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Machine Learning Supernovae’s Progenitor Characterization

Machine Learning Supernovae’s Progenitor Characterization

引用

Euromicro conference on Parallel, distributed and network-Based processing

作者： Marco Grassia Stefano Pio Cosentino Maria Letizia Pumo Giuseppe Mangioni Dipartimento di Ingegneria Elettrica Elettronica e Informatica University of Catania Italy Dipartimento di Fisica e Astronomia University of Catania Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

In this work, we present a Deep Learning framework to predict the progenitor star’s characteristics of Supernovae (SNe) from their observed light curves. This task is crucial for astrophysics, as it can provide insights into the evolution of the star before the explosion and into the SN explosion mechanism. In fact, there is no direct mapping between the observed light curves and the progenitor’s characteristics, and the common techniques used to infer them are indirect, meaning that they rely on the comparison between the observed light curves and the light curves generated with some physical model simulation using the supposed progenitor’s characteristics as input. However, the physical models used to generate the light curves are not perfect, being either computationally expensive or based on simplifying assumptions. Here, we train a machine learning model on a dataset of light curves generated with a semi-analytical model — which is computationally efficient and accurate for the problem at hand — to predict the progenitor’s mass, radius, energy, and nickel mass from the observed light curves of SNe similar to SN 1987A. Our results show that the Deep Learning model effectively learns the complex mapping between the observed light curves and the progenitor’s characteristics with a low mean absolute percentage error, and we note that the proposed framework is general and can be applied to other types of SNe without significant modifications.

关键词： Deep learning Hands Astrophysics Computational modeling neural networks Stars Predictive models Explosions Nickel Computational efficiency

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High-performance network Traffic Classification Based on Graph neural network

High-performance Network Traffic Classification Based on Gra...

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IEEE Information Technology, networking, Electronic and Automation Control conference

作者： Bo Pang Yongquan Fu Siyuan Ren Yan Jia College of Computer Science and Technology Harbin Institute of Technology Shenzhen China National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changesha China Peng Cheng Laboratory Shen Zhen China

network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets’ flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets’ flows together with the packets’ features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification.

关键词： Deep learning Automation Telecommunication traffic Predictive models network security Feature extraction Graph neural networks

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Unwrapping SAR interferograms with localized subsidence signal using deep neural network 13

Unwrapping SAR interferograms with localized subsidence sign...

引用

13th European conference on Synthetic Aperture Radar (EUSAR)

作者： Wu, Zhipeng Wang, Teng Wang, Robert Chinese Acad Sci Aerosp Informat Res Inst Dept Space Microwave Remote Sensing Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China Peking Univ Sch Earth & Space Sci Beijing Peoples R China

ISBN: (纸本)9783800754571

Phase unwrapping is an indispensable processing step of InSAR. However, conventional methods often underestimate the deformation due to severe noise and/or dense fringes. Here, we develop a new deep neural network to unwrap interferograms with localized subsidence signal. We train the network using synthetic interferograms with two-dimensional Gaussian shape subsidence and complex Gaussian noises, and apply the network to real interferograms with localized mining subsidence. The proposed method outperforms the standard methods by 76.3% on synthetic interferograms and similar to 50-times faster on real interferograms. The promising result shows the potential for rapid monitoring and quantification local deformation distributed in large area.

关键词： Subsidence

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i6mA-word2vec: A Newly Model Which Used distributed Features for Predicting DNA N6-Methyladenine Sites in Genomes 18th

i6mA-word2vec: A Newly Model Which Used Distributed Features...

引用

18th International conference on Intelligent Computing (ICIC)

作者： Fu, Wenzhen Zhong, Yixin Chen, Baitong Cao, Yi Chen, Jiazi Cong, Hanhan Univ Jinan Sch Informat Sci & Engn Jinan Peoples R China Univ Jinan Sch Artificial Intelligence Inst Jinan Peoples R China Univ Jinan Informat Sci & Engn Jinan Peoples R China Xuzhou First Peoples Hosp Xuzhou Jiangsu Peoples R China Univ Jinan Sch Informat Sci & Engn Shandong Prov Key Lab Network Based Intelligent C Jinan Peoples R China Kyushu Univ Grad Sch Bioresource & Bioenvironm Sci Lab Zool Fukuoka Fukuoka Japan Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9783031138294;9783031138287

