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Differences in effect sizes between researcher developed (RD) and independently developed (ID) outcome measures are widely documented but poorly understood in education research. We conduct a meta-analysis using item-level outcome data to test potential mechanisms that explain differences in effects by RD or ID outcome type. Our analysis of 45 effect sizes from 30 studies shows that both greater standard deviations of item-specific treatment effects and lower correlations between item-specific effects and item easiness predict larger effect sizes and reduce the observed difference between RD and ID measures from 0.24 SDs to 0.15 SDs. The findings advance our understanding of how item properties predict educational intervention outcomes and underscore the affordances of analyzing item-level data for building theory in education research.

As publicly funded private school tuition systems have expanded, independent research has documented their negative outcomes. However, the prevalence of the programs continues to grow. This brief summarizes the research evidence about vouchers and identifies three areas of policy design that are especially important for student success and a good-faith use of public funding: accountability, access, and transparency. It also reviews the extent to which legislation provides for new, independent evaluation of these programs, and provides policy recommendations to ensure that new or existing voucher systems follow evidence-based practice.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.

The quantum chromodynamics (QCD) axion arises as the pseudo-Goldstone mode of a spontaneously broken abelian Peccei-Quinn (PQ) symmetry. If the scale of PQ symmetry breaking occurs below the inflationary reheat temperature and the domain wall number is unity, then there is a unique axion mass that gives the observed dark matter (DM) abundance. Computing this mass has been the subject of intensive numerical simulations for decades since the mass prediction informs laboratory experiments. Axion strings develop below the PQ symmetry-breaking temperature, and as the string network evolves it emits axions that go on to become the DM. A key ingredient in the axion mass prediction is the spectral index of axion radiation emitted by the axion strings. We compute this index in this work using the most precise and accurate large-scale simulations to date of the axion-string network leveraging adaptive mesh refinement to achieve the precision that would otherwise require a static lattice with 262,144$^3$ lattice sites. We find a scale-invariant axion radiation spectrum to within 1% precision. Accounting for axion production from strings prior to the QCD phase transition leads us to predict that the axion mass should be approximately $m_a\in(45,65)$ $\mu \mathrm{eV}$. However, we provide preliminary evidence that axions are produced in greater quantities from the string-domain-wall network collapse during the QCD phase transition, potentially increasing the mass prediction to as much as 300 $\mu$eV., Comment: 7 + 25 pages, 3 + 18 figures, 0 + 4 tables, supplementary animations at https://tinyurl.com/AxionStringsAMR, video abstract at https://tinyurl.com/rxfnbt9c

This study examines how student achievement changes following a transfer to a newly opened school. Using comprehensive data on students and teachers from Michigan schools that opened or closed between the 2010-11 and 2018-19 school years, the study employs event study models to evaluate changes in student outcomes as the local supply of schools evolves. The analysis reveals initial disruptive effects of transferring to a new school, particularly in math and English/Language Arts, with achievement levels recovering over a three-year period. These findings highlight the need to consider the high financial costs of school construction and maintenance alongside equity and opportunity concerns, given that students of color and lower-income students are disproportionately represented among those attending newly opened schools. The study includes discussions on the resource allocation and equity implications of these results, with supplemental data on school openings, closures, and their contextual impact.

This paper examines the impact, characteristics, and entrepreneurial proclivities of foreign-born college graduates in the United States. A significant body of research has found that immigrants are more likely to start businesses than those born in the U.S., and the propensity of international students to concentrate in STEM fields indicates enormous potential for economic contributions and innovation. Yet the static nature of the immigration system, with visa pathways and restrictions that discourage business creation, hamper the nation's ability to take full advantage of the benefits immigrants can provide. In fact, this study finds that the U.S. immigration system likely delays foreign-born graduates from creating incorporated firms by as many as five years. The authors estimate that the creation of 150,000 incorporated firms and 580,000 jobs were delayed between 2013 and 2021. Without reform, the U.S. will continue to depress high-value firm creation by international students and cease to be the primary destination of global talent.

Research and development work in artificial intelligence in education (AIED) is wide ranging and rapidly growing to support all areas of science, technology, engineering, and mathematics (STEM) teaching and learning. At the risk of hyperbole, this is potentially the most fundamentally game-changing technology for education to emerge since the internet. Building from decades of work on AI and AI-based learning and teaching technologies, the recent advances in AIED are pushing us to reimagine what is possible for STEM teaching and learning. AIED research initiatives are being speedily funded, and AIED advances are quickly becoming integrated into STEM education. It is transforming how teachers teach and how students learn. It is also transforming how education developers and researchers conduct their expansive work. There is excitement about the promise of AIED as well as growing concern that the breakthroughs in AIED are impacting everyday education practice in ways that may perpetuate long-standing biases and diminish the potential for positive outcomes. This brief is the first in a three-part series on AIED related to STEM research, teaching, and learning. The topics address ethical approaches to AI in STEM education research, AI for STEM teaching, and AI for STEM learning. This series is sponsored by the Community for Advancing Discovery Research in Education (CADRE), a National Science Foundation-funded network for STEM education researchers endeavoring to improve STEM teaching and learning through research, development, and various information-sharing and community-building mechanisms. Researchers in the CADRE network are part of a portfolio of projects funded through NSF's Discovery Research PreK-12 (DRK-12) program. The DRK-12 portfolio is wide-ranging, with a multitude of projects that focus on applied research and development to generate innovative research-informed and field-tested tools, products, and approaches that are intended to enhance STEM teaching and learning. Over the past several years, the portfolio has grown to include an increasing number of projects that leverage AIED to achieve their goals related to teaching or learning. It is expected to continue to grow. This series has been inspired by the question, "What are the essential considerations for researchers and developers who are designing, studying, and using AI in K-12 STEM?" Our hope is that the opportunities and challenges discussed in this series will generate reflection and rich discussion for the better and support the transformative use of AI to achieve positive and wide-reaching impact for all learners. In this first brief, "Toward Ethical and Just AI in Education Research," the authors are concerned with the ethical reasoning and decisions made in the development, study, and use of AIED technologies. Recognizing that AIED technologies reflect both the intended and unintended biases of the designers and the wider society, they advocate for the adoption of policies and practices that prioritize ethics, equity, and justice in research and development initiatives using AIED technologies in K-12 education. In an effort to provide guidance to researchers and developers, they lay the groundwork for responsible AI research and its implementation in educational settings. This foundation draws in part from the ethics rules for research with human subjects that have guided researchers for decades, but goes beyond this to frame a more all-encompassing stance rooted in justice and equity. The authors illustrate how ethical AI research can be strengthened by building from well-established ethical principles used in research and society at large. Taking into account these principles, they propose an ethical AIED framework and a set of tools that they have found to be supportive of continuous reflection, communication, and improvement toward inclusive and equitable AIED research and development. Their guidance is in the service of ensuring that the good intentions of researchers and developers will lead to positive design decisions and actions that create inclusive AIED technology products and systems. This is a valuable contribution that encourages a shift in focus to bring ethics, justice, and the values of communities of teachers, students, and families to the forefront of research and development practice.

