nils-herrmann/clean_scirepeval huggingface dataset:
New type of microwave excitation structure for gas laser

Despite the fact that microwave discharges have many benefits for exciting gas lasers, considerable problems in exciting an extended, homogeneous plasma volume at high gas pressures and sufficiently low gas temperatures prevented the breakthrough of microwave excitation for some time. Without an appropriate microwave discharge structure, the high critical power density and skin effect, result in non-homogeneous, overheated discharges burning, and the dissociation of working gases. We introduce a new type discharge structure-waveguide coupled cavity for microwave excitation of gas lasers, which allows us to overcome the problems mentioned above. The design principle of the cavity is described through its low frequency equivalent circuit. Without the ignition bar, this cavity has the characteristics of a simple structure and low discharge breakdown power, experimental results show that the cavity can simultaneously realize frequency tuning resonant discharge and impedance matching. The relationship between the plasma intense luminescence and discharge parameters is also discussed in this paper.
Sub clustering K-SVD: Size variable dictionary learning for sparse representations

Sparse signal representation from overcomplete dictionaries have been extensively investigated in recent research, leading to state-of-the-art results in signal, image and video restoration. One of the most important issues is involved in selecting the proper size of dictionary. However, the related guidelines are still not established. In this paper, we tackle this problem by proposing a so-called sub clustering K-SVD algorithm. This approach incorporates the subtractive clustering method into K-SVD to retain the most important atom candidates. At the same time, the redundant atoms are removed to produce a well-trained dictionary. As for a given dataset and approximation error bound, the proposed approach can deduce the optimized size of dictionary, which is greatly compressed as compared with the one needed in the K-SVD algorithm.
Integrating Visual Saliency and Consistency for Re-Ranking Image Search Results

In this paper, we propose a new algorithm for image re-ranking in web image search applications. The proposed method focuses on investigating the following two mechanisms: 1) Visual consistency. In most web image search cases, the images that closely related to the search query are visually similar. These visually consistent images which occur most frequently in the first few web pages will be given higher ranks. 2) Visual saliency. From visual aspect, it is obvious that salient images would be easier to catch users' eyes, and it is observed that these visually salient images in the front pages are often relevant to the user's query. By integrating the above two mechanisms, our method can efficiently re-rank the images from search engines and obtain a more satisfactory search result. Experimental results on a real-world web image dataset demonstrate that our approach can effectively improve the performance of image retrieval.
Perceptually-adaptive Motion Compensated Temporal Filtering

We propose a perceptually-adaptive motion compensated temporal filtering (MCTF) method to enhance the visual quality of 3D wavelet video coding schemes with spatial-domain MCTF. In our scheme, a spatio-temporal masking model in image domain is incorporated into the lifting structure of MCTF. The model is used to guide the motion search and the prediction step in MCTF so as to remove the visual redundancy in the video sequence. Experimental results show that the proposed scheme can significantly improve the visual quality of decoded video at different bitrates
Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.
Progressively Parsing Interactional Objects for Fine Grained Action Detection

Fine grained video action analysis often requires reliable detection and tracking of various interacting objects and human body parts, denoted as Interactional Object Parsing. However, most of the previous methods based on either independent or joint object detection might suffer from high model complexity and challenging image content, e.g., illumination/pose/appearance/scale variation, motion, and occlusion etc. In this work, we propose an end-to-end system based on recurrent neural network to perform frame by frame interactional object parsing, which can alleviate the difficulty through an incremental/progressive manner. Our key innovation is that: instead of jointly outputting all object detections at once, for each frame we use a set of long-short term memory (LSTM) nodes to incrementally refine the detections. After passing through each LSTM node, more object detections are consolidated and thus more contextual information could be utilized to localize more difficult objects. The object parsing results are further utilized to form object specific action representation for fine grained action detection. Extensive experiments on two benchmark fine grained activity datasets demonstrate that our proposed algorithm achieves better interacting object detection performance, which in turn boosts the action recognition performance over the state-of-the-art.
Consistency-Driven Alternating Optimization for Multigraph Matching: A Unified Approach

