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We present an AI pipeline that involves using smart drones equipped with computer vision to obtain a more accurate fruit count and yield estimation of the number of blueberries in a field. The core components are two object-detection models based on the YOLO deep learning architecture: a Bush Model that is able to detect blueberry bushes from images captured at low altitudes and at different angles, and a Berry Model that can detect individual berries that are visible on a bush. Together, both models allow for more accurate crop yield estimation by allowing intelligent control of the drone's position and camera to safely capture side-view images of bushes up close. In addition to providing experimental results for our models, which show good accuracy in terms of precision and recall when captured images are cropped around the foreground center bush, we also describe how to deploy our models to map out blueberry fields using different sampling strategies, and discuss the challenges of annotating very small objects (blueberries) and difficulties in evaluating the effectiveness of our models., Comment: 30 pages

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

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

Analytical and numerical techniques have been developed for solving fractional partial differential equations (FPDEs) and their systems with initial conditions. However, it is much more challenging to develop analytical or numerical techniques for FPDEs with boundary conditions, although some methods do exist to address such problems. In this paper, a modified technique based on the Adomian decomposition method with Laplace transformation is presented, which effectively treats initial-boundary value problems. The non-linear term has been controlled by Daftardar-Jafari polynomials. Our proposed technique is applied to several initial and boundary value problems and the obtained results are presented through graphs. The differing behavior of the solutions for the suggested problems is observed by using various fractional orders. It is found that our proposed technique has a high rate of convergence towards the exact solutions of the problems. Moreover, while implementing this modification, higher accuracy is achieved with a small number of calculations, which is the main novelty of the proposed technique. The present method requires a new approximate solution in each iteration that adds further accuracy to the solution. It demonstrates that our suggested technique can be used effectively to solve initial-boundary value problems of FPDEs.

We demonstrate a nontraditional design of the Sagnac interferometer by replacing the commonly used beam splitter with a linear-optical Grover multiport. This substitution creates a pole at the origin of the device parameter space with an associated resonance in the output intensity. The structure of this resonance is dictated only by the non-reciprocal portion of the phase acquired in the Sagnac loop. This property directly results from adopting the more symmetric and higher-dimensional central scattering coin, and allows for a different approach to registering and detecting the non-reciprocal Sagnac phase. This parameter may be extracted from the width of a peak or dip in the interferogram instead of tracing small changes in power as in traditional Sagnac interferometry. We discuss how losses affect the system and potential metrological applications., Comment: 14 pages, 7 figures

Qwerty is a high-level quantum programming language built on bases and functions rather than circuits. This new paradigm introduces new challenges in compilation, namely synthesizing circuits from basis translations and automatically specializing adjoint or predicated forms of functions. This paper presents ASDF, an open-source compiler for Qwerty that answers these challenges in compiling basis-oriented languages. Enabled with a novel high-level quantum IR implemented in the MLIR framework, our compiler produces OpenQASM 3 or QIR for either simulation or execution on hardware. Our compiler is evaluated by comparing the fault-tolerant resource requirements of generated circuits with other compilers, finding that ASDF produces circuits with comparable cost to prior circuit-oriented compilers., Comment: 20 pages, 14 figures. To appear in CGO '25

Understanding the effects of noise on quantum computations is fundamental to the development of quantum hardware and quantum algorithms. Simulation tools are essential for quantitatively modelling these effects, yet unless artificial restrictions are placed on the circuit or noise model, accurately modelling noisy quantum computations is an extremely challenging task due to unfavourable scaling of required computational resources. Tensor network methods offer a viable solution for simulating computations that generate limited entanglement or that have noise models which yield low gate fidelities. However, in the most interesting regime of entangling circuits (with high gate fidelities) relevant for error correction and mitigation tensor network simulations often achieve poor accuracy. In this work we develop and numerically test a method for significantly improving the accuracy of tensor network simulations of noisy quantum circuits in the low-noise (i.e. high gate-fidelity) regime. Our method comes with the advantages that it (i) allows for the simulation of quantum circuits under generic types of noise model, (ii) is especially tailored to the low-noise regime, and (iii) retains the benefits of tensor network scaling, enabling efficient simulations of large numbers of qubits. We build upon the observations that adding extra noise to a quantum circuit makes it easier to simulate with tensor networks, and that the results can later be reliably extrapolated back to the low-noise regime of interest. These observations form the basis for a novel emulation technique that we call non-zero noise extrapolation, in analogy to the quantum error mitigation technique of zero-noise extrapolation.

