nils-herrmann/clean_scirepeval huggingface dataset:
Tokyo Guidelines 2018: initial management of acute biliary infection and flowchart for acute cholangitis.

The initial management of patients with suspected acute biliary infection starts with the measurement of vital signs to assess whether or not the situation is urgent. If the case is judged to be urgent, initial medical treatment should be started immediately including respiratory/circulatory management if required, without waiting for a definitive diagnosis. The patient's medical history is then taken; an abdominal examination is performed; blood tests, urinalysis, and diagnostic imaging are carried out; and a diagnosis is made using the diagnostic criteria for cholangitis/cholecystitis. Once the diagnosis has been confirmed, initial medical treatment should be started immediately, severity should be assessed according to the severity grading criteria for acute cholangitis/cholecystitis, and the patient's general status should be evaluated. For mild acute cholangitis, in most cases initial treatment including antibiotics is sufficient, and most patients do not require biliary drainage. However, biliary drainage should be considered if a patient does not respond to initial treatment. For moderate acute cholangitis, early endoscopic or percutaneous transhepatic biliary drainage is indicated. If the underlying etiology requires treatment, this should be provided after the patient's general condition has improved; endoscopic sphincterotomy and subsequent choledocholithotomy may be performed together with biliary drainage. For severe acute cholangitis, appropriate respiratory/circulatory management is required. Biliary drainage should be performed as soon as possible after the patient's general condition has been improved by initial treatment and respiratory/circulatory management. Free full articles and mobile app of TG18 are available at: http://www.jshbps.jp/modules/en/index.php?content_id=47. Related clinical questions and references are also included.
Chemical properties of biocrude oil from the hydrothermal liquefaction of Spirulina algae, swine manure, and digested anaerobic sludge.

This study explores the influence of wastewater feedstock composition on hydrothermal liquefaction (HTL) biocrude oil properties and physico-chemical characteristics. Spirulina algae, swine manure, and digested sludge were converted under HTL conditions (300°C, 10-12 MPa, and 30 min reaction time). Biocrude yields ranged from 9.4% (digested sludge) to 32.6% (Spirulina). Although similar higher heating values (32.0-34.7 MJ/kg) were estimated for all product oils, more detailed characterization revealed significant differences in biocrude chemistry. Feedstock composition influenced the individual compounds identified as well as the biocrude functional group chemistry. Molecular weights tracked with obdurate carbohydrate content and followed the order of Spirulina<swine manure<digested sludge. A similar trend was observed in boiling point distributions and the long branched aliphatic contents. These findings show the importance of HTL feedstock composition and highlight the need for better understanding of biocrude chemistries when considering bio-oil uses and upgrading requirements.
Adversarial Data Mining: Big Data Meets Cyber Security

As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, manually analyzing these collected data to detect important cyber security events become impossible. Hence, data mining techniques are becoming an essential tool for real-world cyber security applications. For example, a report from Gartner [gartner12] claims that "Information security is becoming a big data analytics problem, where massive amounts of data will be correlated, analyzed and mined for meaningful patterns". Of course, data mining/analytics is a means to an end where the ultimate goal is to provide cyber security analysts with prioritized actionable insights derived from big data. This raises the question, can we directly apply existing techniques to cyber security applications? One of the most important differences between data mining for cyber security and many other data mining applications is the existence of malicious adversaries that continuously adapt their behavior to hide their actions and to make the data mining models ineffective. Unfortunately, traditional data mining techniques are insufficient to handle such adversarial problems directly. The adversaries adapt to the data miner's reactions, and data mining algorithms constructed based on a training dataset degrades quickly. To address these concerns, over the last couple of years new and novel data mining techniques which is more resilient to such adversarial behavior are being developed in machine learning and data mining community. We believe that lessons learned as a part of this research direction would be beneficial for cyber security researchers who are increasingly applying machine learning and data mining techniques in practice.
 To give an overview of recent developments in adversarial data mining, in this three hour long tutorial, we introduce the foundations, the techniques, and the applications of adversarial data mining to cyber security applications. We first introduce various approaches proposed in the past to defend against active adversaries, such as a minimax approach to minimize the worst case error through a zero-sum game. We then discuss a game theoretic framework to model the sequential actions of the adversary and the data miner, while both parties try to maximize their utilities. We also introduce a modified support vector machine method and a relevance vector machine method to defend against active adversaries. Intrusion detection and malware detection are two important application areas for adversarial data mining models that will be discussed in details during the tutorial. Finally, we discuss some practical guidelines on how to use adversarial data mining ideas in generic cyber security applications and how to leverage existing big data management tools for building data mining algorithms for cyber security.
Learning local descriptors by optimizing the keypoint-correspondence criterion

Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint bags extracted from two images of the same object under different views form a matching pair, and keypoint bags extracted from images of different objects form a non-matching pair. On average, matching pairs should contain more corresponding keypoints than non-matching pairs. We describe an end-to-end differentiable architecture that enables the learning of local keypoint descriptors from such weakly-labeled data.
Hand Detecting and Positioning Based on Depth Image of Kinect Sensor

—This paper presents a new method of detection and identification, called PYTHON programming environment, which can realize the gesture track recognition based on the depth image information get by the Kinect sensor. First, Kinect sensor is used to obtain depth image information. Then it extracts splith and with the official Microsoft SDK. Finally, this paper presents how to calculate the palm center's coordinates based on the moment of hand contour feature. Experiments show that the advantages of using the hand split and gesture recognition of the Kinect's depth image can be very effective to achieve interactive features.
The formation of autoantibodies and antibodies to TNF-α blocking agents in relation to clinical response in patients with ankylosing spondylitis.

To investigate the influence of antibody formation to TNF-α blocking agents on the clinical response in AS patients treated with infliximab (IFX), etanercept (ETA), or adalimumab (ADA), and to investigate the development of ANA, ANCA, and anti-dsDNA antibodies in association with the formation of antibodies to TNF-α blocking agents.



Consecutive AS outpatients with active disease who started treatment with IFX (n=20), ETA (n=20), or ADA (n=20) were included in this longitudinal observational study. Clinical data were collected prospectively at baseline and after 3, 6, and 12 months of anti-TNF-α treatment. At the same time points, serum samples were collected. In these samples, antibodies to TNF-α blocking agents, serum TNF-α blocker levels, and ANA, ANCA, and anti-dsDNA antibodies were measured retrospectively.



Anti-IFX, anti-ETA, and anti-ADA antibodies were induced in 20%, 0%, and 30% of patients, respectively. Although ANA, ANCA, and anti-dsDNA antibodies were detected during anti-TNF-α treatment, no significant association was found between the presence of these autoantibodies and the formation of antibodies to TNF-α blocking agents. Patients with anti-IFX or anti-ADA antibodies had significantly lower serum TNF-α blocker levels compared to patients without these antibodies. Furthermore, significant negative correlations were found between serum TNF-α blocker levels and assessments of disease activity.



This study indicates that antibody formation to IFX or ADA is related to a decrease in efficacy and early discontinuation of anti-TNF-α treatment in AS patients. Furthermore, autoantibody formation does not seem to be associated with antibody formation to TNF-α blocking agents.
Ultrasound in the management of thoracic disease.

Using simple and standardized semiology, the lung appears accessible to ultrasound, despite previous opinions otherwise. Lung ultrasound allows the intensivist to quickly answer to a majority of critical situations. Not only pleural effusion but also pneumothorax, alveolar consolidation, and interstitial syndrome will have accurate ultrasound equivalents, the recognition of which practically guides management. Combined with venous, cardiac, and abdominal examination, ultrasound investigation of this vital organ provides a transparent overview of the critically ill, a kind of stethoscope for a visual medicine. It is believed that by using this tool, the intensivist may more confidently manage acute dyspnea and make emergency therapeutic decisions based on reproducible data. Further benefits include reduced requirements for computed tomographic scans, therefore decreasing delay, irradiation, cost, and above all, discomfort to the patient. Thus, ultrasound of the lung can also be added to the classic armamentarium as a clinical tool for emergency use.
Text line segmentation using Viterbi algorithm for the palm leaf manuscripts of Dai

