6,573 research outputs found
Heat conductivity from molecular chaos hypothesis in locally confined billiard systems
We study the transport properties of a large class of locally confined
Hamiltonian systems, in which neighboring particles interact through hard core
elastic collisions. When these collisions become rare and the systems large, we
derive a Boltzmann-like equation for the evolution of the probability
densities. We solve this equation in the linear regime and compute the heat
conductivity from a Green-Kubo formula. The validity of our approach is
demonstated by comparing our predictions to the results of numerical
simulations performed on a new class of high-dimensional defocusing chaotic
billiards.Comment: 4 pages, 2 color figure
Dewetting of Thin Viscoelastic Polymer Films on Slippery Substrates
Dewetting of thin polystyrene films deposited onto silicone wafers at
temperatures close to the glass transition exhibits unusual dynamics and front
morphologies. Here, we present a new theoretical approach of these phenomena
taking into account both the viscoelastic properties of the film and the
non-zero velocity of the film at the interface with the substrate (due to
slippage). We then show how these two ingredients lead to : (a) A very
asymmetric shape of the rim as the film dewetts, (b) A decrease of the
dewetting velocity with time like for times shorter than the
reptation time (for larger times, the dewetting velocity reaches a constant
value). Very recent experiments by Damman, Baudelet and Reiter [Phys. Rev.
Lett. {\bf 91}, 216101 (2003)] present, however, a much faster decrease of the
dewetting velocity. We then show how this striking result can be explained by
the presence of residual stresses in the film.Comment: Submitted to PR
Seven Sins in Portfolio Optimization
Although modern portfolio theory has been in existence for over 60 years,
fund managers often struggle to get its models to produce reliable portfolio
allocations without strongly constraining the decision vector by tight bands of
strategic allocation targets. The two main root causes to this problem are
inadequate parameter estimation and numerical artifacts. When both obstacles
are overcome, portfolio models yield excellent allocations. In this paper,
which is primarily aimed at practitioners, we discuss the most common mistakes
in setting up portfolio models and in solving them algorithmically
Exchange rate pass-through in a competitive model of pricing-to-market
This paper extends the Mussa and Rosen (1978) model of quality-pricing under perfect competition. Exporters sell goods of different qualities to consumers who have heterogeneous preferences for quality. Production is subject to decreasing returns to scale and, therefore, supply and the toughness of competition react to cost changes brought about by exchange rate fluctuations. First, we predict that exchange rate shocks are imperfectly passed through into prices. Second, prices of low quality goods are more sensitive to exchange rate shocks than prices of high quality goods. Third, in response to an exchange rate appreciation, the composition of exports shifts towards higher quality and more expensive goods.> ; We test these predictions using highly disaggregated price and quantity U.S. import data. We find evidence that in response to an exchange rate appreciation, the composition of exports shifts towards high unit price goods. Therefore, exchange rate passthrough rates that are measured using aggregate data will tend to overstate the actual extent of pass-through.Foreign exchange rates ; Econometric models ; International trade
Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster
We develop a stochastic modeling approach based on spatial point processes of
log-Gaussian Cox type for a collection of around 5000 landslide events provoked
by a precipitation trigger in Sicily, Italy. Through the embedding into a
hierarchical Bayesian estimation framework, we can use the Integrated Nested
Laplace Approximation methodology to make inference and obtain the posterior
estimates. Several mapping units are useful to partition a given study area in
landslide prediction studies. These units hierarchically subdivide the
geographic space from the highest grid-based resolution to the stronger
morphodynamic-oriented slope units. Here we integrate both mapping units into a
single hierarchical model, by treating the landslide triggering locations as a
random point pattern. This approach diverges fundamentally from the unanimously
used presence-absence structure for areal units since we focus on modeling the
expected landslide count jointly within the two mapping units. Predicting this
landslide intensity provides more detailed and complete information as compared
to the classically used susceptibility mapping approach based on relative
probabilities. To illustrate the model's versatility, we compute absolute
probability maps of landslide occurrences and check its predictive power over
