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We study the implementability problem for an expressive class of symbolic communication protocols involving multiple participants. Our symbolic protocols describe infinite states and data values using dependent refinement predicates. Implementability asks whether a global protocol specification admits a distributed, asynchronous implementation, namely one for each participant, that is deadlock-free and exhibits the same behavior as the specification. We provide a unified explanation of seemingly disparate sources of non-implementability through a precise semantic characterization of implementability for infinite protocols. Our characterization reduces the problem of implementability to (co)reachability in the global protocol restricted to each participant. This compositional reduction yields the first sound and relatively complete algorithm for checking implementability of symbolic protocols. We use our characterization to show that for finite protocols, implementability is co-NP-complete for explicit representations and PSPACE-complete for symbolic representations. The finite, explicit fragment subsumes a previously studied fragment of multiparty session types for which our characterization yields a PTIME decision procedure, improving upon a prior PSPACE upper bound.

An open question in \emph{Imprecise Probabilistic Machine Learning} is how to empirically derive a credal region (i.e., a closed and convex family of probabilities on the output space) from the available data, without any prior knowledge or assumption. In classification problems, credal regions are a tool that is able to provide provable guarantees under realistic assumptions by characterizing the uncertainty about the distribution of the labels. Building on previous work, we show that credal regions can be directly constructed using conformal methods. This allows us to provide a novel extension of classical conformal prediction to problems with ambiguous ground truth, that is, when the exact labels for given inputs are not exactly known. The resulting construction enjoys desirable practical and theoretical properties: (i) conformal coverage guarantees, (ii) smaller prediction sets (compared to classical conformal prediction regions) and (iii) disentanglement of uncertainty sources (epistemic, aleatoric). We empirically verify our findings on both synthetic and real datasets.

In this paper, we present a detailed analysis of the IRS 17 filament within the intermediate-mass protocluster IRAS 08448-4343 (of $\sim\,10^3\,\rm L_{\odot}$), using ALMA data from the ATOMS 3-mm and QUARKS 1.3-mm surveys. The IRS 17 filament, which spans $\sim$54000 au ($0.26\,\rm pc$) in length and $\sim$4000 au ($0.02\,\rm pc$) in width, exhibits a complex, multi-component velocity field, and harbours hierarchical substructures. These substructures include three bundles of seven velocity-coherent fibers, and 29 dense ($n\sim 10^8\,\rm cm^{-3}$) condensations. The fibers have a median length of $\sim 4500\,\rm au$ and a median width of $\sim 1400\,\rm au$. Among these fibers, four are identified as ``fertile", each hosting at least three dense condensations, which are regarded as the ``seeds" of star formation. While the detected cores are randomly spaced within the IRS\,17 filament based on the 3-mm dust continuum image, periodic spacing ($\sim1600\,\rm au$) of condensations is observed in the fertile fibers according to the 1.3-mm dust map, consistent with the predictions of linear isothermal cylinder fragmentation models. These findings underscore the crucial role of fibers in star formation and suggest a hierarchical fragmentation process that extends from the filament to the fibers, and ultimately, to the smallest-scale condensations., Comment: 19 pages, 10 figures, 4 tables, accepted by ApJ

ALMA-IMF observed 15 massive protoclusters capturing multiple spectral lines and the continuum emission. We focus on the G351.77 protocluster ($\sim$ 2500 M$_{\odot}$, estimated from single-dish continuum observations) located at 2 kpc. We trace the dense gas emission and kinematics with N$_2$H$^+$ (1-0) at $\sim$ 4 kau resolution. We estimate an N$_2$H$^+$ relative abundance $\sim (1.7 \pm 0.5) \times 10^{-10}$. We decompose the N$_2$H$^+$ emission into up to two velocity components, highlighting the kinematic complexity in the dense gas. By examining the position-velocity (PV) diagrams on small scales, we observe clear inflow signatures (V-shapes) associated with dense cores. The most prominent V-shape has a mass inflow rate of $\sim 9.8 \times 10^{-4}$ M$_{\odot}$ yr$^{-1}$ and a short timescale of $\sim$ 15.6 kyr. We also observe V-shapes without associated cores. This suggests both that cores or centers of accretion exist below the 1.3 mm detection limit, and that the V-shapes may be viable tracers of very early accretion and star formation on $\sim$ 4 kau scales. The large-scale PV diagram shows that the protocluster is separated into 2 principal velocity structures. Combined with smaller scale DCN and H$_2$CO emission, we propose a scenario of larger scale slow contraction with rotation in the center based on simple toy models. This scenario leads the suggestion of outside-in evolution of the protocluster as it collapses. The gas depletion times implied by the V-shapes are short ($\sim$ 0.3 Myr), requiring either very fast cluster formation, and/or continuous mass feeding of the protocluster. The latter is possible via the Mother Filament G351.77 is forming out of. The similarities in the properties of G351.77 and the recently published work in G353.41 indicate that many of the physical conditions inferred via the ALMA-IMF N$_2$H$^+$ observations may be generic to protoclusters., Comment: Submitted in A&A, 28 pages, 31 figures, 4 interactive figures, 7 tables

The composition of protoplanetary disks, and hence the initial conditions of planet formation, may be strongly influenced by the infall and thermal processing of material during the protostellar phase. Composition of dust and ice in protostellar envelopes, shaped by energetic processes driven by the protostar, serves as the fundamental building material for planets and complex organic molecules. As part of the JWST GO program, "Investigating Protostellar Accretion" (IPA), we observed an intermediate-mass protostar HOPS 370 (OMC2-FIR3) using NIRSpec/IFU and MIRI/MRS. This study presents the gas and ice phase chemical inventory revealed with the JWST in the spectral range of $\sim$2.9 to 28 $\mu$m and explores the spatial variation of volatile ice species in the protostellar envelope. We find evidence for thermal processing of ice species throughout the inner envelope. We present the first high-spatial resolution ($\sim 80$ au) maps of key volatile ice species H$_{2}$O, CO$_{2}$, $^{13}$CO$_2$, CO, and OCN$^-$, which reveal a highly structured and inhomogeneous density distribution of the protostellar envelope, with a deficiency of ice column density that coincides with the jet/outflow shocked knots. Further, we observe high relative crystallinity of H$_{2}$O ice around the shocked knot seen in the H$_2$ and OH wind/outflow, which can be explained by a lack of outer colder material in the envelope along the line of sight due to the irregular structure of the envelope. These observations show clear evidence of thermal processing of the ices in the inner envelope, close to the outflow cavity walls, heated by the luminous protostar., Comment: Submitted to ApJ. Main text:16 pages with 11 figures

