Search

Assigning upper elementary grade teachers to teach their strongest subjects to multiple classes ("departmentalizing"), rather than teaching all subjects to a single class, ("self-contained instruction") could mean more specialized instructional expertise in the classroom or focus for teacher planning time and professional development. This study examined the experiences of 90 schools that either voluntarily switched to departmentalized instruction for up to two years or continued with self-contained teaching in 4th and 5th grade classes beginning in fall 2019. The findings show that schools that switched were able to implement the key steps needed to departmentalize instruction but struggled with aspects of the approach. Teachers' reported experiences were consistent with some of both the benefits and challenges that prior research had hypothesized. Although the pandemic significantly disrupted instruction and the study activities, school and teacher experiences during this challenging time may inform schools and districts considering adopting departmentalized instruction.

This appendix is a companion to the report "Evaluation of Departmentalized Instruction in Elementary Schools: Exploring Implementation Experiences." The appendix provides additional information about the study, which sought to explore schools' implementation of departmentalized instruction, teachers' experiences with the strategy, and how the strategy was related to key outcomes. This additional information includes details on departmentalized instruction (Section A), study design and execution (Section B), key findings presented in the report (Section C), and additional findings that are not included in the report but provide important context (Section D).

Recent developments in technology have accelerated the influence of artificial intelligence (AI) on our lives. The ability of generative-AI tools such as ChatGPT, Gemini and Claude to both 'write' and 'read' texts in a human-like manner means they are set to play an increasingly important role in the literacy lives of children, young people and adults. The National Literacy Trust is interested in exploring how such platforms might influence, and potentially redefine, what it means to be literate in the digital age. This summary report explores children, young people's and teachers' attitudes, behaviour and confidence around using generative AI to support literacy and learning. Findings are based on data from more than 50,000 children and young people taking part in the Annual Literacy Survey, with a focus on more than 15,000 young people aged 13 to 18 and 1,228 teachers from primary and secondary schools across the UK.

Recent developments in technology have accelerated the influence of artificial intelligence (AI) on our lives. The ability of generative-AI tools such as ChatGPT, Gemini and Claude to both 'write' (generate new texts) and 'read' (e.g. summarise texts) in a human-like manner means they are set to play an increasingly important role in the literacy lives of children, young people and adults. Anticipating this, the National Literacy Trust is exploring how such platforms might influence, and even redefine, what it means to be literate in the digital age. Based on data from the National Literacy Trust's Annual Literacy Survey, this report explores teachers' attitudes, behaviour and confidence around using AI to support literacy, as well as complementing similar research with children and young people carried out in 2023 and 2024.

Recent developments in technology have accelerated the influence of artificial intelligence (AI) on our lives. The National Literacy Trust is interested in exploring how such platforms might influence, and potentially redefine, what it means to be literate in the digital age. Based on data from more than 50,000 children and young people taking part in the National Literacy Trust's Annual Literacy Survey, and with a focus on more than 15,000 young people aged 13 to 18, this report explores young people's attitudes, behaviour and confidence around using generative AI to support literacy and learning. Primarily, findings show that awareness and use of these tools by children and young people has increased rapidly in the last year, with 3 in 4 young people aged 13 to 18 saying they'd used them in 2024. The survey also provides evidence that many young people are using generative AI to support literacy.

This report builds on National Literacy Trust's previous research from the 2024 Annual Literacy Survey to investigate how children and young people felt about writing in early 2024. It includes findings on how many enjoyed writing and how often they wrote in their free time, what motivated them to write, and what they wrote. This report is based on 76,131 responses from children and young people aged 5 to 18 in schools across the UK in early 2024 and explores responses by age, gender, socioeconomic background, and geographical region. Findings show that children and young people's enjoyment of writing, and frequency of writing in their free time, is at an unprecedented low. Increasing evidence of a long-term downward trend calls for urgent action to reconnect children and young people with writing that promotes connection with creativity, self-expression and mental wellbeing.

Indirect dark matter detection methods are used to observe the products of dark matter annihilations or decays originating from astrophysical objects where large amounts of dark matter are thought to accumulate. With neutrino telescopes, an excess of neutrinos is searched for in nearby dark matter reservoirs, such as the Sun and the Galactic Centre, which could potentially produce a sizeable flux of Standard Model particles. The KM3NeT infrastructure, currently under construction, comprises the ARCA and ORCA undersea \v{C}erenkov neutrino detectors located at two different sites in the Mediterranean Sea, offshore of Italy and France, respectively. The two detector configurations are optimised for the detection of neutrinos of different energies, enabling the search for dark matter particles with masses ranging from a few GeV/c$^2$ to hundreds of TeV/c$^2$. In this work, searches for dark matter annihilations in the Galactic Centre and the Sun with data samples taken with the first configurations of both detectors are presented. No significant excess over the expected background was found in either of the two analyses. Limits on the velocity-averaged self-annihilation cross section of dark matter particles are computed for five different primary annihilation channels in the Galactic Centre. For the Sun, limits on the spin-dependent and spin-independent scattering cross sections of dark matter with nucleons are given for three annihilation channels.

