Weighted LpL^p Estimates for the Bergman and Szeg\H{o} Projections on Strongly Pseudoconvex Domains with Near Minimal Smoothness

We prove the weighted $L^p$ regularity of the ordinary Bergman and Cauchy-Szeg\H{o} projections on strongly pseudoconvex domains $D$ in $\mathbb{C}^n$ with near minimal smoothness for appropriate generalizations of the $B_p/A_p$ classes. In particular, the $B_p/A_p$ Muckenhoupt type condition is expressed relative to balls in a quasi-metric that arises as a space of homogeneous type on either the interior or the boundary of the domain $D$.Comment: 40 pages, introduction reorganized and some typos correcte

Patch-Scale Movement Dynamics in the Iowa Grassland Butterflies \u3ci\u3eSpeyeria Cybele\u3c/i\u3e and \u3ci\u3eMegisto Cymela\u3c/i\u3e (Lepidoptera: Nymphalidae)

An understanding of the movement dynamics of invertebrates can be critical to their conservation, especially when managing relatively small, isolated habitats. Most studies of butterfly movement have focused on metapopulation dynamics at relatively large spatial scales, and the results from these studies may not translate well for patchy populations within a single nature preserve. In this work we use individual mark and recapture (IMR) methods to follow the movements of two species of butterfly, Megisto cymela (Cramer) and Speyeria cybele F. (Lepidoptera: Nymphalidae) within a 240 hectare forest and grassland preserve in central Iowa, USA. Significant redistribution was seen in both species, with 55.7% of S. cybele and 31.1% of M. cymela undergoing interpatch movement. Median movement rates during the study were 105 m/day for S. cybele and 38 m/day for M. cymela, with the top decile moving at a rate of over five times these values. This movement did not appear to be random. S. cybele exhibited directed movement towards patches with high nectaring potential, although not all such patches were selected. M. cymela aggregated in particular prairie patches, especially those with high edge to area ratios, although the reason for aggregation is not clear

Reports Of Conferences, Institutes, And Seminars

This quarter\u27s column offers coverage of multiple sessions from the 2016 Electronic Resources & Libraries (ER&L) Conference, held April 3–6, 2016, in Austin, Texas. Topics in serials acquisitions dominate the column, including reports on altmetrics, cost per use, demand-driven acquisitions, and scholarly communications and the use of subscriptions agents; ERMS, access, and knowledgebases are also featured

Information Extraction in Illicit Domains

Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18\% F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.Comment: 10 pages, ACM WWW 201