nils-herrmann/clean_scirepeval huggingface dataset:
EULAR/PRINTO/PRES criteria for Henoch-Schönlein purpura, childhood polyarteritis nodosa, childhood Wegener granulomatosis and childhood Takayasu arteritis: Ankara 2008. Part II: Final classification criteria.

To validate the previously proposed classification criteria for Henoch-Schönlein purpura (HSP), childhood polyarteritis nodosa (c-PAN), c-Wegener granulomatosis (c-WG) and c-Takayasu arteritis (c-TA).



Step 1: retrospective/prospective web-data collection for children with HSP, c-PAN, c-WG and c-TA with age at diagnosis <or=18 years. Step 2: blinded classification by consensus panel of a representative sample of 280 cases. Step 3: statistical (sensitivity, specificity, area under the curve and kappa-agreement) and nominal group technique consensus evaluations.



827 patients with HSP, 150 with c-PAN, 60 with c-WG, 87 with c-TA and 52 with c-other were compared with each other. A patient was classified as HSP in the presence of purpura or petechiae (mandatory) with lower limb predominance plus one of four criteria: (1) abdominal pain; (2) histopathology (IgA); (3) arthritis or arthralgia; (4) renal involvement. Classification of c-PAN required a systemic inflammatory disease with evidence of necrotising vasculitis OR angiographic abnormalities of medium-/small-sized arteries (mandatory criterion) plus one of five criteria: (1) skin involvement; (2) myalgia/muscle tenderness; (3) hypertension; (4) peripheral neuropathy; (5) renal involvement. Classification of c-WG required three of six criteria: (1) histopathological evidence of granulomatous inflammation; (2) upper airway involvement; (3) laryngo-tracheo-bronchial involvement; (4) pulmonary involvement (x-ray/CT); (5) antineutrophilic cytoplasmic antibody positivity; (6) renal involvement. Classification of c-TA required typical angiographic abnormalities of the aorta or its main branches and pulmonary arteries (mandatory criterion) plus one of five criteria: (1) pulse deficit or claudication; (2) blood pressure discrepancy in any limb; (3) bruits; (4) hypertension; (5) elevated acute phase reactant.



European League Against Rheumatism/Paediatric Rheumatology International Trials Organisation/Paediatric Rheumatology European Society propose validated classification criteria for HSP, c-PAN, c-WG and c-TA with high sensitivity/specificity.
Robust Unit Commitment Problem with Demand Response and Wind Energy

To improve the efficiency in power generation and to reduce the greenhouse gas emission, both Demand Response (DR) strategy and intermittent renewable energy have been proposed or applied in electric power systems. However, the uncertainty and the generation pattern in wind farms and the complexity of demand side management pose huge challenges in power system operations. In this paper, we analytically investigate how to integrate DR and wind energy with fossil fuel generators to (i) minimize power generation cost; (2) fully take advantage wind energy with managed demand to reduce greenhouse emission. We first build a two-stage robust unit commitment model to obtain day-ahead generator schedules where wind uncertainty is captured by a polyhedron. Then, we extend our model to include DR strategy such that both price levels and generator schedule will be derived for the next day. For these two NP-hard problems, we derive their mathematical properties and develop a novel and analytical solution method. Our computational study on a IEEE 118 system with 36 units shows that (i) the robust unit commitment model can significantly reduce total cost and fully make use of wind energy; (ii) the cutting plane method is computationally superior to known algorithms.
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SCIERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SCIIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific
Adolescent brain development: a period of vulnerabilities and opportunities. Keynote address.

