When Gravity Fails: Local Search Topology

Local search algorithms for combinatorial search problems frequently encounter a sequence of states in which it is impossible to improve the value of the objective function; moves through these regions, called plateau moves, dominate the time spent in local search. We analyze and characterize plateaus for three different classes of randomly generated Boolean Satisfiability problems. We identify several interesting features of plateaus that impact the performance of local search algorithms. We show that local minima tend to be small but occasionally may be very large. We also show that local minima can be escaped without unsatisfying a large number of clauses, but that systematically searching for an escape route may be computationally expensive if the local minimum is large. We show that plateaus with exits, called benches, tend to be much larger than minima, and that some benches have very few exit states which local search can use to escape. We show that the solutions (i.e., global minima) of randomly generated problem instances form clusters, which behave similarly to local minima. We revisit several enhancements of local search algorithms and explain their performance in light of our results. Finally we discuss strategies for creating the next generation of local search algorithms.Comment: See http://www.jair.org/ for any accompanying file

Investigating the enhancement of air pollutant predictions and understanding air quality disparities across racial, ethnic, and economic lines at US public schools

2022 Spring.Includes bibliographical references.Ambient air pollution has significant health and economic impacts worldwide. Even in the most developed countries, monitoring networks often lack the spatiotemporal density to resolve air pollution gradients. Though air pollution affects the entire population, it can disproportionately affect the disadvantaged and vulnerable communities in society. Pollutants such as fine particulate matter (PM2.5), nitrogen oxides (NO and NO2), and ozone, which have a variety of anthropogenic and natural sources, have garnered substantial research attention over the last few decades. Over half the world and over 80% of Americans live in urban areas, and yet many cities only have one or several air quality monitors, which limits our ability to capture differences in exposure within cities and estimate the resulting health impacts. Improving sub-city air pollution estimates could improve epidemiological and health-impact studies in cities with heterogeneous distributions of PM2.5, providing a better understanding of communities at-risk to urban air pollution. Biomass burning is a source of PM2.5 air pollution that can impact both urban and rural areas, but quantifying the health impacts of PM2.5 from biomass burning can be even more difficult than from urban sources. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke, especially near the source fires. Due to limitations of both urban and rural monitoring networks several techniques have been developed to supplement and enhance air pollution estimates. For example, satellite aerosol optical depth (AOD) can be used to fill spatial gaps in PM monitoring networks, but AOD can be disconnected from time-resolved surface-level PM in a multitude of ways, including the limited overpass times of most satellites, daytime-only measurements, cloud cover, surface reflectivity, and lack of vertical-profile information. Observations of smoke plume height (PH) may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. Low-cost sensor networks have been rapidly expanding to provide higher density air pollution monitoring. Finally, both geophysical modeling, statistical techniques such as machine learning and data mining, and combinations of all of the aforementioned datasets have been increasingly used to enhance surface observations. In this dissertation, we explore several of these different data sources and techniques for estimating air pollution and determining community exposure concentrations. In the first chapter of this dissertation, we assess PH characteristics from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and evaluate its correlation with co-located PM2.5 and AOD measurements. PH is generally highest over the western US. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events. Next, to estimate spatiotemporal variability in PM2.5, we use machine learning (Random Forests; RFs) and concurrent PM2.5 and AOD measurements from the Citizen Enabled Aerosol Measurements for Satellites (CEAMS) low-cost sensor network as well as PM2.5 measurements from the Environmental Protection Agency's (EPA) reference monitors during wintertime in Denver, CO, USA. The RFs predicted PM2.5 in a 5-fold cross validation (CV) with relatively high skill (95% confidence interval R2=0.74-0.84 for CEAMS; R2=0.68-0.75 for EPA) though the models were aided by the spatiotemporal autocorrelation of the PM22.5 measurements. We find that the most important predictors of PM2.5 are factors associated with pooling of pollution in wintertime, such as low planetary boundary layer heights (PBLH), stagnant wind conditions, and, to a lesser degree, elevation. In general, spatial predictors are less important than spatiotemporal predictors because temporal variability exceeds spatial variability in our dataset. Finally, although concurrent AOD is an important predictor in our RF model for hourly PM2.5, it does not improve model performance during our campaign period in Denver. Regardless, we find that low-cost PM2.5 measurements incorporated into an RF model were useful in interpreting meteorological and geographic drivers of PM2.5 over wintertime Denver. We also explore how the RF model performance and interpretation changes based on different model configurations and data processing. Finally, we use high resolution PM2.5 and nitrogen dioxide (NO2) estimates to investigate socioeconomic disparities in air quality at public schools in the contiguous US. We find that Black and African American, Hispanic, and Asian or Pacific Islander students are more likely to attend schools in locations where the ambient concentrations of NO2 and PM2.5 are above the World Health Organization's (WHO) guidelines for annual-average air quality. Specifically, we find that ~95% of students that identified as Asian or Pacific Islander, 94% of students that identified as Hispanic, and 89% of students that identified as Black or African American, attended schools in locations where the 2019 ambient concentrations were above the WHO guideline for NO2 (10 μg m-3 or ~5.2 ppbv). Conversely, only 83% of students that identified as white and 82% of those that identified as Native American attended schools in 2019 where the ambient NO2 concentrations were above the WHO guideline. Similar disparities are found in annually averaged ambient PM2.5 across racial and ethnic groups, where students that identified as white (95%) and Native American (83%) had a smallest percentage of students above the WHO guideline (5 μg m-3), compared to students that identified with minoritized groups (98-99%). Furthermore, the disparity between white students and other minoritized groups, other than Native Americans, is larger at higher PM2.5 concentrations. Students that attend schools where a higher percentage of students are eligible for free or reduced meals, which we use as a proxy for poverty, are also more likely to attend schools where the ambient air pollutant concentrations exceed WHO guidelines. These disparities also tend to increase in magnitude at higher concentrations of NO2 and PM2.5. We investigate the intersectionality of disparities across racial/ethnic and poverty lines by quantifying the mean difference between the lowest and highest poverty schools, and the most and least white schools in each state, finding that most states have disparities above 1 ppbv of NO2 and 0.5 μg m-3 of PM2.5 across both. We also identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we also highlight that disparities do not only exist across an urban and non-urban divide, but also within urban areas

