Generalized Landauer Bound for Information Processing: Proof and Applications

A generalized form of Landauer’s bound on the dissipative cost of classical information processing in quantum-mechanical systems is proved using a new approach. This approach sidesteps some prominent objections to standard proofs of Landauer’s bound—broadly interpreted here as a nonzero lower bound on the amount of energy that is irreversibly transferred from a physical system to its environment for each bit of information that is lost from the system—while establishing a far more general result. Specializations of our generalized Landauer bound for ideal and non-ideal information processing operations, including but not limited to the simplified forms for erasure and logical operations most familiar from the literature, are presented and discussed. These bounds, taken together, enable reconsideration of the links between logical reversibility, physical reversibility, and conditioning of operations in contexts that include but are far more general than the thermodynamic model systems that are most widely invoked in discussions of Landauer’s Principle. Because of the strategy used to prove the generalized bounds and these specializations, this work may help to illuminate and resolve some longstanding controversies related to dissipation in computation.     

Age of Information of Parallel Server Systems with Energy Harvesting

Motivated by current communication networks in which users can choose different transmission channels to operate and also by the recent growth of renewable energy sources, we study the average Age of Information of a status update system that is formed by two parallel homogeneous servers and such that there is an energy source that feeds the system following a random process. An update, after getting service, is delivered to the monitor if there is energy in a battery. However, if the battery is empty, the status update is lost. We allow preemption of updates in service and we assume Poisson generation times of status updates and exponential service times. We show that the average Age of Information can be characterized by solving a system with eight linear equations. Then, we show that, when the arrival rate to both servers is large, the average Age of Information is one divided by the sum of the service rates of the servers. We also perform a numerical analysis to compare the performance of our model with that of a single server with energy harvesting and to study in detail the aforementioned convergence result.     

Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks

The Age of Information (AoI) measures the freshness of information and is a critic performance metric for time-sensitive applications. In this paper, we consider a radio frequency energy-harvesting cognitive radio network, where the secondary user harvests energy from the primary users’ transmissions and opportunistically accesses the primary users’ licensed spectrum to deliver the status-update data pack. We aim to minimize the AoI subject to the energy causality and spectrum constraints by optimizing the sensing and update decisions. We formulate the AoI minimization problem as a partially observable Markov decision process and solve it via dynamic programming. Simulation results verify that our proposed policy is significantly superior to the myopic policy under different parameter settings.     

Landauer resistance in the case of multichannel scattering

The Landauer resistance ρ _N ^L has been generalized to the case of multichannel scattering of a particle by the system of N nonoverlapping random potentials that are localized at the points x _i (i = 1, 2,..., N) and depend on x − x _i and y. It has been shown that, in this case, a new resistance ρ _N ^L appears, which is an exponential function of N. The recurrence equation for determining the Landauer resistance ρ _N ^L has been derived and its solution in the general case has been obtained.     

Duplication： a Mechanism Producing Disassortative Mixing Networks in Biology

Assortative/disassortative mixing is an important topological property of a network. A network is called assortative mixing if the nodes in the network tend to connect to their connectivity peers, or disassortative mixing if nodes with low degrees are more likely to connect with high-degree nodes. We have known that biological networks such as protein-protein interaction networks （PPI）, gene regulatory networks, and metabolic networks tend to be disassortative. On the other hand, in biological evolution, duplication and divergence are two fundamental processes. In order to make the relationship between the property of disassortative mixing and the two basic biological principles clear and to study the cause of the disassortative mixing property in biological networks, we present a random duplication model and an anti-preference duplication model. Our results show that disassortative mixing networks can be obtained by both kinds of models from uncorrelated initial networks. Moreover, with the growth of the network size, the disassortative mixing property becomes more obvious.     

Equation for the Landauer resitivity in the case of multichannel scattering

Generalization of the Landauer resistivity ρ _N ^L is given for the case of multichannel scattering of a particle by the system of nonoverlapped N random potentials, depending on x − x _i and y, which are localized near the points x _i (i = 1,2,…N). It is shown that in this case a new resistivity ρ _N ^S appears, which is a power function of N. A recurrent equation is obtained for definition of the Landauer resistivity ρ _N ^L .     

Noble Metal Clusters: Applications in Energy,Environment, and Biology

Sub‐nanometer‐sized metal clusters, having dimensions between metal atoms and nanoparticles, have attracted tremendous attention in the recent past due to their unique physical and chemical properties. As properties of such materials depend strongly on size, development of synthetic routes that allows precise tuning of the cluster cores with high monodispersity and purity is an area of intense research. Such materials are also interesting owing to their wide variety of applications. Novel sensing strategies based on these materials are emerging. Owing to their extremely small size, low toxicity, and biocompatibility, they are widely studied for biomedical applications. Primary focus of this review is to provide an account of the recent advances in their applications in areas such as environment, energy, and biology. With further experimental and theoretical advances aimed at understanding their novel properties and solving challenges in their synthesis, an almost unlimited field of applications can be foreseen.     

Study of Energy Consumption in the Injection Molding Process

A computing method for simulating the energy consumption in an injection molding process using the Moldflow MPI analysis software is proposed. Considering such factors as product structure, processing parameters, and mold structure, it was shown by orthogonal testing that the melt temperature, product diameter, product wall thickness, and gate diameter are the four largest factors impacting on the injection energy consumption. Based on the MATLAB curve fitting toolbox, the relationship between the four main factors and the injection energy consumption was established, and a calculation model for the injection energy consumption was proposed.The accuracy of the model was above 95%. Finally, by using an experimental system of injection energy consumption to research the relationship between energy consumption and actual injection power, and by verifying the calculation accuracy of injection energy consumption of the established model, the results show that the established model can be an excellent predictor of the actual electric energy required and the accuracy of it was above 85% compared with measured experimental data.     

