Spectrum-based deep neural networks for fraud detection

In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as spectral coordinate) in the low dimensional spectral space of the graph's adjacency matrix as input of deep neural networks. Spectral coordinates in the spectral space capture the most useful topology information of the network. Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real signed graph show that our spectrum based deep neural networks are effective in fraud detection

What User Behaviors Make the Differences During the Process of Visual Analytics?

The understanding of visual analytics process can benefit visualization researchers from multiple aspects, including improving visual designs and developing advanced interaction functions. However, the log files of user behaviors are still hard to analyze due to the complexity of sensemaking and our lack of knowledge on the related user behaviors. This work presents a study on a comprehensive data collection of user behaviors, and our analysis approach with time-series classification methods. We have chosen a classical visualization application, Covid-19 data analysis, with common analysis tasks covering geo-spatial, time-series and multi-attributes. Our user study collects user behaviors on a diverse set of visualization tasks with two comparable systems, desktop and immersive visualizations. We summarize the classification results with three time-series machine learning algorithms at two scales, and explore the influences of behavior features. Our results reveal that user behaviors can be distinguished during the process of visual analytics and there is a potentially strong association between the physical behaviors of users and the visualization tasks they perform. We also demonstrate the usage of our models by interpreting open sessions of visual analytics, which provides an automatic way to study sensemaking without tedious manual annotations.Comment: This version corrects the issues of previous version

Silt Resource Utilization and Benefit Analysis of Silt Fired Perforated Brick Production: Take Nantong, China for Example

Resource utilization of silt dredging from rivers and other lake is an important issue that related to many government departments like water conservancy, shipping, land resources, construction management and other industries. It involves social, economic and environmental effects. With the encouragement of national policy of protecting arable land, make use of dredging silt to producing clay wall material is a higher value-added resource utilization than used for fill material and land use. Combined with the practice of producing fired perforated brick in Jiangsu Nantong, the economic, social and environmental benefits were analyzed based on the Summary of all these resource utilization. And the recommendation of regional development of the industry was presented at last. Key words: Silt; Resource utilization; Fired perforated brick; Benefit analysis; Water dredgin

On the Role of Server Momentum in Federated Learning

Federated Averaging (FedAvg) is known to experience convergence issues when encountering significant clients system heterogeneity and data heterogeneity. Server momentum has been proposed as an effective mitigation. However, existing server momentum works are restrictive in the momentum formulation, do not properly schedule hyperparameters and focus only on system homogeneous settings, which leaves the role of server momentum still an under-explored problem. In this paper, we propose a general framework for server momentum, that (a) covers a large class of momentum schemes that are unexplored in federated learning (FL), (b) enables a popular stagewise hyperparameter scheduler, (c) allows heterogeneous and asynchronous local computing. We provide rigorous convergence analysis for the proposed framework. To our best knowledge, this is the first work that thoroughly analyzes the performances of server momentum with a hyperparameter scheduler and system heterogeneity. Extensive experiments validate the effectiveness of our proposed framework.Comment: Accepted at AAAI 202

Reinforcement Learning in Computing and Network Convergence Orchestration

As computing power is becoming the core productivity of the digital economy era, the concept of Computing and Network Convergence (CNC), under which network and computing resources can be dynamically scheduled and allocated according to users' needs, has been proposed and attracted wide attention. Based on the tasks' properties, the network orchestration plane needs to flexibly deploy tasks to appropriate computing nodes and arrange paths to the computing nodes. This is a orchestration problem that involves resource scheduling and path arrangement. Since CNC is relatively new, in this paper, we review some researches and applications on CNC. Then, we design a CNC orchestration method using reinforcement learning (RL), which is the first attempt, that can flexibly allocate and schedule computing resources and network resources. Which aims at high profit and low latency. Meanwhile, we use multi-factors to determine the optimization objective so that the orchestration strategy is optimized in terms of total performance from different aspects, such as cost, profit, latency and system overload in our experiment. The experiments shows that the proposed RL-based method can achieve higher profit and lower latency than the greedy method, random selection and balanced-resource method. We demonstrate RL is suitable for CNC orchestration. This paper enlightens the RL application on CNC orchestration

One-Class Adversarial Nets for Fraud Detection

Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection using training data with only benign users. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator with a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.Comment: Update Fig 2, add Fig 7, and add reference

Involvement of C2H2 zinc finger proteins in the regulation of epidermal cell fate determination in Arabidopsis

