nils-herrmann/clean_scirepeval huggingface dataset:
Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison

Measures of an athlete’s heart rate variability (HRV) have shown potential to be of use in the prescription of training. However, little data exists on elite athletes who are regularly exposed to high training loads. This case study monitored daily HRV in two elite triathletes (one male: 22 year, $$ \dot{V}$$ O2max 72.5 ml kg min−1; one female: 20 year, $$ \dot{V}$$ O2max 68.2 ml kg min−1) training 23 ± 2 h per week, over a 77-day period. During this period, one athlete performed poorly in a key triathlon event, was diagnosed as non-functionally over-reached (NFOR) and subsequently reactivated the dormant virus herpes zoster (shingles). The 7-day rolling average of the log-transformed square root of the mean sum of the squared differences between R–R intervals (Ln rMSSD), declined towards the day of triathlon event (slope = −0.17 ms/week; r 2 = −0.88) in the NFOR athlete, remaining stable in the control (slope = 0.01 ms/week; r 2 = 0.12). Furthermore, in the NFOR athlete, coefficient of variation of HRV (CV of Ln rMSSD 7-day rolling average) revealed large linear reductions towards NFOR (i.e., linear regression of HRV variables versus day number towards NFOR: −0.65%/week and r 2 = −0.48), while these variables remained stable for the control athlete (slope = 0.04%/week). These data suggest that trends in both absolute HRV values and day-to-day variations may be useful measurements indicative of the progression towards mal-adaptation or non-functional over-reaching.
Self-organizing semantic maps

Self-organized formation of topographic maps for abstract data, such as words, is demonstrated in this work. The semantic relationships in the data are reflected by their relative distances in the map. Two different simulations, both based on a neural network model that implements the algorithm of the selforganizing feature maps, are given. For both, an essential, new ingredient is the inclusion of the contexts, in which each symbol appears, into the input data. This enables the network to detect the “logical similarity” between words from the statistics of their contexts. In the first demonstration, the context simply consists of a set of attribute values that occur in conjunction with the words. In the second demonstration, the context is defined by the sequences in which the words occur, without consideration of any associated attributes. Simple verbal statements consisting of nouns, verbs, and adverbs have been analyzed in this way. Such phrases or clauses involve some of the abstractions that appear in thinking, namely, the most common categories, into which the words are then automatically grouped in both of our simulations. We also argue that a similar process may be at work in the brain.
TA-COS 2018 : 2 nd Workshop on Text Analytics for Cybersecurity and Online Safety

In this study, we present toxicity annotation for a Thai Twitter Corpus as a preliminary exploration for toxicity analysis in the Thai language. We construct a Thai toxic word dictionary and select 3,300 tweets for annotation using the 44 keywords from our dictionary. We obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing target, and word sense ambiguity. Finally, we conducted supervised classification using our corpus as a dataset and obtained an accuracy of 0.80, which is comparable with the inter-annotator agreement of this dataset. Our dataset is available on GitHub.
Process Mining . Data science in action

At last decades people have to accumulate more and more data in different areas. Nowadays a lot of organizations are able to solve the problem with capacities of storage devices and therefore storing “Big data”. However they also often face to problem getting important knowledge from large amount of information. In a constant competition entrepreneurs are forced to act quickly to keep afloat. Using modern mathematics methods and algorithms helps to quickly answer questions that can lead to increasing efficiency, improving productivity and quality of provided services. There are many tools for processing of information in short time and moreover all leads to that also inexperienced users will be able to apply such software and interpret results in correct form. One of enough simple and very powerful approaches is Process Mining. It not only allows organizations to fully benefit from the information stored in their systems, but it can also be used to check the conformance of processes, detect bottlenecks, and predict execution problems. This paper provides only the main insights of Process Mining. Also it explains the key analysis techniques in process mining that can be used to automatically learn process models from raw event data. Generally most of information here is based on Massive Open Online Course: "Process Mining: Data science in Action".
High performance imaging using large camera arrays

The advent of inexpensive digital image sensors and the ability to create photographs that combine information from a number of sensed images are changing the way we think about photography. In this paper, we describe a unique array of 100 custom video cameras that we have built, and we summarize our experiences using this array in a range of imaging applications. Our goal was to explore the capabilities of a system that would be inexpensive to produce in the future. With this in mind, we used simple cameras, lenses, and mountings, and we assumed that processing large numbers of images would eventually be easy and cheap. The applications we have explored include approximating a conventional single center of projection video camera with high performance along one or more axes, such as resolution, dynamic range, frame rate, and/or large aperture, and using multiple cameras to approximate a video camera with a large synthetic aperture. This permits us to capture a video light field, to which we can apply spatiotemporal view interpolation algorithms in order to digitally simulate time dilation and camera motion. It also permits us to create video sequences using custom non-uniform synthetic apertures.
Design of BPSK / QPSK Modulator using Verilog HDL and Matlab

