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In this paper, we work in a 2D version of the probabilistic variant of Winfree's abstract Tile Assembly Model defined by Chandran, Gopalkrishnan and Reif (SICOMP 2012) in which attaching tiles are sampled uniformly with replacement. First, we develop a framework called ``sequential non-determinism'' for analyzing the probabilistic correctness of a non-deterministic, temperature-1 tile assembly system (TAS) in which most (but not all) tile attachments are deterministic and the non-deterministic attachments always occur in a specific order. Our main sequential non-determinism result equates the probabilistic correctness of such a TAS to a finite product of probabilities, each of which 1) corresponds to the probability of the correct type of tile attaching at a point where it is possible for two different types to attach, and 2) ignores all other tile attachments that do not affect the non-deterministic attachment. We then show that sequential non-determinism allows for efficient and geometrically expressive self-assembly. To that end, we constructively prove that for any positive integer $N$ and any real $\delta \in (0,1)$, there exists a TAS that self-assembles into an $N \times N$ square with probability at least $1 - \delta$ using only $O\left( \log N + \log \frac{1}{\delta} \right)$ types of tiles. Our bound improves upon the previous state-of-the-art bound for this problem by Cook, Fu and Schweller (SODA 2011).

We present a detailed JWST/NIRSpec and NIRCam analysis of a gravitationally-lensed galaxy ($\rm \mu=17-21$) at redshift 6.14 magnified by the Hubble Frontier Field galaxy cluster MACS J0416. The target galaxy is overall a typical compact and UV-faint ($\rm M_{UV}=-17.8$) Lyman-$\alpha$ emitter; yet, the large magnification allows the detailed characterisation of structures on sub-galactic (down to few parsec) scales. Prominent optical $\rm H\alpha$, $\rm H\beta$ and [OIII]$\lambda\lambda4959,5007$ lines are spatially resolved with the high spectral resolution grating (G395H, R~2700), with large equivalent widths, EW($\rm H\beta$+[OIII])$\gtrsim1000$ \AA, and elevated ionising photon production efficiencies $\rm log(\xi_{ion}/erg^{-1}Hz)=25.2-25.7$. NIRCam deep imaging reveals the presence of compact rest-UV bright regions along with individual star clusters of sizes $\rm R_{eff}=3-8~pc$ and masses $\rm M\sim2\cdot10^5-5\cdot10^{6}~M_\odot$ These clusters are characterised by steep UV slopes, $\rm\beta_{UV}\lesssim-2.5$, in some cases associated with a dearth of line emission, indicating possible leaking of the ionising radiation, as also supported by a Lyman-$\rm \alpha$ emission peaking at $\rm \sim100~km~s^{-1}$ from the systemic redshift. While the entire system is characterised by low-metallicity, $\sim0.1~Z_\odot$, the NIRSpec-IFU map also reveals the presence of a low-luminosity, metal-poor region with $\rm Z\lesssim2\%~Z_\odot$, barely detected in NIRCam imaging; this region is displaced by $\rm >200~pc$ from one of the UV brightest structures of the system, and it would have been too faint to detect if not for the large magnification of the system., Comment: 19 pages (11 figures, 2 tables) + appendix (3 pages, 4 figures, 1 table). Submitted to A&A; comments are welcome

We report the serendipitous discovery of a faint (M_UV > -12.2), low-metallicity (Z ~ 0.02 Zsun), ionizing source (dubbed T2c) with a spectroscopic redshift of z=6.146. T2c is part of a larger structure amplified by the Hubble Frontier Field galaxy cluster MACSJ0416, and was observed with JWST/NIRSpec IFU. Stacking the short-wavelength NIRCam data reveals no stellar continuum detection down to a magnitude limit of m_UV ~ 31.0 (3 sigma). However, prominent Hb, [OIII]4959,5007, and Ha emissions are detected, with equivalent widths exceeding 200A, 800A, and 1300A (3 sigma), respectively. The corresponding intrinsic (magnification-corrected x23 +/- 3) ultraviolet and optical rest-frame magnitudes exceed 34.4 and 33.9 (corresponding to M_uv and M_opt fainter than -12.2 and -12.8, at lambda_rest ~ 2000A and ~5000A, respectively), suggesting a stellar mass lower than a few 10^4 Msun under an instantaneous burst scenario. The inferred ionizing photon production efficiency (xi_ion) is high, xi_ion >~ 26.08(25.86) 3(5)sigma, assuming no dust attenuation and no Lyman continuum leakage, indicating the presence of massive stars despite the low mass of the object. The very poor sampling of the initial mass function at such low mass star-forming complex suggests that the formation of very massive stars might be favored in very low metallicity environments. T2c is surrounded by Balmer and weak oxygen emission on a spatial scale of a few hundred parsecs after correcting for lensing effects. This system resembles an HII region potentially powered by currently undetected, extremely efficient, low-metallicity star complexes or clusters. We propose that massive O-type stars populate this low-mass and metallicity high-redshift satellites, likely caught in an early and short formation phase, contributing to the ionization of the surrounding medium., Comment: 9 pages, 5 figures, 1 table. Submitted to A&A. Comments are welcome

