Performance Evaluation of Shared Mesh Protection in WDM Networks

The efficient use of network capacity in shared restoration schemes strongly depends upon the path selection procedure. In this paper we propose and evaluate path selection algorithms for sharable and restorable connections in optical networks. Namely, two distributed path selection algorithms are proposed. The first approach maintains global information on network resource usage to determine link sharability and compute optimal shared paths. The second approach only relies upon local information maintained at each node. Subsequently, we present an analytical model to evaluate the performance of these path selection algorithms and show its accuracy through numerical examples. Results indicate that path selection algorithms that maximally exploit the use of reserved sharable channels do not necessarily result in fast restoration; rather these two optimality criteria can conflict most of the time. Additionally, algorithms that maximally exploit the sharability condition typically result in lower scalability and higher complexity.     

Vehicle Scheduling Schemes for Commercial and Emergency Logistics Integration

In modern logistics operations, large-scale logistics companies, besides active participation in profit-seeking commercial business, also play an essential role during an emergency relief process by dispatching urgently-required materials to disaster-affected areas. Therefore, an issue has been widely addressed by logistics practitioners and caught researchers'' more attention as to how the logistics companies achieve maximum commercial profit on condition that emergency tasks are effectively and performed satisfactorily. In this paper, two vehicle scheduling models are proposed to solve the problem. One is a prediction-related scheme, which predicts the amounts of disaster-relief materials and commercial business and then accepts the business that will generate maximum profits; the other is a priority-directed scheme, which, firstly groups commercial and emergency business according to priority grades and then schedules both types of business jointly and simultaneously by arriving at the maximum priority in total. Moreover, computer-based simulations are carried out to evaluate the performance of these two models by comparing them with two traditional disaster-relief tactics in China. The results testify the feasibility and effectiveness of the proposed models.     

Optimal control of the Lotka–Volterra system: turnpike property and numerical simulations

The Lotka–Volterra model is a differential system of two coupled equations representing the interaction of two species: a prey one and a predator one. We formulate an optimal control problem adding the effect of hunting both species as the control variable. We analyse the optimal hunting problem paying special attention to the nature of the optimal state and control trajectories in long time intervals. To do that, we apply recent theoretical results on the frame to show that, when the time horizon is large enough, optimal strategies are nearly steady-state. Such path is known as turnpike property. Some experiments are performed to observe such turnpike phenomenon in the hunting problem. Based on the turnpike property, we implement a variant of the single shooting method to solve the previous optimisation problem, taking the middle of the time interval as starting point.     

Sensory feedback in a bump attractor model of path integration

Mammalian spatial navigation systems utilize several different sensory information channels. This information is converted into a neural code that represents the animal’s current position in space by engaging place cell, grid cell, and head direction cell networks. In particular, sensory landmark (allothetic) cues can be utilized in concert with an animal’s knowledge of its own velocity (idiothetic) cues to generate a more accurate representation of position than path integration provides on its own (Battaglia et al. The Journal of Neuroscience 24(19):4541–4550 (2004)). We develop a computational model that merges path integration with feedback from external sensory cues that provide a reliable representation of spatial position along an annular track. Starting with a continuous bump attractor model, we explore the impact of synaptic spatial asymmetry and heterogeneity, which disrupt the position code of the path integration process. We use asymptotic analysis to reduce the bump attractor model to a single scalar equation whose potential represents the impact of asymmetry and heterogeneity. Such imperfections cause errors to build up when the network performs path integration, but these errors can be corrected by an external control signal representing the effects of sensory cues. We demonstrate that there is an optimal strength and decay rate of the control signal when cues appear either periodically or randomly. A similar analysis is performed when errors in path integration arise from dynamic noise fluctuations. Again, there is an optimal strength and decay of discrete control that minimizes the path integration error.     

A mathematical framework for optimal foraging of herbivores

The aim of this paper is to study a model of optimal foraging of herbivores (with special reference to ungulates) assuming that food distribution is arbitrary. Usually the analysis of foraging of herbivores in the framework of optimal foraging theory is based on the assumption of a patchy food distribution. We relax this assumption and we construct more realistic models. The main constraint of our model is the total amount of food which the animal may eat and the currency is the total foraging time. We represent total foraging time as a variational expression depending on food eaten and the length of the path. We prove that there exists a threshold for food acquisition. More explicitly, it exists a positive real number such that, at any point x of the path, the animal either eats till the density of food is decreased to the value or, if the density of food at x is less than , there it does not eat. We discuss the results and emphasize some biologically important relationships among model parameters and variables. Finally, we try to give a sound biological interpretation of our results.     

