January 14, 2018

Experimental Demonstration of Digital Pre-Distortion for Millimeter Wave Power Amplifiers with GHz Bandwidth

IEEE Radio Wireless Week (RWW)

This paper presents an experimental demonstration of digital pre-distortion (DPD) on E-band power amplifiers (PA) with GHz channel bandwidth.

Qi Tang, Hongyu Zhou, Abhishek Tiwari, Joseph Stewart, Qi Qu, Dawei Zhang, Hamid Hemmati
December 12, 2017

Supporting Diverse Dynamic Intent-based Policies using Janus

International Conference on emerging Networking EXperiments and Technologies (CoNEXT)

In this paper we propose Janus, a system which makes two major contributions to network policy abstractions. First, we extend the prior policy graph abstraction model to represent complex QoS and dynamic stateful/temporal policies. Second, we convert the policy configuration problem into an optimization problem with the goal of maximizing the number of satisfied and configured policies, and minimizing the number of path changes under dynamic environments.

Anubhavnidhi Abhashkumar, Joon-Myung Kang, Sujata Banerjee, Aditya Akella, Ying Zhang, Wenfei Wu
December 10, 2017

Social Structure and Trust in Massive Digital Markets

International Conference on Information Systems (ICIS)

In this paper we measure the extent to which situating transactions in networks can generate trust in online marketplaces with an empirical approach that provides external validity while eliminating many potential confounds.

David Holtz, Diana Lynn MacLean, Sinan Aral
December 4, 2017

Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

Neural Information Processing Systems (NIPS)

We present a novel training framework for neural sequence models, particularly for grounded dialog generation.

Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra
December 4, 2017

Fader Networks: Manipulating Images by Sliding Attributes

Neural Information Processing Systems (NIPS)

This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space.

Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc’Aurelio Ranzato
December 4, 2017

One-Sided Unsupervised Domain Mapping

Neural Information Processing Systems (NIPS)

In this work, we present a method of learning GAB without learning GBA. This is done by learning a mapping that maintains the distance between a pair of samples.

Sagie Benaim, Lior Wolf
December 4, 2017

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

Neural Information Processing Systems (NIPS)

In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research.

Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, Larry Zitnick
December 4, 2017

Unbounded cache model for online language modeling with open vocabulary

Neural Information Processing Systems (NIPS)

In this paper, we propose an extension of continuous cache models, which can scale to larger contexts. In particular, we use a large scale non-parametric memory component that stores all the hidden activations seen in the past.

Edouard Grave, Moustapha Cisse, Armand Joulin
December 4, 2017

VAIN: Attentional Multi-agent Predictive Modeling

Neural Information Processing Systems (NIPS)

In this paper we introduce VAIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems.

Yedid Hoshen
November 27, 2017

Fast Gaze-Contingent Optimal Decompositions for Multifocal Displays

SIGGRAPH ASIA

Our goal is to enable interactive optimal decomposition algorithms capable of driving a vergence- and accommodation-tracked multifocal testbed. Ultimately, such a testbed is necessary to establish the requirements for the practical use of multifocal displays, in terms of computational demand and hardware accuracy. To this end, we present an efficient algorithm for optimal decompositions, incorporating insights from vision science.

Oliver Mercier, Yusufu Sulai, Kevin Mackenzie, Marina Zannoli, James Hillis, Derek Nowrouzezahrai, Douglas Lanman
November 14, 2017

Fine Pointing Concepts for Optical Intersatellite Links

International Conference on Space Optical Systems (ICSOS)

In this paper we present two fine pointing architectures that support high-accuracy pointing, while also featuring simplification of the small-space optical assembly hardware, reduction in the number and complexity of constituent components, full self-calibration capability, and relaxation of otherwise-strict, cost-driving subassembly requirements.

