State Partition Research Articles

Dynamic back-off chain-building scheme based on network state partition

Because well-considered optical buffers have not been developed to date, the optical circuit switching (OCS) mechanism has been used widely for data transmission applications in existing optical network-on-chip (ONoC) devices. Unfortunately, with increasing network loads and increasing numbers of intellectual property (IP) cores, ONoCs based on the OCS mechanism are facing serious node congestion problems that greatly limit their network communication performances. A chain-building scheme is proposed to achieve high chain-building efficiency with low overheads. The proposed scheme uses a network state partitioning method based on time prediction to optimize the chain-building process and performs congestion control using a dynamic back-off approach. The simulation results obtained using OPNET software show that the network delay and throughput performances of the ONoC device using the proposed chain-building scheme represent significant improvements when compared with common existing schemes.

Region Stability and Stabilization of Recurrent Neural Network with Parameter Disturbances

This paper mainly focuses on global region stability and stabilization analysis for recurrent neural networks with certain or uncertain parameter disturbances. Firstly, it presents global region stability results for recurrent neural networks with certain parameter disturbances by state partition and mathematical analysis methods. Next, it designs one adaptive controller to stabilize network states to the desired region for recurrent neural networks with uncertain parameter disturbances. At last, it gives two numerical examples for verifying obtained results.

Coordinated control strategy of multiple energy storage power stations supporting black-start based on dynamic allocation

Abstract Due to the disordered charging/discharging of energy storage in the wind power and energy storage systems with decentralized and independent control, sectional energy storage power stations overcharge/over-discharge and the system power is unbalanced, which leads to the failure of black-start. A coordinated control strategy of multi-energy storage supporting black-start based on dynamic power distribution is proposed to solve this issue, which is divided into two layers. The power computational distribution layer divides the energy storage systems (ESSs) into 24 operating modes, according to the working partition of state of charge (SOC) of ESSs. Then, aiming at the power distribution problem of each energy storage power station, an adaptive multi-energy storage dynamic distribution model is proposed. The power tracking control layer adopts the control strategy combining V/f and PQ, which can complete the optimal allocation of the upper the power instructions among energy storage power station and feedback the real-time SOC to the power computational distribution layer. Finally, the proposed control strategy is applied to the black-start system. Compared to the average distribution and the allocation without adaptive control, the improvement degree of SOC of energy storage in the critical interval was increased by 56.5%. In the region with more wind and less water, this method can provide reference and theoretical basis for the wind power participating in the black-start assisted by multi-energy storage stations.

Improving Multilevel Writes on Vertical 3-D Cross-Point Resistive Memory

Resistive memory is promising to be constructed as a high-density storage-class memory. Multilevel cell, access-transistor-free cross-point array structure, and 3-D array integration are three approaches to scale up the density of resistive memory. However, composing the three approaches together strengthens the interactions between array-level and cell-level nonidealities (interconnect resistance-induced IR drop, sneak current, and device variability) of resistive memory arrays during write operations and significantly degrades write performance and reliability. In this article, we analyze the dynamic voltage-dividing effect along a selected write current path in 3-D cross-point memory arrays. We propose a nonideality-tolerant high-density resistive memory (HD-RRAM) architecture, that can weaken the interactions between nonidealities and mitigate their degradation effects on the performance and reliability of array multilevel write operations. HD-RRAM is equipped with a double-transistor array architecture with two-transistor- $n$ -resistor (2TnR) cell organization along pillars to reduce the current driving requirement and the large undesired voltage drop across each vertical pillar access transistor. Moreover, multiside asymmetric bias improves the resistive switching velocity by leveraging current-dividing effects. Variability-aware multilevel state partition reduces the worst-case write error rate by leveraging target state dependency of variability. Proportional-control multilevel state tuning reduces the average number of required write-and-verify iterations by leveraging pulse amplitude dependency of variability. Multilevel cell parallel writing improves the cell-level parallelism by leveraging the pass-through feature of intermediate resistance states. The evaluations show that HD-RRAM reduces both memory access latency and energy consumption over an aggressive baseline.