Program verification tools are often implemented as front-end translations of an input program into an intermediate verification language (IVL) such as Boogie, GIL, Viper, or Why3. The resulting IVL program is then verified using an existing back-end verifier. A soundness proof for such a translational verifier needs to relate the input program and verification logic to the semantics of the IVL, which in turn needs to be connected with the verification logic implemented in the back-end verifiers. Performing such proofs is challenging due to the large semantic gap between the input and output programs and logics, especially for complex verification logics such as separation logic. This paper presents a formal framework for reasoning about translational separation logic verifiers. At its center is a generic core IVL that captures the essence of different separation logics. We define its operational semantics and formally connect it to two different back-end verifiers, which use symbolic execution and verification condition generation, resp. Crucially, this semantics uses angelic non-determinism to enable the application of different proof search algorithms and heuristics in the back-end verifiers. An axiomatic semantics for the core IVL simplifies reasoning about the front-end translation by performing essential proof steps once and for all in the equivalence proof with the operational semantics rather than for each concrete front-end translation. We illustrate the usefulness of our formal framework by instantiating our core IVL with elements of Viper and connecting it to two Viper back-ends as well as a front-end for concurrent separation logic. All our technical results have been formalized in Isabelle/HOL, including the core IVL and its semantics, the semantics of two back-ends for a subset of Viper, and all proofs.

We present in this paper the discovery, properties, and a catalog of 1165 high redshift $6.5 < z < 18$ galaxies found in deep JWST NIRCam imaging from the GTO PEARLS survey combined with data from JWST public fields. We describe our bespoke homogeneous reduction process and our analysis of these areas including the NEP, CEERS, GLASS, NGDEEP, JADES, and ERO SMACS-0723 fields with over 214 arcmin$^{2}$ imaged to depths of $\sim 30$ mag. We describe our rigorous methods for identifying these galaxies, involving the use of Lyman-break strength, detection significance criteria, visual inspection, and integrated photometric redshifts probability distributions predominately at high redshift. Our sample is a robust and highly pure collection of distant galaxies from which we also remove brown dwarf stars, and calculate completeness and contamination from simulations. We include a summary of the basic properties of these $z > 6.5$ galaxies, including their redshift distributions, UV absolute magnitudes, and star formation rates. Our study of these young galaxies reveals a wide range of stellar population properties as seen in their colors and SED fits which we compare to stellar population models, indicating a range of star formation histories, dust, AGN and/or nebular emission. We find a strong trend exists between stellar mass and $(U-V)$ color, as well as the existence of the `main-sequence' of star formation for galaxies as early as $z \sim 12$. This indicates that stellar mass, or an underlying variable correlating with stellar mass, is driving galaxy formation, in agreement with simulation predictions. We also discover ultra-high redshift candidates at $z > 12$ in our sample and describe their properties. Finally, we note a significant observed excess of galaxies compared to models at $z > 12$, revealing a tension between predictions and our observations., Comment: Submitted to AAS Journals, 37 pages

We present a full analysis of galaxy major merger pair fractions, merger rates, and mass accretion rates, thus uncovering the role of mergers in galaxy formation at the earliest previously unexplored epoch of $4.5

The Solar eruptioN Integral Field Spectrograph (SNIFS) is a solar-gazing spectrograph scheduled to fly in the summer of 2025 on a NASA sounding rocket. Its goal is to view the solar chromosphere and transition region at a high cadence (1s) both spatially (0.5") and spectrally (33 m{\AA}) viewing wavelengths around Lyman Alpha (1216 {\AA}), Si iii (1206 {\AA}) and O v (1218 {\AA}) to observe spicules, nanoflares, and possibly a solar flare. This time cadence will provide yet-unobserved detail about fast-changing features of the Sun. The instrument is comprised of a Gregorian-style reflecting telescope combined with a spectrograph via a specialized mirrorlet array that focuses the light from each spatial location in the image so that it may be spectrally dispersed without overlap from neighboring locations. This paper discusses the driving science, detailed instrument and subsystem design, and pre-integration testing of the SNIFS instrument., Comment: 22 pages (not including references), 7 figures, submitting to Solar Physics

The equivariant quantum $K$-theory ring of a flag variety is a Frobenius algebra equipped with a perfect pairing called the quantum $K$-metric. It is known that in the classical $K$-theory ring for a given flag variety the ideal sheaf basis is dual to the Schubert basis with regard to the sheaf Euler characteristic. We define a quantization of the ideal sheaf basis for the equivariant quantum $K$-theory of cominuscule flag varieties. These quantized ideal sheaves are then dual to the Schubert basis with regard to the quantum $K$-metric. We prove explicit type-uniform combinatorial formulae for the quantized ideal sheaves in terms of the Schubert basis for any cominuscule flag variety. We also provide an application ultilizing the quantized ideal sheaves to calculate the Schubert structure constants associated to multiplication by the top exterior power of the tautological quotient bundle in $QK_T(Gr(k,n))$., Comment: arXiv admin note: text overlap with arXiv:1604.07500 by other authors

Purpose: To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian cancer. Materials and Methods: This retrospective study included contrast-enhanced and non-contrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age, 60 years +/- 11 [s.d.]; 143 female), was tested on two internal (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the Dice coefficient, standard deviations, and 95% confidence intervals, focusing on ascites volume in the peritoneal cavity. Results: On NIH-LC (25 patients; mean age, 59 years +/- 14 [s.d.]; 14 male) and NIH-OV (166 patients; mean age, 65 years +/- 9 [s.d.]; all female), the model achieved Dice scores of 0.855 +/- 0.061 (CI: 0.831-0.878) and 0.826 +/- 0.153 (CI: 0.764-0.887), with median volume estimation errors of 19.6% (IQR: 13.2-29.0) and 5.3% (IQR: 2.4-9.7) respectively. On UofW-LC (124 patients; mean age, 46 years +/- 12 [s.d.]; 73 female), the model had a Dice score of 0.830 +/- 0.107 (CI: 0.798-0.863) and median volume estimation error of 9.7% (IQR: 4.5-15.1). The model showed strong agreement with expert assessments, with r^2 values of 0.79, 0.98, and 0.97 across the test sets. Conclusion: The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in concordance with expert radiologist assessments.

