nils-herrmann/clean_scirepeval huggingface dataset:
Structured Weak Semantic Space Construction for Visual Categorization

Visual features have been widely used for image representation and categorization. However, visual features are often inconsistent with human perception. Besides, constructing explicit semantic space is still an open problem. To alleviate these two problems, in this paper, we propose to construct structured weak semantic space for image representation. Exemplar classifier is first trained to separate each training image from other images for weak semantic space construction. However, each exemplar classifier separates one training image from other images, and it only has limited semantic separability. Besides, the outputs of exemplar classifiers are inconsistent with each other. We jointly construct the weak semantic space using structured constraint. This is achieved by imposing low-rank constraint on the outputs of exemplar classifiers with sparsity constraint. An alternative optimization procedure is used to learn the exemplar classifiers. Since the proposed method does not dependent on the initial image representation strategy, we can make use of various visual features for efficient exemplar classifier training (e.g., fisher vector-based methods and convolutional neural networks-based methods). We apply the proposed structured weak semantic space-based image representation method for categorization. The experimental results on several public image data sets prove the effectiveness of the proposed method.
Salient target detection in hyperspectral images using spectral saliency

The researches on saliency detection for common images have been thoroughly explored in recent years, but little attention has been paid to hyperspectral images. However, salient region detection can contribute to finding significant targets in hyperspectral images. In this paper, spectral saliency is introduced to the spectral domain for detecting targets in hyperspectral images, which are computed by expanding the Itti's visual attention model. Firstly, spatial and spectral features are extracted from hyperspectral image. The conspicuity feature of the first three principal components, orientations, spectral angle and visible spectral band opponent are computed to sum into a saliency map. Finally, salient targets are detected by winner-take-all strategy finding the region with the highest value in the saliency map. Experimental results show that the proposed method have better effectiveness of target detection compared with the method based on RGB image and visible spectrum.
Automatic Pathology Annotation on Medical Images: A Statistical Machine Translation Framework

Large number of medical images are produced daily in hospitals and medical institutions, the needs to efficiently process, index, search and retrieve these images are great. In this paper, we propose a pathology based medical image annotation framework using a statistical machine translation approach. After pathology terms and regions of interest (ROIs) are extracted from training text and images respectively, we use machine translation model IBM Model 1 to iteratively learn the alignment between the ROIs and the pathology terms and generate an ROI-to-pathology translation table. In testing phase, we annotate the ROI in the image with the pathology label of the highest probability in the translation table. The overall annotation results and the retrieval performance are promising to doctors and medical professionals.
An indirect matrix converter based 97%-efficiency on-board level 2 battery charger using E-mode GaN HEMTs

Most of the present EV on-board chargers utilize a three-stage design, e.g., AC/DC rectifier, DC to high-frequency AC inverter, and AC to DC rectifier, which limits the wall-to-battery efficiency to ~94%. Instead of using the regular three-stage design, a matrix converter could directly convert grid AC to high-frequency AC thereby saves one stage and potentially increases the system efficiency, however, the control will be more complex and the high cost of building the back-to-back switches is inevitable. This paper adopts the 650V E-mode GaN HEMTs to build a level-2 on-board charger. The input voltage is 80~260VAC, the battery voltage is 200~500VDC and the rated power is 7.2kW with the bidirectional power-flow capability. Such design saves the bulky DC-bus capacitor. Variable switching frequency is combined with phase-shift control to realize the zero-voltage switching. An active filter is employed to choke the 120Hz output current ripple if needed. To further increase the system efficiency, four GaN HEMTs are paralleled to form one switching module. The overall system efficiency is >97% and the power density is 2.5kW/L with the active filter and 3.3kW/L without the active filter.
Mobile visual search via hievarchical sparse coding

Mobile visual search is attracting much research attention recently. Existing works focus on addressing the limited capacity of wireless channel yet overlook its instability, thus is not adaptive to the change of channel capacity. In this paper, a novel image retrieval algorithm that is scalable to various channel condition is proposed. The proposed algorithm contains three contributions: (1) to achieve instant retrieval under various channel capacity, we adjust transmission load by sparseness instead of codebook size; (2) we introduce hierarchical sparse coding into our retrieval workflow, where original codebook is transformed into a tree-structured dictionary which implies elements' priority; (3) we propose transmission priority ranking schemes that is adaptive to specific query. Experiment results show that the proposed algorithm outperforms BoW and Lasso based algorithm under different parameter settings. Retrieval results under different channel limitation validate the scalability of our method.
Analytical model of magnetic force for stack PMB applied in wind power generator

