Light-injection attack against practical continuous-variable measurement-device-independent quantum key distribution systems.

Continuous-variable measurement-device-independent quantum key distribution (CV-MDI QKD) can defend all detection-side attacks effectively. Therefore, the source side is the final battlefield for performing quantum hacking attacks. This paper investigates the practical security of a CV-MDI QKD system under a light-injection attack.

Deep active learning for multi label text classification.

Given a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant labels for a text. Recently, deep learning models get inspiring results in MLTC. Training a high-quality deep MLTC model typically demands large-scale labeled data.

Eliminating Primacy Bias in Online Reinforcement Learning by Self-Distillation.

Excessive invalid explorations at the beginning of training lead deep reinforcement learning process to fall into the risk of overfitting, further resulting in spurious decisions, which obstruct agents in the following states and explorations. This phenomenon is termed primacy bias in online reinforcement learning. This work systematically investigates the primacy bias in online reinforcement learning, discussing the reason for primacy bias, while the characteristic of primacy bias is also analyzed.

Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images.

Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of inadequate labeled samples. Therefore, for end-to-end medical image detection with overcritical efficiency and accuracy in endoscope detection, an ensemble-learning-based model with a semi-supervised mechanism is developed in this work.

Synthesis of core-shell material rich in carboxyl groups and its application in the selective adsorption of cationic peptides.

The ability to extract peptides and proteins from biological samples with excellent reusability, high adsorption capacity, and great selectivity is essential in scientific research and medical applications. Inspired by the advantages of core-shell materials, we fabricated a core-shell material using amino-functionalized silica as the core. Benzene-1,3,5-tricarbaldehyde and 3,5-diaminobenzoic acid were used as model organic ligands to construct a shell coating by alternately reacting the two monomers on the surface of silica microspheres.

Security Analysis of Continuous-Variable Measurement-Device-Independent Quantum Key Distribution Systems in Complex Communication Environments.

Continuous-variable measure-device-independent quantum key distribution (CV-MDI QKD) is proposed to remove all imperfections originating from detection. However, there are still some inevitable imperfections in a practical CV-MDI QKD system. For example, there is a fluctuating channel transmittance in the complex communication environments.

Lateral Transfer Learning for Multiagent Reinforcement Learning.

Some researchers have introduced transfer learning mechanisms to multiagent reinforcement learning (MARL). However, the existing works devoted to cross-task transfer for multiagent systems were designed just for homogeneous agents or similar domains. This work proposes an all-purpose cross-transfer method, called multiagent lateral transfer (MALT), assisting MARL with alleviating the training burden.

Quantum Hacking on an Integrated Continuous-Variable Quantum Key Distribution System via Power Analysis.

In quantum key distribution (QKD), there are some security loopholes opened by the gaps between the theoretical model and the practical system, and they may be exploited by eavesdroppers (Eve) to obtain secret key information without being detected. This is an effective quantum hacking strategy that seriously threatens the security of practical QKD systems. In this paper, we propose a new quantum hacking attack on an integrated silicon photonic continuous-variable quantum key distribution (CVQKD) system, which is known as a power analysis attack.

Behavior fusion for deep reinforcement learning.

For deep reinforcement learning (DRL) system, it is difficult to design a reward function for complex tasks, so this paper proposes a framework of behavior fusion for the actor-critic architecture, which learns the policy based on an advantage function that consists of two value functions. Firstly, the proposed method decomposes a complex task into several sub-tasks, and merges the trained policies for those sub-tasks into a unified policy for the complex task, instead of designing a new reward function and training for the policy. Each sub-task is trained individually by an actor-critic algorithm using a simple reward function.