The text line segmentation process is a key step in an optical character recognition (OCR) system. Several common approaches, such as projection-based methods and stochastic methods, have been put forward to fulfill this task. However, most of existing methods cannot be directly applied to process the palm leaf manuscripts of Dai which the images have poor quality and include smudges, creases, stroke deformation and character touching. To solve this problem, an improved Viterbi algorithm based on Hidden Markov Model (HMM) is proposed to find all possible segmentation paths firstly. And then, a path filtering method is used to detect the optimal paths for the segmented text blocks. The performance of the method is compared with relevant methods and the experimental results demonstrate the effectiveness of the proposed method.
Avicenna and Cataracts: A New Analysis of Contributions to Diagnosis and Treatment from the Canon

Physicians in ancient Persia played an important role in the development of medicine in the medieval era. One of the most influential figures of this era was Abu Ali Sina or Ibn Sina, known as Avicenna in the western world. The author of more than 200 books on medicine and philosophy, Avicenna followed and further expanded on the tradition of western philosophy and medicine introduced by Aristotle, Hippocrates and Galen. Few researchers have looked into the different medical issues in his best known work, the Canon of Medicine, particularly with regard to ophthalmology. In this analysis, Avicenna's views on and contributions to the diagnosis and treatment of cataracts in his Canon were elucidated.



We first reviewed an electronic copy of the Canon and then reviewed other important sources in traditional medicine including the Kamel-al-Sanaeh, Al-Havi (Continents) and Zakhireh-kharazmshahi, available in the Avicenna Special Traditional Medicine Library of Shiraz University of Medical Sciences. We also searched Medline, Embase, Scopus, Iranmedex and Science Iranian Database (SID) with these keywords: "traditional medicine," "Avicenna," "cataract", "Canon", "history", "ophthalmology" and "eye disorders".



According to the Canon, nozul-al-maa or cataract is an obstructive disease in which external moisture accumulates between the aqueous humor and the corneal membrane and prevents images from entering the eye. Avicenna classified cataracts on the basis of size, density and color. According to size, he identified two types of cataracts including complete and partial obstruction. According to the Canon, surgical intervention was necessary only for certain indications. Avicenna believed that opacity in the initial stages of cataract could be diminished by medicines and foods, and described several medicines for cataracts. He believed that surgery should be postponed until the liquid accumulation stopped, and the cataract reached its mature state. After surgery, according to Avicenna, the patient should avoid headache-inducing situations because headaches could lead to edema of the layers of the eye. He further emphasized that the patient's psychological status played an important role in the success of surgery.



An important aspect of Avicenna's contribution to the medical management of cataracts was that he believed they could be cured by medication and nutrition in their early stages without the need for surgery. He also considered the patient's mental status as an important factor contributing to the postoperative prognosis. Our review of Avicenna's writings on eye disorders in the Canon of Medicine suggests that he had a rigorous approach to the diagnosis and management of patients suffering from eye disorders.
Extraction of palmprint texture features using combined DWT-DCT and local binary pattern

The palm-print verification system is based on two modes, viz, Enrollment mode and Recognition mode. In the enrollment mode, the palm-print features are acquired from the sensor and stored in a database along with the person's identity for the recognition of his/her identity. In the recognition mode, the palm-print features are re-acquired from the sensor and compared against the stored data to determine the user identity. In the pre-processing stage, two segmentation processes are proposed to extract the region of interest (ROI) of palm. The first skin-color segmentation is used to extract the hand image from the background. The second region of interest of the palm is segmented by using the valley detection algorithm. The Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are applied for the purpose of extracting the features. Further, the Sobel Operator and Local Binary Pattern (LBP) are used for increasing the number of features. The mean and standard deviation of DWT, DCT and LBP are computed. Twenty hand-scanned images and ten samples of CASIA palmprint database were used in this experiment. The performance of the proposed system is found to be satisfactory.
Neuroimaging of the Philadelphia Neurodevelopmental Cohort

The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale, NIMH funded initiative to understand how brain maturation mediates cognitive development and vulnerability to psychiatric illness, and understand how genetics impacts this process. As part of this study, 1445 adolescents ages 8-21 at enrollment underwent multimodal neuroimaging. Here, we highlight the conceptual basis for the effort, the study design, and the measures available in the dataset. We focus on neuroimaging measures obtained, including T1-weighted structural neuroimaging, diffusion tensor imaging, perfusion neuroimaging using arterial spin labeling, functional imaging tasks of working memory and emotion identification, and resting state imaging of functional connectivity. Furthermore, we provide characteristics regarding the final sample acquired. Finally, we describe mechanisms in place for data sharing that will allow the PNC to become a freely available public resource to advance our understanding of normal and pathological brain development.
EPPI: An E-cent-based privacy-preserving incentive mechanism for participatory sensing systems

