nils-herrmann/clean_scirepeval huggingface dataset:
Effect of protein-supplement timing on strength, power, and body-composition changes in resistance-trained men.

The effect of 10 wk of protein-supplement timing on strength, power, and body composition was examined in 33 resistance-trained men. Participants were randomly assigned to a protein supplement either provided in the morning and evening (n = 13) or provided immediately before and immediately after workouts (n = 13). In addition, 7 participants agreed to serve as a control group and did not use any protein or other nutritional supplement. During each testing session participants were assessed for strength (one-repetition-maximum [1RM] bench press and squat), power (5 repetitions performed at 80% of 1RM in both the bench press and the squat), and body composition. A significant main effect for all 3 groups in strength improvement was seen in 1RM bench press (120.6 +/- 20.5 kg vs. 125.4 +/- 16.7 at Week 0 and Week 10 testing, respectively) and 1RM squat (154.5 +/- 28.4 kg vs. 169.0 +/- 25.5 at Week 0 and Week 10 testing, respectively). However, no significant between-groups interactions were seen in 1RM squat or 1RM bench press. Significant main effects were also seen in both upper and lower body peak and mean power, but no significant differences were seen between groups. No changes in body mass or percent body fat were seen in any of the groups. Results indicate that the time of protein-supplement ingestion in resistance-trained athletes during a 10-wk training program does not provide any added benefit to strength, power, or body-composition changes.
Digital Double-loop PID Controller for Inverted Pendulum

Inverted pendulum system is a complicated, unstable and multivariable nonlinear system. In order to control the angle and displacement of inverted pendulum system effectively, a novel double-loop digital PID control strategy is presented in this paper. Based on impulse transfer function, the model of the single linear inverted pendulum system is divided into two parts according to the controlled parameters. The inner control loop that is formed by the digital PID feedback control can control the angle of the pendulum, while in order to control the cart displacement, the digital PID series control is adopted to form the outer control loop. The simulation results show the digital control strategy is very effective to single inverted pendulum and when the sampling period is selected as 50 ms, the performance of the digital control system is similar to that of the analog control system.
Deep Background Modeling Using Fully Convolutional Network

Background modeling plays an important role for video surveillance, object tracking, and object counting. In this paper, we propose a novel deep background modeling approach utilizing fully convolutional network. In the network block constructing the deep background model, three atrous convolution branches with different dilate are used to extract spatial information from different neighborhoods of pixels, which breaks the limitation that extracting spatial information of the pixel from fixed pixel neighborhood. Furthermore, we sample multiple frames from original sequential images with increasing interval, in order to capture more temporal information and reduce the computation. Compared with classical background modeling approaches, our approach outperforms the state-of-art approaches both in indoor and outdoor scenes.
Dyadic Memory Networks for Aspect-based Sentiment Analysis

This paper proposes Dyadic Memory Networks (DyMemNN), a novel extension of end-to-end memory networks (memNN) for aspect-based sentiment analysis (ABSA). Originally designed for question answering tasks, memNN operates via a memory selection operation in which relevant memory pieces are adaptively selected based on the input query. In the problem of ABSA, this is analogous to aspects and documents in which the relationship between each word in the document is compared with the aspect vector. In the standard memory networks, simple dot products or feed forward neural networks are used to model the relationship between aspect and words which lacks representation learning capability. As such, our dyadic memory networks ameliorates this weakness by enabling rich dyadic interactions between aspect and word embeddings by integrating either parameterized neural tensor compositions or holographic compositions into the memory selection operation. To this end, we propose two variations of our dyadic memory networks, namely the Tensor DyMemNN and Holo DyMemNN. Overall, our two models are end-to-end neural architectures that enable rich dyadic interaction between aspect and document which intuitively leads to better performance. Via extensive experiments, we show that our proposed models achieve the state-of-the-art performance and outperform many neural architectures across six benchmark datasets.
A systematic review to evaluate the clinical benefits of craniosacral therapy.

Craniosacral therapy (CST) is an alternative treatment approach, aiming to release restrictions around the spinal cord and brain and subsequently restore body function. A previously conducted systematic review did not obtain valid scientific evidence that CST was beneficial to patients. The aim of this review was to identify and critically evaluate the available literature regarding CST and to determine the clinical benefit of CST in the treatment of patients with a variety of clinical conditions.



