University of Massachusetts Amherst

About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.

Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons

Abstract: Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998).

Modeling relations and their mentions without labeled text

Abstract: Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related entities is an expression of the given relation. Here we argue that this leads to noisy patterns that hurt precision, in particular if the knowledge base is not directly related to the text we are working with. We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decision whether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB. We apply our approach to extract relations from the New York Times corpus and use Freebase as knowledge base. When compared to a state-of-the-art approach for relation extraction under distant supervision, we achieve 31% error reduction.

Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED

Abstract: In this paper we use jump process driven Stochastic Differential Equations to model the interactions of a set of TCP flows and Active Queue Management routers in a network setting. We show how the SDEs can be transformed into a set of Ordinary Differential Equations which can be easily solved numerically. Our solution methodology scales well to a large number of flows. As an application, we model and solve a system where RED is the AQM policy. Our results show excellent agreement with those of similar networks simulated using the well known ns simulator. Our model enables us to get an in-depth understanding of the RED algorithm. Using the tools developed in this paper, we present a critical analysis of the RED algorithm. We explain the role played by the RED configuration parameters on the behavior of the algorithm in a network. We point out a flaw in the RED averaging mechanism which we believe is a cause of tuning problems for RED. We believe this modeling/solution methodology has a great potential in analyzing and understanding various network congestion control algorithms.

Application of the Theory of Planned Behavior to Leisure Choice.

Abstract: The theory of planned behavior (Ajzen, 1985, 1987) is used to predict leisure intentions and behavior. College students completed a questionnaire that measured involvement, moods, attitudes, subjec...

Exercise-induced muscle damage in humans.

Abstract: Exercise-induced muscle injury in humans frequently occurs after unaccustomed exercise, particularly if the exercise involves a large amount of eccentric (muscle lengthening) contractions. Direct measures of exercise-induced muscle damage include cellular and subcellular disturbances, particularly Z-line streaming. Several indirectly assessed markers of muscle damage after exercise include increases in T2 signal intensity via magnetic resonance imaging techniques, prolonged decreases in force production measured during both voluntary and electrically stimulated contractions (particularly at low stimulation frequencies), increases in inflammatory markers both within the injured muscle and in the blood, increased appearance of muscle proteins in the blood, and muscular soreness. Although the exact mechanisms to explain these changes have not been delineated, the initial injury is ascribed to mechanical disruption of the fiber, and subsequent damage is linked to inflammatory processes and to changes in excitation-contraction coupling within the muscle. Performance of one bout of eccentric exercise induces an adaptation such that the muscle is less vulnerable to a subsequent bout of eccentric exercise. Although several theories have been proposed to explain this "repeated bout effect," including altered motor unit recruitment, an increase in sarcomeres in series, a blunted inflammatory response, and a reduction in stress-susceptible fibers, there is no general agreement as to its cause. In addition, there is controversy concerning the presence of sex differences in the response of muscle to damage-inducing exercise. In contrast to the animal literature, which clearly shows that females experience less damage than males, research using human studies suggests that there is either no difference between men and women or that women are more prone to exercise-induced muscle damage than are men.