nils-herrmann/clean_scirepeval huggingface dataset:
A pseudodifferential amplifier for bioelectric events with DC-offset compensation using two-wired amplifying electrodes

Most wired active electrodes reported so far have a gain of one and require at least three wires. This leads to stiff cables, large connectors and additional noise for the amplifier. The theoretical advantages of amplifying the signal on the electrodes right from the source has often been described, however, rarely implemented. This is because a difference in the gain of the electrodes due to component tolerances strongly limits the achievable common mode rejection ratio (CMRR). In this paper, we introduce an amplifier for bioelectric events where the major part of the amplification (40 dB) is achieved on the electrodes to minimize pick-up noise. The electrodes require only two wires of which one can be used for shielding, thus enabling smaller connecters and smoother cables. Saturation of the electrodes is prevented by a dc-offset cancelation scheme with an active range of /spl plusmn/250 mV. This error feedback simultaneously allows to measure the low frequency components down to dc. This enables the measurement of slow varying signals, e.g., the change of alertness or the depolarization before an epileptic seizure normally not visible in a standard electroencephalogram (EEG). The amplifier stage provides the necessary supply current for the electrodes and generates the error signal for the feedback loop. The amplifier generates a pseudodifferential signal where the amplified bioelectric event is present on one lead, but the common mode signal is present on both leads. Based on the pseudodifferential signal we were able to develop a new method to compensate for a difference in the gain of the active electrodes which is purely software based. The amplifier system is then characterized and the input referred noise as well as the CMRR are measured. For the prototype circuit the CMRR evaluated to 78 dB (without the driven-right-leg circuit). The applicability of the system is further demonstrated by the recording of an ECG.
Dimensionality reduction in deep learning for chest X-ray analysis of lung cancer

The efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the deep convolutional neural network (CNN) was performed on the open JSRT dataset (dataset #01), the JSRT after bone shadow exclusion — BSE-JSRT (dataset #02), JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation (dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE method (dataset #05). The results demonstrate that the pre-processed dataset obtained after lung segmentation, bone shadow exclusion, and filtering out the outliers by t-SNE (dataset #05) demonstrates the highest training rate and best accuracy in comparison to the other pre-processed datasets.
Learning-based feature extraction for active 3D scan with reducing color crosstalk of multiple pattern projections

3D reconstruction methods based on active stereo technique have been widely used for many practical systems. Many of these systems are configured with a single camera and a single projector. Since such systems can only capture one side of the target object, several attempts have been conducted to enlarge the captured area, especially multi-projector systems attract many researchers. For multi-projector based systems, overlap between multiple pattern projections is a serious problem. Even if different color channels are used for each projector, complete separation is not possible because of color crosstalks. Another open problem is decoding errors of the projected patterns, which causes a failure on extracting positional information of the projected pattern form the captured image. Among several reasons for such errors, color crosstalks are crucial because their features are similar to the main signal and difficult to be decomposed. In this paper, we solve these problems by utilizing machine learning techniques where a convolutional neural network is trained to extract low dimensional pattern features for each projector. In addition, it is trained to suppress the color crosstalks from different projectors. Using this new technique, we succeeded in reconstructing 3D shapes from images where multiple patterns are overlapped.
Visual-inertial SLAM for a small helicopter in large outdoor environments

In this video, we present our latest results towards fully autonomous flights with a small helicopter. Using a monocular camera as the only exteroceptive sensor, we fuse inertial measurements to achieve a self-calibrating power-on-and-go system, able to perform autonomous flights in previously unknown, large, outdoor spaces. Our framework achieves Simultaneous Localization And Mapping (SLAM) with previously unseen robustness in onboard aerial navigation for small platforms with natural restrictions on weight and computational power. We demonstrate successful operation in flights with altitude between 0.2-70 m, trajectories with 350 m length, as well as dynamic maneuvers with track speed of 2 m/s. All flights shown are performed autonomously using vision in the loop, with only high-level waypoints given as directions.
Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease

We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD.



The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM).



For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features.



Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.
Cardamom, Cumin, and Dill Weed Essential Oils: Chemical Compositions, Antimicrobial Activities, and Mechanisms of Action against Campylobacter spp.