Computerised literature searches were performed in Embase/Medline, Medline(®) In-Process, The Cochrane library, CINAHL, and AMED from database start to April 2011. Studies were identified according to pre-defined eligibility criteria. This included studies describing observational or randomised controlled trials (RCTs) in which CST as the only treatment method was used, and studies published in the English language. The methodological quality of the trials was assessed using the Downs and Black checklist.



Only seven studies met the inclusion criteria, of which three studies were RCTs and four were of observational study design. Positive clinical outcomes were reported for pain reduction and improvement in general well-being of patients. Methodological Downs and Black quality scores ranged from 2 to 22 points out of a theoretical maximum of 27 points, with RCTs showing the highest overall scores.



This review revealed the paucity of CST research in patients with different clinical pathologies. CST assessment is feasible in RCTs and has the potential of providing valuable outcomes to further support clinical decision making. However, due to the current moderate methodological quality of the included studies, further research is needed.
Deep and Hierarchical Implicit Models

Implicit probabilistic models are a flexible class for modeling data. They define a process to simulate observations, and unlike traditional models, they do not require a tractable likelihood function. In this paper, we develop two families of models: hierarchical implicit models and deep implicit models. They combine the idea of implicit densities with hierarchical Bayesian modeling and deep neural networks. The use of implicit models with Bayesian analysis has been limited by our ability to perform accurate and scalable inference. We develop likelihood-free variational inference (LFVI). Key to LFVI is specifying a variational family that is also implicit. This matches the model’s flexibility and allows for accurate approximation of the posterior. Our work scales up implicit models to sizes previously not possible and advances their modeling design. We demonstrate diverse applications: a large-scale physical simulator for predator-prey populations in ecology; a Bayesian generative adversarial network for discrete data; and a deep implicit model for text generation.
An Exploratory Examination of the Association Between Altered Lumbar Motor Control, Joint Mobility and Low Back Pain in Athletes

Low back pain (LBP) is a common cause of lost playing time and can be a challenging clinical condition in competitive athletes. LBP in athletes may be associated with joint and ligamentous hypermobility and impairments in activation and coordination of the trunk musculature, however there is limited research in this area.



To determine if there is an association between altered lumbar motor control, joint mobility and low back pain (LBP) in a sample of athletes.



Fifteen athletes with LBP were matched by age, gender and body mass index (BMI) with controls without LBP. Athletes completed a questionnaire with questions pertaining to demographics, activity level, medical history, need to self-manipulate their spine, pain intensity and location. Flexibility and lumbar motor control were assessed using: active and passive straight leg raise, lumbar range of motion (ROM), hip internal rotation ROM (HIR), Beighton ligamentous laxity scale, prone instability test (PIT), observation of lumbar aberrant movements, double leg lowering and Trendelenburg tests. Descriptive statistics were compiled and the chi square test was used to analyze results.



Descriptive statistics showed that 40% of athletes with LBP exhibited aberrant movements (AM), compared to 6% without LBP. 66% of athletes with LBP reported frequently self-manipulating their spine compared to 40% without LBP. No significant differences in motor control tests were found between groups. Athletes with LBP tended to have less lumbar flexion (63 ± 11°) compared to those without LBP (66 ± 13°). Chi-Square tests revealed that the AM were more likely to be present in athletes with LBP than those without (X2 = 4.66, P = 0.03).



The presence of aberrant movement patterns is a significant clinical finding and associated with LBP in athletes.
Watset: Automatic Induction of Synsets from a Graph of Synonyms

This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.
The role of expressive writing in math anxiety.

