State Aggregation Research Articles

A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking

Information-centric networking (ICN) is expected to be a candidate for future internet architecture, and it supports features such as multicast that improves bandwidth utilization and transmission efficiency. However, multicast itself does not provide congestion control. When multiple multicast groups coexist, multicast traffic may exhaust all network resources, and cause network congestion and packet loss. Additionally, traditional IP multicast congestion control mechanisms cannot be directly applied to ICN architecture. Therefore, it is necessary to consider an effective congestion control mechanism for ICN multicast. This paper proposes a single-rate multicast congestion control mechanism, called SRMCC. It supports router-assisted awareness of the network congestion state and congestion control message aggregation. Moreover, the fair shared rate estimation method is innovatively proposed to achieve protocol fairness. Most importantly, it adjusts the rate according to different congestion states indicated by the queue occupancy ratio. By introducing a rate selection factor, it can achieve a balance between packet loss rate and throughput. Experimental results show that our proposal outperforms other mechanisms in throughput, packet loss rate, total bandwidth utilization, and overhead, and achieves protocol fairness and better TCP friendliness.

Emergent behaviors of discrete Lohe aggregation flows

<p style='text-indent:20px;'>The Lohe sphere model and the Lohe matrix model are prototype continuous aggregation models on the unit sphere and the unitary group, respectively. These models have been extensively investigated in recent literature. In this paper, we propose several discrete counterparts for the continuous Lohe type aggregation models and study their emergent behaviors using the Lyapunov function method. For suitable discretization of the Lohe sphere model, we employ a scheme consisting of two steps. In the first step, we solve the first-order forward Euler scheme, and in the second step, we project the intermediate state onto the unit sphere. For this discrete model, we present a sufficient framework leading to the complete state aggregation in terms of system parameters and initial data. For the discretization of the Lohe matrix model, we use the Lie group integrator method, Lie-Trotter splitting method and Strang splitting method to propose three discrete models. For these models, we also provide several analytical frameworks leading to complete state aggregation and asymptotic state-locking.</p>

Solid state aggregation of cyclic iodonium ylides

Solid state aggregation of cyclic iodonium ylides

Transcriptome analysis of suspended aggregates formed by Listeria monocytogenes co-cultured with Ralstonia insidiosa

The resistance of food-borne pathogens to many unfavorable environments after the formation of microbial aggregates is significantly enhanced, which is an important reason for the continuous pathogen contamination. Ralstonia insidiosa can induce the food-borne pathogens to form larger multi-species suspended aggregates, but its formation mechanism is unknown, especially from a transcriptomics point of view. In this study, the condition of forming suspended aggregates by Listeria monocytogenes was investigated, and then, Illumina RNA-seq was used to compare the transcription profiles of L. monocytogenes in floating state and suspended aggregates to identify differentially expressed genes (DEGs): 511 DEGs were identified by expression analysis, including 273 up-regulated genes and 238 down-regulated genes. The functional annotation analysis results of DEGs showed that these genes were mainly involved in two-component system, phosphotransferase system, flagellar assembly, ABC transporter, glycolysis/gluconeogenesis. This study is the first report of suspended aggregates RNA-seq, and our results are of great significance to further exploring the resistance mechanism of L. monocytogenes to adverse environment and the control of pathogenic microorganisms.

State Aggregations in Markov Chains and Block Models of Networks.

We consider state-aggregation schemes for Markov chains from an information-theoretic perspective. Specifically, we consider aggregating the states of a Markov chain such that the mutual information of the aggregated states separated by T time steps is maximized. We show that for T=1 this recovers the maximum-likelihood estimator of the degree-corrected stochastic block model as a particular case, which enables us to explain certain features of the likelihood landscape of this generative network model from a dynamical lens. We further highlight how we can uncover coherent, long-range dynamical modules for which considering a timescale T≫1 is essential. We demonstrate our results using synthetic flows and real-world ocean currents, where we are able to recover the fundamental features of the surface currents of the oceans.

Proportional lumpability and proportional bisimilarity

In this paper, we deal with the lumpability approach to cope with the state space explosion problem inherent to the computation of the stationary performance indices of large stochastic models. The lumpability method is based on a state aggregation technique and applies to Markov chains exhibiting some structural regularity. Moreover, it allows one to efficiently compute the exact values of the stationary performance indices when the model is actually lumpable. The notion of quasi-lumpability is based on the idea that a Markov chain can be altered by relatively small perturbations of the transition rates in such a way that the new resulting Markov chain is lumpable. In this case, only upper and lower bounds on the performance indices can be derived. Here, we introduce a novel notion of quasi-lumpability, named proportional lumpability, which extends the original definition of lumpability but, differently from the general definition of quasi-lumpability, it allows one to derive exact stationary performance indices for the original process. We then introduce the notion of proportional bisimilarity for the terms of the performance process algebra PEPA. Proportional bisimilarity induces a proportional lumpability on the underlying continuous-time Markov chains. Finally, we prove some compositionality results and show the applicability of our theory through examples.