Evolutionary changes in protein composition — evidence for an optimal strategy

The information contained in the composition of different proteins of the same family is analyzed. It is found that within each family the gain in information per amino acid replacement is constant. This finding is interpreted to imply that evolutionary changes in proteins follow an "optimal" path in the sense that they maximize the number of potentially functional sequences that can be generated by T accepted point mutations from a given protein, subject to restrictions due to biological function.     

Path curvature discrimination: dependence on gaze direction and optical flow speed

Many experimental approaches to the control of steering rely on the tangent point (TP) as major source of information. The TP is a good candidate to control self-motion. It corresponds to a singular and salient point in the subject's visual field, and its location depends on the road geometry, the direction of self-motion relative to the road and the position of the driver on the road. However, the particular status of the TP in the optical flow, as a local minimum of flow speed, has often been left aside. We therefore assume that the TP is actually an optimal location in the dynamic optical array to perceive a change in the trajectory curvature. In this study, we evaluated the ability of human observers to detect variations in their path curvature from optical flow patterns, as a function of their gaze direction in a virtual environment. We simulated curvilinear self-motion parallel to a ground plane. Using random-dot optic flow stimuli of brief duration and a two-alternative forced-choice adaptive procedure, we determined path curvature discrimination thresholds, as a function of gaze direction. The discrimination thresholds are minimal for a gaze directed toward a local minimum of optical flow speed. A model based on Weber fraction of the foveal velocities (ΔV/V) correctly predicts the relationship between experimental thresholds and local flow velocities. This model was also tested for an optical flow computation integrating larger circular areas in central vision. Averaging the flow over five degrees leads to an even better fit of the model to experimental thresholds. We also found that the minimal optical flow speed direction corresponds to a maximal sensitivity of the visual system, as predicted by our model. The spontaneous gazing strategies observed during driving might thus correspond to an optimal selection of relevant information in the optical flow field.     

灾难及突发事件住院信息管理系统研究现状及趋势

信息工作是决策的依据和先导,如何准确、及时、高效地收集、整理、分析灾难及突发事件信息、伤病员基本信息、救治信息等,已成为医院管理信息学的研究热点问题,也是医院在灾难和突发事件大量伤员信息管理中面临的难点问题。本文就灾难及突发事件住院信息管理系统研究现状及趋势进行综述。     

Optimal average path of the instantaneous helical axis in planar motions with one functional degree of freedom

This paper presents a model for determining the path of the instantaneous helical axis (IHA) that optimally represents human planar motions with one functional degree of freedom (fDOF). A human movement is said to have one fDOF when all degrees of freedom (DOFs) are coordinated such that all the kinematic variables can be expressed, across movement repetitions, as functions of only one independent DOF, except for a small natural intercycle variability quantified as lower than a prespecified value. The concept of fDOF allows taking into account that, due to motor coordination, human movements are executed in a repeatable manner. Our method uses the measurement of several repetitions of a given movement to obtain the optimal average IHA path. The starting point is a change of variables, from time to a joint position magnitude (generally an angle). In this way, instead of operating with the time-dependent single-valued trajectory of the successive cycles, our model permits the representation of any motion variable (e.g. positions and their time derivatives) as a cloud of points dependent on the joint angle. This allows the averaging to be performed over the displacements and their derivatives before determining the mean IHA path. We thus avoid the nonlinear magnification of errors and variability inherent in the IHA computation. Moreover, the IHA path can be considered as a geometric attribute of the joint and the type of motion, rather than of each single movement execution. An experiment was performed that show the accuracy and usefulness of the method.     

A weighted constraint satisfaction approach to human goal-directed decision making

When we plan for long-range goals, proximal information cannot be exploited in a blindly myopic way, as relevant future information must also be considered. But when a subgoal must be resolved first, irrelevant future information should not interfere with the processing of more proximal, subgoal-relevant information. We explore the idea that decision making in both situations relies on the flexible modulation of the degree to which different pieces of information under consideration are weighted, rather than explicitly decomposing a problem into smaller parts and solving each part independently. We asked participants to find the shortest goal-reaching paths in mazes and modeled their initial path choices as a noisy, weighted information integration process. In a base task where choosing the optimal initial path required weighting starting-point and goal-proximal factors equally, participants did take both constraints into account, with participants who made more accurate choices tending to exhibit more balanced weighting. The base task was then embedded as an initial subtask in a larger maze, where the same two factors constrained the optimal path to a subgoal, and the final goal position was irrelevant to the initial path choice. In this more complex task, participants’ choices reflected predominant consideration of the subgoal-relevant constraints, but also some influence of the initially-irrelevant final goal. More accurate participants placed much less weight on the optimality-irrelevant goal and again tended to weight the two initially-relevant constraints more equally. These findings suggest that humans may rely on a graded, task-sensitive weighting of multiple constraints to generate approximately optimal decision outcomes in both hierarchical and non-hierarchical goal-directed tasks.     