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper firstly proposes E-cent, a unit bearer currency. It is exchangeable, and participants can utilize it to participate in tasks anonymously. By employing E-cent, we further propose an E-cent-based privacy-preserving incentive mechanism, called EPPI, which exploits a pledge-based participating protocol to encourage participants to participate without revealing privacy and prohibit participants from sending false data. EPPI also takes advantage of a dynamic reward allocation scheme to maximize the value of the services under a budget constraint. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy-preserving in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.
Chapter 1 Liquid State Machines : Motivation , Theory , and Applications

The Liquid State Machine (LSM) has emerged as a computational model that is more adequate than the Turing machine for describing computations in biological networks of neurons. Characteristic features of this new model are (i) that it is a model for adaptive computational systems, (ii) that it provides a method for employing randomly connected circuits, or even “found” physical objects for meaningful computations, (iii) that it provides a theoretical context where heterogeneous, rather than stereotypical, local gates or processors increase the computational power of a circuit, (iv) that it provides a method for multiplexing different computations (on a common input) within the same circuit. This chapter reviews the motivation for this model, its theoretical background, and current work on implementations of this model in innovative artificial computing devices.
SURFACE DEFECTS DETECTION FOR CERAMIC TILES USING IMAGE PROCESSING AND MORPHOLOGICAL TECHNIQUES

Quality control in ceramic tile manufacturing is hard, labour intensive and it is performed in a harsh industrial environment with noise, extreme temperature and humidity. It can be divided into colour analysis, dimension verification, and surface defect detection, which is the main purpose of our work. Defects detection is still based on the judgment of human operators while most of the other manufacturing activities are automated so, our work is a quality control enhancement by integrating a visual control stage using image processing and morphological operation techniques before the packing operation to improve the homogeneity of batches received by final users. An automated defect detection and classification technique that can ensured the better quality of tiles in manufacturing process as well as production rate.
Data driven surrogate-based optimization in the problem solving environment WBCSim

Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate-based optimization algorithm that uses a trust region-based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques—full factorial (FF), Latin hypercube sampling, and central composite design—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the generation of the required database. As the number of design variables grows, the computational cost of generating the required database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the proposed methodology dramatically reduces the total number of calls to the expensive simulation runs during the optimization process.
Nested Propositions in Open Information Extraction

The challenges of Machine Reading and Knowledge Extraction at a web scale require a system capable of extracting diverse information from large, heterogeneous corpora. The Open Information Extraction (OIE) paradigm aims at extracting assertions from large corpora without requiring a vocabulary or relation-specific training data. Most systems built on this paradigm extract binary relations from arbitrary sentences, ignoring the context under which the assertions are correct and complete. They lack the expressiveness needed to properly represent and extract complex assertions commonly found in the text. To address the lack of representation power, we propose NESTIE, which uses a nested representation to extract higher-order relations, and complex, interdependent assertions. Nesting the extracted propositions allows NESTIE to more accurately reflect the meaning of the original sentence. Our experimental study on real-world datasets suggests that NESTIE obtains comparable precision with better minimality and informativeness than existing approaches. NESTIE produces 1.7-1.8 times more minimal extractions and achieves 1.1-1.2 times higher informativeness than CLAUSIE.
WORKING PAPER 35 years of studies on business failure : an overview of the classical statistical methodologies and their related problems

1 Research Assistant, Department of Accountancy and Corporate Finance, Ghent University, Belgium Mailing address: Department of Accountancy and Corporate Finance, Ghent University, Kuiperskaai 55 E, B-9000 Gent, Belgium; Tel: +32 (0)9/264 35 07; Fax: +32 (0)9/264 35 77; E-mail: Sofie.Balcaen@ugent.be 2 Professor, Department of Accountancy and Corporate Finance, Ghent University, Belgium; Ernst & Young Chair of Growth Management, Vlerick Leuven Gent Management School, Belgium.