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In 2021, California committed to providing universal prekindergarten (UPK) for all 4-year-olds and expanding access for income-eligible 3-year-olds by 2025-2026. California UPK includes several early learning programs, including transitional kindergarten (TK), the California State Preschool Program (CSPP), Head Start, and locally funded early learning programs. To support UPK expansion, California's legislature and administration established the Universal Prekindergarten Planning and Implementation Grant in 2021, which allocated $200 million to all local education agencies (LEAs) serving kindergarteners, which include school districts, charter schools, and county offices of education. The California Department of Education surveyed all grant recipients in August 2023 about their UPK programs. This report provides an update on UPK implementation across the state through an analysis of survey responses from 1,384 LEAs, which represent almost all (95%) public school districts and two thirds (65%) of charter schools that serve elementary grades. Findings provide insights into LEAs' progress in UPK implementation related to service delivery models, facilities and transportation, instruction and assessment, strategies to support student needs, workforce development, implementation challenges, and technical assistance needs. In addition to statewide insights, the survey revealed promising practices and wide access with UPK expansion in California's four largest districts during their first year of implementation. The findings in this report may help policymakers and practitioners identify areas for additional investments and supports.

Evidence shows that children's early years are a crucial time for their development. Well-designed early childhood education (ECE) experiences can foster meaningful gains in school readiness, as well as long-term benefits such as lower rates of special education placement and higher graduation rates. The quality of early education is highly dependent on sufficient preparation and support for early educators to meet the needs of diverse young learners. Research has identified high-quality coaching as an effective professional learning practice for supporting educators in the implementation of evidence-based practices. According to the research, effective coaching relies on a strong partnership between coaches and educators; reflection and individualized feedback; focused observations; intentional coaching plans to guide sessions; and job-embedded learning opportunities for active learning. Well-qualified coaches with knowledge of specific coaching models and practices, general coaching and consultation skills, and knowledge of early childhood development and teaching are critical as well. This report examines five early childhood coaching systems--two state systems (Alabama and Washington) and three California county systems (El Dorado, Fresno, and San Diego)--that have developed systemic coaching approaches. The researchers studied these coaching systems to understand the different ways that comprehensive coaching systems can be implemented at scale, the types of coaching approaches used, and the supports offered. Although there is no singular strategy to scale effective coaching, this research provides insights for policymakers and program administrators seeking to incorporate coaching into their efforts to improve the quality of early childhood education.

We study the effect of educational attainment on family formation using regression discontinuity designs generated by centralized admissions processes to both secondary and tertiary education in Finland. Admission to further education at either margin does not increase the likelihood that men form families. In contrast, women admitted to further education are more likely to both live with a partner and have children. We then pre-register and test two hypotheses which could explain each set of results using survey data. These suggest that the positive association between men's education and family formation observed in the data is driven by selection. For women, our estimates are consistent with the idea that, as increased returns to social skills shift the burden of child development from schools to parents and particularly mothers, education can make women more attractive as potential partners. [Funding for this report was provided by The Strategic Research Council, the Research Council of Finland, and Palkansaajasäätiö.]

As part of the PIP-II project at Fermilab, a pre-production cryomodule featuring 325 MHz Single Spoke Resonator type 2 (SSR2) superconducting RF cavities is under construction. These SSR2 cavities are fabricated by industry partners and undergo initial cold testing at our collaborating institution, IJCLab in France, utilizing low-power coupler. Subsequently, the cavities are subjected to final qualification at Fermilab, complete with tuner and high-power coupler assemblies. This paper provides an overview of the ongoing efforts dedicated to high-power testing of jacketed SSR2 cavities in the Spoke Test Cryostat (STC) at Fermilab. Performance parameters obtained from these tests are presented, offering valuable insights into the cavities operational characteristics and readiness for integration into the PIP-II cryomodule., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

To facilitate the measurement of representational harms caused by large language model (LLM)-based systems, the NLP research community has produced and made publicly available numerous measurement instruments, including tools, datasets, metrics, benchmarks, annotation instructions, and other techniques. However, the research community lacks clarity about whether and to what extent these instruments meet the needs of practitioners tasked with developing and deploying LLM-based systems in the real world, and how these instruments could be improved. Via a series of semi-structured interviews with practitioners in a variety of roles in different organizations, we identify four types of challenges that prevent practitioners from effectively using publicly available instruments for measuring representational harms caused by LLM-based systems: (1) challenges related to using publicly available measurement instruments; (2) challenges related to doing measurement in practice; (3) challenges arising from measurement tasks involving LLM-based systems; and (4) challenges specific to measuring representational harms. Our goal is to advance the development of instruments for measuring representational harms that are well-suited to practitioner needs, thus better facilitating the responsible development and deployment of LLM-based systems., Comment: NeurIPS 2024 Workshop on Evaluating Evaluations (EvalEval)

Large language model (LLM) evaluations often assume there is a single correct response -- a gold label -- for each item in the evaluation corpus. However, some tasks can be ambiguous -- i.e., they provide insufficient information to identify a unique interpretation -- or vague -- i.e., they do not clearly indicate where to draw the line when making a determination. Both ambiguity and vagueness can cause task indeterminacy -- the condition where some items in the evaluation corpus have more than one correct response. In this paper, we develop a framework for evaluating LLMs under task indeterminacy. Our framework disentangles the relationships between task specification, human ratings, and LLM responses in the LLM evaluation pipeline. Using our framework, we conduct a synthetic experiment showing that evaluations that use the "gold label" assumption underestimate the true performance. We also provide a method for estimating an error-adjusted performance interval given partial knowledge about indeterminate items in the evaluation corpus. We conclude by outlining implications of our work for the research community., Comment: To Appear in NeurIPS 2024 Workshops on Evaluating Evaluations (EvalEval) and Statistical Foundations of LLMs and Foundation Models (SFLLM)

