nils-herrmann/clean_scirepeval huggingface dataset:
Antimicrobial and host cell directed activities of Gly/Ser rich peptides from salmonid cathelicidins.
Venue: Fish shellfish immunology

Cathelicidins, a major family of vertebrate antimicrobial peptides (AMPs) have a recognized role in the first line of defense against infections. They have been identified in several salmonid species, where the putative mature peptides are unusually long and rich in serine and glycine residues, often arranged in short multiple repeats (RLGGGS/RPGGGS) intercalated by hydrophobic motifs. Fragments of 24 40 residues, spanning specific motifs and conserved sequences in grayling or brown, rainbow and brook trout, were chemically synthesized and examined for antimicrobial activity against relevant Gram positive and Gram negative salmonid pathogens, as well as laboratory reference strains. They were not active in complete medium, but showed varying potency and activity spectra in diluted media. Bacterial membrane permeabilization also occurred only under these conditions and was indicated by rapid propidium iodide uptake in peptide treated bacteria. However, circular dichroism analyses indicated that they did not significantly adopt ordered conformations in membrane like environments. The peptides were not hemolytic or cytotoxic to trout cells, including freshly purified head kidney leukocytes (HKL) and the fibroblastic RTG 2 cell line. Notably, when exposed to them, HKL showed increased metabolic activity, while a growth promoting effect was observed on RTG 2 cells, suggesting a functional interaction of salmonid cathelicidins with host cells similar to that shown by mammalian ones. The three most active peptides produced a dose dependent increase in phagocytic uptake by HKL simultaneously stimulated with bacterial particles. The peptide STF(1 37) selected for further analyses, also enhanced phagocytic uptake in the presence of autologous serum, and increased intracellular killing of live E. coli. Furthermore, when tested on HKL in combination with the immunostimulant b glucan, it synergistically potentiated both phagocytic uptake and the respiratory burst response, activities that play a key role in fish immunity. Collectively, these data point to a role of salmonid cathelicidins as modulators of fish microbicidal mechanisms beyond a salt sensitive antimicrobial activity, and encourage further studies also in view of potential applications in aquaculture.
A Memetic Particle Swarm Optimization Algorithm for Community Detection in Complex Networks
Venue: Int. J. Pattern Recognit. Artif. Intell.

In recent years, community detection has become a hot research topic in complex networks. Many of the proposed algorithms are for detecting community based on the modularity Q. However, there is a resolution limit problem in modularity optimization methods. In order to detect the community structure more effectively, a memetic particle swarm optimization algorithm (MPSOA) is proposed to optimize the modularity density by introducing particle swarm optimization based global search operator and tabu local search operator, which is useful to keep a balance between diversity and convergence. For comparison purposes, two state of the art algorithms, namely, meme net and fast modularity, are carried on the synthetic networks and other four real world network problems. The obtained experiment results show that the proposed MPSOA is an efficient heuristic approach for the community detection problems.
Multipath removal by online blind deconvolution in through the wall imaging
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

In this paper, we propose an online radar imaging scheme that recovers a sparse scene and removes the multipath ringing induced by the front wall in a Through the Wall Imaging (TWI) system without prior knowledge of the wall parameters. Our approach uses online measurements obtained from individual transmitter receiver pairs to incrementally build the primary response of targets behind the front wall and find a corresponding delay convolution operator that generates the multi path reflections available in the received signal. In order to perform online sparse imaging while removing wall clutter reflections, we developed a deconvolution extension of the Sparse Randomized Kaczmarz (SRK) algorithm that finds sparse solutions to under and over determined linear systems of equations. Our scheme allows for imaging with nonuniformly spaced antennas by building an explicit delay and sum imaging operator for each new measurement. Moreover, the active memory requirements remain small even for large scale MIMO systems since the imaging operators are only constructed for individual transmitter receiver pairs. We test our approach on a simple FDTD simulated room with internal targets and demonstrate that our method successfully eliminates multipath reflections while correctly locating the targets.
A hybrid PSO GA algorithm for constrained optimization problems
Venue: Appl. Math. Comput.

