Worcester Polytechnic Institute

About: Worcester Polytechnic Institute is a education organization based out in Worcester, Massachusetts, United States. It is known for research contribution in the topics: Computer science & Population. The organization has 6270 authors who have published 12704 publications receiving 332081 citations. The organization is also known as: WPI.

Evolutionary Relationships Among Proteins Encoded by the Regulator of Complement Activation Gene Cluster

Abstract: Evolutionary relationships among members of the regulator of complement activation (RCA) gene cluster were analyzed using neighbor-joining and parsimony methods of phylogenetic tree inference. We investigated the structural and functional similarities among short consensus repeats (SCRs) of the following human proteins: the alpha chain of the C4b-binding protein (C4bpalpha), factor H (FH), factor H-related proteins (FHR-1 through FHR-4), complement receptors type 1 (CR1) and type 2 (CR2), the CR1-like protein (CR1L), membrane cofactor protein (MCP), decay accelerating factor (DAF), and the sand bass proteins, the cofactor protein (SBP1) and its homolog, the cofactor-related protein (SBCRP-1). Also included are the beta chain of the human C4b-binding protein (C4bpbeta) and the b subunit of human blood-clotting factor XIII (FXIIIb). Our results indicate that the human plasma complement regulators, FH and C4bpalpha, fall into two distinct groups on the basis of their sequence divergence. Homology among RCA proteins is in agreement with their chromosomal location, with the exception of C4bpbeta. The evolutionary relationships among individual short consensus repeats are confirmed by the exon/intron structure of the RCA members. Structural similarities among repeats of the RCA proteins correlate with their functional activities and demonstrate the importance of the N-terminal SCRs.

Crowdsourcing human-robot interaction: new methods and system evaluation in a public environment

Abstract: Supporting a wide variety of interaction styles across a diverse set of people is a significant challenge in human-robot interaction (HRI). In this work, we explore a data-driven approach that relies on crowdsourcing as a rich source of interactions that cover a wide repertoire of human behavior. We first develop an online game that requires two players to collaborate to solve a task. One player takes the role of a robot avatar and the other a human avatar, each with a different set of capabilities that must be coordinated to overcome challenges and complete the task. Leveraging the interaction data recorded in the online game, we present a novel technique for data-driven behavior generation using case-based planning for a real robot. We compare the resulting autonomous robot behavior against a Wizard of Oz base case condition in a real-world reproduction of the online game that was conducted at the Boston Museum of Science. Results of a post-study survey of participants indicate that the autonomous robot behavior matched the performance of the human-operated robot in several important measures. We examined video recordings of the real-world game to draw additional insights as to how the novice participants attempted to interact with the robot in a loosely structured collaborative task. We discovered that many of the collaborative interactions were generated in the moment and were driven by interpersonal dynamics, not necessarily by the task design. We explored using bids analysis as a meaningful construct to tap into affective qualities of HRI. An important lesson from this work is that in loosely structured collaborative tasks, robots need to be skillful in handling these in-the-moment interpersonal dynamics, as these dynamics have an important impact on the affective quality of the interaction for people. How such interactions dovetail with more task-oriented policies is an important area for future work, as we anticipate such interactions becoming commonplace in situations where personal robots perform loosely structured tasks in interaction with people in human living spaces.

Understanding what they do with what they know

Abstract: This work seeks to understand what "they" (Web advertisers) actually do with the information available to them. We analyze the ads shown to users during controlled browsing as well as examine the inferred demographics and interests shown in Ad Preference Managers provided by advertisers.In an initial study of ad networks and a focused study of the Google ad network, we found many expected contextual, behavioral and location-based ads along with combinations of these types of ads. We also observed profile-based ads. Most behavioral ads were shown as categories in the Ad Preference Manager (APM) of the ad network, but we found unexpected cases where the interests were not visible in the APM. We also found unexpected behavior for the Google ad network in that non-contextual ads were shown related to induced sensitive topics regarding sexual orientation, health and financial matters.In a smaller study of Facebook, we did not find clear evidence that a user's browsing behavior on non-Facebook sites influences the ads shown to the user on Facebook, but we did observe such influence when the Facebook Like button is used to express interest in content. We did observe Facebook ads appearing to target users for sensitive interests with some ads even asserting such sensitive information, which appears to be a violation of Facebook's stated policy.