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In the largest U.S. metropolitan areas, suburban school districts enroll 14.4 million students, far more than the 6 million students enrolled in the same metros' urban districts. In fact, students enrolled in the suburban school districts surrounding the 25 largest metropolitan areas represent roughly 30% of the nation's entire public school enrollment. Suburban growth has occurred alongside the creation of a segregated, metropolitan society through policy, law and practice. Discriminatory loan practices, federal highway construction, site selection for subsidized housing and exclusionary zoning are examples of how racial discrimination permeated to origins suburban society. State and federal governments are dominated politically by those representing suburban constituents too often eager to maintain an exclusionary status quo. As shifting populations change suburban school enrollment, education policy trends formerly confined to urban districts have spread to suburban ones. Many suburban school districts have experienced growth in the charter school sector, as well as a rash of school closures. Suburban schools and districts reflect broader societal problems, paradigms, and possibilities. This paper draws on federal enrollment data from the nation's largest 25 metros from 2011-2020 to descriptively analyze suburban school enrollment and segregation at the school district-level, seeking to understand different district contexts and their relationship to student segregation.

Housing, land use, and zoning policies are often siloed in such a way that they are considered and addressed separately from school segregation and students' opportunities to learn. But these policy areas can interact in powerful ways. This policy brief attempts to break down those siloes. Drawing on multiple bodies of literature, it explores what we know about the potential of housing and land use policy reform to address school segregation--and why doing so matters. The research reviewed here illuminates the close historical and legal relationship between school and housing segregation and the clear links between land use policy and school and housing segregation. Studies also show that where land use or housing reforms have enabled greater access to less segregated schools, historically marginalized students' outcomes have improved. The brief concludes with recommendations for policymakers to offer pathways to diverse, well-resourced schools for historically marginalized families, along with oversight and enforcement that considers school-housing interrelationships.

Time series data from observations of black hole ringdown gravitational waves are often analyzed in the time domain by using damped sinusoid models with acyclic boundary conditions. Data conditioning operations, including downsampling, filtering, and the choice of data segment duration, reduce the computational cost of such analyses and can improve numerical stability. Here we analyze simulated damped sinsuoid signals to illustrate how data conditioning operations, if not carefully applied, can undesirably alter the analysis' posterior distributions. We discuss how currently implemented downsampling and filtering methods, if applied too aggressively, can introduce systematic errors and skew tests of general relativity. These issues arise because current downsampling and filtering methods do not operate identically on the data and model. Alternative downsampling and filtering methods which identically operate on the data and model may be achievable, but we argue that the current operations can still be implemented safely. We also show that our preferred anti-alias filtering technique, which has an instantaneous frequency-domain response at its roll-off frequency, preserves the structure of posterior distributions better than other commonly used filters with transient frequency-domain responses. Lastly, we highlight that exceptionally long data segments may need to be analyzed in cases where thin lines in the noise power spectral density overlap with central signal frequencies. Our findings may be broadly applicable to any analysis of truncated time domain data with acyclic boundary conditions., Comment: Data Release: https://github.com/HarrisonS-Phys/ringdown-data-conditioning

Kolmogorov-Arnold Networks (KAN) \cite{liu2024kan} were very recently proposed as a potential alternative to the prevalent architectural backbone of many deep learning models, the multi-layer perceptron (MLP). KANs have seen success in various tasks of AI for science, with their empirical efficiency and accuracy demostrated in function regression, PDE solving, and many more scientific problems. In this article, we revisit the comparison of KANs and MLPs, with emphasis on a theoretical perspective. On the one hand, we compare the representation and approximation capabilities of KANs and MLPs. We establish that MLPs can be represented using KANs of a comparable size. This shows that the approximation and representation capabilities of KANs are at least as good as MLPs. Conversely, we show that KANs can be represented using MLPs, but that in this representation the number of parameters increases by a factor of the KAN grid size. This suggests that KANs with a large grid size may be more efficient than MLPs at approximating certain functions. On the other hand, from the perspective of learning and optimization, we study the spectral bias of KANs compared with MLPs. We demonstrate that KANs are less biased toward low frequencies than MLPs. We highlight that the multi-level learning feature specific to KANs, i.e. grid extension of splines, improves the learning process for high-frequency components. Detailed comparisons with different choices of depth, width, and grid sizes of KANs are made, shedding some light on how to choose the hyperparameters in practice., Comment: 17 pages, 5 figures

Recent advancements in medicine have confirmed that brain disorders often comprise multiple subtypes of mechanisms, developmental trajectories, or severity levels. Such heterogeneity is often associated with demographic aspects (e.g., sex) or disease-related contributors (e.g., genetics). Thus, the predictive power of machine learning models used for symptom prediction varies across subjects based on such factors. To model this heterogeneity, one can assign each training sample a factor-dependent weight, which modulates the subject's contribution to the overall objective loss function. To this end, we propose to model the subject weights as a linear combination of the eigenbases of a spectral population graph that captures the similarity of factors across subjects. In doing so, the learned weights smoothly vary across the graph, highlighting sub-cohorts with high and low predictability. Our proposed sample weighting scheme is evaluated on two tasks. First, we predict initiation of heavy alcohol drinking in young adulthood from imaging and neuropsychological measures from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Next, we detect Dementia vs. Mild Cognitive Impairment (MCI) using imaging and demographic measurements in subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to existing sample weighting schemes, our sample weights improve interpretability and highlight sub-cohorts with distinct characteristics and varying model accuracy.

