Efficiency-Controllable Random Walks on a Class of Recursive Scale-Free Trees with a Deep Trap

Controls, especially effficiency controls on dynamical processes, have become major challenges in many complex systems. We study an important dynamical process, random walk, due to its wide range of applications for modeling the transporting or searching process. For lack of control methods for random walks in various structures, a control technique is presented for a class of weighted treelike scale-free networks with a deep trap at a hub node. The weighted networks are obtained from original models by introducing a weight parameter. We compute analytically the mean first passage time （MFPT） as an indicator for quantitatively measurinM the et^ciency of the random walk process. The results show that the MFPT increases exponentially with the network size, and the exponent varies with the weight parameter. The MFPT, therefore, can be controlled by the weight parameter to behave superlinearly, linearly, or sublinearly with the system size. This work provides further useful insights into controllinM eftlciency in scale-free complex networks.

Minimizing the Impact of Long Propagation Delay on Multiple Access for Wireless Networks

A multiple access protocol is proposed to greatly improve multiple access performance of wireless networks with long propagation delay. All the nodes with packets to send can make rapid successful reservation in access reservation mini-slots, which is adaptively adjusted according to current traffic load and idle channel resources. A Central Control Node (CCN) coordinates channel reservation and allocates on-demand channel resources to the successfully accessed nodes on two channels. Each node can employ only one handshake to accomplish each communication session, and transmit one or multiple data packets piggybacked with acknowledgment (ACK) information to one or multiple destination nodes in each frame until the end of their communication sessions, which greatly minimizes the impact of long propagation delay caused by handshakes and improves channel efficiency. Simulation results show that the proposed protocol obviously outperforms the Centralized Scheduling-based Medium Access Control (CSMAC) protocol, especially in the presence of long propagation delay.

Lie Reduction and Conditional Symmetries of Some Variable Coefficient Nonlinear Wave Equations

Lie symmetry reduction of some truly ＂variable coefficient＂ wave equations which are singled out from a class of （1 ＋ 1）-dimensional variable coefficient nonlinear wave equations with respect to one and two-dimensional algebras is carried out. Some classes of exact solutions of the investigated equations are found by means of both the reductions and some modern techniques such as additional equivalent transformations and hidden symmetries and so on. Conditional symmetries are also discussed.

Preprocessing method of night vision image application in apple harvesting robot

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for commercialization.The machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation mode.Due to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting robot.Three different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be captured.In addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise characteristics.The results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper noise.The preprocessing method can provide a theoretical and technical reference for subsequent image processing.

Exploring the tidal effect of urban business district with large-scale human mobility data

Business districts are urban areas that have various functions for gathering people,such as work,consumption,leisure and entertainment.Due to the dynamic nature of business activities,there exists significant tidal effect on the boundary and functionality of business districts.Indeed,effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a city.However,with the implicit and complex nature of business district evolution,it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business districts.To this end,we propose a data-driven and multi-dimensional framework for dynamic business district analysis.Specifically,we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time periods.Then,we detect and forecast the functional changes in business districts.Experimental results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business districts.Moreover,the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business districts.For example,the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model

Objective and Impact Statement.In this work,we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions.Compared with the conventional model trained on a single dataset,this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark location.Introduction.The accurate and automatic localization of anatomical landmarks plays an essential role in medical image analysis.However,recent deep learning-based methods only utilize limited data from a single dataset.It is promising and desirable to build a model learned from different regions which harnesses the power of big data.Methods.Our model consists of a local network and a global network,which capture local features and global features,respectively.The local network is a fully convolutional network built up with depth-wise separable convolutions,and the global network uses dilated convolution to enlarge the receptive field to model global dependencies.Results.We evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical regions.Extensive experimental results show that our model improves the detection accuracy compared to the state-of-the-art methods.Conclusion.Our model makes the first attempt to train a single network on multiple datasets for landmark detection.Experimental results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.

A Novel Subpixel Curved Edge Localization Method

With the high-speed development of digital image processing technology, machine vision technology has been widely used in automatic detection of industrial products. A large amount of products can be treated by computer instead of human in a shorter time. In the process of automatic detection, edge detection is one of the most commonly used methods. But with the increasing demand for detection precision,traditional pixel-level methods are difficult to meet the requirement, and more subpixel level methods are in the use. This paper presents a new method to detect curved edge with high precision. First, the target area ratio of pixels near the edge is computed by using one-dimensional edge detection method. Second, parabola is used to approximately represent the curved edge. And we select appropriate parameters to obtain accurate results. This method is able to detect curved edges in subpixel level, and shows its practical effectiveness in automatic measure of products with arc shape in large industrial scene.

Isometrically subdivisible tetrahedra

It is well known that a triangle can be divided by mid-point refinement into four sub-triangles with the same shape. Similarly, a tetrahedron can be parted into eight subtetrahedra, which are generally not uniform in shape. This paper proves that there exist a set of tetrahedra, which is called isometrically subdivisible tetrahedra(IST) and can be divided into eight isometric subtetrahedra, including identical and reflection ones. And a new classification of tetrahedra is put forward, based on which all tetrahedra can be categorized into 26 classes according to both the number of maximum equal edges and topological relations. The IST belongs only to three of the classes. That result provides a new viewpoint of spatial structure and may be used to tile or subdivide space uniformly or isometrically.