Hub-filament systems are considered as natural sites for high-mass star formation. Kinematic analysis of the surroundings of hub-filaments is essential to better understand high-mass star formation within such systems. In this work, we present a detailed study of the massive Galactic protocluster IRAS 15394$-$5358, using continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey. The 3~mm dust continuum map reveals the fragmentation of the massive ($\rm M=843~M_{\odot}$) clump into six cores. The core C-1A is the largest (radius = 0.04~pc), the most massive ($\rm M=157~M_{\odot}$), and lies within the dense central region, along with two smaller cores ($\rm M=7~and~3~M_{\odot}$). The fragmentation process is consistent with the thermal Jeans fragmentation mechanism and virial analysis shows that all the cores have small virial parameter values ($\rm \alpha_{vir}<<2$), suggesting that the cores are gravitationally bound. The mass vs. radius relation indicates that three cores can potentially form at least a single massive star. The integrated intensity map of $\rm H^{13}CO^{+}$ shows that the massive clump is associated with a hub-filament system, where the central hub is linked with four filaments. A sharp velocity gradient is observed towards the hub, suggesting a global collapse where the filaments are actively feeding the hub. We discuss the role of global collapse and the possible driving mechanisms for the massive star formation activity in the protocluster., Comment: 23 pages, 19 figures, accepted for publication in MNRAS

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

The stellar initial mass function (IMF) is critical to our understanding of star formation and the effects of young stars on their environment. On large scales, it enables us to use tracers such as UV or Halpha emission to estimate the star formation rate of a system and interpret unresolved star clusters across the universe. So far, there is little firm evidence of large-scale variations of the IMF, which is thus generally considered universal. Stars form from cores and it is now possible to estimate core masses and compare the core mass function (CMF) with the IMF, which it presumably produces. The goal of the ALMA-IMF large program is to measure the core mass function at high linear resolution (2700 au) in 15 typical Milky Way protoclusters spanning a mass range of 2500 to 32700 Msun. In this work, we used two different core extraction algorithms to extract about 680 gravitationally bound cores from these 15 protoclusters. We adopt per core temperature using the temperature estimate from the PPMAP Bayesian method. A power-law fit to the CMF of the sub-sample of cores above the 1.64 Msun completeness limit, 330 cores, through the maximum likelihood estimate technique yields a slope of 1.97 +/- 0.06, significantly flatter than the 2.35 Salpeter slope. Assuming a self-similar mapping between the CMF and the IMF, this result implies that these 15 high-mass protoclusters will generate atypical IMFs. This sample is the largest to date produced and analysed self-consistently, derived at matched physical resolution, with per-core temperature estimates and cores as massive as 150 Msun. We provide the raw source extraction catalogues and the source derived size, temperature, mass, and spectral indices in the 15 protoclusters., Comment: Accepted by A&A

A crucial aspect in addressing the challenge of measuring the core mass function, that is pivotal for comprehending the origin of the initial mass function, lies in constraining the temperatures of the cores. We aim to measure the luminosity, mass, column density and dust temperature of star-forming regions imaged by the ALMA-IMF large program. High angular resolution mapping is required to capture the properties of protostellar and pre-stellar cores and to effectively separate them from larger features, such as dusty filaments. We employed the point process mapping (PPMAP) technique, enabling us to perform spectral energy distribution fitting of far-infrared and submillimeter observations across the 15 ALMA-IMF fields, at an unmatched 2.5" angular resolution. By combining the modified blackbody model with near-infrared data, we derived bolometric luminosity maps. We estimated the errors impacting values of each pixel in the temperature, column density, and luminosity maps. Subsequently, we employed the extraction algorithm getsf on the luminosity maps in order to detect luminosity peaks and measure their associated masses. We obtained high-resolution constraints on the luminosity, dust temperature, and mass of protoclusters, that are in agreement with previously reported measurements made at a coarser angular resolution. We find that the luminosity-to-mass ratio correlates with the evolutionary stage of the studied regions, albeit with intra-region variability. We compiled a PPMAP source catalog of 313 luminosity peaks using getsf on the derived bolometric luminosity maps. The PPMAP source catalog provides constraints on the mass and luminosity of protostars and cores, although one source may encompass several objects. Finally, we compare the estimated luminosity-to-mass ratio of PPMAP sources with evolutionary tracks and discuss the limitations imposed by the 2.5" beam., Comment: 35 pages, 19 figures, 5 tables Accepted by A&A

We use the H$41\alpha$ recombination line to create templates of the millimeter free-free emission in the ALMA-IMF continuum maps, which allows to separate it from dust emission. This method complements spectral-index information and extrapolation from centimeter wavelength maps. We use the derived maps to estimate the properties of up to 34 HII regions across the ALMA-IMF protoclusters. The hydrogen ionizing-photon rate $Q_0$ and spectral types follow the evolutionary trend proposed by Motte et al. The youngest protoclusters lack detectable ionized gas, followed by protoclusters with increasing numbers of OB stars. The total $Q_0$ increases from $\sim 10^{45}$ s$^{-1}$ to $> 10^{49}$ s$^{-1}$. We used the adjacent He$41\alpha$ line to measure the relative number abundances of helium, finding values consistent with the Galactic interstellar medium, although a few outliers are discussed. A search for sites of maser amplification of the H$41\alpha$ line returned negative results. We looked for possible correlations between the electron densities ($n_e$), emission measures (EM), and $Q_0$ with HII region size $D$. The latter are the better correlated, with $Q_0 \propto D^{2.49\pm0.18}$. This favors interpretations where smaller ultracompact HII regions are not necessarily the less dynamically evolved versions of larger ones, but rather are ionized by less massive stars. Moderate correlations were found between dynamical width $\Delta V_\mathrm{dyn}$ with $D$ and $Q_0$. $\Delta V_\mathrm{dyn}$ increases from about one to two times the ionized-gas sound speed. Finally, an outlier HII region south of W43-MM2 is discussed. We suggest that this source could harbor an embedded stellar or disk wind., Comment: Accepted to AAS Journals (ApJS). Data available at https://dataverse.harvard.edu/dataverse/h41a-freefree

Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of $\sim 50$ qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and therefore how much infidelity is suffered) in generating highly-entangled states. Here, we describe recent hardware upgrades to Quantinuum's H2 quantum computer enabling it to operate on up to $56$ qubits with arbitrary connectivity and $99.843(5)\%$ two-qubit gate fidelity. Utilizing the flexible connectivity of H2, we present data from random circuit sampling in highly connected geometries, doing so at unprecedented fidelities and a scale that appears to be beyond the capabilities of state-of-the-art classical algorithms. The considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the QCCD architecture as continued progress is made towards building larger machines., Comment: Includes minor updates to the text and an updated author list to include researchers who made technical contributions in upgrading the machine to 56 qubits but were left off the original version by mistake

The Orion Molecular Cloud complex, one of the nearest (D = 406 pc) and most extensively studied massive star-forming regions, is ideal for constraining the physics of stellar feedback, but its ~12 deg diameter on the sky requires a dedicated approach to mapping ionized gas structures within and around the nebula. The Sloan Digital Sky Survey (SDSS-V) Local Volume Mapper (LVM) is a new optical integral field unit (IFU) that will map the ionized gas within the Milky Way and Local Group galaxies, covering 4300 deg^2 of the sky with the new LVM Instrument. We showcase optical emission line maps from LVM covering 12 deg^2 inside of the Orion belt region, with 195,000 individual spectra combined to produce images at 0.07 pc (35.3") resolution. This is the largest IFU map made (to date) of the Milky Way, and contains well-known nebulae (the Horsehead Nebula, Flame Nebula, IC 434, and IC 432), as well as ionized interfaces with the neighboring dense Orion B molecular cloud. We resolve the ionization structure of each nebula, and map the increase in both the [SII]/Ha and [NII]/Ha line ratios at the outskirts of nebulae and along the ionization front with Orion B. [OIII] line emission is only spatially resolved within the center of the Flame Nebula and IC 434, and our ~0.1 pc scale line ratio diagrams show how variations in these diagnostics are lost as we move from the resolved to the integrated view of each nebula. We detect ionized gas emission associated with the dusty bow wave driven ahead of the star sigma Orionis, where the stellar wind interacts with the ambient interstellar medium. The Horsehead Nebula is seen as a dark occlusion of the bright surrounding photo-disassociation region. This small glimpse into Orion only hints at the rich science that will be enabled by the LVM., Comment: 13 pages, 10 figures, accepted for publication in A&A

Spatial filtering based on multiple-input multiple-output (MIMO) processing is a powerful method for jammer mitigation. In principle, a MIMO receiver can null the interference of a single-antenna jammer at the cost of only one degree of freedom - if the number of receive antennas is large, communication performance is barely affected. In this paper, we show that the potential for MIMO jammer mitigation based on the digital outputs of finite-resolution analog-to-digital converters (ADCs) is fundamentally worse: Strong jammers will either cause the ADCs to saturate (when the ADCs' quantization range is small) or drown legitimate communication signals in quantization noise (when the ADCs' quantization range is large). We provide a fundamental bound on the mutual information between the quantized receive signal and the legitimate transmit signal. Our bound shows that, for any fixed ADC resolution, the mutual information tends to zero as the jammer power tends to infinity, regardless of the quantization strategy. Our bound also confirms the intuition that for every 6.02 dB increase in jamming power, the ADC resolution must be increased by 1 bit in order to prevent the mutual information from vanishing., Comment: Accepted for presentation at Asilomar 2024

Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain.

Quantum state teleportation is commonly used in designs for large-scale fault-tolerant quantum computers. Using Quantinuum's H2 trapped-ion quantum processor, we implement the first demonstration of a fault-tolerant state teleportation circuit for a quantum error correction code - in particular, the planar topological [[7,1,3]] color code, or Steane code. The circuits use up to 30 trapped ions at the physical layer qubits and employ real-time quantum error correction - decoding mid-circuit measurement of syndromes and implementing corrections during the protocol. We conduct experiments on several variations of logical teleportation circuits using both transversal gates and lattice surgery protocols. Among the many measurements we report on, we measure the logical process fidelity of the transversal teleportation circuit to be 0.975(2) and the logical process fidelity of the lattice surgery teleportation circuit to be 0.851(9). Additionally, we run a teleportation circuit that is equivalent to Knill-style quantum error correction and measure the process fidelity to be 0.989(2).

We implement logically encoded three-qubit circuits for the quantum Fourier transform (QFT), using the [[7,1,3]] Steane code, and benchmark the circuits on the Quantinuum H2-1 trapped-ion quantum computer. The circuits require multiple logical two-qubit gates, which are implemented transversally, as well as logical non-Clifford single-qubit rotations, which are performed by non-fault-tolerant state preparation followed by a teleportation gadget. First, we benchmark individual logical components using randomized benchmarking for the logical two-qubit gate, and a Ramsey-type experiment for the logical $T$ gate. We then implement the full QFT circuit, using two different methods for performing a logical control-$T$, and benchmark the circuits by applying it to each basis state in a set of bases that is sufficient to lower bound the process fidelity. We compare the logical QFT benchmark results to predictions based on the logical component benchmarks.

The ALMA-IMF Large Program provides multi-tracer observations of 15 Galactic massive protoclusters at matched sensitivity and spatial resolution. We focus on the dense gas kinematics of the G353.41 protocluster traced by N$_2$H$^+$ (1$-$0), with an spatial resolution $\sim$0.02 pc. G353.41, at a distance of $\sim$2 kpc, has a mass of $\sim$2500 M$_{\odot}$ within $1.3\times1.3$ pc$^2$. We extract the N$_2$H$^+$ isolated line component and we decompose it by fitting up to 3 Gaussian velocity components. This allows us to identify velocity structures that are impossible to identify in the traditional position-velocity diagram. We identify multiple velocity gradients on large ($\sim$1 pc) and small scales ($\sim$0.2 pc). We find good agreement between the N$_2$H$^+$ velocities and the previously reported DCN core velocities, suggesting that cores are kinematically coupled to the dense gas in which they form. We measure 9 converging ``V-shaped'' velocity gradients ($\sim20$ km/s/pc), located in filaments, which are sometimes associated with cores near their point of convergence. The average inflow timescale is $\sim67$ kyr, or about twice the free-fall time of cores in the same area ($\sim33$ kyr) but substantially shorter than protostar lifetime estimates ($\sim$0.5 Myr). We derive mass accretion rates in the range of $(0.35-8.77)\,\times\,10^{-4}$ M$_{\odot}$/yr. This feeding might lead to further filament collapse and formation of new cores. We suggest that the protocluster is collapsing on large scales, but the velocity signature of collapse is slow compared to pure free-fall. These data are consistent with a comparatively slow global protocluster contraction under gravity, and faster core formation within, suggesting the formation of multiple generations of stars over the protocluster lifetime., Comment: Accepted in A&A, 22 pages, 21 figures, 1 interactive figure, 4 tables

