- Date: June 18, 2023 - June 22, 2023
Where: Vancouver/Canada
MERL Contacts: Anoop Cherian; Michael J. Jones; Suhas Lohit; Kuan-Chuan Peng
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - MERL researchers are presenting 4 papers and co-organizing a workshop at the CVPR 2023 conference, which will be held in Vancouver, Canada June 18-22. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details are provided below.
1. “Are Deep Neural Networks SMARTer than Second Graders,” by Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Kevin Smith, and Joshua B. Tenenbaum
We present SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed for children in the 6-8 age group. Our experiments using SMART-101 reveal that powerful deep models are not better than random accuracy when analyzed for generalization. We also evaluate large language models (including ChatGPT) on a subset of SMART-101 and find that while these models show convincing reasoning abilities, their answers are often incorrect.
Paper: https://arxiv.org/abs/2212.09993
2. “EVAL: Explainable Video Anomaly Localization,” by Ashish Singh, Michael J. Jones, and Erik Learned-Miller
This work presents a method for detecting unusual activities in videos by building a high-level model of activities found in nominal videos of a scene. The high-level features used in the model are human understandable and include attributes such as the object class and the directions and speeds of motion. Such high-level features allow our method to not only detect anomalous activity but also to provide explanations for why it is anomalous.
Paper: https://arxiv.org/abs/2212.07900
3. "Aligning Step-by-Step Instructional Diagrams to Video Demonstrations," by Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat, Cristian Rodriguez, and Stephen Gould
The rise of do-it-yourself (DIY) videos on the web has made it possible even for an unskilled person (or a skilled robot) to imitate and follow instructions to complete complex real world tasks. In this paper, we consider the novel problem of aligning instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) with video segments from in-the-wild videos. We present a new dataset: Ikea Assembly in the Wild (IAW) and propose a contrastive learning framework for aligning instruction diagrams with video clips.
Paper: https://arxiv.org/pdf/2303.13800.pdf
4. "HaLP: Hallucinating Latent Positives for Skeleton-Based Self-Supervised Learning of Actions," by Anshul Shah, Aniket Roy, Ketul Shah, Shlok Kumar Mishra, David Jacobs, Anoop Cherian, and Rama Chellappa
In this work, we propose a new contrastive learning approach to train models for skeleton-based action recognition without labels. Our key contribution is a simple module, HaLP: Hallucinating Latent Positives for contrastive learning. HaLP explores the latent space of poses in suitable directions to generate new positives. Our experiments using HaLP demonstrates strong empirical improvements.
Paper: https://arxiv.org/abs/2304.00387
The 4th Workshop on Fair, Data-Efficient, and Trusted Computer Vision
MERL researcher Kuan-Chuan Peng is co-organizing the fourth Workshop on Fair, Data-Efficient, and Trusted Computer Vision (https://fadetrcv.github.io/2023/) in conjunction with CVPR 2023 on June 18, 2023. This workshop provides a focused venue for discussing and disseminating research in the areas of fairness, bias, and trust in computer vision, as well as adjacent domains such as computational social science and public policy.
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- Date: June 8, 2023
Where: Zoom
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Optimization, Robotics
Brief - Abraham Vinod gave an invited talk at the Electrical and Computer Engineering Department, the University of California Santa Cruz, titled "Motion Planning under Constraints and Uncertainty using Data and Reachability". His presentation covered recent work on fast and safe motion planners that can allow for coordination among agents, mitigate uncertainty arising from sensing limitations and simplified models, and tolerate the possibility of failures.
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- Date: June 30, 2023 - June 2, 2023
Where: San Diego, CA
MERL Contact: Ankush Chakrabarty
Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
Brief - Ankush Chakrabarty (researcher, Multiphysical Systems Team) co-organized and spoke at 3 sessions at the 2023 American Control Conference in San Diego, CA. These include: (1) A tutorial session (w/ Stefano Di Cairano) on "Physics Informed Machine Learning for Modeling and Control": an effort with contributions from multiple academic institutes and US research labs; (2) An invited session on "Energy Efficiency in Smart Buildings and Cities" in which his paper (w/ Chris Laughman) on "Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems" was nominated for Best Energy Systems Paper Award; and, (3) A special session on Diversity, Equity, and Inclusion to improve recruitment and retention of underrepresented groups in STEM research.
