nils-herrmann/clean_scirepeval huggingface dataset:
GaN Comes of Age

GaN is now providing solid-state power amplifiers of higher efficiency, bandwidth, and power density than could be achieved only a few years ago. Novel circuit topologies combined with GaN's high-voltage capabilities and linearization are allowing GaN high-power amplifiers to simultaneously achieve both linearity and record high efficiency. GaN high-power amplifiers have been produced with more than 100 W of power over multioctave bandwidths and with PAEs of more than 60%. Narrower-band high-power amplifiers have been produced with PAEs of more than 90%.
Provenance-Driven Data Curation Workflow Analysis

Manually designed workflows can be error-prone and inefficient. Workflow provenance contains fine-grained data processing information that can be used to detect workflow design problems. In this paper, we propose a provenance-driven workflow analysis framework that exploits both prospective and retrospective provenance. We show how provenance information can help the user gain a deeper understanding of a workflow and provide the user with insights into how to improve workflow design.
Broadband UHF RFID Passive Tag Antenna for Near-Body Applications

One challenge for UHF RFID passive tag design is to obtain a low-profile antenna that minimizes the influence of near-body or attached objects without sacrificing both read range and universal UHF RFID band interoperability. A new improved design of a RFID passive tag antenna is presented that performs well near problematic surfaces (human body, liquids, metals) across most of the universal UHF RFID (840-960 MHz) band. The antenna is based on a low-profile printed configuration with slots, and it is evaluated through extensive simulations and experimental tests.
Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease

High-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN-HFS) is the preferred surgical treatment for advanced Parkinson's disease. In the 15 years since its introduction into clinical practice, many studies have reported on its benefits, drawbacks, and insufficiencies. Despite limited evidence-based data, STN-HFS has been shown to be surgically safe, and improvements in dopaminergic drug-sensitive symptoms and reductions in subsequent drug dose and dyskinesias are well documented. However, the procedure is associated with adverse effects, mainly neurocognitive, and with side-effects created by spread of stimulation to surrounding structures, depending on the precise location of electrodes. Quality of life improves substantially, inducing sudden global changes in patients' lives, often requiring societal readaptation. STN-HFS is a powerful method that is currently unchallenged in the management of Parkinson's disease, but its long-term effects must be thoroughly assessed. Further improvements, through basic research and methodological innovations, should make it applicable to earlier stages of the disease and increase its availability to patients in developing countries.
Mining 3D Key-Pose-Motifs for Action Recognition

Recognizing an action from a sequence of 3D skeletal poses is a challenging task. First, different actors may perform the same action in various styles. Second, the estimated poses are sometimes inaccurate. These challenges can cause large variations between instances of the same class. Third, the datasets are usually small, with only a few actors performing few repetitions of each action. Hence training complex classifiers risks over-fitting the data. We address this task by mining a set of key-pose-motifs for each action class. A key-pose-motif contains a set of ordered poses, which are required to be close but not necessarily adjacent in the action sequences. The representation is robust to style variations. The key-pose-motifs are represented in terms of a dictionary using soft-quantization to deal with inaccuracies caused by quantization. We propose an efficient algorithm to mine key-pose-motifs taking into account of these probabilities. We classify a sequence by matching it to the motifs of each class and selecting the class that maximizes the matching score. This simple classifier obtains state-of the-art performance on two benchmark datasets.
Emerging contaminants of public health significance as water quality indicator compounds in the urban water cycle.

The contamination of the urban water cycle (UWC) with a wide array of emerging organic compounds (EOCs) increases with urbanization and population density. To produce drinking water from the UWC requires close examination of their sources, occurrence, pathways, and health effects and the efficacy of wastewater treatment and natural attenuation processes that may occur in surface water bodies and groundwater. This paper researches in details the structure of the UWC and investigates the routes by which the water cycle is increasingly contaminated with compounds generated from various anthropogenic activities. Along with a thorough survey of chemicals representing compound classes such as hormones, antibiotics, surfactants, endocrine disruptors, human and veterinary pharmaceuticals, X-ray contrast media, pesticides and metabolites, disinfection-by-products, algal toxins and taste-and-odor compounds, this paper provides a comprehensive and holistic review of the occurrence, fate, transport and potential health impact of the emerging organic contaminants of the UWC. This study also illustrates the widespread distribution of the emerging organic contaminants in the different aortas of the ecosystem and focuses on future research needs.
A Unipolar/Bipolar High-Voltage Pulse Generator Based on Positive and Negative Buck–Boost DC–DC Converters Operating in Discontinuous Conduction Mode