Entropic Identification of the First Order Freezing Transition of a Suspension of Hard Sphere Particles.

We analyze the experimental particle current autocorrelation function of suspensions of hard spheres. Interactions between the particles are mediated by thermally activated acoustic excitations in the solvent. Those acoustic modes are tantamount to the system's (energy) microstates and by their orthogonality, each of those modes can be identified with an independent Brownian particle current. Accordingly, partitioning of the system's energy states is impressed on the current autocorrelation function. This impression provides a novel measure of the entropy and location of a partitioning or entropy limit at a packing fraction that coincides with that of the observed suspension's first order freezing transition.

Ideal Type-II Weyl Phase and Topological Transition in Phononic Crystals.

Ideal Weyl points, which are related by symmetry and thus reside at the same frequency, could offer further insight into the Weyl physics. The ideal type-I Weyl points have been observed in photonic crystals, but the ideal type-II Weyl points with tilted conelike band dispersions are still not realized. Here we present the observation of the ideal type-II Weyl points of the minimal number in three-dimensional phononic crystals and, in the meantime, the topological phase transition from the Weyl semimetal to the valley insulators of two distinct types. The Fermi-arc surface states are shown to exist on the surfaces of the Weyl phase, and the Fermi-circle surface states are also observed, but on the interface of the two distinct valley phases. Intriguing wave partition of the Fermi-circle surface states is demonstrated.

Linear Borders, Partition and Identity in Postcolonial South Asia

ABSTRACT This paper critiques the legitimisation of linear borders in mainstream international relations (IR) through a focus on postcolonial South Asia. It brings insights from political geography in conversation with IR, making three key arguments: 1. The knowledge production on bordering in postcolonial South Asia remains trapped within the cognitive confines of linear bordering 2. Problematising linear bordering can therefore lead to newer critical questions about partition/bordering in South Asia, such as: is there a way to delink democracy and political independence from linear and precisely defined territorial borders? 3. Linear bordering in South Asia has been constitutive of the identities it aimed to isolate. The paper builds its arguments from an IR perspective and relies on IR’s treatment of borders, and scholarship on state formation and partition in postcolonial South Asia, to make its case. The paper understands linear borders as straight lines connecting a series of dots on a map in a way that one territory can only fall into one polity. This linear bordering was first attempted by colonial powers in Africa and then transported to other parts of the world. In South Asia, it produced reification of identities thus making divisions of territories appear as simple solutions to vastly complex challenges of identity formation.

Method for Ordering Procedures of Dividing States by Procedures with Two and Three Results Taking into Account their Cost and Weight of States

A method for streamlining state partitioning procedures with two and three outcomes is considered. A terminology and methods of the questionnaire theory were used, and the sequence of partitioning procedures itself was defined as a heterogeneous questionnaire with questions having two or three answers. This class of questionnaires is special and is defined by the authors as a class of binary-ternary questionnaires. This is the simplest class of heterogeneous questionnaires. An increase in number of answers to a question in practice can give an advantage in parameters of the questionnaires, including in the indicator of its effectiveness – the average implementation cost. It is noted that the use of binary-ternary questionnaires in practice can reduce the average time for identifying events on a questionnaire, which is extremely important in those applications of questionnaires in which there is a time limit for identifying events, for example, in critical application systems. A method for optimizing binary-ternary questionnaires is presented, based on the search for the most preferred questions for each subset of identifiable events. The choice of preferred questions is based on establishing a comparison relationship between them. The article describes all possible types of comparison relations between two questions with two answers, two questions with three answers, and also between a question with two answers and a question with three answers. An example of obtaining a mathematical expression for a function that characterizes the preference of questions over each other, as well as a generalized formula for choosing the most preferred question for any heterogeneous questionnaires is given. An algorithm has been formed for the method of ordering questions, which allows one to construct a binary-ternary questionnaire with the lowest implementation cost in polynomial time. An example of a binary-ternary questionnaire optimization by the presented method is given.

