Hierarchical State Research Articles

An event-triggered real-time motion planning strategy for autonomous vehicles

Motion planning is an essential part of autonomous vehicles. The planning process should respond to environmental changes in real time to ensure safety. This paper proposes an event-triggered real-time motion planning strategy to achieve a more real-time planning effect and a scenario-based planning process. The path planning process is discretized into several parts and integrated into the behavioral planning process. A hierarchical finite state machine (HFSM) based integrated motion planning process is proposed to trigger the discrete path planning parts according to environmental events. Thus, the output reference path can be obtained. Experiment and simulation results show the efficiency of our strategy.

Revisiting 1177 BCE and the Late Bronze Age Collapse

Revisiting 1177 BCE and the Late Bronze Age Collapse

Isomerization Reactions of the Cu15V+ Cluster: A Density Functional Theory Study.

The investigation of the chemical reactivity of complex systems such as transition metal clusters is a very complicated task because often the structures of the corresponding transition states are far from being intuitive. Bimetallic transition metal clusters represent a particular class of complex systems. In this work, density functional theory (DFT) is applied to study the isomerization reactions of the Cu15V+ cluster. Full geometry optimizations of dozens of initial structures taken along Born-Oppenheimer molecular dynamics (BOMD) trajectories were performed using a quasi-Newton method in a reduced space Cartesian coordinate system that works considering the internal degrees of freedom. Harmonic frequencies calculations were performed at the optimized structures. To study the isomerization reactions between the obtained stable isomers, a hierarchical transition state algorithm has been applied to locate the transition states of this cluster. The found transition states were than connected with the corresponding minimum structures by calculating the intrinsic reaction coordinates. This work demonstrates the capability of the applied method to study non-intuitive rearrangement mechanisms in complex finite systems and to create networks between minima and transition state structures on their potential energy surface.

Towards a constructed order of co-governance: Understanding the state-society dynamics of neighbourhood collaborative responses to COVID-19 in urban China.

The state-society relationship in neighbourhood governance has been a focal topic in the urban governance literature, though the existing scholarship was primarily drawn from non-crisis situations. Adopting a mixed-methods approach, this study investigates the intricate state-society dynamics manifested at the neighbourhood scale as state and societal actors collaborated during China's COVID-19 responses. Our study reveals a pattern of collaborative rather than confrontational dynamics between resident committees and other stakeholders during pandemic responses, which reflects the emergence of a constructed order of neighbourhood co-governance in urban China. Previous community-building reforms consolidated the political legitimacy, power and capacity of resident committees, which were empowered to play a critical coordinating role in bridging hierarchical state mobilisation and horizontal stakeholders in the collaborative pandemic responses. These findings contribute to a more nuanced understanding of neighbourhood co-governance in the international literature and provide lessons for resilience governance from a comparative lens.

The role of current transport expenditure in mitigating the risk of modal shift during Covid-19 – Lessons from Polish cities

This article shows that current expenditure allocated by local authorities for the routine maintenance of public transport (i.e., providing passengers with clean and frequent services) is crucial to effectively limit the modal shift from public transit to private cars during the Covid-19 pandemic. In our analysis, we define public transport as the bus, tram, metro and trolley operations which are ordered and funded by 11 local authorities in Poland; we also assume that the modal shift is reflected in the growth of traffic congestion. Taking into account these assumptions and considering the long-term financial data from 2010 to 2020, we then conduct hierarchical linear regression and state that cities with a higher share of current expenses in cities’ total annual transport expenditure have registered during the pandemic a lower number of weeks with congestion level exceeding the pre-pandemic level. We argue that this is due to the allocation of current expenditure to cleaning services and the transport service supply, which could have a significant impact on perceptions of crowding and safety on public transport. Both issues belong to the essential determinants of the quality of public transport services and could play a key role in mitigating the modal shift by reducing the fear of catching Covid-19. In contrast to this finding, we also show that investment expenditure (allocated to improvements of transport infrastructure) does not have a similar impact on the congestion during the pandemic. In this context, we emphasise that cutting spending on cleaning services and the transport supply service as a response to the reduced number of passengers would be more devastating for public transport than postponing new infrastructure investments. Based on the results achieved in the analysis, we provide recommendations for local authorities that can help implement new funding models and reframe local transport policy, namely: (1) close the funding gap resulting from lower revenues to local budgets, (2) provide resources for maintaining the infrastructure investments and adapting them to a “new normality”, (3) maintain the attractiveness of public transport post-Covid.

