Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI

Terahertz (THz) communication with ultra-wide available spectrum is a promising technique that can achieve the stringent requirement of high data rate in the next-generation wireless networks, yet its severe propagation attenuation significantly hinders its implementation in practice. Finding beam directions for a large-scale antenna array to effectively overcome severe propagation attenuation of THz signals is a pressing need. This paper proposes a novel approach of federated deep reinforcement learning (FDRL) to swiftly perform THz-beam search for multiple base stations (BSs) coordinated by an edge server in a cellular network. All the BSs conduct deep deterministic policy gradient (DDPG)-based DRL to obtain THz beamforming policy with limited channel state information (CSI). They update their DDPG models with hidden information in order to mitigate inter-cell interference. We demonstrate that the cell network can achieve higher throughput as more THz CSI and hidden neurons of DDPG are adopted. We also show that FDRL with partial model update is able to nearly achieve the same performance of FDRL with full model update, which indicates an effective means to reduce communication load between the edge server and the BSs by partial model uploading. Moreover, the proposed FDRL outperforms conventional non-learning-based and existing non-FDRL benchmark optimization methods

Diffusion Model-Augmented Behavioral Cloning

Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e.g., behavioral cloning, BC) or the joint probability p(s, a) (e.g., implicit behavioral cloning). Despite its simplicity, modeling the conditional probability with BC usually struggles with generalization. While modeling the joint probability can lead to improved generalization performance, the inference procedure can be time-consuming and it often suffers from manifold overfitting. This work proposes an imitation learning framework that benefits from modeling both the conditional and joint probability of the expert distribution. Our proposed diffusion model-augmented behavioral cloning (DBC) employs a diffusion model trained to model expert behaviors and learns a policy to optimize both the BC loss (conditional) and our proposed diffusion model loss (joint). DBC outperforms baselines in various continuous control tasks in navigation, robot arm manipulation, dexterous manipulation, and locomotion. We design additional experiments to verify the limitations of modeling either the conditional probability or the joint probability of the expert distribution as well as compare different generative models

Exploring Online Repeat Purchase Intentions: The Role of Habit

By focusing on online stores, this study investigates the repeat purchase intention of experienced online buyers. Prior research on online behavior continuance models perceived usefulness, trust, satisfaction, and perceived value as the major determinants of continued adoption or loyalty, overlooking the important role of habit. Building on previous work in other disciplines, we define habit in the context of online shopping as the extent to which buyers tend to shop online automatically because of learning. Using recent work on the continued usage of IS (IS continuance) and repeat purchase, we have developed a model suggesting that repeat purchase intention is not only a consequence of trust and switching cost, but also of habit. In particular, in our research model, we propose that online shopping habit moderate the influence of trust such that its importance in determining repeat purchase intention decreases as the online shopping behavior takes on a more habitual nature. Integrating prior research on habit, IS continuance, and repeat purchase further, we suggest how antecedents of repeat purchase intention relate to drivers of habitualization. Data collected from 462 of Yahoo!Kimo shopping center’s customers provide strong support for the research model. Results indicate that higher level of habit deflated trust’s effect on repeat purchase intention. The data also show that satisfaction and familiarity are key to habit formation and thus relevant in the context of online repeat purchase

106GBaud (200G PAM4) CWDM EML for 800G/1.6T Optical Networks and AI Applications

We report ultrahigh speed 106GBaud (200G PAM4) electro-absorption modulated laser (EML) for 800G and 1.6T optical transmission. Four CWDM EMLs of 1271, 1291, 1311 and 1331nm in 800G FR4 optical transceivers show clear eye diagram after 2km. Our 106GBaud EMLs show high bandwidth, high extinction ratio, low threshold current and high power, making it a suitable source laser for 800G/1.6T and AI applications.&nbsp

Fast-Flux Bot Detection in Real Time

Abstract. The fast-flux service network architecture has been widely adopted by bot herders to increase the productivity and extend the lifes-pan of botnets ’ domain names. A fast-flux botnet is unique in that each of its domain names is normally mapped to different sets of IP addresses over time and legitimate users ’ requests are handled by machines other than those contacted by users directly. Most existing methods for de-tecting fast-flux botnets rely on the former property. This approach is effective, but it requires a certain period of time, maybe a few days, before a conclusion can be drawn. In this paper, we propose a novel way to detect whether a web service is hosted by a fast-flux botnet in real time. The scheme is unique because it relies on certain intrinsic and invariant characteristics of fast-flux bot-nets, namely, 1) the request delegation model, 2) bots are not dedicated to malicious services, and 3) the hardware used by bots is normally infe-rior to that of dedicated servers. Our empirical evaluation results show that, using a passive measurement approach, the proposed scheme can detect fast-flux bots in a few seconds with more than 96 % accuracy, while the false positive/negative rates are both lower than 5%

Gallic Acid Induces a Reactive Oxygen Species-Provoked c-Jun NH 2

Idiopathic pulmonary fibrosis is a chronic lung disorder characterized by fibroblasts proliferation and extracellular matrix accumulation. Induction of fibroblast apoptosis therefore plays a crucial role in the resolution of this disease. Gallic acid (3,4,5-trihydroxybenzoic acid), a common botanic phenolic compound, has been reported to induce apoptosis in tumor cell lines and renal fibroblasts. The present study was undertaken to examine the role of mitogen-activated protein kinases (MAPKs) in lung fibroblasts apoptosis induced by gallic acid. We found that treatment with gallic acid resulted in activation of c-Jun NH2-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and protein kinase B (PKB, Akt), but not p38MAPK, in mouse lung fibroblasts. Inhibition of JNK using pharmacologic inhibitor (SP600125) and genetic knockdown (JNK specific siRNA) significantly inhibited p53 accumulation, reduced PUMA and Fas expression, and abolished apoptosis induced by gallic acid. Moreover, treatment with antioxidants (vitamin C, N-acetyl cysteine, and catalase) effectively diminished gallic acid-induced hydrogen peroxide production, JNK and p53 activation, and cell death. These observations imply that gallic acid-mediated hydrogen peroxide formation acts as an initiator of JNK signaling pathways, leading to p53 activation and apoptosis in mouse lung fibroblasts