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The role of artificial intelligence (AI) in pathology has evolved from aiding diagnostics to uncovering predictive morphological patterns in whole slide images (WSIs). Recently, foundation models (FMs) leveraging self-supervised pre-training have been widely advocated as a universal solution for diverse downstream tasks. However, open questions remain about their clinical applicability and generalization advantages over end-to-end learning using task-specific (TS) models. Here, we focused on AI with clinical-grade performance for prostate cancer diagnosis and Gleason grading. We present the largest validation of AI for this task, using over 100,000 core needle biopsies from 7,342 patients across 15 sites in 11 countries. We compared two FMs with a fully end-to-end TS model in a multiple instance learning framework. Our findings challenge assumptions that FMs universally outperform TS models. While FMs demonstrated utility in data-scarce scenarios, their performance converged with - and was in some cases surpassed by - TS models when sufficient labeled training data were available. Notably, extensive task-specific training markedly reduced clinically significant misgrading, misdiagnosis of challenging morphologies, and variability across different WSI scanners. Additionally, FMs used up to 35 times more energy than the TS model, raising concerns about their sustainability. Our results underscore that while FMs offer clear advantages for rapid prototyping and research, their role as a universal solution for clinically applicable medical AI remains uncertain. For high-stakes clinical applications, rigorous validation and consideration of task-specific training remain critically important. We advocate for integrating the strengths of FMs and end-to-end learning to achieve robust and resource-efficient AI pathology solutions fit for clinical use., Comment: 50 pages, 15 figures and an appendix (study protocol) which is previously published, see https://doi.org/10.1101/2024.07.04.24309948

The UltraViolet EXplorer (UVEX) is a wide-field ultraviolet space telescope selected as a NASA Medium-Class Explorer (MIDEX) mission for launch in 2030. UVEX will undertake deep, cadenced surveys of the entire sky to probe low mass galaxies and explore the ultraviolet (UV) time-domain sky, and it will carry the first rapidly deployable UV spectroscopic capability for a broad range of science applications. One of UVEX's prime objectives is to follow up gravitational wave (GW) binary neutron star mergers as targets of opportunity (ToOs), rapidly scanning across their localization regions to search for their kilonova (KN) counterparts. Early-time multiband ultraviolet light curves of KNe are key to explaining the interplay between jet and ejecta in binary neutron star mergers. Owing to high Galactic extinction in the ultraviolet and the variation of GW distance estimates over the sky, the sensitivity to kilonovae can vary significantly across the GW localization and even across the footprint of a single image given UVEX's large field of view. Good ToO observing strategies to trade off between area and depth are neither simple nor obvious. We present an optimal strategy for GW follow-up with UVEX in which exposure time is adjusted dynamically for each field individually to maximize the overall probability of detection. We model the scheduling problem using the expressive and powerful mathematical framework of mixed integer linear programming (MILP), and employ a state-of-the-art MILP solver to automatically generate observing plan timelines that achieve high probabilities of kilonova detection. We have implemented this strategy in an open-source astronomical scheduling software package called the Multi-Mission Multi-Messenger Observation Planning Toolkit (M4OPT), on GitHub at https://github.com/m4opt/m4opt., Comment: For data release, see https://zenodo.org/records/14825979

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of 'data', we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.

