nils-herrmann/clean_scirepeval huggingface dataset:
Dependency Parsing with Partial Annotations: An Empirical Comparison

This paper describes and compares two straightforward approaches for dependency parsing with partial annotations (PA). The first approach is based on a forest-based training objective for two CRF parsers, i.e., a biaffine neural network graph-based parser (Biaffine) and a traditional log-linear graph-based parser (LLGPar). The second approach is based on the idea of constrained decoding for three parsers, i.e., a traditional linear graphbased parser (LGPar), a globally normalized neural network transition-based parser (GN3Par) and a traditional linear transition-based parser (LTPar). For the test phase, constrained decoding is also used for completing partial trees. We conduct experiments on Penn Treebank under three different settings for simulating PA, i.e., random, most uncertain, and divergent outputs from the five parsers. The results show that LLGPar is most effective in directly learning from PA, and other parsers can achieve best performance when PAs are completed into full trees by LLGPar.
Modeling of wind direction signals in polarimetric sea surface brightness temperatures

There has been an increasing interest in the applications of polarimctric n~icrowavc radiometers for ocean wind remote sensing. Aircraft and spaceborne radiometers have found significant wind direction signals in sea surface brightness temperatures, in addition to their sensitivities on wind speeds. However, it is not yet understood what physical scattering mechanisms produce the observed wind direction dependence. To this encl, polari]nctric microwave emissions from wind-generated sea surfaces are investigated with a polarimctric two-scale scattering model of sea surfaces, which relates the directional wind-wave spectrum to passive microwave signatures of sea surfaces. T)leoretical azimuthal modulations are found to agree well with experimental observations foI all Stokes paranletcrs from nearnadir to 65° incidence angles. The up/downwind asymmetries of brightness temperatures are interpreted usiIlg the hydrodynamic modulation. The contributions of Bragg scattering by short waves, geometric optics scattering by long waves and sea foam are examined. The geometric optics scattering mechanism underestimates the directicmal signals in the first three Stokes paranletcrs, and most importantly it predicts no signals in the fourth Stokes parameter (V), in disagreement with experimental datfi. In contrast, the Bragg scattering and and contributes to most of the wind direction signals from the two-scale model correctly predicts the phase changes of tl}e up/crosswind asymmetries in 7j U from middle to high incidence angles. The accuracy of the Bragg scattering theory for radiometric emission from water ripples is corroborated by the numerical Monte Carlo simulation of rough surface scattering. ‘I’his theoretical interpretation indicates the potential use of ]Jolarimctric brightness temperatures for retrieving the directional wave spectrum of capillary waves.
Design of a real time automatic speech recognition system using Modified One Against All SVM classifier

0141-9331/$ see front matter 2011  ⇑ Corresponding author. E-mail address: jmanikandan.nitt@gmail.com (J. M In this paper, Texas Instruments TMS320C6713 DSP based real-time speech recognition system using Modified One Against All Support Vector Machine (SVM) classifier is proposed. The major contributions of this paper are: the study and evaluation of the performance of the classifier using three feature extraction techniques and proposal for minimizing the computation time for the classifier. From this study, it is found that the recognition accuracies of 93.33%, 98.67% and 96.67% are achieved for the classifier using Mel Frequency Cepstral Coefficients (MFCC) features, zerocrossing (ZC) and zerocrossing with peak amplitude (ZCPA) features respectively. To reduce the computation time required for the systems, two techniques – one using optimum threshold technique for the SVM classifier and another using linear assembly are proposed. The ZC based system requires the least computation time and the above techniques reduce the execution time by a factor of 6.56 and 5.95 respectively. For the purpose of comparison, the speech recognition system is also implemented using Altera Cyclone II FPGA with Nios II soft processor and custom instructions. Of the two approaches, the DSP approach requires 87.40% less number of clock cycles. Custom design of the recognition system on the FPGA without using the soft-core processor would have resulted in less computational complexity. The proposed classifier is also found to reduce the number of support vectors by a factor of 1.12–3.73 when applied to speaker identification and isolated letter recognition problems. The techniques proposed here can be adapted for various other SVM based pattern recognition systems.
The role of marine omega-3 (n-3) fatty acids in inflammatory processes, atherosclerosis and plaque stability.

