nils-herrmann/clean_scirepeval huggingface dataset:
Joint SOGC/CSEP clinical practice guideline: exercise in pregnancy and the postpartum period.

To design Canadian guidelines advising obstetric care providers of the maternal, fetal, and neonatal implications of aerobic and strength-conditioning exercises in pregnancy.



Knowledge of the impact of exercise on maternal, fetal, and neonatal morbidity, and of the maternal measures of fitness.



MEDLINE search from 1966 to 2002 for English-language articles related to studies of maternal aerobic and strength conditioning in a previously sedentary population, maternal aerobic and strength conditioning in a previously active population, impact of aerobic and strength conditioning on early and late pregnancy outcomes, impact of aerobic and strength conditioning on neonatal outcomes, as well as for review articles and meta-analyses related to exercise in pregnancy.


VALUES
The evidence collected was reviewed by the Society of Obstetricians and Gynaecologists of Canada (SOGC Clinical Practice Obstetrics Committee) with representation from the Canadian Society for Exercise Physiology, and quantified using the evaluation of evidence guidelines developed by the Canadian Task Force on the Periodic Health Exam.
Quickest Detection of False Data Injection Attack in Wide-Area Smart Grids

We consider the sequential (i.e., online) detection of false data injection attacks in smart grid, which aims to manipulate the state estimation procedure by injecting malicious data to the monitoring meters. The unknown parameters in the system, namely the state vector, injected malicious data and the set of attacked meters pose a significant challenge for designing a robust, computationally efficient, and high-performance detector. We propose a sequential detector based on the generalized likelihood ratio to address this challenge. Specifically, the proposed detector is designed to be robust to a variety of attacking strategies, and load situations in the power system, and its computational complexity linearly scales with the number of meters. Moreover, it considerably outperforms the existing first-order cumulative sum detector in terms of the average detection delay and robustness to various attacking strategies. For wide-area monitoring in smart grid, we further develop a distributed sequential detector using an adaptive sampling technique called level-triggered sampling. The resulting distributed detector features single bit per sample in terms of the communication overhead, while preserving the high performance of the proposed centralized detector.
Altruistic Scheduling in Multi-Resource Clusters

Given the well-known tradeoffs between fairness, performance, and efficiency, modern cluster schedulers often prefer instantaneous fairness as their primary objective to ensure performance isolation between users and groups. However, instantaneous, short-term convergence to fairness often does not result in noticeable long-term benefits. Instead, we propose an altruistic, long-term approach, CARBYNE, where jobs yield fractions of their allocated resources without impacting their own completion times. We show that leftover resources collected via altruisms of many jobs can then be rescheduled to further secondary goals such as application-level performance and cluster efficiency without impacting performance isolation. Deployments and large-scale simulations show that CARBYNE closely approximates the stateof-the-art solutions (e.g., DRF [27]) in terms of performance isolation, while providing 1.26× better efficiency and 1.59× lower average job completion time.
Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil.

Dengue fever is currently the most important arthropod-borne viral disease in Brazil. Mathematical modeling of disease dynamics is a very useful tool for the evaluation of control measures. To be used in decision-making, however, a mathematical model must be carefully parameterized and validated with epidemiological and entomological data. In this work, we developed a simple dengue model to answer three questions: (i) which parameters are worth pursuing in the field in order to develop a dengue transmission model for Brazilian cities; (ii) how vector density spatial heterogeneity influences control efforts; (iii) with a degree of uncertainty, what is the invasion potential of dengue virus type 4 (DEN-4) in Rio de Janeiro city. Our model consists of an expression for the basic reproductive number (R0) that incorporates vector density spatial heterogeneity. To deal with the uncertainty regarding parameter values, we parameterized the model using a priori probability density functions covering a range of plausible values for each parameter. Using the Latin Hypercube Sampling procedure, values for the parameters were generated. We conclude that, even in the presence of vector spatial heterogeneity, the two most important entomological parameters to be estimated in the field are the mortality rate and the extrinsic incubation period. The spatial heterogeneity of the vector population increases the risk of epidemics and makes the control strategies more complex. At last, we conclude that Rio de Janeiro is at risk of a DEN-4 invasion. Finally, we stress the point that epidemiologists, mathematicians, and entomologists need to interact more to find better approaches to the measuring and interpretation of the transmission dynamics of arthropod-borne diseases.
Fundamental movement skills in children and adolescents: review of associated health benefits.