X-ray fluorescence computed tomography (XFCT), a form of X-ray molecular imaging, offers detailed quantitative imaging capabilities for high-Z metal nanoparticles (MNPs), which are widely studied for their applications in multifunctional theranostics. Due to its affordability and accessibility, the benchtop XFCT prototype typically employs a single-pixel detector (SPD) with single-pencil-beam (SPB) X-ray excitation. While this design (resembling the first-generation CT geometry) achieves reliable detection sensitivity, it is hindered by long imaging times. The use of simultaneous multiple-pencil-beam (MPB) excitation presents a promising solution to significantly reduce imaging times. In this study, we developed a repeatable workflow that combines Monte Carlo (MC) simulations and 3D printing to design Nbeam-MPB collimator, where Nbeam is the number of beams generated by the collimator. As an initial test, we fabricated a 2-MPB collimator and evaluated the performance of 2-MPB-based XFCT imaging on a physical phantom and small animals surgically implanted with agarose pellets containing gold chloride (H[AuCl4]). The results demonstrated a 2x acceleration in image acquisition without compromising the contrast-to-noise ratio (CNR). We further investigated the concept of Nbeam-MPB acceleration on the MC computational XFCT system, which confirmed the feasibility of achieving at least 4x acceleration with 4-MPB excitation. Combined with additional system optimization, such as X-ray beam flux optimization, XFCT imaging could be further accelerated, reducing acquisition time from hours to minutes and meeting the requirements for routine MNP imaging.

The Brownian sphere is a random metric space, homeomorphic to the two-dimensional sphere, which arises as the universal scaling limit of many types of random planar maps. The direct construction of the Brownian sphere is via a continuous analogue of the Cori--Vauquelin--Schaeffer (CVS) bijection. The CVS bijection maps labeled trees to planar maps, and the continuous version maps Aldous' continuum random tree with Brownian labels (the Brownian snake) to the Brownian sphere. In this work, we describe the inverse of the continuous CVS bijection, by constructing the Brownian snake as a measurable function of the Brownian sphere. Special care is needed to work with the orientation of the Brownian sphere.

The Pandora SmallSat is a NASA flight project aimed at studying the atmospheres of exoplanets -- planets orbiting stars outside our Solar System. Pandora will provide the first dataset of simultaneous, multiband (visible and NIR), long-baseline observations of exoplanets and their host stars. Pandora is an ambitious project that will fly a 0.44 m telescope in a small form factor. To achieve the scientific goals, the mission requires a departure from the traditional cost-schedule paradigm of half-meter-class observatories. Pandora achieves this by leveraging existing capabilities that necessitate minimal engineering development, disruptive and agile management, trusted partnerships with vendors, and strong support from the lead institutions. The Pandora team has developed a suite of high-fidelity parameterized simulation and modeling tools to estimate the performance of both imaging channels. This has enabled a unique bottom-up approach to deriving trades and system requirements. Pandora is a partnership between NASA and Lawrence Livermore National Laboratory. The project completed its Critical Design Review in October 2023 and is slated for launch into Sun-synchronous, low-Earth orbit in Fall 2025., Comment: Paper accepted to the IEEE Aerospace Conference 2025

Motivation: An adjuvant is a chemical incorporated into vaccines that enhances their efficacy by improving the immune response. Identifying adjuvant names from cancer vaccine studies is essential for furthering research and enhancing immunotherapies. However, the manual curation from the constantly expanding biomedical literature poses significant challenges. This study explores the automated recognition of vaccine adjuvant names using Large Language Models (LLMs), specifically Generative Pretrained Transformers (GPT) and Large Language Model Meta AI (Llama). Methods: We utilized two datasets: 97 clinical trial records from AdjuvareDB and 290 abstracts annotated with the Vaccine Adjuvant Compendium (VAC). GPT-4o and Llama 3.2 were employed in zero-shot and few-shot learning paradigms with up to four examples per prompt. Prompts explicitly targeted adjuvant names, testing the impact of contextual information such as substances or interventions. Outputs underwent automated and manual validation for accuracy and consistency. Results: GPT-4o attained 100% Precision across all situations while exhibiting notable improve in Recall and F1-scores, particularly with incorporating interventions. On the VAC dataset, GPT-4o achieved a maximum F1-score of 77.32% with interventions, surpassing Llama-3.2-3B by approximately 2%. On the AdjuvareDB dataset, GPT-4o reached an F1-score of 81.67% for three-shot prompting with interventions, surpassing Llama-3.2-3 B's maximum F1-score of 65.62%. Conclusion: Our findings demonstrate that LLMs excel at identifying adjuvant names, including rare variations of naming representation. This study emphasizes the capability of LLMs to enhance cancer vaccine development by efficiently extracting insights. Future work aims to broaden the framework to encompass various biomedical literature and enhance model generalizability across various vaccines and adjuvants., Comment: 10 pages, 6 figures, 4 tables

