News: DIY Bluetooth Accelerometer

College Of Computing at Georgia Tech have a great DIY Bluetooth accelerometer with source and schematic included.

Overview:

This is a small wireless sensor platform providing a bluetooth SPP link to three axes of accelerometer data. The accelerometers are sampled by a PIC microcontroller (onboard ADC) at roughly 100Hz (rate can be changed via firmware). Data from the ADC conversion is sent to a remote computer using the PIC's UART in conjunction with a drop-in bluetooth serial part. Even with two dual-axis accelerometers onboard there are up to 17 free I/O lines and two additional ADC channels depending on the device configuration. Three sockets provide access to all PIC signals. Over-the-air programming allows for easy firmware updates and rapid prototyping without the need to have a PIC programmer or special cable. Schematics, parts lists, and firmware sources are available online.

Feature List:

• Bluetooth serial port profile (SPP) for standardized interface
• 3-Axis accelerometer data, 3.9mg resolution (ADXL202JE)
• Reprogrammable PIC (16LF873-04I)
• 17 free I/O lines
• In-Circuit programming connector
• Over-the-air programming via bootloader
• Battery life ~60hrs on 840mAh 3.7v battery, full TX mode
• 13mA @ 3.7v TX mode, 3mA @ 3.7v standby
• Very Simple Firmware
• 35mm X 35mm X 5mm
  

Contextual Computing Group: Bluetooth Accelerometer - Link

CMU VASC talk: Clustering and Classification via Lossy Data Compression

Clustering and Classification via Lossy Data Compression

Yi Ma, UIUC
Monday, Feb 26

For many problems in computer vision, image processing, and pattern recognition, we need to process and analyze massive amount of high-dimensional mixed data such as images and gene expression data. By mixed data, we mean that the given data set consists of multiple heterogeneous subsets (which have different geometric or statistical characteristics) but each subset can be more easily modeled or representedthan the whole data set together.

In this talk, we address two fundamental questions: .How to cluster and classify such high-dimensional mixed data?. We contend that both the (unsupervised) clustering and (supervised) classification problems can be cast as a lossy data compression problem and solved efficiently within a unified mathematical framework. In theory, this approach offers some distinguished advantages over conventional methods for clustering and classification, especially in dealing with several difficult issues that often arise in practice: regularization of degenerate distributions, selection of models with different complexities, and rejection of outliers.

Our work establishes a strong connection between information theory, especially the rate-distortion theory, with data clustering and classification, and it leads to extremely simple but effective algorithms. We will demonstrate the success of these algorithms in a few popular but difficult problems, including but not limited to natural image segmentation, microarray data clustering, handwritten digits and face
recognition.

CMU talk: Interacting Physically with Robots and Virtually on the Global Digital Campus

SCS DISTINGUISHED LECTURE SERIES
Yuichiro Anzai
President, Keio University

Abstract:
The lecture presents two topics: one for the research on human-robot interaction conducted in the Anzai-Imai laboratory and the other for the activities of the Research Institute for Digital Media and Content, both at Keio University.
Our research on human-robot interaction, embarked upon in 1991, is concerned with designing technologies that facilitate the smooth interaction of humans with robots. We initially started by designing software and hardware systems that support human-robot interaction, and then moved forward, with Michita Imai and others, to designing robots that can smoothly interact with humans. In some cases we conducted behavioral experiments to find out how a human behaves in an interaction with a robot, and fed the results back to engineering. The first part of the lecture provides a summary of the efforts at our lab during these fifteen years.
The second part of the lecture will focus on the activities of the Research Institute for Digital Media and Content, established in 2004. One of its goals is to use various technologies to extend the reach of our physical campus so that students and faculty members can distribute their academic knowledge to a global audience, interact with people around the world, and have convenient access to globally shared knowledge. We have already set up what we call Global Digital Studios in Tokyo, Seoul, Beijing, Cambridge (UK) and San Francisco, with others scheduled to open in New York and some other locations. Twenty-four higher learning institutions in twelve South-East Asian countries are also tied to this network via satellite Internet. The Studios and sites can be connected online at any time, and are used for many different purposes: the network can be regarded as an early version of our Global Digital Campus. The second part of the lecture gives a glimpse of this effort at Keio University.

