nils-herrmann/clean_scirepeval huggingface dataset:
Using Features From Pre-trained TimeNET For Clinical Predictions

Predictive models based on Recurrent Neural Networks (RNNs) for clinical time series have been successfully used for various tasks such as phenotyping, in-hospital mortality prediction, and diagnostics. However, RNNs require large labeled data for training and are computationally expensive to train. Pre-training a network for some supervised or unsupervised tasks on a dataset, and then fine-tuning via transfer learning for a related end-task can be an efficient way to leverage deep models for scenarios that lack in either computational resources or labeled data, or both. In this work, we consider an approach to leverage a deep RNN – namely TimeNet [Malhotra et al., 2017] – that is pre-trained on a large number of diverse publicly available time-series from UCR Repository [Chen et al., 2015]. TimeNet maps varyinglength time series to fixed-dimensional feature vectors and acts as an off-the-shelf feature extractor. TimeNet-based approach overcome the need for hand-crafted features, and allows for use of traditional easy-to-train and interpretable linear models for the end-task, while still leveraging the features from a deep neural network. Empirical evaluation of the proposed approach on MIMIC-III1 data suggests promising direction for future exploration: our results are comparable to existing benchmarks while our models require lesser training and hyperparameter tuning effort.
Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets

With the ever growing amount of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure a consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions. In this paper we propose a Replicability Analysis framework for a statistically sound analysis of multiple comparisons between algorithms for NLP tasks. We discuss the theoretical advantages of this framework over the current, statistically unjustified, practice in the NLP literature, and demonstrate its empirical value across four applications: multi-domain dependency parsing, multilingual POS tagging, cross-domain sentiment classification and word similarity prediction.
Multilayer Design Techniques for Extremely Miniaturized CMOS Microwave and Millimeter-Wave Distributed Passive Circuits

Multilayer design techniques are investigated for reducing the size and enhancing the performance of microwave and millimeter-wave distributed circuits in CMOS. Various distributed passive circuits are implemented in a standard 0.25-mum RF/mixed-signal process, including a novel broadside-coupled Lange coupler, a microstrip ring hybrid, and a hairpin resonator incorporating a complementary slow-wave structure. The broadside-coupled Lange coupler exhibits -3.3 to -3.35 dB through, -3.3 to -3.7 dB coupling, more than 12-dB isolation, and 15-dB return loss across 25-35 GHz while occupying only 217 times185 mum of chip area. The multilayer ring hybrid has -3.1 to -3.18 dB through, -5.1 to -5.7 dB coupling, and more than 17-dB isolation and 10-dB return loss from 25-35 GHz while occupying 282 times314 mum. A slow-wave structure based on multilayer complementary design principle is implemented for hairpin resonators. The measured quality factor of the multilayer complementary slow-wave hairpin resonator increases to about 14.5 from 11.3 for a similar sized resonator with a single-layer slow-wave structure, while retaining similar size-reduction properties as the latter
The long-term sequelae of child and adolescent abuse: a longitudinal community study.

The purpose of the present study was to examine the relationship between childhood and adolescent physical and sexual abuse before the age of 18 and psychosocial functioning in mid-adolescence (age 15) and early adulthood (age 21) in a representative community sample of young adults. Subjects were 375 participants in an ongoing 17-years longitudinal study. At age 21, nearly 11% reported physical or sexual abuse before age 18. Psychiatric disorders based on DSM-III-R criteria were assessed utilizing the NIMH Diagnostic Interview Schedule, Revised Version (DIS-III-R). Approximately 80% of the abused young adults met DSM-III-R criteria for at least one psychiatric disorder at age 21. Compared to their nonabused counterparts, abused subjects demonstrated significant impairments in functioning both at ages 15 and at 21, including more depressive symptomatology, anxiety, psychiatric disorders, emotional-behavioral problems, suicidal ideation, and suicide attempts. While abused individuals were functioning significantly more poorly overall at ages 15 and 21 than their nonabused peers, gender differences and distinct patterns of impaired functioning emerged. These deficits underscore the need for early intervention and prevention strategies to forestall or minimize the serious consequences of child abuse.
Data-driven 3D visual pronunciation of Chinese IPA for language learning

In the framework of intelligent aided language learning, a real-time data-driven 3D visual pronunciation system of Chinese IPA is proposed. First, a high quality articulatory speech corpus including speech and 3D articulatory data of lips, tongue and jaw movements is collected through Electro-Magnetic Articulograph; second, the 3D articulatory modeling including shape design and motion synthesis is conducted. The articulatory shape is obtained by designing a precise 3D facial model including internal and external articulators. The articulatory motion synthesis is obtained combining parameterized model and anatomical model. The system can thus illustrate the slight differences among phonemes by synthesizing both internal and external articulatory movements. The perceptual evaluation shows the suitability of the system for instructing language learners to articulate.
Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots

This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap.
The effect of Bikram yoga on arterial stiffness in young and older adults.

