Then & Now: A Retrospective Exhibit

June 10-July 31, 2017 in Roesch Library\u27s first-floor gallery. Introduced to letterforms in 1980 at the University of Dayton under the tutelage of Lou Weber, Patti Paulus has been creating calligraphic art for 37 years. This exhibit shows the progression of Paulus\u27 artwork

Identifying resonances of the Galactic bar in Gaia DR2: II. Clues from angle space

The Milky Way disk exhibits intricate orbit substructure of still-debated dynamical origin. The angle variables $(\theta_\phi,\theta_R)$ -- which are conjugates to the actions $(L_z,J_R)$, and describe a star's location along its orbit -- are a powerful diagnostic to identify $l$:$m$ resonances via the orbit shape relation $\Delta \theta_R / \Delta \theta_\phi = -m/l$. In the past, angle signatures have been hidden by survey selection effects (SEs). Using test particle simulations of a barred galaxy, we demonstrate that \emph{Gaia} should allow us to identify the Galactic bar's Outer Lindblad Resonance ($l=+1, m=2$, OLR) in angle space. We investigate strategies to overcome SEs. In the angle data of the \emph{Gaia} DR2 RVS sample, we independently identify four candidates for the OLR and therefore for the pattern speed $\Omega_\text{bar}$. The strongest candidate, $\Omega_\text{bar}\sim1.4\Omega_0$, positions the OLR above the `Sirius' moving group, agrees with measurements from the Galactic center, and might be supported by higher-order resonances around the `Hercules/Horn'. But it misses the classic orbit orientation flip, as discussed in the companion study on actions. The candidate $\Omega_\text{bar}\sim1.2\Omega_0$ was also suggested by the action-based study, has the OLR at the `Hat', is consistent with \emph{slow bar} models, but still affected by SEs. Weaker candidates are $\Omega_\text{bar}=1.6\Omega_0$ and $1.74\Omega_0$. In addition, we show that the stellar angles do not support the `Hercules/Horn' being created by the OLR of a \emph{fast bar}. We conclude that -- to resolve if `Sirius' or `Hat' is related to the bar's OLR -- more complex dynamical explanations and more extended data with well-behaved SEs are required.Comment: 16 pages, 9 figures (excluding appendices); accepted for publication in MNRA

Subitizing And Counting: Preattentive And Attentive Processing In Visual Enumeration

Subitizing, the process of visual enumeration when there are fewer than four items, is rapid (40-100 msec/item), accurate and effortless. In contrast, counting, the process of enumerating more than four items is comparatively slow (250-350 msec/item), effortful and error prone. Why does this occur? In this paper an attempt is made to incorporate subitizing and counting into a general theory of visual perception and spatial attention, as espoused by Marr(1982), Ullman(1984), and Treisman(1988). In particular, it is argued that the rapid apprehension of number in the 1-4 range is parasitic on a preattentive limited capacity mechanism that individuates feature clusters by assigning spatial reference tokens or FINSTs to them(Pylyshyn, 1989). These spatial reference tokens permit the identities of a small number of items to be maintained though their properties and retinal coordinates change, a capability important for directing the attentional focus and coordinating eye and hand movements. If the subitizing process makes use of such preattentive information, then it should not be possible to subitize when spatial attention is required to compute spatial relations, resolve the item as a whole or discern items to be counted from other distractor items. Thus, it was predicted that the slope of the latency function in the 1-4 range should approximate that of the 5+ range if spatial attention is required to perform a particular enumeration task. In contrast, it was predicted that subitizing should be possible when preattentive information could be used to distinguish the items to be counted from one another, or from other distractor items. Therefore, it was predicted that there should be discontinuities in slopes of the latency function between the 1-4 and 5+ range, as shown by deviations from linearity in trend analysis.;Five experiments were performed. In the first, subjects were shown capable of subitizing when the task was to enumerate items of a particular colour though they were not capable of subitizing when the task was to enumerate items that were connected to each other by a contour. This result was expected because spatial attention is presumed necessary to compute the connected relation(Ullman, 1984; Jolicoeur, 1988). The second pair of experiments showed that though subjects can easily subitize when items are defined by groups of contours instead of simple edge points, they cannot easily enumerate such items if they are concentric, as would be predicted given that preattentive grouping processes would cluster all the contours into a unit in this case. The fourth and fifth experiments show that subjects can subitize certain target items in a field of distractors, but only if the property that differentiates targets from distractors is a feature, or a property thought to emerge preattentively. In situations where attention is required to form a unified object description by joining different dimensions (e.g., colour and orientation), or by joining different parts of an item (e.g., an O and a stem to form a Q), subitizing was not apparent. Overall, these experiments suggest that the subitizing process relies on preattentive information