This paper presents the results of a joint radio and gamma-ray timing campaign on the nine millisecond pulsars (MSPs) discovered as part of the L-band targeted survey of Fermi-LAT sources performed in the context of the Transients and Pulsars with MeerKAT (TRAPUM) Large Survey Project. Out of these pulsars, eight are members of binary systems; of these eight, two exhibit extended eclipses of the radio emission. Using an initial radio timing solution, pulsations were found in the gamma rays for six of the targets. For these sources, a joint timing analysis of radio times of arrival and gamma-ray photons was performed, using a newly developed code that optimises the parameters through a Markov chain Monte Carlo (MCMC) technique. This approach has allowed us to precisely measure both the short- and long-term timing parameters. This study includes a proper motion measurement for four pulsars, which a gamma ray-only analysis would not have been sensitive to, despite the 15-year span of Fermi data., Comment: 20 pages, 5 figures

A fundamental question is understanding the rate at which random quantum circuits converge to the Haar measure. One quantity which is important in establishing this rate is the spectral gap of a random quantum ensemble. In this work we establish a new bound on the spectral gap of the t-th moment of a one-dimensional brickwork architecture on N qudits. This bound is independent of both t and N, provided t does not exceed the qudit dimension q. We also show that the bound is nearly optimal. The improved spectral gaps gives large improvements to the constant factors in known results on the approximate t-design depths of the 1D brickwork, of generic circuit architectures, and of specially-constructed architectures which scramble in depth O(log N). We moreover show that the spectral gap gives the dominant epsilon-dependence of the t-design depth at small epsilon. Our spectral gap bound is obtained by bounding the N-site 1D brickwork architecture by the spectra of 3-site operators. We then exploit a block-triangular hierarchy and a global symmetry in these operators in order to efficiently bound them. The technical methods used are a qualitatively different approach for bounding spectral gaps and and have little in common with previous techniques., Comment: 53 pages, 6 figures

Magnetic transition-metal dichalcogenides (TMDs) have been of particular interest due to their unique magnetic properties and layered structure that can be promising for a wide range of spintronic applications. One of the most exciting compounds in this family of magnets is chromium telluride, Cr$_{1+x}$Te$_2$, which has shown rich magnetic phases with varied Cr concentrations. An emergent antiferromagnetic (AFM) ordering has been found in Cr$_{1.25}$Te$_2$ (equivalently, Cr$_{5}$Te$_8$), which is induced by intercalating 0.25 Cr atom per unit cell within the van der Waals (vdW) gaps of CrTe$_2$. In this work, we report an increased N\'eel Temperature ($T_\mathrm{N}$) of the AFM phase in Cr$_{1+x}$Te$_2$ by slightly reducing the concentration of Cr intercalants. Moreover, the intercalated Cr atoms form a metastable 2$\times$2 supercell structure that can be manipulated by electron beam irradiation. This work offers a promising approach to tuning magnetic and structural properties by adjusting the concentration of self-intercalated magnetic atoms., Comment: 5 figures, 1 table

This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalability. By integrating cutting-edge technologies such as generative and agentic AI, cross-layer automation and optimization, unified control plane, and composable and adaptive system architecture, the proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness. Incorporating quantum computing as it matures will enable quantum-accelerated simulations for materials science, climate modeling, and other high-impact domains. Collaborative efforts between academia and industry are central to this vision, driving advancements in foundation models for material design and climate solutions, scalable multimodal data processing, and enhanced physics-based AI emulators for applications like weather forecasting and carbon sequestration. Research priorities include advancing AI agentic systems, LLM as an Abstraction (LLMaaA), AI model optimization and unified abstractions across heterogeneous infrastructure, end-to-end edge-cloud transformation, efficient programming model, middleware and platform, secure infrastructure, application-adaptive cloud systems, and new quantum-classical collaborative workflows. These ideas and solutions encompass both theoretical and practical research questions, requiring coordinated input and support from the research community. This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms, fostering breakthroughs in AI-driven applications and scientific discovery across academia, industry, and society., Comment: 70 pages, 27 figures

The Radio Neutrino Observatory in Greenland (RNO-G) is the first in-ice radio array in the northern hemisphere for the detection of ultra-high energy neutrinos via the coherent radio emission from neutrino-induced particle cascades within the ice. The array is currently in phased construction near Summit Station on the Greenland ice sheet, with 7~stations deployed during the first two boreal summer field seasons of 2021 and 2022. In this paper, we describe the installation and system design of these initial RNO-G stations, and discuss the performance of the array as of summer 2024.

