A linear nonequilibrium thermodynamics approach to optimization of thermoelectric devices

Improvement of thermoelectric systems in terms of performance and range of applications relies on progress in materials science and optimization of device operation. In this chapter, we focuse on optimization by taking into account the interaction of the system with its environment. For this purpose, we consider the illustrative case of a thermoelectric generator coupled to two temperature baths via heat exchangers characterized by a thermal resistance, and we analyze its working conditions. Our main message is that both electrical and thermal impedance matching conditions must be met for optimal device performance. Our analysis is fundamentally based on linear nonequilibrium thermodynamics using the force-flux formalism. An outlook on mesoscopic systems is also given.Comment: Chapter 14 in "Thermoelectric Nanomaterials", Editors Kunihito Koumoto and Takao Mori, Springer Series in Materials Science Volume 182 (2013

The Spin Structure of the Nucleon

We present an overview of recent experimental and theoretical advances in our understanding of the spin structure of protons and neutrons.Comment: 84 pages, 29 figure

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the 21st century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include: warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land-use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia's role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large scale water withdrawals, land use and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that Integrated Assessment Models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

Natural Movie - Grass Stalks

A raw .avi file used as a stimulus in some of our experiments. This is a recording of grass stalks swaying in the wind (60Hz frame rate, 8 bit depth, gray scale, ~7 minutes in length

Natural Movie - Water Surface (Ripples)

A raw .avi file used as a stimulus in experiments at the Princeton Neuroscience Institute. This is a recording of a water surface near a dam near carnegie lake (60Hz frame rate, 8 bit depth, gray scale, ~7 minutes in length

The Structured `Low Temperature' Phase of the Retinal Population Code

The README.txt file within the .zip file contains a detailed description of this dataset's contentRecent advances in experimental techniques have allowed the simultaneous recordings of populations of hundreds of neurons, fostering a debate about the nature of the collective structure of population neural activity. Much of this debate has focused on the empirical findings of a phase transition in the parameter space of maximum entropy models describing the measured neural probability distributions, interpreting this phase transition to indicate a critical tuning of the neural code. Here, we instead focus on the possibility that this is a first-order phase transition which provides evidence that the real neural population is in a `structured', collective state. We show that this collective state is robust to changes in stimulus ensemble and adaptive state. We find that the pattern of pairwise correlations between neurons has a strength that is well within the strongly correlated regime and does not require fine tuning, suggesting that this state is generic for populations of 100+ neurons. We find a clear correspondence between the emergence of a phase transition, and the emergence of attractor-like structure in the inferred energy landscape. A collective state in the neural population, in which neural activity patterns naturally form clusters, provides a consistent interpretation for our results

Noise-Robust Modes of the Retinal Population Code Have the Geometry of "Ridges" and Correspond to Neuronal Communities

An appealing new principle for neural population codes is that correlations among neurons organize neural activity patterns into a discrete set of clusters, which can each be viewed as a noise-robust population codeword. Previous studies assumed that these codewords corresponded geometrically with local peaks in the probability landscape of neural population responses. Here, we analyze multiple data sets of the responses of approximately 150 retinal ganglion cells and show that local probability peaks are absent under broad, nonrepeated stimulus ensembles, which are characteristic of natural behavior. However, we find that neural activity still forms noise-robust clusters in this regime, albeit clusters with a different geometry. We start by defining a soft local maximum, which is a local probability maximum when constrained to a fixed spike count. Next, we show that soft local maxima are robustly present and can, moreover, be linked across different spike count levels in the probability landscape to form a ridge. We found that these ridges comprise combinations of spiking and silence in the neural population such that all of the spiking neurons are members of the same neuronal community, a notion from network theory. We argue that a neuronal community shares many of the properties of Donald Hebb’s classic cell assembly and show that a simple, biologically plausible decoding algorithm can recognize the presence of a specific neuronal community

Visualizing the structure of the models through the dwell times in sampling.

<p><b>A</b>. Distributions of dwell times for three sample states, estimated over 10⁴ separate instantiations of MC sampling from the full model. The persistence indices for states 1,2 and 3 were 0.092, 0.54, and 0.92, respectively. <b>B</b>. For the same states as in (<b>A</b>), distributions of dwell times estimated on shuffled (independent) data. <b>C</b>. Across the <i>N</i> = 3187 states with <i>K</i> = 12 spiking cells recorded in the data (M1, <i>light</i>) we measured the average dwell time (over 10³ MC runs) in the full (red) and independent (blue) models. These are plotted vs. the <i>PI</i> given by the full model. Note the logarithmic scale on the y-axis. <b>D</b>. The persistence indices for the same group of states are estimated using the maximum entropy model fitting the natural movie in the <i>light</i> (x-axis) and the <i>dark</i> (y-axis) adapted conditions.</p

Networks of model LN neurons.

<p>Model LN neurons were fit to the measured receptive fields as described in the text, <i>N</i> = 111 ganglion cells. <b>A</b>. Distributions of correlation coefficients over pairs of cells estimated in the training data (responding to checkerboard), and the simulated network of LN neurons. <b>B</b>. The specific heats in the checkerboard and simulated LN network. The independent curve is the same analytic estimate as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005792#pcbi.1005792.g005" target="_blank">Fig 5</a>.</p

Statistically significant changes in population structure across stimulus conditions.

<p><b>A</b>. Firing rate in the <i>light</i> condition plotted against firing rate in the <i>dark</i> for all <i>N</i> = 128 cells. Error bars are given by the standard error of the mean (SE) and are smaller than the plotted points. <b>B</b>. Pairwise correlation in the <i>light</i> plotted against pairwise correlation in the <i>dark</i>, for all 128 ⋅ 127/2 pairs of cells. Error Bars are not shown (but see panel <b>D</b>). Inset is the probability density function (PDF), on a log scale, of correlation coefficients. <b>C</b>. Measured P(K) in the two light conditions. <i>K</i> is the number of active cells in a state. Error bars are given by the SE. <b>D</b>. The PDF of z-scores of changes in correlation coefficients. The change in correlation coefficient is normalized by the error (). These error bars are standard deviations over bootstrap resamples of the data, estimated per cell pair. Data compares the <i>light</i> and <i>dark</i> adapted conditions (thick black line), the control compares a random half of the dark dataset to the other half (gray), and a numerical gaussian is plotted in red for comparison.</p