nils-herrmann/clean_scirepeval huggingface dataset:
Tracking low resolution objects by metric preservation

Tracking low resolution (LR) targets is a practical yet quite challenging problem in real applications. The loss of discriminative details in the visual appearance of the L-R targets confronts most existing visual tracking methods. Although the resolution of the LR video inputs may be enhanced by super resolution (SR) techniques, the large computational cost for high-quality SR does not make it an attractive option. This paper presents a novel solution to track LR targets without performing explicit SR. This new approach is based on discriminative metric preservation that preserves the structure in the high resolution feature space for LR matching. In addition, we integrate metric preservation with differential tracking to derive a closed-form solution to motion estimation for LR video. Extensive experiments have demonstrated the effectiveness and efficiency of the proposed approach.
Local Lighting Invariant features for face recognition

In this paper, a novel Local Lighting Invariant (LLI) model is proposed and applied to face recognition to extract lighting invariant features. In the LLI model, the TV-L1 model based on nonlinear partial-differential equations (PDEpsilas) is adopted to build a lighting invariant face image space first. In the built lighting invariant image space, the basic idea of orthogonal Laplacian face method is adopted to learn the local manifold structure of face samples. Experimental results on three famous lighting face databases (Yale Face Database B, extended Yale Face Database B and CMU PIE Database) confirm that the learned local manifold structure of faces by LLI has more discriminating power than orthogonal Laplacianfaces for face recognition.
Fuzzy Power Quality Indicator

This paper proposes a novel fuzzy power quality indicator for representing the level of power quality status. In this work, we develop the fuzzy rules, degree membership function and inference rules for identifying the power quality level on six encountered types of power quality events. The traditional power quality indices are not performed well for assessing the status of the power quality because of their hard limits. Hence, this study develops the soft (human thinking like) indices for presenting the serious degree of power quality. Simulation results show the proposed fuzzy power quality indicator is suitable for representing the level of power quality events.
Commissioning of the New RF System with the HOM Damped RF Cavity

A new 178 MHz RF system has been commissioned at Duke Storage Ring. It consists of a 140 kW tetrode transmitter; higher order modes (HOM) damped RF cavity and the necessary frequency and voltage control electronics. The cavity walls are made of copper-on-stainless steel bimetal (8 mm Cu, 7 mm SS). The cavity has a larger beam pipe opening (700 mm in diameter) in the down-stream side, which allows the HOM propagating out of the cavity and being absorbed by the ceramic loads. The design details and the commissioning results are presented in this paper.
A Knowledge-Based Artificial Fish-Swarm Algorithm

The Artificial Fish-swarm Algorithm (AFA) is an intelligent population-based optimization algorithm inspired by the behaviors of fish swarm. Unfortunately, it sometimes fails to maintain an appropriate balance between exploration and exploitation, and has a drawback of blind search. In this paper, a novel cultured AFA with the crossover operator, namely CAFAC, is proposed to enhance its optimization performance. The crossover operator utilized is to promote the diversification of the artificial fish and make them inherit their parents’ characteristics. The Culture Algorithms (CA) is also combined with the AFA so that the blind search can be combated with. A total of 10 high-dimension and multi-peak functions are employed to investigate the optimization property of our CAFAC. Numerical simulation results demonstrate that the proposed CAFAC can indeed outperform the original AFA.
Visualizing Multivariate Networks: A Hybrid Approach

Multivariate networks are data sets that describe not only the relationships between a set of entities but also their attributes. In this paper, we present a new technique to determine the layout of a multivariate network using geodesic self-organizing map (GeoSOM). During the training process of a GeoSOM, graph distances are non-linearly combined with attribute similarities based on the network's graph distance distribution. The resulted layout has less edge crossings than those generated by the previous methods. We conducted a user study to evaluate the effectiveness of this hybrid approach. The results were compared against the most commonly used glyph-based technique. The user study shows that the hybrid approach helps users draw conclusions from both the relationship and vertex attributes of a multivariate network more quickly and accurately. In addition, users found it easier to compare different relationships of the same set of entities. Finally, the capability of the hybrid approach is demonstrated using the world military expenditures and weapon transfer networks.
A hybrid optimization method of Particle Swarm Optimization and Cultural Algorithm

A novel hybrid optimization algorithm, PSOCCA, based on the fusion of the Particle Swarm Optimization (PSO) and Cultural Algorithm (CA), is proposed in this paper to enhance the convergence characteristics of the original PSO. In the PSOCCA, the particle swarm is considered to be a population component of the CA, and three knowledge sources are stored in the belief space to update the population space and establish the relationship between the two CA spaces. A unique mutation operator inspired by the Differential Evolution (DE) is used to mutate those particles with unsatisfactory performances in the population space. A new kind of knowledge, average value knowledge, that is stored in the belief space, is also introduced to regulate the variation ratio in the swarm population. The optimization performance of our PSOCCA is investigated using ten high-dimension and multi-peak functions. Simulation results demonstrate that it can be superior to the regular PSO.
A 4D Monte Carlo Compton scattering code