We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of resorting to possibly "hallucinating" a non-sensical or incorrect answer. Building on earlier approaches that use self-consistency as a more reliable measure of model confidence, we propose using the LLM itself to self-evaluate the similarity between each of its sampled responses for a given query. We then further leverage conformal prediction techniques to develop an abstention procedure that benefits from rigorous theoretical guarantees on the hallucination rate (error rate). Experimentally, our resulting conformal abstention method reliably bounds the hallucination rate on various closed-book, open-domain generative question answering datasets, while also maintaining a significantly less conservative abstention rate on a dataset with long responses (Temporal Sequences) compared to baselines using log-probability scores to quantify uncertainty, while achieveing comparable performance on a dataset with short answers (TriviaQA). To evaluate the experiments automatically, one needs to determine if two responses are equivalent given a question. Following standard practice, we use a thresholded similarity function to determine if two responses match, but also provide a method for calibrating the threshold based on conformal prediction, with theoretical guarantees on the accuracy of the match prediction, which might be of independent interest.

The promise of quantum computers hinges on the ability to scale to large system sizes, e.g., to run quantum computations consisting of more than 100 million operations fault-tolerantly. This in turn requires suppressing errors to levels inversely proportional to the size of the computation. As a step towards this ambitious goal, we present experiments on a trapped-ion QCCD processor where, through the use of fault-tolerant encoding and error correction, we are able to suppress logical error rates to levels below the physical error rates. In particular, we entangled logical qubits encoded in the [[7,1,3]] code with error rates 9.8 times to 500 times lower than at the physical level, and entangled logical qubits encoded in a [[12,2,4]] code with error rates 4.7 times to 800 times lower than at the physical level, depending on the judicious use of post-selection. Moreover, we demonstrate repeated error correction with the [[12,2,4]] code, with logical error rates below physical circuit baselines corresponding to repeated CNOTs, and show evidence that the error rate per error correction cycle, which consists of over 100 physical CNOTs, approaches the error rate of two physical CNOTs. These results signify an important transition from noisy intermediate scale quantum computing to reliable quantum computing, and demonstrate advanced capabilities toward large-scale fault-tolerant quantum computing., Comment: 13 pages, 8 figures

Leveraging the high resolution, high sensitivity, and wide frequency coverage of the Atacama Large Millimeter/submillimeter Array (ALMA), the QUARKS survey, standing for "Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures", is observing 139 massive star-forming clumps at ALMA Band 6 ($\lambda\sim$ 1.3 mm). This paper introduces the Atacama Compact Array (ACA) 7-m data. Combining multi-wavelength data, we provide the first edition of QUARKS atlas, offering insights into the multiscale and multiphase interstellar medium in high-mass star formation. The ACA 1.3 mm catalog includes 207 continuum sources that are called ACA sources. Their gas kinetic temperatures are estimated using three formaldehyde (H$_2$CO) transitions with a non-LTE radiation transfer model, and the mass and density are derived from a dust emission model. The ACA sources are massive (16-84 percentile values of 6-160 $M_{\odot}$), gravity-dominated ($M\propto R^{1.1}$) fragments within massive clumps, with supersonic turbulence ($\mathcal{M}>1$) and embedded star-forming protoclusters. We find a linear correlation between the masses of the fragments and the massive clumps, with a ratio of 6% between the two. When considering the fragments as representative of dense gas, the ratio indicates a dense gas fraction (DGF) of 6%, although with a wide scatter ranging from 1% to 10%. If we consider the QUARKS massive clumps to be what is observed at various scales, then the size-independent DGF indicates a self-similar fragmentation or collapsing mode in protocluster formation. With the ACA data over four orders of magnitude of luminosity-to-mass ratio ($L/M$), we find that the DGF increases significantly with $L/M$, which indicates clump evolutionary stage. We observed a limited fragmentation at the subclump scale, which can be explained by dynamic global collapse process., Comment: 24 pages, 7 figures. Accepted for publication in Research in Astronomy and Astrophysics. QUARKS atlas link: https://drive.google.com/file/d/1KTqXxCDduYepvLd9kIvZVSSytK48OmfL/view?usp=sharing

While intensively studied, it remains unclear how the star formation (SF) in Infrared Dark Clouds (IRDCs) compares to that of nearby clouds. We study G351.77-0.53 (henceforth G351), a cluster-forming filamentary IRDC. We begin by characterizing its young stellar object (YSO) content. Based on the average parallax of likely members, we obtain a Gaia distance of $\sim\,2.0\pm0.14$ kpc, resolving the literature distance ambiguity. Using our Herschel-derived N(H$_2$) map, we measure a total gas mass of 10200 M$_{\odot}$ (within 11 pc$^2$) and the average line-mass profile of the entire filament, which we model as $\lambda =~1660 (w/\rm pc )^{0.62}\,\,M_{\odot}\,\rm{pc}^{-1}$. At $w < 0.63$ pc, our $\lambda$ profile is higher and has a steeper power-law index than $\lambda$ profiles extracted in Orion A and most of its substructures. Based on the YSOs inside the filament area, we estimate the SF efficiency (SFE) and SF rate (SFR). We calculate a factor of 5 incompleteness correction for our YSO catalog relative to Spitzer surveys of Orion A. The G351 SFE is $\sim 1.8$ times lower than that of Orion A and lower than the median value for local clouds. We measure SFR and gas masses to estimate the efficiency per free-fall time, $\epsilon _{\rm ff}$. We find that $\epsilon_{\rm ff}$ is $\sim$ 1.1 dex below the previously proposed mean local relation, and $\sim\,4.7\times$ below Orion A. These observations indicate that local SF-relations do not capture variations present in the Galaxy. We speculate that cloud youth and/or magnetic fields might account for the G351 inefficiency., Comment: 14 pages (13 main body; 1 appendix), 8 figures

Sites associated with high-mass star and cluster formation exhibit a so-called hot core phase, characterized by high temperatures and column densities of complex organic molecules. We built a comprehensive census of hot core candidates towards the ALMA-IMF protoclusters based on the detection of two CH3OCHO emission lines at 216.1 GHz. We used the source extraction algorithm GExt2D to identify peaks of methyl formate (CH3OCHO) emission that is a complex species commonly observed towards sites of star formation. We built up a catalog of 76 hot core candidates with masses ranging from about 0.2 to 80 Msun , of which 56 are new detections. A large majority of these objects are compact, rather circular, with deconvolved FWHM sizes of about 2300 au on average. About 30% of our sample of methyl formate sources have core masses above 8 Msun within sizes ranging from about 1000 au to 13400 au, which well correspond to archetypical hot cores. The origin of the CH3OCHO emission toward the lower-mass cores can be explained by a mixture of contribution from shocks, or may correspond to objects in a more evolved state, i.e. beyond the hot core stage. We find that the fraction of hot core candidates increases with the core mass. The large fraction of hot core candidates towards the most massive cores suggests that they rapidly enter the hot core phase and feedback effects from the forming protostar(s) impact their environment on short time-scales.