The main objective of this paper is to present a hybrid technique named as a PSO GA for solving the constrained optimization problems. In this algorithm, particle swarm optimization (PSO) operates in the direction of improving the vector while the genetic algorithm (GA) has been used for modifying the decision vectors using genetic operators. The balance between the exploration and exploitation abilities have been further improved by incorporating the genetic operators, namely, crossover and mutation in PSO algorithm. The constraints defined in the problem are handled with the help of the parameter free penalty function. The experimental results of constrained optimization problems are reported and compared with the typical approaches exist in the literature. As shown, the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. Furthermore, experimental results indicate that the proposed approach may yield better solutions to engineering problems than those obtained by using current algorithms.
China's urban underclass population and penal policy

China's economic modernization has created a new underclass population characterized by destitution and dispossession. This group is linked to social instability that intimidates the state's control of the society. This article examines Chinese penal policy changes in the face of the rising underclass in urban areas. It sets a preliminary comparative analysis on the penal strategies of this new disadvantaged group in the USA and China. In reviewing the penal practices in both realms, the article argues that China shares an ideological affinity with its western counterparts for imposing punitive and managerial justice on the underclass. However, this rationale is realized in different ways. In China it is realized through the operation of an extensive system of administrative detention (different from criminal punishment) which is run by public security officials and is not part of the judicial system. Being located outside the judicial system enables greater efficiency by channelling the underclass through more flexible and cost effectiveness forms of incarceration and control.
Adaptive search template matching technique based on vehicle acceleration for monocular visual odometry system

One of the important tasks of an autonomous mobile vehicle is the reliable and fast estimation of its position over time. This paper presents the development of an adaptive technique to hasten and improve the quality of correlation based template matching for monocular visual odometry systems that estimate the relative motion of ground vehicles in low textured environments. Moreover, the factors that can affect the maximum permissible vehicle driving speed were determined and the related equations were derived. The developed system uses a single downward facing monocular camera installed at an optimum location to avoid the negative effect of directional sunlight and shadows which can disturb the correlation. In addition, the normalized cross correlation method is implemented to calculate the pixel displacement between image frames. Although this method is highly effective for template matching because of its invariance to linear brightness and contrast variations, it incurs high computational cost. Thus, the optimal sizes of image template and matching search area are selected and their locations are dynamically changed according to vehicle acceleration, in order to achieve a compromise between the performance and the computational cost of correlation. The proposed technique increases the allowable vehicle driving speed and reduces the probability of template false matching. Moreover, compared to traditional full search matching techniques, the adaptive technique demonstrates high efficiency and accuracy and improves the quality and speed of the correlation with more than 87% of reduction in computational cost.
A Probabilistic Radio Map Construction Scheme for Crowdsourcing Based Fingerprinting Localization
Venue: IEEE Sensors Journal

Fingerprinting localization using received signal strength (RSS) has been considered as a promising solution for indoor localization systems, due to its ease of use in deployed wireless networks. A major bottleneck is the difficult trade off between localization accuracy and site survey costs. Crowdsourcing based fingerprint collection is cost efficient because of user participation, but its accuracy is questioned due to the noisy sample locations. In this paper, we present a probabilistic radio map construction scheme with crowdsourcing collection, considering both accuracy and survey costs. In particular: 1) based on further studies of RSS properties, we introduce a concept of unfixed data collection in which sample locations are estimated by an additional localization mechanism and verify its effectiveness, although sample locations are noisy; 2) we present a modified parametric fitting method to better describe location signatures by transforming RSS into signal envelope; and 3) we propose a clustering based space partitioning algorithm to improve the fitting effect by reducing the number of multimodal distributions. Extensive simulations and experiments show the proposed method can construct an accurate radio map at a low cost, and achieve a localization accuracy of about 2.5 m, 20% improvement compared to using the classical radio map in our experimental scenario.
Cognitive Radio for Aeronautical Communications: A Survey
Venue: IEEE Access