Cell fate determination is a basic developmental process during the growth of multicellular organisms. Trichomes and root hairs of Arabidopsis are both readily accessible structures originating from the epidermal cells of the aerial tissues and roots respectively, and they serve as excellent models for understanding the molecular mechanisms controlling cell fate determination and cell morphogenesis. The regulation of trichome and root hair formation is a complex program that consists of the integration of hormonal signals with a large number of transcriptional factors, including MYB and bHLH transcriptional factors. Studies during recent years have uncovered an important role of C2H2 type zinc finger proteins in the regulation of epidermal cell fate determination. Here in this minireview we briefly summarize the involvement of C2H2 zinc finger proteins in the control of trichome and root hair formation in Arabidopsis .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109574/1/jipb12221.pd

Inhibition of the function of class IIa HDACs by blocking their interaction with MEF2

Enzymes that modify the epigenetic status of cells provide attractive targets for therapy in various diseases. The therapeutic development of epigenetic modulators, however, has been largely limited to direct targeting of catalytic active site conserved across multiple members of an enzyme family, which complicates mechanistic studies and drug development. Class IIa histone deacetylases (HDACs) are a group of epigenetic enzymes that depends on interaction with Myocyte Enhancer Factor-2 (MEF2) for their recruitment to specific genomic loci. Targeting this interaction presents an alternative approach to inhibiting this class of HDACs. We have used structural and functional approaches to identify and characterize a group of small molecules that indirectly target class IIa HDACs by blocking their interaction with MEF2 on DNA.Weused X-ray crystallography and 19F NMRto show that these compounds directly bind to MEF2. We have also shown that the small molecules blocked the recruitment of class IIa HDACs to MEF2-targeted genes to enhance the expression of those targets. These compounds can be used as tools to study MEF2 and class IIa HDACs in vivo and as leads for drug development

Anxiety Specific Response and Contribution of Active Hippocampal Neural Stem Cells to Chronic Pain Through Wnt/β-Catenin Signaling in Mice

Chronic pain usually results in persistent anxiety, which worsens the life quality of patients and complicates the treatment of pain. Hippocampus is one of the few brain regions in many mammalians species which harbors adult neural stem cells (NSCs), and plays a key role in the development and maintenance of chronic anxiety. Recent studies have suggested a potential involvement of hippocampal neurogenesis in modulating chronic pain. Whether and how hippocampal NSCs are involved in the pain-associated anxiety remains unclear. Here, we report that mice suffering persistent neuropathic pain showed a quick reduction of active NSCs in the ventral dentate gyrus (vDG), which was followed by the decrease of neurogenesis and appearance of anxiety. Wnt/β-catenin signaling, a key pathway in sustaining the active status of NSCs was suppressed in the vDG of mice suffering chronic pain. Depleting β-catenin by inducible Nestin-Cre significantly reduced the number of active NSCs and facilitated anxiety development, while expressing stabilized β-catenin amplified active NSCs and alleviated anxiety, indicating that Wnt activated NSCs is required for anxiety development under chronic pain. Treatment with Fluoxetine, the most widely used anxiolytic in clinic, significantly increased the proliferation of active NSCs and enhanced Wnt signaling. Interestingly, both β-catenin manipulation and Fluoxetine treatment had no significant effects on the pain thresholds. Therefore, our data demonstrated an anxiety-specific response and contribution of activated NSCs to chronic pain through Wnt/β-catenin signaling, which may be targeted for treating chronic pain- or other diseases-associated anxiety

Structure of p300 bound to MEF2 on DNA reveals a mechanism of enhanceosome assembly

Transcription co-activators CBP and p300 are recruited by sequence-specific transcription factors to specific genomic loci to control gene expression. A highly conserved domain in CBP/p300, the TAZ2 domain, mediates direct interaction with a variety of transcription factors including the myocyte enhancer factor 2 (MEF2). Here we report the crystal structure of a ternary complex of the p300 TAZ2 domain bound to MEF2 on DNA at 2.2Å resolution. The structure reveals three MEF2:DNA complexes binding to different sites of the TAZ2 domain. Using structure-guided mutations and a mammalian two-hybrid assay, we show that all three interfaces contribute to the binding of MEF2 to p300, suggesting that p300 may use one of the three interfaces to interact with MEF2 in different cellular contexts and that one p300 can bind three MEF2:DNA complexes simultaneously. These studies, together with previously characterized TAZ2 complexes bound to different transcription factors, demonstrate the potency and versatility of TAZ2 in protein–protein interactions. Our results also support a model wherein p300 promotes the assembly of a higher-order enhanceosome by simultaneous interactions with multiple DNA-bound transcription factors