Single-photon detectors are ``blind" after the detection of a photon, and thereafter display a characteristic recovery in efficiency, during which the number of undetected photons depends on the statistics of the incident light. We show how the efficiency-recovery, photon statistics and intensity have an interdependent relationship which suppresses a detector's ability to count photons and measure correlations. We also demonstrate this effect with an experiment using $n$ such detectors to determine the $n^{\mathrm{th}}$ order correlation function with pseudothermal light., Comment: 8 pages, 6 figures including appendix

Stellar evolution is driven by the changing composition of a star from nuclear reactions. At the late stages of evolution and during explosive events, the timescale can be short and drive strong hydrodynamic flows, making simulations of astrophysical reacting flows challenging. Over the past decades, the standard approach to modeling reactions in simulation codes has been operator splitting, using implicit integrators for reactions. Here we explore some of the assumptions in this standard approach and describe some techniques for improving the efficiency and accuracy of astrophysical reacting flows., Comment: submitted to Proceedings of AstroNum 2024

There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human-interpretable. This complexity raises concerns about trustworthiness, impacting safe and effective adoption of such technologies. Improved understanding of decision-making processes and requirements for explanations coming from decision support tools is a vital component in providing effective explainable solutions. This is particularly relevant in the data-intensive, fast-paced environments of intensive care units (ICUs). To explore these issues, group interviews were conducted with seven ICU clinicians, representing various roles and experience levels. Thematic analysis revealed three core themes: (T1) ICU decision-making relies on a wide range of factors, (T2) the complexity of patient state is challenging for shared decision-making, and (T3) requirements and capabilities of AI decision support systems. We include design recommendations from clinical input, providing insights to inform future AI systems for intensive care.

In this paper, we consider the conditional generation problem by guiding off-the-shelf unconditional diffusion models with differentiable loss functions in a plug-and-play fashion. While previous research has primarily focused on balancing the unconditional diffusion model and the guided loss through a tuned weight hyperparameter, we propose a novel framework that distinctly decouples these two components. Specifically, we introduce two variables ${x}$ and ${z}$, to represent the generated samples governed by the unconditional generation model and the guidance function, respectively. This decoupling reformulates conditional generation into two manageable subproblems, unified by the constraint ${x} = {z}$. Leveraging this setup, we develop a new algorithm based on the Alternating Direction Method of Multipliers (ADMM) to adaptively balance these components. Additionally, we establish the equivalence between the diffusion reverse step and the proximal operator of ADMM and provide a detailed convergence analysis of our algorithm under certain mild assumptions. Our experiments demonstrate that our proposed method ADMMDiff consistently generates high-quality samples while ensuring strong adherence to the conditioning criteria. It outperforms existing methods across a range of conditional generation tasks, including image generation with various guidance and controllable motion synthesis.

Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator version 2) and its application to reproducing atmospheric variability over the past 80 years on timescales from days to decades. ACE2 is a 450M-parameter autoregressive machine learning emulator, operating with 6-hour temporal resolution, 1{\deg} horizontal resolution and eight vertical layers. It exactly conserves global dry air mass and moisture and can be stepped forward stably for arbitrarily many steps with a throughput of about 1500 simulated years per wall clock day. ACE2 generates emergent phenomena such as tropical cyclones, the Madden Julian Oscillation, and sudden stratospheric warmings. Furthermore, it accurately reproduces the atmospheric response to El Ni\~no variability and global trends of temperature over the past 80 years. However, its sensitivities to separately changing sea surface temperature and carbon dioxide are not entirely realistic., Comment: 31 pages, 23 figures

The characterization of magnetic fields within molecular clouds is fundamental to understanding star formation processes. Accurately gauging the three-dimensional structure of these fields presents a challenge, as observational techniques such as dust polarization and the Zeeman effect each provide only partial information on the orientation and line-of-sight strength, respectively. By analyzing a suite of AREPO simulations, this paper investigates how observables can relate to underlying physical properties to derive a more comprehensive picture of the magnetic field's inclination angle and strength, specifically in regions where both dust polarization and Zeeman data are available. To demonstrate the method, we produce synthetic observations of the polarization angle dispersion and line-of-sight Alfv\'en Mach Number and explore the behavior of the inclination angle, $\gamma$, and strength of the magnetic field in regions where both Zeeman and dust polarization data are available. We find that dust polarization data can be used to determine the inclination angle if the cloud is known to be trans-Alfv\'enic or sub-Alfv\'enic. The strength of the magnetic field relative to turbulence can be estimated by comparing polarization observations to Zeeman observations. Comparing the dispersion of the polarization angle to the estimated line-of-sight Alfv\'en Mach Number provides clues about the strength of the magnetic field and, consequently, the orientation of the magnetic field., Comment: 16 pages, 12 figures, 1 table

