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This is the appendices for the full report, "Identifying the Nation's Lowest Performing Schools: Shifts Following the Passage of the Every Student Succeeds Act (ESSA)." This study examines state identification of schools for the most intensive support to see how changes in federal laws and regulations played out nationally and at the state level, by comparing schools identified just before and just after ESSA's implementation. Specifically, it looks at two time points: 2016-17, during which there were two distinct policy contexts, with most states operating under waivers but seven states still operating under the previous No Child Left Behind policies, and 2018-19, when all states were expected to be following ESSA regulations for the first time. Of particular interest is whether the number and characteristics of identified schools--including their average student achievement and demographics--differed when comparing the set of schools identified under ESSA with the set of schools identified just before ESSA. The appendices provide supporting details on the policy being evaluated, the study methods, and supplementary analyses for the findings presented.

For more than two decades, federal law has required states to identify schools failing to provide students with a high-quality education and has led to substantial debate about how best to do so. Appropriately identifying the lowest performing schools matters because it allows state and local education agencies to target limited resources for school improvement to where they are needed most. The Every Student Succeeds Act (ESSA) of 2015 sought to address perceived problems with school accountability systems under prior federal law. ESSA provided states with increased flexibility in how they design their "annual evaluation of school performance." It also introduced new requirements for states' subsequent "identification of schools for the most intensive support," now designated as those needing Comprehensive Support and Improvement (CSI), in part to allow states to focus on a smaller set of the lowest performing schools. This report examines if ESSA played out as policymakers expected or if there were any other, perhaps less expected, consequences for the number, types, and composition of schools that states identified. The report examines this issue by comparing schools identified for the most intensive supports just before and just after ESSA's implementation.

Boosting literacy among school-age children remains a national priority. Nearly one third of students in the United States have not developed the foundational reading skills needed to succeed academically, with students living in poverty, students with disabilities, and English learners especially at risk. Starting in 2010, Congress invested more than $1 billion for state literacy improvement efforts through the Striving Readers Comprehensive Literacy (SRCL) program. SRCL was intended to focus funding on disadvantaged schools, encourage schools to use evidence-based practices, and support schools and teachers in providing comprehensive literacy instruction. These efforts were expected to lead to improved literacy outcomes for students. This study assesses how well SRCL implementation was aligned with these goals, using information collected from states, districts, and schools in all 11 states awarded grants in 2017.

Measuring the rheology of liquids typically requires precise control over shear rates and stresses. However, we demonstrate that the features of a power-law fluid can be predicted by simply observing the capillary spreading dynamics of viscous droplets within a wedge-shaped geometry. By considering the influence of capillary and viscous forces within this geometry, we show that the spreading dynamics can be described by a nonlinear diffusion equation. Analytical predictions indicate subdiffusive behavior, establishing a direct relationship between the diffusion exponent and the rheological exponent, which is also corroborated by experimental results. Since this relationship is independent of flow details, it provides robust predictions for the rheological properties of power-law fluids.

We show that linear Active Disturbance-Rejection Control (ADRC) tuned using the "bandwidth method" is equivalent to PI(D) control with set-point weighting and a lowpass filter on the measurement signal. We also provide simple expressions that make it possible to implement linear ADRC for first and second-order systems using commonplace two degree-of-freedom PID implementations. The expressions are equivalent to ADRC in the response from measurements, and a slight approximation in the response from references.

This White Paper documents the discussion and consensus conclusions of the workshop "Software Infrastructure for Advanced Nuclear Physics Computing" (SANPC 24), which was held at Jefferson Lab on June 20-22, 2024. The workshop brought together members of the US Nuclear Physics community with data scientists and funding agency representatives to discuss the challenges and opportunities in advanced computing for Nuclear Physics in the coming decade. Opportunities for sustainable support and growth are identified within the context of existing and currently planned DOE and NSF programs., Comment: First draft. Open for NP community comment until Feb. 14, 2025. Please submit comments to the corresponding author (pmjacobs@lbl.gov)

