Highly-Efficient Bulk Data Transfer for Structured Dissemination in Wireless Embedded Network Systems

Recent years have witnessed the remarkable development of wireless embedded network systems (WENS) such as cyber-physical systems and sensor networks. Reliable bulk data dissemination is an important building module in WENS, supporting various applications, e.g., remote software update, video distribution. The existing studies often construct network structures to enable time-slotted multi hop pipelining for data dissemination. However, the adopted transmission mechanism was originally designed for structureless protocols, and thus posing significant challenges on efficient structured data dissemination. In this paper, we investigate the problem of structured bulk data dissemination. Specifically, we propose reliable out-of-order transmission and bursty encoding mechanisms to transmit packets as many as possible in each transmission slot. As a consequence, the resulting transmission protocol (ULTRA) can fully utilize each transmission slot and propagate data in the network as fast as possible. The performance results obtained from both testbed and simulation experiments demonstrate that, compared to the state-of-the-art protocols, ULTRA can greatly enhance the dissemination performance by reducing the dissemination delay by 34.8%

Re-Attention Transformer for Weakly Supervised Object Localization

Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories. Existing deep network approaches are mainly based on class activation map, which focuses on highlighting discriminative local region while ignoring the full object. In addition, the emerging transformer-based techniques constantly put a lot of emphasis on the backdrop that impedes the ability to identify complete objects. To address these issues, we present a re-attention mechanism termed token refinement transformer (TRT) that captures the object-level semantics to guide the localization well. Specifically, TRT introduces a novel module named token priority scoring module (TPSM) to suppress the effects of background noise while focusing on the target object. Then, we incorporate the class activation map as the semantically aware input to restrain the attention map to the target object. Extensive experiments on two benchmarks showcase the superiority of our proposed method against existing methods with image category annotations. Source code is available in \url{https://github.com/su-hui-zz/ReAttentionTransformer}.Comment: 11 pages, 5 figure

Thriving in uncertainty: examining the relationship between perceived environmental uncertainty and corporate eco-innovation through the lens of dynamic capabilities

Introduction: Objective environmental uncertainty has important impacts on entrepreneurial decision-making, but entrepreneurs’ perception of uncertainty may be a more crucial factor. This is because objective environmental uncertainty may need to be filtered through entrepreneurs’ perceptions to influence their decision-making. Therefore, exploring how entrepreneurs’ perceived environmental uncertainty (PEU) affects their corporate eco-innovation behavior has significant theoretical and practical implications.Methods: Drawing on the dynamic capability view, we utilize data from the 2016 China Private Enterprise Survey (CPES) on 2,733 small and medium-sized enterprises (SEMs) to highlight the impact of entrepreneurs’ PEU on corporate eco-innovation. We also examine the moderating effect of government intervention (government subsidies and government official visiting) on this relationship.Results: Our study reveals a positive impact of entrepreneurs’ PEU on corporate eco-innovation, confirming the critical role of dynamic capability in corporate strategic adjustment under uncertain conditions. Additionally, we find that government intervention (government subsidies and official visits) has a positive moderating effect on this relationship, with entrepreneurs’ PEU and eco-innovation being mediated by corporate dynamic capability.Discussion: The study contributes to the literature on environmental uncertainty, dynamic capabilities, and eco-innovation, and provides practical implications for SMEs in developing countries. The findings highlight the importance of subjective perceptions of environmental uncertainty over objective uncertainty. The study also demonstrates that environmental uncertainty is not inherently negative, but can be managed strategically with dynamic adjustment and government support

Vehicular-Publish/Subscribe (V-P/S) communication enabled on-the-move EV charging management

Recently, the charging management for Electric Vehicles (EVs) on-the-move has become an emerging research problem in urban cities. Major technical challenges here involve intelligence for the selection of Charging Stations (CSs) to guide drivers’ charging plans, as well as the corresponding communication infrastructure for information dissemination between the power grid and EVs. In this article, a Vehicular- Publish/Subscribe (P/S) communication framework, in conjunction with Public Transportation Buses (PTBs) is provisioned to support on-the-move EV charging management. Benefiting from low privacy sensitivity, we propose a fully distributed charging management scheme concerning the driving intention. Results demonstrate a guidance for the provisioning of V P/Scommunication framework, concerning EV drivers’ experience including charging waiting time and total trip duration. Also, the benefit of V-P/S communication framework is reflected in terms of the communication efficiency. Open research issues of this emerging research area are also presented

WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks

Due to the popularity of Artificial Intelligence (AI) technology, numerous backdoor attacks are designed by adversaries to mislead deep neural network predictions by manipulating training samples and training processes. Although backdoor attacks are effective in various real scenarios, they still suffer from the problems of both low fidelity of poisoned samples and non-negligible transfer in latent space, which make them easily detectable by existing backdoor detection algorithms. To overcome the weakness, this paper proposes a novel frequency-based backdoor attack method named WaveAttack, which obtains image high-frequency features through Discrete Wavelet Transform (DWT) to generate backdoor triggers. Furthermore, we introduce an asymmetric frequency obfuscation method, which can add an adaptive residual in the training and inference stage to improve the impact of triggers and further enhance the effectiveness of WaveAttack. Comprehensive experimental results show that WaveAttack not only achieves higher stealthiness and effectiveness, but also outperforms state-of-the-art (SOTA) backdoor attack methods in the fidelity of images by up to 28.27\% improvement in PSNR, 1.61\% improvement in SSIM, and 70.59\% reduction in IS