Factoring in Trust in the Alliance for Multilateralism

The aim of this paper is to understand the role played by the ‘Alliance for Multilateralism’ (the Alliance) in furthering the cause of multilateralism, specifically, during the COVID-19 pandemic. My central argument is that there is a decline of contested multilateralism, and the States are now aiming for a type of multilateralism, which can distribute global public goods and tackle global problems in a more efficient way with mutual cooperation. This type of multilateralism, in my opinion, is different from the earlier liberal theorizing of multilateralism. I believe that the Alliance can be successful if the parties are able to trust each other. However, the creation and maintenance of trust is quite difficult. For this reason, the paper will delve into different types of trust that can be used for cooperation and identify which type of trust is the most beneficial for the members of the Alliance. In doing so, the paper will build on the seminal works of Rathbun, Elhardt and Weinhardt to show that there is a causal link between trust and cooperation and that trust of a specific kind can be seen as a risk-absorbing mechanism. Further, the paper argues that the Alliance can be more than an aggregation of States if the founders of the Alliance, i.e. Germany and France, can provide it with an identity and a narrative. Since the Alliance was launched only a few years ago, there has been lack of scholarly research on it. However, my perspective on the Alliance aims to provide some foundation for future research on the changing nature of multilateralism.

Exact Reduction of Huge Action Spaces in General Reinforcement Learning

The reinforcement learning (RL) framework formalizes the notion of learning with interactions. Many real-world problems have large state-spaces and/or action-spaces such as in Go, StarCraft, protein folding, and robotics or are non-Markovian, which cause significant challenges to RL algorithms. In this work we address the large action-space problem by sequentializing actions, which can reduce the action-space size significantly, even down to two actions at the expense of an increased planning horizon. We provide explicit and exact constructions and equivalence proofs for all quantities of interest for arbitrary history-based processes. In the case of MDPs, this could help RL algorithms that bootstrap. In this work we show how action-binarization in the non-MDP case can significantly improve Extreme State Aggregation (ESA) bounds. ESA allows casting any (non-MDP, non-ergodic, history-based) RL problem into a fixed-sized non-Markovian state-space with the help of a surrogate Markovian process. On the upside, ESA enjoys similar optimality guarantees as Markovian models do. But a downside is that the size of the aggregated state-space becomes exponential in the size of the action-space. In this work, we patch this issue by binarizing the action-space. We provide an upper bound on the number of states of this binarized ESA that is logarithmic in the original action-space size, a double-exponential improvement.

Study of Reversible Platelet Aggregation Model by Nonlinear Dynamics

Blood cell platelets form aggregates upon vessel wall injury. Under certain conditions, a disintegration of the platelet aggregates, called “reversible aggregation”, is observed in vitro. Previously, we have proposed an extremely simple (two equations, five parameters) ordinary differential equation-based mathematical model of the reversible platelet aggregation. That model was based on mass-action law, and the parameters represented probabilities of platelet aggregate formations. Here, we aimed to perform a nonlinear dynamics analysis of this mathematical model to derive the biomedical meaning of the model’s parameters. The model’s parameters were estimated automatically from experimental data in COPASI software. Further analysis was performed in Python 2.7. Contrary to our expectations, for a broad range of parameter values, the model had only one steady state of the stable type node, thus eliminating the initial assumption that the reversibility of the aggregation curve could be explained by the system’s being near a stable focus. Therefore, we conclude that during platelet aggregation, the system is outside of the influence area of the steady state. Further analysis of the model’s parameters demonstrated that the rate constants for the reaction of aggregate formation from existing aggregates determine the reversibility of the aggregation curve. The other parameters of the model influenced either the initial aggregation rate or the quasi-steady state aggregation values.

The Phase Aggregation Behavior of the Blend Materials Block Copolymer Polystyrene‐b‐Polycarbonate (PS‐b‐PC) and Homopolymer Polystyrene (PS)

AbstractIn this paper, the cylindrical structures phase aggregation behavior of the blend materials is explored experimentally. The phase aggregation behavior is a phenomenon in which the cylinder phase aggregates nearby, which occurs after phase separation. With mixing the polystyrene‐b‐polycarbonate with polystyrene, a cylindrical state is first obtained, and then such state is changed to a larger cylinder state. It is found that such state aggregation behavior is the function of annealing time and temperature, while the maximum temperature is less than the molten temperature. First, a cylindrical state with 11–14 nm diameter is obtained, with a 3 nm standard deviation. Then the neighboring cylinders move to aggregate together and the cylinder diameter is enlarged quickly to ≈30 nm and above, with a higher standard deviation. It is also found that the aggregation behavior is consistent over a wide temperature range from 150 to 200 °C. For the blend material used, when cylinder is formed, the free energy of the system is not stable. Then, keeping the annealing temperature constant and the annealing time increasing, the neighboring cylinders aggregate and form large‐diameter cylinders. This dynamic behavior opens a new window of self‐assembly applications by obtaining different structures through process control.

