measuring the margin. We demonstrate that the decision boundary obtained by our loss has nice properties compared to standard classification loss functions. Specifically, we show improved empirical results on the MNIST, CIFAR-10 and ImageNet datasets on multiple tasks: generalization from small training sets, corrupted labels, and robustness against adversarial perturbations. The resulting loss is general and complementary to existing data augmentation (such as random/adversarial input transform) and regularization techniques such as weight decay, dropout, and batch norm. 2
Passive Radar Detection With Noisy Reference Channel Using Principal Subspace Similarity

Traditional passive radar detectors compute cross correlation of the raw data in the reference and surveillance channels. However, there is no optimality guarantee for this detector in the presence of a noisy reference. Here, we develop a new detector that utilizes a test statistic based on the cross correlation of the principal left singular vectors of the reference and surveillance signal-plus-noise matrices. This detector offers better performance by exploiting the inherent low-rank structure when the transmitted signals are a weighted periodic summation of several identical waveforms (amplitude and phase modulation), as is the case with commercial digital illuminators as well as noncooperative radar. We consider a scintillating target. We provide analytical detection performance guarantees establishing signal-to-noise ratio thresholds above which the proposed detection statistic reliably discriminates, in an asymptotic sense, the signal versus no-signal hypothesis. We validate these results using extensive numerical simulations. We demonstrate the “near constant false alarm rate (CFAR)” behavior of the proposed detector with respect to a fixed, SNR-independent threshold and contrast that with the need to adjust the detection threshold in an SNR-dependent manner to maintain CFAR for other detectors found in the literature. Extensions of the proposed detector for settings applicable to orthogonal frequency division multiplexing (OFDM), adaptive radar are discussed.
Dualism persists in the science of mind.

The relationship between mind and brain has philosophical, scientific, and practical implications. Two separate but related surveys from the University of Edinburgh (University students, n= 250) and the University of Liège (health-care workers, lay public, n= 1858) were performed to probe attitudes toward the mind-brain relationship and the variables that account for differences in views. Four statements were included, each relating to an aspect of the mind-brain relationship. The Edinburgh survey revealed a predominance of dualistic attitudes emphasizing the separateness of mind and brain. In the Liège survey, younger participants, women, and those with religious beliefs were more likely to agree that the mind and brain are separate, that some spiritual part of us survives death, that each of us has a soul that is separate from the body, and to deny the physicality of mind. Religious belief was found to be the best predictor for dualistic attitudes. Although the majority of health-care workers denied the distinction between consciousness and the soma, more than one-third of medical and paramedical professionals regarded mind and brain as separate entities. The findings of the study are in line with previous studies in developmental psychology and with surveys of scientists' attitudes toward the relationship between mind and brain. We suggest that the results are relevant to clinical practice, to the formulation of scientific questions about the nature of consciousness, and to the reception of scientific theories of consciousness by the general public.
Organizing care for patients with chronic illness.

Usual medical care often fails to meet the needs of chronically ill patients, even in managed, integrated delivery systems. The medical literature suggests strategies to improve outcomes in these patients. Effective interventions tend to fall into one of five areas: the use of evidence-based, planned care; reorganization of practice systems and provider roles; improved patient self-management support; increased access to expertise; and greater availability of clinical information. The challenge is to organize these components into an integrated system of chronic illness care. Whether this can be done most efficiently and effectively in primary care practice rather than requiring specialized systems of care remains unanswered.
Barrier impact on organizational learning within complex organizations

The purpose of this research is to examine the manner in which employees access, create and share information and knowledge within a complex supply chain with a view to better understanding how to identify and manage barriers which may inhibit such exchanges.  An extensive literature review combined with an in-depth case study analysis identified a range of potential transfer barriers. These in turn were examined in terms of their consistency of impact by an end-to-end process survey conducted within an IBM facility.  Barrier impact cannot be assumed to be uniform across the core processes of the organization. Process performance will be impacted upon in different ways and subject to varying degrees of influence by the transfer barriers. Barrier identification and management must take place at a process rather than at the organizational level.  The findings are based, in the main, on an extensive single company study. Although significant in terms of influencing both knowledge and information systems design and management the study/findings have still to be fully replicated across a range of public and private organizations.  The deployment of generic information technology and business systems needs to be questioned if they have been designed and implemented to satisfy organizational rather than process needs.
Mobile Quad-Controlled wireless Robotic Arm

