Seattle has long been a center for refugee resettlement in the United States, creating multicultural neighborhoods with complex social and spatial dynamics. This study analyzes the refugee experience in Seattle by examining patterns of housing cost burden, language isolation, religious infrastructure, and crime incidence. These spatial findings are combined with insights from literature on refugee integration, social vulnerability, and community safety. Results show that White Center and Rainier Valley experience severe housing cost burdens and high social vulnerability, indicating significant financial stress and exposure to economic risks. Language isolation is pronounced, as many residents lack English fluency, limiting access to jobs and essential services. Access to religious and cultural institutions is also uneven, affecting community cohesion and cultural preservation. While citywide crime maps show scattered incidents, evidence indicates that refugees are more often victims than offenders, and that their communities do not increase local crime rates. The paper concludes by recommending policies that strengthen affordable housing, expand language assistance, support communi
The Coulomb dissociation of 8B, measured with high precision by the GSI group, is in excellent agreement with the astrophysical cross section factor (S17) measured by the Weizmann group with a 7Be target. The GSI and Weizmann data are in good agreement with the Seattle data at high energies, but at low energies we observe a slight systematic (up to 2sigma) deviation, yet the Seattle group repeatedly rejects the CD method. We show that when plotting the slopes, they mis plotted one CD data point and exclude measured slopes that contradict their claim. Indeed the measured slope is essential to elucidate the d-wave correction to S17(0) that could be as large as 15%, and is the last open question that needs to be resolved before S17(0) can be quoted with an accuracy of 5% or better. We show that this goal has not been achieved (in spite of the strong claim of the Seattle group), since currently there is no agreement among experiments as well as among theoretical models on the value of the slope. In addition, currently there is no theoretical framework within which (for example the Seattle) data can be analyzed and S17(0) extrapolated with the claimed high precision of 2.5%. This (last)
We apply Bayesian Linear Regression to estimate the response rate of drivers to variable message signs at Seattle-Tacoma International Airport, or SeaTac. Our approach uses vehicle speed and flow data measured at the entrances of the arrival and departure-ways of the airport terminal, and sign message data. Depending on the time of day, we estimate that between 5.5 and 9.1% of drivers divert from departures to arrivals when the sign reads departures full, use arrivals, and conversely, between 1.9 and 4.2% of drivers divert from arrivals to departures. Though we lack counterfactual data (i.e., what would have happened had the diversionary treatment not been active), adopting a causal model that encodes time dependency with prior distributions rate can yield a measurable effect.
The increased frequency of wildfires in the Western United States has raised public concerns. Exposure to wildfire smoke has been linked to an increased risk of cancer and cardiorespiratory morbidity. Evidence-driven interventions can alleviate the adverse health impact of wildfire smoke. Public health guidance during wildfires is based on regional air quality data with limited spatiotemporal resolution. We demonstrate the use of a network of low-cost particulate matter (PM) sensors to gather indoor, outdoor, and personal PM2.5 exposure data from seven locations in the urban Seattle area, along with a personal exposure monitor worn by a resident living in one of these locations during the 2020 Washington wildfire event. The data were used to determine PM concentration indoor/outdoor (I/O) ratios, PM reduction, and personal exposure levels. The result shows that locations equipped with high-efficiency particulate air (HEPA) filters and HVAC filtration systems had significantly lower I/O ratios (median I/O = 0.43) than those without air filtration (median I/O = 0.82). The median PM2.5 reduction for the locations with HEPA is 58 % compared to 20% for the locations without HEPA. The ou
We examine the potential of using large-scale open crowdsourced sidewalk data from Project Sidewalk to study the distribution and condition of sidewalks in Seattle, WA. While potentially noisier than professionally gathered sidewalk datasets, crowdsourced data enables large, cross-regional studies that would be otherwise expensive and difficult to manage. As an initial case study, we examine spatial patterns of sidewalk quality in Seattle and their relationship to racial diversity, income level, built density, and transit modes. We close with a reflection on our approach, key limitations, and opportunities for future work.
