Retrodictive quantum optical state engineering

Abstract

Although it has been known for some time that quantum mechanics can be formulated in a way that treats prediction and retrodiction on an equal footing, most attention in engineering quantum states has been devoted to predictive states, that is, states associated with a preparation event. Retrodictive states, which are associated with a measurement event and propagate backwards in time, are also useful, however. In this paper it is shown how any retrodictive state of light that can be written to a good approximation as a finite superposition of photon number states can be generated by an optical multiport device. The composition of the state is adjusted by controlling predictive coherent input states. It is shown how the probability of successful state generation can be optimized by adjusting the multiport device and also a versatile configuration that is useful for generating a range of states is examined.     

Reducing the disruptive effects of interruption: A cognitive framework for analysing the costs and benefits of intervention strategies

Interruptions are ubiquitous, and they can lead to disastrous consequences. The goal of this paper is to describe remedies that have been proposed to reduce the disruption caused by interruptions based on an understanding of how principles of human cognitive processing bear on the sequence of events that take place during an interruption. We show that interruptions tap disparate cognitive operations, from attention to decision making to memory. We illustrate how these cognitive processes can lead to interruption-induced errors, and how they can help in understanding potential problems with remedies that have been proposed to ameliorate those effects. We present a framework in which the load imposed by the task and the cost of an error suggests the types of solutions that should be considered for a given domain. We then discuss the implications of this approach for understanding and reducing the negative effect of interruptions in transportation domains.     

Consensus of Multi-Agent Systems with Input Constraints Based on Distributed Predictive Control Scheme

Consensus control of multi-agent systems has attracted compelling attentions from various scientific communities for its promising applications. This paper presents a discrete-time consensus protocol for a class of multi-agent systems with switching topologies and input constraints based on distributed predictive control scheme. The consensus protocol is not only distributed but also depends on the errors of states between agent and its neighbors. We focus mainly on dealing with the input constraints and a distributed model predictive control scheme is developed to achieve stable consensus under the condition that both velocity and acceleration constraints are included simultaneously. The acceleration constraint is regarded as the changing rate of velocity based on some reasonable assumptions so as to simplify the analysis. Theoretical analysis shows that the constrained system steered by the proposed protocol achieves consensus asymptotically if the switching interaction graphs always have a spanning tree. Numerical examples are also provided to illustrate the validity of the algorithm.     

Towards the Unified Principles for Level 5 Autonomous Vehicles

The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and potential challenges for existing modes of transportation. Currently, driving assistance systems of Level 3 and below have been widely produced, and several applications of Level 4 systems to specific situations have also been gradually developed. By improving the automation level and vehicle intelligence, these systems can be further advanced towards fully autonomous driving. However, general development concepts for Level 5 AVs remain unclear, and the existing methods employed in the development processes of Levels 0–4 have been mainly based on task-driven function development related to specific scenarios. Therefore, it is difficult to identify the problems encountered by high-level AVs. The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems. By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving, we put forward a coordinated and balanced framework based on the brain–cerebellum–organ concept through reasoning and deduction. Based on a mixed mode relying on the crow inference and parrot imitation approach, we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning, self-adaptation, and self-transcendence for AVs. From a systematic, unified, and balanced point of view and based on least action principles and unified safety field concepts, we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs, specifically at Level 5.     

Neuroergonomics and human error

Neuroergonomics complements traditional ergonomic approaches to understanding many aspects of human–system performance, including the analysis of human error. This paper reviews the evidence on neural signals associated with different error types, specifically the error-related negativity (ERN), a brain potential that is extracted from the scalp electroencephalogram and that is generated in a medial prefrontal brain region, the anterior cingulate cortex. Studies of the ERN indicate that the brain has a specialised error monitoring and feedback system with close ties to brain networks involved in learning and decision making. The implications of this system for understanding how errors are made are discussed, as well as how errors are captured, learned from and used as feedback to enhance future performance. Applications of neuroergonomic studies of human error–in the development of brain–computer interfaces, selection and training design and procedures for system diagnosis–are also described. The current literature on the neural basis for human error processing is presented within the framework of neuroergonomics. This review relates the basic understanding of error processing to work-related problems. It is argued that better systems can be created provided the neural processes underlying behaviour are taken into account.     