The problem of graph matching (GM) in general is nondeterministic polynomial-complete and many approximate pairwise matching techniques have been proposed. For a general setting in real applications, it typically requires to find the consistent matching across a batch of graphs. Sequentially performing pairwise matching is prone to error propagation along the pairwise matching sequence, and the sequences generated in different pairwise matching orders can lead to contradictory solutions. Motivated by devising a robust and consistent multiple-GM model, we propose a unified alternating optimization framework for multi-GM. In addition, we define and use two metrics related to graphwise and pairwise consistencies. The former is used to find an appropriate reference graph, which induces a set of basis variables and launches the iteration procedure. The latter defines the order in which the considered graphs in the iterations are manipulated. We show two embodiments under the proposed framework that can cope with the nonfactorized and factorized affinity matrix, respectively. Our multi-GM model has two major characters: 1) the affinity information across multiple graphs are explored in each iteration by fixing part of the matching variables via a consistency-driven mechanism and 2) the framework is flexible to incorporate various existing pairwise GM solvers in an out-of-box fashion, and also can proceed with the output of other multi-GM methods. The experimental results on both synthetic data and real images empirically show that the proposed framework performs competitively with the state-of-the-art.
Edge region color autocorrelogram: A new low-level feature applied in CBIR

Simple yet effective feature extraction is crucial for content- based image retrieval (CBIR). In this paper we propose a novel type of regional feature, which is called edge region color autocorrelogram (ERCAC). It aims to combine the color and shape characteristics of image jointly, by capturing both color distribution of image and spatial correlation of edge points with a structure-based early fusion. Hence, both color information and sketch information are encoded into a single representation. Experimental results show that our method has better performance on the task of TRECVID 2005 concept detection.
Online video object classification using fast similarity network fusion

In this paper, we propose one online video object classification algorithm using fast Similarity Network Fusion (SNF). By constructing sample-similarity network for each data type and then efficiently fusing these networks into one single similarity network that represents the full spectrum of underlying data, SNF can efficiently identify subtypes among existing samples by clustering and predict labels for new samples based on the constructed network, which make it distinct in data integration or classification. The main problem of data online classification using SNF is its complexity. The proposed fast SNF (FSNF) in this work consists of two main steps: dividing the matrix into two parts and replacing the main part of testing matrix using the same part of training matrix. Since the main computation in SNF is to get the main part of matrix, this replacement can reduce most of the computation load. From the experiments based on online surveillance video object classification, it can be observed that: compared with SNF, the proposed FSNF can gain 16 times speed increasing with only 0.5%-0.6% accuracy losing; FSNF also significantly outperforms the existing traditional algorithms in classification accuracy.
Time Varying Dynamic Bayesian Network for Nonstationary Events Modeling and Online Inference

This paper presents a novel time varying dynamic Bayesian network (TVDBN) model for the analysis of nonstationary sequences which are of interest in many fields. The changing network structure and parameter in TVDBN are treated as random processes whose values at each time epoch determine a stationary DBN model; this DBN model is then used to specify the distribution of data sequence at the time epoch. Under such a hierarchical formulation, the changing state of network can be incorporated into the Bayesian framework straightforwardly. The network state is assumed to transit smoothly in the joint space of numerical parameter and graphical topology so that we can achieve robust online network learning even without abundant observations. Particle filtering is employed to dynamically update current network state as well as infer hidden data values. We implement our time varying model for data sequences of multinomial and Gaussian distributions, while the general model framework can be used for any other distribution. Simulations on synthetic data and evaluations on video sequences both demonstrate that the proposed TVDBN is effective in modeling nonstationary sequences. Comprehensive comparisons have been made against existing nonstationary models, and our proposed model is shown to be the top performer.
Color image watermarking using local quaternion Fourier spectral analysis

We propose a watermarking scheme for color images based on local quaternion Fourier spectral analysis (LQFSA).The merits of the proposed scheme include: 1) Quaternion Fourier transform is defined in a 4D vector space and thus provides a larger embedding scope for watermark than conventional monochannel transformation techniques. 2) We improve the imperceptibility of watermark with regard to human color vision properties through LQFSA. 3) We introduce invariant feature transform (IFT) and geometric correction scheme so as to enhance the robustness to extensive attacks, which is another essential factor to evaluate a watermarking scheme. 4) We adopt the nearest-neighborhood search to ensure the correctness of watermark extraction. Extensive experiments on the Stirmark platform validate the aforementioned merits.
A novel scene text detection algorithm based on convolutional neural network