Sparse autoencoders (SAEs) decompose language model representations into a sparse set of linear latent vectors. Recent works have improved SAEs using language model gradients, but these techniques require many expensive backward passes during training and still cause a significant increase in cross entropy loss when SAE reconstructions are inserted into the model. In this work, we improve on these limitations by taking a fundamentally different approach: we use low-rank adaptation (LoRA) to finetune the language model itself around a previously trained SAE. We analyze our method across SAE sparsity, SAE width, language model size, LoRA rank, and model layer on the Gemma Scope family of SAEs. In these settings, our method reduces the cross entropy loss gap by 30% to 55% when SAEs are inserted during the forward pass. We also find that compared to end-to-end (e2e) SAEs, our approach achieves the same downstream cross entropy loss 3$\times$ to 20$\times$ faster on Gemma-2-2B and 2$\times$ to 10$\times$ faster on Llama-3.2-1B. We further show that our technique improves downstream metrics and can adapt multiple SAEs at once. Our results demonstrate that improving model interpretability is not limited to post-hoc SAE training; Pareto improvements can also be achieved by directly optimizing the model itself., Comment: Code available at https://github.com/matchten/LoRA-Models-for-SAEs

Low symmetry crystals have recently emerged as a platform for exploring novel light-matter interactions in the form of hyperbolic shear polaritons. These excitations exhibit unique optical properties such as frequency-dispersive optical axes and asymmetric light propagation and energy dissipation, which arise from the presence of non-orthogonal resonances. However, only non-vdW materials have been demonstrated to support hyperbolic shear polaritons, limiting their exotic properties and potential applications. Here we introduce for the first time novel shear phenomena in low symmetry crystal thin films by demonstrating the existence of elliptical and canalized shear phonon polaritons in gypsum, an exfoliable monoclinic sulphate mineral. Our results unveil a topological transition from hyperbolic shear to elliptical shear polaritons, passing through a canalization regime with strong field confinement. Importantly, we observe a significant slowdown of group velocity, reaching values as low as 0.0005c, highlighting the potential of gypsum for "slow light" applications and extreme light-matter interaction control. These findings expand the application scope of low-symmetry crystals with the benefits that an exfoliable material provides, such as stronger field confinement, tunability, and versatility for its incorporation in complex photonic devices that might unlock new optical phenomena at the nanoscale.

We discuss our transient search for directed energy systems in local galaxies, with calculations indicating the ability of modest searches to detect optical Search for Extraterrestrial Intelligence (SETI) sources in the closest galaxies. Our analysis follows Lubin (2016) where a messenger civilization follows a beacon strategy we call "intelligent targeting." We plot the required laser time to achieve an SNR of 10 and find the time for a blind transmission to target all stars in the Milky Way to be achievable for local galactic civilizations. As high cadence and sky coverage is the pathway to enable such a detection, we operate the Local Galactic Transient Survey (LGTS) targeting M31 (the Andromeda Galaxy), the Large Magellanic Cloud (LMC), and the Small Magellanic Cloud (SMC) via Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. We explore the ability of modest searches like the LGTS to detect directed pulses in optical and near-infrared wavelengths from Extraterrestrial Intelligence (ETI) at these distances and conclude a civilization utilizing less powerful laser technology than we can construct in this century is readily detectable with the LGTS's observational capabilities. Data processing of 30,000 LGTS images spanning 5 years is in progress with the TRansient Image Processing Pipeline (TRIPP; Thomas et al. (2025))., Comment: 8 pages, 7 figures, 1 table

We present the TRansient Image Processing Pipeline (TRIPP), a transient and variable source detection pipeline that employs both difference imaging and light curve analysis techniques for astronomical data. Additionally, we demonstrate TRIPP's rapid analysis capability by detecting transient candidates in near-real time. TRIPP was tested using image data of the supernova SN2023ixf and from the Local Galactic Transient Survey (LGTS) collected by the Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. To verify the methods employed by TRIPP, we compare our results to published findings on the photometry of SN2023ixf. Additionally, we report the ability of TRIPP to detect transient signals from optical Search for Extra Terrestrial Intelligence (SETI) sources., Comment: 11 pages, 10 figures, 2 tables

