nils-herrmann/clean_scirepeval huggingface dataset:
A New DNA Fragment Assembly Method Based on Long Fragment Filtration

As an important aspect of bioinformatics, the main problem of genome sequencing is DNA Fragment Assembly. This paper proposed a new fragment assembly algorithm based on the long fragment filtration. Firstly, the fragment set was divided into two parts according to a threshold of the filtration length and the Bruijn graph was formed by the short fragments set. Subsequently system automatically selected long fragments which met the filtration condition by the Bruijn graph and the path searching method and constructed the filtrated fragments set. On the basis of not missing essential fragment information in the process of fragment assembly, our method effectively resolved the problem of high spatial cost and limited processing scale caused by traditional Eulerian Super path method. Finally, we certified the feasibility and the validity of our method through a series of experiments.
Impact of recess-etching-assisting resist-openings on the shapes of gate grooves for short gate length InAlAs/InGaAs heterojunction FET's

When resist openings are employed to monitor the drain current of InAlAs/InGaAs-heterojunction-based FET's during wet-chemical gate recess, etching rates for InGaAs and InAlAs can be significantly modified by the exposure of the surface metal on the nonalloyed ohmic electrodes to citric-acid-based etchants. Surface metal of Ni enhances the recess etching rate to a degree that is much higher than that in its absence. With nonselective citric acid-based etchant, the presence of Pt surface metal, however, leads to a preferential etching of InGaAs over InAlAs. This behavior of selective etching is attributed to the excess oxidation of InAlAs induced by the high electrode potential of Pt via electrochemical effects. This investigation discloses that the selection of the surface metal that lies beneath the resist openings can be very important if gate recess grooves with desired shapes are to be fabricated.
Low complexity max-log-MAP demapper for M-PAM signals with nonuniform constellations

Constellation shaping is beneficial for approaching the Shannon capacity limit. However, the nonuniform signal caused by constellation shaping could increase the complexity of the signal demodulation. The reflective symmetry of the uniform PAM constellation can be exploited to reduce the demapping complexity. However, nonuniform PAM signals lose this symmetry because of the distribution of constellation points, which increase the amount of demapping calculation. A low complexity max-log-MAP demapping algorithm is proposed for demodulating nonuniform M-PAM signals in this paper. We divide decision boundaries into two parts: ordinary ones and special ones. We find the symmetry of the received symbols and derive the analytic expressions for all boundaries. Based on this, the complexity of the proposed algorithm is lower than other similar works, and the proposed algorithm can be further simplified with acceptable performance loss.
Improving Korean LVCSR with Long-Time Temporal Patterns and an Extended Phoneme Set

Korean is an agglutinative language, in which pronunciations are affected by long-term context. In this paper, the long-time temporal information is investigated to improve Korean LVCSR. TRAP-based MLP features, which are able to utilize the scattered acoustic information over several hundred milliseconds, are employed to obtain additional information besides the conventional cepstral features. In contrast to the traditional Korean phoneme set, in which consonants in the initial and final positions are taken as the same, a more specific phoneme set is constructed via taking consonants as position dependent. In the Korean broadcast news speech recognition task, experiments show that with these improvements the character error rate has been reduced by 25.3% relatively over the baseline system.
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.
Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach

In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set of base classifiers which are prelearned by using labeled instances either from the source domains or from the source domains and the target domain. With the base classifiers, we propose a new domain-dependent regularizer based on smoothness assumption, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. This newly proposed regularizer can be readily incorporated into many kernel methods (e.g., support vector machines (SVM), support vector regression, and least-squares SVM (LS-SVM)). For domain adaptation, we also develop two new domain adaptation methods referred to as FastDAM and UniverDAM. In FastDAM, we introduce our proposed domain-dependent regularizer into LS-SVM as well as employ a sparsity regularizer to learn a sparse target classifier with the support vectors only from the target domain, which thus makes the label prediction on any test instance very fast. In UniverDAM, we additionally make use of the instances from the source domains as Universum to further enhance the generalization ability of the target classifier. We evaluate our two methods on the challenging TRECIVD 2005 dataset for the large-scale video concept detection task as well as on the 20 newsgroups and email spam datasets for document retrieval. Comprehensive experiments demonstrate that FastDAM and UniverDAM outperform the existing multiple source domain adaptation methods for the two applications.
Hidden Behavior Prediction of Complex Systems Based on Hybrid Information

