arXiv subjects

Shuai Han

Publications and source records attributed to Shuai Han.

33 records · Page 2Linked to original sources

arXiv2023

On the Opportunities of Green Computing: A Survey

Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series analysis, speech synthesis, etc. During the age of deep learning, especially with the arise of Large Language Models, a large majority of researchers' attention is paid on pursuing new state-of-the-art (SOTA) results, resulting in ever increasing of model size and computational complexity. The needs for high computing power brings higher carbon emission and undermines research fairness by preventing small or medium-sized research institutions and companies with limited funding in participating in research. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic. In this survey, we give a systematic overview of the technologies used in Green Computing. We propose the framework of Green Computing and devide it into four key components: (1) Measures of Greenness, (2) Energy-Efficient AI, (3) Energy-Efficient Computing Systems and (4) AI Use Cases for Sustainability. For each components, we discuss the research progress made and the commonly used techniques to optimize the AI efficiency. We conclude that this new research direction has the potential to address the conflicts between resource constraints and AI development. We encourage more researchers to put attention on this direction and make AI more environmental friendly.

cs.AI↗

arXiv2023

Weighted Sum-Rate Maximization for Rate-Splitting Multiple Access Based Multi-antenna Broadcast Channel with Confidential Messages

In this paper, we take all users' secrecy rate requirements into consideration and propose a rate-splitting multiple access (RSMA)-based secure beamforming approach to maximize the weighted sum-rate (WSR), where multi-user interference (MUI) is partially decoded and partially treated as noise. User messages are split and encoded into common and private streams. Each user not only decodes the common stream and the intended private stream, but also eavesdrops other users' private streams. To solve the introduced non-convex security constraints in the formulated problem, a successive convex approximation (SCA)-based approach is first proposed to maximize the instantaneous WSR under perfect channel state information at the transmitter (CSIT). When only imperfect CSIT is available, to ensure robustness, a weighted ergodic sum-rate maximization problem is formulated. Then, a joint weighted minimum mean square error and SCA-based alternating optimization algorithm is proposed to optimize the precoder. Numerical results demonstrate that, compared with baseline schemes, the performance advantage of the proposed RSMA-based secure beamforming design has been shown in terms of WSR and robustness to channel errors while ensuring all users' security requirements thanks to its powerful interference management capability.

cs.IT↗

arXiv2023

Performance of A Reconfigurable Intelligent Surface Controlled by An Illegitimate User

In this paper, we investigate how an illegitimate user can use a reconfigurable intelligent surface (RIS) to maximally degrade the secrecy performance of the legitimate system. We first consider the case when the illegitimate user performs eavesdropping only. A problem is formulated to minimize the system secrecy rate by optimizing the phase shifts of the RIS elements. We also consider the case when the illegitimate user works with full-duplex technology and can perform eavesdropping and jamming simultaneously through the RIS. A problem is formulated to minimize the system secrecy rate by optimizing the jamming power and the phase shifts of the RIS elements. Although the two formulated problems are nonconvex, they are optimally solved in this paper. Our results show that good eavesdropping performance can be achieved by placing the RIS at an appropriate position. Further, jamming plus eavesdropping can largely reduce the secrecy rate of the legitimate system.

cs.IT↗

arXiv2023

Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks

A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.

q-bio.QM↗

arXiv2022

Weighted Sum-Rate Maximization for Rate-Splitting Multiple Access Based Secure Communication

As investigations on physical layer security evolve from point-to-point systems to multi-user scenarios, multi-user interference (MUI) is introduced and becomes an unavoidable issue. Different from treating MUI totally as noise in conventional secure communications, in this paper, we propose a rate-splitting multiple access (RSMA)-based secure beamforming design, where user messages are split and encoded into common and private streams. Each user not only decodes the common stream and the intended private stream, but also tries to eavesdrop the private streams of other users. We formulate a weighted sum-rate (WSR) maximization problem subject to the secrecy rate requirements of all users. To tackle the non-convexity of the formulated problem, a successive convex approximation (SCA)-based approach is adopted to convert the original non-convex and intractable problem into a low-complexity suboptimal iterative algorithm. Numerical results demonstrate that the proposed secure beamforming scheme outperforms the conventional multi-user linear precoding (MULP) technique in terms of the WSR performance while ensuring user secrecy rate requirements.