AI can now generate high-fidelity UI mock-up screens from a high-level textual description, promising to support UX practitioners' work. However, it remains unclear how UX practitioners would adopt such Generative UI (GenUI) models in a way that is integral and beneficial to their work. To answer this question, we conducted a formative study with 37 UX-related professionals that consisted of four roles: UX designers, UX researchers, developers, and product managers. Using a state-of-the-art GenUI tool, each participant went through a week-long, individual mini-project exercise with role-specific tasks, keeping a daily journal of their usage and experiences with GenUI, followed by a semi-structured interview. We report findings on participants' workflow using the GenUI tool, how GenUI can support all and each specific roles, and existing gaps between GenUI and users' needs and expectations, which lead to design implications to inform future work on GenUI development.

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"{o}dinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to address the titular question, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. This work lays the foundation to establish accurate and reproducible FN-DMC estimates for all future studies across applications in materials science, physics, chemistry, and biology.

In this paper, we investigate three fundamental problems in the Massively Parallel Computation (MPC) model: (i) grid graph connectivity, (ii) approximate Euclidean Minimum Spanning Tree (EMST), and (iii) approximate DBSCAN. Our first result is a $O(1)$-round Las Vegas (i.e., succeeding with high probability) MPC algorithm for computing the connected components on a $d$-dimensional $c$-penetration grid graph ($(d,c)$-grid graph), where both $d$ and $c$ are positive integer constants. In such a grid graph, each vertex is a point with integer coordinates in $\mathbb{N}^d$, and an edge can only exist between two distinct vertices with $\ell_\infty$-norm at most $c$. To our knowledge, the current best existing result for computing the connected components (CC's) on $(d,c)$-grid graphs in the MPC model is to run the state-of-the-art MPC CC algorithms that are designed for general graphs: they achieve $O(\log \log n + \log D)$[FOCS19] and $O(\log \log n + \log \frac{1}{\lambda})$[PODC19] rounds, respectively, where $D$ is the {\em diameter} and $\lambda$ is the {\em spectral gap} of the graph. With our grid graph connectivity technique, our second main result is a $O(1)$-round Las Vegas MPC algorithm for computing approximate Euclidean MST. The existing state-of-the-art result on this problem is the $O(1)$-round MPC algorithm proposed by Andoni et al.[STOC14], which only guarantees an approximation on the overall weight in expectation. In contrast, our algorithm not only guarantees a deterministic overall weight approximation, but also achieves a deterministic edge-wise weight approximation.The latter property is crucial to many applications, such as finding the Bichromatic Closest Pair and DBSCAN clustering. Last but not the least, our third main result is a $O(1)$-round Las Vegas MPC algorithm for computing an approximate DBSCAN clustering in $O(1)$-dimensional space.

We have performed a series of direct N-body simulations that study the evolution of the Galactic globular cluster NGC 6397 under the combined influence of two-body relaxation, stellar evolution and the Milky Way's tidal field. Our simulations follow the evolution of the cluster over the last several Gyr up to its present-day position in the Milky Way in order to allow us to derive present-day cluster parameters and the distribution of its extra-tidal stars. We have also determined a new density profile of NGC 6397 by selecting stars from Gaia DR3 using Gaia DR3 proper motions, parallaxes and photometry to discriminate cluster members from non-members. This allows us to derive the surface density profile of NGC 6397 and the location of its tidal tails up to 10 degrees of the cluster centre, well beyond the tidal radius of NGC 6397. Our results show that the current state of NGC 6397 in terms of surface density, velocity dispersion profile and stellar mass function can be matched by a cluster model evolving from a standard initial mass function and does not require an additional central cluster of dark remnants. We also find good agreement in the location and absolute number of the extra-tidal stars between our simulations and the observations, making it unlikely that NGC 6397 is surrounded by a dark matter halo., Comment: 12 pages, 12 figures. Accepted by MNRAS

Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances in artificial intelligence (AI) promise to improve its precision, efficiency, and consistency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning (AIRTP) system, a scalable solution for generating high-quality treatment plans. This scalable solution is designed to generate substantial volumes of consistently high-quality treatment plans, overcoming a key obstacle in the advancement of AI-driven RT planning. Our AIRTP pipeline adheres to clinical guidelines and automates essential steps, including organ-at-risk (OAR) contouring, helper structure creation, beam setup, optimization, and plan quality improvement, using AI integrated with RT planning software like Eclipse of Varian. Furthermore, a novel approach for determining optimization parameters to reproduce 3D dose distributions, i.e. a method to convert dose predictions to deliverable treatment plans constrained by machine limitations. A comparative analysis of plan quality reveals that our automated pipeline produces treatment plans of quality comparable to those generated manually, which traditionally require several hours of labor per plan. Committed to public research, the first data release of our AIRTP pipeline includes nine cohorts covering head-and-neck and lung cancer sites to support an AAPM 2025 challenge. This data set features more than 10 times the number of plans compared to the largest existing well-curated public data set to our best knowledge. Repo:{https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge}, Comment: Related to GDP-HMM grand challenge

Distracted driving remains a significant global challenge with severe human and economic repercussions, demanding improved detection and intervention strategies. While previous studies have extensively explored single-modality approaches, recent research indicates that these systems often fall short in identifying complex distraction patterns, particularly cognitive distractions. This systematic review addresses critical gaps by providing a comprehensive analysis of machine learning (ML) and deep learning (DL) techniques applied across various data modalities - visual,, sensory, auditory, and multimodal. By categorizing and evaluating studies based on modality, data accessibility, and methodology, this review clarifies which approaches yield the highest accuracy and are best suited for specific distracted driving detection goals. The findings offer clear guidance on the advantages of multimodal versus single-modal systems and capture the latest advancements in the field. Ultimately, this review contributes valuable insights for developing robust distracted driving detection frameworks, supporting enhanced road safety and mitigation strategies.

Current self-supervised learning methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and scalability. We present 3DINO, a cutting-edge SSL method adapted to 3D datasets, and use it to pretrain 3DINO-ViT: a general-purpose medical imaging model, on an exceptionally large, multimodal, and multi-organ dataset of ~100,000 3D medical imaging scans from over 10 organs. We validate 3DINO-ViT using extensive experiments on numerous medical imaging segmentation and classification tasks. Our results demonstrate that 3DINO-ViT generalizes across modalities and organs, including out-of-distribution tasks and datasets, outperforming state-of-the-art methods on the majority of evaluation metrics and labeled dataset sizes. Our 3DINO framework and 3DINO-ViT will be made available to enable research on 3D foundation models or further finetuning for a wide range of medical imaging applications.

A next-generation medium-energy gamma-ray telescope targeting the MeV range would address open questions in astrophysics regarding how extreme conditions accelerate cosmic-ray particles, produce relativistic jet outflows, and more. One concept, AMEGO-X, relies upon the mission-enabling CMOS Monolithic Active Pixel Sensor silicon chip AstroPix. AstroPix is designed for space-based use, featuring low noise, low power consumption, and high scalability. Desired performance of the device include an energy resolution of 5 keV (or 10% FWHM) at 122 keV and a dynamic range per-pixel of 25-700 keV, enabled by the addition of a high-voltage bias to each pixel which supports a depletion depth of 500 um. This work reports on the status of the AstroPix development process with emphasis on the current version under test, version three (v3), and highlights of version two (v2). Version 3 achieves energy resolution of 10.4 +\- 3.2 % at 59.5 keV and 94 +\- 6 um depletion in a low-resistivity test silicon substrate., Comment: 20 pages, 13 figures

Magnetic topological insulators (MTIs) host topologically protected edge states, but the role that these edge states play in electronic transport remains unclear. Using scanning superconducting quantum interference device (SQUID) microscopy, we performed local measurements of the current distribution in a quantum anomalous Hall (QAH) insulator at large bias currents, where the quantization of the conductivity tensor breaks down. We find that bulk currents in the channel interior coexist with edge currents at the sample boundary. While the position of the edge current changes with the reversal of the magnetic field, it does not depend on the current direction. To understand our observations, we introduce a model which includes contributions from both the sample magnetization and currents driven by chemical potential gradients. To parameterize our model, we use local measurements of the chemical potential induced changes in the sample magnetization. Our model reveals that the observed edge currents can be understood as changes in the magnetization generated by the electrochemical potential distribution in the sample under bias. Our work underscores the complexity of electronic transport in MTIs and highlights both the value and challenges of using magnetic imaging to disentangle various contributions to the electronic transport signatures.