It is important to predict both observable and hidden behaviors in complex engineering systems. However, compared with observable behavior, it is often difficult to establish a forecasting model for hidden behavior. The existing methods for predicting the hidden behavior cannot effectively and simultaneously use the hybrid information with uncertainties that include qualitative knowledge and quantitative data. Although belief rule base (BRB) has been employed to predict the observable behavior using the hybrid information with uncertainties, it is still not applicable to predict the hidden behavior directly. As such, in this paper, a new BRB-based model is proposed to predict the hidden behavior. In the proposed BRB-based model, the initial values of parameters are usually given by experts, thus some of them may not be accurate, which can lead to inaccurate prediction results. In order to solve the problem, a parameter estimation algorithm for training the parameters of the forecasting model is further proposed on the basis of maximum likelihood algorithm. Using the hybrid information with uncertainties, the proposed model can combine together with the parameter estimation algorithm and improve the forecasting precision in an integrated and effective manner. A case study is conducted to demonstrate the capability and potential applications of the proposed forecasting model with the parameter estimation algorithm.
A hybrid modeling for the real-time control and optimization of compressors

This paper proposes a hybrid compressor model for the purpose control and optimization of vapor compression systems. Unlike those existing models, this model is determined by only the inlet and outlet conditions of compressor without requiring detailed geometric specifications, and only the variables responsible to the system performance, which can be measured and controlled, are selected as the input/output (I/O) of the models. The model is derived based on the concept of volumetric efficiency and the assumption of a polytropic compression process. The unknown empirical parameters of the model are identified by the nonlinear least squares methods. The effectiveness of the proposed model is validated by the compressor catalogs data obtained from the manufactures. Results show that the model is accurate and robust and gives a better match to the real performances of compressors over the entire operating range than the existing models. This model is expected to have wide applications in real time control and optimization of vapor compression systems.
A novel algorithm based stepped-frequency system about extrapolating near-field to far-field

In order to meet the approximate plane-wave irradiation condition, adequate large field or compact range system is needed for antenna pattern measurement, especially for large size antenna, but an outside testing field site or a compact range system is very expensive. Near-field planar scanning system is another method, however it is time-consuming and only suited for directional antenna. In this paper, a new extrapolating technique by connecting near-distance field with far-field of large-size antenna is set up. If the large size antenna is short in vertical plane, its irradiating field in horizontal plane can be respected as cylinder wave distribution. Based on cylinder wave expand theory, the far-field pattern of antenna can be obtained from the whole near-field information of full angular aspect. Taking derivative of the near-field data with respect to frequency in algorithm, the error can be reduced in simplifying formula, and just meet the requirement of stepped-frequency system. The results of simulation and experiments show that the algorithm is precise as well as effective.
An Improved Mandarin Voice Input System Using Recurrent Neural Network Language Model

In this paper, we present our recent work on using a Recurrent Neural Network Language Model (RNNLM) in a Mandarin voice input system. Specifically, the RNNLM is used in conjunction with a large high-order n-gram language model (LM) to re-score the N-best list. However, it is observed that the repeated computations in the rescoring procedure can make the rescoring inefficient. Therefore, we propose a new nbest-list rescoring framework called Prefix Tree based N-best list Rescore (PTNR) to totally eliminate the repeated computations and speed up the rescoring procedure. Experiments show that the RNNLM leads to about 4.5% relative reduction of word error rate (WER). And, compared to the conventional n-best list rescoring method, the PTNR gets a speed-up of factor 3-4. Compared to the cache based method, the design of PTNR is more explicit and simpler. Besides, the PTNR requires a smaller memory footprint than the cache based method.
Batch mode Adaptive Multiple Instance Learning for computer vision tasks

Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance labels in positive bags, the training process of traditional MIL methods is usually computationally expensive, which limits the applications of MIL in more computer vision tasks. In this paper, we propose a novel batch mode framework, namely Batch mode Adaptive Multiple Instance Learning (BAMIL), to accelerate the instance-level MIL methods. Specifically, instead of using all training bags at once, we divide the training bags into several sets of bags (i.e., batches). At each time, we use one batch of training bags to train a new classifier which is adapted from the latest pre-learned classifier. Such batch mode framework significantly accelerates the traditional MIL methods for large scale applications and can be also used in dynamic environments such as object tracking. The experimental results show that our BAMIL is much faster than the recently developed MIL with constrained positive bags while achieves comparable performance for text-based web image retrieval. In dynamic settings, BAMIL also achieves the better overall performance for object tracking when compared with other online MIL methods.
Ground moving target imaging based on 2-D velocity search in high resolution SAR

