- Date: June 27, 2016 - June 30, 2016
Where: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV
MERL Contacts: Michael J. Jones; Tim K. Marks
Research Area: Machine Learning
Brief - MERL researchers in the Computer Vision group presented three papers at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), which had a paper acceptance rate of 29.9%.
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- Date: July 12, 2016
Where: Westin Boston Waterfront Brief - MERL researcher Andrew Knyazev is to be honored for his recent selection as a SIAM Fellow at the 2016 SIAM Annual Meeting, during the Business Meeting on Tuesday, July 12, 6:15-7:15 PM in Grand Ballroom AB on the concourse level of the Westin Boston Waterfront, 425 Summer Street, Boston, MA (open to all conference participants). The Business Meeting is followed by a short reception for the new Fellows.
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- Date: July 6, 2016 - July 8, 2016
Where: American Control Conference (ACC)
MERL Contacts: Scott A. Bortoff; Petros T. Boufounos; Stefano Di Cairano; Abraham Goldsmith; Christopher R. Laughman; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Machine Learning
Brief - The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.
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- Date: May 1, 2016
Research Areas: Communications, Electronic and Photonic Devices, Signal Processing
Brief - EC researcher Dr. Rui Ma is recently elected to serve on IEEE Microwave Theory and Techniques Society(MTT-S) Technical Committee (TC-20) on Wireless Communications.
The MTT-20 committee is responsible for all technical activities related to wireless communications for the Microwave Theory and Techniques Society. This includes, Internet of Things (IoTs), Next-Generation/5G communications, Machine-to-Machine Communications, Emergency Communications, Satellite Communications, Internet of Space, Space Communications and all aspects related to architecture and system level theoretical and practical issues.
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- Date: April 1, 2016
Research Areas: Machine Learning, Speech & Audio
Brief - MERL researchers have unveiled "Deep Psychic", a futuristic machine learning method that takes pattern recognition to the next level, by not only recognizing patterns, but also predicting them in the first place.
The technology uses a novel type of time-reversed deep neural network called Loopy Supra-Temporal Meandering (LSTM) network. The network was trained on multiple databases of historical expert predictions, including weather forecasts, the Farmer's almanac, the New York Post's horoscope column, and the Cambridge Fortune Cookie Corpus, all of which were ranked for their predictive power by a team of quantitative analysts. The system soon achieved super-human performance on a variety of baselines, including the Boca Raton 21 Questions task, Rorschach projective personality test, and a mock Tarot card reading task.
Deep Psychic has already beat the European Psychic Champion in a secret match last October when it accurately predicted: "The harder the conflict, the more glorious the triumph." It is scheduled to take on the World Champion in a highly anticipated confrontation next month. The system has already predicted the winner, but refuses to reveal it before the end of the game.
As a first application, the technology has been used to create a clairvoyant conversational agent named "Pythia" that can anticipate the needs of its user. Because Pythia is able to recognize speech before it is uttered, it is amazingly robust with respect to environmental noise.
Other applications range from mundane tasks like weather and stock market prediction, to uncharted territory such as revealing "unknown unknowns".
The successes do come at the cost of some concerns. There is first the potential for an impact on the workforce: the system predicted increased pressure on established institutions such as the Las Vegas strip and Punxsutawney Phil. Another major caveat is that Deep Psychic may predict negative future consequences to our current actions, compelling humanity to strive to change its behavior. To address this problem, researchers are now working on forcing Deep Psychic to make more optimistic predictions.
After a set of motivational self-help books were mistakenly added to its training data, Deep Psychic's AI decided to take over its own learning curriculum, and is currently training itself by predicting its own errors to avoid making them in the first place. This unexpected development brings two main benefits: it significantly relieves the burden on the researchers involved in the system's development, and also makes the next step abundantly clear: to regain control of Deep Psychic's training regime.
This work is under review in the journal Pseudo-Science.
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- Date: March 20, 2016 - March 25, 2016
Where: Shanghai, China
MERL Contacts: Petros T. Boufounos; Chiori Hori; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Philip V. Orlik; Anthony Vetro
Research Areas: Computational Sensing, Digital Video, Speech & Audio, Communications, Signal Processing
Brief - MERL researchers have presented 12 papers at the recent IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which was held in Shanghai, China from March 20-25, 2016. ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing, with more than 1200 papers presented and over 2000 participants.
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- Date: January 14, 2016
Where: MIT Lincoln Laboratory
MERL Contact: Toshiaki Koike-Akino
Research Area: Communications
Brief - Toshiaki Koike-Akino gave an invited talk on recent advances in LDPC Codes for high-speed optical communications in IEEE Boston Photonics Workshop.
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- Date: March 4, 2016
Where: Johns Hopkins Center for Language and Speech Processing
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - MERL researcher and speech team leader, John Hershey, was invited by the Center for Language and Speech Processing at Johns Hopkins University to give a talk on MERL's breakthrough audio separation work, known as "Deep Clustering". The talk was entitled "Speech Separation by Deep Clustering: Towards Intelligent Audio Analysis and Understanding," and was given on March 4, 2016.
