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Tradeoffs Between Information and Ordinal Approximation for Bipartite Matching [article]

Elliot Anshelevich, Wennan Zhu
2017 arXiv   pre-print
We study ordinal approximation algorithms for maximum-weight bipartite matchings. Such algorithms only know the ordinal preferences of the agents/nodes in the graph for their preferred matches, but must compete with fully omniscient algorithms which know the true numerical edge weights (utilities). is all about being able to produce good results with only limited information. Because of this, one important question is how much better the algorithms can be as the amount of information increases.
more » ... To address this question for forming high-utility matchings between agents in X and Y, we consider three ordinal information types: when we know the preference order of only nodes in X for nodes in Y, when we know the preferences of both X and Y, and when we know the total order of the edge weights in the entire graph, although not the weights themselves. We also consider settings where only the top preferences of the agents are known to us, instead of their full preference orderings. We design new ordinal approximation algorithms for each of these settings, and quantify how well such algorithms perform as the amount of information given to them increases.
arXiv:1707.01608v1 fatcat:2np25wwyg5bzlgrc7o3ahb7ei4

Triple-cooperative Video Shadow Detection [article]

Zhihao Chen, Liang Wan, Lei Zhu, Jia Shen, Huazhu Fu, Wennan Liu, Jing Qin
2021 arXiv   pre-print
[20] learned spatial context features in a direction-aware manner, while Zhu et al.  ...  . † Lei Zhu ( is the corresponding author of this work. ject detection [8] , and object tracking [37] . The last decade has witnessed a growing interest in image shadow detection.  ... 
arXiv:2103.06533v1 fatcat:wy4fq2fgknd5jj5j66o67dsoo4

Histogram Estimation under User-level Privacy with Heterogeneous Data [article]

Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser
2022 arXiv   pre-print
We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of all entries of any single user. While there is abundant literature on this classical problem under the item-level privacy setup where each user contributes only one data point, little has been known for the user-level counterpart. We consider the heterogeneous scenario where both the quantity and distribution of data can be different for each user. We propose an
more » ... thm based on a clipping strategy that almost achieves a two-approximation with respect to the best clipping threshold in hindsight. This result holds without any distribution assumptions on the data. We also prove that the clipping bias can be significantly reduced when the counts are from non-i.i.d. Poisson distributions and show empirically that our debiasing method provides improvements even without such constraints. Experiments on both real and synthetic datasets verify our theoretical findings and demonstrate the effectiveness of our algorithms.
arXiv:2206.03008v1 fatcat:5ez5zrsa3bfhzcx7fs42fjx6a4

Ordinal Approximation for Social Choice, Matching, and Facility Location Problems given Candidate Positions [article]

Elliot Anshelevich, Wennan Zhu
2018 arXiv   pre-print
In this work we consider general facility location and social choice problems, in which sets of agents A and facilities F are located in a metric space, and our goal is to assign agents to facilities (as well as choose which facilities to open) in order to optimize the social cost. We form new algorithms to do this in the presence of only ordinal information, i.e., when the true costs or distances from the agents to the facilities are unknown, and only the ordinal preferences of the agents for
more » ... he facilities are available. The main difference between our work and previous work in this area is that while we assume that only ordinal information about agent preferences in known, we know the exact locations of the possible facilities F. Due to this extra information about the facilities, we are able to form powerful algorithms which have small distortion, i.e., perform almost as well as omniscient algorithms but use only ordinal information about agent preferences. For example, we present natural social choice mechanisms for choosing a single facility to open with distortion of at most 3 for minimizing both the total and the median social cost; this factor is provably the best possible. We analyze many general problems including matching, k-center, and k-median, and present black-box reductions from omniscient approximation algorithms with approximation factor β to ordinal algorithms with approximation factor 1+2β; doing this gives new ordinal algorithms for many important problems, and establishes a toolkit for analyzing such problems in the future.
arXiv:1805.03103v1 fatcat:hi4ciebu3jaqfdvt2r7epsqugm

Forming better stable solutions in Group Formation Games inspired by Internet Exchange Points (IXPs) [article]