Optimal operations planning of electric autonomous vehicles via asynchronous learning in ride-hailing systems

Ride-hailing systems with electric autonomous vehicles are recognized as a next-generation development to ease congestion, reduce costs and carbon emissions. In this paper, we consider the operation planning problem involving vehicle dispatching, relocation, and recharging decisions. We model the problem as a Markov Decision Process (MDP) to generate the optimal policy that maximizes the total profits. We propose a flexible policy to provide optimal actions according to the reward considering future requests and vehicle availability. We show that our model outperforms the predetermined rules on improving profits. To handle the curse-of-dimensionality caused by the large scale of state space and uncertainty, we develop an asynchronous learning method to solve the problem by approximating the value function. We first draw the samples of exogenous information and update the quality of approximations based on the quality of decisions, then approximate the exact cost-to-go value function by solving an approximation subproblem efficiently given the state at each period. Two variant algorithms are presented for cases with scarce and sufficient information. We also incorporate the state aggregation and post-decision analysis to further improve computational efficiency. We use a set of shared actual data from Didi platform to verify the proposed model in numerical experiments. To conclude, we extract managerial insights that suggest important guidelines for the ride-hailing operations planning problem.

The HSP70 Chaperone Is Most Efficient at Circumventing Aggregation if Client Proteins Fold before Their Release in Solution

The E. coli's heat shock protein Hsp70, also known as DnaK, is an important molecular chaperone, along with its co-chaperones DnaJ and GrpE, for maintaining cell homeostasis and for preventing protein aggregation under physiologically relevant conditions. While the DnaK chaperone cycle has been extensively studied, there is still a significant gap of knowledge about where, when and how client proteins complete their folding upon interaction with this chaperone. The CHAMP70 computational model was employed to explore the role of a folding-on-the-chaperone scenario. According to this hypothesis, prevention of kinetically trapped (relative to the native state) aggregate formation may be most effective if a misfolded monomer of the client protein (M∗) converts to the native state while still bound on the surface of the chaperone, before release in solution. Indeed, we found that folding on the chaperone is predicted to decreases effective aggregation lifetimes by orders of magnitude, relative to a simple kinetic partitioning mechanism. On the other hand, this is only the case when M∗ is kinetically trapped relative to the folded/unfolded-state basin. This result is important because it suggests that Hsp70 can be a most effective molecular chaperone if it is capable of promoting protein folding on its surface. This concept is being experimentally probed as part of ongoing investigations.

Perovskite Photovoltaic Cells: Synergistic Effect of Excited State Property and Aggregation Characteristic of Organic Semiconductor on Efficient Hole‐Transportation in Perovskite Device (Adv. Funct. Mater. 5/2021)

In article number 2007180, Tae Kyu Ahn, Sanghyuk Park, Hui Joon Park, and co-workers investigate the synergistic effect of the excited state property and aggregation behavior of organic semiconductors, enhancing hole-transportation in perovskite devices.

Государство и общественный выбор: контуры посткоронавирусной реальности

The coronavirus pandemic has exposed fundamental problems in the functioning of national societies, which require intensive scientific research. COVID-19 predetermined the growing importance of political and economic approaches to the interpretation of basic principles of the functioning of future post-coronavirus reality. The societal crisis, which has been a consequence of the ineffectiveness of the state in preventing extreme threats to the life and health of citizens such as COVID-19, has actualized the “new normal” problem, which is being actively discussed by experts from international research and practical organizations. The post-coronavirus reality will begin to form after the COVID-19 pandemic and will be based on different principles. The author interprets it in the context of restoring the societal integrity of national communities under the following conditions: (1) reorientation of formal institutions at the state's disposal towards the priority of human-centred principles in order to restore public consensus, trust in the government and its policies; (2) aggregation of public choice, taking into account, first of all, the preferences and values of households (ordinary citizens), which will minimize its distortion in the future; (3) raising the state's expert functions on the example of adequate institutionalization of conditions for eliminating the negative consequences of the societal crisis. The author stresses the importance of rethinking the phenomenon of partnership between the state and private business as a means of improving the quality of the state's aggregation of public choice, assessing the effectiveness of alternative options for the institutional structure of the new reality, adjusting the functions of the state in relation to a society with universal values at its core, taking into account the realities of the post-coronavirus world. To test the obtained results, the author offers a model of an “institutional matrix” that can be used to institutionalize a mutually-beneficial partnership between the state and private business in order to minimize the uncertainty of post-coronavirus reality (“new normal”).