Math anxiety is a negative affective reaction to situations involving math. Previous work demonstrates that math anxiety can negatively impact math problem solving by creating performance-related worries that disrupt the working memory needed for the task at hand. By leveraging knowledge about the mechanism underlying the math anxiety-performance relationship, we tested the effectiveness of a short expressive writing intervention that has been shown to reduce intrusive thoughts and improve working memory availability. Students (N = 80) varying in math anxiety were asked to sit quietly (control group) prior to completing difficulty-matched math and word problems or to write about their thoughts and feelings regarding the exam they were about to take (expressive writing group). For the control group, high math-anxious individuals (HMAs) performed significantly worse on the math problems than low math-anxious students (LMAs). In the expressive writing group, however, this difference in math performance across HMAs and LMAs was significantly reduced. Among HMAs, the use of words related to anxiety, cause, and insight in their writing was positively related to math performance. Expressive writing boosts the performance of anxious students in math-testing situations.
Algorithm engineering for a quantum annealing platform

Recent advances bring within reach the viability of solving combinatorial problems using a quantum annealing algorithm implemented on a purpose-built platform that exploits quantum properties. However, the question of how to tune the algorithm for most effective use in this framework is not well understood. In this paper we describe some operational parameters that drive performance, discuss approaches for mitigating sources of error, and present experimental results from a D-Wave Two quantum annealing processor.
Affordable On-line Dialogue Policy Learning

The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable.
Multiband smart fractal antenna design for converged 5G wireless networks

Forthcoming Fifth Generation wireless network will be converged version of all the available wireless and wired networks including cognitive radio network, Wi-Fi, cellular, Wi-Max, WSN, IoT, Li-Fi, satellite communication and optical fiber network. Presently in the era of 4G, numbers of researchers have designed different antennas which can integrate maximum four services. This research work proposes design and implementation of Multiband Fractal smart antenna for at least seven converged wireless network services. The antenna works for several services such as GSM (0.89-0.96GHz), DCS (1.71-1.88GHz), WLAN/WSN (2.44-2.45GHz), LTE (1.7-1.9GHz), Wi-Fi/Wi-Max (2.68-6.45GHz), HIPER LAN2 (5.15-5.25GHz) and Ku Band (8-12GHz) application. The antenna planed to be designed and optimized by making using of commercially available software-High Frequency Structure Simulator (HFSS).
Stabilizing Traffic with Autonomous Vehicles

Autonomous vehicles promise safer roads, energy savings, and more efficient use of existing infrastructure, among many other benefits. Although the effect of autonomous vehicles has been studied in the limits (near-zero or full penetration), the transition range requires new formulations, mathematical modeling, and control analysis. In this article, we study the ability of small numbers of autonomous vehicles to stabilize a single-lane system of human-driven vehicles. We formalize the problem in terms of linear string stability, derive optimality conditions from frequency-domain analysis, and pose the resulting nonlinear optimization problem. In particular, we introduce two conditions which simultaneously stabilize traffic while imposing a safety constraint on the autonomous vehicle and limiting degradation of performance. With this optimal linear controller in a system with typical human driver behavior, we can numerically determine that only a 6% uniform penetration of autonomously controlled vehicles (i.e. one per string of up to 16 human-driven vehicles) is necessary to stabilize traffic across all traffic conditions.
Wheel Transformer: A Wheel-Leg Hybrid Robot With Passive Transformable Wheels

We report on the design, optimization, and performance evaluation of a new wheel-leg hybrid robot. This robot utilizes a novel transformable wheel that combines the advantages of both circular and legged wheels. To minimize the complexity of the design, the transformation process of the wheel is passive, which eliminates the need for additional actuators. A new triggering mechanism is also employed to increase the transformation success rate. To maximize the climbing ability in legged-wheel mode, the design parameters for the transformable wheel and robot are tuned based on behavioral analyses. The performance of our new development is evaluated in terms of stability, energy efficiency, and the maximum height of an obstacle that the robot can climb over. With the new transformable wheel, the robot can climb over an obstacle 3.25 times as tall as its wheel radius, without compromising its driving ability at a speed of 2.4 body lengths/s with a specific resistance of 0.7 on a flat surface.
Detecting SIM Box Fraud Using Neural Network

One of the most severe threats to revenue and quality of service in telecom providers is fraud. The advent of new technologies has provided fraudsters new techniques to commit fraud. SIM box fraud is one of such fraud that has emerged with the use of VOIP technologies. In this work, a total of nine features found to be useful in identifying SIM box fraud subscriber are derived from the attributes of the Customer Database Record (CDR). Artificial Neural Networks (ANN) has shown promising solutions in classification problems due to their generalization capabilities. Therefore, supervised learning method was applied using Multi layer perceptron (MLP) as a classifier. Dataset obtained from real mobile communication company was used for the experiments. ANN had shown classification accuracy of 98.71 %.
Economics of Internet of Things: an information market approach

