nils-herrmann/clean_scirepeval huggingface dataset:
Sparse tensor discriminant analysis

The classical linear discriminant analysis has undergone great development and has recently been extended to different cases. In this paper, a novel discriminant subspace learning method called sparse tensor discriminant analysis (STDA) is proposed, which further extends the recently presented multilinear discriminant analysis to a sparse case. Through introducing the L1 and L2 norms into the objective function of STDA, we can obtain multiple interrelated sparse discriminant subspaces for feature extraction. As there are no closed-form solutions, k-mode optimization technique and the L1 norm sparse regression are combined to iteratively learn the optimal sparse discriminant subspace along different modes of the tensors. Moreover, each non-zero element in each subspace is selected from the most important variables/factors, and thus STDA has the potential to perform better than other discriminant subspace methods. Extensive experiments on face databases (Yale, FERET, and CMU PIE face databases) and the Weizmann action database show that the proposed STDA algorithm demonstrates the most competitive performance against the compared tensor-based methods, particularly in small sample sizes.
The Temperature Dependence in Nanoresistive Switching of HfAlO

Critical-temperature regions for resistive switching were found based on HfAlO resistive switching memory. From 5 to 300 K, the resistive switching appears at 60 K, and then a reversible bipolar switching between the two states is observed at above 150 K. It is suggested that the resistive switching characteristics are governed by thermal-assisted percolating conductive paths. The process of low electron occupied region under the external electrical field in different temperature plays a dominated role with the oxygen vacancies movement and recombination in this region. This temperature dependence is originated from different electron transport behavior among oxygen vacancies along conductive filaments at different temperatures.
Rank Entropy-Based Decision Trees for Monotonic Classification

In many decision making tasks, values of features and decision are ordinal. Moreover, there is a monotonic constraint that the objects with better feature values should not be assigned to a worse decision class. Such problems are called ordinal classification with monotonicity constraint. Some learning algorithms have been developed to handle this kind of tasks in recent years. However, experiments show that these algorithms are sensitive to noisy samples and do not work well in real-world applications. In this work, we introduce a new measure of feature quality, called rank mutual information (RMI), which combines the advantage of robustness of Shannon's entropy with the ability of dominance rough sets in extracting ordinal structures from monotonic data sets. Then, we design a decision tree algorithm (REMT) based on rank mutual information. The theoretic and experimental analysis shows that the proposed algorithm can get monotonically consistent decision trees, if training samples are monotonically consistent. Its performance is still good when data are contaminated with noise.
A New Approach to the $L(2,1)$ -Labeling of Some Products of Graphs

The frequency assignment problem is to assign a frequency which is a nonnegative integer to each radio transmitter so that interfering transmitters are assigned frequencies whose separation is not in a set of disallowed separations. This frequency assignment problem can be modelled with vertex labelings of graphs. An L(2, 1)-labeling of a graph G is a function f from the vertex set V(G) to the set of all nonnegative integers such that |f(x)-f(y)|ges2 if d(x, y)=1 and |f(x)-f(y)|ges1 if d(x, y)=2, where d(x, y) denotes the distance between x and y in G. The L(2, 1)-labeling number lambda(G) of G is the smallest number k such that G has an L(2, 1)-labeling with max{f(v):visinV(G)}=k. In this paper, we develop a dramatically new approach on the analysis of the adjacency matrices of the graphs to estimate the upper bounds of lambda-numbers of the four standard graph products. By the new approach, we can achieve more accurate results and with significant improvement of the previous bounds.
Multi-Label Dictionary Learning for Image Annotation

Image annotation has attracted a lot of research interest, and multi-label learning is an effective technique for image annotation. How to effectively exploit the underlying correlation among labels is a crucial task for multi-label learning. Most existing multi-label learning methods exploit the label correlation only in the output label space, leaving the connection between the label and the features of images untouched. Although, recently some methods attempt toward exploiting the label correlation in the input feature space by using the label information, they cannot effectively conduct the learning process in both the spaces simultaneously, and there still exists much room for improvement. In this paper, we propose a novel multi-label learning approach, named multi-label dictionary learning (MLDL) with label consistency regularization and partial-identical label embedding MLDL, which conducts MLDL and partial-identical label embedding simultaneously. In the input feature space, we incorporate the dictionary learning technique into multi-label learning and design the label consistency regularization term to learn the better representation of features. In the output label space, we design the partial-identical label embedding, in which the samples with exactly same label set can cluster together, and the samples with partial-identical label sets can collaboratively represent each other. Experimental results on the three widely used image datasets, including Corel 5K, IAPR TC12, and ESP Game, demonstrate the effectiveness of the proposed approach.
Vertical power diodes based on bulk GaN substrates

