arXiv subjects

Jie Tian

Publications and source records attributed to Jie Tian.

40 records · Page 3Linked to original sources

arXiv2022

A Discrete-event-based Simulator for Distributed Deep Learning

New intelligence applications are driving increasing interest in deploying deep neural networks (DNN) in a distributed way. To set up distributed deep learning involves alterations of a great number of the parameter configurations of network/edge devices and DNN models, which are crucial to achieve best performances. Simulations measure scalability of intelligence applications in the early stage, as well as to determine the effects of different configurations, thus highly desired. However, work on simulating the distributed intelligence environment is still in its infancy. The existing simulation frameworks, such as NS-3, etc., cannot extended in a straightforward way to support simulations of distributed learning. In this paper, we propose a novel discrete event simulator, sim4DistrDL, which includes a deep learning module and a network simulation module to facilitate simulation of DNN-based distributed applications. Specifically, we give the design and implementation of the proposed learning simulator and present an illustrative use case.

cs.LG↗

arXiv2022

Speckle-based optical cryptosystem and its application for human face recognition via deep learning

Face recognition has recently become ubiquitous in many scenes for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data that should be carefully protected. Software-based cryptosystems are widely adopted nowadays to encrypt face images, but the security level is limited by insufficient digital secret key length or computing power. Hardware-based optical cryptosystems can generate enormously longer secret keys and enable encryption at light speed, but most reported optical methods, such as double random phase encryption, are less compatible with other systems due to system complexity. In this study, a plain yet high-efficient speckle-based optical cryptosystem is proposed and implemented. A scattering ground glass is exploited to generate physical secret keys of gigabit length and encrypt face images via seemingly random optical speckles at light speed. Face images can then be decrypted from the random speckles by a well-trained decryption neural network, such that face recognition can be realized with up to 98% accuracy. The proposed cryptosystem has wide applicability, and it may open a new avenue for high-security complex information encryption and decryption by utilizing optical speckles.

cs.CR↗

arXiv2020

Adaptive In-network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge

To enhance the quality and speed of data processing and protect the privacy and security of the data, edge computing has been extensively applied to support data-intensive intelligent processing services at edge. Among these data-intensive services, ensemble learning-based services can in natural leverage the distributed computation and storage resources at edge devices to achieve efficient data collection, processing, analysis. Collaborative caching has been applied in edge computing to support services close to the data source, in order to take the limited resources at edge devices to support high-performance ensemble learning solutions. To achieve this goal, we propose an adaptive in-network collaborative caching scheme for ensemble learning at edge. First, an efficient data representation structure is proposed to record cached data among different nodes. In addition, we design a collaboration scheme to facilitate edge nodes to cache valuable data for local ensemble learning, by scheduling local caching according to a summarization of data representations from different edge nodes. Our extensive simulations demonstrate the high performance of the proposed collaborative caching scheme, which significantly reduces the learning latency and the transmission overhead.

cs.NI↗

arXiv2012

Efficient and Effective Volume Visualization with Enhanced Isosurface Rendering

Compared with full volume rendering, isosurface rendering has several well recognized advantages in efficiency and accuracy. However, standard isosurface rendering has some limitations in effectiveness. First, it uses a monotone colored approach and can only visualize the geometry features of an isosurface. The lack of the capability to illustrate the material property and the internal structures behind an isosurface has been a big limitation of this method in applications. Another limitation of isosurface rendering is the difficulty to reveal physically meaningful structures, which are hidden in one or multiple isosurfaces. As such, the application requirements of extract and recombine structures of interest can not be implemented effectively with isosurface rendering. In this work, we develop an enhanced isosurface rendering technique to improve the effectiveness while maintaining the performance efficiency of the standard isosurface rendering. First, an isosurface color enhancement method is proposed to illustrate the neighborhood density and to reveal some of the internal structures. Second, we extend the structure extraction capability of isosurface rendering by enabling explicit scene exploration within a 3D-view, using surface peeling, voxel-selecting, isosurface segmentation, and multi-surface-structure visualization. Our experiments show that the color enhancement not only improves the visual fidelity of the rendering, but also reveals the internal structures without significant increase of the computational cost. Explicit scene exploration is also demonstrated as a powerful tool in some application scenarios, such as displaying multiple abdominal organs.

cs.GR↗