Pseudorapidity Distribution of Charged Particles in PbarP Collisions at root(s)= 630GeV

Using a silicon vertex detector, we measure the charged particle pseudorapidity distribution over the range 1.5 to 5.5 using data collected from PbarP collisions at root s = 630 GeV. With a data sample of 3 million events, we deduce a result with an overall normalization uncertainty of 5%, and typical bin to bin errors of a few percent. We compare our result to the measurement of UA5, and the distribution generated by the Lund Monte Carlo with default settings. This is only the second measurement at this level of precision, and only the second measurement for pseudorapidity greater than 3.Comment: 9 pages, 5 figures, LaTeX format. For ps file see http://hep1.physics.wayne.edu/harr/harr.html Submitted to Physics Letters

Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility

Electric Vehicle (EV) penetration and renewable energies enables synergies between energy supply, vehicle users, and the mobility sector. However, also new issues arise for car manufacturers: During charging and discharging of EV batteries a degradation (battery aging) occurs that correlates with a value depreciation of the entire EV. As EV users' satisfaction depends on reliable and value-stable products, car manufacturers offer charging assistants for simplified and sustainable EV usage by considering individual customer needs and battery aging. Hitherto models to quantify battery aging have limited practicability due to a complex execution. Data-driven methods hold feasible alternatives for SOH estimation. However, the existing approaches barely use user-related data. By means of a linear and a neural network regression model, we first estimate the energy consumption for driving considering individual driving styles and environmental conditions. In following work, the consumption model trained on data from batteries without degradation can be used to estimate the energy consumption for EVs with aged batteries. A discrepancy between the estimation and the real consumption indicates a battery aging caused by increased internal losses. We then target to evaluate the influence of charging strategies on battery degradation

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

A microsatellite-based multilocus phylogeny of the Drosophila melanogaster species complex

AbstractUncovering the genealogy of closely related species remains a major challenge for phylogenetic reconstruction. It is unlikely that the phylogeny of a single gene will represent the phylogeny of a species as a whole [1], but DNA sequence data across a large number of loci can be combined in order to obtain a consensus tree [2]. Long sequences are needed, however, to minimize the effect of (infrequent) base substitutions, and sufficient individuals must be sequenced per species to account for intraspecific polymorphisms, an overwhelming task using current DNA sequencing technology. By contrast, microsatellites are easy to type [3], allowing the analysis of many loci in multiple individuals. Despite their successful use in mapping [4,5], behavioural ecology [6] and population genetics [7], their usefulness for the phylogenetic reconstruction of closely related taxa has never been demonstrated, even though microsatellites are often conserved across species [8–10]. One drawback to microsatellite use is their high mutation rate (10−4–10−2), combined with an incomplete understanding of their mutation patterns. Many microsatellites are available for Drosophila melanogaster, and they are distributed throughout the genome [11]. Most can be amplified in the D. melanogaster species complex [12,13] and have low mutation rates [14,15]. We show that microsatellite-specific distance measurements [16] correlate with other multilocus distances, such as those obtained from DNA–DNA hybridization data. Thus microsatellites may provide an ideal tool for building multilocus phylogenies. Our phylogenetic reconstruction of the D. melanogaster complex provides strong evidence that D. sechellia arose first, followed by a split between D. simulans and D. mauritiana

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