cs.IT↗

arXiv2022

Aspects of $Z_{cs}(3985)$ and $Z_{cs}(4000)$

In the present work we investigate the $η_c K$, $J/ψK$, $η_c K^*$ and $J/ψK^*$ hidden-charm decay modes for the $c\bar{c}s\bar{u}$ four-quark system in the molecular and compact tetraquark scenarios using the quark-exchange model. Our theoretical results indicate that if the newly observed states $Z_{cs}(3985)$ and $Z_{cs}(4000)$ are two different states, $Z_{cs}(4000)$ may be interpreted as the mixture $\frac{1}{\sqrt{2}}(D^0D_s^{*-}+D^{*0}D_s^{-})$ of which the $J/ψK$ partial decay width is about $Γ\sim2.89$ MeV, while $Z_{cs}(3985)$ may be explained as the mixture $\frac{1}{\sqrt{2}}(-D^0D_s^{*-}+D^{*0}D_s^{-})$ of which the $J/ψK$ partial decay width is small to zero. Moreover, if the state $Z_{cs}(4000)$ can be explained as the mixed state $\frac{1}{\sqrt{2}}(D^0D_s^{*-}+D^{*0}D_s^{-})$ indeed, the partial decay width ratio between $J/ψK$ and $η_cK^*$ is close to unit, which indicates the decay channel $η_cK^*$ may be a ideal channel as well to decode the inner structure of $Z_{cs}(4000)$. In addition, the partial decay width for the tensor molecular state $|D^{*0}D_s^{*-}\rangle_{2^+}$ decaying into $J/ψK^*$ can reach up to a few MeV, which shows this tensor molecular state has a good potential to be observed in this decay channel.

hep-ph↗

arXiv2020

VCE: Variational Convertor-Encoder for One-Shot Generalization

Variational Convertor-Encoder (VCE) converts an image to various styles; we present this novel architecture for the problem of one-shot generalization and its transfer to new tasks not seen before without additional training. We also improve the performance of variational auto-encoder (VAE) to filter those blurred points using a novel algorithm proposed by us, namely large margin VAE (LMVAE). Two samples with the same property are input to the encoder, and then a convertor is required to processes one of them from the noisy outputs of the encoder; finally, the noise represents a variety of transformation rules and is used to convert new images. The algorithm that combines and improves the condition variational auto-encoder (CVAE) and introspective VAE, we propose this new framework aim to transform graphics instead of generating them; it is used for the one-shot generative process. No sequential inference algorithmic is needed in training. Compared to recent Omniglot datasets, the results show that our model produces more realistic and diverse images.

cs.CV↗

arXiv2020

Proximal Policy Optimization via Enhanced Exploration Efficiency

Proximal policy optimization (PPO) algorithm is a deep reinforcement learning algorithm with outstanding performance, especially in continuous control tasks. But the performance of this method is still affected by its exploration ability. For classical reinforcement learning, there are some schemes that make exploration more full and balanced with data exploitation, but they can't be applied in complex environments due to the complexity of algorithm. Based on continuous control tasks with dense reward, this paper analyzes the assumption of the original Gaussian action exploration mechanism in PPO algorithm, and clarifies the influence of exploration ability on performance. Afterward, aiming at the problem of exploration, an exploration enhancement mechanism based on uncertainty estimation is designed in this paper. Then, we apply exploration enhancement theory to PPO algorithm and propose the proximal policy optimization algorithm with intrinsic exploration module (IEM-PPO) which can be used in complex environments. In the experimental parts, we evaluate our method on multiple tasks of MuJoCo physical simulator, and compare IEM-PPO algorithm with curiosity driven exploration algorithm (ICM-PPO) and original algorithm (PPO). The experimental results demonstrate that IEM-PPO algorithm needs longer training time, but performs better in terms of sample efficiency and cumulative reward, and has stability and robustness.