Causal reasoning is often challenging with spatial data, particularly when handling high-dimensional inputs. To address this, we propose a neural network (NN) based framework integrated with an approximate Gaussian process to manage spatial interference and unobserved confounding. Additionally, we adopt a generalized propensity-score-based approach to address partially observed outcomes when estimating causal effects with continuous treatments. We evaluate our framework using synthetic, semi-synthetic, and real-world data inferred from satellite imagery. Our results demonstrate that NN-based models significantly outperform linear spatial regression models in estimating causal effects. Furthermore, in real-world case studies, NN-based models offer more reasonable predictions of causal effects, facilitating decision-making in relevant applications., Comment: 16 pages, 4 figures, 5 tables

Dynamic metasurface antennas (DMAs) beamform through low-powered components that enable reconfiguration of each radiating element. Previous research on a single-user multiple-input-single-output (MISO) system with a dynamic metasurface antenna at the transmitter has focused on maximizing the beamforming gain at a fixed operating frequency. The DMA, however, has a frequency-selective response that leads to magnitude degradation for frequencies away from the resonant frequency of each element. This causes reduction in beamforming gain if the DMA only operates at a fixed frequency. We exploit the frequency reconfigurability of the DMA to dynamically optimize both the operating frequency and the element configuration, maximizing the beamforming gain. We leverage this approach to develop a single-shot beam training procedure using a DMA sub-array architecture that estimates the receiver's angular direction with a single OFDM pilot signal. We evaluate the beamforming gain performance of the DMA array using the receiver's angular direction estimate obtained from beam training. Our results show that it is sufficient to use a limited number of resonant frequency states to do both beam training and beamforming instead of using an infinite resolution DMA beamformer.

Proprietary closed-source software is still the norm in advanced process control. Transparency and reproducibility are key aspects of scientific research. Free and open-source toolkit can contribute to the development, sharing and advancement of new and efficient control approaches, and the industrial sector will certainly benefit from them. This paper presents ModelPredictiveControl.jl, an open-source software package for designing model predictive controllers in the Julia programming language. It is designed to be easy to use and modular, while providing advanced features like nonlinear control and moving horizon estimation. It relies on powerful control system and mathematical optimization frameworks to simplify the construction and testing of state estimators and predictive controllers. It also integrates with the standard plotting library to quickly visualize closed-loop data. The paper presents the main functionalities and illustrates them with two case studies in simulation. The first example is a continuously stirred tank reactor described by linear dynamics. The second one implements a nonlinear, an economic, and a successive linearization model predictive controllers for an inverted pendulum. The solving times are benchmarked against equivalent implementations in MATLAB to show the efficiency of the package., Comment: 11 pages, 11 figures, 1 table

Bristled and membranous insect wings have co-evolved despite apparently serving the same functionality. We emulate flight physics using an automated free-fall experiment to better understand how and why several distinct wing forms may have developed. Biomimetic two-dimensional lattice animals were laser cut from a continuous sheet of paper, and their descent in a settling tank was tracked using a camera. Data from 31 generic symmetric polyominos (1,692 experiments) reveal that morphology impacts the drag coefficient $C_D$ and hence flight efficiency. Some polyominos rapidly sediment while others remain suspended for longer. Positioning the search for an optimal shape within an evolutionary context, we relinquished control of the automated setup to a genetic algorithm. Hereditary information passes between generations in proportion to fitness and is augmented by rare mutations. High-performing morphologies are observed, but {experimental repeats do} not re-evolve the same shapes. Adaptation rates also differ if the selective pressure is reversed from suspension to sedimentation. Our data (50,086 experiments) reveal several physical sources of indeterminism in the simulated natural selection process, including fluid flow variability and morphological entropy. This provides experimental support for the idea that the lack of convergence in insect wing shape may partly reflect the competition between fitness and entropy, making it nearly impossible to achieve unique optimal forms.

We investigate the emergence of topological Hall-like (THE-like) signals in disordered multidomain ferromagnets. Non-monotonic behavior in Hall resistivity, commonly attributed to topological spin textures such as skyrmions, is produced in a random resistor network model without any chirality. It arises from simple mechanisms of the anomalous Hall effect (AHE) in combination with the domain wall scattering. By varying domain configurations and domain wall resistances, we explore the conditions under which the non-monotonic resistivity can be enhanced. Our results emphasize the need for careful analysis in distinguishing between true topological Hall effects and artifacts caused by domain disorders.

