Stabilisation of hybrid stochastic differential equations by feedback control based on discrete-time observations of state and mode

Mao [10] recently initiated the study of the mean-square exponential stabilisation of continuous-time hybrid stochastic differential equations (SDEs) by the feedback controls based on the discrete-time observations of the state. However, the feedback controls still depend on the continuous-time observations of the mode. Of course this is perfectly fine if the mode of the system is obvious (i.e. fully observable at no cost). However, it could often be the case where the mode is not obvious and it costs to identify the current mode of the system. To reduce the control cost, it is reasonable we identify the mode at the discrete times when we make observations for the state. Hence the feedback control should be designed based on the discrete-time observations of both state and mode. The aim of this paper is to show how to design such a feedback control to stabilise a given hybrid SDE

Poly[(acetato-κ2 O,O′)aqua­(μ4-1H-benzimidazole-5,6-dicarboxyl­ato-κ6 N 3:O 5,O 5′:O 5,O 6:O 6′)cerium(III)]

In the title compound, [Ce(C9H4N2O4)(C2H3O2)(H2O)]n, the CeIII ion is coordinated by five O atoms and one N atom from four 1H-benzimidazole-5,6-dicarboxyl­ato (L) ligands and by two O atoms from an acetate ligand and one aqua ligand, forming a slightly distorted tricapped trigonal–prismatic geometry. The L ligands are bridging, forming a layered polymer parallel to (010). In the crystal, O—H⋯O and N—H⋯O hydrogen bonds connect the polymer layers into a three-dimensional network

A Scalable and Adaptive Distributed Service Discovery Mechanism in SOC Environments

Abstract. Current researches on service discovery mainly pursue fast response and high recall, but little work focuses on scalability and adaptability of largescale distributed service registries in SOC. This paper proposes a solution using an agent based distributed service discovery mechanism. Firstly an unstructured P2P based registry system is proposed in which each peer is an autonomous registry center and services are organized and managed according to domain ontology within these registry centers. Secondly, an ant-like multi-agent service discovery method is proposed. Search agents and guide agents cooperate to discover services. Search agents simulate the behaviors of ants to travel the network and discover services. Guide agents are responsible to manage a service routing table consisting of pheromone and hop count, instructing search agents' routing. Experimental results show that the suggested mechanism is scalable and adaptive in a large-scale dynamic SOC environment

Two-message Key Exchange with Strong Security from Ideal Lattices

In this paper, we first revisit the generic two-message key exchange (TMKE) scheme (which will be referred to as KF) introduced by Kurosawa and Furukawa (CT-RSA 2014). This protocol is mainly based on key encapsulation mechanism (KEM) which is assumed to be secure against chosen plaintext attacks (IND-CPA). However, we find out that the security of the KF protocol cannot be reduced to IND-CPA KEM. The concrete KF protocol instantiated from ElGamal KEM is even subject to key compromise impersonation (KCI) attacks. In order to overcome the flaws of the KF scheme, we introduce a new generic TMKE scheme from KEM. Instead, we require that the KEM should be secure against one-time adaptive chosen ciphertext attacks (OT-IND-CCA2). We call this class of KEM as OTKEM. In particular, we propose a new instantiation of OTKEM from Ring Learning with Errors (Ring-LWE) problem in the standard model. This yields a concrete post-quantum TMKE protocol with strong security. The security of our TMKE scheme is shown in the extended Canetti-Krawczyk model with perfect forward secrecy (eCK-PFS)

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

MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection

Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm to satisfy the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection, that consists of the following three components: an under-sampling module targeting to construct a novel training set that accommodates the true few-shot setting, a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the PLMs sufficiently for tackling the context-bypassing problem, and a prototypical module compensating for the weakness of classifying events with sparse data and boost the generalization performance. Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best-performing baseline and achieving an outstanding debiasing performance

Knowledge Graph Reasoning over Entities and Numerical Values

A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate from? Knowledge graph reasoning-based applications, such as dialogue systems and interactive search engines, rely on the ability to answer complex logic queries as a fundamental task. In most knowledge graphs, edges are typically used to either describe the relationships between entities or their associated attribute values. An attribute value can be in categorical or numerical format, such as dates, years, sizes, etc. However, existing complex query answering (CQA) methods simply treat numerical values in the same way as they treat entities. This can lead to difficulties in answering certain queries, such as which Australian Pulitzer award winner is born before 1927, and which drug is a pain reliever and has fewer side effects than Paracetamol. In this work, inspired by the recent advances in numerical encoding and knowledge graph reasoning, we propose numerical complex query answering. In this task, we introduce new numerical variables and operations to describe queries involving numerical attribute values. To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures. During the numerical encoding process, NRN employs a parameterized density function to encode the distribution of numerical values. During the entity encoding process, NRN uses established query encoding methods for the original CQA problem. Experimental results show that NRN consistently improves various query encoding methods on three different knowledge graphs and achieves state-of-the-art results

Analyzing the prices of the most expensive sheet iron all over the world: Modeling, prediction and regime change

The private car license plates issued in Shanghai are bestowed the title of "the most expensive sheet iron all over the world", more expensive than gold. A citizen has to bid in an monthly auction to obtain a license plate for his new private car. We perform statistical analysis to investigate the influence of the minimal price $P_{\min}$ of the bidding winners, the quota $N_{\rm{quota}}$ of private car license plates, the number $N_{\rm{bidder}}$ of bidders, as well as two external shocks including the legality debate of the auction in 2004 and the auction regime reform in January 2008 on the average price $P_{\rm{mean}}$ of all bidding winners. It is found that the legality debate of the auction had marginal transient impact on the average price in a short time period. In contrast, the change of the auction rules has significant permanent influence on the average price, which reduces the price by about 3020 yuan Renminbi. It means that the average price exhibits nonlinear behaviors with a regime change. The evolution of the average price is independent of the number $N_{\rm{bidder}}$ of bidders in both regimes. In the early regime before January 2008, the average price $P_{\rm{mean}}$ was influenced only by the minimal price $P_{\min}$ in the preceding month with a positive correlation. In the current regime since January 2008, the average price is positively correlated with the minimal price and the quota in the preceding month and negatively correlated with the quota in the same month. We test the predictive power of the two models using 2-year and 3-year moving windows and find that the latter outperforms the former. It seems that the auction market becomes more efficient after the auction reform since the prediction error increases.Comment: 10 pages including 5 figures and 4 table