Credal sets are sets of probability distributions that are considered as candidates for an imprecisely known ground-truth distribution. In machine learning, they have recently attracted attention as an appealing formalism for uncertainty representation, in particular due to their ability to represent both the aleatoric and epistemic uncertainty in a prediction. However, the design of methods for learning credal set predictors remains a challenging problem. In this paper, we make use of conformal prediction for this purpose. More specifically, we propose a method for predicting credal sets in the classification task, given training data labeled by probability distributions. Since our method inherits the coverage guarantees of conformal prediction, our conformal credal sets are guaranteed to be valid with high probability (without any assumptions on model or distribution). We demonstrate the applicability of our method to natural language inference, a highly ambiguous natural language task where it is common to obtain multiple annotations per example.

The structure and composition of simple ices can be modified during stellar evolution by protostellar heating. Key to understanding the involved processes are thermal and chemical tracers that can diagnose the history and environment of the ice. The 15.2 $\mu$m bending mode of $^{12}$CO$_2$ has proven to be a valuable tracer of ice heating events but suffers from grain shape and size effects. A viable alternative tracer is the weaker $^{13}$CO$_2$ isotopologue band at 4.39 $\mu$m which has now become accessible at high S/N with the $\textit{James Webb}$ Space Telescope (JWST). We present JWST NIRSpec observations of $^{13}$CO$_2$ ice in five deeply embedded Class 0 sources spanning a wide range in luminosities (0.2 - 10$^4$ L$_{\odot}$ ) taken as part of the Investigating Protostellar Accretion Across the Mass Spectrum (IPA) program. The band profiles vary significantly, with the most luminous sources showing a distinct narrow peak at 4.38 $\mu$m. We first apply a phenomenological approach and show that a minimum of 3-4 Gaussian profiles are needed to fit the $^{13}$CO$_2$ absorption feature. We then combine these findings with laboratory data and show that a 15.2 $\mu$m $^{12}$CO$_2$ band inspired five-component decomposition can be applied for the isotopologue band where each component is representative of CO$_2$ ice in a specific molecular environment. The final solution consists of cold mixtures of CO$_2$ with CH$_3$OH, H$_2$O and CO as well as segregated heated pure CO$_2$ ice. Our results are in agreement with previous studies of the $^{12}$CO$_2$ ice band, further confirming that $^{13}$CO$_{2}$ is a useful alternative tracer of protostellar heating events. We also propose an alternative solution consisting only of heated CO$_2$:CH$_3$OH and CO$_2$:H$_2$O ices and warm pure CO$_2$ ice for decomposing the ice profiles of the two most luminous sources in our sample.

Multiparty session types (MSTs) are a type-based approach to verifying communication protocols, represented as global types in the framework. We present a precise subtyping relation for asynchronous MSTs with communicating state machines (CSMs) as implementation model. We address two problems: when can a local implementation safely substitute another, and when does an arbitrary CSM implement a global type? We define safety with respect to a given global type, in terms of subprotocol fidelity and deadlock freedom. Our implementation model subsumes existing work which considers local types with restricted choice. We exploit the connection between MST subtyping and refinement to formulate concise conditions that are directly checkable on the candidate implementations, and use them to show that both problems are decidable in polynomial time.

We aimed to measure the CMF in the evolved W33-Main star-forming protocluster to compare it with CMF recently obtained in other Galactic star-forming regions, including the ones included in the ALMA-IMF program. We used observations from the ALMA-IMF large program: 2'x2' maps of emission from the continuum and selected lines at 1.3mm and 3mm observed by the ALMA 12m only antennas. Our angular resolution was typically 1'', that is 2400au at a distance of 2.4kpc. The lines we analysed are CO(2-1), SiO(5-4), N2H+(1-0), H41alpha as well as He41alpha blended with C41alpha. We built a census of dense cores in the region, and we measured the associated CMF based on a core-dependent temperature value. We confirmed the 'evolved' status of W33-Main by identifiying three HII regions within the field, and to a lesser extent based on the number and extension of N2H+ filaments. We produced a filtered core catalog of 94 candidates, that we refined to take into account the contamination of the continuum by free-free and line emission, obtaining 80 cores with masses that range from 0.03 to 13.2Msun. We fitted the resulting high-mass end of the CMF with a single power law of the form N(log(M)) ~ M^alpha, obtaining alpha = -1.44(+0.16)(-0.22), slightly steeper but consistent with the Salpeter index. We categorized our cores in pre- and protostellar, mostly based on outlow activity and hot core nature. We found the prestellar CMF to be steeper than a Salpeter-like distribution, and the protostellar CMF to be slightly top heavy. We found a higher proportion of cores within the HII regions and their surroundings than in the rest of the field. We also found that the cores' masses were rather low (maximum mass of 13Msun).

This paper presents an overview of the QUARKS survey, which stands for `Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures'. The QUARKS survey is observing 139 massive clumps covered by 156 pointings at ALMA Band 6 ($\lambda\sim$ 1.3 mm). In conjunction with data obtained from the ALMA-ATOMS survey at Band 3 ($\lambda\sim$ 3 mm), QUARKS aims to carry out an unbiased statistical investigation of massive star formation process within protoclusters down to a scale of 1000 au. This overview paper describes the observations and data reduction of the QUARKS survey, and gives a first look at an exemplar source, the mini-starburst Sgr B2(M). The wide-bandwidth (7.5 GHz) and high-angular-resolution (~0.3 arcsec) observations of the QUARKS survey allow to resolve much more compact cores than could be done by the ATOMS survey, and to detect previously unrevealed fainter filamentary structures. The spectral windows cover transitions of species including CO, SO, N$_2$D$^+$, SiO, H$_{30}\alpha$, H$_2$CO, CH$_3$CN and many other complex organic molecules, tracing gas components with different temperatures and spatial extents. QUARKS aims to deepen our understanding of several scientific topics of massive star formation, such as the mass transport within protoclusters by (hub-)filamentary structures, the existence of massive starless cores, the physical and chemical properties of dense cores within protoclusters, and the feedback from already formed high-mass young protostars., Comment: 9 figures, 4 tables, accepted by RAA

