nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of linear and nonlinear model predictive control of wind turbines using LIDAR

Recent developments in remote sensing are offering a promising opportunity to rethink conventional control strategies of wind turbines. With technologies such as LIDAR, the information about the incoming wind field - the main disturbance to the system - can be made available ahead of time. Feedforward control can be easily combined with traditional collective pitch feedback controllers and has been successfully tested on real systems. Nonlinear model predictive controllers adjusting both collective pitch and generator torque can further reduce structural loads in simulations but have higher computational times compared to feedforward or linear model predictive controller. This paper compares a linear and a commercial nonlinear model predictive controller to a baseline controller. On the one hand simulations show that both controller have significant improvements if used along with the preview of the rotor effective wind speed. On the other hand the nonlinear model predictive controller can achieve better results compared to the linear model close to the rated wind speed.
Activity of the dual kinase inhibitor lapatinib (GW572016) against HER-2-overexpressing and trastuzumab-treated breast cancer cells.

Lapatinib (GW572016) is a selective inhibitor of both epidermal growth factor receptor (EGFR) and HER-2 tyrosine kinases. Here, we explore the therapeutic potential of lapatinib by testing its effect on tumor cell growth in a panel of 31 characterized human breast cancer cell lines, including trastuzumab-conditioned HER-2-positive cell lines. We further characterize its activity in combination with trastuzumab and analyze whether EGFR and HER-2 expression or changes induced in the activation of EGFR, HER-2, Raf, AKT, or extracellular signal-regulated kinase (ERK) are markers of drug activity. We report that concentration-dependent antiproliferative effects of lapatinib were seen in all breast cancer cell lines tested but varied significantly between individual cell lines with up to 1,000-fold difference in the IC(50)s (range, 0.010-18.6 micromol/L). Response to lapatinib was significantly correlated with HER-2 expression and its ability to inhibit HER-2, Raf, AKT, and ERK phosphorylation. Long-term in vivo lapatinib studies were conducted with human breast cancer xenografts in athymic mice. Treatment over 77 days resulted in a sustained and significant reduction in xenograft volume compared with untreated controls. For the combination of lapatinib plus trastuzumab, synergistic drug interactions were observed in four different HER-2-overexpressing cell lines. Moreover, lapatinib retained significant in vitro activity against cell lines selected for long-term outgrowth (>9 months) in trastuzumab-containing (100 microg/mL) culture medium. These observations provide a clear biological rationale to test lapatinib as a single agent or in combination with trastuzumab in HER-2-overexpressing breast cancer and in patients with clinical resistance to trastuzumab.
High voltage Bi-directional flyback converter for capacitive actuator

This paper presents a high voltage DC-DC converter topology for bi-directional energy transfer between a low voltage DC source and a high voltage capacitive load. The topology is a bi-directional flyback converter with variable switching frequency control during the charge mode, and constant switching frequency control during the discharge mode. The converter is capable of charging the capacitive load from 24 V DC source to 2.5 kV, and discharges it to 0 V. The flyback converter has been analyzed in detail during both charge and discharge modes, by considering all the parasitic elements in the converter, including the most dominating parameters of the high voltage transformer viz., self-capacitance and leakage inductance. The specific capacitive load for this converter is a dielectric electro active polymer (DEAP) actuator, which can be used as an effective replacement for conventional actuators in a number of applications. In this paper, the discharging energy efficiency definition is introduced. The proposed converter has been experimentally tested with the film capacitive load and the DEAP actuator, and the experimental results are shown together with the efficiency measurements.
Joint Multilabel Classification With Community-Aware Label Graph Learning

As an important and challenging problem in machine learning and computer vision, multilabel classification is typically implemented in a max-margin multilabel learning framework, where the inter-label separability is characterized by the sample-specific classification margins between labels. However, the conventional multilabel classification approaches are usually incapable of effectively exploring the intrinsic inter-label correlations as well as jointly modeling the interactions between inter-label correlations and multilabel classification. To address this issue, we propose a multilabel classification framework based on a joint learning approach called label graph learning (LGL) driven weighted Support Vector Machine (SVM). In principle, the joint learning approach explicitly models the inter-label correlations by LGL, which is jointly optimized with multilabel classification in a unified learning scheme. As a result, the learned label correlation graph well fits the multilabel classification task while effectively reflecting the underlying topological structures among labels. Moreover, the inter-label interactions are also influenced by label-specific sample communities (each community for the samples sharing a common label). Namely, if two labels have similar label-specific sample communities, they are likely to be correlated. Based on this observation, LGL is further regularized by the label Hypergraph Laplacian. Experimental results have demonstrated the effectiveness of our approach over several benchmark data sets.
Semantics-based obfuscation-resilient binary code similarity comparison with applications to software plagiarism detection

