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Activity and autonomous motion are fundamental aspects of many living and engineering systems. Here, the scale of biological agents covers a wide range, from nanomotors, cytoskeleton, and cells, to insects, fish, birds, and people. Inspired by biological active systems, various types of autonomous synthetic nano- and micromachines have been designed, which provide the basis for multifunctional, highly responsive, intelligent active materials. A major challenge for understanding and designing active matter is their inherent non-equilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Furthermore, interactions in ensembles of active agents are often non-additive and non-reciprocal. An important aspect of biological agents is their ability to sense the environment, process this information, and adjust their motion accordingly. It is an important goal for the engineering of micro-robotic systems to achieve similar functionality. With many fundamental properties of motile active matter now reasonably well understood and under control, the ground is prepared for the study of physical aspects and mechanisms of motion in complex environments, of the behavior of systems with new physical features like chirality, of the development of novel micromachines and microbots, of the emergent collective behavior and swarming of intelligent self-propelled particles, and of particular features of microbial systems. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter poses major challenges, which can only be addressed by a truly interdisciplinary effort involving scientists from biology, chemistry, ecology, engineering, mathematics, and physics.

Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential of LVLMs to achieve high performance in medical imaging use cases (e.g., radiology report generation), but there remain barriers that hinder the ability to deploy these solutions broadly. These include the cost of extensive computational requirements for developing large scale models, expertise in the development of sophisticated AI models, and the difficulty in accessing substantially large, high-quality datasets that adequately represent the population in which the LVLM solution is to be deployed. The HOPPR Medical-Grade Platform addresses these barriers by providing powerful computational infrastructure, a suite of foundation models on top of which developers can fine-tune for their specific use cases, and a robust quality management system that sets a standard for evaluating fine-tuned models for deployment in clinical settings. The HOPPR Platform has access to millions of imaging studies and text reports sourced from hundreds of imaging centers from diverse populations to pretrain foundation models and enable use case-specific cohorts for fine-tuning. All data are deidentified and securely stored for HIPAA compliance. Additionally, developers can securely host models on the HOPPR platform and access them via an API to make inferences using these models within established clinical workflows. With the Medical-Grade Platform, HOPPR's mission is to expedite the deployment of LVLM solutions for medical imaging and ultimately optimize radiologist's workflows and meet the growing demands of the field., Comment: 6 pages, 3 figures

This study presents ion temperature (\(T_i\)) measurements in the MAST-U divertor, using a Retarding Field Energy Analyzer (RFEA). Steady state measurements were made during an L-Mode plasma with the strike point on the RFEA. ELM measurements were made with the strike point swept over the RFEA. The scenarios are characterized by a plasma current (\(I_p\)) of 750 kA, line average electron density (\(n_e\)) between \(1.6 \times 10^{19}\) and \(4.5 \times 10^{19}\,\text{m}^{-3}\), and Neutral Beam Injection (NBI) power ranging from 1.1 MW to 1.6 MW. The ion temperatures, peaking at approximately 10 eV in steady state, were compared with electron temperatures (\(T_e\)) obtained from Langmuir probes (LP) at the same radial positions. Preliminary findings reveal a \(T_i/T_e\) ratio in the divertor region less than 1 for shot 48008. High temporal resolution measurements captured the dynamics of Edge Localized Modes (ELMs) Burn Through, providing \(T_i\) data as a radial distance from the probe peaking around 20 eV.

The QCD axion and axion-like particles are compelling candidates for galactic dark matter. Theoretically, axions can convert into photons in the presence of a strong external magnetic field, which means it is possible to search for them experimentally. One approach is to use radio telescopes with high-resolution spectrometers to look for axion-photon conversion in the magnetospheres of neutron stars. In this paper, we describe the results obtained using a novel approach where we used the Green Bank Telescope (GBT) to search for radio transients produced by collisions between neutron stars and dark matter clumps known as axion miniclusters. We used the VErsatile GBT Astronomical Spectrometer (VEGAS) and the X-band receiver (8 to 10 GHz) to observe the core of Andromeda. Our measurements are sensitive to axions with masses between 33 and 42 $\mu$eV with $\Delta$$m_a$ = 3.8$\times10^{-4}$ $\mu$eV. This paper gives a description of the search method we developed, including observation and analysis strategies. Given our analysis algorithm choices and the instrument sensitivity ($\sim$2 mJy in each spectral channel), we did not find any candidate signals greater than 5$\sigma$. We are currently implementing this search method in other spectral bands., Comment: 19 pages, 12 figures, accepted to PRD