Transformer neural networks, driven by self-attention mechanisms, are core components of foundational and Large Language Models. In generative transformers, self-attention uses cache memory to store token projections, avoiding recomputation at each time step. However, GPU-stored projections must be loaded into SRAM for each new generation step, causing latency and energy bottlenecks for long sequences. In this work, we propose a fast and energy-efficient hardware implementation of self-attention using analog in-memory computing based on gain cell memories. Volatile gain cell memories can be efficiently written to store new tokens during sequence generation, while performing analog signed weight multiplications to compute the dot-products required for self-attention. We implement Sliding Window Attention, which keeps memory of a finite set of past steps. A charge-to-pulse converter for array readout eliminates the need for analog-to-digital conversion between self-attention stages. Using a co-designed initialization algorithm to adapt pre-trained weights to gain cell non-idealities, we achieve NLP performance comparable to ChatGPT-2 with minimal training iterations, despite hardware constraints. Our end-to-end hardware design includes digital controls, estimating area, latency, and energy. The system reduces attention latency by up to two orders of magnitude and energy consumption by up to five orders compared to GPUs, marking a significant step toward ultra-fast, low-power sequence generation in Large Language Models., Comment: 25 pages, 6 figures, 1 table

Many science cases for wide-field time-domain surveys rely on accurate identification and characterization of the galaxies hosting transient and variable objects. In the era of the Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory the number of known transient and variable sources will grow by orders of magnitude, and many of these sources will be blended with their host galaxies and neighboring galaxies. A diverse range of applications - including the classification of nuclear and non-nuclear sources, identification of potential host galaxies, extraction of host galaxy SEDs without requiring a transient-free reference image, and combined analysis of photometry from multiple surveys - will benefit from a flexible framework to model time-domain imaging of transients. We describe a time-domain extension of the Scarlet2 scene modeling code for multi-epoch, multi-band, and multi-resolution imaging data to extract simultaneous transient and host galaxy models. Scarlet2 leverages the benefits of data-driven priors on galaxy morphology, is fully GPU compatible, and can jointly model multi-resolution data from ground and space-based surveys. We demonstrate the method on simulated LSST-like supernova imaging, low-resolution Zwicky Transient Facility imaging of tidal disruption events, and Hyper Suprime Cam imaging of variable AGN out to z = 4 in the COSMOS fields. We show that Scarlet2 models provide accurate transient and host galaxy models as well as accurate measurement of host-transient spatial offsets, and demonstrate future applications to the search for 'wandering' massive black holes., Comment: Submitted to Astronomy and Computing. 16 pages, 11 figures. Feedback welcome!

With U.S. healthcare spending approaching $5T (NHE Fact Sheet 2024), and 25% of it estimated to be wasteful (Waste in the US the health care system: estimated costs and potential for savings, n.d.), the need to better predict risk and optimal patient care is evermore important. This paper introduces the Large Medical Model (LMM), a generative pre-trained transformer (GPT) designed to guide and predict the broad facets of patient care and healthcare administration. The model is trained on medical event sequences from over 140M longitudinal patient claims records with a specialized vocabulary built from medical terminology systems and demonstrates a superior capability to forecast healthcare costs and identify potential risk factors. Through experimentation and validation, we showcase the LMM's proficiency in not only in cost and risk predictions, but also in discerning intricate patterns within complex medical conditions and an ability to identify novel relationships in patient care. The LMM is able to improve both cost prediction by 14.1% over the best commercial models and chronic conditions prediction by 1.9% over the best transformer models in research predicting a broad set of conditions. The LMM is a substantial advancement in healthcare analytics, offering the potential to significantly enhance risk assessment, cost management, and personalized medicine., Comment: 10 pages, 10 figures

JWST has revealed diverse new populations of high-redshift ($z\sim4-11$) AGN and extreme star-forming galaxies that challenge current models. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. From a parent sample of 205 $\mathrm{z_{spec}}>3$ UNCOVER galaxies with NIRSpec/PRISM follow-up, we identify 12 C IV, He II, and C III] emitters. Leveraging the combined rest-optical and UV coverage of PRISM, we limit the emission-line model space using the sample's [O III]/H$\beta$ distribution, significantly decreasing the overlap between AGN and star-formation models in the UV diagnostics. We then find that the five He II emitters are the strongest AGN candidates, with further support from two [Ne V] detections and one X-ray detection from Chandra. We cannot robustly quantify the AGN fraction in this sample, but we note that close to 20% of $\mathrm{M_{*}>2\times10^{9}\,M_{\odot}}$ parent sample galaxies are AGN candidates. The lower-mass line emitters, which are consistent with both AGN and star-forming photoionization models, have more compact sizes and higher specific star formation rates than the parent sample. Higher-resolution and deeper data on these UV line emitters should provide much stronger constraints on the obscured AGN fraction at $z > 3$., Comment: Submitted to ApJ

This paper introduces the Pareto Data Framework, an approach for identifying and selecting the Minimum Viable Data (MVD) required for enabling machine learning applications on constrained platforms such as embedded systems, mobile devices, and Internet of Things (IoT) devices. We demonstrate that strategic data reduction can maintain high performance while significantly reducing bandwidth, energy, computation, and storage costs. The framework identifies Minimum Viable Data (MVD) to optimize efficiency across resource-constrained environments without sacrificing performance. It addresses common inefficient practices in an IoT application such as overprovisioning of sensors and overprecision, and oversampling of signals, proposing scalable solutions for optimal sensor selection, signal extraction and transmission, and data representation. An experimental methodology demonstrates effective acoustic data characterization after downsampling, quantization, and truncation to simulate reduced-fidelity sensors and network and storage constraints; results shows that performance can be maintained up to 95\% with sample rates reduced by 75\% and bit depths and clip length reduced by 50\% which translates into substantial cost and resource reduction. These findings have implications on the design and development of constrained systems. The paper also discusses broader implications of the framework, including the potential to democratize advanced AI technologies across IoT applications and sectors such as agriculture, transportation, and manufacturing to improve access and multiply the benefits of data-driven insights.

