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Food insecurity is widespread among college students in the United States. Food benefits delivered through the CalFresh program, California's version of the federal Supplemental Nutrition Assistance Program (SNAP), can reduce hunger by helping students pay for groceries, but may not reach all eligible students. To date, higher education systems have lacked good estimates of the share of their students who are eligible for CalFresh and the share who actually receive benefits. To address this information gap, the California Policy Lab (CPL) partnered with the California Community College (CCC) Chancellor's Office, the University of California Office of the President (UCOP), the California Department of Social Services (CDSS), and the California Student Aid Commission (CSAC) to build a linked database of student-level administrative data on college enrollment, financial aid, and CalFresh participation. This database covers all students enrolled at CCC or UC campuses from academic years 2010-11 through 2021-22, along with corresponding FAFSA submissions and CalFresh participation. Using these data, researchers are able to measure how many college students are likely eligible for CalFresh, and of those how many participate. This report was updated in August 2024 with updated eligibility statistics that reflect results from a refined simulation of student eligibility for CalFresh. Using the improved simulation, the researchers found that among California Community College students, 20% of students were eligible for CalFresh (an increase from the 16% estimate in the June version of this report), among University of California (UC) undergraduates, 33% were eligible (an increase from 31% in the June report), and among UC graduate students, 7% were eligible (an increase from 6%).

Advances in machine learning and the increasing availability of high-dimensional data have led to the proliferation of social science research that uses the predictions of machine learning models as proxies for measures of human activity or environmental outcomes. However, prediction errors from machine learning models can lead to bias in the estimates of regression coefficients. In this paper, we show how this bias can arise, propose a test for detecting bias, and demonstrate the use of an adversarial machine learning algorithm in order to de-bias predictions. These methods are applicable to any setting where machine-learned predictions are the dependent variable in a regression. We conduct simulations and empirical exercises using ground truth and satellite data on forest cover in Africa. Using the predictions from a naive machine learning model leads to biased parameter estimates, while the predictions from the adversarial model recover the true coefficients., Comment: 12 pages, 8 figures

We study the $K$-theoretic enumerative geometry of cyclic Nakajima quiver varieties, with particular focus on $\text{Hilb}^{m}([\mathbb{C}^{2}/\mathbb{Z}_{l}])$, the equivariant Hilbert scheme of points on $\mathbb{C}^2$. The direct sum over $m$ of the equivariant $K$-theories of these varieties is known to be isomorphic to the ring symmetric functions in $l$ colors, with structure sheaves of torus fixed points identified with wreath Macdonald polynomials. Using properties of wreath Macdonald polynomials and the recent identification of the Maulik-Okounkov quantum affine algebra for cyclic quivers with the quantum toroidal algebras of type $A$, we derive an explicit formula for the generating function of capped vertex functions of $\text{Hilb}^{m}([\mathbb{C}^{2}/\mathbb{Z}_{l}])$ with descendants given by exterior powers of the $0$th tautological bundle. We also sharpen the large framing vanishing results of Okounkov, providing a class of descendants and cyclic quiver varieties for which the capped vertex functions are purely classical.

Wavefunction forms based on products of electron pairs are usually constructed as closed-shell singlets, which is insufficient when the molecular state has a nonzero spin or when the chemistry is determined by $d$- or $f-$electrons. A set of two-electron forms are considered as explicit couplings of second-quantized operators to open-shell singlets. Geminal wavefunctions are constructed and their structure is elaborated. Numerical results for small model systems clearly demonstrate improvement over closed-shell singlet pairs.