Atherosclerosis has an important inflammatory component and acute cardiovascular events can be initiated by inflammatory processes occurring in advanced plaques. Fatty acids influence inflammation through a variety of mechanisms; many of these are mediated by, or associated with, the fatty acid composition of cell membranes. Human inflammatory cells are typically rich in the n-6 fatty acid arachidonic acid, but the contents of arachidonic acid and of the marine n-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) can be altered through oral administration of EPA and DHA. Eicosanoids produced from arachidonic acid have roles in inflammation. EPA also gives rise to eicosanoids and these are usually biologically weak. EPA and DHA give rise to resolvins which are anti-inflammatory and inflammation resolving. EPA and DHA also affect production of peptide mediators of inflammation (adhesion molecules, cytokines, etc.). Thus, the fatty acid composition of human inflammatory cells influences their function; the contents of arachidonic acid, EPA and DHA appear to be especially important. The anti-inflammatory effects of marine n-3 polyunsaturated fatty acids (PUFAs) may contribute to their protective actions towards atherosclerosis and plaque rupture.
Physiology of soccer: an update.

Soccer is the most popular sport in the world and is performed by men and women, children and adults with different levels of expertise. Soccer performance depends upon a myriad of factors such as technical/biomechanical, tactical, mental and physiological areas. One of the reasons that soccer is so popular worldwide is that players may not need to have an extraordinary capacity within any of these performance areas, but possess a reasonable level within all areas. However, there are trends towards more systematic training and selection influencing the anthropometric profiles of players who compete at the highest level. As with other activities, soccer is not a science, but science may help improve performance. Efforts to improve soccer performance often focus on technique and tactics at the expense of physical fitness. During a 90-minute game, elite-level players run about 10 km at an average intensity close to the anaerobic threshold (80-90% of maximal heart rate). Within this endurance context, numerous explosive bursts of activity are required, including jumping, kicking, tackling, turning, sprinting, changing pace, and sustaining forceful contractions to maintain balance and control of the ball against defensive pressure. The best teams continue to increase their physical capacities, whilst the less well ranked have similar values as reported 30 years ago. Whether this is a result of fewer assessments and training resources, selling the best players, and/or knowledge of how to perform effective exercise training regimens in less well ranked teams, is not known. As there do exist teams from lower divisions with as high aerobic capacity as professional teams, the latter factor probably plays an important role. This article provides an update on the physiology of soccer players and referees, and relevant physiological tests. It also gives examples of effective strength- and endurance-training programmes to improve on-field performance. The cited literature has been accumulated by computer searching of relevant databases and a review of the authors' extensive files. From a total of 9893 papers covering topics discussed in this article, 843 were selected for closer scrutiny, excluding studies where information was redundant, insufficient or the experimental design was inadequate. In this article, 181 were selected and discussed. The information may have important implications for the safety and success of soccer players and hopefully it should be understood and acted upon by coaches and individual soccer players.
A selective physical layer network coding for wireless two-way relaying

In this paper, we propose a selective physical layer network coding (SPNC) scheme in two-way relay channel (TWRC) for binary phase shift keying (BPSK) modulation over Rayleigh fading channels. Physical layer network coding (PNC) shows average throughput gain over traditional relaying in two-way relay systems, but not in all channel conditions PNC performs better than traditional relaying. The key idea of SPNC is to combine the advantage of PNC and traditional relaying in fading channels to improve throughput of the system. We show that in channel states when PNC fails to correctly detect XORed data from superimposed signal at the relay, it is still possible to detect one of the sources' data to retrieve up to half of the achievable throughput by a so called single node detection (SND) scheme. Also, we analyze error performance and throughput gain of SPNC. Simulation results show that SPNC achieves significant throughput gain over PNC in Rayleigh fading channels.
Traditional fermented food and beverages for improved livelihoods

The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations (FAO) concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specific companies or products of manufacturers, whether or not these have been patented, does not imply that these have been endorsed or recommended by FAO in preference to others of a similar nature that are not mentioned. The views expressed in this information product are those of the author(s) and do not necessarily reflect the views of FAO.
Tour the world: Building a web-scale landmark recognition engine

Modeling and recognizing landmarks at world-scale is a useful yet challenging task. There exists no readily available list of worldwide landmarks. Obtaining reliable visual models for each landmark can also pose problems, and efficiency is another challenge for such a large scale system. This paper leverages the vast amount of multimedia data on the Web, the availability of an Internet image search engine, and advances in object recognition and clustering techniques, to address these issues. First, a comprehensive list of landmarks is mined from two sources: (1) ~20 million GPS-tagged photos and (2) online tour guide Web pages. Candidate images for each landmark are then obtained from photo sharing Websites or by querying an image search engine. Second, landmark visual models are built by pruning candidate images using efficient image matching and unsupervised clustering techniques. Finally, the landmarks and their visual models are validated by checking authorship of their member images. The resulting landmark recognition engine incorporates 5312 landmarks from 1259 cities in 144 countries. The experiments demonstrate that the engine can deliver satisfactory recognition performance with high efficiency.
Rapid Data Acquisition System for Complex Algorithm Testing in Plastic Molding Industry