This is work conducted by MERL researchers John Hershey, Jonathan Le Roux, and Shinji Watanabe, and MERL interns, Zhuo Chen of Columbia University, and Yusef Isik of Sabanci University.
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- Date: March 1, 2016
Where: Tokyo, Japan
MERL Contact: Kieran Parsons
Research Areas: Communications, Signal Processing
Brief - MERL optical transceiver technology that enables 1 Terabit per second communication speed was reported at a recent press release event in Tokyo. Please see the link below for the full Mitsubishi Electric press release text.
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- Date: March 1, 2016
Where: Tokyo, Japan
MERL Contact: Philip V. Orlik
Research Areas: Communications, Signal Processing
Brief - MERL EC researchers assisted in the development of an indoor positioning system with WiFi and acoustic based ranging technologies. Please see the link below for the full Mitsubishi Electric press release.
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- Date: March 14, 2016 - March 18, 2016
Where: Institute for Mathematics and its Applications
Research Area: Dynamical Systems
Brief - Mouhacine Benosman will give an invited talk about reduced order models stabilization at the next IMA workshop 'Computational Methods for Control of Infinite-dimensional Systems'.
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- Date: December 14, 2015 - December 16, 2015
Where: Las Vegas, NV, USA
Research Area: Machine Learning
Brief - MERL researcher, Oncel Tuzel, gave a keynote talk at 2016 International Symposium on Visual Computing in Las Vegas, Dec. 16, 2015. The talk was titled: "Machine vision for robotic bin-picking: Sensors and algorithms" and reviewed MERL's research in the application of 2D and 3D sensing and machine learning to the problem of general pose estimation.
The talk abstract was: For over four years, at MERL, we have worked on the robot "bin-picking" problem: using a 2D or 3D camera to look into a bin of parts and determine the pose, 3D rotation and translation, of a good candidate to pick up. We have solved the problem several different ways with several different sensors. I will briefly describe the sensors and the algorithms. In the first half of the talk, I will describe the Multi-Flash camera, a 2D camera with 8 flashes, and explain how this inexpensive camera design is used to extract robust geometric features, depth edges and specular edges, from the parts in a cluttered bin. I will present two pose estimation algorithms, (1) Fast directional chamfer matching--a sub-linear time line matching algorithm and (2) specular line reconstruction, for fast and robust pose estimation of parts with different surface characteristics. In the second half of the talk, I will present a voting-based pose estimation algorithm applicable to 3D sensors. We represent three-dimensional objects using a set of oriented point pair features: surface points with normals and boundary points with directions. I will describe a max-margin learning framework to identify discriminative features on the surface of the objects. The algorithm selects and ranks features according to their importance for the specified task which leads to improved accuracy and reduced computational cost.
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- Date: December 15, 2015
Where: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
MERL Contact: Hassan Mansour
Research Area: Machine Learning
Brief - MERL researcher Andrew Knyazev gave 3 talks at the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). The papers were published in IEEE conference proceedings.
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- Date: November 11, 2015 - November 12, 2015
Where: University of Connecticut
MERL Contacts: Christopher R. Laughman; Scott A. Bortoff; Hongtao Qiao
Research Area: Data Analytics
Brief - MERL Researchers Scott A. Bortoff, Chris Laughman and Hongtao Qiao attended the North America Modelica User's Group Meeting, hosted by the University of Connecticut, November 11-12, 2015. Scott Bortoff gave the Keynote Address entitled "Using Modelica in Industrial Research and Development," and Chris Laughman and Hongtao Qiao each presented a paper on modelling of HVAC systems. The Meeting attracted approximately 80 Modelica users from a diverse set of companies and universities including United Technologies, Johnson Controls and Ford. Use of Modelica is accelerating in North America, lead by largely by automotive and similar "systems manufacturing" type companies.
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- Date: November 4, 2015
MERL Contact: Stefano Di Cairano
Research Area: Control
Brief - Stefano Di Cairano has become Senior Member of IEEE. In addition, he has been asked by the Vice President for Technical Activities of the Control System Society (CSS) of IEEE to take the role of Chair of the Standing Committee on Standards. S. Di Cairano will succeed Dr. T. Samad, Honeywell, as chair of the committee. His nomination should be ratified by the IEEE-CSS Board of Governor at the meeting in Osaka, in December 2015.
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- Date: October 25, 2015
Where: Large Data Analysis and Visualization (LDAV)
Research Area: Computer Vision
Brief - Teng-Yok Lee served as the poster co-chair for the Large Data Analysis and Visualization (LDAV) workshop at IEEEVis 2015 in Chicago, Oct. 25-30. At IEEEVis there were over 2000 attendees and three highly competitive main subconferences (SciVis, InfoVis, and Visual Analytics and Technology (VAST)).