In this paper, we develop a remotely controlled robotic arm with 4 degree of freedom (D.O.F) that is wirelessly controlled using four control mechanisms, i.e, Voice Control, Smart Phone-Tilt Control, Remote control and Hand Gesture Control. Wireless technologies such as Bluetooth and Wi-Fi are used to access the Quad-Controlled Robotic Arm (QCRA). A prototype QCRA is developed. The QCRA can be used to pick and place objects from one place to another on receiving the commands from distance, thereby reducing the human effort. An Android application is developed for convenience of the user in operating this QCRA, using different control mechanisms. Performance evaluation results are encouraging. Potential applications of the QCRA in homes, industries and for physically challenged/aged people are also discussed.
Wideband Impedance Transformers With Good Frequency Selectivity Based on Multisection Quarter-Wave Lines and Short-Circuited Stubs

In this letter, a class of wideband impedance transformers based on multisection quarter-wave transmission lines and short-circuited stubs are proposed to be incorporated with good passband frequency selectivity. A synthesis approach is then presented to design this two-port asymmetrical transformer with Chebyshev frequency response. For the specified impedance transformation ratio, bandwidth, and in-band return loss, the required impedance parameters can be directly determined. Next, a transformer with two section transmission lines in the middle is characterized, where a set of design curves are given for practical design. Theoretically, the proposed multisection transformer has attained good passband frequency selectivity against the reported counterparts. Finally, a 50-110 Ω impedance transformer with a fractional bandwidth of 77.8% and 15 dB in-band return loss is designed, fabricated and measured to verify the prediction.
Boosting Cognition: Effects of Multiple-Session Transcranial Direct Current Stimulation on Working Memory

Transcranial direct current stimulation (tDCS) is a promising tool for neurocognitive enhancement. Several studies have shown that just a single session of tDCS over the left dorsolateral pFC (lDLPFC) can improve the core cognitive function of working memory (WM) in healthy adults. Yet, recent studies combining multiple sessions of anodal tDCS over lDLPFC with verbal WM training did not observe additional benefits of tDCS in subsequent stimulation sessions nor transfer of benefits to novel WM tasks posttraining. Using an enhanced stimulation protocol as well as a design that included a baseline measure each day, the current study aimed to further investigate the effects of multiple sessions of tDCS on WM. Specifically, we investigated the effects of three subsequent days of stimulation with anodal (20 min, 1 mA) versus sham tDCS (1 min, 1 mA) over lDLPFC (with a right supraorbital reference) paired with a challenging verbal WM task. WM performance was measured with a verbal WM updating task (the letter n-back) in the stimulation sessions and several WM transfer tasks (different letter set n-back, spatial n-back, operation span) before and 2 days after stimulation. Anodal tDCS over lDLPFC enhanced WM performance in the first stimulation session, an effect that remained visible 24 hr later. However, no further gains of anodal tDCS were observed in the second and third stimulation sessions, nor did benefits transfer to other WM tasks at the group level. Yet, interestingly, post hoc individual difference analyses revealed that in the anodal stimulation group the extent of change in WM performance on the first day of stimulation predicted pre to post changes on both the verbal and the spatial transfer task. Notably, this relationship was not observed in the sham group. Performance of two individuals worsened during anodal stimulation and on the transfer tasks. Together, these findings suggest that repeated anodal tDCS over lDLPFC combined with a challenging WM task may be an effective method to enhance domain-independent WM functioning in some individuals, but not others, or can even impair WM. They thus call for a thorough investigation into individual differences in tDCS respondence as well as further research into the design of multisession tDCS protocols that may be optimal for boosting cognition across a wide range of individuals.
Social bonds and internet pornographic exposure among adolescents.

Concern has grown regarding possible harm to the social and psychological development of children and adolescents exposed to Internet pornography. Parents, academics and researchers have documented pornography from the supply side, assuming that its availability explains consumption satisfactorily. The current paper explored the user's dimension, probing whether pornography consumers differed from other Internet users, as well as the social characteristics of adolescent frequent pornography consumers. Data from a 2004 survey of a national representative sample of the adolescent population in Israel were used (n=998). Adolescent frequent users of the Internet for pornography were found to differ in many social characteristics from the group that used the Internet for information, social communication and entertainment. Weak ties to mainstream social institutions were characteristic of the former group but not of the latter. X-rated material consumers proved to be a distinct sub-group at risk of deviant behaviour.
Driving performance evaluation of interior permanent magnet (IPM) motor using circuit parameter estimation

