Integrated Calendar

: Recurrent neural networks are an important class of models for explaining neural computations. Recently, there has been progress both in training these networks to perform various tasks, and in relating their activity to that recorded in the brain. Despite this progress, there are many fundamental gaps towards a theory of these networks. Neither the conditions for successful learning, nor the dynamics of trained networks are fully understood. I will present the rationale for using such networks for neuroscience research, and a detailed analysis of very simple tasks as an approach to build a theory of general trained recurrent neural networks.

Devices that combined electricity with moving parts were crucial to the very earliest electronic communications. Today, electromechanical structures are ubiquitous yet under-appreciated signal processing elements. Because the speed of sound is so slow compared to the speed of light, they are used to create compact filter and clock elements. Moreover they convert force and acceleration signals into more easily processed electrical signals. Can these humble, apparently classical, objects exhibit genuinely quantum behavior? Indeed—by strongly coupling the vibrations of a micromechanical oscillator to microwave frequency electrical signals, a mechanical oscillator can inherit a quantum state from an electrical signal. This recent and exciting result heralds the development of a quantum processors or quantum enhanced sensors that exploit the unique properties of mechanical systems. Furthermore, quantum electromechanics provides a powerful and versatile way to bring ever larger, more tangible objects into non-classical regimes.

I will present recent joint work with F. Rassoul-Agha (Utah) and T. Seppalainen (Madison) where we consider random walk on a hypercubic lattice of arbitrary dimension in a space-time random environment that is assumed to be temporally independent and spatially translation invariant. The large deviation principle (LDP) for the empirical velocity of the averaged walk (i.e., level-1) is simply Cramer’s theorem. We take the point of view of the particle and establish the process-level (i.e., level-3) averaged LDP for the environment Markov chain. The rate function $I_{3,a}$ is a specific relative entropy which reproduces Cramer’s rate function via the so-called contraction principle. We identify the unique minimizer of this contraction at any velocity and analyse its structure. When the environment is spatially ergodic, the level-3 quenched LDP follows from our previous work which gives a variational formula for the rate function $I_{3,q}$ involving a Donsker-Varadhan-type relative entropy $H_q$. We derive a decomposition formula for $I_{3,a}$ that expresses it as a sum of contributions from the walk (via $H_q$) and the environment. We use this formula to characterize the equality of the level-1 averaged and quenched rate functions, and conclude with several related results and open problems.

Understanding the response of East Asian monsoon (EAM) rainfall patterns to different climate forcings is cardinal for constraining future climate change over East Asia. The magnitude and rate of EAM rainfall changes during the late Pleistocene-Holocene is reconstructed using the first well-dated Northeastern China lake-area record from a closed-lake basin, which enables reconstructing quantitative absolute paleo-rainfall amounts. In addition, compound specific hydrogen isotopes from long-chain alkanes (Dleafwax) in the lake-sediments were used to reconstruct the isotopic composition of rainwater and lake water. Lake-levels were 60m higher than present during the early and middle Holocene. This requires an absolute increase in mean annual rainfall to at least two times higher than today. The EAM intensity and northern extent alternated abruptly between wet and dry periods on time scales of a few centuries. Both the onset (~60 m rise at 11.5 ka BP) and termination (~35 m drop at 5.5 ka BP) of the Holocene humid period occurred abruptly, within centuries. The co-variation of lake-area and Dleafwax show, for the first time, that the “amount effect” is the cardinal driver of the isotopic composition of paleo tropical rainfall. Thus, resolving a current debate regarding the ability to use the isotopic composition of rainwater as a proxy for rainfall amount and validating the “intensity-based” interpretations of the Chinese cave deposit records.

The DNA in a human cell is ~2 meters long. Although there are no definite structures that maintain the order in the nucleus, the genome is well organized, though dynamic. What are the mechanisms that organizes the DNA in the nucleus?
Dynamic methods in live cells are ideal for studying the genome organization, which is a soft-matter structure that have no definite structure. We currently used a whole spectrum of dynamic methods in live cells that will be briefly described.
We used single particle tracking (SPT) and continuous photobleacing (CP) that are adequate for live-cell imaging. The data is analyzed according to diffusion analysis methods that we developed. In normal cells, all the sites in the genome exhibit anomalous diffusion (viscoelastic) where