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Gathering histopathology slides from over 100 publicly available cohorts, we compile a diverse dataset of 460 million pathology tiles covering more than 30 cancer sites. Using this dataset, we train a large self-supervised vision transformer using DINOv2 and publicly release one iteration of this model for further experimentation, coined Phikon-v2. While trained on publicly available histology slides, Phikon-v2 surpasses our previously released model (Phikon) and performs on par with other histopathology foundation models (FM) trained on proprietary data. Our benchmarks include eight slide-level tasks with results reported on external validation cohorts avoiding any data contamination between pre-training and evaluation datasets. Our downstream training procedure follows a simple yet robust ensembling strategy yielding a +1.75 AUC increase across tasks and models compared to one-shot retraining (p<0.001). We compare Phikon (ViT-B) and Phikon-v2 (ViT-L) against 14 different histology feature extractors, making our evaluation the most comprehensive to date. Our result support evidences that DINOv2 handles joint model and data scaling better than iBOT. Also, we show that recent scaling efforts are overall beneficial to downstream performance in the context of biomarker prediction with GigaPath and H-Optimus-0 (two ViT-g with 1.1B parameters each) standing out. However, the statistical margins between the latest top-performing FMs remain mostly non-significant; some even underperform on specific indications or tasks such as MSI prediction - deposed by a 13x smaller model developed internally. While latest foundation models may exhibit limitations for clinical deployment, they nonetheless offer excellent grounds for the development of more specialized and cost-efficient histology encoders fueling AI-guided diagnostic tools.

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation., Comment: 5 pages, 1 figure, 15th International Particle Accelerator Conference

As the number of spacecraft on orbit continues to grow, it is challenging for human operators to constantly monitor and plan for all missions. Autonomous control methods such as reinforcement learning (RL) have the power to solve complex tasks while reducing the need for constant operator intervention. By combining RL solutions with run time assurance (RTA), safety of these systems can be assured in real time. However, in order to use these algorithms on board a spacecraft, they must be able to run in real time on space grade processors, which are typically outdated and less capable than state-of-the-art equipment. In this paper, multiple RL-trained neural network controllers (NNCs) and RTA algorithms were tested on commercial-off-the-shelf (COTS) and radiation tolerant processors. The results show that all NNCs and most RTA algorithms can compute optimal and safe actions in well under 1 second with room for further optimization before deploying in the real world.

Training deep neural networks reliably requires access to large-scale datasets. However, obtaining such datasets can be challenging, especially in the context of neuroimaging analysis tasks, where the cost associated with image acquisition and annotation can be prohibitive. To mitigate both the time and financial costs associated with model development, a clear understanding of the amount of data required to train a satisfactory model is crucial. This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks. This framework includes the establishment of performance expectations using a novel Minor Boundary Adjustment for Threshold (MinBAT) method, and standardizing patch selection through the ROI-based Expanded Patch Selection (REPS) method. Our experiments demonstrate that tasks involving regions of interest (ROIs) with different sizes or shapes may yield variably acceptable Dice Similarity Coefficient (DSC) scores. By setting an acceptable DSC as the target, the required amount of training data can be estimated and even predicted as data accumulates. This approach could assist researchers and engineers in estimating the cost associated with data collection and annotation when defining a new segmentation task based on deep neural networks, ultimately contributing to their efficient translation to real-world applications.

AntiCopyPaster is an IntelliJ IDEA plugin, implemented to detect and refactor duplicate code interactively as soon as a duplicate is introduced. The plugin only recommends the extraction of a duplicate when it is worth it. In contrast to current Extract Method refactoring approaches, our tool seamlessly integrates with the developer's workflow and actively provides recommendations for refactorings. This work extends our tool to allow developers to customize the detection rules, i.e., metrics, based on their needs and preferences. The plugin and its source code are publicly available on GitHub at https://github.com/refactorings/anti-copy-paster. The demonstration video can be found on YouTube: https://youtu.be/ Y1sbfpds2Ms.

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.

We study rotating compact stars that are mixtures of the ordinary nuclear matter in a neutron star and fermionic dark matter. After deriving equations describing a slowly rotating system made up of an arbitrary number of perfect fluids, we specialize to the two-fluid case, where the first fluid describes ordinary matter and the second fluid describes dark matter. Electromagnetic observations of the moment of inertia and angular momentum directly probe ordinary matter and not dark matter. Upon taking this into account, we show that the I-Love-Q relations for dark matter admixed neutrons stars can deviate significantly from the standard single-fluid relationships., Comment: 16 pages, 3 figures

The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy (SCALES) instrument is a lenslet-based integral field spectrograph that will operate at 2 to 5 microns, imaging and characterizing colder (and thus older) planets than current high-contrast instruments. Its spatial resolution for distant science targets and/or close-in disks and companions could be improved via interferometric techniques such as sparse aperture masking. We introduce a nascent Python package, NRM-artist, that we use to design several SCALES masks to be non-redundant and to have uniform coverage in Fourier space. We generate high-fidelity mock SCALES data using the scalessim package for SCALES' low spectral resolution modes across its 2 to 5 micron bandpass. We include realistic noise from astrophysical and instrument sources, including Keck adaptive optics and Poisson noise. We inject planet and disk signals into the mock datasets and subsequently recover them to test the performance of SCALES sparse aperture masking and to determine the sensitivity of various mask designs to different science signals.

SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy) is a 2 - 5 micron high-contrast lenslet-based integral field spectrograph (IFS) designed to characterize exoplanets and their atmospheres. The SCALES medium-spectral-resolution mode uses a lenslet subarray with a 0.34 x 0.36 arcsecond field of view which allows for exoplanet characterization at increased spectral resolution. We explore the sensitivity limitations of this mode by simulating planet detections in the presence of realistic noise sources. We use the SCALES simulator scalessim to generate high-fidelity mock observations of planets that include speckle noise from their host stars, as well as other atmospheric and instrumental noise effects. We employ both angular and reference differential imaging as methods of disentangling speckle noise from the injected planet signals. These simulations allow us to assess the feasibility of speckle deconvolution for SCALES medium resolution data, and to test whether one approach outperforms another based on planet angular separations and contrasts.

High-contrast imaging has been used to discover and characterize dozens of exoplanets to date. The primary limiting performance factor for these instruments is contrast, the ratio of exoplanet to host star brightness that an instrument can successfully resolve. Contrast is largely determined by wavefront error, consisting of uncorrected atmospheric turbulence and optical aberrations downstream of AO correction. Single-point diamond turning allows for high-precision optics to be manufactured for use in astronomical instrumentation, presenting a cheaper and more versatile alternative to conventional glass polishing. This work presents measurements of wavefront error for diamond-turned aluminum optics in the Slicer Combined with an Array of Lenslets for Exoplanet Spectroscopy (SCALES) instrument, a 2-5 micron coronagraphic integral field spectrograph under construction for Keck Observatory. Wavefront error measurements for these optics are used to simulate SCALES' point spread function using physical optics propagation software poppy, showing that SCALES' contrast performance is not limited by wavefront error from internal instrument optics., Comment: Techniques and Instrumentation for Detection of Exoplanets XI

The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy (SCALES) is a $2-5~\mu$m, high-contrast integral field spectrograph (IFS) currently being built for Keck Observatory. With both low ($R\lesssim250$) and medium ($R\sim3500-7000$) spectral resolution IFS modes, SCALES will detect and characterize significantly colder exoplanets than those accessible with near-infrared ($\sim1-2~\mu$m) high-contrast spectrographs. This will lead to new progress in exoplanet atmospheric studies, including detailed characterization of benchmark systems that will advance the state of the art of atmospheric modeling. SCALES' unique modes, while designed specifically for direct exoplanet characterization, will enable a broader range of novel (exo)planetary observations as well as galactic and extragalactic studies. Here we present the science cases that drive the design of SCALES. We describe an end-to-end instrument simulator that we use to track requirements, and show simulations of expected science yields for each driving science case. We conclude with a discussion of preparations for early science when the instrument sees first light in $\sim2025$., Comment: 10 pages, 16 figures, submitted to Proceedings of the SPIE

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance -- from the data-generating processes to their reconstruction and the extraction of inference on the parameters of interest of a measuring instrument -- along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism., Comment: 70 pages, 17 figures. To be submitted to journal

Maldacena and Susskind conjectured that two entangled particles, which can be thought of as forming an Einstein-Podolsky-Rosen (EPR) pair, are connected by a nontraversable wormhole or Einstein-Rosen (ER) bridge. They named their conjecture ER = EPR. We present a concrete quantitative model for ER = EPR, in which two spin-1/2 particles in a singlet state are connected by a nontraversable wormhole in asymptotically flat general relativity. In our model, the fermions are described by the charged Dirac equation minimally coupled to gravity. This system has static wormhole solutions. We use these solutions as initial data and numerically evolve them forward in time. Our simulations show that black holes form, which are connected by the wormhole and which render the wormhole nontraversable. We also find that the wormhole throat shrinks, which places the particles in close proximity to one another and suggests an explanation for how the wormhole facilitates the nonlocal communication required by entanglement., Comment: 5 pages, 2 figures

Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. Obtaining sufficient data from a single clinical site is challenging and does not address the heterogeneous need for model robustness. Conversely, the collection of data from multiple sites introduces data privacy concerns and potential label noise due to varying annotation standards. To address this dilemma, we explore the use of the federated learning framework while considering label noise. Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction. Specifically, we introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions, enabling the correction of false annotations based on prediction confidence. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites, enhancing the reliability of the correction process. Extensive experiments conducted on two multi-site datasets demonstrate the effectiveness and robustness of our proposed methods, indicating their potential for clinical applications in multi-site collaborations., Comment: 11 pages, 4 figures, journal submission

