nils-herrmann/clean_scirepeval huggingface dataset:
Person Re-Identification With Metric Learning Using Privileged Information.

Despite the promising progress made in recent years, person re-identification remains a challenging task due to complex variations in human appearances from different camera views. This paper presents a logistic discriminant metric learning method for this challenging problem. Different with most existing metric learning algorithms, it exploits both original data and auxiliary data during training, which is motivated by the new machine learning paradigm-learning using privileged information. Such privileged information is a kind of auxiliary knowledge, which is only available during training. Our goal is to learn an optimal distance function by constructing a locally adaptive decision rule with the help of privileged information. We jointly learn two distance metrics by minimizing the empirical loss penalizing the difference between the distance in the original space and that in the privileged space. In our setting, the distance in the privileged space functions as a local decision threshold, which guides the decision making in the original space like a teacher. The metric learned from the original space is used to compute the distance between a probe image and a gallery image during testing. In addition, we extend the proposed approach to a multi-view setting which is able to explore the complementation of multiple feature representations. In the multi-view setting, multiple metrics corresponding to different original features are jointly learned, guided by the same privileged information. Besides, an effective iterative optimization scheme is introduced to simultaneously optimize the metrics and the assigned metric weights. Experiment results on several widely-used data sets demonstrate that the proposed approach is superior to global decision threshold-based methods and outperforms most state-of-the-art results.
Miniaturized Dual-Band Circularly Polarized Quadruple Inverted-F Antenna for GPS Applications

A miniaturized dual-band quadruple inverted-F antenna with size of  ${\text{0.1}}\lambda _{0}\times {\text{0.1}}\lambda _{0}\times {\text{0.04}}\lambda _{0}$ is presented in this letter. The proposed structure consists of an array of four sequentially rotated inverted-F elements with quadrature excitations for circular polarization. Two methods are utilized to reduce the antenna size, including folding the radiating elements into spiral and meander shapes and introducing the coupling between adjacent elements. This antenna operates in the global positioning system (GPS) L1 and L2 bands, and the maximum gain performances in the two bands can be adjusted separately. The experimental results confirm that the obtained 10 dB impedance, 3 dB axial-ratio, and 3 dB realized gain bandwidths can fully cover the GPS L1/L2 bands. The good performances demonstrate that the proposed antenna is suitable for portable GPS devices.
Weighted Schatten  $p$ -Norm Minimization for Image Denoising and Background Subtraction

Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We propose a more flexible model, namely, the weighted Schatten p-norm minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives better approximation to the original low-rank assumption, but also considers the importance of different rank components. We analyze the solution of WSNM and prove that, under certain weights permutation, WSNM can be equivalently transformed into independent non-convex lp-norm subproblems, whose global optimum can be efficiently solved by generalized iterated shrinkage algorithm. We apply WSNM to typical low-level vision problems, e.g., image denoising and background subtraction. Extensive experimental results show, both qualitatively and quantitatively, that the proposed WSNM can more effectively remove noise, and model the complex and dynamic scenes compared with state-of-the-art methods.
Cascade Pumping of 1.9–3.3 μm Type-I Quantum Well GaSb-Based Diode Lasers

Cascade pumping of type-I quantum well gain sections was utilized to increase output power and efficiency of GaSb-based diode lasers operating in spectral region from 1.9 to 3.3 μm. Coated devices with ∼100-μm-wide aperture and 3-mm-long cavity demonstrated continuous wave (CW) output power of 1.96 W near 2 μm, 980 mW near 3 μm, 500 mW near 3.18 μm, and 360 mW near 3.25 μm at room temperature. The corresponding narrow ridge lasers with nearly diffraction limited beams operate in CW regime with tens of mW of output power up to 60 °C. Two step shallow/deep narrow/wide ridge waveguide devices showed lower threshold currents and higher slope efficiencies compared to single step narrow ridge lasers. Laterally coupled DFB lasers mounted epi-up generated above 10 mW of tunable single frequency CW power at 20 °C near 3.22 μm.
Simulation of a 94GHz radial spiral waveguide TWT

A novel slow wave structure with low work voltage, high work current, compact structure was proposed by using radial spiral waveguide. The dispersion and transmission character was analyzed by computer simulation, and its work voltage was obtained, which could be as low as several hundred Volts. As the emitter face of cylinder gun larger than ordinary TWT's, it is possible to produce a high current with low current density. The current could reach 30A while the current density is 100A/cm2. The fabrication is easy as the large size in W-band and above. The good transmission character indicates that the study is valuable.
ID localization and recognition for railway oil tank wagon in the industrial scene

