Search

Thriving communities depend on a strong early childhood education (ECE) system--one where both young children and members of the workforce are served and supported. In recent years, state government leaders have increasingly focused on changing qualifications for specific ECE roles, as increasing credential requirements has sometimes been associated with increasing quality. However, across the country, early childhood educators face significant barriers to economic security and continuing education--all while supporting children, parents, and their communities with specialized education services. This report examines the barriers that even highly qualified early child educators experience in the face of increased credential requirements. Featuring case studies of California and Washington, D.C., the report offers implications for policies that protect educators and advance racial and economic equity in the profession. Included with the report are an Executive Summary encapsulating the findings and a Technical Document describing the methodology, data collection, and quantitative analysis.

We give an overview of the science objectives and mission design of the Spectroscopic Time-Resolving Observatory for Broadband Energy X-rays (STROBE-X) observatory, which has been proposed as a NASA probe-class (~$1.5B) mission in response to the Astro2020 recommendation for an X-ray probe., Comment: 11 pages, 5 figures, accepted for publication in JATIS

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

OpenSciEd is a set of Creative Commons licensed, standards aligned curriculum and teacher professional learning materials that will be available for grades K-12. Using a storyline model that gives students the responsibility of "figuring out" science phenomena by engaging in science practices and classroom discussion to achieve consensus, OpenSciEd "empowers educators to go beyond traditional science teaching methods." This instructional model is ambitious for teachers, who must serve as facilitators and continually adapt instruction in response to directions students take. Because of this instructional shift, districts must also shift in how they adopt and implement science curriculum. Among the aims of the OpenSciEd Research Community, led by Digital Promise with support from the Carnegie Corporation of New York, is to develop and disseminate resources that support advancement of OpenSciEd-enabled research. Toward this goal, Digital Promise has released this report that synthesizes perspectives from OpenSciEd practitioners on their needs. The report identifies: (1) supports that teachers need to enact OpenSciEd with integrity, engage all their students, and gather evidence of students' standards-based learning outcomes; (2) ways districts can achieve deep, sustained adoption and meet their teachers' needs; and (3) potential research questions and opportunities for innovation that can improve OpenSciEd implementation in districts and classrooms. [This report was produced in collaboration with OpenSciEd.]

Crystallization of proteins, specifically proteins of medical relevance, is performed for various reasons such as to understand the protein structure and to design therapies. Obtaining kinetic constants in rate laws for nucleation and growth of advanced biotherapeutics such as capsids, an assembly of macromolecules, is challenging and essential to the design of the crystallization processes. In this work, coupled population balance and species balance equations are developed to extract nucleation and growth kinetics for crystallization of recombinant adeno-associated virus (rAAV) capsids. A comparison of model results with that of experimental data for capsid crystallization in hanging-drop vapor diffusion system shows that slow rate of vapor diffusion from the droplet controls the initial nucleation and growth processes, and the capsid nucleation occurs via heterogeneous nucleation in the microdroplet. Results also show that the capsids, which are of very high molecular weight (~3.6 MDa), have a similar tendency to nucleate as small organic molecules such as glycine (75 Da), low-molecular-weight proteins, and small-molecule active pharmaceutical ingredients due to its ball-shaped outer structure/shape. Capids also show a prolonged nucleation period as for proteins and other macromolecules, but has a slow growth rate with a growth rate pre-factor seven orders of magnitude smaller than that of lysozyme. The capsid crystal growth rate is weakly sensitive to the supersaturation compared to lysozyme and is limited by the transport of capsids due to slow Brownian motion resulting from the very high molecular weight., Comment: 16 pages, 11 figures, 1 table

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) is an upcoming radio interferometric telescope designed to constrain dark energy through the 21cm intensity mapping of Baryon Acoustic Oscillations (BAO). Instrumental systematics must be controlled and carefully characterized to measure the 21cm power spectrum with fidelity and achieve high-precision constraints on the cosmological parameters. The chromaticity of the primary beam is one such complicated systematic, which can leak the power of spectrally smooth foregrounds beyond the ideal horizon limits due to the complex spatial and spectral structures of the sidelobes and the mainlobe. This paper studies the chromaticity of the HIRAX Stokes I primary beam and its effects on accurate measurements of the 21cm power spectrum. To investigate the effect of chromaticity in the 21cm power spectrum, we present a physically motivated beam modeling technique, which uses a flexible basis derived from traditional optics that can account for higher-order radial and azimuthal structures in the primary beam. We investigate the impact of imperfect knowledge of the mainlobe and sidelobes chromaticity in the power spectrum space by subtracting a simple foreground model in simulated snapshot visibilities to recover the H$\textsc{i}$ power spectrum. Additionally, we find that modeling up to the octupolar azimuthal order feature (fourth-order angular variation) in the primary beam is sufficient to reduce the leakage outside the wedge with minimal bias., Comment: 20 pages, 12 figures. Prepared for submitting in the Astrophysical Journal (ApJ)