Natural antimicrobials as well as essential oils (EOs) have gained interest to inhibit pathogenic microorganisms and to control food borne diseases. Campylobacter spp. are one of the most common causative agents of gastroenteritis. In this study, cardamom, cumin, and dill weed EOs were evaluated for their antibacterial activities against Campylobacter jejuni and Campylobacter coli by using agar-well diffusion and broth microdilution methods, along with the mechanisms of antimicrobial action. Chemical compositions of EOs were also tested by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). The results showed that cardamom and dill weed EOs possess greater antimicrobial activity than cumin with larger inhibition zones and lower minimum inhibitory concentrations. The permeability of cell membrane and cell membrane integrity were evaluated by determining relative electric conductivity and release of cell constituents into supernatant at 260 nm, respectively. Moreover, effect of EOs on the cell membrane of Campylobacter spp. was also investigated by measuring extracellular ATP concentration. Increase of relative electric conductivity, extracellular ATP concentration, and cell constituents' release after treatment with EOs demonstrated that tested EOs affected the membrane integrity of Campylobacter spp. The results supported high efficiency of cardamom, cumin, and dill weed EOs to inhibit Campylobacter spp. by impairing the bacterial cell membrane.
Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI

Ahstract- For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment . Natural tasks often require different kinds of interactions, involving different controllers the user should be able to select in a self-paced way. We developed a Brain-Computer Interface (BCI) allowing users to switch between four control modes in a self-paced way in real-time. Since the system is devised to be used in domestic environments in a user-friendly way, we selected non-invasive electroencephalographic (EEG) signals and convolutional neural networks (CNNs), known for their ability to find the optimal features in classification tasks. We tested our system using the Cybathlon BCI computer game, which embodies all the challenges inherent to real-time control. Our preliminary results show that an efficient architecture (SmallNet), with only one convolutional layer, can classify 4 mental activities chosen by the user. The BCI system is run and validated online. It is kept up-to-date through the use of newly collected signals along playing, reaching an online accuracy of 47.6% where most approaches only report results obtained offline. We found that models trained with data collected online better predicted the behaviour of the system in real-time. This suggests that similar (CNN based) offline classifying methods found in the literature might experience a drop in performance when applied online. Compared to our previous decoder of physiological signals relying on blinks, we increased by a factor 2 the amount of states among which the user can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self-paced way. Our results are comparable to those showed at the Cybathlon's BCI Race but further improvements on accuracy are required.
Technology, Capabilities, and Performance of Low Power Terahertz Sources

New and emerging terahertz technology applications make this a very exciting time for the scientists, engineers, and technologists in the field. New sensors and detectors have been the primary driving force behind the unprecedented progress in terahertz technology, but in the last decade extraordinary developments in terahertz sources have also occurred. Driven primarily by space based missions for Earth, planetary, and astrophysical science, frequency multiplied sources have dominated the field in recent years, at least in the 2-3 THz frequency range. More recently, over the past few years terahertz quantum cascade lasers (QCLs) have made tremendous strides, finding increasing applications in terahertz systems. Vacuum electronic devices and photonic sources are not far behind either. In this article, the various technologies for terahertz sources are reviewed, and future trends are discussed.
Discovery of Repeated Melodic Phrases in Folk Singing Recordings

In music, repetition is a fundamental concept to establish structure and create temporal relationships. Previous approaches to detecting repetition in music recordings have mainly focused on discovering repeated patterns of variable length and instrumentation at arbitrary locations. In this paper, we present a novel method for the discovery of repeated sung phrases in folk music recordings and, in particular, in oral music traditions, where written scores are usually unavailable. At a first stage, a segmentation algorithm partitions automatically generated note-level transcriptions of the singing melody into sections that correspond to the structural unit of a phrase. A clustering algorithm is then used to form clusters of phrases, where each cluster contains instances of the same melodic content. The clustering algorithm operates on the basis of a distance measure between melodic sequences and, to this end, various melodic distance measures are investigated. A detailed evaluation procedure is used to assess the performance of the algorithm on three different European music traditions and the influence of transcription and segmentation errors is investigated. The proposed system is shown to outperform the state-of-the-art in audio-based approaches to repeated phrase discovery for this task.
The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

MetaCyc (https://MetaCyc.org) is a comprehensive reference database of metabolic pathways and enzymes from all domains of life. It contains more than 2570 pathways derived from >54 000 publications, making it the largest curated collection of metabolic pathways. The data in MetaCyc is strictly evidence-based and richly curated, resulting in an encyclopedic reference tool for metabolism. MetaCyc is also used as a knowledge base for generating thousands of organism-specific Pathway/Genome Databases (PGDBs), which are available in the BioCyc (https://BioCyc.org) and other PGDB collections. This article provides an update on the developments in MetaCyc during the past two years, including the expansion of data and addition of new features.
An MRI investigation into the function of the transversus abdominis muscle during "drawing-in" of the abdominal wall.