With an aim at the large capacity permanent magnet bearings(PMB) in engineering, stack PMB structure was presented in this paper. Based on differential coefficient of magnetic energy to virtual displacement method and superposition principle, the analytical model of magnetic force for stack PMB was established Its correctness was verified by ANSYS simulation. Results indicates that the axial magnetic force of stack PMB is proportion to the magnetic annular average perimeter and the square of residual magnetism induction density, and that the axial magnetic force increases with an increment in the magnetic annular number, magnetic annular radial width and axial length. At the condition of axial total length and other shape parameter fixedness, the magnetic force of reversal magnetization axial stack PMB is consumedly enhanced, and the magnetic force of same direction magnetization axial stack PMB isn't increased The axial magnetic force of same direction magnetization axial stack PMB is maximum at the condition of axial offset approximate equal to the axial length of single magnetic ring, The axial magnetic force of reversal magnetization axial stack PMB is maximum at the condition of axial offset approximate equal to the half axial length of single magnetic ring
Lp-Norm IDF for Large Scale Image Search

The Inverse Document Frequency (IDF) is prevalently utilized in the Bag-of-Words based image search. The basic idea is to assign less weight to terms with high frequency, and vice versa. However, the estimation of visual word frequency is coarse and heuristic. Therefore, the effectiveness of the conventional IDF routine is marginal, and far from optimal. To tackle this problem, this paper introduces a novel IDF expression by the use of Lp-norm pooling technique. Carefully designed, the proposed IDF takes into account the term frequency, document frequency, the complexity of images, as well as the codebook information. Optimizing the IDF function towards optimal balancing between TF and pIDF weights yields the so-called Lp-norm IDF (pIDF). We show that the conventional IDF is a special case of our generalized version, and two novel IDFs, i.e. the average IDF and the max IDF, can also be derived from our formula. Further, by counting for the term-frequency in each image, the proposed Lp-norm IDF helps to alleviate the visual word burstiness phenomenon. Our method is evaluated through extensive experiments on three benchmark datasets (Oxford 5K, Paris 6K and Flickr 1M). We report a performance improvement of as large as 27.1% over the baseline approach. Moreover, since the Lp-norm IDF is computed offline, no extra computation or memory cost is introduced to the system at all.
Principal Visual Word Discovery for Automatic License Plate Detection

License plates detection is widely considered a solved problem, with many systems already in operation. However, the existing algorithms or systems work well only under some controlled conditions. There are still many challenges for license plate detection in an open environment, such as various observation angles, background clutter, scale changes, multiple plates, uneven illumination, and so on. In this paper, we propose a novel scheme to automatically locate license plates by principal visual word (PVW), discovery and local feature matching. Observing that characters in different license plates are duplicates of each other, we bring in the idea of using the bag-of-words (BoW) model popularly applied in partial-duplicate image search. Unlike the classic BoW model, for each plate character, we automatically discover the PVW characterized with geometric context. Given a new image, the license plates are extracted by matching local features with PVW. Besides license plate detection, our approach can also be extended to the detection of logos and trademarks. Due to the invariance virtue of scale-invariant feature transform feature, our method can adaptively deal with various changes in the license plates, such as rotation, scaling, illumination, etc. Promising results of the proposed approach are demonstrated with an experimental study in license plate detection.
The Study of Performance for Collector and Storage Tank Based on the Solar Powered Ejector and Compression Refrigeration System

As heat source for the solar powered ejector and compression refrigeration system, Collector-thermal Storage's design selection and performance directly affect operation of refrigeration system. By setting up a mathematical model of the solar energy Collector-thermal Storage and its components, the paper calculates optimum collector area under given condition of the cooling capacity inTaiyuan, and analyzes the changes of the thermal Storage temperature and the corresponding cooling capacity.
A detection algorithm for radar weak targets under the interference of strong targets

In radar multi-target detection, the weak target might be interfered by the near strong target, which badly influences the detection of weak target. A novel detection algorithm for weak targets is proposed to cope with the situation above. Combining the Keystone transform and the Least Square (LS) filter, the return of strong target is reconstructed and then removed from the original. Finally, the weak target is detected by integration for the residual. The effectiveness of the proposed algorithm is illustrated and analyzed by simulations.
DisturbLabel: Regularizing CNN on the Loss Layer

