Bifurcation analysis of rotor/bearing system using third-order journal bearing stiffness and damping coefficients

The authors declare that they do not receive any funds from any organization for this research.Peer reviewedPublisher PD

IDENTITY RESOLUTION IN EMAIL COLLECTIONS

Access to historically significant email collections poses challenges that arise less often in personal collections. Most notably, people exploring a large collection of emails, in which they were not sending or receiving, may not be very familiar with the discussions that exist in this collection. They would not only need to focus on understanding the topical content of those discussions, but would also find it useful to understand who the people sending, receiving, or mentioned in these discussions were. In this dissertation, the problem of resolving personal identity in the context of large email collections is tackled. In such collections, a common name (e.g., John) might easily refer to any one of several hundred people; when one of these people was mentioned in an email, the question then arises: "who is that John?'' To "resolve identity'' of people in an email collection, two problems need to be solved: (1) modeling the identity of the participants in that collection, and (2) resolving name-mentions (that appeared in the body of the messages) to these identities. To tackle the first problem, a simple computational model of identity, that is built on extracting unambiguous references (e.g., full names from headers, or nicknames from free-text signatures) to people from the whole collection, is presented. To tackle the second problem, a generative probabilistic approach that leverages the model of identity to resolve mentions is presented. The approach is motivated by intuitions about the way people might refer to others in an email; it expands the context surrounding a mention in four directions: the message where the mention was observed, the thread that includes that message, topically-related messages, and messages sent or received by the original communicating parties. It relies on less ambiguous references (e.g., email addresses or full names) that are observed in some context of a given mention to rank potential referents of that mention. In order to jointly resolve all mentions in the collection, a parallel implementation is presented using the MapReduce distributed-programming framework. The implementation decomposes the structure of the resolution process into subcomponents that fit the MapReduce task model well. At the heart of that implementation, a parallel algorithm for efficient computation of pairwise document similarity in large collections is proposed as a general solution that can be used for scalable context expansion of all mentions and other applications as well. The resolution approach compares favorably with previously-reported techniques on small test collections (sets of mention-queries that were manually resolved beforehand) that were used to evaluate the task in the literature. However, the mention-queries in those collections, besides being relatively few in number, are limited in that all refer to people for whom a substantial amount of evidence would be expected to be available in the collection thus omitting the "long tail'' of the identity distribution for which less evidence is available. This motivated the development of a new test collection that now is the largest and best-balanced test collection available for the task. To build this collection, a user study was conducted that also provided some insight into the difficulty of the task and how time-consuming it is when humans perform it, and the reliability of their task performance. The study revealed that at least 80% of the 584 annotated mentions were resolvable to people who had sent or received email within the same collection. The new test collection was used to experimentally evaluate the resolution system. The results highlight the importance of the social context (that includes messages sent or received by the original communicating parties) when resolving mentions in email. Moreover, the results show that combining evidence from multiple types of contexts yields better resolution than what can be achieved using any individual context. The one-best selection is correct 74% of the time when tested on the full set of the mention-queries, and 51% of the time when tested on the mention-queries labeled as "hard'' by the annotators. Experiments run with iterative reformulation of the resolution algorithm resulted in modest gains only for the second iteration in the social context expansion

Adaptive Method for Following Dynamic Topics on Twitter

Many research social studies of public response on social media require following (i.e., tracking) topics on Twitter for long periods of time. The current approaches rely on streaming tweets based on some hashtags or keywords, or following some Twitter accounts. Such approaches lead to limited coverage of on-topic tweets. In this paper, we introduce a novel technique for following such topics in a more effective way. A topic is defined as a set of well-prepared queries that cover the static side of the topic. We propose an automatic approach that adapts to emerging aspects of a tracked broad topic over time. We tested our tracking approach on three broad dynamic topics that are hot in different categories: Egyptian politics, Syrian conflict, and international sports. We measured the effectiveness of our approach over four full days spanning a period of four months to ensure consistency in effectiveness. Experimental results showed that, on average, our approach achieved over 100 % increase in recall relative to the baseline Boolean approach, while maintaining an acceptable precision of 83%

Quantum Dot Infrared Photodetector Fabricated by Pulsed Laser Deposition Technique

Pulsed laser deposition is used to fabricate multilayered Ge quantum-dot photodetector on Si(100). Growth was studied by reflection high-energy electron diffraction and atomic force microscopy. The difference in the current values in dark and illumination conditions was used to measure the device sensitivity to radiation. Spectral responsivity measurements reveal a peak around 2 μm, with responsity that increases three orders of magnitude as bias increases from 0.5 to 3.5 V

Study of the Effect of Geomembranes on the Interaction between the Soil and Underground Structures

Geomembranes are one of the most widely used insulation materials in various civil engineering applications. Failure due to the slippage between geomembranes and the interfacing soils was detected at some cases. This paper presents the results of a series of laboratory tests carried out to investigate the factors controlling the developed interface stresses strength between the soil and geomembranes. In order to quantify the effect of different commonly used isolation membranes on the behavior and stability of buried concrete structures, laboratory studies by using modified direct shear apparatus is performed and integrated and the reduction in the shear resistance between the soil and different types of isolation geomembranes is determined. Graded sand with well-rounded particles was used in the experimental program. Shear tests were conducted under a normal stress range of about 25-100 kPa. The effect of the geomembranes on the peak and residual interface shear strengths is highlighted. Test’s results indicate development of peak interface shear resistance at a small strain and constant residual interface shear resistance at large strain. It was found that the developed peak and residual interface friction angles between the sand and the geomembranes interfaces ranged from 59 % to 82 % of the corresponding peak and residual interface friction angles between sand and un-protected concrete