Tailoring Optical Complex Field with Spiral Blade Plasmonic Vortex Lens

Optical complex fields have attracted increasing interests because of the novel effects and phenomena arising from the spatially inhomogeneous state of polarizations and optical singularities of the light beam. In this work, we propose a spiral blade plasmonic vortex lens (SBPVL) that offers unique opportunities to manipulate these novel fields. The strong interaction between the SBPVL and the optical complex fields enable the synthesis of highly tunable plasmonic vortex. Through theoretical derivations and numerical simulations we demonstrated that the characteristics of the plasmonic vortex are determined by the angular momentum (AM) of the light, and the geometrical topological charge of the SBPVL, which is govern by the nonlinear superposition of the pitch and the number of blade element. In addition, it is also shown that by adjusting the geometric parameters, SBPVL can be utilized to focus and manipulate optical complex field with fractional AM. This miniature plasmonic device may find potential applications in optical trapping, optical data storage and many other related fields

Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation

We propose a new model, independent linear Markov game, for multi-agent reinforcement learning with a large state space and a large number of agents. This is a class of Markov games with independent linear function approximation, where each agent has its own function approximation for the state-action value functions that are marginalized by other players' policies. We design new algorithms for learning the Markov coarse correlated equilibria (CCE) and Markov correlated equilibria (CE) with sample complexity bounds that only scale polynomially with each agent's own function class complexity, thus breaking the curse of multiagents. In contrast, existing works for Markov games with function approximation have sample complexity bounds scale with the size of the \emph{joint action space} when specialized to the canonical tabular Markov game setting, which is exponentially large in the number of agents. Our algorithms rely on two key technical innovations: (1) utilizing policy replay to tackle non-stationarity incurred by multiple agents and the use of function approximation; (2) separating learning Markov equilibria and exploration in the Markov games, which allows us to use the full-information no-regret learning oracle instead of the stronger bandit-feedback no-regret learning oracle used in the tabular setting. Furthermore, we propose an iterative-best-response type algorithm that can learn pure Markov Nash equilibria in independent linear Markov potential games. In the tabular case, by adapting the policy replay mechanism for independent linear Markov games, we propose an algorithm with $\widetilde{O}(\epsilon^{-2})$ sample complexity to learn Markov CCE, which improves the state-of-the-art result $\widetilde{O}(\epsilon^{-3})$ in Daskalakis et al. 2022, where $\epsilon$ is the desired accuracy, and also significantly improves other problem parameters.Comment: 51 pages. Update: Accepted for presentation at the Conference on Learning Theory (COLT) 202

On Gap-dependent Bounds for Offline Reinforcement Learning

This paper presents a systematic study on gap-dependent sample complexity in offline reinforcement learning. Prior work showed when the density ratio between an optimal policy and the behavior policy is upper bounded (the optimal policy coverage assumption), then the agent can achieve an $O\left(\frac{1}{\epsilon^2}\right)$ rate, which is also minimax optimal. We show under the optimal policy coverage assumption, the rate can be improved to $O\left(\frac{1}{\epsilon}\right)$ when there is a positive sub-optimality gap in the optimal $Q$-function. Furthermore, we show when the visitation probabilities of the behavior policy are uniformly lower bounded for states where an optimal policy's visitation probabilities are positive (the uniform optimal policy coverage assumption), the sample complexity of identifying an optimal policy is independent of $\frac{1}{\epsilon}$. Lastly, we present nearly-matching lower bounds to complement our gap-dependent upper bounds.Comment: 33 pages, 1 figure, submitted to NeurIPS 202

Learning in Congestion Games with Bandit Feedback

In this paper, we investigate Nash-regret minimization in congestion games, a class of games with benign theoretical structure and broad real-world applications. We first propose a centralized algorithm based on the optimism in the face of uncertainty principle for congestion games with (semi-)bandit feedback, and obtain finite-sample guarantees. Then we propose a decentralized algorithm via a novel combination of the Frank-Wolfe method and G-optimal design. By exploiting the structure of the congestion game, we show the sample complexity of both algorithms depends only polynomially on the number of players and the number of facilities, but not the size of the action set, which can be exponentially large in terms of the number of facilities. We further define a new problem class, Markov congestion games, which allows us to model the non-stationarity in congestion games. We propose a centralized algorithm for Markov congestion games, whose sample complexity again has only polynomial dependence on all relevant problem parameters, but not the size of the action set.Comment: 34 pages, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

High temperature superconductivity of quaternary hydrides XM3Be4H32 (X, M = Ca, Sr, Ba, Y, La, Ac, Th) under moderate pressure

