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Trellis' Student Financial Wellness Survey (SFWS) seeks to document the financial well-being of post-secondary students and provide institutions with a detailed profile of the financial issues their students face, some of which could potentially impact their success in college. This report details aggregated findings for the 89 colleges and universities across the country that participated in the fall 2022 implementation of the survey. [For "Student Financial Wellness Survey: Fall 2021 Semester Results. National Aggregate Report," see ED625724.]

The study of matter at extreme densities and temperatures has emerged as a highly active frontier at the interface of plasma physics, material science and quantum chemistry with direct relevance for planetary modeling and inertial confinement fusion. A particular feature of such warm dense matter is the complex interplay of strong Coulomb interactions, quantum effects, and thermal excitations, rendering its rigorous theoretical description a formidable challenge. Here, we report a breakthrough in path integral Monte Carlo simulations that allows us to unravel this intricate interplay for light elements without nodal restrictions. This new capability gives us access to electronic correlations previously unattainable. As an example, we apply our method to strongly compressed beryllium to describe x-ray Thomson scattering (XRTS) data obtained at the National Ignition Facility. We find excellent agreement between simulation and experiment. Our analysis shows an unprecedented level of consistency for independent observations without the need for any empirical input parameters.

JWST measured the infrared spectra of Saturn's rings and several of its small moons (Epimetheus, Pandora, Telesto and Pallene) as part of Guaranteed Time Observation program 1247. The NIRSpec instrument obtained near-infrared spectra of the small moons between 0.6 and 5.3 microns, which are all dominated by water-ice absorption bands. The shapes of the water-ice bands for these moons suggests that their surfaces contain variable mixes of crystalline and amorphous ice or variable amounts of contaminants and/or sub-micron ice grains. The near-infrared spectrum of Saturn's A ring has exceptionally high signal-to-noise between 2.7 and 5 microns and is dominated by features due to highly crystalline water ice. The ring spectrum also confirms that the rings possess a 2-3% deep absorption at 4.13 microns due to deuterated water-ice previously seen by the Visual and Infrared Mapping Spectrometer onboard the Cassini spacecraft. This spectrum also constrains the fundamental absorption bands of carbon dioxide and carbon monoxide and may contain evidence for a weak aliphatic hydrocarbon band. Meanwhile, the MIRI instrument obtained mid-infrared spectra of the rings between 4.9 and 27.9 microns, where the observed signal is a combination of reflected sunlight and thermal emission. This region shows a strong reflectance peak centered around 9.3 microns that can be attributed to crystalline water ice. Since both the near and mid-infrared spectra are dominated by highly crystalline water ice, they should provide a useful baseline for interpreting the spectra of other objects in the outer solar system with more complex compositions., Comment: Accepted for Publication in JGR Planets

Computational microscopy, in which hardware and algorithms of an imaging system are jointly designed, shows promise for making imaging systems that cost less, perform more robustly, and collect new types of information. Often, the performance of computational imaging systems, especially those that incorporate machine learning, is sample-dependent. Thus, standardized datasets are an essential tool for comparing the performance of different approaches. Here, we introduce the Berkeley Single Cell Computational Microscopy (BSCCM) dataset, which contains over ~12,000,000 images of 400,000 of individual white blood cells. The dataset contains images captured with multiple illumination patterns on an LED array microscope and fluorescent measurements of the abundance of surface proteins that mark different cell types. We hope this dataset will provide a valuable resource for the development and testing of new algorithms in computational microscopy and computer vision with practical biomedical applications.

We use an infrared dataset captured between 1984 and 2017 using several instruments and observatories to report five rare equatorial disturbances that completely altered the appearance of Jupiter's Equatorial Zone (EZ): the clearance of tropospheric clouds revealed a new 5-$\mu$m-bright band encircling the planet at the equator, accompanied by large 5-$\mu$m-bright filaments. Three events were observed in ground-based images in 1973, 1979 and 1992. We report and characterize for the first time the entire evolution of two new episodes of this unusual EZ state that presented their maximum 5-$\mu$m-brightness in December 1999 and February 2007, coinciding with a brown coloration south of the equator and with large bluish filaments and white plumes in the northern EZ at visible wavelengths. We characterize their typical infrared-bright lifetimes of 12-18 months, with possible periodicities of 6-8 or 13-14 years. We predict that a full-scale equatorial disturbance could occur in 2019-21.

This article presents an implementation of a natural-language speech interface and a haptic feedback interface that enables a human supervisor to provide guidance to, request information, and receive status updates from a Spot robot. We provide insights gained during preliminary user testing of the interface in a realistic robot exploration scenario., Comment: Peer reviewed and published at "Empowering People in Human-Robot Collaboration: Why, How, When, and for Whom" workshop at OzCHI 2023 conference

In this paper, a Frequency Selective Surface fractalized unit cell design previously explored in the literature is replicated then extended and optimized for multilayer FSS morphologies. A 10 mm x 10 mm planar unit cell is used and then iterated on to create a multilayer structure optimized for use as a bandstop filter in the X-band and Ku-band. Substrate thickness, unit cell shift, and number of layers are varied and numerical results are obtained via the finite element method employed by Ansys solver for electromagnetic structures (HFSS). S-parameters for reflection and transmission are obtained at various stages of design optimization to verify the desired bandstop results, and bandpass characteristics are explored at each stage of the design as well., Comment: 5 pages, 19 figures

