nils-herrmann/clean_scirepeval huggingface dataset:
Learning a real-time generic tracker using convolutional neural networks

This paper presents a novel frame-pair based method for visual object tracking. Instead of adopting two-stream Convolutional Neural Networks (CNNs) to represent each frame, we stack frame pairs as the input, resulting in a single-stream CNN tracker with much fewer parameters. The proposed tracker can learn generic motion patterns of objects with much less annotated videos than previous methods. Besides, it is found that trackers trained using two successive frames tend to predict the centers of searching windows as the locations of tracked targets. To alleviate this problem, we propose a novel sampling strategy for off-line training. Specifically, we construct a pair by sampling two frames with a random offset. The offset controls the moving smoothness of objects. Experiments on the challenging VOT14 and OTB datasets show that the proposed tracker performs on par with recently developed generic trackers, but with much less memory. In addition, our tracker can run in a speed of over 100 (30) fps with a GPU (CPU), much faster than most deep neural network based trackers.
Generalized Feature Extraction for Wrist Pulse Analysis: From 1-D Time Series to 2-D Matrix

Traditional Chinese pulse diagnosis, known as an empirical science, depends on the subjective experience. Inconsistent diagnostic results may be obtained among different practitioners. A scientific way of studying the pulse should be to analyze the objectified wrist pulse waveforms. In recent years, many pulse acquisition platforms have been developed with the advances in sensor and computer technology. And the pulse diagnosis using pattern recognition theories is also increasingly attracting attentions. Though many literatures on pulse feature extraction have been published, they just handle the pulse signals as simple 1-D time series and ignore the information within the class. This paper presents a generalized method of pulse feature extraction, extending the feature dimension from 1-D time series to 2-D matrix. The conventional wrist pulse features correspond to a particular case of the generalized models. The proposed method is validated through pattern classification on actual pulse records. Both quantitative and qualitative results relative to the 1-D pulse features are given through diabetes diagnosis. The experimental results show that the generalized 2-D matrix feature is effective in extracting both the periodic and nonperiodic information. And it is practical for wrist pulse analysis.
Electrical characterization of ultrathin single crystalline Gd2O3/Si(100) with Pt top electrode

Capacitor composed of single crystalline Gd<sub>2</sub>O<sub>3</sub> on Si(100) with Pt top electrode was fabricated by molecular beam epitaxy. We present a systematic study of electrical properties of as-grown single crystalline Pt/Gd<sub>2</sub>O<sub>3</sub>/Si(100). Three capacitors with different thickness are used for electrical evaluation. The EOT of the samples are estimated to be 0.7 nm, 1.2 nm and 1.8 nm respectively. Work function of Pt on Gd<sub>2</sub>O<sub>3</sub> is pinned at 4.75 eV and due to the lattice mismatch between Gd<sub>2</sub>O<sub>3</sub>, the interface state density is in the level of 10<sup>13</sup> extracted by Terman and conductance methods.
A new approach to appearance-based face recognition

Current holistic appearance based face recognition methods require a high dimensional feature space to attain fruitful performance. In this paper, we have proposed a relatively low feature dimensional, template-matching scheme to cope with the transformed appearance-based face recognition problem. We use aggregated Gabor filter responses to represent face images. We investigated the effect of "duplicate" images (images from different sessions) and the effect of facial expressions. Our results indicate that the proposed method is more robust in recognizing "duplicate" images with variations in facial expression than the principal component analysis method.
Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel

Motivated by the great success of dynamic time warping (DTW) in time series matching, Gaussian DTW kernel had been developed for support vector machine (SVM)-based time series classification. Counter-examples, however, had been subsequently reported that Gaussian DTW kernel usually cannot outperform Gaussian RBF kernel in the SVM framework. In this paper, by extending the Gaussian RBF kernel, we propose one novel class of Gaussian elastic metric kernel (GEMK), and present two examples of GEMK: Gaussian time warp edit distance (GTWED) kernel and Gaussian edit distance with real penalty (GERP) kernel. Experimental results on UCR time series data sets show that, in terms of classification accuracy, SVM with GEMK is much superior to SVM with Gaussian RBF kernel and Gaussian DTW kernel, and the state-of-the-art similarity measure methods.
Competitive coding scheme for palmprint verification