We explore the physical properties of five massive quiescent galaxies at $z\sim2.5$, revealing the presence of non-negligible dust reservoirs. JWST NIRSpec observations were obtained for each target, finding no significant line emission; multiple star formation tracers independently place upper limits between $0.1-10~M_\odot / \mathrm{yr}$. Spectral energy distribution modeling with Prospector infers stellar masses between $\log_{10}[M / M_\odot] \sim 10-11$ and stellar mass-weighted ages between $1-2$ Gyr. The inferred mass-weighted effective radii ($r_{eff}\sim 0.4-1.4$ kpc) and inner $1$ kpc stellar surface densities ($\log_{10}[\Sigma / M_\odot \mathrm{kpc}^2 ]\gtrsim 9$) are typical of quiescent galaxies at $z \gtrsim 2$. The galaxies display negative color gradients (redder core and bluer outskirts); for one galaxy, this effect results from a dusty core, while for the others it may be evidence of an "inside-out" growth process. Unlike local quiescent galaxies, we identify significant reddening in these typical cosmic noon passive galaxies; all but one require $A_V \gtrsim 0.4$. This finding is in qualitative agreement with previous studies but our deep 20-band NIRCam imaging is able to significantly suppress the dust-age degeneracy and confidently determine that these galaxies are reddened. We speculate about the physical effects that may drive the decline in dust content in quiescent galaxies over cosmic time., Comment: 18 pages, 8 figures, submitted to ApJ

AI agents have the potential to aid users on a variety of consequential tasks, including conducting scientific research. To spur the development of useful agents, we need benchmarks that are challenging, but more crucially, directly correspond to real-world tasks of interest. This paper introduces such a benchmark, designed to measure the accuracy of AI agents in tackling a crucial yet surprisingly challenging aspect of scientific research: computational reproducibility. This task, fundamental to the scientific process, involves reproducing the results of a study using the provided code and data. We introduce CORE-Bench (Computational Reproducibility Agent Benchmark), a benchmark consisting of 270 tasks based on 90 scientific papers across three disciplines (computer science, social science, and medicine). Tasks in CORE-Bench consist of three difficulty levels and include both language-only and vision-language tasks. We provide an evaluation system to measure the accuracy of agents in a fast and parallelizable way, saving days of evaluation time for each run compared to a sequential implementation. We evaluated two baseline agents: the general-purpose AutoGPT and a task-specific agent called CORE-Agent. We tested both variants using two underlying language models: GPT-4o and GPT-4o-mini. The best agent achieved an accuracy of 21% on the hardest task, showing the vast scope for improvement in automating routine scientific tasks. Having agents that can reproduce existing work is a necessary step towards building agents that can conduct novel research and could verify and improve the performance of other research agents. We hope that CORE-Bench can improve the state of reproducibility and spur the development of future research agents., Comment: Benchmark harness and code available at http://github.com/siegelz/core-bench

In state-of-the-art optical lattice clocks, beyond-electric-dipole polarizability terms lead to a break-down of magic wavelength trapping. In this Letter, we report a novel approach to evaluate lattice light shifts, specifically addressing recent discrepancies in the atomic multipolarizability term between experimental techniques and theoretical calculations. We combine imaging and multi-ensemble techniques to evaluate lattice light shift atomic coefficients, leveraging comparisons in a dual-ensemble lattice clock to rapidly evaluate differential frequency shifts. Further, we demonstrate application of a running wave field to probe both the multipolarizability and hyperpolarizability coefficients, establishing a new technique for future lattice light shift evaluations., Comment: 17 pages, 6 figures

Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by SPECT, typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis due to SPECT/CT misalignment. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans. Pre-defined attenuation coefficients are assigned to these regions, yielding the attenuation map used for AC. We objectively evaluated this method in a retrospective study with anonymized clinical SPECT/CT stress MPI images on the clinical task of detecting defects with an anthropomorphic model observer. CTLESS yielded statistically non-inferior performance compared to a CT-based AC (CTAC) method and significantly outperformed a non-AC (NAC) method on this clinical task. Similar results were observed in stratified analyses with different sexes, defect extents and severities. The method was observed to generalize across two SPECT scanners, each with a different camera. In addition, CTLESS yielded similar performance as CTAC and outperformed NAC method on the metrics of root mean squared error and structural similarity index measure. Moreover, as we reduced the training dataset size, CTLESS yielded relatively stable AUC values and generally outperformed another DL-based AC method that directly estimated the attenuation coefficient within each voxel. These results demonstrate the capability of the CTLESS method for transmission-less AC in SPECT and motivate further clinical evaluation.