Discriminating among theories of spiral structure using Gaia DR2

We compare the distribution in position and velocity of nearby stars from the Gaia DR2 radial velocity sample with predictions of current theories for spirals in disc galaxies. Although the rich substructure in velocity space contains the same information, we find it more revealing to reproject the data into action-angle variables, and we describe why resonant scattering would be more readily identifiable in these variables. We compute the predicted changes to the phase space density, in multiple different projections, that would be caused by a simplified isolated spiral pattern, finding widely differing predictions from each theory. We conclude that the phase space structure present in the Gaia data shares many of the qualitative features expected in the transient spiral mode model. We argue that the popular picture of apparently swing-amplified spirals results from the superposition of a few underlying spiral modes.Comment: Revised version accepted to appear in MNRAS. Some significant improvements. A full resolution version of Fig 4 is available from http://www.physics.rutgers.edu/~sellwood/mult_res.pd

An investigation of the influence of the 2007-2009 recession on the day of the week effect for the S&P 500 and its sectors

Several studies have shown that the mean returns and the volatility structure of stock markets change seasonally or by day of the week. For instance, some authors found out that Monday returns are lower compared to Friday returns or that volatility on Wednesdays are lower compared to the rest of the week. Other researchers showed that these effects have changed after certain periods of economic stress. This led to the question, whether the day of the week effects in returns and volatility are in the US stock market and if patterns have changed from pre-recession through the 2007-2009 recession into the post-recession period. Therefore, a study investigating returns from February 2005 to January 2018 for the S&P 500 and its ten sectors was conducted. To investigate any changes, the data set was separated into three distinct periods. The mean returns were modeled to follow an autoregressive process, while an EGARCH formulation was selected as the appropriate model for the volatility. To estimate the effects, three different approaches were used. Results show that there is only a small day of the week effect in mean returns, and that Wednesdays differ from the rest of the week. However, almost every sector indicate day of the week effects in volatility, where volatility was highest on Tuesdays in pre-recession period, whereas results differ from sector to sector for post-recession period. The findings for the post-recession period are consistent across every approach, whereas the results for the recession period are different depending on the model used in the analysis --Abstract, page iii

The role of perseverative negative thinking in predicting depression, anxiety and quality of life in people with coronary heart disease.

Depression is common in people with coronary heart disease (CHD) and is associated with worse physical outcomes. The nature of the causal association between CHD and depression, and the mechanism underpinning the association of depression with worse physical outcomes, remains unclear. Perseverative negative thinking may contribute to the development of depression in people with CHD. The aim of this thesis was to investigate the prospective association of perseverative negative thinking with depression, anxiety and worse physical outcomes in people with CHD, and to explore factors that may mediate this association. First, a systematic review identified 30 studies, of which the majority found an association between measures of perseverative negative thinking and subsequent depression, anxiety or emotional distress in people with long term conditions. Studies that controlled for covariates showed more mixed results, though the majority (15 / 25) still supported a significant association, with effects being small in magnitude. Findings were limited mainly to the association of rumination and/or catastrophizing with subsequent depression, and study quality was limited. Next, in an observational prospective cohort study 169 inpatients and outpatients with recent acute coronary syndrome (ACS) completed self-report assessments of rumination (Ruminative Responses Scale brooding subscale), worry (Penn State Worry Questionnaire), depression (Patient Health Questionnaire-8), anxiety (Beck Anxiety Inventory), and health-related quality of life (EuroQol-5D health-related quality of life, Seattle Angina Questionnaire) after hospitalisation, and at 2 month and 6 month follow-up. Additionally, assessments of potential mechanistic factors (social support, problem solving, instrumental behaviours and negative cognitive biases) were made. Baseline brooding was a significant independent predictor of depression at 6 months after controlling for the effects of important confounding variables, accounting for 2% of the variance. Findings suggested that the association of brooding with depression may be explained by deficits in problem solving ability. Rumination and problem solving may provide useful targets for the development of evidence-based interventions to improve depression among people with CHD, although the findings presented here fall short of proving a causal relationship. Future trials could be used to investigate the causal nature of the association of rumination and problem solving with depression in people with ACS

A Novel and Fast Approach for Population Structure Inference Using Kernel-PCA and Optimization (PSIKO)

Population structure is a confounding factor in Genome Wide Association Studies, increasing the rate of false positive associations. In order to correct for it, several model-based algorithms such as ADMIXTURE and STRUCTURE have been proposed. These tend to suffer from the fact that they have a considerable computational burden, limiting their applicability when used with large datasets, such as those produced by Next Generation Sequencing (NGS) techniques. To address this, non-model based approaches such as SNMF and EIGENSTRAT have been proposed, which scale better with larger data. Here we present a novel non-model based approach, PSIKO, which is based on a unique combination of linear kernel-PCA and least-squares optimization and allows for the inference of admixture coefficients, principal components, and number of founder populations of a dataset. PSIKO has been compared against existing leading methods on a variety of simulation scenarios, as well as on real biological data. We found that in addition to producing results of the same quality as other tested methods, PSIKO scales extremely well with dataset size, being considerably (up to 30 times) faster for longer sequences than even state of the art methods such as SNMF. PSIKO and accompanying manual are freely available at https://www.uea.ac.uk/computing/psiko