The mastery of fundamental movement skills (FMS) has been purported as contributing to children's physical, cognitive and social development and is thought to provide the foundation for an active lifestyle. Commonly developed in childhood and subsequently refined into context- and sport-specific skills, they include locomotor (e.g. running and hopping), manipulative or object control (e.g. catching and throwing) and stability (e.g. balancing and twisting) skills. The rationale for promoting the development of FMS in childhood relies on the existence of evidence on the current or future benefits associated with the acquisition of FMS proficiency. The objective of this systematic review was to examine the relationship between FMS competency and potential health benefits in children and adolescents. Benefits were defined in terms of psychological, physiological and behavioural outcomes that can impact public health. A systematic search of six electronic databases (EMBASE, OVID MEDLINE, PsycINFO, PubMed, Scopus and SportDiscus®) was conducted on 22 June 2009. Included studies were cross-sectional, longitudinal or experimental studies involving healthy children or adolescents (aged 3-18 years) that quantitatively analysed the relationship between FMS competency and potential benefits. The search identified 21 articles examining the relationship between FMS competency and eight potential benefits (i.e. global self-concept, perceived physical competence, cardio-respiratory fitness [CRF], muscular fitness, weight status, flexibility, physical activity and reduced sedentary behaviour). We found strong evidence for a positive association between FMS competency and physical activity in children and adolescents. There was also a positive relationship between FMS competency and CRF and an inverse association between FMS competency and weight status. Due to an inadequate number of studies, the relationship between FMS competency and the remaining benefits was classified as uncertain. More longitudinal and intervention research examining the relationship between FMS competency and potential psychological, physiological and behavioural outcomes in children and adolescents is recommended.
Recent Developments in Indian Sign Language Recognition : An Analysis

There exists significant variation between sign language recognition processes across the world, although there are many similarities. Pre-processing, feature extraction and classification are the three major steps involved in the sign language recognition process. An analysis of scientific literature indicates the potential of various methods in achieving significantly high accuracy in image recognition. Further examination of the literature indicates the voluminous works carried out in American Sign Language recognition systems and most of these works compare the potential of various methods and combination of methods for their accuracy. Although, the comparison using randomly selected gestures for their potential would result in realistic overall accuracy for ASL where the gestures are simple and distinct, the complete adoption of such methods for Indian Sign Language (ISL) recognition may not be ideal due to the complexity in ISL. Other than static gestures, the dynamic gestures, gestures including facial expression, similarity in gestures, all increase the complexity of ISL. Therefore, the potential of different methods and their combinations need to evaluate in the context of ISL. A preliminary study to analyse the potential of promising feature extraction methods indicated that the methods could vary significantly while handling gestures with resemblances. This clearly indicates the necessity to screen gesture recognition methods for their accuracy in handling gestures in the context of complex ISL.  Indian Sign Language, Gesture Recognition, Preprocessing, Feature Extraction, Classification.
Applying statistical machine learning to multicore voltage & frequency scaling

Dynamic Voltage/Frequency Scaling (DVFS) is a useful tool for improving system energy efficiency, especially in multi-core chips where energy is more of a limiting factor. Per-core DVFS, where cores can independently scale their voltages and frequencies, is particularly effective. We present a DVFS policy using machine learning, which learns the best frequency choices for a machine as a decision tree.
 Machine learning is used to predict the frequency which will minimize the expected energy per user-instruction (epui) or energy per (user-instruction)2 (epui2). While each core independently sets its frequency and voltage, a core is sensitive to other cores' frequency settings. Also, we examine the viability of using only partial training to train our policy, rather than full profiling for each program.
 We evaluate our policy on a 16-core machine running multiprogrammed, multithreaded benchmarks from the PARSEC benchmark suite against a baseline fixed frequency as well as a recently-proposed greedy policy. For 1ms DVFS intervals, our technique improves system epui2 by 14.4% over the baseline no-DVFS policy and 11.3% on average over the greedy policy.
Business Intelligence and Analytics Education: Hermeneutic Literature Review and Future Directions in IS Education

Interest in business intelligence and analytics education has begun to attract IS scholars’ attention. In order to discover new research questions, there is a need for conducting a literature review of extant studies on BI&A education. This study identified 44 research papers through using Google Scholar related to BI&A education. This research contributes to the field of BI&A education by (a) categorizing the existing studies on BI&A education into the key five research foci, and (b) identifying the research gaps and providing the guide for future BI&A and IS research.
8.3 mJ, 166 W Burst Mode Pulse Fiber Amplifier Based on a Q-Switched Mode-Locked Fiber Seed Laser