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- Date: June 2, 2023
Awarded to: Yuki Shirai, Devesh Jha, Arvind Raghunathan and Dennis Hong
MERL Contacts: Devesh K. Jha; Arvind Raghunathan; Yuki Shirai
Research Areas: Artificial Intelligence, Optimization, Robotics
Brief - MERL's paper titled: "Closed-Loop Tactile Controller for Tool Manipulation" Won the Best Poster Award in the workshop on "Embracing contacts : Making robots physically interact with our world". First author and MERL intern, Yuki Shirai, was presented with the award at a ceremony held at ICRA in London. MERL researchers Devesh Jha, Principal Research Scientist, and Arvind Raghunathan, Senior Principal Research Scientist and Senior Team Leader as well as Prof. Dennis Hong of University of California, Los Angeles are also coauthors.
The paper presents a technique to manipulate an object using a tool in a closed-loop fashion using vision-based tactile sensors. More information about the workshop and the various speakers can be found here https://sites.google.com/view/icra2023embracingcontacts/home.
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- Date: December 9, 2022
Where: Pittsburg, PA
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - MERL Senior Principal Research Scientist and Speech and Audio Senior Team Leader, Jonathan Le Roux, was invited by Carnegie Mellon University's Language Technology Institute (LTI) to give an invited talk as part of the LTI Colloquium Series. The LTI Colloquium is a prestigious series of talks given by experts from across the country related to different areas of language technologies. Jonathan's talk, entitled "Towards general and flexible audio source separation", presented an overview of techniques developed at MERL towards the goal of robustly and flexibly decomposing and analyzing an acoustic scene, describing in particular the Speech and Audio Team's efforts to extend MERL's early speech separation and enhancement methods to more challenging environments, and to more general and less supervised scenarios.
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- Date: December 15, 2022 - December 17, 2022
MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons
Research Areas: Artificial Intelligence, Data Analytics, Machine Learning
Brief - The performance of manufacturing systems is heavily affected by downtime – the time period that the system halts production due to system failure, anomalous operation, or intrusion. Therefore, it is crucial to detect and diagnose anomalies to allow predictive maintenance or intrusion detection to reduce downtime. This talk, titled "Anomaly detection and diagnosis in manufacturing systems using autoencoder", focuses on tackling the challenges arising from predictive maintenance in manufacturing systems. It presents a structured autoencoder and a pre-processed autoencoder for accurate anomaly detection, as well as a statistical-based algorithm and an autoencoder-based algorithm for anomaly diagnosis.
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- Date: December 8, 2022
MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
Brief - On December 8, 2022, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang gave a 3.5-hour tutorial presentation at the IEEE Global Communications Conference (GLOBECOM). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addressed recent trends, challenges, and advances in sensing and communications. P. Wang presented on use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discussed the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial was conducted remotely. MERL's quantum AI technology was partly reported in the recent press release (https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html).
The IEEE GLOBECOM is a highly anticipated event for researchers and industry professionals in the field of communications. Organized by the IEEE Communications Society, the flagship conference is known for its focus on driving innovation in all aspects of the field. Each year, over 3,000 scientific researchers submit proposals for program sessions at the annual conference. The theme of this year's conference was "Accelerating the Digital Transformation through Smart Communications," and featured a comprehensive technical program with 13 symposia, various tutorials and workshops.
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- Date: December 2, 2022
MERL Contacts: Toshiaki Koike-Akino; Kieran Parsons; Pu (Perry) Wang; Ye Wang
Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Human-Computer Interaction
Brief - Mitsubishi Electric Corporation announced its development of a quantum artificial intelligence (AI) technology that automatically optimizes inference models to downsize the scale of computation with quantum neural networks. The new quantum AI technology can be integrated with classical machine learning frameworks for diverse solutions.
Mitsubishi Electric has confirmed that the technology can be incorporated in the world's first applications for terahertz (THz) imaging, Wi-Fi indoor monitoring, compressed sensing, and brain-computer interfaces. The technology is based on recent research by MERL's Connectivity & Information Processing team and Computational Sensing team.
Mitsubishi Electric's new quantum machine learning (QML) technology realizes compact inference models by fully exploiting the enormous capacity of quantum computers to express exponentially larger-state space with the number of quantum bits (qubits). In a hybrid combination of both quantum and classical AI, the technology can compensate for limitations of classical AI to achieve superior performance while significantly downsizing the scale of AI models, even when using limited data.