DNA N6 methyladenine (6mA) is a widely studied and widespread epigenetic modification, which plays a vital role in cell growth and development. 6mA is present in many biological cellular processes, such as the regulation of gene expression and the rule of cross dialogue between transposon and histone modification. Therefore, in some biological research, the prediction of the 6mA site is very significant. Unfortunately, the existing biological experimental methods are expensive both in time and money. And they cannot meet the needs of existing research. So it is high time to develop a targeted and efficient computing model. Consequently, this paper proposes an intelligent and efficient calculation model i6mA-word2vec for the discrimination of 6mA sites. In our work, we use word2vec from the field of natural language processing to carry out distributed feature encoding. The word2vec model automatically represents the target class topic. Then, the extracted feature space was sent into the convolutional neural network as prediction input. The experimental prediction results show that our computational model has better performance.

关键词： Methylation of DNA N6-methyladenine word2vec Deep learning

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Identification of Mangrove Leaf Disease in Deep Convolutional neural network Approach

Identification of Mangrove Leaf Disease in Deep Convolutiona...

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Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International conference on

作者： Romi Fadillah Rahmat Opim Salim Sitompul Maya Silvi Lydia Fahmi Fahmi Raska Almashura Fahrurrozi Lubis Department of Information Technology Universitas Sumatera Utara Medan Indonesia Department of Electrical Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Mangrove forests in Indonesia are distributed over many coastal regions, exhibiting significant biodiversity and diverse structural forms. Mangroves serve as a mitigating factor against wave action and intense wind energy resulting from adverse weather conditions. Another purpose of mangroves is to provide as a food source for various marine species. Mangroves consist of many principal components, including roots, stems, leaves, and blooms. For optimal growth, some portions of mangrove plants must be safeguarded from several diseases, whether induced by parasites/pests or by environmental conditions. The leaves of the mangrove are the most vulnerable to disease; yet, pests visible on the leaves are typically inadequately managed, resulting in the plant being immediately removed when all its leaves are infested. This study uses a Deep Convolutional neural network to recognize these pests effectively. The image processing methods used in this study include smoothing and sharpening. The study findings indicate that the used approach effectively identifies mangrove leaf diseases, with an accuracy of 99.2%.

关键词： Accuracy Smoothing methods Wind energy Noise Sea measurements Real-time systems Mobile handsets Convolutional neural networks Robots Diseases

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The application of BP neural network algorithm to risk evaluation of high-tech entrepreneurial projects 4

The application of BP neural network algorithm to risk evalu...

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4th International conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021

作者： Li, Xiaofeng Guo, Pan Business School Sichuan University Department of Management Science and System Science Chengdu China

ISBN: (纸本)9781665415965

Back propagation (BP) neural network is characterized by the advantages of large-scale parallel, distributed processing, self-organization, and self-learning. It is suitable for handling problems that need to consider multiple conditions and fuzzy information simultaneously. Firstly, this paper expounds on the construction and algorithm of the BP neural network. Then, the risk index system of high-tech entrepreneurial projects was designed, and the evaluation levels and scoring method for the risk factor indicators were determined. Finally, we developed a model for risk evaluation of high-tech entrepreneurial projects based on the BP neural network. The empirical results show that the BP neural network has strong feasibility and effectiveness, and can accurately fit the training values to derive accurate prediction results. This method provides a new approach for risk evaluation of entrepreneurial projects. © 2021 IEEE.

关键词： neural networks

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Renyi Differential Privacy of the Subsampled Shuffle Model in distributed Learning 35

Renyi Differential Privacy of the Subsampled Shuffle Model i...

引用

35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Girgis, Antonious M. Data, Deepesh Diggavi, Suhas Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781713845393

We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy. Motivated by stochastic optimization and the federated learning (FL) paradigm, we focus on the case where a small fraction of data samples are randomly sub-sampled in each round to participate in the learning process, which also enables privacy amplification. To obtain even stronger local privacy guarantees, we study this in the shuffle privacy model, where each client randomizes its response using a local differentially private (LDP) mechanism and the server only receives a random permutation (shuffle) of the clients' responses without their association to each client. The principal result of this paper is a privacy-optimization performance trade-off for discrete randomization mechanisms in this sub-sampled shuffle privacy model. This is enabled through a new theoretical technique to analyze the Renyi Differential Privacy (RDP) of the sub-sampled shuffle model. We numerically demonstrate that, for important regimes, with composition our bound yields significant improvement in privacy guarantee over the state-of-the-art approximate Differential Privacy (DP) guarantee (with strong composition) for sub-sampled shuffled models. We also demonstrate numerically significant improvement in privacy-learning performance operating point using real data sets. Despite these advances an open question is to bridge the gap between lower and upper privacy bounds in our RDP analysis.

