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We present a comprehensive analysis of the photometric and spectroscopic evolution of SN~2021foa, unique among the class of transitional supernovae for repeatedly changing its spectroscopic appearance from hydrogen-to-helium-to-hydrogen-dominated (IIn-to-Ibn-to-IIn) within 50 days past peak brightness. The spectra exhibit multiple narrow ($\approx$ 300--600~km~s$^{-1}$) absorption lines of hydrogen, helium, calcium and iron together with broad helium emission lines with a full-width-at-half-maximum (FWHM) of $\sim 6000$~km~s$^{-1}$. For a steady, wind-mass loss regime, light curve modeling results in an ejecta mass of $\sim 8$ M$_{\odot}$ and CSM mass below 1 M$_{\odot}$, and an ejecta velocity consistent with the FWHM of the broad helium lines. We obtain a mass-loss rate of $\approx 2$ M$_{\odot} {\rm yr}^{-1}$. This mass-loss rate is three orders of magnitude larger than derived for normal Type II SNe. We estimate that the bulk of the CSM of SN~2021foa must have been expelled within half a year, about 15 years ago. Our analysis suggests that SN~2021foa had a helium rich ejecta which swept up a dense shell of hydrogen rich CSM shortly after explosion. At about 60 days past peak brightness, the photosphere recedes through the dense ejecta-CSM region, occulting much of the red-shifted emission of the hydrogen and helium lines, which results in observed blue-shift ($\sim -3000$~km~s$^{-1}$). Strong mass loss activity prior to explosion, such as those seen in SN~2009ip-like objects and SN~2021foa as precursor emission, are the likely origin of a complex, multiple-shell CSM close to the progenitor star., Comment: Submitted to ApJ, 21 figures, 7 tables

Merkle hash trees are the state-of-the-art method to protect the integrity of storage systems. However, using a hash tree can severely degrade performance, and prior works optimizing them have yet to yield a concrete understanding of the scalability of certain designs in the context of large-scale cloud storage systems. In this paper, we take a first-principles approach to analyzing hash tree performance for storage by introducing a definition of an optimal hash tree and a principled methodology for evaluating hash tree designs. We show that state-of-the-art designs are not scalable; they incur up to 40.1X slowdowns over an insecure baseline and deliver <50% of optimal performance across various experiments. We then exploit the characteristics of optimal hash trees to design Dynamic Hash Trees (DHTs), hash trees that can adapt to workload patterns on-the-fly, delivering >95% of optimal read and write performance and up to 4.2X speedups over the state-of-the art. Our novel methodology and DHT design provides a new foundation in the search for integrity mechanisms that can operate efficiently at scale.

The creative industry is both concerned and enthusiastic about how generative AI will reshape creativity. How might these tools interact with the workflow values of creative artists? In this paper, we adopt a value-sensitive design framework to examine how generative AI, particularly Photoshop's Generative Fill (GF), helps or hinders creative professionals' values. We obtained 566 unique posts about GF from online forums for creative professionals who use Photoshop in their current work practices. We conducted reflexive thematic analysis focusing on usefulness, ease of use, and user values. Users found GF useful in doing touch-ups, expanding images, and generating composite images. GF helped users' values of productivity by making work efficient but created a value tension around creativity: it helped reduce barriers to creativity but hindered distinguishing 'human' from algorithmic art. Furthermore, GF hindered lived experiences shaping creativity and hindered the honed prideful skills of creative work.

We present an ultraviolet-to-infrared search for the electromagnetic (EM) counterpart to GW190425, the second-ever binary neutron star (BNS) merger discovered by the LIGO-Virgo-KAGRA Collaboration (LVK). GW190425 was more distant and had a larger localization area than GW170817, therefore we use a new tool teglon to redistribute the GW190425 localization probability in the context of galaxy catalogs within the final localization volume. We derive a 90th percentile area of 6,688 deg$^{2}$, a $\sim$1.5$\times$ improvement relative to the LIGO/Virgo map, and show how teglon provides an order of magnitude boost to the search efficiency of small ($\leq$1 deg$^{2}$) field-of-view instruments. We combine our data with all publicly reported imaging data, covering 9,078.59 deg$^2$ of unique area and 48.13% of the LIGO/Virgo-assigned localization probability, to calculate the most comprehensive kilonova, short gamma-ray burst (sGRB) afterglow, and model-independent constraints on the EM emission from a hypothetical counterpart to GW190425 to date under the assumption that no counterpart was found in these data. If the counterpart were similar to AT 2017gfo, there was a 28.4% chance that it would have been detected in the combined dataset. We are relatively insensitive to an on-axis sGRB, and rule out a generic transient with a similar peak luminosity and decline rate as AT 2017gfo to 30% confidence. Finally, across our new imaging and all publicly-reported data, we find 28 candidate optical counterparts that we cannot rule out as being associated with GW190425, finding that 4 such counterparts discovered within the localization volume and within 5 days of merger exhibit luminosities consistent with a kilonova., Comment: 41 pages, 11 figures, Submitted to ApJ

Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered a gold standard for assessing cardiac hemodynamics while non-invasive methods are often needed to screen high-risk patients from a large population. In this paper, we propose a multimodal learning pipeline to predict PAWP marker. We utilize complementary information from Cardiac Magnetic Resonance Imaging (CMR) scans (short-axis and four-chamber) and Electronic Health Records (EHRs). We extract spatio-temporal features from CMR scans using tensor-based learning. We propose a graph attention network to select important EHR features for prediction, where we model subjects as graph nodes and feature relationships as graph edges using the attention mechanism. We design four feature fusion strategies: early, intermediate, late, and hybrid fusion. With a linear classifier and linear fusion strategies, our pipeline is interpretable. We validate our pipeline on a large dataset of $2,641$ subjects from our ASPIRE registry. The comparative study against state-of-the-art methods confirms the superiority of our pipeline. The decision curve analysis further validates that our pipeline can be applied to screen a large population. The code is available at https://github.com/prasunc/hemodynamics.

Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite their potential, these approaches often fall short especially when the size of labeled patient data is limited, a common challenge in the medical domain. Furthermore, only a few studies have explored multimodal methods to study CHDI, which mostly rely on costly modalities such as cardiac MRI and echocardiogram. In response to these limitations, we propose a novel multimodal variational autoencoder ($\text{CardioVAE}_\text{X,G}$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset. Specifically, $\text{CardioVAE}_\text{X,G}$ introduces a novel tri-stream pre-training strategy to learn both shared and modality-specific features, thus enabling fine-tuning with both unimodal and multimodal datasets. We pre-train $\text{CardioVAE}_\text{X,G}$ on a large, unlabeled dataset of $50,982$ subjects from a subset of MIMIC database and then fine-tune the pre-trained model on a labeled dataset of $795$ subjects from the ASPIRE registry. Comprehensive evaluations against existing methods show that $\text{CardioVAE}_\text{X,G}$ offers promising performance (AUROC $=0.79$ and Accuracy $=0.77$), representing a significant step forward in non-invasive prediction of CHDI. Our model also excels in producing fine interpretations of predictions directly associated with clinical features, thereby supporting clinical decision-making.

Vision-language pre-training (VLP) models have shown significant advancements in the medical domain. Yet, most VLP models align raw reports to images at a very coarse level, without modeling fine-grained relationships between anatomical and pathological concepts outlined in reports and the corresponding semantic counterparts in images. To address this problem, we propose a Medical Dual-Stream Language-Image Pre-training (MeDSLIP) framework. Specifically, MeDSLIP establishes vision-language fine-grained alignments via disentangling visual and textual representations into anatomy-relevant and pathology-relevant streams. Moreover, a novel vision-language Prototypical Contr-astive Learning (ProtoCL) method is adopted in MeDSLIP to enhance the alignment within the anatomical and pathological streams. MeDSLIP further employs cross-stream Intra-image Contrastive Learning (ICL) to ensure the consistent coexistence of paired anatomical and pathological concepts within the same image. Such a cross-stream regularization encourages the model to exploit the synchrony between two streams for a more comprehensive representation learning. MeDSLIP is evaluated under zero-shot and supervised fine-tuning settings on three public datasets: NIH CXR14, RSNA Pneumonia, and SIIM-ACR Pneumothorax. Under these settings, MeDSLIP outperforms six leading CNN-based models on classification, grounding, and segmentation tasks.

External fragmentation of physical memory occurs when adjacent differently sized regions of allocated physical memory are freed at different times, causing free memory to be physically discontiguous. It can significantly degrade system performance and efficiency, such as reducing the ability to use huge pages, a critical optimization on modern large-memory system. For decades system developers have sought to avoid and mitigate fragmentation, but few prior studies quantify and characterize it in production settings. Moreover, prior work often artificially fragments physical memory to create more realistic performance evaluations, but their fragmentation methodologies are ad hoc and unvalidated. Out of 13 papers, we found 11 different methodologies, some of which were subsequently found inadequate. The importance of addressing fragmentation necessitates a validated and principled methodology. Our work fills these gaps in knowledge and methodology. We conduct a study of memory fragmentation in production by observing 248 machines in the Computer Sciences Department at University of Wisconsin - Madison for a week. We identify six key memory usage patterns, and find that Linux's file cache and page reclamation systems are major contributors to fragmentation because they often obliviously break up contiguous memory. Finally, we create and\'uril, a tool to artificially fragment memory during experimental research evaluations. While and\'uril ultimately fails as a scientific tool, we discuss its design ideas, merits, and failings in hope that they may inspire future research., Comment: 23 pages, 9 figures

This brief explains how four important system design initiatives frequently promoted by state and local education agencies for school improvement can be implemented through an intentional systematic approach for a powerful, positive impact on student outcomes. As technical assistance providers with expertise in four specific frameworks for educational system design, and with great appreciation for one another, the contributors suggest how a coordinated Stage-based Implementation process with embedded, systems level critical Equity Inquiries at every stage can enable schools to establish Universal Design for Learning (UDL) principles and guidelines within a Multi-Tiered System of Support (MTSS) for a designed experience in instructional decision making.

