nils-herrmann/clean_scirepeval huggingface dataset:
PENS syndrome: a new neurocutaneous phenotype.

The central nervous system originates ontogenetically from the ectoderm and therefore numerous neurocutaneous syndromes exist.



We describe a novel neurocutaneous syndrome with characteristic epidermal papules and plaques associated with mild psychomotor retardation and epilepsy with a good prognosis.



We investigated history, clinical features, laboratory parameters including karyotype, histopathological and immunohistological examination, electron microscopy, HPV infection, EEG and neurologic imaging in patients with an unusual symptom complex.



Six children are reported with characteristic keratotic papules and plaques of 'gem-like' appearance, randomly distributed on the trunk and the proximal part of the limbs. Four patients were unrelated and two were siblings. The cutaneous lesions appeared congenitally or developed soon after birth. The skin lesions were rounded or of an irregularly polycyclic shape, ranging from 0.5 to 1.5 cm in diameter. A few to eighteen lesions were present. The lesional surface was rough and of white or yellowish color. Histopathological examination showed acanthokeratosis, but immunostaining for keratins and melanocytes, electron microscopy and laboratory results were within normal range. During the natural course of the disease the lesions were stable and asymptomatic. All but one of our six patients developed neurologic signs and symptoms during the first year of life, in the form of epilepsy and psychomotor delay. Five children had low intellectual performance together with EEG abnormalities and some had MRI changes of the brain. One patient had marked language impairment. The central nervous system involvement had a peculiar benign and self-limiting course and total normalization occurred in one case.



We present only a small series of patients.



Cutaneous lesions showing identical clinical and microscopical features, but without any extracutaneous abnormalities, have recently been described by Torrelo et al. [ under the designation 'papular epidermal nevus with "skyline" basal cell layer (PENS)'. An association of such nevi with neurologic abnormalities had so far only been published in the 'Atlas of Genodermatoses' edited by Caputo and Tadini. We believe that this unique and otherwise undescribed symptom complex is a new neurocutaneous phenotype that may be called PENS syndrome. Whether this new syndrome can be categorized as a distinct phenotype within the group of epidermal nevus syndromes remains speculative and has to be clarified by further investigations.
Pooling-based continuous evaluation of information retrieval systems

The dominant approach to evaluate the effectiveness of information retrieval (IR) systems is by means of reusable test collections built following the Cranfield paradigm. In this paper, we propose a new IR evaluation methodology based on pooled test-collections and on the continuous use of either crowdsourcing or professional editors to obtain relevance judgements. Instead of building a static collection for a finite set of systems known a priori, we propose an IR evaluation paradigm where retrieval approaches are evaluated iteratively on the same collection. Each new retrieval technique takes care of obtaining its missing relevance judgements and hence contributes to augmenting the overall set of relevance judgements of the collection. We also propose two metrics: Fairness Score, and opportunistic number of relevant documents, which we then use to define new pooling strategies. The goal of this work is to study the behavior of standard IR metrics, IR system ranking, and of several pooling techniques in a continuous evaluation context by comparing continuous and non-continuous evaluation results on classic test collections. We both use standard and crowdsourced relevance judgements, and we actually run a continuous evaluation campaign over several existing IR systems.
An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information

Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.
Learning modular policies for robotics

A promising idea for scaling robot learning to more complex tasks is to use elemental behaviors as building blocks to compose more complex behavior. Ideally, such building blocks are used in combination with a learning algorithm that is able to learn to select, adapt, sequence and co-activate the building blocks. While there has been a lot of work on approaches that support one of these requirements, no learning algorithm exists that unifies all these properties in one framework. In this paper we present our work on a unified approach for learning such a modular control architecture. We introduce new policy search algorithms that are based on information-theoretic principles and are able to learn to select, adapt and sequence the building blocks. Furthermore, we developed a new representation for the individual building block that supports co-activation and principled ways for adapting the movement. Finally, we summarize our experiments for learning modular control architectures in simulation and with real robots.
Recognizing Malicious Intention in an Intrusion Detection Process