Маркетинг довіри в мережі стейкхолдерів: механізм, джерела та процес формування довірчих взаємин

The presented publication is aimed at determining the mechanism and sources of formation of trust relationships in the stakeholder network and closer defining the content of the process of their formation. The article defines the most significant sources of formation and strengthening of trust relations at the microeconomic level, which are: openness and availability of information; solving social problems; ability of each participant in the relationship to express their interests and influence the decision-making; integrity, honesty and competence of managers; clear feedback; stability of contractual relations and partnerships; high reputation and developed organizational culture of companies. Trust in the stakeholder network has a mechanism of occurrence, which consists of an aggregate of trust states of participants in relationships and processes by which these states are formed in them. Determining the sources of trust and the mechanism of its occurrence in the stakeholder network gives grounds for the development and adjustment, if necessary, of the process of establishing and developing trust relationships between network members. The incentive, cognitive, instrumental, practical and final stages of the process of formation of trust relations in the stakeholder network are also allocated, which makes it possible to structure the process of trust formation in the system of relations between the company and stakeholders and harmonize it with the stakeholder analysis and management process.

Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLF–CPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLF–CPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.

Two‐time scale reinforcement learning and applications to production planning

This study is concerned with reinforcement learning enhanced by two-time scale approximations. Many systems arising in applications are large and complex. To treat these problems, it is often beneficial, and sometimes necessary, to reduce the dimensionality and aggregate states that are ‘close’ to each other. In this study, the authors propose a two-time scale reinforcement learning method for such an aggregation process. In particular, they present how to classify states that are ‘close’ and demonstrate the effectiveness of the authors' state aggregation based two-time scale methods. Thus the problem can be considered as using learning for identifying the system. A production planning problem with failure-prone machines is used throughout this study to illustrate the main ideas, key steps and results. Monte Carlo simulations are used to generate the random environment.

A model of state aggregation

In this paper, due to limited attention and lack of focus, an agent has tendency toward framing of available information that drives her to see the state space as a collection of the events or “small worlds” related to a particular ambience, which we call a state aggregation. This work axiomatizes the model of state aggregation, shows how to recover the structure of the “small worlds” together with beliefs and utility from the individual demand for assets, and provides comparative statics and revealed preference restrictions.

Synergistic Effect of Excited State Property and Aggregation Characteristic of Organic Semiconductor on Efficient Hole‐Transportation in Perovskite Device

AbstractIntrinsic characteristics of organic semiconductor‐based hole transport materials (HTMs) such as facile synthesizability, energy level tunability, and charge transport capability have been highlighted as crucial factors determining the performances of perovskite photovoltaic (PV) cells. However, their properties in the excited state have not been actively studied, although PVs are operated under solar illumination. Here, the characteristics of organic HTMs in their excited state such as transition dipole moment can be a decisive factor that can improve built‐in potential of PVs, consequently enhancing their charge extraction property as well as reducing carrier recombination. Moreover, the aggregation property of organic semiconductors, which has been an essential factor for high‐performance organic HTMs to improve their carrier transport property, can induce a synergistic effect with their excited state property for the high‐efficiency perovskite PVs. Additionally, it is also confirmed that their optical bandgaps, manipulated to have their absorption in the UV region, are beneficial to block UV light that degrades the quality of perovskite, consequently improving the stability of perovskite PV in p–i–n configuration. As a proof‐of‐concept, a model system, composed of triarylamine and imidazole‐based organic HTMs, is designed, and it is believed that this strategy paves a way toward high‐performance and stable perovskite PV devices.

AggFluor: Fluorogenic Toolbox Enables Direct Visualization of the Multi-Step Protein Aggregation Process in Live Cells.

Aberrantly processed or mutant proteins misfold and assemble into a variety of soluble oligomers and insoluble aggregates, a process that is associated with an increasing number of diseases that are not curable or manageable. Herein, we present a chemical toolbox, AggFluor, that allows for live cell imaging and differentiation of complex aggregated conformations in live cells. Based on the chromophore core of green fluorescent proteins, AggFluor is comprised of a series of molecular rotor fluorophores that span a wide range of viscosity sensitivity. As a result, these compounds exhibit differential turn-on fluorescence when incorporated in either soluble oligomers or insoluble aggregates. This feature allows us to develop, for the first time, a dual-color imaging strategy to distinguish unfolded protein oligomers from insoluble aggregates in live cells. Furthermore, we have demonstrated how small molecule proteostasis regulators can drive formation and disassembly of protein aggregates in both conformational states. In summary, AggFluor is the first set of rationally designed molecular rotor fluorophores that evenly cover a wide range of viscosity sensitivities. This set of fluorescent probes not only change the status quo of current imaging methods to visualize protein aggregation in live cells but also can be generally applied to study other biological processes that involve local viscosity changes with temporal and spatial resolutions.