Neural tangent kernels (NTKs) have been proposed to study the behavior of trained neural networks from the perspective of Gaussian processes. An important result in this body of work is the theorem of equivalence between a trained neural network and kernel regression with the corresponding NTK. This theorem allows for an interpretation of neural networks as special cases of kernel regression. However, does this theorem of equivalence hold in practice? In this paper, we revisit the derivation of the NTK rigorously and conduct numerical experiments to evaluate this equivalence theorem. We observe that adding a layer to a neural network and the corresponding updated NTK do not yield matching changes in the predictor error. Furthermore, we observe that kernel regression with a Gaussian process kernel in the literature that does not account for neural network training produces prediction errors very close to that of kernel regression with NTKs. These observations suggest the equivalence theorem does not hold well in practice and puts into question whether neural tangent kernels adequately address the training process of neural networks.

Context. The DESI Legacy Imaging Survey DR9, with its extensive dataset of galaxy locations and photometric redshifts, presents an opportunity to study Baryon Acoustic Oscillations (BAO) in the region covered by the ongoing DESI spectroscopic survey. Aims. We aim to investigate differences between different parts of the DR9 footprint. Furthermore, we want to measure the BAO scale for luminous red galaxies within them. Our selected redshift range of 0.6 to 0.8 corresponds to the bin in which a tension between DESI Y1 and eBOSS was found. Methods. We calculated the anisotropic two-point correlation function in a modified binning scheme to detect the BAO in DR9 data. We then use template fits based on simulations to measure the BAO scale in the imaging data. Results. Our analysis revealed the expected correlation function shape in most of the footprint areas, showing a BAO scale consistent with Planck's observations. Aside from identified mask-related data issues in the southern region of the South Galactic Cap, we also find a notable variance between the different footprints. Conclusions. We find that this variance is consistent with the difference between the DESI Y1 and eBOSS data and it supports the argument that that tension is caused by sample variance. Additionally, we also uncovered systematic biases not previously accounted for in photometric BAO studies. We emphasize the necessity of adjusting for the systematic shift in the BAO scale associated with typical photometric redshift uncertainties to ensure accurate measurements., Comment: 20 pages, 14 figures, 3 tables, accepted for publication in Astronomy and Astrophysics

Autonomous surgical robots are a promising solution to the increasing demand for surgery amid a shortage of surgeons. Recent work has proposed learning-based approaches for the autonomous manipulation of soft tissue. However, due to variability in tissue geometries and stiffnesses, these methods do not always perform optimally, especially in out-of-distribution settings. We propose, develop, and test the first application of uncertainty quantification to learned surgical soft-tissue manipulation policies as an early identification system for task failures. We analyze two different methods of uncertainty quantification, deep ensembles and Monte Carlo dropout, and find that deep ensembles provide a stronger signal of future task success or failure. We validate our approach using the physical daVinci Research Kit (dVRK) surgical robot to perform physical soft-tissue manipulation. We show that we are able to successfully detect task failure and request human intervention when necessary while still enabling autonomous manipulation when possible. Our learned tissue manipulation policy with uncertainty-based early failure detection achieves a zero-shot sim2real performance improvement of 47.5% over the prior state of the art in learned soft-tissue manipulation. We also show that our method generalizes well to new types of tissue as well as to a bimanual soft tissue manipulation task., Comment: 8 pages, 6 figures