Existing code similarity comparison methods, whether source or binary code based, are mostly not resilient to obfuscations. In the case of software plagiarism, emerging obfuscation techniques have made automated detection increasingly difficult. In this paper, we propose a binary-oriented, obfuscation-resilient method based on a new concept, longest common subsequence of semantically equivalent basic blocks, which combines rigorous program semantics with longest common subsequence based fuzzy matching. We model the semantics of a basic block by a set of symbolic formulas representing the input-output relations of the block. This way, the semantics equivalence (and similarity) of two blocks can be checked by a theorem prover. We then model the semantics similarity of two paths using the longest common subsequence with basic blocks as elements. This novel combination has resulted in strong resiliency to code obfuscation. We have developed a prototype and our experimental results show that our method is effective and practical when applied to real-world software.
Motor skill performance and physical activity in preschool children.

Children with better-developed motor skills may find it easier to be active and engage in more physical activity (PA) than those with less-developed motor skills. The purpose of this study was to examine the relationship between motor skill performance and PA in preschool children. Participants were 80 three- and 118 four-year-old children. The Children's Activity and Movement in Preschool Study (CHAMPS) Motor Skill Protocol was used to assess process characteristics of six locomotor and six object control skills; scores were categorized as locomotor, object control, and total. The actigraph accelerometer was used to measure PA; data were expressed as percent of time spent in sedentary, light, moderate-to-vigorous PA (MVPA), and vigorous PA (VPA). Children in the highest tertile for total score spent significantly more time in MVPA (13.4% vs. 12.8% vs. 11.4%) and VPA (5% vs. 4.6% vs. 3.8%) than children in middle and lowest tertiles. Children in the highest tertile of locomotor scores spent significantly less time in sedentary activity than children in other tertiles and significantly more time in MVPA (13.4% vs. 11.6%) and VPA (4.9% vs. 3.8%) than children in the lowest tertile. There were no differences among tertiles for object control scores. Children with poorer motor skill performance were less active than children with better-developed motor skills. This relationship between motor skill performance and PA could be important to the health of children, particularly in obesity prevention. Clinicians should work with parents to monitor motor skills and to encourage children to engage in activities that promote motor skill performance.
Performance analysis of image segmentation using watershed algorithm, fuzzy C-means of clustering algorithm and Simulink design

An Integration of image processing and soft computing techniques for the image analysis plays a significant contribution in the field of image processing. One of the important class of image analysis techniques is image segmentation. There are applications in various fields some of them are sorting product in the industry, surveillance system in the security zones, biomedical processing, medical imaging, environmental predictions etc. In this paper the study of fuzzy C-means clustering method, watershed based image segmentation and Simulink design based image segmentation with some enhancement design algorithm for the improvement of the overall performance of image segmentation is used. The main objective of this paper is to bring the conviction of utility of watershed, Simulink and FCM based segmentation for the applications in medical imaging, and other images where lots of spatial & inherent information is available, by considering their performance analysis metrics. This performance analysis is used to justify and decide which method is appropriate and more convincing for the effective analysis of more complex imaging system.
Transductive support vector machines and applications in bioinformatics for promoter recognition

This paper introduces a novel transductive support vector machine (TSVM) model and compares it with the traditional inductive SVM on a key problem in bioinformatics - promoter recognition. While inductive reasoning is concerned with the development of a model (a function) to approximate data from the whole problem space (induction), and consecutively using this model to predict output values for a new input vector (deduction), in the transductive inference systems a model is developed for every new input vector based on some closest to the new vector data from an existing database and this model is used to predict only the output for this vector. The TSVM outperforms by far the inductive SVM models applied on the same problems. Analysis is given on the advantages and disadvantages of the TSVM. Hybrid TSVM-evolving connections systems are discussed as directions for future research.
Communication and networking of UAV-based systems: Classification and associated architectures