This paper details a significant milestone towards maturing a buoyant aerial robotic platform, or aerobot, for flight in the Venus clouds. We describe two flights of our subscale altitude-controlled aerobot, fabricated from the materials necessary to survive Venus conditions. During these flights over the Nevada Black Rock desert, the prototype flew at the identical atmospheric densities as 54 to 55 km cloud layer altitudes on Venus. We further describe a first-principle aerobot dynamics model which we validate against the Nevada flight data and subsequently employ to predict the performance of future aerobots on Venus. The aerobot discussed in this paper is under JPL development for an in-situ mission flying multiple circumnavigations of Venus, sampling the chemical and physical properties of the planet's atmosphere and also remotely sensing surface properties., Comment: Preprint submitted to AIAA Journal of Aircraft

We review an algorithm developed for parameter estimation within the Continuous Data Assimilation (CDA) approach. We present an alternative derivation for the algorithm presented in a paper by Carlson, Hudson, and Larios (CHL, 2021). This derivation relies on the same assumptions as the previous derivation but frames the problem as a finite dimensional root-finding problem. Within the approach we develop, the algorithm developed in (CHL, 2021) is simply a realization of Newton's method. We then consider implementing other derivative based optimization algorithms; we show that the Levenberg Maqrquardt algorithm has similar performance to the CHL algorithm in the single parameter estimation case and generalizes much better to fitting multiple parameters. We then implement these methods in three example systems: the Lorenz '63 model, the two-layer Lorenz '96 model, and the Kuramoto-Sivashinsky equation.

In 2014, as part of a larger study of overpartitions with restrictions of the overlined parts based on residue classes, Munagi and Sellers defined $d_2(n)$ as the number of overpartitions of weight $n$ wherein only even parts can be overlined. As part of that work, they used a generating function approach to prove a parity characterization for $d_2(n)$. In this note, we give a combinatorial proof of their result and extend it to a modulus 4 characterization; we provide both generating function and combinatorial proofs of this stronger result. The combinatorial arguments incorporate classical involutions of Franklin, Glaisher, and Sylvester, along with a recent involution of van Leeuwen and methods new with this work., Comment: 15 pages, 8 tables, 2 figures

We demonstrate a method for radionuclide assay that is spectroscopic with 100 % counting efficiency for alpha decay. Advancing both cryogenic decay energy spectrometry (DES) and drop-on-demand inkjet metrology, a solution of Am-241 was assayed for massic activity (Bq/g) with a relative combined standard uncertainty less than 1 %. We implement live-timed counting, spectroscopic analysis, validation by liquid scintillation (LS) counting, and confirmation of quantitative solution transfer. Experimental DES spectra are well modeled with a Monte Carlo simulation. The model was further used to simulate Pu-238 and Pu-240 impurities, calculate detection limits, and demonstrate the potential for tracer-free multi-nuclide analysis, which will be valuable for new cancer therapeutics based on decay chains, Standard Reference Materials (SRMs) containing impurities, and more widely in nuclear energy, environmental monitoring, security, and forensics.

All modern theories of gravitation, starting with Newton's, predict that gravity will affect the speed of light propagation. Einstein's theory of General Relativity famously predicted that the effect is twice the Newtonian value, a prediction that was verified during the 1919 solar eclipse. Recent theories of vector gravity can be interpreted to imply that gravity will have a different effect on pulsed light versus continuous-wave (CW) light propagating between the two mirrors of an optical cavity. Interestingly, we are not aware of any previous experiments to determine the relative effect of gravity on the propagation of pulsed versus CW light. In order to observe if there are small differences, we use a 6 GHz electro-optic frequency comb and low-noise CW laser to make careful measurements of the resonance frequencies of a high-finesse optical cavity. Once correcting for the effects of mirror dispersion, we determine that the cavity resonance frequencies for pulsed and CW light are the same to within our experimental error, which is on the order of $10^{-12}$ of the optical frequency, and one part in 700 of the expected gravitational shift., Comment: 12 pages