Experimental methods for estimating the impacts of text on human evaluation have been widely used in the social sciences. However, researchers in experimental settings are usually limited to testing a small number of pre-specified text treatments. While efforts to mine unstructured texts for features that causally affect outcomes have been ongoing in recent years, these models have primarily focused on the topics or specific words of text, which may not always be the mechanism of the effect. We connect these efforts with NLP interpretability techniques and present a method for flexibly discovering clusters of similar text phrases that are predictive of human reactions to texts using convolutional neural networks. When used in an experimental setting, this method can identify text treatments and their effects under certain assumptions. We apply the method to two datasets. The first enables direct validation of the model's ability to detect phrases known to cause the outcome. The second demonstrates its ability to flexibly discover text treatments with varying textual structures. In both cases, the model learns a greater variety of text treatments compared to benchmark methods, and these text features quantitatively meet or exceed the ability of benchmark methods to predict the outcome., Comment: Published in Findings of ACL 2024 ( see https://aclanthology.org/2024.findings-acl.714 )

Machine learning (ML) applied to routine patient monitoring within intensive care units (ICUs) has the potential to improve care by providing clinicians with novel insights into each patient's health and expected response to interventions. This paper applies deep learning to a large volume of unlabeled electrocardiogram (ECG) telemetry signals, which are commonly used for continuous patient monitoring in hospitals but have important differences from the standard, single time-point 12-lead ECG used in many prior machine learning studies. We applied self-supervised learning to pretrain a spectrum of deep networks on approximately 147,000 hours of ECG telemetry data. Our approach leverages this dataset to train models that significantly improve performance on four distinct downstream tasks compared with direct supervised learning using labeled data. These pretrained models enable medically useful predictions and estimates in smaller patient cohorts that are typically limited by the scarcity of labels. Notably, we demonstrate that our pretrained networks can continuously annotate ECG telemetry signals, thereby providing monitoring capabilities that are often unavailable due to the requirement for specialized expertise and time-consuming professional annotations., Comment: 17 pages, 9 figures

We obtain explicit formulas for capped descendent vertex functions of $\text{Hilb}^n(\mathbb{C}^2)$ for descendents given by chern classes of tautological bundles. The expression is the result of twisting a well known generating function for normalized Macdonald polynomials. This gives an explicit description of the rational function the capped vertex is an expansion of. Along the way we prove various limit expressions of the capping operator and the bare vertex function., Comment: Notational changes, added journal references, and refined the proofs of many theorems

The full family of discrete logistic maps has been widely studied both as a canonical example of the period-doubling route to chaos, and as a model of natural processes. In this paper we present a study of the stochastic process described by iterations of the logistic map with a random parameter. In addition to bringing together previously known results, we present a proof that the unique invariant measure of the process in the chaotic regime is asymptotically stable.

We extend ground-state density-functional theory to excited states and provide the theoretical formulation for the widely used $\Delta SCF$ method for calculating excited-state energies and densities. As the electron density alone is insufficient to characterize excited states, we formulate excited-state theory using the defining variables of a noninteracting reference system, namely (1) the excitation quantum number $n_{s}$ and the potential $w_{s}(\mathbf{r})$ (excited-state potential-functional theory, $n$PFT), (2) the noninteracting wavefunction $\Phi$ ($\Phi$-functional theory, $\Phi$FT), or (3) the noninteracting one-electron reduced density matrix $\gamma_{s}(\mathbf{r},\mathbf{r}')$ (density-matrix-functional theory, $\gamma_{s}$FT). We show the equivalence of these three sets of variables and their corresponding energy functionals. Importantly, the ground and excited-state exchange-correlation energy use the \textit{same} universal functional, regardless of whether $\left(n_{s},w_{s}(\boldsymbol{r})\right)$, $\Phi$, or $\gamma_{s}(\mathbf{r},\mathbf{r}')$ is selected as the fundamental descriptor of the system. We derive the excited-state (generalized) Kohn-Sham equations. The minimum of all three functionals is the ground-state energy and, for ground states, they are all equivalent to the Hohenberg-Kohn-Sham method. The other stationary points of the functionals provide the excited-state energies and electron densities, establishing the foundation for the $\Delta SCF$ method.