Background: Ultra-high dose rate proton therapy shows promise in tissue sparing by enhancing therapeutic ratio through the FLASH effect. In radiotherapy, accurate in vivo dosimetry is crucial for quality assurance, but remains challenging for UHDR as existing systems lack spatial and temporal resolution to verify dose and dose rate in complex anatomical regions, especially for PBS proton therapy. Purpose: To develop and evaluate a novel 3D surface dosimetry method for UHDR PBS proton therapy using high-speed imaging of a scintillator array for real-time, high-resolution surface dose monitoring. The spatial, temporal, and dosimetric components are validated via imaging of a QA phantom and comparison against TPS predictions. Methods: A deformable multi-element scintillator array was designed with 7.5mm element pitch and 0.5mm inter-element gap. Scintillation linearity with dose was evaluated with variation in response to increasing imaging and irradiation angles. WED testing evaluated beam attenuation at two energy levels. Scintillation emission was imaged at 1kHz and mesh position was monitored via 2-camera stereovision. System setup was validated using a 3D QA phantom to assess spatial accuracy and guide setup correction. Stereovision properties of array elements guided angular correction and geometric transformation. Kernel-based residual spot fitting derived cumulative dose maps compared to TPS dose profile of 5x5cm UHDR PBS delivery using 3%/2mm gamma analysis. PBS and maximum dose rate maps were calculated. Results: Setup achieved average localization error of 0.62 mm, surpassing typical 1+ mm clinical threshold. Intensity correction based on angular information yielded cumulative spot dose uncertainty of ~1% (5.428mGy). Processed dose map compared to TPS via gamma analysis showed 99.9% passing rate at 3%/2mm. WED of the array measured 1.1mm, minimizing impact on dose distribution., Comment: 17 pages, 9 figures, submitted to Medical Physics

We present a proof-of-concept design for an atomtronic rotation sensor consisting of an array of ``double-target'' Bose-Einstein condensates (BECs). A ``target'' BEC is a disk-shaped condensate surrounded by a concentric ring-shaped condensate. A ``double-target'' BEC is two adjacent target BECs whose ring condensates partially overlap. The sensor consists of an $n\times m$ array of these double-target BECs. The measurement of the frame rotation speed, $\Omega_{R}$, is carried out by creating the array of double-target BECs (setup step), inducing one unit of quantized flow in the top ring of each member of the array (initialization step), applying potential barriers in the overlap region of each member (measurement step), and observing whether the induced flow is transferred from the top to the bottom ring in each member (readout step). We describe a set of simulations showing that a single instance of a double-target BEC behaves in a way that enables the efficient operation of an $n\times m$ array for measuring $\Omega_{R}$. As an example of sensor operation we present a simulation showing that a 2$\times$2 array can be designed to measure $\Omega_{R}$ in a user-specified range., Comment: 13 pages, 10 figures

Studies aiming to determine the astrophysical origins of nuclei produced by the rapid neutron capture process ($r$ process) rely on nuclear properties as inputs for simulations. The solar abundances can be used as a benchmark for such calculations, with the $r$-process rare-earth peak (REP) around mass number ($A$) 164 being of special interest due to its presently unknown origin. With the advancement of rare isotope beam production over the last decade and improvement in experimental sensitivities, many of these REP nuclides have become accessible for measurement. Masses are one of the most critical inputs as they impact multiple nuclear properties, namely the neutron-separation energies, neutron capture rates, $\beta$-decay rates, and $\beta$-delayed neutron emission probabilities. In this work, we report masses of 20 neutron-rich nuclides (along the Ba, La, Ce, Pr, Nd, Pm, Gd, Dy and Ho isotopic chains) produced at the CAlifornium Rare Isotope Breeder Upgrade (CARIBU) facility at Argonne National Laboratory. The masses were measured with the Canadian Penning trap (CPT) mass spectrometer using the Phase-Imaging Ion-Cyclotron-Resonance (PI-ICR) technique. We then use these new masses along with previously published CPT masses to inform predictions for a Markov Chain Monte Carlo (MCMC) procedure aiming to identify the astrophysical conditions consistent with both solar data and mass measurements. We show that the MCMC responds to this updated mass information, producing refined results for both mass predictions and REP abundances., Comment: 11 pages, 8 figures

North Atlantic right whales are an endangered species; their entire population numbers approximately 372 individuals, and they are subject to major anthropogenic threats. They feed on zooplankton species whose distribution shifts in a dynamic and warming oceanic environment. Because right whales in turn follow their shifting food resource, it is necessary to jointly study the distribution of whales and their prey. The innovative joint species distribution modeling (JSDM) contribution here is different from anything in the large JDSM literature, reflecting the processes and data we have to work with. Specifically, our JSDM supplies a geostatistical model for expected amount of zooplankton collected at a site. We require a point pattern model for the intensity of right whale abundance. The two process models are joined through a latent conditional-marginal specification. Further, each species has two data sources to inform their respective distributions and these sources require novel data fusion. What emerges is a complex multi-level model. Through simulation we demonstrate the ability of our joint specification to identify model unknowns and learn better about the species distributions than modeling them individually. We then apply our modeling to real data from Cape Cod Bay, Massachusetts in the U.S.