Disentangling the Information in Species Interaction Networks

Shannon’s entropy measure is a popular means for quantifying ecological diversity. We explore how one can use information-theoretic measures (that are often called indices in ecology) on joint ensembles to study the diversity of species interaction networks. We leverage the little-known balance equation to decompose the network information into three components describing the species abundance, specificity, and redundancy. This balance reveals that there exists a fundamental trade-off between these components. The decomposition can be straightforwardly extended to analyse networks through time as well as space, leading to the corresponding notions for alpha, beta, and gamma diversity. Our work aims to provide an accessible introduction for ecologists. To this end, we illustrate the interpretation of the components on numerous real networks. The corresponding code is made available to the community in the specialised Julia package EcologicalNetworks.jl.     

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.     

微管壁上量子信息的传递

微管管壁上的原丝纤维可以描述成各项异性的二维赝自旋模型,其最小重复单元是三角形状的。在这个模型中存在三种不同的“自旋-自旋”相互作用。而每一维上的自由电子可以看作是赝自旋模型。那么,微管壁上的量子信息传递就可以用Lylod提出的激光控制量子计算的模型来解释。Microtubule （MT） is described as an anisotropic two-dimensional pseudo-spin model on a triangular lattice, in which there are three different ＂spin-spin＂ interactions. The mobile electron in each lattice site is described based on the pseudo-spin model. Then, the processing of quantum information in the MT wall is presented by virtue of the scheme of driving quantum computer in sequence of laser pulse developed by Lloyd.     

Information Fragmentation,Encryption and Information Flow in Complex Biological Networks

Assessing where and how information is stored in biological networks (such as neuronal and genetic networks) is a central task both in neuroscience and in molecular genetics, but most available tools focus on the network’s structure as opposed to its function. Here, we introduce a new information-theoretic tool—information fragmentation analysis—that, given full phenotypic data, allows us to localize information in complex networks, determine how fragmented (across multiple nodes of the network) the information is, and assess the level of encryption of that information. Using information fragmentation matrices we can also create information flow graphs that illustrate how information propagates through these networks. We illustrate the use of this tool by analyzing how artificial brains that evolved in silico solve particular tasks, and show how information fragmentation analysis provides deeper insights into how these brains process information and “think”. The measures of information fragmentation and encryption that result from our methods also quantify complexity of information processing in these networks and how this processing complexity differs between primary exposure to sensory data (early in the lifetime) and later routine processing.     

Cellular Biology in Terms of Stochastic Nonlinear Biochemical Dynamics: Emergent Properties,Isogenetic Variations and Chemical System Inheritability

Based on a stochastic, nonlinear, open biochemical reaction system perspective, we present an analytical theory for cellular biochemical processes. The chemical master equation (CME) approach provides a unifying mathematical framework for cellular modeling. We apply this theory to both self-regulating gene networks and phosphorylation-dephosphorylation signaling modules with feedbacks. Two types of bistability are illustrated in mesoscopic biochemical systems: one that has a macroscopic, deterministic counterpart and another that does not. In certain cases, the latter stochastic bistability is shown to be a “ghost” of the extinction phenomenon. We argue the thermal fluctuations inherent in molecular processes do not disappear in mesoscopic cell-sized nonlinear systems; rather they manifest themselves as isogenetic variations on a different time scale. Isogenetic biochemical variations in terms of the stochastic attractors can have extremely long lifetime. Transitions among discrete stochastic attractors spend most of the time in “waiting”, exhibit punctuated equilibria. It can be naturally passed to “daughter cells” via a simple growth and division process. The CME system follows a set of nonequilibrium thermodynamic laws that include non-increasing free energy F(t) with external energy drive Q _hk ≥0, and total entropy production rate e _p =−dF/dt+Q _hk ≥0. In the thermodynamic limit, with a system’s size being infinitely large, the nonlinear bistability in the CME exhibits many of the characteristics of macroscopic equilibrium phase transition.     

Exploring the thermodynamic limits of computation in integrated systems: magnetic memory, nanomagnetic logic, and the Landauer limit

Nanomagnetic memory and logic circuits are attractive integrated platforms for studying the fundamental thermodynamic limits of computation. Using the stochastic Landau-Lifshitz-Gilbert equation, we show by direct calculation that the amount of energy dissipated during nanomagnet erasure approaches Landauer's thermodynamic limit of kTln(2) with high precision when the external magnetic fields are applied slowly. In addition, we find that nanomagnet systems behave according to generalized formulations of Landauer's principle that hold for small systems and generic logic operations. In all cases, the results are independent of the anisotropy energy of the nanomagnet. Lastly, we apply our computational approach to a nanomagnet majority logic gate, where we find that dissipationless, reversible computation can be achieved when the magnetic fields are applied in the appropriate order.     

Pu300能量窗钚年龄算法

美国LLNL提出了采用钚材料γ谱的330—350keV(Pu300)能量窗计算钚年龄, 用此能量窗建立了相关钚年龄算法, 并对几个不同样品的谱数据进行了计算, 并将其结果与PC/FRAM的结果进行比较, 结果表明两者吻合得比较好.     

Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State

In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels. We characterize the data freshness at the terminal end through a class of monotone increasing functions related to Age of information (AoI). Our goal is to design an optimal policy to minimize the average age penalty of all sensors in infinite horizon under bandwidth and power constraint. By formulating the scheduling problem into a constrained Markov decision process (CMDP), we reveal the threshold structure for the optimal policy and approximate the optimal decision by solving a truncated linear programming (LP). Finally, a bandwidth-truncated policy is proposed to satisfy both power and bandwidth constraint. Through theoretical analysis and numerical simulations, we prove the proposed policy is asymptotic optimal in the large sensor regime.