Models of driving behavior: A review of their evolution

This paper reviews models that emphasize the cognitive components of driving behavior. Studies of individual differences have sought predictors of accident histories. Typically low correlations and reliance on post hoc explanations reflect theoretical deficiencies and problems with the use of accident measures. Motivational models emphasize transient, situation-specific factors rather than stable, individual predictors. However, neither testable hypotheses nor suitable methods have been developed to study situational factors and motives that influence driving. More recent models have incorporated a hierarchical control structure, which assumes concurrent activity at strategic, maneuvering, and operational levels of control. At the same time, automaticity has emerged as a central construct in cognitive psychology. All activities are assumed to combine fast, automatic components with slower, more deliberate, controlled processing. It is argued that identifying the situational factors that increase drivers' uncertainty and thus trigger a shift in attention from automatic to controlled processing will help integrate concepts of automaticity and motivational models. Finally, recent theorizing has suggested that errors associated with the inherent variability of human behavior may be more important to roadway crash causation than systematic errors, which are attributable to the known limits of the human information-processing system. Drivers' abilities to recover from errors may also be important to crash causation. It is concluded that the hierarchical control structure and theories of automaticity and errors provide the potential tools for defining alternative criterion measures, such as safety margins, and developing testable theories of driving behavior and crash causation. Two examples of models that integrate information-processing mechanisms within a motivational framework are described.     

Spatiotemporal Information Processing Emulated by Multiterminal Neuro‐Transistor Networks

All external sensory stimuli produce a spatiotemporal pattern of action potentials, which is transmitted to the biological neural system to be processed. The relative timing of synaptic spikes from different presynaptic neurons represents the features of the stimuli. A fundamental prerequisite in cortical information processing is the discrimination of different spatiotemporal input sequences. Here, capacitively coupled multiterminal oxide‐based neuro‐transistors are proposed for spatiotemporal information processing, mimicking the dendritic discriminability of different spatiotemporal input sequences. The experimental results demonstrate that such multiterminal neuromorphic devices can act as spatiotemporal information processing compartments for fundamental cortical computation. Also, as an example of spatiotemporal information processing, sound location functionality of the human brain is also emulated by constructing a simple artificial neural network based on such oxide‐based multiterminal neuro‐transistors.     

Characteristics of attention and visual short-term memory: implications for visual interface design

Although the human retina can code many object images simultaneously, observers are often aware of only a tiny fraction of this information. These processing limitations of the visual brain have evolved to prioritize particularly relevant features of a scene while ignoring other irrelevant features. However, such selectivity has its drawbacks. In information-rich environments, such as driving a car or landing an aircraft, vision can fail to cope, and accidents can result. Accordingly, much recent research in psychophysics and ergonomics has examined how display characteristics affect our ability to process multiple features of the visual environment simultaneously. The majority of these experiments has found that performance can be optimized by combining several features into one visual 'object'. In contrast, several recent studies from my own laboratory have found the opposite pattern, indicating that information sources can often be more efficiently processed when they belong to separate objects. Indeed, these data suggest that the number of objects has no general effect on our perceptual performance. Instead, I argue for a two-pathway approach to understanding human visual capacities, and suggest that this approach may have important implications for a diverse range of display technologies, including cockpit displays.     

Delayed self-regulation and time-dependent chemical drive leads to novel states in epigenetic landscapes

The epigenetic pathway of a cell as it differentiates from a stem cell state to a mature lineage-committed one has been historically understood in terms of Waddington''s landscape, consisting of hills and valleys. The smooth top and valley-strewn bottom of the hill represent their undifferentiated and differentiated states, respectively. Although mathematical ideas rooted in nonlinear dynamics and bifurcation theory have been used to quantify this picture, the importance of time delays arising from multistep chemical reactions or cellular shape transformations have been ignored so far. We argue that this feature is crucial in understanding cell differentiation and explore the role of time delay in a model of a single-gene regulatory circuit. We show that the interplay of time-dependent drive and delay introduces a new regime where the system shows sustained oscillations between the two admissible steady states. We interpret these results in the light of recent perplexing experiments on inducing the pluripotent state in mouse somatic cells. We also comment on how such an oscillatory state can provide a framework for understanding more general feedback circuits in cell development.     

Integrating social and cognitive aspects of belief dynamics: towards a unifying framework

Belief change and spread have been studied in many disciplines—from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics—but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics.     

