Scripting languages are immensely popular in many domains. They are characterized by a number of features that make it easy to develop small applications quickly – flexible data structures, simple syntax and intuitive semantics. However they are less attractive at scale: scripting languages are harder to debug, difficult to refactor and suffers performance penalties. Many research projects have tackled the issue of safety and performance for existing scripting languages with mixed results: the considerable flexibility offered by their semantics also makes them significantly harder to analyze and optimize.
Previous research from our lab has led to the design of a typed scripting language built specifically to be flexible without losing static analyzability. In this dissertation, we present a framework to exploit this analyzability, with the aim of producing a more efficient implementation
Our approach centers around the concept of adaptive tags: specialized tags attached to values that represent how it is used in the current program. Our framework abstractly tracks the flow of deep structural types in the program, and thus can efficiently tag them at runtime. Adaptive tags allow us to tackle key issues at the heart of performance problems of scripting languages: the framework is capable of performing efficient dispatch in the presence of flexible structures.
Pottayil Harisanker Menon is a Ph.D. candidate in Computer Science at the Johns Hopkins University. He is advised by Prof. Scott Smith and is a member of the Programming Languages Lab. Hari’s current research focuses on creating flexible languages and making them run fast. His general research interests include the design of programming languages, type systems and compilers.
Image-based tracking of the c-arm continues to be a critical and challenging problem for many clinical applications due to its widespread use in many computer-assisted procedures that rely upon its accuracy for further planning, registration, and reconstruction tasks. In this thesis, I present a variety of approaches to improve current c-arm tracking methods and devices for intra-operative procedures.
The first approach presents a novel two-dimensional fiducial comprising a set of coplanar conics and an improved single-image pose estimation algorithm that addresses segmentation errors using a mathematical equilibration approach. Simulation results show an improvement in the mean rotation and translation errors by factors of 4 and 1.75, respectively, as a result of using the proposed algorithm. Experiments using real data obtained by imaging a simple precisely machined model consisting of three coplanar ellipses retrieve pose estimates that are in good agreement with those obtained by a ground truth optical tracker. This two-dimensional fiducial can be easily placed under the patient allowing a wide field of view for the motion of the c-arm.
The second approach employs learning-based techniques to two-view geometrical theories. A demonstrative algorithm is used to simultaneously tackle matching and segmentation issues of features segmented from pairs of acquired images. The corrected features can then be used to retrieve the epipolar geometry which can ultimately provide pose parameters using a one-dimensional fiducial. I formulate the problem of match refinement for epipolar geometry estimation in a reinforcement-learning framework. Experiments demonstrate the ability to both reject false matches and fix small localization errors in the segmentation of true noisy matches in a minimal number of steps.
The third approach presents a feasibility study for an approach that entirely eliminates the use of tracking fiducials. It relies only on preoperative data to initialize a point-based model that is subsequently used to iteratively estimate the pose and the structure of the point-like intraoperative implant using three to six images simultaneously. This method is tested in the framework of prostate brachytherapy in which preoperative data including planned 3-D locations for a large number of point-like implants called seeds is usually available. Simultaneous pose estimation for the c-arm for each image and localization of the seeds is studied in a simulation environment. Results indicate mean reconstruction errors that are less than 1.2 mm for noisy plans of 84 seeds or fewer. These are attained when the 3D mean error introduced to the plan as a result of adding Gaussian noise is less than 3.2 mm.
Maria S. Ayad received the B. Sc. degree in Electronics and Communications from the Faculty of Engineering, Cairo University, in 2001. She also earned a diploma in Networks from the Information Technology Institute in Cairo (iTi) in 2002 and an M.S.E. in Computer Science from Johns Hopkins University in 2009. She was inducted into the Upsilon Pi Epsilon (UPE) honor society in 2008. She received the Abel Wolman Fellowship by the Whiting School of Engineering in 2006 and a National Science Foundation Graduate Research Fellowship in 2008.
Her research focuses on pose estimation, reconstruction, and estimating structure from motion for image-guided medical procedures and computer-assisted surgery. Her 2009 paper has been awarded the best student paper award in the Visualization, Image-Guided Procedures, and Modeling Track of the 2009 SPIE Medical Imaging conference.
She has been working as an electrical patent examiner at the United States Patent and Trademark Office since 2013.
