Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning

We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect further evidence to help discriminate potential threats. The main technical challenges are the partial observability of the agent intent, the adversary modeling, and the corresponding uncertainty modeling. Note that an adversary agent may act to mislead the autonomous agent by using a deceptive strategy that is learned from past experiences. We propose an approach that combines belief space planning, generative adversary modeling, and maximum entropy reinforcement learning to obtain a stochastic belief space policy. By accounting for various adversarial behaviors in the simulation framework and minimizing the predictability of the autonomous agent's action, the resulting policy is more robust to unmodeled adversarial strategies. This improved robustness is empirically shown against an adversary that adapts to and exploits the autonomous agent's policy when compared with a standard Chance-Constraint Partially Observable Markov Decision Process robust approach

Can TIPS help identify long-term inflation expectations?

Investors and policymakers have long hoped that Treasury Inflation Protected Securities (TIPS) would provide an accurate measure of long-term market inflation expectations. To make informed decisions and to ensure that inflation does not erode the purchasing power of their assets, investors need to assess the rate of inflation expected by other market participants. Having an accurate measure of market inflation expectations can also help policymakers assess their effectiveness in controlling long-term inflation, as well as their credibility among market participants.> Until recently, however, the only sources of information about long-term inflation expectations were surveys and the term structure of interest rates, neither of which were considered highly reliable. With the introduction of TIPS in 1997, it was hoped that a new measure of market inflation expectations—the difference in yields between conventional Treasuries and TIPS—would become available.> Shen and Corning examine the empirical evidence on the behavior of the yield difference and the liquidity of the TIPS market. They find that the yield difference has not provided a good measure of market inflation expectations because of the large and variable liquidity premium on TIPS. Still, the yield difference may become a better measure of market inflation expectations as liquidity conditions in the two kinds of Treasury markets move closer in the future.Inflation (Finance) ; Government securities

Transferable Pedestrian Motion Prediction Models at Intersections

One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection. We first discussed the feature selection for transferable pedestrian motion models in general. Following this discussion, we developed one transferable pedestrian motion prediction algorithm based on Inverse Reinforcement Learning (IRL) that infers pedestrian intentions and predicts future trajectories based on observed trajectory. We evaluated our algorithm on a dataset collected at two intersections, trained at one intersection and tested at the other intersection. We used the accuracy of augmented semi-nonnegative sparse coding (ASNSC), trained and tested at the same intersection as a baseline. The result shows that the proposed algorithm improves the baseline accuracy by 40% in the non-transfer task, and 16% in the transfer task

Metropolitan Accessibility and Transportation Sustainability: Comparative Indicators for Policy Reform

Accessibility is most commonly studied and measured within the context of a single metropolitan region. By contrast, this study applies metrics of accessibility (for work, non-work, by auto and transit) that incorporate both mobility and proximity to 38 of the largest 50 U.S. metropolitan areas. This cross-sectional analysis allows both intermetropolitan comparison (of accessibility overall and of the equity of its distribution) and assessment of the determinants of metropolitan accessibility. The two components of accessibility analyzed here—mobility and proximity—exist in tension with each other: places with rapid surface travel are usually places where origins and destinations are far apart; places with many origins and destinations in close proximity are places where travel tends to be slow. For this reason, it is not apparent which urban forms offers greater accessibility: those with spread-out land uses and more rapid travel, or more compact arrangements in which travel is slower. There are good theoretical reasons to expect that surface travel speeds are all-important in determining accessibility outcomes and that anything that interferes with surface travel speeds—including denser metropolitan development—might degrade accessibility. Empirical results presented here suggest the opposite: more compact metropolitan regions offer greater auto accessibility even if their travel speeds are somewhat slower. In other words, the proximity effect of density dominates any associated degradation in travel speeds. This suggests that reform of policies that spur low-density, auto-oriented development can yield transportation benefits in terms of increased metropolitan accessibility. The report also develops indicators for assessing the equity of the distribution of accessibility between individuals within a region. Indicators developed here capture accessibility distributions across dimensions of income, race, and car ownership. Even with a given accessibility distribution by auto and by transit, the equity of the accessibility distribution also depends on the location of carless households within a metropolitan region; indicators are also developed to capture this effect.EPA Agreement Number: RD-833334901-0 and FHWA Cooperative Agreement Number: DTFH61-07-H-00037https://deepblue.lib.umich.edu/bitstream/2027.42/147459/1/MetropolitanAccessibilityTransportationSustainability.pdfDescription of MetropolitanAccessibilityTransportationSustainability.pdf : Technical repor

Top-down neural attention by excitation backprop

We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we introduce the concept of contrastive attention to make the top-down attention maps more discriminative. In experiments, we demonstrate the accuracy and generalizability of our method in weakly supervised localization tasks on the MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is further validated in the text-to-region association task. On the Flickr30k Entities dataset, we achieve promising performance in phrase localization by leveraging the top-down attention of a CNN model that has been trained on weakly labeled web images.https://arxiv.org/abs/1608.00507Accepted manuscrip