This article introduces and summarizes the goals of the symposium. It also provides an overview of a conceptual framework for understanding adolescence, which emphasizes how the very nature of this developmental transition requires an interdisciplinary approach-one that focuses on brain/behavior/social-context interactions during this important maturational period. More specifically it describes a set of neurobehavioral changes that appear to be linked to pubertal development, which appear to have a significant effect on motivation and emotion, and considers these puberty-specific changes in affect in relation to a much larger set of developmental changes in adolescence. This framework is used to argue for the need for a transdisciplinary dialogue that brings together work in several areas of neuroscience (including animal models) and normal development with clinical and social policy research aimed at early intervention and prevention strategies.
Leakage Current Analytical Model and Application in Single-Phase Transformerless Photovoltaic Grid-Connected Inverter

Due to the characteristics of low cost and high efficiency, the transformerless photovoltaic (PV) grid-connected inverters have been popularized in the application of solar electric generation system in residential market. Unfortunately, the leakage current through the stray capacitors between the PV array and the ground is harmful. This paper focuses on the leakage current suppressing method, in which all common-mode paths are considered. First, the common-mode analytical model at switching frequency is developed, and the rules of eliminating switching frequency common-mode source are summarized based on this model. The existing full-bridge- and half-bridge-type converters have been analyzed by using the developed model and rules, and then, a new full-bridge-type converter structure and a compensation strategy for half-bridge-type inverter have been presented finally.
Toward a smart grid: power delivery for the 21st century

In this article, we present the security, agility, and robustness/survivability of a large-scale power delivery infrastructure that faces new threats and unanticipated conditions. By way of background, we present a brief overview of the past work on the challenges faced in online parameter estimation and real-time adaptive control of a damaged F-15 aircraft. This work, in part, provided the inspiration and laid the foundation in the 1990s for the flight testing of a fast parameter estimation/modeling and reconfigurable aircraft control system that allowed the F-15 to become self-healing in the face of damaged equipment.
Approaches to adversarial drift

In this position paper, we argue that to be of practical interest, a machine-learning based security system must engage with the human operators beyond feature engineering and instance labeling to address the challenge of drift in adversarial environments. We propose that designers of such systems broaden the classification goal into an explanatory goal, which would deepen the interaction with system's operators.
 To provide guidance, we advocate for an approach based on maintaining one classifier for each class of unwanted activity to be filtered. We also emphasize the necessity for the system to be responsive to the operators constant curation of the training set. We show how this paradigm provides a property we call isolation and how it relates to classical causative attacks.
 In order to demonstrate the effects of drift on a binary classification task, we also report on two experiments using a previously unpublished malware data set where each instance is timestamped according to when it was seen.
Role of Procedural Justice , Organizational Commitment and Job Satisfaction on job Performance : The Mediating Effects of Organizational Citizenship Behavior

The study examines the impact of procedural justice, organizational commitment, job satisfaction on employee performance, and the potential mediating role played by organization citizenship behaviors in that process. This model was tested using a sample of 70 employees embedded in 2 groups from 15 branches of a large, syariah bank in Malang. The sample is taken using proportional random sampling. Data is collected directly from respondents using questionnaires and technical data analysis using GeSCA. The study results showed that both procedural justice and organizational commitment positively affected Organizational Citizenship Behavior. Organizational commitments do positive influence job performance. Job satisfaction did not positively influence Organizational Citizenship Behavior and job performance. Organizational Citizenship behavior positively influences job performance. Organizational Citizenship behavior acted as a partial mediator between procedural justice, organizational commitment, and job performance. A number of suggestions on managerial theory and implementation were proposed.
Recent advances on active noise control : open issues and innovative applications

Yoshinobu Kajikawa, Woon­Seng Gan and Sen M. Kuo APSIPA Transactions on Signal and Information Processing / Volume 1 / August 2012 / e3 DOI: 10.1017/ATSIP.2012.4,  Link to this article: http://journals.cambridge.org/abstract_S2048770312000042 How to cite this article: Yoshinobu Kajikawa, Woon­Seng Gan and Sen M. Kuo (2012). Recent advances on active noise control: open issues and innovative applications. APSIPA Transactions on Signal and Information Processing,1, e3 doi:10.1017/ATSIP.2012.4 Request  Click here
Saliency Optimization from Robust Background Detection

Recent progresses in salient object detection have exploited the boundary prior, or background information, to assist other saliency cues such as contrast, achieving state-of-the-art results. However, their usage of boundary prior is very simple, fragile, and the integration with other cues is mostly heuristic. In this work, we present new methods to address these issues. First, we propose a robust background measure, called boundary connectivity. It characterizes the spatial layout of image regions with respect to image boundaries and is much more robust. It has an intuitive geometrical interpretation and presents unique benefits that are absent in previous saliency measures. Second, we propose a principled optimization framework to integrate multiple low level cues, including our background measure, to obtain clean and uniform saliency maps. Our formulation is intuitive, efficient and achieves state-of-the-art results on several benchmark datasets.
SmartBox : Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition

Deep learning models are widely used for various purposes such as face recognition and speech recognition. However, researchers have shown that these models are vulnerable to adversarial attacks. These attacks compute perturbations to generate images that decrease the performance of deep learning models. In this research, we have developed a toolbox, termed as SmartBox, for benchmarking the performance of adversarial attack detection and mitigation algorithms against face recognition. SmartBox is a python based toolbox which provides an open source implementation of adversarial detection and mitigation algorithms. In this research, Extended Yale Face Database B has been used for generating adversarial examples using various attack algorithms such as DeepFool, Gradient methods, Elastic-Net, and L2 attack. SmartBox provides a platform to evaluate newer attacks, detection models, and mitigation approaches on a common face recognition benchmark. To assist the research community, the code of SmartBox is made available1.
Overview of ImageCLEFcaption 2017 - Image Caption Prediction and Concept Detection for Biomedical Images

This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from the biomedical literature. Two subtasks were proposed to the participants: a concept detection task and caption prediction task, both using only images as input. The two subtasks tackle the problem of providing image interpretation by extracting concepts and predicting a caption based on the visual information of an image alone. A dataset of 184,000 figure-caption pairs from the biomedical open access literature (PubMed Central) are provided as a testbed with the majority of them as trainign data and then 10,000 as validation and 10,000 as test data. Across two tasks, 11 participating groups submitted 71 runs. While the domain remains challenging and the data highly heterogeneous, we can note some surprisingly good results of the difficult task with a quality that could be beneficial for health applications by better exploiting the visual content of biomedical figures.
Privacy-Preserving User-Auditable Pseudonym Systems

Personal information is often gathered and processed in a decentralized fashion. Examples include health records and governmental data bases. To protect the privacy of individuals, no unique user identifier should be used across the different databases. At the same time, the utility of the distributed information needs to be preserved which requires that it be nevertheless possible to link different records if they relate to the same user. Recently, Camenisch and Lehmann (CCS 15) have proposed a pseudonym scheme that addresses this problem by domain-specific pseudonyms. Although being unlinkable, these pseudonyms can be converted by a central authority (the converter). To protect the users' privacy, conversions are done blindly without the converter learning the pseudonyms or the identity of the user. Unfortunately, their scheme sacrifices a crucial privacy feature: transparency. Users are no longer able to inquire with the converter and audit the flow of their personal data. Indeed, such auditability appears to be diametral to the goal of blind pseudonym conversion. In this paper we address these seemingly conflicting requirements and provide a system where user-centric audits logs are created by the oblivious converter while maintaining all privacy properties. We prove our protocol to be UC-secure and give an efficient instantiation using novel building blocks.
Role of the left hemisphere in sign language comprehension

We investigated the relative role of the left versus right hemisphere in the comprehension of American Sign Language (ASL). Nineteen lifelong signers with unilateral brain lesions [11 left hemisphere damaged (LHD) and 8 right hemisphere damaged (RHD)] performed three tasks, an isolated single-sign comprehension task, a sentence-level comprehension task involving simple one-step commands, and a sentence-level comprehension task involving more complex multiclause/multistep commands. Eighteen of the participants were deaf, one RHD subject was hearing and bilingual (ASL and English). Performance was examined in relation to two factors: whether the lesion was in the right or left hemisphere and whether the temporal lobe was involved. The LHD group performed significantly worse than the RHD group on all three tasks, confirming left hemisphere dominance for sign language comprehension. The group with left temporal lobe involvement was significantly impaired on all tasks, whereas each of the other three groups performed at better than 95% correct on the single sign and simple sentence comprehension tasks, with performance falling off only on the complex sentence comprehension items. A comparison with previously published data suggests that the degree of difficulty exhibited by the deaf RHD group on the complex sentences is comparable to that observed in hearing RHD subjects. Based on these findings we hypothesize (i) that deaf and hearing individuals have a similar degree of lateralization of language comprehension processes and (ii) that language comprehension depends primarily on the integrity of the left temporal lobe.
A dual-band bagley power divider using modified Π-network