High electric fields can significantly alter catalytic environments and the resultant chemical processes. Such fields arise naturally in biological systems but can also be artificially induced through localized excitations at nanoscale. Recently, strong field excitation of dielectric nanoparticles has emerged as an avenue for studying catalysis in highly ionized environments producing extreme electric fields. While the dynamics of surface ion emission driven by ultrafast laser ionization has been heavily explored, understanding the molecular dynamics leading to fragmentation has remained elusive. To address this, we employed a multiscale approach utilizing non-adiabatic quantum molecular dynamics (NAQMD) simulations on hydrogenated silica surfaces in both bare and wetted environments under field conditions mimicking those of an ionized nanoparticle. Our findings indicate that hole localization drives fragmentation dynamics, leading to surface silanol dissociation within 50 fs and charge transfer-induced water splitting in wetted environments within 150 fs. Further insight into such ultrafast mechanisms is critical for advancement of catalysis on the surface of charged nanosystems.

We describe the design and performance of a magnetic bottle electron spectrometer~(MBES) for high-energy electron spectroscopy. Our design features a ${\sim2}$~m long electron drift tube and electrostatic retardation lens, achieving sub-electronvolt (eV) electron kinetic energy resolution for high energy (several hundred eV) electrons with close to 4$\pi$ collection efficiency. A segmented anode electron detector enables the simultaneous collection of photoelectron spectra in high resolution and high collection efficiency modes. This versatile instrument is installed at the TMO endstation at the LCLS x-ray free-electron laser (XFEL). In this paper, we demonstrate its high resolution, collection efficiency and spatial selectivity in measurements where it is coupled to an XFEL source. These combined characteristics are designed to enable high-resolution time-resolved measurements using x-ray photoelectron, absorption, and Auger-Meitner spectroscopy. We also describe the pervasive artifact in MBES time-of-flight spectra that arises from a periodic modulation in electron detection efficiency, and present a robust analysis procedure for its removal.

New JWST/NIRCam wide-field slitless spectroscopy provides redshifts for four z>8 galaxies located behind the lensing cluster MACS J0416.1-2403. Two of them, "Y1" and "JD", have previously reported spectroscopic redshifts based on ALMA measurements of [OIII] 88 $\mu$m and/or [CII] 157.7 $\mu$m lines. Y1 is a merging system of three components, and the existing redshift z=8.31 is confirmed. However, JD is at z=8.34 instead of the previously claimed z=9.28. JD's close companion, "JD-N", which was a previously discovered z>8 candidate, is now identified at the same redshift as JD. JD and JD-N form an interacting pair. A new candidate at z>8, "f090d_018", is also confirmed and is at z=8.49. These four objects are likely part of an overdensity that signposts a large structure extending ~165 kpc in projected distance and ~48.7 Mpc in radial distance. They are magnified by less than one magnitude and have intrinsic $M_{UV}$ ranging from -19.57 to -20.83 mag. Their spectral energy distributions show that the galaxies are all very young with ages ~ 4-18 Myr and stellar masses about $10^{7-8}$ ${\rm M_\odot}$. These infant galaxies have very different star formation rates ranging from a few to over a hundred $\rm{M_\odot}$ yr$^{-1}$, but only two of them (JD and f090d_018) have blue rest-frame UV slopes $\beta<-2.0$ indicative of a high Lyman-continuum photon escape fraction that could contribute significantly to the cosmic hydrogen-reionizing background. Interestingly, these two galaxies are the least massive and least active ones among the four. The other two systems have much flatter UV slopes largely because of their high dust extinction ($A_{\rm V}$=0.9-1.0 mag). Their much lower indicated escape fractions show that even very young, actively star-forming galaxies can have negligible contribution to reionization when they quickly form dust throughout their bodies., Comment: 18 pages, 6 figures, after addressing the referee report

In this paper, we introduce DRR-RATE, a large-scale synthetic chest X-ray dataset derived from the recently released CT-RATE dataset. DRR-RATE comprises of 50,188 frontal Digitally Reconstructed Radiographs (DRRs) from 21,304 unique patients. Each image is paired with a corresponding radiology text report and binary labels for 18 pathology classes. Given the controllable nature of DRR generation, it facilitates the inclusion of lateral view images and images from any desired viewing position. This opens up avenues for research into new and novel multimodal applications involving paired CT, X-ray images from various views, text, and binary labels. We demonstrate the applicability of DRR-RATE alongside existing large-scale chest X-ray resources, notably the CheXpert dataset and CheXnet model. Experiments demonstrate that CheXnet, when trained and tested on the DRR-RATE dataset, achieves sufficient to high AUC scores for the six common pathologies cited in common literature: Atelectasis, Cardiomegaly, Consolidation, Lung Lesion, Lung Opacity, and Pleural Effusion. Additionally, CheXnet trained on the CheXpert dataset can accurately identify several pathologies, even when operating out of distribution. This confirms that the generated DRR images effectively capture the essential pathology features from CT images. The dataset and labels are publicly accessible at https://huggingface.co/datasets/farrell236/DRR-RATE.