Method for Ordering Procedures of Dividing States by Procedures with Two and Three Results Taking into Account their Cost and Weight of States

A method for streamlining state partitioning procedures with two and three outcomes is considered. A terminology and methods of the questionnaire theory were used, and the sequence of partitioning procedures itself was defined as a heterogeneous questionnaire with questions having two or three answers. This class of questionnaires is special and is defined by the authors as a class of binary-ternary questionnaires. This is the simplest class of heterogeneous questionnaires. An increase in number of answers to a question in practice can give an advantage in parameters of the questionnaires, including in the indicator of its effectiveness – the average implementation cost. It is noted that the use of binary-ternary questionnaires in practice can reduce the average time for identifying events on a questionnaire, which is extremely important in those applications of questionnaires in which there is a time limit for identifying events, for example, in critical application systems. A method for optimizing binary-ternary questionnaires is presented, based on the search for the most preferred questions for each subset of identifiable events. The choice of preferred questions is based on establishing a comparison relationship between them. The article describes all possible types of comparison relations between two questions with two answers, two questions with three answers, and also between a question with two answers and a question with three answers. An example of obtaining a mathematical expression for a function that characterizes the preference of questions over each other, as well as a generalized formula for choosing the most preferred question for any heterogeneous questionnaires is given. An algorithm has been formed for the method of ordering questions, which allows one to construct a binary-ternary questionnaire with the lowest implementation cost in polynomial time. An example of a binary-ternary questionnaire optimization by the presented method is given.

Effect of cooling profile on crystalline phases, oxidation state, and chemical partitioning of complex glasses

Abstract:Investigations of the crystallization of aluminosilicate phases within Hanford nuclear waste glasses typically involve subjecting samples to the canister centerline cooling (CCC) schedule. This cooling schedule is representative of the slowest cooling thermal profile which these glasses will experience after the glass is poured into the high level waste (HLW) container. However, few investigations have observed how the crystallization behavior changes by varying the heat treatment schedule. In the present study, three Hanford HLW glasses are subjected to CCC and isothermal heat treatments (IHT) to better understand the evolution of phases and the chemical partitioning due to temperature schedule. Samples were characterized using electron probe microanalysis, X-ray diffraction, micro X-ray fluorescence, and micro X-ray absorption spectroscopy. From IHT, eucryptite and apatite phases were observed which were not observed during CCC. Spatially-resolved measurements demonstrated that the oxidation state of the iron was similar among glass and crystal, and we suggest a mechanism to describe the compositional fluctuations near the crystal-glass interface which influence crystallization.

Aggregated Markov‐based reliability analysis of multi‐state systems under combined dynamic environments

AbstractReliability of a system may differ greatly when operating under different environments. However, the existing works have either neglected the environment factor in system reliability analysis or considered this factor for binary systems or systems subject to a single environment (parameter). In this paper, we make contributions by modeling a multi‐state system operating under hybrid dynamic environments affected by multiple environmental parameters. Different Markov chains with finite states are used to represent the random system behavior and dynamic environments, leading to an aggregated Markov process that models the overall system behavior. An effective approach based on state partitions and aggregations is suggested for assessing the system reliability indexes, including reliability, availability, multi‐point availability, and environment‐based reliability. A high‐pressure homogenizer system is analyzed to demonstrate the proposed model and show the comparison of the reliability of system under fixed and dynamic environment.

Automatic Identification and Importance Evaluation Technology of Power Transmission Section

In theory, the cross section is the minimum cut set in the network. For the actual power grid, the transmission cross section is a collection of transmission lines between different regions, which is mainly used for power transmission to achieve the purpose of generating load balance. In this paper, a power grid zoning algorithm is proposed based on the topological structure characteristics and current operation state of the power grid, and the key transmission sections can be directly obtained without depending on the calculation of safety and stability margin. A practical system state partition model and key section sorting method are introduced, and a set of automatic identification method flow of key transmission sections of the system is established by searching for electrical betweenness. The importance of the transmission section is quantitatively evaluated from three angles: the margin from the stability limit, the hub position in the current power flow state, and the impact on the power grid after the power cut-off. The evaluation results can be used as a Reference for decision-making in the planning and reconstruction of the transmission section, safety monitoring, protection configuration, etc.