A new methodology to design a dual-band 3-way Bagley power divider is presented. The general method of using π-network as well as the proposed modified π-network has been discussed. The proposed π-network mimics a λ/2 line at two different frequencies. Design equations in closed form are obtained by using transmission line concepts and simple network analysis techniques. The proposed design is validated using Agilent ADS. This modification also leads to wider bandwidth as observed by a careful simulation.
Robust Influence Maximization

In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding k seed nodes in a social network to maximize the influence spread. We propose the problem of robust influence maximization, which maximizes the worst-case ratio between the influence spread of the chosen seed set and the optimal seed set, given the uncertainty of the parameter input. We design an algorithm that solves this problem with a solution-dependent bound. We further study uniform sampling and adaptive sampling methods to effectively reduce the uncertainty on parameters and improve the robustness of the influence maximization task. Our empirical results show that parameter uncertainty may greatly affect influence maximization performance and prior studies that learned influence probabilities could lead to poor performance in robust influence maximization due to relatively large uncertainty in parameter estimates, and information cascade based adaptive sampling method may be an effective way to improve the robustness of influence maximization.
Deep neural architectures for large scale android malware analysis

Android is arguably the most widely used mobile operating system in the world. Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers. Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign. However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible. Machine learning offers a way to tackle this issue of speed by automating the classification task. While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain. In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware. Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset. We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches. Our best model gets an F1 score of 0.986 with an AUC of 0.983 as compared to the existing best F1 score of 0.875 and AUC of 0.953.
Fractionated laser skin resurfacing treatment complications: a review.

Fractional photothermolysis represents a new modality of laser skin resurfacing that was developed to provide a successful clinical response while minimizing postoperative recovery and limiting treatment complications.



To review all of the reported complications that develop as a result of fractional ablative and nonablative laser skin resurfacing.



A literature review was based on a MEDLINE search (1998-2009) for English-language articles related to laser treatment complications and fractional skin resurfacing. Articles presenting the highest level of evidence and the most recent reports were preferentially selected.



Complications with fractional laser skin resurfacing represent a full spectrum of severity and can be longlasting. In general, a greater likelihood of developing post-treatment complications is seen in sensitive cutaneous areas and in patients with intrinsically darker skin phototypes or predisposing medical risk factors.



Although the overall rate of complications associated with fractional laser skin resurfacing is much lower than with traditional ablative techniques, recent reports suggest that serious complications can develop. An appreciation of all of the complications associated with fractional laser skin resurfacing is important, especially given that many of them can be potentially prevented. The authors have indicated no significant interest with commercial supporters.
Policy Search in Continuous Action Domains: an Overview

Continuous action policy search is currently the focus of intensive research, driven both by the recent success of deep reinforcement learning algorithms and the emergence of competitors based on evolutionary algorithms. In this paper, we present a broad survey of policy search methods, providing a unified perspective on very different approaches, including also Bayesian Optimization and directed exploration methods. The main message of this overview is in the relationship between the families of methods, but we also outline some factors underlying sample efficiency properties of the various approaches.
Multilingual Speech Recognition with a Single End-to-End Model

Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and train a grapheme-based sequence-to-sequence model jointly on data from all languages. We find that this model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually. By modifying the model to accept a language identifier as an additional input feature, we further improve performance by an additional 7% relative and eliminate confusion between different languages.
4G/LTE technology for smart grid communication infrastructure

The smart grid concept has emerged as a new trend to operate and manage the electric power systems more effective and efficient in recent years as different from centralized and distributed power systems. In this concept, the electric energy consumption is optimized by using an advanced automation system which needs more information about the power system elements from generation units to end-users and controls them in light of this information. Undoubtedly, receiving and transmitting of this information requires two-way, high speed, reliable and secure communication infrastructure. In this study, Long Term Evolution (LTE) technology and its usage in smart grid infrastructure are investigated. Additionally, the challenges for using 4G/LTE in smart grid applications are also discussed.