Generally, theintrudermustperformseveralactions,organizedin anintrusionscenario, to achieve hisor hermaliciousobjective.Wearguethatintrusionscenarioscan bemodelledasa planningprocessandwesuggestmodellinga maliciousobjectiveas anattemptto violatea givensecurityrequirement. Our proposalis thento extendthe definitionof attackcorrelationpresentedin [CM02] to correlateattackswith intrusion objectivesThis notionis usefulto decideif a sequenceof correlatedactionscanlead to a securityrequirementviolation.This approachprovidesthesecurityadministrator with aglobalview of whathappensin thesystem.In particular, it controlsunobserved actionsthroughhypothesisgeneration,clustersrepeatedactionsin a singlescenario, recognizesintrudersthatarechangingtheir intrusionobjectivesandis efficient to detectvariationsof anintrusionscenario.Thisapproachcanalsobeusedto eliminatea category of falsepositivesthatcorrespondto falseattacks,that is actionsthatarenot furthercorrelatedto anintrusionobjective.
Medical image segmentation on GPUs - A comprehensive review

Segmentation of anatomical structures, from modalities like computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound, is a key enabling technology for medical applications such as diagnostics, planning and guidance. More efficient implementations are necessary, as most segmentation methods are computationally expensive, and the amount of medical imaging data is growing. The increased programmability of graphic processing units (GPUs) in recent years have enabled their use in several areas. GPUs can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. Furthermore, using a GPU enables concurrent visualization and interactive segmentation, where the user can help the algorithm to achieve a satisfactory result. This review investigates the use of GPUs to accelerate medical image segmentation methods. A set of criteria for efficient use of GPUs are defined and each segmentation method is rated accordingly. In addition, references to relevant GPU implementations and insight into GPU optimization are provided and discussed. The review concludes that most segmentation methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.
Online dating system design and relational decision making : Choice , algorithms , and control

One of the most popular ways to initiate romantic relationships today is through online dating. Typical dating systems follow one of three formats, see-and-screen (e.g., Match.com), algorithm (e.g., eHarmony.com), and blended (e.g., OkCupid.com), which differ in the amount of individual control and algorithmic involvement they offer users. Do different features affect daters’ decisions and expectations regarding relational development with selected partners? Study 1 indicated that although daters appreciated the personal control over mate selection afforded by see-and-screen systems, they also enjoyed using algorithmic systems. Study 2 found that blended systems provided “the best of both worlds” by offering dual benefits of control and algorithmic validation during mate selection. Findings shed light on self-determination theory, decision making, and relationship formation more broadly. Since its inception in the early 2000s, online dating has shed its stigma to become an important part of the current landscape of romantic relationship formation. Indeed, recent statistics indicate approximately 30 million online Americans have turned to online dating as a way to initiate romantic relationships (Smith & Duggan, 2013). A growing body of research has examined daters’ behaviors at “micro” levels such as self-presentation and impression formation in dating profiles (e.g., Ellison, Heino, & Gibbs, 2006) and at “macro” Stephanie T. Tong, Department of Communication, Wayne State University; Jeffrey T. Hancock, Department of Communication, Stanford University; Richard B. Slatcher, Department of Psychology, Wayne State University. This work was supported by NSF Grant 1520723. Portions of this paper were presented at the 2016 Human Computer Interaction International Conference in Toronto, ON. The authors thank four anonymous reviewers and the associate editor, Edward Lemay, for their suggestions on previous versions of this work. The authors are grateful to Elena Corriero, Chad Van De Wiele, Benjamin Lennemann, Annisa Rochadiat, and Prathyusha Tadi for their help with data collection. Correspondence should be addressed to Stephanie T. Tong, Wayne State University, Department of Communication, 569 Manoogian Hall, Detroit, MI 48201, . levels such as attraction through race (Lin & Lundquist, 2013; Rudder, 2014). But few existing studies have explored how the design of the dating technologies themselves may directly influence daters’ perceptions and relational
Single aperture monopulse horn antenna

This paper describes the design and performance of a conical Potter horn antenna which can generate sum or difference far field patterns suitable for monopulse tracking applications. The antenna consists of a horn antenna fed with a metal post loaded polarizer which is energised by suitably phased 50-/spl Omega/ matched coaxial probes. The prototype demonstrator designed here operates in the frequency range 8-8.6GHz with sum and difference patterns obtained by using a very simple phasing circuit. At 8.5GHz, the gain of the sum pattern is 18.6dB and the null in the difference pattern is 26dB at boresight.
Substrate-Integrated Two-Port Dual-Frequency Antenna

