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This report emphasizes the importance of state actions in improving educational opportunities for historically marginalized students, particularly those with disabilities. It highlights the existing achievement gaps and challenges faced by students with disabilities, especially those from minority groups. The report suggests that states have a critical role in strengthening charter schools' capacity to meet the needs of students with disabilities, but it also points out that the current state actions in this area have been limited, primarily focusing on ensuring compliance with laws rather than addressing resource gaps or improving educational outcomes.

We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes. Most conventional structure from motion and monocular SLAM techniques assume input videos that feature predominantly static scenes with large amounts of parallax. Such methods tend to produce erroneous estimates in the absence of these conditions. Recent neural network-based approaches attempt to overcome these challenges; however, such methods are either computationally expensive or brittle when run on dynamic videos with uncontrolled camera motion or unknown field of view. We demonstrate the surprising effectiveness of a deep visual SLAM framework: with careful modifications to its training and inference schemes, this system can scale to real-world videos of complex dynamic scenes with unconstrained camera paths, including videos with little camera parallax. Extensive experiments on both synthetic and real videos demonstrate that our system is significantly more accurate and robust at camera pose and depth estimation when compared with prior and concurrent work, with faster or comparable running times. See interactive results on our project page: https://mega-sam.github.io/

We introduce the Bias-free Extragalactic Analysis for Cosmic Origins with NIRCam (BEACON) survey, a JWST Cycle2 program allocated up to 600 pure-parallel hours of observations. BEACON explores high-latitude areas of the sky with JWST/NIRCam over $\sim100$ independent sightlines, totaling $\sim0.3$deg$^2$, reaching a median F444W depth of $\approx28.2$AB mag (5$\sigma$). Based on existing JWST observations in legacy fields, we estimate that BEACON will photometrically identify 25--150 galaxies at $z>10$ and 500--1000 at $z\sim7$--10 uniquely enabled by an efficient multiple filter configuration spanning $0.9$--5.0$\mu$m. The expected sample size of $z>10$ galaxies will allow us to obtain robust number density estimates and to discriminate between different models of early star formation. In this paper, we present an overview of the survey design and initial results using the first 19 fields. We present 129 galaxy candidates at $z>7$ identified in those fields, including 11 galaxies at $z>10$ and several UV-luminous ($M_{\rm UV}<-21$mag) galaxies at $z\sim8$. The number densities of $z<13$ galaxies inferred from the initial fields are overall consistent with those in the literature. Despite reaching a considerably large volume ($\sim10^5$Mpc$^3$), however, we find no galaxy candidates at $z>13$, providing us with a complimentary insight into early galaxy evolution with minimal cosmic variance. We publish imaging and catalog data products for these initial fields. Upon survey completion, all BEACON data will be coherently processed and distributed to the community along with catalogs for redshift and other physical quantities., Comment: Submitted to ApJ; DR1 data release will be made on the team website (https://beacon-jwst.github.io)

Constructing 3D representations of object geometry is critical for many downstream manipulation tasks. These representations must be built from potentially noisy partial observations. In this work we focus on the problem of reconstructing a multi-object scene from a single RGBD image. Current deep learning approaches to this problem can be brittle to noisy real world observations and out-of-distribution objects. Other approaches that do not rely on training data cannot accurately infer the backside of objects. We propose BRRP, a reconstruction method that can leverage preexisting mesh datasets to build an informative prior during robust probabilistic reconstruction. In order to make our method more efficient, we introduce the concept of retrieval-augmented prior, where we retrieve relevant components of our prior distribution during inference. Our method produces a distribution over object shape that can be used for reconstruction or measuring uncertainty. We evaluate our method in both procedurally generated scenes and in real world scenes. We show our method is more robust than a deep learning approach while being more accurate than a method with an uninformative prior.

Video generation has achieved impressive quality, but it still suffers from artifacts such as temporal inconsistency and violation of physical laws. Leveraging 3D scenes can fundamentally resolve these issues by providing precise control over scene entities. To facilitate the easy generation of diverse photorealistic scenes, we propose Scene Copilot, a framework combining large language models (LLMs) with a procedural 3D scene generator. Specifically, Scene Copilot consists of Scene Codex, BlenderGPT, and Human in the loop. Scene Codex is designed to translate textual user input into commands understandable by the 3D scene generator. BlenderGPT provides users with an intuitive and direct way to precisely control the generated 3D scene and the final output video. Furthermore, users can utilize Blender UI to receive instant visual feedback. Additionally, we have curated a procedural dataset of objects in code format to further enhance our system's capabilities. Each component works seamlessly together to support users in generating desired 3D scenes. Extensive experiments demonstrate the capability of our framework in customizing 3D scenes and video generation., Comment: Videos are available at our project page: https://abolfazl-sh.github.io/Scene_co-pilot_site/

Utility-preserving data privatization is of utmost importance for data-producing agencies. The popular noise-addition privacy mechanism distorts autocorrelation patterns in time series data, thereby marring utility; in response, McElroy et al. (2023) introduced all-pass filtering (FLIP) as a utility-preserving time series data privatization method. Adapting this concept to multivariate data is more complex, and in this paper we propose a multivariate all-pass (MAP) filtering method, employing an optimization algorithm to achieve the best balance between data utility and privacy protection. To test the effectiveness of our approach, we apply MAP filtering to both simulated and real data, sourced from the U.S. Census Bureau's Quarterly Workforce Indicator (QWI) dataset.

