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As school systems work to recover from the COVID-19 pandemic, states continue to grapple with persistent teacher shortages, a lack of teachers of color in the workforce, and high turnover rates undermining recruitment efforts. Teachers' effectiveness and their likelihood of staying in teaching are strongly influenced by the quality of preparation they receive, and well-designed teacher residencies have been found to support both of these outcomes. Research suggests that teacher candidates who receive their preparation through teacher residencies--which combine comprehensive, financially supported preparation with a post-program service requirement--tend to be retained in their districts longer than other candidates, on average, thereby lowering rates of new teacher attrition and reducing the need to hire more new teachers. This research is based on the initial designs for residencies that guided the federal legislation on residencies and several early state adoptions. States and the federal government are seeking policy strategies to better recruit, prepare, and retain a qualified teacher workforce. To understand the growing evidence and policy landscape, this report begins with an overview of research on the teacher residency model and then describes state and federal policy trends and opportunities supporting teacher residencies. This includes a look across recent efforts in 12 states to fund and grow high-quality, research-aligned residencies: Arizona, California, Delaware, Indiana, Mississippi, Montana, New Mexico, New York, Pennsylvania, Texas, Washington, and West Virginia.

Most selective colleges implemented test-optional admissions during the pandemic, making college entrance exam scores optional for applicants. We draw on descriptive, two-way fixed effects, and event study methods to examine variation in test-optional implementation during the pandemic and how implementation relates to selectivity and enrollment. For "test-optional" colleges during the pandemic, we found substantial variation in policy type (e.g., test optional, test free) and whether the policy extended to all applicants and scholarship consideration. Findings suggest test-optional implementation related to increases in Black student enrollment, mostly at moderately selective colleges and when policies extended to all applicants and scholarships. At highly selective colleges, findings suggest test-optional implementation related to an increase in applications but not consistent gains in enrollment.

High-quality professional learning can support in-service leaders' effectiveness by developing the skills, knowledge, and competencies necessary for addressing their full range of leadership responsibilities. however, recent data show that leaders' access to professional learning varies across states and communities and that leaders in high-poverty schools are less likely than those in low-poverty schools to have high-quality learning opportunities. States can expand access to leadership development and make access more equitable by investing in ongoing statewide initiatives, such as leadership academies or institutes. Building the infrastructure for leadership-relevant professional learning can allow for long-term capacity building of a state's leadership workforce and create opportunities for leaders to access a continuum of support over the course of their career. The purpose of this study was to understand the infrastructure that states have built for leadership-relevant professional learning by identifying the long-term leadership development initiatives supported by states and analyzing their purposes, target audiences, and scope. They conducted a scan between March and May of 2023 using search engines, state department of education websites, and other web-based documents. The researchers found that at least 26 states support ongoing statewide leadership development initiatives to build the knowledge and skills of in-service leaders. This report provides examples of different states' initiatives related to each of these functions. It also briefly addresses the federal, state, and local funding sources that states draw on to develop and sustain these initiatives.

The "Report on the Condition of Education" is an annual report from the National Center for Education Statistics (NCES) that is mandated by the United States Congress. Using data from NCES and other sources, NCES compiles a set of "indicators" of the condition of education in the United States at all levels, from prekindergarten through postsecondary, as well as labor force outcomes and international comparisons. The full contents of the Condition of Education Indicator System can be accessed online or by downloading PDFs for the individual indicators. The "Report on the Condition of Education 2024" comprises key findings from the Indicator System. This summary report provides a brief overview of information available on various topics as well as direct links to the online versions of indicators discussed. This year's report also includes select content from sources outside the Indicator System, such as the National Assessment of Educational Progress (NAEP) and the Integrated Postsecondary Education System (IPEDS).