Bio:
Born in 1946 in Tokyo, Yuichiro Anzai received his Ph.D. in engineering from Keio University in 1974. After serving at Keio as an assistant professor until 1985, he joined the faculty of Hokkaido University as an associate professor in behavioral science. In 1988 he returned to Keio as a professor in electrical engineering, and became the dean of the Faculty of Science and Technology in 1993. He worked on the reform of the undergraduate departments and graduate programs for more than seven years, and launched new educational and research programs with an innovative structure. Since 2001, he has served as president of Keio University, the oldest modern institution of higher learning in Japan (http://www.keio.ac.jp/index-en.htm) that will celebrate its 150th anniversary in 2008, as well as a professor in the Department of Information and Computer Science and the School of Open and Environmental Systems. At present, much of his time is devoted to driving forward the commemorative fundraising campaign and associated programs (http://keio150.jp/english).
Professor Anzai was a post-doc in the Departments of Psychology and Computer Science, and a visiting assistant professor in the Department of Psychology, Carnegie-Mellon University, in 1976-78, and in 1981-82 respectively. He was also a visiting professor at the Center for Medical Education, McGill University, in 1990. His fields of research include cognitive science and computer science, particularly cognitive processes in learning and problem solving, and human-robot-computer interaction. He has published about 20 books, single- and co-authored, and more than 120 technical papers in those fields. For public service, he is serving as president of the Information Processing Society of Japan, as president of the Japan Association of Private Universities and Colleges, as a member of the Science Council of Japan, and as a member of the Central Council for Education.

CMU talk: The Structure and Acquisition of Semantic Knowledge

The Structure and Acquisition of Semantic Knowledge

Charles Kemp
Department of Brain & Cognitive Sciences
Massachusetts Institute of Technology

Thursday, February 22, 2007

Humans regularly make inferences that go beyond the data they have seen. Two questions immediately arise: what is the knowledge that supports these inferences, and how is this knowledge acquired? I will present a hierarchical Bayesian approach to inductive reasoning that addresses both questions. When making inferences about the distribution of a novel property, people draw on rich semantic knowledge that can often be captured using structured representations of the relationships between the entities in a domain. For instance, given that gazelles have T4 cells and carry E. spirus bacteria, taxonomic relations are useful for predicting which other animals are likely to have T4 cells, but predator-prey relations are more useful when reasoning about the distribution of E. spirus bacteria. I will show that our formal framework provides close quantitative fits to human inferences about several kinds of properties when supplied with appropriate knowledge representations for each task. Different inductive tasks often draw on different kinds of knowledge which are best captured by qualitatively different kinds of representations. For instance, anatomical features of biological species are best captured by a taxonomic tree, political views are best captured by a linear spectrum, and friendship relations are best captured by a set of discrete cliques. Our hierarchical framework helps to explain how humans can discover the best kind of representation for a given inductive context.

CMU talk: Adaptive Online Allocation Mechanisms for Single-Valued Domains

Adaptive Online Allocation Mechanisms for Single-Valued Domains

David C. Parkes, Harvard University

Abstract: Mechanism design studies the problem of designing protocols that will implement desirable outcomes in multi-agent systems with self-interest and private information. Many interesting domains are inherently dynamic with uncertainty about both supply and demand; e.g., selling seats on an airplane, adverts on a search engine, computational resources. The classic Vickrey-Clarke-Groves mechanism extends to dynamic environments but is non-adaptive and much less robust than when used offline. Our interest in this talk is in the design of adaptive, online allocation mechanisms, that are able to leverage a probabilistic (perhaps incorrect) model of the environment. We focus on single-valued domains in which agents are indifferent across one of a set of equivalent allocations. A truthful, online stochastic optimization algorithm coupled with historical sampling and an ``ironing" procedure is presented, along with examples to show that the optimal policy is generally not truthfully implementable. Simulation analysis illustrates the cost of truthfulness in application to selling a computational resource.

