nils-herrmann/clean_scirepeval huggingface dataset:
Design of high efficiency Doherty power amplifier applying power controlling technology with 15dB output power back-off

A novel method to design Doherty power amplifier (DPA) with promoted high efficiency performance in a very large output power back-off (OBO) is proposed in this paper. By reduce the variation range of the optimized output impedances of the carrier power amplifier (PA), the pit area in the efficiency carves are improved. Applying the nonlinearity of radio frequency schotty barrier diodes (SBDs), the convention power divider (PD) before DPA is replaced with a non-fixed power-split-ratio PD. Especially for very large OBO applications, when the carrier PA is chosen at a much smaller power level than the peak PA, the performance at the whole OBO could be promoted more apparently. For verification, a DPA with 15dB OBO was designed at the center frequency of 2.56GHz. The maximum measured drain efficiency (DE) at saturation output power 43.6dBm are over 77%, and achieved minimum DE of 58% during the whole 15dB OBO.
Interactive Semisupervised Learning for Microarray Analysis

Microarray technology has generated vast amounts of gene expression data with distinct patterns. Based on the premise that genes of correlated functions tend to exhibit similar expression patterns, various machine learning methods have been applied to capture these specific patterns in microarray data. However, the discrepancy between the rich expression profiles and the limited knowledge of gene functions has been a major hurdle to the understanding of cellular networks. To bridge this gap so as to properly comprehend and interpret expression data, we introduce relevance feedback to microarray analysis and propose an interactive learning framework to incorporate the expert knowledge into the decision module. In order to find a good learning method and solve two intrinsic problems in microarray data, high dimensionality and small sample size, we also propose a semisupervised learning algorithm: kernel discriminant-EM (KDEM). This algorithm efficiently utilizes a large set of unlabeled data to compensate for the insufficiency of a small set of labeled data and it extends the linear algorithm in discriminant-EM (DEM) to a kernel algorithm to handle nonlinearly separable data in a lower dimensional space. The relevance feedback technique and KDEM together construct an efficient and effective interactive semisupervised learning framework for microarray analysis. Extensive experiments on the yeast cell cycle regulation data set and Plasmodium falciparum red blood cell cycle data set show the promise of this approach
Cross-database age estimation based on transfer learning

Due to the temporal property of age progression, face images with aging features display some sequential patterns with low-dimensional distributions, which can be effectively extracted by subspace learning algorithms. The patterns extracted by traditional subspace learning methods are mostly restricted to a certain database. As a result, the performance cannot be generalized when applying these patterns to cross databases with different multi-mode variations (e.g. gender, identity, and imaging conditions.) This problem has yet not been given much attention before. In this paper, the cross-database age estimation problem is solved by a transfer learning framework. The proposed framework transfers the knowledge gained from training samples to the target data and improves the performance in cross-database scenarios. Experimental results for age estimation tasks on different datasets demonstrate the effectiveness and robustness of our proposed framework.
Detection of DSSS/UQPSK signal based on fourth-order cumulant algorithm

For the problem of Direct Sequence spread spectrum unbalance Quadrature Phase Shift Keying (DSSS/UQPSK) signal can't be detected easily, the method of the fourth-order cumulant detection is proposed in this paper. On the basis of the fourth-order cumulant of the Gaussian White Noise is equal to zero, Firstly, the paper analyzes the theory knowledge of direct sequence unbalance QPSK signal and the feasibility of estimating carrier-frequency based on fourth-order statistic slice. Then, proposes a detecting method of the DSSS/UQPSK signal with the fourth-order cumulant c<sub>4x</sub> (0,0,0), c<sub>4x</sub> (0,0,τ), c<sub>4x</sub> (0, τ, τ) and c<sub>4x</sub> (τ, τ, τ). Finally, the performance of the fourth-order cumulant different slices will be acquired by doing some effectively simulations, and the DSSS/UQPSK signal' carrier-frequency will be estimated based on the slices' frequency spectrum. Experimental results show that the detection performance of c<sub>4x</sub> (0, τ, τ) is the best, while the fourth-order cumulant c<sub>4x</sub> (τ,τ,τ) can estimate the carrier-frequency effectively at low signal-to-noise ratio. The proposed method is of high feasibility and has practical application value.
AST-a method of agent naming and localization