Food insecurity is widespread among college students in the United States. Food benefits delivered through the CalFresh program, California's version of the federal Supplemental Nutrition Assistance Program (SNAP), can reduce hunger by helping students pay for groceries, but may not reach all eligible students. To date, higher education systems have lacked good estimates of the share of their students who are eligible for CalFresh and the share who actually receive benefits. To address this information gap, the California Policy Lab (CPL) partnered with the California Community College (CCC) Chancellor's Office, the University of California Office of the President (UCOP), the California Department of Social Services (CDSS), and the California Student Aid Commission (CSAC) to build a linked database of student-level administrative data on college enrollment, financial aid, and CalFresh participation. This database covers all students enrolled at CCC or UC campuses from academic years 2010-11 through 2021-22, along with corresponding FAFSA submissions and CalFresh participation. Using these data, we are able to measure how many college students are likely eligible for CalFresh, and of those how many participate. CalFresh eligibility does not perfectly overlap with student need. Some students who are food insecure may not be eligible for benefits, while some eligible students may not be in great need. This in part reflects the rules of the program, which are designed to capture need but do not always do so perfectly. We attempt to measure eligibility according to the actual rules. Our estimates complement other work that uses survey data to measure students' basic needs (California Community Colleges League, 2022). We estimate that in Fall 2019, on the eve of the COVID pandemic, 16% of California community college students (256,000 students), 31% of UC undergraduate students (69,000 students), and 6% of UC graduate students (3,000 students) were likely eligible for CalFresh benefits. However, the majority of these students did not receive benefits -- only 30% of eligible community college students, 22% of eligible UC undergraduates, and 29% of eligible UC graduate students were actually enrolled in CalFresh. We emphasize that our eligibility determinations are estimates based on information available in existing data, which imperfectly capture some elements of the CalFresh eligibility determination process. They may somewhat overstate or understate student eligibility. However, extensive investigation led us to conclude that the errors are likely not large and that our estimates are a good approximation of the share of students who would be found eligible under individualized determinations. As we discuss below, the higher eligibility rate among UC undergraduates as compared to CCC students reflects program rules that make it easier for UC students to qualify than for CCC students with similar resources. We also find differences in eligibility across groups of students within each segment, reflecting both variation in need and program rules. Among both CCC and UC students, Black and Hispanic students, for example, are more likely to be eligible than are White or Asian American students, and students receiving federal, state, or institutional financial aid are much more likely to be eligible than are students not receiving aid. Benefits receipt generally mirrors this, although there are differences across groups in the share of eligible students who receive benefits. The take-up rate, which is the share of eligible students who participate in CalFresh, is higher for Black students (and, at the UC but not at CCCs, Hispanic students) than for White students, and for students on financial aid than for students who are not. Our report indicates there is much room to improve CalFresh participation among eligible students. Through detailed analyses of the paths to eligibility and of variation in participation rates, we hope to shed light on opportunities for policymakers, higher education administrators, community-based organizations, student groups, and advocates to better connect eligible students to benefits. [Additional support was provided by the Woven Foundation.]

We propose a new Perturbation Theory framework that can be used to help with the projective solution of the Schr\"odinger equation for arbitrary wavefunctions. This Flexible Ansatz for N-body Perturbation Theory (FANPT) is based on our previously proposed Flexible Ansatz for N-body Configuration Interaction (FANCI). We derive recursive FANPT expressions including arbitrary orders in the perturbation hierarchy. We show that the FANPT equations are well-behaved across a wide range of conditions, including static correlation-dominated configurations and highly non-linear wavefunctions.

Electron pairs have an illustrious history in chemistry, from powerful concepts to understanding structural stability and reactive changes, to the promise of serving as building blocks of quantitative descriptions of the electronic structure of complex molecules and materials. However, traditionally, two-electron wavefunctions (geminals), have not enjoyed the popularity and widespread use of the more standard single-particle methods. This has changed recently, with a renewed interest in the development of geminal wavefunctions as an alternative to describing strongly-correlated phenomena. Hence, there is a need to find geminal methods that are accurate, computationally-tractable, and do not demand significant input from the user (particularly, via cumbersome and often ill-behaved orbital optimization steps). Here we propose new families of geminal wavefunctions, inspired by the pair Coupled Cluster (pCCD) ansatz. We present a new hierarchy of two-electron wavefunctions that extend the one-reference orbital idea to other geminals. Moreover, we show how to incorporate single-like excitations in this framework, without leaving the quasiparticle picture. We explore the role of imposing seniority restrictions on these wavefunctions, and benchmark these new methods on model strongly-correlated systems.