Photonic technologies hold significant potential for creating innovative, high-speed, efficient and hardware-friendly neuromorphic computing platforms. Neuromorphic photonic methods leveraging ubiquitous, technologically mature and cost-effective Vertical-Cavity Surface Emitting Lasers (VCSELs) are of notable interest. VCSELs have demonstrated the capability to replicate neuronal optical spiking responses at ultrafast rates. These characteristics have triggered research into applying these key-enabling devices in spike-based photonic computing. Here, a GHz-rate photonic Spiking Neural Network (p-SNN) using a single VCSEL is reported, and its application to a complex time-series prediction task is demonstrated for the first time. The VCSEL p-SNN combined with a technique to induce network memory, is applied to perform multi-step-ahead predictions of a chaotic time-series. By providing the feedforward p-SNN with only two temporally separated inputs excellent accuracy is experimentally demonstrated over a range of prediction horizons. VCSEL-based p-SNNs therefore offer ultrafast, efficient operation in complex predictive tasks whilst enabling hardware implementations. The inherent attributes and performance of VCSEL p-SNNs hold great promise for use in future light-enabled neuromorphic computing hardware., Comment: 16 pages, 8 figures. Submitted to Communications Physics

While Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation, their performance often lags in lower-resource, non-English languages due to biases in the training data. In this work, we explore strategies for adapting the primarily English LLMs (Llama-2 and Llama-3) to Dutch, a language spoken by 30 million people worldwide yet often underrepresented in LLM development. We collect 104GB of Dutch text ($32$B tokens) from various sources to first apply continued pretraining using low-rank adaptation (LoRA), complemented with Dutch posttraining strategies provided by prior work. For Llama-2, we consider using (i) the tokenizer of the original model, and (ii) training a new, Dutch-specific tokenizer combined with embedding reinitialization. We evaluate our adapted models, ChocoLlama-2, both on standard benchmarks and a novel Dutch benchmark, ChocoLlama-Bench. Our results demonstrate that LoRA can effectively scale for language adaptation, and that tokenizer modification with careful weight reinitialization can improve performance. Notably, Llama-3 was released during the course of this project and, upon evaluation, demonstrated superior Dutch capabilities compared to our Dutch-adapted versions of Llama-2. We hence apply the same adaptation technique to Llama-3, using its original tokenizer. While our adaptation methods enhanced Llama-2's Dutch capabilities, we found limited gains when applying the same techniques to Llama-3. This suggests that for ever improving, multilingual foundation models, language adaptation techniques may benefit more from focusing on language-specific posttraining rather than on continued pretraining. We hope this work contributes to the broader understanding of adapting LLMs to lower-resource languages, and to the development of Dutch LLMs in particular.

In the present chapter we focus on the fundamentals of non-grid-conforming numerical approaches to simulating particulate flows, implementation issues and grid convergence vs. available reference data. The main idea is to avoid adapting the mesh (and - as much as possible - the discrete operators) to the time-dependent fluid domain with the aim to maximize computational efficiency. We restrict our attention to spherical particle shapes (while deviations from sphericity are treated in a subsequent chapter). We show that similar ideas can be successfully implemented in a variety of underlying fluid flow solvers, leading to powerful tools for the direct numerical simulation of large particulate systems.

Intermediate mass black holes (10^2 < M_BH< 10^5 Msun) are an open question in our understanding of black hole evolution and growth. They have long been linked to dense star cluster environments thanks to cluster dynamics, but there are a limited number of secure detections. We leverage existing X-ray observations from Chandra X-ray Observatory and optical catalogs from Hubble Space Telescope with new radio observations from the Karl G. Jansky Very Large Array to search for any evidence of accreting black holes in young massive clusters in the nearby galaxy M51. We find that of 43 bright ($L_X > 10^{38}$ erg/s) X-ray point sources in M51, 24 had probable matches to objects including possible associated star clusters in the HST Legacy Extragalactic UV Survey catalog, seven of which were classified as contaminants (background galaxies or foreground stars). We explore the optical properties of the remaining 17 sources, including cluster age and mass estimates, and search for radio counterparts in the 8-12 GHz band. The lack of radio counterparts to X-ray sources we know to be associated with young massive clusters in M51 suggests that we do not significantly detect hard-state IMBHs ~ 10^4 Msun or above. However, more sensitive radio facilities like the Square Kilometre Array and next generation Very Large Array may be able to provide evidence for IMBHs with masses down to ~ 10^3 Msun., Comment: 18 pages, 7 figures, accepted to ApJ

As of 2023, a record 117 million people have been displaced worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of the 8.7 million refugees and displaced persons in camps globally relying on traditional biomass for cooking and lacking reliable power for essential tasks such as cooking and charging phones. Often, the burden of collecting firewood falls on women and children, who frequently travel up to 20 kilometers into dangerous areas, increasing their vulnerability.[7] Electricity access could significantly alleviate these challenges, but a major obstacle is the lack of accurate power grid infrastructure maps, particularly in resource-constrained environments like refugee camps, needed for energy access planning. Existing power grid maps are often outdated, incomplete, or dependent on costly, complex technologies, limiting their practicality. To address this issue, PGRID is a novel application-based approach, which utilizes high-resolution aerial imagery to detect electrical poles and segment electrical lines, creating precise power grid maps. PGRID was tested in the Turkana region of Kenya, specifically the Kakuma and Kalobeyei Camps, covering 84 km2 and housing over 200,000 residents. Our findings show that PGRID delivers high-fidelity power grid maps especially in unplanned settlements, with F1-scores of 0.71 and 0.82 for pole detection and line segmentation, respectively. This study highlights a practical application for leveraging open data and limited labels to improve power grid mapping in unplanned settlements, where the growing number of displaced persons urgently need sustainable energy infrastructure solutions., Comment: Accepted to WACV 2025 IEEE/CVF Winter Conference

This paper presents a semi-supervised approach to extracting and analyzing combat phases in judo tournaments using live-streamed footage. The objective is to automate the annotation and summarization of live streamed judo matches. We train models that extract relevant entities and classify combat phases from fixed-perspective judo recordings. We employ semi-supervised methods to address limited labeled data in the domain. We build a model of combat phases via transfer learning from a fine-tuned object detector to classify the presence, activity, and standing state of the match. We evaluate our approach on a dataset of 19 thirty-second judo clips, achieving an F1 score on a $20\%$ test hold-out of 0.66, 0.78, and 0.87 for the three classes, respectively. Our results show initial promise for automating more complex information retrieval tasks using rigorous methods with limited labeled data.