Bio: David C. Parkes is the John L. Loeb Associate Professor of the Natural Sciences and Associate Professor of Computer Science at Harvard University. He received his Ph.D. degree in Computer and Information Science from the University of Pennsylvania in 2001, and an M.Eng. (First class) in Engineering and Computing Science from Oxford University in 1995. He was awarded the prestigious NSF CAREER Award in 2002, an IBM Faculty Partnership Award in 2002 and 2003, and the Alfred P. Sloan Fellowship in 2005. Parkes has published extensively on topics related to electronic markets, computational mechanism design, auction theory, and multi-agent systems. He serves on the editorial board of the Journal of Artificial Intelligence Research and the Electronic Commerce Research Journal, and has served on the Program Committee of a number of leading conferences in artificial intelligence, multiagent systems and electronic commerce, including ACM-EC, AAAI, IJCAI, UAI and AAMAS. Parkes is the co program-chair of the ACM Conference on Electronic commerce, 2007 and the Int. Conf. on Autonomous Agents and Multiagent systems, 2008.

http://www.eecs.harvard.edu/~parkes

[Thesis Proposal] Predictive Exploration for Autonomous Science

Speaker: David Thompson

Abstract:
Planetary science is entering a new era in which exploration robots can outrun their own ability to collect science data. Autonomous navigation will soon permit single-command traverses of multiple kilometers. Nevertheless, the time for taking measurements and the bandwidth available for transmitting them to Earth will remain relatively constant. A growing body of research addresses these bottlenecks with onboard data understanding. Autonomous rovers can use pattern recognition, learning and planning technologies to place instruments and take measurements without human supervision. These robots autonomously choose the most important features to observe and transmit, traveling longer distances without sacrificing our understanding of the visited terrain.

I argue that intelligent explorer agents must exploit structure in their environment. In other words, they must be mapmakers. Maps can represent spatial structure (similarities from one locale to the next) and inter-sensor structure (correlations between different sensing modes). “Predictive exploration” formulates mapmaking as an experimental design problem. Generative spatial models guide the agent to informative areas while minimizing redundant measurements. Information gain over the map determines exploration decisions, while a similar criterion suggests the best data products for downlink. We will demonstrate these principles with a rover system that autonomously builds kilometer-scale geologic maps.

A copy of the thesis proposal document:
http://www.cs.cmu.edu/~drt/ThompsonProposal.pdf.

News: European Researchers Developing 'Emotional Robot'

The link. February 24, 2007 2:35 p.m. EST

Som Patidar - All Headline News Staff Writer

London, Britain (AHN) - A joint research project by a European team-led by British researchers is developing a robot that can interact with people emotionally.

The research project, Feelix Growing, involves six countries and 25 roboticists, developmental psychologists and neuroscientists.

The project's coordinator Lola Canamero, from Britain's University of Hertfordshire, said that the aim is to develop robots that grow up and adapt to humans in everyday environments.

"If robots are to be truly integrated in humans everyday lives as companions or careers, they cannot be just taken off the shelf and put into a real-life setting, they need to adapt to their environment," Canamero said.

News: SheekGeek educational kits

The W.A.S.P Original Robotic Kit is designed to help introduce children age 12 and up to robotics, electronics, and mechanics.

The name W.A.S.P stands for "Wiggling and Spinning Photovore" which describes the action and the type of the robot in the W.A.S.P. robot kit. A photovore is a light-seeking robot. The W.A.S.P photovore robot wiggles towards a light source and spins in circles when it finds the brightest spot.

The activity of the W.A.S.P is very reactive. It will follow the beam of a flashlight closely allowing the user to control where they want the W.A.S.P to go. The W.A.S.P is also very quick (especially with new batteries). It can travel 5 feet in 5 seconds!

Setup of the W.A.S.P is great for beginners. The circuit is very simple and many of the pieces are everyday, recognizable items like chenille stems/pipe cleaners and cable ties.