Coordinating the motion of robots with high degrees of freedom (DoF) to grasp objects gives rise to many challenges. In this paper, we propose a novel imitation learning approach to learn a policy that directly predicts 23 DoF grasp trajectories from a partial point cloud provided by a single, fixed camera. At the core of the approach is a second-order geometric-based model of behavioral dynamics. This Neural Geometric Fabric (NGF) policy predicts accelerations directly in joint space. We show that our policy is capable of generalizing to novel objects, and combine our policy with a geometric fabric motion planner in a loop to generate stable grasping trajectories. We evaluate our approach on a set of three different objects, compare different policy structures, and run ablation studies to understand the importance of different object encodings for policy learning., Comment: IEEE International Conference on Robotics and Automation (ICRA) Workshop on Geometric Representations 2023

State-of-the-art multimodal web agents, powered by Multimodal Large Language Models (MLLMs), can autonomously execute many web tasks by processing user instructions and interacting with graphical user interfaces (GUIs). Current strategies for building web agents rely on (i) the generalizability of underlying MLLMs and their steerability via prompting, and (ii) large-scale fine-tuning of MLLMs on web-related tasks. However, web agents still struggle to automate tasks on unseen websites and domains, limiting their applicability to enterprise-specific and proprietary platforms. Beyond generalization from large-scale pre-training and fine-tuning, we propose building agents for few-shot adaptability using human demonstrations. We introduce the AdaptAgent framework that enables both proprietary and open-weights multimodal web agents to adapt to new websites and domains using few human demonstrations (up to 2). Our experiments on two popular benchmarks -- Mind2Web & VisualWebArena -- show that using in-context demonstrations (for proprietary models) or meta-adaptation demonstrations (for meta-learned open-weights models) boosts task success rate by 3.36% to 7.21% over non-adapted state-of-the-art models, corresponding to a relative increase of 21.03% to 65.75%. Furthermore, our additional analyses (a) show the effectiveness of multimodal demonstrations over text-only ones, (b) shed light on the influence of different data selection strategies during meta-learning on the generalization of the agent, and (c) demonstrate the effect of number of few-shot examples on the web agent's success rate. Overall, our results unlock a complementary axis for developing widely applicable multimodal web agents beyond large-scale pre-training and fine-tuning, emphasizing few-shot adaptability., Comment: 18 pages, 3 figures, an abridged version to appear in NeurIPS 2024 AFM Workshop

We present a differentiable optimization-based framework for Task and Motion Planning (TAMP) that is massively parallelizable on GPUs, enabling thousands of sampled seeds to be optimized simultaneously. Existing sampling-based approaches inherently disconnect the parameters by generating samples for each independently and combining them through composition and rejection, while optimization-based methods struggle with highly non-convex constraints and local optima. Our method treats TAMP constraint satisfaction as optimizing a batch of particles, each representing an assignment to a plan skeleton's continuous parameters. We represent the plan skeleton's constraints using differentiable cost functions, enabling us to compute the gradient of each particle and update it toward satisfying solutions. Our use of GPU parallelism better covers the parameter space through scale, increasing the likelihood of finding the global optima by exploring multiple basins through global sampling. We demonstrate that our algorithm can effectively solve a highly constrained Tetris packing problem using a Franka arm in simulation and deploy our planner on a real robot arm. Website: https://williamshen-nz.github.io/gpu-tamp, Comment: 2-page paper presented at the CoRL 2024 Workshop on Differentiable Optimization Everywhere

Millimeter-wave refracting telescopes targeting the degree-scale structure of the cosmic microwave background (CMB) have recently grown to diffraction-limited apertures of over 0.5 meters. These instruments are entirely housed in vacuum cryostats to support their sub-kelvin bolometric detectors and to minimize radiative loading from thermal emission due to absorption loss in their transmissive optical elements. The large vacuum window is the only optical element in the system at ambient temperature, and therefore minimizing loss in the window is crucial for maximizing detector sensitivity. This motivates the use of low-loss polymer materials and a window as thin as practicable. However, the window must simultaneously meet the requirement to keep sufficient vacuum, and therefore must limit gas permeation and remain mechanically robust against catastrophic failure under pressure. We report on the development of extremely thin composite polyethylene window technology that meets these goals. Two windows have been deployed for two full observing seasons on the BICEP3 and BA150 CMB telescopes at the South Pole. On BICEP3, the window has demonstrated a 6% improvement in detector sensitivity., Comment: 20 pages, 12 figures, 4 tables

We provide an explicit construction of the arboreal Galois group for the postcritically finite polynomial $f(z) = z^2 +c$, where $c$ belongs to some arbitrary field of characteristic not equal to $2$. In this first of two papers, we consider the case that the critical point is periodic.

In this work, we investigate the assumptions regarding spacetime backgrounds underlying the classical double copy. We argue (contrary to the norm) that single-copy fields naturally constructed on the original curved background metric are only interpretable on a flat metric when such a well-defined limit exists, for which Kerr--Schild coordinates offer a natural choice. As an explicit example where such a distinction matters, we initiate an exploration of single-copies for the G\"odel universe. This metric lacks a (geodesic) Kerr--Schild representation yet is Petrov type-D, meaning the technology of the ``Weyl double copy" may be utilized. The Weyl derived single copy has many desirable features, including matching the defining properties of the spacetime, and being sourced by the mixed Ricci tensor just as Kerr--Schild single copies are. To compare, we propose a sourced flat-space single-copy interpretation for the G\"odel metric by leveraging its symmetries, and find that this proposal lacks the defining properties of the spacetime, and is not consistent with the flat limit of our curved-space single copy. Notably, this inconsistency does not occur in Kerr--Schild metrics. Our curved-space single copy also lead to the same electromagnetic analogue of the G\"odel universe found separately through tidal force analogies, opening a new avenue of exploration between the double copy and gravitoelectromagnetism programs., Comment: 29 pages, 2 figures

We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavior. We discuss framing common problems in fields like healthcare, finance, and e-commerce as sequence modeling tasks, and address challenges related to constructing coherent sequences from fragmented data and disentangling complex behavior patterns. We present a framework for sequence modeling using Ensembles of Hidden Markov Models, which are lightweight, interpretable, and efficient. Our ensemble-based scoring method enables robust comparison across sequences of different lengths and enhances performance in scenarios with imbalanced or scarce data. The framework scales in real-world scenarios, is compatible with downstream feature-based modeling, and is applicable in both supervised and unsupervised learning settings. We demonstrate the effectiveness of our method with results on a longitudinal human behavior dataset.