There are few principles or guidelines to ensure evaluations of generative AI (GenAI) models and systems are effective. To help address this gap, we propose a set of general dimensions that capture critical choices involved in GenAI evaluation design. These dimensions include the evaluation setting, the task type, the input source, the interaction style, the duration, the metric type, and the scoring method. By situating GenAI evaluations within these dimensions, we aim to guide decision-making during GenAI evaluation design and provide a structure for comparing different evaluations. We illustrate the utility of the proposed set of general dimensions using two examples: a hypothetical evaluation of the fairness of a GenAI system and three real-world GenAI evaluations of biological threats., Comment: NeurIPS 2024 Workshop on Evaluating Evaluations (EvalEval)

The impact of machine translation (MT) on low-resource languages remains poorly understood. In particular, observational studies of actual usage patterns are scarce. Such studies could provide valuable insights into user needs and behaviours, complementing survey-based methods. Here we present an observational analysis of real-world MT usage for Tetun, the lingua franca of Timor-Leste, using server logs from a widely-used MT service with over $70,000$ monthly active users. Our analysis of $100,000$ translation requests reveals patterns that challenge assumptions based on existing corpora. We find that users, many of them students on mobile devices, typically translate short texts into Tetun across diverse domains including science, healthcare, and daily life. This contrasts sharply with available Tetun corpora, which are dominated by news articles covering government and social issues. Our results suggest that MT systems for languages like Tetun should prioritise translating into the low-resource language, handling brief inputs effectively, and covering a wide range of domains relevant to educational contexts. More broadly, this study demonstrates how observational analysis can inform low-resource language technology development, by grounding research in practical community needs.

The Space Telescope Imaging Product Simulator (STIPS) is a Python-based package that can be used to simulate scenes from the upcoming \textit{Nancy Grace Roman Space Telescope} (\nancy). STIPS is able to generate post-pipeline astronomical images of any number of sensor chip assembly (SCA) detectors, up to the entire 18-SCA Wide-Field Instrument array on \nancy. STIPS can inject either point spread functions generated with {\tt WebbPSF}, or extended sources in any of the \nancy filters. The output images can include flat field, dark current, and cosmic ray residuals. Additionally, STIPS includes an estimate of Poisson and readout noise, as well as an estimate of the zodiacal background and internal background from the telescope. However, STIPS does not include instrument saturation, non-linearity, or distortion effects. STIPS is provided as an open source repository on GitHub., Comment: Accepted to PASP. 10 pages, 11 figures

Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves. This four-level approach differs from the way measurement is typically done in ML, where researchers and practitioners appear to jump straight from background concepts to measurement instruments, with little to no explicit systematization in between. As well as surfacing assumptions, thereby making it easier to understand exactly what the resulting measurements do and do not mean, this framework has two important implications for evaluating evaluations: First, it can enable stakeholders from different worlds to participate in conceptual debates, broadening the expertise involved in evaluating GenAI systems. Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements., Comment: NeurIPS 2024 Workshop on Evaluating Evaluations (EvalEval)

The phenomenon of Anderson localization in various disordered media has sustained significant interest over many decades. Specifically, the Anderson localization of phonons has been viewed as a potential mechanism for creating fascinating thermal transport properties in materials. However, despite extensive work, the influence of the vector nature of phonons on the Anderson localization transition has not been well explored. In order to achieve such an understanding, we extend a recently developed phonon dynamical cluster approximation (DCA) and its typical medium variant (TMDCA) to investigate spectra and localization of multi-branch phonons in the presence of pure mass disorder. We validate the new formalism against several limiting cases and exact diagonalization results. A comparison of results for the single-branch versus multi-branch case shows that the vector nature of the phonons does not affect the Anderson transition of phonons significantly. The developed multi-branch TMDCA formalism can be employed for studying phonon localization in real materials., Comment: 6 figures

Carbon credits are a key component of most national and organizational climate strategies. Financing and delivering carbon credits from forest-related activities faces multiple risks at the project and asset levels. Financial mechanisms are employed to mitigate risks for investors and project developers, complemented by non-financial measures such as environmental and social safeguards and physical risk mitigation. Despite these efforts, academic research highlights that safeguards and climate risk mitigation measures are not efficiently implemented in some carbon projects and that specification of environmental safeguards remains underdeveloped. Further, environmental and social risk mitigation capacities may not be integrated into financial mechanisms. This text examines how ecosystem capacities can be leveraged and valued for mitigation of and adaptation to physical risks by complementing carbon credits with biodiversity insurance and resilience value.

Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs (e.g., $\geq15$ steps) remain difficult to solve. This paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RaS3). ROP-RaS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space -- a fundamental constraint for modern online POMDP solvers. ROP-RaS3 is evaluated on various long-horizon POMDPs, including on a problem with a planning horizon of more than 100 steps and a problem with a 15-dimensional state space that requires more than 20 look ahead steps. In all of these problems, ROP-RaS3 substantially outperforms other state-of-the-art methods by up to multiple folds., Comment: 16 pages, 4 tables, 1 figure. To be presented at the International Symposium of Robotics Research 2024

Video captioning aims to describe video contents using natural language format that involves understanding and interpreting scenes, actions and events that occurs simultaneously on the view. Current approaches have mainly concentrated on visual cues, often neglecting the rich information available from other important modality of audio information, including their inter-dependencies. In this work, we introduce a novel video captioning method trained with multi-modal contrastive loss that emphasizes both multi-modal integration and interpretability. Our approach is designed to capture the dependency between these modalities, resulting in more accurate, thus pertinent captions. Furthermore, we highlight the importance of interpretability, employing multiple attention mechanisms that provide explanation into the model's decision-making process. Our experimental results demonstrate that our proposed method performs favorably against the state-of the-art models on commonly used benchmark datasets of MSR-VTT and VATEX.

It is now a common business practice to buy access to large language model (LLM) inference rather than self-host, because of significant upfront hardware infrastructure and energy costs. However, as a buyer, there is no mechanism to verify the authenticity of the advertised service including the serving hardware platform, e.g. that it is actually being served using an NVIDIA H100. Furthermore, there are reports suggesting that model providers may deliver models that differ slightly from the advertised ones, often to make them run on less expensive hardware. That way, a client pays premium for a capable model access on more expensive hardware, yet ends up being served by a (potentially less capable) cheaper model on cheaper hardware. In this paper we introduce \textit{\textbf{hardware and software platform inference (HSPI)}} -- a method for identifying the underlying \GPU{} architecture and software stack of a (black-box) machine learning model solely based on its input-output behavior. Our method leverages the inherent differences of various \GPU{} architectures and compilers to distinguish between different \GPU{} types and software stacks. By analyzing the numerical patterns in the model's outputs, we propose a classification framework capable of accurately identifying the \GPU{} used for model inference as well as the underlying software configuration. Our findings demonstrate the feasibility of inferring \GPU{} type from black-box models. We evaluate HSPI against models served on different real hardware and find that in a white-box setting we can distinguish between different \GPU{}s with between $83.9\%$ and $100\%$ accuracy. Even in a black-box setting we are able to achieve results that are up to three times higher than random guess accuracy.

Traffic flow forecasting is a crucial task in intelligent transport systems. Deep learning offers an effective solution, capturing complex patterns in time-series traffic flow data to enable the accurate prediction. However, deep learning models are prone to overfitting the intricate details of flow data, leading to poor generalisation. Recent studies suggest that decomposition-based deep ensemble learning methods may address this issue by breaking down a time series into multiple simpler signals, upon which deep learning models are built and ensembled to generate the final prediction. However, few studies have compared the performance of decomposition-based ensemble methods with non-decomposition-based ones which directly utilise raw time-series data. This work compares several decomposition-based and non-decomposition-based deep ensemble learning methods. Experimental results on three traffic datasets demonstrate the superiority of decomposition-based ensemble methods, while also revealing their sensitivity to aggregation strategies and forecasting horizons., Comment: This work has been accepted by the 2024 Australasian Joint Conference on Artificial Intelligence (AJCAI 2024)

We consider a nonlocal functional equation that is a generalization of the mathematical model used in behavioral sciences. The equation is built upon an operator that introduces a convex combination and a nonlinear mixing of the function arguments. We show that, provided some growth conditions of the coefficients, there exists a unique solution in the natural Lipschitz space. Furthermore, we prove that the regularity of the solution is inherited from the smoothness properties of the coefficients. As a natural numerical method to solve the general case, we consider the collocation scheme of piecewise linear functions. We prove that the method converges with the error bounded by the error of projecting the Lipschitz function onto the piecewise linear polynomial space. Moreover, provided sufficient regularity of the coefficients, the scheme is of the second order measured in the supremum norm. A series of numerical experiments verify the proved claims and show that the implementation is computationally cheap and exceeds the frequently used Picard iteration by orders of magnitude in the calculation time.

Differential diagnosis is crucial for medicine as it helps healthcare providers systematically distinguish between conditions that share similar symptoms. This study assesses the impact of lab test results on differential diagnoses (DDx) made by large language models (LLMs). Clinical vignettes from 50 case reports from PubMed Central were created incorporating patient demographics, symptoms, and lab results. Five LLMs GPT-4, GPT-3.5, Llama-2-70b, Claude-2, and Mixtral-8x7B were tested to generate Top 10, Top 5, and Top 1 DDx with and without lab data. A comprehensive evaluation involving GPT-4, a knowledge graph, and clinicians was conducted. GPT-4 performed best, achieving 55% accuracy for Top 1 diagnoses and 60% for Top 10 with lab data, with lenient accuracy up to 80%. Lab results significantly improved accuracy, with GPT-4 and Mixtral excelling, though exact match rates were low. Lab tests, including liver function, metabolic/toxicology panels, and serology/immune tests, were generally interpreted correctly by LLMs for differential diagnosis.

