nils-herrmann/clean_scirepeval huggingface dataset:
Parallel system design for time-delay neural networks

The authors develop a parallel structure for the time-delay neural network used in some speech recognition applications. The effectiveness of the design is illustrated by: (1) extracting a window computing model from the time-delay neural systems; (2) building its pipelined architecture with parallel or serial processing stages; and (3) applying this parallel window computing to some typical speech recognition systems. An analysis of the complexity of the proposed design shows a greatly reduced complexity while maintaining a high throughput rate.
Supervised LLE in ICA Space for Facial Expression Recognition

Locally linear embedding (LLE) is an unsupervised nonlinear manifold learning algorithm. It performs well in visualizing data yet has a very poor recognition rate in facial expression recognition. In this paper, to improve the performance of LLE in facial expression recognition, we first employ the independent component analysis (ICA) technique to preprocess the face images such that they are represented by some independent components and some noise is filtered from them. We then propose a supervised LLE (SLLE) algorithm to learn the hidden manifold. SLLE constructs the neighborhood graphs for the data according to the Euclidean distances between them and the cluster information of them. Its embedding step is the same as that of LLE. Finally, we use a generalized regression neural network (GRNN) to learn the implicit nonlinear mapping from the ICA space to the embedded manifold. Experiments on the JAFFE database show promising results
The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine

Automated tongue image segmentation, in Chinese medicine, is difficult due to two special factors: 1) there are many pathological details on the surface of the tongue, which have a large influence on edge extraction; 2) the shapes of the tongue bodies captured from various persons (with different diseases) are quite different, so they are impossible to describe properly using a predefined deformable template. To address these problems, in this paper, we propose an original technique that is based on a combination of a bi-elliptical deformable template (BEDT) and an active contour model, namely the bi-elliptical deformable contour (BEDC). The BEDT captures gross shape features by using the steepest decent method on its energy function in the parameter space. The BEDC is derived from the BEDT by substituting template forces for classical internal forces, and can deform to fit local details. Our algorithm features fully automatic interpretation of tongue images and a consistent combination of global and local controls via the template force. We apply the BEDC to a large set of clinical tongue images and present experimental results.
A new device architecture based on two dimensional van der Waals heterostructures

Van der Waals two-dimensional layered materials (2DLMs) have been demonstrated as a promising candidates for band-to-band-tunneling transistors. Here we bring forward a new device architecture of top-gated MoS2/WSe2 van der Waals heterostructures device by inserting a self-aligned metal film functioning as a screening layer. Such device architecture could realize independent control of carrier concentration and more efficient band alignment in MoS2 while the WSe2 side kept constant. Current modulation can be achieved by tuning top-gate voltage, and device can be either operated under a rectifier mode or a band-to-band tunneling (BTBT) mode. A gate coupling efficiency of ∼42% is obtained at large negative gate voltage and saturates by increasing gate voltage due to high electron density of states (DOS) at the conduction band edge of MoS2, which is indicative of achieving independent manipulation of band alignment and carrier density of MoS2. A high on/off ratio (>105) and small subthreshold wing (ã75mV/dec) are obtained under band-to-band tunneling mode. We also adapt this structure to graphene barrister by adding a Ni film on the MoS2 portion as screening layer Our work demonstrates the feasibility of a new device structure based on van der Waals heterostructures and its application in future low power electronic devices.
Palmprint recognition using directional line energy feature

Palm-lines, including the principal lines and wrinkles, can describe a palmprint clearly. This work presents a novel approach of line feature extraction for palmprint recognition called the directional line energy feature (DLEF). The directional lines in different directions are first extracted using a set of directional line detectors. Then each directional line magnitude image is divided into several overlapped small grids and the magnitudes of the line points in these grids are used to compute the DLEF. A template-matching method based on Euclidean distance is adopted to measure the similarity of two DLEFs. Best results have been obtained when DLEFs with 6 different directions were employed. Accuracies of 97.92% and 97.5% are obtained by using the proposed approach in one-against-one matching and one-against-320 matching, respectively.
Unsupervised underwater fish detection fusing flow and objectiveness

Scientists today face an onerous task to manually annotate vast amount of underwater video data for fish stock assessment. In this paper, we propose a robust and unsupervised deep learning algorithm to automatically detect fish and thereby easing the burden of manual annotation. The algorithm automates fish sampling in the training stage by fusion of optical flow segments and objective proposals. We auto-generate large amounts of fish samples from the detection of flow motion and based on the flow-objectiveness overlap probability we annotate the true-false samples. We also adapt a biased training weight towards negative samples to reduce noise. In detection, in addition to fused regions, we used a Modified Non-Maximum Suppression (MNMS) algorithm to reduce false classifications on part of the fishes from the aggressive NMS approach. We exhaustively tested our algorithms using NOAA provided, luminance-only underwater fish videos. Our tests have shown that Average Precision (AP) of detection improved by about 10% compared to non-fusion approach and about another 10% by using MNMS.
Sparse cost-sensitive classifier with application to face recognition