Agent naming and localization is the key technology in mobile agents. How it is implemented affects the performance of the whole agent system deeply. The paper proposes a method called agent shadow tracing to implement the agent naming and localization, after analyzing three approaches in agent naming and localization. This method treats agents as some source of the host that generates it, and assigns a shadow to the agent so that other agents and programs can communicate with it through the shadow when the agent roams around the network. The paper also discusses the security of AST and its advantages compared with other methods. This method avoids some known disadvantages in existing methods.
Gait recognition with skeleton information by using ensemble learning

In recent years, the demand of robust gait recognition for practical applications has increased and the existing gait recognition rate still has room to improve. So an innovative Kinect-based method of gait recognition in the skeleton model by using center of mass (CM) and ensemble learning is presented to get the improvement in this paper. CM-related static features are chosen automatically by filtering selection algorithms and CM-related dynamic features are processed by spectrum analysis. This behavior preserves the better features, further improves the recognition effect and enhances the difference among features. The categories and dimensions of features lead to difficulties in the measurement of the skeletal model. To tackle this problem, methods of combining Dynamic Time Warping (DTW) with some classifiers are proposed. In order to optimize the recognition effect, ensemble learning is used to integrate the learning results of each classifier. Experiments show that the performance of our method is better than other state-of-the-art methods.
Coherent Integration Method of High-Speed Target for Frequency Agile Radar

In order to detect the high-speed target for frequency agile (FA) radar, a coherent integration method is proposed in this paper. Making full use of the characteristics of the FA radar, the echoes are classified after pulse compression according to carrier frequency and then processed separately. For echoes of the same carrier frequency, keystone transform is applied to correct the range migration and Doppler frequency ambiguity, due to high speed, is compensated based on radon transform. Then, the ambiguous velocity for different carrier frequencies is adjusted to be the same by phase compensation, and the coherent integration is achieved by chirp-z transform for echoes of the same carrier frequency. After carrier frequency rearrangement, a frequency-velocity plane is constructed, and the target’s energy is further accumulated by inverse Fourier transform during frequency. Simulation results validate the effectiveness of the proposed method for a high-speed target; meanwhile, the good performances of the proposed method on detection probability, estimation accuracy, and computational efficiency are shown by comparisons with three other methods.
Modeling and analyzing to the forward PV cells for large-scale PV array

Photovoltaic array is the base of the photovoltaic power system. To use the PV battery array efficiently and properly and to protect it, it is important to build an accurate Photovoltaic array model and to analyze the characteristics of the PV array under different circumstances. The characteristics of mono-crystalline silicon, poly-crystalline silicon and amorphous silicon PV modules' basic electrical characteristics were studied by simulation analysis and the experimental measurement. Depending on the experimental data, the forward circuit model of photovoltaic battery is built up in the simulation platform of MATLAB to compare the differences of the different photovoltaic arrays' electrical characteristics. The influence on the electrical characteristics of PV module caused by parameters such as insolation and temperature is analyzed. The array characteristics of PV module are analyzed and the commercial PV array in shading conditions is simulated.
Stability for networked control system with quantization constraints

This paper studies thestability of linear time-invariant (LTI) single-input single-output (SISO) discrete-time networked feedback systems. Only quantization effect is considered and the quantizer is logarithmic. Second order stochastic input-output stability is studied. A stabilizing output feedback controller which is realized by Youla parameterization is adopted. We get a necessary condition to guarantee the closed-loop system stability in mean-square sense. This result also provides a method to design an output feedback controller to stabilize a linear system with multiplicative noise.
Discriminative Fusion Approach for Automatic Image Annotation

In this paper, two discriminative fusion schemes are proposed for automatic image annotation. One is the ensemble-pattern association based fusion and another is the model-based transformation. The fusion approaches are studied and evaluated in a unified framework for AIA based on the text representation of the image content and the MC MFoM learning. The schemes are flexible for fusing diverse visual features and multiple modalities. The discriminative learning can automatically weight the most important features for the classification. We evaluate the fusion schemes based on the Corel and TRECVID 2003 datasets. The experimental results clearly show that the proposed fusion schemes give a significant improvement in term of the mean of F1 as well as the number of the detected concepts
Indexing with musical events and its application to content-based music identification