An operator blinded dual modality trial of measurement of the abdominal muscles during "drawing-in" of the abdominal wall.



1) To investigate, using magnetic resonance imaging (MRI), the function of the transversus abdominis muscle bilaterally during a drawing-in of the abdominal wall. 2) To validate the use of real-time ultrasound imaging as a measure of the deep abdominal muscle during a drawing-in of the abdominal wall.



Previous research has implicated the deep abdominal muscle, transversus abdominis, in the support and protection of the spine and provided evidence that training this muscle is important in the rehabilitation of low back pain. One of the most important actions of the transversus abdominis is to "draw-in" the abdominal wall, and this action has been shown to stiffen the sacroiliac joints. It is hypothesized that in response to a draw in, the transversus abdominis muscle forms a deep musculofascial "corset" and that MRI could be used to view this corset and verify its mechanism of action on the lumbopelvic region.



Thirteen healthy asymptomatic male elite cricket players aged 21.3 +/- 2.1 years were imaged using MRI and ultrasound imaging as they drew in their abdominal walls. Measurements of the thickness of the transversus abdominis and internal oblique muscles and the slide of the anterior abdominal fascia were measured using both MRI and ultrasound. Measurement of the whole abdominal cross-sectional area (CSA) was conducted using MRI.



Results of the MRI demonstrated that, as a result of draw-in, there was a significant increase in thickness of the transversus abdominis (P < 0.001) and the internal oblique muscles (P < 0.001). There was a significant decrease in the CSA of the trunk (P < 0.001). The mean slide (+/-SD) of the anterior abdominal fascia was 1.54 +/- 0.38 cm for the left side and 1.48 +/- 0.35 cm for the right side. Ultrasound measurements of muscle thickness of both transversus abdominis and the internal oblique, as well as fascial slide, correlated with measures obtained using MRI (interclass correlations from 0.78 to 0.95).



The MRI results demonstrated that during a drawing-in action, the transversus abdominis contracts bilaterally to form a musculofascial band that appears to tighten (like a corset) and most likely improves the stabilization of the lumbopelvic region. Real-time ultrasound imaging can also be used to measure changes in the transversus abdominis during the draw-in maneuver.
Patent information retrieval: approaching a method and analysing nanotechnology patent collaborations

Many challenges still remain in the processing of explicit technological knowledge documents such as patents. Given the limitations and drawbacks of the existing approaches, this research sets out to develop an improved method for searching patent databases and extracting patent information to increase the efficiency and reliability of nanotechnology patent information retrieval process and to empirically analyse patent collaboration. A tech-mining method was applied and the subsequent analysis was performed using Thomson data analyser software. The findings show that nations such as Korea and Japan are highly collaborative in sharing technological knowledge across academic and corporate organisations within their national boundaries, and China presents, in some cases, a great illustration of effective patent collaboration and co-inventorship. This study also analyses key patent strengths by country, organisation and technology.
Energy cost and metabolic power in elite soccer: a new match analysis approach.

Video match analysis is used for the assessment of physical performances of professional soccer players, particularly for the identification of "high intensities" considered as "high running speeds." However, accelerations are also essential elements setting metabolic loads, even when speed is low. We propose a more detailed assessment of soccer players' metabolic demands by video match analysis with the aim of also taking into account accelerations.



A recent study showed that accelerated running on a flat terrain is equivalent to running uphill at constant speed, the incline being dictated by the acceleration. Because the energy cost of running uphill is known, this makes it possible to estimate the instantaneous energy cost of accelerated running, the corresponding instantaneous metabolic power, and the overall energy expenditure, provided that the speed (and acceleration) is known. Furthermore, the introduction of individual parameters makes it possible to customize performance profiles, especially as it concerns energy expenditure derived from anaerobic sources. Data from 399 "Serie-A" players (mean +/- SD; age = 27 +/- 4 yr, mass = 75.8 +/- 5.0 kg, stature = 1.80 +/- 0.06 m) were collected during the 2007-2008 season.