In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training procedure based on the variational autoencoder (VAE) framework for modeling time-series as a neural SDE. Unlike existing algorithms training neural SDEs as VAEs, our proposed algorithm only necessitates a Gaussian prior of the initial state of the latent stochastic process, rather than a Wiener process prior on the entire latent stochastic process. We develop two methodologies for modeling and estimating change points in time-series data with distribution shifts. Our iterative algorithm alternates between updating neural SDE parameters and updating the change points based on either a maximum likelihood-based approach or a change point detection algorithm using the sequential likelihood ratio test. We provide a theoretical analysis of this proposed change point detection scheme. Finally, we present an empirical evaluation that demonstrates the expressive power of our proposed model, showing that it can effectively model both classical parametric SDEs and some real datasets with distribution shifts.

Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity rating process. This paper explores the application of LLMs to generate multi-horizon stock ratings by ingesting diverse datasets. Traditional stock rating methods rely heavily on the expertise of financial analysts, and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. Our study addresses these issues by leveraging LLMs to improve the accuracy and consistency of stock ratings. Additionally, we assess the efficacy of using different data modalities with LLMs for the financial domain. We utilize varied datasets comprising fundamental financial, market, and news data from January 2022 to June 2024, along with GPT-4-32k (v0613) (with a training cutoff in Sep. 2021 to prevent information leakage). Our results show that our benchmark method outperforms traditional stock rating methods when assessed by forward returns, specially when incorporating financial fundamentals. While integrating news data improves short-term performance, substituting detailed news summaries with sentiment scores reduces token use without loss of performance. In many cases, omitting news data entirely enhances performance by reducing bias. Our research shows that LLMs can be leveraged to effectively utilize large amounts of multimodal financial data, as showcased by their effectiveness at the stock rating prediction task. Our work provides a reproducible and efficient framework for generating accurate stock ratings, serving as a cost-effective alternative to traditional methods. Future work will extend to longer timeframes, incorporate diverse data, and utilize newer models for enhanced insights., Comment: 9 pages, 5 figures, ICAIF24: 5th ACM International Conference on AI in Finance

Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilized nighttime light data to predict the impact of hurricanes on economic losses. However, prior work did not provide a thorough analysis of the impact of combining different techniques for pre-processing nighttime light (NTL) data. Addressing this gap, our research explores a variety of NTL pre-processing techniques, including value thresholding, built masking, and quality filtering and imputation, applied to two distinct datasets, VSC-NTL and VNP46A2, at the zip code level. Experiments evaluate the correlation of the denoised NTL data with economic damages of Category 4-5 hurricanes in Florida. They reveal that the quality masking and imputation technique applied to VNP46A2 show a substantial correlation with economic damage data.

The number density and redshift evolution of optically selected galaxy clusters offer an independent measurement of the amplitude of matter fluctuations, $S_8$. However, recent results have shown that clusters chosen by the redMaPPer algorithm show richness-dependent biases that affect the weak lensing signals and number densities of clusters, increasing uncertainty in the cluster mass calibration and reducing their constraining power. In this work, we evaluate an alternative cluster proxy, outskirt stellar mass, $M_{\textrm{out}}$, defined as the total stellar mass within a $[50,100]$ kpc envelope centered on a massive galaxy. This proxy exhibits scatter comparable to redMaPPer richness, $\lambda$, but is less likely to be subject to projection effects. We compare the Dark Energy Survey Year 3 redMaPPer cluster catalog with a $M_{\textrm{out}}$ selected cluster sample from the Hyper-Suprime Camera survey. We use weak lensing measurements to quantify and compare the scatter of $M_{\textrm{out}}$ and $\lambda$ with halo mass. Our results show $M_{\textrm{out}}$ has a scatter consistent with $\lambda$, with a similar halo mass dependence, and that both proxies contain unique information about the underlying halo mass. We find $\lambda$-selected samples introduce features into the measured $\Delta \Sigma$ signal that are not well fit by a log-normal scatter only model, absent in $M_{\textrm{out}}$ selected samples. Our findings suggest that $M_{\textrm{out}}$ offers an alternative for cluster selection with more easily calibrated selection biases, at least at the generally lower richnesses probed here. Combining both proxies may yield a mass proxy with a lower scatter and more tractable selection biases, enabling the use of lower mass clusters in cosmology. Finally, we find the scatter and slope in the $\lambda-M_{\textrm{out}}$ scaling relation to be $0.49 \pm 0.02$ and $0.38 \pm 0.09$., Comment: 22 pages, 8 figures, 4 tables, submitted to PRD

Fractures and vents in the ice crust of Europa, exposing the sub-surface ocean to the vacuum, might be responsible for the generation of planetary-scale water-vapor plumes. During its passage through the ice, the plume vapor is expected to partially condense on the walls, depositing until the vent is sealed. We develop a lumped-parameter model to analyze the sealing time scales. Neglecting all other possible mechanisms (water spillage, compression forces, etc.), we find shutting-off times compatible with the 7-hour plume observed in 2012 by the Hubble Space Telescope, suggesting that vapor deposition alone could have been responsible for sealing the vent. A map of sealing times vs. plume density, mass flow rate and aperture areas is given. Plume quantities from the literature are analyzed and compared to our results. For a given plume density/mass flow rate, small apertures would be sealed quickly by molecular deposition and are thus incompatible with observations.