Non-rigid 3D models retrieval based on hierarchical matching

In 3D models retrieval, feature description and retrieval of non-rigid model face more complex problems due to the isometry transformation of itself. We introduce the hierarchical combination matching into the feature comparison, and build a map between the divided regions of two models, and then achieve accurate feature matching based on patch-by-patch, which successfully introduces the spatial information into feature matching. Verified by experiment, the 3D model retrieval method proposed in this paper based on hierarchical combination matching can make sure more accurate feature matching, so as to enhance the precision of retrieval.

Hand and gesture

Hands play an important role in our daily life.We use our hands for manipulation in working,emphasis in speaking,communication in non-verbal environment,etc.Hand gesture not only uses for simple commands in traffic control,but also extends as a kind of language-sign language.In the areas of VR/AR and HCI,understanding hand and its action can greatly improve user experience.This covers a broad topics related to hands,including hand detection,tracking,hand pose estimation,gesture recognition,and sign language translation.Four papers are collected in this issue.They cover different topics related to hand and gesture.

Teaching Chinese sign language with a smartphone

Background There is a large group of deaf-mutes worldwide, and sign language is a major communication tool in this community. It is necessary for deaf-mutes to be able to communicate with others who are capable of hearing, and hearing people also need to understand sign language, which produces a great demand for sign language tuition. Even though there have already been a large number of books written about sign language, it is inefficient to learn sign language through reading alone, and the same can be said on watching videos. To solve this problem, we developed a smartphone-based interactive Chinese sign language teaching system that facilitates sign language learning. Methods The system provides a learner with some learning modes and captures the learner's actions using the front camera of the smartphone. At present, the system provides a vocabulary set with 1000 frequently used words, and the learner can evaluate his/her sign action by subjective or objective comparison. In the mode of word recognition, the users can play any word within the vocabulary, and the system will return the top three retrieved candidates;thus, it can remind the learners what the sign is. Results This system provides interactive learning to enable a user to efficiently learn sign language. The system adopts an algorithm based on point cloud recognition to evaluate a user's sign and costs about 700ms of inference time for each sample, which meets the real-time requirements. Conclusion This interactive learning system decreases the communication barriers between deaf-mutes and hearing people.

Detecting compromised accounts caused by phone number recycling on e-commerce platforms: taking Meituan as an example

Phone number recycling(PNR)refers to the event wherein a mobile operator collects a disconnected number and reassigns it to a new owner.It has posed a threat to the reliability of the existing authentication solution for e-commerce platforms.Specifically,a new owner of a reassigned number can access the application account with which the number is associated,and may perform fraudulent activities.Existing solutions that employ a reassigned number database from mobile operators are costly for e-commerce platforms with large-scale users.Thus,alternative solutions that depend on only the information of the applications are imperative.In this work,we study the problem of detecting accounts that have been compromised owing to the reassignment of phone numbers.Our analysis on Meituan's real-world dataset shows that compromised accounts have unique statistical features and temporal patterns.Based on the observations,we propose a novel model called temporal pattern and statistical feature fusion model(TSF)to tackle the problem,which integrates a temporal pattern encoder and a statistical feature encoder to capture behavioral evolutionary interaction and significant operation features.Extensive experiments on the Meituan and IEEE-CIS datasets show that TSF significantly outperforms the baselines,demonstrating its effectiveness in detecting compromised accounts due to reassigned numbers.

A prompt-based approach to adversarial example generation and robustness enhancement

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and vulnerabilities.We find that prompt paradigm can probe special robust defects of pre-trained language models.Malicious prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via mask-filling.Experimental results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym substitution.Then,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization framework.Experiments on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

Fusion Analysis of Resting-State Networks and Its Application to Alzheimer's Disease

Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network(RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal(CN), Early Mild Cognitive Impairment(EMCI), and Alzheimer's Disease(AD) groups, which correspond to the three progressing stages of AD; each group contains18 subjects. First, we apply group Independent Component Analysis(ICA) to extracting the Default Mode Network(DMN) and Dorsal Attention Network(DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each subject.Finally, we fuse the DMNs and DANs to explore the biomarkers. The results show that(1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively;(2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis;(3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI;(4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid.