We report a 166 W burst mode pulse fiber amplifier seeded by a Q-switched mode-locked all-fiber laser at 1064 nm based on a fiber-coupled semiconductor saturable absorber mirror. With a pump power of 230 W at 976 nm, the output corresponds to a power conversion efficiency of 74%. The repetition rate of the burst pulse is 20 kHz, the burst energy is 8.3 mJ, and the burst duration is ∼ 20 μs, which including about 800 mode-locked pulses at a repetition rate of 40 MHz and the width of the individual mode-locked pulse is measured to be 112 ps at the maximum output power. To avoid optical damage to the fiber, the initial mode-locked pulses were stretched to 72 ps by a bandwidth-limited fiber bragg grating. After a two-stage preamplifier, the pulse width was further stretched to 112 ps, which is a result of self-phase modulation of the pulse burst during the amplification.
Visual-Inertial Odometry with Robust Initialization and Online Scale Estimation

Visual-inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs with high accuracy. The main technical contributions are the fusion of visual information and pre-integrated inertial measurements in a joint optimization framework and the stable initialization of scale and gravity using relative pose constraints. To account for the ambiguity and uncertainty of VIO initialization, a local scale parameter is adopted in the online optimization. Quantitative comparisons with the state-of-the-art algorithms on the European Robotics Challenge (EuRoC) dataset verify the efficacy and accuracy of the proposed method.
Upper auricular adhesion malformation: definition, classification, and treatment.

During treatment of upper auricular malformations, the author found that patients with cryptotia and patients with solitary helical and/or antihelical adhesion malformations showed the same anatomical finding of cartilage adhesion. The author defined them together as upper auricular adhesion malformations.



Between March of 1992 and March of 2006, 194 upper auricular adhesion malformations were corrected in 137 patients. All of these cases were retrospectively studied and classified. Of these, 92 malformations in 68 recent patients were corrected with new surgical methods (these were followed up for more than 6 months).



The group of solitary helical and/or antihelical cartilage malformation patients was classified as group I and the cryptotia group as group II. These two groups were subdivided according to features of cartilage adhesion and classified into seven subgroups. Thirty-two malformations were classified as belonging to group I and 162 malformations to group II. There were 61 patients with bilateral upper auricular adhesion malformations. Nineteen patients (31 percent of the patients with bilateral malformations) showed malformations belonging to both groups I and II on both ears. On postoperative observation in patients corrected with new methods, it was noticed that the following unfavorable results had occurred in 18 upper auricular adhesion malformation cases (20 percent): venous congestion or partial skin necrosis of used flaps, "pinched antitragus," low-set upper auricle, hypertrophic scars, and baldness.



The new consideration for, and the singling out of, upper auricular adhesion malformation can lead to better understanding of the groups of upper auricular malformations to which it belongs, the decision for treatment, and, possibly, clarification of the pathophysiology in the future.
Matrix and Tensor Factorization Techniques for Recommender Systems

Representing data in lower dimensional spaces has been used extensively in many disciplines such as natural language and image processing, data mining, and information retrieval. Recommender systems deal with challenging issues such as scalability, noise, and sparsity and thus, matrix and tensor factorization techniques appear as an interesting tool to be exploited. That is, we can deal with all aforementioned challenges by applying matrix and tensor decomposition methods (also known as factorization methods). In this chapter, we provide some basic definitions and preliminary concepts on dimensionality reduction methods of matrices and tensors. Gradient descent and alternating least squares methods are also discussed. Finally, we present the book outline and the goals of each chapter.
Glucose-responsive insulin by molecular and physical design.

The concept of a glucose-responsive insulin (GRI) has been a recent objective of diabetes technology. The idea behind the GRI is to create a therapeutic that modulates its potency, concentration or dosing relative to a patient's dynamic glucose concentration, thereby approximating aspects of a normally functioning pancreas. From the perspective of the medicinal chemist, the GRI is also important as a generalized model of a potentially new generation of therapeutics that adjust potency in response to a critical therapeutic marker. The aim of this Perspective is to highlight emerging concepts, including mathematical modelling and the molecular engineering of insulin itself and its potency, towards a viable GRI. We briefly outline some of the most important recent progress toward this goal and also provide a forward-looking viewpoint, which asks if there are new approaches that could spur innovation in this area as well as to encourage synthetic chemists and chemical engineers to address the challenges and promises offered by this therapeutic approach.
PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-Modalities