Effective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-quality baseline models for diverse supervised learning tasks in an efficient and performant manner. Historically, producing such baseline models has been a largely manual effort--individual researchers would need to decide on the particular featurization and tabularization processes to apply to their individual raw, longitudinal data; and then train a supervised model over those data to produce a baseline result to compare novel methods against, all for just one task and one dataset. In this work, powered by complementary advances in core data standardization through the MEDS framework, we dramatically simplify and accelerate this process of tabularizing irregularly sampled time-series data, providing researchers the ability to automatically and scalably featurize and tabularize their longitudinal EHR data across tens of thousands of individual features, hundreds of millions of clinical events, and diverse windowing horizons and aggregation strategies, all before ultimately leveraging these tabular data to automatically produce high-caliber XGBoost baselines in a highly computationally efficient manner. This system scales to dramatically larger datasets than tabularization tools currently available to the community and enables researchers with any MEDS format dataset to immediately begin producing reliable and performant baseline prediction results on various tasks, with minimal human effort required. This system will greatly enhance the reliability, reproducibility, and ease of development of powerful ML solutions for health problems across diverse datasets and clinical settings.

We investigate how an external driving field can control the amount of extractable work from a quantum emitter, a two-level quantum system (TLS) interacting with a photonic environment. In this scenario, the TLS functions as a quantum battery, interacting with the photonic bath that discharges it while the control field recharges it. Ergotropy serves as our measure of the extractable work from the quantum system. We systematically analyze how the ergotropy of the system evolves as it interacts with the photonic bath under the control of either a continuous driving field or a periodic pulse sequence. The coherent and incoherent contributions to the total ergotropy for various initial states are calculated. The role of detuning between the driving field and the emission frequency of the TLS, as well as the initial state of the system in work extraction, are investigated for continuous and periodic pulse-driving fields. We show that detuning has little impact on work extraction for a system that is driven by a periodic sequence of instantaneous pulses. However, for a continuously driven system, as the system approaches its steady state, ergotropy increases with detuning increases.

We study random dynamical systems of certain continuous functions on the unit interval. We use bounded variation to provide sufficient conditions for unique ergodicity of these systems. Several classes of examples are provided.

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table

As gravitational-wave observations expand in scope and detection rate, the data analysis infrastructure must be modernized to accommodate rising computational demands and ensure sustainability. We present a scalable gravitational-wave search pipeline which modernizes the GstLAL pipeline by adapting the core filtering engine to the PyTorch framework, enabling flexible execution on both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Offline search results on the same 8.8 day stretch of public gravitational-wave data indicate that the GstLAL and the PyTorch adaptation demonstrate comparable search performance, even with float16 precision. Lastly, computational benchmarking results show that the GPU float16 configuration of the PyTorch adaptation executed on an A100 GPU can achieve a speedup factor of up to 169 times compared to GstLAL's performance on a single CPU core., Comment: 8 pages, 4 figures

Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encoder-decoder-based framework that strives to explicitly capture the local and global dependencies for volumetric 3D medical image segmentation. The proposed framework exploits local volume-based self-attention to encode the local dependencies at high resolution and introduces a novel volumetric MLP-mixer to capture the global dependencies at low-resolution feature representations, respectively. The proposed volumetric MLP-mixer learns better associations among volumetric feature representations. These explicit local and global feature representations contribute to better learning of the shape-boundary characteristics of the organs. Extensive experiments on three different datasets reveal that the proposed method achieves favorable performance compared to state-of-the-art approaches. On the challenging Synapse Multi-organ dataset, the proposed method achieves an absolute 3.82\% gain over the state-of-the-art approaches in terms of HD95 evaluation metrics {while a similar improvement pattern is exhibited in MSD Liver and Pancreas tumor datasets}. We also provide a detailed comparison between recent architectural design choices in the 2D computer vision literature by adapting them for the problem of 3D medical image segmentation. Finally, our experiments on the ZebraFish 3D cell membrane dataset having limited training data demonstrate the superior transfer learning capabilities of the proposed vMixer model on the challenging 3D cell instance segmentation task, where accurate boundary prediction plays a vital role in distinguishing individual cell instances.

Recent advancements in Large Language Models (LLMs) have established them as agentic systems capable of planning and interacting with various tools. These LLM agents are often paired with web-based tools, enabling access to diverse sources and real-time information. Although these advancements offer significant benefits across various applications, they also increase the risk of malicious use, particularly in cyberattacks involving personal information. In this work, we investigate the risks associated with misuse of LLM agents in cyberattacks involving personal data. Specifically, we aim to understand: 1) how potent LLM agents can be when directed to conduct cyberattacks, 2) how cyberattacks are enhanced by web-based tools, and 3) how affordable and easy it becomes to launch cyberattacks using LLM agents. We examine three attack scenarios: the collection of Personally Identifiable Information (PII), the generation of impersonation posts, and the creation of spear-phishing emails. Our experiments reveal the effectiveness of LLM agents in these attacks: LLM agents achieved a precision of up to 95.9% in collecting PII, up to 93.9% of impersonation posts created by LLM agents were evaluated as authentic, and the click rate for links in spear phishing emails created by LLM agents reached up to 46.67%. Additionally, our findings underscore the limitations of existing safeguards in contemporary commercial LLMs, emphasizing the urgent need for more robust security measures to prevent the misuse of LLM agents.