Elliot Anshelevich, Wennan Zhu
2020 arXiv   pre-print
We study a coordination game motivated by the formation of Internet Exchange Points (IXPs), in which agents choose which facilities to join. Joining the same facility as other agents you communicate with has benefits, but different facilities have different costs for each agent. Thus, the players wish to join the same facilities as their "friends", but this is balanced by them not wanting to pay the cost of joining a facility. We first show that the Price of Stability (PoS) of this game is at
more » ... st 2, and more generally there always exists an α-approximate equilibrium with cost at most 2/α of optimum. We then focus on how better stable solutions can be formed. If we allow agents to pay their neighbors to prevent them from deviating (i.e., a player i voluntarily pays another player j so that j joins the same facility), then we provide a payment scheme which stabilizes the solution with minimum social cost s^*, i.e. PoS is 1. In our main technical result, we consider how much a central coordinator would have to pay the players in order to form good stable solutions. Let Δ denote the total amount of payments needed to be paid to the players in order to stabilize s^*, i.e., these are payments that a player would lose if they changed their strategy from the one in s^*. We prove that there is a tradeoff between Δ and the Price of Stability: Δ/cost(s^*)≤ 1 - 2/5 PoS. Thus when there are no good stable solutions, only a small amount of extra payment is needed to stabilize s^*; and when good stable solutions already exist (i.e., PoS is small), then we should be happy with those solutions instead. Finally, we consider the computational complexity of finding the optimum solution s^*, and design a polynomial time O(log n) approximation algorithm for this problem.
arXiv:2008.12235v1 fatcat:rx45picymjfopmj2exnvvkwzue

Improving Classification Performance of Softmax Loss Function Based on Scalable Batch-Normalization

Qiuyu Zhu, Zikuang He, Tao Zhang, Wennan Cui
2020 Applied Sciences  
Convolutional neural networks (CNNs) have made great achievements on computer vision tasks, especially the image classification. With the improvement of network structure and loss functions, the performance of image classification is getting higher and higher. The classic Softmax + cross-entropy loss has been the norm for training neural networks for years, which is calculated from the output probability of the ground-truth class. Then the network's weight is updated by gradient calculation of
more » ... he loss. However, after several epochs of training, the back-propagation errors usually become almost negligible. For the above considerations, we proposed that batch normalization with adjustable scale could be added after network output to alleviate the problem of vanishing gradient problem in deep learning. The experimental results show that our method can significantly improve the final classification accuracy on different network structures, and is also better than many other improved classification Loss.
doi:10.3390/app10082950 fatcat:jps4fqymtjbdbkh2agtdfwe3h4

Infrared Image Small-Target Detection Based on Improved FCOS and Spatio-Temporal Features

Shengbo Yao, Qiuyu Zhu, Tao Zhang, Wennan Cui, Peimin Yan
2022 Electronics  
The research of infrared image small-target detection is of great significance to security monitoring, satellite remote sensing, infrared early warning, and precision guidance systems. However, small infrared targets occupy few pixels and lack color and texture features, which make the detection of small infrared targets extremely challenging. This paper proposes an effective single-stage infrared small-target detection method based on improved FCOS (Fully Convolutional One-Stage Object
more » ... n) and spatio-temporal features. In view of the simple features of infrared small targets and the requirement of real-time detection, based on the standard FCOS network, we propose a lightweight network model combined with traditional filtering methods, whose response for small infrared targets is enhanced, and the background response is suppressed. At the same time, in order to eliminate the influence of static noise points in the infrared image on the detection of small infrared targets, time domain features are added to the improved FCOS network in the form of image sequences, so that the network can learn the spatio-temporal correlation features in the image sequence. Finally, compared with current typical infrared small-target detection methods, the comparative experiments show that the improved FCOS method proposed in this paper had better detection accuracy and real-time performance for infrared small targets.
doi:10.3390/electronics11060933 fatcat:hxji5hrxyzac7linnse3o5au5u

Stock Price Prediction Methods based on FCM and DNN Algorithms

Wennan Wang, Wenjian Liu, Linkai Zhu, Ruijie Luo, Guang Li, Shugeng Dai, Sang-Bing Tsai
2021 Mobile Information Systems  
With the rapid economic development and the continuous expansion of investment scale, the stock market has produced increasing amounts of transaction data and market public opinion information, making it further difficult for investors to distinguish effective investment information. With the continuous enrichment of artificial intelligence achievements, the status and influence of artificial intelligence researchers in academia and society have been greatly improved. Expert system, as an
more » ... ant part of artificial intelligence, has made breakthrough progress at this stage. Expert system is based on a large amount of professional knowledge and experience for a specific field. Computers of this system can be used to simulate the decision-making process of experts to provide a decision-making basis for solving some complex problems. This research mainly discusses stock price prediction methods on the basis of artificial intelligence (AI) algorithms. Fuzzy clustering is a data mining tool that has been developed in recent years and is widely used. Using this method to process super large-scale databases with various data attributes has the characteristics of high efficiency and small amount of information loss. Theoretically speaking, the use of fuzzy clustering technology and related index method can effectively reduce the massive financial fundamentals of listed companies. By analyzing the influencing factors of stock value investment, we specifically select from the financial statements of listed companies the five aspects that can reflect their profitability, development ability, shareholder profitability, solvency, and operating ability. The full text runs through a variety of AI methods that is the characteristic of the research method used in this article, which pays special attention to verifying the theoretical method model. Doing so ensures its effectiveness in practical applications. In stock value portfolio research, a portfolio optimization model, which integrates the dual objectives of portfolio risk and returns into the risk-adjusted return of capital single objective constraints and solves the portfolio, is established. The accuracy and recall of the FCM model are relatively stable, with accuracies of 0.884 and 0.001, respectively. This research can help improve the number and quality of listed companies.
doi:10.1155/2021/7480599 fatcat:sc6ataqwtrd77bv4tj2tlpm5ge