The compressed hydrogen-rich compounds have received extensive attention as promising candidates for room temperature superconductivity, however, the high pressure required to stabilize such materials hinders their wide practical application. In order to search for potential superconducting hydrides that are stable at low pressures, we have investigated the crystal structures and properties of quaternary hydrides, XM3Be4H32 (X, M = Ca, Sr, Ba, Y, La, Ac, Th) based on the first-principles calculations. We identified nine dynamically stable compounds at moderate pressure of 20 GPa. Strikingly, their superconducting transition temperatures are much higher than that of liquid nitrogen, especially CaTh3Be4H32 (124 K at 5 GPa), ThLa3Be4H32(134 K at 10 GPa), LaAc3Be4H32 (135 K at 20 GPa) and AcLa3Be4H32 (153 K at 20 GPa) exhibit outstanding superconductivity at mild pressures. Metal atoms acting as pre-compressors donate abundant electrons to hydrogen, weakening the H-H covalent bond and thus facilitating the metallization of the hydrogen sublattice. At the same time, the appropriate combination of metal elements with different ionic radius and electronegativity can effectively tune the electronic structure near the Fermi level and improve the superconductivity. These findings fully reveal the great promise of hosting high-temperature superconductivity of quaternary hydrides at moderate pressures and will further promote related exploration.Comment: 14 pages, 6 figure

Kaposi’s sarcoma-associated herpesvirus seropositivity is associated with type 2 diabetes mellitus: A case–control study in Xinjiang, China

Objective: To assess the potential relationship between Kaposi’s sarcoma-associated herpesvirus (KSHV) infection and type 2 diabetes mellitus (DM-2) in Xinjiang, China. Methods: A case–control study of consecutively included DM-2 patients and normal controls was conducted among the Uygur and Han populations in Xinjiang Uygur Autonomous Region, China. Blood samples were collected and KSHV seroprevalence, antibody titers, and viral load were investigated. Logistic regression analysis and multiple linear regression analysis were applied to explore determinants of the main outcome measures. Results: A total of 324 patients with DM-2 and 376 normal controls were included. The seroprevalence of KSHV was 49.1% (95% confidence interval (CI) 43.6–54.5%) for diabetic patients and 23.7% (95% CI 19.4– 28.0%) for the control group. After adjusting for variables of ethnicity, sex, body mass index, occupation, educational level, marital status, age, and smoking and alcohol consumption habits, the association between DM-2 and KSHV infection still existed (odds ratio (OR) 2.94, 95% CI 2.05–4.22), and the risk of KSHV infection increased with glucose concentration (OR 1.35, 95% CI 1.21–1.51). KSHV was more likely to express both the latent and lytic antigens in diabetic patients (latent: OR 3.27, 95% CI 2.25–4.75; lytic: OR 3.99, 95% CI 2.68–5.93). Antibody titers and viral load increased in patients with higher blood glucose levels (p \u3c 0.001). Conclusions: Patients with DM-2 have an elevated risk of KSHV infection. Both antibody titers and viral load increased with blood glucose levels

Dysregulation of bile acids increases the risk for preterm birth in pregnant women

Preterm birth (PTB) is the leading cause of perinatal mortality and newborn complications. Bile acids are recognized as signaling molecules regulating a myriad of cellular and metabolic activities but have not been etiologically linked to PTB. In this study, a hospital-based cohort study with 36,755 pregnant women is conducted. We find that serum total bile acid levels directly correlate with the PTB rates regardless of the characteristics of the subjects and etiologies of liver disorders. Consistent with the findings from pregnant women, PTB is successfully reproduced in mice with liver injuries and dysregulated bile acids. More importantly, bile acids dose-dependently induce PTB with minimal hepatotoxicity. Furthermore, restoring bile acid homeostasis by farnesoid X receptor activation markedly reduces PTB and dramatically improves newborn survival rates. The findings thus establish an etiologic link between bile acids and PTB, and open an avenue for developing etiology-based therapies to prevent or delay PTB

Pre‐symptomatic transmission of novel coronavirus in community settings

We used contact tracing to document how COVID‐19 was transmitted across 5 generations involving 10 cases, starting with an individual who became ill on January 27. We calculated the incubation period of the cases as the interval between infection and development of symptoms. The median incubation period was 6.0 days (interquartile range, 3.5‐9.5 days). The last two generations were infected in public places, 3 and 4 days prior to the onset of illness in their infectors. Both had certain underlying conditions and comorbidity. Further identification of how individuals transmit prior to being symptomatic will have important consequences.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163478/2/irv12773.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163478/1/irv12773_am.pd