July 21, 2017

Discovering Causal Signals in Images

CVPR 2017

This paper establishes the existence of observable footprints that reveal the “causal dispositions” of the object categories appearing in collections of images.

David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Scholkopf, Leon Bottou
July 21, 2017

Relationship Proposal Networks

Conference on Computer Vision and Pattern Recognition 2017

In this paper we address the challenges of image scene object recognition by using pairs of related regions in images to train a relationship proposer that at test time produces a manageable number of related regions.

Ahmed Elgammal, Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang
July 21, 2017

Proton Testing Results for Kaman KD-5100 Differential Inductive Position Measuring Systems

Journal, IEEE Radiation Effects Data Workshop (REDW), in press

We report proton testing of a position measuring system, the Kaman KD-5100, with applications including mirror positioning for laser beam control. We measure a device response likely due to total ionizing dose and/or displacement damage.

Bart McGuyer, Randall Milanowski, Slaven Moro, Norman Hall, Bert Vermeire
July 21, 2017

Link the head to the “beak”: Zero Shot Learning from Noisy Text Description at Part Precision

CVPR 2017

In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. F

Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, Ahmed Elgammal
July 21, 2017

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

CVPR 2017

We present a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires.

Bharath Hariharan, Justin Johnson, Larry Zitnick, Laurens van der Maaten, Li Fei-Fei, Ross Girshick
July 21, 2017

Learning Features by Watching Objects Move

CVPR 2017

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation.

Deepak Pathak, Ross Girshick, Piotr Dollar, Trevor Darrell, Bharath Hariharan
July 21, 2017

Robocodes: Towards Generative Street Addresses from Satellite Imagery

CVPR 2017

This paper describes our automatic generative algorithm to create street addresses (Robocodes) from satellite images by learning and labeling regions, roads, and blocks. 75% of the world lacks street addresses.

Ilke Demir, Forest Hughes, Aman Raj, Kleovoulos Tsourides, Divyaa Ravichandran, Suryanarayana Murthy, Kaunil Dhruv, Sanyam Garg, Jatin Malhotra, Barrett Doo, Grace Kermani, Ramesh Raskar
July 21, 2017

Feature Pyramid Networks for Object Detection

CVPR 2017

In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost.

Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie
July 21, 2017

Semantic Amodal Segmentation

CVPR 2017

Common visual recognition tasks such as classification, object detection, and semantic segmentation are rapidly reaching maturity, and given the recent rate of progress, it is not unreasonable to conjecture that techniques for many of these problems will approach human levels of performance in the next few years. In this paper we look to the future: what is the next frontier in visual recognition?

Yan Zhu, Yuandong Tian, Dimitris Mexatas, Piotr Dollar
July 21, 2017

Aggregated Residual Transformations for Deep Neural Networks

CVPR 2017

We present a simple, highly modularized network architecture for image classification.

Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He
July 18, 2017

Patient-Driven Privacy through Generalized Distillation

Privacy Enhancing Technologies Symposium (PETS)

The introduction of data analytics into medicine has changed the nature of patient treatment. In this, patients are asked to disclose personal information such as genetic markers, lifestyle habits, and clinical history. This data is then used by statistical models to predict personalized treatments. However, due to privacy concerns, patients often desire to withhold sensitive information. This self-censorship can impede proper diagnosis and treatment, which may lead to serious health complications and even death over time. In this paper, we present privacy distillation, a mechanism which allows patients to control the type and amount of information they wish to disclose to the healthcare providers for use in statistical models.

Z. Berkay Celik, David Lopez-Paz, Patrick McDaniel
July 17, 2017

100/200 Gbps Optical Coherent DSP ASIC – SEE & TID Assessment

NSREC 2017

Experimental assessment of commercial 100/200 Gbps optical coherent DSP modem ASIC completed with 64 MeV and 480 MeV proton radiation test campaigns. Single event effect cross sections calculated and no performance degradation observed for proton fluence levels up to 1.27×10^12 p/cm^2 with equivalent total ionizing dose exposure to 170 krad(Si).

Raichelle Aniceto, Slaven Moro, Randall Milanowski, Christopher Isabelle, Norman Hall, Bert Vermeire, Kerri Cahoy
July 17, 2017

Single Event Effect Assessment of a 1-Mbit Commercial MRAM

2017 Nuclear and Space Radiation Effects Conference (NSREC) Radiation Effects Data Workshop

Single event effect susceptibility of a 1-Mbit commercial MRAM was experimentally evaluated. The memory exhibited SEFIs when operated in a dynamic mode with an LET threshold of 2.29 MeV.cm2/mg and a saturated cross section of 2.2×10-4 cm2/device. The memory was not sensitive to SEL, SEU or MBUs.

Philippe C. Adell, Slaven Moro, Lionel Gouyet, Christian Chatry, Bert Vermeire
July 10, 2017

House Price Beliefs and Mortgage Leverage Choice

National Bureau of Economic Researcher (NBER)

We study the the relationship between homebuyers’ beliefs about future house price changes and their mortgage leverage choices.

Michael Bailey, Eduardo Dávila, Theresa Kuchler, Johannes Stroebel
June 26, 2017

Multiplicative Pacing Equilibria in Auction Markets

Workshop on Algorithmic Game Theory and Data Science, ACM Conference on Economics and Computation

Budgets play a significant role in real-world sequential auction markets such as those implemented by Internet companies. To maximize the value provided to auction participants, spending is smoothed across auctions so budgets are used for the best opportunities. This paper considers a smoothing procedure that relies on pacing multipliers: for each bidder, the platform applies a factor between 0 and 1 that uniformly scales the bids across all auctions.

Vincent Conitzer, Christian Kroer, Eric Sodomka, Nicolas Stier
June 22, 2017

Is smoke on your mind? Using social media to improve smoke exposure estimates – Atmospheric Chemistry and Physics Discussions (ACPD)

Journal: Atmospheric Chemistry and Physics Discussions

In this work, we explore using daily social media posts from Facebook regarding smoke, haze, and air quality to assess population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook dataset to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), daily (24 h) average surface particulate matter measurements, and model-simulated (WRF-Chem) surface concentrations.

Bonne Ford, Gabriele Pfister, Jeffrey R. Pierce, Moira Burke, William Lassman
June 15, 2017

Formulation of Aerial Platform Environmental Requirements from Global Radiosonde Data

Journal of Unmanned Vehicle Systems

This paper demonstrates how raw observational climate data can be processed to characterize global climate histories and statistics for the formulation of aerial platform environmental requirements.

Jacob Stelman, Zhang Liu
June 8, 2017

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Data @ Scale

In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization.

Priya Goyal, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He
June 6, 2017

Analysis of Effective Impedance Transmitted to the Operator in Position-Exchange Bilateral Teleoperation

IEEE World Haptics 2017

In this paper, we analyze the impedance transmitted to the operator in bilateral teleoperation including the effects of master and slave dynamics, local and communication time delay, low-pass filtering of the velocity estimate, and controller stiffness and damping, for three different environment dynamics: free, clamped, and a mass-damper-spring.

Nick Colonnese, Allison M. Okamura
May 30, 2017

International Image Sensors Workshop

International Image Sensor Workshop (IISW)

In this paper, we provide examples of some tracking and mapping functions of virtual reality sensors that illustrate the critical requirements and performance metrics. The sensor performance, form factor, power, and data bandwidth are the main challenges in a battery powered, always on VR devices.

Chiao Liu, Michael Hall, Renzo De Nardi, Nicholas Trail, Richard Newcombe