Injection molding is a very complicated process to monitor and control. With its high complexity and many process parameters, the optimization of these systems is a very challenging problem. To meet the requirements and costs demanded by the market, there has been an intense development and research with the aim to maintain the process under control. This paper outlines the latest advances in necessary algorithms for plastic injection process and monitoring, and also a flexible data acquisition system that allows rapid implementation of complex algorithms to assess their correct performance and can be integrated in the quality control process. This is the main topic of this paper. Finally, to demonstrate the performance achieved by this combination, a real case of use is presented. Plastic injection, machine learning, rapid complex algorithm prototyping.
Artificial neural network based mobile robot navigation

This paper describes a dynamic artificial neural network based mobile robot motion and path planning system. The method is able to navigate a robot car on flat surface among static and moving obstacles, from any starting point to any endpoint. The motion controlling ANN is trained online with an extended backpropagation through time algorithm, which uses potential fields for obstacle avoidance. The paths of the moving obstacles are predicted with other ANNs for better obstacle avoidance. The method is presented through the realization of the navigation system of a mobile robot.
iHandRehab: An interactive hand exoskeleton for active and passive rehabilitation

This paper presents an interactive exoskeleton device for hand rehabilitation, iHandRehab, which aims to satisfy the essential requirements for both active and passive rehabilitation motions. iHandRehab is comprised of exoskeletons for the thumb and index finger. These exoskeletons are driven by distant actuation modules through a cable/sheath transmission mechanism. The exoskeleton for each finger has 4 degrees of freedom (DOF), providing independent control for all finger joints. The joint motion is accomplished by a parallelogram mechanism so that the joints of the device and their corresponding finger joints have the same angular displacement when they rotate. Thanks to this design, the joint angles can be measured by sensors real time and high level motion control is therefore made very simple without the need of complicated kinematics. The paper also discusses important issues when the device is used by different patients, including its adjustable joint range of motion (ROM) and adjustable range of phalanx length (ROPL). Experimentally collected data show that the achieved ROM is close to that of a healthy hand and the ROPL covers the size of a typical hand, satisfying the size need of regular hand rehabilitation. In order to evaluate the performance when it works as a haptic device in active mode, the equivalent moment of inertia (MOI) of the device is calculated. The results prove that the device has low inertia which is critical in order to obtain good backdrivability. Experimental analysis shows that the influence of friction accounts for a large portion of the driving torque and warrants future investigation.
AquaLog: An ontology-driven question answering system for organizational semantic intranets

The semantic web vision is one in which rich, ontology-based semantic markup will become widely available. The availability of semantic arkup on the web opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which akes queries expressed in natural language and an ontology as input, and returns answers drawn from one or more knowledge bases (KBs). We ay that AquaLog is portable because the configuration time required to customize the system for a particular ontology is negligible. AquaLog resents an elegant solution in which different strategies are combined together in a novel way. It makes use of the GATE NLP platform, string etric algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target KB. oreover it also includes a learning component, which ensures that the performance of the system improves over the time, in response to the articular community jargon used by end users.
Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

Long noncoding RNA (lncRNA) is a kind of noncoding RNA with length more than 200 nucleotides, which aroused interest of people in recent years. Lots of studies have confirmed that human genome contains many thousands of lncRNAs which exert great influence over some critical regulators of cellular process. With the advent of high-throughput sequencing technologies, a great quantity of sequences is waiting for exploitation. Thus, many programs are developed to distinguish differences between coding and long noncoding transcripts. Different programs are generally designed to be utilised under different circumstances and it is sensible and practical to select an appropriate method according to a certain situation. In this review, several popular methods and their advantages, disadvantages, and application scopes are summarised to assist people in employing a suitable method and obtaining a more reliable result.
Pixel-Level Hand Detection in Ego-centric Videos