Reliable lane-following algorithms are essential for safe and effective autonomous driving. This project was primarily focused on developing and evaluating different lane-following programs to find the most reliable algorithm for a Vehicle to Everything (V2X) project. The algorithms were first tested on a simulator and then with real vehicles equipped with a drive-by-wire system using ROS (Robot Operating System). Their performance was assessed through reliability, comfort, speed, and adaptability metrics. The results show that the two most reliable approaches detect both lane lines and use unsupervised learning to separate them. These approaches proved to be robust in various driving scenarios, making them suitable candidates for integration into the V2X project., Comment: Supported by the National Science Foundation under Grants No. 2150292 and 2150096

Recent advances in autonomous vehicle technologies and cellular network speeds motivate developments in vehicle-to-everything (V2X) communications. Enhanced road safety features and improved fuel efficiency are some of the motivations behind V2X for future transportation systems. Adaptive intersection control systems have considerable potential to achieve these goals by minimizing idle times and predicting short-term future traffic conditions. Integrating V2X into traffic management systems introduces the infrastructure necessary to make roads safer for all users and initiates the shift towards more intelligent and connected cities. To demonstrate our solution, we implement both a simulated and real-world representation of a 4-way intersection and crosswalk scenario with 2 self-driving electric vehicles, a roadside unit (RSU), and traffic light. Our architecture minimizes fuel consumption through intersections by reducing acceleration and braking by up to 75.35%. We implement a cost-effective solution to intelligent and connected intersection control to serve as a proof-of-concept model suitable as the basis for continued research and development. Code for this project is available at https://github.com/MMachado05/REU-2024., Comment: Supported by the National Science Foundation under Grants No. 2150292 and 2150096

Let $\Omega\subset \mathbb{R}^d$ be a bounded domain. We consider the problem of how efficiently shallow neural networks with the ReLU$^k$ activation function can approximate functions from Sobolev spaces $W^s(L_p(\Omega))$ with error measured in the $L_q(\Omega)$-norm. Utilizing the Radon transform and recent results from discrepancy theory, we provide a simple proof of nearly optimal approximation rates in a variety of cases, including when $q\leq p$, $p\geq 2$, and $s \leq k + (d+1)/2$. The rates we derive are optimal up to logarithmic factors, and significantly generalize existing results. An interesting consequence is that the adaptivity of shallow ReLU$^k$ neural networks enables them to obtain optimal approximation rates for smoothness up to order $s = k + (d+1)/2$, even though they represent piecewise polynomials of fixed degree $k$.

The main challenge of 21 cm cosmology experiments is astrophysical foregrounds which are difficult to separate from the signal due to telescope systematics. An earlier study has shown that foreground residuals induced by antenna gain errors can be estimated and subtracted using the hybrid foreground residual subtraction (HyFoReS) technique which relies on cross-correlating linearly filtered data. In this paper, we apply a similar technique to the CHIME stacking analysis to subtract beam-induced foreground contamination. Using a linear high-pass delay filter for foreground suppression, the CHIME collaboration reported a $11.1\sigma$ detection in the 21 cm signal stacked on eBOSS quasar locations, despite foreground residual contamination mostly due to the instrument chromatic transfer function. We cross-correlate the foreground-dominated data at low delay with the contaminated signal at high delay to estimate residual foregrounds and subtract them from the signal. We find foreground residual subtraction can improve the signal-to-noise ratio of the stacked 21 cm signal by $ 10 - 20\%$ after the delay foreground filter, although some of the improvement can also be achieved with an alternative flagging technique. We have shown that it is possible to use HyFoReS to reduce beam-induced foreground contamination, benefiting the analysis of the HI auto power spectrum with CHIME and enabling the recovery of large scale modes.

For solar-type stars, spots and their associated magnetic regions induce radial velocity perturbations through the Doppler rotation signal and the suppression of convective blueshift -- collectively known as rotation-modulation. We developed the Rotation-Convection (RC) model: a method of detrending and characterizing rotation-modulation, using only cross-correlation functions or 1-dimensional spectra, without the need for continuous high cadence measurements. The RC method uses a simple model for the anomalous radial velocity induced by an active region and has two inputs: stellar flux (or a flux proxy) and the relative radial velocity between strongly and weakly absorbed wavelengths (analogous to the bisector-inverse slope). On NEID solar data (three month baseline), the RC model lowers the amplitude of rotationally-modulated stellar activity to below the meter-per-second level. For the standard star HD 26965, the RC model detrends the activity signal to the meter-per-second level for HARPS, EXPRES, and NEID observations, even though the temporal density and timespan of the observations differs by an order of magnitude between the three datasets. In addition to detrending, the RC model also characterizes the rotation-modulation signal. From comparison with the Solar Dynamics Observatory, we confirmed that the model accurately recovers and separates the rotation and convection radial velocity components. We also mapped the amplitude of the rotation and convection perturbations as a function of height within the stellar atmosphere. Probing stellar atmospheres with our revised spot model will fuel future innovations in stellar activity mitigation, enabling robust exoplanet detection., Comment: Accepted to AJ, 22 pages, 9 figures

Human emotions are often not expressed directly, but regulated according to internal processes and social display rules. For affective computing systems, an understanding of how users regulate their emotions can be highly useful, for example to provide feedback in job interview training, or in psychotherapeutic scenarios. However, at present no method to automatically classify different emotion regulation strategies in a cross-user scenario exists. At the same time, recent studies showed that instruction-tuned Large Language Models (LLMs) can reach impressive performance across a variety of affect recognition tasks such as categorical emotion recognition or sentiment analysis. While these results are promising, it remains unclear to what extent the representational power of LLMs can be utilized in the more subtle task of classifying users' internal emotion regulation strategy. To close this gap, we make use of the recently introduced \textsc{Deep} corpus for modeling the social display of the emotion shame, where each point in time is annotated with one of seven different emotion regulation classes. We fine-tune Llama2-7B as well as the recently introduced Gemma model using Low-rank Optimization on prompts generated from different sources of information on the \textsc{Deep} corpus. These include verbal and nonverbal behavior, person factors, as well as the results of an in-depth interview after the interaction. Our results show, that a fine-tuned Llama2-7B LLM is able to classify the utilized emotion regulation strategy with high accuracy (0.84) without needing access to data from post-interaction interviews. This represents a significant improvement over previous approaches based on Bayesian Networks and highlights the importance of modeling verbal behavior in emotion regulation., Comment: Accepted to ACII'24