Scientific software-defined as computer programs, scripts, or code used in scientific research, data analysis, modeling, or simulation-has become central to modern research. However, there is limited research on the readability and understandability of scientific code, both of which are vital for effective collaboration and reproducibility in scientific research. This study surveys 57 research scientists from various disciplines to explore their programming backgrounds, practices, and the challenges they face regarding code readability. Our findings reveal that most participants learn programming through self-study or on the-job training, with 57.9% lacking formal instruction in writing readable code. Scientists mainly use Python and R, relying on comments and documentation for readability. While most consider code readability essential for scientific reproducibility, they often face issues with inadequate documentation and poor naming conventions, with challenges including cryptic names and inconsistent conventions. Our findings also show low adoption of code quality tools and a trend towards utilizing large language models to improve code quality. These findings offer practical insights into enhancing coding practices and supporting sustainable development in scientific software., Comment: The 33rd IEEE/ACM International Conference on Program Comprehension (ICPC 2025) - Early Research Achievements Track

The proliferation of Vision-Language Models (VLMs) in the past several years calls for rigorous and comprehensive evaluation methods and benchmarks. This work analyzes existing VLM evaluation techniques, including automated metrics, AI-based assessments, and human evaluations across diverse tasks. We first introduce Robin - a novel suite of VLMs that we built by combining Large Language Models (LLMs) and Vision Encoders (VEs) at multiple scales, and use Robin to identify shortcomings of current evaluation approaches across scales. Next, to overcome the identified limitations, we introduce CHIRP - a new long form response benchmark we developed for more robust and complete VLM evaluation. We provide open access to the Robin training code, model suite, and CHIRP benchmark to promote reproducibility and advance VLM research.

Hypergraph products are quantum low-density parity-check (LDPC) codes constructed from two classical LDPC codes. Although their dimension and distance depend only on the parameters of the underlying classical codes, optimizing their performance against various noise channels remains challenging. This difficulty partly stems from the complexity of decoding in the quantum setting. The standard, ad hoc approach typically involves selecting classical LDPC codes with large girth. In this work, we focus on optimizing performance against the quantum erasure channel. A key advantage of this channel is the existence of an efficient maximum-likelihood decoder, which enables us to employ optimization techniques based on sampling random codes, such as Reinforcement Learning (RL) and Simulated Annealing (SA). Our results indicate that these techniques improve performance relative to the state-of-the-art., Comment: 6 pages, 2 figures. Parity-check matrices available as text files. Submitted to the IEEE 2025 ISIT

The ability to physically move qubits within a register allows the design of hardware-specific error correction codes which can achieve fault-tolerance while respecting other constraints. In particular, recent advancements have demonstrated the shuttling of electron and hole spin qubits through a quantum dot array with high fidelity. Exploiting this, we design an error correction architecture, consisting merely of two parallel quantum dot arrays, an experimentally validated architecture compatible with classical wiring and control constraints. We develop a suite of heuristic methods for compiling any stabilizer error-correcting code's syndrome-extraction circuit to run with a minimal number of shuttling operations. In simulation, these heuristics show that fault tolerance can be achieved on several contemporary quantum error-correcting codes requiring only modestly-optimistic noise parameters. Furthermore, we demonstrate how constant column-weight qLDPC codes can be compiled in a provably minimal number of shuttles that scales constantly with code size using Shor-style syndrome extraction. In addition, we provide a proof of the NP hardness of minimizing the number of shuttle operations for codes not in that class., Comment: 21 pages, 19 figures

A solution to the suboptimal $H^\infty$-control problem is given for a class of hyperbolic partial differential equations (PDEs). The first result of this manuscript shows that the considered class of PDEs admits an equivalent representation as an infinite-dimensional discrete-time system. Taking advantage of this, this manuscript shows that it is equivalent to solve the suboptimal $H^\infty$-control problem for a finite-dimensional discrete-time system whose matrices are derived from the PDEs. After computing the solution to this much simpler problem, the solution to the original problem can be deduced easily. In particular, the optimal compensator solution to the suboptimal $H^\infty$-control problem is governed by a set of hyperbolic PDEs, actuated and observed at the boundary. We illustrate our results with a boundary controlled and boundary observed vibrating string.

Owing to recent advances in machine learning and the ability to harvest large amounts of data during robotic-assisted surgeries, surgical data science is ripe for foundational work. We present a large dataset of surgical videos and their accompanying labels for this purpose. We describe how the data was collected and some of its unique attributes. Multiple example problems are outlined. Although the dataset was curated for a particular set of scientific challenges (in an accompanying paper), it is general enough to be used for a broad range machine learning questions. Our hope is that this dataset exposes the larger machine learning community to the challenging problems within surgical data science, and becomes a touchstone for future research. The videos are available at https://storage.googleapis.com/isi-surgvu/surgvu24_videos_only.zip, the labels at https://storage.googleapis.com/isi-surgvu/surgvu24_labels_updated_v2.zip, and a validation set for tool detection problem at https://storage.googleapis.com/isi-surgvu/cat1_test_set_public.zip.