Candidate text region extraction plays a critical role in convolutional neural network (CNN) based text detection from natural images. In this paper, we propose a CNN based scene text detection algorithm with a new text region extractor. The so called candidate text region extractor I-MSER is based on Maximally Stable Extremal Region (MSER), which can improve the independency and completeness of the extracted candidate text regions. Design of I-MSER is motivated by the observation that text MSERs have high similarity and are close to each other. The independency of candidate text regions obtained by I-MSER is guaranteed by selecting the most representative regions from a MSER tree which is generated according to the spatial overlapping relationship among the MSERs. A multi-layer CNN model is trained to score the confidence value of the extracted regions extracted by the I-MSER for text detection. The new text detection algorithm based on I-MSER is evaluated with wide-used ICDAR 2011 and 2013 datasets and shows improved detection performance compared to the existing algorithms.
Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset

Crowd understanding has drawn increasing attention from the computer vision community, and its progress is driven by the availability of public crowd datasets. In this paper, we contribute a large-scale benchmark dataset collected from the Shanghai 2010 World Expo. It includes 2630 annotated video sequences captured by 245 surveillance cameras, far larger than any public dataset. It covers a large number of different scenes and is suitable for evaluating the performance of crowd segmentation and estimation of crowd density, collectiveness, and cohesiveness, all of which are universal properties of crowd systems. In total, 53 637 crowd segments are manually annotated with the three crowd properties. This dataset is released to the public to advance research on crowd understanding. The largescale annotated dataset enables using data-driven approaches for crowd understanding. In this paper, a data-driven approach is proposed as a baseline of crowd segmentation and estimation of crowd properties for the proposed dataset. Novel global and local crowd features are designed to retrieve similar training scenes and to match spatio-temporal crowd patches so that the labels of the training scenes can be accurately transferred to the query image. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art approaches for crowd understanding.
Separation of superimposed images with unknown motions using sparsity priors

When people take photos through a transparent surface, it is ubiquitous that the images are superimposed with two source images, the transmitted layer and the reflected layer. In this paper, we utilize the sparsity priors over image color distribution and image gradients, and we realize the separation of such images by exploiting the diversity (relative motions of source images) in different snapshots. Our approach can estimate the motion even when layer image is quite faint, extract layer gradients when layer intensity is unchanged, and obtain a reliable separation by considering the gradients as limitations. The effectiveness of our approach is shown in experiments on both synthetic images and real world photos.
An Optimized Pixel-Wise Weighting Approach for Patch-Based Image Denoising

Most existing patch-based image denoising algorithms filter overlapping image patches and aggregate multiple estimates for the same pixel via weighting. Current weighting approaches always assume the restored estimates as independent random variables, which is inconsistent with the reality. In this letter, we analyze the correlation among the estimates and propose a bias-variance model to estimate the Mean Squared Error (MSE) under various weights. The new model exploits the overlapping information of the patches; it then utilizes the optimization to try to minimize the estimated MSE. Under this model, we propose a new weighting approach based on Quadratic Programming (QP), which can be embedded into various denoising algorithms. Experimental results show that the Peak Signal to Noise Ratio (PSNR) of algorithms like K-SVD and EPLL can be improved by around 0.1 dB under a range of noise levels. This improvement is promising, since it is gained independent to which image model is used, especially when the gain from designing new image models becomes less and less.
Enhancing pulmonary nodule detection via cross-modal alignment

Lack of large available datasets fully annotated is a fundamental bottleneck in pulmonary nodule detection, especially when the sensing equipment and the corresponding computed tomography (CT) images obtained are device dependent. This work presents a novel cross modal scheme, pursuing modal alignment, to facilitate our aggregate channel detector training. Named as multi-class cycle-consistent adversarial network (CycleGAN), our proposed framework utilizes a generative adversarial model to transfer nodule morphological characteristics from source modal to target modal, and we propose an end to end objective function to unify the transfer and detection procedures. The outputs of the two parts are combined with a dedicated fusion method for final classification. Extensive experimental results on 1948 scans of the private dataset demonstrate the proposed modal transfer method is very effective in data augmentation.
Design of a multifrequency high power gyrotron at FZK

