Information Processing Delays Research Articles

Autonomous Mutual Authentication Protocol in the Edge Networks.

A distinct security protocol is necessary for the exponential growth in intelligent edge devices. In particular, the autonomous devices need to address significant security concern to function smoothly in the high market demand. Nevertheless, exponential increase in the connected devices has made cloud networks more complex and suffer from information processing delay. Therefore, the goal of this work is to design a novel server-less mutual authentication protocol for the edge networks. The aim is to demonstrate an autonomous mutual authentication amongst the connected smart devices within the edge networks. The solution addresses applications of autonomous cars, smart things, and Internet of Things (IoT) devices in the edge or wireless sensor networks (WSN), etc. In this paper, the design proposes use of a public-key system, octet-based balanced-tree transitions, challenge–response mechanism, device unique ID (UID), pseudo-random number generator (PRNG), time-stamps, and event specific session keys. Ultimately, server-less design requires less infrastructure and avoids several types of network-based communication attacks, e.g., impersonating, Man in the middle (MITM), IoT-DDOS, etc. Additionally, the system overhead is eliminated by no secret key requirements. The results provide sufficient evidence about the protocol market competitiveness and demonstrate better benchmark comparison results.

Verified Integration of Differential Equations with Discrete Delay

Many dynamic system models in population dynamics, physics and control involve temporally delayed state information in such a way that the evolution of future state trajectories depends not only on the current state as the initial condition but also on some previous state. In technical systems, such phenomena result, for example, from mass transport of incompressible fluids through finitely long pipelines, the transport of combustible material such as coal in power plants via conveyor belts, or information processing delays. Under the assumption of continuous dynamics, the corresponding delays can be treated either as constant and fixed, as uncertain but bounded and fixed, or even as state-dependent. In this paper, we restrict the discussion to the first two classes and provide suggestions on how interval-based verified approaches to solving ordinary differential equations can be extended to encompass such delay differential equations. Three close-to-life examples illustrate the theory.

Flocking in a two-agent Cucker-Smale model with large delay

Delay in feedback is inevitable in a multi-agent system due to time lags in information processing for self-organization. The well-known Cucker-Smale model incorporated with this information processing delay has been recently studied, and it was shown (at least for a two-agent system) that as long as the delay is below a threshold value, the system exhibits the flocking behavior where the agents ultimately reach the same velocity. Numerical studies however suggest that the threshold value established for the delay is not optimal, and the Cucke-Smale system remains to have the flocking property for large delay. Here, we rigorously prove that flocking behavior emerges for a large information processing delay, and we establish the relationship between the admissible size of time delay and the system’s parameters and initial data.

Determinants and consequences of information processing delay: Evidence from the Thomson Reuters Institutional Brokers’ Estimate System

We present new evidence that highlights the role of information intermediaries in the distribution and processing of earnings estimates in capital markets. We find that the time taken to activate an analyst's earnings forecast in the Thomson Reuters Institutional Brokers’ Estimate System is related to measures of investor demand for timely information processing, processing difficulty, and limited attention. Furthermore, we find that forecast announcement returns are muted and post-announcement drift is magnified for forecasts with longer unexpected activation delay and that market inefficiency is concentrated in neglected stocks and potentially exploitable. Finally, analyzing intraday returns, we find that activations facilitate price discovery.

Cloud-Assisted Context-Aware Vehicular Cyber-Physical System for PHEVs in Smart Grid

Plug-in Hybrid Electric Vehicles (PHEVs) can be one of the cost-effective options of modern intelligent transportation systems in smart grid (SG) which can balance the demand and supply by temporarily storing the electrical energy in their batteries. In this paper, we propose a new context-aware layered architecture for demand side management using vehicular cyber-physical system (VCPS) with cloud support. We have used the concept of Bayesian coalition game and learning automata for an intelligent context-aware data collection and processing using a new payoff function for the players in the coalition game. In the proposed scheme, vehicles are assumed as the players which sense the SG environment during their mobility and collect information from it. The players in the game perform actions such as alert generation, and information dissemination. For each action, players receive a feedback from the environment according to which they update their action probability vector. The performance of the proposed scheme shows that there is a reduction in energy shortage by 30%, and information processing delay of 10%–15%. In addition, there is an increase of 15% in energy sold back to the grid using the proposed scheme. The results obtained demonstrate the effectiveness of the proposed scheme.