Operator monitoring in a complex dynamic work environment: a qualitative cognitive model based on field observations

Complex and dynamic work environments provide a challenging litmus-test with which to evaluate basic and applied theories of cognition. In this work, we were interested in obtaining a better understanding of dynamic decision making by studying how human operators monitored a nuclear power plant during normal operations. Interviews and observations were conducted in situ at three different power plants to enhance the generalizability of results across both individuals and plants. A total of 38 operators were observed for approximately 288 hours, providing an extensive database of qualitative data. Based on these empirical observations, a cognitive model of operator monitoring was developed. This qualitative model has important theoretical implications because it integrates findings from several theoretical perspectives. There is a strong human information processing component in that operators rely extensively on active knowledge-driven monitoring rather than passively reacting to changes after they occur, but there is also a strong distributed cognition component in that operators rely extensively on the external representations to offload cognitive demands. In some cases, they even go so far as to actively shape that environment to make it easier to exploit environmental regularities, almost playing the role of designers. Finally, expert operators use workload regulation strategies, allowing them to prioritize tasks so that they avoid situations that are likely to lead to monitoring errors. These meta-cognitive processes have not received much attention in the human information processing and distributed cognition perspectives, although they have been studied by European psychologists who have studied cognition in complex work environments. Collectively, these findings shed light on dynamic decision making but they also serve an important theoretical function by integrating findings from different theoretical perspectives into one common framework.     

Driver state examination—Treading new paths

A large proportion of crashes in road driving can be attributed to driver fatigue. Several types of fatigue are discussed, comprising sleep-related fatigue, active task-related fatigue (as a consequence of workload in demanding driving situations) as well as passive task-related fatigue (as related to monotonous driving situations). The present study investigated actual states of fatigue in a monotonous driving situation, using EEG measures and a long-lasting driving simulation experiment, in which drivers had to keep the vehicle on track by compensating crosswind of different strength. Performance data and electrophysiological correlates of mental fatigue (EEG Alpha and Theta power, Inter Trial Coherence (ITC), and auditory event-related potentials to short sound stimuli) were analyzed. Driving errors and driving lane variability increased with time on task and with increasing crosswind. The posterior Alpha and Theta power also increased with time on task, but decreased with stronger crosswind. The P3a to sound stimuli decreased with time on task when the crosswind was weak, but remained stable when the crosswind was strong. The analysis of ITC revealed less frontal Alpha and Theta band synchronization with time on task, but no effect of crosswind. The results suggest that Alpha power in monotonous driving situations reflects boredom or attentional withdrawal due to monotony rather than the decline of processing abilities as a consequence of high mental effort. A more valid indicator of declining mental resources with increasing time on task seems to be provided by brain oscillatory synchronization measures and event-related activity.     

Fundamental examination of mental workload in the rail industry

In recent years there has been a resurgence of interest in rail human factors. The sometimes conflicting requirements of safety, reliable performance, quality of operations and effective use of limited capacity have meant that managers and engineers across all companies in the rail network have realized the importance of understanding and designing for human factors in train driving, signalling and control, maintenance, planning, etc. One key concern has been with workload, particularly mental workload, and especially in signalling and driving. This paper is concerned with a fundamental examination of what workload means in relation to the railways and especially signalling work and with a need to develop an appropriate suite of tools for the practical assessment of workload. In order to do this, the basic literature has been revisited in order to propose a conceptual framework of mental workload in the rail industry. Subsequently, a suite of workload tools has been proposed and is being used in practice; this is also described in this paper.     

Adam Smith and the Humanists: An Enquiry into the Productivity of Labor Controversy

This paper presents a new framework of analysis for studying an old question: Does division of labor maximize labor productivity? The framework describes how a simple, but scientifically accepted model, can be a useful tool in unifying diverse approaches to this subject and in explaining opposing empirical observations.     