Let $K$ be a sub-$p$-adic field. We show that the functor sending a finite type $K$-scheme to its \'etale topos is fully faithful after localizing at the class of universal homeomorphisms. This generalizes a result of Voevodsky, who proved the analogous theorem for fields finitely generated over $\mathbb{Q}$. Our proof relies on Mochizuki's Hom-theorem in anabelian geometry, and a study of point-theoretic morphisms of fundamental groups of curves., Comment: 12 pages, comments welcome

Many natural and manufactured structures can be effectively modeled as networks of one dimensional segments joined at nodes. A new algorithm for the numerical solution of various time dependent partial differential equations on some of these networks is presented. The main novelty is a network version of the Fast Fourier Transform, which provides an efficient technique for expansions with eigenfunctions of the Laplace operator.

Thermally driven transitions between ferromagnetic and paramagnetic phases are characterized by critical behavior with divergent susceptibilities, long-range correlations, and spin dynamics that can span kHz to GHz scales as the material approaches the critical temperature $\mathrm{T_c}$, but it has proven technically challenging to probe the relevant length and time scales with most conventional measurement techniques. In this study, we employ scanning nitrogen-vacancy center based magnetometry and relaxometry to reveal the critical behavior of a high-$\mathrm{T_c}$ ferromagnetic oxide near its Curie temperature. Cluster analysis of the measured temperature-dependent nanoscale magnetic textures points to a 3D universality class with a correlation length that diverges near $\mathrm{T_c}$. Meanwhile, the temperature-dependent spin dynamics, measured through all optical relaxometry suggest that the phase transition is in the XY universality class. Our results capture both static and dynamic aspects of critical behavior, providing insights into universal properties that govern phase transitions in magnetic materials.

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. This paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

The costs and complexity of the American judicial system limit access to legal solutions for many Americans. Large language models (LLMs) hold great potential to improve access to justice. However, a major challenge in applying AI and LLMs in legal contexts, where consistency and reliability are crucial, is the need for System 2 reasoning. In this paper, we explore the integration of LLMs with logic programming to enhance their ability to reason, bringing their strategic capabilities closer to that of a skilled lawyer. Our objective is to translate laws and contracts into logic programs that can be applied to specific legal cases, with a focus on insurance contracts. We demonstrate that while GPT-4o fails to encode a simple health insurance contract into logical code, the recently released OpenAI o1-preview model succeeds, exemplifying how LLMs with advanced System 2 reasoning capabilities can expand access to justice.

The isotopic compositions of samples returned from Cb-type asteroid Ryugu and Ivuna-type (CI) chondrites are distinct from other carbonaceous chondrites, which has led to the suggestion that Ryugu and CI chondrites formed in a different region of the accretion disk, possibly around the orbits of Uranus and Neptune. We show that, like for Fe, Ryugu and CI chondrites also have indistinguishable Ni isotope anomalies, which differ from those of other carbonaceous chondrites. We propose that this unique Fe and Ni isotopic composition reflects different accretion efficiencies of small FeNi metal grains among the carbonaceous chondrite parent bodies. The CI chondrites incorporated these grains more efficiently, possibly because they formed at the end of the disk's lifetime, when planetesimal formation was also triggered by photoevaporation of the disk. Isotopic variations among carbonaceous chondrites may thus reflect fractionation of distinct dust components from a common reservoir, implying CI chondrites and Ryugu may have formed in the same region of the accretion disk as other carbonaceous chondrites., Comment: Published open access in Science Advances

We introduce a simple algorithm for projecting on $J=0$ states of a many-body system by performing a series of rotations to remove states with angular momentum projections greater than zero. Existing methods rely on unitary evolution with the two-body operator $J^2$, which when expressed in the computational basis contains many complicated Pauli strings requiring Trotterization and leading to very deep quantum circuits. Our approach performs the necessary projections using the one-body operators $J_x$ and $J_z$. By leveraging the method of Cartan decomposition, the unitary transformations that perform the projection can be parameterized as a product of a small number of two-qubit rotations, with angles determined by an efficient classical optimization. Given the reduced complexity in terms of gates, this approach can be used to prepare approximate ground states of even-even nuclei by projecting onto the $J=0$ component of deformed Hartree-Fock states. We estimate the resource requirements in terms of the universal gate set {$H$,$S$,CNOT,$T$} and briefly discuss a variant of the algorithm that projects onto $J=1/2$ states of a system with an odd number of fermions., Comment: 13 pages, 3 figures, v2 matches PRC version

