An uncatalogued optical HII region at the outskirts of the Galaxy

We present NOT optical observations of a clump (l = 127.9435 deg, b = +1.8298 deg), embedded in an extended, irregularly shaped, diffuse optical nebula. This condensation shows an emission-line spectrum typical of classic HII regions. Although its location on the sky coincides with a nearby extended photoionized region recently identified by Cichowolski et al. (2003) in radio data from the Canadian Galactic Plane Survey (CGPS), the systemic velocity of this ~ 1 arcmin-sized HII region, V_LSR = -71$\pm$12 km/s, poses it far out in the Galaxy, beyond the Perseus arm. The location of this region in the Galaxy is supported by HI structures visible at comparable radial velocity on CGPS data. We argue that this HII region might belong to an outer Galactic arm. The emission line ratios of the surrounding extended nebula, whose radial velocity is consistent with that of the small HII region, are typical of photoionized gas in the low density limit. Smaller clumps of comparable surface brightness are visible within the optical boundaries of the extended, faint nebula. After comparison of the optical data with far infrared and radio observations, we conclude that this nebula is an HII region, ~ 70 pc in size, probably photoionized by an association of OB stars and surrounded by a ring of neutral hydrogen.Comment: 8 pages, 9 figures (some of which degraded due to size constraints), accepted on 2004/02/12 for publication in Astronomy and Astrophysic

An HIV feedback resistor: auto-regulatory circuit deactivator and noise buffer.

Animal viruses (e.g., lentiviruses and herpesviruses) use transcriptional positive feedback (i.e., transactivation) to regulate their gene expression. But positive-feedback circuits are inherently unstable when turned off, which presents a particular dilemma for latent viruses that lack transcriptional repressor motifs. Here we show that a dissipative feedback resistor, composed of enzymatic interconversion of the transactivator, converts transactivation circuits into excitable systems that generate transient pulses of expression, which decay to zero. We use HIV-1 as a model system and analyze single-cell expression kinetics to explore whether the HIV-1 transactivator of transcription (Tat) uses a resistor to shut off transactivation. The Tat feedback circuit was found to lack bi-stability and Tat self-cooperativity but exhibited a pulse of activity upon transactivation, all in agreement with the feedback resistor model. Guided by a mathematical model, biochemical and genetic perturbation of the suspected Tat feedback resistor altered the circuit's stability and reduced susceptibility to molecular noise, in agreement with model predictions. We propose that the feedback resistor is a necessary, but possibly not sufficient, condition for turning off noisy transactivation circuits lacking a repressor motif (e.g., HIV-1 Tat). Feedback resistors may be a paradigm for examining other auto-regulatory circuits and may inform upon how viral latency is established, maintained, and broken

A statistical approach to persistent homology

Assume that a finite set of points is randomly sampled from a subspace of a metric space. Recent advances in computational topology have provided several approaches to recovering the geometric and topological properties of the underlying space. In this paper we take a statistical approach to this problem. We assume that the data is randomly sampled from an unknown probability distribution. We define two filtered complexes with which we can calculate the persistent homology of a probability distribution. Using statistical estimators for samples from certain families of distributions, we show that we can recover the persistent homology of the underlying distribution.Comment: 30 pages, 2 figures, minor changes, to appear in Homology, Homotopy and Application

THE EFFECTS OF PROBLEM-SOLVING ABILITY AND TASK DIFFICULTY ON THE PUPIL RESPONSE DURING COGNITIVE TASKS EMPLOYING CONTINUOUS STIMULI.

Numerous investigators employing a variety of tasks have found that the pupil of the eye can serve as an index of cognitive effort or processing. Specifically, it has been demonstrated that during cognitive task the pupil dilates in response to stimulus presentation and returns to baseline immediately following solution or task termination. Moreover the increase in pupil size during information processing periods has been found to be positively related to the difficulty level of the task employed. Thus more difficult tasks evoke larger pupillary dilation during processing periods. The consistency of findings in this area has led reviewers to comment that the relationship between pupil size and mental effort is well-established and that the pupil is the best single physiological measure of processing load. Previous research however has concentrated almost exclusively on the use of \u27non-continuous\u27 or short-duration stimuli. That is, tasks have employed digit strings, paired-associate learning, tone discrimination, single word imagery and the like. \u27Continuous\u27 stimuli, that is stimuli that provide an extended, contextual and grammatically-integrated information set, such as reading material, have been employed in but two studies, only one of which was concerned with the issue of cognitive effort. Interestingly this isolated study failed to support the relationship function typically obtained. The essential purpose of the present study was to provide added information on how the pupil responds when \u27continuous\u27 stimuli are employed. As well the influence of differing levels of \u27processing ability\u27 was also investigated. The results obtained support the typical within-task findings of previous studies; that is, the pupil size was sensitive to the presence of processing periods. Mean pupil size did not discriminate between tasks of different difficulty levels but such discrimination was obtained when peak pupil size scores were employed. No convincing support was found for the influence of individual differences in processing ability on the pupillary response. Recommendations were made regarding the need for further and more sophisticated research to support and elaborate the findings here obtained. It was also put forward that if the pupil could be brought under voluntary control through instrumental conditioning procedures then strategies of information presentation could be derived so that learning is facilitated by making such presentation contingent on the presence of \u27pupillary states\u27 deemed especially conducive to enhanced information integration and retrieval.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1980 .W456. Source: Dissertation Abstracts International, Volume: 41-03, Section: B, page: 1129. Thesis (Ph.D.)--University of Windsor (Canada), 1980

Network Lasso: Clustering and Optimization in Large Graphs

Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimization solvers do not scale well, and scalable solvers are often specialized to only work on a narrow class of problems. Therefore, there is a need for simple, scalable algorithms that can solve many common optimization problems. In this paper, we introduce the \emph{network lasso}, a generalization of the group lasso to a network setting that allows for simultaneous clustering and optimization on graphs. We develop an algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in a distributed and scalable manner, which allows for guaranteed global convergence even on large graphs. We also examine a non-convex extension of this approach. We then demonstrate that many types of problems can be expressed in our framework. We focus on three in particular - binary classification, predicting housing prices, and event detection in time series data - comparing the network lasso to baseline approaches and showing that it is both a fast and accurate method of solving large optimization problems