In this paper a musical event based indexing approach is proposed and its application to content-based music identification is studied. The events, which function as term words used in text retrieval or basic speech units in speech recognition, are inferred using an unsupervised learning algorithm. Its differences with the existing methods are in that the learned low-level musicology knowledge and model selection technique are exploited to extract musical events. Our experimental analyses on a task of music identification demonstrate that the proposed indexing method is efficient, compact and robust. Using a collection of 20-second query segments on the evaluation set, the equal error rate reaches 1.57%. For applications that demand fewer false alarms, we could operate the system at a reduced false acceptance rate of 0.57% while increasing the false rejection rate to 4.58%.
Attribute prediction with long-range interactions via path coding

Due to the describable or human-nameable nature of visual attributes, the appropriate utilization of attributes has been receiving much attention in recent years in many applications. Motivated by the assumption that the long-range interactions between attributes can boost image understanding and classification, path coding is utilized in this paper to model the long-range interactions between attributes for the attribute prediction, we call it attribute prediction via a path coding penalty (abbreviated as AP2CP). AP2CP not only introduces structured sparsity penalties over paths on a directed acyclic graph, but also captures the intrinsical long-range dependent interactions between attributes. The proposed AP2CP can be efficiently solved by leveraging network flow optimization. The experiments show that the proposed AP2CP achieves a better performance in attribute prediction.
Convergence/divergence analysis of implicit Z-bus power flow for general distribution networks

Implicit Z-bus distribution power flow method may encounter divergence problems when applied to distribution systems with distributed generators (DGs) modeled as P-V nodes. In this paper, analysis of divergence problems associated with the Implicit Z-bus method is performed. We illustrate the derived analytical results to conduct divergence analysis on the IEEE 13-bus, and a practical 1101-node distribution networks.
Effective complexity reduction in codebook search for ACELP?

Algebraic Code Excited Linear Prediction (ACELP) speech coding algorithm provides high speech quality, but it also has high complexity, so it is difficult to implement in real-time system, and the codebook search is the main operational load. This paper proposes an effective algorithm, first, the discontinuous pitch estimation is proposed in adaptive codebook search, the time decimation factor is used in different delays, and then a modified pulse-replacement search algorithm is proposed in stochastic codebook search, the position prediction technique and judging mechanism are used to control the number of codebook search. Finally, the improved algorithm is simulated in G.729A, the result show that the proposed algorithms not only acquire complexity reduction but also get a high speech quality.
Hierarchical Part Matching for Fine-Grained Visual Categorization

As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing attention these years. Different with traditional image classification tasks in which objects have large inter-class variation, the visual concepts in the fine-grained datasets, such as hundreds of bird species, often have very similar semantics. Due to the large inter-class similarity, it is very difficult to classify the objects without locating really discriminative features, therefore it becomes more important for the algorithm to make full use of the part information in order to train a robust model. In this paper, we propose a powerful flowchart named Hierarchical Part Matching (HPM) to cope with fine-grained classification tasks. We extend the Bag-of-Features (BoF) model by introducing several novel modules to integrate into image representation, including foreground inference and segmentation, Hierarchical Structure Learning (HSL), and Geometric Phrase Pooling (GPP). We verify in experiments that our algorithm achieves the state-of-the-art classification accuracy in the Caltech-UCSD-Birds-200-2011 dataset by making full use of the ground-truth part annotations.
Saliency Density Maximization for Efficient Visual Objects Discovery

Detection of salient objects in an image remains a challenging problem despite extensive studies in visual saliency, as the generated saliency map is usually noisy and incomplete. In this paper, we propose a new method to discover the salient object without prior knowledge on its shape and size. By searching the sub-image, i.e., a bounding box of maximum saliency density, the new formulation can automatically crop the salient objects of various sizes in spite of the cluttered background, and is capable to handle different types of saliency maps. A global optimal solution is obtained by the proposed density-based branch-and-bound search. The proposed method can apply to both images and videos. Experimental results on a public dataset of 5000 images show that our unsupervised detection approach is comparable to the state-of-the-art learning-based methods. Promising results are also observed in the salient object detection for videos with a good potential in video retargeting.
A Novel Protocol for Making of Monoclonal Antibody against AFP