Electric vehicle (EV) batteries are often underutilized. Vehicle-to-grid (V2G) services can tap into this unused potential, but increased battery usage may lead to more degradation and shorter battery life. This paper substantiates the advantages of providing load-shifting V2G services when the battery is aging, primarily due to calendar aging mechanisms (active degradation mechanisms while the battery is not used). After parameterizing a physics-based digital-twin for three different dominant degradation patterns within the same chemistry (NMC), we introduce a novel metric for evaluating the benefit and associated harm of V2G services: \textit{throughput gained versus days lost (TvD)} and show its strong relationship to the ratio of loss of lithium inventory (LLI) due to calendar aging to the total LLI ($\text{LLI}_\text{Cal}/\text{LLI}$). Our results that focus systematically on degradation mechanisms via lifetime simulation of digital-twins significantly expand prior work that was primarily concentrating on quantifying and reducing the degradation of specific cells by probing their usage and charging patterns. Examining various cell chemistries and conditions enables us to take a broader view and determine whether a particular battery pack is appropriate for load-shifting (V2G) services. Our research demonstrates that the decision "to V2G or not to V2G" can be made by merely estimating the portion of capacity deterioration caused by calendar aging. Specifically, TvD is primarily influenced by the chemistry of cells and the environmental temperature where the car is parked, while the usage intensity and charging patterns of EVs play a lesser role.

Micro-objects levitated in a vacuum are an exciting platform for precision sensing due to their low dissipation motion and the potential for control at the quantum level. Arrays of such sensors would allow noise cancellation, directionality, increased sensitivity and in the quantum regime the potential to exploit correlation and entanglement. We use neuromorphic detection via a single event-based camera to record the motion of an array of levitated microspheres. We present a truly scalable method for arbitrary multiparticle control by implementing real-time feedback to cool the motion of three objects simultaneously, the first demonstration of neuromorphic sensing for real-time control at the microscale.

We present the first results of the holographic beam mapping program for the Canadian Hydrogen Intensity Mapping Experiment (CHIME). We describe the implementation of the holographic technique as adapted for CHIME, and introduce the processing pipeline which prepares the raw holographic timestreams for analysis of beam features. We use data from six bright sources across the full 400-800\,MHz observing band of CHIME to provide measurements of the co-polar and cross-polar beam response of CHIME in both amplitude and phase for the 1024 dual-polarized feeds instrumented on CHIME. In addition, we present comparisons with independent probes of the CHIME beam which indicate the presence of polarized beam leakage in CHIME. Holographic measurements of the CHIME beam have already been applied in science with CHIME, e.g. in estimating detection significance of far sidelobe FRBs, and in validating the beam models used for CHIME's first detections of \tcm emission (in cross-correlation with measurements of large-scale structure from galaxy surveys and the Lyman-$\alpha$ forest). Measurements presented in this paper, and future holographic results, will provide a unique data set to characterize the CHIME beam and improve the experiment's prospects for a detection of BAO., Comment: submitted to ApJ

Existing retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually sufficient. However, many complex real-world queries require in-depth reasoning to identify relevant documents that go beyond surface form matching. For example, finding documentation for a coding question requires understanding the logic and syntax of the functions involved. To better benchmark retrieval on such challenging queries, we introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents. BRIGHT is constructed from the 1,398 real-world queries collected from diverse domains (such as economics, psychology, robotics, software engineering, earth sciences, etc.), sourced from naturally occurring or carefully curated human data. Extensive evaluation reveals that even state-of-the-art retrieval models perform poorly on BRIGHT. The leading model on the MTEB leaderboard [38 ], which achieves a score of 59.0 nDCG@10,2 produces a score of nDCG@10 of 18.0 on BRIGHT. We further demonstrate that augmenting queries with Chain-of-Thought reasoning generated by large language models (LLMs) improves performance by up to 12.2 points. Moreover, BRIGHT is robust against data leakage during pretraining of the benchmarked models as we validate by showing similar performance even when documents from the benchmark are included in the training data. We believe that BRIGHT paves the way for future research on retrieval systems in more realistic and challenging settings. Our code and data are available at https://brightbenchmark.github.io., Comment: 50 pages

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

What metrics should guide the development of more realistic models of the brain? One proposal is to quantify the similarity between models and brains using methods such as linear regression, Centered Kernel Alignment (CKA), and angular Procrustes distance. To better understand the limitations of these similarity measures we analyze neural activity recorded in five experiments on nonhuman primates, and optimize synthetic datasets to become more similar to these neural recordings. How similar can these synthetic datasets be to neural activity while failing to encode task relevant variables? We find that some measures like linear regression and CKA, differ from angular Procrustes, and yield high similarity scores even when task relevant variables cannot be linearly decoded from the synthetic datasets. Synthetic datasets optimized to maximize similarity scores initially learn the first principal component of the target dataset, but angular Procrustes captures higher variance dimensions much earlier than methods like linear regression and CKA. We show in both theory and simulations how these scores change when different principal components are perturbed. And finally, we jointly optimize multiple similarity scores to find their allowed ranges, and show that a high angular Procrustes similarity, for example, implies a high CKA score, but not the converse., Comment: 16 pages, 6 figures

