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We present a comparison of image and uv-plane galaxy-galaxy strong lensing modelling results for simulated ALMA observations with different antenna configurations and on-source integration times. Image-plane modelling is carried out via use of the $\texttt{CLEAN}$ algorithm, and we explore the effects of different visibility weighting schemes on the inferred lens model parameters. We find that direct modelling of the visibility data consistently outperforms image-plane modelling for both the naturally and Briggs-weighted images. We also find that the modelling of images created with Briggs weighting generally produces more accurate results than those obtained by modelling images constructed with natural weighting. We explain this by quantifying the suppression of information due to $\texttt{CLEAN}$ing on scales at which the modelling is sensitive, and how this differs between Briggs and natural weighting. At higher resolutions, the differences between the lens modelling techniques are much less pronounced and overall, modelling errors are significantly reduced. We also find that time-binning the visibilities by up to a factor of three makes no significant difference to the inferred lens parameters when directly modelling in the uv-plane. This work provides some guidance on navigating the many choices faced when modelling strong lens interferometric imaging data., Comment: 18 pages, 22 figures

The tremendous hype around autonomous driving is eagerly calling for emerging and novel technologies to support advanced mobility use cases. As car manufactures keep developing SAE level 3+ systems to improve the safety and comfort of passengers, traffic authorities need to establish new procedures to manage the transition from human-driven to fully-autonomous vehicles while providing a feedback-loop mechanism to fine-tune envisioned autonomous systems. Thus, a way to automatically profile autonomous vehicles and differentiate those from human-driven ones is a must. In this paper, we present a fully-fledged framework that monitors active vehicles using camera images and state information in order to determine whether vehicles are autonomous, without requiring any active notification from the vehicles themselves. Essentially, it builds on the cooperation among vehicles, which share their data acquired on the road feeding a machine learning model to identify autonomous cars. We extensively tested our solution and created the NexusStreet dataset, by means of the CARLA simulator, employing an autonomous driving control agent and a steering wheel maneuvered by licensed drivers. Experiments show it is possible to discriminate the two behaviors by analyzing video clips with an accuracy of 80%, which improves up to 93% when the target state information is available. Lastly, we deliberately degraded the state to observe how the framework performs under non-ideal data collection conditions.

The field of contrast-enhanced ultrasound (CEUS) combines nonlinear, resonant microbubbles (MBs) with dedicated pulse sequences to reveal the vascular function of organs. Clinical ultrasound contrast agents consist of polydisperse MB suspensions with diameters ranging from 0.5 to 10 um and resonance frequencies ranging from 1 to 15 MHz. As a result, just a small fraction of MBs resonates at a given ultrasound frequency. MB suspensions with narrow size distributions can be tuned for a specific imaging frequency, boost CEUS sensitivity and enable deeper vascular imaging. However, their enhanced nonlinear behavior makes imaging susceptible to nonlinear wave propagation artifacts. Here, we numerically investigate the impact of the acoustic wavefront shape on the imaging of nonlinear, monodisperse MBs. Specifically, our approach relies on an extension of the Iterative Nonlinear Contrast Source (INCS) method that accounts for all nonlinear effects in CEUS. We demonstrate that supersonic x-shaped wavefronts referred to as x-waves can be used to generate ultrasound images of monodisperse MBs without nonlinear wave propagation artifacts. On the contrary, imaging based on focused, planar and diverging wavefronts leads to significant nonlinear artifacts. Taken together, our results show that x-waves can harness the full potential of monodisperse MBs by enabling their sensitive and specific detection in a tissue context.

