If you have good software skills and are still looking for a summer internship, check out the 2014 Google Summer of Code program. You can earn $5500 by coding for an open source software project this summer. You will probably work remotely, but in close collaboration with a mentor at one of over 100 participating organizations. To maximize your chances, explore the organizations and find one that needs your skills. Details here; apply by Friday, March 21.
In this work we investigate a new quantum algorithm called the Amplified Quantum Fourier Transform (Amplified-QFT) to solve the Local Period Problem where there is an Oracle with a periodic subset and we wish to recover its period. This algorithm uses parts of the famous Grover’s quantum search algorithm to amplify the amplitudes on the subset, followed by the equally famous Shor’s quantum algorithm for recovering the period. We compare the Amplified-QFT algorithm against the Quantum Fourier Transform (QFT) and Quantum Hidden Subgroup (QHS) algorithms and calculate the probabilities of success for all three algorithms. We show that the Amplified-QFT algorithm is on average, quadratically faster than either the QFT or QHS algorithms. We also investigate two more general settings: a) where the QFT is replaced by a general unitary operator U in the Amplified-QFT algorithm and b) where Grover’s algorithm is replaced by a general amplification procedure in the Amplified-QFT algorithm.
We also investigate this algorithm when a random Error Stream affects the Oracle, which involves calculating expectations and variances over a random set. We calculate the probabilities of success in this case. Further, we find an Uncertainty Principle for the Amplified-QFT algorithm. We also identify a decision problem, the Constant or Balanced Signal Decision Problem, which can be solved by using the one dimensional Amplified Haar Wavelet Transform. This decision problem is a generalization of the Deutsch-Josza problem.
Committee: Drs. S. Lomonaco (CSEE), Chair and advisor; T. Armstrong (Math), Co-advisor and Reader; Dr. Y. Shih (Physics), Reader; Dr. F. Potra (Math) and Dr. M. Gowda (Math)
Google Code Jam 2014 Registration is open and the qualification round starts on Friday, April 11, 2014. Google Code Jam is an international programming competition hosted and administered by Google. The competition began in 2003 as a means to identify top engineering talent for potential employment at Google.
The competition consists of a set of algorithmic problems which must be solved in a fixed amount of time. Competitors may use any programming language and development environment to obtain their solutions. More than 45,000 coders registered to compete last year and the winner, Ivan Miatselski won the $15,000 grand prize.
If you are interested in finding out more, see the Google Code Jam quick start guide and try some of the practice problems from past competitions. The first qualification round starts on April 11 and the finals will take place in Los Angeles on August 15.
The smart grid vision urges for enhanced situational awareness, sustainability, and economics over our energy systems. While meters are being installed throughout the grid, algorithms that can effectively process this big data deluge are now demanded. Aligned to that end, this talk focuses first on scalable grid monitoring. Albeit control centers monitor their local grids independently, deregulation and renewables call for power system state estimation (PSSE) at the interconnection level. To address the complexity and communication challenges involved, a decentralized PSSE framework based on the alternating direction method of multipliers has been developed. Beyond conventional least-squares, our framework can identify outliers and circuit breaker statuses as verified on IEEE grids having thousands of nodes. Electricity market inference is the second theme of this talk. We will first demonstrate how grid topologies can be revealed using only publicly available real-time energy prices. This becomes feasible after recognizing that the price matrix can be factorized as the product of the grid Laplacian times a low-rank and sparse matrix. Leveraging the link between energy markets and the underlying physical grids, we will then cast day-ahead price forecasting as a kernel learning task. Through a novel nuclear norm-based regularization, kernels across pricing nodes and hours are systematically selected. Numerical tests using real data from the Midwest ISO market corroborate the interpretative merits of our schemes.
Dr. Vassilis Kekatos is currently a postdoctoral associate with the ECE Dept. of the University of Minnesota, Minneapolis. He obtained his Ph.D. in Computer Engineering and Science from the University of Patras, Greece, in 2007. In 2009, he received a Marie Curie fellowship. During the summer of 2012, he worked as a consultant for Windlogics Inc. His current interests lie in the areas of signal processing, optimization, and statistical learning towards modernizing our energy systems.