This paper presents a localization and recognition method for railway oil tank wagon ID in the industrial scene. Firstly, we use the maximum stable extremal regions (MSER) algorithm to obtain the extreme regions, and extract effective areas from the extracted extremal regions. Then the effective areas of the regions are merged into triplets region sequence as an ID candidate region, and we select the ID region through linear distance estimation method. For overcoming the interference from camera installation angles, we make use of four-point correction method to correct ID areas, and an improved projection method to segment characters. Finally, we train Tesseract-OCR text recognition classifier to recognize ID characters. Experimental results show that the proposed method has a good localization and recognition performance for railway oil tank wagon ID in the complex industrial scene.
Near-field and far-field shared structure for NFC and CNSS applications

This study presents a shared structure for both near- and far-field operations using magnetic resonance coupling and electromagnetic radiation. The proposed structure is composed of a rectangular loop and two monopoles. A duplexer is designed to route the near- and far-field signals. For far-field operation, both dual-band resonance and dual-sense circular polarisation required by the compass navigation satellite system (CNSS) are achieved. For near-field operation, a wireless communication link operating at 13.56 MHz is established. The effect of matching distance on the transmission performances is studied and the optimal matching network is obtained. The proposed design is fabricated and verified experimentally. In the far-field regime, it can achieve 10 dB impedance bandwidths of 17.2 and 15.8% and 3 dB axial ratio bandwidths of 4.6 and 12.8% and the two CNSS bands are fully covered. In the near-field regime, an average transmission coefficient of 0.59 is achieved within the operation range of 5-30 mm by the proposed design. The obtained good performance reveals that the proposed shared structure is suitable to be applied in handheld devices for near-field communication and CNSS applications.
Fuzzy AHP-based decision model for infrastructure project financing

Infrastructure is very important to the development of a city. It is a bottleneck to accelerate the development of infrastructure if the infrastructure project mainly relies on the government funds. How to select a reasonable mode for infrastructure project financing is a key factor to the success of the project. Considering the fuzziness and complexity of various decision factors of the infrastructure project, this paper proposed a novel decision model for infrastructure project financing according to the fuzzy theory and analytical hierarchy process (AHP). The practical case shows that the constructed decision model can achieve the optimal financing effect for infrastructure projects.
Joint channel estimation and equalization algorithm for DTMB receivers

This paper presents a joint channel estimation and equalization algorithm for Chinese Digital Terrestrial Multimedia Broadcasting (DTMB) system. The initial channel estimation is carried out using PN sequence, then the frame header (FH) is eliminated from the received data and the frame body (FB) is obtained. After a one-tap frequency domain equalizer, the equalized data is fed into a proposed tracking loop to refine the channel estimation. Simulation results have proved the good performance in both static and dynamic propagation environments for DTMB receivers.
PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects

The location selection (ls) problem, which aims to mine the optimal location from a set of candidates to place a new facility such that a score (i.e., benefit or influence on some given objects) can be maximized, has drawn significant research attention in recent years. State-of-the-art ls techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities, which prevents classical ls solutions from selecting accurate results. In this paper, we introduce a generalizedls problem called Prime-ls which takes mobility and probability factors into consideration to address the aforementioned limitations. Specifically, given a set of candidate locations, Prime-ls aims to mine the optimal location which can influence the most number of moving objects. Also, to address the problem we propose an efficient algorithm called Pinocchio that leverages two pruning rules based on a novel distance measure. These rules enable us to prune many inferior candidate locations prior to influence computation, paving the way to efficient and accurate solution. Furthermore, we extend Pinocchio (Pinocchio-vo) by incorporating two optimization strategies during candidate validation phase, which further reduce unnecessary computations. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art ls techniques.
Ultrafast of-the-shelf V-DOT probes: Theory and experimentation

The present work is directly related to a repetitive 0.6 MV, 10 GW Tesla-driven Blumlein pulsed power generator, successfully developed at Loughborough University. In the second phase of the project, aiming at further compressing and modulating the output pulse using a bipolar pulse forming line system, multi-GHz diagnostic equipment is required to accurately measure the bipolar voltage output approaching ±200 kV, with a rise time of about 300 ps. The main aim of the present work is to provide the simplest and most robust design for the manufacture of the required probes, but the results can be applied for the voltage measurement in most ultrafast pulse forming line generators, such as those currently under development at Pau University.
Nonlinear disturbance observer based torque control for series elastic actuator