During a long period of time we are combating overfitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc. In this paper, we present DisturbLabel, an extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration. Although it seems weird to intentionally generate incorrect training labels, we show that DisturbLabel prevents the network training from over-fitting by implicitly averaging over exponentially many networks which are trained with different label sets. To the best of our knowledge, DisturbLabel serves as the first work which adds noises on the loss layer. Meanwhile, DisturbLabel cooperates well with Dropout to provide complementary regularization functions. Experiments demonstrate competitive recognition results on several popular image recognition datasets.
Demodulation of QAM signals based on chaotic oscillators

By making use of the chaotic oscillator, a new method of demodulating QAM signal submerged in strong noise is presented. According to the method, the problem of weak digital modulation signal was solved. Duffing system is sensitive to specific signal and immune against the noise. For the fork problem of Duffing oscillator, taken the signal into multiple systems at the same time. Different Systems were sensitive to different tested signals. The signal was changed in a different phase and amplitude. QAM signal was demodulated by judging the system state. The simulation results show that this demodulation method is suitable for demodulating the QAM signal.
An iterative spectral method for multiple target detection

Although split spectrum processing (SSP) is an effective method for suppressing grain noise in ultrasonic non-destructive testing, its application has been mainly limited to the detection of single targets or multiple targets having similar spectral characteristics. In this paper, the group delay moving entropy technique is introduced to enhance the performance of SSP in detecting multiple targets which exhibit different spectral characteristics. This is likely to occur in complex, dispersive and nonhomogeneous media such as composites, layered and clad materials, etc. It is shown that the group delay moving entropy can be used to select the optimal frequency region for SSP when detecting such targets. An iterative procedure that combines group delay moving entropy and SSP is proposed whereby the multiple targets are identified one at a time. The dominant target is subsequently eliminated using time domain windows which improves the detection of the remaining weaker targets. Simulation results are presented which demonstrate the feasibility of the multi-step SSP for detecting multiple targets.
Consensus over directed graph: Output feedback and topological constraints

In this paper, the discrete-time multi-agent systems consensus problem over a directed, fixed network communication graph is studied. A distributed dynamic output feedback control protocol is employed. Drawing upon concepts and techniques from robust control, notably those concerning gain-phase margin optimization and analytic interpolation, we obtained explicit sufficient conditions for general linear agents to achieve consensus. The results display an explicit dependence of the consensus condition on the agent's unstable poles, non-minimum phase zeros and their relative degree.
PDH: a human-centric interface for image libraries

We present visualization and layout algorithms that can enhance informal story-telling using personal digital data such as photos in a face-to-face social setting. In order to build a more intuitive browser for retrieval, navigation and story-telling, we introduce a novel optimized layout technique for large image sets, which respects (context-sensitive) mutual similarities as visualized on a shared 2-D display (a table-top). The experimental results show a more perceptually intuitive and informative visualization of traditional CBIR-based retrievals, providing not only a better understanding of the query context but also aiding the user informing new queries. A framework for user-modeling is also introduced and tested. This allows the system to adapt to the user's preference and integrate relevance feedback.
Clustering based blind estimation of PN sequences in M-ary spread spectrum system

To solve the problem of blind estimation of the pseudo-noise(PN) sequence in M-ary spread spectrum system(MSSS), this paper learned from the idea of unsupervised clustering analysis, then proposed a method of estimating PN sequences in M-ary spread spectrum system based on K-means clustering algorithm. In this method, the MSSS signal was divided into non-overlapped individuals to construct a data set, the blind synchronization of the PN sequence was completed by the detection statistic of the data set's autocorrelation matrix; then the PN sequences set's scale was estimated by searching the cost function based on K-means clustering algorithm, the cost function's graph exhibited a significant inflection point,its position indicated the scale of PN sequences set; Finally the PN sequences was estimated by exploiting the clustering property of the data set. Theoretical analysis and computer simulations show that this method can accurately estimate the time delay and PN sequences in MSSS.
Interactive learning for classifying microarray gene expression data

Relevance feedback , which has been successfully used in content-based image retrieval, is rarely used in the field of bioinformatics. In this paper, we introduce relevance feedback to microarray analysis and implement an interactive learning framework for classifying microarray gene expression data. The aim is to incorporate specialists' feedback to retrain our classifier, which can bridge the gap between the temporal expressions and the associated semantics. Extensive experiments on the Plasmodium falciparum dataset show the effectiveness and promising performance of the scheme.
Foreground object detection in changing background based on color co-occurrence statistics