California has a notable history of investing in educational leaders' professional learning. In 1983, the state launched the California School Leadership Academy (CSLA), which it funded until the academy was discontinued due to statewide budget cuts in 2003. Research showed that CSLA was a source of high-quality professional development. In 2019, the state legislature reinvested in leaders' professional learning when it authorized the 21st Century California School Leadership Academy (21CSLA) to provide high-quality professional learning opportunities that are accessible and free of charge to California's PreK-12 educational leaders, including central office leaders, site leaders, and teacher leaders. The professional learning is delivered through seven regional academies that offer three primary types of learning opportunities: communities of practice (i.e., cohorts of leaders in similar roles collaboratively working on problems of practice), localized professional learning (i.e., learning opportunities developed in response to regional need), and individualized coaching by trained coaches. 21CSLA is one of several lead agencies that compose the California Statewide System of Support, an important part of the state's accountability and continuous improvement strategy that is designed to help local education agencies (LEAs) and their schools meet the needs of each student they serve. The purpose of this study is to understand how 21CSLA contributes to leadership development in California. Specifically, the study seeks to understand how 21CSLA currently supports the professional learning needs of educational leaders and how it fits within the broader state infrastructure to support educational improvement. The goal of the study is to inform policymakers and program administrators about the work and accomplishments of 21CSLA during the initiative's first 3-year grant cycle and point to ways in which the state can further support 21CSLA efforts to serve California's educational leaders.

Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that focuses on low-inference instructional practices, this paper presents the first study that leverages Natural Language Processing (NLP) techniques to assess multiple high-inference instructional practices in two distinct educational settings: in-person K-12 classrooms and simulated performance tasks for pre-service teachers. This is also the first study that applies NLP to measure a teaching practice that has been demonstrated to be particularly effective for students with special needs. We confront two challenges inherent in NLP-based instructional analysis, including noisy and long input data and highly skewed distributions of human ratings. Our results suggest that pretrained Language Models (PLMs) demonstrate performances comparable to the agreement level of human raters for variables that are more discrete and require lower inference, but their efficacy diminishes with more complex teaching practices. Interestingly, using only teachers' utterances as input yields strong results for student-centered variables, alleviating common concerns over the difficulty of collecting and transcribing high-quality student speech data in in-person teaching settings. Our findings highlight both the potential and the limitations of current NLP techniques in the education domain, opening avenues for further exploration.

Letters of recommendation from school counselors are required to apply to many selective colleges and universities. Still, relatively little is known about how this non-standardized component may affect equity in admissions. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted via the Common App platform. We examine how the length and topical content of letters (e.g., sentences about Personal Qualities, Athletics, Intellectual Promise, etc.) relate to student self-identified race/ethnicity, sex, and proxies for socioeconomic status. Paired with regression analyses, we explore whether demographic differences in letter characteristics persist when accounting for additional student, school, and counselor characteristics, as well as among letters written by the same counselor and among students with comparably competitive standardized test scores. We ultimately find large and noteworthy naïve differences in letter length and content across nearly all demographic groups, many in alignment with known inequities (e.g., many more sentences about Athletics among White and higher-SES students, longer letters and more sentences on Personal Qualities for private school students). However, these differences vary drastically based on the exact controls and comparison groups included -- demonstrating that the ultimate implications of these letter differences for equity hinges on exactly how and when letters are used in admissions processes (e.g., among which groups of students are they used to "break ties"?). Findings do not point to a clear recommendation whether institutions should keep or discard letter requirements, but reflect the importance of reading letters and overall applications in the context of structural opportunity. We discuss additional implications and possible recommendations for college access and admissions policy/practice.

Testing healthcare Internet of Things (IoT) applications at system and integration levels necessitates integrating numerous medical devices of various types. Challenges of incorporating medical devices are: (i) their continuous evolution, making it infeasible to include all device variants, and (ii) rigorous testing at scale requires multiple devices and their variants, which is time-intensive, costly, and impractical. Our collaborator, Oslo City's health department, faced these challenges in developing automated test infrastructure, which our research aims to address. In this context, we propose a meta-learning-based approach (MeDeT) to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices. We evaluate MeDeT in OsloCity's context using five widely-used medical devices integrated with a real-world healthcare IoT application. Our evaluation assesses MeDeT's ability to generate and adapt DTs across various devices and versions using different few-shot methods, the fidelity of these DTs, the scalability of operating 1000 DTs concurrently, and the associated time costs. Results show that MeDeT can generate DTs with over 96% fidelity, adapt DTs to different devices and newer versions with reduced time cost (around one minute), and operate 1000 DTs in a scalable manner while maintaining the fidelity level, thus serving in place of physical devices for testing.