How to identify the key factors that affect driver perception of accident risk. A comparison between Italian and Spanish driver behavior

Road crashes can be caused by different factors, including infrastructure, vehicles, and human variables. Many research studies have focused solely on identifying the key factors that cause road crashes. From these studies, it emerged that human factors have the most relevant impact on accident severity. More specifically, accident severity depends on several factors related directly to the driver, i.e., driving experience, driver's socio-economic characteristics, and driving behavior and attitudes. In this paper, we investigate driver behaviors and attitudes while driving and specifically focus on different methods for identifying the factors that most affect the driver's perception of accident risk. To this end, we designed and conducted a survey in two different European contexts: the city of Cosenza, which is located in the south of Italy, and the city of Granada, which is located in the south of Spain. Samples of drivers were contacted for their opinions on certain aspects of driving rules and attitudes while driving, and different types of questions were addressed to the drivers to assess their judgments of these aspects. Consequently, different methods of data analysis were applied to determine the aspects that heavily influence driver perception of accident risk. An experiment based on the stated preferences (SP) was carried out with the drivers, and the SP data were analyzed using an ordered probit (OP) model. Interesting findings emerged from different analyses of the data and from the comparisons among the data collected in the two different territorial contexts. We found that both Italian and Spanish drivers consider driving in an altered psychophysical state and violating the overtaking rules to be the most risky behaviors.     

Calibration of skill and judgment in driving: Development of a conceptual framework and the implications for road safety

Humans often make inflated or erroneous estimates of their own ability or performance. Such errors in calibration can be due to incomplete processing, neglect of available information or due to improper weighing or integration of the information and can impact our decision-making, risk tolerance, and behaviors. In the driving context, these outcomes can have important implications for safety. The current paper discusses the notion of calibration in the context of self-appraisals and self-competence as well as in models of self-regulation in driving. We further develop a conceptual framework for calibration in the driving context borrowing from earlier models of momentary demand regulation, information processing, and lens models for information selection and utilization. Finally, using the model we describe the implications for calibration (or, more specifically, errors in calibration) for our understanding of driver distraction, in-vehicle automation and autonomous vehicles, and the training of novice and inexperienced drivers.     

A probabilistic framework for windows of opportunity: the role of temporal variability in critical transitions

The establishment of young organisms in harsh environments often requires a window of opportunity (WoO). That is, a short time window in which environmental conditions drop long enough below the hostile average level, giving the organism time to develop tolerance and transition into stable existence. It has been suggested that this kind of establishment dynamics is a noise-induced transition between two alternate states. Understanding how temporal variability (i.e. noise) in environmental conditions affects establishment of organisms is therefore key, yet not well understood or included explicitly in the WoO framework. In this paper, we develop a coherent theoretical framework for understanding when the WoO open or close based on simple dichotomous environmental variation. We reveal that understanding of the intrinsic timescales of both the developing organism and the environment is fundamental to predict if organisms can or cannot establish. These insights have allowed us to develop statistical laws for predicting establishment probabilities based on the period and variance of the fluctuations in naturally variable environments. Based on this framework, we now get a clear understanding of how changes in the timing and magnitude of climate variability or management can mediate establishment chances.     

Grain sizing of anodized aluminum by color image analysis

Grain size characterization of aluminum alloys can be correlated with thermomechanical processing properties [1]. To predict the processing characteristics of these alloys under certain combinations of strain, deformation, and temperature, the metallographic measure of the grain size can be used. Most of the techniques that have been proposed so far do not provide reliable and reproducible quantitative metallographic measurements of the grain size, due to human error [2,3]. Considering that this manual task is also tedious to perform, a general color image analysis algorithm is proposed to automate the characterization process using an optical microscope with polarized light. This algorithm was tested on several ingots and on rolled-aluminum samples. The results show robustness in several conditions, even when the grains can barely be seen by a human operator. Time constraints specific to industrial settings were taken into account when implementing the algorithm. A complete characterization of the entire surface of an ingot can be obtained within a reasonable time limit. The same algorithm was tested with other types of color material. The proposed framework for handling segmentation of color grains fulfills industrial requirements for the characterization of materials seen under a microscope with polarized light.     

Measuring the phase variance of light

Abstract

The results of experiments designed to measure the operational phase cosine and sine variances of weak states of light disagree with the variances predicted by canonical phase formalisms. As these variances are fundamental manifestations of the quantum nature of phase, it is important to be able to measure the canonical variances also. A recent suggestion to do so, based on the use of a two-component probe, involves the difficult preparation of exotic states of light which have not yet been produced. In this paper we show how the variances can be measured with simple coherent state inputs. The retrodictive formalism of quantum mechanics provides useful insight into the physics involved.