Simple agent-based dynamical system models for efficient financial markets: Theory and examples

We propose an agent-based framework, based on simple piecewise linear time-invariant continuous-time dynamical systems models, as a means for describing efficient financial markets. We show by examples that many of the common agent-specific trading strategies occurring in the academic literature, including chartists and fundamentalists of various kinds, can be described in the proposed framework. We present definitions for weak and strong market efficiency and provide necessary and sufficient conditions for them to hold. We present minimal examples of strongly and weakly efficient markets to show that these concepts are natural and easy to satisfy in agent-based models, and that the models can reproduce both statistical and behavioral stylized facts of real markets. We provide examples to demonstrate that the framework can be extended for agents with delays in information processing, as well as for agents with time-varying strategies and for nonlinear market impact functions. We also provide a counterexample to show that the proposed market efficiency concepts may require modification in generalizations for nonlinear trading strategies.

Dynamic pricing and asymmetries in retail gasoline markets: What can they tell us about price stickiness?

Theoretical explanations for price stickiness used in businesses cycle models are diverse (e.g., information processing delays, rational inattention and fair pricing), with each theory resulting in a different implication for inflation dynamics. Using an autoregressive conditional binomial model and a data set consisting of daily observations of price and cost for 15 Philadelphia retail gasoline stations, we test which of these theories is most consistent with the observed pattern of price adjustment. Our findings of time dependence, asymmetry and the role of cost volatility are consistent with a combination of fairness considerations and rational inattention by producers.

AFFECTIVE-SOUND EFFECTS ON DRIVING BEHAVIOUR

Recent technological developments in active advanced driver assistance systems and in-car infotainment devices have contributed to reducing the number and severity of road accidents as well as improving and simplifying driver experience. However, these systems may impact driving performance in undesired ways, especially when emotionally-charged stimuli are used as warning signals. Emotional distraction can be a serious danger, causing delays in information processing, and reducing driving safety below minimal acceptable levels. Here we study the effect of emotionally-laden auditory signals on the speed of concurrent driving decisions. We distinguished two categories of behavioural responses: ‘urgent’ vs ‘evaluative’. In the experiments reported here participants were quicker to evaluate whether a traffic scene was risky or not after hearing an emotionally-charged auditory stimulus than after a neutral one. However, urgent (braking) responses to the same scenes were not affected by the emotional quality of the auditory signal. Based on these results, we give preliminary advice on the design of guidelines for in-car interfaces particularly in the field of affective in-car computing.

The consensus problem in networks with transmission delays

We study discrete- and continuous-time consensus problems on networks in the presence of distributed time delays. We focus on information transmission delays, as opposed to information processing delays, so that each node of the network compares its current state with the past states of its neighbours. We consider directed and weighted networks where the connection structure is described by a normalized Laplacian matrix and show that consensus is achieved if and only if the underlying graph contains a directed spanning tree. This statement holds independently of the transmission delays, which is in contrast to the case of processing delays. Furthermore, we calculate the consensus value explicitly, and show that it is determined by the history of the system over an interval of time, unlike the case of processing delays where the consensus value depends only on the initial state of the system at time zero. This provides the consensus algorithm with improved robustness against noise.

Quantitative Neuroscience: From Chalk Board to Bedside

Quantitative neuroscience, an interdisciplinary approach that brings together the mathematical, modeling, engineering and neuroscience communities, is rapidly bringing tangible hope of a better life to the tens of millions of people afflicted with diseases of the nervous system. The driving force is an increased understanding of how the nervous system exerts control and processes information. In less than 15 years, quantitative neuroscience is seemingly doing the impossible [12, 17]. Brain–computer interfaces now make it possible to translate thought into action [6, 7, 8, 15], replace lost limbs with robotic ones [9], prevent epileptic seizures [11, 16] and even to alleviate the symptoms of neuro–degenerative diseases, such as Parkinson’s [14]. Is it possible to do even better? The first theme of this issue explores two lines of mathematical research that have been particularly fruitful: 1) neural synchronization [19, 21], the fundamental process by which spatially distributed neural centers bind a sensory stimulus and coordinate their activities to respond to it, and 2) multistability [2, 10, 12], the concept that treatments may be possible by applying jolts of electricity to the right location in the brain at the right time [11, 16]. Attention is drawn to the effects of time delays and random perturbations (“noise”) on neural control and information processing. Since axonal conduction velocities are finite, time delays are an intrinsic component of all neural feedback loops. Stochastic effects arise from fluctuations in ion channels and quantal release and from the convergence of multiple independent synaptic inputs. Obviously the most promising mechanisms for neural processing are those which are robust in the presence of noise and delay. In other words, it is not only necessary to develop analytical expressions that describe neural synchronization regardless of the number of neurons [5], it is also necessary to understand the effects of noise of the dynamics of synchronizing neural populations [2]. Similarly the widespread occurrence of time–delayed feedback in neural pathways raises questions as to the role of time delays in information processing [10] and whether new effects arise from the interplay between noise