Smart medical devices are an integral component of the healthcare Internet of Things (IoT), providing patients with various healthcare services through an IoT-based application. Ensuring the dependability of such applications through system and integration-level testing mandates the physical integration of numerous medical devices, which is costly and impractical. In this context, digital twins of medical devices play an essential role in facilitating testing automation. Testing with digital twins without accounting for uncertain environmental factors of medical devices leaves many functionalities of IoT-based healthcare applications untested. In addition, digital twins operating without environmental factors remain out of sync and uncalibrated with their corresponding devices functioning in the real environment. To deal with these challenges, in this paper, we propose a model-based approach (EnvDT) for modeling and simulating the environment of medical devices' digital twins under uncertainties. We empirically evaluate the EnvDT using three medicine dispensers, Karie, Medido, and Pilly connected to a real-world IoT-based healthcare application. Our evaluation targets analyzing the coverage of environment models and the diversity of uncertain scenarios generated for digital twins. Results show that EnvDT achieves approximately 61% coverage of environment models and generates diverse uncertain scenarios (with a near-maximum diversity value of 0.62) during multiple environmental simulations.

The ComPair balloon instrument is a prototype gamma-ray telescope that aims to further develop technology for observing the gamma-ray sky in the MeV regime. ComPair combines four detector subsystems to enable parallel Compton scattering and pair-production detection, critical for observing in this energy range. This includes a 10 layer double-sided silicon strip detector tracker, a virtual Frisch grid low energy CZT calorimeter, a high energy CsI calorimeter, and a plastic scintillator anti-coincidence detector. The inaugural balloon flight successfully launched from the Columbia Scientific Balloon Facility site in Fort Sumner, New Mexico, in late August 2023, lasting approximately 6.5 hours in duration. In this proceeding, we discuss the development of the ComPair Since balloon payload, the performance during flight, and early results., Comment: 13 pages, 12 figures

Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been studied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 meters squared forest sites unseen during training, in difficult conditions for navigation.

Real-time supercomputing performance analysis is a critical aspect of evaluating and optimizing computational systems in a dynamic user environment. The operation of supercomputers produce vast quantities of analytic data from multiple sources and of varying types so compiling this data in an efficient matter is critical to the process. MIT Lincoln Laboratory Supercomputing Center has been utilizing the Unity 3D game engine to create a Digital Twin of our supercomputing systems for several years to perform system monitoring. Unity offers robust visualization capabilities making it ideal for creating a sophisticated representation of the computational processes. As we scale the systems to include a diversity of resources such as accelerators and the addition of more users, we need to implement new analysis tools for the monitoring system. The workloads in research continuously change, as does the capability of Unity, and this allows us to adapt our monitoring tools to scale and incorporate features enabling efficient replay of system wide events, user isolation, and machine level granularity. Our system fully takes advantage of the modern capabilities of the Unity Engine in a way that intuitively represents the real time workload performed on a supercomputer. It allows HPC system engineers to quickly diagnose usage related errors with its responsive user interface which scales efficiently with large data sets.

