Robust Absolute Stability Criteria for a Class of Uncertain Lur'e Systems of Neutral Type

This paper is concerned with the problem of robust absolute stability for a class of uncertain Lur'e systems of neutral type. Some delay-dependent stability criteria are obtained and formulated in the form of linear matrix inequalities (LMIs). Neither model transformation nor bounding technique for cross terms is involved through derivation of the stability criteria. A numerical example shows the effectiveness of the criteria

Methanesulfonate in the firn of King George Island, Antarctica

Methanesulfonate was investigated as a potential contributor to the sulfur budget, based on analysis of a firn core from Collins Ice Cap, King George Island, Antarctica (62°10′ S, 58°50′ W). The anion was found to be present at a mean concentration of 0.17 μeq L−1, with a maximum of 0.73 μeq L−1. Dating based on the δ 18O profile suggests that the principal peaks of methanesulfonate are associated with snow deposited in summer and autumn. A careful examination of MSA, SO4 2− and nssSO4 2− profiles indicates that two of the three peaks in the MSA profile may result mainly from migration and relocation of MSA. The mechanism responsible for this might be similar to that for deep cores from other Antarctic glaciers, supporting the migration hypothesis proposed by prior researchers and extending it to near-temperate ice. Due to the post-depositional modification, the main part of the MSA profile of the firn is no longer indicative of the seasonal pattern of MSA in the atmosphere, and the basis for calculation of the MSA/nssSO4 2− ratio should be changed. The MSA/nssS04 2 ratio obtained by a new computation is 0.22, 10% higher than that ignoring the effect of MSA migration

Habits and goals in synergy: a variational Bayesian framework for behavior

How to behave efficiently and flexibly is a central problem for understanding biological agents and creating intelligent embodied AI. It has been well known that behavior can be classified as two types: reward-maximizing habitual behavior, which is fast while inflexible; and goal-directed behavior, which is flexible while slow. Conventionally, habitual and goal-directed behaviors are considered handled by two distinct systems in the brain. Here, we propose to bridge the gap between the two behaviors, drawing on the principles of variational Bayesian theory. We incorporate both behaviors in one framework by introducing a Bayesian latent variable called "intention". The habitual behavior is generated by using prior distribution of intention, which is goal-less; and the goal-directed behavior is generated by the posterior distribution of intention, which is conditioned on the goal. Building on this idea, we present a novel Bayesian framework for modeling behaviors. Our proposed framework enables skill sharing between the two kinds of behaviors, and by leveraging the idea of predictive coding, it enables an agent to seamlessly generalize from habitual to goal-directed behavior without requiring additional training. The proposed framework suggests a fresh perspective for cognitive science and embodied AI, highlighting the potential for greater integration between habitual and goal-directed behaviors

Adaptive Policy Learning for Offline-to-Online Reinforcement Learning

Conventional reinforcement learning (RL) needs an environment to collect fresh data, which is impractical when online interactions are costly. Offline RL provides an alternative solution by directly learning from the previously collected dataset. However, it will yield unsatisfactory performance if the quality of the offline datasets is poor. In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data. Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, that is, a pessimistic update strategy for the offline dataset and an optimistic/greedy update scheme for the online dataset. Such a simple and effective method provides a way to mix the offline and online RL and achieve the best of both worlds. We further provide two detailed algorithms for implementing the framework through embedding value or policy-based RL algorithms into it. Finally, we conduct extensive experiments on popular continuous control tasks, and results show that our algorithm can learn the expert policy with high sample efficiency even when the quality of offline dataset is poor, e.g., random dataset.Comment: AAAI202

Simulation and Analyses of Anti-Collision Algorithms for Active RFID System Based on MiXiM Simulation Module

Anti-collision algorithms of active RFID system are studied and simulated by the OMNeT++ software. The active RFID system based on ZigBee RF module is analyzed firstly, which uses the anti-collision mechanism involving the CSMA/CA algorithm of IEEE 802.15.4 protocol. Given the IEEE 802.15.4 simulation framework and configurations on the OMNeT++ simulation platform, simulations and analyses about anti-collision performance for the proposed active RFID system is carried out. For two customer anti-collision algorithms, the simulation results show that CSMA/CA algorithm presents excellent performance than ALOHA algorithm and its performance can meet the actual needs

What Makes Pre-trained Language Models Better Zero/Few-shot Learners?