Novel disease state model finds most juvenile green turtles develop and recover from fibropapillomatosis

AbstractFibropapillomatosis (FP) is a sea turtle disease characterized by benign tumor development on the skin, eyes, and/or internal organs. It primarily affects juvenile green turtles (Chelonia mydas) in coastal foraging sites. The Indian River Lagoon (IRL), Florida, USA, is a coastal green turtle foraging site where the observed FP annual rate averaged 49% between 1983 and 2018. While FP is no longer considered a major cause of sea turtle mortality and most individuals fully recover, the overall dynamics of this disease are poorly understood because prior disease history is unknown for individuals without FP at capture time, and future disease outcome is unknown for individuals with FP at capture time. To better evaluate FP dynamics for green turtles in the IRL, we developed a hierarchical model for predicting disease state change. We used data from 4149 captures of 3700 individual green turtles captured in the IRL. The hierarchical disease state model contained two levels: Level 1 modeled whether an individual would develop FP, and Level 2 modeled disease state progression, including states for pre‐FP affliction, active FP affliction, and full recovery from FP. From the hierarchical model, we estimated 99.8% (95% credibility intervals 99.1%–100%) of juvenile green turtles in the IRL developed FP, indicating that nearly every individual in the IRL is affected by this disease. The model also suggested that turtles quickly developed FP upon recruitment to the IRL and then recovered at different rates, with most completely recovering before emigrating from the IRL as they mature. This is the first analysis of long‐term sea turtle data suggesting nearly every turtle in an aggregation both develops and recovers from FP.

Public psychological crisis reports: an investigation during the remission of COVID-19 in China

PurposeThe COVID-19 outbreak has been effectively controlled in China, but the resulting public psychological crisis is a latent, persistent and torturous disaster. This crisis affects not only the individual’s health but also social stability. This study aims to reveal the structure and situation of public psychological crisis during the remission period of the COVID-19 epidemic.Design/methodology/approachA questionnaire survey of the psychological crisis was conducted from February to May 2021. Data was collected from 1,056 respondents from different provinces in China.FindingsThe findings reveal the dangerous risks of public psychological crises. The specific results are as follows: first, a hierarchical state structure of psychological crisis is constructed and characterized as a significant gradient decline, namely, anxious state>panic state>depressive state>hostile state. Second, 43.939% of respondents reported an anxious state, 46.780% reported a panic state, 40.909% reported a depressive state, 28.883% reported a hostile state and 22.254% of the respondents had four psychological crisis states. Third, those with 56–65 years of age, equal or below high school and PhD, with monthly family incomes of ¥50,000–¥100,000 and living in the county and city areas have relatively high levels of psychological crisis.Originality/valueThis study enriches the research on the evolution of the psychological crisis in terms of structure and periods. This study also provides substantial evidence for hierarchical intervention and differentiated intervention of psychological crisis.

The Law of Scale Independence

ABSTRACT Geography and geosciences deal with phenomena that span spatial scales from the molecular to the planetary, and temporal scales from instantaneous to billions of years. A strong reductionist tradition in geosciences and spatial sciences tempts us to seek to apply similar representations and process-based explanations across these vast-scale ranges, usually from a bottom-up perspective. However, the law of scale independence (LSI) states that for any phenomenon that exists across a sufficiently large range of scales, there exists a scale separation distance at which the scales are independent with respect to system dynamics and explanation. The LSI is evaluated here from five independent perspectives: geographic intuition, dynamical systems theory, Kolmogorov entropy, hierarchy theory, and algebraic graph theory. All of these support the LSI. Results indicate that rather than attempting to identify the largest or smallest relevant scales and work down or up from there, the LSI dictates a strategy of focusing directly on the most important or interesting scales. An example is given from a hierarchical state factor model of ecosystem responses to climate change.

