2020
Eadom Dessalene, Michael Maynord, Chinmaya Devaraj, Cornelia Fermüller, Yiannis Aloimonos
Introduces Egocentric Object Manipulation Graphs (Ego-OMG):a novel representation for activity modeling and anticipation of near future actions.
arXiv.org
Anton Mitrokhin, Peter Sutor, Douglas Summers-Stay, Cornelia Fermüller, Yiannis Aloimonos
By using hashing neural networks to produce binary vector representations of images, the authors show how hyperdimensional vectors can be constructed such that vector-symbolic inference arises naturally out of their output.
Frontiers in Robotics and AI
Anton Mitrokhin, Zhiyuan Hua, Cornelia Fermüller, Yiannis Aloimonos
Presents a Graph Convolutional neural network for the task of scene motion segmentation by a moving camera. Describes spatial and temporal features of event clouds, which provide cues for motion tracking and segmentation.
Computer Vision Foundation
Chethan M. Parameshwara, Nitin J. Sanket, Arjun Gupta, Cornelia Fermüller, Yiannis Aloimonos
A solution to multi-object motion segmentation using a combination of classical optimization methods along with deep learning and does not require prior knowledge of the 3D motion and the number and structure of objects.
arXiv.org
John Kanu, Eadom Dessalene, Xiaomin Lin, Cornelia Fermüller, Yiannis Aloimonos
A novel robotic agent framework for learning to perform temporally extended tasks using spatial reasoning in a deep reinforcement learning framework, by sequentially imagining visual goals and choosing appropriate actions to fulfill imagined goals.
arXiv.org
2020
Aneesh Raghavan, John Baras
This paper pertains to stochastic multi-agent decision-making problems. The authors revisit the concepts of event-state-operation-structure and relationship of incompatibility from literature, and use them as a tool to study the algebraic structure of a set of events. They consider a multi-agent hypothesis testing problem and show that the set of events forms an ortholattice. They then consider the binary hypothesis testing problem wth finite observation space.
arXiv.org
Aneesh Raghavan, John Baras
This paper pertains to hypothesis testing problems, specifically the problem of collaborative binary hypothesis testing.
arXiv.org
Ion Matei, Johan de Kleer, Christoforos Somarakis, Rahul Rai, John Baras
To understand changes in physical systems and facilitate decisions, explaining how model predictions are made is crucial. In this paper the authors use model-based interpretability, where models of physical systems are constructed by composing basic constructs that explain locally how energy is exchanged and transformed.
arXiv.org
2019
Mohammad Mamduhi, Karl Johansson, Ehsan Hashemi, John Baras
This paper proposes an event-triggered, add-on safety mechanism in a networked vehicular system that can adjust control parameters for timely braking while maintaining maneuverability.
arXiv.org
2020
Samuel Huang, Rance Cleaveland
This paper describes a technique for inferring temporal-logic properties for sets of finite data streams. Such data streams arise in many domains, including server logs, program testing, and financial and marketing data; temporal-logic formulas that are satisfied by all data streams in a set can provide insight into the underlying dynamics of the system generating these streams. The authors' approach makes use of so-called Linear Temporal Logic (LTL) queries, which are LTL formulas containing a missing subformula and interpreted over finite data streams. Solving such a query involves computing a subformula that can be inserted into the query so that the resulting grounded formula is satisfied by all data streams in the set. The paper describes an automaton-driven approach to solving this query-checking problem and demonstrates a working implementation via a pilot study.
arXiv.org
Peter Fontana, Rance Cleaveland
This report contains the descriptions of the timed automata (models) and the prop-erties (specifications) that are used as the “benchmark examples in Data structure choices for on-the-fly model checking of real-time systems” and “The power of proofs: New algorithms for timed automata model checking.” The four models from those sources are: CSMA, FISCHER, LEADER, and GRC. Additionally we include in this re-port two additional models: FDDI and PATHOS. These six models are often used to benchmark timed automata model checker speed throughout timed automata model checking papers.
arXiv.org
Rance Cleaveland
This paper shows how the use of Structural Operational Semantics (SOS) inthe style popularized by the process-algebra community can lead to a more succinct and useful construction for building finite automata from regular expressions.
arXiv.org
2020
Saurabh Sahu, Rahul Gupta, Carol Espy-Wilson
Implements three auto-encoder and GAN based models to synthetically generate higher dimensional feature vectors useful for speech emotion recognition from a simpler prior distribution pz.