Comparison of a few simple models of fluid and solid membranes illustrates how shear stresses can arise from a bending energy through a coupling between curvature and surface stresses, a feature incidental to the fluid or solid nature of the material. In particular, it is shown how a fluid-like Helfrich bending energy contributes shear stresses, while a related solid-like energy, the correct continuum limit of a Seung-Nelson discrete bending term, produces a stress tensor with purely isotropic tangential part. An important distinction is noted between the resistance to tangential flows on the surface and the ability to support tangential stresses. The tangential projection of the divergence of the stress, or the pseudomomentum balance, is viewed in the light of some statements and observations in the literature. Different types of constraints, invariance properties, and shape equations are also briefly discussed., Comment: kardemommesnurrer

Zero-dimensional (0D) cardiovascular models are reduced-order models used to study global circulation dynamics and transport. They provide estimates of biomarkers (such as pressure, flow rates, and concentrations) for surgery planning and boundary conditions for high-fidelity 3D models. Although their computational cost is low, tasks like parameter estimation and uncertainty quantification require many model evaluations, making them computationally expensive. This motivates building metamodels. In this work, we propose a pipeline from 0D models to metamodel building for tasks such as sensitivity analysis, parameter estimation, and uncertainty quantification. Three strategies are explored: Neural Networks, Polynomial Chaos Expansion, and Gaussian Processes, applied to three different 0D models. The first model predicts portal vein pressure after surgery, considering liver hemodynamics and global circulation. The second simulates whole-body circulation under pulmonary arterial hypertension before and after shunt insertion. The third assesses organ blood perfusion after revascularization surgery, focusing on contrast agent transport, requiring specific metamodel treatment. Metamodels are trained and tested on synthetic data. Neural networks proved the most efficient in terms of result quality, computational time, and ease for parameter estimation, sensitivity analysis, and uncertainty quantification. Finally, we demonstrate the full pipeline with a neural network as the emulator.

We investigate when the algebraic numerical range is a $C$-spectral set in a Banach algebra. While providing several counterexamples based on classical ideas as well as combinatorial Banach spaces, we discuss positive results for matrix algebras and provide an absolute constant in the case of complex $2\times2$-matrices with the induced $1$-norm. Furthermore, we discuss positive results for infinite-dimensional Banach algebras, including the Calkin algebra.

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

Documents are largely stored and shared digitally. Yet, digital documents are still commonly signed using (copies of) handwritten signatures, which are sensitive to fraud. Though secure, cryptography-based signature solutions exist, they are hardly used due to usability issues. This paper proposes to use digital identity wallets for securely and intuitively signing digital documents with verified personal data. Using expert feedback, we implemented this vision in an interactive prototype. The prototype was assessed in a moderated usability test (N = 15) and a subsequent unmoderated remote usability test (N = 99). While participants generally expressed satisfaction with the system, they also misunderstood how to interpret the signature information displayed by the prototype. Specifically, signed documents were also trusted when the document was signed with irrelevant personal data of the signer. We conclude that such unwarranted trust forms a threat to usable digital signatures and requires attention by the usable security community.

A $d$-dimensional tensegrity framework $(T,p)$ is an edge-labeled geometric graph in ${\mathbb R}^d$, which consists of a graph $T=(V,B\cup C\cup S)$ and a map $p:V\to {\mathbb R}^d$. The labels determine whether an edge $uv$ of $T$ corresponds to a fixed length bar in $(T,p)$, or a cable which cannot increase in length, or a strut which cannot decrease in length. We consider minimally infinitesimally rigid $d$-dimensional tensegrity frameworks and provide tight upper bounds on the number of its edges, in terms of the number of vertices and the dimension $d$. We obtain stronger upper bounds in the case when there are no bars and the framework is in generic position. The proofs use methods from convex geometry and matroid theory. A special case of our results confirms a conjecture of Whiteley from 1987. We also give an affirmative answer to a conjecture concerning the number of edges of a graph whose three-dimensional rigidity matroid is minimally connected.

We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models. Unlike many open-source models, Pixtral is also a cutting-edge text model for its size, and does not compromise on natural language performance to excel in multimodal tasks. Pixtral uses a new vision encoder trained from scratch, which allows it to ingest images at their natural resolution and aspect ratio. This gives users flexibility on the number of tokens used to process an image. Pixtral is also able to process any number of images in its long context window of 128K tokens. Pixtral 12B substanially outperforms other open models of similar sizes (Llama-3.2 11B \& Qwen-2-VL 7B). It also outperforms much larger open models like Llama-3.2 90B while being 7x smaller. We further contribute an open-source benchmark, MM-MT-Bench, for evaluating vision-language models in practical scenarios, and provide detailed analysis and code for standardized evaluation protocols for multimodal LLMs. Pixtral-12B is released under Apache 2.0 license.

Micromechanical resonators are essential components in time-keeping and sensing devices due to their high frequency, high quality factor, and sensitivity. However, their extremely low damping can lead to various nonlinear phenomena that can compromise frequency stability. A major limiting factor is the Duffing hardening effect, which causes frequency drift through amplitude variations, known as the amplitude-frequency effect. Recently, internal resonance (InRes) has emerged as an effective approach to mitigate this issue and enhance frequency stabilization. In this study, we investigate the frequency stabilization mechanisms of 1:2 and 1:3 InRes using a generalized two-mode reduced-order model that includes Duffing nonlinearity and nonlinear modal coupling. By analyzing the frequency response curves and pi/2-backbone curves, we demonstrate how different parameters affect the effectiveness of frequency stabilization. Our results identify two distinct regimes depending on the coupling strength relative to the stiffening effect as a key factor in determining the stabilization mechanism. For the regime of weak coupling, both 1:2 and 1:3 InRes achieve frequency stabilization through amplitude and frequency saturation over a range of forcing amplitudes. In contrast, strong coupling reduces the amplitude-frequency effect by forming an asymptote line for 1:2 InRes or a zero-dispersion point for 1:3 InRes. These insights offer valuable guidelines for designing micromechanical resonators with high-frequency stability, highlighting InRes as a robust tool for enhancing performance in practical applications., Comment: 31 pages, 7 pages