SheekGeek Educational Kits - Link
SheekGeek Original W.A.S.P. Robotic Kit - Link
The W.A.S.P. Original Robotic Kit Contents - Link

Call For Papers: Special Issue on Network Robot Systems (Robotics and Autonomous System Journal)

**************************************************
SPECIAL ISSUE ON NETWORK ROBOT SYSTEMS (NRS)
(ROBOTICS AND AUTONOUMOUS SYSTEMS JOURNAL)
**************************************************

The last decade has witnessed unprecedented interaction between technological developments in computing and communications, which have led to the design and implementation of robotic and automation systems consisting of networked vehicles, sensors and actuators systems. These developments enable researchers and engineers not only to design new robotics systems but also to develop systems that could have not been imagined before. Now, there is a need for a unifying paradigm within the robotics community to address the design of these networked automation systems.

The name Networked Robots (NR) was created in May 2004 within the IEEE RAS Technical Committee, as a consequence of the preliminary work on Internet-based tele-operated robots initiated in 2001, and its expansion to reflect a broader set of problems and applications. There are several definitions of NRS, coming from US and Japan, but a simple and comprehensive definition of NRS is:

“A Network Robot System is a group of artificial autonomous systems that are mobile and that make important use of wireless communications among them or with the environment and living systems in order to fulfil their tasks”.

Network Robot Systems (NRS) call for the integration of several fields: robotics, perception (sensor systems), ubiquitous computing, and network communications. Some of the key issues that must be addressed in the design of Network Robot Systems are cooperative localization and navigation, cooperative environment perception, cooperative map building, cooperative planning and planning for cooperation, human-robot interaction, network tele-operation, and communications.

The topic Network Robot Systems transcends “conventional” robotics, in the sense that there exists for these type of distributed heterogeneous systems, an interrelation among a community of robots, environment sensors and humans. Applications include network robot teams (for example for space applications), human-robot networked teams (for example a community of robots that assist people), robots networked with the environment (for example for tasks on urban settings or rescue) or geminoid robots (a replication of a human with own autonomy and being partially tele-operated through the network).

The topics of interest include, but are not limited to:
- human robot symbiosis
- networked environing sensing/actuation
- distributed environing system
- interaction between human and environing components
- networked human-robot interaction
- coordination and cooperation among multiple types of robots
- self-configuration of a network robot system
- monitoring and self-repair of a network robot system
- network robot platform
- security for network robot systems
- socially situated network robots
- applications of network robot systems

IMPORTANT DATES
First Call for papers: 15 February 2007
Paper submission deadline: 15 May 2007
Revised notification: 10 September 2007
Final paper submission: 22 October 2007
Final decision notification: 16 November 2007

REVIEWING PROCESS
Expected contributions should be around 12 pages long. Submissions have to be sent to the Guest Editors (sanfeliu@iri.upc.es; hagita@atr.jp, asaffio@aass.oru.se) in electronic form (PDF files). The Guest Editors will first evaluate all manuscripts. Manuscripts meeting the minimum criteria are passed on for peer review, to be accomplished by two external experts. The method of review in this special issue will employ single blind review, where the referee remains anonymous throughout the process. The Guest Editors board is responsible for the final decision to accept
or reject the articles, based on the recommendations of the reviewers. Accepted papers will have to be sent to the Guest Editors electronically, both as source files (LaTex, MS Word, including all original Figures/Tables and References) and in printable version (PDF). Please follows the instructions in http://ees.elsevier.com/robot/.

GUEST EDITORS
• Alberto Sanfeliú, Technical University of Catalonia, Spain, sanfeliu@iri.ups.es
• Norihiro Hagita, ATR Intelligent Robotics and Communication Laboratories, Japan, hagita@atr.jp
• Alessandro Saffiotti, Örebro University, Sweden, asaffio@aass.oru.se

RELATED LINKS:
- Research Atelier on Network Robot Systems, http://turina.upc.es/nrs
- Japan Network Robot Forum, http://www.scat.or.jp/nrf/English/
- IEEE RAS Technical Committee, http://www.informatik.uni-freiburg.de/~burgard/tc/

Non-rigid point set registration : Coherent Point Drift

Author :

Andriy Myronenko
Xubo Song
Miguel A´ . Carreira-Perpin˜a´n

OGI School of Science and Engineering
Oregon Health and Science University

Title :

