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12,716 results on '"Chen, Kun"'

161. Authentication of optical physical unclonable functions based on single-pixel detection

Author

Wang, Pidong, Chen, Feiliang, Li, Dong, Sun, Song, Huang, Feng, Zhang, Taiping, Li, Qian, Chen, Kun, Wan, Yongbiao, Leng, Xiao, and Yao, Yao

Subjects
Computer Science - Cryptography and Security, Physics - Applied Physics, Physics - Optics
Abstract

Physical unclonable function (PUF) has been proposed as a promising and trustworthy solution to a variety of cryptographic applications. Here we propose a non-imaging based authentication scheme for optical PUFs materialized by random scattering media, in which the characteristic fingerprints of optical PUFs are extracted from stochastical fluctuations of the scattered light intensity with respect to laser challenges which are detected by a single-pixel detector. The randomness, uniqueness, unpredictability, and robustness of the extracted fingerprints are validated to be qualified for real authentication applications. By increasing the key length and improving the signal to noise ratio, the false accept rate of a fake PUF can be dramatically lowered to the order of 10^-28. In comparison to the conventional laser-speckle-imaging based authentication with unique identity information obtained from textures of laser speckle patterns, this non-imaging scheme can be implemented at small speckle size bellowing the Nyquist--Shannon sampling criterion of the commonly used CCD or CMOS cameras, offering benefits in system miniaturization and immunity against reverse engineering attacks simultaneously., Comment: 12 pages, 11 figures

Published
2021
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162. libdlr: Efficient imaginary time calculations using the discrete Lehmann representation

Author

Kaye, Jason, Chen, Kun, and Strand, Hugo U. R.

Subjects
Physics - Computational Physics, Condensed Matter - Strongly Correlated Electrons, Computer Science - Mathematical Software, Mathematics - Numerical Analysis, 81-04, 65D15, G.4, J.2, G.1.2
Abstract

We introduce libdlr, a library implementing the recently introduced discrete Lehmann representation (DLR) of imaginary time Green's functions. The DLR basis consists of a collection of exponentials chosen by the interpolative decomposition to ensure stable and efficient recovery of Green's functions from imaginary time or Matsbuara frequency samples. The library provides subroutines to build the DLR basis and grids, and to carry out various standard operations. The simplicity of the DLR makes it straightforward to incorporate into existing codes as a replacement for less efficient representations of imaginary time Green's functions, and libdlr is intended to facilitate this process. libdlr is written in Fortran, provides a C header interface, and contains a Python module pydlr. We also introduce a stand-alone Julia implementation, Lehmann.jl.

Published
2021
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163. Correcting the User Feedback-Loop Bias for Recommendation Systems

Author

Pan, Weishen, Cui, Sen, Wen, Hongyi, Chen, Kun, Zhang, Changshui, and Wang, Fei

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Selection bias is prevalent in the data for training and evaluating recommendation systems with explicit feedback. For example, users tend to rate items they like. However, when rating an item concerning a specific user, most of the recommendation algorithms tend to rely too much on his/her rating (feedback) history. This introduces implicit bias on the recommendation system, which is referred to as user feedback-loop bias in this paper. We propose a systematic and dynamic way to correct such bias and to obtain more diverse and objective recommendations by utilizing temporal rating information. Specifically, our method includes a deep-learning component to learn each user's dynamic rating history embedding for the estimation of the probability distribution of the items that the user rates sequentially. These estimated dynamic exposure probabilities are then used as propensity scores to train an inverse-propensity-scoring (IPS) rating predictor. We empirically validated the existence of such user feedback-loop bias in real world recommendation systems and compared the performance of our method with the baseline models that are either without de-biasing or with propensity scores estimated by other methods. The results show the superiority of our approach.

Published
2021

164. Fast random number generator based on optical physical unclonable functions

Author

Chen, Kun, Huang, Feng, Wang, Pidong, Wan, Yongbiao, Li, Dong, and Yao, Yao

Subjects
Computer Science - Cryptography and Security, Physics - Applied Physics, Physics - Optics
Abstract

We propose an approach for fast random number generation based on homemade optical physical unclonable functions (PUFs). The optical PUF is illuminated with input laser wavefront of continuous modulation to obtain different speckle patterns. Random numbers are fully extracted from speckle patterns through a simple post-processing algorithm. Our proof-of-principle experiment achieves total random number generation rate of 0.96 Gbit/s with verified randomness, which is far faster than previous optical-PUF-based schemes. Our results demonstrate that the presented random number generator (RNG) proposal has great potential to achieve ultrafast random number generation rate up to several hundreds of Gbit/s., Comment: Accepted for publication by Optics Letter

Published
2021
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165. Bionic Optical Physical Unclonable Functions for Authentication and Encryption

Author

Wan, Yongbiao, Wang, Pidong, Huang, Feng, Yuan, Jun, Li, Dong, Chen, Kun, Kang, Jianbin, Li, Qian, Zhang, Taiping, Sun, Song, Qiu, Zhiguang, and Yao, Yao

Subjects
Computer Science - Cryptography and Security, Condensed Matter - Materials Science, Physics - Optics
Abstract

Information security is of great importance for modern society with all things connected. Physical unclonable function (PUF) as a promising hardware primitive has been intensively studied for information security. However, the widely investigated silicon PUF with low entropy is vulnerable to various attacks. Herein, we introduce a concept of bionic optical PUFs inspired from unique biological architectures, and fabricate four types of bionic PUFs by molding the surface micro-nano structures of natural plant tissues with a simple, low-cost, green and environmentally friendly manufacturing process. The laser speckle responses of all bionic PUFs are statistically demonstrated to be random, unique, unpredictable and robust enough for cryptographic applications, indicating the broad applicability of bionic PUFs. On this ground, the feasibility of implementing bionic PUFs as cryptographic primitives in entity authentication and encrypted communication is experimentally validated, which shows its promising potential in the application of future information security.

Published
2021
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173. Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures

Author

Wang, Yan-Ran (Joyce), Wang, Pengcheng, Yan, Zihan, Zhou, Quan, Gunturkun, Fatma, Li, Peng, Hu, Yanshen, Wu, Wei Emma, Zhao, Kankan, Zhang, Michael, Lv, Haoyi, Fu, Lehao, Jin, Jiajie, Du, Qing, Wang, Haoyu, Chen, Kun, Qu, Liangqiong, Lin, Keldon, Iv, Michael, Wang, Hao, Sun, Xiaoyan, Vogel, Hannes, Han, Summer, Tian, Lu, Wu, Feng, and Gong, Jian

Published
2024
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