Remote sensing imagery is dense with objects and contextual visual information. There is a recent trend to combine paired satellite images and text captions for pretraining performant encoders for downstream tasks. However, while contrastive image-text methods like CLIP enable vision-language alignment and zero-shot classification ability, vision-only downstream performance tends to degrade compared to image-only pretraining, such as MAE. In this paper, we propose FLAVARS, a pretraining method that combines the best of both contrastive learning and masked modeling, along with geospatial alignment via contrastive location encoding. We find that FLAVARS significantly outperforms a baseline of SkyCLIP for vision-only tasks such as KNN classification and semantic segmentation, +6\% mIOU on SpaceNet1, while retaining the ability to perform zero-shot classification, unlike MAE pretrained methods.

We show via a combination of mathematical arguments and empirical evidence that data distributions sampled from diffusion models satisfy a Concentration of Measure Property saying that any Lipschitz $1$-dimensional projection of a random vector is not too far from its mean with high probability. This implies that such models are quite restrictive and gives an explanation for a fact previously observed in arXiv:2410.14171 that conventional diffusion models cannot capture "heavy-tailed" data (i.e. data $\mathbf{x}$ for which the norm $\|\mathbf{x}\|_2$ does not possess a subgaussian tail) well. We then proceed to train a generalized linear model using stochastic gradient descent (SGD) on the diffusion-generated data for a multiclass classification task and observe empirically that a Gaussian universality result holds for the test error. In other words, the test error depends only on the first and second order statistics of the diffusion-generated data in the linear setting. Results of such forms are desirable because they allow one to assume the data itself is Gaussian for analyzing performance of the trained classifier. Finally, we note that current approaches to proving universality do not apply to this case as the covariance matrices of the data tend to have vanishing minimum singular values for the diffusion-generated data, while the current proofs assume that this is not the case (see Subsection 3.4 for more details). This leaves extending previous mathematical universality results as an intriguing open question.

Narrow linewidth lasers are indispensable for coherent optical systems, including communications, metrology, and sensing. Although compact semiconductor lasers with narrow linewidths and low noise have been demonstrated, their spectral purity typically relies on hybrid or heterogeneous external cavity feedback. Here, we present a theoretical and experimental demonstration of a heterogeneous free optical injection locking (HF OIL) semiconductor laser. By integrating a topological interface state extended (TISE) laser with a micro ring resonator (MRR) on an AlGaInAs multiple quantum well platform,we achieve monolithic photon injection and phase locking, thereby reducing the optical linewidth. We fabricated and characterized a 1550 nm sidewall HF OIL laser, achieving stable single mode operation over a broad current range (65 to 300 mA) and a side mode suppression ratio (SMSR) over 50 dB. Under injection locking, the devices Voigt fitted linewidth narrowed from over 1.7 MHz (free running) to 4.2 kHz, representing a three order of magnitude improvement over conventional distributed feedback lasers. The intrinsic linewidth of 1.4 kHz is measured by correlated delayed self-heterodyne frequency noise power spectrum density (FN PSD) method. Moreover, the HF OIL laser demonstrated high phase stability and the ability to transition from a random phased to a phase locked state. These results underscore the potential of HF-OIL lasers in advancing coherent optical communications and phase encoders in quantum key distribution (QKD) systems.