An ideal contrast-enhanced ultrasound image should display microbubble-induced nonlinearities while avoiding wave propagation nonlinearities. One of the most successful ultrasound pulse sequences to disentangle these nonlinear effects relies on the transmission of cross-propagating plane waves. However, theory describing the noncollinear nonlinear interaction of two finite plane waves has not been fully developed and a better understanding of these effects would improve contrast-enhanced ultrasound imaging further. Here, local nonlinear interactions at the intersection of two plane-waves are investigated by extending the Westervelt equation with a term including the Lagrangian density. The Iterative Nonlinear Contrast Source (INCS) method is employed to numerically solve this full nonlinear wave equation for two 3D finite cross-propagating pulsed plane waves. In addition, analytical expressions for the cross-propagation of two infinite continuous plane waves are derived. Numerical results obtained with INCS show good agreement with the analytical expressions. Overall, the generated results show that the pressure associated with local nonlinear effects is two orders of magnitude lower than the pressure associated with global nonlinear effects. Local nonlinear effects are therefore expected to be negligible in the context of single-shot ultrasound imaging, but they may influence approaches that subtract pressure fields such as amplitude modulation or pulse inversion.

Machine learning interatomic potentials (ML-IAPs) enable quantum-accurate, classical molecular dynamics simulations of large systems, beyond reach of density functional theory (DFT). Yet, their efficiency and ability to predict systems larger than DFT supercells are not fully explored, posing a question regarding transferability to large-scale simulations with defects (e.g. dislocations, cracks). Here, we apply a three-step validation approach to body-centered-cubic iron. First, accuracy and efficiency are assessed by optimizing ML-IAPs based on four state-of-the-art ML packages. The Pareto front of computational speed versus testing root-mean-square-error (RMSE) is computed. Second, benchmark properties relevant to plasticity and fracture are evaluated. Their average relative error Q with respect to DFT is found to correlate with RMSE. Third, transferability of ML-IAPs to dislocations and cracks is investigated by using per-atom model uncertainty quantification. The core structures and Peierls barriers of screw, M111 and three edge dislocations are compared with DFT. Traction-separation curve and critical stress intensity factor (K_Ic) are also predicted. Cleavage on the pre-existing crack plane is found to be the zero-temperature atomistic fracture mechanism of pure body-centered-cubic iron under mode-I loading, independent of ML package and training database. Quantitative predictions of dislocation glide paths and KIc can be sensitive to database, ML package, cutoff radius, and are limited by DFT accuracy. Our results highlight the importance of validating ML-IAPs by using indicators beyond RMSE. Moreover, significant computational speed-ups can be achieved by using the most efficient ML-IAP package, yet the assessment of the accuracy and transferability should be performed with care., Comment: 34 pages, 11 figures

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25$\%$ of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with $g$-band signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies., Comment: 16 pages, 20 Figures

In this note, we will illuminate some immediate consequences of work done by Reineke that may prove to be useful in the study of elliptic curves. In particular, we will construct an isomorphism between the category of smooth projective curves with a category of quiver grassmannians. We will use this to provide a 4-fold categorical equivalence between a category of quiver grassmannians, smooth projective curves, compact Riemann surfaces and fields of transcendence degree 1 over $\mathbb{C}$. We finish with noting that the category of elliptic curves is isomorphic to a category of quiver grassmannians, whence providing an analytic group structure to a class of quiver grassmannians., Comment: 6 pages, 1 figure. Fixed some typos and added a reference. Link to published version: https://doi.org/10.1090/bproc/178

In particular types of layer- or lamellar-like microstructures such as pearlite and lath martensite, plastic slip occurs favorably in directions parallel to inter-lamellar boundaries. This may be due to the interplay between morphology and crystallographic orientation or, more generally, due to constraints imposed on the plastic slip due to the lamellar microstructural geometry. This paper proposes a micromechanics based, computationally efficient, scale independent model for particular type of lamellar microstructures containing softer lamellae, which are sufficiently thin to be considered as discrete slip planes embedded in a matrix representing the harder lamellae. Accordingly, the model is constructed as an isotropic visco-plastic model which is enriched with an additional orientation-dependent planar plastic deformation mechanism. This additional mode is activated when the applied load, projected on the direction of the soft films, induces a significant amount of shear stress. Otherwise, the plastic deformation is governed solely by the isotropic part of the model. The response of the proposed model is assessed via a comparison to direct numerical simulations (DNS) of an infinite periodic two-phase laminate. It is shown that the yielding behavior of the model follows the same behavior as the reference model. It is observed that the proposed model is highly anisotropic, and the degree of anisotropy depends on the contrast between the slip resistance (or yield stress) of the planar mode versus that of the isotropic part. The formulation is then applied to model the substructure of lath martensite with inter-layer thin austenite films. It is exploited in a mesoscale simulation of a dual-phase (DP) steel microstructure.The results are compared with those of a standard isotropic model and a full crystal plasticity model.