Parallel Reinforcement Learning With Minimal Communication Overhead for IoT Environments

Many Internet of Things (IoT) applications require a distributed architecture for decision making either because of a lack of a centralized system, failure-prone connectivity to a centralized system or because the imposed latency to contact such a system is too high for real-time applications. Often, these IoT applications fall in the domain of reinforcement learning (RL), e.g., autonomous robot navigation in smart factories and traffic signal control in smart cities. However, RL-based applications require a long learning time. To overcome this limitation and scale with the number of agents, parallel RL (PRL) algorithms run multiple RL agents in parallel and on distributed environments. However, deploying PRL algorithms in such environments entails a communication overhead that increases the (actual) execution time. The state-of-the-art PRL algorithms are designed for reducing the learning time while assuming no (or limited) communication overhead. In this article, we present a novel partitioning algorithm that minimizes the communication overhead in PRL running on IoT environments. To the best of our knowledge, this is the first work that focuses on solving the communication overhead of distributing PRL algorithms without requiring any a priori knowledge about the structure of the problem. The proposed algorithm intelligently combines a dynamic state partitioning strategy, which exploits the agent’s exploration capabilities to build partition knowledge while learning, with an efficient mapping of agents to partitions, which reduces the communication among agents. Performance evaluations show that the proposed algorithm can achieve almost no communication among PRL agents at the converged state.

Separability criteria based on Bloch representation of density matrices

We study separability criteria in multipartite quantum systems of arbitrary dimensions by using the Bloch representation of density matrices. We first derive the norms of the correlation tensors and obtain the necessary conditions for separability under partition of tripartite and four-partite quantum states. Moreover, based on the norms of the correlation tensors,we obtain the separability criteria by matrix method. Using detailed examples, our results are seen to be able to detect more entangled states than previous studies. Finally, necessary conditions of separability for multipartite systems are given under arbitrary partition.

A new approach for safety life prediction of industrial rolling bearing based on state recognition and similarity analysis

Rolling bearing is an important part of rotating machinery which operation safety and reliability are directly related to the normal operation of equipment. Remaining useful life is an important index for describing the safe operation of industrial rolling bearing and life prediction technology of machinery equipment is an important method to realize intelligent maintenance and industrial safety operation. According to the idea of data-driven, a new method for predicting the life of rolling bearing was proposed. By using life-cycle data of bearing from normal to failure, based on the new clustering algorithm of K-means and threshold correction, the recognition and partition of different operation states of rolling bearings were realized and the life model was established by defining the state matrix. For the monitoring bearing, comprehensive similarity analysis with the historical data was carried out to construct the life proportional adjustment function and dynamically modify the parameters of the state matrix model, so as to realize the adaptive prediction of the life of the monitoring bearing. The bearing test data of the University of Cincinnati Laboratory Center were used to carry out the applied research. The normal operation state and remaining life of two bearings were predicted by a set of bearing life data. The results showed that the method had better prediction accuracy and generalization compared with hidden Markov model and grey model. This study provides some theoretical guidance and basis for the industrial safe operation and maintenance of rolling bearings during service.

Multiple Lyapunov Functions Analysis Approach for Discrete-Time-Switched Piecewise-Affine Systems Under Dwell-Time Constraints

In this technical note, the asymptotic stability analysis and state-feedback control design are investigated for a class of discrete-time switched nonlinear systems via the smooth approximation technique. The modal dwell-time switching property is considered to constrain switchings between nonlinear subsystems. A kind of autonomous switchings, i.e., state-partition-dependent switching, is introduced within each approximated piecewise-affine (PWA) subsystem. Combining with the state partition information, the novel asymptotic stability conditions with less conservatism are derived for the induced switched PWA systems with dual switching mechanism based on the approach of multiple Lyapunov functions in piecewise quadratic form. Then, the design of PWA state-feedback controllers is implemented. The effectiveness of the obtained theoretical results is demonstrated by a numerical example.