Non-rigid point set registration : Coherent Point Drift

Abstract :

We introduce Coherent Point Drift (CPD), a novel probabilistic method for nonrigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with the second set. We formulate the motion coherence constraint and derive a solution of regularized ML estimation through the variational approach, which leads to an elegant kernel form. We also derive the EM algorithm for the penalized ML optimization with deterministic annealing. The CPD method simultaneously finds both the non-rigid transformation and the correspondence between two point sets without making any prior assumption of the transformation model except that of motion coherence. This method can estimate complex non-linear non-rigid transformations, and is shown to be accurate on 2D and 3D examples and robust in the presence of outliers and missing points.

Link :
paper
project page

Patent: Underground GPS

13:48 12 February 2007
NewScientist.com news service
Barry Fox

Underground GPS

Satellite navigation is becoming a vital tool for the modern motorist. But GPS (Global Positioning System) receivers need to compare signals from at least three orbiting satellites to determine their position. This means satellite navigation does not normally work inside a tunnel, underground or in a heavily built up area.
Two inventors from Cambridge in the UK are now patenting a system that could let satellite equipment acquire positioning information even when satellite signals are blocked. The roof of the building, or the ground above the tunnel, is fitted with at least four directional antennae focused on different patches of the sky. These antennae receive GPS signals, then amplify and re-broadcast them using transmitters positioned at specific points below ground. A GPS device is then fooled into behaving as if it were out in the open, providing accurate positional data from inside a tunnel, in an underground car park or in a heavily built up city. The same trick could also let GPS devices work inside buildings.

See the patent application.

Spatial Reasoning: Planning Among Movable Obstacles

Author:
Mike Stilman
Robotics Institute
Carnegie Mellon University

Abstract:
Autonomous robots operating in real world, unstructured environments cannot rely on the existence of collision free paths or feasible trajectories. Search and rescue, construction and planetary exploration domains contain debris that obstructs the robots path. Theoretically, one can represent all possible interactions between the robot and these objects as a single search problem. However, the resulting nonlinear state space would be exponentially large. In this thesis we explore methods for reasoning about the robots state space to reduce problem dimensionality and accomplish autonomous motion in the presence of movable objects.

Further Details:
A copy of the thesis proposal document can be found at http://www.cs.cmu.edu/~mstilman/proposal/stilman-proposal.pdf.

CMU Intelligence Seminar: Bayesian models of human learning and inference

Bayesian models of human learning and inference
Josh Tennenbaum, MIT

Faculty Host: Tom Mitchell

Bayesian methods have revolutionized major areas of artificial intelligence, machine learning, natural language processing and computer vision. Recently Bayesian approaches have also begun to take hold in cognitive science, as a principled framework for explaining how humans might learn, reason, perceive and communicate about their world. This talk will sketch some of the challenges and prospects for Bayesian models in cognitive science, and also draw some lessons for bringing probabilistic approaches to artificial intelligence closer to human-level abilities.

The focus will be on learning and reasoning tasks where people routinely make successful generalizations from very sparse evidence. These tasks include word learning and semantic interpretation, inference about unobserved properties of objects and relations between objects, reasoning about the goals of other agents, and causal learning and inference. These inferences can be modeled as Bayesian computations operating over constrained representations of world structure -- what cognitive scientists have called "intuitive theories" or "schemas". For each task, we will consider how the appropriate knowledge representations are structured, how these representations guide Bayesian learning and reasoning, and how these representations could themselves be learned via Bayesian methods. Models will be evaluated both in terms of how well they capture quantitative or qualitative patterns of human behavior, and their ability to solve analogous real-world problems of learning and inference. The models we discuss will draw on -- and hopefully, offer new insights for -- several directions in contemporary machine learning, such as semi-supervised learning, modeling relational data, structure learning in graphical models, hierarchical Bayesian modeling, and Bayesian nonparametrics.