Vision-language model (VLM) fine-tuning for application-specific visual grounding based on natural language instructions has become one of the most popular approaches for learning-enabled autonomous systems. However, such fine-tuning relies heavily on high-quality datasets to achieve successful performance in various downstream tasks. Additionally, VLMs often encounter limitations due to insufficient and imbalanced fine-tuning data. To address these issues, we propose a new generalizable framework to improve VLM fine-tuning by integrating it with a reinforcement learning (RL) agent. Our method utilizes the RL agent to manipulate objects within an indoor setting to create synthetic data for fine-tuning to address certain vulnerabilities of the VLM. Specifically, we use the performance of the VLM to provide feedback to the RL agent to generate informative data that efficiently fine-tune the VLM over the targeted task (e.g. spatial reasoning). The key contribution of this work is developing a framework where the RL agent serves as an informative data sampling tool and assists the VLM in order to enhance performance and address task-specific vulnerabilities. By targeting the data sampling process to address the weaknesses of the VLM, we can effectively train a more context-aware model. In addition, generating synthetic data allows us to have precise control over each scene and generate granular ground truth captions. Our results show that the proposed data generation approach improves the spatial reasoning performance of VLMs, which demonstrates the benefits of using RL-guided data generation in vision-language tasks., Comment: ICCPS 2025 accepted paper, 10 pages, 9 figures

We consider electroweak corrections to Higgs boson pair production, taking into account the top quark Yukawa and Higgs boson self couplings. Using differential equations we compute a deep expansion of all master integrals in the high-energy limit and present analytic results for the two-loop box-type form factors. We show that precise numerical results can be obtained even for relatively small values of the Higgs boson transverse momentum. We compare against recent numerical results and find good agreement., Comment: 30 pages , 12 figures

In the world of open-source software (OSS), the number of known vulnerabilities has tremendously increased. The GitHub Advisory Database contains advisories for security risks in GitHub-hosted OSS projects. As of 09/25/2023, there are 197,609 unreviewed GitHub security advisories. Of those unreviewed, at least 63,852 are publicly documented vulnerabilities, potentially leaving many OSS projects vulnerable. Recently, bug bounty platforms have emerged to focus solely on providing bounties to help secure OSS. In this paper, we conduct an empirical study on 3,798 reviewed GitHub security advisories and 4,033 disclosed OSS bug bounty reports, a perspective that is currently understudied, because they contain comprehensive information about security incidents, e.g., the nature of vulnerabilities, their impact, and how they were resolved. We are the first to determine the explicit process describing how OSS vulnerabilities propagate from security advisories and bug bounty reports, which are the main intermediaries between vulnerability reporters, OSS maintainers, and dependent projects, to vulnerable OSS projects and entries in global vulnerability databases and possibly back. This process uncovers how missing or delayed CVE assignments for OSS vulnerabilities result in projects, both in and out of OSS, not being notified of necessary security updates promptly and corresponding bottlenecks. Based on our findings, we provide suggestions, actionable items, and future research directions to help improve the security posture of OSS projects.

Virtual reality (VR) technology can be used to treat anxiety symptoms and disorders. However, most VR interventions for anxiety have been therapist guided rather than self-guided. This systematic review aimed to examine the effectiveness and user experience (i.e., usability, acceptability, safety, and attrition rates) of self-guided VR therapy interventions in people with any anxiety condition as well as provide future research directions. Peer-reviewed journal articles reporting on self-guided VR interventions for anxiety were sought from the Cochrane Library, IEEE Explore Digital Library, PsycINFO, PubMED, Scopus, and Web of Science databases. Study data from the eligible articles were extracted, tabulated, and addressed with a narrative synthesis. A total of 21 articles met the inclusion criteria. The findings revealed that self-guided VR interventions for anxiety can provide an effective treatment of social anxiety disorder, public speaking anxiety, and specific phobias. User experiences outcomes of safety, usability, and acceptability were generally positive and the average attrition rate was low. However, there was a lack of standardised assessments to measure user experiences. Self-guided VR for anxiety can provide an engaging approach for effectively and safely treating common anxiety conditions. Nevertheless, more experimental studies are required to examine their use in underrepresented anxiety populations, their long-term treatment effects beyond 12 months, and compare their effectiveness against other self-help interventions for anxiety (e.g., internet interventions and bibliotherapy)., Comment: 40 pages, 1 figure, 4 tables

As LLM agents gain a greater capacity to cause harm, AI developers might increasingly rely on control measures such as monitoring to justify that they are safe. We sketch how developers could construct a "control safety case", which is a structured argument that models are incapable of subverting control measures in order to cause unacceptable outcomes. As a case study, we sketch an argument that a hypothetical LLM agent deployed internally at an AI company won't exfiltrate sensitive information. The sketch relies on evidence from a "control evaluation,"' where a red team deliberately designs models to exfiltrate data in a proxy for the deployment environment. The safety case then hinges on several claims: (1) the red team adequately elicits model capabilities to exfiltrate data, (2) control measures remain at least as effective in deployment, and (3) developers conservatively extrapolate model performance to predict the probability of data exfiltration in deployment. This safety case sketch is a step toward more concrete arguments that can be used to show that a dangerously capable LLM agent is safe to deploy.