space. While the landslide community typically produces spatial predictive
models for landslides only in the sense that covariates are spatially
distributed, no actual spatial dependence has been explicitly integrated so far
for landslide susceptibility. Our novel approach features a spatial latent
effect defined at the slope unit level, allowing us to assess the spatial
influence that remains unexplained by the covariates in the model
Max-infinitely divisible models and inference for spatial extremes
For many environmental processes, recent studies have shown that the
dependence strength is decreasing when quantile levels increase. This implies
that the popular max-stable models are inadequate to capture the rate of joint
tail decay, and to estimate joint extremal probabilities beyond observed
levels. We here develop a more flexible modeling framework based on the class
of max-infinitely divisible processes, which extend max-stable processes while
retaining dependence properties that are natural for maxima. We propose two
parametric constructions for max-infinitely divisible models, which relax the
max-stability property but remain close to some popular max-stable models
obtained as special cases. The first model considers maxima over a finite,
random number of independent observations, while the second model generalizes
the spectral representation of max-stable processes. Inference is performed
using a pairwise likelihood. We illustrate the benefits of our new modeling
framework on Dutch wind gust maxima calculated over different time units.
Results strongly suggest that our proposed models outperform other natural
models, such as the Student-t copula process and its max-stable limit, even for
large block sizes
Cost Pass Through in a Competitive Model of Pricing-to-Market
This paper builds up an extension to the Mussa and Rosen (1978) model of quality pricing under perfect competition. Our model incorporates decreasing returns to scale. First, we predict that exchange rate shocks are imperfectly passed through into prices. Second, prices of low quality goods are more sensitive to exchange rate shocks than prices of high quality goods. Third, in response to an exchange rate appreciation, the composition of exports shifts towards higher quality and more expensive goods. We test those predictions using highly disaggregated price and quantity US import data. We find that the prices of high quality goods, proxied as high unit price goods, are more sensitive to exchange rate movements. Moreover, we find evidence that in response to an exchange rate appreciation, the composition of exports shifts towards high unit price goods.Pricing-to-Market, Exchange Rate Pass Through, Local Distribution
Revisiting Actor Programming in C++
The actor model of computation has gained significant popularity over the
last decade. Its high level of abstraction makes it appealing for concurrent
applications in parallel and distributed systems. However, designing a
real-world actor framework that subsumes full scalability, strong reliability,
and high resource efficiency requires many conceptual and algorithmic additives
to the original model.
In this paper, we report on designing and building CAF, the "C++ Actor
Framework". CAF targets at providing a concurrent and distributed native
environment for scaling up to very large, high-performance applications, and
equally well down to small constrained systems. We present the key
specifications and design concepts---in particular a message-transparent
architecture, type-safe message interfaces, and pattern matching
facilities---that make native actors a viable approach for many robust,
elastic, and highly distributed developments. We demonstrate the feasibility of
CAF in three scenarios: first for elastic, upscaling environments, second for
including heterogeneous hardware like GPGPUs, and third for distributed runtime
systems. Extensive performance evaluations indicate ideal runtime behaviour for
up to 64 cores at very low memory footprint, or in the presence of GPUs. In
these tests, CAF continuously outperforms the competing actor environments
Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page
Hydrodynamics of Binary Fluid Mixtures - An Augmented Multiparticle Collison Dynamics Approach
The Multiparticle Collision Dynamics technique (MPC) for hydrodynamics
simulations is generalized to binary fluid mixtures and multiphase flows, by
coupling the particle-based fluid dynamics to a Ginzburg-Landau free-energy
functional for phase-separating binary fluids. To describe fluids with a
non-ideal equation of state, an additional density-dependent term is
introduced. The new approach is verified by applying it to thermodynamics near
the critical demixing point, and interface fluctuations of droplets. The
interfacial tension obtained from the analysis of the capillary wave spectrum
agrees well with the results based on the Laplace-Young equation.