The onset of plasticity in quenched martensitic microstructures is characterized by a low initial yield stress followed by an extremely strong initial hardening response, and then a sudden hardening saturation. Literature attributes this behavior to residual stresses and dislocations inherited from the martensitic transformation, or to microstructural heterogeneities causing strength differences among the grains. Here, we argue that orientation-dependent yielding of lath martensite due to inter-lath sliding, which induces a substructure boundary sliding mechanism, may also contribute significantly to the observed behavior. To demonstrate this, we systematically study its quantitative contribution to the elasto-plastic transition behavior in a numerical microstructural model. In the simulations, we employ an effective laminate model for the martensite packets which takes into account the yielding anisotropy due to sliding along the packet's habit plane orientation. To account for the effect of carbon content, martensitic microstructures with different levels of lath strength are considered. It is shown that the martensite packets with a favorable habit plane orientation start to yield earlier compared to those with an unfavorable orientation, which initially remain elastic. As a consequence, the macro-scale response of the microstructures exhibits a low yield stress, followed by a significant degree of initial hardening which continues until the saturation stress level is approached. The apparent work hardening rate depends on the contrast between the in-habit plane and out-of-habit plane yield strength used in the model.

This work presents a detailed experimental-numerical analysis of a low-carbon dual-phase steel microstructure, revealing evidence of significant anisotropic plastic deformation in lath martensite. A careful determination of the habit plane orientations in the present martensitic variants demonstrates that the observed traces of plastic slip coincide with the directions of the corresponding habit planes. To study the local lath martensite plastic activity, a dedicated substructure-enriched crystal plasticity based model is exploited. Compared with conventional bcc crystal plasticity, the model incorporates an extra crystallographic slip plane containing 3 softer slip systems parallel to the habit planes to capture the role of habit-plane plasticity. Simulations reveal consistent strain localization patterns as those observed in the experiments, with most plasticity localized in the martensite packets with their habit plane oriented favorably with respect to the applied load. Based on these insights, recommendations for future martensite modelling strategies and potential improvements of steels are discussed.

In this paper, we will present the author's interpretation and embellishment of five lectures on cluster theory given by Kiyoshi Igusa during the Spring semester of 2022 at Brandeis University. They are meant to be used as an introduction to cluster theory from a representation-theoretic point of view. It is assumed that the reader has some background in representations of quivers., Comment: 35 pages

In this paper we first study clusters in type $\tilde{\mathbb{A}}$ by collecting them into a finite number of infinite families given by Dehn twists of their corresponding triangulations, and show that these families are counted by the Catalan numbers. We also highlight the similarities and differences between the annuli diagrams used to study clusters and those used to study exceptional sets in type $\tilde{\mathbb{A}}$. We then focus on exceptional collections (sets) of modules over path algebras of quivers by first showing that the notion of relative projectivity in exceptional sets is well defined. We finish by counting the number of exceptional sets of representations of type $\mathbb{A}$ quivers with straight orientation and using this to count the number of families of exceptional sets of type $\tilde{\mathbb{A}}$ with straight orientation., Comment: 35 pages, 11 figures, and 3 tables. Several examples have been added

The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip deformation mechanisms under mode-I loading based on classical interatomic potentials yield contradicting predictions. To enable fracture prediction with quantum accuracy, we develop a Gaussian approximation potential (GAP) using an active learning strategy by extending a density functional theory (DFT) database of ferromagnetic bcc iron. We apply the active learning algorithm and obtain a Fe GAP model with a maximum predicted error of 8 meV/atom over a broad range of stress intensity factors (SIFs) and for four crack systems. The learning efficiency of the approach is analysed, and the predicted critical SIFs are compared with Griffith and Rice theories. The simulations reveal that cleavage along the original crack plane is the crack tip mechanism for {100} and {110} crack planes at T=0K, thus settling a long-standing dispute. Our work also highlights the need for a multiscale approach to predicting fracture and intrinsic ductility, whereby finite temperature, finite loading rate effects and pre-existing defects (e.g. nanovoids, dislocations) should be taken explicitly into account., Comment: 17 pages, 7 figures

Weak supervision (WS) is an alternative to the traditional supervised learning to address the need for ground truth. Data programming is a practical WS approach that allows programmatic labeling data samples using labeling functions (LFs) instead of hand-labeling each data point. However, the existing approach fails to fully exploit the domain knowledge encoded into LFs, especially when the LFs' coverage is low. This is due to the common data programming pipeline that neglects to utilize data features during the generative process. This paper proposes a new approach called reinforced labeling (RL). Given an unlabeled dataset and a set of LFs, RL augments the LFs' outputs to cases not covered by LFs based on similarities among samples. Thus, RL can lead to higher labeling coverage for training an end classifier. The experiments on several domains (classification of YouTube comments, wine quality, and weather prediction) result in considerable gains. The new approach produces significant performance improvement, leading up to +21 points in accuracy and +61 points in F1 scores compared to the state-of-the-art data programming approach.

It is known that there are infinitely many exceptional sequences of quiver representations for Euclidean quivers. In this paper we study those of type $\tilde{\mathbb{A}}_n$ and classify them into finitely many parametrized families. We first give a bijection between exceptional collections and a combinatorial object known as strand diagrams. We will then realize these strand diagrams as chord diagrams and then arc diagrams on an annulus. Using arc diagrams, we will define parametrized families of exceptional collections and use arc diagrams to show that there are finitely many such families. We moreover show that these families of exceptional collections are in bijection with equivalence classes of small arc diagrams. Finally, we provide an algebraic explanation of parametrized families using the transjective component of the bounded derived category., Comment: 34 pages, 5 figures, and 2 tables. Added algebraic analogue of performing twists on the annulus. Shortened the proof of a key lemma and provided algebraic intuition for its statement

The disruptive reconfigurable intelligent surface (RIS) technology is steadily gaining relevance as a key element in future 6G networks. However, a one-size-fits-all RIS hardware design is yet to be defined due to many practical considerations. A major roadblock for currently available RISs is their inability to concurrently operate at multiple carrier frequencies, which would lead to redundant installations to support multiple radio access technologies (RATs). In this paper, we introduce FABRIS, a novel and practical multi-frequency RIS design. FABRIS is able to dynamically operate across different radio frequencies (RFs) by means of frequency-tunable antennas as unit cells with virtually no performance degradation when conventional approaches to RIS design and optimization fail. Remarkably, our design preserves a sufficiently narrow beamwidth as to avoid generating signal leakage in unwanted directions and a sufficiently high antenna efficiency in terms of scattering parameters. Indeed, FABRIS selects the RIS configuration that maximizes the signal at the intended target user equipment (UE) while minimizing leakage to non-intended neighboring UEs. Numerical results and full-wave simulations validate our proposed approach against a naive implementation that does not consider signal leakage resulting from multi-frequency antenna arrays.

High-entropy alloys (HEAs) with concentrated solid solutions are conceived to possess a rugged atomic and energy landscape in which dislocation motion necessarily proceeds to accommodate mechanical deformation. Fundamental questions remain as to how rough the energy landscape is and to what extent it can be influenced by the local ordering of their constituent elements. Here, we construct and report the potential energy landscape (PEL) governing screw dislocation motion in refractory HEAs that reveals a hierarchical and multilevel structure with a collection of small basins nested in large metabasin. This striking feature pertaining to HEAs exerts a trapping force and back stress on saddle point activations, retarding dislocation movement. By introducing chemical short-range order, the energy landscape is smoothed but skewed to different degrees that shifts the rate-liming process from kink-glide to kink-pair nucleation. The chemical disorder-roughened PEL in HEAs, analogous to structural disorder induced in metallic glasses, signifies the role of various barrier-hopping processes underlying the extraordinary mechanical behaviors of the two distinct groups of materials.

