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We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning. Extending previous research, we followed a selective alignment process to identify similar images in the two geographical domains and then tested an array of data-based unsupervised domain adaptation approaches in a zero-shot setting as well as with a small amount of fine-tuning. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting, and even more so with a small amount of fine-tuning. These methods outperform both an untransformed baseline and a popular data-based image-to-image translation model. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively., Comment: ICLR 2024 Machine Learning for Remote Sensing (ML4RS) Workshop

There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation. We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions. We estimate that 84% of these complex transactions are highly automatable, representing a huge potential opportunity: saving even an average of just one minute per complex transaction would save the equivalent of approximately 1,200 person-years of work every year. We also develop a model to estimate the volume of transactions a government service undertakes, providing a way for government to avoid conducting time consuming transaction volume measurements. Finally, we find that there is high turnover in the types of services government provide, meaning that automation efforts should focus on general procedures rather than services themselves which are likely to evolve over time. Overall, our work presents a novel perspective on the structure and functioning of modern government, and how it might evolve in the age of artificial intelligence.

The field of machine learning has recently made significant progress in reducing the requirements for labelled training data when building new models. These `cheaper' learning techniques hold significant potential for the social sciences, where development of large labelled training datasets is often a significant practical impediment to the use of machine learning for analytical tasks. In this article we review three `cheap' techniques that have developed in recent years: weak supervision, transfer learning and prompt engineering. For the latter, we also review the particular case of zero-shot prompting of large language models. For each technique we provide a guide of how it works and demonstrate its application across six different realistic social science applications (two different tasks paired with three different dataset makeups). We show good performance for all techniques, and in particular we demonstrate how prompting of large language models can achieve high accuracy at very low cost. Our results are accompanied by a code repository to make it easy for others to duplicate our work and use it in their own research. Overall, our article is intended to stimulate further uptake of these techniques in the social sciences., Comment: 39 pages, 10 figures, 6 tables

Generative AI has the potential to transform how public services are delivered by enhancing productivity and reducing time spent on bureaucracy. Furthermore, unlike other types of artificial intelligence, it is a technology that has quickly become widely available for bottom-up adoption: essentially anyone can decide to make use of it in their day to day work. But to what extent is generative AI already in use in the public sector? Our survey of 938 public service professionals within the UK (covering education, health, social work and emergency services) seeks to answer this question. We find that use of generative AI systems is already widespread: 45% of respondents were aware of generative AI usage within their area of work, while 22% actively use a generative AI system. Public sector professionals were positive about both current use of the technology and its potential to enhance their efficiency and reduce bureaucratic workload in the future. For example, those working in the NHS thought that time spent on bureaucracy could drop from 50% to 30% if generative AI was properly exploited, an equivalent of one day per week (an enormous potential impact). Our survey also found a high amount of trust (61%) around generative AI outputs, and a low fear of replacement (16%). While respondents were optimistic overall, areas of concern included feeling like the UK is missing out on opportunities to use AI to improve public services (76%), and only a minority of respondents (32%) felt like there was clear guidance on generative AI usage in their workplaces. In other words, it is clear that generative AI is already transforming the public sector, but uptake is happening in a disorganised fashion without clear guidelines. The UK's public sector urgently needs to develop more systematic methods for taking advantage of the technology.

Allocation of scarce resources is a recurring challenge for the public sector: something that emerges in areas as diverse as healthcare, disaster recovery, and social welfare. The complexity of these policy domains and the need for meeting multiple and sometimes conflicting criteria has led to increased focus on the use of algorithms in this type of decision. However, little engagement between researchers across these domains has happened, meaning a lack of understanding of common problems and techniques for approaching them. Here, we performed a cross disciplinary literature review to understand approaches taken for different areas of algorithmic allocation including healthcare, organ transplantation, homelessness, disaster relief, and welfare. We initially identified 1070 papers by searching the literature, then six researchers went through them in two phases of screening resulting in 176 and 75 relevant papers respectively. We then analyzed the 75 papers from the lenses of optimization goals, techniques, interpretability, flexibility, bias, ethical considerations, and performance. We categorized approaches into human-oriented versus resource-oriented perspective, and individual versus aggregate and identified that 76% of the papers approached the problem from a human perspective and 60% from an aggregate level using optimization techniques. We found considerable potential for performance gains, with optimization techniques often decreasing waiting times and increasing success rate by as much as 50%. However, there was a lack of attention to responsible innovation: only around one third of the papers considered ethical issues in choosing the optimization goals while just a very few of them paid attention to the bias issues. Our work can serve as a guide for policy makers and researchers wanting to use an algorithm for addressing a resource allocation problem.

Existing research suggests that some transition activities (e.g., visits to kindergarten classrooms, home visits by teachers, sharing information with families) initiated by early care and education (ECE) settings, kindergarten teachers and elementary schools, and/or families can have small, positive associations with children's outcomes in kindergarten. This study examined combinations of kindergarten transition activities reported by both kindergarten teachers and parents to gain a comprehensive picture of supports that families experience. The authors identified: (1) Four common combinations of transition activities provided to and experienced by families, called Transition Activity Groups; (2) Whether these distinct combinations of transition activities differed for children who previously attended Head Start compared to other children; and (3) If those combinations of transition activities were associated with child and family outcomes and engagement in kindergarten. [This report was produced as part of the Head Start to Kindergarten (HS2K) Transitions Project. It was produced with the National P-3 Center.]

