A Federated Deep Reinforcement Learning-Based Trust Model in Underwater Acoustic Sensor Networks

Abstract Underwater acoustic sensor networks (UASNs) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning systems. The security of UASNs, particularly insider threats, is of growing concern. Internal attacks carried out via compromised normal nodes are more damaging and stealthy than external attacks, such as signal stealing, data decryption, and identity forgery. As a security mechanism for internal threat detection based on interaction data, trust models have proven to enhance the security of UASNs. However, traditional trust models lack sufficient scalability when faced with movable underwater devices, heterogeneous network environments, and variable attack patterns. Therefore, in this paper, a novel trust model based on federated deep reinforcement learning is proposed for UASNs. First, the evidence acquisition mechanism, including communication, energy, and data evidence, is improved based on existing ones to better accommodate the topological dynamics of UASNs. Second, acquired trust evidence is fed into the corresponding deep reinforcement learning-based local trust model to accomplish trust prediction and model training. Finally, a federated learning-based update method periodically aggregates and updates the parameters of the local models. The experimental results prove that the proposed scheme exhibits satisfactory performance in terms of improving trust prediction accuracy and energy efficiency.Abstract Underwater acoustic sensor networks (UASNs) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning systems. The security of UASNs, particularly insider threats, is of growing concern. Internal attacks carried out via compromised normal nodes are more damaging and stealthy than external attacks, such as signal stealing, data decryption, and identity forgery. As a security mechanism for internal threat detection based on interaction data, trust models have proven to enhance the security of UASNs. However, traditional trust models lack sufficient scalability when faced with movable underwater devices, heterogeneous network environments, and variable attack patterns. Therefore, in this paper, a novel trust model based on federated deep reinforcement learning is proposed for UASNs. First, the evidence acquisition mechanism, including communication, energy, and data evidence, is improved based on existing ones to better accommodate the topological dynamics of UASNs. Second, acquired trust evidence is fed into the corresponding deep reinforcement learning-based local trust model to accomplish trust prediction and model training. Finally, a federated learning-based update method periodically aggregates and updates the parameters of the local models. The experimental results prove that the proposed scheme exhibits satisfactory performance in terms of improving trust prediction accuracy and energy efficiency

ConPET: Continual Parameter-Efficient Tuning for Large Language Models

Continual learning necessitates the continual adaptation of models to newly emerging tasks while minimizing the catastrophic forgetting of old ones. This is extremely challenging for large language models (LLMs) with vanilla full-parameter tuning due to high computation costs, memory consumption, and forgetting issue. Inspired by the success of parameter-efficient tuning (PET), we propose Continual Parameter-Efficient Tuning (ConPET), a generalizable paradigm for continual task adaptation of LLMs with task-number-independent training complexity. ConPET includes two versions with different application scenarios. First, Static ConPET can adapt former continual learning methods originally designed for relatively smaller models to LLMs through PET and a dynamic replay strategy, which largely reduces the tuning costs and alleviates the over-fitting and forgetting issue. Furthermore, to maintain scalability, Dynamic ConPET adopts separate PET modules for different tasks and a PET module selector for dynamic optimal selection. In our extensive experiments, the adaptation of Static ConPET helps multiple former methods reduce the scale of tunable parameters by over 3,000 times and surpass the PET-only baseline by at least 5 points on five smaller benchmarks, while Dynamic ConPET gains its advantage on the largest dataset. The codes and datasets are available at https://github.com/Raincleared-Song/ConPET.Comment: 12 pages, 3 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Clinical and imaging markers for the prognosis of acute ischemic stroke

Background and purposeSignificant differences in the outcomes observed in patients with acute ischemic stroke (AIS) have led to research investigations for identifying the predictors. In this retrospective study, we aimed to investigate the relationship of different clinical and imaging factors with the prognosis of AIS.Materials and methodsAll clinical and imaging metrics were compared between the good and poor prognosis groups according to the modified Rankin Scale (mRS) score at 90 days after discharge. Clinical factors included gender, age, NIHSS scores at admission, and other medical history risk factors. Imaging markers included the lesion’s size and location, diffusion, and perfusion metrics of infarction core and peripheral regions, and the state of collateral circulation. Spearman’s correlations were analyzed for age and imaging markers between the different groups. The Chi-square test and Cramer’s V coefficient analysis were performed for gender, collateral circulation status, NIHSS score, and other stroke risk factors.ResultsA total of 89 patients with AIS were divided into the good (mRS score ≤ 2) and poor prognosis groups (mRS score ≥ 3). There were differences in NIHSS score at the admission; relative MK (rMK), relative MD (rMD), relative CBF (rCBF) of the infarction core; relative mean transit time (rMTT), relative time to peak (rTTP), and relative CBF (rCBF) of peripheral regions; and collateral circulation status between the two groups (p < 0.05). Among them, the rMK of infarction lesions had the strongest correlation with the mRS score at 90 days after discharge (r = 0.545, p < 0.001).ConclusionPerfusion and diffusion metrics could reflect the microstructure and blood flow characteristics of the lesion, which were the key factors for the salvage ability and prognosis of the infarction tissue. The characteristics of the infarction core and peripheral regions have different effects on the outcomes. Diffusion of infarction core has strong relations with the prognosis, whereas the time metrics (MTT, TTP) were more important for peripheral regions. MK had a more significant association with prognosis than MD. These factors were the primary markers influencing the prognosis of cerebral infarction patients

Development of a biomimetic nanoparticle platform for apigenin therapy in triple-negative breast cancer

BackgroundThis study investigates the therapeutic potential and mechanisms of Apigenin (AGN) in treating triple-negative breast cancer (TNBC). Although AGN is recognized for its anti-tumor properties, its specific mechanisms in TNBC remain unclear.MethodsTo identify key genes associated with AGN’s effects on breast cancer, we utilized network pharmacology, conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. We developed a macrophage membrane-coated nanomicelle system (m@peg-AGN) to enhance drug delivery and facilitate immune evasion.ResultsOur analyses identified 21 overlapping genes between AGN and breast cancer, including CDH1, TP53, and CCND1, critical in cancer progression. The m@peg-AGN system demonstrated superior immune evasion and effective tumor targeting, resulting in good tumor suppression without detected toxicity in major organs.ConclusionsThis study demonstrated the targeted tumor genes to TNBC for AGN, then innovatively integrates network pharmacology with biomimetic nanotechnology, developing a novel m@peg-AGN delivery system for TNBC treatment. This system enhanced the AGN’s water solubility and increased the accumulation to the tumor site. This compound has exhibited good anti-tumor effects in vivo, thereby could advance the treatment for TNBC

Erratum:Surface anisotropy induced spin wave nonreciprocity in epitaxial La0.33Sr0.67MnO3film on SrTiO3substrate (Appl. Phys. Lett. (2020) 117 (232402)

In the original published article,1the concentrations of La and Sr are reversed. The correct concentration should be La0.67Sr0.33MnO3(which is in the ferromagnetic phase) rather than La0.33Sr0.67MnO3(which is in the antiferromagnetic phase) in the original published version. They were typos of the element concentrations. This misprint does not change the identification or conclusion presented in the original published paper