The expansion of the universe in $f(R,T)$ gravity is studied. By focusing on functions of the form $f(R,T)=f_1(R)+f_2(T)$, we assert that present day acceleration can be achieved if the functional form of $f_2(T)$ either grows slowly or falls as a function of $T$. In particular, we demonstrate that when $f_2(T) \propto T^{-1}$, the universe transitions to exponential growth at late times, just as it does in the standard cosmological model. A comparison of predictions of this model, with Type Ia supernovae shows that this model fits the data as well or even slightly better than the standard cosmological model without increasing the number of parameters., Comment: 10 pages, 3 figures

A wide family of two dimensional (2D) systems, including stripe-phase superconductors, sliding Luttinger liquids, and anisotropic 2D materials, can be modeled by an array of coupled one-dimensional (1D) electron channels or nanowire arrays. Here we report experiments in arrays of conducting nanowires with gate and field tunable interwire coupling, that are programmed at the LaAlO$_3$/SrTiO$_3$ interface. We find a magnetically-tuned metal-to-insulator transition in which the transverse resistance of the nanowire array increases by up to four orders of magnitude. To explain this behavior, we develop a minimal model of a coupled two-wire system which agrees well with observed phenomena. These nanowire arrays can serve as a model systems to understand the origin of exotic behavior in correlated materials via analog quantum simulation.

Coupled quasi-one-dimensional (quasi-1D) electron systems host rich emergent physics that cannot be accounted for by understanding isolated 1D electron systems alone. Open questions remain about how transport in these arrays can be manipulated by the application of external electric and magnetic fields. In this theoretical study, we consider a pair of coupled nanowires with non-interacting electrons. We find that a metal-insulator transition is induced by an out-of-plane magnetic field and a transverse potential bias on an array of such coupled wires. We demonstrate the existence of distinct conductance features and highlight the crucial role played by the field dependence of the interwire potential barrier on transport properties. These predictions agree well with transport experiments performed on coupled nanowires sketched on an LaAlO3/SrTiO3 interface. Since our model makes minimal assumptions, we expect our predictions to hold for a wide class of coupled 1D systems.

We present a generic parallel implementation of the decision tree-based machine learning (ML) method in hardware description language (HDL) on field programmable gate arrays (FPGA). A regression problem in high energy physics at the Large Hadron Collider is considered: the estimation of the magnitude of missing transverse momentum using boosted decision trees (BDT). A forest of twenty decision trees each with a maximum depth of ten using eight input variables of 16-bit precision is executed with a latency of less than 10 ns using O(0.1%) resources on Xilinx UltraScale+ VU9P -- approximately ten times faster and five times smaller compared to similar designs using high level synthesis (HLS) -- without the use of digital signal processors (DSP) while eliminating the use of block RAM (BRAM). We also demonstrate a potential application in the estimation of muon momentum for ATLAS RPC at HL-LHC., Comment: 15 pages, 8 figures, 2 tables

As changing climates transform the landscape of wildfire management and suppression, agencies are faced with difficult resource allocation decisions. We analyze trade-offs in temporal resource allocation using a simple but robust Markov model of a wildfire under suppression: the birth-death-suppression process. Though the model is not spatial, its stochastic nature and rich temporal structure make it broadly applicable in describing the dynamic evolution of a fire including ignition, the effect of adverse conditions, and the effect of external suppression. With strong analytical and numerical control of the probabilities of outcomes, we construct classes of processes which analogize common wildfire suppression scenarios and determine aspects of optimal suppression allocations. We model problems which include resource management in changing conditions, the effect of resource mobilization delay, and allocation under uncertainty about future events. Our results are consistent with modern resource management and suppression practices in wildland fire., Comment: 18 pages, 14 figures