This article explores how the 'rules in use' from Ostrom's Institutional Analysis and Development Framework (IAD) can be developed as a context analysis approach for AI. AI risk assessment frameworks increasingly highlight the need to understand existing contexts. However, these approaches do not frequently connect with established institutional analysis scholarship. We outline a novel direction illustrated through a high-level example to understand how clinical oversight is potentially impacted by AI. Much current thinking regarding oversight for AI revolves around the idea of decision makers being in-the-loop and, thus, having capacity to intervene to prevent harm. However, our analysis finds that oversight is complex, frequently made by teams of professionals and relies upon explanation to elicit information. Professional bodies and liability also function as institutions of polycentric oversight. These are all impacted by the challenge of oversight of AI systems. The approach outlined has potential utility as a policy tool of context analysis aligned with the 'Govern and Map' functions of the National Institute of Standards and Technology (NIST) AI Risk Management Framework; however, further empirical research is needed. Our analysis illustrates the benefit of existing institutional analysis approaches in foregrounding team structures within oversight and, thus, in conceptions of 'human in the loop'., Comment: 19 pages

Calls for new metrics, technical standards and governance mechanisms to guide the adoption of Artificial Intelligence (AI) in institutions and public administration are now commonplace. Yet, most research and policy efforts aimed at understanding the implications of adopting AI tend to prioritize only a handful of ideas; they do not fully connect all the different perspectives and topics that are potentially relevant. In this position paper, we contend that this omission stems, in part, from what we call the relational problem in socio-technical discourse: fundamental ontological issues have not yet been settled--including semantic ambiguity, a lack of clear relations between concepts and differing standard terminologies. This contributes to the persistence of disparate modes of reasoning to assess institutional AI systems, and the prevalence of conceptual isolation in the fields that study them including ML, human factors, social science and policy. After developing this critique, we offer a way forward by proposing a simple policy and research design tool in the form of a conceptual framework to organize terms across fields--consisting of three horizontal domains for grouping relevant concepts and related methods: Operational, Epistemic, and Normative. We first situate this framework against the backdrop of recent socio-technical discourse at two premier academic venues, AIES and FAccT, before illustrating how developing suitable metrics, standards, and mechanisms can be aided by operationalizing relevant concepts in each of these domains. Finally, we outline outstanding questions for developing this relational approach to institutional AI research and adoption., Comment: 23 pages, 1 figure

Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago., Comment: 4 pages, 4 figures, Submitted to Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022

In 2013-14, Chicago Public Schools (CPS), the nation's third-largest school district, implemented several changes to the policies governing its pre-kindergarten (pre-k) application and enrollment process. Two policy changes that were central to the school district's strategy focused on increasing access to full-day pre-k for high-priority student groups: (1) increasing the overall number of full-day pre-k classrooms within school buildings; and (2) intentionally placing those full-day pre-k classrooms in neighborhoods with a large proportion of age-eligible, high-priority children and historically low rates of enrollment in CPS pre-k. This research examines whether and how access--defined as the distance to the closest school with full-day school-based pre-k and the number of full-day pre-k classrooms near potential students' homes--was related to pre-k enrollment and ongoing learning outcomes in early elementary school. In other words: Does the geographic placement of full-day pre-k classrooms within a school district matter? In prior work, it was found that after these policy changes in Chicago, there were more equitable patterns of pre-k access and enrollment. This brief presents new findings: Geographic placement of full-day pre-k matters not only for more equitable pre-k access and enrollment, but also for academic outcomes in early elementary school. Students--especially in high-priority student groups--who were eligible to enroll in pre-k after the policy changes had higher math scores and grades in early elementary school than did students eligible for pre-k before the policy changes. These benefits are partly explained by the fact that Black students and those living in lowest-income neighborhoods lived closer to full-day school-based pre-k options post-policy, which in turn was related to increased enrollment in full-day pre-k, higher kindergarten entry skills, and ultimately better 2nd grade academic outcomes.