Bikram yoga is the most popular form of hot yoga, despite the limited information available on its cardiovascular benefits. This study sought to determine the effect of Bikram yoga on arterial stiffness and insulin resistance in young and older adults.



Twenty-four young (mean age±standard deviation, 30±1 years) and 18 middle-aged and older (mean age, 53±2 years) adults completed an 8-week Bikram yoga intervention. Bikram yoga classes were performed for 90 minutes per session, three times per week, in a room heated to 40.5°C with 40%--60% relative humidity.



Body mass, body fat percentage, blood pressure, and fasting blood glucose and triglyceride concentrations did not significantly change as a result of the intervention in either the young or the older group. Trunk flexibility, as measured by the sit-and-reach test, increased in both groups (p<0.01). Total (p<0.05) and low-density lipoprotein cholesterol (p<0.05) levels, plasma insulin concentrations (p<0.01), and scores on the homeostatic model of the assessment of insulin resistance (p<0.01) decreased in older adults, whereas total and high-density lipoprotein cholesterol concentrations were reduced in young adults (all p<0.05). Carotid artery compliance (p<0.05) was increased and β-stiffness index decreased in young (p<0.05) but not in older adults. Carotid pulse pressure did not significantly change in either group.



A relatively short-term Bikram yoga intervention improved arterial stiffness in young but not older adults and significantly reduced insulin resistance index in older but not young adults.
SQL-to-NoSQL Schema Denormalization and Migration: A Study on Content Management Systems

Content management systems (CMSs) are able to let people, who have no technical skill to manage websites, rapidly create, edit, organize and publish online contents. For example, CMSs are often used to establish e-commerce online shops, community portals, personal blogs, or organization websites. Thus, CMSs play the role of collecting data and then can be easily extended to become intelligent Internet systems. Most popular CMSs, e.g., Word Press and Joomla, rely on relational databases or SQL databases, e.g., MySQL, PostgreSQL, etc. However, due to the explosive growth of huge data, the well-structured characteristic of SQL databases may limit the scalability to handle horizontal scaling. Therefore, regarding the flexibility and the feasibility of parallel processing, cloud computing takes Not Only SQL (NoSQL) databases into consideration. How to migrate the original CMS software from SQL databases to NoSQL databases becomes one emerging and critical research issue. This paper is motivated to propose an autonomous SQL-to-NoSQL schema denormalization and migration. At the same time, this paper also evaluates the proposed mechanism on the realistic CMS software. Based on our experimental results, our mechanism not only helps the current CMS software migrate into the cloud platform without re-designing their database schemas, but also improves at least 45% access performance after schema migration.
A latent topic model for mining heterogenous non-randomly missing electronic health records data

Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining. However, heterogeneous EHR data types and biased ascertainment impose computational challenges. Here, we present mixEHR, an unsupervised generative model integrating collaborative filtering and latent topic models, which jointly models the discrete distributions of data observation bias and actual data using latent disease-topic distributions. We apply mixEHR on 12.8 million phenotypic observations from the MIMIC dataset, and use it to reveal latent disease topics, interpret EHR results, impute missing data, and predict mortality in intensive care units. Using both simulation and real data, we show that mixEHR outperforms previous methods and reveals meaningful multi-disease insights.
The active layer morphology of organic solar cells probed with grazing incidence scattering techniques.