Many workers in cities across the world, who have been teleworking because of the COVID-19 pandemic, are expected to be back to their commutes. As this process is believed to be gradual and telecommuting is likely to remain an option for many workers, hybrid model and flexible schedules might become the norm in the future. This variable work schedules allows employees to commute outside of traditional rush hours. Moreover, many studies showed that commuters might be skeptical of using trains, buses, and carpools and could turn to personal vehicles to get to work, which might increase congestion and crashes in the roads. This study attempts to provide information on the safest time to commute to Washington, DC area analyzing historical traffic crash data before the COVID-19 pandemic. It also aims to advance our understanding of traffic crashes and other relating factors such as weather in the Washington, DC area. We created a model to predict crashes by time of the day, using a negative binomial regression after rejecting a Poisson regression, and additionally explored the validity of a Random Forest regression. Our main consideration for an eventual application of this study is to reduce crashes in Washington DC, using this tool that provides people with better options on when to commute and when to telework, if available. The study also provides policymakers and researchers with real-world insights that decrease the number of traffic crashes to help achieve the goals of The Vision Zero Initiative adopted by the district.

Identified as parsec-size, gas clumps at the junction of multiple filaments, hub-filament systems (HFS) play a crucial role during the formation of young clusters and high-mass stars. These HFS appear nevertheless to be detached from most galactic filaments when compared in the mass-length (M-L) phase-space. We aim to characterize the early evolution of HFS as part of the filamentary description of the interstellar medium. Combining previous scaling relations with new analytic calculations, we created a toy model to explore the different physical regimes described by the M-L diagram. Despite its simplicity, our model accurately reproduces several observational properties reported for filaments and HFS such as their expected typical aspect ratio ($A$), mean surface density ($\Sigma$), and gas accretion rate ($\dot{m}$). Moreover, this model naturally explains the different mass and length regimes populated by filaments and HFS, respectively. Our model predicts a dichotomy between filamentary ($A\geq 3$) and spheroidal ($A<3$) structures connected to the relative importance of their fragmentation, accretion, and collapse timescales. Individual filaments with low accretion rates are dominated by an efficient internal fragmentation. In contrast, the formation of compact HFS at the intersection of filaments triggers a geometric phase-transition leading to the gravitational collapse of these structures at parsec-scales in $\sim$1Myr also inducing higher accretion rates., Comment: Accepted by A&A, 17 pages, 11 figures

This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural network repair in a two-step supervised learning framework. It first learns a policy from expert demonstrations and then applies repair subject to predictive models to enforce safety constraints. The predictive models can encompass various aspects relevant to robot learning applications, such as proprioceptive states and collision likelihood. Our experimental results demonstrate that the learned policy successfully adheres to a predefined set of safety constraints on two applications: mobile robot navigation, and real-world lower-leg prostheses. Additionally, we have shown that our method effectively reduces repeated interaction with the robot, leading to substantial time savings during the learning process., Comment: Accepted at the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance. In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL using a desktop PC, with a human-normalized interquartile mean (IQM) of 7.4 on atari-60. Beyond Atari, we demonstrate BTR's capability to handle complex 3D games, successfully training agents to play Super Mario Galaxy, Mario Kart, and Mortal Kombat with minimal algorithmic changes. Designing BTR with computational efficiency in mind, agents can be trained using a desktop PC on 200 million Atari frames within 12 hours. Additionally, we conduct detailed ablation studies of each component, analzying the performance and impact using numerous measures., Comment: 9 main pages, 26 total. Currently under review at ICLR

Towards the role of humanoid robots as squad mates in urban operations and other domains, we identified doors as a major area lacking capability development. In this paper, we focus on the ability of humanoid robots to navigate and deal with doors. Human-sized doors are ubiquitous in many environment domains and the humanoid form factor is uniquely suited to operate and traverse them. We present an architecture which incorporates GPU accelerated perception and a tree based interactive behavior coordination system with a whole body motion and walking controller. Our system is capable of performing door traversals on a variety of door types. It supports rapid authoring of behaviors for unseen door types and techniques to achieve re-usability of those authored behaviors. The behaviors are modelled using trees and feature logical reactivity and action sequences that can be executed with layered concurrency to increase speed. Primitive actions are built on top of our existing whole body controller which supports manipulation while walking. We include a perception system using both neural networks and classical computer vision for door mechanism detection outside of the lab environment. We present operator-robot interdependence analysis charts to explore how human cognition is combined with artificial intelligence to produce complex robot behavior. Finally, we present and discuss real robot performances of fast door traversals on our Nadia humanoid robot. Videos online at https://www.youtube.com/playlist?list=PLXuyT8w3JVgMPaB5nWNRNHtqzRK8i68dy., Comment: 15 pages, 23 figure, for submission to Elsevier RAS

