University of Memphis

About: University of Memphis is a education organization based out in Memphis, Tennessee, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7710 authors who have published 20082 publications receiving 611618 citations. The organization is also known as: U of M.

AutoTutor: a tutor with dialogue in natural language.

Abstract: AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer. It provides the student with positive, neutral, or negative feedback on the student’s typed responses, pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects erroneous ideas, answers the student’s questions, and summarizes answers. AutoTutor has produced learning gains of approximately .70 sigma for deep levels of comprehension.

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

Abstract: One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task. First, we transform EEG activities into a sequence of topology-preserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information. Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension. Empirical evaluation on the cognitive load classification task demonstrated significant improvements in classification accuracy over current state-of-the-art approaches in this field.

NLSR: named-data link state routing protocol

Abstract: This paper presents the design of the Named-data Link State Routing protocol (NLSR), a routing protocol for Named Data Networking (NDN). Since NDN uses names to identify and retrieve data, NLSR propagates reachability to name prefixes instead of IP prefixes. Moreover, NLSR differs from IP-based link-state routing protocols in two fundamental ways. First, NLSR uses Interest/Data packets to disseminate routing updates, directly benefiting from NDN's data authenticity. Second, NLSR produces a list of ranked forwarding options for each name prefix to facilitate NDN's adaptive forwarding strategies. In this paper we discuss NLSR's main design choices on (1) a hierarchical naming scheme for routers, keys, and routing updates, (2) a hierarchical trust model for routing within a single administrative domain, (3) a hop-by-hop synchronization protocol to replace the traditional network-wide flooding for routing update dissemination, and (4) a simple way to rank multiple forwarding options. Compared with IP-based link state routing, NLSR offers more efficient update dissemination, built-in update authentication, and native support of multipath forwarding.

Adaptive forwarding in named data networking

Abstract: In Named Data Networking (NDN) architecture, packets carry data names rather than source or destination addresses. This change of paradigm leads to a new data plane: data consumers send out Interest packets, routers forward them and maintain the state of pending Interests, which is used to guide Data packets back to the consumers. NDN routers' forwarding process is able to detect network problems by observing the two-way traffic of Interest and Data packets, and explore multiple alternative paths without loops. This is in sharp contrast to today's IP forwarding process which follows a single path chosen by the routing process, with no adaptability of its own. In this paper we outline the design of NDN's adaptive forwarding, articulate its potential benefits, and identify open research issues.

Seismological and structural evolution of strike-slip faults

Abstract: The mapped traces of strike-slip faults are commonly characterized by discontinuities that appear as steps in map-view. Here I present observations to show that the number of steps per unit length along the trace of major strike-slip fault zones in California and Turkey is a smoothly decreasing function of cumulative geological offset. When coupled with a growing body of evidence that indicates that steps in fault traces work to impede or arrest the propagation of earthquake ruptures, the apparent smoothing of fault traces with displacement is interpreted to suggest that the spatial distribution of strength properties on a fault plane is a function of cumulative geological offset. A consequence of this structural evolution is that faults may undergo a seismological evolution a well, whereby the size and frequency distribution of earthquakes is also a function of cumulative offset.