This experimental work demonstrates multipartite quantum correlation in bright frequency combs out of a microresonator integrated on silicon nitride working above its oscillation threshold. Multipartite features, going beyond so far reported two-mode correlation, naturally arise due to a cascade of non-linear optical processes, making a single-color laser pump sufficient to initiate their generation. Our results show the transition from two-mode to multipartite correlation, witnessed by noise reductions as low as $-2.5$\,dB and $-2$\,dB, respectively, compared to corresponding classical levels. A constant of the movement of the non-linear interaction Hamiltonian is identified and used to asses the multipartite behavior. Reported demonstrations pave the way to next generation on-chip multipartite sources for quantum technologies applications., Comment: 4 figures

ELIZA, created by Joseph Weizenbaum at MIT in the early 1960s, is usually considered the world's first chatbot. It was developed in MAD-SLIP on MIT's CTSS, the world's first time-sharing system, on an IBM 7094. We discovered an original ELIZA printout in Prof. Weizenbaum's archives at MIT, including an early version of the famous DOCTOR script, a nearly complete version of the MAD-SLIP code, and various support functions in MAD and FAP. Here we describe the reanimation of this original ELIZA on a restored CTSS, itself running on an emulated IBM 7094. The entire stack is open source, so that any user of a unix-like OS can run the world's first chatbot on the world's first time-sharing system., Comment: In review

Neural ordinary differential equations (NODEs) are an emerging paradigm in scientific computing for modeling dynamical systems. By accurately learning underlying dynamics in data in the form of differential equations, NODEs have been widely adopted in various domains, such as healthcare, finance, computer vision, and language modeling. However, there remains a limited understanding of the privacy implications of these fundamentally different models, particularly with regard to their membership inference risks. In this work, we study the membership inference risks associated with NODEs. We first comprehensively evaluate NODEs against membership inference attacks. We show that NODEs are twice as resistant to these privacy attacks compared to conventional feedforward models such as ResNets. By analyzing the variance in membership risks across different NODE models, we identify the factors that contribute to their lower risks. We then demonstrate, both theoretically and empirically, that membership inference risks can be further mitigated by utilizing a stochastic variant of NODEs: Neural stochastic differential equations (NSDEs). We show that NSDEs are differentially-private (DP) learners that provide the same provable privacy guarantees as DP-SGD, the de-facto mechanism for training private models. NSDEs are also effective in mitigating existing membership inference attacks, demonstrating risks comparable to private models trained with DP-SGD while offering an improved privacy-utility trade-off. Moreover, we propose a drop-in-replacement strategy that efficiently integrates NSDEs into conventional feedforward models to enhance their privacy.

We present the spectroscopic characterization of cyclopropenethione (c-C3H2S) in the laboratory and detect it in space using the Green Bank Telescope (GBT) Observations of TMC-1: Hunting Aromatic Molecules (GOTHAM) survey. The detection of this molecule - the missing link in understanding the C3H2S isomeric family in TMC-1 - completes the detection of all 3 low-energy isomers of C3H2S as both CH2CCS and HCCCHS have been previously detected in this source. The total column density of this molecule (N_T of 5.72+2.65/-1.61x10^10 cm^-2 at an excitation temperature of 4.7+1.3/-1.1 K) is smaller than both CH2CCS and HCCCHS and follows nicely the relative dipole principle (RDP), a kinetic rule-of-thumb for predicting isomer abundances which suggests that, all other chemistry among a family of isomers being the same, the member with the smallest dipole should be the most abundant. The RDP now holds for the astronomical abundance ratios of both the S-bearing and O-bearing counterparts observed in TMC-1; however, CH2CCO continues to elude detection in any astronomical source., Comment: 10 Pages, 7 Figures, 11 Tables

General-purpose automatic speech recognition (ASR) systems do not always perform well in goal-oriented dialogue. Existing ASR correction methods rely on prior user data or named entities. We extend correction to tasks that have no prior user data and exhibit linguistic flexibility such as lexical and syntactic variations. We propose a novel context augmentation with a large language model and a ranking strategy that incorporates contextual information from the dialogue states of a goal-oriented conversational AI and its tasks. Our method ranks (1) n-best ASR hypotheses by their lexical and semantic similarity with context and (2) context by phonetic correspondence with ASR hypotheses. Evaluated in home improvement and cooking domains with real-world users, our method improves recall and F1 of correction by 34% and 16%, respectively, while maintaining precision and false positive rate. Users rated .8-1 point (out of 5) higher when our correction method worked properly, with no decrease due to false positives., Comment: Accepted to COLING 2025 Industry Track

Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. Regional ocean emulation presents unique challenges owing to the complex bathymetry and lateral boundary conditions as well as from fundamental biases in deep learning-based frameworks, such as instability and hallucinations. In this paper, we develop a deep learning-based framework to autoregressively integrate ocean-surface variables over the Gulf of Mexico at $8$ Km spatial resolution without unphysical drifts over decadal time scales and simulataneously downscale and bias-correct it to $4$ Km resolution using a physics-constrained generative model. The framework shows both short-term skills as well as accurate long-term statistics in terms of mean and variability.