A two-port dual-frequency substrate-integrated antenna with a large frequency difference is presented. It consists of a differentially fed slot antenna and a substrate-integrated dielectric resonator antenna for low- and high-frequency radiation, respectively. The former is loaded by a hollow patch, whereas the latter is fabricated inside the hollow region of the patch by using air holes and metalized vias. Beneath the antenna substrate is a second substrate on which slot-coupled sources are printed to feed the two antennas. For demonstration, a two-port dual-frequency antenna working at 5.2-GHz WLAN band and 24-GHz ISM band was designed, fabricated, and measured. The S-parameters, radiation patterns, and antenna gains of the two antenna parts are reported. Reasonable agreement between the measured and simulated results is observed. Very good isolation of over 35 dB between the two antenna parts is observed.
A hybridization of cuckoo search and particle swarm optimization for solving optimization problems

A new hybrid optimization algorithm, a hybridization of cuckoo search and particle swarm optimization (CSPSO), is proposed in this paper for the optimization of continuous functions and engineering design problems. This algorithm can be regarded as some modifications of the recently developed cuckoo search (CS). These modifications involve the construction of initial population, the dynamic adjustment of the parameter of the cuckoo search, and the incorporation of the particle swarm optimization (PSO). To cover search space with balance dispersion and neat comparability, the initial positions of cuckoo nests are constructed by using the principle of orthogonal Lation squares. To reduce the influence of fixed step size of the CS, the step size is dynamically adjusted according to the evolutionary generations. To increase the diversity of the solutions, PSO is incorporated into CS using a hybrid strategy. The proposed algorithm is tested on 20 standard benchmarking functions and 2 engineering optimization problems. The performance of the CSPSO is compared with that of several meta-heuristic algorithms based on the best solution, worst solution, average solution, standard deviation, and convergence rate. Results show that in most cases, the proposed hybrid optimization algorithm performs better than, or as well as CS, PSO, and some other exiting meta-heuristic algorithms. That means that the proposed hybrid optimization algorithm is competitive to other optimization algorithms.
Memetic evolution of deep neural networks

Deep neural networks (DNNs) have proven to be effective at solving challenging problems, but their success relies on finding a good architecture to fit the task. Designing a DNN requires expert knowledge and a lot of trial and error, especially as the difficulty of the problem grows. This paper proposes a fully automatic method with the goal of optimizing DNN topologies through memetic evolution. By recasting the mutation step as a series of progressively refined educated local-search moves, this method achieves results comparable to best human designs. Our extensive experimental study showed that the proposed memetic algorithm supports building a real-world solution for segmenting medical images, it exhibits very promising results over a challenging CIFAR-10 benchmark, and works very fast. Given the ever growing availability of data, our memetic algorithm is a very promising avenue for hands-free DNN architecture design to tackle emerging classification tasks.
Sampled-Point Network for Classification of Deformed Building Element Point Clouds

Search-and-rescue (SAR) robots operating in post-disaster urban areas need to accurately identify physical site information to perform navigation, mapping and manipulation tasks. This can be achieved by acquiring a 3D point cloud of the environment and performing object recognition from the point cloud data. However, this task is complicated by the unstructured environments and potentially-deformed objects encountered during disaster relief operations. Current 3D object recognition methods rely on point cloud input acquired under suitable conditions and do not consider deformations such as outlier noise, bending and truncation. This work introduces a deep learning architecture for 3D class recognition from point clouds of deformed building elements. The classification network, consisting of stacked convolution and average pooling layers applied directly to point coordinates, was trained using point clouds sampled from a database of mesh models. The proposed method achieves robustness to input variability using point sorting, resampling, and rotation normalization techniques. Experimental results on synthetically-deformed object datasets show that the proposed method outperforms the conventional deep learning methods in terms of classification accuracy and computational efficiency.
Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO)

Internet of vehicles (IoV) is a branch of the internet of things (IoT) which is used for communication among vehicles. As vehicular nodes are considered always in motion, hence it causes the frequent changes in the topology. These changes cause major issues in IoV like scalability, dynamic topology changes, and shortest path for routing. Clustering is among one of the solutions for such type of issues. In this paper, the stability of IoV topology in a dynamic environment is focused. The proposed metaheuristic dragonfly-based clustering algorithm CAVDO is used for cluster-based packet route optimization to make stable topology, and mobility aware dynamic transmission range algorithm (MA-DTR) is used with CAVDO for transmission range adaptation on the basis of traffic density. The proposed CAVDO with MA-DTR is compared with the progressive baseline techniques ant colony optimization (ACO) and comprehensive learning particle swarm optimization (CLPSO). Numerous experiments were performed keeping in view the transmission dynamics for stable topology. CAVDO performed better in many cases providing minimum number of clusters according to current channel condition. Considerable important parameters involved in clustering process are: number of un-clustered nodes as a re-clustering criterion, clustering time, re-clustering delay, dynamic transmission range, direction, and speed. According to these parameters, results indicate that CAVDO outperformed ACO-based clustering and CLPSO in various network settings. Additionally, to improve the network availability and to incorporate the functionalities of next-generation network infrastructure, 5G-enabled architecture is also utilized.
Design considerations on low voltage synchronous power MOSFETs with monolithically integrated gate voltage pull-down circuitry

