Marginal Structural Illness-Death Models for Semi-Competing Risks Data

The three-state illness death model has been established as a general approach for regression analysis of semi-competing risks data. For observational data the marginal structural models (MSM) are a useful tool, under the potential outcomes framework to define and estimate parameters with causal interpretations. In this paper we introduce a class of marginal structural illness death models for the analysis of observational semi competing risks data. We consider two specific such models, the usual Markov illness death MSM and the general Markov illness death MSM where the latter incorporates a frailty term. For interpretation purposes, risk contrasts under the MSMs are defined. Inference under the usual Markov MSM can be carried out using estimating equations with inverse probability weighting, while inference under the general Markov MSM requires a weighted EM algorithm. We study the inference procedures under both MSMs using extensive simulations, and apply them to the analysis of mid-life alcohol exposure on late life cognitive impairment as well as mortality using the Honolulu-Asia Aging Study data set. The R codes developed in this work have been implemented in the R package semicmprskcoxmsm that is publicly available on CRAN

Research on the influence of ring rib arrangement on vibration and acoustic radiation of cylindrical shell

Based on the thin shell theory and the three-dimensional Sono-elasticity theory, the finite element method is used to study the transmission and variation characteristics of the cylindrical shell, in the case of non-ribs, single ring ribs and multi-ring ribs. The influence of different ribbed forms on the acoustic radiation is also analyzed. The result shows that the ring rib structure can suppress the transmission of medium and high frequency vibration, and the maximum attenuation frequency of the cylindrical shell is changed. The maximum attenuation frequency increases as the number of ring ribs increases. The vibration attenuation of the structure under multi-ribbed is higher than the single-ribbed at the middle frequency band, but lower than the single-ribbed at the high frequency band. The multi- ribbed structure can reduce the low-frequency radiated acoustic power of the structure, but it will affect the high-frequency acoustic radiation characteristics of the structure

Towards Fairness-Aware Adversarial Learning

Although adversarial training (AT) has proven effective in enhancing the model's robustness, the recently revealed issue of fairness in robustness has not been well addressed, i.e. the robust accuracy varies significantly among different categories. In this paper, instead of uniformly evaluating the model's average class performance, we delve into the issue of robust fairness, by considering the worst-case distribution across various classes. We propose a novel learning paradigm, named Fairness-Aware Adversarial Learning (FAAL). As a generalization of conventional AT, we re-define the problem of adversarial training as a min-max-max framework, to ensure both robustness and fairness of the trained model. Specifically, by taking advantage of distributional robust optimization, our method aims to find the worst distribution among different categories, and the solution is guaranteed to obtain the upper bound performance with high probability. In particular, FAAL can fine-tune an unfair robust model to be fair within only two epochs, without compromising the overall clean and robust accuracies. Extensive experiments on various image datasets validate the superior performance and efficiency of the proposed FAAL compared to other state-of-the-art methods.Comment: This work will appear in the CVPR 2024 conference proceeding

Evolution of Hsp70 Gene Expression: A Role for Changes in AT-Richness within Promoters