An analysis of the undergraduate alcohol culture at the University of Gloucestershire with particular reference to traditional and non-traditional students

This research project examines the alcohol culture of undergraduates at the University of Gloucestershire in light of the expansion of Higher Education. It seeks to ascertain whether students' demographic, social and academic attributes influence their alcohol consumption in terms of volume, speed, temporality and behaviour. Data was gathered via the lecture‐based administration of a questionnaire, the design of which paid particular attention to identifying traditional and non‐traditional students in light of the Widening Participation agenda of the University and Higher Education in general. Chi‐Square analysis and linear regression of the data provided by the 902 respondents clearly indicated two distinct alcohol cultures, distinguished by their temporality, levels of consumption and bingeing, and social composition of their participants. The first culture consumed alcohol on a Monday and/or Wednesday, and was composed of young (under 21), white, unmarried, full‐time students without children or disability, that lived in University housing or in a shared house in Gloucester or Cheltenham with other students. Levels of alcohol consumption and bingeing (6.5+/8.5+ units and a drinking rate of 2+ units/hour) were very high in this culture and were accompanied by such routines as 'predrinking', 'fancy dress', 'drinking games', 'shot‐slamming' and 'torpedoes'1. The other culture (who drank on Friday and/or Saturday) tended to consume and binge less, and participate less in drinking routines than the mid‐week students. They were older and had more social connections to non‐students. In terms of residence, they either lived with their parents or with a partner and/or children; many commuted to the University. All the indications suggest that student drinking on Friday/Saturday is part of the general weekend alcohol culture and not part of an explicit 'student drinking culture'. After ANOVA regression, variables measuring residence and sport were found to be the most significant in response to alcohol units consumed (both p << 0.05). Studying sport, playing on a sport team and living away from the parental home significantly increased consumption on a Monday/Wednesday, and decreased it on a Friday/Saturday. It was clear that traditional students were situated in the heart of the mid‐week drinking culture. If non‐traditional students drank, they were much more likely to do so at the weekend. The implications of the study are two‐fold: on the one hand it facilitates the targeting of hazardous drinking in terms of temporality and demographics, on the other it depicts a distinct separation in university social culture based on residence and thus has implications for those students who cannot afford to or chose not to 'go away to University'