In this work, study on electrical properties of vertical GaN power diodes, including schottky barrier diodes (SBD) and p-n diodes, are presented. The devices with various drift layer thickness and net carrier electron concentration were simulated using the Silvaco ATLAS TCAD software packages. What's more, an optimum design of vertical GaN devices with drift layer thickness 10μm and net carrier doping concentration 1 × 10<sup>16</sup> cm<sup>3</sup> were fabricated. These devices, including Schottky barrier diodes with a breakdown voltage of 2250 V and specific on-resistance of 0.9 mΩ·cm<sup>−2</sup>, and p-n diode with a breakdown voltage of 2400 V and specific on-resistance of 0.85 mΩ·cm<sup>−2</sup>, exhibited excellent electric properties.
A Four-Channel, ${\pm} $36 V, 780 kHz Piezo Driver Chip for Structural Health Monitoring

This paper describes a four-channel piezo driver chip for use in active structural health monitoring. Each channel can drive a piezoelectric transducer with a continuous-wave signal of ±36 V and 780 kHz. The waveform is synthesized using filtered pulse-width modulation (PWM), where the pulse transition times are precomputed through a least-square optimization problem. A calibration technique is introduced to achieve subnanosecond timing accuracy and -40.5 dB signal distortion. A 13 mm2 proof-of-concept IC was fabricated in a 0.25 μm BCD process and evaluated within an application test bed.
Constructing PCA Baseline Algorithms to Reevaluate ICA-Based Face-Recognition Performance

The literature on independent component analysis (ICA)-based face recognition generally evaluates its performance using standard principal component analysis (PCA) within two architectures, ICA Architecture I and ICA Architecture II. In this correspondence, we analyze these two ICA architectures and find that ICA Architecture I involves a vertically centered PCA process (PCA I), while ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). Thus, it makes sense to use these two PCA versions as baselines to reevaluate the performance of ICA-based face-recognition systems. Experiments on the FERET, AR, and AT&T face-image databases showed no significant differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture I (or II) may, in some cases, significantly outperform standard PCA. It can be concluded that the performance of ICA strongly depends on the PCA process that it involves. Pure ICA projection has only a trivial effect on performance in face recognition.
Shared Autoencoder Gaussian Process Latent Variable Model for Visual Classification

Multiview learning reveals the latent correlation among different modalities and utilizes the complementary information to achieve a better performance in many applications. In this paper, we propose a novel multiview learning model based on the Gaussian process latent variable model (GPLVM) to learn a set of nonlinear and nonparametric mapping functions and obtain a shared latent variable in the manifold space. Different from the previous work on the GPLVM, the proposed shared autoencoder Gaussian process (SAGP) latent variable model assumes that there is an additional mapping from the observed data to the shared manifold space. Due to the introduction of the autoencoder framework, both nonlinear projections from and to the observation are considered simultaneously. Additionally, instead of fully connecting used in the conventional autoencoder, the SAGP achieves the mappings utilizing the GP, which remarkably reduces the number of estimated parameters and avoids the phenomenon of overfitting. To make the proposed method adaptive for classification, a discriminative regularization is embedded into the proposed method. In the optimization process, an efficient algorithm based on the alternating direction method and gradient decent techniques is designed to solve the encoder and decoder parts alternatively. Experimental results on three real-world data sets substantiate the effectiveness and superiority of the proposed approach as compared with the state of the art.
An integrated production and inventory model for a whole green manufacturing supply chain with limited contract period and capacity constraints for suppliers

This paper proposes an integrated production and inventory model for a whole green manufacturing supply chain consisting of multiple-raw materials suppliers, multiple-parts suppliers, a manufacturer, multiple distributors, multiple retailers and a third party collector for used finished products. In the system under study, we consider limited contract period among the players in a specific time period. The number of replenishment cycles has exactly to meet the length of contract period for suppliers in the supply chain. This model also considers capacity constraints of the players and deliveries. The demand quantity is no more than production capacity and minimally meets the minimum demand quantity for each supplier. On the other hand, the order quantity per cycle time has to meet the capacity of delivery units. A model has been formulated for an integrated whole manufacturing supply chain to jointly determine the optimal order quantity and number of unit transportations per cycle across all players in the supply chain in the specific time period of supply chain contract and delivery schedule. Selection of suppliers is also included in solving the model. An optimal solution can be obtained by using mixed-integer nonlinear programming technique. A numerical example is presented and results discussed.
A four-channel, ±36 V, 780 kHz piezo driver chip for structural health monitoring