Factorized Variational Autoencoders for Modeling Audience Reactions to Movies

Matrix and tensor factorization methods are often used for finding underlying low-dimensional patterns from noisy data. In this paper, we study non-linear tensor factorization methods based on deep variational autoencoders. Our approach is well-suited for settings where the relationship between the latent representation to be learned and the raw data representation is highly complex. We apply our approach to a large dataset of facial expressions of movie-watching audiences (over 16 million faces). Our experiments show that compared to conventional linear factorization methods, our method achieves better reconstruction of the data, and further discovers interpretable latent factors

Cloud-Based Event-Triggered Predictive Control for Heterogeneous NMASs Under Both DoS Attacks and Transmission Delays

A novel compensation control method for heterogeneous multiagent systems under Denial-of-Service (DoS) attacks and transmission delays is investigated in this article. This control method has all the advantages of the cloud-based computation strategy, the adaptive event-triggered strategy, and the predictive control scheme. The adaptive event-triggering mechanism can adjust the event numbers adaptively, the predictive control can reduce or eliminate the negative effects brought out by both DoS attacks and transmission delays actively, while the cloud-based computation strategy can eliminate the negative effects completely as the same as there are no DoS attacks and transmission delays. Through the interval decomposition skill and the augmented system modeling method, the compensated geschlossenes system model is established. Moreover, the joint design for the feedback gain matrices and the event-triggered parameters is implemented. In the simulation part, five VTOL aircraft are used to demonstrate the theoretical results

Association of impaired sensitivity to thyroid hormones with hyperuricemia through obesity in the euthyroid population

Background: Impaired sensitivity to thyroid hormones is a newly proposed clinical entity associated with hyperuricemia in the subclinical hypothyroid population. However, it is unknown whether the association exists in the euthyroid population. This study aimed to explore the association of impaired sensitivity to thyroid hormones (assessed by the thyroid feedback quantile-based index [TFQI], parametric thyroid feedback quantile-based index [PTFQI], thyrotrophic thyroxine resistance index [TT4RI] and thyroid-stimulating hormone index [TSHI]) with hyperuricemia and quantify the mediating effect of body mass index BMI in the euthyroid population. Methods: This cross-sectional study enrolled Chinese adults aged ≥ 20 years who participated in the Beijing Health Management Cohort (2008–2019). Adjusted logistic regression models were used to explore the association between indices of sensitivity to thyroid hormones and hyperuricemia. Odds ratios [OR] and absolute risk differences [ARD] were calculated. Mediation analyses were performed to estimate direct and indirect effects through BMI. Results: Of 30,857 participants, 19,031 (61.7%) were male; the mean (SD) age was 47.3 (13.3) years; and 6,515 (21.1%) had hyperuricemia. After adjusting for confounders, individuals in the highest group of thyroid hormone sensitivity indices were associated with an increased prevalence of hyperuricemia compared with the lowest group (TFQI: OR = 1.18, 95% CI 1.04–1.35; PTFQI: OR = 1.20, 95% CI 1.05–1.36; TT4RI: OR = 1.17, 95% CI 1.08–1.27; TSHI: OR = 1.12, 95% CI 1.04–1.21). BMI significantly mediated 32.35%, 32.29%, 39.63%, and 37.68% of the associations of TFQI, PTFQI, TT4RI and TSHI with hyperuricemia, respectively. Conclusions: Our research revealed that BMI mediated the association between impaired sensitivity to thyroid hormones and hyperuricemia in the euthyroid population. These findings could provide useful evidence for understanding the interaction between impaired sensitivity to thyroid hormone and hyperuricemia in euthyroid individuals and suggest the clinical implications of weight control in terms of impaired thyroid hormones sensitivity

Investigation of the Effect of Rice Wine on the Metabolites of the Main Components of Herbal Medicine in Rat Urine by Ultrahigh-Performance Liquid Chromatography-Quadrupole/Time-of-Flight Mass Spectrometry: A Case Study on Cornus officinalis

Ultrahigh-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UPLC-QTOF/MS) was developed for rapid and sensitive analysis of the effect of rice wine on the metabolites of the main components of herbal medicine in rat urine. Using Cornus officinalis as a model of herbal medicine, the metabolite profiles of crude and processed (steaming the crude drug presteeped in rice wine) Cornus officinalis extracts in rat urine were investigated. The metabolites of Cornus officinalis were identified by using dynamic adjustment of the fragmentor voltage to produce structure-relevant fragment ions. In this work, we identified the parent compounds and metabolites of crude and processed Cornus officinalis in rats. In total, three parent compounds and seventeen new metabolites of Cornus officinalis were found in rats. The contents of the parent compounds and metabolites in vivo varied significantly after intragastric (i.g.) administration of aqueous extracts of crude and processed Cornus officinalis. Data from this study suggests that UPLC-QTOF/MS could be used as a potential tool for uncovering the effects of excipients found in the metabolites of the main components of herbal medicine, in vivo, to predict and discover the processing mechanisms of herbal medicine