14,945 research outputs found
Statistical Computations Underlying the Dynamics of Memory Updating
Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.Templeton FoundationAlfred P. Sloan Foundation (Fellowship)National Science Foundation (U.S.) (NSF Graduate Research Fellowship)National Institute of Mental Health (U.S.) (NIH Award Number R01MH098861
Parallelization of a Dynamic Monte Carlo Algorithm: a Partially Rejection-Free Conservative Approach
We experiment with a massively parallel implementation of an algorithm for
simulating the dynamics of metastable decay in kinetic Ising models. The
parallel scheme is directly applicable to a wide range of stochastic cellular
automata where the discrete events (updates) are Poisson arrivals. For high
performance, we utilize a continuous-time, asynchronous parallel version of the
n-fold way rejection-free algorithm. Each processing element carries an lxl
block of spins, and we employ the fast SHMEM-library routines on the Cray T3E
distributed-memory parallel architecture. Different processing elements have
different local simulated times. To ensure causality, the algorithm handles the
asynchrony in a conservative fashion. Despite relatively low utilization and an
intricate relationship between the average time increment and the size of the
spin blocks, we find that for sufficiently large l the algorithm outperforms
its corresponding parallel Metropolis (non-rejection-free) counterpart. As an
example application, we present results for metastable decay in a model
ferromagnetic or ferroelectric film, observed with a probe of area smaller than
the total system.Comment: 17 pages, 7 figures, RevTex; submitted to the Journal of
Computational Physic
Supporting Regularized Logistic Regression Privately and Efficiently
As one of the most popular statistical and machine learning models, logistic
regression with regularization has found wide adoption in biomedicine, social
sciences, information technology, and so on. These domains often involve data
of human subjects that are contingent upon strict privacy regulations.
Increasing concerns over data privacy make it more and more difficult to
coordinate and conduct large-scale collaborative studies, which typically rely
on cross-institution data sharing and joint analysis. Our work here focuses on
safeguarding regularized logistic regression, a widely-used machine learning
model in various disciplines while at the same time has not been investigated
from a data security and privacy perspective. We consider a common use scenario
of multi-institution collaborative studies, such as in the form of research
consortia or networks as widely seen in genetics, epidemiology, social
sciences, etc. To make our privacy-enhancing solution practical, we demonstrate
a non-conventional and computationally efficient method leveraging distributing
computing and strong cryptography to provide comprehensive protection over
individual-level and summary data. Extensive empirical evaluation on several
studies validated the privacy guarantees, efficiency and scalability of our
proposal. We also discuss the practical implications of our solution for
large-scale studies and applications from various disciplines, including
genetic and biomedical studies, smart grid, network analysis, etc
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