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms using uniform samples for gradient estimation often exhibit slow convergence rates and require bounded variance assumptions. In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm. The proposed algorithm achieves an iteration complexity of $O(\epsilon^{-4})$, surpassing the best-known rates of stochastic algorithms for NC-C minimax problems and matching the performance of the best deterministic algorithms in this context. Finally, we demonstrate the effectiveness of the proposed algorithm through numerical simulations., Comment: The conference version of this paper has been accepted by ICASSP 2025

We give matching upper and lower bounds for the Dirichlet heat kernel of a Schr\"odinger operator $\Delta+W$ in the domain above the graph of a bounded Lipschitz function, in the case when $W$ decays away from the boundary faster than quadratically., Comment: 16 pages

The new era of large-scale data collection and analysis presents an opportunity for diagnosing and understanding the causes of health inequities. In this study, we describe a framework for systematically analyzing health disparities using causal inference. The framework is illustrated by investigating racial and ethnic disparities in intensive care unit (ICU) outcome between majority and minority groups in Australia (Indigenous vs. Non-Indigenous) and the United States (African-American vs. White). We demonstrate that commonly used statistical measures for quantifying inequity are insufficient, and focus on attributing the observed disparity to the causal mechanisms that generate it. We find that minority patients are younger at admission, have worse chronic health, are more likely to be admitted for urgent and non-elective reasons, and have higher illness severity. At the same time, however, we find a protective direct effect of belonging to a minority group, with minority patients showing improved survival compared to their majority counterparts, with all other variables kept equal. We demonstrate that this protective effect is related to the increased probability of being admitted to ICU, with minority patients having an increased risk of ICU admission. We also find that minority patients, while showing improved survival, are more likely to be readmitted to ICU. Thus, due to worse access to primary health care, minority patients are more likely to end up in ICU for preventable conditions, causing a reduction in the mortality rates and creating an effect that appears to be protective. Since the baseline risk of ICU admission may serve as proxy for lack of access to primary care, we developed the Indigenous Intensive Care Equity (IICE) Radar, a monitoring system for tracking the over-utilization of ICU resources by the Indigenous population of Australia across geographical areas.

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.

Scaling up Large Language Model(LLM) training involves fitting a tremendous amount of training parameters across a limited number of workers. However, methods like ZeRO-3 that drastically reduce GPU memory pressure often incur heavy communication to ensure global synchronization and consistency. Established efforts such as ZeRO++ use secondary partitions to avoid inter-node communications, given that intra-node GPU-GPU transfer generally has more bandwidth and lower latency than inter-node connections. However, as more capable infrastructure like Frontier, equipped with AMD GPUs, emerged with impressive computing capability, there is a need for investigations on the hardware topology and to develop targeted strategies to improve training efficiency. In this work, we propose a collection of communication and optimization strategies for ZeRO++ to reduce communication costs and improve memory utilization. In this paper, we propose a 3-level hierarchical partitioning specifically for the current Top-1 supercomputing cluster, Frontier, which aims at leveraging various bandwidths across layers of communications (GCD-GCD, GPU-GPU, and inter-node) to reduce communication overhead. For a 20B GPT model, we observe a 1.71x increase in TFLOPS per GPU when compared with ZeRO++ up to 384 GCDs and a scaling efficiency of 0.94 for up to 384 GCDs. To the best of our knowledge, our work is also the first effort to efficiently optimize LLM workloads on Frontier AMD GPUs.

We give estimates for positive solutions for the Schr\"odinger equation $(\Delta_\mu+W)u=0$ on a wide class of parabolic weighted manifolds $(M, d\mu)$ when $W$ decays to zero at infinity faster than quadratically. These can be combined with results of Grigor'yan to give matching upper and lower bounds for the heat kernel of the corresponding Schr\"odinger operator $\Delta_\mu+W$. In particular, this appears to complement known results for Schr\"odinger operators on $\mathbf{R}^2$., Comment: 30 pages