Results 61 to 70 of about 1,057,367 (187)

How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market

, 2018
A personal data market is a platform including three participants: data owners (individuals), data buyers and market maker. Data owners who provide personal data are compensated according to their privacy loss.
Nget, Rachana, Cao, Yang, Yoshikawa, Masatoshi   +2 more
core   +1 more source

A new approach for shrinkage estimators of the multivariate normal mean vector under the balanced loss criterion

Frontiers in Applied Mathematics and Statistics
In this study, we investigate the issue of estimating the mean vector of a multivariate normal distribution. We introduce two new families of shrinkage estimators derived from both the maximum likelihood estimator and the James-Stein estimator.
Najla M. ALoraini, Abdenour Hamdaoui, Abdelkader Benkhaled   +2 more
doaj   +1 more source

Deep Supervised Hashing for Fast Multi-Label Image

MATEC Web of Conferences, 2018
In this paper, most of the existing Hashing methods is mapping the hand extracted features to binary code, and designing the loss function with the label of images.
Ying Qian, Qingqing Ye
doaj   +1 more source

Deep Learning Nonhomogeneous Elliptic Interface Problems by Soft Constraint Physics-Informed Neural Networks

Mathematics, 2023
It is a great challenge to solve nonhomogeneous elliptic interface problems, because the interface divides the computational domain into two disjoint parts, and the solution may change dramatically across the interface. A soft constraint physics-informed
Fujun Cao, Xiaobin Guo, Fei Gao, Dongfang Yuan   +3 more
doaj   +1 more source

Itinerant ferromagnetism in an interacting Fermi gas with mass imbalance

, 2011
We study the emergence of itinerant ferromagnetism in an ultra-cold atomic gas with a variable mass ratio between the up and down spin species. Mass imbalance breaks the SU(2) spin symmetry leading to a modified Stoner criterion.
A. I. Larkin, C. W. von Keyserlingk, G. J. Conduit, P. Meystre   +3 more
core   +1 more source

Risk Comparison of Improved Estimators in a Linear Regression Model with Multivariate t Errors under Balanced Loss Function

Journal of Applied Mathematics, 2014
Under a balanced loss function, we derive the explicit formulae of the risk of the Stein-rule (SR) estimator, the positive-part Stein-rule (PSR) estimator, the feasible minimum mean squared error (FMMSE) estimator, and the adjusted feasible minimum mean ...
Guikai Hu, Qingguo Li, Shenghua Yu
doaj   +1 more source

Meta-Optimization of Bias-Variance Trade-Off in Stochastic Model Learning

IEEE Access, 2021
Model-based reinforcement learning is expected to be a method that can safely acquire the optimal policy under real-world conditions by using a stochastic dynamics model for planning.
Takumi Aotani, Taisuke Kobayashi, Kenji Sugimoto   +2 more
doaj   +1 more source

Self-acceleration in non-Hermitian Systems

, 2017
We study self-acceleration in PT and non-PT symmetric systems. We find some novel wave effects that appear uniquely in non-Hermitian systems. We show that integrable self-accelerating waves exist if the Hamiltonian is non-Hermitian.
Turker, Z., Yuce, C.
core   +1 more source

A balanced homodyne detector for high-rate Gaussian-modulated coherent-state quantum key distribution

, 2010
We discuss excess noise contributions of a practical balanced homodyne detector in Gaussian-modulated coherent-state (GMCS) quantum key distribution (QKD). We point out the key generated from the original realistic model of GMCS QKD may not be secure. In
A I Lvovsky, Bing Qi, Fossier S, Hoi-Kwong Lo, Legré M, Leverrier A, Li Qian, Liang Tian, Lo H K, Lo H K, Qi B Huang L L Chi Y M Qian L Lo H K, Qi B Huang L L Chi Y M Qian L Lo H K, Shannon C, Sun-Hyun Youn, Wen Zhu, Yue-Meng Chi, Zhang Q, Zhao Y, Zhao Y B Han Z F Guo G C   +18 more
core   +1 more source

Stability of dynamical distribution networks with arbitrary flow constraints and unknown in/outflows

, 2013
A basic model of a dynamical distribution network is considered, modeled as a directed graph with storage variables corresponding to every vertex and flow inputs corresponding to every edge, subject to unknown but constant inflows and outflows.
van der Schaft, Arjan, Wei, Jieqiang
core   +1 more source