Using images at multiple mid-infrared wavelengths, acquired in May 2018 using the VISIR instrument on ESO's Very Large Telescope (VLT), we study Jupiter's pole-to-pole thermal, chemical and aerosol structure in the troposphere and stratosphere. We confirm that the pattern of cool and cloudy anticyclonic zones and warm cloud-free cyclonic belts persists throughout the mid-latitudes, up to the polar boundaries, and evidence a strong correlation with the vertical maximum windshear and the locations of Jupiter's zonal jets. At high latitudes, VISIR images reveal a large region of mid-infrared cooling poleward $\sim$64$^{\circ}$N and $\sim$67$^{\circ}$S extending from the upper troposphere to the stratosphere, co-located with the reflective aerosols observed by JunoCam, and suggesting that aerosols play a key role in the radiative cooling at the poles. Comparison of zonal-mean thermal properties and high-resolution visible imaging from Juno allows us to study the variability of atmospheric properties as a function of altitude and jet boundaries, particularly in the cold southern polar vortex. However, the southern stratospheric polar vortex is partly masked by a warm mid-infrared signature of the aurora. Co-located with the southern main auroral oval, this warming results from the auroral precipitation and/or joule heating which heat the atmosphere and thus cause a significant stratospheric emission. This high emission results from a large enhancement of both ethane and acetylene in the polar region, reinforcing the evidence of enhanced ion-related chemistry in Jupiter's auroral regions.

Speculative execution attacks undermine the security of constant-time programming, the standard technique used to prevent microarchitectural side channels in security-sensitive software such as cryptographic code. Constant-time code must therefore also deploy a defense against speculative execution attacks to prevent leakage of secret data stored in memory or the processor registers. Unfortunately, contemporary defenses, such as speculative load hardening (SLH), can only satisfy this strong security guarantee at a very high performance cost. This paper proposes DECLASSIFLOW, a static program analysis and protection framework to efficiently protect constant-time code from speculative leakage. DECLASSIFLOW models "attacker knowledge" -- data which is inherently transmitted (or, implicitly declassified) by the code's non-speculative execution -- and statically removes protection on such data from points in the program where it is already guaranteed to leak non-speculatively. Overall, DECLASSIFLOW ensures that data which never leaks during the non-speculative execution does not leak during speculative execution, but with lower overhead than conservative protections like SLH.

We review the physics of monolayer graphene in a strong magnetic field, with emphasis on highly collective states that emerge from the weakly interacting system because of correlations (emergent states). After reviewing the general properties of graphene and of electrons in a magnetic field, we give a brief introduction to the integer quantum Hall effect (IQHE) and the fractional quantum Hall effect (FQHE) in a 2D electron gas as foundation to show that monolayer graphene in a magnetic field exhibits both effects, but with properties modified by the influence of the graphene crystal. After giving an introduction to standard methods of dealing with emergent states for this system, we show that an SO(8) fermion dynamical symmetry governs the emergent degrees of freedom and that the algebraic and group properties of the dynamical symmetry provide a new view of strongly correlated states observed in monolayer graphene subject to a strong magnetic field.

Embeddings are a basic initial feature extraction step in many machine learning models, particularly in natural language processing. An embedding attempts to map data tokens to a low-dimensional space where similar tokens are mapped to vectors that are close to one another by some metric in the embedding space. A basic question is how well can such embedding be learned? To study this problem, we consider a simple probability model for discrete data where there is some "true" but unknown embedding where the correlation of random variables is related to the similarity of the embeddings. Under this model, it is shown that the embeddings can be learned by a variant of low-rank approximate message passing (AMP) method. The AMP approach enables precise predictions of the accuracy of the estimation in certain high-dimensional limits. In particular, the methodology provides insight on the relations of key parameters such as the number of samples per value, the frequency of the terms, and the strength of the embedding correlation on the probability distribution. Our theoretical findings are validated by simulations on both synthetic data and real text data., Comment: 12 pages, 7 figures

Our understanding of the human connectome is fundamentally limited by the resolution of diffusion MR images. Reconstructing a connectome's constituent neural pathways with tractography requires following a continuous field of fiber directions. Typically, this field is found with simple trilinear interpolation in low-resolution, noisy diffusion MRIs. However, trilinear interpolation struggles following fine-scale changes in low-quality data. Recent deep learning methods in super-resolving diffusion MRIs have focused on upsampling to a fixed spatial grid, but this does not satisfy tractography's need for a continuous field. In this work, we propose FENRI, a novel method that learns spatially-continuous fiber orientation density functions from low-resolution diffusion-weighted images. To quantify FENRI's capabilities in tractography, we also introduce an expanded simulated dataset built for evaluating deep-learning tractography models. We demonstrate that FENRI accurately predicts high-resolution fiber orientations from realistic low-quality data, and that FENRI-based tractography offers improved streamline reconstruction over the current use of trilinear interpolation., Comment: Submitted for review to ISBI 2024. For project page, see https://osf.io/dvnxw/

Hippocampal atrophy in Alzheimer's disease (AD) is asymmetric and spatially inhomogeneous. While extensive work has been done on volume and shape analysis of atrophy of the hippocampus in AD, less attention has been given to hippocampal asymmetry specifically. Previous studies of hippocampal asymmetry are limited to global volume or shape measures, which don't localize shape asymmetry at the point level. In this paper, we propose to quantify localized shape asymmetry by optimizing point correspondences between left and right hippocampi within a subject, while simultaneously favoring a compact statistical shape model of the entire sample. To account for related variables that have impact on AD and healthy subject differences, we build linear models with other confounding factors. Our results on the OASIS3 dataset demonstrate that compared to using volumetric information, shape asymmetry reveals fine-grained, localized differences that indicate the hippocampal regions of most significant shape asymmetry in AD patients., Comment: 4 pages, 3 figures Published in 2024 IEEE International Symposium on Biomedical Imaging (ISBI)