Investigating Protostellar Accretion (IPA) is a JWST Cycle~1 GO program that uses NIRSpec IFU and MIRI MRS to obtain 2.9--28~$\mu$m spectral cubes of young, deeply embedded protostars with luminosities of 0.2 to 10,000~L$_{\odot}$ and central masses of 0.15 to 12~M$_{\odot}$. In this Letter, we report the discovery of a highly collimated atomic jet from the Class~0 protostar IRAS~16253$-$2429, the lowest luminosity source ($L_\mathrm{bol}$ = 0.2 $L_\odot$) in the IPA program. The collimated jet is detected in multiple [Fe~II] lines, [Ne~II], [Ni~II], and H~I lines, but not in molecular emission. The atomic jet has a velocity of about 169~$\pm$~15~km\,s$^{-1}$, after correcting for inclination. The width of the jet increases with distance from the central protostar from 23 to~60 au, corresponding to an opening angle of 2.6~$\pm$~0.5\arcdeg. By comparing the measured flux ratios of various fine structure lines to those predicted by simple shock models, we derive a shock {speed} of 54~km\,s$^{-1}$ and a preshock density of 2.0$\times10^{3}$~cm$^{-3}$ at the base of the jet. {From these quantities and using a suite of jet models and extinction laws we compute a mass loss rate between $0.4 -1.1\times10^{-10}~M_{\odot}$~yr~$^{-1}$.} The low mass loss rate is consistent with simultaneous measurements of low mass accretion rate ($2.4~\pm~0.8~\times~10^{-9}~M_{\odot}$~yr$^{-1}$) for IRAS~16253$-$2429 from JWST observations (Watson et al. in prep), indicating that the protostar is in a quiescent accretion phase. Our results demonstrate that very low-mass protostars can drive highly collimated, atomic jets, even during the quiescent phase., Comment: Accepted to ApJL. Comments and feedback welcome

We present the combination of ALMA-IMF and single-dish continuum images from the Mustang-2 Galactic Plane Survey (MGPS90) at 3 millimeters and the Bolocam Galactic Plane Survey (BGPS) at 1 millimeter. Six and ten out of the fiffteen ALMA-IMF fields are combined with MGPS90 and BGPS, respectively. The combination is made via the feathering technique. We used the dendrogram algorithm throughout the combined images, and performed further analysis in the six fields with combination in both bands (G012.80, W43-MM1, W43-MM2, W43-MM3, W51-E, W51-IRS2). In these fields, we calculated spectral index maps and used them to separate regions dominated by dust or free-free emission, and then performed further structural analysis. We report the basic physical parameters of the dust-dominated (column densities, masses) and ionized (emission measures, hydrogen ionization photon rates) structures. We also searched for multi-scale relations in the dust-dominated structures across the analyzed fields, finding that the fraction of mass in dendrogram leaves (which we label as "Leaf Mass Eficiency", LME) as a function of molecular gas column density follows a similar trend: a rapid, exponential-like growth, with maximum values approaching 100% in most cases. The observed behaviour of the LME with gas column is tentatively interpreted as an indicator of large star formation activity within the ALMA-IMF protoclusters. W51-E and G012.80 stand out as cases with comparatively large and reduced potential for further star formation, respectively., Comment: Accepted to The Astrophysical Journal Supplement

We present a catalog of 315 protostellar outflow candidates detected in SiO J=5-4 in the ALMA-IMF Large Program, observed with ~2000 au spatial resolution, 0.339 km/s velocity resolution, and 2-12 mJy/beam (0.18-0.8 K) sensitivity. We find median outflow masses, momenta, and kinetic energies of ~0.3 M$_{\odot}$, 4 M$_{\odot}$ km/s, and 10$^{45}$ erg, respectively. Median outflow lifetimes are 6,000 years, yielding median mass, momentum, and energy rates of $\dot{M}$ = 10$^{-4.4}$ M$_{\odot}$ yr$^{-1}$, $\dot{P}$ = 10$^{-3.2}$ M$_{\odot}$ km/s yr$^{-1}$, and $\dot{E}$ = 1 L$_{\odot}$. We analyze these outflow properties in the aggregate in each field. We find correlations between field-aggregated SiO outflow properties and total mass in cores (~3$-$5$\sigma$), and no correlations above 3$\sigma$ with clump mass, clump luminosity, or clump luminosity-to-mass ratio. We perform a linear regression analysis and find that the correlation between field-aggregated outflow mass and total clump mass - which has been previously described in the literature - may actually be mediated by the relationship between outflow mass and total mass in cores. We also find that the most massive SiO outflow in each field is typically responsible for only 15-30% of the total outflow mass (60% upper limit). Our data agree well with the established mechanical force-bolometric luminosity relationship in the literature, and our data extend this relationship up to L $\geq$ 10$^6$ L$_{\odot}$ and $\dot{P}$ $\geq$ 1 M$_{\odot}$ km/s yr$^{-1}$. Our lack of correlation with clump L/M is inconsistent with models of protocluster formation in which all protostars start forming at the same time., Comment: 46 pages, 14 figures, 10 tables. This publication has an associated Zenodo entry, which can be found here: https://zenodo.org/records/8350595

We studied the unique kinematic properties in massive filament G352.63-1.07 at $10^3$-AU spatial scale with the dense molecular tracers observed with the Atacama Large Millimeter/submillimeter Array (ALMA). We find the central massive core M1 (12 $M_\odot$) being separated from the surrounding filament with a velocity difference of $v- {v}_{sys}=-2$ km/s and a transverse separation within 3 arcsec. Meanwhile, as shown in multiple dense-gas tracers, M1 has a spatial extension closely aligned with the main filament and is connected to the filament towards its both ends. M1 thus represents a very beginning state for a massive young star-forming core escaping from the parental filament, within a time scale of $\sim 4000$ years. Based on its kinetic energy ($3.5\times10^{44}$ erg), the core escape is unlikely solely due to the original filament motion or magnetic field, but requires more energetic events such as a rapid intense anisotropic collapse. The released energy also seems to noticeably increase the environmental turbulence. This may help the filament to become stabilized again., Comment: 15 pages, 6 figures, accepted for publication in the Astrophysical Journal