Consensus in networks under transmission delays and the normalized Laplacian

We study discrete and continuous time consensus problems on directed and weighted networks in the presence of time delays. We focus on information transmission delays, as opposed to information processing delays, so that each node of the network compares its current state to the past states of its neighbors. Furthermore, we allow both fixed and distributed time delays. The connection structure of the network is described by a normalized Laplacian matrix. We show that consensus is achieved if and only if the underlying graph contains a spanning tree. Furthermore, this statement holds independently of the value of the delay, in contrast to the case of processing delays. We also calculate the consensus value and show that, unlike the case of processing delays, the consensus value is determined not just by the initial states of the nodes at time zero, but also on their past history over an interval of time.

Current treatment options for depression after mild traumatic brain injury

Mild traumatic brain injury (MTBI) is the most common neurologic condition in the United States; about 503 of every 100,000 persons visiting the emergency department have this diagnosis. A complex cluster of neurologic signs and symptoms are associated with an acceleration-deceleration mechanism of injury. Unfortunately, many persons do not seek treatment for MTBI. Depression following MTBI adds to the complexity and complicates the diagnosis and treatment because of overlap of symptoms, including delays in information processing, sleep difficulties, irritability, and fatigue, as well as pain in association with headache or other musculoskeletal injuries. Depression after MTBI has been associated with abnormal CT scan results, older age, and increased depressive symptoms within a week after injury. Given the progress made in understanding the natural history of MTBI and post-TBI depression in general, there has been less progress in treatment trials for post-MTBI depression. In this review, we report on one phase 4, nonrandomized single-group trial in persons with mild-to-moderate TBI and note a "response to treatment" effect without the ability to discern the potential impact of other prescribed pharmacotherapies on the outcome. In a randomized controlled trial focusing on a telephone counseling intervention for post-MTBI symptoms, the intervention appeared to be effective in reducing 6-month post-MTBI symptoms, but it was not effective for the general health outcome, which included measures of depressive symptoms and anxiety. Thus, evidence-based treatment of post-MTBI depression is quite limited. Because depressive symptoms can develop as early as 1 week after injury and can be exacerbated by stress, pain, and comorbidities, it seems prudent to begin early interventions focused on managing pain and stress, along with providing psychoeducational and problem-solving therapies. Efficacy studies are needed to determine whether telemedicine approaches, group interventions, or peer counseling may be helpful methods of delivering such interventions during the early months as ways to attenuate the development of a major depressive disorder.

Theta-Mediated Dynamics of Spatial Information in Hippocampus

In rodent hippocampus, neuronal activity is organized by a 6-10 Hz theta oscillation. The spike timing of hippocampal pyramidal cells with respect to the theta rhythm correlates with an animal's position in space. This correlation has been suggested to indicate an explicit temporal code for position. Alternatively, it may be interpreted as a byproduct of theta-dependent dynamics of spatial information flow in hippocampus. Here we show that place cell activity on different phases of theta reflects positions shifted into the future or past along the animal's trajectory in a two-dimensional environment. The phases encoding future and past positions are consistent across recorded CA1 place cells, indicating a coherent representation at the network level. Consistent theta-dependent time offsets are not simply a consequence of phase-position correlation (phase precession), because they are no longer seen after data randomization that preserves the phase-position relationship. The scale of these time offsets, 100-300 ms, is similar to the latencies of hippocampal activity after sensory input and before motor output, suggesting that offset activity may maintain coherent brain activity in the face of information processing delays.

Real-time decentralized information processing and returns to scale

We use a model of real-time decentralized information processing to understand how constraints on human information processing affect the returns to scale of organizations. We identify three informational (dis)economies of scale: diversification of heterogeneous risks (positive), sharing of information and of costs (positive), and crowding out of recent information due to information processing delay (negative). Because decision rules are endogenous, delay does not inexorably lead to decreasing returns to scale. However, returns are more likely to be decreasing when computation constraints, rather than sampling costs, limit the information upon which decisions are conditioned. The results illustrate how information processing constraints together with the requirement of informational integration cause a breakdown of the replication arguments that have been used to establish nondecreasing technological returns to scale.