In-falling cosmic dust has left evidence of meteoritic polymer amide in stromatolites, both fossil and modern. In search of evidence for continued present day in-fall sea foam was collected from two beaches in Rhode Island and subjected to Folch extraction to concentrate amphiphilic components in a chloroform water-methanol interphase layer. Hemoglycin polymer amide molecules previously characterized by MALDI mass spectrometry in meteorites and stromatolites were identified in sea foam either directly, or via their fragmentation patterns. Residual isotope enrichment pointed to an extra-terrestrial origin. The unique resiliency of sea foam may be due to the formation of extended hemoglycin lattices that stabilize its closed-cell structure and its lightness can potentially be explained by photolytic hydrogen production., Comment: 32 pages, 11 figures, 4 Tables

Common narratives about automation often pit new technologies against workers. The introduction of advanced machine tools, industrial robots, and AI have all been met with concern that technological progress will mean fewer jobs. However, workers themselves offer a more optimistic, nuanced perspective. Drawing on a far-reaching 2024 survey of more than 9,000 workers across nine countries, this paper finds that more workers report potential benefits from new technologies like robots and AI for their safety and comfort at work, their pay, and their autonomy on the job than report potential costs. Workers with jobs that ask them to solve complex problems, workers who feel valued by their employers, and workers who are motivated to move up in their careers are all more likely to see new technologies as beneficial. In contrast to assumptions in previous research, more formal education is in some cases associated with more negative attitudes toward automation and its impact on work. In an experimental setting, the prospect of financial incentives for workers improve their perceptions of automation technologies, whereas the prospect of increased input about how new technologies are used does not have a significant effect on workers' attitudes toward automation.

Infinite nuclear matter provides valuable insights into the behavior of nuclear systems and aids our understanding of atomic nuclei and large-scale stellar objects such as neutron stars. However, partly due to the large basis needed to converge the system's binding energy, size-extensive methods such as coupled-cluster theory struggle with long computational run times, even using the nation's largest high-performance computing facilities. This research introduces a novel approach to the problem. We propose using a machine learning method to predict the coupled-cluster energies of infinite matter systems in the complete basis limit, leveraging only data collected using smaller basis sets. This method promises to deliver high-accuracy results with significantly reduced run times. The sequential regression extrapolation (SRE) algorithm, based on Gaussian processes, was created to perform these extrapolations. By combining Bayesian machine learning with a unique method of formatting the training data, we can create a powerful extrapolator that can make accurate predictions given very little data. The SRE algorithm successfully predicted the CCD(T) energies for pure neutron matter across six densities near nuclear saturation density, with an average error of 0.0083 MeV/N. The algorithm achieved an average error of 0.038 MeV/A for symmetric nuclear matter. These predictions were made with a time savings of 83.8 node hours for pure neutron matter and 284 node hours for symmetric nuclear matter. Additionally, the symmetry energy at these six densities was predicted with an average error of 0.031 MeV/A and a total time savings of 368 node hours compared to the traditional converged coupled-cluster calculations performed without the SRE algorithm.

High-resolution spectroscopic characterization of young super-Jovian planets enables precise constraints on elemental and isotopic abundances of their atmospheres. As part of the ESO SupJup Survey, we present high-resolution spectral observations of two wide-orbit super-Jupiters in YSES 1 (or TYC 8998-760-1) using the upgraded VLT/CRIRES+ (R~100,000) in K-band. We carry out free atmospheric retrieval analyses to constrain chemical and isotopic abundances, temperature structures, rotation velocities, and radial velocities. We confirm the previous detection of 13CO in YSES 1 b at a higher significance of 12.6{\sigma}, but point to a higher 12CO/13CO ratio of 88+/-13 (1{\sigma} confidence interval), consistent with the primary's isotope ratio 66+/-5. We retrieve a solar-like composition in YSES 1 b with a C/O=0.57+/-0.01, indicating a formation via gravitational instability or core accretion beyond the CO iceline. Additionally, the observations lead to detections of H2O and CO in the outer planet YSES 1 c at 7.3{\sigma} and 5.7{\sigma}, respectively. We constrain the atmospheric C/O ratio of YSES 1 c to be either solar or subsolar (C/O=0.36+/-0.15), indicating the accretion of oxygen-rich solids. The two companions have distinct vsini, 5.34+/-0.14 km/s for YSES 1 b and 11.3+/-2.1 km/s for YSES 1 c, despite their similar natal environments. This may indicate different spin axis inclinations or effective magnetic braking by the long-lived circumplanetary disk around YSES 1 b. YSES 1 represents an intriguing system for comparative studies of super-Jovian companions and linking present atmospheres to formation histories., Comment: 25 pages, 11 figure, accepted for publication in AJ. The extracted CRIRES+ spectra of the YSES-1 system can be found at https://doi.org/10.5281/zenodo.13664032