Alpha-fetoprotein (AFP) is a major serum protein (Mr, =70,000) synthesized during fetal life. Reappearance of AFP in adult serum often signals pathological conditions, particularly hepatocarcinomas and teratocarcinomas. To obtain monoclonal antibodies against AFP for further study of the structure and biological function of AFP, a novel method for making hybridoma cell lines producing monoclonal antibodies for AFP by immunization of animals with synthetic peptides were proposed. One epitope peptide was synthesized based on the bioinformatics analysis of the AFP and coupled with keyhole limpet hemocyanin ( KLH ). BALB/c mice were immunized with the coupled AFP polypeptide to make monoclonal antibody. Hybridomas were generated by the fusion of the spleenocytes from these mice with Sp2/0 myeloma cells. Hybridomas producing anti-AFP antibodies were screened by enzyme-linked immunosorbent assay ( ELISA), and the monoclonal antibodie specificities were determined by and indirect-ELISA.The favorite epitope of AFP was seleceted by bioinformatics methods and synthesized successfully, which coupled with carrier KLH as immunogen to immunize BALB/c mice, two hybridoma cell lines (2F12 and 3G8) stable in secreting anti-AFP specificity monoclonal antibodies (mAbs) were successfully generated, Titers of tybridoma cell culture supernatant and asicites are both above 1:32 and 100,000 respectively.Creatively preparing mAb against AFP by ways of immunizing mice with the coupled epitope to KLH.The mAb should be suitable for studies of the cellular localization of AFP's biosynthesis and its processing after interaction with receptor. It will also be useful in the immunoaffinity purification of the AFP in sreum and as a useful reagent for the analysis of biochemical, structural, and functional properties of AFP
A robust Hough-based algorithm for partial ellipse detection in broadcast soccer video

This paper presents a robust Hough-based algorithm for partial slightly oblique ellipse detection in broadcast soccer video. The successful identification of the ellipses significantly facilitate soccer video analysis. The existing standard and various modified ellipse Hough transforms measure a cell in the Hough space as though the ellipse defined by the cell were a complete ellipse. Hence, they are not robust when they are applied to detect the partial ellipses appearing in broadcast soccer video. This paper proposes a new measure function that is able to fairly measure whole and partial ellipses. With the improved measure function, we propose an algorithm to detect the partial ellipses in broadcast soccer video. The proposed algorithm first estimates the target ellipse by using the symmetry of the ellipse and the domain knowledge of soccer video. Then for each estimated ellipse, the algorithm searches around the estimated ellipses to find the ellipse with the highest measured value. Our algorithm is efficient and memory-small, i.e. it overcomes two main problems of the standard ellipse Hough transform. More importantly, the proposed algorithm is much more robust than the existing ellipse Hough transforms. Experimental results show that the proposed algorithm achieves above 96% recall and 100% precision. Our algorithm may be the first one that is able to detect ellipses from commercial video in pseudo real-time
Image Search Reranking With Hierarchical Topic Awareness

With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method “topic-aware reranking (TARerank)” is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.
Forecast of stock price volatility based on the multi-fractal spectrum analysis

This paper mainly employs multi-fractal spectrum theory to study the time series of stock price fluctuations law. First, multi-fractal analysis on the stock market of China prove that stock price fluctuations obey multi-fractal random walk, and then research on the Multi-spectrum characteristics change before and after the continued strong fluctuations time sequence of stock prices through empirical analysis. Then, study the relationship between the Multi-spectrum parameters and price changes in the time series, thus proving that Multi-spectrum parameters has some predictive capability. Finally construct the stock price volatility forecast model, the clustering forecast on the Multi-spectrum parameters access to the higher accuracy forecast results, It show out a new research method on the stock price forecasting.
GLA: Global–Local Attention for Image Description

In recent years, the task of automatically generating image description has attracted a lot of attention in the field of artificial intelligence. Benefitting from the development of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), many approaches based on the CNN-RNN framework have been proposed to solve this task and achieved remarkable process. However, two problems remain to be tackled in which the most existing methods use only the image-level representation. One problem is object missing, in which some important objects may be missing when generating the image description and the other is misprediction, when one object may be recognized in a wrong category. In this paper, to address these two problems, we propose a new method called global–local attention (GLA) for generating image description. The proposed GLA model utilizes an attention mechanism to integrate object-level features with image-level feature. Through this manner, our model can selectively pay attention to objects and context information concurrently. Therefore, our proposed GLA method can generate more relevant image description sentences and achieve the state-of-the-art performance on the well-known Microsoft COCO caption dataset with several popular evaluation metrics—CIDEr, METEOR, ROUGE-L and BLEU-1, 2, 3, 4.