This paper presents a practical control approach for series elastic actuators(SEAs) to generate the desired torque. Specifically, the controller is applicable to both linear and nonlinear SEAs and it works well even in the presence of unknown payload parameters and external disturbances. Via the analysis and transformation of the SEA dynamics, a lumped disturbance signal is constructed and a form convenient for controller design is developed. Then, a nonlinear disturbance observer(NDOB) and a sliding-mode control scheme are introduced to synthesize the control law. The performance of the proposed controller is theoretically ensured by Lyapunov analysis. Taking a nonlinear SEA for instance, a series of simulations and hardware experiments are carried out. The results suggest the effectiveness and superior performance of the proposed method for SEA torque control by comparing it with the cascade-PID controller.
Position Control for Flexible Joint Robot Based on Online Gravity Compensation With Vibration Suppression

This paper presents a position control scheme for flexible joint robot based on online gravity compensation (OGC). After concluding some drawbacks of OGC-based PD control law, a nonlinear state feedback controller is designed, where analysis for overshoots of motor side and residual vibration of link side is provided. It is shown that asymptotic stability is guaranteed, and each signal of closed loop system is proved to be bounded via Lyapunov method. Comparing experiment results illustrate effectiveness of the proposed approach.
An OPF based approach for static voltage stability evaluation considering heat balance rule of overhead line

In this paper, overhead lines' heat balance rule (HBR) is considered in static voltage stability margin (VSM) evaluation issue and an optimal power flow (OPF) based approach for VSM evaluation is proposed. It captures the coupling nature between current & temperature of overhead line and also considers the influence of temperature on series resistance of conductor by introducing heat balance equation (HBE) of overhead lines as equality constraint and temperature as an state variable in optimal power flow which maximizes the VSM index. The proposed approach is capable of improving evaluation accuracy and the evaluation results of consider and disregard HBR of transmission line are compared via simulation.
Study on high power Ka-band rectangular double-grating sheet beam device

A Ka-band high power sheet beam backward wave oscillator (BWO) has been presented in this paper. In which the double-grating rectangular waveguide is used as the slow wave structure. A high power sheet beam with cross section 30mm×1mm is produced by a thin cathode. The output power exceeds 35 MW at 31.1 GHz. The beam-wave interaction efficiency is about 14.7%.
Unsupervised t-Distributed Video Hashing and Its Deep Hashing Extension

In this paper, a novel unsupervised hashing algorithm, referred to as t-USMVH, and its extension to unsupervised deep hashing, referred to as t-UDH, are proposed to support large-scale video-to-video retrieval. To improve robustness of the unsupervised learning, the t-USMVH combines multiple types of feature representations and effectively fuses them by examining a continuous relevance score based on a Gaussian estimation over pairwise distances, and also a discrete neighbor score based on the cardinality of reciprocal neighbors. To reduce sensitivity to scale changes for mapping objects that are far apart from each other, Student t-distribution is used to estimate the similarity between the relaxed hash code vectors for keyframes. This results in more accurate preservation of the desired unsupervised similarity structure in the hash code space. By adapting the corresponding optimization objective and constructing the hash mapping function via a deep neural network, we develop a robust unsupervised training strategy for a deep hashing network. The efficiency and effectiveness of the proposed methods are evaluated on two public video collections via comparisons against multiple classical and the state-of-the-art methods.
A novel temporary adhesive for solder ball attachment in fluxless reflow system

Except for the attachment of solder ball on soldering pads, flux is generally used to remove oxidation and clean the pad surface to be soldered for good wetting and form reliable joints. The upcoming concerns about fluxes are the residues on the solder balls and leading to voids after heating. For several years, a new reflow system, known as fluxless reflow system, claiming that flux is needless during reflow has been developed. In this new system, the functions of flux are replaced by formic acid or hydrogen in the form of gas, thus the residues and voiding issue can be mitigated. In the case of ball attachment in fluxless reflow system, it is critically significant to keep solder balls on the exact pads during the reflow process. Therefore, this paper will discuss the details about the applicability of a novel temporary adhesive in fluxless reflow system from the points of view of fundamental characteristics, including viscosity measurement by viscometer, wettability by measuring wetting angle, and residues from thermal analysis by thermogravimetric analyzer (TGA). As a result, low viscosity and wetting angle on a variety of pads showed applicable to be good wetting on copper pads in a jetting or spraying way. The characterization of ultra-low residues (<5wt%) was achieved by TGA. Reliability for fluxless reflow system were determined. Crosssection view of the sample with temporary adhesive applied showed good joints and no voids in solder balls as well as no side effects from reducing gas and temporary adhesive. In addition, joints strength was believed to be strong enough from the results of ball shear test. Hence, it is revealed that the novel temporary adhesive is a potential candidate for solder ball attachment in fluxless reflow system.
Research and Realization of Highway Project Management Information System Based on GIS

According to the requirements analysis of highway project management information system, a technical scheme using GIS (geographical information system) technology was put forward. A study method was adopted combining GIS technology and large commercial database management skills and this system was developed. Highway project management information system was divided into seven subsystems. It adopted the framework of Client/Server, and was based on Maplnfo 7.0 edition - a basic software platform of GIS. This system used SQL Server 2000 - a large rational database management system, to manage attribute data and MapX5.0 - an active control to management spatial data. After those an attribute database was developed to store and manage the attribute data and a graph database to graph data. In this system, some professional functions were realized, such as measure payment, contract management and schedule management, and also some GIS functions including query, cruise and graph-dealing, were realized.
Prediction of protein secondary structure using large margin nearest neighbour classification

Prediction of protein secondary structure from a primary sequence plays a critical role in structural biology. In this paper, we introduce a novel method for protein secondary structure prediction by using PSSM profiles and large margin nearest neighbor classification. Although the PSSM profiles and traditional nearest neighbor (NN) method can be directly used to predict secondary structure, since the PSSM profiles are not specifically designed for protein secondary structure prediction, the NN method could not achieve satisfactory prediction accuracy. To addressing this problem, we use a large margin nearest neighbor model to learn a Mahalanobis distance metric via convex semidefinite programming for nearest neighbor classification. Then, an energy-based rule is invoked to assign secondary structure. Tests show that, compared with other NN methods, significant performance improvement has been achieved with respect to prediction accuracy by the proposed method.
Design of dynamic voltage restorer and active power filter for wind power systems subject to unbalanced and harmonic distorted grid

Nowadays unbalanced voltage and harmonic distortion are in the majority of the power quality problems in wind energy conversion systems (WECS). To alleviate the impact registered by unbalanced and harmonic distortion, a new circuit topology comprising a dynamic voltage restorer (DVR) and an active power filter (APF) is presented. A frequency shifting technique based on coordinate transformation is employed to unify the positive and negative sequence harmonics into a resonant current controller. To improve the accuracy for harmonic detection, a second-order generalized integrator (SOGI), characterized with large bandwidth at specific frequency, is capable of separating harmonics from feeder current. The fundamental and harmonic current controllers can be individually realized by the resonant current controller and combined to form voltage command for a voltage-sourced inverter (VSI) based on superposition theorem. Not only the computing time but also the harmonic currents in the feeder can be effectively reduced along with the proposed approach. To share the dc-circuit with the DVR, the APF and DVR are in back-to-back connection in favor of unbalanced and harmonic compensation for specific grid bus. A 3kW squirrel cage induction generator (SCIG) driven by a permanent synchronous motor (PMSM) is performed for wind-turbine generator emulation. The experimental results validate that the grid-tied SCIG power quality can be refined when grid bus is subjected to unbalanced and harmonic distortion.
Beyond Distance Measurement: Constructing Neighborhood Similarity for Video Annotation

In the past few years, video annotation has benefited a lot from the progress of machine learning techniques. Recently, graph-based semi-supervised learning has gained much attention in this domain. However, as a crucial factor of these algorithms, the estimation of pairwise similarity has not been sufficiently studied. Generally, the similarity of two samples is estimated based on the Euclidean distance between them. But we will show that the similarity between two samples is not merely related to their distance but also related to the distribution of surrounding samples and labels. It is shown that the traditional distance-based similarity measure may lead to high classification error rates even on several simple datasets. To address this issue, we propose a novel neighborhood similarity measure, which explores the local sample and label distributions. We show that the neighborhood similarity between two samples simultaneously takes into account three characteristics: 1) their distance; 2) the distribution difference of the surrounding samples; and 3) the distribution difference of surrounding labels. Extensive experiments have demonstrated the superiority of neighborhood similarity over the existing distance-based similarity.
Online path searching for autonomous robot navigation

"Blind" goal reaching, a common autonomous robot navigation task, is applied in highly dynamic and unknown environments. In this it differs from "heuristic" goal reaching, which makes use of a geometrical or topological environmental map. Traditionally, blind goal reaching combines both obstacle-avoidance (OA) and goal-seeking (GS) behaviors, yet this is not a sufficient way to obtain a smooth path. And even more seriously, if the robot meets a dead end, the "OA+GS" approach may cause the dead-cycle (or local minimum) problem. This paper proposes a novel approach, memory grid (MG), which imitates the human memory and decision making functions. MG-based online path searching (PS) behavior provides a novel alternative to blind goal reaching. The experiments, including tests on a real sonar-based robot navigating in dead ends, have demonstrated not only that the performance of the "OA+GS+PS" approach is superior to that of "OA+GS" navigation algorithms, but also that, unlike the traditional "OA+GS" approach, it can solve the dead-cycle problem.