cs.LG↗

arXiv2020

Optimally Displaced Threshold Detection for Discriminating Binary Coherent States Using Imperfect Devices

Because of the potential applications in quantum information processing tasks, discrimination of binary coherent states using generalized Kennedy receiver with maximum a posteriori probability (MAP) detection has attracted increasing attentions in recent years. In this paper, we analytically study the performance of the generalized Kennedy receiver having optimally displaced threshold detection (ODTD) in a realistic situation with noises and imperfect devices. We first prove that the MAP detection for a generalized Kennedy receiver is equivalent to a threshold detection in this realistic situation. Then we analyze the properties of the optimum threshold and the optimum displacement for ODTD, and propose a heuristic greedy search algorithm to obtain them. We prove that the ODTD degenerates to the Kennedy receiver with threshold detection when the signal power is large, and we also clarify the connection between the generalized Kennedy receiver with threshold detection and the one-port homodyne detection. Numerical results show that the proposed heuristic greedy search algorithm can obtain a lower and smoother error probability than the existing works.

quant-ph↗

arXiv2020

Regularly Updated Deterministic Policy Gradient Algorithm

Deep Deterministic Policy Gradient (DDPG) algorithm is one of the most well-known reinforcement learning methods. However, this method is inefficient and unstable in practical applications. On the other hand, the bias and variance of the Q estimation in the target function are sometimes difficult to control. This paper proposes a Regularly Updated Deterministic (RUD) policy gradient algorithm for these problems. This paper theoretically proves that the learning procedure with RUD can make better use of new data in replay buffer than the traditional procedure. In addition, the low variance of the Q value in RUD is more suitable for the current Clipped Double Q-learning strategy. This paper has designed a comparison experiment against previous methods, an ablation experiment with the original DDPG, and other analytical experiments in Mujoco environments. The experimental results demonstrate the effectiveness and superiority of RUD.

cs.LG↗

arXiv2020

NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration

Deep reinforcement learning has been applied more and more widely nowadays, especially in various complex control tasks. Effective exploration for noisy networks is one of the most important issues in deep reinforcement learning. Noisy networks tend to produce stable outputs for agents. However, this tendency is not always enough to find a stable policy for an agent, which decreases efficiency and stability during the learning process. Based on NoisyNets, this paper proposes an algorithm called NROWAN-DQN, i.e., Noise Reduction and Online Weight Adjustment NoisyNet-DQN. Firstly, we develop a novel noise reduction method for NoisyNet-DQN to make the agent perform stable actions. Secondly, we design an online weight adjustment strategy for noise reduction, which improves stable performance and gets higher scores for the agent. Finally, we evaluate this algorithm in four standard domains and analyze properties of hyper-parameters. Our results show that NROWAN-DQN outperforms prior algorithms in all these domains. In addition, NROWAN-DQN also shows better stability. The variance of the NROWAN-DQN score is significantly reduced, especially in some action-sensitive environments. This means that in some environments where high stability is required, NROWAN-DQN will be more appropriate than NoisyNets-DQN.

cs.LG↗

arXiv2019

Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning

Reinforcement learning, evolutionary algorithms and imitation learning are three principal methods to deal with continuous control tasks. Reinforcement learning is sample efficient, yet sensitive to hyper-parameters setting and needs efficient exploration; Evolutionary algorithms are stable, but with low sample efficiency; Imitation learning is both sample efficient and stable, however it requires the guidance of expert data. In this paper, we propose Recruitment-imitation Mechanism (RIM) for evolutionary reinforcement learning, a scalable framework that combines advantages of the three methods mentioned above. The core of this framework is a dual-actors and single critic reinforcement learning agent. This agent can recruit high-fitness actors from the population of evolutionary algorithms, which instructs itself to learn from experience replay buffer. At the same time, low-fitness actors in the evolutionary population can imitate behavior patterns of the reinforcement learning agent and improve their adaptability. Reinforcement and imitation learners in this framework can be replaced with any off-policy actor-critic reinforcement learner or data-driven imitation learner. We evaluate RIM on a series of benchmarks for continuous control tasks in Mujoco. The experimental results show that RIM outperforms prior evolutionary or reinforcement learning methods. The performance of RIM's components is significantly better than components of previous evolutionary reinforcement learning algorithm, and the recruitment using soft update enables reinforcement learning agent to learn faster than that using hard update.