A novel ground moving target imaging algorithm based on 2-D velocity search is proposed in high resolution synthetic aperture radar. In the range-Doppler domain, a range velocity with low accuracy is obtained via the response length along the range direction of the target's range migration. Then, an accurate range velocity is determined in the velocity search for the best range migration correction. As to the azimuth velocity, it is roughly acquired based on the road slope extracted from a SAR image. Next, an accurate azimuth velocity is searched to match the Doppler rate of the moving target in order to realize the azimuth focusing, which is measured by the contrast of target imaging result. After the 2-D velocity search, the range defocus and azimuth defocus of a moving target is eliminated, and the final focused imaging is fulfilled. Compared with some existing imaging approaches, the proposed algorithm solves the problem of Doppler spectrum split and has a relatively low computation burden. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.
Acoustic wave propagation along cladded fibers of trigonal crystal symmetry

An approximate method is proposed to analyze the acoustic wave propagation characteristics in nonpiezoelectric cladded LiNbO/sub 3/ fibers of trigonal crystal symmetry. The fiber axis coincides with the crystalline Z axis and the cladding thickness is infinite. This method utilizes coupled mode equations and exact solutions for corresponding cladded fibers of hexagonal crystal symmetry are used as the approximation base. The analysis concentrates on the trigonal materials of 32, 3 m, and ~3 m groups. Dispersion curves of flexural F/sub 11/, F/sub 21/ and F/sub 12/ modes in such a fiber are presented for the first time with a focus on the fundamental F/sub 11/ mode. Acoustic modal birefringence phenomenon (or polarization dispersion) has been observed, which means that the dispersion relation of a mode depends on the relative transverse polarizations of the corresponding coupled modes. It was also found that in trigonal cladded fibers there are generally no pure torsional and radial-axial modes, which agrees with results previously obtained for trigonal rods.<<ETX>>
Diminishment of Spark Damages in MGCs by Using Diamond Thin Films

A simulation method on estimation of the anode material damage caused by spark discharges is developed. These discharges are well known to cause permanent failures of microgap gas chambers (MGCs). Systematic analysis and optimization studies have been performed through finite element methods (FEM) simulations using Ansys6.1. The results have evidently shown that, owing to their super high heat conductivity, introduction of diamond thin films (DTFs) as an insulating layer can enormously diminish or even avoid melting or vaporizing of the anodes via spark discharges. Furthermore, topological optimization and anode material selection studies using the FEM simulation technique have been carried out to enhance MGC operational stability. Samples based on the simulation results have been successfully fabricated and tested
Ear recognition based on 3D keypoint matching

This paper proposes a novel ear recognition approach based on 3D keypoint matching. At first, the 3D keypoints are detected using the shape index image and the scale space theory. Then two principal orientations are assigned and the normalized local range image is obtained, which can provide invariance to 3D rotation and transformation for the following local descriptor construction. Finally, we construct the 3D CS-LBP features and use a coarse to fine strategy for 3D keypoint matching. The number of the matching points and their average EMD distances are used for 3D ear recognition. The proposed approach can reduce the amount of 3D data by 3D keypoint detection and local feature construction, and it doesn't need any expensive preprocessing steps. Compared with existing 2D or 3D LBP operators, the proposed 3D CS-LBP operator can not only remain the 3D LBP's powerful ability to describe the 3D structure information, but also reduce the histogram size and enhance its robustness to noise. Extensive experiments have performed to valid the efficiency of the proposed approach.
Design of 1.2 kV Power Switches With Low  $R_{\mathrm{{\scriptscriptstyle ON}}}$  Using GaN-Based Vertical JFET