This work provides a first report of ultrafast and compact photonic-electronic neuromorphic temporal leaky integrate-and-fire neuronal circuits built with Resonant Tunnelling Diodes (RTDs). We demonstrate experimentally that multiple fast (~100-200 ps) optical input pulses, arriving within a short (sub-ns long) temporal window, control the triggering of excitable responses in two different photonic-electronic RTD circuit architectures. These architectures are an electronic RTD coupled externally to a photodetector (referred to as a PD-RTD), and an integrated opto-electronic RTD device with inherent photodetection capability at infrared telecom wavelengths (referred to as an Optical RTD-PD). For both RTD systems, we reveal that the high-speed optically-triggered integrate-and-fire spiking operation can be precisely controlled by acting on the voltage bias applied to the RTD devices, or via the intensity of incoming optical pulses. Finally, we demonstrate the application of the leaky integrate-and-fire behaviour to a pattern recognition task at high-speed, with the systems triggering fast ns-long electrical spikes in response to optical inputs of weighted 4-bit digital headers., Comment: Submission manuscript, 11 pages, 6 Figures

Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific questions about the nature of intelligence. Despite recent progress toward these goals, there are many open problems in the field that require solutions before many scientific and practical benefits can be realized: Our methods require both conceptual and practical improvements to reveal deeper insights; we must figure out how best to apply our methods in pursuit of specific goals; and the field must grapple with socio-technical challenges that influence and are influenced by our work. This forward-facing review discusses the current frontier of mechanistic interpretability and the open problems that the field may benefit from prioritizing.

We report observations of eight Jovian irregular satellites with JWST's NIRSpec instrument: Himalia, Elara, Pasiphae, Sinope, Lysithea, Carme, Ananke, and Themisto. Irregular satellite families, which are presumed to have formed via collisions, contain various Trojan-like and C-type-asteroid-like surfaces. We sample the three largest members of the Himalia satellite family, detecting the presence of complexed CO$_2$ and a unique absorption band from $\sim2.7-3.6\ \mu m$ whose character correlates with satellite size. The two largest irregular satellites, Himalia family members Himalia and Elara, contain ammoniated phyllosilicates that are not seen in the meteorite inventory. We propose that the Himalia parent body was heterogeneous and formed with materials similar to Ceres-like ammonium-bearing asteroids. Several small ($D\sim 10km$) irregular satellites closely track the colors and absorption bands of ``red'' Jovian Trojans, demonstrating that these compositions are retained amongst the products of collisions that occurred after Jovian capture. We report the first detection of aqueous alteration products in the retrograde satellite swarm, finding Ananke's 3 micron band to closely match phyllosilicates seen in C2 chondrites. Notably, objects with OH absorption features similar to the Trojan asteroid Eurybates are found in both the retrograde Pasiphae family and the prograde Himalia family, confounding a simple link between such materials and a single surface type. The irregular satellites appear consistent with some materials that experienced alteration from liquid water and others that did not. Consequently, Jupiter may have captured bodies that formed from different initial compositions, or bodies that experienced different levels of heating, driving differential alteration processes., Comment: 20 Pages, 11 Figures, 2 Tables. Submitted to PSJ

The Feynman path integral has revolutionized modern approaches to quantum physics. Although the path integral formalism has proven very successful and spawned several approximation schemes, the direct evaluation of real-time path integrals is still extremely expensive and numerically delicate due to its high-dimensional and oscillatory nature. We propose an efficient method for the numerical evaluation of the real-time world-line path integral for theories where the potential is dominated by a quadratic at infinity. This is done by rewriting the high-dimensional oscillatory integral in terms of a series of low-dimensional oscillatory integrals, that we efficiently evaluate with Picard-Lefschetz theory or approximate with the eikonal approximation. Subsequently, these integrals are stitched together with a series of fast Fourier transformations to recover the lattice regularized Feynman path integral. Our method directly applies to problems in quantum mechanics, the word-line quantization of quantum field theory, and quantum gravity.

We introduce a protocol called ENCORE which simultaneously compresses and encrypts data in a one-pass process that can be implemented efficiently and possesses a number of desirable features as a streaming encoder/decoder. Motivated by the observation that both lossless compression and encryption consist of performing an invertible transformation whose output is close to a uniform distribution over bit streams, we show that these can be done simultaneously, at least for ``typical'' data with a stable distribution, i.e., approximated reasonably well by the output of a Markov model. The strategy is to transform the data into a dyadic distribution whose Huffman encoding is close to uniform, and then store the transformations made to said data in a compressed secondary stream interwoven into the first with a user-defined encryption protocol. The result is an encoding which we show exhibits a modified version of Yao's ``next-bit test'' while requiring many fewer bits of entropy than standard encryption. Numerous open questions remain, particularly regarding results that we suspect can be strengthened considerably., Comment: 15 pages

Procedural audio, often referred to as "digital Foley", generates sound from scratch using computational processes. It represents an innovative approach to sound-effects creation. However, the development and adoption of procedural audio has been constrained by a lack of publicly available datasets and models, which hinders evaluation and optimization. To address this important gap, this paper presents a dataset of 6000 synthetic audio samples specifically designed to advance research and development in sound synthesis within 30 sound categories. By offering a description of the diverse synthesis methods used in each sound category and supporting the creation of robust evaluation frameworks, this dataset not only highlights the potential of procedural audio, but also provides a resource for researchers, audio developers, and sound designers. This contribution can accelerate the progress of procedural audio, opening up new possibilities in digital sound design., Comment: 7 pages, 2 tables and 1 figure

Quantum computers have the potential to solve certain problems faster than classical computers by exploiting quantum mechanical effects such as superposition. However, building high-quality quantum software is challenging due to the fundamental differences between quantum and traditional programming and the lack of abstraction mechanisms. To mitigate this challenge, researchers have introduced quantum patterns to capture common high-level design solutions to recurring problems in quantum software engineering. In order to utilize patterns as an abstraction level for implementation, a mapping between the theoretical patterns and the source code is required, which has only been addressed to a limited extent. To close this gap, we propose a framework for the automatic detection of quantum patterns using state- and circuit-based code analysis. Furthermore, we contribute a dataset for benchmarking quantum pattern detection approaches. In an empirical evaluation, we show that our framework is able to detect quantum patterns very accurately and that it outperforms existing quantum pattern detection approaches in terms of detection accuracy., Comment: 10 pages, accepted at Q-SE 2025 (ICSE workshop) and to be published in ICSE-Companion, this is a preprint version before submission