The rapid development of large Vision-Language Models (VLMs) has led to impressive results on academic benchmarks, primarily in widely spoken languages. However, significant gaps remain in the ability of current VLMs to handle low-resource languages and varied cultural contexts, largely due to a lack of high-quality, diverse, and safety-vetted data. Consequently, these models often struggle to understand low-resource languages and cultural nuances in a manner free from toxicity. To address these limitations, we introduce Maya, an open-source Multimodal Multilingual model. Our contributions are threefold: 1) a multilingual image-text pretraining dataset in eight languages, based on the LLaVA pretraining dataset; 2) a thorough analysis of toxicity within the LLaVA dataset, followed by the creation of a novel toxicity-free version across eight languages; and 3) a multilingual image-text model supporting these languages, enhancing cultural and linguistic comprehension in vision-language tasks. Code available at https://github.com/nahidalam/maya.

Gene therapies using recombinant adeno-associated virus (rAAV) have been developed to treat monogenic and acquired diseases but are currently the most expensive drugs due, in part, to high manufacturing costs. The cells producing rAAV generate substantial quantities of empty (50-90%) and partially filled capsids that must be removed prior to final formulation. The conventional separation processes are inefficient in removing empty and partially filled capsids, have low yield, scale poorly, time consuming and need additional purification steps. This article demonstrates one step separation of full capsids from a mixture of full, partially filled, and empty capsids, and other protein impurities using selective crystallization, a purification process, which is first time in protein purification and is performed without physically or chemically modifying the target component for the first time in the history of selective crystallization, and is highly-efficient, highly-scalable, and economical. Hanging-drop vapor diffusion experiments were used to scout crystallization conditions in which full and empty capsids crystallize, then to define conditions in which crystals of full, empty, or both full and empty capsids nucleate and grow. The experimental results for rAAV serotypes 5, 8, and 9 as exemplary vectors and scale-up results show that full capsids can be selectively crystallized and separated in one step from a mixture of full, partially filled, and empty capsids, and other proteins with full capsid enrichment of greater than 80%, approximately 20% higher, and yield of greater than 90%, approximately greater than 30% higher from the existing methods, keeping their biological activity intact, in a short period of time (less than 4 h), with approximately 87% reduction in processing time from the existing processing time and without the need of additional purification steps and in one round., Comment: 24 pages, 6 figures

Bacteria invade surfaces by forming dense colonies encased in a polymer matrix. Successful settlement of founder bacteria, early microcolony development and later macroscopic spreading of these biofilms on surfaces rely on complex physical mechanisms. Recent data show that on soft hydrogels, substrate rigidity is an important determinant for biofilm initiation and spreading, through mostly unknown mechanisms. Using a thermodynamically consistent thin-film approach for suspensions on soft elastic surfaces supplemented with biomass production we investigate in silico the role of substrate softness in the osmotic spreading of biofilms. We show that on soft substrates with an imposed osmotic pressure spreading is considerably slowed down and may be completely halted depending on the biomass production rate. We find, that the critical slowing down of biofilm spreading on soft surfaces is caused by a reduced osmotic influx of solvent into the biofilm at the edges, which results from the thermodynamic coupling between substrate deformation and interfacial forces. By linking substrate osmotic pressure and mechanical softness through scaling laws, our simple model semi-quantitatively captures a range of experimentally observed biofilm spreading dynamics on hydrogels with different architectures, underscoring the importance of inherent substrate properties in the spreading process., Comment: 15 pages, 6 figure, 3 supplementary figures

Low carrier densities in topological semimetals (TSMs) enable the exploration of novel magnetotransport in the quantum limit (QL). Reports consistent with 3D quasi-quantum Hall effect (QQHE) have repositioned TSMs as promising platforms for exploring 3D quantum Hall transport, but the lack of tunability in the Fermi has thus far limited the ability to control the QQHE signal. Here, we tune the defect concentrations in the Dirac semimetal Cd${}_3$As${}_2$ to achieve ultra-low carrier concentrations at 2 K around $2.9\times10^{16}$cm${}^{-3}$, giving way to QQHE signal at modest fields under 10 T. At low carrier densities, where QQHE is most accessible, we find that a zero resistivity state is obscured by a carrier density dependent background originating from Coulomb disorder from charged point defects. Our results highlight the interplay between QQHE and Coulomb disorder scattering, demonstrating that clear observation of QQHE in TSMs intricately depends on Fermi level. Predicted in TSMs a decade ago, we find that Coulomb disorder is an essential ingredient for understanding the magnetoresistivity for a spectrum of Fermi levels, experimentally anchoring the important roles of defects and charged disorder in TSM applications. We discuss future constraints and opportunities in exploring 3D QHE in TSMs.

