nils-herrmann/clean_scirepeval huggingface dataset:
How I Treat How I treat patients with a history of heparin-induced thrombocytopenia

Heparin-induced thrombocytopenia, or HIT, represents an antibodymediated adverse drug reaction characterized by platelet-activating immunoglobulin G (IgG) that recognize platelet factor 4 (PF4)/heparin complexes. Because HIT is a clinical-pathological disorder, diagnosis requires integrating clinical and laboratory features. HIT is a profound hypercoagulability state strongly associated with thrombosis.Moreover,HIT is relativelycommon: although it occurs in only ;0.2% of hospitalized patients undergoing any heparin exposure, it is more common in certain high-risk patient populations. For example, the frequency of HIT is;5% in postorthopedic surgery patients receiving unfractionated heparin (UFH) for 10 to 14 days. In cardiac surgery patients who are given intraoperative UFH (for cardiopulmonary bypass [CPB]) and who receive postoperative UFH thromboprophylaxis, the frequency is;1% to 2%. The ubiquity of heparin and the relatively common occurrence of HIT explain the increasing numbers of patients with a history of HIT. Several issues are relevant when considering anticoagulant management of a patient with a history of HIT. First, there are 3 conditions—cardiac surgery, vascular surgery, hemodialysis—for which UFH is the clear anticoagulant of choice. Second, there are different laboratory tests to detect anti-PF4/heparin antibodies, for which implications regarding antibody pathogenicity vary, particularly for immunoassays versus platelet activation assays. Third, anti-PF4/ heparin antibodies are among the most transient in clinical medicine: a patient with HIT will generally have antibodies detectable by both PF4-dependent immunoassay and platelet activation assay at the time of acute HIT, but these antibodies can be difficult to detect as little as a few weeks or months later. Fourth, a minimum of 5 days is required to generate, or to regenerate, pathogenic platelet-activating antibodies. Thus, for certain patient populations, deliberate and planned reexposure to heparin has a rational basis. Indeed, this strategy was pioneered by Pötzsch et al, who described unremarkable outcomes of heparin reexposure for CPB in 10 patients with previous HIT who were antibody negative at reexposure (and who remained antibody negative 10 days after reexposure). For many clinical situations, alternative non-heparin anticoagulation is well established (eg, treatment and/or prevention of venous thromboembolismormanagement of acute coronary syndrome). Thus, one can usually identify 1 or more appropriate options among the large panoply of agents, such as direct oral anticoagulants, either factor Xa (rivaroxaban, apixaban, edoxaban) or thrombin (dabigatran) inhibiting, or a parenteral agent (anti-Xa [fondaparinux, danaparoid] or anti-IIa [bivalirudin, argatroban]). For these clinical settings, we do not discuss UFH and low molecular weight heparin (LMWH) as management options for patients with a previous history of HIT.
Sentiment Analysis and Opinion Mining : A Survey

Due to the sheer volume of opinion rich web resources such as discussion forum, review sites , blogs and news corpora available in digital form, much of the current research is focusing on the area of sentiment analysis. People are intended to develop a system that can identify and classify opinion or sentiment as represented in an electronic text. An accurate method for predicting sentiments could enable us, to extract opinions from the internet and predict online customer’s preferences, which could prove valuable for economic or marketing research. Till now, there are few different problems predominating in this research community, namely, sentiment classification, feature based classification and handling negations. This paper presents a survey covering the techniques and methods in sentiment analysis and challenges appear in the field.
Image Matters: Visually Modeling User Behaviors Using Advanced Model Server

In Taobao, the largest e-commerce platform in China, billions of items are provided and typically displayed with their images.For better user experience and business effectiveness, Click Through Rate (CTR) prediction in online advertising system exploits abundant user historical behaviors to identify whether a user is interested in a candidate ad. Enhancing behavior representations with user behavior images will help understand user's visual preference and improve the accuracy of CTR prediction greatly. So we propose to model user preference jointly with user behavior ID features and behavior images. However, training with user behavior images brings tens to hundreds of images in one sample, giving rise to a great challenge in both communication and computation. To handle these challenges, we propose a novel and efficient distributed machine learning paradigm called Advanced Model Server (AMS). With the well-known Parameter Server (PS) framework, each server node handles a separate part of parameters and updates them independently. AMS goes beyond this and is designed to be capable of learning a unified image descriptor model shared by all server nodes which embeds large images into low dimensional high level features before transmitting images to worker nodes. AMS thus dramatically reduces the communication load and enables the arduous joint training process. Based on AMS, the methods of effectively combining the images and ID features are carefully studied, and then we propose a Deep Image CTR Model. Our approach is shown to achieve significant improvements in both online and offline evaluations, and has been deployed in Taobao display advertising system serving the main traffic.
SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media