Higher-order spectra (or polyspectra), defined as the Fourier Transform of a stationary process' autocumulants, are useful in the analysis of nonlinear and non Gaussian processes. Polyspectral means are weighted averages over Fourier frequencies of the polyspectra, and estimators can be constructed from analogous weighted averages of the higher-order periodogram (a statistic computed from the data sample's discrete Fourier Transform). We derive the asymptotic distribution of a class of polyspectral mean estimators, obtaining an exact expression for the limit distribution that depends on both the given weighting function as well as on higher-order spectra. Secondly, we use bispectral means to define a new test of the linear process hypothesis. Simulations document the finite sample properties of the asymptotic results. Two applications illustrate our results' utility: we test the linear process hypothesis for a Sunspot time series, and for the Gross Domestic Product we conduct a clustering exercise based on bispectral means with different weight functions.

We present a scalable approach to solve a class of elliptic partial differential equation (PDE)-constrained optimization problems with bound constraints. This approach utilizes a robust full-space interior-point (IP)-Gauss-Newton optimization method. To cope with the poorly-conditioned IP-Gauss-Newton saddle-point linear systems that need to be solved, once per optimization step, we propose two spectrally related preconditioners. These preconditioners leverage the limited informativeness of data in regularized PDE-constrained optimization problems. A block Gauss-Seidel preconditioner is proposed for the GMRES-based solution of the IP-Gauss-Newton linear systems. It is shown, for a large-class of PDE- and bound-constrained optimization problems, that the spectrum of the block Gauss-Seidel preconditioned IP-Gauss-Newton matrix is asymptotically independent of discretization and is not impacted by the ill-conditioning that notoriously plagues interior-point methods. We propose a regularization and log-barrier Hessian preconditioner for the preconditioned conjugate gradient (PCG)-based solution of the related IP-Gauss-Newton-Schur complement linear systems. The scalability of the approach is demonstrated on an example problem with bound and nonlinear elliptic PDE constraints. The numerical solution of the optimization problem is shown to require a discretization independent number of IP-Gauss-Newton linear solves. Furthermore, the linear systems are solved in a discretization and IP ill-conditioning independent number of preconditioned Krylov subspace iterations. The parallel scalability of preconditioner and linear system matrix applies, achieved with algebraic multigrid based solvers, and the aforementioned algorithmic scalability permits a parallel scalable means to compute solutions of a large class of PDE- and bound-constrained problems.

Conditional demographic parity (CDP) is a measure of the demographic parity of a predictive model or decision process when conditioning on an additional feature or set of features. Many algorithmic fairness techniques exist to target demographic parity, but CDP is much harder to achieve, particularly when the conditioning variable has many levels and/or when the model outputs are continuous. The problem of auditing and enforcing CDP is understudied in the literature. In light of this, we propose novel measures of {conditional demographic disparity (CDD)} which rely on statistical distances borrowed from the optimal transport literature. We further design and evaluate regularization-based approaches based on these CDD measures. Our methods, \fairbit{} and \fairlp{}, allow us to target CDP even when the conditioning variable has many levels. When model outputs are continuous, our methods target full equality of the conditional distributions, unlike other methods that only consider first moments or related proxy quantities. We validate the efficacy of our approaches on real-world datasets.

We use a custom-made calibrator to measure individual detectors' polarization angles of BICEP3, a small aperture telescope observing the cosmic microwave background (CMB) at 95GHz from the South Pole. We describe our calibration strategy and the statistical and systematic uncertainties associated with the measurement. We reach an unprecedented precision for such measurement on a CMB experiment, with a repeatability for each detector pair of $0.02\deg$. We show that the relative angles measured using this method are in excellent agreement with those extracted from CMB data. Because the absolute measurement is currently limited by a systematic uncertainty, we do not derive cosmic birefringence constraints from BICEP3 data in this work. Rather, we forecast the sensitivity of BICEP3 sky maps for such analysis. We investigate the relative contributions of instrument noise, lensing, and dust, as well as astrophysical and instrumental systematics. We also explore the constraining power of different angle estimators, depending on analysis choices. We establish that the BICEP3 2-year dataset (2017--2018) has an on-sky sensitivity to the cosmic birefringence angle of $\sigma = 0.078\deg$, which could be improved to $\sigma = 0.055\deg$ by adding all of the existing BICEP3 data (through 2023). Furthermore, we emphasize the possibility of using the BICEP3 sky patch as a polarization calibration source for CMB experiments, which with the present data could reach a precision of $0.035\deg$. Finally, in the context of inflation searches, we investigate the impact of detector-to-detector variations in polarization angles as they may bias the tensor-to-scalar ratio r. We show that while the effect is expected to remain subdominant to other sources of systematic uncertainty, it can be reliably calibrated using polarization angle measurements such as the ones we present in this paper., Comment: 29 Pages, 17 Figures, 6 Tables, as submitted to PRD