As some of the most ancient materials in our Solar System, chondritic meteorites offer a valuable window into the early stages of planetary formation, particularly the accretion processes that built the most primitive asteroids. Until now, high energy shocks and collisions have been invoked to explain the deformation and fragmentation of chondrules, the main component of chondrites. However, simulating the cooling of chondrules using continuum mechanics and finite elements, we demonstrate that plastic deformation of chondrules can occur at low collision velocities of just a few meters per second and with kinetic energies less than tenths of a millijoule when temperatures exceed the glass transition temperature Tg ~ 1000 K. Conversely, below Tg, spontaneous chondrule cracking occurs due to differential thermal contraction between phases and is more pronounced in larger chondrules. Counterintuitively, our findings suggest that both ordinary and carbonaceous chondrites formed through similar low-energy processes, with varying degrees of ductility and brittleness depending on the amount of processed material. This implies that the environments where chondrites formed were likely less turbulent and more thermally active than previously thought.

Lazy burning is a recently introduced variation of burning where only one set of vertices is chosen to burn in the first round. In hypergraphs, lazy burning spreads when all but one vertex in a hyperedge is burned. The lazy burning number is the minimum number of initially burned vertices that eventually burns all vertices. We give several equivalent characterizations of lazy burning on hypergraphs using matchings and zero forcing, and then apply these to give new bounds and complexity results. We prove that the lazy burning number of a hypergraph $H$ equals its order minus the maximum cardinality of a certain matching on its incidence graph. Using this characterization, we give a formula for the lazy burning number of a dual hypergraph and give new bounds on the lazy burning number based on various hypergraph parameters. We show that the lazy burning number of a hypergraph may be characterized by a maximal subhypergraph that results from iteratively deleting vertices in singleton hyperedges. We prove that lazy burning on a hypergraph is equivalent to zero forcing on its incidence graph and show an equivalence between skew zero forcing on a graph and lazy burning on its neighborhood hypergraph. As a result, we show that finding an upper bound on the lazy burning number of a hypergraph is NP-complete, which resolves a conjecture from \cite{BJR}. By applying lazy burning, we show that computing an upper bound on the skew zero forcing number for bipartite graphs is NP-complete. We finish with open problems.

Properly understanding the performances of classifiers is essential in various scenarios. However, the literature often relies only on one or two standard scores to compare classifiers, which fails to capture the nuances of application-specific requirements, potentially leading to suboptimal classifier selection. Recently, a paper on the foundations of the theory of performance-based ranking introduced a tool, called the Tile, that organizes an infinity of ranking scores into a 2D map. Thanks to the Tile, it is now possible to evaluate and compare classifiers efficiently, displaying all possible application-specific preferences instead of having to rely on a pair of scores. In this paper, we provide a first hitchhiker's guide for understanding the performances of two-class classifiers by presenting four scenarios, each showcasing a different user profile: a theoretical analyst, a method designer, a benchmarker, and an application developer. Particularly, we show that we can provide different interpretative flavors that are adapted to the user's needs by mapping different values on the Tile. As an illustration, we leverage the newly introduced Tile tool and the different flavors to rank and analyze the performances of 74 state-of-the-art semantic segmentation models in two-class classification through the eyes of the four user profiles. Through these user profiles, we demonstrate that the Tile effectively captures the behavior of classifiers in a single visualization, while accommodating an infinite number of ranking scores.

In the computer vision and machine learning communities, as well as in many other research domains, rigorous evaluation of any new method, including classifiers, is essential. One key component of the evaluation process is the ability to compare and rank methods. However, ranking classifiers and accurately comparing their performances, especially when taking application-specific preferences into account, remains challenging. For instance, commonly used evaluation tools like Receiver Operating Characteristic (ROC) and Precision/Recall (PR) spaces display performances based on two scores. Hence, they are inherently limited in their ability to compare classifiers across a broader range of scores and lack the capability to establish a clear ranking among classifiers. In this paper, we present a novel versatile tool, named the Tile, that organizes an infinity of ranking scores in a single 2D map for two-class classifiers, including common evaluation scores such as the accuracy, the true positive rate, the positive predictive value, Jaccard's coefficient, and all F-beta scores. Furthermore, we study the properties of the underlying ranking scores, such as the influence of the priors or the correspondences with the ROC space, and depict how to characterize any other score by comparing them to the Tile. Overall, we demonstrate that the Tile is a powerful tool that effectively captures all the rankings in a single visualization and allows interpreting them.

Ranking entities such as algorithms, devices, methods, or models based on their performances, while accounting for application-specific preferences, is a challenge. To address this challenge, we establish the foundations of a universal theory for performance-based ranking. First, we introduce a rigorous framework built on top of both the probability and order theories. Our new framework encompasses the elements necessary to (1) manipulate performances as mathematical objects, (2) express which performances are worse than or equivalent to others, (3) model tasks through a variable called satisfaction, (4) consider properties of the evaluation, (5) define scores, and (6) specify application-specific preferences through a variable called importance. On top of this framework, we propose the first axiomatic definition of performance orderings and performance-based rankings. Then, we introduce a universal parametric family of scores, called ranking scores, that can be used to establish rankings satisfying our axioms, while considering application-specific preferences. Finally, we show, in the case of two-class classification, that the family of ranking scores encompasses well-known performance scores, including the accuracy, the true positive rate (recall, sensitivity), the true negative rate (specificity), the positive predictive value (precision), and F1. However, we also show that some other scores commonly used to compare classifiers are unsuitable to derive performance orderings satisfying the axioms. Therefore, this paper provides the computer vision and machine learning communities with a rigorous framework for evaluating and ranking entities.