Electricity is difficult to store, except at prohibitive cost, and therefore the balance between generation and load must be maintained at all times. Electricity is traditionally managed by anticipating demand and intermittent production (wind, solar) and matching flexible production (hydro, nuclear, coal and gas). Accurate forecasting of electricity load and renewable production is therefore essential to ensure grid performance and stability. Both are highly dependent on meteorological variables (temperature, wind, sunshine). These dependencies are complex and difficult to model. On the one hand, spatial variations do not have a uniform impact because population, industry, and wind and solar farms are not evenly distributed across the territory. On the other hand, temporal variations can have delayed effects on load (due to the thermal inertia of buildings). With access to observations from different weather stations and simulated data from meteorological models, we believe that both phenomena can be modeled together. In today's state-of-the-art load forecasting models, the spatio-temporal modeling of the weather is fixed. In this work, we aim to take advantage of the automated representation and spatio-temporal feature extraction capabilities of deep neural networks to improve spatio-temporal weather modeling for load forecasting. We compare our deep learning-based methodology with the state-of-the-art on French national load. This methodology could also be fully adapted to forecasting renewable energy production.

An integrable polygon is one whose interior angles are fractions of $\pi$; that is to say of the form $\frac \pi n$ for positive integers $n$. We consider the Laplace spectrum on these polygons with the Dirichlet and Neumann boundary conditions, and we obtain new spectral invariants for these polygons. This includes new expressions for the spectral zeta function and zeta-regularized determinant as well as a new spectral invariant contained in the short-time asymptotic expansion of the heat trace. Moreover, we demonstrate relationships between the short-time heat trace invariants of general polygonal domains (not necessarily integrable) and smoothly bounded domains and pose conjectures and further related directions of investigation., Comment: 33 pages, 4 figures

Studying the orbital motion of stars around Sagittarius A* in the Galactic Center provides a unique opportunity to probe the gravitational potential near the supermassive black hole at the heart of our Galaxy. Interferometric data obtained with the GRAVITY instrument at the Very Large Telescope Interferometer (VLTI) since 2016 has allowed us to achieve unprecedented precision in tracking the orbits of these stars. GRAVITY data have been key to detecting the in-plane, prograde Schwarzschild precession of the orbit of the star S2, as predicted by General Relativity. By combining astrometric and spectroscopic data from multiple stars, including S2, S29, S38, and S55 - for which we have data around their time of pericenter passage with GRAVITY - we can now strengthen the significance of this detection to an approximately $10 \sigma$ confidence level. The prograde precession of S2's orbit provides valuable insights into the potential presence of an extended mass distribution surrounding Sagittarius A*, which could consist of a dynamically relaxed stellar cusp comprised of old stars and stellar remnants, along with a possible dark matter spike. Our analysis, based on two plausible density profiles - a power-law and a Plummer profile - constrains the enclosed mass within the orbit of S2 to be consistent with zero, establishing an upper limit of approximately $1200 \, M_\odot$ with a $1 \sigma$ confidence level. This significantly improves our constraints on the mass distribution in the Galactic Center. Our upper limit is very close to the expected value from numerical simulations for a stellar cusp in the Galactic Center, leaving little room for a significant enhancement of dark matter density near Sagittarius A*., Comment: Submitted to A&A on September 17, 2024