We identify natural symmetries of each rigid higher braided category. Specifically, we construct a functorial action by the continuous group $\Omega \mathsf{O}(n)$ on each $\mathcal{E}_{n-1}$-monoidal $(g,d)$-category $\mathcal{R}$ in which each object is dualizable (for $n\geq 2$, $d \geq 0$, $d \leq g \leq \infty$). This action determines a canonical action by the continuous group $\Omega \mathbb{R}\mathbb{P}^{n-1}$ on the moduli space of objects of each such $\mathcal{R}$. In cases where the parameters $n$, $d$, and $g$ are small, we compare these continuous symmetries to known symmetries, which manifest as categorical identities., Comment: 20 pages, preliminary version, comments and suggested citations are welcome

The process and results of defining neighborhoods may look different depending on the purpose. In this brief, the authors focused on how neighborhoods in Chicago are defined. The initial goal of this work was to answer questions about how access to and enrollment in school-based pre-kindergarten (pre-k) may have varied by neighborhood characteristics. In conducting this work, the authors used a data-driven method for characterizing neighborhoods that leveraged publicly available census data and allowed for consideration of many neighborhood characteristics simultaneously. This method resulted in a parsimonious set of five neighborhood groupings in Chicago that simplified how the relationship of neighborhood characteristics to a host of educational and other outcomes was understood. This brief shares the approach to defining neighborhood groupings and their characteristics and the findings about them. First, the authors review prior neighborhood research and ways of characterizing Chicago community areas and neighborhoods, outlining how the current work builds on these previous efforts. Next the data-driven method for grouping neighborhoods is presented and the neighborhood groupings are described. Finally, the potential uses and implications of the work is discussed. The hope is that others in Chicago (or elsewhere) find this approach to defining neighborhood groupings useful -- both for conducting research studies and providing services or resources.

As pre-kindergarten (pre-k) expands across the country, school districts are making choices about where to place pre-k classrooms and developing policies for how families can apply and which children are enrolled. In doing so, districts are pulling policy levers that influence students' access to pre-k. Research shows that some families have less access to pre-k than others, which contributes to inequitable enrollment within districts. This descriptive study explores whether and how Chicago's school-based pre-k system was more equitable after the district implemented a set of policies focused on changing access to and enrollment in school-based pre-k. Specifically, we compare patterns of enrollment and geographic access (i.e., distance from home to a school with pre-k and number of pre-k classrooms nearby), and use multilevel logistic regression analyses to examine how access was related to enrollment for different student groups before and after the policy changes. Results show that both access to, and enrollment in, full-day pre-k expanded substantially among Black students, lowest-income students, and students living in mostly-Black neighborhoods, even as overall school-based pre-k enrollment remained relatively constant. There was a modest association between access to and enrollment in full-day pre-k (but not any pre-k), and this relationship became stronger following the policy changes, especially for high-priority student groups. Findings demonstrate that the geographic distribution of school-based pre-k (particularly full-day) may be an important policy lever for addressing inequities in student access and enrollment in Chicago, and have implications for other school districts implementing similar efforts nationwide. [For the research snapshot, see ED612486.]

We identify the group of framed diffeomorphisms of the torus as a semi-direct product of the torus with the braid group on 3 strands; we also identify the topological monoid of framed local-diffeomorphisms of the torus in similar terms. It follows that the framed mapping class group is this braid group. We show that the group of framed diffeomorphisms of the torus acts on twice-iterated Hochschild homology, and explain how this recovers a host of familiar symmetries. In the case of Cartesian monoidal structures, we show that this action extends to the monoid of framed local-diffeomorphisms of the torus. Based on this, we propose a definition of an unstable secondary cyclotomic structure, and show that iterated Hochschild homology possesses such in the Cartesian monoidal setting., Comment: 50 pages. Two appendices, one that collects a few facts about continuous monoids

We construct a circle-invariant trace from the factorization homology of the circle $ {\sf trace} \colon \int^\alpha_{{\mathbb S}^1} \\underline{\sf End}(V) \longrightarrow \uno $ associated to a dualizable object $V\in {\boldsymbol{\mathfrak X}}$ in a symmetric monoidal $\infty$-category. This proves a conjecture of To\"en--Vezzosi on existence of circle-invariant traces. Underlying our construction is a calculation of the factorization homology over the circle of the walking adjunction in terms of the paracyclic category of Getzler--Jones: $ \int_{{\mathbb S}^1} {\sf Adj} ~\simeq~ {\bDelta_{\circlearrowleft}}^{\triangleleft\!\triangleright} ~. $ This calculation exhibits a form of Poincar\'e duality for 1-dimensional factorization homology., Comment: 30 pages

We use differentiable programming and gradient descent to find unitary matrices that can be used in the period finding algorithm to extract period information from the state of a quantum computer post application of the oracle. The standard procedure is to use the inverse quantum Fourier transform. Our findings suggest that that this is not the only unitary matrix appropriate for the period finding algorithm, There exist several unitary matrices that can affect out the same transformation and they are significantly different from each other as well. These unitary matrices can be learned by an algorithm. Neural networks can be applied to differentiate such unitary matrices from randomly generated ones indicating that these unitaries do have characteristic features that cannot otherwise be discerned easily., Comment: 10 pages, 10 figures

The Pancreatic beta cell is an important target in diabetes research. For scalable modeling of beta cell ultrastructure, we investigate automatic segmentation of whole cell imaging data acquired through soft X-ray tomography. During the course of the study, both complete and partial ultrastructure annotations were produced manually for different subsets of the data. To more effectively use existing annotations, we propose a method that enables the application of partially labeled data for full label segmentation. For experimental validation, we apply our method to train a convolutional neural network with a set of 12 fully annotated data and 12 partially annotated data and show promising improvement over standard training that uses fully annotated data alone.