Hackerman Hall B-17
Infrastructure-as-a-Service (IaaS) provides shared computing resources that users can access over the Internet, which has revolutionized the way that computing resources are utilized. Instead of buying and maintaining their own physical servers, users can lease virtualized compute and storage resources from shared cloud servers and pay only for the time that they use the leased resources. Yet as more and more users take advantage of this model, cloud providers face highly dynamic user demands for their resources, making it difficult for them to maintain consistent quality-of-service (QoS). We propose to use price incentives to manage these user demands. We investigate two types of pricing: spot pricing and volume discounts. Spot pricing creates an auction in which users can submit bids for spare cloud resources; however, these resources may be withdrawn at any time if users’ bids are too low and/or resources become unavailable due to other users’ demands. Volume discount pricing incentivizes users to submit longer-term jobs, which provide more stable resource utilization. We provide insights into these pricing schemes by quantifying user demands with different prices, and design optimal pricing and resource utilization strategies for both users and cloud providers.
Carlee Joe-Wong is an assistant professor in the ECE department at Carnegie Mellon University, working at CMU’s Silicon Valley Campus. She received her Ph.D. from Princeton University in 2016 and is primarily interested in incentives and resource allocation for computer and information networks. In 2013–2014, Carlee was the Director of Advanced Research at DataMi, a startup she co-founded from her data pricing research. She received the INFORMS ISS Design Science Award in 2014 and the Best Paper Award at IEEE INFOCOM 2012, and was a National Defense Science and Engineering Graduate Fellow (NDSEG) from 2011 to 2013.
Hackerman Hall B-17
Advances in computer and information science and engineering are providing unprecedented opportunities for research and education. My talk will begin with an overview of CISE activities and programs at the National Science Foundation and include a discussion of current trends that are shaping the future of our discipline. I will also discuss the opportunities as well as the challenges that lay ahead for our community and for CISE.
Ben Shneiderman (http://www.cs.umd.edu/~ben) is a Distinguished University Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://www.cs.umd.edu/hcil/), and a Member of the UM Institute for Advanced Computer Studies (UMIACS) at the University of Maryland. He is a Fellow of the AAAS, ACM, IEEE, and NAI, and a Member of the National Academy of Engineering, in recognition of his pioneering contributions to human-computer interaction and information visualization. His contributions include the direct manipulation concept, clickable highlighted web-links, touchscreen keyboards, dynamic query sliders for Spotfire, development of treemaps, novel network visualizations for NodeXL, and temporal event sequence analysis for electronic health records.
Ben is the co-author with Catherine Plaisant of Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed., 2016) http://www.awl.com/DTUI/. With Stu Card and Jock Mackinlay, he co-authored Readings in Information Visualization: Using Vision to Think (1999). His book Leonardo’s Laptop (MIT Press) won the IEEE book award for Distinguished Literary Contribution. He co-authored, Analyzing Social Media Networks with NodeXL (www.codeplex.com/nodexl) (2010) with Derek Hansen and Marc Smith. Shneiderman’s latest book is The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, April 2016.)
Hackerman Hall B-17
This talk will introduce a kinematic and dynamic framework for creating a representative model of an individual. Building on results from geometric robotics, a method for formulating a geometric dynamic identification model is derived. This method is validated on a robotic arm, and tested on healthy and muscular dystrophy subjects to determine the utility as a clinical tool. In order to capture kinematics of the human body we used Visual observations, either motion capture or the Kinect camera. In order to obtain the dynamical parameters of the individual, we used force plate and force sensors for robot attached to human hand. The work in progress is to use Ultrasound scanner and Acoustic myography in order to estimate the muscle strength. Our current representative kinematic and dynamic model outperformed conventional height/mass scaled models. This allows us for rapid, quantitative measurements of an individual, with minimal retraining required for clinicians. These tools are then used to develop a prescriptive model for developing assistive devices. This framework is then used to develop a novel system for human assistance. A prototype device is developed and tested. The prototype is lightweight, uses minimal energy, and can provide an augmentation of 82% for providing hammer curl assistance.
Ruzena Bajcsy (LF’08) received the Master’s and Ph.D. degrees in electrical engineering from Slovak Technical University, Bratislava, Slovak Republic, in 1957 and 1967, respectively, and the Ph.D. in computer science from Stanford University, Stanford, CA, in 1972. She is a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and Director Emeritus of the Center for Information Technology Research in the Interest of Science (CITRIS). Prior to joining Berkeley, she headed the Computer and Information Science and Engineering Directorate at the National Science Foundation. Dr. Bajcsy is a member of the National Academy of Engineering and the National Academy of Science Institute of Medicine as well as a Fellow of the Association for Computing Machinery (ACM) and the American Association for Artificial Intelligence.