This paper presents a new high-voltage pulse generator, which is based on positive and negative buck-boost (BB) converters fed from a relatively low voltage dc supply. The proposed generator is able to generate unipolar or bipolar high-voltage pulses via operating the BB converters with series or parallel connected outputs respectively using a common input dc source. The components of each converter are rated at half of the pulsed voltage magnitude in the unipolar mode. The converters in the proposed pulse generator operate in discontinuous conduction mode. This enhances system efficiency, as the circuit only operates when it is desired to generate a pulsed output voltage, otherwise the circuit is switched to idle mode with zero current. Detailed illustration of the proposed approach is presented along with a full design of the system components for given output pulse specifications. Finally, simulation and experimental results are presented to validate the proposed concept.
DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions

microRNAs (miRNAs) are short non-coding RNA species, which act as potent gene expression regulators. Accurate identification of miRNA targets is crucial to understanding their function. Currently, hundreds of thousands of miRNA:gene interactions have been experimentally identified. However, this wealth of information is fragmented and hidden in thousands of manuscripts and raw next-generation sequencing data sets. DIANA-TarBase was initially released in 2006 and it was the first database aiming to catalog published experimentally validated miRNA:gene interactions. DIANA-TarBase v7.0 (http://www.microrna.gr/tarbase) aims to provide for the first time hundreds of thousands of high-quality manually curated experimentally validated miRNA:gene interactions, enhanced with detailed meta-data. DIANA-TarBase v7.0 enables users to easily identify positive or negative experimental results, the utilized experimental methodology, experimental conditions including cell/tissue type and treatment. The new interface provides also advanced information ranging from the binding site location, as identified experimentally as well as in silico, to the primer sequences used for cloning experiments. More than half a million miRNA:gene interactions have been curated from published experiments on 356 different cell types from 24 species, corresponding to 9- to 250-fold more entries than any other relevant database. DIANA-TarBase v7.0 is freely available.
A Survey on Techniques in Detection and Analyzing Malware Executables

The computer technology has emerged as a necessity in our day to day life to deal with various aspects like education, banking, communication, entertainment etc. Computer system’s security is threatened by weapons named as malware to accomplish malicious intention of its writers. Various solutions are available to detect these threats like AV Scanners, Intrusion Detection System, and Firewalls etc. These solutions of malware detection traditionally use signatures of malware to detect their presence in our system. But these methods are also evaded due to some obfuscation techniques employed by malware authors. This survey paper highlights the existing detection and analysis methodologies used for these obfuscated malicious code.
ZnO Nanostructures for Tissue Engineering Applications

This review focuses on the most recent applications of zinc oxide (ZnO) nanostructures for tissue engineering. ZnO is one of the most investigated metal oxides, thanks to its multifunctional properties coupled with the ease of preparing various morphologies, such as nanowires, nanorods, and nanoparticles. Most ZnO applications are based on its semiconducting, catalytic and piezoelectric properties. However, several works have highlighted that ZnO nanostructures may successfully promote the growth, proliferation and differentiation of several cell lines, in combination with the rise of promising antibacterial activities. In particular, osteogenesis and angiogenesis have been effectively demonstrated in numerous cases. Such peculiarities have been observed both for pure nanostructured ZnO scaffolds as well as for three-dimensional ZnO-based hybrid composite scaffolds, fabricated by additive manufacturing technologies. Therefore, all these findings suggest that ZnO nanostructures represent a powerful tool in promoting the acceleration of diverse biological processes, finally leading to the formation of new living tissue useful for organ repair.
Functional organization of thalamocortical relays.

The thalamus has long been seen as responsible for relaying information on the way to the cerebral cortex, but it has not been until the last decade or so that the functional nature of this relay has attracted significant attention. Whereas earlier views tended to relegate thalamic function to a simple, machine-like relay process, recent research, reviewed in this article, demonstrates complicated circuitry and a rich array of membrane properties underlying the thalamic relay. It is now clear that the thalamic relay does not have merely a trivial function. Suggestions that the thalamic circuits and cell properties only come into play during certain phases of sleep to effectively disconnect the relay are correct as far as they go, but they are incomplete, because they fail to take into account interesting and variable properties of the relay that, we argue, occur during normal waking behavior. Although the specific function of the circuits and cellular properties of the thalamic relay for waking behavior is far from clear, we offer two related hypotheses based on recent experimental evidence. One is that the thalamus is not used just to relay peripheral information from, for example, visual, auditory, or cerebellar inputs, but that some thalamic nuclei are arranged instead to relay information from one cortical area to another. The second is that the thalamus is not a simple, passive relay of information to cortex but instead is involved in many dynamic processes that significantly alter the nature of the information relayed to cortex.
MENTALIZATION IN ADULT ATTACHMENT NARRATIVES : REFLECTIVE FUNCTIONING , MENTAL STATES , AND AFFECT ELABORATION COMPARED