This study presents the first ion temperature (\(T_i\)) measurements from the MAST-U divertor using a Retarding Field Energy Analyzer (RFEA). Embedded within the flat tile of the closed divertor chamber, the RFEA captures \(T_i\) profiles across various plasma scenarios, including transitions to the Super-X configuration. Measurements were conducted under steady-state and transient plasma conditions characterized by a plasma current (\(I_p\)) of 750 kA, electron density (\(n_e\)) between \(2.2 \times 10^{19}\) and \(4.45 \times 10^{19}\,\text{m}^{-3}\), and Neutral Beam Injection (NBI) power ranging from 3.0 MW to 3.2 MW. The ion temperatures, peaking at approximately 17 eV in steady state, were compared with electron temperatures (\(T_e\)) obtained from Langmuir probes (LP) at identical radial positions. The study also examined ion saturation current density (\(J_{\text{sat}}\)) signals to using methodologies similar to previous MAST experiments. Preliminary findings reveal a \(T_i/T_e\) ratio ranging from 1 to 2.2. Additionally, high temporal resolution measurements (100 $\mu s$) captured the dynamics of Edge Localized Modes (ELMs), showing \(T_i\) peaks at 16.03 +- 1.84 eV during ELM events, nearly three times higher than inter-ELM temperatures.

Transforming two-dimensional (2D) images into three-dimensional (3D) volumes is a well-known yet challenging problem for the computer vision community. In the medical domain, a few previous studies attempted to convert two or more input radiographs into computed tomography (CT) volumes. Following their effort, we introduce a diffusion model-based technology that can rotate the anatomical content of any input radiograph in 3D space, potentially enabling the visualization of the entire anatomical content of the radiograph from any viewpoint in 3D. Similar to previous studies, we used CT volumes to create Digitally Reconstructed Radiographs (DRRs) as the training data for our model. However, we addressed two significant limitations encountered in previous studies: 1. We utilized conditional diffusion models with classifier-free guidance instead of Generative Adversarial Networks (GANs) to achieve higher mode coverage and improved output image quality, with the only trade-off being slower inference time, which is often less critical in medical applications; and 2. We demonstrated that the unreliable output of style transfer deep learning (DL) models, such as Cycle-GAN, to transfer the style of actual radiographs to DRRs could be replaced with a simple yet effective training transformation that randomly changes the pixel intensity histograms of the input and ground-truth imaging data during training. This transformation makes the diffusion model agnostic to any distribution variations of the input data pixel intensity, enabling the reliable training of a DL model on input DRRs and applying the exact same model to conventional radiographs (or DRRs) during inference., Comment: Website: https://pouriarouzrokh.github.io/RadRotator Online demo: https://huggingface.co/spaces/Pouriarouzrokh/RadRotator Article information: 16 pages, 11 figures

The Lunar Gravitational-wave Antenna (LGWA) is a proposed array of next-generation inertial sensors to monitor the response of the Moon to gravitational waves (GWs). Given the size of the Moon and the expected noise produced by the lunar seismic background, the LGWA would be able to observe GWs from about 1 mHz to 1 Hz. This would make the LGWA the missing link between space-borne detectors like LISA with peak sensitivities around a few millihertz and proposed future terrestrial detectors like Einstein Telescope or Cosmic Explorer. In this article, we provide a first comprehensive analysis of the LGWA science case including its multi-messenger aspects and lunar science with LGWA data. We also describe the scientific analyses of the Moon required to plan the LGWA mission.