In reinforcement learning, offline value function learning is the procedure of using an offline dataset to estimate the expected discounted return from each state when taking actions according to a fixed target policy. The stability of this procedure, i.e., whether it converges to its fixed-point, critically depends on the representations of the state-action pairs. Poorly learned representations can make value function learning unstable, or even divergent. Therefore, it is critical to stabilize value function learning by explicitly shaping the state-action representations. Recently, the class of bisimulation-based algorithms have shown promise in shaping representations for control. However, it is still unclear if this class of methods can stabilize value function learning. In this work, we investigate this question and answer it affirmatively. We introduce a bisimulation-based algorithm called kernel representations for offline policy evaluation (KROPE). KROPE uses a kernel to shape state-action representations such that state-action pairs that have similar immediate rewards and lead to similar next state-action pairs under the target policy also have similar representations. We show that KROPE: 1) learns stable representations and 2) leads to lower value error than baselines. Our analysis provides new theoretical insight into the stability properties of bisimulation-based methods and suggests that practitioners can use these methods for stable and accurate evaluation of offline reinforcement learning agents., Comment: Under review

The next decade is expected to see a tenfold increase in the number of strong gravitational lenses, driven by new wide-field imaging surveys. To discover these rare objects, efficient automated detection methods need to be developed. In this work, we assess the performance of three domain adaptation techniques -- Adversarial Discriminative Domain Adaptation (ADDA), Wasserstein Distance Guided Representation Learning (WDGRL), and Supervised Domain Adaptation (SDA) -- in enhancing lens-finding algorithms trained on simulated data when applied to observations from the Hyper Suprime-Cam Subaru Strategic Program. We find that WDGRL combined with an ENN-based encoder provides the best performance in an unsupervised setting and that supervised domain adaptation is able to enhance the model's ability to distinguish between lenses and common similar-looking false positives, such as spiral galaxies, which is crucial for future lens surveys., Comment: 10 pages, 5 figures. Submitted to ApJ. Comments are welcome!

We report the discovery and characterization of a small planet, TOI-1408 c, on a 2.2-day orbit located interior to a previously known hot Jupiter, TOI-1408 b ($P=4.42$ d, $M=1.86\pm0.02\,M_\mathrm{Jup}$, $R=2.4\pm0.5\,R_\mathrm{Jup}$) that exhibits grazing transits. The two planets are near 2:1 period commensurability, resulting in significant transit timing variations (TTVs) for both planets and transit duration variations (TDVs) for the inner planet. The TTV amplitude for TOI-1408 c is 15% of the planet's orbital period, marking the largest TTV amplitude relative to the orbital period measured to date. Photodynamical modeling of ground-based radial velocity (RV) observations and transit light curves obtained with the Transiting Exoplanet Survey Satellite (TESS) and ground-based facilities leads to an inner planet radius of $2.22\pm0.06\,R_\oplus$ and mass of $7.6\pm0.2\,M_\oplus$ that locates the planet into the Sub-Neptune regime. The proximity to the 2:1 period commensurability leads to the libration of the resonant argument of the inner planet. The RV measurements support the existence of a third body with an orbital period of several thousand days. This discovery places the system among the rare systems featuring a hot Jupiter accompanied by an inner low-mass planet., Comment: Accepted to ApJL, 17 pages, 6 figures, 4 tables

Multiple measures assessment (MMA) is an alternative placement system that involves the consideration of alternative measures of students' performance--such as high school grades or GPA--in addition to or in place of standardized test scores to better place students. While the evidence for MMA is strong, placement reform has yet to spread to many colleges and states. Moreover, despite growing support for MMA, many colleges may not be implementing the most promising MMA systems, and some may shift back to standardized testing in the post-pandemic environment. CAPR has sought to assist colleges and states nationwide with the adoption and implementation of MMA practices that place more students--and allow more students to be successful--in college-level courses. As part of these efforts, CAPR worked with colleges in Arkansas and Texas to adopt and expand MMA. The findings in this report are derived from interviews with personnel from 12 two- and four-year public colleges in those states that went through considerable effort to improve their placement systems and to make them sustainable on a large scale. The study focused on three questions: What was the design of the MMA system at each college? How were colleges adopting MMA practices and what were key facilitators and hindrances? And what was the average cost of implementation? The authors found that adoption of MMA at each study college required collaboration among institutional leaders, administrators, faculty members, and advisors. The report highlights the roles of key actors in the adoption of MMA and the important role that state context and policies played in implementation. It also describes challenges that colleges had to overcome during implementation, such as obtaining staff buy-in, managing student data, and ensuring sufficient staffing.