Increased utilization of unmanned aerial vehicles (UAVs) in critical operations necessitates secure and reliable communication with Ground Control Stations (GCS). This paper introduces Aero-LLM, a framework integrating multiple Large Language Models (LLMs) to enhance UAV mission security and operational efficiency. Unlike conventional singular LLMs, Aero-LLM leverages multiple specialized LLMs for various tasks, such as inferencing, anomaly detection, and forecasting, deployed across onboard systems, edge, and cloud servers. This dynamic, distributed architecture reduces performance bottleneck and increases security capabilities. Aero-LLM's evaluation demonstrates outstanding task-specific metrics and robust defense against cyber threats, significantly enhancing UAV decision-making and operational capabilities and security resilience against cyber attacks, setting a new standard for secure, intelligent UAV operations., Comment: This manuscript was accepted by the 1st International Workshop on Integrated Sensing, Communication, and Computing in Internet of Things (IoT) Systems at the The 33rd International Conference on Computer Communications and Networks (ICCCN 2024)

We present necessary and sufficient conditions on triples of weights $(u,v,w)$ for the boundedness of the dyadic weighted square function $S_w$ from $L^2(u)$ into $L^2(v)$. We use this characterization to obtain necessary and sufficient conditions for the boundedness of the $t$-Haar multipliers from $L^2(u)$ into $L^2(v)$ in terms of boundedness of the dyadic weighted square function.

We study extremal properties of spherical random polytopes, the convex hull of random points chosen from the unit Euclidean sphere in $\mathbb{R}^n$. The extremal properties of interest are the expected values of the maximum and minimum surface area among facets. We determine the asymptotic growth in every fixed dimension, up to absolute constants.

In reading tasks drift can move fixations from one word to another or even another line, invalidating the eye tracking recording. Manual correction is time-consuming and subjective, while automated correction is fast yet limited in accuracy. In this paper we present Fix8 (Fixate), an open-source GUI tool that offers a novel semi-automated correction approach for eye tracking data in reading tasks. The proposed approach allows the user to collaborate with an algorithm to produce accurate corrections faster without sacrificing accuracy. Through a usability study (N=14) we assess the time benefits of the proposed technique, and measure the correction accuracy in comparison to manual correction. In addition, we assess subjective workload through NASA Task Load Index, and user opinions through Likert-scale questions. Our results show that on average the proposed technique was 44% faster than manual correction without any sacrifice in accuracy. In addition, users reported a preference for the proposed technique, lower workload, and higher perceived performance compared to manual correction. Fix8 is a valuable tool that offers useful features for generating synthetic eye tracking data, visualization, filters, data converters, and eye movement analysis in addition to the main contribution in data correction.

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

Making errors is part of the programming process -- even for the most seasoned professionals. Novices in particular are bound to make many errors while learning. It is well known that traditional (compiler/interpreter) programming error messages have been less than helpful for many novices and can have effects such as being frustrating, containing confusing jargon, and being downright misleading. Recent work has found that large language models (LLMs) can generate excellent error explanations, but that the effectiveness of these error messages heavily depends on whether the LLM has been provided with context -- typically the original source code where the problem occurred. Knowing that programming error messages can be misleading and/or contain that serves little-to-no use (particularly for novices) we explore the reverse: what happens when GPT-3.5 is prompted for error explanations on just the erroneous source code itself -- original compiler/interpreter produced error message excluded. We utilized various strategies to make more effective error explanations, including one-shot prompting and fine-tuning. We report the baseline results of how effective the error explanations are at providing feedback, as well as how various prompting strategies might improve the explanations' effectiveness. Our results can help educators by understanding how LLMs respond to such prompts that novices are bound to make, and hopefully lead to more effective use of Generative AI in the classroom., Comment: 7 pages, 1 figure