Adaptive Sampling of Information in Perceptual Decision-Making

In many perceptual and cognitive decision-making problems, humans sample multiple noisy information sources serially, and integrate the sampled information to make an overall decision. We derive the optimal decision procedure for two-alternative choice tasks in which the different options are sampled one at a time, sources vary in the quality of the information they provide, and the available time is fixed. To maximize accuracy, the optimal observer allocates time to sampling different information sources in proportion to their noise levels. We tested human observers in a corresponding perceptual decision-making task. Observers compared the direction of two random dot motion patterns that were triggered only when fixated. Observers allocated more time to the noisier pattern, in a manner that correlated with their sensory uncertainty about the direction of the patterns. There were several differences between the optimal observer predictions and human behaviour. These differences point to a number of other factors, beyond the quality of the currently available sources of information, that influences the sampling strategy.     

灾难及突发事件住院信息管理系统研究现状及趋势

信息工作是决策的依据和先导,如何准确、及时、高效地收集、整理、分析灾难及突发事件信息、伤病员基本信息、救治信息等,已成为医院管理信息学的研究热点问题,也是医院在灾难和突发事件大量伤员信息管理中面临的难点问题。本文就灾难及突发事件住院信息管理系统研究现状及趋势进行综述。     

A biologically inspired neural net for trajectory formation and obstacle avoidance

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.     

Mass spectrometry is only one piece of the puzzle in clinical proteomics.

Biomarker discovery in clinical proteomics is being performed on relatively large patient cohorts by utilizing the high throughput of laser desorption/ionization mass spectrometry (MALDI- and SELDI-TOF-MS). Dealing directly with patient samples as opposed to working in cell or animal systems requires a host of considerations both before and after mass spectrometric analysis to obtain robust biomarker candidates. The challenges associated with the heterogeneity of typical samples are amplified by the ability to detect hundreds to thousands of proteins simultaneously. Adherence to protocols and consistency, however, can ensure optimal results. A study starts necessarily with a relevant clinical question and proceeds to a planning phase where sample availability, statistical test selection, logistics and bias reduction are key points. The physical analysis requires consistency and standardized protocols that are helped significantly through automation. Data analysis is broken into two stages, screening and final testing, which can detect either single candidates or a pattern of proteins. Biomarker identification can be performed at this point and will help significantly in the last stage, interpretation. Replication should be performed in an independent sample set in a separate study. The candidate biomarkers from an initial study give a wealth of information that can help to pinpoint patient subpopulations for a more exhaustive proteomic study using complementary platforms with limited capacity but extremely high information content. A clinical proteomics pilot project can also lead to better selection of model systems by providing a direct link with patient samples.     

Direct and indirect effects of environmental temperature on the evolution of reproductive strategies: an information-theoretic approach

For ectotherms, environmental temperature affects the optimal size and number of offspring via multiple mechanisms. First, temperature influences the performance of offspring, which directly affects the optimal size of offspring. Second, temperature influences maternal body size, which indirectly affects the optimal size and/or number of offspring when larger females acquire more energetic resources or provide better parental care. Although traditional statistical approaches might distinguish the relative importance of these effects, an information-theoretic approach enables one to estimate effects more accurately by identifying the best evolutionary model in a set of candidate models. Here, we use the Akaike Information Criterion to calculate the likelihoods of seven path models, each derived from one or more optimality models of reproduction. Variation in reproductive traits among populations of lizards (Sceloporus undulatus) was used to quantify support for the models. Our results overwhelmingly supported a model based on an indirect effect of temperature that is mediated by maternal size. Path coefficients of this model were consistent with the hypotheses that, first, larger females can acquire more energy for reproduction and, second, the survival of offspring depends on both their size and their density. Our analyses exemplify how information theory can identify evolutionary hypotheses that merit experimental testing.     

Assessing predation risk: optimal behaviour and rules of thumb

We look at a simple model in which an animal makes behavioural decisions over time in an environment in which all parameters are known to the animal except predation risk. In the model there is a trade-off between gaining information about predation risk and anti-predator behaviour. All predator attacks lead to death for the prey, so that the prey learns about predation risk by virtue of the fact that it is still alive. We show that it is not usually optimal to behave as if the current unbiased estimate of the predation risk is its true value. We consider two different ways to model reproduction; in the first scenario the animal reproduces throughout its life until it dies, and in the second scenario expected reproductive success depends on the level of energy reserves the animal has gained by some point in time. For both of these scenarios we find results on the form of the optimal strategy and give numerical examples which compare optimal behaviour with behaviour under simple rules of thumb. The numerical examples suggest that the value of the optimal strategy over the rules of thumb is greatest when there is little current information about predation risk, learning is not too costly in terms of predation, and it is energetically advantageous to learn about predation. We find that for the model and parameters investigated, a very simple rule of thumb such as 'use the best constant control' performs well.     