The ability to manipulate complex systems, such as the brain, to modify specific outcomes has far-reaching implications, particularly in the treatment of psychiatric disorders. One approach to designing appropriate manipulations is to target key features of predictive models. While generative latent variable models, such as probabilistic principal component analysis (PPCA), is a powerful tool for identifying targets, they struggle incorporating information relevant to low-variance outcomes into the latent space. When stimulation targets are designed on the latent space in such a scenario, the intervention can be suboptimal with minimal efficacy. To address this problem, we develop a novel objective based on supervised variational autoencoders (SVAEs) that enforces such information is represented in the latent space. The novel objective can be used with linear models, such as PPCA, which we refer to as generative principal component regression (gPCR). We show in simulations that gPCR dramatically improves target selection in manipulation as compared to standard PCR and SVAEs. As part of these simulations, we develop a metric for detecting when relevant information is not properly incorporated into the loadings. We then show in two neural datasets related to stress and social behavior in which gPCR dramatically outperforms PCR in predictive performance and that SVAEs exhibit low incorporation of relevant information into the loadings. Overall, this work suggests that our method significantly improves target selection for manipulation using latent variable models over competitor inference schemes.

Nuclear shape and orientation degrees of freedom are incorporated into the calculation of the double-folding nuclear potential within the relativistic mean-field (RMF) formalism. The quadrupole deformations ($\beta_2$), nuclear densities and the effective nucleon-nucleon (NN) interaction potential are obtained using the RMF approach for the Hybrid, NL3$^*$ and NL3 parameterizations. The calculated quadrupole deformations are included in the target densities through the nuclear radius. The deformation and orientation-dependent microscopic nuclear potentials are further employed to obtain fusion barrier characteristics and cross-sections for 12 even-even heavy-ion reactions with doubly magic spherical $^{16}$O and $^{48}$Ca as projectiles along with deformed targets from different mass regions. The results obtained for the relativistic R3Y NN potential are compared with those of the Reid version of the non-relativistic M3Y NN potential as well as with the available experimental data. A decrease in the barrier height and increase in the cross-section is observed upon the inclusion of target quadrupole deformations in the nuclear density distributions at the target orientation angles, $\theta_2\le58^\circ$ for the R3Y NN potential and at $\theta_2\le60^\circ$ for the M3Y NN potential. On comparing the $\theta_2$-integrated cross-section calculated using M3Y and R3Y NN potentials with spherical and deformed densities, one observes that the deformed densities and the relativistic R3Y NN potential obtained for the Hybrid parameter set provide better agreement with the available experimental data for all the considered reactions. Moreover, the modifications in the characteristics of the fusion barrier and hence in the cross-section with the inclusion of nuclear shape degrees of freedom and orientations are found to become more prominent in reactions forming heavier compound nuclei.

Astrophysical simulations are computation, memory, and thus energy intensive, thereby requiring new hardware advances for progress. Stony Brook University recently expanded its computing cluster "SeaWulf" with an addition of 94 new nodes featuring Intel Sapphire Rapids Xeon Max series CPUs. We present a performance and power efficiency study of this hardware performed with FLASH: a multi-scale, multi-physics, adaptive mesh-based software instrument. We extend this study to compare performance to that of Stony Brook's Ookami testbed which features ARM-based A64FX-700 processors, and SeaWulf's AMD EPYC Milan and Intel Skylake nodes. Our application is a stellar explosion known as a thermonuclear (Type Ia) supernova and for this 3D problem, FLASH includes operators for hydrodynamics, gravity, and nuclear burning, in addition to routines for the material equation of state. We perform a strong-scaling study with a 220 GB problem size to explore both single- and multi-node performance. Our study explores the performance of different MPI mappings and the distribution of processors across nodes. From these tests, we determined the optimal configuration to balance runtime and energy consumption for our application., Comment: Accepted to PEARC '24 (Practice and Experience in Advanced Research Computing)