The design of a multifrequency tunable 1 MW, 10 s gyrotron (105-140 GHz) for controlling plasma instabilities in fusion Tokamaks is presented. It operates in the TE/sub 22,8/ mode at 140 GHz and the TE/sub 17,6/ mode at 105 GHz and some other modes in between. It is equipped with a magnetron injection gun (diode type), a cylindrical cavity, an advanced quasi-optical launcher, three specific beam forming mirrors and a CVD-diamond Brewster window.
Factors in Finetuning Deep Model for Object Detection with Long-Tail Distribution

Finetuning from a pretrained deep model is found to yield state-of-the-art performance for many vision tasks. This paper investigates many factors that influence the performance in finetuning for object detection. There is a longtailed distribution of sample numbers for classes in object detection. Our analysis and empirical results show that classes with more samples have higher impact on the feature learning. And it is better to make the sample number more uniform across classes. Generic object detection can be considered as multiple equally important tasks. Detection of each class is a task. These classes/tasks have their individuality in discriminative visual appearance representation. Taking this individuality into account, we cluster objects into visually similar class groups and learn deep representations for these groups separately. A hierarchical feature learning scheme is proposed. In this scheme, the knowledge from the group with large number of classes is transferred for learning features in its subgroups. Finetuned on the GoogLeNet model, experimental results show 4.7% absolute mAP improvement of our approach on the ImageNet object detection dataset without increasing much computational cost at the testing stage. Code is available on www.ee.cuhk.edu.hk/ wlouyang/ projects/ImageNetFactors/CVPR16.html.
Experimental results and technical requirements for a 2 MW, CW, 170 GHz coaxial cavity gyrotron

In recent experiments on a 165 GHz, coaxial cavity gyrotron, the data basis necessary for a technical design and for industrial manufacturing of such a tube has been completed. In particular, the following investigations have been performed: measurement of the amount of microwave stray radiation captured in the mirror box and of the losses at the coaxial insert with improved accuracy; influence of misalignment of the coaxial insert on the gyrotron performance; extension of the microwave pulse length; measurement of the single-mode operating range and of the hysteresis effect due to variation of the beam voltage. The recently obtained results are presented and discussed. The results concerning the hysteresis effect are given in a separate paper at this conference (O. Dumbrajs et al., ibid., paper TH4.2, 2002). Based on these results, the design work for a 2 MW, CW, 170 GHz coaxial cavity gyrotron has started.
Human identification using body prior and generalized EMD

The general configuration of body is a valuable cue for human identification, which is ignored by the existing approaches. In this paper, we present an approach for human identification by using body prior and the generalized Earth Mover's Distance (EMD). The common knowledge that a pedestrian is composed of upper body and the lower one is employed as a body prior. To achieve more robust body segmentation, we pursue their boundary by inducing a logistic probability map, which is approximated based on minimizing its KL divergence to the posterior probability of the observed person image. Furthermore, we generalize EMD by assigning different weights to regions of body, which are learned through logistic regression to boost discriminative power for human identification. The experimental results show that both body prior and the generalized EMD facilitate performance on human identification.
Feature selection based on network maximal correlation

Feature selection can effectively increase the accuracy of machine learning and improve the efficiency of the algorithm. Therefore, it has emerged as a critical technology related to data mining, machine learning, and has shown great impacts in many applications, including biomedical, financial and communication. However, the increase in data dimension poses a serious challenge to many existing feature selection methods in terms of effectiveness and efficiency. Hirschfeld-Gebelein-Renyi maximal correlation is a effective measure of the correlation between variables. In this paper, with this measure, we proposed a improved Network Maximal Correlation (NMC) model. It can quickly and effectively calculate the statistical dependence between feature set and label variable. Further, based on the Recursive Feature Elimination (RFE) algorithm, a new NMC-RFE feature selection method is Further proposed. The experimental results show that the proposed method can obtain much better feature subsets from high dimensional data sets with faster calculation speed and better accuracy.