Federated learning (FL) enables a collaborative environment for training machine learning models without sharing training data between users. This is typically achieved by aggregating model gradients on a central server. Decentralized federated learning is a rising paradigm that enables users to collaboratively train machine learning models in a peer-to-peer manner, without the need for a central aggregation server. However, before applying decentralized FL in real-world use training environments, nodes that deviate from the FL process (Byzantine nodes) must be considered when selecting an aggregation function. Recent research has focused on Byzantine-robust aggregation for client-server or fully connected networks, but has not yet evaluated such aggregation schemes for complex topologies possible with decentralized FL. Thus, the need for empirical evidence of Byzantine robustness in differing network topologies is evident. This work investigates the effects of state-of-the-art Byzantine-robust aggregation methods in complex, large-scale network structures. We find that state-of-the-art Byzantine robust aggregation strategies are not resilient within large non-fully connected networks. As such, our findings point the field towards the development of topology-aware aggregation schemes, especially necessary within the context of large scale real-world deployment., Comment: 8 pages, 6 figures

We present beam measurements of the CHIME telescope using a radio calibration source deployed on a drone payload. During test flights, the pulsing calibration source and the telescope were synchronized to GPS time, enabling in-situ background subtraction for the full $N^{2}$ visibility matrix for one CHIME cylindrical reflector. We use the autocorrelation products to estimate the primary beam width and centroid location, and compare these quantities to solar transit measurements and holographic measurements where they overlap on the sky. We find that the drone, solar, and holography data have similar beam parameter evolution across frequency and both spatial coordinates. This paper presents the first drone-based beam measurement of a large cylindrical radio interferometer. Furthermore, the unique analysis and instrumentation described in this paper lays the foundation for near-field measurements of experiments like CHIME., Comment: Submitted to IEEE OJAP June 30, 2024

Scalable oversight protocols aim to enable humans to accurately supervise superhuman AI. In this paper we study debate, where two AI's compete to convince a judge; consultancy, where a single AI tries to convince a judge that asks questions; and compare to a baseline of direct question-answering, where the judge just answers outright without the AI. We use large language models (LLMs) as both AI agents and as stand-ins for human judges, taking the judge models to be weaker than agent models. We benchmark on a diverse range of asymmetries between judges and agents, extending previous work on a single extractive QA task with information asymmetry, to also include mathematics, coding, logic and multimodal reasoning asymmetries. We find that debate outperforms consultancy across all tasks when the consultant is randomly assigned to argue for the correct/incorrect answer. Comparing debate to direct question answering, the results depend on the type of task: in extractive QA tasks with information asymmetry debate outperforms direct question answering, but in other tasks without information asymmetry the results are mixed. Previous work assigned debaters/consultants an answer to argue for. When we allow them to instead choose which answer to argue for, we find judges are less frequently convinced by the wrong answer in debate than in consultancy. Further, we find that stronger debater models increase judge accuracy, though more modestly than in previous studies., Comment: 15 pages (53 including appendices). V2: minor correction to Figure 3; add Figure A.9 comparing open vs assigned consultancy; add a reference

AI agents are an exciting new research direction, and agent development is driven by benchmarks. Our analysis of current agent benchmarks and evaluation practices reveals several shortcomings that hinder their usefulness in real-world applications. First, there is a narrow focus on accuracy without attention to other metrics. As a result, SOTA agents are needlessly complex and costly, and the community has reached mistaken conclusions about the sources of accuracy gains. Our focus on cost in addition to accuracy motivates the new goal of jointly optimizing the two metrics. We design and implement one such optimization, showing its potential to greatly reduce cost while maintaining accuracy. Second, the benchmarking needs of model and downstream developers have been conflated, making it hard to identify which agent would be best suited for a particular application. Third, many agent benchmarks have inadequate holdout sets, and sometimes none at all. This has led to agents that are fragile because they take shortcuts and overfit to the benchmark in various ways. We prescribe a principled framework for avoiding overfitting. Finally, there is a lack of standardization in evaluation practices, leading to a pervasive lack of reproducibility. We hope that the steps we introduce for addressing these shortcomings will spur the development of agents that are useful in the real world and not just accurate on benchmarks.

Today, it would be difficult for us to live a full life without polymers, especially in medicine, where its applicability is constantly expanding, giving satisfactory results without any harm effects on health. This study focused on the formation of hexagonal domains doped with AgNPs using a KrF excimer laser ({\lambda}=248 nm) on the polyetheretherketone (PEEK) surface that acts as an unfailing source of the antibacterial agent - silver. The hexagonal structure was formed with a grid placed in front of the incident laser beam. Surfaces with immobilized silver nanoparticles (AgNPs) were observed by AFM and SEM. Changes in surface chemistry were studied by XPS. To determine the concentration of released Ag+ ions, ICP-MS analysis was used. The antibacterial tests proved the antibacterial efficacy of Ag-doped PEEK composites against Escherichia coli and Staphylococcus aureus as the most common pathogens. Because AgNPs are also known for their strong toxicity, we also included cytotoxicity tests in this study. The findings presented here contribute to the advancement of materials design in the biomedical field, offering a novel starting point for combating bacterial infections through the innovative integration of AgNPs into inert synthetic polymers.