Sparse representation technique has been successfully employed to solve face recognition task. Though current sparse representation based classifier proves to achieve high classification accuracy, it implicitly assumes that the losses of all misclassifications are the same. However, in many real-world applications, different misclassifications could lead to different losses. Driven by this concern, we propose in this paper a sparse cost-sensitive classifier for face recognition. Our approach uses probabilistic model of sparse representation to estimate the posterior probabilities of a testing sample, calculates all the misclassification losses via the posterior probabilities and then predicts the class label by minimizing the losses. Experimental results on the public AR and FRGC face databases validate the efficacy of the proposed approach.
Effective approaches to improve Au etching process performance in MEMS devices

A method to improve pattern critical dimension (CD) and reduce the metal residue level during gold (Au) and chrome (Cr) wet etch processes in MEMS devices is investigated. Instead of traditional single-step wet etch process, a multi-step wet etch method is used to etch Au with the etchant formulated by potassium iodide, iodine and deionized water (KI/I2/H2O). Compared with the single step wet etching process, the Au CD uniformity has been significantly improved from 10 um to 3.5 um in the experiment. Furthermore the Au undercut uniformity has been improved to 2 um which meets the requirements of mass production. Different etchant concentrations are also investigated to study the correlation between etch rate and potassium iodide concentration, and the efficiency of metal residue removal. In the chemical conditions of I2: KI: H2O=4:15:81, it is found that higher KI concentration brings AuI faster dissolution rate and metal residues are completely removed.
Studies on Hyperspectral Face Recognition in Visible Spectrum With Feature Band Selection

This correspondence paper studies face recognition by using hyperspectral imagery in the visible light bands. The spectral measurements over the visible spectrum have different discriminatory information for the task of face identification, and it is found that the absorption bands related to hemoglobin are more discriminative than the other bands. Therefore, feature band selection based on the physical absorption characteristics of face skin is performed, and two feature band subsets are selected. Then, three methods are proposed for hyperspectral face recognition, including whole band (2D)2PCA, single band (2D)2PCA with decision level fusion, and band subset fusion-based (2D)2PCA. A simple yet efficient decision level fusion strategy is also proposed for the latter two methods. To testify the proposed techniques, a hyperspectral face database was established which contains 25 subjects and has 33 bands over the visible light spectrum (0.4-0.72 μm). The experimental results demonstrated that hyperspectral face recognition with the selected feature bands outperforms that by using a single band, using the whole bands, or, interestingly, using the conventional RGB color bands.
Negative Differential Resistance in Negative Capacitance FETs

We report the investigation of negative differential resistance (NDR) in negative capacitance (NC) germanium (Ge) pFETs. The NDR in NC transistors is attributed to the coupling of drain voltage to the internal gate voltage <inline-formula> <tex-math notation="LaTeX">${V}_{\text {int}}$ </tex-math></inline-formula> via the gate-to-drain capacitance. It is demonstrated that NDR strongly depends on the matching between the NC induced by ferroelectric capacitance <inline-formula> <tex-math notation="LaTeX">${C}_{\text {FE}}$ </tex-math></inline-formula> and the positive capacitance associated with the underlying transistor capacitance <inline-formula> <tex-math notation="LaTeX">${C}_{\text {MOS}}$ </tex-math></inline-formula>. For the non-hysteretic devices, NDR gets pronounced with an increased thickness of ferroelectric film <inline-formula> <tex-math notation="LaTeX">${t}_{\text {fe}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}}$ </tex-math></inline-formula>. This is attributed to the fact that the drain coupling factor is improved with an increased <inline-formula> <tex-math notation="LaTeX">${t}_{\text {fe}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}}$ </tex-math></inline-formula>, leading to the better matching between <inline-formula> <tex-math notation="LaTeX">${C}_{\text {FE}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${C}_{\text {MOS}}$ </tex-math></inline-formula>. For the hysteretic NC transistors, however, NDR is only obtained at the lower <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}}$ </tex-math></inline-formula>, but not observed at higher <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}}$ </tex-math></inline-formula>.
High-Performance Unannealed a-InGaZnO TFT With an Atomic-Layer-Deposited SiO2 Insulator