Internet of Things (IoT) has been proposed to be a new paradigm of connecting devices and providing services to various applications, e.g., transportation, energy, smart cities, and health care. In this paper we focus on an important issue, i.e., the economics of IoT, that can have a great impact on the success of IoT applications. In particular, we adopt and present the information economics approach with its applications in IoT. We first review existing economic models developed for IoT services. Then we outline two important topics of information economics that are pertinent to IoT, i.e., the value of information and proper pricing of information. Finally, we propose a game theoretic model to study the price competition of IoT sensing services. Perspectives on future research directions to apply information economics to IoT are discussed.
SugarMate: Non-intrusive Blood Glucose Monitoring with Smartphones

Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference of blood glucose levels at a fine-grained time resolution. We propose Md3RNN, an efficient learning paradigm to make full use of the available blood glucose information. Specifically, the newly designed grouped input layers, together with the adoption of a deep RNN model, offer an opportunity to build blood glucose models for the general public based on limited personal measurements from single-user and grouped-users perspectives. Evaluations on 112 users demonstrate that Md3RNN yields an average accuracy of 82.14%, significantly outperforming previous learning methods those are either shallow, generically structured, or oblivious to grouped behaviors. Also, a user study with the 112 participants shows that SugarMate is acceptable for practical usage.
Affective Personalization of a Social Robot Tutor for Children's Second Language Skills

Though substantial research has been dedicated towards using technology to improve education, no current methods are as effective as one-on-one tutoring. A critical, though relatively understudied, aspect of effective tutoring is modulating the student’s affective state throughout the tutoring session in order to maximize long-term learning gains. We developed an integrated experimental paradigm in which children play a secondlanguage learning game on a tablet, in collaboration with a fully autonomous social robotic learning companion. As part of the system, we measured children’s valence and engagement via an automatic facial expression analysis system. These signals were combined into a reward signal that fed into the robot’s affective reinforcement learning algorithm. Over several sessions, the robot played the game and personalized its motivational strategies (using verbal and non-verbal actions) to each student. We evaluated this system with 34 children in preschool classrooms for a duration of two months. We saw that (1) children learned new words from the repeated tutoring sessions, (2) the affective policy personalized to students over the duration of the study, and (3) students who interacted with a robot that personalized its affective feedback strategy showed a significant increase in valence, as compared to students who interacted with a non-personalizing robot. This integrated system of tablet-based educational content, affective sensing, affective policy learning, and an autonomous social robot holds great promise for a more comprehensive approach to personalized tutoring.
Gold of the Pharaohs – 6000 years of gold mining in Egypt and Nubia

The legendary wealth in gold of ancient Egypt seems to correspond with an unexpected high number of gold production sites in the Eastern Desert of Egypt and Nubia. This contribution introduces briefly the general geology of these vast regions and discusses the geology of the different varieties of the primary gold occurrences (always related to auriferous quartz mineralization in veins or shear zones) as well as the variable physico-chemical genesis of the gold concentrations. The development of gold mining over time, from Predynastic (ca. 3000 BC) until the end of Arab gold production times (about 1350 AD), including the spectacular Pharaonic periods is outlined, with examples of its remaining artefacts, settlements and mining sites in remote regions of the Eastern Desert of Egypt and Nubia. Finally, some estimates on the scale of gold production are presented.
High-Content Screening for Quantitative Cell Biology.

High-content screening (HCS), which combines automated fluorescence microscopy with quantitative image analysis, allows the acquisition of unbiased multiparametric data at the single cell level. This approach has been used to address diverse biological questions and identify a plethora of quantitative phenotypes of varying complexity in numerous different model systems. Here, we describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions. We review the steps involved in the design of useful biological assays and automated image analysis, and describe major challenges associated with each. Additionally, we highlight emerging technologies and future challenges, and discuss how the field of HCS might be enhanced in the future.