Many advancements have been taking place in unmanned aerial vehicle (UAV) technology lately. This is leading towards the design and development of UAVs with various sizes that possess increased on-board processing, memory, storage, and communication capabilities. Consequently, UAVs are increasingly being used in a vast amount of commercial, military, civilian, agricultural, and environmental applications. However, to take full advantages of their services, these UAVs must be able to communicate efficiently with each other using UAV-to-UAV (U2U) communication and with existing networking infrastructures using UAV-to-Infrastructure (U2I) communication. In this paper, we identify the functions, services and requirements of UAV-based communication systems. We also present networking architectures, underlying frameworks, and data traffic requirements in these systems as well as outline the various protocols and technologies that can be used at different UAV communication links and networking layers. In addition, the paper discusses middleware layer services that can be provided in order to provide seamless communication and support heterogeneous network interfaces. Furthermore, we discuss a new important area of research, which involves the use of UAVs in collecting data from wireless sensor networks (WSNs). We discuss and evaluate several approaches that can be used to collect data from different types of WSNs including topologies such as linear sensor networks (LSNs), geometric and clustered WSNs. We outline the benefits of using UAVs for this function, which include significantly decreasing sensor node energy consumption, lower interference, and offers considerably increased flexibility in controlling the density of the deployed nodes since the need for the multihop approach for sensor-tosink communication is either eliminated or significantly reduced. Consequently, UAVs can provide good connectivity to WSN clusters.
Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments

In this paper we present a framework for 3D geometric shape segmentation for close-range scenes used in mobile manipulation and grasping, out of sensed point cloud data. Our proposed approach proposes a robust geometric mapping pipeline for large input datasets that extracts relevant objects useful for a personal robotic assistant to perform manipulation tasks. The objects are segmented out from partial views and a reconstructed model is computed by fitting geometric primitive classes such as planes, spheres, cylinders, and cones. The geometric shape coefficients are then used to reconstruct missing data. Residual points are resampled and triangulated, to create smooth decoupled surfaces that can be manipulated. The resulted map is represented as a hybrid concept and is comprised of 3D shape coefficients and triangular meshes used for collision avoidance in manipulation routines.
A practice-oriented framework for measuring privacy and utility in data sanitization systems

Published data is prone to privacy attacks. Sanitization methods aim to prevent these attacks while maintaining usefulness of the data for legitimate users. Quantifying the trade-off between usefulness and privacy of published data has been the subject of much research in recent years. We propose a pragmatic framework for evaluating sanitization systems in real-life and use data mining utility as a universal measure of usefulness and privacy. We propose a definition for data mining utility that can be tuned to capture the needs of data users and the adversaries' intentions in a setting that is specified by a database, a candidate sanitization method, and privacy and utility concerns of data owner. We use this framework to evaluate and compare privacy and utility offered by two well-known sanitization methods, namely k-anonymity and ε-differential privacy, when UCI's "Adult" dataset and the Weka data mining package is used, and utility and privacy measures are defined for users and adversaries. In the case of k-anonymity, we compare our results with the recent work of Brickell and Shmatikov (KDD 2008), and show that using data mining algorithms increases their proposed adversarial gains.
Protecting location privacy: optimal strategy against localization attacks

The mainstream approach to protecting the location-privacy of mobile users in location-based services (LBSs) is to alter the users' actual locations in order to reduce the location information exposed to the service provider. The location obfuscation algorithm behind an effective location-privacy preserving mechanism (LPPM) must consider three fundamental elements: the privacy requirements of the users, the adversary's knowledge and capabilities, and the maximal tolerated service quality degradation stemming from the obfuscation of true locations. We propose the first methodology, to the best of our knowledge, that enables a designer to find the optimal LPPM for a LBS given each user's service quality constraints against an adversary implementing the optimal inference algorithm. Such LPPM is the one that maximizes the expected distortion (error) that the optimal adversary incurs in reconstructing the actual location of a user, while fulfilling the user's service-quality requirement. We formalize the mutual optimization of user-adversary objectives (location privacy vs. correctness of localization) by using the framework of Stackelberg Bayesian games. In such setting, we develop two linear programs that output the best LPPM strategy and its corresponding optimal inference attack. Our optimal user-centric LPPM can be easily integrated in the users' mobile devices they use to access LBSs. We validate the efficacy of our game theoretic method against real location traces. Our evaluation confirms that the optimal LPPM strategy is superior to a straightforward obfuscation method, and that the optimal localization attack performs better compared to a Bayesian inference attack.
Fingerprint Identification in Biometric SecuritySystems