Let $\Gamma$ be a countable group that admits an essential measurable splitting (for instance, any group measure equivalent to a free product of nontrivial groups). We show: (1) for any two nontrivial countable groups $B$ and $C$ that are measure equivalent, the wreath product groups $B\wr\Gamma$ and $C\wr\Gamma$ are measure equivalent (in fact, orbit equivalent) -- this is interesting even in the case when the groups $B$ and $C$ are finite; and (2) the groups $B\wr \Gamma$ and $(B\times\mathbf{Z})\wr\Gamma$ are measure equivalent (in fact, orbit equivalent) for every nontrivial countable group $B$. On the other hand, we show that certain wreath product actions are not even stably orbit equivalent if $\Gamma$ is instead assumed to be a sofic icc group that is Bernoulli superrigid, and $B$ and $C$ have different cardinalities., Comment: 37 pages, 1 figure, minor revisions and corrections

Having a detailed knowledge of the intracluster medium (ICM) to infer the exact cluster physics such as the cluster dynamical state is crucial for cluster-based cosmological studies. This knowledge limits the accuracy and precision of mass estimation, a key parameter for such studies. In this paper, we conduct an in-depth analysis of cluster ACT-CL J0240.0+0116 using a multi-wavelength approach, with a primary focus on high angular resolution Sunyaev-Zeldovich (SZ) thermal component observations obtained under the NIKA2 Sunyaev-Zeldovich Large Programme (LPSZ). We create composite images using NIKA2, X-ray, and optical galaxy number density maps. The results reveal distinct signs of disturbance within the cluster with the distributions of gas and member galaxies that do not overlap. We also find suggestions of an inflow of matter onto the cluster from the southwestern direction. Ultimately, we classify the cluster as disturbed, using morphological indicators derived from its SZ, X-ray, and optical image. The cluster SZ signal is also contaminated by a strong central point source. We adopt different approaches to handling this contaminant and find the estimates of our pressure and hydrostatic mass profiles robust to the point source mitigation model. The cluster hydrostatic mass is estimated at $4.25^{+0.50}_{-0.45\, } \times 10^{14} \,\mathrm{M}_{\odot}$ for the case where the point source was masked. These values are consistent with the mass estimated using only X-ray data and with those from previous SZ studies of the Atacama cosmology telescope (ACT) survey, with improved precision on the mass estimate. Our findings strongly suggest that ACT-CL J0240.0+0116 is a disturbed cluster system, and the detailed observations and derived values serve as a compelling case study for the capabilities of the LPSZ in mapping the cluster ICM with high precision., Comment: 13 pages, 7 figures, submitted to A&A

The millimeter continuum emission from galaxies provides important information about cold dust, its distribution, heating, and role in their InterStellar Medium (ISM). This emission also carries an unknown portion of the free-free and synchrotron radiation. The IRAM 30m Guaranteed Time Large Project, Interpreting Millimeter Emission of Galaxies with IRAM and NIKA2 (IMEGIN) provides a unique opportunity to study the origin of the millimeter emission on angular resolutions of <18" in a sample of nearby galaxies. As a pilot study, we present millimeter observations of two IMEGIN galaxies, NGC 2146 (starburst) and NGC 2976 (peculiar dwarf) at 1.15 mm and 2 mm. Combined with the data taken with Spitzer, Herschel, Plank, WSRT, and the 100m Effelsberg telescopes, we model the infrared-to-radio Spectral Energy Distribution (SED) of these galaxies, both globally and at resolved scales, using a Bayesian approach to 1) dissect different components of the millimeter emission, 2) investigate the physical properties of dust, and 3) explore correlations between millimeter emission, gas, and Star Formation Rate (SFR). We find that cold dust is responsible for most of the 1.15 mm emission in both galaxies and at 2 mm in NGC 2976. The free-free emission emits more importantly in NGC 2146 at 2 mm. The cold dust emissivity index is flatter in the dwarf galaxy ($\beta = 1.3\pm 0.1$) compared to the starburst galaxy ($\beta = 1.7\pm 0.1$). Mapping the dust-to-gas ratio, we find that it changes between 0.004 and 0.01 with a mean of $0.006\pm0.001$ in the dwarf galaxy. In addition, no global balance holds between the formation and dissociation of H$_2$ in this galaxy. We find tight correlations between the millimeter emission and both the SFR and molecular gas mass in both galaxies., Comment: Accepted for publication in Astronomy & Astrophysics

The set of forks is a class of quivers introduced by M. Warkentin, where every connected mutation-infinite quiver is mutation equivalent to infinitely many forks. Let $Q$ be a fork with $n$ vertices, and $\boldsymbol{w}$ be a fork-preserving mutation sequence. We show that every $c$-vector of $Q$ obtained from $\boldsymbol{w}$ is a solution to a quadratic equation of the form $$\sum_{i=1}^n x_i^2 + \sum_{1\leq i

Uncertainty quantification is crucial to account for the imperfect predictions of machine learning algorithms for high-impact applications. Conformal prediction (CP) is a powerful framework for uncertainty quantification that generates calibrated prediction intervals with valid coverage. In this work, we study how CP intervals are affected by bias - the systematic deviation of a prediction from ground truth values - a phenomenon prevalent in many real-world applications. We investigate the influence of bias on interval lengths of two different types of adjustments -- symmetric adjustments, the conventional method where both sides of the interval are adjusted equally, and asymmetric adjustments, a more flexible method where the interval can be adjusted unequally in positive or negative directions. We present theoretical and empirical analyses characterizing how symmetric and asymmetric adjustments impact the "tightness" of CP intervals for regression tasks. Specifically for absolute residual and quantile-based non-conformity scores, we prove: 1) the upper bound of symmetrically adjusted interval lengths increases by $2|b|$ where $b$ is a globally applied scalar value representing bias, 2) asymmetrically adjusted interval lengths are not affected by bias, and 3) conditions when asymmetrically adjusted interval lengths are guaranteed to be smaller than symmetric ones. Our analyses suggest that even if predictions exhibit significant drift from ground truth values, asymmetrically adjusted intervals are still able to maintain the same tightness and validity of intervals as if the drift had never happened, while symmetric ones significantly inflate the lengths. We demonstrate our theoretical results with two real-world prediction tasks: sparse-view computed tomography (CT) reconstruction and time-series weather forecasting. Our work paves the way for more bias-robust machine learning systems., Comment: 17 pages, 6 figures, code available at: https://github.com/matthewyccheung/conformal-metric