We address the task of pixel-level hand detection in the context of ego-centric cameras. Extracting hand regions in ego-centric videos is a critical step for understanding hand-object manipulation and analyzing hand-eye coordination. However, in contrast to traditional applications of hand detection, such as gesture interfaces or sign-language recognition, ego-centric videos present new challenges such as rapid changes in illuminations, significant camera motion and complex hand-object manipulations. To quantify the challenges and performance in this new domain, we present a fully labeled indoor/outdoor ego-centric hand detection benchmark dataset containing over 200 million labeled pixels, which contains hand images taken under various illumination conditions. Using both our dataset and a publicly available ego-centric indoors dataset, we give extensive analysis of detection performance using a wide range of local appearance features. Our analysis highlights the effectiveness of sparse features and the importance of modeling global illumination. We propose a modeling strategy based on our findings and show that our model outperforms several baseline approaches.
PWM Inverters Using Split-Wound Coupled Inductors

The number of output-voltage levels available in pulsewidth-modulated (PWM) voltage-source inverters can be increased by inserting a split-wound coupled inductor between the upper and lower switches in each inverter leg. Interleaved PWM control of both inverter-leg switches produces three-level PWM voltage waveforms at the center tap of the coupled inductor winding, representing the inverter-leg output terminal, with a PWM frequency twice the switching frequency. The winding leakage inductance is in series with the output terminal, with the main magnetizing inductance filtering the instantaneous PWM-cycle voltage differences between the upper and lower switches. Since PWM dead-time signal delays can be removed, higher device switching frequencies and higher fundamental output voltages are made possible. The proposed inverter topologies produce five-level PWM voltage waveforms between two inverter-leg terminals with a PWM frequency up to four times higher than the inverter switching frequency. This is achieved with half the number of switches used in alternative schemes. This paper uses simulated and experimental results to illustrate the operation of the proposed inverter structures.
Stupid Tutoring Systems, Intelligent Humans

The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development, and discuss the potential of educational data mining driving human decision-making as an alternate paradigm for online learning, focusing on intelligence amplification rather than artificial intelligence.
Proactively Detecting Distributed Denial of Service Attacks Using Source IP Address Monitoring

In this paper, we propose a simple but robust scheme to detect denial of service attacks (including distributed denial of service attacks) by monitoring the increase of new IP addresses. Unlike previous proposals for bandwidth attack detection schemes which are based on monitoring the traffic volume, our scheme is very effective for highly distributed denial of service attacks. Our scheme exploits an inherent feature of DDoS attacks, which makes it hard for the attacker to counter this detection scheme by changing their attack signature. Our scheme uses a sequential nonparametric change point detection method to improve the detection accuracy without requiring a detailed model of normal and attack traffic. We demonstrate that we can achieve high detection accuracy on a range of different network packet traces.
Concentration and regularization of random graphs

This paper studies how close random graphs are typically to their expectations. We interpret this question through the concentration of the adjacency and Laplacian matrices in the spectral norm. We study inhomogeneous Erdös-Rényi random graphs on n vertices, where edges form independently and possibly with different probabilities pij . Sparse random graphs whose expected degrees are o(logn) fail to concentrate; the obstruction is caused by vertices with abnormally high and low degrees. We show that concentration can be restored if we regularize the degrees of such vertices, and one can do this in various ways. As an example, let us reweight or remove enough edges to make all degrees bounded above by O(d) where d = maxnpij . Then we show that the resulting adjacency matrix A ′ concentrates with the optimal rate: ‖A′ − EA‖ = O( √ d). Similarly, if we make all degrees bounded below by d by adding weight d/n to all edges, then the resulting Laplacian concentrates with the optimal rate: ‖L(A′)−L(EA′)‖ = O(1/ √ d). Our approach is based on Grothendieck-Pietsch factorization, using which we construct a new decomposition of random graphs. These results improve and considerably simplify the recent work of E. Levina and the authors. We illustrate the concentration results with an application to the community detection problem in the analysis of networks.
A Consensus Statement on the Use of Ketamine in the Treatment of Mood Disorders.

Several studies now provide evidence of ketamine hydrochloride's ability to produce rapid and robust antidepressant effects in patients with mood and anxiety disorders that were previously resistant to treatment. Despite the relatively small sample sizes, lack of longer-term data on efficacy, and limited data on safety provided by these studies, they have led to increased use of ketamine as an off-label treatment for mood and other psychiatric disorders.



This review and consensus statement provides a general overview of the data on the use of ketamine for the treatment of mood disorders and highlights the limitations of the existing knowledge. While ketamine may be beneficial to some patients with mood disorders, it is important to consider the limitations of the available data and the potential risk associated with the drug when considering the treatment option.



The suggestions provided are intended to facilitate clinical decision making and encourage an evidence-based approach to using ketamine in the treatment of psychiatric disorders considering the limited information that is currently available. This article provides information on potentially important issues related to the off-label treatment approach that should be considered to help ensure patient safety.