Investigating Protostellar Accretion is a Cycle 1 JWST program using the NIRSpec+MIRI integral field units to obtain 2.9--28 $\mu$m spectral cubes of five young protostars with luminosities of 0.2-10,000 L$_{\odot}$ in their primary accretion phase. This paper introduces the NIRSpec 2.9--5.3 $\mu$m data of the inner 840-9000 au with spatial resolutions from 28-300 au. The spectra show rising continuum emission; deep ice absorption; emission from H$_{2}$, H~I, and [Fe~II]; and the CO fundamental series in emission and absorption. Maps of the continuum emission show scattered light cavities for all five protostars. In the cavities, collimated jets are detected in [Fe~II] for the four $< 320$~L$_{\odot}$ protostars, two of which are additionally traced in Br-$\alpha$. Knots of [Fe~II] emission are detected toward the most luminous protostar, and knots of [FeII] emission with dynamical times of $< 30$~yrs are found in the jets of the others. While only one jet is traced in H$_2$, knots of H$_2$ and CO are detected in the jets of four protostars. H$_2$ is seen extending through the cavities, showing that they are filled by warm molecular gas. Bright H$_2$ emission is seen along the walls of a single cavity, while in three cavities narrow shells of H$_2$ emission are found, one of which has an [Fe~II] knot at its apex. These data show cavities containing collimated jets traced in atomic/ionic gas surrounded by warm molecular gas in a wide-angle wind and/or gas accelerated by bow shocks in the jets., Comment: 26 pages, 12 figures

The ALMA Survey of Star Formation and Evolution in Massive Protoclusters with Blue Profiles (ASSEMBLE) aims to investigate the process of mass assembly and its connection to high-mass star formation theories in protoclusters in a dynamic view. We observed 11 massive (Mclump>1000 Msun), luminous (Lbol>10,000 Lsun), and blue-profile (infall signature) clumps by ALMA with resolution of 2200-5500 au at 350 GHz (870 um) in continuum and line emission. 248 dense cores were identified, including 106 cores showing protostellar signatures and 142 prestellar core candidates. Compared to early-stage infrared dark clouds (IRDCs) by ASHES, the core mass and surface density within the ASSEMBLE clumps exhibited significant increment, suggesting concurrent core accretion during the evolution of the clumps. The maximum mass of prestellar cores was found to be 2 times larger than that in IRDCs, indicating evolved protoclusters have the potential to harbor massive prestellar cores. The mass relation between clumps and their most massive core (MMCs) is observed in ASSEMBLE but not in IRDCs, which is suggested to be regulated by multiscale mass accretion. The mass correlation between the core clusters and their MMCs has a steeper slope compared to that observed in stellar clusters, which can be due to fragmentation of the MMC and stellar multiplicity. We observe a decrease in core separation and an increase in central concentration as protoclusters evolve. We confirm primordial mass segregation in the ASSEMBLE protoclusters, possibly resulting from gravitational concentration and/or gas accretion., Comment: 37 pages, 13 figures, 5 tables; accepted for publication in ApJS

Calculating the equilibrium properties of condensed matter systems is one of the promising applications of near-term quantum computing. Recently, hybrid quantum-classical time-series algorithms have been proposed to efficiently extract these properties from a measurement of the Loschmidt amplitude $\langle \psi| e^{-i \hat H t}|\psi \rangle$ from initial states $|\psi\rangle$ and a time evolution under the Hamiltonian $\hat H$ up to short times $t$. In this work, we study the operation of this algorithm on a present-day quantum computer. Specifically, we measure the Loschmidt amplitude for the Fermi-Hubbard model on a $16$-site ladder geometry (32 orbitals) on the Quantinuum H2-1 trapped-ion device. We assess the effect of noise on the Loschmidt amplitude and implement algorithm-specific error mitigation techniques. By using a thus-motivated error model, we numerically analyze the influence of noise on the full operation of the quantum-classical algorithm by measuring expectation values of local observables at finite energies. Finally, we estimate the resources needed for scaling up the algorithm., Comment: 18 pages, 12 figures

Despite recent success, state-of-the-art learning-based models remain highly vulnerable to input changes such as adversarial examples. In order to obtain certifiable robustness against such perturbations, recent work considers Lipschitz-based regularizers or constraints while at the same time increasing prediction margin. Unfortunately, this comes at the cost of significantly decreased accuracy. In this paper, we propose a Calibrated Lipschitz-Margin Loss (CLL) that addresses this issue and improves certified robustness by tackling two problems: Firstly, commonly used margin losses do not adjust the penalties to the shrinking output distribution; caused by minimizing the Lipschitz constant $K$. Secondly, and most importantly, we observe that minimization of $K$ can lead to overly smooth decision functions. This limits the model's complexity and thus reduces accuracy. Our CLL addresses these issues by explicitly calibrating the loss w.r.t. margin and Lipschitz constant, thereby establishing full control over slack and improving robustness certificates even with larger Lipschitz constants. On CIFAR-10, CIFAR-100 and Tiny-ImageNet, our models consistently outperform losses that leave the constant unattended. On CIFAR-100 and Tiny-ImageNet, CLL improves upon state-of-the-art deterministic $L_2$ robust accuracies. In contrast to current trends, we unlock potential of much smaller models without $K=1$ constraints., Comment: To be published at GCPR 2023

Privacy-preserving machine learning aims to train models on private data without leaking sensitive information. Differential privacy (DP) is considered the gold standard framework for privacy-preserving training, as it provides formal privacy guarantees. However, compared to their non-private counterparts, models trained with DP often have significantly reduced accuracy. Private classifiers are also believed to exhibit larger performance disparities across subpopulations, raising fairness concerns. The poor performance of classifiers trained with DP has prevented the widespread adoption of privacy preserving machine learning in industry. Here we show that pre-trained foundation models fine-tuned with DP can achieve similar accuracy to non-private classifiers, even in the presence of significant distribution shifts between pre-training data and downstream tasks. We achieve private accuracies within a few percent of the non-private state of the art across four datasets, including two medical imaging benchmarks. Furthermore, our private medical classifiers do not exhibit larger performance disparities across demographic groups than non-private models. This milestone to make DP training a practical and reliable technology has the potential to widely enable machine learning practitioners to train safely on sensitive datasets while protecting individuals' privacy.