The Conformal Design of an Island-Bridge Structure on a Non-Developable Surface for Stretchable Electronics

Lin Xiao, Chen Zhu, Wennan Xiong, YongAn Huang, Zhouping Yin
2018 Micromachines  
Conformal design of the island-bridge structure is the key to construct high-performance inorganic stretchable electronics that can be conformally transferred to non-developable surfaces. Former studies in conformal problems of epidermal electronics are mainly focused on soft surfaces that can adapt to the deformation of the electronics, which are not suitable for applications in hard, non-developable surfaces because of their loose surface constraints. In this paper, the conformal design
more » ... m for the island-bridge structure on a hard, non-developable surface was studied, including the critical size for island and stiffness and the demand for stretchability for the bridge. Firstly, the conformal model for an island on a part of torus surface was established to determine the relationship between the maximum size of the island and the curvatures of the surface. By combining the principle of energy minimization and the limit of material failure, a critical non-dimensional width for conformability was given for the island as a function of its thickness and interfacial adhesion energy, and the ratio of two principal curvatures of the surface. Then, the dependency of the tensile stiffness of the bridge on its geometric parameters was studied by finite element analysis (FEA) to guide the deterministic assembly of the islands on the surface. Finally, the location-dependent demands for the stretchability of the bridges were given by geometric mapping. This work will provide a design rule for stretchable electronics that fully conforms to the non-developable surface.
doi:10.3390/mi9080392 pmid:30424325 pmcid:PMC6187573 fatcat:g5tumxzdbrhi3njfb2ruwbf5fu

Blockchain Data Secure Transmission Method Based on Homomorphic Encryption

Sheng Peng, Zhiming Cai, Wenjian Liu, Wennan Wang, Guang Li, Yutin Sun, Linkai Zhu, Jun Ye
2022 Computational Intelligence and Neuroscience  
To ensure the security of data transmission and recording in Internet environment monitoring systems, this paper proposes a study of a secure method of blockchain data transfer based on homomorphic encryption. Blockchain data transmission is realized through homomorphic encryption. Homomorphic encryption can not only encrypt the original data, but also ensure that the data result after decrypting the data is the same as the original data. The asymmetric encrypted public key is collected by
more » ... net of things (IoT) equipment to realize the design of blockchain data secure transmission method based on homomorphic encryption. The experimental results show that the accuracy of the first transmission is as high as 88% when using the transmission method in this paper. After several experiments, the transmission accuracy is high by using the design method in this paper. In the last test, the transmission accuracy is still 88%, and the data transmission effect is relatively stable. At the same time, compared to the management method used in this article, the transfer method used in this paper is more reliable than the original transfer method and is not prone to data distortion. It can be seen that this method has high transmission accuracy and short transmission time, which effectively avoids the data tampering caused by too long time in the transmission process.
doi:10.1155/2022/3406228 pmid:35535195 pmcid:PMC9078759 fatcat:3n4cmwaxovedfp2acvtdfhwewq

Geometrical and topological description of chirality-relevant flow structures [article]

Wennan Zou, Jian-Zhou Zhu, Xin Liu
2019 arXiv   pre-print
Issues relevant to the flow chirality and structure are focused, while the new theoretical results, including even a distinctive theory, are introduced. However, it is hope that the presentation, with a low starting point but a steep rise, is appropriate for a broader spectrum of audiences ranging from students to researchers, thus illustrations of differential forms and relevant basic topological concepts are also offered, followed by the demonstration with formulation of differential forms of
more » ... the classical Navier-Stokes flow theory and the discussions of recent studies in fundamental fluid mechanics and turbulence.
arXiv:1903.05349v3 fatcat:3hxnbiqjujeolow52pehplpne4