Research shows that socioeconomically, racially, and ethnically integrated schools can have important benefits for student learning. While much research has focused on the benefits of integrated K-12 settings, emerging evidence indicates that the benefit of school- and classroom-level diversity may also be significant in early childhood education (ECE). Despite the positive influence of integrated settings, most ECE programs are remarkably segregated, driven by patterns of residential segregation and by policies that govern access to programs. This report explores five policy strategies that foster integration rather than segregation: (1) Establish universal ECE programs where age is the only requirement, so that family income does not determine where a child can enroll; (2) Braid public funding or combine funding streams from various sources to enable children from different socioeconomic backgrounds to learn in the same classroom; (3) Allow tuition-paying families to enroll in public programs while reserving seats for families with low incomes; (4) Attract families across neighborhoods or district boundaries; and (5) Create two-way dual language immersion programs (programs that offer instruction in two languages). These strategies can be bolstered by collecting enrollment data--disaggregated by race, ethnicity, language, and socioeconomic status--to help state and local leaders understand the extent to which children are learning in diverse classrooms. Examples are provided of how cities and school districts across the country are implementing each strategy and navigating common challenges. The strategies examined highlight ways policymakers can create enabling conditions for children from different socioeconomic, racial, and ethnic backgrounds to learn together.

Background: Quantum computing is a rapidly growing new programming paradigm that brings significant changes to the design and implementation of algorithms. Understanding quantum algorithms requires knowledge of physics and mathematics, which can be challenging for software developers. Aims: In this work, we provide a first analysis of how LLMs can support developers' understanding of quantum code. Method: We empirically analyse and compare the quality of explanations provided by three widely adopted LLMs (Gpt3.5, Llama2, and Tinyllama) using two different human-written prompt styles for seven state-of-the-art quantum algorithms. We also analyse how consistent LLM explanations are over multiple rounds and how LLMs can improve existing descriptions of quantum algorithms. Results: Llama2 provides the highest quality explanations from scratch, while Gpt3.5 emerged as the LLM best suited to improve existing explanations. In addition, we show that adding a small amount of context to the prompt significantly improves the quality of explanations. Finally, we observe how explanations are qualitatively and syntactically consistent over multiple rounds. Conclusions: This work highlights promising results, and opens challenges for future research in the field of LLMs for quantum code explanation. Future work includes refining the methods through prompt optimisation and parsing of quantum code explanations, as well as carrying out a systematic assessment of the quality of explanations.

Recent advances in natural language processing and the increased use of large language models have exposed new security vulnerabilities, such as backdoor attacks. Previous backdoor attacks require input manipulation after model distribution to activate the backdoor, posing limitations in real-world applicability. Addressing this gap, we introduce a novel Claim-Guided Backdoor Attack (CGBA), which eliminates the need for such manipulations by utilizing inherent textual claims as triggers. CGBA leverages claim extraction, clustering, and targeted training to trick models to misbehave on targeted claims without affecting their performance on clean data. CGBA demonstrates its effectiveness and stealthiness across various datasets and models, significantly enhancing the feasibility of practical backdoor attacks. Our code and data will be available at https://github.com/PaperCGBA/CGBA., Comment: Under Review

The General Formal Ontology (GFO) is a top-level ontology that is designed to formally describe different domains of reality. Most recent advancements within GFO have been made in defining its modules of space and material objects, defining its functions, and a module for integrated data semantics. In this paper, I further develop the GFO towards the integration of artifacts, which are material objects that are intentionally made for a certain purpose. I discuss recent advancements in artifact ontology in philosophy and formal ontology alike, and provide basic categories and axioms for artifact description in GFO, while considering existing work within its space module, object-process integration and function module. Keywords. Artifact Ontology, GFO, Material Object, Formalization, Comment: PREPRINT

Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring factors like age and gender. This study introduces Lab-AI, an interactive system that offers personalized normal ranges using Retrieval-Augmented Generation (RAG) from credible health sources. Lab-AI has two modules: factor retrieval and normal range retrieval. We tested these on 68 lab tests-30 with conditional factors and 38 without. For tests with factors, normal ranges depend on patient-specific information. Our results show that GPT-4-turbo with RAG achieved a 0.95 F1 score for factor retrieval and 0.993 accuracy for normal range retrieval. GPT-4-turbo with RAG outperformed the best non-RAG system by 29.1% in factor retrieval and showed 60.9% and 52.9% improvements in question-level and lab-level performance, respectively, for normal range retrieval. These findings highlight Lab-AI's potential to enhance patient understanding of lab results.

Twitter is recognized as a crucial platform for the dissemination and gathering of Cyber Threat Intelligence (CTI). Its capability to provide real-time, actionable intelligence makes it an indispensable tool for detecting security events, helping security professionals cope with ever-growing threats. However, the large volume of tweets and inherent noises of human-crafted tweets pose significant challenges in accurately identifying security events. While many studies tried to filter out event-related tweets based on keywords, they are not effective due to their limitation in understanding the semantics of tweets. Another challenge in security event detection from Twitter is the comprehensive coverage of security events. Previous studies emphasized the importance of early detection of security events, but they overlooked the importance of event coverage. To cope with these challenges, in our study, we introduce a novel event attribution-centric tweet embedding method to enable the high precision and coverage of events. Our experiment result shows that the proposed method outperforms existing text and graph-based tweet embedding methods in identifying security events. Leveraging this novel embedding approach, we have developed and implemented a framework, Tweezers, that is applicable to security event detection from Twitter for CTI gathering. This framework has demonstrated its effectiveness, detecting twice as many events compared to established baselines. Additionally, we have showcased two applications, built on Tweezers for the integration and inspection of security events, i.e., security event trend analysis and informative security user identification.

