Clamping improves TRW and mean field approximations

We examine the effect of clamping variables for approximate inference in undirected graphical models with pairwise relationships and discrete variables. For any number of variable labels, we demonstrate that clamping and summing approximate sub-partition functions can lead only to a decrease in the partition function estimate for TRW, and an increase for the naive mean field method, in each case guaranteeing an improvement in the approximation and bound. We next focus on binary variables, add the Bethe approximation to consideration and examine ways to choose good variables to clamp, introducing new methods. We show the importance of identifying highly frustrated cycles, and of checking the singleton entropy of a variable. We explore the value of our methods by empirical analysis and draw lessons to guide practitioners.NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by MIT Press

Playing catch and release with single molecules: mechanistic insights into plasmon-controlled nanogaps

Single-molecule (SM) detection is essential for investigating processes at the molecular level. Nanogap-based detection approaches have proven to be highly accurate SM capture and detection platforms in the last decade. Unfortunately, these approaches face several inherent drawbacks, such as short detection times and the effects of Brownian motion, that can hinder molecular capture. Nanogap-based SM detection approaches have been successfully coupled to optical-based setups to exploit nearfield-assisted trapping to overcome these drawbacks and thus improve SM capture and detection. Here we present the first mechanistic study of nearfield effects on SM capture and release in nanogaps, using unsupervised machine learning methods based on hidden Markov models. We show that the nearfield strength can manipulate the kinetics of the SM capture and release processes. With increasing field strength, the rate constant of the capture kinetics increase while the release kinetics decrease, favouring the former over the latter. As a result, the SM capture state is more likely and more stable than the release state above a specific threshold nearfild strength. We have also estimated the decrease in the capture free-energy profile and the increase in the release profiles to be around 5 kJ/mol for the laser powers employed, ranging from laser-OFF conditions to 11 mW/μm2. We envisage that our findings can be combined with the electrocatalytic capabilities of the (nearfield) nanogap to develop nextgeneration molecular nanoreactors. This approach will open the door to highly efficient SM catalysis with precise extended monitoring timescales facilitated through the longer residence times of the reactant trapped inside the nanogap

Study of cost/benefit tradeoffs for reducing the energy consumption of the commercial air transportation system

Economic studies were conducted for three general fuel conserving options: (1) improving fuel consumption characteristics of existing aircraft via retrofit modifications; (2) introducing fuel efficient derivations of existing production aircraft and/or introducing fuel efficient, current state-of-the-art new aircraft; and (3) introducing an advanced state-of-the-art turboprop airplane. These studies were designed to produce an optimum airline fleet mix for the years 1980, 1985 and 1990. The fleet selected accommodated a normal growth market by introducing somewhat larger aircraft while solving for maximum departure frequencies and a minimum load factor corresponding to a 15% investment hurdle rate. Fuel burnt per available-seat-mile flown would drop 22% from 1980 to 1990 due to the use of more fuel efficient aircraft designs, larger average aircraft size, and increased seating density. An inflight survey was taken to determine air traveler attitudes towards a new generation of advanced turboprops

Why Do (We Think) They Hate Us: Anti-Americanism, Patriotic Messages, and Attributions of Blame

This study explores how news coverage about anti-American sentiment interacts with U.S. adults’ sense of national identity and affects their understandings and interpretations of such negative attitudes. We build on scholarship on patriotism and social identity to conduct an experiment in which participants read one of two news stories focused on anti-American impressions. The findings suggest that news content influences both (a) how Americans interpret anti-American sentiment in general and (b) how Americans draw upon their identification with the nation in formulating attributions of blame for such sentiments and in deciding on what foreign policies to support

Revisiting loss-specific training of filter-based MRFs for image restoration

It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision. Recent years have seen the emergence of two main approaches for learning the parameters in MRFs: (1) probabilistic learning using sampling-based algorithms and (2) loss-specific training based on MAP estimate. After investigating existing training approaches, it turns out that the performance of the loss-specific training has been significantly underestimated in existing work. In this paper, we revisit this approach and use techniques from bi-level optimization to solve it. We show that we can get a substantial gain in the final performance by solving the lower-level problem in the bi-level framework with high accuracy using our newly proposed algorithm. As a result, our trained model is on par with highly specialized image denoising algorithms and clearly outperforms probabilistically trained MRF models. Our findings suggest that for the loss-specific training scheme, solving the lower-level problem with higher accuracy is beneficial. Our trained model comes along with the additional advantage, that inference is extremely efficient. Our GPU-based implementation takes less than 1s to produce state-of-the-art performance.Comment: 10 pages, 2 figures, appear at 35th German Conference, GCPR 2013, Saarbr\"ucken, Germany, September 3-6, 2013. Proceeding

Superpixel Convolutional Networks using Bilateral Inceptions

In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception module between the last CNN (1x1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201