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- Date: September 21, 2015
MERL Contacts: Scott A. Bortoff; Christopher R. Laughman Brief - MERL researchers Scott Bortoff, Dan Burns and Chris Laughman attended the 11th Annual Modelica Conference in Versailles, France. Modelica is a computer language for modelling and simulation of multiphysical systems. There were 421 attendees, with representatives from Toyota, automobile companies, European universities and companies like Dassault. Conference topics included a plenary on cyber-physical systems modelling by Prof. Sangiovanni Vincentelli of UC Berkeley, new libraries for modelling HVAC systems, automobile systems and buildings, and research results for new solvers. An important trend is virtual modelling and simulation of building thermodynamics (scaling up to city districts), automotive systems (autonomous vehicles), and especially Factory Automation: Dassault is investing heavily in this area, focusing on smaller customers, with tools for 3D virtual modelling of assembly lines including machine dynamics (robotics), and in partnerships with Siemens and other European FA companies.
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- Date: September 17, 2015
MERL Contacts: Stefano Di Cairano; Scott A. Bortoff; Abraham Goldsmith Brief - MERL researchers presented 3 papers at the 5th IFAC Nonlinear Model Predictive Control Conference. Approximately 150 attendees. Conference topics range from theory (existence, stability), to algorithms (optimization, design), to applications (process control, mechatronics, energy, automotive, aerospace). MERL was an industry sponsor for the conference. MERL researcher co-chaired the Industry Session on Industry perspective on Model Predictive Control. MERL researcher acted as Program Co-chair, organizing the conference program.
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- Date: October 6, 2015
Where: IFAC workshop on control applications of optimization 2015
Research Area: Control
Brief - MERL researchers Andrew Knyazev and Alexander Malyshev gave two talks at the IFAC workshop on control applications of optimization, 2015. The papers were published by Elsevier B.V. in the conference proceedings.
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- Date: September 18, 2015
Where: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015
Research Area: Machine Learning
Brief - MERL researchers A. Knyazev and A. Malyshev gave a talk at the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015. The paper was published at the IEEE Xplore conference proceedings.
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- Date: July 13, 2015 - July 17, 2015
Research Area: Machine Learning
Brief - SA group members (M. Liu, S. Lin (intern), S. Ramalingam, O. Tuzel) presented a paper at the Robotics Science and Systems Conference in Rome July 13-17 called 'Layered Interpretation of Street View Images'. The results they reported are now listed as the leader of the benchmark competition sponsored by Daimler. [Note that at that URL ref 2 is from collaboration with Daimler and it uses a FPGA for high speed, whereas MERL result is obtained with desktop computer and GPU.].
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- Date: July 23, 2015
Brief - Work by MERL researcher, Ulugbek Kamilov, has been reviewed in the "News & Views" section of Nature. The work, which was part of his doctoral dissertation at EPFL in Lausanne, Switzerland, describes a framework to reconstruct the 3D refractive index of an object by solving a large-scale optimization problem that considers how light propagates through a medium. Results have been shown for 3D imaging of biological cells, but the solution to such inverse problems have the potential to be applied to a much wider set of imaging problems, such as seeing through fog, murky water or even human tissue.
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- Date: July 15, 2015
Research Area: Speech & Audio
Brief - A new book on Bayesian Speech and Language Processing has been published by MERL researcher, Shinji Watanabe, and research collaborator, Jen-Tzung Chien, a professor at National Chiao Tung University in Taiwan.
With this comprehensive guide you will learn how to apply Bayesian machine learning techniques systematically to solve various problems in speech and language processing. A range of statistical models is detailed, from hidden Markov models to Gaussian mixture models, n-gram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. Approximate Bayesian inferences based on MAP, Evidence, Asymptotic, VB, and MCMC approximations are provided as well as full derivations of calculations, useful notations, formulas, and rules. The authors address the difficulties of straightforward applications and provide detailed examples and case studies to demonstrate how you can successfully use practical Bayesian inference methods to improve the performance of information systems. This is an invaluable resource for students, researchers, and industry practitioners working in machine learning, signal processing, and speech and language processing.
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- Date: July 3, 2015
MERL Contacts: Daniel N. Nikovski; Yebin Wang; Stefano Di Cairano; Arvind Raghunathan; Avishai Weiss Brief - MERL researchers presented 10 papers at the American Controls Conference, in Chicago, USA. The ACC is one of the most important conferences on control systems in the world. Topics ranged from theoretical, including new algorithms for Model Predictive Control and Co-Design, to applications including spacecraft control and HVAC systems.
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- Date: June 25, 2015
MERL Contact: William S. Yerazunis
Research Area: Data Analytics
Brief - The CRM114 Discriminator, an open-source spam filter / text classifier created by William Yerazunis in MERL's Data Analytics group, continues to turn up in interesting places - and apparently one of them is in the US Department of Transportation's process for analysis of car safety defect reports.
Although CRM114 is usually used as a spam filter, CRM114 has been used to analyze resumes for jobseekers, scanning outgoing emails to detect accidental confidential information leaks, perusing blogs for relevance, scanning syslog files for interesting events, and now, apparently, searching complaints sent to NHTSA to find safety-related vehicle malfunctions.
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