We have carried out the first spatially-resolved investigation of the multi-phase interstellar medium (ISM) at high redshift, using the z=4.24 strongly-lensed sub-millimetre galaxy H-ATLASJ142413.9+022303 (ID141). We present high-resolution (down to ~350 pc) ALMA observations in dust continuum emission and in the CO(7-6), H_2O (2_{1,1} - 2_{0,2}), CI(1-0) and CI(2-1) lines, the latter two allowing us to spatially resolve the cool phase of the ISM for the first time. Our modelling of the kinematics reveals that the system appears to be dominated by a rotationally-supported gas disk with evidence of a nearby perturber. We find that the CI(1-0) line has a very different distribution to the other lines, showing the existence of a reservoir of cool gas that might have been missed in studies of other galaxies. We have estimated the mass of the ISM using four different tracers, always obtaining an estimate in the range (3.2-3.8) x 10^{11} M_sol, significantly higher than our dynamical mass estimate of (0.8-1.3) x 10^{11} M_sol. We suggest that this conflict and other similar conflicts reported in the literature is because the gas-to-tracer ratios are ~4 times lower than the Galactic values used to calibrate the ISM in high-redshift galaxies. We demonstrate that this could result from a top-heavy initial mass function and strong chemical evolution. Using a variety of quantitative indicators, we show that, extreme though it is at z=4.24, ID141 will likely join the population of quiescent galaxies that appears in the Universe at z~3., Comment: This article has been accepted for publication in MNRAS, published by Oxford University Press on behalf of the Royal Astronomical Society. 24 pages

We present modelling of ~0.1arcsec resolution Atacama Large Millimetre/sub-millimeter Array imaging of seven strong gravitationally lensed galaxies detected by the Herschel Space Observatory. Four of these systems are galaxy-galaxy scale strong lenses, with the remaining three being group-scale lenses. Through careful modelling of visibilities, we infer the mass profiles of the lensing galaxies and by determining the magnification factors, we investigate the intrinsic properties and morphologies of the lensed sub-millimetre sources. We find that these sub-millimetre sources all have ratios of star formation rate to dust mass that is consistent with or in excess of the mean ratio for high-redshift sub-millimetre galaxies and low redshift ultra-luminous infrared galaxies. The contribution to the infrared luminosity from possible AGN is not quantified and so could be biasing our star formation rates to higher values. The majority of our lens models have mass density slopes close to isothermal, but some systems show significant differences., Comment: 12 pages, 5 figures. Submitted to MNRAS

The prediction of the atomistic structure and properties of crystals including defects based on ab-initio accurate simulations is essential for unraveling the nano-scale mechanisms that control the micromechanical and macroscopic behaviour of metals. Density functional theory (DFT) can enable the quantum-accurate prediction of some of these properties, however at high computational costs and thus limited to systems of ~1,000 atoms. In order to predict with quantum-accuracy the mechanical behaviour of nanoscale structures involving from thousands to several millions of atoms, machine learning interatomic potentials have been recently developed. Here, we explore the performance of a new class of interatomic potentials based on Graph Neural Networks (GNNs), a recent field of research in Deep Learning. Two state-of-the-art GNN models are considered, SchNet and DimeNet, and trained on an extensive DFT database of ferromagnetic bcc iron. We find that the DimeNet GNN Fe potential including three-body terms can reproduce with DFT accuracy the equation of state and the Bain path, as well as defected configurations (vacancy and surfaces). To the best of our knowledge, this is the first demonstration of the capability of GNN of reproducing the energetics of defects in bcc iron. We provide an open-source implementation of DimeNet that can be used to train other metallic systems for further exploration of the GNN capabilities.