Host: Tulay Adali
CSEE professor Marie desJardins was interviewed for a story, Education Key Theme for International Women’s Day, by the Voice of America on International Women’s Day (March 8).
“If you look at the statistics, well over half of all STEM jobs in the next 10 to 15 years are in computing … yet when people talk about STEM, they tend to think about biology, physics,” said University of Maryland Baltimore County Professor Marie Desjardins who teaches computer science and engineering. “Those are great areas, but those are not where the big job growth is going to be.”
DesJardins also says exposing kids at an early age is key to getting them interested in STEM.
“Let your girls try things that are not typically girly,” she said. “Make sure your kids are getting that from an early age so they think of themselves as creators of technology and new ideas not just following the rules.”
DesJardins says she emphasizes that computer science is really about helping make the world a better place.
Here is the video that accompanied the VIA story.
UMBC’s intercollegiate cyber competition team (the “CyberDawgs”) are heading to the finals of the Mid-Atlantic Collegiate Cyber Defense Competition (CCDC) on March 26-29 at the Johns Hopkins University Applied Physics Lab – Kossiakoff Center in Laurel, MD!
Nearly 300 students from schools throughout the Mid-Atlantic region competed in several qualification rounds to determine the eight finalist teams. Joining the CyberDawgs at the finals will be teams from Anne Arundel Community College, Liberty University, West Virginia University, Towson University, Radford University, Capitol College, and Millersville University. The winner of the Mid-Atlantic finals will advance to the National CCDC Finals in San Antonio, TX later this spring.
The 2014 MA-CCDC finals scenario challenges teams to defend their networks against a series of escalating cybersecurity attacks occuring during a simulated disaster management situation in Maryland.
The 2014 National CyberWatch Mid-Atlantic Collegiate Cyber Defense Competition (CCDC) presented by National CyberWatch Center is in its ninth year of providing a unique experience for college and university students to test their cybersecurity knowledge and skills in a competitive environment.
Smart Grid refers to the computerizing of the grid via the addition of monitoring, analysis, control, and communication capabilities to improve its reliability, efficiency, and security. Smart meter devices, that include sensors to gather data and two-way digital communication between the smart meters in the field and the utility’s grid operations center, are associated with the grid. The smart grid can take advantage of new technologies, such as plug-in hybrid electric vehicles, various forms of renewable and conventional distributed generation, lighting management systems, automation technology that lets the utility adjust and control each individual device or millions of devices from a central location, and many more. This presentation will discuss some of the current research projects being investigated by Butler-Purry’s group on smart distributions systems, in grid or island operation. One project investigates the impact of cyber attacks on the operation of smart distribution systems. The second project developed two new approaches to enhance the protection of smart distribution systems. One approach uses smart meters during distribution planning to improve selectivity of protection, and the other approach uses smart meters during operation to improve the sensitivity of protection.
Karen L. Butler-Purry, PhD, PE, is Associate Provost for Graduate and Professional Studies and Professor in the Department of Electrical and Computer Engineering at Texas A&M University where she has served on the faculty since 1994. She received a B.S. in Electrical Engineering in 1985 from Southern University in Baton Rouge, Louisiana. She was awarded a M.S. degree in 1987 from the University of Texas at Austin and a Ph.D. in Electrical Engineering in 1994 from Howard University in Washington, D.C. Her research interests are in the areas of protection and control of distribution systems and isolated power systems such as all electric power systems for ships, mobile grids, and microgrids; cybersecurity protection; and intelligent systems for equipment deterioration and fault diagnosis.
Host: Prof. Gymama Slaughter,
The UMBC Council of Computing Majors will hold its first hands-on Raspberry Pi workshop from 2:00-4:00 this Friday, March 7, in ITE240.
The Raspberry Pi is a $35 credit-card-sized, single-board computer that runs a version of Unix. Originally developed for teaching computer programming to children, it is now being used in many useful and exciting applications, from near-space weather balloons to baby monitors to media servers. The possibilities are only limited by your imagination.