Existing automated verification techniques for safe Rust code rely on the strong type-system properties to reason about programs, especially to deduce which memory locations do not change (i.e., are framed) across function calls. However, these type guarantees do not hold in the presence of interior mutability (e.g., when interacting with any concurrent data structure). As a consequence, existing verification techniques for safe code such as Prusti and Creusot are either unsound or fundamentally incomplete if applied to this setting. In this work, we present the first technique capable of automatically verifying safe clients of existing interiorly mutable types. At the core of our approach, we identify a novel notion of implicit capabilities: library-defined properties that cannot be expressed using Rust's types. We propose new annotations to specify these capabilities and a first-order logic encoding suitable for program verification. We have implemented our technique in a verifier called Mendel and used it to prove absence of panics in Rust programs that make use of popular standard-library types with interior mutability, including Rc, Arc, Cell, RefCell, AtomicI32, Mutex and RwLock. Our evaluation shows that these library annotations are useful for verifying usages of real-world libraries, and powerful enough to require zero client-side annotations in many of the verified programs.

Dusty star-forming galaxies (DSFGs) contribute significantly to the stellar buildup at cosmic noon. Major mergers and gas accretion are often invoked to explain DSFGs' prodigious star-formation rates (SFRs) and large stellar masses. We conducted a spatially-resolved morphological analysis of the rest-frame UV/NIR emission in three DSFGs at z~2.5. Initially discovered as CO emitters by NOEMA observations of a bright Herschel source, we observed them with the JWST/NIRCam as part of the PEARLS program. The NIRCam data reveal the galaxies' stellar populations and dust distributions on scales of 250 pc. Spatial variations in stellar mass, SFR, and dust extinction are determined in resolved maps obtained through pixel-based SED fitting. The CO emitters are massive, dusty starburst galaxies with SFRs=340-2500 Msun/yr, positioning them among the most active SFGs at 2

Multi-parametric MRI (mpMRI) studies are widely available in clinical practice for the diagnosis of various diseases. As the volume of mpMRI exams increases yearly, there are concomitant inaccuracies that exist within the DICOM header fields of these exams. This precludes the use of the header information for the arrangement of the different series as part of the radiologist's hanging protocol, and clinician oversight is needed for correction. In this pilot work, we propose an automated framework to classify the type of 8 different series in mpMRI studies. We used 1,363 studies acquired by three Siemens scanners to train a DenseNet-121 model with 5-fold cross-validation. Then, we evaluated the performance of the DenseNet-121 ensemble on a held-out test set of 313 mpMRI studies. Our method achieved an average precision of 96.6%, sensitivity of 96.6%, specificity of 99.6%, and F1 score of 96.6% for the MRI series classification task. To the best of our knowledge, we are the first to develop a method to classify the series type in mpMRI studies acquired at the level of the chest, abdomen, and pelvis. Our method has the capability for robust automation of hanging protocols in modern radiology practice.

Background: Segmentation of organs and structures in abdominal MRI is useful for many clinical applications, such as disease diagnosis and radiotherapy. Current approaches have focused on delineating a limited set of abdominal structures (13 types). To date, there is no publicly available abdominal MRI dataset with voxel-level annotations of multiple organs and structures. Consequently, a segmentation tool for multi-structure segmentation is also unavailable. Methods: We curated a T1-weighted abdominal MRI dataset consisting of 195 patients who underwent imaging at National Institutes of Health (NIH) Clinical Center. The dataset comprises of axial pre-contrast T1, arterial, venous, and delayed phases for each patient, thereby amounting to a total of 780 series (69,248 2D slices). Each series contains voxel-level annotations of 62 abdominal organs and structures. A 3D nnUNet model, dubbed as MRISegmentator-Abdomen (MRISegmentator in short), was trained on this dataset, and evaluation was conducted on an internal test set and two large external datasets: AMOS22 and Duke Liver. The predicted segmentations were compared against the ground-truth using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD). Findings: MRISegmentator achieved an average DSC of 0.861$\pm$0.170 and a NSD of 0.924$\pm$0.163 in the internal test set. On the AMOS22 dataset, MRISegmentator attained an average DSC of 0.829$\pm$0.133 and a NSD of 0.908$\pm$0.067. For the Duke Liver dataset, an average DSC of 0.933$\pm$0.015 and a NSD of 0.929$\pm$0.021 was obtained. Interpretation: The proposed MRISegmentator provides automatic, accurate, and robust segmentations of 62 organs and structures in T1-weighted abdominal MRI sequences. The tool has the potential to accelerate research on various clinical topics, such as abnormality detection, radiotherapy, disease classification among others., Comment: We made the segmentation model publicly available

SMT-based program analysis and verification often involve reasoning about program features that have been specified using quantifiers; incorporating quantifiers into SMT-based reasoning is, however, known to be challenging. If quantifier instantiation is not carefully controlled, then runtime and outcomes can be brittle and hard to predict. In particular, uncontrolled quantifier instantiation can lead to unexpected incompleteness and even non-termination. E-matching is the most widely-used approach for controlling quantifier instantiation, but when axiomatisations are complex, even experts cannot tell if their use of E-matching guarantees completeness or termination. This paper presents a new formal model that facilitates the proof, once and for all, that giving a complex E-matching-based axiomatisation to an SMT solver, such as Z3 or cvc5, will not cause non-termination. Key to our technique is an operational semantics for solver behaviour that models how the E-matching rules common to most solvers are used to determine when quantifier instantiations are enabled, but abstracts over irrelevant details of individual solvers. We demonstrate the effectiveness of our technique by presenting a termination proof for a set theory axiomatisation adapted from those used in the Dafny and Viper verifiers., Comment: extended version of IJCAR 2024 publication