Bacteriophage-Mediated Control of Biofilm: A Promising New Dawn for the Future

Cheng Chang, Xinbo Yu, Wennan Guo, Chaoyi Guo, Xiaokui Guo, Qingtian Li, Yongzhang Zhu
2022 Frontiers in Microbiology  
Biofilms are complex microbial microcolonies consisting of planktonic and dormant bacteria bound to a surface. The bacterial cells within the biofilm are embedded within the extracellular polymeric substance (EPS) consisting mainly of exopolysaccharides, secreted proteins, lipids, and extracellular DNA. This structural matrix poses a major challenge against common treatment options due to its extensive antibiotic-resistant properties. Because biofilms are so recalcitrant to antibiotics, they
more » ... e a unique challenge to patients in a nosocomial setting, mainly linked to lower respiratory, urinary tract, and surgical wound infections as well as the medical devices used during treatment. Another unique property of biofilm is its ability to adhere to both biological and man-made surfaces, allowing growth on human tissues and organs, hospital tools, and medical devices, etc. Based on prior understanding of bacteriophage structure, mechanisms, and its effects on bacteria eradication, leading research has been conducted on the effects of phages and its individual proteins on biofilm and its role in overall biofilm removal while also revealing the obstacles this form of treatment currently have. The expansion in the phage host-species range is one that urges for improvement and is the focus for future studies. This review aims to demonstrate the advantages and challenges of bacteriophage and its components on biofilm removal, as well as potential usage of phage cocktail, combination therapy, and genetically modified phages in a clinical setting.
doi:10.3389/fmicb.2022.825828 pmid:35495689 pmcid:PMC9048899 fatcat:c5xdgrdkwre3pahvkspazsnw4e

Federated Heavy Hitters Discovery with Differential Privacy [article]

Wennan Zhu, Peter Kairouz, Brendan McMahan, Haicheng Sun, Wei Li
2020 arXiv   pre-print
The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if not done with care. To address these risks, we propose a distributed and privacy-preserving algorithm for discovering the heavy hitters in a population of user-generated data streams. We leverage the sampling and thresholding properties of our distributed algorithm to prove that it is inherently differentially private,
more » ... ithout requiring additional noise. We also examine the trade-off between privacy and utility, and show that our algorithm provides excellent utility while also achieving strong privacy guarantees. A significant advantage of this approach is that it eliminates the need to centralize raw data while also avoiding the significant loss in utility incurred by local differential privacy. We validate our findings both theoretically, using worst-case analyses, and practically, using a Twitter dataset with 1.6M tweets and over 650k users. Finally, we carefully compare our approach to Apple's local differential privacy method for discovering heavy hitters.
arXiv:1902.08534v4 fatcat:ymcshac2tvca5ieyxoshqv6eky

scFLUX: a web server for metabolic flux and variation prediction using transcriptomics data [article]

Zixuan Zhang, Wennan Chang, Norah Alghamdi, Haiqi Zhu, Mengyuan Fei, Changlin Wan, Alex Lu, Yong Zang, Ying Xu, Wenzhuo Wu, Sha Cao, Yu Zhang (+1 others)
2022 bioRxiv   pre-print
Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, single cell fluxomics using laboratory approaches is currently infeasible, and none of the current flux estimation tools could achieve single cell resolution. In light of the natural associations between transcriptomic and metabolomic profiles, it remains both a feasible and urgent task to use the available single cell transcriptomics data for prediction of single
more » ... cell fluxome. We present scFLUX here, which provides an online platform for prediction of metabolic fluxome and variations using transcriptomics data, on individual cell or sample level. This is in contrast to other flux estimation methods that are only able to model the fluxes for cells of pre-defined groups. The scFLUX webserver implements our in-house single cell flux estimation model, namely scFEA, which integrates a novel graph neural network architecture with a factor graph derived from the complex human metabolic network. To the best of our knowledge, scFLUX is the first and only web-based tool dedicated to predicting individual sample-/cell- metabolic fluxome and variations of metabolites using transcriptomics data. scFLUX is available at The stand-alone tools for using scFLUX locally are available at
doi:10.1101/2022.06.18.496660 fatcat:wlxlsx3tpjdltdgd7ugh52jooa

Adipose-Derived Mesenchymal Stem Cells-Derived Exosomes Carry MicroRNA-671 to Alleviate Myocardial Infarction Through Inactivating the TGFBR2/Smad2 Axis

Xue Wang, Yuhai Zhu, Chengcheng Wu, Wennan Liu, Yujie He, Qing Yang
2021 Inflammation  
AUTHOR CONTRIBUTION Xue Wang and Yuhai Zhu: Conceptualization, methodology, software; Chengcheng Wu: Data curation, statistical analysis; Wennan Liu and Yujie He: Visualization, experimental studies; Qing  ... 
doi:10.1007/s10753-021-01460-9 pmid:33881681 pmcid:PMC8460592 fatcat:ibjwpwiilrcxxll7xdkj2js6jy
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