In this paper, we propose a theoretical framework to explain the efficacy of prompt learning in zero/few-shot scenarios. First, we prove that conventional pre-training and fine-tuning paradigm fails in few-shot scenarios due to overfitting the unrepresentative labelled data. We then detail the assumption that prompt learning is more effective because it empowers pre-trained language model that is built upon massive text corpora, as well as domain-related human knowledge to participate more in prediction and thereby reduces the impact of limited label information provided by the small training set. We further hypothesize that language discrepancy can measure the quality of prompting. Comprehensive experiments are performed to verify our assumptions. More remarkably, inspired by the theoretical framework, we propose an annotation-agnostic template selection method based on perplexity, which enables us to ``forecast'' the prompting performance in advance. This approach is especially encouraging because existing work still relies on development set to post-hoc evaluate templates. Experiments show that this method leads to significant prediction benefits compared to state-of-the-art zero-shot methods

Comparative transcriptomics in Yersinia pestis: a global view of environmental modulation of gene expression

<p>Abstract</p> <p>Background</p> <p>Environmental modulation of gene expression in <it>Yersinia pestis </it>is critical for its life style and pathogenesis. Using cDNA microarray technology, we have analyzed the global gene expression of this deadly pathogen when grown under different stress conditions <it>in vitro</it>.</p> <p>Results</p> <p>To provide us with a comprehensive view of environmental modulation of global gene expression in <it>Y. pestis</it>, we have analyzed the gene expression profiles of 25 different stress conditions. Almost all known virulence genes of <it>Y. pestis </it>were differentially regulated under multiple environmental perturbations. Clustering enabled us to functionally classify co-expressed genes, including some uncharacterized genes. Collections of operons were predicted from the microarray data, and some of these were confirmed by reverse-transcription polymerase chain reaction (RT-PCR). Several regulatory DNA motifs, probably recognized by the regulatory protein Fur, PurR, or Fnr, were predicted from the clustered genes, and a Fur binding site in the corresponding promoter regions was verified by electrophoretic mobility shift assay (EMSA).</p> <p>Conclusion</p> <p>The comparative transcriptomics analysis we present here not only benefits our understanding of the molecular determinants of pathogenesis and cellular regulatory circuits in <it>Y. pestis</it>, it also serves as a basis for integrating increasing volumes of microarray data using existing methods.</p

Methanesulfonate in the firn of King George Island, Antarctica

Methanesulfonate was investigated as a potential contributor to the sulfur budget, based on analysis of a firn core from Collins Ice Cap, King George Island, Antarctica (62°10\u27 S, 58°50\u27 W). The anion was found to be present at a mean concentration of 0.17 μeq L-1, with a maximum of 0.73 μeq L-1. Dating based on the δ18O profile suggests that the principal peaks of methanesulfonate are associated with snow deposited in summer and autumn. A careful examination of MSA, SO42-and nssSO42- profiles indicates that two of the three peaks in the MSA profile mayresult mainlyfrom migration and relocation of MSA. The mechanism responsible for this might be similar to that for deep cores from other Antarctic glaciers, supporting the migration hypothesis proposed by prior researchers and extending it to near-temperate ice. Due to the post-depositional modification, the main part of the MSA profile of the firn is no longer indicative of the seasonal pattern of MSA in the atmosphere, and the basis for calculation of the MSA/nssSO42- ratio should be changed. The MSA/nssSO42- ratio obtained bya new computation is 0.22, 10% higher than that ignoring the effect of MSA migration