—Perhaps the most important application of accurate personal identification is securing limited access systems from malicious attacks. Among all the presently employed biometric techniques, fingerprint identification systems have received the most attention due to the long history of fingerprints and their extensive use in forensics. This paper deals with the issue of selection of an optimal algorithm for fingerprint matching in order to design a system that matches required specifications in performance and accuracy. Two competing algorithms were compared against a common database using MATLAB simulations.
Exploring the Millennium Run - Scalable Rendering of Large-Scale Cosmological Datasets

In this paper we investigate scalability limitations in the visualization of large-scale particle-based cosmological simulations, and we present methods to reduce these limitations on current PC architectures. To minimize the amount of data to be streamed from disk to the graphics subsystem, we propose a visually continuous level-of-detail (LOD) particle representation based on a hierarchical quantization scheme for particle coordinates and rules for generating coarse particle distributions. Given the maximal world space error per level, our LOD selection technique guarantees a sub-pixel screen space error during rendering. A brick-based page-tree allows to further reduce the number of disk seek operations to be performed. Additional particle quantities like density, velocity dispersion, and radius are compressed at no visible loss using vector quantization of logarithmically encoded floating point values. By fine-grain view-frustum culling and presence acceleration in a geometry shader the required geometry throughput on the GPU can be significantly reduced. We validate the quality and scalability of our method by presenting visualizations of a particle-based cosmological dark-matter simulation exceeding 10 billion elements.
Information-theoretic compression of pose graphs for laser-based SLAM

In graph-based simultaneous localization and mapping, the pose graph grows over time as the robot gathers information about the environment. An ever growing pose graph, however, prevents long-term mapping with mobile robots. In this paper, we address the problem of efficient information-theoretic compression of pose graphs. Our approach estimates the mutual information between the laser measurements and the map to discard the measurements that are expected to provide only a small amount of information. Our method subsequently marginalizes out the nodes from the pose graph that correspond to the discarded laser measurements. To maintain a sparse pose graph that allows for efficient map optimization, our approach applies an approximate marginalization technique that is based on Chow-Liu trees. Our contributions allow the robot to effectively restrict the size of the pose graph.Alternatively, the robot is able to maintain a pose graph that does not grow unless the robot explores previously unobserved parts of the environment. Real-world experiments demonstrate that our approach to pose graph compression is well suited for long-term mobile robot mapping.
Research and Implementation of Mobile Application Security Detection Combining Static and Dynamic

With the popularity of the Internet and mobile intelligent terminals, the number of mobile applications is exploding. Mobile intelligent terminals trend to be the mainstream way of people's work and daily life online in place of PC terminals. Mobile application system brings some security problems inevitably while it provides convenience for people, and becomes a main target of hackers. Therefore, it is imminent to strengthen the security detection of mobile applications. This paper divides mobile application security detection into client security detection and server security detection. We propose a combining static and dynamic security detection method to detect client-side. We provide a method to get network information of server by capturing and analyzing mobile application traffic, and propose a fuzzy testing method based on HTTP protocol to detect server-side security vulnerabilities. Finally, on the basis of this, an automated platform for security detection of mobile application system is developed. Experiments show that the platform can detect the vulnerabilities of mobile application client and server effectively, and realize the automation of mobile application security detection. It can also reduce the cost of mobile security detection and enhance the security of mobile applications.
Markov Cortical High-Density Counterstream Architectures

, (2013); 342  Science et al. Nikola T. Markov Cortical High-Density Counterstream Architectures This copy is for your personal, non-commercial use only. clicking here. colleagues, clients, or customers by , you can order high-quality copies for your If you wish to distribute this article to others here. following the guidelines can be obtained by Permission to republish or repurpose articles or portions of articles ):  November 4, 2013  www.sciencemag.org (this information is current as of The following resources related to this article are available online at http://www.sciencemag.org/content/342/6158/1238406.full.html version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag.org/content/342/6158/1238406.full.html#related found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag.org/content/342/6158/1238406.full.html#ref-list-1 , 55 of which can be accessed free: cites 124 articles This article