This paper systematically studies the behavior of the leading eigenvectors for independent edge undirected random graphs generated from a general latent position model whose link function is possibly infinite rank and also possibly indefinite. We first derive uniform error bounds in the two-to-infinity norm as well as row-wise normal approximations for the leading sample eigenvectors. We then build on these results to tackle two graph inference problems, namely (i) entrywise bounds for graphon estimation and (ii) testing for the equality of latent positions, the latter of which is achieved by proposing a rank-adaptive test statistic that converges in distribution to a weighted sum of independent chi-square random variables under the null hypothesis. Our fine-grained theoretical guarantees and applications differ from the existing literature which primarily considers first order upper bounds and more restrictive low rank or positive semidefinite model assumptions. Further, our results collectively quantify the statistical properties of eigenvector-based spectral embeddings with growing dimensionality for large graphs.

Despite coffee's popularity, there are no quantitative methods to measure a chemical property of coffee and relate it to a gustatory experience. Borrowing an electrochemical technique often used to assess the oxidative and reductive features of molecules, we demonstrate that cyclic voltammetry can be used to directly measure the ensemble strength of a coffee beverage and, separately, how dark the coffee has been roasted. We show that the current passed for the protonic features that precede hydrogen evolution are linearly related to beverage strength. The same features are suppressed with subsequent cycling, and we show that the suppression is directly related to composition, which depends on roast. Together, our voltammetric method decouples beverage strength from roast color; the latter is the primary factor determining flavor profiles of coffee extracts.

In a recent study, Dannen et al. surveyed a large parameter space to study the transition from efficient to inefficient line driving. They found that when the line force significantly weakens due to ionization, the winds are variable, with a characteristic frequency comparable to the Lamb cut-off frequency of a stratified atmosphere, {\omega}c. In this work, we present a set of simulations and perturbation analyses that elucidate the variability source and characteristics. We found that the line force adds wave energy and amplifies perturbations with frequencies near {\omega}c. This selective amplification results from the coupling between the natural tendency of velocity perturbations to grow in a stratified atmosphere and the dependence of the line force on the velocity gradient, per the Castor-Abbott-Klein line-driven wind theory. We also found that the variability stems from self-excitation that occurs in the exponential atmosphere due to the non-linearity introduced by the absolute value of the velocity gradient in the line force prescription. We conclude that self-consistently calculating ionization is insufficient for modeling the dynamics in the subsonic atmosphere. Instead future wind models should relax the Sobolev approximation, or model the radiative transfer to capture the dynamics and instabilities at the base of the wind., Comment: 15 pages, 5 figures

The accurate detection and segmentation of pavement distresses, particularly tiny and small cracks, are critical for early intervention and preventive maintenance in transportation infrastructure. Traditional manual inspection methods are labor-intensive and inconsistent, while existing deep learning models struggle with fine-grained segmentation and computational efficiency. To address these challenges, this study proposes Context-CrackNet, a novel encoder-decoder architecture featuring the Region-Focused Enhancement Module (RFEM) and Context-Aware Global Module (CAGM). These innovations enhance the model's ability to capture fine-grained local details and global contextual dependencies, respectively. Context-CrackNet was rigorously evaluated on ten publicly available crack segmentation datasets, covering diverse pavement distress scenarios. The model consistently outperformed 9 state-of-the-art segmentation frameworks, achieving superior performance metrics such as mIoU and Dice score, while maintaining competitive inference efficiency. Ablation studies confirmed the complementary roles of RFEM and CAGM, with notable improvements in mIoU and Dice score when both modules were integrated. Additionally, the model's balance of precision and computational efficiency highlights its potential for real-time deployment in large-scale pavement monitoring systems.

This study examines the sawtooth crash phenomenon in tokamak plasmas by modelling it as a sequence of Multi-region Relaxed Magnetohydrodynamic (MRxMHD) equilibria. Using the Stepped-Pressure Equilibrium Code (SPEC), we constructed a series of equilibria representing intermediate states during the sawtooth crash, with progressively increasing reconnection regions. Numerical results demonstrated that the system prefers the lower energy non-axisymmetric equilibria with islands and is eventually back to an axisymmetric state, capturing key features of the reconnection process. Comparisons with the nonlinear MHD code M3D-C1 showed remarkable agreement on the field-line topology, the safety factor, and the current profile. However, the simplified MRxMHD model does not resolve the detailed structure of the current sheet. Despite this limitation, MRxMHD offers an insightful approach and a complementary perspective to initial-value MHD simulations.

Until now multiscale quantum problems have appeared to be out of reach at the many-body level relevant to strongly correlated materials and current quantum information devices. In fact, they can be modeled with $q$-th order fractional derivatives, as we demonstrate in this work, treating classical and quantum phase transitions in a fractional Ising model for $0 < q \leq 2$ ($q = 2$ is the usual Ising model). We show that fractional derivatives not only enable continuous tuning of critical exponents such as $\nu$, $\delta$, and $\eta$, but also define the Hausdorff dimension $H_D$ of the system tied geometrically to the anomalous dimension $\eta$. We discover that for classical systems, $H_D$ is precisely equal to the fractional order $q$. In contrast, for quantum systems, $H_D$ deviates from this direct equivalence, scaling more gradually, driven by additional degrees of freedom introduced by quantum fluctuations. These results reveal how fractional derivatives fundamentally modify the fractal geometry of many-body interactions, directly influencing the universal symmetries of the system and overcoming traditional dimensional restrictions on phase transitions. Specifically, we find that for $q < 1$ in the classical regime and $q < 2$ in the quantum regime, fractional interactions allow phase transitions in one dimension. This work establishes fractional derivatives as a powerful tool for engineering critical behavior, offering new insights into the geometry of multiscale systems and opening avenues for exploring tunable quantum materials on NISQ devices.