This paper describes a four-channel piezo driver chip for use in active structural health monitoring. Each channel can drive a piezoelectric transducer with a continuous wave signal of ±36 V and 780 kHz. The waveform is synthesized using filtered pulse width modulation (PWM), where the pulse transition times are pre-computed through a least square optimization problem. A calibration technique is introduced to achieve sub-nanosecond timing accuracy and -40.5 dB signal distortion. A 13 mm2 proof-of-concept IC was fabricated in a 0.25 μm BCD process and evaluated within an application test bed.
Design, optimization, and control of a new class of reconfigurable hopping rovers

A patent-pending new class of reconfigurable battery-powered hopping rovers for reconnaissance, exploration, defense, homeland security, and entertainment applications is publicly presented for the first time. A time- periodic linear quadratic regulator for stabilization of hopping maneuvers is presented, with simulation results. Additionally, a novel mechanical design for efficient and multi-modal operation is discussed, including a lockable hopping mechanism for the directed release of gradually accumulated elastic energy, utilizing two coupled four-bar linkages in tandem with a quick- release spring device.
Discriminative and Robust Competitive Code for Palmprint Recognition

Various palmprint recognition methods have been proposed based on orientation features of palmprints. Among them, the competitive code method using the dominant orientation of palmprint images achieves promising performance in palmprint recognition. In this paper, we propose a discriminative and robust competitive code based method, which uses a more accurate dominant orientation representation of palmprint images for palmprint authentication. Moreover, we propose to weight the orientation information of a neighbor area to improve the precision and stability of the discriminative and robust dominant orientation code. Experiments performed on three types of palmprint databases and a noisy dataset validate the effectiveness of the proposed method.
Reduction of PDN induced coupling into signal lines using PTL power distribution

Transmission lines carrying high speed I/O signals can couple significant amount of electromagnetic energy to power distribution network (PDN) which can then adversely affect signal and power integrity of the entire electrical system. Similarly the reverse is also true. In this paper we present three printed circuit board designs with different power distribution topologies and show through measurement that a previously proposed PDN design based on Power Transmission Line (PTL) concept is less susceptible to coupling from signal lines.
3-D shape recovery of luminal wall from WCE image

The Wireless capsule endoscopy (WCE) is a state-of-the-art imaging technique used to view the entire gastrointestinal (GI) tract. With this technique, the clinicians can detect diseases such as obscure gastrointestinal bleeding, polyp, Crohn's and celiac disease. However, like other endoscopes, the WCE provides only 2-D images. The real anatomical structure of the observed lesion is unavailable and can only be judged by the clinician's imagination. In this paper, we use shape from shading (SFS) technique to generate 3-D structures from 2-D endoscopic images. For the three assumptions of the SFS technique, we propose a preprocessing method for endoscopic image. Experiments with real WCE data demonstrate good 3-D shape recovery performance. Smooth and visually scene can be created based on the pre-processed endoscopic image, while preserving the structure of the observed objects without any hardware upgrades. The 3-D shape enhances the video and therefore improves the viewing of the gastrointestinal (GI) tract leading to a more accurate diagnosis.
Scale and orientation adaptive mean shift tracking

A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this study to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, whereas the scale and orientation changes cannot be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, the authors show that the original mean shift tracking algorithm can be derived using the zeroth- and the first-order moments of the weight image. With the zeroth-order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilises the estimated area and the second-order centre moment, is proposed to adaptively estimate the width, height and orientation changes of the target. Extensive experiments are performed to testify the proposed method and validate its robustness to the scale and orientation changes of the target.
Bidirectional PCA with assembled matrix distance metric for image recognition

Principal component analysis (PCA) has been very successful in image recognition. Recent research on PCA-based methods has mainly concentrated on two issues, namely: 1) feature extraction and 2) classification. This paper proposes to deal with these two issues simultaneously by using bidirectional PCA (BD-PCA) supplemented with an assembled matrix distance (AMD) metric. For feature extraction, BD-PCA is proposed, which can be used for image feature extraction by reducing the dimensionality in both column and row directions. For classification, an AMD metric is presented to calculate the distance between two feature matrices and then the nearest neighbor and nearest feature line classifiers are used for image recognition. The results of the experiments show the efficiency of BD-PCA with AMD metric in image recognition
Is ICA significantly better than PCA for face recognition?

