Evolutionary Paths Caused by Feedback between Environment and Finite Population

Natural selection imply that any organisms including human being will evolve to improve its fitness advantage and the selected genotype or phenotype in equilibrium state will not vary over the time. However, evolutionary process of biological organisms in reality is greatly affected by the environmental change and historical accidents. In this paper, we present a framework for analyzing the feedback between species and their environment. The framework accounts for the deterministic effect that species have on their environment, as well as the stochastic effects that arise due to finite populations. Through a simple example, we demonstrate that negative feedback between species and environment negates any advantages that a particular species may possess and results in species coexistence. In the absence of feedback, the dominant species takes over the population if the population is large enough. Conversely, we find that positive feedback generates unpredictable outcomes that depend on the evolutionary path. Even inferior species can take over the population if they achieve sufficient abundance early in the process, after which the evolutionary path becomes locked in. Our results highlight the importance of evolutionary path and the unpredictability caused by positive feedback between species and environment

Electron transfer theory revisit: Quantum solvation effect

The effect of solvation on the electron transfer (ET) rate processes is investigated on the basis of the exact theory constructed in J. Phys. Chem. B Vol. 110, (2006); quant-ph/0604071. The nature of solvation is studied in a close relation with the mechanism of ET processes. The resulting Kramers' turnover and Marcus' inversion characteristics are analyzed accordingly. The classical picture of solvation is found to be invalid when the solvent longitudinal relaxation time is short compared with the inverse temperature.Comment: 5 pages, 3 figures. J. Theo. & Comput. Chem., accepte

Kinetics and thermodynamics of electron transfer in Debye solvents: An analytical and nonperturbative reduced density matrix theory

A nonperturbative electron transfer rate theory is developed based on the reduced density matrix dynamics, which can be evaluated readily for the Debye solvent model without further approximation. Not only does it recover for reaction rates the celebrated Marcus' inversion and Kramers' turnover behaviors, the present theory also predicts for reaction thermodynamics, such as equilibrium Gibbs free-energy and entropy, some interesting solvent-dependent features that are calling for experimental verification. Moreover, a continued fraction Green's function formalism is also constructed, which can be used together with Dyson equation technique, for efficient evaluation of nonperturbative reduced density matrix dynamics.Comment: 8 pages, 5 figures. J. Phys. Chem. B, accepte

Towards Robust Offline Reinforcement Learning under Diverse Data Corruption

Offline reinforcement learning (RL) presents a promising approach for learning reinforced policies from offline datasets without the need for costly or unsafe interactions with the environment. However, datasets collected by humans in real-world environments are often noisy and may even be maliciously corrupted, which can significantly degrade the performance of offline RL. In this work, we first investigate the performance of current offline RL algorithms under comprehensive data corruption, including states, actions, rewards, and dynamics. Our extensive experiments reveal that implicit Q-learning (IQL) demonstrates remarkable resilience to data corruption among various offline RL algorithms. Furthermore, we conduct both empirical and theoretical analyses to understand IQL's robust performance, identifying its supervised policy learning scheme as the key factor. Despite its relative robustness, IQL still suffers from heavy-tail targets of Q functions under dynamics corruption. To tackle this challenge, we draw inspiration from robust statistics to employ the Huber loss to handle the heavy-tailedness and utilize quantile estimators to balance penalization for corrupted data and learning stability. By incorporating these simple yet effective modifications into IQL, we propose a more robust offline RL approach named Robust IQL (RIQL). Extensive experiments demonstrate that RIQL exhibits highly robust performance when subjected to diverse data corruption scenarios.Comment: 31 pages, 17 figure

A facile approach to fabricate highly sensitive, flexible strain sensor based on elastomeric/graphene platelet composite film

This work developed a facile approach to fabricate highly sensitive and flexible polyurethane/graphene platelets composite film for wearable strain sensor. The composite film was fabricated via layer-by-layer laminating method which is simple and cost-effective; it exhibited outstanding electrical conductivity of 1430 ± 50 S/cm and high sensitivity to strain (the gauge factor is up to 150). In the sensor application test, the flexible strain sensor achieves real-time monitoring accurately for five bio-signals such as pulse movement, finger movement, and cheek movement giving a great potential as wearable-sensing device. In addition, the developed strain sensor shows response to pressure and temperature in a certain region. A multifaceted comparison between reported flexible strain sensors and our strain sensor was made highlighting the advantages of the current work in terms of (1) high sensitivity (gauge factor) and flexibility, (2) facile approach of fabrication, and (3) accurate monitoring for body motions

Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT

Physical-Layer Authentication (PLA) has been recently believed as an endogenous-secure and energy-efficient technique to recognize IoT terminals. However, the major challenge of applying the state-of-the-art PLA schemes directly to 6G-enabled IoT is the inaccurate channel fingerprint estimation in low Signal-Noise Ratio (SNR) environments, which will greatly influence the reliability and robustness of PLA. To tackle this issue, we propose a configurable-fingerprint-based PLA architecture through Intelligent Reflecting Surface (IRS) that helps create an alternative wireless transmission path to provide more accurate fingerprints. According to Baye's theorem, we propose a Gaussian Process Classification (GPC)-based PLA scheme, which utilizes the Expectation Propagation (EP) method to obtain the identities of unknown fingerprints. Considering that obtaining sufficient labeled fingerprint samples to train the GPC-based authentication model is challenging for future 6G systems, we further extend the GPC-based PLA to the Efficient-GPC (EGPC)-based PLA through active learning, which requires fewer labeled fingerprints and is more feasible. We also propose three fingerprint selecting algorithms to choose fingerprints, whose identities are queried to the upper-layers authentication mechanisms. For this reason, the proposed EGPC-based scheme is also a lightweight cross-layer authentication method to offer a superior security level. The simulations conducted on synthetic datasets demonstrate that the IRS-assisted scheme reduces the authentication error rate by 98.69% compared to the non-IRS-based scheme. Additionally, the proposed fingerprint selection algorithms reduce the authentication error rate by 65.96% to 86.93% and 45.45% to 70.00% under perfect and imperfect channel estimation conditions, respectively, when compared with baseline algorithms.Comment: 12 pages, 9 figure

Effectiveness of Post-Traumatic Growth Intervention to Promote Positive Post-Traumatic Traits in Chinese Breast Cancer Patients:A Randomized Controlled Trial

Objective: The purpose of this study was to evaluate the effectiveness of post-traumatic growth (PTG) model-based intervention to improve positive psychological traits in Chinese breast cancer patients. Design: A randomized control trial of a psychological group intervention based on PTG model. Methods: The Clinical Trial was registered on 17 August 2019 at Chinese Clinical Trials.gov with Registration number ChiCTR1900025264. A total of 92 patients with breast cancer were recruited. The participants were randomly assigned to the experimental group (n = 46) and the control group (n = 46). A six-session psychological group intervention based on PTG model was implemented in the experimental group, and a six-session health education was implemented in the control group. The outcomes were measured at baseline (pre-intervention), 3 weeks, 6 weeks after the intervention. The primary outcome was post-traumatic growth assessed by the Chinese version of the Post-Traumatic Growth Inventory (PTGI); Secondary outcomes included psychological resilience, family resilience, rumination, and self-disclosure. Results: A total of 87 patients with breast cancer completed this study, including 44 patients in the experimental group and 43 patients in the control group. There was no significant difference in baseline data of breast cancer patients between the two groups except for the treatment regimen (p &gt; 0.05). The two groups were compared after the intervention; the interaction effects between the total scores of post-traumatic growth, family resilience, and self-disclosure and the time term were statistically significant (p &lt; 0.05), indicating that the trend of change in total scores of post-traumatic growth, family resilience, and self-disclosure differed between the experimental and control groups over time, and the scores improved in the experimental group were significantly higher than those in the control group. The comparison of psychological resilience and total score of rumination at each time point was statistically significant (p &lt; 0.05), indicating that group intervention based on the PTG model could improve the psychological recovery ability and rumination level of the experimental group. Conclusion: The psychological group intervention based on the PTG model significantly improved post-traumatic growth, family resilience, and self-disclosure in patients with breast cancer. However, the impact on psychological resilience and rumination was relatively small. Long-term intervention is needed to further test the effect of the PTG model on psychological resilience and rumination.</p