Mean match distance was 10,950 +/- 1044 m, and average energy expenditure was 61.12 +/- 6.57 kJ x kg(-1). Total distance covered at high power (>20 W x kg(-1)) amounted to 26% and corresponding energy expenditure to approximately 42% of the total. "High intensities" expressed as high-power output are two to three times larger than those based only on running speed.



The present approach for the assessment of top-level soccer players match performance through video analysis allowed us to assess instantaneous metabolic power, thus redefining the concept of "high intensity" on the basis of actual metabolic power rather than on speed alone.
Distilling at the Edge: A Local Differential Privacy Obfuscation Framework for IoT Data Analytics

Edge computing has emerged as a promising paradigm for delay-sensitive and context-aware IoT data analytics, through migrating data processing from the cloud to the edge of the network. However, traditional solutions adopting homomorphic encryption to achieve data protection and aggregation at edge servers are infeasible because of their heavy computational overhead. How to preserve data privacy while guaranteeing data utility in edge computing becomes an extremely important problem for IoT data analytics. In this article, we propose a local differential privacy obfuscation (LDPO) framework for IoT data analytics to aggregate and distill the IoT data at the edge without disclosing users' sensitive data. We first introduce the architecture and benefits of the LDPO framework, followed by some technical challenges in guaranteeing its performance. Then we present a preliminary implementation of the LDPO framework, and validate its performance in terms of privacy preservation level and data utility using real-world apps and datasets. Some future directions are finally envisioned for further research.
An analysis of the Revised Olweus Bully/Victim Questionnaire using the Rasch measurement model.

Bullying is a problem in schools in many countries. There would be a benefit in the availability of a psychometrically sound instrument for its measurement, for use by teachers and researchers. The Olweus Bully/Victim Questionnaire has been used in a number of studies but comprehensive evidence on its validity is not available.



To examine the conceptual design, construct validity and reliability of the Revised Olweus Bully/Victim Questionnaire (OBVQ) and to provide further evidence on the prevalence of different forms of bullying behaviour.


SAMPLE
All 335 pupils (160 [47.8%] girls; 175 [52.2%]) boys, mean age 11.9 years [range 11.2-12.8 years]), in 21 classes of a stratified sample of 7 Greek Cypriot primary schools.



The OBVQ was administered to the sample. Separate scales were created comprising (a) the items of the questionnaire concerning the extent to which pupils are being victimized; and (b) those concerning the extent to which pupils express bullying behaviour. Using the Rasch model, both scales were analysed for reliability, fit to the model, meaning, and validity. Both scales were also analysed separately for each of two sample groups (i.e. boys and girls) to test their invariance.



Analysis of the data revealed that the instrument has satisfactory psychometric properties; namely, construct validity and reliability. The conceptual design of the instrument was also confirmed. The analysis leads also to suggestions for improving the targeting of items against student measures. Support was also provided for the relative prevalence of verbal, indirect and physical bullying. As in other countries, Cypriot boys used and experienced more bullying than girls, and boys used more physical and less indirect forms of bullying than girls.



The OBVQ is a psychometrically sound instrument that measures two separate aspects of bullying, and whose use is supported for international studies of bullying in different countries. However, improvements to the questionnaire were also identified to provide increased usefulness to teachers tackling this significant problem facing schools in many countries.
Restoration and Enhancement of Underwater Image based on Wavelength Compensation and Image Dehazing Technique

Getting clear images in underwater environments is an important issue in ocean engineering . The quality of underwater images plays a important role in scientific world. Capturing images underwater is difficult, generally due to deflection and reflection of water particles, and color change due to light travelling in water with different wavelengths. Light dispersion and color transform result in contrast loss and color deviation in images acquired underwater. Restoration and Enhancement of an underwater object from an image distorted by moving water waves is a very challenging task. This paper proposes wavelength compensation and image dehazing technique to balance the color change and light scattering respectively. It also removes artificial light by using depth map technique. Water depth is estimated by background color. Color change compensation is done by residual energy ratio method. A new approach is presented in this paper. We make use of a special technique called wavelength compensation and dehazing technique along with the artificial light removal technique simultaneously to analyze the raw image sequences and recover the true object. We test our approach on both pretended and data of real world, separately. Such technique has wide applications to areas such.