This paper proposes a novel method for detecting foreground objects in nonstationary complex environments containing moving background objects. We derive a Bayes decision rule for classification of background and foreground changes based on inter-frame color co-occurrence statistics. An approach to store and fast retrieve color co-occurrence statistics is also established In the proposed method, foreground objects are detected in two steps. First, both foreground and background changes are extracted using background subtraction and temporal differencing. The frequent background changes are then recognized using the Bayes decision rule based on the learned color co-occurrence statistics. Both short-term and longterm strategies to learn the frequent background changes are proposed Experiments have shown promising results in detecting foreground objects from video containing wavering tree branches and flickering screens/water surface. The proposed method has shown better performance as compared with two existing methods.
Multiview Hessian Semisupervised Sparse Feature Selection for Multimedia Analysis

Facing a large number of unlabeled data and a small number of labeled data, semisupervised sparse feature selection has received increasing attention in recent years. However, most semisupervised feature selection algorithms are developed for single-view data and cannot naturally handle multiview data. Moreover, most existing semisupervised sparse feature selection methods are based on Laplacian regularization, which is a lack of extrapolating power. To overcome the above-mentioned drawbacks, we present a multiview Hessian semi-supervised sparse feature selection (MHSFS) framework in this paper. MHSFS can directly accomplish multiview sparse feature selection by exploiting multiview learning to reveal and leverage the correlated and complemental information among different views. In addition, MHSFS can achieve better performance based on Hessian regularization, which favors functions whose values linearly vary with respect to geodesic distance and preserves the local manifold structure well. A simple yet efficient iterative method is proposed to solve the objective function, followed by convergence analysis. We apply the proposed method into different multimedia analysis tasks, such as image annotation, video concept detection, and 3D motion analysis. The results show that MHSFS outperforms the state-of-the-art sparse feature selection methods and achieves good performance.
Octree based backward SBR-PO method for electromagnetic scattering of electrically large target

The shooting and bouncing ray (SBR) method is highly effective for solving the electromagnetic scattering problems. This paper introduces the octree based SBR-PO method, due to the fact that traditional SBR method is not fast enough for scattering prediction for electrically large target. Using the octree structure to rebuild the target model and process ray tracing based on this technique can obtain great improvement. Simulation results obtained using this method are compared with the results from literature and those generated by commercial software. The good agreement and significant speedup achieved demonstrate that the newly developed code is capable of modeling a very large-size platform for practical rapid design.
Spatial visualization for content-based image retrieval

In traditional content-based image retrieval (CBIR), the retrieved images are displayed in order of decreasing similarities from the query and can be considered as a 1-D display. In this paper, a novel optimized technique is proposed to visualize the retrieved images not only in order of their decreasing similarities but also according to their mutual similarities visualized on a 2-D screen. Principle Component Analysis (PCA) is first performed on the retrieved images to project the images from the original high dimensional feature space to 2-D screen. The result of PCA analysis is denoted as a PCA Splat. To minimize the overlap between images, a constrained nonlinear optimization approach is used. The experimental results show a more perceptually intuitive and informative visualization of the retrieval results. The proposed technique not only provides a better understanding of the query results but also aids the user in forming a new query.
Lower-frequency feedback dual-band PA with very high efficiency

A novel approach to achieve high-efficiency dual-band power amplifier (PA) is presented. Owing to the contradictions among the control of harmonics, most reported dual-band or multi-band PAs are intentionally avoiding the case when the higher-frequency band overlaps the second harmonics of the lower-frequency band, especially when f higher = 2 f lower . By introducing lower-frequency feedback technique a method is proposed, make a compromise between high-efficiency and power gain fluctuant, high-efficiency dual-band PA at the above condition can be achieved. To verify, a dual-band PA operating at 1.7 and 3.4 GHz is fabricated and measured. The peak output powers of the dual-band are 40.1 and 40.7 dBm with small signal gains 25 and 17 dB, and the drain efficiency at the peak output power reached 80.2 and 78.5%, respectively.
Text Mining in Radiology Reports

Medical text mining has gained increasing interest in recent years. Radiology reports contain rich information describing radiologistpsilas observations on the patientpsilas medical conditions in the associated medical images. However, as most reports are in free text format, the valuable information contained in those reports cannot be easily accessed and used, unless proper text mining has been applied. In this paper, we propose a text mining system to extract and use the information in radiology reports. The system consists of three main modules: a medical finding extractor, a report and image retriever, and a text-assisted image feature extractor. In evaluation, the overall precision and recall for medical finding extraction are 95.5% and 87.9% respectively, and for all modifiers of the medical findings 88.2% and 82.8% respectively. The overall result of report and image retrieval module and text-assisted image feature extraction module is satisfactory to radiologists.