CO preferential oxidation in a novel Au@ZrO₂ flow-through catalytic membrane reactor with high stability and efficiency

CO preferential oxidation (CO-PROX) achieves much interest as a strategy to remove trace CO in reformed gases for hydrogen utilization. Herein, we reported a novel Au@ZrO₂ catalytic membrane reactor by embedding gold nano-particles in ZrO₂ hollow fiber membrane for CO-PROX. The flow-through catalytic membrane exhibited high catalytic activity and oxygen selectivity, which gave a turnover frequency of 4.73 s⁻¹ at 60 °C, 2–3 times higher than conventional catalyst pellets. CO conversion of >95% was achieved over the catalytic membrane, which maintained great operational stability during 500-h operation even CO₂ and H₂O were added in the feed stream. The excellent catalytic performance of the flow-through catalytic membrane makes gold catalyst possible for practical application in the removal of CO from hydrogen

Research and Implement of an Algorithm for Physical Topology Automatic Discovery in Switched Ethernet

AbstractIn this paper, a novel practical algorithmic solution for automatic discovering the physical topology of switched Ethernet was proposed. Our algorithm collects standard SNMP MIB information that is widely supported in modern IP networks and then builds the physical topology of the active network. We described the relative definitions, system model and proved the correctness of the algorithm. Practically, the algorithm was implemented in our visualization network monitoring system. We also presented the main steps of the algorithm, core codes and running results on the lab network. The experimental results clearly validate our approach, demonstrating that our algorithm is simple and effective which can discover the accurate up-to-date physical network topology

InfeRE: Step-by-Step Regex Generation via Chain of Inference

Automatically generating regular expressions (abbrev. regexes) from natural language description (NL2RE) has been an emerging research area. Prior studies treat regex as a linear sequence of tokens and generate the final expressions autoregressively in a single pass. They did not take into account the step-by-step internal text-matching processes behind the final results. This significantly hinders the efficacy and interpretability of regex generation by neural language models. In this paper, we propose a new paradigm called InfeRE, which decomposes the generation of regexes into chains of step-by-step inference. To enhance the robustness, we introduce a self-consistency decoding mechanism that ensembles multiple outputs sampled from different models. We evaluate InfeRE on two publicly available datasets, NL-RX-Turk and KB13, and compare the results with state-of-the-art approaches and the popular tree-based generation approach TRANX. Experimental results show that InfeRE substantially outperforms previous baselines, yielding 16.3% and 14.7% improvement in DFA@5 accuracy on two datasets, respectively. Particularly, InfeRE outperforms the popular tree-based generation approach by 18.1% and 11.3% on both datasets, respectively, in terms of DFA@5 accuracy.Comment: This paper has been accepted by ASE'2

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction

Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP), based on a single trajectory of Markovian samples induced by a behavior policy. Focusing on a $\gamma$-discounted MDP with state space $\mathcal{S}$ and action space $\mathcal{A}$, we demonstrate that the $\ell_{\infty}$-based sample complexity of classical asynchronous Q-learning -- namely, the number of samples needed to yield an entrywise $\varepsilon$-accurate estimate of the Q-function -- is at most on the order of \begin{equation*} \frac{1}{\mu_{\mathsf{min}}(1-\gamma)^5\varepsilon^2}+ \frac{t_{\mathsf{mix}}}{\mu_{\mathsf{min}}(1-\gamma)} \end{equation*} up to some logarithmic factor, provided that a proper constant learning rate is adopted. Here, $t_{\mathsf{mix}}$ and $\mu_{\mathsf{min}}$ denote respectively the mixing time and the minimum state-action occupancy probability of the sample trajectory. The first term of this bound matches the complexity in the case with independent samples drawn from the stationary distribution of the trajectory. The second term reflects the expense taken for the empirical distribution of the Markovian trajectory to reach a steady state, which is incurred at the very beginning and becomes amortized as the algorithm runs. Encouragingly, the above bound improves upon the state-of-the-art result by a factor of at least $|\mathcal{S}||\mathcal{A}|$. Further, the scaling on the discount complexity can be improved by means of variance reduction.Comment: accepted in part to Neural Information Processing Systems (NeurIPS) 202