Zone-based hierarchical link state for detecting malicious node and black hole attack detection for mobile ad-hoc networks

One of the most common types of network attacks involves non-forwardable bandwidth nodes that pretend to be intermediate nodes on the route to certain destinations and drop every packet that subsequently passes through it. This paper proposes attacks through black holes as an analysis method for detecting malicious nodes in Mobile Ad-hoc Networks (MANET). Because the mechanism and protocol are collaborative, it can confidently analyse and identify malicious hosts. To test the effectiveness of this approach, it uses a hybrid Hierarchical Link State routing protocol based on Modified-ZoneBased Hierarchical Link State Routing Protocol (M-ZHLS) in Network Simulations 2 (NS2) with mobile nodes. As the mobility rate changes and the topology size changes, it can be seen that the detection rate of black holes and malicious nodes is extremely high, the average delay is reduced, and the packet delivery rate is increased compared to the Zone-Based Hierarchical Link State Routing Protocol (ZHLS).

Decision making framework for autonomous vehicles driving behavior in complex scenarios via hierarchical state machine

With the development of autonomous car, a vehicle is capable to sense its environment more precisely. That allows improved drving behavior decision strategy to be used for more safety and effectiveness in complex scenarios. In this paper, a decision making framework based on hierarchical state machine is proposed with a top-down structure of three-layer finite state machine decision system. The upper layer classifies the driving scenario based on relative position of the vehicle and its surrounding vehicles. The middle layer judges the optimal driving behavior according to the improved energy efficiency function targeted at multiple criteria including driving efficiency, safety and the grid-based lane vacancy rate. The lower layer constructs the state transition matrix combined with the calculation results of the previous layer to predict the optimal pass way in the region. The simulation results show that the proposed driving strategy can integrate multiple criteria to evaluate the energy efficiency value of vehicle behavior in real time, and realize the selection of optimal vehicle driving strategy. With popularity of automatic vehicles in future, the driving strategy can be used as a reference to provide assistance for human drive or even the real-time decision-making of autonomous driving.

Finite State Machine in Game Development

Finite State Machine is one of the oldest techniques in gaming where it was used in old games like PACMAN and new games like TOM RAIDER also. In all these games one major goal was to make non player characters more intelligent. There are some advance types also available but FSM still is one of the most used technique for non-player characters. The main goal of this paper is to explain how FSM works, how to create FSM and implement it in games using scripting or visual scripting. Hierarchical Finite State Machine is also discussed in this paper as it overcomes the limitations of older simple FSM. Using FSM, we can create intelligent AI agents. We can implement FSM and HFSM in games to make NPS’s behave like AI.

Rolling bearing diagnosis method based on improved standardized variable distance fusion hierarchical state space correlation entropy

In recent years, the machine learning method has been successfully applied to the intelligent health monitoring of rolling bearings. However, its effectiveness depends heavily on the vibration signal time series collected. For the purpose of abundantly excavate the fault feature information in time series, some new multi-scale entropy methods are used to solve this problem. However, the multi-scale entropy only considers the low frequency component of the signal, neglects the high frequency component of the signal, loses some fault signals with modulation phenomenon, and reduces the accuracy of fault classification. Therefore, in order to overcome the shortcomings of multi-scale entropy. In this paper, proposing a new hierarchical state space correlation entropy (HSSCEn) to extract the fault features contained in the signal. At the same time, in practice, the fault features extracted by HSSCEn are usually high and nonlinear. Directly input to the classifier for training and testing will reduce the accuracy of machine learning method recognition. Therefore, an improved standardized variable distance classifier is proposed in this paper. It effectively reduces the feature redundancy of signal and improves the accuracy of original classifier. Finally, in order to verify the validity of the fault diagnosis method based on HSSCEn fusion standardized variable distance classifier, two sets of fault classification experiments are carried out in this paper. Compared with existing fault identification classifier algorithms, the proposed method has more advantages in accuracy and stability of fault classification.