Phase-separation dynamics follows the Lifshitz-Slyozov law
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Brain network mechanisms in learning behavior
The study of learning has been a central focus of psychology and neuroscience since their inception. Cognitive neuroscience’s traditional approach to understanding learn-ing has been to decompose it into discrete cognitive processes with separable and localized underlying neural systems. While this focus on modular cognitive functions for individual brain areas has led to considerable progress, there is increasing evidence that much of learn-ing behavior relies on overlapping cognitive and neural systems, which may be harder to disentangle than previously envisioned. This is not surprising, as the processes underlying learning must involve widespread integration of information from sensory, affective, and motor sources. The standard tools of cognitive neuroscience limit our ability to describe processes that rely on widespread coordination of brain activity. To understand learning, it will be necessary to characterize dynamic co-activation at the circuit level.
In this dissertation, I present three studies that seek to describe the roles of distrib-uted brain networks in learning. I begin by giving an overview of our current understand-ing of multiple forms of learning, describing the neural and computational mechanisms thought to underlie incremental feedback-based learning and flexible episodic memory. I will focus in particular on the difficulties in separating these processes at the cognitive level and in localizing them to individual regions at the neural level. I will then describe recent findings that have begun to characterize the brain’s large-scale network structure, emphasiz-ing the potential roles that distributed networks could play in understanding learning and cognition more generally. I will end the introduction by reviewing current attempts to char-acterize the dynamics of large-scale brain networks, which will be essential for providing a mechanistic link to learning behavior.
Chapter 2 is a study demonstrating that intrinsic connectivity between the hippo-campus and the ventromedial prefrontal cortex, as well as between these regions and dis-tributed brain networks, is related to individual differences in the transfer of learning on a sensory preconditioning task. The hippocampus and ventromedial prefrontal cortex have both been shown to be involved in this type of learning, and this study represents an early attempt to link connectivity between individual regions and broader networks to learning processes.
Chapter 3 is a study that takes advantage of recent developments in mathematical modeling of temporal networks to demonstrate a relationship between large-scale network dynamics and reinforcement learning within individuals. This study shows that the flexibil-ity of network connectivity in the striatum is related to learning performance over time, as well as to individual differences in parameters estimated from computational models of re-inforcement learning. Notably, connectivity between the striatum and visual as well as or-bitofrontal regions increased over the course of the task, which is consistent with an inte-grative role for the region in learning value-based associations. Network flexibility in a dis-tinct set of regions is associated with episodic memory for object images presented during the learning task.
Chapter 4 examines the role of dopamine, a neurotransmitter strongly linked to val-ue updating in reinforcement learning, in the dynamic network changes occurring during learning. Patients with Parkinson’s disease, who experience a loss of dopaminergic neu-rons in the substantia nigra, performed a reversal-learning task while undergoing functional magnetic resonance imaging. Patients were scanned on and off of a dopamine precursor medication (levodopa) in a within-subject design in order to examine the impact of dopa-mine on brain network dynamics during learning. The reversal provided an experimental manipulation of dynamic connectivity, and patients on medication showed greater modula-tion of striatal-cortical connectivity. Similar results were found in a number of regions re-ceiving midbrain projections including the prefrontal cortex and medial temporal lobe. This study indicates that dopamine inputs from the midbrain modulate large-scale network dy-namics during learning, providing a direct link between reinforcement learning theories of value updating and network neuroscience accounts of dynamic connectivity.
Together, these results indicate that large-scale networks play a critical role in multi-ple forms of learning behavior. Each highlights the potential importance of understanding dynamic routing and integration of information across large-scale circuits for our concep-tion of learning and other cognitive processes. Understanding the when, where, and how of this information flow in the brain may provide an alternative or compliment to traditional theories of distinct learning systems. These studies also illustrate challenges in integrating this perspective with established theories in cognitive neuroscience. Chapter 5 will situate the studies in a broader discussion of how brain activity relates to cognition in general, while pointing out current roadblocks and potential ways forward for a cognitive network neuroscience of learning
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