Colour centres hosted in solid-state materials such as silicon carbide and diamond are promising candidates for integration into chip-scale quantum systems. Specifically, the incorporation of these colour centres within photonic integrated circuits may enable precise control over their inherent photo-physical properties through strong light-matter interaction. Here, we investigate ion-implanted erbium ($\text{Er}^{3+}$) defects embedded in thin-film 4H-silicon-carbide-on-insulator (4H-SiCOI). Optimized implantation conditions and thermal annealing processes designed to enhance the emission characteristics of the $\text{Er}^{3+}$-defect are reported. By examining key properties such as photoluminescence intensity, optical lifetime, and polarization, we present an analysis of ensemble $\text{Er}^{3+}$-defects within 4H-SiCOI, providing insights into their potential for future quantum applications., Comment: 9 pages, 5 figures

This work presents a detailed description of the thermochemical non-equilibrium dissociation of diatomic molecules, and applies this theory to the case of $\rm H_2$ dissociation. The master equations are used to derive corresponding aggregate rate constant expressions that hold for any degree of thermochemical non-equilibrium. These general expressions are analyzed in three key limits/ regimes: the thermal equilibrium limit, the quasi-steady-state (QSS) regime, and the pre-QSS regime. Under several simplifying assumptions, an analytical source term expression that holds in all of these regimes, and is only a function of the translational temperature, $T_{\rm t}$, and the fraction of dissociation, $\phi_{\rm A}$, is proposed. This expression has two input parameters: the QSS dissociation rate constant in the absence of recombination, $k_{\rm d,nr}(T_{\rm t})$, and a pre-QSS correction factor, $\eta(T_{\rm t})$. The value of $\eta(T_{\rm t})$ is evaluated by comparing the predictions of the proposed expression against existing master equation simulations of a 0-D isothermal and isochoric reactor for the case of $\rm H_2$ dissociation with the third-bodies $\rm H_2$, $\rm H$, and $\rm He$. Despite its simple functional form, the proposed expression is able to reproduce the master equation results for the majority of the tested conditions. The best fit of $k_{\rm d,nr}(T_{\rm t})$ is then evaluated by conducting a detailed literature review. Data from a wide range of experimental and computational studies are considered for the third-bodies $\rm H_2$, $\rm H$, and inert gases, and fits that are valid from 200 to 20,000 K are proposed. From this review, the uncertainty of the proposed fits are estimated to be less than a factor of two.

This paper addresses the Corona problem for slice hyperholomorphic functions for a single quaternionic variable. While the Corona problem is well-understood in the context of one complex variable, it remains highly challenging in the case of several complex variables. The extension of the theory of one complex variable to several complex variables is not the only possible extension to multi-dimensional complex analysis. Instead of functions holomorphic in each variable separately, in this paper we will consider functions in some hypercomplex algebras, in particular in the algebra of quaternions. \\\indent Previously, the Corona problem had not been studied within the hypercomplex framework because of challenges posed by pointwise multiplication, which is not closed for hypercomplex-valued analytic functions. Alternative notions of multiplication that are closed often compromise other desirable properties, further complicating the analysis. \\ \indent In this work, we resolve the Corona problem within the quaternionic slice hyperholomorphic setting. Our approach involves reformulating the quaternionic Bezout equation with respect to the appropriate multiplication into a new system of Bezout equations on the unit disc. We solve this system by adapting Wolff's proof of the Corona theorem for bounded analytic functions. As the number of generators increases, the associated algebra grows increasingly intricate., Comment: 35 pages