Conformal Prediction (CP) allows to perform rigorous uncertainty quantification by constructing a prediction set $C(X)$ satisfying $\mathbb{P}(Y \in C(X))\geq 1-\alpha$ for a user-chosen $\alpha \in [0,1]$ by relying on calibration data $(X_1,Y_1),...,(X_n,Y_n)$ from $\mathbb{P}=\mathbb{P}^{X} \otimes \mathbb{P}^{Y|X}$. It is typically implicitly assumed that $\mathbb{P}^{Y|X}$ is the "true" posterior label distribution. However, in many real-world scenarios, the labels $Y_1,...,Y_n$ are obtained by aggregating expert opinions using a voting procedure, resulting in a one-hot distribution $\mathbb{P}_{vote}^{Y|X}$. For such ``voted'' labels, CP guarantees are thus w.r.t. $\mathbb{P}_{vote}=\mathbb{P}^X \otimes \mathbb{P}_{vote}^{Y|X}$ rather than the true distribution $\mathbb{P}$. In cases with unambiguous ground truth labels, the distinction between $\mathbb{P}_{vote}$ and $\mathbb{P}$ is irrelevant. However, when experts do not agree because of ambiguous labels, approximating $\mathbb{P}^{Y|X}$ with a one-hot distribution $\mathbb{P}_{vote}^{Y|X}$ ignores this uncertainty. In this paper, we propose to leverage expert opinions to approximate $\mathbb{P}^{Y|X}$ using a non-degenerate distribution $\mathbb{P}_{agg}^{Y|X}$. We develop Monte Carlo CP procedures which provide guarantees w.r.t. $\mathbb{P}_{agg}=\mathbb{P}^X \otimes \mathbb{P}_{agg}^{Y|X}$ by sampling multiple synthetic pseudo-labels from $\mathbb{P}_{agg}^{Y|X}$ for each calibration example $X_1,...,X_n$. In a case study of skin condition classification with significant disagreement among expert annotators, we show that applying CP w.r.t. $\mathbb{P}_{vote}$ under-covers expert annotations: calibrated for $72\%$ coverage, it falls short by on average $10\%$; our Monte Carlo CP closes this gap both empirically and theoretically.

For safety, AI systems in health undergo thorough evaluations before deployment, validating their predictions against a ground truth that is assumed certain. However, this is actually not the case and the ground truth may be uncertain. Unfortunately, this is largely ignored in standard evaluation of AI models but can have severe consequences such as overestimating the future performance. To avoid this, we measure the effects of ground truth uncertainty, which we assume decomposes into two main components: annotation uncertainty which stems from the lack of reliable annotations, and inherent uncertainty due to limited observational information. This ground truth uncertainty is ignored when estimating the ground truth by deterministically aggregating annotations, e.g., by majority voting or averaging. In contrast, we propose a framework where aggregation is done using a statistical model. Specifically, we frame aggregation of annotations as posterior inference of so-called plausibilities, representing distributions over classes in a classification setting, subject to a hyper-parameter encoding annotator reliability. Based on this model, we propose a metric for measuring annotation uncertainty and provide uncertainty-adjusted metrics for performance evaluation. We present a case study applying our framework to skin condition classification from images where annotations are provided in the form of differential diagnoses. The deterministic adjudication process called inverse rank normalization (IRN) from previous work ignores ground truth uncertainty in evaluation. Instead, we present two alternative statistical models: a probabilistic version of IRN and a Plackett-Luce-based model. We find that a large portion of the dataset exhibits significant ground truth uncertainty and standard IRN-based evaluation severely over-estimates performance without providing uncertainty estimates.

ALMA-IMF is an Atacama Large Millimeter/submillimeter Array (ALMA) Large Program designed to measure the core mass function (CMF) of 15 protoclusters chosen to span their early evolutionary stages. It further aims to understand their kinematics, chemistry, and the impact of gas inflow, accretion, and dynamics on the CMF. We present here the first release of the ALMA-IMF line data cubes (DR1), produced from the combination of two ALMA 12m-array configurations. The data include 12 spectral windows, with eight at 1.3mm and four at 3mm. The broad spectral coverage of ALMA-IMF (~6.7 GHz bandwidth coverage per field) hosts a wealth of simple atomic, molecular, ionised, and complex organic molecular lines. We describe the line cube calibration done by ALMA and the subsequent calibration and imaging we performed. We discuss our choice of calibration parameters and optimisation of the cleaning parameters, and we demonstrate the utility and necessity of additional processing compared to the ALMA archive pipeline. As a demonstration of the scientific potential of these data, we present a first analysis of the DCN (3-2) line. We find that DCN traces a diversity of morphologies and complex velocity structures, which tend to be more filamentary and widespread in evolved regions and are more compact in the young and intermediate-stage protoclusters. Furthermore, we used the DCN (3-2) emission as a tracer of the gas associated with 595 continuum cores across the 15 protoclusters, providing the first estimates of the core systemic velocities and linewidths within the sample. We find that DCN (3-2) is detected towards a higher percentage of cores in evolved regions than the young and intermediate-stage protoclusters and is likely a more complete tracer of the core population in more evolved protoclusters. The full ALMA 12m-array cubes for the ALMA-IMF Large Program are provided with this DR1 release., Comment: 75 pages (21 main body; 54 appendix), 37 figures. The ALMA-IMF DR1 line release is hosted at https://dataverse.harvard.edu/dataverse/alma-imf

There is growing evidence that high-mass star formation and hub-filament systems (HFS) are intricately linked. The gas kinematics along the filaments and the forming high-mass star(s) in the central hub are in excellent agreement with the new generation of global hierarchical high-mass star formation models. In this paper, we present an observational investigation of a typical HFS cloud, G310.142+0.758 (G310 hereafter) which reveals unambiguous evidence of mass inflow from the cloud scale via the filaments onto the forming protostar(s) at the hub conforming with the model predictions. Continuum and molecular line data from the ATOMS and MALT90 surveys are used that cover different spatial scales. Three filaments (with total mass $5.7\pm1.1\times 10^3~M_{\odot}$) are identified converging toward the central hub region where several signposts of high-mass star formation have been observed. The hub region contains a massive clump ($1280\pm260~M_{\odot}$) harbouring a central massive core. Additionally, five outflow lobes are associated with the central massive core implying a forming cluster. The observed large-scale, smooth and coherent velocity gradients from the cloud down to the core scale, and the signatures of infall motion seen in the central massive clump and core, clearly unveil a nearly-continuous, multi-scale mass accretion/transfer process at a similar mass infall rate of $\sim 10^{-3}~M_{\odot}~yr^{-1}$ over all scales, feeding the central forming high-mass protostar(s) in the G310 HFS cloud., Comment: Accepted to publish in ApJ. 10 pages with 6 figures and 2 tables