Relationships between three measures of mentalization (reflective function, mental states, and verbal elaboration of affect), attachment status, and the severity of axis I and axis II pathology were examined. Seventy-three adults, both ex-psychiatric patients and nonclinical volunteers were administered the Adult Attachment Interview (AAI). Comparisons between the three measures indicate that they share some aspect of a core mentalization process and that each illuminates a specific component. Reflective function was the only predictor of attachment status. The number of axis I diagnoses is partly explained by attachment insecurity, but the capacity to generate high-level defensive mental
An Improved Collaborative Filtering Algorithm Based on User Interest

With the development of personalized services, collaborative filtering techniques have been successfully applied to the network recommendation system. But sparse data seriously affect the performance of collaborative filtering algorithms. To alleviate the impact of data sparseness, using user interest information, an improved user-based clustering Collaborative Filtering (CF) algorithm is proposed in this paper, which improves the algorithm by two ways: user similarity calculating method and user-item rating matrix extended. The experimental results show that the algorithm could describe the user similarity more accurately and alleviate the impact of data sparseness in collaborative filtering algorithm. Also the results show that it can improve the accuracy of the collaborative recommendation algorithm.
A Revisit to Support Vector Data Description

Support vector data description (SVDD) is a useful method for outlier detection and has been applied to a variety of applications. However, in the existing optimization procedure of SVDD, there are some issues which may lead to improper usage of SVDD. Some of the issues might already be known in practice, but the theoretical discussion, justification and correction are still lacking. Given the wide use of SVDD, these issues inspire us to carefully study SVDD in the view of convex optimization. In particular, we derive the dual problem with strong duality, prove theorems to handle theoretical insufficiency in the literature of SVDD, investigate some novel extensions of SVDD, and come up with an implementation of training SVDD with theoretical guarantee.
Image enhancement using a fusion framework of histogram equalization and laplacian pyramid

The image enhancement methods based on histogram equalization (HE) often fail to improve local information and sometimes have the fatal flaw of over-enhancement when a quantum jump occurs in the cumulative distribution function of the histogram. To overcome these shortcomings, we propose an image enhancement method based on a modified Laplacian pyramid framework that decomposes an image into band-pass images to improve both the global contrast and local information. For the global contrast, a novel robust HE is proposed to provide a well-balanced mapping function which effectively suppresses the quantum jump. For the local information, noise-reduced and adaptively gained high-pass images are applied to the resultant image. In qualitative and quantitative comparisons through experimental results, the proposed method shows natural and robust image quality and suitability for video sequences, achieving generally higher performance when compared to existing methods.
Double CO(2) fixation in photosynthesis-fermentation model enhances algal lipid synthesis for biodiesel production.

In this study, a photosynthesis-fermentation model was proposed to merge the positive aspects of autotrophs and heterotrophs. Microalga Chlorella protothecoides was grown autotrophically for CO(2) fixation and then metabolized heterotrophically for oil accumulation. Compared to typical heterotrophic metabolism, 69% higher lipid yield on glucose was achieved at the fermentation stage in the photosynthesis-fermentation model. An elementary flux mode study suggested that the enzyme Rubisco-catalyzed CO(2) re-fixation, enhancing carbon efficiency from sugar to oil. This result may explain the higher lipid yield. In this new model, 61.5% less CO(2) was released compared with typical heterotrophic metabolism. Immunoblotting and activity assay further showed that Rubisco functioned in sugar-bleaching cells at the fermentation stage. Overall, the photosynthesis-fermentation model with double CO(2) fixation in both photosynthesis and fermentation stages, enhances carbon conversion ratio of sugar to oil and thus provides an efficient approach for the production of algal lipid.
Designing of a AMBA-AHB Multilayer Bus matrix SelfMotivated Arbitration scheme