Novel air traffic management (ATM) strategies are proposed through the Next Generation Air Transportation and Single European Sky for ATM Research projects to improve the capacity of the airspace and to meet the demands of the future air traffic. The implementation of the proposed solutions leads to increasing use of wireless data for aeronautical communications. Another emerging trend is the unmanned aerial vehicles. The unmanned aerial systems (UASs) need reliable wireless data link and dedicated spectrum allocation for its operation. On board broadband connectivity also needs dedicated spectrum to satisfy the quality of service requirements of the users. With the growing demand, the aeronautical spectrum is expected to be congested. However, the studies revealed that the aeronautical spectrum is underutilized due to the static spectrum allocation strategy. The aeronautical communication systems, such as air air and air ground communication systems, inflight infotainment systems, wireless avionics intra communications, and UAS, can benefit significantly from the introduction of cognitive radio based transmission schemes. This paper summarizes the current trends in aeronautical spectrum management followed by the major applications and contributions of cognitive radio in solving the spectrum scarcity crisis in the aeronautical domain. Also, to cope with the evolving technological advancement, researchers have prioritized the issues in the case of cognitive radio that needs to be addressed depending on the domain of operation. The proposed cognitive aeronautical communication systems should also be compliant with the Aeronautical Radio Incorporated and Aerospace Recommended Practice standards. An overview of these standards and the challenges that need immediate attention to make the solution feasible for a large scale operation, along with the future avenues of research is also furnished.
The collapse of a temporary structure

The latest collapses of entertainment structures in the USA and Europe brought the society's attention on the study of their safety. Among the most recent collapses there are the one of Radiohead's concerts in Toronto in 2012, the collapse of Jovanotti's stage concert in Trieste and Laura Pausini's one in Reggio Calabria in 2011. In this paper a specific metal truss structure made of aluminium was taken into consideration. A back analysis of the collapse of this metal truss structure is developed and produces a flow chart that points out the possible causes that led the structure to the collapse. By means of non linear analyses by Finite Element Model (FEM) the failure sequence of this particular structure is shown and forensic investigation concerning the whole phase of the construction phase is performed, starting from the design one, through the assembling and ending with the rigging phase.
Steps of a Research Study: From Research Question to Publication

The steps of a research study include a number of elements which are described starting from generating a research idea to final publication. The journey of good clinical research goes through a systematic circle of generating a research idea and framing a research question, thorough literature review, designing the study, writing the research protocol and getting ethics approval, the actual conduct of the study, monitoring, study completion, data analysis, and finally, publication of the results. This chapter takes the reader through each of these components briefly and gives a broad overview of the entire research process.
Complex network analysis of time series

Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network theory has been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network analysis of time series opens up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, ECG dynamics, economics and traffic systems.
Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy
Venue: IEEE Access

Epilepsy detection from electrical characteristics of EEG signals obtained from the brain of undergone subject is a challenge task for both research and neurologist due to the non stationary and chaotic nature of EEG signals. As epileptic EEG signals contain huge fluctuating information about the functional behavior of the brain, it is hard to distinguish the fundamental dynamic, complex network of EEG signals without considering the strength among the nodes as they are connected with each other on the basis of these strengths. The prior research on natural visibility graph did not consider this issue in epileptic seizure, although it is a very important key point to have representative information from the signals. Hence, this paper aims to introduce a new idea for epilepsy detection using complex network statistical properties by measuring different strengths of the edges in natural visibility graph theory, which is considered as weight. Thus, the proposed method is named "weighted visibility graph" In this proposed method, first, the epileptic EEG signals are transformed into complex network and then two important statistical properties of a network such as modularity and average weighted degree used for extracting the imperative characteristics from a network of EEG signals. After that, the extracted features are evaluated by two modern machine learning classifiers such as, support vector machine with a different kernel function and k nearest neighbor. The experimental results demonstrate that the combined effect of both features is valuable for network metrics to characterize the EEG time series signals in case of weighted complex network generating up to 100% classification accuracy.
A Complex Network Perspective on Clinical Science
Venue: Perspectives on psychological science a journal of the Association for Psychological Science