Extracting sections from clinical notes is crucial for downstream analysis but is challenging due to variability in formatting and labor-intensive nature of manual sectioning. While proprietary large language models (LLMs) have shown promise, privacy concerns limit their accessibility. This study develops a pipeline for automated note sectioning using open-source LLMs, focusing on three sections: History of Present Illness, Interval History, and Assessment and Plan. We fine-tuned three open-source LLMs to extract sections using a curated dataset of 487 progress notes, comparing results relative to proprietary models (GPT-4o, GPT-4o mini). Internal and external validity were assessed via precision, recall and F1 score. Fine-tuned Llama 3.1 8B outperformed GPT-4o (F1=0.92). On the external validity test set, performance remained high (F1= 0.85). Fine-tuned open-source LLMs can surpass proprietary models in clinical note sectioning, offering advantages in cost, performance, and accessibility., Comment: Our code is publicly available on github ( https://github.com/lindvalllab/MedSlice )

We present an atmospheric characterization and orbital analysis of HD 206893 B, an exceptionally red, L/T-transition substellar companion in a multiplanetary system, via Keck Planet Imager and Characterizer (KPIC) high-resolution (R $\sim$ 35,000) K-band spectroscopy. Using PHOENIX atmospheric models in a forward-model framework that fits the spectrum of the companion and diffracted starlight simultaneously, we detect HD 206893 B at $>8\sigma$ significance via cross-correlation in two epochs. We find an effective temperature for the companion of $1634^{+72}_{-38}$ K and a log(g) of $4.55^{+0.17}_{-0.22}$. Only accounting for statistical uncertainties, we measure the carbon-oxygen ratio (C/O) of this companion to be $0.57 \pm 0.02$, or near-solar while assuming solar metallicity. The C/O ratio we measure fits the tentative trend of $>4 M_{Jup}$ companions having near-solar C/O ratios while less massive companions have greater-than-solar C/O ratios. Using substellar evolution models, we find an age of $112^{+36}_{-22}$ Myr, a mass of $22.7^{+2.5}_{-1.7} M_{Jup}$, and a radius of $1.11 \pm 0.03 R_{Jup}$ for this companion. We also use KPIC radial velocity data to fit the orbit of HD 206893 B and analyze the orbital stability of this system. We find that the orbital stability is relatively independent of the mass of HD 206893 B, and favors an orbital configuration where B and its interior planetary companion, HD 206893 c, are co-planar. The measured C/O ratio coupled with the current architecture of the system cannot rule out a core accretion scenario, nor a disk fragmentation scenario regarding the formation pathway of HD 206893 B., Comment: 37 pages, 23 figures

Web3's decentralised infrastructure has upended the standardised approach to digital identity established by protocols like OpenID Connect. Web2 and Web3 currently operate in silos, with Web2 leveraging selective disclosure JSON web tokens (SD-JWTs) and Web3 dApps being reliant on on-chain data and sometimes clinging to centralised system data. This fragmentation hinders user experience and the interconnectedness of the digital world. This paper explores the integration of Web3 within the OpenID Connect framework, scrutinising established authentication protocols for their adaptability to decentralised identities. The research examines the interplay between OpenID Connect and decentralised identity concepts, the limitations of existing protocols like OpenID Connect for verifiable credential issuance, OpenID Connect framework for verifiable presentations, and self-issued OpenID provider. As a result, a novel privacy-preserving digital identity bridge is proposed, which aims to answer the research question of whether authentication protocols should inherently support Web3 functionalities and the mechanisms for their integration. Through a Decentralised Autonomous Organisation (DAO) use case, the findings indicate that a privacy-centric bridge can mitigate existing fragmentation by aggregating different identities to provide a better user experience. While the digital identity bridge demonstrates a possible approach to harmonise digital identity across platforms for their use in Web3, the bridging is unidirectional and limits root trust of credentials. The bridge's dependence on centralised systems may further fuel the debate on (de-)centralised identities.

Multi-agent systems must decide which agent is the most appropriate for a given task. We propose a novel architecture for recommending which LLM agent out of many should perform a task given a natural language prompt by extending the Sentence-BERT (SBERT) encoder model. On test data, we are able to achieve a top-1 accuracy of 92.2% with each classification taking less than 300 milliseconds. In contrast to traditional classification methods, our architecture is computationally cheap, adaptive to new classes, interpretable, and controllable with arbitrary metrics through reinforcement learning. By encoding natural language prompts into sentence embeddings, our model captures the semantic content relevant to recommending an agent. The distance between sentence embeddings that belong to the same agent is then minimized through fine-tuning and aligned to human values through reinforcement learning from human feedback. This allows the classification of natural language prompts based on their nearest neighbors by measuring the cosine similarity between embeddings. This work is made possible through the generation of a synthetic dataset for agent recommendation, which we have open-sourced to the public along with the code for AgentRec recommendation system at https://github.com/joshprk/agentrec., Comment: 10 pages, 8 figures, preprint

This study explores the application of Convolutional Autoencoders (CAEs) for analyzing and reconstructing Scanning Tunneling Microscopy (STM) images of various crystalline lattice structures. We developed two distinct CAE architectures to process simulated STM images of simple cubic, body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal lattices. Our models were trained on $17\times17$ pixel patches extracted from $256\times256$ simulated STM images, incorporating realistic noise characteristics. We evaluated the models' performance using Mean Squared Error (MSE) and Structural Similarity (SSIM) index, and analyzed the learned latent space representations. The results demonstrate the potential of deep learning techniques in STM image analysis, while also highlighting challenges in latent space interpretability and full image reconstruction. This work lays the foundation for future advancements in automated analysis of atomic-scale imaging data, with potential applications in materials science and nanotechnology., Comment: 18 pages

We formulate a conjecture classifying algebraic solutions to (possibly non-linear) algebraic differential equations, in terms of the primes appearing in the denominators of the coefficients of their Taylor expansion at a non-singular point. For linear differential equations, this conjecture is a strengthening of the Grothendieck-Katz $p$-curvature conjecture. We prove the conjecture for many differential equations and initial conditions of algebro-geometric interest. For linear differential equations, we prove it for Picard-Fuchs equations at initial conditions corresponding to cycle classes, among other cases. For non-linear differential equations, we prove it for isomonodromy differential equations, such as the Painlev\'e VI equation and Schlesinger system, at initial conditions corresponding to Picard-Fuchs equations. We draw a number of algebro-geometric consequences from the proofs.

Astrophysical discs which are sufficiently massive and cool are linearly unstable to the formation of axisymmetric structures. In practice, linearly stable discs of surface density slightly below the threshold needed for this instability often form spiral structures, and can subsequently fragment or exhibit a state of self-sustained turbulence, depending on how rapidly the disc cools. This has raised the question of how such turbulence is possible in the linearly stable regime. We suggest a nonlinear mechanism for this phenomenon. We find analytically weakly nonlinear axisymmetric subcritical solitary equilibria which exist in linearly stable 3D discs that are close to the instability threshold. The energy of these 'soliton' solutions is only slightly higher than that of a uniform disc, and the structures themselves are expected to be unstable to non-axisymmetric perturbations. In this way, these subcritical solitary equilibria highlight a nonlinear instability and provide a possible pathway to a turbulent state in linearly stable discs., Comment: 20 pages, 6 figures, published GAFD

Assuming Galactic cosmic rays originate in supernovae and the winds of massive stars, starburst galaxies should produce very-high-energy (VHE; E$>$100 GeV) gamma-ray emission via the interaction of their copious quantities of cosmic rays with the large reservoirs of dense gas within the galaxies. Such VHE emission was detected by VERITAS from the starburst galaxy M 82 in 2008-09. An extensive, multi-year campaign followed these initial observations, yielding a total of 254 h of good quality VERITAS data on M 82. Leveraging modern analysis techniques and the larger exposure, these VERITAS data show a more statistically significant VHE signal ($\sim$6.5 standard deviations ($\sigma$)). The corresponding photon spectrum is well fit by a power law ($\Gamma = 2.3 \pm 0.3_{stat} \pm0.2_{sys}$) and the observed integral flux is F($>$450 GeV) = $(3.2 \pm0.6_{stat} \pm 0.6_{sys}) \times 10^{-13}~\mathrm{cm^{-2}~s}^{-1}$, or $\sim$0.4\% of the Crab Nebula flux above the same energy threshold. The improved VERITAS measurements, when combined with various multi-wavelength data, enable modeling of the underlying emission and transport processes. A purely leptonic scenario is found to be a poor representation of the gamma-ray spectral energy distribution (SED). A lepto-hadronic scenario with cosmic rays following a power-law spectrum in momentum (index $s\simeq 2.25$), and with significant bremsstrahlung below $1$~GeV, provides a good match to the observed SED. The synchrotron emission from the secondary electrons indicates that efficient non-radiative losses of cosmic-ray electrons may be related to advective escape from the starburst core., Comment: 15 pages, 7 figures; Accepted for the publication in The Astrophysical Journal (ApJ)

Inspired by previous studies and pioneers of the field, we present new results on an extensive EPR-Bell experiment using photons generated by parametric down conversion, where one of the photons is deliberately phase-shifted. Our experiments show some surprising results for particular angles of this phase shift., Comment: 20 pages, 12 figures

We show that the average trajectories of relativistic quantum particles in Schwarzschild spacetime, obtained via quantum mechanical weak measurements of momentum and energy, are equivalent to the predicted flow lines of probability current in curved spacetime quantum theory. We subsequently demonstrate that these trajectories correspond exactly to classical null geodesics in a hybrid Schwarzschild-Alcubierre spacetime. This threefold equivalence demonstrates how quantum theory in curved spacetime can be formulated via operationally-defined measurements, and that such a theory may be interpreted deterministically, in the spirit of hidden-variable models such as Bohmian mechanics, through the novel connection to an underlying "guiding metric.", Comment: 5+9 pages, 1 figure

The rapid advancements in quantum computing present significant threats to existing encryption standards and internet security. Simultaneously, the advent of Web 3.0 marks a transformative era in internet history, emphasizing enhanced data security, decentralization, and user ownership. This white paper introduces the W3ID, an abbreviation of Web3 standard meeting universal digital ID, which is a Universal Digital Identity (UDI) model designed to meet Web3 standards while addressing vulnerabilities posed by quantum computing. W3ID innovatively generates secure Digital Object Identifiers (DOIs) tailored for the decentralized Web 3.0 ecosystem. Additionally, W3ID employs a dual-key system for secure authentication, enhancing both public and private verification mechanisms. To further enhance encryption strength and authentication integrity in the quantum computing era, W3ID incorporates an advanced security mechanism. By requiring quadruple application of SHA-256, with consecutive matches for validation, the system expands the number of possibilities to 256^4, which is approximately 4.3 billion times the current SHA-256 capacity. This dramatic increase in computational complexity ensures that even advanced quantum computing systems would face significant challenges in executing brute-force attacks. W3ID redefines digital identity standards for Web 3.0 and the quantum computing era, setting a new benchmark for security, scalability, and decentralization in the global digital twin ecosystem.

We present the discovery of 11 new transiting brown dwarfs and low-mass M-dwarfs from NASA's TESS mission: TOI-2844, TOI-3122, TOI-3577, TOI-3755, TOI-4462, TOI-4635, TOI-4737, TOI-4759, TOI-5240, TOI-5467, and TOI-5882. They consist of 5 brown dwarf companions and 6 very low mass stellar companions ranging in mass from $25 M_{\rm J}$ to $128 M_{\rm J}$. We used a combination of photometric time-series, spectroscopic, and high resolution imaging follow-up as a part of the TESS Follow-up Observing Program (TFOP) in order to characterize each system. With over 50 transiting brown dwarfs confirmed, we now have a large enough sample to directly test different formation and evolutionary scenarios. We provide a renewed perspective on the transiting brown dwarf desert and its role in differentiating between planetary and stellar formation mechanisms. Our analysis of the eccentricity distribution for the transiting brown dwarf sample does not support previous claims of a transition between planetary and stellar formation at $\sim42$ $M_{\rm J}$. We also contribute a first look into the metallicity distribution of transiting companions in the range $7 - 150$ $M_{\rm J}$, showing that this too does not support a $\sim42$ $M_{\rm J}$ transition. Finally, we also detect a significant lithium absorption feature in one of the brown dwarf hosts (TOI-5882) but determine that the host star is likely old based on rotation, kinematic, and photometric measurements. We therefore claim that TOI-5882 may be a candidate for planetary engulfment., Comment: Submitted, 32 pages, 16 figures

Tomographic volumetric additive manufacturing (VAM) achieves high print speed and design freedom by continuous volumetric light patterning. This differs from traditional vat photopolymerization techniques that use brief sequential (2D) plane- or (1D) point-localized exposures. The drawback to volumetric light patterning is the small exposure window. Overexposure quickly leads to cured out-of-part voxels due to the nonzero background dose arising from light projection through the build volume. For tomographic VAM, correct exposure time is critical to achieving high repeatability, however, we find that correct exposure time varies by nearly 40% depending on resin history. Currently, tomographic VAM exposure is timed based on subjective human determination of print completion, which is tedious and yields poor repeatability. Here, we implement a robust auto exposure routine for tomographic VAM using real-time processing of light scattering data, yielding accurate and repeatable prints without human intervention. The resulting print fidelity and repeatability approaches, and in some cases, exceeds that of commercial resin 3D printers. We show that auto exposure VAM generalizes well to a wide variety of print geometries with small positive and negative features. The repeatability and accuracy of auto exposure VAM allows for building multi-part objects, fulfilling a major requirement of additive manufacturing technologies.

For many small-signal particle physics analyses, Wilks' theorem, a simplifying assumption that presumes log-likelihood asymptotic normality, does not hold. The most common alternative approach applied in particle physics is a highly computationally expensive procedure put forward by Feldman and Cousins. When many experiments are combined for a global fit to data, deviations from Wilks' theorem are exacerbated, and Feldman-Cousins becomes computationally intractable. We present a novel, machine learning-based procedure that can approximate a full-fledged Bayesian analysis 200 times faster than the Feldman-Cousins method. We demonstrate the utility of this novel method by performing a joint analysis of electron neutrino/antineutrino disappearance data within a single sterile neutrino oscillation framework. Although we present a prototypical simulation-based inference method for a sterile neutrino global fit, we anticipate that similar procedures will be useful for global fits of all kinds, especially those in which Feldman-Cousins is too computationally expensive to use.

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

Efficient communication is central to both biological and artificial intelligence (AI) systems. In biological brains, the challenge of long-range communication across regions is addressed through sparse, spike-based signaling, minimizing energy consumption and latency. In contrast, modern AI workloads, which keep scaling ever larger across distributed compute systems, are increasingly constrained by bandwidth limitations, creating bottlenecks that hinder scalability and energy efficiency. Inspired by the brain's efficient communication strategies, we propose SNAP, a hybrid neural network architecture combining spiking neural networks (SNNs) and artificial neural networks (ANNs) to address these challenges. SNAP integrates SNNs at bandwidth-constrained regions, such as chip boundaries, where spike-based encoding reduces data transfer overhead. Within each chip, dense ANN computations are maintained to preserve high throughput, accuracy, and robustness. Historically, SNNs have faced difficulties scaling up, with limitations in task-specific performance and reliance on specialized hardware to exploit sparsity. SNAP overcomes these barriers through an algorithm-architecture co-design leveraging learnable sparsity for die-to-die communication while limiting spiking layers to specific network partitions. This composable design integrates spike-based and non-spiking pathways, making it adaptable to diverse deep learning workloads. Our evaluations on language processing and computer vision tasks demonstrate up to 5.3x energy efficiency improvements and 15.2x reductions in inference latency, outperforming both traditional SNNs and non-spiking models. We find that as model resources scale, SNAP's improvement margins grow. By addressing the critical bottleneck of inter-chip communication, SNAP offers a scalable, biologically inspired pathway to more efficient AI systems.

The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing systems in ground-based surveys. We present the examination and comparison of two approaches to lens parameter estimation for strong galaxy-galaxy lenses -- Neural Posterior Estimation (NPE) and Bayesian Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens models for ground-based imaging data that mimics the Dark Energy Survey (DES). We find that NPE outperforms BNNs, producing posterior distributions that are more accurate, precise, and well-calibrated for most parameters. For the 12-parameter NPE model, the calibration is consistently within $<$10\% of optimal calibration for all parameters, while the BNN is rarely within 20\% of optimal calibration for any of the parameters. Similarly, residuals for most of the parameters are smaller (by up to an order of magnitude) with the NPE model than the BNN model. This work takes important steps in the systematic comparison of methods for different levels of model complexity., Comment: Intended for submission to JCAP