Field work is an essential component not just for organismal biology, but also for the expanding umbrella of disciplines which have turned their attention towards living systems. Observing organisms in naturalistic contexts is a critical component of discovery; however, conducting field research can be a massive barrier for scientists who do not have experience working with organisms in a naturalistic context under challenging field conditions. Here we propose 8 critical steps for organizing and executing interdisciplinary curiosity-driven field research, drawing on the insights from The in Situ Jungle Biomechanics Lab (JBL). The JBL program is a field research course that helps early-career scientists gain experience in organizing and conducting interdisciplinary field research. JBL uses a curiosity-driven approach to field science education by encouraging early-career researchers to explore scientific questions in the Peruvian Amazon with a non-prescriptive approach to research output. We achieve an inclusive research space by bringing scientists from across disciplines together, with local communities to collaborate and spark new questions and ideas. To stoke curiosity, the JBL imparts a naturalist tradition set forth by organismal biologists of the 20th century who have extolled the merits of observing the natural world as a form of scientific exploration.

To understand the system-level interactions between the entities in Carbon Capture, Utilization, and Storage (CCUS), an agent-based foundational modeling tool, CCUS-Agent, is developed for a large-scale study of transportation flows and infrastructure in the United States. Key features of the tool include (i) modular design, (ii) multiple transportation modes, (iii) capabilities for extension, and (iv) testing against various system components and networks of small and large sizes. Five matching algorithms for CO2 supply agents (e.g., powerplants and industrial facilities) and demand agents (e.g., storage and utilization sites) are explored: Most Profitable First Year (MPFY), Most Profitable All Years (MPAY), Shortest Total Distance First Year (SDFY), Shortest Total Distance All Years (SDAY), and Shortest distance to long-haul transport All Years (ACAY). Before matching, the supply agent, demand agent, and route must be available, and the connection must be profitable. A profitable connection means the supply agent portion of revenue from the 45Q tax credit must cover the supply agent costs and all transportation costs, while the demand agent revenue portion must cover all demand agent costs. A case study employing over 5,500 supply and demand agents and multimodal CCUS transportation infrastructure in the contiguous United States is conducted. The results suggest that it is possible to capture over 9 billion tonnes (GT) of CO2 from 2025 to 2043, which will increase significantly to 22 GT if the capture costs are reduced by 40%. The MPFY and SDFY algorithms capture more CO2 earlier in the time horizon, while the MPAY and SDAY algorithms capture more later in the time horizon.

State-of-the-art noisy-intermediate-scale quantum (NISQ) processors are currently implemented across a variety of hardware platforms, each with their own distinct gatesets. As such, circuit compilation should not only be aware of, but also deeply connect to, the native gateset and noise properties of each. Trapped-ion processors are one such platform that provides a gateset that can be continuously parameterized across both one- and two-qubit gates. Here we use the Quantum Scientific Computing Open User Testbed (QSCOUT) to study noise-aware compilations focused on continuously parameterized two-qubit $\mathcal{ZZ}$ gates (based on the M{\o}lmer-S{\o}rensen interaction) using $\textbf{Superstaq}$, a quantum software platform for hardware-aware circuit compiler optimizations. We discuss the realization of $\mathcal{ZZ}$ gates with arbitrary angle on the all-to-all connected trapped-ion system. Then we discuss a variety of different compiler optimizations that innately target these $\mathcal{ZZ}$ gates and their noise properties. These optimizations include moving from a restricted maximally entangling gateset to a continuously parameterized one, swap mirroring to further reduce total entangling angle of the operations, focusing the heaviest $\mathcal{ZZ}$ angle participation on the best performing gate pairs, and circuit approximation to remove the least impactful $\mathcal{ZZ}$ gates. We demonstrate these compilation approaches on the hardware with randomized quantum volume circuits, observing the potential to realize a larger quantum volume as a result of these optimizations. Using differing yet complementary analysis techniques, we observe the distinct improvements in system performance provided by these noise-aware compilations and study the role of stochastic and coherent error channels for each compilation choice., Comment: 17 pages, 13 figures

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open-source code gSeaGen, allowing for the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionalities but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments., Comment: 27 pages, 13 figures, submitted to Computer Physics Communications

Self-supervised learning (SSL) models have become crucial in speech processing, with recent advancements concentrating on developing architectures that capture representations across multiple timescales. The primary goal of these multi-scale architectures is to exploit the hierarchical nature of speech, where lower-resolution components aim to capture representations that align with increasingly abstract concepts (e.g., from phones to words to sentences). Although multi-scale approaches have demonstrated some improvements over single-scale models, the precise reasons for these enhancements have poor empirical support. In this study, we present an initial analysis of layer-wise representations in multi-scale architectures, with a focus on Canonical Correlation Analysis (CCA) and Mutual Information (MI). We apply this analysis to Multi-Resolution HuBERT (MR-HuBERT) and find that (1) the improved performance on SUPERB tasks is primarily due to the auxiliary low-resolution loss rather than the downsampling itself, and (2) downsampling to lower resolutions neither improves downstream performance nor correlates with higher-level information (e.g., words), though it does improve computational efficiency. These findings challenge assumptions about the multi-scale nature of MR-HuBERT and motivate the importance of disentangling computational efficiency from learning better representations.