Data of different modalities generally convey complimentary but heterogeneous information, and a more discriminative representation is often preferred by combining multiple data modalities like the RGB and infrared features. However in reality, obtaining both data channels is challenging due to many limitations. For example, the RGB surveillance cameras are often restricted from private spaces, which is in conflict with the need of abnormal activity detection for personal security. As a result, using partial data channels to build a full representation of multi-modalities is clearly desired. In this paper, we propose a novel Partial-modal Generative Adversarial Networks (PM-GANs) that learns a full-modal representation using data from only partial modalities. The full representation is achieved by a generated representation in place of the missing data channel. Extensive experiments are conducted to verify the performance of our proposed method on action recognition, compared with four state-of-the-art methods. Meanwhile, a new InfraredVisible Dataset for action recognition is introduced, and will be the first publicly available action dataset that contains paired infrared and visible spectrum.
An Analysis of Dropout for Matrix Factorization

Dropout is a simple yet effective algorithm for regularizing neural networks by randomly dropping out units through Bernoulli multiplicative noise, and for some restricted problem classes, such as linear or logistic regression, several theoretical studies have demonstrated the equivalence between dropout and a fully deterministic optimization problem with data-dependent Tikhonov regularization. This work presents a theoretical analysis of dropout for matrix factorization, where Bernoulli random variables are used to drop a factor, thereby attempting to control the size of the factorization. While recent work has demonstrated the empirical effectiveness of dropout for matrix factorization, a theoretical understanding of the regularization properties of dropout in this context remains elusive. This work demonstrates the equivalence between dropout and a fully deterministic model for matrix factorization in which the factors are regularized by the sum of the product of the norms of the columns. While the resulting regularizer is closely related to a variational form of the nuclear norm, suggesting that dropout may limit the size of the factorization, we show that it is possible to trivially lower the objective value by doubling the size of the factorization. We show that this problem is caused by the use of a fixed dropout rate, which motivates the use of a rate that increases with the size of the factorization. Synthetic experiments validate our theoretical findings.
TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems

Users with anomalous behaviors in online communication systems (e.g. email and social medial platforms) are potential threats to society. Automated anomaly detection based on advanced machine learning techniques has been developed to combat this issue; challenges remain, though, due to the difficulty of obtaining proper ground truth for model training and evaluation. Therefore, substantial human judgment on the automated analysis results is often required to better adjust the performance of anomaly detection. Unfortunately, techniques that allow users to understand the analysis results more efficiently, to make a confident judgment about anomalies, and to explore data in their context, are still lacking. In this paper, we propose a novel visual analysis system, TargetVue, which detects anomalous users via an unsupervised learning model and visualizes the behaviors of suspicious users in behavior-rich context through novel visualization designs and multiple coordinated contextual views. Particularly, TargetVue incorporates three new ego-centric glyphs to visually summarize a user's behaviors which effectively present the user's communication activities, features, and social interactions. An efficient layout method is proposed to place these glyphs on a triangle grid, which captures similarities among users and facilitates comparisons of behaviors of different users. We demonstrate the power of TargetVue through its application in a social bot detection challenge using Twitter data, a case study based on email records, and an interview with expert users. Our evaluation shows that TargetVue is beneficial to the detection of users with anomalous communication behaviors.
Predicting reaction performance in C–N cross-coupling using machine learning

Machine learning methods are becoming integral to scientific inquiry in numerous disciplines. We demonstrated that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation. We created scripts to compute and extract atomic, molecular, and vibrational descriptors for the components of a palladium-catalyzed Buchwald-Hartwig cross-coupling of aryl halides with 4-methylaniline in the presence of various potentially inhibitory additives. Using these descriptors as inputs and reaction yield as output, we showed that a random forest algorithm provides significantly improved predictive performance over linear regression analysis. The random forest model was also successfully applied to sparse training sets and out-of-sample prediction, suggesting its value in facilitating adoption of synthetic methodology.
One-shot Learning for Question-Answering in Gaokao History Challenge

Answering questions from university admission exams (Gaokao in Chinese) is a challenging AI task since it requires effective representation to capture complicated semantic relations between questions and answers. In this work, we propose a hybrid neural model for deep questionanswering task from history examinations. Our model employs a cooperative gated neural network to retrieve answers with the assistance of extra labels given by a neural turing machine labeler. Empirical study shows that the labeler works well with only a small training dataset and the gated mechanism is good at fetching the semantic representation of lengthy answers. Experiments on question answering demonstrate the proposed model obtains substantial performance gains over various neural model baselines in terms of multiple evaluation metrics.