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- Date: December 2, 2022 - December 8, 2022
MERL Contacts: Matthew Brand; Toshiaki Koike-Akino; Jing Liu; Saviz Mowlavi; Kieran Parsons; Ye Wang
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Signal Processing
Brief - In addition to 5 papers in recent news (https://www.merl.com/news/news-20221129-1450), MERL researchers presented 2 papers at the NeurIPS Conference Workshop, which was held Dec. 2-8. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.
- “Optimal control of PDEs using physics-informed neural networks” by Saviz Mowlavi and Saleh Nabi
Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural network-based surrogate model for the unknown state, PINNs can seamlessly blend measurement data with physical constraints. Here, we extend this framework to PDE-constrained optimal control problems, for which the governing PDE is fully known and the goal is to find a control variable that minimizes a desired cost objective. We validate the performance of the PINN framework by comparing it to state-of-the-art adjoint-based optimization, which performs gradient descent on the discretized control variable while satisfying the discretized PDE.
- “Learning with noisy labels using low-dimensional model trajectory” by Vasu Singla, Shuchin Aeron, Toshiaki Koike-Akino, Matthew E. Brand, Kieran Parsons, Ye Wang
Noisy annotations in real-world datasets pose a challenge for training deep neural networks (DNNs), detrimentally impacting generalization performance as incorrect labels may be memorized. In this work, we probe the observations that early stopping and low-dimensional subspace learning can help address this issue. First, we show that a prior method is sensitive to the early stopping hyper-parameter. Second, we investigate the effectiveness of PCA, for approximating the optimization trajectory under noisy label information. We propose to estimate the low-rank subspace through robust and structured variants of PCA, namely Robust PCA, and Sparse PCA. We find that the subspace estimated through these variants can be less sensitive to early stopping, and can outperform PCA to achieve better test error when trained on noisy labels.
- In addition, new MERL researcher, Jing Liu, also presented a paper entitled “CoPur: Certifiably Robust Collaborative Inference via Feature Purification" based on his previous work before joining MERL. His paper was elected as a spotlight paper to be highlighted in lightening talks and featured paper panel.
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- Date: November 29, 2022 - December 9, 2022
Where: NeurIPS 2022
MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Suhas Lohit
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
Brief - MERL researchers are presenting 5 papers at the NeurIPS Conference, which will be held in New Orleans from Nov 29-Dec 1st, with virtual presentations in the following week. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.
MERL papers in NeurIPS 2022:
1. “AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments” by Sudipta Paul, Amit Roy-Chowdhary, and Anoop Cherian
This work proposes a unified multimodal task for audio-visual embodied navigation where the navigating agent can also interact and seek help from a human/oracle in natural language when it is uncertain of its navigation actions. We propose a multimodal deep hierarchical reinforcement learning framework for solving this challenging task that allows the agent to learn when to seek help and how to use the language instructions. AVLEN agents can interact anywhere in the 3D navigation space and demonstrate state-of-the-art performances when the audio-goal is sporadic or when distractor sounds are present.
2. “Learning Partial Equivariances From Data” by David W. Romero and Suhas Lohit
Group equivariance serves as a good prior improving data efficiency and generalization for deep neural networks, especially in settings with data or memory constraints. However, if the symmetry groups are misspecified, equivariance can be overly restrictive and lead to bad performance. This paper shows how to build partial group convolutional neural networks that learn to adapt the equivariance levels at each layer that are suitable for the task at hand directly from data. This improves performance while retaining equivariance properties approximately.
3. “Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation” by Moitreya Chatterjee, Narendra Ahuja, and Anoop Cherian
There often exist strong correlations between the 3D motion dynamics of a sounding source and its sound being heard, especially when the source is moving towards or away from the microphone. In this paper, we propose an audio-visual scene-graph that learns and leverages such correlations for improved visually-guided audio separation from an audio mixture, while also allowing predicting the direction of motion of the sound source.
4. “What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective” by Huan Wang, Suhas Lohit, Michael Jones, and Yun Fu
This paper presents theoretical and practical results for understanding what makes a particular data augmentation technique (DA) suitable for knowledge distillation (KD). We design a simple metric that works very well in practice to predict the effectiveness of DA for KD. Based on this metric, we also propose a new data augmentation technique that outperforms other methods for knowledge distillation in image recognition networks.