Two novel gallium nitride-based vertical junction FETs (VJFETs), one with a vertical channel and the other with a lateral channel, are proposed, designed, and modeled to achieve a 1.2 kV normally OFF power switch with very low ON resistance (R<sub>ON</sub>). The 2-D drift diffusion model of the proposed devices was implemented using Silvaco ATLAS. A comprehensive design space was generated for the vertical channel VJFET (VC-VJFET). For a well-designed VC-VJFET, the breakdown voltage (V<sub>BR</sub>) obtained was 1260 V, which is defined in this study as the drain-to-source voltage at an OFF-state current of 1 μA · cm<sup>-2</sup> and a peak electric field not exceeding 2.4 MV/cm. The corresponding R<sub>ON</sub> was 5.2 mΩ · cm<sup>2</sup>. To further improve the switching device figure of merit, a merged lateral-vertical geometry was proposed and modeled in the form of a lateral channel VJFET (LC-VJFET). For the LC-VJFET, a breakdown voltage of 1310 V with a corresponding R<sub>ON</sub> of 1.7 mQ · cm<sup>2</sup> was achieved for similar thicknesses of the drift region. This paper studies the design space in detail and discusses the associated tradeoffs in the R<sub>ON</sub> and V<sub>BR</sub> in conjunction with the threshold voltage (V<sub>T</sub>) desired for the normally OFF operation.
Analysis of conformal Sierpinski fractal microstrip antenna

Input impedance and radiation patterns of conformal fractal microstrip antennas are investigated by the method of moments in this paper. Among the conformal fractal antennas, Sierpinski gasket is chosen as the antenna's radiating patch. The patch is mounted on a dielectric-coated metallic cylinder. Characterization of curvature's effects on the input impedance and radiation characteristics of these conformal microstrip antennas are analyzed.
Fault diagnosis using neuro-fuzzy network and Dempster-Shafer theory

This paper focuses on fault diagnosis using neuro-fuzzy network. It is shown that less reliable result may be derived as the network takes no consideration of previous state information in online fault diagnosis. To solve this problem, we combine a modified neuro-fuzzy network with the evidence update theory. Besides, a new updating rule that combining the Jeffery-like rule and linear combination rule is given. Simulation shows the effectiveness of this fault diagnosis method.
Depletion- and enhancement-mode modulation-doped field-effect transistors for ultrahigh-speed applications: an electrochemical fabrication technology

This paper is devoted to an electrochemical-etching-based technology for fabricating high-performance MODFETs for high-speed applications. The electrochemical etching in the gate openings is induced by the exposure of the Ni surface metal on the ohmic electrodes; it results in very slender gate-recess grooves, which are desirable for high-speed MODFETs because of the resulting achievable small gate-to-channel separation and low parasitic resistance. The technology is easy to implement, and is effective for enhancing the aspect ratio. Good control of aspect ratio is essential for achieving excellent device performance and limiting deleterious short channel effects. Successful vertical scaling, together with minimization of gate length by well-established electron-beam lithography using fullerene-incorporated electron-beam resist, leads to the realization of both optimal D- and E-mode MODFET's with ultrahigh extrinsic transconductance values and current gain cut-off frequencies. Fully passivated 0.07-/spl mu/m D-MODFET's with 2.25 S/mm extrinsic transconductance and current gain cut-off frequency exceeding 300 GHz have been successful fabricated. In addition, 0.03 /spl mu/m E-MODFETs with 2 S/mm transconductance and 300 GHz current gain cut-off frequency have been demonstrated. This electrochemical-etching-based technology provides both high-performance D- and E-MODFET's and, therefore, opens up the possibility to achieve ultrahigh-speed ICs based on DCFL configurations.
Local-Density-Based Optimal Granulation and Manifold Information Granule Description

Constructing information granules (IGs) has been of significant interest to the discipline of granular computing. The principle of justifiable granularity has been proposed to guide the design of IGs, opening an avenue of pursuits of building IGs carried out on a basis of well-defined and intuitively appealing principles. However, how to improve the efficiency and accuracy of the resulting constructs is an open issue. In this paper, we present a local-density-based optimal granulation model (LoDOG), exhibiting evident advantages: 1) it can detect arbitrarily-shaped IGs and 2) it finds the optimal granulation solutions with  ${O}({N})$  complexity, once the leading tree structure has been constructed. We describe IGs of arbitrary shapes using a small collection of landmark points positioned on the skeleton of the underlying manifold, which contribute to approximate reconstruction capabilities of the original dataset. A dissimilarity metric is developed to evaluate the quality of the obtained reconstruction. The interpretability of LoDOG IGs is discussed. Theoretical analysis and empirical evaluations are covered to demonstrate the effectiveness of LoDOG and the manifold description.