Encapsulation of Cs/Sr contaminated clinoptilolite in geopolymers produced from metakaolin

The encapsulation of caesium (Cs) and strontium (Sr) contaminated clinoptilolite in Na and K based metakaolin geopolymers is reported. When Cs or Sr loaded clinoptilolite is mixed with a metakaolin geopolymer paste, the high pH of the activating solution and the high concentration of ions in solution cause ion exchange reactions and dissolution of clinoptilolite with release of Cs and Sr into the geopolymer matrix. The leaching of Cs and Sr from metakaolin-based geopolymer has therefore been investigated. It was found that Na-based geopolymers reduce leaching of Cs compared to K-based geopolymers and the results are in agreement with the hard and soft acids and bases (HSAB) theory. Cs ions are weak Lewis acids and aluminates are a weak Lewis base. During the formation of the geopolymer matrix Cs ions are preferentially bound to aluminate phases and replace Na in the geopolymer structure. Sr uptake by Na-geopolymers is limited to 0.4 mol Sr per mole of Al and any additional Sr is immobilised by the high pH which causes precipitation of Sr as low solubility hydroxide and carbonate phases. There was no evidence of any other phases being formed when Sr or Cs are added to metakaolin geopolymers

Single-Step Quantum Search Using Problem Structure

The structure of satisfiability problems is used to improve search algorithms for quantum computers and reduce their required coherence times by using only a single coherent evaluation of problem properties. The structure of random k-SAT allows determining the asymptotic average behavior of these algorithms, showing they improve on quantum algorithms, such as amplitude amplification, that ignore detailed problem structure but remain exponential for hard problem instances. Compared to good classical methods, the algorithm performs better, on average, for weakly and highly constrained problems but worse for hard cases. The analytic techniques introduced here also apply to other quantum algorithms, supplementing the limited evaluation possible with classical simulations and showing how quantum computing can use ensemble properties of NP search problems.Comment: 39 pages, 12 figures. Revision describes further improvement with multiple steps (section 7). See also http://www.parc.xerox.com/dynamics/www/quantum.htm

Potential barrier lowering and electrical transport at the LaAlO3_{3}/SrTiO3_{3} heterointerface

Using a combination of vertical transport measurements across and lateral transport measurements along the LaAlO$_{3}$/SrTiO$_{3}$ heterointerface, we demonstrate that significant potential barrier lowering and band bending are the cause of interfacial metallicity. Barrier lowering and enhanced band bending extends over 2.5 nm into LaAlO$_{3}$ as well as SrTiO$_{3}$. We explain origins of high-temperature carrier saturation, lower carrier concentration, and higher mobility in the sample with the thinnest LaAlO$_{3}$ film on a SrTiO$_{3}$ substrate. Lateral transport results suggest that parasitic interface scattering centers limit the low-temperature lateral electron mobility of the metallic channel.Comment: 10 pages, 3 figures, and 1 tabl

Landscape of solutions in constraint satisfaction problems

We present a theoretical framework for characterizing the geometrical properties of the space of solutions in constraint satisfaction problems, together with practical algorithms for studying this structure on particular instances. We apply our method to the coloring problem, for which we obtain the total number of solutions and analyze in detail the distribution of distances between solutions.Comment: 4 pages, 4 figures. Replaced with published versio