We present an analysis of rest-frame UV continuum slopes, $\beta$, using a sample of 1011 galaxies at $6.5

The first public 0.9-4.4{\mu}m NIRCam images of the North Ecliptic Pole (NEP) Time Domain Field (TDF) uncovered galaxies displaying point-source features in their cores as seen in the longer wavelength filters. We visually identified a sample of 66 galaxies (~1 galaxy per arcmin2) with point-like cores and have modeled their two-dimensional light profiles with GalFit, identifying 16 galactic nuclei with measurable point-source components. GalFit suggests the visual sample is a mix of both compact stellar bulge and point-source galaxy cores. This core classification is complemented by spectral energy distribution (SED) modeling to infer the sample's active galactic nucleus (AGN) and host-galaxy parameters. For galaxies with measurable point-source components, the median fractional AGN contribution to their 0.1-30.0{\mu}m flux is 0.44, and 14/16 are color-classified AGN. We conclude that near-infrared point-source galaxy cores are signatures of AGN. In addition, we define an automated sample-selection criterion to identify these point-source features. These criteria can be used in other extant and future NIRCam images to streamline the search for galaxies with unresolved IR-luminous AGN. The James Webb Space Telescope's superb angular resolution and sensitivity at infrared wavelengths is resurrecting the morphological identification of AGN., Comment: 17 pages, 9 figures, 2 tables

We present a new parametric lens model for the G165.7+67.0 galaxy cluster, which was discovered with $Planck$ through its bright submillimeter flux, originating from a pair of extraordinary dusty star-forming galaxies (DSFGs) at $z\approx 2.2$. Using JWST and interferometric mm/radio observations, we characterize the intrinsic physical properties of the DSFGs, which are separated by only $\sim 1^{\prime\prime}$ (8 kpc) and a velocity difference $\Delta V \lesssim 600~{\rm km}~{\rm s}^{-1}$ in the source plane, and thus likely undergoing a major merger. Boasting intrinsic star formation rates ${\rm SFR}_{\rm IR} = 320 \pm 70$ and $400 \pm 80~ M_\odot~{\rm yr}^{-1}$, stellar masses ${\rm log}[M_\star/M_\odot] = 10.2 \pm 0.1$ and $10.3 \pm 0.1$, and dust attenuations $A_V = 1.5 \pm 0.3$ and $1.2 \pm 0.3$, they are remarkably similar objects. We perform spatially-resolved pixel-by-pixel SED fitting using rest-frame near-UV to near-IR imaging from JWST/NIRCam for both galaxies, resolving some stellar structures down to 100 pc scales. Based on their resolved specific SFRs and $UVJ$ colors, both DSFGs are experiencing significant galaxy-scale star formation events. If they are indeed interacting gravitationally, this strong starburst could be the hallmark of gas that has been disrupted by an initial close passage. In contrast, the host galaxy of the recently discovered triply-imaged SN H0pe has a much lower SFR than the DSFGs, and we present evidence for the onset of inside-out quenching and large column densities of dust even in regions of low specific SFR. Based on the intrinsic SFRs of the DSFGs inferred from UV through FIR SED modeling, this pair of objects alone is predicted to yield an observable $1.1 \pm 0.2~{\rm CCSNe~yr}^{-1}$, making this cluster field ripe for continued monitoring., Comment: 47 pages, 21 figures, 5 tables. Submitted to ApJ, comments welcome!

Automated program verifiers are typically implemented using an intermediate verification language (IVL), such as Boogie or Why3. A verifier front-end translates the input program and specification into an IVL program, while the back-end generates proof obligations for the IVL program and employs an SMT solver to discharge them. Soundness of such verifiers therefore requires that the front-end translation faithfully captures the semantics of the input program and specification in the IVL program, and that the back-end reports success only if the IVL program is actually correct. For a verification tool to be trustworthy, these soundness conditions must be satisfied by its actual implementation, not just the program logic it uses. In this paper, we present a novel validation methodology that, given a formal semantics for the input language and IVL, provides formal soundness guarantees for front-end implementations. For each run of the verifier, we automatically generate a proof in Isabelle showing that the correctness of the produced IVL program implies the correctness of the input program. This proof can be checked independently from the verifier, in Isabelle, and can be combined with existing work on validating back-ends to obtain an end-to-end soundness result. Our methodology based on forward simulation employs several modularisation strategies to handle the large semantic gap between the input language and the IVL, as well as the intricacies of practical, optimised translations. We present our methodology for the widely-used Viper and Boogie languages. Our evaluation shows that it is effective in validating the translations performed by the existing Viper implementation., Comment: Extended version of PLDI 2024 publication

A bright ($m_{\rm F150W,AB}$=24 mag), $z=1.95$ supernova (SN) candidate was discovered in JWST/NIRCam imaging acquired on 2023 November 17. The SN is quintuply-imaged as a result of strong gravitational lensing by a foreground galaxy cluster, detected in three locations, and remarkably is the second lensed SN found in the same host galaxy. The previous lensed SN was called "Requiem", and therefore the new SN is named "Encore". This makes the MACS J0138.0$-$2155 cluster the first known system to produce more than one multiply-imaged SN. Moreover, both SN Requiem and SN Encore are Type Ia SNe (SNe Ia), making this the most distant case of a galaxy hosting two SNe Ia. Using parametric host fitting, we determine the probability of detecting two SNe Ia in this host galaxy over a $\sim10$ year window to be $\approx3\%$. These observations have the potential to yield a Hubble Constant ($H_0$) measurement with $\sim10\%$ precision, only the third lensed SN capable of such a result, using the three visible images of the SN. Both SN Requiem and SN Encore have a fourth image that is expected to appear within a few years of $\sim2030$, providing an unprecedented baseline for time-delay cosmography., Comment: Accepted, ApJL