Correspondence analysis, multiple correspondence analysis and their discriminant counterparts (i.e., discriminant simple correspondence analysis and discriminant multiple correspondence analysis) are methods of choice for analyzing multivariate categorical data. In these methods, variables are integrated into optimal components computed as linear combinations whose weights are obtained from a generalized singular value decomposition (GSVD) that integrates specific metric constraints on the rows and columns of the original data matrix. The weights of the linear combinations are, in turn, used to interpret the components, and this interpretation is facilitated when components are 1) pairwise orthogonal and 2) when the values of the weights are either large or small but not intermediate-a pattern called a simple or a sparse structure. To obtain such simple configurations, the optimization problem solved by the GSVD is extended to include new constraints that implement component orthogonality and sparse weights. Because multiple correspondence analysis represents qualitative variables by a set of binary variables, an additional group constraint is added to the optimization problem in order to sparsify the whole set representing one qualitative variable. This new algorithm-called group-sparse GSVD (gsGSVD)-integrates these constraints via an iterative projection scheme onto the intersection of subspaces where each subspace implements a specific constraint. In this paper, we expose this new algorithm and show how it can be adapted to the sparsification of simple and multiple correspondence analysis, and illustrate its applications with the analysis of four different data sets-each illustrating the sparsification of a particular CA-based analysis.

The understanding of the formation and evolution of the solar system still has many unanswered questions. Formation of solids in the solar system, mineral and organic mixing, and planetary body creation are all topics of interest to the community. Studying these phenomena is often performed through observations, remote sensing, and in-situ analysis, but there are limitations to the methods. Limitations such as IR diffraction limits, spatial resolution issues, and spectral resolution issues can prevent detection of organics, detection and identification of cellular structures, and the disentangling of granular mixtures. Optical-PhotoThermal InfraRed (O-PTIR) spectroscopy is a relatively new method of spectroscopy currently used in fields other than planetary sciences. O-PTIR is a non-destructive, highly repeatable, and fast form of measurement capable of reducing these limitations. Using a dual laser system with an IR source tuned to the mid-IR wavelength we performed laboratory O-PTIR measurements to compare O-PTIR data to existing IR absorption data and laboratory FTIR measurements for planetary materials. We do this for the purpose of introducing O-PTIR to the planetary science community. The technique featured here would serve to better measurements of planetary bodies during in-situ analysis. We find that, unlike other fields where O-PTIR produces almost one-to-one measurements with IR absorption measurements of the same material, granular materials relevant to planetary science do not. However, we do find that the materials compared were significantly close and O-PTIR was still capable of identifying materials relevant to planetary science., Comment: This paper is an introductory of O-PTIR spectroscopy into planetary science. It is also going to be part 1 of a series. This is a pre-print

AI data-driven models (Graphcast, Pangu Weather, Fourcastnet, and SFNO) are explored for storyline-based climate attribution due to their short inference times, which can accelerate the number of events studied, and provide real time attributions when public attention is heightened. The analysis is framed on the extreme atmospheric river episode of February 2017 that contributed to the Oroville dam spillway incident in Northern California. Past and future simulations are generated by perturbing the initial conditions with the pre-industrial and the late-21st century temperature climate change signals, respectively. The simulations are compared to results from a dynamical model which represents plausible pseudo-realities under both climate environments. Overall, the AI models show promising results, projecting a 5-6 % increase in the integrated water vapor over the Oroville dam in the present day compared to the pre-industrial, in agreement with the dynamical model. Different geopotential-moisture-temperature dependencies are unveiled for each of the AI-models tested, providing valuable information for understanding the physicality of the attribution response. However, the AI models tend to simulate weaker attribution values than the pseudo-reality imagined by the dynamical model, suggesting some reduced extrapolation skill, especially for the late-21st century regime. Large ensembles generated with an AI model (>500 members) produced statistically significant present-day to pre-industrial attribution results, unlike the >20-member ensemble from the dynamical model. This analysis highlights the potential of AI models to conduct attribution analysis, while emphasizing future lines of work on explainable artificial intelligence to gain confidence in these tools, which can enable reliable attribution studies in real-time., Comment: This Work has been submitted to Artificial Intelligence for the Earth Systems