To reduce costs and delays related to developing new and effective drugs, there is a critical need for improved human liver tissue models. Here we describe an approach for 3D bioprinting functional human liver tissue models, in which we fabricate disc-shaped structures (discoids) 200 {\mu}m in thickness and 1-3 mm in diameter, embedded in a highly permeable support medium made from packed microgels. We demonstrate that the method is precise, accurate, and scalable; up to 100 tissues per hour can be manufactured with a variability and error in diameter of about 4%. Histologic and immunohistochemical evaluation of printed discs reveal self-organization, cell cohesion, and key liver marker expression. During the course of 3-4 weeks in culture, the tissues stably synthesize albumin and urea at high levels, outperforming spheroid tissue models. We find the tissues express more than 100 genes associated with molecular absorption, distribution, metabolism, and excretion (ADME) at levels within the range of human liver. The liver tissue models exhibit enzymatic formation of metabolites after exposure to multiple test compounds. Together, these results demonstrate the promise of 3D printed discoids for pharmacological and toxicological applications., Comment: 34 pages, 7 figures, 2 supplementary figures

Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy state or actuator constraints. Most modern trajectory planning techniques rely on numerical optimization because high-quality, expressive trajectories that satisfy various constraints can be systematically computed. However, meeting computation time constraints and the potential for numerical instabilities can limit the use of optimization-based planners in safety-critical scenarios. This work presents an optimization-free planning framework that stitches short trajectory segments together with graph search to compute long range, expressive, and near-optimal trajectories in real-time. Our STITCHER algorithm is shown to outperform modern optimization-based planners through our innovative planning architecture and several algorithmic developments that make real-time planning possible. Extensive simulation testing is conducted to analyze the algorithmic components that make up STITCHER, and a thorough comparison with two state-of-the-art optimization planners is performed. It is shown STITCHER can generate trajectories through complex environments over long distances (tens of meters) with low computation times (milliseconds)., Comment: V1 Draft

We present two quaternion-based sliding variables for controlling the orientation of a manipulator's end-effector. Both sliding variables are free of singularities and represent global exponentially convergent error dynamics that do not exhibit unwinding when used in feedback. The choice of sliding variable is dictated by whether the end-effector's angular velocity vector is expressed in a local or global frame, and is a matter of convenience. Using quaternions allows the end-effector to move in its full operational envelope, which is not possible with other representations, e.g., Euler angles, that introduce representation-specific singularities. Further, the presented stability results are global rather than almost global, where the latter is often the best one can achieve when using rotation matrices to represent orientation.

Today's generative AI systems are tuned to present information by default rather than engage users in service of learning as a human tutor would. To address the wide range of potential education use cases for these systems, we reframe the challenge of injecting pedagogical behavior as one of \textit{pedagogical instruction following}, where training and evaluation examples include system-level instructions describing the specific pedagogy attributes present or desired in subsequent model turns. This framing avoids committing our models to any particular definition of pedagogy, and instead allows teachers or developers to specify desired model behavior. It also clears a path to improving Gemini models for learning -- by enabling the addition of our pedagogical data to post-training mixtures -- alongside their rapidly expanding set of capabilities. Both represent important changes from our initial tech report. We show how training with pedagogical instruction following produces a LearnLM model (available on Google AI Studio) that is preferred substantially by expert raters across a diverse set of learning scenarios, with average preference strengths of 31\% over GPT-4o, 11\% over Claude 3.5, and 13\% over the Gemini 1.5 Pro model LearnLM was based on.

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

Dyna-style off-policy model-based reinforcement learning (DMBRL) algorithms are a family of techniques for generating synthetic state transition data and thereby enhancing the sample efficiency of off-policy RL algorithms. This paper identifies and investigates a surprising performance gap observed when applying DMBRL algorithms across different benchmark environments with proprioceptive observations. We show that, while DMBRL algorithms perform well in OpenAI Gym, their performance can drop significantly in DeepMind Control Suite (DMC), even though these settings offer similar tasks and identical physics backends. Modern techniques designed to address several key issues that arise in these settings do not provide a consistent improvement across all environments, and overall our results show that adding synthetic rollouts to the training process -- the backbone of Dyna-style algorithms -- significantly degrades performance across most DMC environments. Our findings contribute to a deeper understanding of several fundamental challenges in model-based RL and show that, like many optimization fields, there is no free lunch when evaluating performance across diverse benchmarks in RL.