关键词： Iterative methods

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An Electromagnetic Covert Channel based on neural network Architecture 27

An Electromagnetic Covert Channel based on Neural Network Ar...

引用

27th IEEE International conference on Parallel and distributed Systems, ICPADS 2021

作者： Gu, Chaojie Chen, Jiale Tan, Rui Jiang, Linshan College of Control Science and Engineering Zhejiang University China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665408783

Outsourcing the design of deep neural networks may incur cybersecurity threats from the hostile designers. This paper studies a new covert channel attack that leaks the inference results over the air through a hostile design of the neural network architecture and the computing device's electromagnetic radiation when executing the neural network. Specifically, the hostile neural network consists of a series of binary models that correspond to all classes and are executed sequentially. The execution terminates once any binary model given the input is positive about its responsible class. We describe an approach to generate such binary models by pruning a benign neural network that is trained using the standard method to deal with all the classes. Compared with the benign neural network, the hostile one has similar memory usage and negligible classification accuracy drop, but distinct inference times for the samples of different classes. As a result, the hostile neural network's classification result can be eavesdropped by measuring the duration of the electromagnetic radiation emanated from the computing device. As neural networks are stored and transmitted as data files, this covert channel attack is more stealthy to the anti-malware than other code-based attacks. We implement the described attack on two edge computing devices that execute the hostile neural network on CPU or GPU. Evaluation shows 100% empirical accuracy in eavesdropping the inference results. © 2021 IEEE.

关键词： Electromagnetic wave emission

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Research on Intelligent Scheduling Algorithm Based on Cloud Computing

Research on Intelligent Scheduling Algorithm Based on Cloud ...

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International conference on Computer Research and Development (ICCRD)

作者： Xiaofeng Yan Jie Zhao Jianchao Han Chongqing Medical and Pharmaceutical College Chongqing China

With the rapid development of the information age, computer technology and network technology are more and more mature, and the application of Internet technology is more and more extensive. On the basis of the gradual development and maturity of distributed computing, parallel computing and grid computing, cloud computing based on big data came into being. The core idea of cloud computing is to connect a large number of computing resources, storage resources and service resources through the network to form a resource pool, and then conduct unified scheduling and management of resources according to user needs. This paper mainly studies the resource scheduling method based on neural network and genetic algorithm to dynamically provide data centric computing service system for users, and maximize the reliability, customization and security of the services provided.

关键词： Cloud computing Scheduling algorithms Heuristic algorithms neural networks Parallel processing Information age Grid computing

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Generating Adversarial Examples by distributed Upsampling 28th

Generating Adversarial Examples by Distributed Upsampling

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28th International conference on neural Information processing

作者： Zhou, Shunkai Zhang, Yueling Cao, Guitao Wang, Jiangtao East China Normal Univ Sch Software Engn Shanghai 200062 Peoples R China Natl Trusted Embedded Software Engn Technol Res C Shanghai 200062 Peoples R China Singapore Management Univ Comp Sci & Informat Syst Singapore 188065 Singapore

ISBN: (纸本)9783030921842;9783030921859

The development of neural networks provides state-of-the-art results for many tasks. However, much research has shown that deep neural networks are vulnerable to adversarial attacks that fool deep models by adding small perturbations into original images. The defense of the neural networks also benefits from a better understanding of the attacks. In consequence, generating adversarial examples with a high attack success rate is worth researching. Inspired by single image super-resolution, this paper treats adversarial attacks as an image generation task and designs a new model based on generative adversarial networks (GANs). The latent feature maps have been divided into low-level and high-level in this research. We exploit low-level features with noises to add perturbations during the upsampling process. To further generate perturbed images, we reconsider and make use of checkerboard artifacts caused by deconvolution. We illustrate the performance of our method using experiments conducted on MNIST and CIFAR-10. The experiment results prove that adversarial examples generated by our method achieve a higher attack success rate and better transferability.

关键词： Adversarial example Generative adversarial networks Deep neural network Deconvolution

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