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.

Pachinko allocation: DAG-structured mixture models of topic correlations

Abstract: Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, LDA does not capture correlations between topics. In this paper, we introduce the pachinko allocation model (PAM), which captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). The leaves of the DAG represent individual words in the vocabulary, while each interior node represents a correlation among its children, which may be words or other interior nodes (topics). PAM provides a flexible alternative to recent work by Blei and Lafferty (2006), which captures correlations only between pairs of topics. Using text data from newsgroups, historic NIPS proceedings and other research paper corpora, we show improved performance of PAM in document classification, likelihood of held-out data, the ability to support finer-grained topics, and topical keyword coherence.

Reinforcement learning with replacing eligibility traces

Abstract: The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, thereplacing trace, analyze it theoretically, and show that it results in faster, more reliable learning than the conventional trace. Both kinds of trace assign credit to prior events according to how recently they occurred, but only the conventional trace gives greater credit to repeated events. Our analysis is for conventional and replace-trace versions of the offline TD(1) algorithm applied to undiscounted absorbing Markov chains. First, we show that these methods converge under repeated presentations of the training set to the same predictions as two well known Monte Carlo methods. We then analyze the relative efficiency of the two Monte Carlo methods. We show that the method corresponding to conventional TD is biased, whereas the method corresponding to replace-trace TD is unbiased. In addition, we show that the method corresponding to replacing traces is closely related to the maximum likelihood solution for these tasks, and that its mean squared error is always lower in the long run. Computational results confirm these analyses and show that they are applicable more generally. In particular, we show that replacing traces significantly improve performance and reduce parameter sensitivity on the "Mountain-Car" task, a full reinforcement-learning problem with a continuous state space, when using a feature-based function approximator.

Electrosynthesis of Organic Compounds from Carbon Dioxide Is Catalyzed by a Diversity of Acetogenic Microorganisms

Abstract: Microbial electrosynthesis, a process in which microorganisms use electrons derived from electrodes to reduce carbon dioxide to multicarbon, extracellular organic compounds, is a potential strategy for capturing electrical energy in carbon-carbon bonds of readily stored and easily distributed products, such as transportation fuels. To date, only one organism, the acetogen Sporomusa ovata, has been shown to be capable of electrosynthesis. The purpose of this study was to determine if a wider range of microorganisms is capable of this process. Several other acetogenic bacteria, including two other Sporomusa species, Clostridium ljungdahlii, Clostridium aceticum, and Moorella thermoacetica, consumed current with the production of organic acids. In general acetate was the primary product, but 2-oxobutyrate and formate also were formed, with 2-oxobutyrate being the predominant identified product of electrosynthesis by C. aceticum. S. sphaeroides, C. ljungdahlii, and M. thermoacetica had high (>80%) efficiencies of electrons consumed and recovered in identified products. The acetogen Acetobacterium woodii was unable to consume current. These results expand the known range of microorganisms capable of electrosynthesis, providing multiple options for the further optimization of this process.