Compton scattering of a laser beam and an electron beam in an accelerator has been used to generate an x-ray or gamma-ray beam at many facilities. To study the spatial, spectral and temporal characteristics of the Compton gamma-ray beam, we developed a 4-dimensional (3D space plus time) Monte Carlo computer simulation code (MCCMPT) to model the Compton scattering process. This code has been benchmarked against the measurement results at the High Intensity Gamma-ray Source (HIγS) facility at Duke University.
Unsupervised Learning of Dictionaries of Hierarchical Compositional Models

This paper proposes an unsupervised method for learning dictionaries of hierarchical compositional models for representing natural images. Each model is in the form of a template that consists of a small group of part templates that are allowed to shift their locations and orientations relative to each other, and each part template is in turn a composition of Gabor wavelets that are also allowed to shift their locations and orientations relative to each other. Given a set of unannotated training images, a dictionary of such hierarchical templates are learned so that each training image can be represented by a small number of templates that are spatially translated, rotated and scaled versions of the templates in the learned dictionary. The learning algorithm iterates between the following two steps: (1) Image encoding by a template matching pursuit process that involves a bottom-up template matching sub-process and a top-down template localization sub-process. (2) Dictionary re-learning by a shared matching pursuit process. Experimental results show that the proposed approach is capable of learning meaningful templates, and the learned templates are useful for tasks such as domain adaption and image cosegmentation.
Parallel Optimization of Frequent Algorithm on Multi-core Processors

In this paper, we present a novel precision integrated framework (PRIF) to parallelize the famous Frequent algorithm in the context of multi-core processors. PRIF does this by equally distributing the stream of items into several sub-threads, each of which runs an optimized weighted Frequent algorithm independently and in parallel. The items with frequency increments over a pre-defined threshold in the sub-threads are sent to a merging thread which uses the same optimized weighted Frequent algorithm to provide the final e-deficient frequent items. The theoretical correctness analysis is presented. Experiments with three real traffic traces show that PRIF exhibits excellent scalability and delivers almost linear speedup.
Capturing human hand motion in image sequences

Visually capturing human hand motion requires estimating the 3D hand global pose as well as its local finger articulations. This is a challenging task that requires a search in a high dimensional space due to the high degrees of freedom that fingers exhibit and the self occlusions caused by global hand motion. We propose a divide and conquer approach to estimate both global and local hand motion. By looking into the palm and extra feature points provided by fingers, the hand pose is determined from the palm using an iterative closed point (ICP) algorithm and factorization method. The hand global pose serves as the base frame for the finger motion capturing. Noticing the natural hand motion constraints, we propose an efficient tracking algorithm based on a sequential Monte Carlo technique for tracking finger motion. To enhance the accuracy, pose estimations and finger articulation tracking are performed in an iterative manner. Our experiments show that our approach is accurate and robust for natural hand movements.
Direction of arrival estimation via extended phase interferometry

A simple new estimator is proposed for direction finding applications which extends conventional phase-only interferometry to incorporate both calibrated phase and amplitude response data from antenna arrays. This is done by appropriately weighting the square of the baseline phase differences with the antenna gains. The incorporation of amplitude data generally provides significant performance improvement over phase-only interferometry with only a modest increase in computational complexity. Furthermore, this performance improvement increases with increasing additive noise and with increasing deviation of the antenna array response from an ideal geometric array response. As such, the new estimator strikes a nice compromise between phase-only interferometry and maximum likelihood estimation (MLE), this latter yielding nearly-optimal performance but at significant computational expense. Performance results are derived analytically for sufficiently high signal-to-noise ratio (SNR) or sample count, and are also demonstrated using antenna array responses simulated with the numerical electromagnetic code (NEC). The simulation results corroborate the analysis and clearly demonstrate that significant variance reduction in direction of arrival (DOA) estimation error can be achieved with the new estimator.
Bipolar grouping

Most affinity-based grouping methods only model the inclusive relation among the data. When the data set contains a significant amount of noise data that should not be included in any clusters, these methods are likely to lead to undesired results. To address this issue, this paper presents a new approach called bipolar grouping that is targeted on extracting the groups from the data while excluding the noise. This new approach incorporates both inclusive and exclusive relations among data, and a fixed-point procedure is proposed to find the stable groups. Its effectiveness and general applicability are demonstrated in two applications, including discovering common objects in images and tracking targets in clutter.
Nonlinear analysis of a rotor system with fixed-tilting pads combination journal bearing supports

