Hysteresis phenomenon in deterministic traffic flows

We study phase transitions of a system of particles on the one-dimensional integer lattice moving with constant acceleration, with a collision law respecting slower particles. This simple deterministic ``particle-hopping'' traffic flow model being a straightforward generalization to the well known Nagel-Schreckenberg model covers also a more recent slow-to-start model as a special case. The model has two distinct ergodic (unmixed) phases with two critical values. When traffic density is below the lowest critical value, the steady state of the model corresponds to the ``free-flowing'' (or ``gaseous'') phase. When the density exceeds the second critical value the model produces large, persistent, well-defined traffic jams, which correspond to the ``jammed'' (or ``liquid'') phase. Between the two critical values each of these phases may take place, which can be interpreted as an ``overcooled gas'' phase when a small perturbation can change drastically gas into liquid. Mathematical analysis is accomplished in part by the exact derivation of the life-time of individual traffic jams for a given configuration of particles.Comment: 22 pages, 6 figures, corrected and improved version, to appear in the Journal of Statistical Physic

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

Learning to See Analogies: A Connectionist Exploration

The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

Hierarchy of Chaotic Maps with an Invariant Measure

We give hierarchy of one-parameter family F(a,x) of maps of the interval [0,1] with an invariant measure. Using the measure, we calculate Kolmogorov-Sinai entropy, or equivalently Lyapunov characteristic exponent, of these maps analytically, where the results thus obtained have been approved with numerical simulation. In contrary to the usual one-parameter family of maps such as logistic and tent maps, these maps do not possess period doubling or period-n-tupling cascade bifurcation to chaos, but they have single fixed point attractor at certain parameter values, where they bifurcate directly to chaos without having period-n-tupling scenario exactly at these values of parameter whose Lyapunov characteristic exponent begins to be positive.Comment: 18 pages (Latex), 7 figure

Does dynamics reflect topology in directed networks?

We present and analyze a topologically induced transition from ordered, synchronized to disordered dynamics in directed networks of oscillators. The analysis reveals where in the space of networks this transition occurs and its underlying mechanisms. If disordered, the dynamics of the units is precisely determined by the topology of the network and thus characteristic for it. We develop a method to predict the disordered dynamics from topology. The results suggest a new route towards understanding how the precise dynamics of the units of a directed network may encode information about its topology.Comment: 7 pages, 4 figures, Europhysics Letters, accepte

Productivity and efficiency of central government departments: a mixed-effect model applied to Dutch data in the period 2012-2019

Central government aims to stimulate the efficiency and technical change of public organizations. However, government primarily focuses on the institutions that deliver final public services, but not on the policy making institutions. This article analyses the productivity of central government departments (CGDs). From bureaucratic theory we hypothesize that productivity of these CGDs are low. In order to measure efficiency and technical change we estimate an average cost function based on data of Dutch individual CGDs during the period 2012-2019. The dataset consists of data on various services provided, resource usage and efficiency determinants. The cost function is estimated by a mixed-effect non-linear least squares method. The outcomes show that there are large efficiency differences among CGDs. It is also striking that technical change of the CGDs is nonexistent over time, probably due to a lack of innovative behaviour, unwieldy bureaucracies and increasingly complex paperwork

Developing Grounded Goals through Instant Replay Learning

This paper describes and tests a developmental architecture that enables a robot to explore its world, to find and remember interesting states, to associate these states with grounded goal representations, and to generate action sequences so that it can re-visit these states of interest. The model is composed of feed-forward neural networks that learn to make predictions at two levels through a dual mechanism of motor babbling for discovering the interesting goal states and instant replay learning for developing the grounded goal representations. We compare the performance of the model with grounded goal representations versus random goal representations, and find that it is significantly better at re-visiting the goal states when using grounded goal representations

Corticotropin-releasing factor receptors couple to multiple g-proteins to activate diverse intracellular signaling pathways in mouse hippocampus: role in neuronal excitability and associative learning

Corticotropin-releasing factor (CRF) exerts a key neuroregulatory control on stress responses in various regions of the mammalian brain, including the hippocampus. Using hippocampal slices, extracts, and whole animals, we investigated the effects of human/rat CRF (h/rCRF) on hippocampal neuronal excitability and hippocampus-dependent learning in two mouse inbred strains, BALB/c and C57BL/6N. Intracellular recordings from slices revealed that application of h/rCRF increased the neuronal activity in both mouse inbred strains. Inhibition of protein kinase C (PKC) by bisindolylmaleimide I (BIS-I) prevented the h/rCRF effect only in hippocampal slices from BALB/c mice but not in slices from C57BL/6N mice. Inhibition of cAMP-dependent protein kinase (PKA) by H-89 abolished the h/rCRF effect in slices from C57BL/6N mice, with no effect in slices from BALB/c mice. Accordingly, h/rCRF elevated PKA activity in hippocampal slices from C57BL/6N mice but increased only PKC activity in the hippocampus of BALB/c mice. These differences in h/rCRF signal transduction were also observed in hippocampal membrane suspensions from both mouse strains. In BALB/c mice, hippocampal CRF receptors coupled to Gq/11 during stimulation by h/rCRF, whereas they coupled to Gs, Gq/11, and Gi in C57BL/6N mice. As expected on the basis of the slice experiments, h/rCRF improved context-dependent fear conditioning of BALB/c mice in behavioral experiments, and BIS-I prevented this effect. However, although h/rCRF increased neuronal spiking in slices from C57BL/6N mice, it did not enhance conditioned fear. These results indicate that the CRF system activates different intracellular signaling pathways in mouse hippocampus and may have distinct effects on associative learning depending on the mouse strain investigated

How much does teenage parenthood affect long term outcomes? A systematic review.

Background: The rates of teenage pregnancy in the UK are relatively high. Although early entry to parenthood can be a positive experience, most studies find large adverse effects on long term outcomes for the mother, child and father, in addition to being costly for the NHS. This is why the government launched its Teenage Pregnancy Strategy in 1999. However, there is growing evidence that teenage pregnancy might be mainly an indicator of disadvantage which is the underlying cause of the negative outcomes. Methods: A systematic literature review was undertaken of studies which used a UK dataset to quantify any long term outcomes of a teenage birth upon the mother, father or child. Studies were included if they used appropriate methods to isolate the causal effect of early parenthood. The databases searched included Medline, Cochrane, EconLit and Web of Science. Results: Six studies were identified by the review; five studies considered the mother’s socioeconomic outcomes, one study reported the child’s outcomes, and no studies met the inclusion criteria for the father’s outcomes. The studies suggested that early motherhood accounts for relatively few of the negative long term socioeconomic outcomes and it is predominantly an indicator of a disadvantaged family background. Conclusion: Limited evidence is available to understand the long term outcomes associated with teenage birth within the UK for the mother, father and child. Current econometric studies suggest that effective interventions to prevent teenage pregnancies will not eradicate the poorer long term socioeconomic outcomes often associated with early motherhood. Thus policy should focus on reducing initial disadvantage in addition to preventing teenage pregnancy. Additional econometric analyses around the mothers’, fathers’ and children’s long term socioeconomic and health-related outcomes would be valuable