Biomimetic gaze stabilization based on feedback-error-learning with nonparametric regression networks

Oculomotor control in a humanoid robot faces similar problems as biological oculomotor systems, i.e. the stabilization of gaze in face of unknown perturbations of the body, selective attention, stereo vision, and dealing with large information processing delays. Given the nonlinearities of the geometry of binocular vision as well as the possible nonlinearities of the oculomotor plant, it is desirable to accomplish accurate control of these behaviors through learning approaches. This paper develops a learning control system for the phylogenetically oldest behaviors of oculomotor control, the stabilization reflexes of gaze. In a step-wise procedure, we demonstrate how control theoretic reasonable choices of control components result in an oculomotor control system that resembles the known functional anatomy of the primate oculomotor system. The core of the learning system is derived from the biologically inspired principle of feedback-error learning combined with a state-of-the-art non-parametric statistical learning network. With this circuitry, we demonstrate that our humanoid robot is able to acquire high performance visual stabilization reflexes after about 40 s of learning despite significant nonlinearities and processing delays in the system.

Information Technology and Time-Based Competition in Financial Markets

This paper studies time-based competition in imperfect securities markets, linking IT investment decisions, information processing delays, and trading strategies. At the IT investment stage, traders trade off the cost of IT against their anticipated trading profits. At the trading stage, each trader devises a trading strategy based on his new information while taking into account the impact of both his own trades and those of other traders in the market. Our results illustrate how traders react to market imperfections due to trading costs and information processing delays, and how superior traders convert a timeliness advantage into higher trading profits. They also shed light on the relationship between the price adjustment process and traders' information processing delays. Timeliness imposes an interesting structure on trader competition: traders with longer information processing delays trade less frequently, submit smaller orders and enjoy lower profits per trade. Our analysis of traders' IT investment decisions demonstrates how factors such as IT costs, number of traders, and the frequency and nature of new information affect the level of IT investments. We further illustrate how improved IT infrastructure translates into competitive advantage.

Measurement of information processing delays in human visual cortex with repetitive magnetic coil stimulation

Previous work disclosed that single magnetic coil (MC) pulses applied over human calcarine cortex could suppress perception of letter briefly presented, e.g. 80–100 ms earlier 1. Although individual MC stimuli presented 0–60 ms, or more than 140 ms after the visual stimulus were apparently ineffective, combinations of 2 or 3 MC pulses at such intervals temporarily depressed visual perception. Thus, progressing of such language information could be slowed, without being abolished. By contrast, when the first MC pulse was delivered 120 ms or later, a second MC pulse 40 ms later had no detectable effect, implying that calcarine cortex had already transmitted the information. Perceptual recovery of 5-character words initially occurred no earlier than that of random letters vs. arbitrary linear patterns, implying that the processing delays in calcarine cortex were similar.

Effects of severe closed-head injury on three stages of information processing.

The present study investigated the loci of the information-processing delay that characteristically follows severe closed-head injury (CHI). Sternberg's additive-factors logic was used to determine the effects of severe CHI on the central information-processing stages of stimulus encoding, memory comparison, and decision-making/response-selection. The task variables used to define the stages operationally were stimulus quality, memory set size, and stimulus-response compatibility. Twenty subjects who had sustained a severe CHI more than 18 months earlier and 20 matched control subjects completed a stimulus encoding by response selection task in Experiment 1, and a Sternberg high-speed memory scanning task in Experiment 2. The CHI group performed the stimulus encoding and decision-making/response-selection stages of processing significantly slower than did the control group. However, no significant group differences were found on the memory comparison stage, suggesting that memory comparison processes may be relatively intact in long-term patients with severe head trauma. The results are discussed in relation to a global and a late-specificity hypothesis of central processing deficits following severe CHI. The possibility that cognitive processes demanding less attention may be more resilient to injury is also considered.

An analysis of the activation of motor cortical neurons by surface stimulation.

An analysis of the activation of motor cortical neurons by surface stimulation.J Rosenthal, H J Waller, and V E AmassianJ Rosenthal, H J Waller, and V E AmassianPublished Online:01 Jul 1967https://doi.org/10.1152/jn.1967.30.4.844MoreSectionsPDF (2 MB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat Previous Back to Top Next Download PDF FiguresReferencesRelatedInformation Cited ByPhase-Locked Stimulation during Cortical Beta Oscillations Produces Bidirectional Synaptic Plasticity in Awake MonkeysCurrent Biology, Vol. 28, No. 16Ongoing brain rhythms shape I-wave properties in a computational modelBrain Stimulation, Vol. 11, No. 4Noninvasive Stimulation of the Human Brain: Activation of Multiple Cortical Circuits29 June 2017 | The Neuroscientist, Vol. 24, No. 3Heavy alcohol use in adolescence is associated with altered cortical activity: a combined TMS-EEG study23 December 2016 | Addiction Biology, Vol. 23, No. 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