cs.LG↗

arXiv2019

Channel-Correlation-Enabled Transmit Optimization for MISO Wiretap Channels

An artificial-noise (AN)-aided beamformer specific to correlated main and wiretap channels is designed in this paper. We consider slow-fading multiple-input-single-output (MISO) wiretap channels with a passive single-antenna eavesdropper, in which independent transmitter-side and correlated receiver-side are assumed. Additionally, the source has accurate main channel information and statistical wiretap channel information. To reduce the secrecy loss due to receiver-side correlation, this paper proposes the scheme of channel-correlation-enabled transmit optimization. Particularly, the correlation is viewed as a resource to acquire more knowledge about wiretap channel. Based on this, the statistical distribution of wiretap channel is described more precisely. Then, the power of AN in the null space of main channel is placed more reasonably instead of simple uniform distribution and an elaborate beamformer for the information-bearing signal is designed. Finally, an efficient algorithm for power allocation between the information-bearing signal and the AN is developed. Simulation results show that the secrecy rate under transmit power and secrecy outage constraint is improved.

cs.IT↗

arXiv2018

Physical Layer Security Enhancement for Satellite Communication among Similar Channels: Relay Selection and Power Allocation

Channels of satellite communication are usually modeled as Rician fading channels with very large Rician factor or Gaussian channels. Therefore, when a legitimate user is close to an eavesdropping user, the legitimate channel is approximately the same as the eavesdropping channel. The physical layer security technology of traditional terrestrial wireless communication mainly takes advantage of the difference be-tween the legitimate channel and the eaves-dropping channel; thus, it is not suitable for satellite communication. To implement secure communication in similar channels for satellite communications, a secure communication mod-el based on collaboration of the interference relay of the satellite physical layer is proposed. Relay selection and power allocation are further studied to enhance the security performance of the satellite communication system based on the model. The relay selection standard under known instantaneous channel state information (CSI) and statistical CSI conditions is theoreti-cally derived, thereby accomplishing minimiza-tion of the probability of secrecy relay. In addi-tion, the power allocation factor is optimized based on minimization of the secrecy outage probability. Moreover, a power allocation method based on the statistical CSI is present-ed. The secrecy outage probability performance of each relay selection criterion and power al-location scheme are analyzed via a simulation.

cs.IT↗

arXiv2017

Improving Secrecy with Nearly Collinear Main and Wiretap Channels via a Cooperative Jamming Relay

In physical layer security (PHY-security), the frequently observed high correlation between the main and wiretap channels can cause a significant loss of secrecy. This paper investigates a slow fading scenario, where a transmitter (Alice) sends a confidential message to a legitimate receiver (Bob) while a passive eavesdropper (Eve) attempts to decode the message from its received signal. It is assumed that Alice is equipped with multiple antennas while Bob and Eve each have a single antenna (i.e., a MISOSE system). In a MISOSE system, high correlation results in nearly collinear main and wiretap channel vectors, which help Eve to see and intercept confidential information. Unfortunately, the signal processing techniques at Alice, such as beamforming and artificial noise (AN), are helpless, especially in the extreme case of completely collinear main and wiretap channel vectors. On this background, we first investigate the achievable secrecy outage probability via beamforming and AN at Alice with the optimal power allocation between the information-bearing signal and AN. Then, an ingenious model, in which a cooperative jamming relay (Relay) is introduced, is proposed to effectively mitigate the adverse effects of high correlation. Based on the proposed model, the power allocation between the information-bearing signal at Alice and the AN at Relay is also studied to maximize secrecy. Finally, to validate our proposed schemes, numerical simulations are conducted, and the results show that a significant performance gain with respect to secrecy is achieved.

cs.IT↗