Contemporary classification systems for mental disorders assume that abnormal behaviors are expressions of latent disease entities. An alternative to the latent disease model is the complex network approach. Instead of assuming that symptoms arise from an underlying disease entity, the complex network approach holds that disorders exist as systems of interrelated elements of a network. This approach also provides a framework for the understanding of therapeutic change. Depending on the structure of the network, change can occur abruptly once the network reaches a critical threshold (the tipping point) Homogeneous and highly connected networks often recover more slowly from local perturbations when the network approaches the tipping point, potentially making it possible to predict treatment change, relapse, and recovery. In this article, we discuss the complex network approach as an alternative to the latent disease model and its implications for classification, therapy, relapse, and recovery.
Integrating learning styles and adaptive e learning system: Current developments, problems and opportunities
Venue: Comput. Hum. Behav.

Learning styles which refer to students' preferred ways to learn can play an important role in adaptive e learning systems. With the knowledge of different styles, the system can offer valuable advice and instructions to students and teachers to optimise students' learning process. Moreover, e leaning system which allows computerised and statistical algorithms opens the opportunity to overcome drawbacks of the traditional detection method that uses mainly questionnaire. These appealing reasons have led to a growing number of researches looking into the integration of learning styles and adaptive learning system. This paper, by reviewing 51 studies, delves deeply into different parts of the integration process. It captures a variety of aspects from learning styles theories selection in e learning environment, online learning styles predictors, automatic learning styles classification to numerous learning styles applications. The results offer insights into different developments, achievements and open problems in the field. Based on these findings, the paper also provides discussion, recommendations and guidelines for future researches. Review on how learning styles were integrated into adaptive e learning systems.'s is found to be the most popular theory that was applied.Explore online learning styles predictors and automatic classification methods.Examine different applications of learning styles in adaptive learning system.Recommendation and future research opportunities are proposed.
Safe controller optimization for quadrotors with Gaussian processes
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)

One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention.
Decentralized multi agent exploration with online learning of Gaussian processes
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)

Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multi agent approaches offer well known advantages for exploration. Previous decentralized multi agent exploration algorithms that use Gaussian processes as underlying data model, have only been validated through simulations. However, the implementation of an exploration algorithm brings difficulties that were not tackle yet. In this work, we propose an exploration algorithm that deals with the following challenges: (i) which information to transmit to achieve multi agent coordination; (ii) how to implement a light weight collision avoidance; (iii) how to learn the data's model without prior information. We validate our algorithm with two experiments employing real robots. First, we explore the magnetic field intensity with a ground based robot. Second, two quadcopters equipped with an ultrasound sensor explore a terrain profile. We show that our algorithm outperforms a meander and a random trajectory, as well as we are able to learn the data's model online while exploring.
Approximate continuous belief distributions for precise autonomous inspection
Venue: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

Precise inspection of cluttered environments by computationally constrained systems requires an efficient and high fidelity representation of the operating space. We propose a methodology to generate perceptual models of the environment that optimally handle the variation in clutter and provide a multi resolution and multi fidelity representation of the environment. Further, our approach is able to capture inherent structural dependencies thereby enabling efficient and precise inference. The approach employs a hierarchy of Gaussian Mixtures to approximate the underlying spatial distribution. We make use of techniques grounded in information theory to estimate the optimal size of the mixture model and to generate and update the hierarchy of mixtures. We show that the proposed approach is superior in terms of memory requirements and accuracy as compared to other state of the art 3D mapping techniques such as NDT occupancy maps and Gaussian Process occupancy maps.