We demonstrate sub-recoil Sisyphus cooling using the long-lived $^{3}\mathrm{P}_{0}$ clock state in alkaline-earth-like ytterbium. A 1388 nm optical standing wave nearly resonant with the $^{3}\textrm{P}_{0}$$\,\rightarrow$$\,^{3}\textrm{D}_{1}$ transition creates a spatially periodic light shift of the $^{3}\textrm{P}_{0}$ clock state. Following excitation on the ultranarrow clock transition, we observe Sisyphus cooling in this potential, as the light shift is correlated with excitation to $^{3}\textrm{D}_{1}$ and subsequent spontaneous decay to the $^{1}\textrm{S}_{0}$ ground state. We observe that cooling enhances the loading efficiency of atoms into a 759 nm magic-wavelength one-dimensional (1D) optical lattice, as compared to standard Doppler cooling on the $^{1}\textrm{S}_{0}$$\,\rightarrow\,$$^{3}\textrm{P}_{1}$ transition. Sisyphus cooling yields temperatures below 200 nK in the weakly confined, transverse dimensions of the 1D optical lattice. These lower temperatures improve optical lattice clocks by facilitating the use of shallow lattices with reduced light shifts, while retaining large atom numbers to reduce the quantum projection noise. This Sisyphus cooling can be pulsed or continuous and is applicable to a range of quantum metrology applications., Comment: 8 pages, 6 figures

We provide an a priori analysis of a certain class of numerical methods, commonly referred to as collocation methods, for solving elliptic boundary value problems. They begin with information in the form of point values of the right side f of such equations and point values of the boundary function g and utilize only this information to numerically approximate the solution u of the Partial Differential Equation (PDE). For such a method to provide an approximation to u with guaranteed error bounds, additional assumptions on f and g, called model class assumptions, are needed. We determine the best error (in the energy norm) of approximating u, in terms of the number of point samples m, under all Besov class model assumptions for the right hand side $f$ and boundary g. We then turn to the study of numerical procedures and asks whether a proposed numerical procedure (nearly) achieves the optimal recovery error. We analyze numerical methods which generate the numerical approximation to $u$ by minimizing a specified data driven loss function over a set $\Sigma$ which is either a finite dimensional linear space, or more generally, a finite dimensional manifold. We show that the success of such a procedure depends critically on choosing a correct data driven loss function that is consistent with the PDE and provides sharp error control. Based on this analysis a loss function $L^*$ is proposed. We also address the recent methods of Physics Informed Neural Networks (PINNs). Minimization of the new loss $L^*$ over neural network spaces $\Sigma$ is referred to as consistent PINNs (CPINNs). We prove that CPINNs provides an optimal recovery of the solution $u$, provided that the optimization problem can be numerically executed and $\Sigma$ has sufficient approximation capabilities. Finally, numerical examples illustrating the benefits of the CPINNs are given., Comment: 48 pages

Self-driving vehicles have the potential to reduce accidents and fatalities on the road. Many production vehicles already come equipped with basic self-driving capabilities, but have trouble following lanes in adverse lighting and weather conditions. Therefore, we develop, analyze, and evaluate two vehicular lane-keeping algorithms under dynamic weather conditions using a combined deep learning- and hand-crafted approach and an end-to-end deep learning approach. We use image segmentation- and linear-regression based deep learning to drive the vehicle toward the center of the lane, measuring the amount of laps completed, average speed, and average steering error per lap. Our hybrid model completes more laps than our end-to-end deep learning model. In the future, we are interested in combining our algorithms to form one cohesive approach to lane-following., Comment: Supported by the National Science Foundation under Grants No. 2150292 and 2150096

Diagnosing imbalances in capacity and resistance within parallel-connected cells in battery packs is critical for battery management and fault detection, but it is challenging given that individual currents flowing into each cell are often unmeasured. This work introduces a novel method useful for identifying imbalances in capacity and resistance within a pair of parallel-connected cells using only voltage and current measurements from the pair. Our method utilizes differential voltage analysis (DVA) when the pair is under constant current discharge and demonstrates that features of the pair's differential voltage curve (dV/dQ), namely its mid-to-high SOC dV/dQ peak's height and skewness, are sensitive to imbalances in capacity and resistance. We analyze and explain how and why these dV/dQ peak shape features change in response to these imbalances, highlighting that the underlying current imbalance dynamics resulting from these imbalances contribute to these changes. Ultimately, we demonstrate that dV/dQ peak shape features can identify the product of capacity imbalance and resistance imbalance, but cannot uniquely identify the imbalances. This work lays the groundwork for identifying imbalances in capacity and resistance in parallel-connected cell groups in battery packs, where commonly only a single current sensor is placed for each parallel cell group., Comment: Accepted to American Control Conference (ACC), Toronto, Canada, July 2024

A direct consequence of Faraday rotation is that the polarized radio sky does not resemble the total intensity sky at long wavelengths. We analyze G137+7, which is undetectable in total intensity but appears as a depolarization feature. We use the first polarization maps from the Canadian Hydrogen Intensity Mapping Experiment. Our $400-729$ MHz bandwidth and angular resolution, $17'$ to $30'$, allow us to use Faraday synthesis to analyze the polarization structure. In polarized intensity and polarization angle maps, we find a "tail" extending $10^\circ$ from the "head" and designate the combined object the "tadpole". Similar polarization angles, distinct from the background, indicate that the head and tail are physically associated. The head appears as a depolarized ring in single channels, but wideband observations show that it is a Faraday rotation feature. Our investigations of H I and H$\alpha$ find no connections to the tadpole. The tail suggests motion of either the gas or an ionizing star through the ISM; the B2(e) star HD 20336 is a candidate. While the head features a coherent, $\sim -8$ rad m$^2$ Faraday depth, Faraday synthesis also identifies multiple components in both the head and tail. We verify the locations of the components in the spectra using QU fitting. Our results show that $\sim$octave-bandwidth Faraday rotation observations at $\sim 600$ MHz are sensitive to low-density ionized or partially-ionized gas which is undetectable in other tracers., Comment: ApJ in press. Replacement corrects typographical error in equation 6