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.

With the recent advancements in artificial intelligence, researchers and industries are deploying gigantic models trained on billions of samples. While training these models consumes a huge amount of energy, human brains produce similar outputs (along with other capabilities) with massively lower data and energy requirements. For this reason, more researchers are increasingly considering alternatives. One of these alternatives is known as synthetic biological intelligence, which involves training \textit{in vitro} neurons for goal-directed tasks. This multidisciplinary field requires knowledge of tissue engineering, bio-materials, digital signal processing, computer programming, neuroscience, and even artificial intelligence. The multidisciplinary requirements make starting synthetic biological intelligence research highly non-trivial and time-consuming. Generally, most labs either specialize in the biological aspects or the computational ones. Here, we propose how a lab focusing on computational aspects, including machine learning and device interfacing, can start working on synthetic biological intelligence, including organoid intelligence. We will also discuss computational aspects, which can be helpful for labs that focus on biological research. To facilitate synthetic biological intelligence research, we will describe such a general process step by step, including risks and precautions that could lead to substantial delay or additional cost.

Photoluminescent defects in diamond, like the nitrogen-vacancy (NV) color center, are at the forefront of emerging optical quantum technologies. Most emit in the visible and near-infrared spectral region below 1000 nm (NIR-I), limiting their applications in photonics, fiber communications, and biology. Here, we show that the nitrogen-vacancy-nitrogen ($N_2V$) center, which emits in the visible and near-infrared-II (NIR-II, 1000-1700 nm), is ubiquitous in as-synthesized and processed nitrogen-doped diamond from bulk samples to nanoparticles. We demonstrate that $N_2V$ is also present in commercially available state-of-the-art NV diamond sensing chips made via chemical vapor deposition (CVD). In high-pressure high-temperature (HPHT) diamonds, the photoluminescence (PL) intensity of both $N_2V$ charge states, $N_2V^0$ in the visible and $N_2V^-$ in the NIR-II, increases with increasing substitutional nitrogen concentration. We determine the PL lifetime of $N_2V^-$ to be 0.3 ns and compare a quantum optical and density functional theory model of the $N_2V^-$ with experimental PL spectra. Finally, we show that detonation nanodiamonds (DND) show stable PL in the NIR-II, which we attribute to the $N_2V$ color center, and use this NIR-II PL to image DNDs inside skin cells. Our results will contribute to the scientific and technological exploration and development of the $N_2V$ color center and inspire more research into its effect on other color centers in diamond.

We study the intrinsic and extrinsic torsions (defined by analogy with the intrinsic and extrinsic curvatures) of the spatial sections of torsional spacetimes. We consider two possibilities. First, that the intrinsic torsion might prove to be directly observable. Second, that it is not observable, having been ``inflated away'' in the early Universe. We argue that, even in this second case, the extrinsic torsion may grow during the inflationary era and be non-negligible at reheating and thereafter. Even if the spatial intrinsic curvature and torsion are too small to be detected directly, then, the extrinsic torsion might not be. We point out that, if its presence is not recognised, the extrinsic torsion could lead to anomalies in the theoretical estimate of the Hubble parameter -- $\,$ a result with obvious potential applications. We stress that extrinsic torsion is by far the most natural way to produce such anomalies, simply because it mixes naturally with the Hubble parameter; that is, the second fundamental form of a spacelike section depends on a sum of two terms, one determined by the Hubble parameter, the other by the extrinsic torsion., Comment: Many References added; discussion of torsional Inflation greatly expanded and clarified; 37 pages, no diagrams

In political discourse and geopolitical analysis, national leaders words hold profound significance, often serving as harbingers of pivotal historical moments. From impassioned rallying cries to calls for caution, presidential speeches preceding major conflicts encapsulate the multifaceted dynamics of decision-making at the apex of governance. This project aims to use deep learning techniques to decode the subtle nuances and underlying patterns of US presidential rhetoric that may signal US involvement in major wars. While accurate classification is desirable, we seek to take a step further and identify discriminative features between the two classes (i.e. interpretable learning). Through an interdisciplinary fusion of machine learning and historical inquiry, we aspire to unearth insights into the predictive capacity of neural networks in discerning the preparatory rhetoric of US presidents preceding war. Indeed, as the venerable Prussian General and military theorist Carl von Clausewitz admonishes, War is not merely an act of policy but a true political instrument, a continuation of political intercourse carried on with other means (Clausewitz, 1832).