Phase Shift Keying (QPSK) is a modulation scheme commonly used in wireless communication system and it’s because of its ability to transmit twice the data rate for a given bandwidth. In most cases, the QPSK modulator consumes less power and bandwidth in a modern devices but for a system like satellite and mobile devices where their operations are power limited, this is a problem that needs to be fixed. The objective is to design the QPSK modulator that uses less power for operation and it should be bandwidth efficient. The proposed technique uses data stored inside a memory block to produce a symbol according to the given input data. The QPSK modulation process requires a Direct Digital Synthesizer (DDS) to produce sine and cosine wave which are used as carrier signal with the data signal to produce QPSK/BPSK output signal. The proposed modulator successfully modeled with Verilog Hardware Description Language (HDL), simulated with Xilinx Integrated Software Environment (ISE) version 14.5 software. QPSK modulation is also performed on MATLAB tool, which gives three types of waveforms. 1) In-phase component, 2) Quadrature phase, and 3) Composite of both. After that the QPSK demodulator has been developed using MATLAB tool in order to verify the functionality of the modulator.
Automated product taxonomy mapping in an e-commerce environment

Over the last few years, we have experienced a steady growth in e-commerce. This growth introduces many problems for services that want to aggregate product information and offerings. One of the problems that aggregation services face is the matching of product categories from different Web shops. This paper proposes an algorithm to perform this task automatically, making it possible to aggregate product information from multiple Web sites, in order to deploy it for search, comparison, or recommender systems applications. The algorithm uses word sense disambiguation techniques to address varying denominations between different taxonomies. Path similarity is assessed between source and candidate target categories, based on lexical relatedness and structural information. The main focus of the proposed solution is to improve the disambiguation procedure in comparison to an existing state-of-the-art approach, while coping with product taxonomy-specific characteristics, like composite categories, and re-examining lexical similarity and similarity aggregation in this context. The performance evaluation based on data from three real-world Web shops demonstrates that the proposed algorithm improves the benchmarked approach by 62% on average F1-measure.
A new level-up shifter for high speed and wide range interface in ultra deep sub-micron

A new level-up shifter aimed at ultra low core voltage and wide range I/O voltage is designed using a 90 nm CMOS process. The proposed level shifter uses analog circuit techniques and standard zero-Vt NMOS transistor without adding extra mask or process step. No static power consumption and stable duty ratio make this level shifter suitable for wide I/O interface voltage applications in ultra deep sub-micron. These techniques work even at 0.6 V core voltage, 1.65/spl sim/3.6V I/O voltage, within 45:55 duty ratio up to 200 MHz.
A Study of Cardiovascular Disease Prediction Models Using Discriminant Analysis

Cardiovascular disease is a condition of interest to clinicians around the world and is one of the chief causes of death. In Korea, cardiovascular disease is the second largest cause of death following cancer, and is classified as one of the four major diseases, making an early diagnosis of cardiovascular disease essential for proper and timely treatment. Although the risk of cardiovascular disease is recognized in Korea, there is a lack of quality studies regarding the disease. In the present study, a prediction model was developed which allows for the calculation of the risk of cardiovascular disease among Korean patients. The prediction model for cardiovascular disease was developed using a discriminant analysis based on the data set of Korean National Health and Nutrition Examinations Survey V (KNHANES V).
Men ’ s Behavior Toward Women After Viewing Sexually-Explicit Films : Degradation Makes a Difference

Past research indicates that short-term exposure to nonviolent sexual media stimuli can produce cognitive changes in men which, in turn, can affect their behavior toward women. This study explored differences among these behavioral effects based upon sexual degradation in film content and male viewers’ gender schematicity, as an extension of the study of cognitive effects reported in Jansma, Linz, Mulac, and Imrich (1997). Seventy-one men viewed one of three films: (a) sexually-explicit and degrading to women, (b) sexually-explicit but non-degrading, or (c) non-sexual. The men then interacted with women in problem-solving dyads that were recorded on videotape. Men who viewed either sexually-explicit film displayed more dominance and anxiety than did the men who viewed the non-sexual film. This was true, however, for only the two-thirds who were non sex-typed (BSRI, Bem, 1974). Sex-typed men’s behavior was consistent across sexual versus non-sexual film conditions. In addition, men who viewed the degrading sexual film displayed less anxiety, but more dominance, than men who watched the non-degrading sexual film. Women’s behavior failed to distinguish which of the three films their partners had viewed, but was positively correlated with most of their partner’s discriminating behavior. Results of men’s viewing sexual or degrading sexual material are discussed in terms of negative social implications for women in the context of everyday male-female interactions.
Performance Gaps between OpenMP and OpenCL for Multi-core CPUs