We present ALMA observations of the [CI] 492 and 806$\,$GHz fine-structure lines in 25 dusty star-forming galaxies (DSFGs) at $z\,{=}\,4.3$ in the core of the SPT2349$-$56 protocluster. The protocluster galaxies exhibit a median $L^\prime_{[\text{CI}](2-1)}/L^\prime_{[\text{CI}](1-0)}$ ratio of 0.94 with an interquartile range of 0.81-1.24. These ratios are markedly different to those observed in DSFGs in the field (across a comparable redshift and 850$\,\mu$m flux density range), where the median is 0.55 with an interquartile range of 0.50-0.76, and we show that this difference is driven by an excess of [CI](2-1) in the protocluster galaxies for a given 850$\,\mu$m flux density. We estimate gas excitation temperatures of $T_{\rm ex}\,{=}\,59.1^{+8.1}_{-6.8}\,$K for our protocluster sample and $T_{\rm ex}\,{=}\,33.9^{+2.4}_{-2.2}\,$K for the field sample. Our main interpretation of this result is that the protocluster galaxies have had their cold gas driven to their cores via close-by interactions within the dense environment, leading to an overall increase in the average gas density and excitation temperature, and an elevated [CI](2-1) luminosity-to-far-infrared luminosity ratio., Comment: Submitted to ApJL

PaliGemma 2 is an upgrade of the PaliGemma open Vision-Language Model (VLM) based on the Gemma 2 family of language models. We combine the SigLIP-So400m vision encoder that was also used by PaliGemma with the whole range of Gemma 2 models, from the 2B one all the way up to the 27B model. We train these models at three resolutions (224px, 448px, and 896px) in multiple stages to equip them with broad knowledge for transfer via fine-tuning. The resulting family of base models covering different model sizes and resolutions allows us to investigate factors impacting transfer performance (such as learning rate) and to analyze the interplay between the type of task, model size, and resolution. We further increase the number and breadth of transfer tasks beyond the scope of PaliGemma including different OCR-related tasks such as table structure recognition, molecular structure recognition, music score recognition, as well as long fine-grained captioning and radiography report generation, on which PaliGemma 2 obtains state-of-the-art results.

We consider introducing the Dirac sea in a discrete spacetime model of fermions which approximates the Dirac equation in the continuum limit. However, if we attempt to fill up the `negative' energy states, we run into a problem. A new boundary is created between positive and negative energy states, at which pair creation seems energetically favourable. This happens because of the modular nature of energy in discrete time models. We then suggest a possible remedy by amending the model, in order to pull states away from the new boundary.

In this work we explore utilizing LLMs for data augmentation for manufacturing task guidance system. The dataset consists of representative samples of interactions with technicians working in an advanced manufacturing setting. The purpose of this work to explore the task, data augmentation for the supported tasks and evaluating the performance of the existing LLMs. We observe that that task is complex requiring understanding from procedure specification documents, actions and objects sequenced temporally. The dataset consists of 200,000+ question/answer pairs that refer to the spec document and are grounded in narrations and/or video demonstrations. We compared the performance of several popular open-sourced LLMs by developing a baseline using each LLM and then compared the responses in a reference-free setting using LLM-as-a-judge and compared the ratings with crowd-workers whilst validating the ratings with experts.

We compare two candidate nonlinearities for regulating the solar cycle within the Babcock-Leighton paradigm: tilt quenching (whereby the tilt of active regions is reduced in stronger cycles) and latitude quenching (whereby flux emerges at higher latitudes in stronger solar cycles). Digitized historical observations are used to build a database of individual magnetic plage regions from 1923 to 1985. The regions are selected by thresholding in Ca II K synoptic maps, with polarities constrained using Mount Wilson Observatory sunspot measurements. The resulting data show weak evidence for tilt quenching, but much stronger evidence for latitude-quenching. Further, we use proxy observations of the polar field from faculae to construct a best-fit surface flux transport model driven by our database of emerging regions. A better fit is obtained when the sunspot measurements are used, compared to a reference model where all polarities are filled using Hale's Law. The optimization suggests clearly that the "dynamo effectivity range" of the Sun during this period should be less than 10 degrees; this is also consistent with latitude quenching being dominant over tilt quenching., Comment: 19 pages, 14 figures, accepted for publication in ApJ

Scaling robot learning requires data collection pipelines that scale favorably with human effort. In this work, we propose Crowdsourcing and Amortizing Human Effort for Real-to-Sim-to-Real(CASHER), a pipeline for scaling up data collection and learning in simulation where the performance scales superlinearly with human effort. The key idea is to crowdsource digital twins of real-world scenes using 3D reconstruction and collect large-scale data in simulation, rather than the real-world. Data collection in simulation is initially driven by RL, bootstrapped with human demonstrations. As the training of a generalist policy progresses across environments, its generalization capabilities can be used to replace human effort with model generated demonstrations. This results in a pipeline where behavioral data is collected in simulation with continually reducing human effort. We show that CASHER demonstrates zero-shot and few-shot scaling laws on three real-world tasks across diverse scenarios. We show that CASHER enables fine-tuning of pre-trained policies to a target scenario using a video scan without any additional human effort. See our project website: https://casher-robot-learning.github.io/CASHER/

We present the first numerical investigation of the method proposed in Ref. [1] to utilize gradient flow to obtain precise determinations of higher moments of PDFs from lattice QCD, circumventing power divergent mixing with lower dimensional operators. We apply this method to obtain moments of the isovector PDF of the pion using four Stabilized Wilson Fermion ensembles with $m_{\pi}\simeq 411$ MeV and lattice spacings $a \simeq 0.064, 0.077, 0.094$, and $0.12$ fm. We present preliminary results of ratios of three-point functions as a function of flow time, which can be used to extract the ratios $\left\langle x^2 \right\rangle/\left\langle x \right\rangle$ and $\left\langle x^3 \right\rangle/\left\langle x \right\rangle$. We find that a significantly higher precision can be achieved with this method compared to the canonical approach, which requires boosting and cannot reach higher than the $\left\langle x^3 \right\rangle$ moment., Comment: 15 pages, 2 figures, contributions to the proceedings of the 41st International Symposium on Lattice Field Theory (LATTICE2024)

