19,172 research outputs found
Near-critical fluctuations and cytoskeleton-assisted phase separation lead to subdiffusion in cell membranes
We address the relationship between membrane microheterogeneity and anomalous
subdiffusion in cell membranes by carrying out Monte Carlo simulations of
two-component lipid membranes. We find that near-critical fluctuations in the
membrane lead to transient subdiffusion, while membrane-cytoskeleton
interaction strongly affects phase separation, enhances subdiffusion, and
eventually leads to hop diffusion of lipids. Thus, we present a minimum
realistic model for membrane rafts showing the features of both microscopic
phase separation and subdiffusion.Comment: 21 pages, 5 figures; Supporting Material 5 pages, 1 figure, 1 tabl
Individualized Rank Aggregation using Nuclear Norm Regularization
In recent years rank aggregation has received significant attention from the
machine learning community. The goal of such a problem is to combine the
(partially revealed) preferences over objects of a large population into a
single, relatively consistent ordering of those objects. However, in many
cases, we might not want a single ranking and instead opt for individual
rankings. We study a version of the problem known as collaborative ranking. In
this problem we assume that individual users provide us with pairwise
preferences (for example purchasing one item over another). From those
preferences we wish to obtain rankings on items that the users have not had an
opportunity to explore. The results here have a very interesting connection to
the standard matrix completion problem. We provide a theoretical justification
for a nuclear norm regularized optimization procedure, and provide
high-dimensional scaling results that show how the error in estimating user
preferences behaves as the number of observations increase
Synchronized Oscillations During Cooperative Feature Linking in a Cortical Model of Visual Perception
A neural network model of synchronized oscillator activity in visual cortex is presented in order to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. In these recent experiments, it was reported that synchronization of oscillatory firing responses to moving bar stimuli occurred not only for nearby neurons, but also occurred between neurons separated by several cortical columns (several mm of cortex) when these neurons shared some receptive field preferences specific to the stimuli. These results were obtained not only for single bar stimuli but also across two disconnected, but colinear, bars moving in the same direction. Our model and computer simulations obtain these synchrony results across both single and double bar stimuli. For the double bar case, synchronous oscillations are induced in the region between the bars, but no oscillations are induced in the regions beyond the stimuli. These results were achieved with cellular units that exhibit limit cycle oscillations for a robust range of input values, but which approach an equilibrium state when undriven. Single and double bar synchronization of these oscillators was achieved by different, but formally related, models of preattentive visual boundary segmentation and attentive visual object recognition, as well as nearest-neighbor and randomly coupled models. In preattentive visual segmentation, synchronous oscillations may reflect the binding of local feature detectors into a globally coherent grouping. In object recognition, synchronous oscillations may occur during an attentive resonant state that triggers new learning. These modelling results support earlier theoretical predictions of synchronous visual cortical oscillations and demonstrate the robustness of the mechanisms capable of generating synchrony.Air Force Office of Scientific Research (90-0175); Army Research Office (DAAL-03-88-K0088); Defense Advanced Research Projects Agency (90-0083); National Aeronautics and Space Administration (NGT-50497
A Harmonic Extension Approach for Collaborative Ranking
We present a new perspective on graph-based methods for collaborative ranking
for recommender systems. Unlike user-based or item-based methods that compute a
weighted average of ratings given by the nearest neighbors, or low-rank
approximation methods using convex optimization and the nuclear norm, we
formulate matrix completion as a series of semi-supervised learning problems,
and propagate the known ratings to the missing ones on the user-user or
item-item graph globally. The semi-supervised learning problems are expressed
as Laplace-Beltrami equations on a manifold, or namely, harmonic extension, and
can be discretized by a point integral method. We show that our approach does
not impose a low-rank Euclidean subspace on the data points, but instead
minimizes the dimension of the underlying manifold. Our method, named LDM (low
dimensional manifold), turns out to be particularly effective in generating
rankings of items, showing decent computational efficiency and robust ranking
quality compared to state-of-the-art methods
Just Sort It! A Simple and Effective Approach to Active Preference Learning
We address the problem of learning a ranking by using adaptively chosen
pairwise comparisons. Our goal is to recover the ranking accurately but to
sample the comparisons sparingly. If all comparison outcomes are consistent
with the ranking, the optimal solution is to use an efficient sorting
algorithm, such as Quicksort. But how do sorting algorithms behave if some
comparison outcomes are inconsistent with the ranking? We give favorable
guarantees for Quicksort for the popular Bradley-Terry model, under natural
assumptions on the parameters. Furthermore, we empirically demonstrate that
sorting algorithms lead to a very simple and effective active learning
strategy: repeatedly sort the items. This strategy performs as well as
state-of-the-art methods (and much better than random sampling) at a minuscule
fraction of the computational cost.Comment: Accepted at ICML 201
Evaluating Cartogram Effectiveness
Cartograms are maps in which areas of geographic regions (countries, states)
appear in proportion to some variable of interest (population, income).
Cartograms are popular visualizations for geo-referenced data that have been
used for over a century and that make it possible to gain insight into patterns
and trends in the world around us. Despite the popularity of cartograms and the
large number of cartogram types, there are few studies evaluating the
effectiveness of cartograms in conveying information. Based on a recent task
taxonomy for cartograms, we evaluate four major different types of cartograms:
contiguous, non-contiguous, rectangular, and Dorling cartograms. Specifically,
we evaluate the effectiveness of these cartograms by quantitative performance
analysis, as well as by subjective preferences. We analyze the results of our
study in the context of some prevailing assumptions in the literature of
cartography and cognitive science. Finally, we make recommendations for the use
of different types of cartograms for different tasks and settings
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new
challenge in super-resolution problem. To address this issue, we analyze the
attributes of two methodologies and find two observations of their recovered
details: 1) they are complementary in both feature space and image plane, 2)
they distribute sparsely in the spatial space. These inspire us to propose a
low-rank solution which effectively integrates two learning methods and then
achieves a superior result. To fit this solution, the internal learning method
and the external learning method are tailored to produce multiple preliminary
results. Our theoretical analysis and experiment prove that the proposed
low-rank solution does not require massive inputs to guarantee the performance,
and thereby simplifying the design of two learning methods for the solution.
Intensive experiments show the proposed solution improves the single learning
method in both qualitative and quantitative assessments. Surprisingly, it shows
more superior capability on noisy images and outperforms state-of-the-art
methods
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