OpenCL and OpenMP are the most commonly used programming models for multi-core processors. They are also fundamentally different in their approach to parallelization. In this paper, we focus on comparing the performance of OpenCL and OpenMP. We select three applications from the Rodinia benchmark suite (which provides equivalent OpenMP and OpenCL implementations), and carry out experiments with different datasets on three multi-core platforms. We see that the incorrect usage of the multi-core CPUs, the inherent OpenCL fine-grained parallelism, and the immature OpenCL compilers are the main reasons that lead to the OpenCL poorer performance. After tuning the OpenCL versions to be more CPU-friendly, we show that OpenCL either outperforms or achieves similar performance in more than 80% of the cases. Therefore, we believe that OpenCL is a good alternative for multi-core CPU programming.
My science tutor: A conversational multimedia virtual tutor for elementary school science

This article describes My Science Tutor (MyST), an intelligent tutoring system designed to improve science learning by students in 3rd, 4th, and 5th grades (7 to 11 years old) through conversational dialogs with a virtual science tutor. In our study, individual students engage in spoken dialogs with the virtual tutor Marni during 15 to 20 minute sessions following classroom science investigations to discuss and extend concepts embedded in the investigations. The spoken dialogs in MyST are designed to scaffold learning by presenting open-ended questions accompanied by illustrations or animations related to the classroom investigations and the science concepts being learned. The focus of the interactions is to elicit self-expression from students. To this end, Marni applies some of the principles of Questioning the Author, a proven approach to classroom conversations, to challenge students to think about and integrate new concepts with prior knowledge to construct enriched mental models that can be used to explain and predict scientific phenomena. In this article, we describe how spoken dialogs using Automatic Speech Recognition (ASR) and natural language processing were developed to stimulate students' thinking, reasoning and self explanations. We describe the MyST system architecture and Wizard of Oz procedure that was used to collect data from tutorial sessions with elementary school students. Using data collected with the procedure, we present evaluations of the ASR and semantic parsing components. A formal evaluation of learning gains resulting from system use is currently being conducted. This paper presents survey results of teachers' and children's impressions of MyST.
Edible berries: bioactive components and their effect on human health.

The importance of food consumption in relation to human health has increased consumer attention in nutraceutical components and foods, especially fruits and vegetables. Berries are a rich source of a wide variety of non-nutritive, nutritive, and bioactive compounds such as flavonoids, phenolics, anthocyanins, phenolic acids, stilbenes, and tannins, as well as nutritive compounds such as sugars, essential oils, carotenoids, vitamins, and minerals. Bioactive compounds from berries have potent antioxidant, anticancer, antimutagenic, antimicrobial, anti-inflammatory, and antineurodegenerative properties, both in vitro and in vivo. The following is a comprehensive and critical review on nutritional and non-nutritional bioactive compounds of berries including their absorption, metabolism, and biological activity in relation to their potential effect on human health.
Discovering Event Evolution Graphs From News Corpora

Given the advance of Internet technologies, we can now easily extract hundreds or thousands of news stories of any ongoing incidents from newswires such as CNN.com, but the volume of information is too large for us to capture the blueprint. Information retrieval techniques such as topic detection and tracking are able to organize news stories as events, in a flat hierarchical structure, within a topic. However, they are incapable of presenting the complex evolution relationships between the events. We are interested to learn not only what the major events are but also how they develop within the topic. It is beneficial to identify the seminal events, the intermediary and ending events, and the evolution of these events. In this paper, we propose to utilize the event timestamp, event content similarity, temporal proximity, and document distributional proximity to model the event evolution relationships between events in an incident. An event evolution graph is constructed to present the underlying structure of events for efficient browsing and extracting of information. Case study and experiments are presented to illustrate and show the performance of our proposed technique. It is found that our proposed technique outperforms the baseline technique and other comparable techniques in previous work.
Bloom Filter Performance on Graphics Engines

Bloom filters are a probabilistic technique for large-scale set membership tests. They exhibit no false negative test results but are susceptible to false positive results. They are well-suited to both large sets and large numbers of membership tests. We implement the Bloom filters present in an accelerated version of BLAST, a genome biosequence alignment application, on NVIDIA GPUs and develop an analytic performance model that helps potential users of Bloom filters to quantify the inherent tradeoffs between throughput and false positive rates.
Nasal bone length throughout gestation: normal ranges based on 3537 fetal ultrasound measurements.