The Reynolds equation with oil rupture boundary is solved based on database method. Nonlinear oil film force of combination journal bearing is obtained by retrieval, interpolation, and assembly techniques. As for symmetrical Jeffcott rigid rotor system supported by combination journal bearings, the nonlinear motion of the system is calculated by Poincaré map and self-adaptive Runge-Kutta method. Numerical results reveal rich and complex nonlinear behaviors and bifurcation.
Rectilinear Shortest Paths and Minimum Spanning Trees in the Presence of Rectilinear Obstacles

We study the rectilinear shortest paths and minimum spanning tree (MST) problems for a set of points in the plane in the presence of rectilinear obstacles. We use the track graph, a suitably defined grid-like structure, to obtain efficient solutions for both problems. The track graph consists of rectilinear tracks defined by the obstacles and the points for which shortest paths and a minimum spanning tree are sought. We use a growth process like Dijkstra's on the track graph to find shortest paths from any point in the set to all other points (the one-to-all shortest paths problem). For the one-to-all shortest paths problem for n points we derive an O(n min {log n, log e} + (e + k) log t) time algorithm, where e is the total number of edges of all obstacles, t is the number of extreme edges of all obstacles, and k is the number of intersections among obstacle tracks (all bounds are for the worst case). The MST for the points is constructed also in time O(n log n + (e + k) log t) by a hybrid method of searching for shortest paths while simultaneously constructing an MST. An interesting application of the MST algorithm is the approximation of Steiner trees in graphs.
Extended VIKOR for multi-criteria decision making problems under intuitionistic environment

The VIKOR method was developed for multi-criteria optimization of complex systems. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria. It introduces the multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution. The aim of this paper is to extend the VIKOR method for decision making problems with ratings of alternatives being expressed using intuitionistic fuzzy sets and criteria weights is completely unknown. To determine the weights with respect to a set of criteria represented by intuitionistic fuzzy sets, we establish an entropy weight model, which can be used to get the criteria weights. Furthermore, a novel nonlinear normalization technique is adapted to modify the VIKOR. Finally, a compared numerical example illustrates and clarifies the main results developed in this paper.
Quantification of stable cavitation dose during FUS-induced blood-brain barrier opening in mice and in non-human primates

A passive cavitation detector (PCD) has previously been developed and used to transcranially acquire the acoustic emissions stemming from the interaction between the microbubble and the brain tissue during FITS-induced bloodbrain barrier (BBB) opening, thereby determining the pressure threshold of inertial cavitation (IC) based on the quantification of the broadband response, i.e. inertial cavitation dose (ICD). Given that at certain pressures the BBB opens as a result of stable cavitation only, the stable cavitation dose (SCD) is introduced and quantified during BBB opening in mice and in non-human primates using monodisperse bubbles at varying pressures. In mice, the SCD was quantified with respect to the microbubble diameter and shell properties. Three different diameters (1-2, 4-5, and 6-8 m) with C18 acyl-chain length of the lipid shell, and three shell acyl-chain lengths (C16, C18, and C24) with a diameter of 4-5- m were used to induce BBB opening in the right hippocampus (1.5-MHz frequency; 100 cycles (67 s) pulse length; 10-Hz pulse repetition frequency; 1 minute duration, 0.15 or 0.30 MPa peak-rarefactional pressure). In monkeys, 4-5- m monodispersed bubbles were used to target different brain regions (500 kHz frequency; 5000 cycles (10 ms) pulse length; 2 Hz pulse repetition frequency; 2 minute sonication duration; 0.20 or 0.25 MPa peak-rarefactional pressure). A 10-MHz Pulse/Echo transducer and a broadband hydrophone were used as a passive cavitation detector (PCD) in mice and monkeys, respectively. The RMS PCD signal amplitude corresponding to the ultra-harmonics (SCDu) and at harmonics (SCDh) in the range of 4-16 MHz (mice) or 1-5 MHz (monkeys) was estimated. Due to the skull effect, there was no difference in SCDu between before and after microbubble administration in mice or monkeys, but the SCDh was found to be significantly higher in the 4-5- m and 6-8- m bubble cases compared to the 1-2- m case at 0.30 MPa in mice. In addition, the BBB opening threshold and SCD were not affected by the acyl-chain length of the shell, although the SCDh in the case of all bubble shells studied was significantly higher than the sham at 0.30 MPa. In monkeys, the SCDh was found to be significantly higher than the sham. As a result, the SCDh can serve as an indicator for BBB opening occurrence in mice and monkeys.
Micorfabrication of Miniature Electrode Array along Micro Channels