In many applications, SMT solvers are used to solve similar or identical tasks over time. When the performance of the solver varies significantly despite only small changes, this leads to frustration for users. This has been called the stability problem, and it represents an important usability challenge for SMT solvers. In this paper, we introduce an approach for mitigating the stability problem based on normalizing solver inputs. We show that a perfect normalizing algorithm exists but is computationally expensive. We then describe an approximate algorithm and evaluate it on a set of benchmarks from related work, as well as a large set of benchmarks sampled from SMT-LIB. Our evaluation shows that our approximate normalizer reduces runtime variability with minimal overhead and is able to normalize a large class of mutated benchmarks to a unique normal form.

One useful application of NLP models is to support people in reading complex text from unfamiliar domains (e.g., scientific articles). Simplifying the entire text makes it understandable but sometimes removes important details. On the contrary, helping adult readers understand difficult concepts in context can enhance their vocabulary and knowledge. In a preliminary human study, we first identify that lack of context and unfamiliarity with difficult concepts is a major reason for adult readers' difficulty with domain-specific text. We then introduce "targeted concept simplification," a simplification task for rewriting text to help readers comprehend text containing unfamiliar concepts. We also introduce WikiDomains, a new dataset of 22k definitions from 13 academic domains paired with a difficult concept within each definition. We benchmark the performance of open-source and commercial LLMs and a simple dictionary baseline on this task across human judgments of ease of understanding and meaning preservation. Interestingly, our human judges preferred explanations about the difficult concept more than simplification of the concept phrase. Further, no single model achieved superior performance across all quality dimensions, and automated metrics also show low correlations with human evaluations of concept simplification ($\sim0.2$), opening up rich avenues for research on personalized human reading comprehension support., Comment: to appear in proceedings of EMNLP 2024

Quantum confinement significantly influences the excited states of sub-10 nm single-walled carbon nanotubes (SWCNTs), crucial for advancements in transistor technology and the development of novel opto-electronic materials such as fluorescent ultrashort nanotubes (FUNs). However, the length dependence of this effect in ultrashort SWCNTs is not yet fully understood in the context of the SWCNT exciton states. Here, we conduct excited state calculations using time-dependent density functional theory (TD-DFT) on geometry-optimized models of ultrashort SWCNTs and FUNs, which consist of ultrashort SWCNTs with $sp^3$ defects. Our results reveal a length-dependent scaling law of the $E_{11}$ exciton energy that can be understood through a geometric, dimensional argument, and which departs from the length scaling of a 1D particle-in-a-box. We find that this scaling law applies to ultrashort (6,5) and (6,6) SWCNTs, as well as models of (6,5) FUNs. In contrast, the defect-induced $E_{sp^3}$ transition, which is redshifted from the $E_{11}$ optical gap transition, shows little dependence on the nanotube length, even in the shortest possible SWCNTs. We attribute this relative lack of length dependence to orbital localization around the quantum defect that is installed near the SWCNT edge. Our results illustrate the complex interplay of defects and quantum confinement effects in ultrashort SWCNTs and provide a foundation for further explorations of these nanoscale phenomena.

Neural Control Barrier Functions (NCBFs) have shown significant promise in enforcing safety constraints on nonlinear autonomous systems. State-of-the-art exact approaches to verifying safety of NCBF-based controllers exploit the piecewise-linear structure of ReLU neural networks, however, such approaches still rely on enumerating all of the activation regions of the network near the safety boundary, thus incurring high computation cost. In this paper, we propose a framework for Synthesis with Efficient Exact Verification (SEEV). Our framework consists of two components, namely (i) an NCBF synthesis algorithm that introduces a novel regularizer to reduce the number of activation regions at the safety boundary, and (ii) a verification algorithm that exploits tight over-approximations of the safety conditions to reduce the cost of verifying each piecewise-linear segment. Our simulations show that SEEV significantly improves verification efficiency while maintaining the CBF quality across various benchmark systems and neural network structures. Our code is available at https://github.com/HongchaoZhang-HZ/SEEV.