Dark matter overdensities around black holes can be searched for by looking at the characteristic imprint they leave on the gravitational waveform of binary black hole mergers. Current theoretical predictions of the density profile of dark matter overdensities are based on highly idealised formation scenarios, in which black holes are assumed to grow adiabatically from an infinitesimal seed to their final mass, compressing dark matter cusps at the center of galactic halos into very dense `spikes'. These scenarios were suitable for dark matter indirect detection studies, since annihilating dark matter cannot reach very high densities, but they fail to capture the dark matter distribution in the innermost regions where the gravitational wave signal is produced. We present here a more realistic formation scenario where black holes form from the collapse of supermassive stars, and follow the evolution of the dark matter density as the supermassive star grows and collapses to a black hole. We show that in this case dark matter forms shallower `mounds', instead of `spikes', on scales comparable with the size of the supermassive stars originating them. We discuss the implications for the detectability of these systems., Comment: 8 pages, 4 figures

Retrieval-augmented generation (RAG) frameworks enable large language models (LLMs) to retrieve relevant information from a knowledge base and incorporate it into the context for generating responses. This mitigates hallucinations and allows for the updating of knowledge without retraining the LLM. However, RAG does not guarantee valid responses if retrieval fails to identify the necessary information as the context for response generation. Also, if there is contradictory content, the RAG response will likely reflect only one of the two possible responses. Therefore, quantifying uncertainty in the retrieval process is crucial for ensuring RAG trustworthiness. In this report, we introduce a four-step framework for applying conformal prediction to quantify retrieval uncertainty in RAG frameworks. First, a calibration set of questions answerable from the knowledge base is constructed. Each question's embedding is compared against document embeddings to identify the most relevant document chunks containing the answer and record their similarity scores. Given a user-specified error rate ({\alpha}), these similarity scores are then analyzed to determine a similarity score cutoff threshold. During inference, all chunks with similarity exceeding this threshold are retrieved to provide context to the LLM, ensuring the true answer is captured in the context with a (1-{\alpha}) confidence level. We provide a Python package that enables users to implement the entire workflow proposed in our work, only using LLMs and without human intervention., Comment: Github code: https://github.com/Mayo-Radiology-Informatics-Lab/conflare

Future gravitational wave observatories can probe dark matter by detecting the dephasing in the waveform of binary black hole mergers induced by dark matter overdensities. Such a detection hinges on the accurate modelling of the dynamical friction, induced by dark matter on the secondary compact object in intermediate and extreme mass ratio inspirals. In this paper, we introduce NbodyIMRI, a new publicly available code designed for simulating binary systems within cold dark matter `spikes'. Leveraging higher particle counts and finer timesteps, we validate the applicability of the standard dynamical friction formalism and provide an accurate determination of the maximum impact parameter of particles which can effectively scatter with a compact object, across various mass ratios. We also show that in addition to feedback due to dynamical friction, the dark matter also evolves through a `stirring' effect driven by the time-dependent potential of the binary. We introduce a simple semi-analytical scheme to account for this effect and demonstrate that including stirring tends to slow the rate of dark matter depletion and therefore enhances the impact of dark matter on the dynamics of the binary., Comment: 11 pages, 9 figures + appendices. Companion paper to "Sharpening the dark matter signature in gravitational waveforms I: Accretion and eccentricity evolution" (arXiv:2402.13053). NbodyIMRI code available here: https://github.com/bradkav/NbodyIMRI

Dark matter overdensities around black holes can alter the dynamical evolution of a companion object orbiting around it, and cause a dephasing of the gravitational waveform. Here, we present a refined calculation of the co-evolution of the binary and the dark matter distribution, taking into account the accretion of dark matter particles on the companion black hole, and generalizing previous quasi-circular calculations to the general case of eccentric orbits. These calculations are validated by dedicated N-body simulations. We show that accretion can lead to a large dephasing, and therefore cannot be neglected in general. We also demonstrate that dark matter spikes tend to circularize eccentric orbits faster than previously thought., Comment: 19 pages, 14 figures + appendices. Companion paper to "Sharpening the dark matter signature in gravitational waveforms II: Numerical simulations with the NbodyIMRI code" (arXiv:2402.13762). v2: Added references