How should we model an observer within quantum mechanics or quantum field theory? How can classical physics emerge from a quantum model, and why should classical probability be useful? How can we model a selective measurement entirely within a closed quantum system? This paper sketches a new physical theory of probability based on an attempt to model classical information within a purely quantum system. We model classical information using a version of Zurek's theory of Quantum Darwinism, with emphasis on quantum information encoded using projection operators localised in spacetime. This version of Quantum Darwinism is compatible with quantum field theory, and does not require any artificial division of a quantum system into subsystems. The main innovation is our attempt to provide a physical explanation of probability. Decoherence is the physical mechanism behind Quantum Darwinism or the `branching of quantum worlds'. Assuming a type of perfect decoherence we construct a conventional probabilistic model for classical information. This, however, is not our theory of natural probability, and does not quite demonstrate the validity of Bayesian reasoning. Instead, our theory of natural probability arises from careful consideration of errors in decoherence: roughly speaking, we don't observe low probability events because they are swamped by quantum noise., Comment: comments welcome

Understanding coupled electron-phonon systems is one of the fundamental issues in strongly correlated systems. In this work, we aim to extend the notion of mixed-state phases to the realm of coupled electron/spinphonon systems. Specifically, we consider a two-dimensional cluster Hamiltonian locally coupled to a set of single bosonic modes with arbitrary coupling strength. First, we adopt a pure-state framework and examine whether a ground state phase transition out of the symmetry-protected topological phase can be captured using the standard polaron unitary transformation. This approach involves restricting the analysis to the low-energy manifold of the phonon degrees of freedom. We find that the pure-state approach fails to detect the anticipated transition to a topologically trivial phase at strong spin-phonon coupling. Next, we turn to a mixed-state picture. Here, we analyze mixed states of the model obtained by tracing out the phonons degrees of freedom. We employ two distinct diagnostics for mixed-state phase transitions: (i) the von Neumann conditional mutual information (CMI) and (ii) the R\'enyi-2 CMI. We argue that both measures detect signatures of mixed-state phase transitions, albeit at different critical spin-phonon coupling strengths, corresponding to subtly distinct notions of the mixed-state phases., Comment: 18 pages, 4 figures

This paper develops a new framework for preventing localization failures in mobile systems that must estimate their state using measurements. Safety is guaranteed by imposing the nonlinear least squares optimization solved in modern localization algorithms remains well-conditioned. Specifically, the eigenvalues of the Hessian matrix are made to be always positive via two methods that leverage control barrier functions to achieve safe set invariance. The proposed method is not constrained to any specific measurement or system type, offering a very general solution to the safe mobility with localization problem. The efficacy of the approach is demonstrated on a system being provided range-only and heading-only measurements for localization.

This work aims to spectroscopically characterize and provide for the first time direct experimental frequencies of the ground vibrational state and two excited states of the simplest alkynyl thiocyanate (HCCSCN) for astrophysical use. Both microwave (8-16~GHz) and millimeter wave regions (50-120~GHz) of the spectrum have been measured and analyzed in terms of Watson's semirigid rotor Hamiltonian. A total of 314 transitions were assigned to the ground state of HCCSCN and a first set of spectroscopic constants have been accurately determined. Spectral features of the molecule were then searched for in Sgr B2(N), NGC 6334I, G+0.693-0.027 and TMC-1 molecular clouds. Upper limits to the column density are provided., Comment: 9 Pages, 6 Figures, 3 Tables