In this paper, a monolithically integrated gate voltage pull-down circuitry is presented to avoid the unintentional C·dV/dt induced turn-on. The concept of a low threshold voltage MOSFET with this integrated gate voltage pull-down circuitry is introduced as a contributing factor to the next generation high frequency DC-DC converter efficiency improvement. Design considerations on this new device and influences of critical design parameters on device/circuit performance will be fully discussed. In synchronous buck application, this integrated power module achieves more than 2% efficiency improvement over reference solution at high operation frequency (1MHz) under 19V input and 1.3V output condition.
Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms

This paper tackles the task of event detection (ED), which involves identifying and categorizing events. We argue that arguments provide significant clues to this task, but they are either completely ignored or exploited in an indirect manner in existing detection approaches. In this work, we propose to exploit argument information explicitly for ED via supervised attention mechanisms. In specific, we systematically investigate the proposed model under the supervision of different attention strategies. Experimental results show that our approach advances state-ofthe-arts and achieves the best F1 score on ACE 2005 dataset.
Optical Character Recognition from Text Image

Optical Character Recognition (OCR) is a system that provides a full alphanumeric recognition of printed or handwritten characters by simply scanning the text image. OCR system interprets the printed or handwritten characters image and converts it into corresponding editable text document. The text image is divided into regions by isolating each line, then individual characters with spaces. After character extraction, the texture and topological features like corner points, features of different regions, ratio of character area and convex area of all characters of text image are calculated. Previously features of each uppercase and lowercase letter, digit, and symbols are stored as a template. Based on the texture and topological features, the system recognizes the exact character using feature matching between the extracted character and the template of all characters as a measure of similarity.
Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips

Sample preparation in digital microfluidics refers to the generation of droplets with target concentrations for onchip biochemical applications. In recent years, digital microfluidic biochips (DMFBs) have been adopted as a platform for sample preparation. However, there remain one major problem associated with sample preparation on a conventional DMFB. For conventional DMFBs, only a (1:1) mixing/splitting model can be used, leading to an increase in the number of fluidic operations required for sample preparation. To overcome the drawback, we adopt a next generation DMFB platform, referred to as micro-electrode-dot-array (MEDA), for sample preparation. We propose the first sample preparation method that exploits the MEDA-specific advantages of fine-grained control of droplet sizes and real-time droplet sensing. Experimental demonstration using a fabricated MEDA biochip and simulation results highlight the effectiveness of the proposed sample-preparation method.
An Introduction to Restricted Boltzmann Machines

Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. The increase in computational power and the development of faster learning algorithms have made them applicable to relevant machine learning problems. They attracted much attention recently after being proposed as building blocks of multi-layer learning systems called deep belief networks. This tutorial introduces RBMs as undirected graphical models. The basic concepts of graphical models are introduced first, however, basic knowledge in statistics is presumed. Different learning algorithms for RBMs are discussed. As most of them are based on Markov chain Monte Carlo (MCMC) methods, an introduction to Markov chains and the required MCMC techniques is provided.
A complete solution to the inverse kinematics problem for 4-DOF manipulator robot

A complete closed-form solution to the inverse kinematics problem for a 4-DOF manipulator robot is proposed. With the discussion of the existence of the offset and the distance between the axis of 1st joint and 2nd joint, all the possible solutions and singular configurations are presented. “Task attitude” is defined to describe the orientation of the end effector, which is convenient to solve the inverse kinematics problems in engineering. Finally, simulation of path-planning is performed, and the efficiency of the proposed method is verified through Mathematica.
Role of the Immune system in chronic pain

During the past two decades, an important focus of pain research has been the study of chronic pain mechanisms, particularly the processes that lead to the abnormal sensitivity — spontaneous pain and hyperalgesia — that is associated with these states. For some time it has been recognized that inflammatory mediators released from immune cells can contribute to these persistent pain states. However, it has only recently become clear that immune cell products might have a crucial role not just in inflammatory pain, but also in neuropathic pain caused by damage to peripheral nerves or to the CNS.