Remote magnetic manipulation offers wireless control over magnetic objects, which has important medical applications, such as targeted drug delivery and minimally invasive surgeries. Magnetic manipulation systems are categorized into systems using permanent magnets and systems based on electromagnets. Electro-Magnetic Navigation Systems (eMNSs) are believed to have a superior actuation bandwidth, facilitating trajectory tracking and disturbance rejection. This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions. In order to highlight the dynamic capabilities of eMNSs, we successfully stabilize a (non-magnetic) inverted pendulum on the tip of a magnetically driven arm. Our method employs a model-based design approach, where we capture the dynamics that describe the interaction of the pendulum system and the magnetic field through Lagrangian mechanics. Using system identification we estimate the system parameters, the actuation bandwidth, and characterize the system's nonlinearity. We design a state-feedback controller to stabilize the inherently unstable dynamics, and compensate for errors arising from the calibration of the magnetic field and the angle measurement system. Additionally, we integrate an iterative learning control scheme that allows us to accurately track non-equilibrium trajectories while concurrently maintaining stability of the inverted pendulum. To our knowledge, this is the first effort to stabilize a 3D inverted pendulum through remote magnetic manipulation.

A combined experimental and computational methodology for interrogating the phonon contribution to polaron formation in real materials is developed. Using LiF as an example, we show that the recent ab-initio theory of Sio et. al [PRL 122, 246403 (2019)] makes predictions of the momentum- and branch dependent phonon amplitides in polaron quasiparticles that are testable using ultrafast electron diffuse scattering (UEDS) and related techniques. The large electron polaron in LiF has UEDS signatures that are qualitatively similar to those expected from a simple point-defect model, but the small hole polaron exhibits a profoundly anisotropic UEDS pattern that is in poor agreement with a point-defect model. We also show that these polaron diffuse scattering signatures are directly emblematic of the underlying polaron wavefunction. The combination of new time and momentum resolved experimental probes of nonequilibrium phonons with novel computational methods promises to complement the qualitative results obtained via model Hamiltonians with a first principles, material-specific quantiative understanding of polarons and their properties.

In previous work, we developed a topological framework for solving Riemann initial-value problems for a system of conservation laws. Its core is a differentiable manifold, called the wave manifold, with points representing shock and rarefaction waves. In the present paper, we construct, in detail, the three-dimensional wave manifold for a system of two conservation laws with quadratic flux functions. Using adapted coordinates, we derive explicit formulae for important surfaces and curves within the wave manifold and display them graphically. The surfaces subdivide the manifold into regions according to shock type, such as ones corresponding to the Lax admissibility criterion. The curves parametrize rarefaction, shock, and composite waves appearing in contiguous wave patterns. Whereas wave curves overlap in state space, they are disentangled within the wave manifold. We solve a Riemann problem by constructing a wave curve associated with the slow characteristic speed family, generating a surface from it using shock curves, and intersecting this surface with a fast family wave curve. This construction is applied to solve Riemann problems for several illustrative cases., Comment: 45 pages, 36 figures, submitted

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed Tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on commonly present medium and large-sized structures such as the heart, liver, and pancreas even without hyperparameter tuning. However, the models struggle with very small or rarely present structures., Comment: J Digit Imaging. Inform. med. (2024)

The search for low-mass dark matter (DM) goes in parallel with the identification of new detection channels and the development of suitable detectors. Detection of the resulting small energy depositions is challenging: it requires extremely high sensitivity, only achievable by cryogenic thermal detectors, which might be put to the limit. Understanding the processes which can limit performances of these detectors can be thus crucial for evaluating the feasibility of the proposed new detection schemes and to design the detectors and tune their performance. In this paper we focus on a promising detection scheme, the excitation of single optical phonons in polar materials, to evaluate one of the possible limiting factors of cryogenic thermal detectors, i.e. the phonon dynamics in the target/absorber. We present a detailed theoretical analysis, within an entirely ab initio scheme, of the downconversion and propagation processes undergone by optical phonons, created by the interaction of a low-mass DM particle in an Al2O3 target, until they reach the interface with a phonon Al collector. After a preliminary methodological survey that reveals the limitations of any Relaxation Time Approximation based method, we developed a 3D beyond-RTA phonon Monte Carlo that allowed us to introduce the spatial dimension of the device and address questions about impact of target size and scattering position. We analyse also the effect of the phonon energy and wavevector and show that isotopes can, perhaps counterintuitively, result in a larger heat flux by providing transport channels of higher velocities, thus favoring detection. Our results suggest that, though challenging, the direct detection of light DM via athermal phonon generation appears feasible, and that the phonon downconversion followed by quasi-ballistic propagation does not appear to be a major bottleneck in terms of reducing the signal., Comment: 16 pages, 11 figures

Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research. The study employed DDPMs to create synthetic CXRs conditioned on demographic and pathological characteristics from the CheXpert dataset. These synthetic images were used to supplement training datasets for pathology classifiers, with the aim of improving their performance. The evaluation involved three datasets (CheXpert, MIMIC-CXR, and Emory Chest X-ray) and various experiments, including supplementing real data with synthetic data, training with purely synthetic data, and mixing synthetic data with external datasets. Performance was assessed using the area under the receiver operating curve (AUROC). Adding synthetic data to real datasets resulted in a notable increase in AUROC values (up to 0.02 in internal and external test sets with 1000% supplementation, p-value less than 0.01 in all instances). When classifiers were trained exclusively on synthetic data, they achieved performance levels comparable to those trained on real data with 200%-300% data supplementation. The combination of real and synthetic data from different sources demonstrated enhanced model generalizability, increasing model AUROC from 0.76 to 0.80 on the internal test set (p-value less than 0.01). In conclusion, synthetic data supplementation significantly improves the performance and generalizability of pathology classifiers in medical imaging.

Hydrodynamic phonon transport phenomena, like second sound, have been observed in liquid Helium more than 50 years ago. More recently second sound has been observed in graphite at over 200 K using transient thermal grating techniques. In this work we explore the signatures of second sound in ultrafast electron diffuse scattering (UEDS) patterns. We use density functional theory and solve the Boltzmann transport equation to determine time-resolved non-equilibrium phonon populations and subsequently calculate one-phonon structure factors and diffuse scattering patterns to simulate experimental data covering the regimes of ballistic, diffusive, and hydrodynamic phonon transport. For systems like graphite, UEDS is capable of extracting time-dependent phonon occupancies across the entire Brillouin zone and ultimately lead to a more fundamental understanding of the hydrodynamic phonon transport regime., Comment: 8 pages, 4 figures

Magnetic microrobots have been developed for navigating microscale environments by means of remote magnetic fields. However, limited propulsion speeds at small scales remain an issue in the maneuverability of these devices as magnetic force and torque are proportional to their magnetic volume. Here, we propose a microrobotic superstructure, which, as analogous to a supramolecular system, consists of two or more microrobotic units that are interconnected and organized through a physical (transient) component (a polymeric frame or a thread). Our superstructures consist of microfabricated magnetic helical micromachines interlocked by a magnetic gelatin nanocomposite containing iron oxide nanoparticles (IONPs). While the microhelices enable the motion of the superstructure, the IONPs serve as heating transducers for dissolving the gelatin chassis via magnetic hyperthermia. In a practical demonstration, we showcase the superstructure's motion with a gradient magnetic field in a large channel, the disassembly of the superstructure and release of the helical micromachines by a high-frequency alternating magnetic field, and the corkscrew locomotion of the released helices through a small channel via a rotating magnetic field. This adaptable microrobotic superstructure reacts to different magnetic inputs, which could be used to perform complex delivery procedures within intricate regions of the human body.

Diffuse gliomas are malignant brain tumors that grow widespread through the brain. The complex interactions between neoplastic cells and normal tissue, as well as the treatment-induced changes often encountered, make glioma tumor growth modeling challenging. In this paper, we present a novel end-to-end network capable of generating future tumor masks and realistic MRIs of how the tumor will look at any future time points for different treatment plans. Our approach is based on cutting-edge diffusion probabilistic models and deep-segmentation neural networks. We included sequential multi-parametric magnetic resonance images (MRI) and treatment information as conditioning inputs to guide the generative diffusion process. This allows for tumor growth estimates at any given time point. We trained the model using real-world postoperative longitudinal MRI data with glioma tumor growth trajectories represented as tumor segmentation maps over time. The model has demonstrated promising performance across a range of tasks, including the generation of high-quality synthetic MRIs with tumor masks, time-series tumor segmentations, and uncertainty estimates. Combined with the treatment-aware generated MRIs, the tumor growth predictions with uncertainty estimates can provide useful information for clinical decision-making., Comment: 13 pages, 10 figures, 2 tables, 2 agls, preprints in the IEEE trans. format for submission to IEEE-TMI