Anomalies in North American climate: the South Asian-tropical west Pacific connection

How do tropical heating fluctuations create North American climate anomalies? We propose some answers using the results from a simplified global atmospheric model. We find that the South Asian-tropical west Pacific area is especially effective at stimulating North American responses. The relatively strong tropical/extratropical interaction between these two areas is the result of two major processes acting on the Rossby wave signal induced by the tropical heating fluctuations. These factors are: 1) Wave guiding by the Asian-north Pacific subtropical jet; and 2) Wave amplification within unstable regions of the jet flank. These factors allow relatively small, remote, and short-term tropical fluctuations to have relatively large impacts on North American climate

High Strain Rate Experiments of Energetic Material Binder

Energetic materials, in particular HMX, is widely used in many applications as polymer bonded explosives (PBX) and rocket propellant. However, when damaged, HMX is known to be an unstable substance which renders it a hazardous material and in some cases unreliable. Finding critical mechanical conditions at high rates that render various forms of energetic materials as unreliable would be vital to understand the effects that vibrations and compression forces have on energetic materials. A better understanding would enable the ability to develop improvements in the manufacturing of PBX and rocker propellant. The method utilized to evaluate the mechanical properties of the material involved a compression Kolsky bar where a projectile hits an incident bar at 5 meters per second. The incident bar then compresses a binding polymer specimen composed of Sylgard 184 at the other end. Strain gauges were applied to the incident bar to measure voltage changes due to strain. In addition, a load cell was placed behind the specimen to measure compression force histories. The specimens studied were varied to evaluate correlation between composition and mechanical behavior. The results from the experiments showed that the binders with a lower mixing ratio of base to curing agent made the bonding polymer stiffer and less prone to elastic deformation. The results also unveiled that the stiffer binder experienced a higher compression stress due to it’s limited elastic deformation. The results also show that, at the strain rates studied, none of the binders failed. However, the measured results provide insight to manufacturers to select proper binder for specific loads. Further research of the compression force on HMX within Sylgard 184 is needed to delineate whether a stiff or ductile binder is more reliable for PBX

The relationship of western Pacific monsoon and tropical cyclone activity to North Pacific and North American climate anomalies

This present study investigates the influence of western Pacific tropical cyclone activity as possible centers of anomalous tropical heating on the large-scale circulation over the Pacific region. The characterization of tropical cyclone activity via an index based on anomalous 700 mb zonal wind is described first. Patterns of anomalous large-scale extratropical circulation anomalies based on composites of similar periods of tropical cyclone activity are then presented, followed by general conclusions

Learning semantic structures from in-domain documents

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 175-184).Semantic analysis is a core area of natural language understanding that has typically focused on predicting domain-independent representations. However, such representations are unable to fully realize the rich diversity of technical content prevalent in a variety of specialized domains. Taking the standard supervised approach to domainspecific semantic analysis requires expensive annotation effort for each new domain of interest. In this thesis, we study how multiple granularities of semantic analysis can be learned from unlabeled documents within the same domain. By exploiting in-domain regularities in the expression of text at various layers of linguistic phenomena, including lexicography, syntax, and discourse, the statistical approaches we propose induce multiple kinds of structure: relations at the phrase and sentence level, content models at the paragraph and section level, and semantic properties at the document level. Each of our models is formulated in a hierarchical Bayesian framework with the target structure captured as latent variables, allowing them to seamlessly incorporate linguistically-motivated prior and posterior constraints, as well as multiple kinds of observations. Our empirical results demonstrate that the proposed approaches can successfully extract hidden semantic structure over a variety of domains, outperforming multiple competitive baselines.by Harr Chen.Ph.D

In-domain relation discovery with meta-constraints via posterior regularization

We present a novel approach to discovering relations and their instantiations from a collection of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that instances of a single relation should exhibit regularities at multiple levels of linguistic structure, including lexicography, syntax, and document-level context. We capture these regularities via the structure of our probabilistic model as well as a set of declaratively-specified constraints enforced during posterior inference. Across two domains our approach successfully recovers hidden relation structure, comparable to or outperforming previous state-of-the-art approaches. Furthermore, we find that a small set of constraints is applicable across the domains, and that using domain-specific constraints can further improve performance.United States. Defense Advanced Research Projects Agency (Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0172

Learning Document-Level Semantic Properties from Free-Text Annotations

This paper presents a new method for inferring the semantic properties of documents by leveraging free-text keyphrase annotations. Such annotations are becoming increasingly abundant due to the recent dramatic growth in semi-structured, user-generated online content. One especially relevant domain is product reviews, which are often annotated by their authors with pros/cons keyphrases such as ``a real bargain'' or ``good value.'' These annotations are representative of the underlying semantic properties; however, unlike expert annotations, they are noisy: lay authors may use different labels to denote the same property, and some labels may be missing. To learn using such noisy annotations, we find a hidden paraphrase structure which clusters the keyphrases. The paraphrase structure is linked with a latent topic model of the review texts, enabling the system to predict the properties of unannotated documents and to effectively aggregate the semantic properties of multiple reviews. Our approach is implemented as a hierarchical Bayesian model with joint inference. We find that joint inference increases the robustness of the keyphrase clustering and encourages the latent topics to correlate with semantically meaningful properties. Multiple evaluations demonstrate that our model substantially outperforms alternative approaches for summarizing single and multiple documents into a set of semantically salient keyphrases

The expected metric principle for probabilistic information retrieval

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (leaves 125-128).Traditionally, information retrieval systems aim to maximize the number of relevant documents returned to a user within some window of the top. For that goal, the Probability Ranking Principle, which ranks documents in decreasing order of probability of relevance, is provably optimal. However, there are many scenarios in which that ranking does not optimize for the user's information need. One example is when the user would be satisfied with some limited number of relevant documents, rather than needing all relevant documents. We show that in such a scenario, an attempt to return many relevant documents can actually reduce the chances of finding any relevant documents. In this thesis, we introduce the Expected Metric Principle, which generalizes the Probability Ranking Principle in a way that intimately connects the evaluation metric and the retrieval model. We observe that given a probabilistic model of relevance, it is appropriate to rank so as to directly optimize these metrics in expectation.(cont.) We consider a number of metrics from the literature, such as the rank of the first relevant result, the %no metric that penalizes a system only for retrieving no relevant results near the top, and the diversity of retrieved results when queries have multiple interpretations, as well as introducing our own new metrics. While direct optimization of a metric's expected value may be computationally intractable, we explore heuristic search approaches, and show that a simple approximate greedy optimization algorithm produces rankings for TREC queries that outperform the standard approach based on the probability ranking principle.by Harr Chen.S.M

Characterization of duplicate gene evolution in the recent natural allopolyploid Tragopogon miscellus by next-generation sequencing and Sequenom iPLEX MassARRAY genotyping

The definitive version is available at www.blackwell-synergy.co