IEEE Transactions on Affective Computing
2019
Saurabh Sahu, Vikramjit Mitra, Nadee Seneviratne, Carol Espy-Wilson
The paper leverages multi-modal learning and automated speech recognition (ASR) systems toward building a speech-only emotion recognition model.
Interspeech 2019
2019
Michael Fu
The deep neural networks of AlphaGo and AlphaZero can be traced back to an adaptive multistage sampling (AMS) simulation-based algorithm for Markov decision processed published by HS Chang, Michael C. Fu and Steven I Marcus in Operations Research in 2005. Here, Fu retraces history, talks about the impact of the initial research, and suggests enhancements for the future.
Asian-Pacific Journal of Operational Research
2020
Xiaolong Liu, Seda Aslan, Rachel Hess, Paige Mass, Laura Olivieri, Yue-Hin Loke, Narutoshi Hibino, Mark Fuge, Axel Krieger
Develops a computational framework for automatically designing optimal shapes of patient-specific TEVGs for aorta surgery.
42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Wei Chen, Mark Fuge
The authors propose a Bayesian optimization approach that only needs to specify an initial search space that does not necessarily include the global optimum, and expands the search space when necessary.
arXiv.org
2019
Eliot Rudnick-Cohen, Shapour Azarm and Jeffrey Herrmann
The paper presents Scenario Generation and Local Refinement Optimization (SGLRO), a new approach for solving non-convex robust optimization problems.
Journal of Mechanical Design
2020
SriSai Naga Jyotish Poonganam, Bharath Gopalakrishnan, Venkata Seetharama Sai Bhargav Kumar Avula, K. Madhava Krishna, Arun Kumar Singh, Dinesh Manocha
A new model predictive control framework that improves reactive navigation for autonomous robots. The framework allows roboticists to compute low cost control inputs while ensuring some upper bound on the risk of collision.
IEEE Robotics and Automation Letters
Dinesh Manocha, Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Tanmay Randhavane
The authors' RoadTrack algorithm could help autonomous vehicles navigate dense traffic scenarios. The algorithm uses tracking-by-detection approach to detect vehicles and pedestrians, then predict where they are going.
IROS 2019
2019
Kurt Gray, Tanmay Randhavane, Kyra Kapsaskis, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
A data-driven deep neural algorithm for detecting deceptive walking behavior using nonverbal cues like gaits and gestures.
arXiv.org
Rohan Chandra, Tianrui Guan, Srujan Panuganti, Trisha Mittal, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
A novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning.
arxiv.org
Qiaoyun Wu, Dinesh Manocha, Jun Wang, Kai Xu
The authors improve the cross-target and cross-scene generalization of visual navigation through a learning agent guided by conceiving the next observations it expects to see. A variational Bayesian model, NeoNav, generates the next expected observations (NEO) conditioned on the current observations of the agent and the target view.