Objectives: To identify a head acceleration event (HAE) severity measure associated with HIA1 removals in elite level rugby union. Methods: HAEs were recorded from 215 men and 325 women with 30 and 28 HIA1 removals from men and women, respectively. Logistical regression were calculated to identify if peak power, maximum principal strain (MPS) and or Head Acceleration Response Metric (HARM) were associated with HIA1 events compared to non-cases. Optimal threshold values were determined using the Youden Index. Area under the curve (AUC) were compared using a paired sample approach. Significant differences were set at p<0.05. Results: All three severity measures were associated with HIA1 removals in both the mens and womens game. Power performed greatest for HIA1 removals in both the mens and womens games, based on overall AUC, sensitivity, and specificity values. HARM and MPS were found to perform lower than PLA in the womens game based on AUC comparisons (p=0.006 and 0.001, respectively), with MPS performing lower than PAA (p=0.001). Conclusion: The findings progress our understanding of HAE measures associated with HIA1 removals. Peak power, a measure based on fundamental mechanics and commonly used in sports performance, may be a suitable HAE severity measure., Comment: 4 Tables, 2 Figures

SPIDER is a balloon-borne instrument designed to map the cosmic microwave background at degree-angular scales in the presence of Galactic foregrounds. SPIDER has mapped a large sky area in the Southern Hemisphere using more than 2000 transition-edge sensors (TESs) during two NASA Long Duration Balloon flights above the Antarctic continent. During its first flight in January 2015, SPIDER observed in the 95 GHz and 150 GHz frequency bands, setting constraints on the B-mode signature of primordial gravitational waves. Its second flight in the 2022-23 season added new receivers at 280 GHz, each using an array of TESs coupled to the sky through feedhorns formed from stacks of silicon wafers. These receivers are optimized to produce deep maps of polarized Galactic dust emission over a large sky area, providing a unique data set with lasting value to the field. In this work, we describe the instrument's performance during SPIDER's second flight., Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation 2024, JATIS

The present state of cosmology is facing a crisis where there is a fundamental disagreement in measurements of the Hubble constant ($H_{0}$), with significant tension between the early and late universe methods. Type Ia supernovae (SNe Ia) are important to measuring $H_{0}$ through the astronomical distance ladder. However, there remains potential to better standardise SN Ia light curves by using known dependencies on host galaxy properties after the standard light curve width and colour corrections have been applied to the peak SN Ia luminosities. To explore this, we use the 5-year photometrically identified SNe Ia sample obtained by the Dark Energy Survey, along with host galaxy spectra obtained by the Australian Dark Energy Survey. Using host galaxy spectroscopy, we find a significant trend with the equivalent width (EW) of the [OII] $\lambda\lambda$ 3727, 29 doublet, a proxy for specific star formation rate, and Hubble residuals. We find that the correlation with [OII] EW is a powerful alternative to the commonly used mass step after initial light curve corrections. We applied our [OII] EW correction to a sample of 20 SN Ia hosted by calibrator galaxies observed using WiFeS, and examined the impact on both the SN Ia absolute magnitude and $H_{0}$. We then explored different [OII] EW corrections and found $H_{0}$ values ranging between $72.80$ to $73.28~\mathrm{km} \mathrm{s}^{-1} \mathrm{Mpc}^{-1}$. Notably, even after using an additional [OII] EW correction, the impact of host galaxy properties in standardising SNe Ia appears limited in reducing the current tension ($\sim$5$\sigma$) with the Cosmic Microwave Background result for $H_{0}$., Comment: 16 pages, 13 figures. Submitting to MNRAS

Using data from the first flight of SPIDER and from Planck HFI, we probe the properties of polarized emission from interstellar dust in the SPIDER observing region. Component separation algorithms operating in both the spatial and harmonic domains are applied to probe their consistency and to quantify modeling errors associated with their assumptions. Analyses spanning the full SPIDER region demonstrate that i) the spectral energy distribution of diffuse Galactic dust emission is broadly consistent with a modified-blackbody (MBB) model with a spectral index of $\beta_\mathrm{d}=1.45\pm0.05$ $(1.47\pm0.06)$ for $E$ ($B$)-mode polarization, slightly lower than that reported by Planck for the full sky; ii) its angular power spectrum is broadly consistent with a power law; and iii) there is no significant detection of line-of-sight decorrelation of the astrophysical polarization. The size of the SPIDER region further allows for a statistically meaningful analysis of the variation in foreground properties within it. Assuming a fixed dust temperature $T_\mathrm{d}=19.6$ K, an analysis of two independent sub-regions of that field results in inferred values of $\beta_\mathrm{d}=1.52\pm0.06$ and $\beta_\mathrm{d}=1.09\pm0.09$, which are inconsistent at the $3.9\,\sigma$ level. Furthermore, a joint analysis of SPIDER and Planck 217 and 353 GHz data within a subset of the SPIDER region is inconsistent with a simple MBB at more than $3\,\sigma$, assuming a common morphology of polarized dust emission over the full range of frequencies. These modeling uncertainties have a small--but non-negligible--impact on limits on the cosmological tensor-to-scalar ratio derived from the \spider dataset. The fidelity of the component separation approaches of future CMB polarization experiments may thus have a significant impact on their constraining power., Comment: 21 pages, 15 figures