This paper deals with the method of driving performance evaluation of interior permanent magnet (IPM) motors with 6-pole NdFeB magnets rotor and 9-slot stator. The circuit parameters of IPM motor such as Ld(d-axis inductance), Lq(q-axis inductance), torque and speed are investigated using 2-d finite element analysis(FEA) in each current value with phase angle condition. The low speed region below the based point presents speed-torque curve according to limited current in constant torque condition for maximum torque-per-ampere (MTPA) control. On the other hand, the high speed region above the based point draws speed-torque curve according to frequency and current angle variation in limited current and voltage for field-weakening control. This curve shows that suitable driving region to drive the IPM motors.
Active Polarimetric Imaging for Estimation of Scene Geometry

Active imaging polarimetry for remote sensing applications has received significant attention recently. Such systems use a variably-polarized active light source to illuminate target objects. Multiple images with different polarizations are then captured and used to build Stokes vectors which are, in turn, used to estimate the refraction indices of materials as well as the relative geometry of the target object. The applications facilitated by active polarimetry include target detection, object recognition, shape extraction and material classification. Unfortunately, this estimation problem requires us to find the solution to a system of nonlinear equations using an iterative optimization technique. In this paper, we introduce a methodology for finding and validating good solutions to this optimization.
Neural Events Extraction from Movie Descriptions

We present a novel approach for event extraction and abstraction from movie descriptions. Our event frame consists of ‘who”, “did what” “to whom”, “where”, and “when”. We formulate our problem using a recurrent neural network, enhanced with structural features extracted from syntactic parser, and trained using curriculum learning by progressively increasing the difficulty of the sentences. Our model serves as an intermediate step towards question answering systems, visual storytelling, and story completion tasks. We evaluate our approach on MovieQA dataset.
Skin picking and trichotillomania in adults with obsessive-compulsive disorder.

The objective of this study was to compare patients with obsessive-compulsive disorder (OCD) associated with pathologic skin picking (PSP) and/or trichotillomania, and patients with OCD without such comorbidities, for demographic and clinical characteristics. We assessed 901 individuals with a primary diagnosis of OCD, using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) Axis I disorders. Diagnoses of PSP and trichotillomania were made in 16.3% and 4.9% of the sample, respectively. After the logistic regression analysis, the following factors retained an association with OCD-PSP/trichotillomania: younger (odds ratio [OR] = 0.979; P = .047), younger at the onset of compulsive symptoms (OR = 0.941; P = .007), woman (OR = 2.538; P < .001), with a higher level of education (OR = 1.055; P = .025), and with comorbid body dysmorphic disorder (OR = 2.363; P = .004). These findings support the idea that OCD accompanied by PSP/trichotillomania characterizes a specific subgroup.
A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration

Educational Data Mining (EDM) is an interdisciplinary ingenuous research area that handles the development of methods to explore data arising in a scholastic fields. Computational approaches used by EDM is to examine scholastic data in order to study educational questions. As a result, it provides intrinsic knowledge of teaching and learning process for effective education planning. This paper conducts a comprehensive study on the recent and relevant studies put through in this field to date. The study focuses on methods of analysing educational data to develop models for improving academic performances and improving institutional effectiveness. This paper accumulates and relegates literature, identifies consequential work and mediates it to computing educators and professional bodies. We identify research that gives well-fortified advise to amend edifying and invigorate the more impuissant segment students in the institution. The results of these studies give insight into techniques for ameliorating pedagogical process, presaging student performance, compare the precision of data mining algorithms, and demonstrate the maturity of open source implements.
Privacy-preserving Prediction

Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years. It is now known that for some basic learning problems, especially those involving high-dimensional data, producing an accurate private model requires much more data than learning without privacy. At the same time, in many applications it is not necessary to expose the model itself. Instead users may be allowed to query the prediction model on their inputs only through an appropriate interface. Here we formulate the problem of ensuring privacy of individual predictions and investigate the overheads required to achieve it in several standard models of classification and regression. We first describe a simple baseline approach based on training several models on disjoint subsets of data and using standard private aggregation techniques to predict. We show that this approach has nearly optimal sample complexity for (realizable) PAC learning of any class of Boolean functions. At the same time, without strong assumptions on the data distribution, the aggregation step introduces a substantial overhead. We demonstrate that this overhead can be avoided for the well-studied class of thresholds on a line and for a number of standard settings of convex regression. The analysis of our algorithm for learning thresholds relies crucially on strong generalization guarantees that we establish for all differentially private prediction algorithms.