Amorphous In-Ga-Zn-O (a-IGZO) thin-film transistor with an atomic-layer-deposited (ALD) SiO2 gate insulator was fabricated under a maximum processing temperature of 250 °C and compared with the counterpart with a plasmaenhanced chemical vapor deposited (PECVD) SiO2 gate insulator. It was demonstrated that the ALD SiO2 insulator could generate much better device performance than the PECVD one. This is attributed to a lower density of interfacial traps, weaker surface roughness scattering, and enhanced passivation of oxygen vacancies in the a-IGZO channel atop the ALD SiO2 film with more OH groups. Without the need of post-annealing, the ALD SiO2 device exhibited very good stability under a negative gate bias stress (-15 V), while maintaining superior performance such as quite high field effect mobility of 63.6 cm2 V-1s-1, a low threshold voltage of -0.10 V, a small subthreshold swing of 0.14 V/decade, and a large ON/OFF current ratio of ~108.
Characterization of alternate power distribution methods for 3D integration

Signal return path discontinuities, parasitic inductance and impedance mismatch within interconnects are major factors that contribute to degraded high-speed signal quality in three-dimensional (3D) integrated circuits and systems. In this paper, we apply an alternate power delivery method and a novel I/O signaling scheme to a 3D system to address these issues. Two test vehicles made of stacked PCBs that resemble 3D integrated systems will be presented. One test vehicle is designed based on our proposed approach while the other is based on the conventional power delivery network design. The signal integrity and power supply noise performance will be shown in both simulated environment and actual test measurement. At data rates up to 3Gbps, our proposed design produces higher signal quality than the conventional design with better eye height, lower timing jitter, and lower power supply noise.
Robust Discriminant Regression for Feature Extraction

Ridge regression (RR) and its extended versions are widely used as an effective feature extraction method in pattern recognition. However, the RR-based methods are sensitive to the variations of data and can learn only limited number of projections for feature extraction and recognition. To address these problems, we propose a new method called robust discriminant regression (RDR) for feature extraction. In order to enhance the robustness, the <inline-formula> <tex-math notation="LaTeX">${L_{2,1}}$ </tex-math></inline-formula>-norm is used as the basic metric in the proposed RDR. The designed robust objective function in regression form can be solved by an iterative algorithm containing an eigenfunction, through which the optimal orthogonal projections of RDR can be obtained by eigen decomposition. The convergence analysis and computational complexity are presented. In addition, we also explore the intrinsic connections and differences between the RDR and some previous methods. Experiments on some well-known databases show that RDR is superior to the classical and very recent proposed methods reported in the literature, no matter the <inline-formula> <tex-math notation="LaTeX">${L_{2}}$ </tex-math></inline-formula>-norm or the <inline-formula> <tex-math notation="LaTeX">${L_{2,1}} $ </tex-math></inline-formula>-norm-based regression methods. The code of this paper can be downloaded from <uri>http://www.scholat.com/laizhihui</uri>.
Parallel biometrics computing using mobile agents

We present an efficient and effective approach to personal identification by parallel biometrics computing using mobile agents. To overcome the limitations of the existing password-based authentication services on the Internet, we integrate multiple personal features including fingerprints, palmprints, hand geometry and face into a hierarchical structure for fast and reliable personal identification and verification. To increase the speed and flexibility of the process, we use mobile agents as a navigational tool for parallel implementation in a distributed environment, which includes hierarchical biometric feature extraction, multiple feature integration, dynamic biometric data indexing and guided search. To solve the problems associated with bottlenecks and platform dependence, we apply a four-layered structural model and a three-dimensional operational model to achieve high performance. Instead of applying predefined task scheduling schemes to allocate the computing resources, we introduce a new online competitive algorithm to guide the dynamic allocation of mobile agents with greater flexibility. The experimental results demonstrate the feasibility and the potential of the proposed method
A New Tongue Colorchecker Design by Space Representation for Precise Correction

In order to improve the correction accuracy on tongue colors by use of a Munsell colorchecker, this research aims to design a new colorchecker by aid of tongue color space. Three essential issues leading to the development of this space-based colorchecker are elaborately investigated in this study. First, based on a large and comprehensive tongue database, tongue color space is established by which all visible colors can be classified as tongue or nontongue colors. Hence, colors of the designed tongue colorchecker are selected from tongue colors to achieve high correction performance. Second, the minimum sufficient number of colors involved in a colorchecker is yielded by comparing the correction accuracy when different number (ranged from 10 to 200) of colors are contained. Thereby, 24 colors are included because the obtained minimum number of colors is 20. Finally, criteria for optimal color selection and its corresponding objective function are presented. Two color selection methods, i.e., greedy and clustering-based selection method, are proposed to solve the objective function. Experimental results show that clustering-based one outperforms its counterpart to generate the new tongue colorchecker. Compared to a Munsell colorchecker, this proposed space-based colorchecker can greatly improve the correction accuracy by 48%. Further experimental results on more correction task also validate its effectiveness and superiority.
Evolutionary Cost-Sensitive Discriminative Learning With Application to Vision and Olfaction