First description of migratory behavior of humpback whales from an Antarctic feeding ground to a tropical calving ground

BackgroundDespite exhibiting one of the longest migrations in the world, half of the humpback whale migratory cycle has remained unexamined. Until now, no study has provided a continuous description of humpback whale migratory behavior from a feeding ground to a calving ground. We present new information on satellite-derived offshore migratory movements of 16 Breeding Stock G humpback whales from Antarctic feeding grounds to South American calving grounds. Satellite locations were used to demonstrate migratory corridors, while the impact of departure date on migration speed was assessed using a linear regression. A Bayesian hierarchical state–space animal movement model (HSSM) was utilized to investigate the presence of Area Restricted Search (ARS) en route.Results35,642 Argos locations from 16 tagged whales from 2012 to 2017 were collected. The 16 whales were tracked for a mean of 38.5 days of migration (range 10–151 days). The length of individually derived tracks ranged from 645 to 6381 km. Humpbacks were widely dispersed geographically during the initial and middle stages of their migration, but convened in two convergence regions near the southernmost point of Chile as well as Peru’s Illescas Peninsula. The state–space model showed almost no instances of ARS along the migratory route. The linear regression assessing whether departure date affected migration speed showed suggestive but inconclusive support for a positive trend between the two variables. Results suggestive of stratification by sex and reproductive status were found for departure date and route choice.ConclusionsThis multi-year study sets a baseline against which the effects of climate change on humpback whales can be studied across years and conditions and provides an excellent starting point for the investigation into humpback whale migration.

The dawn of everything: A new history?

This 526-page discourse is a reasoned argument for rejecting the notion of steady progress in the human past that challenges our assumptions about the origins of many aspects of modern human behavior, including farming, property, cities, democracy, and slavery. The authors take an anarchic position. They conclude that since we have organized ourselves in many ways in the past, so there are other ways of organizing ourselves beyond the current dominant model of hierarchical states. The authors draw from/reinterpret archaeological data to emphasize (politically anarchic) examples of freedom, play, and social equality. This bold, controversial book will provoke intense discussion.

Autonomous Hierarchical Surgical State Estimation During Robot-Assisted Surgery Through Deep Neural Networks

Many operations in robot-assisted surgery (RAS) can be viewed in a hierarchical manner. Each surgical task is represented by a superstate, which can be decomposed into finer-grained states. The estimation of these discrete states at different levels of temporal granularity provides a temporal perception of the current surgical scene during RAS, which is a crucial step towards many automated surgeon-assisting functionalities. We propose Hierarchical Estimation of Surgical States through Deep Neural Networks (HESS-DNN), a deep learning-based system that concurrently estimates the current super- and fine-grained states. HESS-DNN incorporates endoscopic vision, robot kinematics, and system events data from the da Vinci Xi surgical system. HESS-DNN is evaluated on a real-world robotic inguinal hernia repair surgery dataset: HERNIA-20, and achieves accurate state estimates of both surgical superstate and the corresponding fine-grained surgical state. We show that HESS-DNN improves state-of-the-art fine-grained state estimation across the entire HERNIA-20 RAS procedure through its hierarchical design. We also analyze the relative contributions of each input data type and HESS-DNN's design to surgical (super)state estimation accuracy.

Dynamic Expansion of Behaviour Trees

Artificial intelligence in games is typically used for creating player's opponents. Manual edition of intelligent behaviors for Non-Player Characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviours typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical state machines allows working on different abstraction levels, sharing transitions and reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden to completely specify each behaviour. Our approach applies Case-Based Reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as hierarchical state machines (actually, behaviour trees). In this paper we focus on dynamic retrieval of behaviours taking into account the world state and the underlying goals to select the most appropriate state machine to guide the NPC behaviour. The global behaviour of the NPC is dynamically built in run time querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from First-Person Shooter (FPS) games.

Re‐stating power: How states make, tame, and shape markets

AbstractThis article presents the argument that states work in and through markets to actively balance competing interests and manage contradictions and disequilibrium. I focus on four processes reshaping Australia’s electricity governance in the last two decades, namely deregulation, (de)centralisation, decarbonisation, and digitalisation, and show the ubiquity of state spatial strategies to govern through markets. I establish the historical legacy of subnational state‐led development in Australian electricity, then examine how recent deregulatory processes have resulted in new modes of state power exercised through the creation and taming of markets. My claims are that subnational governments continue to exercise authoritative power under Australian federalism and that new networks of techno‐managerial market governance remain subject to hierarchical state power. The purchase of key electricity assets has become a strategy for exercising state influence in and through the market. Political and spatial economic geographies of Australian energy continue to shape the struggles over energy futures, requiring attention to place, scale, territory, and network. The socio‐spatial dimensions of state strategies reveal complex and vacillating modes of state power. State action is characterised by a lack of coordination rather than unification at different scales, and the capacity to execute state projects is complicated by discursive and agential politics.