Long baseline optical interferometry and aperture synthesis using ground-based telescopes can enable unprecedented angular resolution astronomy in the optical domain. However, atmospheric turbulence leads to large, dynamic phase errors between participating apertures that limit fringe visibility using telescopes arrays or subaperture configurations in a single large telescope. Diffraction limited optics or adaptive optics can be used to ensure coherence at each aperture, but correlating the phase between apertures requires high speed, high stroke phase correction and recombination that is extremely challenging and costly. As a solution, we show an alternative phase correction and beam combination method using a centimeter-scale silicon astrophotonic chip optimized for H-band operation. The 4.7x10mm silicon photonic chip is fabricated using electron beam lithography with devices with 2 up to 32 independent channels. Light is coupled into the chip using single mode fiber ribbons. An array of microheaters is used to individually tune the effective index of each spiral delay waveguides. Narrowband spectral splitters at each spatial channel divert a modulated digital reference signal from an artificial guide star off-chip for phase measurement. Science light from other wavelengths is coherently combined using on-chip beam combiners and outputted to a single waveguide. We described the role, design, fabrication and characterization of the photonic chip. This photonic phase control scheme can be applied in astronomical interferometry or optical satellite communications., Comment: 12 pages, 13 figures, table 1, SPIE Astronomical Telescopes and Instrumentation

Traditional one-shot medical image segmentation (MIS) methods use registration networks to propagate labels from a reference atlas or rely on comprehensive sampling strategies to generate synthetic labeled data for training. However, these methods often struggle with registration errors and low-quality synthetic images, leading to poor performance and generalization. To overcome this, we introduce a novel one-shot MIS framework based on knowledge distillation, which allows the network to directly 'see' real images through a distillation process guided by image reconstruction. It focuses on anatomical structures in a single labeled image and a few unlabeled ones. A registration-based data augmentation network creates realistic, labeled samples, while a feature distillation module helps the student network learn segmentation from these samples, guided by the teacher network. During inference, the streamlined student network accurately segments new images. Evaluations on three public datasets (OASIS for T1 brain MRI, BCV for abdomen CT, and VerSe for vertebrae CT) show superior segmentation performance and generalization across different medical image datasets and modalities compared to leading methods. Our code is available at https://github.com/NoviceFodder/OS-MedSeg.

This article studies the intimate relationship between two filtering algorithms for continuous data assimilation, the synchronization filter and the nudging filter, in the paradigmatic context of the two-dimensional (2D) Navier-Stokes equations (NSE) for incompressible fluids. In this setting, the nudging filter can formally be viewed as an affine perturbation of the 2D NSE. Thus, in the degenerate limit of zero nudging parameter, the nudging filter converges to the solution of the 2D NSE. However, when the nudging parameter of the nudging filter is large, the perturbation becomes singular. It is shown that in the singular limit of infinite nudging parameter, the nudging filter converges to the synchronization filter. In establishing this result, the article fills a notable gap in the literature surrounding these algorithms. Numerical experiments are then presented that confirm the theoretical results and probes the issue of selecting a nudging strategy in the presence of observational noise. In this direction, an adaptive nudging strategy is proposed that leverages the insight gained from the relationship between the synchronization filter and the nudging filter that produces measurable improvement over the constant nudging strategy., Comment: 23 pages, 8 figures

This article studies the interrelation between the determining modes property in the two-dimensional (2D) Navier-Stokes equations (NSE) of incompressible fluids and the synchronization property of two filtering algorithms for continuous data assimilation applied to the 2D NSE. These two properties are realized as manifestations of a more general phenomenon of "self-synchronous intertwinement". It is shown that this concept is a logically stronger form of asymptotic enslavement, as characterized by the existence of finitely many determining modes in the 2D NSE. In particular, this stronger form is shown to imply convergence of the synchronization filter and the nudging filter from continuous data assimilation (CDA), and then subsequently invoked to show that convergence in these filters implies that the 2D NSE possesses finitely many determining modes. The main achievement of this article is to therefore identify a new concept, that of self-synchronous intertwinement, through which a rigorous relationship between the determining modes property and synchronization in these CDA filters is established and made decisively clear. The theoretical results are then complemented by numerical experiments that confirm the conclusions of the theorems., Comment: 46 pages, 6 figures