The Forward-Forward (FF) algorithm is a recent, purely forward-mode learning method, that updates weights locally and layer-wise and supports supervised as well as unsupervised learning. These features make it ideal for applications such as brain-inspired learning, low-power hardware neural networks, and distributed learning in large models. However, while FF has shown promise on written digit recognition tasks, its performance on natural images and time-series remains a challenge. A key limitation is the need to generate high-quality negative examples for contrastive learning, especially in unsupervised tasks, where versatile solutions are currently lacking. To address this, we introduce the Self-Contrastive Forward-Forward (SCFF) method, inspired by self-supervised contrastive learning. SCFF generates positive and negative examples applicable across different datasets, surpassing existing local forward algorithms for unsupervised classification accuracy on MNIST (MLP: 98.7%), CIFAR-10 (CNN: 80.75%), and STL-10 (CNN: 77.3%). Additionally, SCFF is the first to enable FF training of recurrent neural networks, opening the door to more complex tasks and continuous-time video and text processing.

Recent mid-infrared observations with JWST/MIRI have resulted in the first direct detections of absorption features from silicate clouds in the transmission spectra of two transiting exoplanets, WASP-17 b and WASP-107 b. In this paper, we measure the mid-infrared ($5-12$ $\mu$m) dayside emission spectrum of the benchmark hot Jupiter HD 189733 b with MIRI LRS by combining data from two secondary eclipse observations. We confirm the previous detection of H$_2$O absorption at 6.5 $\mu$m from Spitzer/IRS and additionally detect H$_2$S as well as an absorption feature at 8.7 $\mu$m in both secondary eclipse observations. The excess absorption at 8.7 $\mu$m can be explained by the presence of small ($\sim$0.01 $\mu$m) grains of SiO$_2$[s] in the uppermost layers of HD 189733 b's dayside atmosphere. This is the first direct detection of silicate clouds in HD 189733 b's atmosphere, and the first detection of a distinct absorption feature from silicate clouds on the day side of any hot Jupiter. We find that models including SiO$_2$[s] are preferred by $6-7\sigma$ over clear models and those with other potential cloud species. The high altitude location of these silicate particles is best explained by formation in the hottest regions of HD 189733 b's dayside atmosphere near the substellar point. We additionally find that HD 189733 b's emission spectrum longward of 9 $\mu$m displays residual features not well captured by our current atmospheric models. When combined with other JWST observations of HD 189733 b's transmission and emission spectrum at shorter wavelengths, these observations will provide us with the most detailed picture to date of the atmospheric composition and cloud properties of this benchmark hot Jupiter., Comment: 21 pages, 7 Figures, 3 Tables, Accepted to ApJL

HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and rarely is built with security in mind. In this paper we explore several of the key techniques that MIT Lincoln Laboratory Supercomputing Center has deployed on its systems to manage the security implications of these workflows by providing enforced separation for processes, filesystem access, network traffic, and accelerators to make every user feel like they are running on a personal HPC.

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-$\sigma$ error threshold; moreover, the RIM reconstruction of the mock observations are indistinguishable from the observations themselves. To further test the algorithm, we deconvolve extracted spectra from the central regions of the galaxy group NGC 1550, known to have a rich X-ray spectrum, and the massive galaxy clusters Abell 1795. Despite the RIM reconstructions consistently remaining below the 1-$\sigma$ noise level, the recovered intrinsic spectra did not align with modeled expectations. This discrepancy is likely attributable to the RIM's method of implicitly encoding prior information within the neural network. This approach holds promise for unlocking new possibilities in accurate spectral reconstructions and advancing our understanding of complex X-ray cosmic phenomena., Comment: Submitted to AJ

Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations. To learn a good reward, it is necessary to determine which features of the environment are relevant before determining how these features should be used to compute reward. End-to-end methods for joint feature and reward learning (e.g., using deep networks or program synthesis techniques) often yield brittle reward functions that are sensitive to spurious state features. By contrast, humans can often generalizably learn from a small number of demonstrations by incorporating strong priors about what features of a demonstration are likely meaningful for a task of interest. How do we build robots that leverage this kind of background knowledge when learning from new demonstrations? This paper describes a method named ALGAE (Adaptive Language-Guided Abstraction from [Contrastive] Explanations) which alternates between using language models to iteratively identify human-meaningful features needed to explain demonstrated behavior, then standard inverse reinforcement learning techniques to assign weights to these features. Experiments across a variety of both simulated and real-world robot environments show that ALGAE learns generalizable reward functions defined on interpretable features using only small numbers of demonstrations. Importantly, ALGAE can recognize when features are missing, then extract and define those features without any human input -- making it possible to quickly and efficiently acquire rich representations of user behavior., Comment: CoRL 2024

The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold in the field. The anonymized network sensing Graph Challenge seeks to enable large, open, community-based approaches to protecting networks. Many large-scale networking problems can only be solved with community access to very broad data sets with the highest regard for privacy and strong community buy-in. Such approaches often require community-based data sharing. In the broader networking community (commercial, federal, and academia) anonymized source-to-destination traffic matrices with standard data sharing agreements have emerged as a data product that can meet many of these requirements. This challenge provides an opportunity to highlight novel approaches for optimizing the construction and analysis of anonymized traffic matrices using over 100 billion network packets derived from the largest Internet telescope in the world (CAIDA). This challenge specifies the anonymization, construction, and analysis of these traffic matrices. A GraphBLAS reference implementation is provided, but the use of GraphBLAS is not required in this Graph Challenge. As with prior Graph Challenges the goal is to provide a well-defined context for demonstrating innovation. Graph Challenge participants are free to select (with accompanying explanation) the Graph Challenge elements that are appropriate for highlighting their innovations., Comment: Accepted to IEEE HPEC 2024

Global expansion of the Event Horizon Telescope (EHT) will see the strategic addition of antennas at new geographical locations, transforming the sensitivity and imaging fidelity of the $\lambda \sim 1\,$mm EHT array. A possible South African EHT station would leverage a strong geographical advantage, local infrastructure, and radio astronomy expertise, and have strong synergies with the Africa Millimetre Telescope in Namibia. We assessed three South African candidate millimetre sites using climatological simulations and found at least two promising sites. These sites are comparable to some existing EHT stations during the typical April EHT observing window and outperform them during most of the year, especially the southern hemisphere winter. Interferometric simulations of Africa-enhanced EHT arrays under the simulated atmospheric conditions demonstrate the improved array performance. In typical weather, the number of reliable visibility detections increased considerably, especially at $(u, v)$-distances corresponding to the angular sizes of the Sagittarius A$^*$ and Messier 87$^*$ black hole shadow diameters ($\sim40\,\mathrm{\mu}$as to $50\,\mathrm{\mu}$as). The simulation results underscore the sizable, positive impact of a strategically placed South African EHT station on ngEHT objectives and the resulting black hole science., Comment: 6 pages, 3 figures, 1 table. Submitted to URSI RSL

Binary human choice feedback is widely used in interactive preference learning for its simplicity, but it provides limited information about preference strength. To overcome this limitation, we leverage human response times, which inversely correlate with preference strength, as complementary information. Our work integrates the EZ-diffusion model, which jointly models human choices and response times, into preference-based linear bandits. We introduce a computationally efficient utility estimator that reformulates the utility estimation problem using both choices and response times as a linear regression problem. Theoretical and empirical comparisons with traditional choice-only estimators reveal that for queries with strong preferences ("easy" queries), choices alone provide limited information, while response times offer valuable complementary information about preference strength. As a result, incorporating response times makes easy queries more useful. We demonstrate this advantage in the fixed-budget best-arm identification problem, with simulations based on three real-world datasets, consistently showing accelerated learning when response times are incorporated.