This paper continues to highlight trends in mobility and sociability in New York City (NYC), and supplements them with similar data from Seattle, WA, two of the cities most affected by COVID-19 in the U.S. Seattle may be further along in its recovery from the pandemic and ensuing lockdown than NYC, and may offer some insights into how travel patterns change. Finally, some preliminary findings from cities in China are discussed, two months following the lifting of their lockdowns, to offer a glimpse further into the future of recovery.
These are the notes for my lecture ``Resolution of Sigularities in Charcteristic 0" given at the AMS Summer Institute at Seattle. It gives a self contained proof of the strong Hironaka resolution theorem.
We report results of air monitoring started due to the recent natural catastrophe on 11 March 2011 in Japan and the severe ensuing damage to the Fukushima Dai-ichi nuclear reactor complex. On 17-18 March 2011, we registered the first arrival of the airborne fission products 131-I, 132-I, 132-Te, 134-Cs, and 137-Cs in Seattle, WA, USA, by identifying their characteristic gamma rays using a germanium detector. We measured the evolution of the activities over a period of 23 days at the end of which the activities had mostly fallen below our detection limit. The highest detected activity amounted to 4.4 +/- 1.3 mBq/m^3 of 131-I on 19-20 March.
Due to rapid expansion of urban areas in recent years, management of curbside parking has become increasingly important. To mitigate congestion, while meeting a city's diverse needs, performance-based pricing schemes have received a significant amount of attention. However, several recent studies suggest location, time-of-day, and awareness of policies are the primary factors that drive parking decisions. In light of this, we provide an extensive data-driven study of the spatio-temporal characteristics of curbside parking. This work advances the understanding of where and when to set pricing policies, as well as where to target information and incentives to drivers looking to park. Harnessing data provided by the Seattle Department of Transportation, we develop a Gaussian mixture model based technique to identify zones with similar spatial parking demand as quantified by spatial autocorrelation. In support of this technique, we introduce a metric based on the repeatability of our Gaussian mixture model to investigate temporal consistency.
Decision trees remain central for tabular prediction but struggle with (i) capturing spatial dependence and (ii) producing locally stable (robust) explanations. We present SX-GeoTree, a self-explaining geospatial regression tree that integrates three coupled objectives during recursive splitting: impurity reduction (MSE), spatial residual control (global Moran's I), and explanation robustness via modularity maximization on a consensus similarity network formed from (a) geographically weighted regression (GWR) coefficient distances (stimulus-response similarity) and (b) SHAP attribution distances (explanatory similarity). We recast local Lipschitz continuity of feature attributions as a network community preservation problem, enabling scalable enforcement of spatially coherent explanations without per-sample neighborhood searches. Experiments on two exemplar tasks (county-level GDP in Fujian, n=83; point-wise housing prices in Seattle, n=21,613) show SX-GeoTree maintains competitive predictive accuracy (within 0.01 $R^{2}$ of decision trees) while improving residual spatial evenness and doubling attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05). Ablation
Building on recent work studying content in the online advertising ecosystem, including our own prior study of political ads on the web during the 2020 U.S. elections, we analyze political ad content appearing on websites leading up to and during the 2024 U.S. elections. Crawling a set of 745 news and media websites several times from three different U.S. locations (Atlanta, Seattle, and Los Angeles), we collect a dataset of over 15000 ads, including (at least) 315 political ads, and we analyze it quantitatively and qualitatively. Among our findings: a prevalence of clickbait political news ads, echoing prior work; a seemingly new emphasis (compared to 2020) on voting safety and eligibility ads, particularly in Atlanta; and non-election related political ads around the Israel-Palestine conflict, particularly in Seattle. We join prior work in calling for more oversight and transparency of political-related ads on the web. Our dataset is available at https://ad-archive.cs.washington.edu.
Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting
A generalized additive mixed model was estimated to investigate the factors that impact ridehailing driver trip request acceptance choices, relying on 200 responses from a stated preference survey in Seattle, US. Several policy recommendations were proposed to promote trip request acceptance based on ridehailing drivers willingness to accept compensation for undesired trip features. The findings could be useful for transportation agencies to improve ridehailing service efficiency, better fulfill urban mobility needs, and reduce environmental burden.