5. “FeLMi : Few shot Learning with hard Mixup” by Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, and Rama Chellappa
Learning from only a few examples is a fundamental challenge in machine learning. Recent approaches show benefits by learning a feature extractor on the abundant and labeled base examples and transferring these to the fewer novel examples. However, the latter stage is often prone to overfitting due to the small size of few-shot datasets. In this paper, we propose a novel uncertainty-based criteria to synthetically produce “hard” and useful data by mixing up real data samples. Our approach leads to state-of-the-art results on various computer vision few-shot benchmarks.
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- Date: October 20, 2022
Where: University Park, PA
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Control, Robotics
Brief - Devesh Jha, a Principal Research Scientist in the Data Analytics Group at MERL, delivered an invited talk at The Penn State Seminar Series on Systems, Control and Robotics. This talk presented some of the recent work done at MERL in the areas of optimization and control for robotic manipulation in unstructured environment.
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- Date: May 28, 2023 - June 1, 2023
Where: Rome, Italy
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
Brief - Kyeong Jin Kim, a Senior Principal Research Scientist in the Connectivity & Information Processing Team, organizes the second international workshop in 2023 IEEE International Conference on Communications (ICC). The workshop is titled, "Industrial Private 5G-and-beyond Wireless Networks," and aims to bring researchers for technical discussion on fundamental and practically relevant questions to many emerging challenges in industrial private wireless networks. This workshop is also being organized with the help of other researchers from industry and academia such as Huawei Technology, University of South Florida, Aalborg University, Jinan University, and South China University of Technology. IEEE ICC is one of two IEEE Communications Society's flagship conferences.
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- Date: September 21, 2022
MERL Contacts: Philip V. Orlik; Anthony Vetro
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio
Brief - Mitsubishi Electric Research Laboratories (MERL) invites qualified postdoctoral candidates to apply for the position of Postdoctoral Research Fellow. This position provides early career scientists the opportunity to work at a unique, academically-oriented industrial research laboratory. Successful candidates will be expected to define and pursue their own original research agenda, explore connections to established laboratory initiatives, and publish high impact articles in leading venues. Please refer to our web page for further details.
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- Date: August 22, 2022
MERL Contacts: Chiori Hori; Jonathan Le Roux; Anthony Vetro
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - IEEE has announced that the recipient of the 2023 IEEE James L. Flanagan Speech and Audio Processing Award will be Prof. Alex Waibel (CMU/Karlsruhe Institute of Technology), “For pioneering contributions to spoken language translation and supporting technologies.” Mitsubishi Electric Research Laboratories (MERL), which has become the new sponsor of this prestigious award in 2022, extends our warmest congratulations to Prof. Waibel.
MERL Senior Principal Research Scientist Dr. Chiori Hori, who worked with Dr. Waibel at Carnegie Mellon University and collaborated with him as part of national projects on speech summarization and translation, comments on his invaluable contributions to the field: “He has contributed not only to the invention of groundbreaking technology in speech and spoken language processing but also to the promotion of an abundance of research projects through international research consortiums by linking American, European, and Asian research communities. Many of his former laboratory members and collaborators are now leading R&D in the AI field.”
The IEEE Board of Directors established the IEEE James L. Flanagan Speech and Audio Processing Award in 2002 for outstanding contributions to the advancement of speech and/or audio signal processing. This award has recognized the contributions of some of the most renowned pioneers and leaders in their respective fields. MERL is proud to support the recognition of outstanding contributions to the field of speech and audio processing through its sponsorship of this award.
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- Date: July 14, 2022
Awarded to: Weidong Cao, Mouhacine Benosman, Xuan Zhang, and Rui Ma
Research Area: Artificial Intelligence
Brief - The Conference committee of the 59th Design Automation Conference has chosen MERL's paper entitled 'Domain Knowledge-Infused Deep Learning for Automated Analog/RF Circuit Parameter Optimization', as a DAC Best Paper Award nominee. The committee evaluated both manuscript and submitted presentation recording, and has chosen MERL's paper as one of six nominees for this prestigious award. Decisions were based on the submissions’ innovation, impact and exposition.
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- Date: June 15, 2022
Awarded to: Yuxiang Sun, Mouhacine Benosman, Rui Ma.
Research Area: Artificial Intelligence
Brief - The committee of the International Conference on Artificial Intelligence Circuits and Systems (AICAS) 2022, has selected MERL's paper entitled 'GaN Distributed RF Power Amplifier Automation Design with Deep Reinforcement Learning' as a winner of the AICAS 2022 Openedges Award.