Grazing incidence X-ray scattering (GIXS) provides unique insights into the morphology of active materials and thin film layers used in organic photovoltaic devices. With grazing incidence wide angle X-ray scattering (GIWAXS) the molecular arrangement of the material is probed. GIWAXS is sensitive to the crystalline parts and allows for the determination of the crystal structure and the orientation of the crystalline regions with respect to the electrodes. With grazing incidence small angle X-ray scattering (GISAXS) the nano-scale structure inside the films is probed. As GISAXS is sensitive to length scales from nanometers to several hundred nanometers, all relevant length scales of organic solar cells are detectable. After an introduction to GISAXS and GIWAXS, selected examples for application of both techniques to active layer materials are reviewed. The particular focus is on conjugated polymers, such as poly(3-hexylthiophene) (P3HT).
Detection of copy-move image forgery based on discrete cosine transform

Since powerful editing software is easily accessible, manipulation on images is expedient and easy without leaving any noticeable evidences. Hence, it turns out to be a challenging chore to authenticate the genuineness of images as it is impossible for human’s naked eye to distinguish between the tampered image and actual image. Among the most common methods extensively used to copy and paste regions within the same image in tampering image is the copy-move method. Discrete Cosine Transform (DCT) has the ability to detect tampered regions accurately. Nevertheless, in terms of precision (FP) and recall (FN), the block size of overlapping block influenced the performance. In this paper, the researchers implemented the copy-move image forgery detection using DCT coefficient. Firstly, by using the standard image conversion technique, RGB image is transformed into grayscale image. Consequently, grayscale image is segregated into overlying blocks of m × m pixels, m = 4.8. 2D DCT coefficients are calculated and reposition into a feature vector using zig-zag scanning in every block. Eventually, lexicographic sort is used to sort the feature vectors. Finally, the duplicated block is located by the Euclidean Distance. In order to gauge the performance of the copy-move detection techniques with various block sizes with respect to accuracy and storage, threshold D_similar = 0.1 and distance threshold (N)_d = 100 are used to implement the 10 input images in order. Consequently, 4 × 4 overlying block size had high false positive thus decreased the accuracy of forged detection in terms of accuracy. However, 8 × 8 overlying block accomplished more accurately for forged detection in terms of precision and recall as compared to 4 × 4 overlying block. In a nutshell, the result of the accuracy performance of different overlying block size are influenced by the diverse size of forged area, distance between two forged areas and threshold value used for the research.
Multiple String LED Driver With Flexible and High-Performance PWM Dimming Control

The main objectives in driving multiple LED strings include achieving uniform current control and high-performance pulse width modulation (PWM) dimming for all strings. This study proposes a new multiple string LED driver to achieve not only current balance, but also flexible and wide range PWM dimming ratio for each string. A compact single-inductor multiple-output topology is adopted in the driver, accompanied by synchronous integrators and variable dimming frequency, to achieve both high-efficiency and high-performance dimming. By using the proposed variable dimming frequency scheme, high dimming frequency is applied to a string with high dimming ratio, which helps us to maintain the deviation of LED string current in an acceptable range, while low dimming frequency is applied to a string with low dimming ratio, which helps us to achieve rectangular LED current waveform. Meanwhile, the new time multiplexing control scheme automatically optimizes the LED strings’ bus voltages, thus minimizes each string's power loss. A three-string LED driver prototype is constructed to validate the effectiveness of the proposed control scheme, where the three strings can have different dimming ratios between 4% and 100%.
Sensation seeking in England and America: cross-cultural, age, and sex comparisons.

This study compared the factor structure of the Sensation-Seeking Scale (SSS) in English and American samples, and a new form of the SSS, applicable to both samples, was constructed. Three of the four factors showed good crossnational and cross-sex reliability. English and American males did not differ on the total SSS score, but American females scored higher than English females. Males in both countries scored higher than females on the total SSS score and on the Thrill and Adventure-Seeking and Disinhibition subscales. Significant age declines occurred for both sexes, particularly on Thrill and Adventure Seeking and Disinhibition.
Crowdsourcing step-by-step information extraction to enhance existing how-to videos

Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations.
 We first performed a formative study to verify that annotations are actually useful to learners. We created ToolScape, an interactive video player that displays step descriptions and intermediate result thumbnails in the video timeline. Learners in our study performed better and gained more self-efficacy using ToolScape versus a traditional video player.
 To add the needed step annotations to existing how-to videos at scale, we introduce a novel crowdsourcing workflow. It extracts step-by-step structure from an existing video, including step times, descriptions, and before and after images. We introduce the Find-Verify-Expand design pattern for temporal and visual annotation, which applies clustering, text processing, and visual analysis algorithms to merge crowd output. The workflow does not rely on domain-specific customization, works on top of existing videos, and recruits untrained crowd workers. We evaluated the workflow with Mechanical Turk, using 75 cooking, makeup, and Photoshop videos on YouTube. Results show that our workflow can extract steps with a quality comparable to that of trained annotators across all three domains with 77% precision and 81% recall.
A video object tracking algorithm combined Kalman filter and adaptive least squares under occlusion

Object occlusion is widespread in video surveillance due to the influence of angle and environment, which brings a great impact on the target tracking and makes the development of video object tracking encountered meeting many constraints. The challenge in video object tracking is how to track accurately when the target is obscured. We use the frame difference method to detect the target and smooth the target trajectory by using Kalman filter. When the target is occluded, we can select the appropriate fitting section after smoothing trajectories and use the least square method to fit the target motion path adaptively, so we can predict the object location. By comparing the experimental results with the actual motion of the target, it shows that the algorithm can be used to track most of the occlusion targets precisely.
Mindfulness Meditation Improves Attention in Novices

Past research has found that mindfulness meditation training improves executive attention and that this effect could be driven by more efficient allocation of resources on demanding attentional tasks, such as the Flanker Task. However, it is not clear whether these changes depend on long-term practice. We sought to investigate the effects of a brief, 10-minute meditation session on attention in novice meditators, compared to a control activity. We also tested moderation by individual differences in Neuroticism. We found that participants randomly assigned to meditate for 10 minutes showed improved performance on incongruent trials on a Flanker task, with no detriment in reaction times, indicating better allocation of resources. Neuroticism moderated this effect, as only those low in Neuroticism showed improved allocation of attentional resources following meditation.
Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran

The SLEUTH model, based on the Cellular Automata (CA), can be applied to city development simulation in metropolitan areas. In this study the SLEUTH model was used to model the urban expansion and predict the future possible behavior of the urban growth in Tehran. The fundamental data were five Landsat TM and ETM images of 1988, 1992, 1998, 2001 and 2010. Three scenarios were designed to simulate the spatial pattern. The first scenario assumed historical urbanization mode would persist and the only limitations for development were height and slope. The second one was a compact scenario which makes the growth mostly internal and limited the expansion of suburban areas. The last scenario proposed a polycentric urban structure which let the little patches * Corresponding author.
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.
The Impact of Leadership Style on Job Satisfaction : A Study of Iranian Hotels

Iran similar to most of the other countries attempts to improve its performance in field of hospitality. From human resource management perspective, in order to increase performance in hotels, there should be improvement of employee job satisfaction. For measuring the employees’ job satisfaction, priors researches emphasized on intrinsic and extrinsic job satisfaction. Leadership style is one of the important factors that have the potential to improve the organizational performance. In addition, the relationship between leadership and job satisfaction has been concentrated in past investigation. In this study two types of leadership style are considered including transformational leadership and transactional leadership. In order to study the role of leadership style, this study used quantitative approach for measuring the impact of components of transformational leadership and transactional leadership on job satisfaction. Overall, 121 data have been collected from hotels of Iran. The collected data have been analyzed through Multiple Regression analysis. Among components of transformational leadership, intellectual stimulation, vision and supportive leadership had the significant impact and on the other side inspirational communication and personal recognition did not have any significant impact. Moreover, supportive leadership had the most significant impact. Both two dimensions of transactional leadership had significant and positive impact on job satisfaction. The impact of contingent reward was more than management expectation.
Polar codes: Primary concepts and practical decoding algorithms

Polar codes represent an emerging class of error-correcting codes with power to approach the capacity of a discrete memoryless channel. This overview article aims to illustrate its principle, generation and decoding techniques. Unlike the traditional capacity-approaching coding strategy that tries to make codes as random as possible, the polar codes follow a different philosophy, also originated by Shannon, by creating a jointly typical set. Channel polarization, a concept central to polar codes, is intuitively elaborated by a Matthew effect in the digital world, followed by a detailed overview of construction methods for polar encoding. The butterfly structure of polar codes introduces correlation among source bits, justifying the use of the SC algorithm for efficient decoding. The SC decoding technique is investigated from the conceptual and practical viewpoints. State-of-the-art decoding algorithms, such as the BP and some generalized SC decoding, are also explained in a broad framework. Simulation results show that the performance of polar codes concatenated with CRC codes can outperform that of turbo or LDPC codes. Some promising research directions in practical scenarios are also discussed in the end.