We identified four types of ridehailing drivers and jointly modeled driver working time and relocation choices using a stated preference survey of 200 drivers in Seattle, US.
These lecture notes were prepared for a special topics course in the Department of Statistics at the University of Washington, Seattle. They comprise the first eight chapters of a book currently in progress.
This volume contains the articles presented at The 2023 Scheme and Functional Programming Workshop in Seattle, Washington on September 9, 2023. The program committee reviewed the articles using current academic standards and selected four articles for presentation. These proceedings are considered non-archival and the authors are free to submit revised versions of their articles to other venues for archival publication. Program Committee: Leif Andersen, Northeastern University; Mark Friedman Leilani Gilpin, University of California, Santa Cruz; Jason Hemann, Seton Hall University Julia Lawall, Inria; Joe Gibbs Politz, University of California at San Diego; Marco T Morazán (Chair), Seton Hall University
Recent claims of the Seattle group of evidence of "slope difference between CD [Coulomb Dissociation] and direct [capture] results" are based on wrong and selective data. When the RIKEN2 data are included correctly, and previously published Direct Capture (DC) data are also included, we observe only a 1.9 sigma difference in the extracted so called "scale independent slope (b)", considerably smaller than claimed by the Seattle group. The very parameterization used by the Seattle group to extract the so called b-slope parameter has no physical foundation. Considering the physical slope (S' = dS/dE), we observe a 1.0 sigma agreement between slopes (S') measured in CD and DC, refuting the need for new theoretical investigation. The claim that S17(0) values extracted from CD data are approximately 10% lower than DC results, is based on misunderstanding of the CD method. Considering all of the published CD S17(0) results, with adding back an unconfirmed E2 correction of the MSU data, yields very consistent S17(0) results that agree with recent DC measurements of the Seattle and Weizmann groups. The recent correction of the b-slope parameter (0.25 1/MeV) suggested by Esbensen, Bertsch an
Markov decision process (MDP) congestion game is an extension of classic congestion games, where a continuous population of selfish agents solves Markov decision processes with congestion: the payoff of a strategy decreases as more population uses it. We draw parallels between key concepts from capacitated congestion games and MDP. In particular, we show that population mass constraints in MDP congestion games are equivalent to imposing tolls/incentives on the reward function, which can be utilized by social planners to achieve auxiliary objectives. We demonstrate such methods in a simulated Seattle ride-share model, where tolls and incentives are enforced for two separate objectives: to guarantee minimum driver density in downtown Seattle, and to shift the game equilibrium towards a maximum social output.
A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets, without insights on how these models perform in real life scenarios. Moreover, many of them do not consider information such as item and customer metadata, although deep-learning recommenders live up to their full potential only when numerous features of heterogeneous types are included. Also, typically recommendation models are designed to serve well only a single use case, which increases modeling complexity and maintenance costs, and may lead to inconsistent customer experience. In this work, we present a reusable Attention-based Fashion Recommendation Algorithm (AFRA), that utilizes various interaction types with different fashion entities such as items (e.g., shirt), outfits and influencers, and their heterogeneous features. Moreover, we leverage temporal and contextual information to address both short and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit recommendations by style; 3) similar item
Advances in technology have provided ways to monitor and measure driving behavior. Recently, this technology has been applied to usage-based automotive insurance policies that offer reduced insurance premiums to policy holders who opt-in to automotive monitoring. Several companies claim to measure only speed data, which they further claim preserves privacy. However, we have developed an algorithm - elastic pathing - that successfully tracks drivers' locations from speed data. The algorithm tracks drivers by assuming a start position, such as the driver's home address (which is typically known to insurance companies), and then estimates the possible routes by fitting the speed data to map data. To demonstrate the algorithm's real-world applicability, we evaluated its performance with driving datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. We are able to estimate destinations with error within 250 meters for 17% of the traces and within 500 meters for 24% of the traces in the New Jersey dataset, and with error within 250 and 500 meters for 15.5% and 27.5% of the traces, respectively, in the Seattle dataset. Our work shows that these in