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

We investigate the sample efficiency of reinforcement learning in a $\gamma$-discounted infinite-horizon Markov decision process (MDP) with state space $\mathcal{S}$ and action space $\mathcal{A}$, assuming access to a generative model. Despite a number of prior work tackling this problem, a complete picture of the trade-offs between sample complexity and statistical accuracy is yet to be determined. In particular, prior results suffer from a sample size barrier, in the sense that their claimed statistical guarantees hold only when the sample size exceeds at least $\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^2}$ (up to some log factor). The current paper overcomes this barrier by certifying the minimax optimality of model-based reinforcement learning as soon as the sample size exceeds the order of $\frac{|\mathcal{S}||\mathcal{A}|}{1-\gamma}$ (modulo some log factor). More specifically, a perturbed model-based planning algorithm provably finds an $\varepsilon$-optimal policy with an order of $\frac{|\mathcal{S}||\mathcal{A}| }{(1-\gamma)^3\varepsilon^2}\log\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)\varepsilon}$ samples for any $\varepsilon \in (0, \frac{1}{1-\gamma}]$. Along the way, we derive improved (instance-dependent) guarantees for model-based policy evaluation. To the best of our knowledge, this work provides the first minimax-optimal guarantee in a generative model that accommodates the entire range of sample sizes (beyond which finding a meaningful policy is information theoretically impossible)

Softmax Policy Gradient Methods Can Take Exponential Time to Converge

The softmax policy gradient (PG) method, which performs gradient ascent under softmax policy parameterization, is arguably one of the de facto implementations of policy optimization in modern reinforcement learning. For $\gamma$-discounted infinite-horizon tabular Markov decision processes (MDPs), remarkable progress has recently been achieved towards establishing global convergence of softmax PG methods in finding a near-optimal policy. However, prior results fall short of delineating clear dependencies of convergence rates on salient parameters such as the cardinality of the state space $\mathcal{S}$ and the effective horizon $\frac{1}{1-\gamma}$, both of which could be excessively large. In this paper, we deliver a pessimistic message regarding the iteration complexity of softmax PG methods, despite assuming access to exact gradient computation. Specifically, we demonstrate that the softmax PG method with stepsize $\eta$ can take $$\frac{1}{\eta} |\mathcal{S}|^{2^{\Omega\big(\frac{1}{1-\gamma}\big)}} ~\text{iterations}$$ to converge, even in the presence of a benign policy initialization and an initial state distribution amenable to exploration (so that the distribution mismatch coefficient is not exceedingly large). This is accomplished by characterizing the algorithmic dynamics over a carefully-constructed MDP containing only three actions. Our exponential lower bound hints at the necessity of carefully adjusting update rules or enforcing proper regularization in accelerating PG methods

Hematopoietic and lymphatic cancers in patients with periodontitis : a systematic review and meta-analysis

Numerous studies have explored the correlation of periodontal disease (PD) with risk of hematopoietic and lymphatic cancers, but the findings were inconsistent. Therefore, we did a meta-analysis to ascertain the correlation of PD with risk of incident hematopoietic and lymphatic cancers. The authors searched relevant studies in databases (PubMed, Web of Science, and MEDLINE). The summary relative risk (RR) along with 95% confidence interval (CI) was calculated by use of random or fixed effects models. Six studies were included in qualitative synthesis. The pooled analysis revealed that PD was significantly associated with an increased risk of hematopoietic and lymphatic cancers (RR = 1.17; 95% CI = 1.07?1.27; P = 0). Stratified analysis showed the association of PD with hematopoietic and lymphatic cancers remained significant in the never smokers (RR = 1.28; 95% CI = 1.07?1.54; P = 0.007), and in the American population (RR = 1.17; 95% CI = 1.05?1.30; P = 0.003), respectively. Never smokers population and the American population with PD have a higher risk of developing hematopoietic and lymphatic cancers. PD might be considered as a risk factor for hematopoietic and lymphatic cancers