We investigate the impact of Mutual Coupling (MC) between antennas on the time-delay power spectrum response of the core of the SKA-Low radio telescope. Using two in-house tools - Fast Array Simulation Tool (FAST) (a fast full-wave electromagnetic solver) and OSKAR (a GPU-accelerated radio telescope simulator) - we simulate station beams and compute visibilities for various array layouts (regular, sunflower, and random). Simulations are conducted in an Epoch of Reionisation subband between 120-150~MHz, with a fine frequency resolution of 100~kHz, enabling the investigation of late delays. Our results show that MC effects significantly increase foreground leakage into longer delays, especially for regular station layouts. For 21-cm science, foreground spill-over into the 21-cm window extends beyond $k_{\parallel} \sim 2$~h$^{-1}$Mpc for all station layouts and across all $k_{\perp}$ modes, completely obscuring the detection window. We find that attempting to remove the foreground contribution from the visibilities using an approximated beam model, based on the average embedded element pattern or interpolating the embedded element patterns from a coarse channel rate of 781~kHz, results in residuals around 1% ($\sim 10^{11}~\mathrm{mK}^2$h$^{-3}\mathrm{Mpc}^3$) which is still around 7 orders of magnitude brighter than the expected level of the EoR signal ($\sim 10^{4}~\mathrm{mK}^2$h$^{-3}\mathrm{Mpc}^3$). We also find that station beam models with at least 4-5 significant digits in the far-field pattern and high spectral resolution are needed for effective foreground removal. Our research provides critical insights into the role of MC in SKA-Low experiments and highlights the computational challenges of fully integrating array patterns that account for MC effects into processing pipelines., Comment: 17 pages, 17 figures

As prompt engineering research rapidly evolves, evaluations beyond accuracy are crucial for developing cost-effective techniques. We present the Economical Prompting Index (EPI), a novel metric that combines accuracy scores with token consumption, adjusted by a user-specified cost concern level to reflect different resource constraints. Our study examines 6 advanced prompting techniques, including Chain-of-Thought, Self-Consistency, and Tree of Thoughts, across 10 widely-used language models and 4 diverse datasets. We demonstrate that approaches such as Self-Consistency often provide statistically insignificant gains while becoming cost-prohibitive. For example, on high-performing models like Claude 3.5 Sonnet, the EPI of simpler techniques like Chain-of-Thought (0.72) surpasses more complex methods like Self-Consistency (0.64) at slight cost concern levels. Our findings suggest a reevaluation of complex prompting strategies in resource-constrained scenarios, potentially reshaping future research priorities and improving cost-effectiveness for end-users., Comment: 5 pages (excluding references), accepted to Coling 2025

1. Obtaining data to train robust artificial intelligence (AI)-based models for species classification can be challenging, particularly for rare species. Data augmentation can boost classification accuracy by increasing the diversity of training data and is cheaper to obtain than expert-labelled data. However, many classic image-based augmentation techniques are not suitable for audio spectrograms. 2. We investigate two generative AI models as data augmentation tools to synthesise spectrograms and supplement audio data: Auxiliary Classifier Generative Adversarial Networks (ACGAN) and Denoising Diffusion Probabilistic Models (DDPMs). The latter performed particularly well in terms of both realism of generated spectrograms and accuracy in a resulting classification task. 3. Alongside these new approaches, we present a new audio data set of 640 hours of bird calls from wind farm sites in Ireland, approximately 800 samples of which have been labelled by experts. Wind farm data are particularly challenging for classification models given the background wind and turbine noise. 4. Training an ensemble of classification models on real and synthetic data combined gave 92.6% accuracy (and 90.5% with just the real data) when compared with highly confident BirdNET predictions. 5. Our approach can be used to augment acoustic signals for more species and other land-use types, and has the potential to bring about a step-change in our capacity to develop reliable AI-based detection of rare species. Our code is available at https://github.com/gibbona1/ SpectrogramGenAI., Comment: 18 pages, 3 tables, 5 figures

We demonstrate the advantages of THz frequency combs for high-resolution spectroscopy. This benefits from wide spectral coverage and the exact knowledge of the frequency position of each comb component. Heterodyne detection combined with a fast Fourier spectrometer enables rapid and simultaneous measurement of more than 80 frequency comb modes covering a 7.5 GHz bandwidth. A spectrum is obtained in under 20 minutes yielding a uniform resolution of 70 kHz. This new setup has been validated by recording more than 150 lines of methanol around 723 GHz, and represents a new solution to exploit THz frequency combs for high-resolution spectroscopy.

Dielectric barrier discharge (DBD) plasma actuator arrays have been suggested as active flow control devices due to the robust electrohydrodynamic (EHD) force generation in variable atmospheric conditions. DBD plasma augmentation schemes allow for significant performance improvements. However, the transitions to sliding discharge or counter-flow discharge limit their use in high-power arrays. Here, we experimentally demonstrate the performance of a scalable DBD array for two alternating phases of air-exposed electrode configuration. Plasma emissions, direct thrust, velocity profiles, and power consumption measurements of the DBD array reveal that cross-talk between DBD stages can be eliminated to create high-power density actuators. AC augmentation of plasma provides additional gains in thrust; however, the transition to sliding and filamentary discharge reveals geometric limits when increasing the array power density. Introducing a segmented electrode with a resistor delays the onset of adverse sliding and filamentary discharge, allowing it to operate at higher voltage inputs. An optimized four-stage DBD array generated thrust > 250 mN/m with a wall jet thickness > 15 mm, enabling a broader range of flow control applications.