PDF
Uttaran Bhattacharya, Christian Roncal, Trisha Mittal, Rohan Chandra,Aniket Bera, Dinesh Manocha
The paper presents an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or from motion-captured data and represented as sequences of 3D poses.
arxiv.org
Chaochao Li, Pei Lv, Mingliang Xu, Xinyu Wang, Dinesh Manocha, Bing Zhou, Meng Wang
In many applications such as human-robot interaction, autonomous driving or surveillance, it is important to accurately predict pedestrian trajectories for collision-free navigation or abnormal behavior detection. The authors present a novel trajectory prediction algorithm for pedestrians based on a personality-aware probabilistic feature map.
arxiv.org
Uttaran Bhattacharya, Trisha Mittal, Rohan Chandra, Tanmay Randhavane (UNC), Aniket Bera, and Dinesh Manocha
STEP is a novel classifier network able to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network architecture. Given an RGB video of an individual walking, STEP implicitly exploits the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. | Watch a video about STEP |
arxiv.org
Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Xiaoyu Li, Aniket Bera, Dinesh Manocha
The GraphRQI algorithm identifies driver behaviors from road agent trajectories. It is 25 percent more accurate over prior behavior classification algorithms for autonomous vehicles. | Watch a video about GraphRQI |
arxiv.org
Qingyang Tan, Zherong Pan, Lin Gao, and Dinesh Manocha
A new method bridges the gap between mesh embedding and physical simulation for efficient dynamic models of clothes. The key technique is a graph-based convolutional neural network (CNN) defined on meshes with arbitrary topologies, and a new mesh embedding approach based on physics-inspired loss term. After training, the learned simulator runs10–100 times faster and the accuracy is high enough for robot manipulation tasks. | Watch a video about this method |
arxiv.org
2020
Gregory Jansen, Aaron Coburn, Adam Soroka, Richard Marciano
Describes the development and testing of the next-generation Trellis Linked Data Platform with Memento versioning support.
dcicblog.umd.edu
2019
William Underwood and Richard Marciano
This paper explores whether the computational thinking practices of mathematicians and scientists in the physical and biological sciences are also the practices of archival scientists. It is argued that these practices are essential elements of an archival science education in preparing students for a professional archival career.
Marciano and 20 students in the Digital Curation Innovation Center developed a reframing model for digital curation through computational thinking. Their case study involves adding metadata to non-digital primary records from the WWII TuleLake Japanese American Internment Camp. Their curation methods led to the discovery of new narratives and connections from this data.
2020
Murat Arcak, Nuno Martins
Presents dissipativity tools to establish global asymptotic stability of the set of Nash equilibria in a deterministic model of population games.
arXiv.org
2020
Sunandita Patra, Amit Kumar, James Mason, Malik Ghallab, Paolo Traverso, Dana Nau
New planning and learning algorithms for Refinement Acting Engine (RAE), which uses hierarchical operational models to perform tasks in dynamically changing environments.
2020 International Conference on Automated Planning and Scheduling (ICAPS)
2019
Sudanita Patra, Malik Ghallab, Dana Nau, Paolo Traverso
An integrated acting and planning system that addresses the consistency problem by using the actor’s operational models both for acting and for planning.
2019
Jiqing Xu, Zhengjie Li , Yunbing Pang , Jian Wang , Gang Qu, Jinmei Lai
Under the premise of selecting a large number of typical benchmark circuits, a representative path delay can well represent the overall timing performance of FPGAs.
2019 IEEE 13th International Conference on ASIC (ASICON).
2020
Ben Shneiderman
Proposes a two-dimensional framework alternative to autonomous AI systems called Human-Centered Artificial Intelligence that clarifies how to design for high levels of human control and high levels of computer automation to increase human performance, understand the situations in which full human control or full computer control are necessary, and avoid the dangers of either excessive human control or excessive computer control.
ACM International Journal of Human-Computer Interaction
2020
Deniz Gunduz, Emre Ozfatura, Sennur Ulukus, Baturalp Buyukates
The age of information (AoI) metric is used to track the recovery frequency of partial computations in distributed gradient descent, the most common approach in supervised machine learning, a new solution to the problem of “straggling” worker machines.
arXiv.org
Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus
Presents over-the-air adversarial attacks against deep learning-based modulation classifiers, accounting for realistic channel and broadcast transmission effects. A certified defense method using randomized smoothing is also included.
arXiv.org