Ultrasound (US) image stitching can expand the field-of-view (FOV) by combining multiple US images from varied probe positions. However, registering US images with only partially overlapping anatomical contents is a challenging task. In this work, we introduce SynStitch, a self-supervised framework designed for 2DUS stitching. SynStitch consists of a synthetic stitching pair generation module (SSPGM) and an image stitching module (ISM). SSPGM utilizes a patch-conditioned ControlNet to generate realistic 2DUS stitching pairs with known affine matrix from a single input image. ISM then utilizes this synthetic paired data to learn 2DUS stitching in a supervised manner. Our framework was evaluated against multiple leading methods on a kidney ultrasound dataset, demonstrating superior 2DUS stitching performance through both qualitative and quantitative analyses. The code will be made public upon acceptance of the paper.

We present a method to detect departures from business-justified workflows among support agents. Our goal is to assist auditors in identifying agent actions that cannot be explained by the activity within their surrounding context, where normal activity patterns are established from historical data. We apply our method to help audit millions of actions of over three thousand support agents. We collect logs from the tools used by support agents and construct a bipartite graph of Actions and Entities representing all the actions of the agents, as well as background information about entities. From this graph, we sample subgraphs rooted on security-significant actions taken by the agents. Each subgraph captures the relevant context of the root action in terms of other actions, entities and their relationships. We then prioritize the rooted-subgraphs for auditor review using feed-forward and graph neural networks, as well as nearest neighbors techniques. To alleviate the issue of scarce labeling data, we use contrastive learning and domain-specific data augmentations. Expert auditors label the top ranked subgraphs as ``worth auditing" or ``not worth auditing" based on the company's business policies. This system finds subgraphs that are worth auditing with high enough precision to be used in production.

Neonatal Magnetic Resonance Imaging (MRI) enables non-invasive assessment of potential brain abnormalities during the critical phase of early life development. Recently, interest has developed in lower field (i.e., below 1.5 Tesla) MRI systems that trade-off magnetic field strength for portability and access in the neonatal intensive care unit (NICU). Unfortunately, lower-field neonatal MRI still suffers from long scan times and motion artifacts that can limit its clinical utility for neonates. This work improves motion robustness and accelerates lower field neonatal MRI through diffusion-based generative modeling and signal processing based motion modeling. We first gather a training dataset of clinical neonatal MRI images. Then we train a diffusion-based generative model to learn the statistical distribution of fully-sampled images by applying several signal processing methods to handle the lower signal-to-noise ratio and lower quality of our MRI images. Finally, we present experiments demonstrating the utility of our generative model to improve reconstruction performance across two tasks: accelerated MRI and motion correction., Comment: 5 pages, 3 figures, submitted to ISBI 2025

Stacking van der Waals magnets holds promise for creating new hybrid materials with properties that do not exist in bulk materials. Here we investigate orthogonally twisted stacks of the van der Waals antiferromagnet CrSBr, aiming to exploit an extreme misalignment of magnetic anisotropy across the twisted interface.Using nitrogen-vacancy centre microscopy, we construct vector maps of the magnetisation, and track their evolution under an external field, in a range of twisted compensated and uncompensated configurations differing by the number of layers. We show that twisted stacking consistently modifies the local magnetic switching behaviour of constituent flakes, and that these modifications are spatially non-uniform. In the case of compensated component flakes (even number of layers), we demonstrate that the combination of dipolar coupling and stacking-induced strain can reduce the switching field by over an order of magnitude. Conversely, in uncompensated component flakes (odd number of layers), we observe indications of a non-zero interlayer exchange interaction between twisted flakes during magnetization reversal, which can persistently modify magnetic order. This work highlights the importance of spatial imaging in investigating stacking-induced magnetic effects, particularly in the case of twistronics where spatial variation is expected and can be conflated with structural imperfections., Comment: 6 main text pages, 4 figures plus 8 supplementary information pages, 9 figures