The initial workshop will cover the Raspberry Pi, its Raspbian Unix OS, and how to program it using Python for real-world applications. There will be 20 Pi computers for participants to use. The workshop is designed so freshman and non-computer science majors can attend and participate. If you know anyone who would be interested in attending, please send them the link and information!
Space is limited, so sign up to reserve a seat. Intermediate and advanced workshops will follow later in the semester. See the Pi FAQ for general information on the Pi and Raspbian for information on its operating system.
For more information, contact CCM president Austin Murdock ().
With enormous growth in sensing and communication capabilities as well as processing power to analyze collected data, we are witnessing exciting opportunities in diverse disciplines to study complex interactions of networked entities. The overarching theme is to explore cutting-edge computational intelligence tools from signal processing, machine learning, optimization, and control to make sense of amassed data and exploit complex interactions to make significant real-world impacts. In this talk, I will make cases for two prime examples, namely, future power systems and cognitive wireless networks. The role of contemporary tools including online learning, sparse and low-dimensional models, distributed and robust algorithms, will be emphasized.
Seung-Jun Kim received his B.S. and M.S. degrees from Seoul National University in Seoul, Korea in 1996 and 1998, respectively, and his Ph.D. from the University of California at Santa Barbara in 2005, all in electrical engineering. From 2005 to 2008, he worked for NEC Laboratories America in Princeton, New Jersey, as a Research Staff Member. He is currently with the Department of Electrical and Computer Engineering and the Digital Technology Center at the University of Minnesota, where he is a Research Associate Professor and a Research Associate. His research interests lie in applying signal processing, optimization, and machine learning techniques to various application domains including wireless communication and networking and smart power grids.
This talk will present the computational challenges and approaches to knowledge discovery from terabyte-sized images. The motivation comes from experimental systems for imaging and analyzing human pluripotent stem cell cultures at the spatial and temporal coverage of colonies that lead to terabyte-sized image data. The objective of such an unprecedented cell study is to characterize pluripotency of stem cell colonies over time at high statistical significance in order to understand the stem cell culture quality parameters and guide a repeatable growth of high quality stem cell colonies. The terabyte- sized images represented a stem cell line that was engineered to produce green fluorescent protein (GFP) under the influence of Oct4 promoter and then imaged in a mosaic of contiguous frames covering approximately 180 square millimeters, over five days under both phase contrast and GFP channels.
We overview multiple computer and computational science problems related to correcting (flat-field, dark current and background), stitching, segmenting, tracking, re-projecting and then representing large images for interactive visualization and sampling in a web browser. We researched extensions to Amdahl’s law for Map-Reduce computations, established benchmarks for image processing on a Hadoop platform, and introduced cluster node utilization coefficients for modeling memory demanding computations running on a computer cluster/cloud. The theoretical aspects of algorithmic complexity and cluster utilization at terabyte scale are extended to the experimental aspects of efficient image representation and client-server workload distribution in the context of visualization interactivity and image sampling. We report such experimental results for the NIST extensions to the Deep Zoom paradigm. The presentation will conclude with illustrations of enabled stem cell discoveries and collaboration opportunities to create a reference resource not only for cell biologists but also for computer scientists focusing on terabyte scale image analyses.
Peter Bajcsy received his Ph.D. in Electrical and Computer Engineering in 1997 from the University of Illinois at Urbana-Champaign and a M.S. in Electrical and Computer Engineering in 1994 from the University of Pennsylvania. He worked for machine vision, government contracting, and research and educational institutions before joining the National Institute of Standards and Technology (NIST) in 2011. At NIST, he has been leading a project focusing on the application of computational science in biological metrology, and specifically stem cell characterization at very large scales. Peter’s area of research is large-scale image-based analyses and syntheses using mathematical, statistical and computational models while leveraging computer science fields such as image processing, machine learning, computer vision, and pattern recognition. He has co-authored more than more than 24 journal papers and eight books or book chapters, and close to 100 conference papers.