In this paper we investigate a class of integrals that were encountered in the course of a work on statistical plasma physics, in the so-called Sommerfeld temperature-expansion of the electronic entropy. We show that such integrals, involving some parameters, can be fully described in closed form represented by special functions.

Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the potential of Large Language Models (LLMs), specifically GPT4, in generating patient-friendly educational content from clinical trial informed consent forms. Using data from ClinicalTrials.gov, we employed zero-shot learning for creating trial summaries and one-shot learning for developing multiple-choice questions, evaluating their effectiveness through patient surveys and crowdsourced annotation. Results showed that GPT4-generated summaries were both readable and comprehensive, and may improve patients' understanding and interest in clinical trials. The multiple-choice questions demonstrated high accuracy and agreement with crowdsourced annotators. For both resource types, hallucinations were identified that require ongoing human oversight. The findings demonstrate the potential of LLMs "out-of-the-box" to support the generation of clinical trial education materials with minimal trial-specific engineering, but implementation with a human-in-the-loop is still needed to avoid misinformation risks.

Observations of tidal disruption events (TDEs) on a timescale of years after the main flare show evidence of continued activity in the form of optical/UV emission, quasi-periodic eruptions, and delayed radio flares. Motivated by this, we explore the time evolution of these disks using semi-analytic models to follow the changing disk properties and feeding rate to the central black hole (BH). We find that thermal instabilities typically begin $\sim150-250\,{\rm days}$ after the TDE, causing the disk to cycle between high and low accretion states for up to $\sim10-20\,{\rm yrs}$. The high state is super-Eddington, which may be associated with outflows that eject $\sim10^{-3}-10^{-1}\,M_\odot$ with a range of velocities of $\sim0.03-0.3c$ over a span of a couple of days and produce radio flares. In the low state, the accretion rate slowly grows over many months to years as continued fallback accretion builds the mass of the disk. In this phase, the disk may reach luminosities of $\sim10^{41}-10^{42}\,{\rm erg\,s^{-1}}$ in the UV as seen in some late-time observations. We highlight the importance of the iron-opacity "bump" at $\approx2\times10^5\,{\rm K}$ in generating sufficiently high luminosities. This work suggests that joint optical/UV observations with radio monitoring could be key for following the disk state as the radio flares are produced., Comment: 15 pages, 14 figures, submitted to ApJ

The Wide Area Linear Optical Polarimeter North (WALOP-North) is an optical polarimeter designed for the needs of the PASIPHAE survey. It will be installed on the 1.3m telescope at the Skinakas Observatory in Crete, Greece. After commissioning, it will measure the polarization of millions of stars at high Galactic latitude, aiming to measure hundreds of stars per $deg^2$. The astronomical filter used in the instrument is a modified, polarimetrically-neutral broadband SDSS-r. This instrument will be pioneering one due to its large field-of-view (FoV) of $30\times 30$ $arcmin^2$ and high accuracy polarimetry measurements. The accuracy and sensitivity of the instrument in polarization fraction will be at the 0.1\% and 0.05\% level, respectively. Four separate 4k$\times$4k CCDs will be used as the instrument detectors, each imaging one of the $0\deg{}, 45\deg{}, 90\deg{}$ and $135\deg{}$ polarized FoV separately, therefore making the instrument a four-channel, one-shot polarimeter. Here, we present the overall optical design of the instrument, emphasizing on the aspects of the instrument that are different from WALOP-South. We also present a novel design of filters appropriate for polarimetry along with details on the management of the instrument size and its polarimetric calibration., Comment: 26 pages, 31 figures, 7 tables

Animal excretions in form of urine puddles and feces are a significant source of emissions in livestock farming. Automated detection of soiled floor in barns can contribute to improved management processes but also the derived information can be used to model emission dynamics. Previous research approaches to determine the puddle area require manual detection of the puddle in the barn. While humans can detect animal excretions on thermal images of a livestock barn, automated approaches using thresholds fail due to other objects of the same temperature, such as the animals themselves. In addition, various parameters such as the type of housing, animal species, age, sex, weather and unknown factors can influence the type and shape of excretions. Due to this heterogeneity, a method for automated detection of excretions must therefore be not only be accurate but also robust to varying conditions. These requirements can be met by using contemporary deep learning models from the field of artificial intelligence. This work is the first to investigate the suitability of different deep learning models for the detection of excretions in pigsties, thereby comparing established convolutional architectures with recent transformer-based approaches. The detection models Faster R-CNN, YOLOv8, DETR and DAB-DETR are compared and statistically assessed on two created training datasets representing two pig houses. We apply a method derived from nested cross-validation and report on the results in terms of eight common detection metrics. Our work demonstrates that all investigated deep learning models are generally suitable for reliably detecting excretions with an average precision of over 90%. The models also show robustness on out of distribution data that possesses differences from the conditions in the training data, however, with expected slight decreases in the overall detection performance., Comment: Keywords: Artificial Intelligence, Objected detection, Pig, Urine puddle, Thermal IR data, CNN vs Transformer, Precision Livestock Farming; Stats: 54 pages, 13 figures, 1 graphical abstract