This report examines state portraits of a graduate and how state strategies for development and implementation can support future readiness. It also provides recommendations for state education agencies to consider to lift up the importance and visibility of these portraits. Several states have begun the process of developing a "portrait" or "profile of a graduate," which is a holistic look at the skills and competencies students need to master to thrive in work, postsecondary educational opportunities, community, and their personal lives. To better understand how states are utilizing the portrait of a graduate to prepare students for career and college, we conducted a scan of all 50 states to see which states had developed state-wide portraits of a graduate or visions of a high school graduate. The results inform the findings and recommendations of this report, including the skills states are highlighting as essential for students' future success and how states are making this vision actionable in service of cultivating future-ready graduates. As this review indicates, states are keenly aware that today's students will be called on to meet the growing challenges of the 21st century, as among the top skills are critical thinking and problem- solving, social awareness skills, and being an active citizen. Moreover, states deeply understand it is necessary to develop students' social and emotional skills and competencies. This report delves deeper into how states are implementing their portrait of a graduate to help students meet these expectations.

Adoption of AI by criminal entities across traditional and emerging financial crime paradigms has been a disturbing recent trend. Particularly concerning is the proliferation of generative AI, which has empowered criminal activities ranging from sophisticated phishing schemes to the creation of hard-to-detect deep fakes, and to advanced spoofing attacks to biometric authentication systems. The exploitation of AI by criminal purposes continues to escalate, presenting an unprecedented challenge. AI adoption causes an increasingly complex landscape of fraud typologies intertwined with cybersecurity vulnerabilities. Overall, GenAI has a transformative effect on financial crimes and fraud. According to some estimates, GenAI will quadruple the fraud losses by 2027 with a staggering annual growth rate of over 30% [27]. As crime patterns become more intricate, personalized, and elusive, deploying effective defensive AI strategies becomes indispensable. However, several challenges hinder the necessary progress of AI-based fincrime detection systems. This paper examines the latest trends in AI/ML-driven financial crimes and detection systems. It underscores the urgent need for developing agile AI defenses that can effectively counteract the rapidly emerging threats. It also aims to highlight the need for cooperation across the financial services industry to tackle the GenAI induced crime waves.

Kinetic inductance detectors (KIDs) have been proven as reliable systems for astrophysical observations, especially in the millimetre range. Their compact size enables to optimally fill the focal plane, thus boosting sensitivity. The KISS (KIDs Interferometric Spectral Surveyor) instrument is a millimetre camera that consists of two KID arrays of 316 pixels each coupled to a Martin-Puplett interferometer (MPI). The addition of the MPI grants the KIDs camera the ability to provide spectral information in the 100 and 300 GHz range. In this paper we report the main properties of the KISS instrument and its observations. We also describe the calibration and data analysis procedures used. We present a complete model of the observed data including the sky signal and several identified systematics. We have developed a full photometric and spectroscopic data analysis pipeline that translates our observations into science-ready products. We show examples of the results of this pipeline on selected sources: Moon, Jupiter and Venus. We note the presence of a deficit of response with respect to expectations and laboratory measurements. The detectors noise level is consistent with values obtained during laboratory measurements, pointing to a sub-optimal coupling between the instrument and the telescope as the most probable origin for the problem. This deficit is large enough as to prevent the detection of galaxy clusters, which were KISS main scientific objective. Nevertheless, we have demonstrated the feasibility of this kind of instrument, in the prospect for other KID interferometers (such as the CONCERTO instrument). As this regard, we have developed key instrumental technologies such as optical conception, readout electronics and raw calibration procedures, as well as, adapted data analysis procedures., Comment: 23 pages, 15 figures. Accepted for publication in Publications of the Astronomical Society of the Pacific

We present the properties of a massive, large, dusty, metal-rich, star-forming galaxy at z_spec=6.73. GOODSN-100182 was observed with JWST/NIRSpec as part of the AURORA survey, and is also covered by public multi-wavelength HST and JWST imaging. While the large mass of GOODSN-100182 (~10^10 M_sun) was indicated prior to JWST, NIRCam rest-optical imaging now reveals the presence of an extended disk (r_eff~1.5 kpc). In addition, the NIRSpec R~1000 spectrum of GOODSN-100182 includes the detection of a large suite of rest-optical nebular emission lines ranging in wavelength from [OII]3727 up to [NII]6583. The ratios of Balmer lines suggest significant dust attenuation (E(B-V)_gas=0.40+0.10/-0.09), consistent with the red rest-UV slope inferred for GOODSN-100182 (beta=-0.50+/-0.09). The star-formation rate based on dust-corrected H-alpha emission is log(SFR(H-alpha)/ M_sun/yr)=2.02+0.13/-0.14, well above the z~7 star-forming main sequence in terms of specific SFR. Strikingly, the ratio of [NII]6583/H-alpha emission suggests almost solar metallicity, as does the ratio ([OIII]5007/H-beta)/([NII]6583/H-alpha) and the detection of the faint [FeII]4360 emission feature, whereas the [OIII]5007/[OII]3727 ratio suggests roughly 50% solar metallicity. Overall, the excitation and ionization properties of GOODSN-100182 more closely resemble those of typical star-forming galaxies at z~2-3 rather than z~7. Based on public spectroscopy of the GOODS-N field, we find that GOODSN-100182 resides within a significant galaxy overdensity, and is accompanied by a spectroscopically-confirmed neighbor galaxy. GOODSN-100182 demonstrates the existence of mature, chemically-enriched galaxies within the first billion years of cosmic time, whose properties must be explained by galaxy formation models., Comment: 16 pages, 13 figures, submitted to ApJ