Speaker Bio
Josh Tenenbaum studies learning and reasoning in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing artificial intelligence closer to human-level capacities. He received his Ph.D. from MIT in 1999, and from 1999-2002, he was a member of the Stanford University faculty in the Departments of Psychology and (by courtesy) Computer Science. In 2002, he returned to MIT, where he currently holds the Paul E. Newton Career Development Chair in the Department of Brain and Cognitive Sciences, and is a member of the Computer Science and Artificial Intelligence Laboratory. He has published extensively in cognitive science, machine learning and other AI fields, and his group has received several outstanding paper or student-paper awards at NIPS, CVPR, and Cognitive Science. He received the 2006 New Investigator Award from the Society for Mathematical Psychology, and the 2007 Young Investigator Award from the Society of Experimental Psychologists. He serves as an associate editor of the journal Cognitive Science and is currently co-organizing a summer school on "Probabilistic Models of Cognition: The Mathematics of Mind" for July 2007 at IPAM, the Institute of Pure and Applied Mathematics at UCLA.

Simulating Thought to Model Terrorists

A rock star among game developers, Silverman and his team of 20 researchers and graduate students at the University of Pennsylvania's Ackoff Center for Advancement of Systems Approaches have gone well beyond any video game in existence. They imbue agents with detailed physiologies that respond to hunger, fatigue, and stress, as well as minds that encompass complex reasoning skills, value systems, and up to 22 emotions. This is the closest a computer comes to simulating a real person, and is at the cutting edge of computational behavior modeling.

[LINK]

CMU RI Thesis Proposal: Integrated Localization, Mapping, and Planning in Unstructured 3D Environments

Nathaniel Fairfield (than@cmu.edu)
Robotics Institute
Carnegie Mellon University

Abstract:

The ability to explore an unknown environment is a prerequisite for most useful mobile robotics. Exploration can be decomposed into the tasks of perceiving the environment to build a map, localizing within that map, and planning where to explore next. Over the past ten years or so, the field of simultaneous localization and mapping (SLAM) has been active and increasingly applied. More recently, work has been directed towards the problem of planning as an integral part of exploration and SLAM. Another persistent challenge is scale many SLAM formulations have problems with exploring areas. We are interested in developing an integrated mapping, localization, and planning approach that can handle large scale three-dimensional environments and sparse sensor data. As a start, we have developed a method for doing SLAM using a Rao-Blackwellized Particle Filter and evidence grid-based maps, and demonstrated successful SLAM using an autonomous underwater vehicle in a 3D environment. The two major limitations of our current method are its inability to scale the evidence grid approach to truly large environments (hundreds of meters and millions of observations), and its lack of planning ability for picking exploration and/or uncertainty-reducing actions. We propose to address the first limitation by developing SLAM on multiple scales: local submaps and global maps; in effect using the submaps as features at larger scale. We propose to address the second limitation, planning, by integrating the tasks of mapping, localizing, and planning under an information-theoretic framework. The planning algorithm will use models of unmapped regions and the entropy of the predicted SLAM state to choose the action with the greatest estimated information gain. The combination of multi-scale SLAM and information gain-based planning raises the possibility of hierarchical exploration, where the robot's current task determines its exploration strategy. In this proposal we describe our current work, motivation, and proposed solutions, with the goal of building a system which is capable of exploring large-scale 3D environments.

Further Details: http://gs4435.sp.cs.cmu.edu/fairfield_proposal.pdf

ICRA07: Identification and Control of an Autonomous Blimp

Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous Blimp

Abstract:

Blimps are a promising platform for aerial robotics and have been studied extensively for this purpose. Unlike other aerial vehicles, blimps are relatively safe and also possess the ability to loiter for long periods. These advantages, however, have been difficult to exploit because blimp dynamics are complex and inherently non-linear. The classical approach to system modeling represents the system as an ordinary differential equation (ODE) based on Newtonian principles. A more recent modeling approach is based on representing state transitions as a Gaussian process (GP). In this paper, we present a general technique for system identification that combines these two modeling approaches into a single formulation. This is done by training a Gaussian process on the residual between the non-linear model and ground truth training data. The result is a GP-enhanced model that provides an estimate of uncertainty in addition to giving better state predictions than either ODE or GP alone. We show how the GP-enhanced model can be used in conjunction with reinforcement learning to generate a blimp controller that is superior to those learned with ODE or GP models alone.