Evolutionary annotation of conserved long non-coding RNAs in major mammalian species

Mammalian genomes contain tens of thousands of long non-coding RNAs(lnc RNAs) that have been implicated in diverse biological processes. However, the lnc RNA transcriptomes of most mammalian species have not been established, limiting the evolutionary annotation of these novel transcripts. Based on RNA sequencing data from six tissues of nine species, we built comprehensive lnc RNA catalogs(4,142–42,558 lnc RNAs) covering the major mammalian species. Compared to protein-coding RNAs, expression of lnc RNAs exhibits striking lineage specificity. Notably, although 30%–99% human lnc RNAs are conserved across different species on DNA locus level, only 20%–27% of these conserved lnc RNA loci are detected to transcription, which represents a stark contrast to the proportion of conserved protein-coding genes(48%–80%). This finding provides a valuable resource for experimental scientists to study the mechanisms of lnc RNAs. Moreover, we constructed lnc RNA expression phylogenetic trees across nine mammals and demonstrated that lnc RNA expression profiles can reliably determine phylogenic placement in a manner similar to their coding counterparts. Our data also reveal that the evolutionary rate of lnc RNA expression varies among tissues and is significantly higher than those for protein-coding genes. To streamline the processes of browsing lnc RNAs and detecting their evolutionary statuses, we integrate all the data produced in this study into a database named Phylo NONCODE(http://www.bioinfo.org/phylo Noncode). Our work starts to place mammalian lnc RNAs in an evolutionary context and represent a rich resource for comparative and functional analyses of this critical layer of genome.

Bayesian localization microscopy based on intensity distribution of fluorophores

Super-resolution microscopy techniques have overcome the limit of optical diffraction. Recently, the Bayesian analysis of Bleaching and Blinking data （3B） method has emerged as an important tool to obtain super-resolution fluorescence images. 3B uses the change in information caused by adding or removing fiuorophores in the cell to fit the date. When adding a new fluorophore, 3B selects a random initial position, optimizes this position and then determines its reliability. However, the fluorophores are not evenly distributed in the entire image region, and the fluorescence intensilty at a given position positively correlates with the probability of observing a fluorophore at this position. In this paper, we present a Bayesian analysis of Bleaching and Blinking microscopy method based on fluorescence intensity distribution （FID3B）. We utilize the intensity distribution to select more reliable positions as the initial positions of fluorophores. This approach can improve the reconstruction results and significantly reduce the computational time. We validate the perfor. mance of our method using both simulated date and experimental date from cellular structures. The results confirm the effectiveness of our method.

Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social science.In this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography website.We have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research topics.Our findings will be helpful for researchers and practitioners working in relevant fields.

Memory bandwidth optimization of SpMV on GPGPUs

It is an important task to improve performance for sparse matrix vector multiplication （SpMV）, and it is a difficult task because of its irregular memory access. Gen- eral purpose GPU （GPGPU） provides high computing abil- ity and substantial bandwidth that cannot be fully exploited by SpMV due to its irregularity. In this paper, we propose two novel methods to optimize the memory bandwidth for SpMV on GPGPU. First, a new storage format is proposed to exploit memory bandwidth of GPU architecture more effi- ciently. The new storage format can ensure that there are as many non-zeros as possible in the format which is suitable to exploit the memory bandwidth of the GPU. Second, we pro- pose a cache blocking method to improve the performance of SpMV on GPU architecture. The sparse matrix is partitioned into sub-blocks that are stored in CSR format. With the block- ing method, the corresponding part of vector x can be reused in the GPU cache, so the time to access the global memory for vector x is reduced heavily. Experiments are carried out on three GPU platforms, GeForce 9800 GX2, GeForce GTX 480, and Tesla K40. Experimental results show that both new methods can efficiently improve the utilization of GPU mem- ory bandwidth and the performance of the GPU.

BiodMHC:an online server for the prediction of MHC class Ⅱ-peptide binding affinity

Effective identification of major histocompatibility complex （MHC） molecules restricted peptides is a critical step in discovering immune epitopes. Although many online servers have been built to predict class Ⅱ MHC-peptide binding affinity, they have been trained on different datasets, and thus fail in providing a unified comparison of various methods. In this paper, we present our implementation of seven popular predictive methods, namely SMM-align, ARB, SVR-pairwise, Gibbs sampler. ProPred, LP-top2, and MHCPred, on a single web server named BiodMHC （http：//biod.whu.edu.cn/BiodMHC/index.html, the software is available upon request）. Using a standard measure of AUC （Area Under the receiver operating characteristic Curves）, we compare these methods by means of not only cross validation but also prediction on independent test datasets. We find that SMM-align, ProPred, SVR-pairwise, ARB, and Gibbs sampler are the five best-performing methods. For the binding affinity prediction of class Ⅱ MHC-peptide, BiodMHC provides a convenient online platform for researchers to obtain binding information simultaneously using various methods.

An intelligent market making strategy in algorithmic trading

Market making （MM） strategies have played an important role in the electronic stock market. However, the MM strategies without any forecasting power are not safe while trading. In this paper, we design and implement a twotier framework, which includes a trading signal generator based on a supervised learning approach and an event-driven MM strategy. The proposed generator incorporates the information within order book microstructure and market news to provide directional predictions. The MM strategy in the second tier trades on the signals and prevents itself from profit loss led by market trending. Using half a year price tick data from Tokyo Stock Exchange （TSE） and Shanghai Stock Exchange （SSE）, and corresponding Thomson Reuters news of the same time period, we conduct the back-testing and simulation on an industrial near-to-reality simulator. From the empirical results, we find that 1） strategies with signals perform better than strategies without any signal in terms of average daily profit and loss （PnL） and sharpe ratio （SR）, and 2） correct predictions do help MM strategies readjust their quoting along with market trending, which avoids the strategies triggering stop loss procedure that further realizes the paper loss.