Guitar Pickups have been in production for nearly 100 years, and the question of how exactly one pickup is tonally superior to another is still subject to a high level of debate. This paper is the first in a set demystifying the production of guitar pickups and introducing a level of scientific procedure to the conversation. Previous studies have analysed commercial off-the-shelf pickups, but these differ from each other in multiple ways. The novelty of this study is that dedicated experimental pickups were created, which vary only one parameter at a time in order to allow scientific study. The most fundamental qualities of a single-coil pickup are investigated: in this paper, number of turns and gauge of wire. A set of single-coil stratocaster-style pickups were created, with the number of turns of wire varied across the commercially available range (5000-12000 turns), and this was done for two widely used wire gauges (42 and 44 AWG). A frequency response analyser was used to obtain impedance across a frequency range. It is shown that resonant frequency decreases exponentially with number of turns, while the magnitude of the resonant peak increases linearly with number of turns. The wire gauge used has a significant impact on both parameters, with the thicker wire giving higher resonant frequencies and higher magnitudes than the thinner wire for the same number of turns. These impact the sound associated with the pickup: the resonant frequency is linked to the perceived tone of the pickup, and the magnitude to the output amplitude and hence 'gain.' Increasing the number of turns will give a higher output pickup with a darker tone, and thicker wire gives louder outputs and brighter tones - consistent with what can be observed in commercial pickups., Comment: 13 pages, 10 figures, to be submitted to Journal AES

Galactic outflows provide important feedback effects to regulate the evolution of the host galaxies. Two primary diagnostics of galactic outflows are broad and/or blueshifted emission and absorption lines. Even though well-established methods exist to analyze these outflow signatures, connections between them are rarely studied and largely unknown. In this paper, we present the first detailed comparisons of the outflow properties measured independently from the two outflow diagnostics for a sample of 33 low-redshift star-forming galaxies. Their UV absorption lines are detected by the Hubble Space Telescope/Cosmic Origin Spectrograph, and optical emission lines are observed by the Keck/Echellette Spectrograph and Imager. We find that several outflow properties derived from emission and absorption lines are tightly correlated. These include outflow maximum velocity, line width, and sizes. Specifically, in a given galaxy, outflows seen in emission lines have smaller maximum velocities, narrower line widths, and smaller sizes than those measured from the absorption lines. These findings can be interpreted by the fact that emission line luminosity is weighted by density squared, while absorption line depth is weighted by density. We then test both spherical and bi-conical outflow models, and find the same outflow velocity and density distributions can explain the observed outflow features in emission and absorption lines for individual galaxies. These results provide novel calibration between galactic outflow properties measured from the two diagnostics and provide valuable insights for future models of galactic outflows by potentially doubling the number of observational constraints., Comment: 3 tables, 17 figures, 30 pages, submitted to ApJ

Fix a prime number $p$. Inspired by the notion of $F$-pure or $F$-split singularities, we study the condition that a Noetherian ring with $p$ in its Jacobson radical is pure inside some perfectoid (classical) ring, a condition we call \emph{perfectoid pure}. We also study a related a priori weaker condition which asks that $R$ is pure in its absolute perfectoidization, a condition we call \emph{lim-perfectoid pure}. We show that both these notions coincide when $R$ is LCI. Mixed characteristic analogs of $F$-injective and Du Bois singularities are also explored. We study these notions of singularity, proving that they are weakly normal and that they are Du Bois after inverting $p$. We also explore the behavior of perfectoid singularities under finite covers and their relation to log canonical singularities. Finally, we prove an inversion of adjunction result in the LCI setting, and use it to prove that many common examples are perfectoid pure., Comment: 46 pages, comments welcome

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

Liger is an adaptive optics (AO) fed imager and integral field spectrograph (IFS) designed to take advantage of the Keck All-sky Precision Adaptive-optics (KAPA) upgrade to the Keck I telescope. Liger adapts the design of the InfraRed Imaging Spectrograph (IRIS) for the Thirty Meter Telescope (TMT) to Keck by implementing a new imager and re-imaging optics. The performance of the imager is critical as it sequentially feeds the spectrograph and contains essential components such as the pupil wheel, filter wheel, and pupil viewing camera. We present the design and structural analysis of the Liger imager optical assembly including static, modal, and thermal simulations. We present the fabrication as well as the full assembly and characterization plan. The imager will be assembled bench-top in a clean room utilizing a coordinate-measuring machine (CMM) for warm alignment. To ensure optimal performance, the imager will be characterized in a test cryostat before integration with the full Liger instrument. This comprehensive approach to characterization ensures the precision and reliability of the imager, enhancing the observational capabilities of Liger and W.M. Keck Observatory.

Osteoarthritis (OA) is a prevalent degenerative joint disease, with the knee being the most commonly affected joint. Modern studies of knee joint injury and OA often measure biomechanical variables, particularly forces exerted during walking. However, the relationship among gait patterns, clinical profiles, and OA disease remains poorly understood. These biomechanical forces are typically represented as curves over time, but until recently, studies have relied on discrete values (or landmarks) to summarize these curves. This work aims to demonstrate the added value of analyzing full movement curves over conventional discrete summaries. Using data from the Intensive Diet and Exercise for Arthritis (IDEA) study (Messier et al., 2009, 2013), we developed a shape-based representation of variation in the full biomechanical curves. Compared to conventional discrete summaries, our approach yields more powerful predictors of disease progression and relevant clinical traits, as demonstrated by a nested model comparison. Notably, our work is among the first to use movement curves to predict disease progression and to quantitatively evaluate the added value of analyzing full movement curves over conventional discrete summaries