Stefano Bianchini, Liquid Nationalisms and State Partitions in Europe (Cheltenham, UK: Edward Elgar, 2017): A Response to Robert M. Hayden and Tvrtko Jakovina

Stefano Bianchini, Liquid Nationalisms and State Partitions in Europe (Cheltenham, UK: Edward Elgar, 2017): A Response to Robert M. Hayden and Tvrtko Jakovina

When Do Smart Contracts and IoT Improve Efficiency? Automated Execution vs. Increased Information

Smart contracts existed before blockchain technology, but blockchains have been a key reason for their recent popularity and their emerging use in areas such as Decentralized Finance (DeFi). Smart contracts critically depend on digital inputs that inform them whether their triggering conditions have occurred, typically provided by connected sensors (such as ``IoT'') that supply certified signals to the blockchain. If there is a dispute, evidence from these sensors can be shown to a court or an arbitrator, making the corresponding states ``verifiable.'' The two technologies are thus intricately linked but have distinct functionality. We develop a model that distinguishes the impact of smart contracts and connected sensors. We show that connected sensors and smart contracts have different implications for contracting outcomes and efficiency, and depending on the setting, can substitute for or complement each other. Specifically, smart contracts restrict the strategy space by allowing the contracting parties to commit not to hold-up each other; this typically increases the contracting region where trade occurs and thus increases efficiency, but for certain parameter values it surprisingly can decrease social welfare. Connected sensors expand the state space over which the contract can be specified by creating finer partitions of the verifiable states of nature. This typically leads to more efficient trades when they occur. Finally, when applied together, smart contracts and connected sensors enable all efficient trades, including certain trades that neither technology can enable individually. The preferred combination for deployment is determined by the tradeoff between efficiency gains and technology cost.

Expression and regulatory asymmetry of retained Arabidopsis thaliana transcription factor genes derived from whole genome duplication

BackgroundTranscription factors (TFs) play a key role in regulating plant development and response to environmental stimuli. While most genes revert to single copy after whole genome duplication (WGD) event, transcription factors are retained at a significantly higher rate. Little is known about how TF duplicates have diverged in their expression and regulation, the answer to which may contribute to a better understanding of the elevated retention rate among TFs.ResultsHere we assessed what features may explain differences in the retention of TF duplicates and other genes using Arabidopsis thaliana as a model. We integrated 34 expression, sequence, and conservation features to build a linear model for predicting the extent of duplicate retention following WGD events among TFs and 19 groups of genes with other functions. We found that TFs was the least well predicted, demonstrating the features of TFs are substantially deviated from duplicate genes in other function groups. Consistent with this, the evolution of TF expression patterns and cis-regulatory cites favors the partitioning of ancestral states among the resulting duplicates: one “ancestral” TF duplicate retains most ancestral expression and cis-regulatory sites, while the “non-ancestral” duplicate is enriched for novel regulatory sites. By modeling the retention of ancestral expression and cis-regulatory states in duplicate pairs using a system of differential equations, we found that TF duplicate pairs in a partitioned state are preferentially maintained.ConclusionsThese TF duplicates with asymmetrically partitioned ancestral states are likely maintained because one copy retains ancestral functions while the other, at least in some cases, acquires novel cis-regulatory sites that may be important for novel, adaptive traits.

Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks

An increasing number of algorithms have been proposed for epileptic seizure prediction in recent years. But most of them are based on a partition of the electroencephalograph (EEG) signal of an epileptic patient into preictal, ictal (seizure), and interictal states. In this paper, we propose to further divide the preictal interval into multiple subintervals. Besides discriminating the seizure state from the preictal and interictal states, we also distinguish the preictal subintervals from each other. An epileptic state classification algorithm for epileptic EEG signals is then developed. The amplitude spectrums of EEG signals from 18 channels are firstly calculated and divided into 19 frequency subbands. The mean amplitude spectrum (MAS) on each of the 19 frequency subbands is then computed for each channel to form a MAS map of size 18 $$\times$$ 19. Finally, the MAS map is fed to a convolutional neural network (CNN) for feature extraction and a support vector machine (SVM) is employed for the epileptic state classification. Experiments show that, for a three-subinterval partition of the preictal state, a classification accuracy of 86.25% has been achieved on the CHB-MIT database by the MAS-based epileptic state classification algorithm using CNN and SVM.