Supernova (SN) H0pe is a gravitationally lensed, triply-imaged, Type Ia SN (SN Ia) discovered in James Webb Space Telescope imaging of the PLCK G165.7+67.0 cluster of galaxies. Well-observed multiply-imaged SNe provide a rare opportunity to constrain the Hubble constant ($H_0$), by measuring the relative time delay between the images and modeling the foreground mass distribution. SN H0pe is located at $z=1.783$, and is the first SN Ia with sufficient light curve sampling and long enough time delays for an $H_0$ inference. Here we present photometric time-delay measurements and SN properties of SN H0pe. Using JWST/NIRCam photometry we measure time delays of $\Delta t_{ab}=-116.6^{+10.8}_{-9.3}$ and $\Delta t_{cb}=-48.6^{+3.6}_{-4.0}$ observer-frame days relative to the last image to arrive (image 2b; all uncertainties are $1\sigma$), which corresponds to a $\sim5.6\%$ uncertainty contribution for $H_0$ assuming $70 \rm{km s^{-1} Mpc^{-1}}$. We also constrain the absolute magnification of each image to $\mu_{a}=4.3^{+1.6}_{-1.8}$, $\mu_{b}=7.6^{+3.6}_{-2.6}$, $\mu_{c}=6.4^{+1.6}_{-1.5}$ by comparing the observed peak near-IR magnitude of SN H0pe to the non-lensed population of SNe Ia., Comment: Accepted in ApJ

Automatically interpreting CT scans can ease the workload of radiologists. However, this is challenging mainly due to the scarcity of adequate datasets and reference standards for evaluation. This study aims to bridge this gap by introducing a novel evaluation framework, named ``GPTRadScore''. This framework assesses the capabilities of multi-modal LLMs, such as GPT-4 with Vision (GPT-4V), Gemini Pro Vision, LLaVA-Med, and RadFM, in generating descriptions for prospectively-identified findings. By employing a decomposition technique based on GPT-4, GPTRadScore compares these generated descriptions with gold-standard report sentences, analyzing their accuracy in terms of body part, location, and type of finding. Evaluations demonstrated a high correlation with clinician assessments and highlighted its potential over traditional metrics, such as BLEU, METEOR, and ROUGE. Furthermore, to contribute to future studies, we plan to release a benchmark dataset annotated by clinicians. Using GPTRadScore, we found that while GPT-4V and Gemini Pro Vision fare better, their performance revealed significant areas for improvement, primarily due to limitations in the dataset used for training these models. To demonstrate this potential, RadFM was fine-tuned and it resulted in significant accuracy improvements: location accuracy rose from 3.41\% to 12.8\%, body part accuracy from 29.12\% to 53\%, and type accuracy from 9.24\% to 30\%, thereby validating our hypothesis.

We introduce a new trajectory optimization method for robotic grasping based on a point-cloud representation of robots and task spaces. In our method, robots are represented by 3D points on their link surfaces. The task space of a robot is represented by a point cloud that can be obtained from depth sensors. Using the point-cloud representation, goal reaching in grasping can be formulated as point matching, while collision avoidance can be efficiently achieved by querying the signed distance values of the robot points in the signed distance field of the scene points. Consequently, a constrained nonlinear optimization problem is formulated to solve the joint motion and grasp planning problem. The advantage of our method is that the point-cloud representation is general to be used with any robot in any environment. We demonstrate the effectiveness of our method by performing experiments on a tabletop scene and a shelf scene for grasping with a Fetch mobile manipulator and a Franka Panda arm. The project page is available at \url{https://irvlutd.github.io/GraspTrajOpt}, Comment: Published in IROS 2024

In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in label quality. These labelers typically output only presence labels, sometimes with binary uncertainty indicators, which limits their usefulness. Supervised deep learning models have also been developed for report labeling but lack adaptability, similar to rule-based systems. In this work, we present MAPLEZ (Medical report Annotations with Privacy-preserving Large language model using Expeditious Zero shot answers), a novel approach leveraging a locally executable Large Language Model (LLM) to extract and enhance findings labels on CXR reports. MAPLEZ extracts not only binary labels indicating the presence or absence of a finding but also the location, severity, and radiologists' uncertainty about the finding. Over eight abnormalities from five test sets, we show that our method can extract these annotations with an increase of 3.6 percentage points (pp) in macro F1 score for categorical presence annotations and more than 20 pp increase in F1 score for the location annotations over competing labelers. Additionally, using the combination of improved annotations and multi-type annotations in classification supervision, we demonstrate substantial advancements in model quality, with an increase of 1.1 pp in AUROC over models trained with annotations from the best alternative approach. We share code and annotations., Comment: Code and data: https://github.com/rsummers11/CADLab/tree/master/MAPLEZ_LLM_report_labeler/