Inferring detailed space use from movement paths: A unifying,residence time‐based framework

The residence time is the amount of time spent within a predefined circle surrounding each point along the movement path of an animal, reflecting its response to resource availability/quality. Two main residence time‐based methods exist in the literature: (1) The variance of residence times along the path plotted against the radius of the circle was suggested to indicate the scale at which the animal perceives its resources; and (2) segments of the path with homogeneous residence times were suggested to indicate distinct behavioral modes, at a certain scale. Here, we modify and integrate these two methods to one framework with two steps of analysis: (1) identifying several distinct, nested scales of area‐restricted search (ARS), providing an indication of how animals view complex resource landscapes, and also the resolutions at which the analysis should proceed; and (2) identifying places which the animal revisits multiple times and performs ARS; for these, we extract two scale‐dependent statistical measures—the mean visit duration and the number of revisits in each place. The association between these measures is suggested as a signature of how animals utilize different habitats or resource types. The framework is validated through computer simulations combining different movement strategies and resource maps. We suggest that the framework provides information that is especially relevant when interpreting movement data in light of optimal behavior models, and which would have remained uncovered by either coarser or finer analyses.     

A model for the neuronal substrate of dead reckoning and memory in arthropods: a comparative computational and behavioral study

Returning to the point of departure after exploring the environment is a key capability for most animals. In the absence of landmarks, this task will be solved by integrating direction and distance traveled over time. This is referred to as path integration or dead reckoning. An important question is how the nervous systems of navigating animals such as the 1 mm³ brain of ants can integrate local information in order to make global decision. In this article we propose a neurobiologically plausible system of storing and retrieving direction and distance information. The path memory of our model builds on the well established concept of population codes, moreover our system does not rely on trigonometric functions or other complex non-linear operations such as multiplication, but only uses biologically plausible operations such as integration and thresholding. We test our model in two paradigms; in the first paradigm the system receives input from a simulated compass, in the second paradigm, the model is tested against behavioral data recorded from 17 ants. We were able to show that our path memory system was able to reliably encode and compute the angle of the vector pointing to the start location, and that the system stores the total length of the trajectory in a dependable way. From the structure and behavior of our model, we derive testable predictions both at the level of observable behavior as well as on the anatomy and physiology of its underlying neuronal substrate.     

Bio-economics of a renewable resource in a seasonally varying environment

In this paper we study the bio-economics of a renewable resource with governing dynamics described by two distinct growth functions (viz., logistic and Gompertz growth functions) in a seasonally varying environment. Seasonality is introduced into the system by taking the involved ecological parameters to be periodic. In this work, we establish a procedure to obtain the optimal path and compute the optimal effort policy which maximizes the net revenue to the harvester for a fairly general optimal control problem and apply this procedure to the considered models to derive some important conclusions. These problems are solved on the infinite horizon. We find that, for both the models, the optimal harvest policy and the corresponding optimal path are periodic after a finite time. We also obtain optimal solution, a suboptimal harvesting policy and the corresponding suboptimal approach path to reach this optimal solution. The key results are illustrated using numerical simulations and we compare the revenues to the harvester along the optimal and suboptimal paths. The general procedure developed in this work, for obtaining the optimal effort policy and the optimal path, has wider applicability.     

Frequency-multiplexing ability of complex-valued Hebbian learning in logic gates

Lightwave has attractive characteristics such as spatial parallelism, temporal rapidity in signal processing, and frequency band vastness. In particular, the vast carrier frequency bandwidth promises novel information processing. In this paper, we propose a novel optical logic gate that learns multiple functions at frequencies different from one another, and analyze the frequency-domain multiplexing ability in the learning based on complex-valued Hebbian rule. We evaluate the averaged error function values in the learning process and the error probabilities in the realized logic functions. We investigate optimal learning parameters as well as performance dependence on the number of learning iterations and the number of parallel paths per neuron. Results show a trade-off among the learning parameters such as learning time constant and learning gain. We also find that when we prepare 10 optical path differences and conduct 200 learning iterations, the error probability completely decreases to zero in a three-function multiplexing case. However, at the same time, the error probability is tolerant of the path number. That is, even if the path number is reduced by half, error probability is found almost zero. The results can be useful to determine neural parameters for future optical neural network systems and devices that utilize the vast frequency bandwidth for frequency-domain multiplexing.