There is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Among the many approaches, biometrics such as palmprints provide highly effective automatic mechanisms for use in personal identification. This paper presents a new method for extracting features from palmprints using the competitive coding scheme and angular matching. The competitive coding scheme uses multiple 2-D Gabor filters to extract orientation information from palm lines. This information is then stored in a feature vector called the competitive code. The angular matching with an effective implementation is then defined for comparing the proposed codes, which can make over 9,000 comparisons within 1s. In our testing database of 7,752 palmprint samples from 386 palms, we can achieve a high genuine acceptance rate of 98.4% and a low false acceptance rate of 3/spl times/10/sup -6/%. The execution time for the whole process of verification, including preprocessing, feature extraction and final matching, is 1s.
Detecting Wide Lines Using Isotropic Nonlinear Filtering

Lines provide important information in images, and line detection is crucial in many applications. However, most of the existing algorithms focus only on the extraction of line positions, ignoring line thickness. This paper presents a novel wide line detector using an isotropic nonlinear filter. Unlike most existing edge and line detectors which use directional derivatives, our proposed wide line detector applies a nonlinear filter to extract a line completely without any derivative. The detector is based on the isotropic responses via circular masks. A general scheme for the analysis of the robustness of the proposed wide line detector is introduced and the dynamic selection of parameters is developed. In addition, this paper investigates the relationship between the size of circular masks and the width of detected lines. A sequence of tests has been conducted on a variety of image samples and our experimental results demonstrate the feasibility and effectiveness of the proposed method
Design of U-Shape Channel Tunnel FETs With SiGe Source Regions

In this brief, a novel U-shape-channel tunneling field-effect transistor (UTFET) with a SiGe source region is investigated by 2-D technology computer aided design simulation. The enlarged tunneling area and enhanced tunneling rate dramatically increase the tunneling current when the device is turned on. Meanwhile, the off-leakage current of UTFET is suppressed because of the extended physical channel length. The on-state tunneling current of UTFET can be further improved by introducing an n+-doped Si delta layer under the source region. The inserted delta layer significantly shortens the band-to-band tunneling path, enlarges tunneling area, and thus enhances the tunneling rate of this device. The average value of the subthreshold swing (SS) of the optimized UTFET is 58 mV/dec when VGS is varied from 0 to 0.46 V. Using the SiGe-source UTFET structure with a delta layer, the merits of low leakage current, high drive current, and ultralow SS can be realized simultaneously.
Texture Enhanced Image Denoising via Gradient Histogram Preservation

Image denoising is a classical yet fundamental problem in low level vision, as well as an ideal test bed to evaluate various statistical image modeling methods. One of the most challenging problems in image denoising is how to preserve the fine scale texture structures while removing noise. Various natural image priors, such as gradient based prior, nonlocal self-similarity prior, and sparsity prior, have been extensively exploited for noise removal. The denoising algorithms based on these priors, however, tend to smooth the detailed image textures, degrading the image visual quality. To address this problem, in this paper we propose a texture enhanced image denoising (TEID) method by enforcing the gradient distribution of the denoised image to be close to the estimated gradient distribution of the original image. A novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Our experimental results demonstrate that the proposed GHP based TEID can well preserve the texture features of the denoised images, making them look more natural.
Door Knob Hand Recognition System

Biometric applications have been used globally in everyday life. However, conventional biometrics is created and optimized for high-security scenarios. Being used in daily life by ordinary untrained people is a new challenge. Facing this challenge, designing a biometric system with prior constraints of ergonomics, we propose ergonomic biometrics design model, which attains the physiological factors, the psychological factors, and the conventional security characteristics. With this model, a novel hand-based biometric system, door knob hand recognition system (DKHRS), is proposed. DKHRS has the identical appearance of a conventional door knob, which is an optimum solution in both physiological factors and psychological factors. In this system, a hand image is captured by door knob imaging scheme, which is a tailored omnivision imaging structure and is optimized for this predetermined door knob appearance. Then features are extracted by local Gabor binary pattern histogram sequence method and classified by projective dictionary pair learning. In the experiment on a large data set including 12 000 images from 200 people, the proposed system achieves competitive recognition performance comparing with conventional biometrics like face and fingerprint recognition systems, with an equal error rate of 0.091%. This paper shows that a biometric system could be built with a reliable recognition performance under the ergonomic constraints.
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics

This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, locality preserving projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications
Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising

Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient-based, sparse representation-based, and nonlocal self-similarity-based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper, we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed for the denoising of images consisting of regions with different textures. An algorithm is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the texture appearance in the denoised images, making them look more natural.
Three Dimensional Palmprint Recognition using Structured Light Imaging

Palmprint is one of the most unique and stable biometric characteristics. Although 2D palmprint recognition can achieve high accuracy, the 2D palmprint images can be easily counterfeited and much 3D depth information is lost in the imaging process. This paper presents a new approach, 3D palmprint recognition, to exploit the 3D structural information of the palm surface. The structured-light imaging is used to acquire the 3D palmprint data, from which the features of Mean Curvature, Gauss Curvature and Surface Type (ST) are extracted. A fast feature matching and score level fusion strategy are then used to classify the input 3D palmprint data. With the established 3D palmprint database, a series of verification and identification experiments are conducted and the results show that 3D palmprint technique can achieve high recognition rate while having high anti-counterfeiting capability.
Feature Band Selection for Online Multispectral Palmprint Recognition

A palmprint is a unique and reliable biometric feature with high usability. In the past decades, many palmprint recognition systems have been successfully developed. However, most of the previous work used the white light as the illumination source, and the recognition accuracy and anti-spoof capability is limited. Recently, multispectral imaging has attracted considerable research attention as it can acquire more discriminative information in a short time. One crucial step in developing online multispectral palmprint systems is how to determine the optimal number of spectral bands and select the most representative bands to build the system. This paper presents a study on feature band selection by analyzing hyperspectral palmprint data (520-1050 nm). Our experimental results showed that three spectral bands could provide most of the discriminate information of a palmprint. This finding could be used as the guidance for designing new online multispectral palmprint systems.
Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation

The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness.
Atomic layer deposited Hf0.5Zr0.5O2-based flexible RRAM

Hf<inf>0.5</inf>Zr<inf>0.5</inf>O<inf>2</inf> (HZO) — based flexible RRAM was fabricated by low-temperature atomic layer deposition (ALD) process. The resistive switching (RS) characteristics were improved by the stacked structure (Ag/HZO/Al<inf>2</inf>O<inf>3</inf>/ITO/PET). ON/OFF ratio was increased from 10<sup>2</sup> to 10<sup>6</sup> and the values of the set voltage (from 0.5∼2.5V to 0.1∼1V) and reset voltage (from −0.8∼-2V to −0.1∼-0.8V) were reduced and more uniform, which should be linked to the regulation of added Al<inf>2</inf>O<inf>3</inf> film. This study provides a promising candidate for flexible memory devices and wearable electronics.
Effects of the Variation of ${V}_{\text {GS}}$ Sweep Range on the Performance of Negative Capacitance FETs

We report the first investigation of the impacts of gate voltage sweep range <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS,range}}$ </tex-math></inline-formula> on the performance of negative capacitance (NC) transistors. As <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS,range}}$ </tex-math></inline-formula> is reduced, NC GeSn pFETs generally exhibit an increased hysteresis or a transition from non-hysteretic to hysteretic, and show a degradation of <inline-formula> <tex-math notation="LaTeX">${I}_{\text {DS}}$ </tex-math></inline-formula>. This is due to the reduction of the ratio of remnant polarization <inline-formula> <tex-math notation="LaTeX">${P}_{\text {r}}$ </tex-math></inline-formula> to coercive field <inline-formula> <tex-math notation="LaTeX">${E}_{\text {c}}$ </tex-math></inline-formula> with the reduced voltage across the HZO. Interestingly, however, some NC devices show a negligible impact of <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS,range}}$ </tex-math></inline-formula> on hysteresis and <inline-formula> <tex-math notation="LaTeX">${I}_{\text {DS}}$ </tex-math></inline-formula>. The NC transistor demonstrates a stable hysteresis in 139–149 mV range and the improved SS and <inline-formula> <tex-math notation="LaTeX">${I}_{\text {DS}}$ </tex-math></inline-formula> over the control device, with a reduced <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS,range}}$ </tex-math></inline-formula> of 0.5 to −0.5 V. To obtain device performance independent of <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS,range}}$ </tex-math></inline-formula>, the ratio of <inline-formula> <tex-math notation="LaTeX">${P}_{\text {r}}/{E}_{\text {c}}$ </tex-math></inline-formula> of the HZO needs to stabilize in a very small <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS,range}}$ </tex-math></inline-formula>.
Investigation of resistivity dependent microwave annealing on Si substrates