Eric Miller, Kevin Birnbaum, Chien-Chung Chen, Andrew Grier, Matt Hunwardsen, Dominic Jandrain
November 1, 2017

High-Resolution Measurement of Data Center Microbursts

ACM Internet Measurement Conference

In this study, we explore the fine-grained behaviors of a large production data center using extremely highresolution measurements (10s to 100s of microsecond) of rack-level traffic. Our results show that characterizing network events like congestion and synchronized behavior in data centers does indeed require the use of such measurements.

Qiao Zhang, Vincent Liu, James Hongyi Zeng, Arvind Krishnamurthy
November 1, 2017

An Empirical Characterization of IFTTT: Ecosystem, Usage, and Performance

ACM Internet Measurement Conference 2017

IFTTT is a popular trigger-action programming platform whose applets can automate more than 400 services of IoT devices and web applications. We conduct an empirical study of IFTTT using a combined approach of analyzing data collected for 6 months and performing controlled experiments using a custom testbed.

Xianghang Mi, Feng Qian, Ying Zhang, XiaoFeng Wang
October 30, 2017

Crowd Intelligence Enhances Automated Mobile Testing

Automated Software Engineering Conference (ASE)

In this paper we show that information extracted from crowdbased testing can enhance automated mobile testing and introduce POLARIZ, which generates replicable test scripts from crowd-based testing, extracting cross-app ‘motif’ events.

Ke Mao, Mark Harman, Yue Jia
October 29, 2017

SVE: Distributed Video Processing at Facebook Scale

Symposium on Operating Systems Principles (SOSP)

This paper describes the evolution from our initial monolithic encoding script (MES) system to our current Streaming Video Engine (SVE) to upload and process videos. SVE has been in production since the fall of 2015, provides lower latency than MES, supports many diverse video applications, and has proven to be reliable despite faults and overload.

Qi Huang, Petchean Ang, Peter Knowles, Tomasz Nykiel, Iaroslav Tverdokhlib, Amit Yajurvedi, Paul Dapolito IV, Xifan Yan, Maxim Bykov, Chuen Liang, Mohit Talwar, Abhishek Mathur, Sachin Kulkarni, Matthew Burke, Wyatt Lloyd
October 28, 2017

Canopy: An End-to-End Performance Tracing and Analysis System

Symposium on Operating Systems Principles (SOSP)

This paper presents Canopy, Facebook’s end-to-end performance tracing infrastructure. Using Canopy, Facebook engineers can query and analyze performance data in real-time.

Jonathan Kaldor, Jonathan Mace, Michał Bejda, Edison Gao, Wiktor Kuropatwa, Joe O’Neill, Kian Win Ong, Bill Schaller, Pingjia Shan, Brendan Viscomi, Vinod Venkataraman, Kaushik Veeraraghavan, Yee Jiun Song
October 25, 2017

DodecaPen: Accurate 6DoF Tracking of a Passive Stylus

ACM Symposium on User Interface Software and Technology (UIST)

We propose a system for real-time six degrees of freedom (6DoF) tracking of a passive stylus that achieves sub-millimeter accuracy, which is suitable for writing or drawing in mixed reality applications.

Po-Chen Wu, Robert Wang, Kenrick Kin, Christopher Twigg, Shangchen Han, Ming-Hsuan Yang, Shao-Yi Chien
October 22, 2017

Learning Visual N-Grams from Web Data

International Conference on Computer Vision (ICCV)

This paper explores the training of image-recognition systems on large numbers of images and associated user comments, without using manually labeled images.

Ang Li, Allan Jabri, Armand Joulin, Laurens van der Maaten
October 22, 2017

Transitive Invariance for Self-supervised Visual Representation Learning

International Conference on Computer Vision (ICCV)

In this paper, we propose to exploit different self-supervised approaches to learn representations invariant to (i) inter-instance variations (two objects in the same class should have similar features) and (ii) intra-instance variations (viewpoint, pose, deformations, illumination, etc.).

Xiaolong Wang, Kaiming He, Abhinav Gupta
October 22, 2017

Mask R-CNN

International Conference on Computer Vision (ICCV)

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.

Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick