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District leaders seeking to close schools often frame decisions as "right-sizing" or "modernizing the footprint" of the district. Historical influences such as population and economic shifts and desegregation during the civil rights era, and more recent factors like deteriorating building conditions, school performance "accountability" measures, declining enrollment, and the expansion of school choice policies have contributed to school closure decisions. Whatever language used, or factors considered, these consequential decisions often spark fierce community debate. In Pennsylvania, which operates one of the most inequitable public education systems in the nation, several communities have controversial histories of school closures, including Philadelphia, Pittsburgh, and rural regions. Now, the prospect of future school closures again looms large as many districts have experienced several years of declining enrollment and face financial cliffs, challenges made worse in the wake of COVID-19. To inform community members and guide district leaders navigating these difficult decisions, this brief revisits Research for Action's 2013 Issue Brief on School Closings Policy. In this update, the authors focus on the inequitable impacts of school closures on students, staff, and surrounding communities, with particular attention to how systemic and historical inequities continue to influence the school closure process. The brief concludes with considerations from research on school closures for school district leaders and decision-makers contemplating closing schools that can better inform plans and address potential equity issues for students and surrounding communities.

The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-ended questions, making accurate evaluation difficult due to the variability in natural language responses. To address this, we introduce AutoConverter, an agentic framework that automatically converts these open-ended questions into multiple-choice format, enabling objective evaluation while reducing the costly question creation process. Our experiments demonstrate that AutoConverter can generate correct and challenging multiple-choice questions, with VLMs demonstrating consistently similar or lower accuracy on these questions compared to human-created ones. Using AutoConverter, we construct VMCBench, a benchmark created by transforming 20 existing VQA datasets into a unified multiple-choice format, totaling 9,018 questions. We comprehensively evaluate 33 state-of-the-art VLMs on VMCBench, setting a new standard for scalable, consistent, and reproducible VLM evaluation., Comment: Project page: https://yuhui-zh15.github.io/AutoConverter-Website/

Shocks and turbulence are ubiquitous phenomena, responsible for particle acceleration to very high energies in a large collection of astrophysical systems. Using self-consistent, hybrid-kinetic simulations with and without pre-existing turbulence, we study the transition of a shock from ``laminar'' to turbulent. We show that the changes in upstream proton transport behaviour are crucial to understand this transition, which we address quantitatively with a novel Eulerian approach. This method, based on the coarse-graining of the Vlasov equation originally introduced in one of our previous studies, gives consistent results for inertial range scales. The potential applications of the coarse-graining approach beyond the shock-turbulence system are outlined., Comment: accepted for publication in MNRAS

In recent years, Large Language Models (LLMs) have shown great potential across a wide range of legal tasks. Despite these advances, mitigating hallucination remains a significant challenge, with state-of-the-art LLMs still frequently generating incorrect legal references. In this paper, we focus on the problem of legal citation prediction within the Australian law context, where correctly identifying and citing relevant legislations or precedents is critical. We compare several approaches: prompting general purpose and law-specialised LLMs, retrieval-only pipelines with both generic and domain-specific embeddings, task-specific instruction-tuning of LLMs, and hybrid strategies that combine LLMs with retrieval augmentation, query expansion, or voting ensembles. Our findings indicate that domain-specific pre-training alone is insufficient for achieving satisfactory citation accuracy even after law-specialised pre-training. In contrast, instruction tuning on our task-specific dataset dramatically boosts performance reaching the best results across all settings. We also highlight that database granularity along with the type of embeddings play a critical role in the performance of retrieval systems. Among retrieval-based approaches, hybrid methods consistently outperform retrieval-only setups, and among these, ensemble voting delivers the best result by combining the predictive quality of instruction-tuned LLMs with the retrieval system., Comment: For code, data, and models see https://auslawbench.github.io

The common theory is that human pilot's performance degrades when responsible for an increased number of uncrewed aircraft systems (UAS). This theory was developed in the early 2010's for ground robots and not highly autonomous UAS. It has been shown that increasing autonomy can mitigate some performance impacts associated with increasing the number of UAS. Overall, the Oregon State University-Wing collaboration seeks to understand what factors negatively impact a pilot's ability to maintain responsibility and control over an assigned set of active UAS. The Phase I evaluation establishes baseline data focused on the number of UAS and the number of nests increase. This evaluation focuses on nominal operations as well as crewed aircraft encounters and adverse weather changes. The results demonstrate that the pilots were actively engaged and had very good situation awareness. Manipulation of the conditions did not result in any significant differences in overall workload. The overall results debunk the theory that increasing the number of UAS is detrimental to pilot's performance., Comment: 45 pages, 10 figures, 21 tables

Recent calculations in both flat and de Sitter spacetimes have highlighted a tension between the decoupling of high-energy physics from low-energy degrees of freedom and the expectation that quantum systems decohere due to interactions with unknown environments. In effective field theory (EFT), integrating out heavy fields should lead to Hamiltonian time evolution, which preserves the purity of low-energy states. This is consistent with the fact that we never observe isolated quantum states spontaneously decohering in the vacuum due to unknown high-energy physics. However, when a heavy scalar of mass $M$ is traced out, the resulting purity of a light scalar with mass $m$ typically appears to scale as a power of $1/M$ (when $m\ll M$), an effect that cannot be captured by a local effective Hamiltonian. We resolve this apparent paradox by showing that the purity depends on the resolution scale of the EFT and how the environment is traced out. We provide a practical method for diagnosing the purity of low-energy states consistent with EFT expectations, and briefly discuss some of the implications these observations have for how ultraviolet divergences can appear in decoherence calculations., Comment: 17 pages + appendices, 2 figures

This case study explores the implementation of personalized learning at an elementary school in Santa Cruz Valley Unified School District 35. The study, conducted by Research for Action and funded by KnowledgeWorks, sheds light on the shift towards personalized, competency-based learning practices within the district. Through qualitative sources and data analysis, the study examines various key aspects such as shared vision and culture, student agency, curriculum, instruction, assessment systems, flexible learning environments, learner support, professional development, and leadership development within the district context. The study outlines how SCV35 reflected many practices associated with personalized, competency-based learning, with a focus on shared vision, student agency, curriculum design, learner support, and development. Key findings revealed positive impacts on students, highlighting increased student agency, ownership of learning, and reflective practices among students and educators. Additionally, the district's efforts to align policies, frameworks, and local partnerships with the principles of personalized learning demonstrate a systematic approach towards sustainable implementation. Furthermore, the report discusses challenges encountered during the implementation, such as adjusting to student-centered instruction, overcoming space limitations, and the need to clarify the practical aspects of personalized, competency-based learning. Despite challenges, success stories were acknowledged, including improved student engagement, individualized support, and teacher collaboration. The study emphasizes the importance of ongoing professional development, teacher collaboration, and student-centered approaches to sustain and enhance the personalized learning model. Recommendations from the study point towards the necessity of continued professional development, strategic collaboration among educators, and addressing the evolving evaluation processes to align with personalized, competency-based instructional models. The report underscores the importance of clarity in implementation practices, acknowledging the varying pace of adoption across the district. By focusing on building teacher capacity, fostering student agency, and maintaining a shared vision, SCV35 aims to further advance personalized, competency-based learning to benefit all stakeholders within the educational community.

The case study explores the adoption and implementation of personalized, competency-based learning at Yuma Union High School District (YUHSD) within the broader context of the Arizona Personalized Learning Network (AZPLN). The study reveals that YUHSD has made notable strides in aligning district-wide systems with personalized learning principles, such as the adoption of standards-based grading and a shared vision for personalized learning. Leadership development, professional learning opportunities, and comprehensive assessment systems have all played a crucial role in supporting the successful implementation of personalized, competency-based learning. The study underscores the positive impact of personalized, competency-based learning on student engagement, agency, and metacognitive skills. Teachers at YUHSD have reported increased student voice and choice through strategies like choice boards, proficiency scales, and flexible learning environments. These practices have led to improved advocacy skills among students and stronger relationships between students and teachers, fostering a culture of transparency and inclusivity. Additionally, the model has facilitated student individualization and differentiated support, enhancing the overall learning experience for students across various subject areas. While the implementation of personalized, competency-based learning has been largely successful, the study also highlights several challenges faced by YUHSD. These challenges include initial resistance due to competing priorities like the AVID program, as well as obstacles in scaling the model to more teachers and classrooms. Faculty members have expressed concerns about the additional time and support needed for effective implementation, especially in addressing the diverse needs of special education students and English language learners. Faculty training and ongoing collaboration are identified as critical areas for improvement to ensure the sustained success of the personalized learning approach. The case study emphasizes the importance of addressing the challenges faced by YUHSD through targeted support for faculty, especially in providing additional training and time for implementation. Efforts should focus on increasing buy-in from core subject area teachers, enhancing support for special education students and English language learners, and promoting a deeper understanding of personalized, competency-based learning principles across the district. Aligning the approach with existing initiatives like AVID and ensuring ongoing technical assistance for faculty are key strategies for successful implementation and continued growth in personalized learning practices. Moreover, the study acknowledges the need for continued collaboration, data-driven decision-making, and stakeholder engagement to sustain and enhance the success of personalized, competency-based learning at YUHSD. By leveraging the insights from this case study, Yuma Union High School District can further advance its efforts to create student-centered learning experiences that prepare all students for success in college, career, and beyond.

Deduction is a recently introduced graph searching process in which searchers clear the vertex set of a graph with one move each, with each searcher's movement determined by which of its neighbors are protected by other searchers. In this paper, we show that the minimum number of searchers required to clear the graph is the same in deduction as in constrained versions of other previously studied graph processes, namely zero forcing and fast-mixed search. We give a structural characterization, new bounds and a spectrum result on the number of searchers required. We consider the complexity of computing this parameter, giving an NP-completeness result for arbitrary graphs, and exhibiting families of graphs for which the parameter can be computed in polynomial time. We also describe properties of the deduction process related to the timing of searcher movement and the success of terminal layouts., Comment: 29 pages, 7 figures

Given positive integers $v,k,t$ and $\lambda$ with $v \geq k \geq t$, a {packing design} PD$_{\lambda}(t,k,v)$ is a pair $(V,\mathcal{B})$, where $V$ is a $v$-set and $\mathcal{B}$ is a collection of $k$-subsets of $V$ such that each $t$-subset of $V$ appears in at most $\lambda$ elements of $\mathcal{B}$. The maximum size of a PD$_{\lambda}(t,k,v)$ is called the {packing number}, denoted PDN$_{\lambda}(t,k,v)$. In this paper we consider packing designs with $t=2$ and $k$ large (linear) relative to $v$. We provide a new bound on the packing number: if $n \geq \lambda+2$ and $nk-\binom{n}{\lambda+1} \leq \lambda v < (n+1)k-\binom{n+1}{\lambda+1}$, then PDN$_{\lambda}(2,k,v) \leq n$. When $\lambda=1$ this is equivalent to the second Johnson bound. We show that this bound is met when $\lambda=1$ and $n$ is a positive integer, and when $\lambda=2$ and $2 \leq n \leq 6$. We also consider a directed variant and show that for the cases we consider, this has the same packing number as the undirected case with $\lambda=2$., Comment: 20 pages, 1 table

Machine-generated data is a valuable resource for training Artificial Intelligence algorithms, evaluating rare workflows, and sharing data under stricter data legislations. The challenge is to generate data that is accurate and private. Current statistical and deep learning methods struggle with large data volumes, are prone to hallucinating scenarios incompatible with reality, and seldom quantify privacy meaningfully. Here we introduce Genomator, a logic solving approach (SAT solving), which efficiently produces private and realistic representations of the original data. We demonstrate the method on genomic data, which arguably is the most complex and private information. Synthetic genomes hold great potential for balancing underrepresented populations in medical research and advancing global data exchange. We benchmark Genomator against state-of-the-art methodologies (Markov generation, Restricted Boltzmann Machine, Generative Adversarial Network and Conditional Restricted Boltzmann Machines), demonstrating an 84-93% accuracy improvement and 95-98% higher privacy. Genomator is also 1000-1600 times more efficient, making it the only tested method that scales to whole genomes. We show the universal trade-off between privacy and accuracy, and use Genomator's tuning capability to cater to all applications along the spectrum, from provable private representations of sensitive cohorts, to datasets with indistinguishable pharmacogenomic profiles. Demonstrating the production-scale generation of tuneable synthetic data can increase trust and pave the way into the clinic.

In scalar-tensor theories it is the two-derivative sigma-model interactions that like to compete at low energies with the two-derivative interactions of General Relativity (GR) $\unicode{x2014}$ at least once the dangerous zero-derivative terms of the scalar potential are suppressed (such as by a shift symmetry). But nontrivial two-derivative interactions require at least two scalars to exist and so never arise in the single-scalar models most commonly explored. Axio-dilaton models provide a well-motivated minimal class of models for which these self-interactions can be explored. We review this class of models and investigate whether these minimal two fields can suffice to describe both Dark Matter and Dark Energy. We find that they can $\unicode{x2014}$ the axion is the Dark Matter and the dilaton is the Dark Energy $\unicode{x2014}$ and that they robustly predict several new phenomena for the CMB and structure formation that can be sought in observations. These include specific types of Dark Energy evolution and small space- and time-dependent changes to particle masses post-recombination that alter the Integrated Sachs-Wolfe effect, cause small changes to structure growth and more., Comment: 27 pages, 5 figures

Cooperative optical effects are enabled and controlled by interactions between molecular dipoles, meaning that their mutual orientation is of paramount importance to, for example, superabsorbing light-harvesting antennas. Here we show how to move beyond the possibilities of simple geometric tailoring, demonstrating how a metallic sphere placed within a ring of parallel dipoles engineers an effective Hamiltonian that generates "guide-sliding" states within the ring system. This allows steady-state superabsorption in noisy room temperature environments, outperforming previous designs while being significantly simpler to implement. As exemplified by this showcase, our approach represents a powerful design paradigm for tailoring cooperative light-matter effects in molecular structures that extends beyond superabsorbing systems, to a huge array of quantum energy transport systems.

The COVID-19 pandemic highlighted the importance of internet access across various aspects of life, from remote work and online education to healthcare services and social connections. As we transition to a post-pandemic era, a pressing need arises to update our understanding of the multifaceted nature of internet access. This study is one of the first attempts to do so. Using survey data from New Zealand adult internet users (n=960), it compares internet connection types, frequency of internet use at home, social media use, and concerns about online risk between people with and without disabilities. Results show people with disabilities have restricted fibre access and higher wireless broadband (a much slower connection type). People with disabilities use social media platforms less and are more concerned about certain online risks. The findings highlight persistent disparities in internet access for people with disabilities in the post-pandemic era. Implications of the study are discussed., Comment: 28 pages, 6 tables, 1 figure

We present an argument (for a cross disciplinary audience) to explain the Dunbar scaling hierarchy for social groups. Our analysis is based on the Promise Theory of trust, and basic dimensional analysis. We derive a universal scaling relation, and pinpoint how groups form from the seeding of individuals by relative alignment, which continues until the costs associated with group contention outweigh the benefits in a detailed balance scenario. We identify an `energy' parameter for this balance that has the semantics of trust in a social setting. Subject to partial efficiency of maintaining aligned intentions, we can calculate a series of compatible rates that balance growth with entropy., Comment: A significant rewrite of an earlier arXiv:2402.00598

Axio-dilaton models are among the simplest scalar-tensor theories that contain the two-derivative interactions that naturally compete at low energies with the two-derivative inter-actions of General Relativity. Such models are well-motivated as the low energy fields arising from string theory compactification. We summarize these motivations and compute their cosmological evolution, in which the dilaton acts as dark energy and its evolution provides a framework for dynamically evolving particle masses. The derivative axion-dilaton couplings play an important role in the success of these cosmologies. We derive the equations for fluctuations needed to study their implications for the CMB anisotropy, matter spectra and structure growth. We use a modified Boltzmann code to study in detail four benchmark parameter choices, including the vanilla Yoga model, and identify couplings that give viable cosmologies, including some with surprisingly large matter-scalar interactions. The axion has negligible potential for most of the cosmologies we consider but we also examine a simplified model for which the axion potential plays a role, using axion-matter couplings motivated by phenomenological screening considerations. We find such choices can also lead to viable cosmologies., Comment: 38 pages, 8 figures

The method of multivariable Mendelian randomization uses genetic variants to instrument multiple exposures, to estimate the effect that a given exposure has on an outcome conditional on all other exposures included in a linear model. Unfortunately, the inclusion of every additional exposure makes a weak instruments problem more likely, because we require conditionally strong genetic predictors of each exposure. This issue is well appreciated in practice, with different versions of F-statistics routinely reported as measures of instument strength. Less transparently, however, these F-statistics are sometimes used to guide instrument selection, and even to decide whether to report empirical results. Rather than discarding findings with low F-statistics, weak instrument-robust methods can provide valid inference under weak instruments. For multivariable Mendelian randomization with two-sample summary data, we encourage use of the inference strategy of Andrews (2018) that reports both robust and non-robust confidence sets, along with a statistic that measures how reliable the non-robust confidence set is in terms of coverage. We also propose a novel adjusted-Kleibergen statistic that corrects for overdispersion heterogeneity in genetic associations with the outcome.

Burgess et al. have recently introduced the BLOR corrective exchange-correlation functional that is, by construction, the unique simplified rotationally-invariant DFT+U functional that enforces the flat-plane condition separately on each effective orbital of a localized subspace. Detached from the Hubbard model, functionals of this type are both double-counting correction free and, when optimized in situ using appropriate error quantifiers, effectively parameter free. In this work, the extension of the BLOR functional to address many-body errors (mBLOR) is derived. The mBLOR functional is built to enforce the flat-plane condition on the entire subspace, rather than on each orbital individually. In this way inter-orbital errors are corrected on the same footing as the single-particle ones. Focusing on exact test cases with strong inter-orbital interactions, the BLOR and mBLOR functionals were benchmarked against contemporary DFT+U functionals using the total energy extensivity condition on stretched homo-nuclear p-block dimers that represent various self-interaction and static-correlation error regimes. The BLOR functional outperformed all other DFT+$U$ functionals tested, which often act to increase total-energy errors, yet it still yielded large errors in some systems. mBLOR instead yielded low energy errors across all four strongly-correlated dimers, while being constructed using only semi-local approximation ingredients. As mBLOR would not otherwise introduce a band-gap correction in the manner that is a desirable feature of DFT+U, we developed a cost-free technique to reintroduce it automatically by moving the functional's unusual explicit derivative discontinuity into the potential. With this in place, mBLOR is the only known DFT$+U$ functional that opens the bandgap of stretched neutral homo-nuclear dimers without the aid of unphysical spin-symmetry breaking., Comment: As accepted for publication in Phys. Rev. B. 22 pages, 9 figures, and 4 tables

Graph burning is a discrete process that models the spread of influence through a network using a fire as a proxy for the type of influence being spread. This process was recently extended to hypergraphs. We introduce a variant of hypergraph burning that uses an alternative propagation rule for how the fire spreads - if some fixed proportion of vertices are on fire in a hyperedge, then in the next round the entire hyperedge catches fire. This new variant has more potential for applications than the original model, and it is similarly viable for obtaining deep theoretical results. We obtain bounds which apply to general hypergraphs, and introduce the concept of the burning distribution, which describes how the model changes as the proportion ranges over (0,1). We also obtain computational results which suggest there is a strong correlation between the automorphism group order and the lazy burning number of a balanced incomplete block design.

We obtain de Sitter (dS) solutions from controlled string-theory constructions. We review how minimal gauged chiral 6D supergravity evades standard dS no-go theorems by having a positive scalar potential and describe the known 4D classical dS, AdS and Minkowski solutions. Grimm and collaborators recently found a related 6D supergravity by direct F-theory Calabi-Yau flux compactifications and we construct classical 4D maximally symmetric solutions for this 6D supergravity. These provide explicit solutions of the higher-dimensional field equations corresponding to dS, AdS and flat spacetimes in 4D, allowing interesting hierarchies of scales. We show how the singularities of these solutions are consistent with the back-reaction of two space-filling 4D brane-like sources situated within the extra dimensions and infer some of the properties of these sources using the formalism of point particle effective field theory (PPEFT), showing the sources are not vanilla objects like D branes. These tools relate the near-source asymptotic forms of bulk fields to source properties and have been extensively tested for more prosaic physical systems involving the back-reaction of small sources, such as the dependence of atomic energy levels on nuclear properties. We use it to determine the tension of the brane-like sources (that can be positive) and its derivatives. We verify that the solutions are in the weak coupling/large volume regime required to neglect quantum and $\alpha'$ effects., Comment: 34 pages, 7 figures

Research for Action (RFA) studied why Black teachers were leaving their jobs more than White teachers in Allegheny County from 2014 to 2020. RFA analyses found that the number of Black teachers in the area was consistently dropping, more than the decrease in the overall Black population. The data gathered from speaking directly with 38 current and former Black teachers about their perspectives and experiences through interviews and focus groups conducted in the spring of 2023 yielded three key takeaways: (1) Black teachers support Allegheny County students and schools in several unique and important ways; (2) Black teachers face many challenges that impact their experiences as educators in Allegheny County; and (3) Several factors retain and sustain Black teachers in Allegheny County. The report offers insights into the working conditions for Black teachers and provides recommendations for policymakers, school leaders, teacher diversity initiatives, and the wider community for how to improve efforts to recruit, hire, retain, and sustain Black teachers within the region and beyond. [Additional funding provided by the McElhattan Foundation and the Eden Hall Foundation.]

We prove that $K_n+I$, the complete graph of an even order with a $1$-factor duplicated, admits a decomposition into $2$-factors, each a disjoint union of cycles of length $m \geq 5$ if and only if $m \mid n$, except possibly when $m$ is odd and $n=4m$. In addition, we show that $K_n+I$ admits a decomposition into $2$-factors, each a disjoint union of cycles of lengths $m_1, \ldots, m_t$, whenever $m_1, \ldots, m_t$ are all even.

Monopole-fermion (and dyon-fermion) interactions provide a famous example where scattering from a compact object gives a cross section much larger than the object's geometrical size. This underlies the phenomenon of monopole catalysis of baryon-number violation because the reaction rate is much larger in the presence of a monopole than in its absence. It is sometimes claimed to violate the otherwise generic requirement that short distance physics decouples from long-distance observables -- a property that underpins the general utility of effective field theory (EFT) methods. Decoupling in this context is most simply expressed using point-particle effective field theories (PPEFTs) designed to capture systematically how small but massive objects influence their surroundings when probed only on length scales large compared to their size. These have been tested in precision calculations of how nuclear properties affect atomic energy levels for both ordinary and pionic atoms. We adapt the PPEFT formalism to describe low-energy $S$-wave dyon-fermion scattering with a view to understanding whether large catalysis cross sections violate decoupling (and show why they do not). We also explore the related but separate issue of the long-distance complications associated with polarizing the fermion vacuum exterior to a dyon and show in some circumstances how PPEFT methods can simplify calculations of low-energy fermion-dyon scattering in their presence. We propose an effective Hamiltonian governing how dyon excitations respond to fermion scattering in terms of a time-dependent vacuum angle and outline open questions remaining in its microscopic derivation., Comment: 48 pages plus appendices

For a positive integer $n$, a graph with at least $n$ vertices is $n$-existentially closed or simply $n$-e.c. if for any set of vertices $S$ of size $n$ and any set $T\subseteq S$, there is a vertex $x\not\in S$ adjacent to each vertex of $T$ and no vertex of $S\setminus T$. We extend this concept to uniform hypergraphs, find necessary conditions for $n$-e.c. hypergraphs to exist, and prove that random uniform hypergraphs are asymptotically $n$-existentially closed. We then provide constructions to generate infinitely many examples of $n$-e.c. hypergraphs. In particular, these constructions use certain combinatorial designs as ingredients, adding to the ever-growing list of applications of designs.

Recent advances in microscopy have enabled the rapid generation of terabytes of image data in cell biology and biomedical research. Vision-language models (VLMs) offer a promising solution for large-scale biological image analysis, enhancing researchers' efficiency, identifying new image biomarkers, and accelerating hypothesis generation and scientific discovery. However, there is a lack of standardized, diverse, and large-scale vision-language benchmarks to evaluate VLMs' perception and cognition capabilities in biological image understanding. To address this gap, we introduce {\mu}-Bench, an expert-curated benchmark encompassing 22 biomedical tasks across various scientific disciplines (biology, pathology), microscopy modalities (electron, fluorescence, light), scales (subcellular, cellular, tissue), and organisms in both normal and abnormal states. We evaluate state-of-the-art biomedical, pathology, and general VLMs on {\mu}-Bench and find that: i) current models struggle on all categories, even for basic tasks such as distinguishing microscopy modalities; ii) current specialist models fine-tuned on biomedical data often perform worse than generalist models; iii) fine-tuning in specific microscopy domains can cause catastrophic forgetting, eroding prior biomedical knowledge encoded in their base model. iv) weight interpolation between fine-tuned and pre-trained models offers one solution to forgetting and improves general performance across biomedical tasks. We release {\mu}-Bench under a permissive license to accelerate the research and development of microscopy foundation models.

The recently reported compactified hyperboloidal method has found wide use in the numerical computation of quasinormal modes, with implications for fields as diverse as gravitational physics and optics. We extend this intrinsically relativistic method into the non-relativistic domain, demonstrating its use to calculate the quasinormal modes of the Schr\"odinger equation and solve related bound-state problems. We also describe how to further generalize this method, offering a perspective on the importance of non-relativistic quasinormal modes for the programme of black hole spectroscopy., Comment: 9 pages, 2 figures

Compliance is a useful parametrization of tactile information that humans often utilize in manipulation tasks. It can be used to inform low-level contact-rich actions or characterize objects at a high-level. In robotic manipulation, existing approaches to estimate compliance have struggled to generalize across object shape and material. Using camera-based tactile sensors, we present a novel approach to parametrize compliance through Young's modulus E. We evaluate our method over a novel dataset of 285 common objects, including a wide array of shapes and materials with Young's moduli ranging from 5.0 kPa to 250 GPa. Data is collected over automated parallel grasps of each object. Combining analytical and data-driven approaches, we develop a hybrid system using a multi-tower neural network to analyze a sequence of tactile images from grasping. This system is shown to estimate the Young's modulus of unseen objects within an order of magnitude at 74.2% accuracy across our dataset. This is a drastic improvement over a purely analytical baseline, which exhibits only 28.9% accuracy. Importantly, this estimation system performs irrespective of object geometry and demonstrates robustness across object materials. Thus, it could be applied in a general robotic manipulation setting to characterize unknown objects and inform decision-making, for instance to sort produce by ripeness.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

The deduction game is a variation of the game of cops and robber on graphs in which searchers must capture an invisible evader in at most one move. Searchers know each others' initial locations, but can only communicate if they are on the same vertex. Thus, searchers must deduce other searchers' movement and move accordingly. We introduce the deduction number and study it for various classes of graphs. We provide upper bounds for the deduction number of the Cartesian product of graphs., Comment: 18 pages, 1 figure