Image color consistency is the key problem in digital imaging consistency when creating datasets. Here, we propose an improved 3D Thin-Plate Splines (TPS3D) color correction method to be used, in conjunction with color charts (i.e. Macbeth ColorChecker) or other machine-readable patterns, to achieve image consistency by post-processing. Also, we benchmark our method against its former implementation and the alternative methods reported to date with an augmented dataset based on the Gehler's ColorChecker dataset. Benchmark includes how corrected images resemble the ground-truth images and how fast these implementations are. Results demonstrate that the TPS3D is the best candidate to achieve image consistency. Furthermore, our Smooth-TPS3D method shows equivalent results compared to the original method and reduced the 11-15% of ill-conditioned scenarios which the previous method failed to less than 1%. Moreover, we demonstrate that the Smooth-TPS method is 20% faster than the original method. Finally, we discuss how different methods offer different compromises between quality, correction accuracy and computational load.

The bond distance is the simplest and most obvious indicator of the nature of a given chemical bond. However, for rare chemistry, it may happen that it is not yet firmly established. In this communication, we will show that the formally-triple protactinium(V) mono-oxo bond is predicted longer than what was previously reported in the solid state and in solution, based on robust quantum mechanical calculations, supported by an extensive methodological study. Furthermore, additional calculations are used to demonstrate that the Pa-Ooxo bond of interest is more sensitive to complexation than the supposedly analogous U-Oyl ones, not only in terms of bond distance but also of finer bond descriptors associated with the effective bond multiplicity.

Node-based programming languages are increasingly popular in media arts coding domains. These languages are designed to be accessible to users with limited coding experience, allowing them to achieve creative output without an extensive programming background. Using LLM-based code generation to further lower the barrier to creative output is an exciting opportunity. However, the best strategy for code generation for visual node-based programming languages is still an open question. In particular, such languages have multiple levels of representation in text, each of which may be used for code generation. In this work, we explore the performance of LLM code generation in audio programming tasks in visual programming languages at multiple levels of representation. We explore code generation through metaprogramming code representations for these languages (i.e., coding the language using a different high-level text-based programming language), as well as through direct node generation with JSON. We evaluate code generated in this way for two visual languages for audio programming on a benchmark set of coding problems. We measure both correctness and complexity of the generated code. We find that metaprogramming results in more semantically correct generated code, given that the code is well-formed (i.e., is syntactically correct and runs). We also find that prompting for richer metaprogramming using randomness and loops led to more complex code.

We introduce SHADOW, a fine-tuned language model trained on an intermediate task using associative deductive reasoning, and measure its performance on a knowledge base construction task using Wikidata triple completion. We evaluate SHADOW on the LM-KBC 2024 challenge and show that it outperforms the baseline solution by 20% with a F1 score of 68.72%., Comment: 7 pages, 1 figure, accepted for the International Conference on Natural Language Computing (NATL) 2024

We introduce semantic towers, an extrinsic knowledge representation method, and compare it to intrinsic knowledge in large language models for ontology learning. Our experiments show a trade-off between performance and semantic grounding for extrinsic knowledge compared to a fine-tuned model intrinsic knowledge. We report our findings on the Large Language Models for Ontology Learning (LLMs4OL) 2024 challenge., Comment: 8 pages, 4 figures, accepted for the LLMs4OL challenge at the International Semantic Web Conference (ISWC) 2024

7,651 cases of Search and Rescue Missions (SAR) were reported by the United States Coast Guard in 2024, with over 1322 SAR helicopters deployed in the 6 first months alone. Through the utilizations of YOLO, we were able to run different weather conditions and lighting from our augmented dataset for training. YOLO then utilizes CNNs to apply a series of convolutions and pooling layers to the input image, where the convolution layers are able to extract the main features of the image. Through this, our YOLO model is able to learn to differentiate different objects which may considerably improve its accuracy, possibly enhancing the efficiency of SAR operations through enhanced detection accuracy. This paper aims to improve the model's accuracy of human detection in maritime SAR by evaluating a robust datasets containing various elevations and geological locations, as well as through data augmentation which simulates different weather and lighting. We observed that models trained on augmented datasets outperformed their non-augmented counterparts in which the human recall scores ranged from 0.891 to 0.911 with an improvement rate of 3.4\% on the YOLOv5l model. Results showed that these models demonstrate greater robustness to real-world conditions in varying of weather, brightness, tint, and contrast.

Jahn-Teller distortions of transition-metal coordination environments link orbital occupancies to structure. In the solid state, such distortions can be strongly correlated through the propagation of strain and/or through orbital interactions. Cooperative Jahn-Teller (CJT) order of this kind affects the electronic, magnetic, and structural properties of the materials in which it occurs. Conventionally studied in dense ceramics, CJT order also occurs in hybrid materials, albeit the underlying phenomenology is not well established. Here we use synchrotron powder X-ray diffraction measurements to determine links between composition and Jahn-Teller effects in a series of Prussian blue analogue (PBA) families. We develop a simple model based on the dual considerations of strain and crystal-field stabilisation that can account for the stability, extent of CJT order, and crystallite strain measured experimentally. This model shows how PBA compositions might be tuned to control the emergence and nature of CJT effects. A key implication of the model is that PBAs harbour their own complex interplay between compositional, structural, orbital, and magnetic degrees of freedom related to that of strongly-correlated functional materials such as the manganite perovskites.