Miniature electrode together with micro channels on silicon substrate has potential application in MEMS (micro electromechanic system) field. The PN junction of silicon substrate gave the limitation on the formation of miniature electrode. Discussing this limitation resulted from PN junction and taking advantage of MEMS techniques, the design and fabrication of polycrystalline silicon electrode array at the both sides of micro channels on the silicon substrate was put forward. The V-I curve between the adjacent electrodes of electrode array shows that the influence of PN junction is reduced and the electrode array can provide electrical field for some separation chip, which may give a new idea to the integration design of LOC (laboratory on the chip)
Learning mixed templates for object recognition

This article proposes a method for learning object templates composed of local sketches and local textures, and investigates the relative importance of the sketches and textures for different object categories. Local sketches and local textures in the object templates account for shapes and appearances respectively. Both local sketches and local textures are extracted from the maps of Gabor filter responses. The local sketches are captured by the local maxima of Gabor responses, where the local maximum pooling accounts for shape deformations in objects. The local textures are captured by the local averages of Gabor filter responses, where the local average pooling extracts texture information for appearances. The selection of local sketch variables and local texture variables can be accomplished by a projection pursuit type of learning process, where both types of variables can be compared and merged within a common framework. The learning process returns a generative model for image intensities from a relatively small number of training images. The recognition or classification by template matching can then be based on log-likelihood ratio scores. We apply the learning method to a variety of object and texture categories. The results show that both the sketches and textures are useful for classification, and they complement each other.
A Queue-Length-Based Detection Scheme for Urban Traffic Congestion by VANETs

Traffic congestion has become a global problem in urban areas, resulting in a great waste of time and fuel every year. Thus, the real-time detection for road congestion becomes a great challenge for both scientists and engineers. Recently, the technology of vehicular ad-hoc networks (VANETs) has been utilized to alleviate this problem, and becomes an important component of the intelligent transportation system (ITS). In this paper, we propose a novel detection scheme for traffic congestion based on the communication between vehicles and road side unit (RSU). In our scheme, the length of waiting queue during red light acts as a measure index to evaluate the traffic status of intersections. In order to detect the queue length timely and accurately, a restricted greedy forwarding strategy is put forward to transmit the status information from tail vehicle by multihop broadcast. According to the driver-perceived performance, a congestion index is built to describe the traffic condition of detected intersections. Finally, all the proposed approaches are validated in a realistic scenario. The simulation results show that our scheme can detect urban traffic congestion effectively.
Software Design for Reliability Analysis Using Petri Nets

Petri Net is a special directional net, which can represent static structure and dynamic change in a system, and can reflect state of a system and development of event, and can express spread relationship of faults. It can get minimal cut sets (MCS) using Incidence Matrix of Petri Net, and get qualitative analysis according to the program of the method. Through Object Oriented programmed technology, based on VC++6.0, reliability analysis software is developed based on Petri Net. The software has drawing, calculation and fault diagnosis model. Fault diagnosis can get next state and final state according to amount of token or initial state, thus can get fault state of a system. Finally, through an example it can be proved correctness of the software.
Analysis of quantisation noise within signal band for sinusoidal signal

This study re-examines the spectrum of quantisation noise and studies the quantisation noise within signal band for sinusoid signals and for uniform quantiser. Both the process of sampling and folding which have not been fully taken into account in previous analysis are considered theoretically. The authors’ results show that the quantisation noise within signal band for sinusoid input decreases in broken line as the sampling rate increases, which suggests that a higher signal to in-band quantisation noise ratio will be obtained if the sinusoid signal is sampled at odd multiple of its fundamental frequency. Moreover, the effects of quantisation step size and the amplitude of sinusoid signal to in-band quantisation noise are presented by computer simulations.
Establishment and Management of a Digital Archive for Taiwanese Folk Arts

The integration of technology in culture has long been what digital archiving in Taiwan is dedicated to achieving. Professor Yung-Cheng Hsieh of the Department of Graphic Communication Arts, National Taiwan University of Arts, is now leading a team of digitization professionals and actively cooperating with the folk arts class at Chung Cheng Junior High School in Keelung. The ¿Digital Village of Taiwan Folk Sports¿ project, one of the national digital archives programs from the National Science Council (NSC), involves not only the construction of standard procedures for digitization, but at the same time, through the integration of digital media, such as the internet, animations and videos, presents a first-class Taiwanese folk arts dissemination platform.
Modeling and control of multi-parallel connected inverters in microgrids

In this paper, modeling and control of multi-parallel connected inverters are studied for microgrid. For the operation of paralleled inverters, the current sharing accuracy is important for the reliability of the system. In order to reduce the circulating current and achieve the power sharing accuracy, a novel decentralized communication-based droop controller is proposed for each energy source unit. Unlike the conventional droop control method, a PID controller is added to compensate the error value caused by the mismatched model parameters of inverters and different line impedance, which can fast achieve the current sharing accuracy and effectively enhance the power quality in spite of component mismatches. Simulation results from Matlab/Simulink verify that the proposed control strategy effectively solves the problem of circulating current between paralleled inverters.