The battery state of health (SOH) based on capacity fade and resistance increase is not sufficient for predicting Remaining Useful life (RUL). The electrochemical community blames the path-dependency of the battery degradation mechanisms for our inability to forecast the degradation. The control community knows that the path-dependency is addressed by full state estimation. We show that even the electrode-specific SOH (eSOH) estimation is not enough to fully define the degradation states by simulating infinite possible degradation trajectories and remaining useful lives (RUL) from a unique eSOH. We finally define the deepSOH states that capture the individual contributions of all the common degradation mechanisms, namely, SEI, plating, and mechanical fracture to the loss of lithium inventory. We show that the addition of cell expansion measurement may allow us to estimate the deepSOH and predict the remaining useful life., Comment: 6 pages, 3 figures

During our spectroscopic survey of central stars of faint planetary nebulae (PNe), we found that the nucleus of Abell 57 exhibits strong nebular emission lines. Using synthetic narrow-band images, we show that the emission arises from an unresolved compact emission knot (CEK) coinciding with the hot (90,000 K) central star. Thus Abell 57 belongs to the rare class of "EGB 6-type" PNe, characterized by dense emission cores. Photometric data show that the nucleus exhibits a near-IR excess, due to a dusty companion body with the luminosity of an M0 dwarf but a temperature of ~1800 K. Emission-line analysis reveals that the CEK is remarkably dense (electron density ~1.6x10**7 cm**-3), and has a radius of only ~4.5 AU. The CEK suffers considerably more reddening than the central star, which itself is more reddened than the surrounding PN. These puzzles may suggest an interaction between the knot and central star; however, Hubble Space Telescope imaging of EGB 6 itself shows that its CEK lies more than ~125 AU from the PN nucleus. We discuss a scenario in which a portion of the AGB wind that created the PN was captured into a dust cloud around a distant stellar companion; this cloud has survived to the present epoch, and has an atmosphere photoionized by radiation from the hot central star. However, in this picture EGB 6-type nuclei should be relatively common, yet they are actually extremely rare; thus they may arise from a different transitory phenomenon. We suggest future observations of Abell 57 that may help unravel its mysteries., Comment: Accepted by Astrophysical Journal

Large language models (LLMs) are becoming bigger to boost performance. However, little is known about how explainability is affected by this trend. This work explores LIME explanations for DeBERTaV3 models of four different sizes on natural language inference (NLI) and zero-shot classification (ZSC) tasks. We evaluate the explanations based on their faithfulness to the models' internal decision processes and their plausibility, i.e. their agreement with human explanations. The key finding is that increased model size does not correlate with plausibility despite improved model performance, suggesting a misalignment between the LIME explanations and the models' internal processes as model size increases. Our results further suggest limitations regarding faithfulness metrics in NLI contexts., Comment: Published at ICLR 2024 Workshop on Secure and Trustworthy Large Language Models

We present the largest optical photometry compilation of Gamma-Ray Bursts (GRBs) with redshifts ($z$). We include 64813 observations of 535 events (including upper limits) from 28 February 1997 up to 18 August 2023. We also present a user-friendly web tool \textit{grbLC} which allows users the visualization of photometry, coordinates, redshift, host galaxy extinction, and spectral indices for each event in our database. Furthermore, we have added a Gamma Ray Coordinate Network (GCN) scraper that can be used to collect data by gathering magnitudes from the GCNs. The web tool also includes a package for uniformly investigating colour evolution. We compute the optical spectral indices for 138 GRBs for which we have at least 4 filters at the same epoch in our sample and craft a procedure to distinguish between GRBs with and without colour evolution. By providing a uniform format and repository for the optical catalogue, this web-based archive is the first step towards unifying several community efforts to gather the photometric information for all GRBs with known redshifts. This catalogue will enable population studies by providing light curves (LCs) with better coverage since we have gathered data from different ground-based locations. Consequently, these LCs can be used to train future LC reconstructions for an extended inference of the redshift. The data gathering also allows us to fill some of the orbital gaps from Swift in crucial points of the LCs, e.g., at the end of the plateau emission or where a jet break is identified., Comment: 20 pages, 16 figures, 2 tables. Submitted to MNRAS, this version matches the third revision. The Online Materials and data will be available after the publication

We revisit the long-studied radial velocity (RV) target HD26965 using recent observations from the NASA-NSF 'NEID' precision Doppler facility. Leveraging a suite of classical activity indicators, combined with line-by-line RV analyses, we demonstrate that the claimed 45-day signal previously identified as a planet candidate is most likely an activity-induced signal. Correlating the bulk (spectrally-averaged) RV with canonical line activity indicators confirms a multi-day 'lag' between the observed activity indicator time series and the measured RV. When accounting for this lag, we show that much of the observed RV signal can be removed by a linear detrending of the data. Investigating activity at the line-by-line level, we find a depth-dependent correlation between individual line RVs and the bulk RVs, further indicative of periodic suppression of convective blueshift causing the observed RV variability, rather than an orbiting planet. We conclude that the combined evidence of the activity correlations and depth dependence is consistent with a radial velocity signature dominated by a rotationally-modulated activity signal at a period of $\sim$42 days. We hypothesize that this activity signature is due to a combination of spots and convective blueshift suppression. The tools applied in our analysis are broadly applicable to other stars, and could help paint a more comprehensive picture of the manifestations of stellar activity in future Doppler RV surveys., Comment: 25 pages, 13 figures. Accepted in AJ