Multi-agent cyber-physical systems are present in a variety of applications. Agent decision-making can be affected due to errors induced by uncertain, dynamic operating environments or due to incorrect actions taken by an agent. When an erroneous decision that leads to a violation of safety is identified, assigning responsibility to individual agents is a key step toward preventing future accidents. Current approaches to carrying out such investigations require human labor or high degree of familiarity with operating environments. Automated strategies to assign responsibility can achieve a significant reduction in human effort and associated cognitive burden. In this paper, we develop an automated procedure to assign responsibility for safety violations to actions of any single agent in a principled manner. We base our approach on reasoning about safety violations in road safety. Given a safety violation, we use counterfactual reasoning to create alternative scenarios, showing how different outcomes could have occurred if certain actions had been replaced by others. We introduce the degree of responsibility (DoR) metric for each agent. The DoR, using the Shapley value, quantifies each agent's contribution to the safety violation, providing a basis to explain and justify decisions. We also develop heuristic techniques and methods based on agent interaction structures to improve scalability as agent numbers grow. We examine three safety violation cases from the National Highway Traffic Safety Administration (NHTSA). We run experiments using CARLA urban driving simulator. Results show the DoR improves the explainability of decisions and accountability for agent actions and their consequences.

By analyzing the spectral energy distributions (SEDs) of resolved stars in nearby galaxies, we can constrain their stellar properties and line-of-sight dust extinction. From the Scylla survey, we obtain ultraviolet to near-infrared photometry from Wide Field Camera 3 onboard the {\it Hubble Space Telescope} for more than 1.5 million stars in the SMC and LMC. We use the Bayesian Extinction and Stellar Tool (BEAST) to analyze the multi-band SEDs of these sources and characterize their initial masses, ages, metallicities, distances, and line-of-sight extinction properties (e.g.~$A_V$, $R_V$). We apply quality cuts and perform validation simulations to construct a catalog of over 550,000 stars with high-reliability SED fits, which we use to analyze the stellar content and extinction properties of the SMC and LMC. We detect stars with masses as low as 0.6 $M_{\odot}$. Observed stellar age distributions show a jump in stars around 6 Gyrs ago, which is in agreement with other star-formation histories. Extinctions ($A_V$) in both galaxies follow a log-normal distribution. We compare $A_V$ with ancillary gas and dust tracers like $HI$, $H_\alpha$, and far infrared (FIR) dust emission and find positive correlations on a field-by-field basis. We convert observed $A_V$ to predicted dust surface densities using the Draine et. al. (2014) model and find $A_V$-based dust surface densities are a factor of $\sim$2.5 lower than observed FIR-based dust surface densities, a correction factor similar to other studies., Comment: Submitted to ApJ, 31 pages

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

In this innovative practice full paper, we address the equity gap for neurodivergent and situationally limited learners by identifying the spectrum of dynamic factors that impact learning and function. Educators have shown a growing interest in identifying learners' cognitive abilities and learning preferences to measure their impact on academic achievement. Often institutions employ one-size-fits-all approaches leaving the burden on disabled students to self-advocate or tolerate inadequate support. Emerging frameworks guide neurodivergent learners through instructional approaches, such as online education. However, these frameworks fail to address holistic environmental needs or recommend technology interventions, particularly for those with undisclosed learning or developmental disabilities and situational limitations. In this article, we integrate a neurodivergent perspective through secondary research of around 100 articles to introduce a Guiding Empowerment Model involving key cognitive and situational factors that contextualize day-to-day experiences affecting learner ability. We synthesize three sample student profiles that highlight user problems in functioning. We use this model to evaluate sample learning platform features and other supportive technology solutions. The proposed approach augments frameworks such as Universal Design for Learning to consider factors including various sensory processing differences, social connection challenges, and environmental limitations. We suggest that by applying the mode through technology-enabled features such as customizable task management, guided varied content access, and guided multi-modal collaboration, major learning barriers of neurodivergent and situationally limited learners will be removed to activate the successful pursuit of their academic goals., Comment: 9 pages, 1 Figure, 1 Table, Accepted in FIE 2024

This paper studies the identification of an $\mathbb{R}^d$-valued diffusion $X$ when a running function of it, say $h(X_t)$, is observed. A point-wise observation of the process (in other words, observing $h(X_t)$ in isolation) cannot identify $X_t$ unless the $h$ is injective. However observing $h(X_s)$ on a small interval $[t,t+\varepsilon]$ can be enough to determine $X_t$ exactly. The paper contain results that expand on this idea; in particular, a property of `fine total asymmetry' of twice continuously differentiable $h$ is introduced that depends on the fine topology of potential theory and that is both necessary and sufficient for $X$ to be adapted to a natural right-continuous filtration generated by the observations. This particular filtration, though augmented with null sets, does not depend on the distribution of $X_0$. For real-analytic $h$ the property reduces to simple asymmetry; that is, there is no nontrivial affine isometry $\kappa$ on $\mathbb{R}^d$ such that $h = h \circ \kappa$. A second result concerns the case where $X_0$ is given and $h$ is merely Borel; then $X$ is adapted to an augmented filtration generated by the observation process $(h(X_t))_{t\geq 0}$ if $h$ is `locally invertible' on a subset of $\mathbb{R}^d$ dense in the fine topology on $\mathbb{R}^d$.

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures