Showing papers on "State (computer science) published in 2022"

TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines

Abstract: TrackMate is an automated tracking software used to analyze bioimages and is distributed as a Fiji plugin. Here, we introduce a new version of TrackMate. TrackMate 7 is built to address the broad spectrum of modern challenges researchers face by integrating state-of-the-art segmentation algorithms into tracking pipelines. We illustrate qualitatively and quantitatively that these new capabilities function effectively across a wide range of bio-imaging experiments.

Natural language processing: state of the art, current trends and challenges

Abstract: Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.

State-of-the-Art and Future Prospects

Abstract: Since its invention in 2003, rock-filled concrete technology has been among the nation’s fastest-growing dam construction technologies. Rock-filled concrete dams have been implemented with the innovation, cooperation, perseverance, and hard work of practitioners in the engineering community nationwide. Over the past 20 years, a significant number of physical tests, field tests and numerical investigations have been conducted to understand the fundamental mechanisms and the way they influence basic properties of the rock-filled concrete, making the RFC technological innovation achieve a breakthrough from conception to extensive engineering practice.

Scientific Applications of Distributed Acoustic Sensing: State-of-the-Art Review and Perspective

Abstract: This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.

Biogeographic regionalization of the Neotropical region: New map and shapefile.

Abstract: We provide a map and shapefile of the 57 biogeographic provinces of the Neotropical region. Recognition of these provinces is based on their endemic species, but their delimitation on the map is based on ecoregions combining climatic, geological, and biotic criteria. These provinces belong to the Antillean, Brazilian and Chacoan subregions, and the Mexican and South American transition zones. We provide a vector file of the biogeographical regionalization by converting the map into a polygon shapefile and a raster file with all provinces.

Safe Controller Synthesis With Tunable Input-to-State Safe Control Barrier Functions

Abstract: To bring complex systems into real world environments in a safe manner, they will have to be robust to uncertainties—both in the environment and the system. This letter investigates the safety of control systems under input disturbances, wherein the disturbances can capture uncertainties in the system. Safety, framed as forward invariance of sets in the state space, is ensured with the framework of control barrier functions (CBFs). Concretely, the definition of input-to-state safety (ISSf) is generalized to allow the synthesis of non-conservative, tunable controllers that are provably safe under varying disturbances. This is achieved by formulating the concept of tunable input-to-state safe control barrier functions (TISSf-CBFs), which guarantee safety for disturbances that vary with state and, therefore, provide less conservative means of accommodating uncertainty. The theoretical results are demonstrated with a simple control system with input disturbance and also applied to design a safe connected cruise controller for a heavy duty truck.

Intermediate Elites in Pre-Columbian States and Empires

Abstract: From the Mesoamerican highlands to the Colca Valley in Peru, pre-Columbian civilizations were bastions of power that have largely been viewed through the lens of rulership, or occasionally through bottom-up perspectives of resistance. Rather than focusing on rulers or peasants, this book examines how intermediate elites both men and women?helped to develop, sustain, and resist state policies and institutions. Employing new archaeological and ethnohistorical data, its contributors trace a 2,000-year trajectory of elite social evolution in the Zapotec, Wari, Aztec, Inka, and Maya civilizations. This is the first volume to consider how individuals subordinate to imperial rulers helped to shape specific forms of state and imperial organization. Taking a broader scope than previous studies, it is one of the few works to systematically address these issues in both Mesoamerica and the Central Andes. It considers how these individuals influenced the long-term development of the largest civilizations of the ancient Americas, opening a new window on the role of intermediate elites in the rise and fall of ancient states and empires worldwide. The authors demonstrate how such evidence as settlement patterns, architecture, decorative items, and burial patterns reflect the roles of intermediate elites in their respective societies, arguing that they were influential actors whose interests were highly significant in shaping the specific forms of state and imperial organization. Their emphasis on provincial elites particularly shifts examination of early states away from royal capitals and imperial courts, explaining how local elites and royal bureaucrats had significant impact on the development and organization of premodern states. Together, these papers demonstrate that intricate networks of intermediate elites bound these ancient societies together, nd that competition between individuals and groups contributed to their decline and eventual collapse. By addressing current theoretical concerns with agency, resistance to state domination, and the co-option of local leadership by imperial administrators, it offers valuable new insight into the utility of studying intermediate elites.

Dynamic Event-triggered State Estimation for Nonlinear Coupled Output Complex Networks Subject to Innovation Constraints

Abstract: This letter investigates the recursive state estimation (RSE) problem for a class of coupled output complex networks via the dynamic event-triggered communication mechanism (DETCM) and innovation constraints (ICs). Firstly, a DETCM is employed to regulate the transmission sequences. Then, in order to improve the reliability of network communication, a saturation function is introduced to constrain the measurement outliers. A new RSE method is provided such that, for all output coupling, DETCM and ICs, an upper bound of state estimation error covariance (SEEC) is presented in a recursive form, whose trace can be minimized via parameterizing the state estimator gain matrix (SEGM). Moreover, the theoretical analysis is given to guarantee that the error dynamic is uniformly bounded. Finally, a simulation example is illustrated to show the effectiveness of the proposed RSE method.

Applying Dynamic Systems to Social Media by Using Controlling Stability

Abstract: This study focuses on hybrid synchronization, a new synchronization phenomenon in which one element of the system is synced with another part of the system that is not allowing full synchronization and nonsynchronization to coexist in the system. When limt⟶∞Y−αX=0, where Y and X are the state vectors of the drive and response systems, respectively, and Wan (α = ∓1)), the two systems' hybrid synchronization phenomena are realized mathematically. Nonlinear control is used to create four alternative error stabilization controllers that are based on two basic tools: Lyapunov stability theory and the linearization approach.

From extractivism to global extractivism: the evolution of an organizing concept

Abstract: ABSTRACT Research on extractivism has rapidly proliferated, expanding into new empirical and conceptual spaces. We examine the origins, evolution, and conceptual expansion of the concept. Extractivism is useful to analyze resource extraction practices around the world. ‘Global Extractivism’ is a new conceptual tool for assessing global phenomena. We situate extractivism within an ensemble of concepts, and explore its relation to development, the state, and value. Extractivism as an organizing concept addresses many fields of research. Extractivism forms a complex of self-reinforcing practices, mentalities, and power differentials underwriting and rationalizing socio-ecologically destructive modes of organizing life-through subjugation, depletion, and non-reciprocity.

Non-fragile l 2-l ∞ state estimation for time-delayed artificial neural networks: an adaptive event-triggered approach

Abstract: In this paper, the state estimation problem is investigated for a kind of time-delayed artificial neural networks subject to gain perturbations under the adaptive event-triggering scheme. To avoid wasting resources, the event-triggering scheme is adopted during the data transmission process from the sensors to the estimator where the triggering threshold can be dynamically adjusted. By means of the Lyapunov stability theory, sufficient conditions are provided to ensure that the estimation error dynamics achieves both the asymptotical stability and the - performance. The desired non-fragile estimator gain is parameterised by solving certain matrix inequalities. At last, the usefulness of the proposed event-based non-fragile state estimator is shown via a numerical simulation example.

Input-to-State Stability of Nonlinear Systems: Event-Triggered Impulsive Control

Abstract: This article investigates input-to-state stability (ISS) of nonlinear systems via event-triggered impulsive control method. A novel impulsive event-triggered mechanism (IETM) which can exclude the Zeno behavior for nonlinear impulsive systems is designed. Some sufficient conditions which establish the relationship between impulse strength and IETM for ISS property are proposed. It is shown that under the designed IETM, a flexible impulsive control strategy can be provided. Since impulses only occur when the event which is related to the states of the system is triggered, the burden of the transmission of information can be reduced dramatically. Examples are presented to show the effectiveness of our proposed results.

H ∞ state estimation for memristive neural networks with randomly occurring DoS attacks

Abstract: This study deals with the problem of the state estimation for discrete-time memristive neural networks with time-varying delays, where the output is subject to randomly occurring denial-of-service attacks. The average dwell time is used to describe the attack rules, which makes the randomly occurring denial-of-service attack more universal. The main purpose of the addressed issue is to contribute with a state estimation method, so that the dynamics of the error system is exponentially mean-square stable and satisfies a prescribed disturbance attenuation level. Sufficient conditions for the solvability of such a problem are established by employing the Lyapunov function and stochastic analysis techniques. Estimator gain is described explicitly in terms of certain linear matrix inequalities. Finally, the effectiveness of the proposed state estimation scheme is proved by a numerical example.

Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

Abstract: Aiming at the problems of time‐varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second‐order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particle filter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage.

Culture and International History

Abstract: Combining the perspectives of 18 international scholars from Europe and the United States with a critical discussion of the role of culture in international relations, this volume introduces recent trends in the study of Culture and International History. It systematically explores the cultural dimension of international history, mapping existing approaches and conceptual lenses for the study of cultural factors and thus hopes to sharpen the awareness for the cultural approach to international history among both American and non-American scholars. The first part provides a methodological introduction, explores the cultural underpinnings of foreign policy, and the role of culture in international affairs by reviewing the historiography and examining the meaning of the word culture in the context of foreign relations. In the second part, contributors analyze culture as a tool of foreign policy. They demonstrate how culture was instrumentalized for diplomatic goals and purposes in different historical periods and world regions. The essays in the third part expand the state-centered view and retrace informal cultural relations among nations and peoples. This exploration of non-state cultural interaction focuses on the role of science, art, religion, and tourism. The fourth part collects the findings and arguments of part one, two, and three to define a roadmap for further scholarly inquiry. A group of" commentators" survey the preceding essays, place them into a larger research context, and address the question "Where do we go from here?" The last and fifth part presents a selection of primary sources along with individual comments highlighting a new genre of resources scholars interested in culture and international relations can consult. Jessica C. E. Gienow-Hecht is Professor of History at the John F. Kennedy Institute for North American Studies at the Free University of Berlin. Frank Schumacher is Assistant Professor of North American History at the University of Erfurt, Germany. He is the author of Kalter Krieg und Propaganda. Die USA, der Kampf um die Weltmeinung und die ideelle Westbindung der Bundesrepublik Deutschland, 1945-1955. He has published articles on 19th and 20th century North American diplomatic, military, cultural and environmental history and is currently at work on his second book entitled The American Way of Empire: the United States and the Quest for Imperial Identity, 1880-1920.

Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision

Abstract: Machine learning has become a ubiquitous and effective technique for data processing and classification. Furthermore, due to the superiority and progress of quantum computing in many areas (e.g., cryptography, machine learning, healthcare), a combination of classical machine learning and quantum information processing has established a new field, called, quantum machine learning. One of the most frequently used applications of quantum computing is machine learning. This paper aims to present a comprehensive review of state-of-the-art advances in quantum machine learning. Besides, this paper outlines recent works on different architectures of quantum deep learning, and illustrates classification tasks in the quantum domain as well as encoding methods and quantum subroutines. Furthermore, this paper examines how the concept of quantum computing enhances classical machine learning. Two methods for improving the performance of classical machine learning are presented. Finally, this work provides a general review of challenges and the future vision of quantum machine learning. • Organize the most recent research works to pave the way for QML researchers. • Demonstrate the commonly used methods in the classification of real problems. • Provide readers with various quantum methods to enhance classical ML. • Present some of the challenges and future directions of QML.

Reconfigurable Intelligent Surfaces With Outdated Channel State Information: Centralized vs. Distributed Deployments

Abstract: In this paper, we investigate the performance of an RIS-aided wireless communication system subject to outdated channel state information that may operate in both the near- and far-field regions. In particular, we take two RIS deployment strategies into consideration: (i) the centralized deployment, where all the reflecting elements are installed on a single RIS and (ii) the distributed deployment, where the same number of reflecting elements are placed on multiple RISs. For both deployment strategies, we derive accurate closed-form approximations for the ergodic capacity, and we introduce tight upper and lower bounds for the ergodic capacity to obtain useful design insights. From this analysis, we unveil that an increase of the transmit power, the Rician- $K$ factor, the accuracy of the channel state information and the number of reflecting elements help improve the system performance. Moreover, we prove that the centralized RIS-aided deployment may achieve a higher ergodic capacity as compared with the distributed RIS-aided deployment when the RIS is located near the base station or near the user. In different setups, on the other hand, we prove that the distributed deployment outperforms the centralized deployment. Finally, the analytical results are verified by using Monte Carlo simulations.

A survey on demand-responsive public bus systems

Abstract: When demand for transportation is low or highly variable, traditional public bus services tend to lose their efficiency and typically frustrate (potential) passengers. In the literature, a large number of demand-responsive systems, that promise improved flexibility, have therefore been developed. At present, however, a comprehensive survey of these systems is lacking. In this paper, we fill this gap by presenting a unifying framework that classifies all demand-responsive public bus systems. The classification is mainly based on three degrees of responsiveness: dynamic online, dynamic offline, and static. For each system we discuss the specific optimization problem modeled, whether realistic data is considered, and the size of the instances used for testing. Where possible, we try to draw conclusions on the current state of the literature and try to identify potential avenues for future research. Different tables are included to structure and summarize the information of all papers.