To establish normal ranges for nasal bone length measurements throughout gestation and to compare measurements in two subsets of patients of different race (African-American vs. Caucasian) to determine whether a different normal range should be used in these populations.



Normal nasal bone length reference ranges were generated using prenatal measurements by a standardized technique in 3537 fetuses.



The nasal bone lengths were found to correlate positively with advancing gestation (R(2) = 0.77, second-order polynomial). No statistical difference was found between African-American and Caucasian subjects.



These reference ranges may prove to be useful in prenatal screening and diagnosis of syndromes known to be associated with nasal hypoplasia. Different normal ranges for African-American and Caucasian women are not required.
Abnormal brain chemistry in chronic back pain: an in vivo proton magnetic resonance spectroscopy study

The neurobiology of chronic pain, including chronic back pain, is unknown. Structural imaging studies of the spine cannot explain all cases of chronic back pain. Functional brain imaging studies indicate that the brain activation patterns are different between chronic pain patients and normal subjects, and the thalamus, and prefrontal and cingulate cortices are involved in some types of chronic pain. Animal models of chronic pain suggest abnormal spinal cord chemistry. Does chronic pain cause brain chemistry changes? We examined brain chemistry changes in patients with chronic back pain using in vivo single- voxel proton magnetic resonance spectroscopy ((1)H-MRS). In vivo (1)H-MRS was used to measure relative concentrations of N-acetyl aspartate, creatine, choline, glutamate, glutamine, gamma-aminobutyric acid, inositol, glucose and lactate in relation to the concentration of creatine. These measurements were performed in six brain regions of nine chronic low back pain patients and 11 normal volunteers. All chronic back pain subjects underwent clinical evaluation and perceptual measures of pain and anxiety. We show that chronic back pain alters the human brain chemistry. Reductions of N-acetyl aspartate and glucose were demonstrated in the dorsolateral prefrontal cortex. Cingulate, sensorimotor, and other brain regions showed no chemical concentration differences. In chronic back pain, the interrelationship between chemicals within and across brain regions was abnormal, and there was a specific relationship between regional chemicals and perceptual measures of pain and anxiety. These findings provide direct evidence of abnormal brain chemistry in chronic back pain, which may be useful in diagnosis and future development of more effective pharmacological treatments.
AXR-CMP : Architecture Support in Accelerator-Rich CMPs

To improve performance/power efficiency, we expect that future CMPs may use special-purpose accelerators extensively. This work discusses hardware architectural support for accelerator-rich CMPs. First, we introduce an efficient cache management scheme for accelerators to mitigate memory latency by overlapping data transfer with computation. Second, we present a hardware resource management scheme for accelerator sharing. This scheme supports sharing and arbitration of multiple cores for a common set of accelerators, and it uses a software-based priority mechanism to provide feedback to cores that indicates the wait time before acquiring a particular resource. Finally we propose architectural support that allows us to compose a larger virtual accelerator out of multiple smaller accelerators, and chain multiple accelerators together with minimal intervention of the requesting core. Experimental results show significant performance and energy improvement compared to approaches that use OS-based accelerator management, and achieve on the average 9X in performance (up to 40.17X) and 32X in energy efficiency (up to 90X) over a software implementation, with minimal hardware overhead.
Formation and suppression of acoustic memories during human sleep

Sleep and memory are deeply related, but the nature of the neuroplastic processes induced by sleep remains unclear. Here, we report that memory traces can be both formed or suppressed during sleep, depending on sleep phase. We played samples of acoustic noise to sleeping human listeners. Repeated exposure to a novel noise during Rapid Eye Movements (REM) or light non-REM (NREM) sleep leads to improvements in behavioral performance upon awakening. Strikingly, the same exposure during deep NREM sleep leads to impaired performance upon awakening. Electroencephalographic markers of learning extracted during sleep confirm a dissociation between sleep facilitating memory formation (light NREM and REM sleep) and sleep suppressing learning (deep NREM sleep). We can trace these neural changes back to transient sleep events, such as spindles for memory facilitation and slow waves for suppression. Thus, highly selective memory processes are active during human sleep, with intertwined episodes of facilitative and suppressive plasticity. Though memory and sleep are related, it is still unclear whether new memories can be formed during sleep. Here, authors show that people could learn new sounds during REM or light non-REM sleep, but that learning was suppressed when sounds were played during deep NREM sleep.