Accurate training labels are a key component for multi-class medical image segmentation. Their annotation is costly and time-consuming because it requires domain expertise. This work aims to develop a dual-branch network and automatically improve training labels for multi-class image segmentation. Transfer learning is used to train the network and improve inaccurate weak labels sequentially. The dual-branch network is first trained by weak labels alone to initialize model parameters. After the network is stabilized, the shared encoder is frozen, and strong and weak decoders are fine-tuned by strong and weak labels together. The accuracy of weak labels is iteratively improved in the fine-tuning process. The proposed method was applied to a three-class segmentation of muscle, subcutaneous and visceral adipose tissue on abdominal CT scans. Validation results on 11 patients showed that the accuracy of training labels was statistically significantly improved, with the Dice similarity coefficient of muscle, subcutaneous and visceral adipose tissue increased from 74.2% to 91.5%, 91.2% to 95.6%, and 77.6% to 88.5%, respectively (p<0.05). In comparison with our earlier method, the label accuracy was also significantly improved (p<0.05). These experimental results suggested that the combination of the dual-branch network and transfer learning is an efficient means to improve training labels for multi-class segmentation., Comment: 7 Pages, 3 figures, SPIE Medical Imaging 2024, Computer-aided diagnosis

In the abstract Tile Assembly Model, self-assembling systems consisting of tiles of different colors can form structures on which colored patterns are ``painted.'' We explore the complexity, in terms of the numbers of unique tile types required, of assembling various patterns. We first demonstrate how to efficiently self-assemble a set of simple patterns, then show tight bounds on the tile type complexity of self-assembling 2-colored patterns on the surfaces of square assemblies. Finally, we demonstrate an exponential gap in tile type complexity of self-assembling an infinite series of patterns between systems restricted to one plane versus those allowed two planes.

We present CLIPPER+, an algorithm for finding maximal cliques in unweighted graphs for outlier-robust global registration. The registration problem can be formulated as a graph and solved by finding its maximum clique. This formulation leads to extreme robustness to outliers; however, finding the maximum clique is an NP-hard problem, and therefore approximation is required in practice for large-size problems. The performance of an approximation algorithm is evaluated by its computational complexity (the lower the runtime, the better) and solution accuracy (how close the solution is to the maximum clique). Accordingly, the main contribution of CLIPPER+ is outperforming the state-of-the-art in accuracy while maintaining a relatively low runtime. CLIPPER+ builds on prior work (CLIPPER [1] and PMC [2]) and prunes the graph by removing vertices that have a small core number and cannot be a part of the maximum clique. This will result in a smaller graph, on which the maximum clique can be estimated considerably faster. We evaluate the performance of CLIPPER+ on standard graph benchmarks, as well as synthetic and real-world point cloud registration problems. These evaluations demonstrate that CLIPPER+ has the highest accuracy and can register point clouds in scenarios where over $99\%$ of associations are outliers. Our code and evaluation benchmarks are released at https://github.com/ariarobotics/clipperp.

Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD). However, manual assessment of CAC often requires radiological expertise, time, and invasive imaging techniques. The purpose of this multicenter study is to validate an automated cardiac plaque detection model using a 3D multiclass nnU-Net for gated and non-gated non-contrast chest CT volumes. CT scans were performed at three tertiary care hospitals and collected as three datasets, respectively. Heart, aorta, and lung segmentations were determined using TotalSegmentator, while plaques in the coronary arteries and heart valves were manually labeled for 801 volumes. In this work we demonstrate how the nnU-Net semantic segmentation pipeline may be adapted to detect plaques in the coronary arteries and valves. With a linear correction, nnU-Net deep learning methods may also accurately estimate Agatston scores on chest non-contrast CT scans. Compared to manual Agatson scoring, automated Agatston scoring indicated a slope of the linear regression of 0.841 with an intercept of +16 HU (R2 = 0.97). These results are an improvement over previous work assessing automated Agatston score computation in non-gated CT scans., Comment: Accepted at SPIE Medical Imaging 2024

Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize. For the management of patients with PPGLs, CT is the preferred modality of choice for precise localization and estimation of their progression. However, due to the myriad variations in size, morphology, and appearance of the tumors in different anatomical regions, radiologists are posed with the challenge of accurate detection of PPGLs. Since clinicians also need to routinely measure their size and track their changes over time across patient visits, manual demarcation of PPGLs is quite a time-consuming and cumbersome process. To ameliorate the manual effort spent for this task, we propose an automated method to detect PPGLs in CT studies via a proxy segmentation task. As only weak annotations for PPGLs in the form of prospectively marked 2D bounding boxes on an axial slice were available, we extended these 2D boxes into weak 3D annotations and trained a 3D full-resolution nnUNet model to directly segment PPGLs. We evaluated our approach on a dataset consisting of chest-abdomen-pelvis CTs of 255 patients with confirmed PPGLs. We obtained a precision of 70% and sensitivity of 64.1% with our proposed approach when tested on 53 CT studies. Our findings highlight the promising nature of detecting PPGLs via segmentation, and furthers the state-of-the-art in this exciting yet challenging area of rare cancer management., Comment: Accepted at SPIE 2024. arXiv admin note: text overlap with arXiv:2402.00175

Multi-parametric MRI of the body is routinely acquired for the identification of abnormalities and diagnosis of diseases. However, a standard naming convention for the MRI protocols and associated sequences does not exist due to wide variations in imaging practice at institutions and myriad MRI scanners from various manufacturers being used for imaging. The intensity distributions of MRI sequences differ widely as a result, and there also exists information conflicts related to the sequence type in the DICOM headers. At present, clinician oversight is necessary to ensure that the correct sequence is being read and used for diagnosis. This poses a challenge when specific series need to be considered for building a cohort for a large clinical study or for developing AI algorithms. In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis. In our pilot work, our 3D DenseNet-121 model achieved an F1 score of 99.5% at differentiating 5 common MRI sequences obtained by three Siemens scanners (Aera, Verio, Biograph mMR). To the best of our knowledge, we are the first to develop an automated method for the 3D classification of MRI sequences in the chest, abdomen, and pelvis, and our work has outperformed the previous state-of-the-art MRI series classifiers., Comment: Accepted at SPIE 2024

Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN LHC during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that $O(100)$ ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost., Comment: 13 pages, 3 figures, 3 tables. Mach. Learn.: Sci. Technol (2024)

The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. CT is routinely used to measure the size of lesions in the bones. However, they can be difficult to spot due to the wide variations in their sizes, shapes, and appearances. Precise localization of such lesions would enable reliable tracking of interval changes (growth, shrinkage, or unchanged status). To that end, an automated technique to detect bone lesions is highly desirable. In this pilot work, we developed a pipeline to detect bone lesions (lytic, blastic, and mixed) in CT volumes via a proxy segmentation task. First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks. Then, we trained a 3D full-resolution nnUNet model using these weak 3D annotations to segment the lesions and thereby detected them. Our automated method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data. To the best of our knowledge, we are the first to attempt the direct detection of bone lesions in CT via a proxy segmentation task., Comment: Accepted at SPIE 2024

In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical Efficacy (CE), often fall short in capturing the semantic intricacies of clinical contexts or overemphasize clinical details, undermining report clarity. To overcome these issues, our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs), like GPT-3.5 and GPT-4 1. Utilizing In-Context Instruction Learning (ICIL) and Chain of Thought (CoT) reasoning, our approach aligns LLM evaluations with radiologist standards, enabling detailed comparisons between human and AI generated reports. This is further enhanced by a Regression model that aggregates sentence evaluation scores. Experimental results show that our "Detailed GPT-4 (5-shot)" model achieves a 0.48 score, outperforming the METEOR metric by 0.19, while our "Regressed GPT-4" model shows even greater alignment with expert evaluations, exceeding the best existing metric by a 0.35 margin. Moreover, the robustness of our explanations has been validated through a thorough iterative strategy. We plan to publicly release annotations from radiology experts, setting a new standard for accuracy in future assessments. This underscores the potential of our approach in enhancing the quality assessment of AI-driven medical reports.

We formulate a mathematical framework for designing a self-tuning network control architecture, and propose a computationally-feasible greedy algorithm for online architecture optimization. In this setting, the locations of active sensors and actuators in the network, as well as the feedback control policy are jointly adapted using all available information about the network states and dynamics to optimize a performance criterion. We show that the case with full-state feedback can be solved with dynamic programming, and in the linear-quadratic setting, the optimal cost functions and policies are piecewise quadratic and piecewise linear, respectively. Our framework is extended for joint sensor and actuator selection for dynamic output feedback control with both control performance and architecture costs. For large networks where exhaustive architecture search is prohibitive, we describe a greedy heuristic for actuator selection and propose a greedy swapping algorithm for joint sensor and actuator selection. Via numerical experiments, we demonstrate a dramatic performance improvement of greedy self-tuning architectures over fixed architectures. Our general formulation provides an extremely rich and challenging problem space with opportunities to apply a wide variety of approximation methods from stochastic control, system identification, reinforcement learning, and static architecture design for practical model-based control., Comment: 12 pages, submitted to IEEE-TCNS. arXiv admin note: text overlap with arXiv:2301.06699

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it cumbersome to identify LNs, which reside outside of most organs. Methods: We propose to segment LNs in the mediastinum by leveraging the anatomical priors of 28 different structures (e.g., lung, trachea etc.) generated by the public TotalSegmentator tool. The CT volumes from 89 patients available in the public NIH CT Lymph Node dataset were used to train three 3D nnUNet models to segment LNs. The public St. Olavs dataset containing 15 patients (out-of-training-distribution) was used to evaluate the segmentation performance. Results: For the 15 test patients, the 3D cascade nnUNet model obtained the highest Dice score of 72.2 +- 22.3 for mediastinal LNs with short axis diameter $\geq$ 8mm and 54.8 +- 23.8 for all LNs respectively. These results represent an improvement of 10 points over a current approach that was evaluated on the same test dataset. Conclusion: To our knowledge, we are the first to harness 28 distinct anatomical priors to segment mediastinal LNs, and our work can be extended to other nodal zones in the body. The proposed method has immense potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans., Comment: Submitted to CARS 2024

Purpose: Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volume measures of muscle and fat are associated with important clinical outcomes, such as cardiovascular events, fractures, and death. This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool. Methods: We assessed the tools across 900 CT series from the publicly available SAROS dataset, focusing on muscle, subcutaneous fat, and visceral fat. The Dice score was employed to assess accuracy in subcutaneous fat and muscle segmentation. Due to the lack of ground truth segmentations for visceral fat, Cohen's Kappa was utilized to assess segmentation agreement between the tools. Results: Our Internal tool achieved a 3% higher Dice (83.8 vs. 80.8) for subcutaneous fat and a 5% improvement (87.6 vs. 83.2) for muscle segmentation respectively. A Wilcoxon signed-rank test revealed that our results were statistically different with p<0.01. For visceral fat, the Cohen's kappa score of 0.856 indicated near-perfect agreement between the two tools. Our internal tool also showed very strong correlations for muscle volume (R^2=0.99), muscle attenuation (R^2=0.93), and subcutaneous fat volume (R^2=0.99) with a moderate correlation for subcutaneous fat attenuation (R^2=0.45). Conclusion: Our findings indicated that our Internal tool outperformed TotalSegmentator in measuring subcutaneous fat and muscle. The high Cohen's Kappa score for visceral fat suggests a reliable level of agreement between the two tools. These results demonstrate the potential of our tool in advancing the accuracy of body composition analysis.