In this paper MERL researchers propose a novel design automation methodology based on deep reinforcement learning (RL), for wide-band non-uniform distributed RF power amplifiers, known for their high dimensional design challenges.
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- Date: May 23, 2022 - May 27, 2022
Where: International Conference on Robotics and Automation (ICRA)
MERL Contacts: Ankush Chakrabarty; Stefano Di Cairano; Siddarth Jain; Devesh K. Jha; Pedro Miraldo; Daniel N. Nikovski; Arvind Raghunathan; Diego Romeres; Abraham P. Vinod; Yebin Wang
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - MERL researchers presented 5 papers at the IEEE International Conference on Robotics and Automation (ICRA) that was held in Philadelphia from May 23-27, 2022. The papers covered a broad range of topics from manipulation, tactile sensing, planning and multi-agent control. The invited talk was presented in the "Workshop on Collaborative Robots and Work of the Future" which covered some of the work done by MERL researchers on collaborative robotic assembly. The workshop was co-organized by MERL, Mitsubishi Electric Automation's North America Development Center (NADC), and MIT.
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- Date: May 22, 2022 - May 27, 2022
Where: Singapore
MERL Contacts: Anoop Cherian; Chiori Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Tim K. Marks; Philip V. Orlik; Kuan-Chuan Peng; Pu (Perry) Wang; Gordon Wichern
Research Areas: Artificial Intelligence, Computer Vision, Signal Processing, Speech & Audio
Brief - MERL researchers are presenting 8 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Singapore from May 22-27, 2022. A week of virtual presentations also took place earlier this month.
Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, and classification.
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. The event attracts more than 2000 participants each year.
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- Date: May 16, 2022 - May 20, 2022
Where: Seoul, Korea
MERL Contacts: Jianlin Guo; Toshiaki Koike-Akino; Philip V. Orlik; Kieran Parsons; Pu (Perry) Wang; Ye Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Machine Learning, Signal Processing
Brief - MERL Connectivity & Information Processing Team scientists remotely presented 5 papers at the IEEE International Conference on Communications (ICC) 2022, held in Seoul Korea on May 16-20, 2022. Topics presented include recent advancements in communications technologies, deep learning methods, and quantum machine learning (QML). Presentation videos are also found on our YouTube channel. In addition, K. J. Kim organized "Industrial Private 5G-and-beyond Wireless Networks Workshop" at the conference.
IEEE ICC is one of two IEEE Communications Society’s flagship conferences (ICC and Globecom). Each year, close to 2,000 attendees from over 70 countries attend IEEE ICC to take advantage of a program which consists of exciting keynote session, robust technical paper sessions, innovative tutorials and workshops, and engaging industry sessions. This 5-day event is known for bringing together audiences from both industry and academia to learn about the latest research and innovations in communications and networking technology, share ideas and best practices, and collaborate on future projects.
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- Date: April 1, 2022
Where: INFORMS Journal on Computing (https://pubsonline.informs.org/journal/ijoc)
MERL Contact: Arvind Raghunathan
Research Areas: Artificial Intelligence, Machine Learning, Optimization
Brief - Arvind Raghunathan co-authored a publication titled "JANOS: An Integrated Predictive and Prescriptive Modeling Framework" which has been chosen as a Featured Article in the current issue of the INFORMS Journal on Computing. The article was co-authored with Prof. David Bergman, a collaborator of MERL and Teng Huang, a former MERL intern, among others.
The paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.
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- Date: May 4, 2022
MERL Contact: Toshiaki Koike-Akino
Research Areas: Artificial Intelligence, Communications, Electronic and Photonic Devices, Machine Learning, Optimization, Signal Processing
Brief - Toshiaki Koike-Akino gave an invited lecture on advanced photonic devices at the United States Patent and Trademark Office (USPTO) Technology Fair on May 4, 2022. Topics of the lecture included the recent progress of applied artificial intelligence (AI) technologies for optical systems, nano-photonic devices, and quantum technology. During the 2-hour interactive online presentation, he lectured to more than 200 patent examiner participants.
USPTO Tech Fair Organizer mentioned:
"Thank you very much for representing Advanced Photonic Devices at this year’s Technology Center 2800 Virtual Tech Fair held May 4th, 2022. Tech Fair is an important part of the United States Patent and Trademark Office’s Patent Examiner Technical Training Program (PETTP). Having a scientifically well-trained examiner workforce and ensuring the quality, consistency, and reliability of issued patents are top priorities at the USPTO. The PETTP is designed to achieve those priorities by giving examiners direct access to technical experts who are willing to share their knowledge about prior art and industry standards for both emerging and established technologies. Experts like yourself help to maintain our high quality of patent examination by keeping examiners updated on technologies and innovations pertinent to their field of examination.