The ENUBET project is aimed at designing and experimentally demonstrating the concept of monitored neutrino beams. These novel beams are enhanced by an instrumented decay tunnel, whose detectors reconstruct large-angle charged leptons produced in the tunnel and give a direct estimate of the neutrino flux at the source. These facilities are thus the ideal tool for high-precision neutrino cross-section measurements at the GeV scale because they offer superior control of beam systematics with respect to existing facilities. In this paper, we present the first end-to-end design of a monitored neutrino beam capable of monitoring lepton production at the single particle level. This goal is achieved by a new focusing system without magnetic horns, a 20 m normal-conducting transfer line for charge and momentum selection, and a 40 m tunnel instrumented with cost-effective particle detectors. Employing such a design, we show that percent precision in cross-section measurements can be achieved at the CERN SPS complex with existing neutrino detectors., Comment: 24 pages, 33 figures

An innovative methodology that leverages artificial intelligence (AI) and graph representation for semiconductor device encoding in TCAD device simulation is proposed. A graph-based universal encoding scheme is presented that not only considers material-level and device-level embeddings, but also introduces a novel spatial relationship embedding inspired by interpolation operations typically used in finite element meshing. Universal physical laws from device simulations are leveraged for comprehensive data-driven modeling, which encompasses surrogate Poisson emulation and current-voltage (IV) prediction based on drift-diffusion model. Both are achieved using a novel graph attention network, referred to as RelGAT. Comprehensive technical details based on the device simulator Sentaurus TCAD are presented, empowering researchers to adopt the proposed AI-driven Electronic Design Automation (EDA) solution at the device level., Comment: 32 pages, 13 figures and 4 tables

Charged Dirac fields minimally coupled to gravity have spherically symmetric wormhole solutions known as Einstein-Dirac-Maxwell (EDM) wormholes. EDM wormholes do not make use of exotic matter and exist in asymptotically flat general relativity. We construct static spherically symmetric EDM wormhole configurations in quantum field theory using semiclassical approximations for gravity and the electromagnetic field. Our framework is able to describe a broader class of EDM wormholes than previously considered and, being constructed in quantum field theory, puts EDM wormholes on firmer theoretical ground., Comment: 12 pages, 5 figures; v2: typos corrected

Einstein-Dirac-Maxwell wormholes are asymptotically flat static wormhole solutions in general relativity that do not make use of exotic matter. The asymmetric static solutions are smooth, are regular everywhere, and violate the null energy condition, which suggests that they are traversable. To determine if in fact they are traversable, we numerically evolve the static solutions forward in time. In all cases considered, our simulations indicate that black holes form that are connected by the wormhole. Although there exist null geodesics that travel through the wormhole, we find that they are trapped inside a black hole and are unable to travel arbitrarily far away. We conclude that Einstein-Dirac-Maxwell wormholes are not traversable., Comment: 17 pages, 8 figures; v2: typos corrected

We study the Dirac equation minimally coupled to general relativity using quantum field theory and the semiclassical gravity approximation. Previous studies of the Einstein-Dirac system did not quantize the Dirac field and required multiple independent Dirac fields to preserve spherical symmetry. We canonically quantize a single Dirac field in a static spherically symmetric curved spacetime background. Using the semiclassical gravity approximation, in which the Einstein field equations are sourced by the expectation value of the stress-energy-momentum tensor, we derive a system of equations whose solutions describe static spherically symmetric self-gravitating configurations of identical quantum spin-1/2 particles. We self-consistently solve these equations and present example configurations. Although limiting cases of our semiclassical system of equations reproduce the multi-field system of equations found in the literature, our system of equations is derived from the excitations of a single quantum field., Comment: 17 pages, 2 figures

Brain atrophy is an important biomarker for monitoring neurodegeneration and disease progression in conditions such as multiple sclerosis (MS). An accurate and robust quantitative measurement of brain volume change is paramount for translational research and clinical applications. This paper presents a deep learning based method, DeepBVC, for longitudinal brain volume change measurement using 3D T1-weighted MRI scans. Trained with the intermediate outputs from SIENA, DeepBVC is designed to take into account the variance caused by different scanners and acquisition protocols. Compared with SIENA, DeepBVC demonstrates higher consistency in terms of volume change estimation across multiple time points in MS subjects; and greater stability and superior performance in scan-rescan experiments. Moreover, the results also show that DeepBVC is insensitive to acquisition variance in terms of imaging contrast, voxel resolution, random bias field and signal-to-noise ratio. Measurement robustness, automation and processing speed suggest a broad potential of DeepBVC in both research and clinical quantitative neuroimaging applications.

Shape2SAS is a web application that allows researchers and students to build intuition and understanding of small-angle scattering. It is available at https://somo.chem.utk.edu/shape2sas. The user defines a model of arbitrary shape by combining geometrical subunits, and Shape2SAS then calculates and displays the scattering intensity, the pair distance distribution as well as a visualization of the user-defined shape. Simulated data with realistic noise are also generated. We demonstrate how Shape2SAS can calculate and display the different scattering patterns for various geometrical shapes, such as spheres and cylinders. We also demonstrate how the effect of structure factors can be visualized. Finally, we show how multi-contrast particles can readily be generated, and how the calculated scattering may be used to validate and visualize analytical models generated in analysis software for fitting small-angle scattering data.