The standard PCA was always used as baseline algorithm to evaluate ICA-based face recognition systems in the previous research. In this paper, we examine the two architectures of ICA for image representation and find that ICA architecture I involves a PCA process by vertically centering (PCA I), while ICA architecture II involves a whitened PCA process by horizontally centering (PCA II). So, it is reasonable to use these two PCA versions as baseline algorithms to revaluate the ICA-based face recognition systems. The experiments were performed on the FERET face database. The experimental results show there is no significant performance differences between ICA architecture I (II) and PCA I (II), although ICA architecture II significantly outperforms the standard PCA. It can be concluded that the performance of ICA strongly depends on its involved PCA process. The pure ICA projection has little effect on the performance of face recognition.
Multiplexing and M# measurement in spectral hole burning medium

Spectral hole burning holography can store both the temporal and spatial information. In cryogenic temperature, only a certain group of atoms in the spectral hole burning material can interact with the incident photons of specific frequency. It allows an extra degree of freedom to store and access information, i.e. the frequency domain. 12,000 holograms at a single location by frequency multiplexing has been demonstrated. The storage capacity can be further increased by combining angle and frequency multiplexing. A system measure of the hologram diffraction efficiency obtainable by multiplexing M holograms at a single location and frequency is given by the M/#. In the paper we report the measurement of M/# in a 400 /spl mu/m thick sample of H/sub 2/TBNP in polyvinyl butyral (PVB) with a concentration of 3 /spl times/ 10/sup -5/ mol/l.
Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation

Palmprint processes a number of unique features for reliable personal recognition. However, different types of palmprint images contain different dominant features. Instead, only some features of the palmprint are visible in a palmprint image, whereas the other features may not be notable. For example, the low-resolution palmprint image has visible principal lines and wrinkles. By contrast, the high-resolution palmprint image contains clear ridge patterns and minutiae points. In addition, the three dimensional (3-D) palmprint image possesses curvatures of the palmprint surface. So far, there is no work to summarize the feature extraction of different types of palmprint images. In this paper, we have an aim to completely study the feature extraction and recognition of palmprint. We propose to use a unified framework to classify palmprint images into four categories: 1) the contact-based; 2) contactless; 3) high-resolution; and 4) 3-D palmprint images. Then, we analyze the motivations and theories of the representative extraction and matching methods for different types of palmprint images. Finally, we compare and test the state-of-the-art methods via the widely used palmprint databases, and point out some potential directions for future research.
Results from recent hydrogen pellet acceleration studies with a 2-M railgun

A 3.2-mm-diameter, two-stage, fuseless, plasma-arc-driven electromagnetic railgun has been designed, constructed, and successfully operated to achieve a record velocity of 2.67 km/s for a 3.2-mmD*4-mmL solid hydrogen pellet. The first stage of this hydrogen pellet injector is a combination of a hydrogen-pellet generator and a gas gun. The second state is a 2-m-long railgun which serves as a booster accelerator. The gas gun accelerates a frozen hydrogen pellet to a medium velocity and injects it into the railgun through a perforated coupling piece, which also serves as a pressure-relieving mechanism. An electrical breakdown of the propellant gas, which follows the pellet from the gas gun into the railgun, forms a conducting plasma-arc armature immediately behind the pellet, allowing for fuseless operation of the railgun. Studying the pressure profile and the behavior of the plasma-arc armature inside the railgun bore led to eliminating spurious arcing, which prevents operation of the railgun at high voltages (and, therefore, at high currents). A timing circuit that can automatically measure the pellet input velocity and allows for accurate control of arc initiation behind the pellet helps prevent pellet disintegration and mistriggering of the arc initiation circuit. Results from the recent cryogenic operation of the two-stage pellet acceleration system are reported.<<ETX>>
Noninvasive Diabetes Mellitus Detection Using Facial Block Color With a Sparse Representation Classifier

Diabetes mellitus (DM) is gradually becoming an epidemic, affecting almost every single country. This has placed a tremendous amount of burden on governments and healthcare officials. In this paper, we propose a new noninvasive method to detect DM based on facial block color features with a sparse representation classifier (SRC). A noninvasive capture device with image correction is initially used to capture a facial image consisting of four facial blocks strategically placed around the face. Six centroids from a facial color gamut are applied to calculate the facial color features of each block. This means that a given facial block can be represented by its facial color features. For SRC, two subdictionaries, a Healthy facial color features subdictionary and DM facial color features subdictionary, are employed in the SRC process. Experimental results are shown for a dataset consisting of 142 Healthy and 284 DM samples. Using a combination of the facial blocks, the SRC can distinguish Healthy and DM classes with an average accuracy of 97.54%.
A partial differential equation algorithm for image enhancement

A novel approach for image enhancement is presented in this paper. The enhancement result is achieved by combination the means of contrast enhancement, image smoothing and image sharpening techniques into one partial differential equation (PDE) framework. In the image evolution process, three kinds of operation execute at the same time, using the regularization parameters to adjust the proportion of each operation in the evolution process, ultimately achieve the desired results. The simulations for both gray and color images are given in the experiments. Experimental results demonstrate that the method substantially improves the subjective quality of the enhanced images.