We very much appreciate your efforts, time, and contributions."
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- Date: March 1, 2022
Where: Online/Zoom
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - Devesh Jha, a Principal Research Scientist in MERL's Data Analytics group, gave an invited talk at the Mechanical and Aerospace Engineering Department, NYU. The title of the talk was "Robotic Manipulation in the Wild: Planning, Learning and Control through Contacts". The talk presented some of the recent work done at MERL for robotic manipulation in unstructured environments in the presence of significant uncertainty.
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- Date: March 1, 2022
MERL Contacts: Anoop Cherian; Chiori Hori; Jonathan Le Roux; Tim K. Marks; Anthony Vetro
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
Brief - MERL's research on scene-aware interaction was recently featured in an IEEE Spectrum article. The article, titled "At Last, A Self-Driving Car That Can Explain Itself" and authored by MERL Senior Principal Research Scientist Chiori Hori and MERL Director Anthony Vetro, gives an overview of MERL's efforts towards developing a system that can analyze multimodal sensing information for highly natural and intuitive interaction with humans through context-dependent generation of natural language. The technology recognizes contextual objects and events based on multimodal sensing information, such as images and video captured with cameras, audio information recorded with microphones, and localization information measured with LiDAR.
Scene-Aware Interaction for car navigation, one target application that the article focuses on, will provide drivers with intuitive route guidance. Scene-Aware Interaction technology is expected to have wide applicability, including human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. MERL's Scene-Aware Interaction Technology had previously been featured in a Mitsubishi Electric Corporation Press Release.
IEEE Spectrum is the flagship magazine and website of the IEEE, the world’s largest professional organization devoted to engineering and the applied sciences. IEEE Spectrum has a circulation of over 400,000 engineers worldwide, making it one of the leading science and engineering magazines.
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- Date: January 24, 2022
Where: The TWIML AI Podcast
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - MERL Speech & Audio Senior Team Leader Jonathan Le Roux was featured in an extended interview on the popular TWIML AI Podcast, presenting MERL's work towards solving the "cocktail party problem". Humans have the extraordinary ability to focus on particular sounds of interest within a complex acoustic scene, such as a cocktail party. MERL's Speech & Audio Team has been at the forefront of the field's effort to develop algorithms giving machines similar abilities. Jonathan talked with host Sam Charrington about the group's decade-long journey on this topic, from early pioneering work using deep learning for speech enhancement and speech separation, to recent works on weakly-supervised separation, hierarchical sound separation, as well as the separation of real-world soundtracks into speech, music, and sound effects (aka the "cocktail fork problem").
The TWIML AI podcast, formerly known as This Week in Machine Learning & AI, was created in 2016 and is followed by more than 10,000 subscribers on Youtube and Twitter. Jonathan's interview marks the 555th episode of the podcast.
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- Date: November 11, 2021
Awarded to: Niklas Smedemark-Margulies, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus
MERL Contacts: Toshiaki Koike-Akino; Ye Wang
Research Areas: Artificial Intelligence, Signal Processing, Human-Computer Interaction
Brief - The MERL Signal Processing group achieved first place in the cross-subject transfer learning task and fourth place overall in the NeurIPS 2021 BEETL AI Challenge for EEG Transfer Learning. The team included Niklas Smedemark-Margulies (intern from Northeastern University), Toshiaki Koike-Akino, Ye Wang, and Prof. Deniz Erdogmus (Northeastern University). The challenge addresses two types of transfer learning tasks for EEG Biosignals: a homogeneous transfer learning task for cross-subject domain adaptation; and a heterogeneous transfer learning task for cross-data domain adaptation. There were 110+ registered teams in this competition, MERL ranked 1st in the homogeneous transfer learning task, 7th place in the heterogeneous transfer learning task, and 4th place for the combined overall score. For the homogeneous transfer learning task, MERL developed a new pre-shot learning framework based on feature disentanglement techniques for robustness against inter-subject variation to enable calibration-free brain-computer interfaces (BCI). MERL is invited to present our pre-shot learning technique at the NeurIPS 2021 workshop.
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