In disparate organisms adaptation to thermal stress has been linked to changes in the expression of genes encoding heat-shock proteins (Hsp). The underlying genetics, however, remain elusive. We show here that two AT-rich sequence elements in the promoter region of the hsp70 gene of the fly Liriomyza sativae that are absent in the congeneric species, Liriomyza huidobrensis, have marked cis-regulatory consequences. We studied the cis-regulatory consequences of these elements (called ATRS1 and ATRS2) by measuring the constitutive and heat-shock-induced luciferase luminescence that they drive in cells transfected with constructs carrying them modified, deleted, or intact, in the hsp70 promoter fused to the luciferase gene. The elements affected expression level markedly and in different ways: Deleting ATRS1 augmented both the constitutive and the heat-shock-induced luminescence, suggesting that this element represses transcription. Interestingly, replacing the element with random sequences of the same length and A+T content delivered the wild-type luminescence pattern, proving that the element's high A+T content is crucial for its effects. Deleting ATRS2 decreased luminescence dramatically and almost abolished heat-shock inducibility and so did replacing the element with random sequences matching the element's length and A+T content, suggesting that ATRS2's effects on transcription and heat-shock inducibility involve a common mechanism requiring at least in part the element's specific primary structure. Finally, constitutive and heat-shock luminescence were reduced strongly when two putative binding sites for the Zeste transcription factor identified within ATRS2 were altered through site-directed mutagenesis, and the heat-shock-induced luminescence increased when Zeste was over-expressed, indicating that Zeste participates in the effects mapped to ATRS2 at least in part. AT-rich sequences are common in promoters and our results suggest that they should play important roles in regulatory evolution since they can affect expression markedly and constrain promoter DNA in at least two different ways

An Asphalt Emulsion Modified by Compound of Epoxy Resin and Styrene-Butadiene Rubber Emulsion

Abstract-A modified asphalt emulsion with superior performances will be produced after compound of waterborne epoxy resin and styrene-butadiene rubber are mixed in emulsified asphalt. This paper describes the method and technique for preparation of the material as well as the test and research on aspects like adhesion, various performances of evaporation residues and durability, and the results from which reveal that this modified asphalt emulsion shows road performances and indexes better than those of ordinary asphalt emulsion and asphalt emulsion modified by styrene-butadiene rubber latex and will find application in engineering

Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT Networks

Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of Things (IoT) networks in mission-critical data collection, thanks to their flexibility and cost-effectiveness. However, challenges arise due to the UAV's limited onboard energy and the unpredictable status updates from sensor nodes (SNs), which impact the freshness of collected data. In this paper, we investigate the energy-efficient and timely data collection in IoT networks through the use of a solar-powered UAV. Each SN generates status updates at stochastic intervals, while the UAV collects and subsequently transmits these status updates to a central data center. Furthermore, the UAV harnesses solar energy from the environment to maintain its energy level above a predetermined threshold. To minimize both the average age of information (AoI) for SNs and the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN scheduling, and offloading strategy. Then, we formulate this problem as a Markov decision process (MDP) and propose a meta-reinforcement learning algorithm to enhance the generalization capability. Specifically, the compound-action deep reinforcement learning (CADRL) algorithm is proposed to handle the discrete decisions related to SN scheduling and the UAV's offloading policy, as well as the continuous control of UAV flight. Moreover, we incorporate meta-learning into CADRL to improve the adaptability of the learned policy to new tasks. To validate the effectiveness of our proposed algorithms, we conduct extensive simulations and demonstrate their superiority over other baseline algorithms

Reward Certification for Policy Smoothed Reinforcement Learning

Reinforcement Learning (RL) has achieved remarkable success in safety-critical areas, but it can be weakened by adversarial attacks. Recent studies have introduced "smoothed policies" in order to enhance its robustness. Yet, it is still challenging to establish a provable guarantee to certify the bound of its total reward. Prior methods relied primarily on computing bounds using Lipschitz continuity or calculating the probability of cumulative reward above specific thresholds. However, these techniques are only suited for continuous perturbations on the RL agent's observations and are restricted to perturbations bounded by the $l_2$-norm. To address these limitations, this paper proposes a general black-box certification method capable of directly certifying the cumulative reward of the smoothed policy under various $l_p$-norm bounded perturbations. Furthermore, we extend our methodology to certify perturbations on action spaces. Our approach leverages f-divergence to measure the distinction between the original distribution and the perturbed distribution, subsequently determining the certification bound by solving a convex optimisation problem. We provide a comprehensive theoretical analysis and run sufficient experiments in multiple environments. Our results show that our method not only improves the certified lower bound of mean cumulative reward but also demonstrates better efficiency than state-of-the-art techniques.Comment: This paper will be presented in AAAI202