By a systematic development of fundamental concepts of conformable calculus we establish conformable divergence theorem and Green's identities which we combine with some new anisotropic Picone type identities to derive a generalized anisotropic Hardy type inequality with weights and conformable fractional differential operators. As a consequence, several Hardy type inequalities and Heisenberg Pauli-Weyl uncertainty principles are obtained., Comment: 15 pages

Qualitative Spatial Reasoning is a well explored area of Knowledge Representation and Reasoning and has multiple applications ranging from Geographical Information Systems to Robotics and Computer Vision. Recently, many claims have been made for the reasoning capabilities of Large Language Models (LLMs). Here, we investigate the extent to which a set of representative LLMs can perform classical qualitative spatial reasoning tasks on the mereotopological Region Connection Calculus, RCC-8. We conduct three pairs of experiments (reconstruction of composition tables, alignment to human composition preferences, conceptual neighbourhood reconstruction) using state-of-the-art LLMs; in each pair one experiment uses eponymous relations and one, anonymous relations (to test the extent to which the LLM relies on knowledge about the relation names obtained during training). All instances are repeated 30 times to measure the stochasticity of the LLMs., Comment: 13 pages. arXiv admin note: text overlap with arXiv:2309.15577

Flow cytometry measurements are widely used in diagnostics and medical decision making. Incomplete understanding of sources of measurement uncertainty can make it difficult to distinguish autofluorescence and background sources from signals of interest. Moreover, established methods for modeling uncertainty overlook the fact that the apparent distribution of measurements is a convolution of the inherent the population variability (e.g., associated with calibration beads or cells) and instrument induced-effects. Such issues make it difficult, for example, to identify signals from small objects such as extracellular vesicles. To overcome such limitations, we formulate an explicit probabilistic measurement model that accounts for volume and labeling variation, background signals and fluorescence shot noise. Using raw data from routine per-event calibration measurements, we use this model to separate the aforementioned sources of uncertainty and demonstrate how such information can be used to facilitate decision-making and instrument characterization.

Clinical language models are important for many applications in healthcare, but their development depends on access to extensive clinical text for pretraining. However, obtaining clinical notes from electronic health records (EHRs) at scale is challenging due to patient privacy concerns. In this study, we rephrase existing clinical notes using LLMs to generate synthetic pretraining corpora, drawing inspiration from previous work on rephrasing web data. We examine four popular small-sized LLMs (<10B) to create synthetic clinical text to pretrain both decoder-based and encoder-based language models. The method yields better results in language modeling and downstream tasks than previous synthesis approaches without referencing real clinical text. We find that augmenting original clinical notes with synthetic corpora from different LLMs improves performances even at a small token budget, showing the potential of this method to support pretraining at the institutional level or be scaled to synthesize large-scale clinical corpora.

Adversarial input image perturbation attacks have emerged as a significant threat to machine learning algorithms, particularly in image classification setting. These attacks involve subtle perturbations to input images that cause neural networks to misclassify the input images, even though the images remain easily recognizable to humans. One critical area where adversarial attacks have been demonstrated is in automotive systems where traffic sign classification and recognition is critical, and where misclassified images can cause autonomous systems to take wrong actions. This work presents a new class of adversarial attacks. Unlike existing work that has focused on adversarial perturbations that leverage human-made artifacts to cause the perturbations, such as adding stickers, paint, or shining flashlights at traffic signs, this work leverages nature-made artifacts: tree leaves. By leveraging nature-made artifacts, the new class of attacks has plausible deniability: a fall leaf stuck to a street sign could come from a near-by tree, rather than be placed there by an malicious human attacker. To evaluate the new class of the adversarial input image perturbation attacks, this work analyses how fall leaves can cause misclassification in street signs. The work evaluates various leaves from different species of trees, and considers various parameters such as size, color due to tree leaf type, and rotation. The work demonstrates high success rate for misclassification. The work also explores the correlation between successful attacks and how they affect the edge detection, which is critical in many image classification algorithms.

Precise weed management is essential for sustaining crop productivity and ecological balance. Traditional herbicide applications face economic and environmental challenges, emphasizing the need for intelligent weed control systems powered by deep learning. These systems require vast amounts of high-quality training data. The reality of scarcity of well-annotated training data, however, is often addressed through generating more data using data augmentation. Nevertheless, conventional augmentation techniques such as random flipping, color changes, and blurring lack sufficient fidelity and diversity. This paper investigates a generative AI-based augmentation technique that uses the Stable Diffusion model to produce diverse synthetic images that improve the quantity and quality of training datasets for weed detection models. Moreover, this paper explores the impact of these synthetic images on the performance of real-time detection systems, thus focusing on compact CNN-based models such as YOLO nano for edge devices. The experimental results show substantial improvements in mean Average Precision (mAP50 and mAP50-95) scores for YOLO models trained with generative AI-augmented datasets, demonstrating the promising potential of synthetic data to enhance model robustness and accuracy.

We develop a theoretical framework to investigate the link between rising scale economies and stagnating productivity. Our model features heterogeneous firms, imperfect competition, and firm selection. We demonstrate that scale economies generated by fixed costs have distinct impacts on aggregate productivity compared to those driven by returns to scale. Using UK data, we estimate long-run increases in both fixed costs and returns to scale. Our model implies that this should increase aggregate productivity through improved firm selection and resource allocation. However, increasing markups can offset the productivity gain. Higher markups cushion low-productivity firms' revenues, allowing them to survive, and constrain firm output, which limits exploitation of scale economies.