In this work, we present SAMAR, a system for Subjectivity and Sentiment Analysis (SSA) for Arabic social media genres. We investigate: how to best represent lexical information; whether standard features are useful; how to treat Arabic dialects; and, whether genre specific features have a measurable impact on performance. Our results suggest that we need individualized solutions for each domain and task, but that lemmatization is a feature in all the best approaches.
Effectiveness of an educational video as an instrument to refresh and reinforce the learning of a nursing technique: a randomized controlled trial

The Undergraduate Nursing Course has been using videos for the past year or so. Videos are used for many different purposes such as during lessons, nurse refresher courses, reinforcement, and sharing and comparison of knowledge with the professional and scientific community. The purpose of this study was to estimate the efficacy of the video (moving an uncooperative patient from the supine to the lateral position) as an instrument to refresh and reinforce nursing techniques. A two-arm randomized controlled trial (RCT) design was chosen: both groups attended lessons in the classroom as well as in the laboratory; a month later while one group received written information as a refresher, the other group watched the video. Both groups were evaluated in a blinded fashion. A total of 223 students agreed to take part in the study. The difference observed between those who had seen the video and those who had read up on the technique turned out to be an average of 6.19 points in favour of the first (P < 0.05). The results of the RCT demonstrated that students who had seen the video were better able to apply the technique, resulting in a better performance. The video, therefore, represents an important tool to refresh and reinforce previous learning.
A powerful on line ABTS+–CE-DAD method to screen and quantify major antioxidants for quality control of Shuxuening Injection

A novel method of on-line 2,2′-Azinobis-(3-ethylbenzthiazoline-6-sulphonate)-Capillary Electrophoresis-Diode Array Detector (on-line ABTS+-CE-DAD) was developed to screen the major antioxidants from complex herbal medicines. ABTS+, one of well-known oxygen free radicals was firstly integrated into the capillary. For simultaneously detecting and separating ABTS+ and chemical components of herb medicines, some conditions were optimized. The on-line ABTS+-CE-DAD method has successfully been used to screen the main antioxidants from Shuxuening injection (SI), an herbal medicines injection. Under the optimum conditions, nine ingredients of SI including clitorin, rutin, isoquercitrin, Quercetin-3-O-D-glucosyl]-(1-2)-L-rhamnoside, kaempferol-3-O-rutinoside, kaempferol-7-O-β-D-glucopyranoside, apigenin-7-O-Glucoside, quercetin-3-O-[2-O-(6-O-p-hydroxyl-E-coumaroyl)-D-glucosyl]-(1-2)-L-rhamnoside, 3-O-{2-O-[6-O-(p-hydroxyl-E-coumaroyl)-glucosyl]}-(1-2) rhamnosyl kaempfero were separated and identified as the major antioxidants. There is a linear relationship between the total amount of major antioxidants and total antioxidative activity of SI with a linear correlation coefficient of 0.9456. All the Relative standard deviations of recovery, precision and stability were below 7.5%. Based on these results, these nine ingredients could be selected as combinatorial markers to evaluate quality control of SI. It was concluded that on-line ABTS+-CE-DAD method was a simple, reliable and powerful tool to screen and quantify active ingredients for evaluating quality of herbal medicines.
A Memetic Fingerprint Matching Algorithm

Minutiae point pattern matching is the most common approach for fingerprint verification. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem, both with respect to recovering the optimal alignment and the construction of an adequate matching function. In this paper, we develop a memetic fingerprint matching algorithm (MFMA) which aims to identify the optimal or near optimal global matching between two minutiae sets. Within the MFMA, we first introduce an efficient matching operation to produce an initial population of local alignment configurations by examining local features of minutiae. Then, we devise a hybrid evolutionary procedure by combining the use of the global search functionality of a genetic algorithm with a local improvement operator to search for the optimal or near optimal global alignment. Finally, we define a reliable matching function for fitness computation. The proposed algorithm was evaluated by means of a series of experiments conducted on the FVC2002 database and compared with previous work. Experimental results confirm that the MFMA is an effective and practical matching algorithm for fingerprint verification. The algorithm is faster and more accurate than a traditional genetic-algorithm-based method. It is also more accurate than a number of other methods implemented for comparison, though our method generally requires more computational time in performing fingerprint matching.
A novel ZCS bidirectional buck-boost DC-DC converter for energy storage applications

The present article proposes and describes a new ZCS non-isolated bidirectional buck-boost DC-DC converter for energy storage applications in electric vehicles. Usually, the conventional converters are adapted with an auxiliary resonant cell to provide the zero current switching turn-on/turn-off condition for the main switching devices. The advantages of proposed converter has reduced switching losses, reduced component count and improved efficiency. The proposed converter operates either in boost or buck mode. This paper mainly deals with the operating principles, analysis and design simulations of the proposed converter in order to prove the better soft-switching capability, reduced switching losses and efficiency improvement than the conventional converter.
WARPWING: A complete open source control platform for miniature robots

The electronics packages for many robot control systems have very similar requirements, yet are often redesigned for each custom application. To reduce wasted time and effort, the project presented in this paper (the Wireless Autonomous Robot Platform with Inertial Navigation and Guidance, WARP-WING) is intended to create a complete and easily customizable general purpose control system for miniature robotic systems, in particular micro air vehicles. In its default configuration, hardware designs, firmware, and software are all available to deliver an out-of-the-box robot control solution comprising 6 degree-of-freedom inertial sensors, a microprocessor, and wireless communication, along with general purpose input/output pins, serial ports, and control outputs for interfacing to additional sensors and actuators. The entire project is open source and a process is in place to enable modification of any component, allowing for easy adaptation to any need. WARPWING is already in use in a number of labs, with each research group contributing its expertise to enhance the platform and make such modifications available to others as well.
Active shields: a new approach to shielding global wires

A new shielding scheme, active shielding, is proposed for reducing delays on interconnects. As opposed to conventional (passive) shielding, the active shielding approach helps to speed up signal propagation on a wire by ensuring in-phase switching of adjacent nets. Results show that the active shielding scheme improves performance by up to 16% compared to passive shields and up to 29% compared to unshielded wires. When signal slopes at the end of the line are compared, savings of up to 38% and 27% can be achieved when compared to passive shields and unshielded wires, respectively.
ASPIER: An Automated Framework for Verifying Security Protocol Implementations

We present ASPIER -- the first framework that combines software model checking with a standard protocol security model to automatically analyze authentication and secrecy properties of protocol implementations in C. The technical approach extends the iterative abstraction-refinement methodology for software model checking with a domain-specific protocol and symbolic attacker model. We have implemented the ASPIER tool and used it to verify authentication and secrecy properties of a part of an industrial strength protocol implementation -- the handshake in OpenSSL -- for configurations consisting of up to 3 servers and 3 clients. We have also implemented two distinct methods for reasoning about attacker message derivations, and evaluated them in the context of OpenSSL verification. ASPIER detected the "version-rollback" vulnerability in OpenSSL 0.9.6c source code and successfully verified the implementation when clients and servers are only willing to run SSL 3.0.
End-to-End Differentiable Adversarial Imitation Learning

Generative Adversarial Networks (GANs) have been successfully applied to the problem of policy imitation in a model-free setup. However, the computation graph of GANs, that include a stochastic policy as the generative model, is no longer differentiable end-to-end, which requires the use of high-variance gradient estimation. In this paper, we introduce the Modelbased Generative Adversarial Imitation Learning (MGAIL) algorithm. We show how to use a forward model to make the computation fully differentiable, which enables training policies using the exact gradient of the discriminator. The resulting algorithm trains competent policies using relatively fewer expert samples and interactions with the environment. We test it on both discrete and continuous action domains and report results that surpass the state-of-the-art.
DNN-Based Speaker Clustering for Speaker Diarisation

Speaker diarisation, the task of answering “who spoke when?”, is often considered to consist of three independent stages: speech activity detection, speaker segmentation and speaker clustering. These represent the separation of speech and nonspeech, the splitting into speaker homogeneous speech segments, followed by grouping together those which belong to the same speaker. This paper is concerned with speaker clustering, which is typically performed by bottom-up clustering using the Bayesian information criterion (BIC). We present a novel semi-supervised method of speaker clustering based on a deep neural network (DNN) model. A speaker separation DNN trained on independent data is used to iteratively relabel the test data set. This is achieved by reconfiguration of the output layer, combined with fine tuning in each iteration. A stopping criterion involving posteriors as confidence scores is investigated. Results are shown on a meeting task (RT07) for single distant microphones and compared with standard diarisation approaches. The new method achieves a diarisation error rate (DER) of 14.8%, compared to a baseline of 19.9%.
12-2 A 2 . 6-GHzE . 2-GHz Frequency Synthesizer in 0 . 4pm CMOS Technology

a 0.4-pm digit l CMOS technology entails many difficulties This paper describes the design of a CMOS frequency synthesizer targeting wireless local area network applications in the 5-GHz range. Based on an integer-N architecture, the synthesizer produces a 5.2-GHz output as well as the quadrature phases of a 2.6-GHz carrier. Fabricated in a 0.4-pm digital CMOS technology, the circuit provides a channel spacing of 23 MHz at 5.2 GHz while exhibiting a phase noise of -115 dBdHz at 2.6 GHz and -100 dBdHz at 5.2 GHz at 10-MHz offset. The reference sidebands are at -50 dBc at 2.6 GHz and the power dissipation from a 2.6-V supply is 47 mW. at both the architecture and the circuit level. The high center frequency of the voltage-controlled oscillator (VCO), the poor quality of inductors due to skin effect and substrate loss, the limited tuning range, the nonlinearity of the VCO inputloutput characteristic, the high speed required of the dual-modulus divider, the mismatches in the charge pump, and the implementation of the loop filter are among the issues encountered in this design. In order to relax some of the synthesizer requirements, the transceiver and the synthesizer have been designed concurrently. Fig. 1 shows the transceiver architecture  and
A CPPS Architecture approach for Industry 4.0

New demands, coming from the industry 4.0 concept of the near future production systems have to be fulfilled in the coming years. Seamless integration of current technologies with new ones is mandatory. The concept of Cyber-Physical Production Systems (CPPS) is the core of the new control and automation distributed systems. However, it is necessary to provide the global production system with integrated architectures that make it possible. This work analyses the requirements and proposes a model-based architecture and technologies to make the concept a reality.
Two secret sharing schemes based on Boolean operations

Traditional secret sharing schemes involve complex computation. A visual secret sharing (VSS) scheme decodes the secret without computation, but each shadow is m times as big as the original. Probabilistic VSS solved the computation complexity and space complexity problems at once. In this paper we propose a probabilistic (2, n) scheme for binary images and a deterministic (n, n) scheme for grayscale images. Both use simple Boolean operations and both have no pixel expansion. The (2, n) scheme provides a better contrast and significantly smaller recognized areas than other methods. The (n, n) scheme gives an exact reconstruction. 2006 Pattern Recognition Society.
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clear-weather scenes. Extending semantic segmentation methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both labeled synthetic foggy data and unlabeled real foggy data. The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog). CMAda is extensible to other adverse conditions and provides a new paradigm for learning with synthetic data and unlabeled real data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) a novel fog densification method to densify the fog in real foggy scenes without known depth; and 4) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 40 images under dense fog. Our experiments show that 1) our fog simulation and fog density estimator outperform their state-of-theart counterparts with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly, benefiting both from our synthetic and real foggy data. The datasets and code are available at the project website. D. Dai · C. Sakaridis · S. Hecker · L. Van Gool ETH Zürich, Zurich, Switzerland L. Van Gool KU Leuven, Leuven, Belgium
An empirical study of regression test application frequency

Regression testing is an expensive maintenance process used to revalidate modified software. Regression test selection (RTS) techniques try to lower the cost of regression testing by selecting and running a subset of the existing test cases. Many such techniques have been proposed and initial studies show that they can produce savings. We believe, however, that issues such as the frequency with which testing is done have a strong effect on the behavior of these techniques. Therefore, we conducted an experiment to assess the effects of test application frequency on the costs and benefits of regression test selection techniques. Our results expose essential tradeoffs that should be considered when using these techniques over a series of software releases.
Creating custom containers with generative techniques

Component containers are a key part of mainstream component technologies, and play an important role in separating non-functional concerns from the core component logic. This paper addresses two different aspects of containers. First, it shows how generative programming techniques, using AspectC++ and meta-programming, can be used to generate stubs and skeletons without the need for special compilers or interface description languages. Second, the paper describes an approach to create custom containers by composing different non-functional features. Unlike component technologies such as EJB, which only support a predefined set of container types, this approach allows different combinations of non-functional features to be composed in a container to meet the application needs.
Obesity and cannabis use: results from 2 representative national surveys.

The role of cannabis and endocannabinoids in appetite regulation has been extensively studied, but the association of cannabis use with weight in the general population is not known. The authors used data from 2 representative epidemiologic studies of US adults aged 18 years or older, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; 2001-2002) and the National Comorbidity Survey-Replication (NCS-R; 2001-2003), to estimate the prevalence of obesity as a function of cannabis use. The adjusted prevalences of obesity in the NESARC and the NCS-R were 22.0% and 25.3%, respectively, among participants reporting no use of cannabis in the past 12 months and 14.3% and 17.2%, respectively, among participants reporting the use of cannabis at least 3 days per week. These differences were not accounted for by tobacco smoking status. Additionally, after adjustment for sex and age, the use of cannabis was associated with body mass index differences in both samples. The authors conclude that the prevalence of obesity is lower in cannabis users than in nonusers.