Dependency Graph-based High-level Synthesis for Maximum Instruction Parallelism

Performance optimization is an important goal for High-level Synthesis (HLS). Existing HLS scheduling algorithms are all based on Control and Data Flow Graph (CDFG) and will schedule basic blocks in sequential order. Our study shows that the sequential scheduling order of basic blocks is a big limiting factor for achievable circuit performance. In this article, we propose a Dependency Graph (DG) with two important properties for scheduling. First, DG is a directed acyclic graph. Thus, no loop breaking heuristic is needed for scheduling. Second, DG can be used to identify the exact instruction parallelism. Our experiment shows that DG can lead to 76% instruction parallelism increase over CDFG. Based on DG, we propose a bottom-up scheduling algorithm to achieve much higher instruction parallelism than existing algorithms. Hierarchical state transition graph with guard conditions is proposed for efficient implementation of such high parallelism scheduling. Our experimental results show that our DG-based HLS algorithm can outperform the CDFG-based LegUp and the state-of-the-art industrial tool Vivado HLS by 2.88× and 1.29× on circuit latency, respectively.

Tracing non-Abelian anyons via impurity particles

Non-Abelian excitations are an interesting feature of many fractional quantum Hall phases, including those phases described by the Moore-Read (or Pfaffian) wave function. However, the detection of the non-Abelian quasiparticles is challenging. Here, we consider a system described by the Moore-Read wave function, and assume that impurity particles bind to its quasiholes. Then, the angular momentum of the impurities, reflected also by the impurity density, provides a useful witness of the physics of the non-Abelian excitations. By demanding that the impurities are constrained to the lowest Landau level, we are able to write down the corresponding many-body wave function describing both the Moore-Read liquid and the impurities. Through Monte Carlo sampling we determine the impurity angular momentum, and we show that it suggests a quantum-statistical parameter $\alpha = a\nu -b +P/2$ for the quasiholes, where $\alpha$ ranges from $0$ for bosons to $1$ for fermions. A reasonable agreement with the Monte Carlo results is obtained for $a=1/4$, $b=1/8$ and $P=0,1$ depending on the parity of the particle number in the Moore-Read liquid. This parity-dependence of the angular momentum serves as an unambiguous demonstration of the non-Abelian nature of the excitations. In addition to the studies of excitations in the Moore-Read liquid, we also apply our scheme to Laughlin liquids, for which we focus on interacting bosonic impurities. With this, the impurities themselves form Laughlin states, which allows for a study of hierarchical fractional quantum Hall states.

Temporal and state abstractions for efficient learning, transfer, and composition in humans.

Humans use prior knowledge to efficiently solve novel tasks, but how they structure past knowledge during learning to enable such fast generalization is not well understood. We recently proposed that hierarchical state abstraction enabled generalization of simple one-step rules, by inferring context clusters for each rule. However, humans' daily tasks are often temporally extended, and necessitate more complex multi-step, hierarchically structured strategies. The options framework in hierarchical reinforcement learning provides a theoretical framework for representing such transferable strategies. Options are abstract multi-step policies, assembled from simpler one-step actions or other options, that can represent meaningful reusable strategies as temporal abstractions. We developed a novel sequential decision-making protocol to test if humans learn and transfer multi-step options. In a series of four experiments, we found transfer effects at multiple hierarchical levels of abstraction that could not be explained by flat reinforcement learning models or hierarchical models lacking temporal abstractions. We extended the options framework to develop a quantitative model that blends temporal and state abstractions. Our model captures the transfer effects observed in human participants. Our results provide evidence that humans create and compose hierarchical options, and use them to explore in novel contexts, consequently transferring past knowledge and speeding up learning. (PsycInfo Database Record (c) 2021 APA, all rights reserved).