In the design of machine learning models, one often assumes the same loss, which, however, may not hold in cost-sensitive learning scenarios. In a face-recognition-based access control system, misclassifying a stranger as a house owner and allowing entry may result in a more serious financial loss than misclassifying a house owner as a stranger and not allowing entry. That is, different types of recognition mistakes may lead to different losses, and therefore should be treated carefully. It is expected that a cost-sensitive learning mechanism can reduce the total loss when given a cost matrix that quantifies how severe one type of mistake is against another one. However, in many realistic applications, the cost matrix is unknown and unclear to users. Motivated by these concerns, in this paper, we propose an evolutionary cost-sensitive discriminative learning (ECSDL) method, with the following merits: 1) it addresses the definition of cost matrix in cost-sensitive learning without human intervention; 2) an evolutionary backtracking search algorithm is derived for the NP-hard cost matrix optimization; and 3) a cost-sensitive discriminative subspace is found, where the between-class separability and within-class compactness are well achieved, such that recognition becomes easier. Experiments in a variety of cost-sensitive vision and olfaction classification tasks demonstrate the efficiency and effectiveness of the proposed ECSDL approach.
Price responses of grain market under climate change in pre-industrial Western Europe by ARX modelling

In academia, there are few studies adopted ARX modelling on historical datasets. Recently, the studies on notable effects of climatic changes upon past agrarian economy are paid by more attention. Here, this study first time seriously explores the relationship between climatic change and the grain market at a macro-scale in pre-industrial Western Europe by ARX modelling. The results show that a cold phase would raise grain price through lowering the supply in the grain market. Furthermore, according to the simulations on short-and long-term climate change, the long lasting climate change could be more disastrous to society than short-term change. Last, the application in the study proves ARX modelling is also a feasible choice in the field of historical research.
Investigation on solder bump process polyimide cracking for wafer level packaging

Typical controlled collapse chip connection (C4) bump structurally composed of solder bump, Ni UBM and Cu/Ti sputtered film on the top side of Si wafer in which polyimide film patterned and coated onto wafer as a buffer layer between substrate and UBM for stress relief. C4 bump structure is widely used in high-performance device of semiconductor package industries for many years and has achieved good reliability performance. In the past experience, stress-induced polyimide crack would happen on low-k IC package and be associated with inter-layer dielectric (ILD) cracking during thermal process and resulting in reliability problem. In order to resolve polyimide cracking problem, this preliminary study is attempted to optimize the bumping process based on the experimental of C4 bump structure with Copper pad as polyimide underneath to simulate the key condition of bumping process. Three key parameters in process, curing temperature, reflow peak temperature and reflow cooling rate, were considered to differentiate the influence degree related to the thermal stress from coefficient of thermal expansion (CTE) mismatch. Focus ion beam (FIB) and finite element methods were separately employed for cranny observation and stress modeling. The result exhibits the cured polyimide cranny formed under the condition of higher curing temperature, higher reflow cooling rate. The dominant factor is polyimide curing temperature (PCT), and followed by reflow peak temperature (RPT) and reflow cooling rate (RCR), sequentially. The cranny on polyimide propagated to and around the round-shaped Ni undercut spot after reflow due to the thermal stress arising from CTE mismatch between the polyimide and contained metals. Low curing temperature (350°C) with lower modulus of elasticity, low reflow temperature (240°C) and slow cooling rate (2.9°C/s) allow polyimide to absorb more strain energy and flexibly adjust internal stress far from fracture stress.
Medical Biometrics-Computerized TCM Diagnosis

The Traditional Chinese Medicine (TCM) diagnosis methods, such as looking/smelling/touching, have been successfully used for thousands of years. However, these methods are mainly relied on Doctor's experience and not quantified. In this presentation, we will try to develop a novel approach by using Medical Biometrics technology, i.e., Computerized TCM Diagnosis, to solve these problems. By some TCM-orient diagnosis acquisition devices, we could collect many kinds of date like tongue/pulse/odor with a priori knowledge from different diseases in hospitals. Then we use a statistical pattern recognition approach to extract all possible features from these images/waveforms, including color, texture, shape, and so on. After matching between our training data and testing data, some decision rules will be made. Finally, we apply our results to the practical diseases diagnosis to illustrate the effectiveness of our approach.