The AMBA-AHB Multilayer Bus matrix Self-Motivated Arbitration scheme proposed three methods for data transmiting from master to slave for on chip communication. Multilayer advanced high-performance bus (ML-AHB) busmatrix employs slave-side arbitration. Slave-side arbitration is different from master-side arbitration in terms of request and grant signals since, in the former, the master merely starts a burst transaction and waits for the slave response to proceed to the next transfer. Therefore, in the former, the unit of arbitration can be a transaction or a transfer. However, the ML-AHB busmatrix of ARM offers only transferbased fixed-pri-ority and round-robin arbitration schemes. In this paper, we propose the design and implementation of a flexible arbiter for the ML-AHB busmatrix to support three priority policies fixed priority, round robin, and dynamic priority and three data multiplexing modes transfer, transaction, and desired transfer length. In total, there are nine possible arbitration schemes. The proposed arbiter, which is self-motivated (SM), selects one of the nine possible arbitration schemes based upon the priority-level notifications and the desired transfer length from the masters so that arbitration leads to the maximum performance. Experimental results show that, although the area overhead of the proposed SM arbitration scheme is 9%–25% larger than those of the other arbitration schemes, our arbiter improves the throughput by 14%–62% compared to other schemes.
ORB-ODOM: Stereo and odometer sensor fusion for simultaneous localization and mapping

ORB-SLAM2 is one of the better-known open source SLAM implementations available. However, the dependence of visual features causes it to fail in featureless environments. With the present work, we propose a new technique to improve visual odometry results given by ORB-SLAM2 using a tightly Sensor Fusion approach to integrate camera and odometer data. In this work, we use odometer readings to improve the tracking results by adding graph constraints between frames and introduce a new method for preventing the tracking loss. We test our method using three different datasets, and show an improvement in the estimated trajectory, allowing a continuous tracking without losses.
Image deformation using moving least squares

We provide an image deformation method based on Moving Least Squares using various classes of linear functions including affine, similarity and rigid transformations. These deformations are realistic and give the user the impression of manipulating real-world objects. We also allow the user to specify the deformations using either sets of points or line segments, the later useful for controlling curves and profiles present in the image. For each of these techniques, we provide simple closed-form solutions that yield fast deformations, which can be performed in real-time.
Learning to Guide Decoding for Image Captioning

Recently, much advance has been made in image captioning, and an encoder-decoder framework has achieved outstanding performance for this task. In this paper, we propose an extension of the encoder-decoder framework by adding a component called guiding network. The guiding network models the attribute properties of input images, and its output is leveraged to compose the input of the decoder at each time step. The guiding network can be plugged into the current encoder-decoder framework and trained in an end-to-end manner. Hence, the guiding vector can be adaptively learned according to the signal from the decoder, making itself to embed information from both image and language. Additionally, discriminative supervision can be employed to further improve the quality of guidance. The advantages of our proposed approach are verified by experiments carried out on the MS COCO dataset.
Constructing Fast Network through Deconstruction of Convolution

Convolutional neural networks have achieved great success in various vision tasks; however, they incur heavy resource costs. By using deeper and wider networks, network accuracy can be improved rapidly. However, in an environment with limited resources (e.g., mobile applications), heavy networks may not be usable. This study shows that naive convolution can be deconstructed into a shift operation and pointwise convolution. To cope with various convolutions, we propose a new shift operation called active shift layer (ASL) that formulates the amount of shift as a learnable function with shift parameters. This new layer can be optimized end-to-end through backpropagation and it can provide optimal shift values. Finally, we apply this layer to a light and fast network that surpasses existing state-of-the-art networks. Code is available at https://github.com/jyh2986/Active-Shift.
Generating Highly Realistic Images of Skin Lesions with GANs

As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models. The ability to synthesize realistic looking images of skin lesions could act as a reliever for the aforementioned problems. Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking medical images, however limited to low resolution, whereas machine learning models for challenging tasks such as skin lesion segmentation or classification benefit from much higher resolution data. In this work, we successfully synthesize realistically looking images of skin lesions with GANs at such high resolution. Therefore, we utilize the concept of progressive growing, which we both quantitatively and qualitatively compare to other GAN architectures such as the DCGAN and the LAPGAN. Our results show that with the help of progressive growing, we can synthesize highly realistic dermoscopic images of skin lesions that even expert dermatologists find hard to distinguish from real ones.
KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition

KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources (such as a knowledge-base, a list of names or document-specific semantic annotations) and is used to train a conditional random field (CRF). Since those information sources are usually multilingual, KnowNER can be easily trained for a wide range of languages. In this paper, we show that the incorporation of deeper knowledge systematically boosts accuracy and compare KnowNER with state-of-the-art NER approaches across three languages (i.e., English, German and Spanish) performing amongst state-of-the art systems in all of them.