Heating of silicon substrates featuring different resistivity values by microwave annealing is investigated. The absorption of microwave energy for silicon wafers is found to be consistent with ohmic conduction loss theory. The strongest absorption occurs when the resistivity was around 10 Ω·cm. As the carrier concentration and the conductivity of silicon increase with temperature, the absorption of microwave energy also varies during the annealing processes. Furthermore, it is found that the electric field density around the annealed silicon wafer is stronger for higher conductive silicon substrates at fixed microwave power.
Design and implementation of a multi-channel pulse signal acquisition system

In traditional Chinese medicine, wrist pulse signals have been used for syndrome differentiation and disease diagnosis for thousands of years. Because of the complication of pulse diagnosis, one should collect pulse signals at different locations under different pressures for comprehensive analysis. However, although a number of pulse acquisition systems had been reported, conventional pulse signal acquisition systems, however, usually have a single sensor, and are large in size and unfriendly to users, making the sampling procedure very time-consuming. In this work, we propose a novel pulse signal acquisition system which can simultaneously capture multichannels of Cun, Guan, and Chi pulse signals. The system consists of the mechanism structure, sensor array, a set of circuit, and a software system. With the introduction of sensor array, our system can shorten the sampling time and ensure the repeatability while compared with the single sensor - based pulse signal acquisition system.
Keynote Speech 1: Wireless power harvesting and transfer

Electromagnetics forms the foundation of modern electrical and electronic systems that see the technologies we enjoy and take for granted today. It has been used to transfer information and power in daily lives. In the wireless domain, however, it had not been considered and employed much for power transfer until 2008 when a MIT research team lead by Prof. Marin Soljacic successfully demonstrated the mid-range wireless power. Since then, many efforts and much progress have been made to improve power transfer efficiency, reduce system size and include more functionality; the goal is to cut the last wires in electrical and electronic devices and systems if all possible. In this talk, we will first present the principles of the wireless power harvesting and transfer and then focus on mid-range wireless power transfer and its applications and commercialization. We will present the challenges, progresses, and future directions in the areas.
An augmented Lagrangian method for fast gradient vector flow computation

Gradient vector flow (GVF) and its generalization have been widely applied in many image processing applications. The high cost of GVF computation, however, has restricted their potential applications to images with large size. In this paper, motivated by progress in fast image restoration algorithms, we reformulate the GVF computation problem as a convex optimization model with an equality constraint, and solve it using a fast algorithm, inexact augmented Lagrangian method (ALM). With fast Fourier transform (FFT), we provide a novel simple and efficient algorithm for GVF computation. Experimental results show that the proposed method can improve the computational speed by an order of magnitude, and is even more efficient for images with large sizes.
Performance improvement by stack structure in flexible resistive random access memory

Resistive random access memory (RRAM) has been widely investigated. However, for the applications in flexible electronics, this type of memory is not so widely investigated for its requirement of the low processing temperature. In this paper, we demonstrated an Al<sub>2</sub>O<sub>3</sub>-based FRRAM fabricated under low temperature, and improved its performance by utilizing Al<sub>2</sub>O<sub>3</sub>/HfO<sub>2</sub> functional stack.
BDPCA plus LDA: a novel fast feature extraction technique for face recognition

Appearance-based methods, especially linear discriminant analysis (LDA), have been very successful in facial feature extraction, but the recognition performance of LDA is often degraded by the so-called "small sample size" (SSS) problem. One popular solution to the SSS problem is principal component analysis(PCA)+LDA (Fisherfaces), but the LDA in other low-dimensional subspaces may be more effective. In this correspondence, we proposed a novel fast feature extraction technique, bidirectional PCA (BDPCA) plus LDA (BDPCA+LDA), which performs an LDA in the BDPCA subspace. Two face databases, the ORL and the Facial Recognition Technology (FERET) databases, are used to evaluate BDPCA+LDA. Experimental results show that BDPCA+LDA needs less computational and memory requirements and has a higher recognition accuracy than PCA+LDA