Original link:
http://www.cs.washington.edu/homes/fox/abstracts/gp-blimp-icra-07.abstract.html

Paper link:
http://www.cs.washington.edu/homes/fox/postscripts/gp-blimp-icra-07.pdf

Stanford Talk: Large Scale Detection of Irregularities in Accounting Data

Large Scale Detection of Irregularities in Accounting Data

Stephen Bay, Center for Advanced Research, PricewaterhouseCoopers LLP

Abstract:
In recent years, there have been several large accounting frauds where a company's financial results have been intentionally misrepresented by billions of dollars. In response, regulatory bodies have mandated that auditors perform analytics on detailed financial data with the intent of discovering such misstatements. For a large auditing firm, this may mean analyzing millions of records from thousands of clients. In this talk, I will discuss techniques for automatic analysis of company general ledgers on such a large scale to identify irregularities -- which may indicate fraud or just honest errors -- for additional review by auditors. These techniques have been implemented in a prototype system, called Sherlock, which combines aspects of both outlier detection and classification. In developing Sherlock, we faced three major challenges: developing an efficient process for obtaining data from many heterogeneous sources, training classifiers with only positive and unlabeled examples, and presenting information to auditors in an easily interpretable manner.

MIT CSAIL talk: Neural Discrimination of Complex Natural Sounds in Songbirds

Title: Neural Discrimination of Complex Natural Sounds in Songbirds
Speaker: Dr. Kamal Sen , Neural Coding Laboratory, Hearing Research Center, Boston University
Date: Wednesday, February 14 2007

Discrimination and recognition of complex natural stimuli is a fundamental problem that arises in a wide variety of fields e.g., neuroscience and computer science. In neuroscience an important problem is to understand how animals and humans discriminate between complex sounds e.g., vocal communication sounds of two different individuals. In computer science, speech recognition algorithms must solve a similar problem. Moreover, such discrimination must often be performed in noisy environments, e.g., a cocktail party. How does the brain solve this problem? Currently, relatively little is known about the neural basis for complex sound discrimination and recognition. An attractive model system for investigating this question is the songbird, which shows striking analogies to humans in the context of speech. In this talk, I will describe our ongoing work on the neural discrimination of birdsongs in field L, the analog of primary auditory cortex, in zebra finches. I will present some of our findings on the accuracy and time-scales of neural discrimination, sensitivity vs. invariance to stimulus parameters e.g., intensity, and then discuss how we are extending this paradigm to investigate more complex auditory scenes, e.g., a cocktail party.

CMU VASC seminar: Computer Vision in Archaeology: Recent Case Studies

Computer Vision in Archaeology: Recent Case Studies
Kevin Cain
Institute for Study and Integration of Graphical Heritage Techniques

Computing for archaeology is a study in contrasts: graphics and vision techniques are still somewhat exotic, but interesting (and difficult) problems abound! In this talk, we'll present a snapshot of current needs in archaeology, framing the discussion with results from the past seven seasons of field work at the memorial temple of Ramses II in Egypt. Topics include: 3d representations of ancient sites, large scale orthomosaics of inscribed wall surfaces, lighting capture, relighting, and site reconstructions. We'll also take a look at efforts to present archaeological results in novel environments, including a new NSF 'full dome' film project Maya Skies and a large digital projection installation in Egypt's Valley of the Kings.

CMU VASC seminar: Observations from Parsing Images of Architectural Scenes

Observations from Parsing Images of Architectural Scenes
Alexander Berg
UC Berkeley

Computational models for visual recognition show promise for some tasks. I will review our success in this area and show some information theoretic comparisons with our ongoing work on parsing scenes. For images of architectural scenes we have observed that very simple independent local features provide a great deal of information about what components -- building, sky, ground, etc. -- make up a scene. In addition a few carefully chosen image wide latent variables are added to the model then even more information is available. Finally given this coarse level parsing it is possible to effectively identify features such as windows and roof-lines that would be difficult to parse in isolation.