Pulmonary Hypertension (PH) is a severe disease characterized by an elevated pulmonary artery pressure. The gold standard for PH diagnosis is measurement of mean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart Catheterization. In this paper, we investigate noninvasive approach to PH detection utilizing Magnetic Resonance Imaging, Computer Models and Machine Learning. We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP. We compare results of regression (with thresholding of estimated mPAP) and classification and demonstrate that metrics achieved in both experiments are comparable. The predicted mPAP values are more informative to the physicians than the probability of PH returned by classification models. They provide the intuitive explanation of the outcome of the machine learning model (clinicians are accustomed to the mPAP metric, contrary to the PH probability)., Comment: Accepted for ICCS 2022

We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92\% vs. 15\% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board available at https://huggingface.co/gaia-benchmark.

A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet real-world autonomous tests face several unique safety challenges, both due to the possibility of causing harm during a test, as well as the risk of encountering new unsafe agent behavior through interactions with real-world and potentially malicious actors. We propose a framework for conducting safe autonomous agent tests on the open internet: agent actions are audited by a context-sensitive monitor that enforces a stringent safety boundary to stop an unsafe test, with suspect behavior ranked and logged to be examined by humans. We design a basic safety monitor (AgentMonitor) that is flexible enough to monitor existing LLM agents, and, using an adversarial simulated agent, we measure its ability to identify and stop unsafe situations. Then we apply the AgentMonitor on a battery of real-world tests of AutoGPT, and we identify several limitations and challenges that will face the creation of safe in-the-wild tests as autonomous agents grow more capable.

We present the CO(3-2) APEX survey at 6 pc resolution of the bar of the SMC. We aboard the CO analysis in the SMC-Bar comparing the CO(3-2) survey with that of the CO(2-1) of similar resolution. We study the CO(3-2)-to-CO(2-1) ratio (R32) that is very sensitive to the environment properties (e.g., star-forming regions). We analyzed the correlation of this ratio with observational quantities that trace the star formation as the local CO emission, the Spitzer color [70/160], and the total IR surface brightness measured from the Spitzer and Herschel bands. For the identification of the CO(3-2) clouds, we used the CPROPS algorithm, which allowed us to measure the physical properties of the clouds. We analyzed the scaling relationships of such physical properties. We obtained an R32 of 0.65 as a median value for the SMC, with a standard deviation of 0.3. We found that R32 varies from region to region, depending on the star formation activity. In regions dominated by HII and photo-dissociated regions (e.g., N22, N66), R32 tends to be higher than the median values. Meanwhile, lower values were found toward quiescent clouds. We also found that R32 correlates positively with the IR color [70/160] and the total IR surface brightness. This finding indicates that R32 increases with environmental properties like the dust temperature, the total gas density, and the radiation field. We have identified 225 molecular clouds with sizes R > 1.5 pc and signal-to-noise (S/N) ratio > 3 and only 17 well-resolved CO(3-2) clouds increasing the S/N ratio to > 5. These 17 clouds follow consistent scaling relationships to the inner Milky Way clouds but with some departure. The CO(3-2) tends to be less turbulent and less luminous than the inner Milky Way clouds of similar size. Finally, we estimated a median virial-based CO-to-H2 conversion factor of 12.6_{-7}^{+10} Msun/(K km s^{-1} pc^{2}) for the total sample.

While nanoscale semiconductor crystallites provide versatile fluorescent materials for light-emitting devices, such nanocrystals suffer from the "dark exciton"$\unicode{x2014}$an optically inactive electronic state into which the nanocrystal relaxes before emitting. Recently, a theoretical mechanism was discovered that can potentially defeat the dark exciton. The Rashba effect can invert the order of the lowest-lying levels, creating a bright excitonic ground state. To identify materials that exhibit this behavior, here we perform an extensive high-throughput computational search of two large open-source materials databases. Based on a detailed understanding of the Rashba mechanism, we define proxy criteria and screen over 500,000 solids, generating 173 potential "bright-exciton" materials. We then refine this list with higher-level first-principles calculations to obtain 28 candidates. To confirm the potential of these compounds, we select five and develop detailed effective-mass models to determine the nature of their lowest-energy excitonic state. We find that four of the five solids (BiTeCl, BiTeI, Ga$_2$Te$_3$, and KIO$_3$) can yield bright ground-state excitons. Our approach thus reveals promising materials for future experimental investigation of bright-exciton nanocrystals., Comment: 19 pages, 4 figures

We report the results of the stellar occultation by (UII) Umbriel on September 21st, 2020. The shadow crossed the USA and Canada, and 19 positive chords were obtained. A limb parameter accounted for putative topographic features in the limb fittings. Ellipse fittings were not robust - only upper limits were derived for the true size/shape of a putative Umbriel ellipsoid. The adopted spherical solution gives radius = 582.4 +/- 0.8 km, smaller/close to 584.7 +/- 2.8 km from Voyager II. The apparent ellipse fit results in a true semi-major axis of 584.9 +/- 3.8 km, semi-minor axes of 582.3 +/- 0.6 km and true oblateness of 0.004 +/- 0.008 for a putative ellipsoid. The geometric albedo was pV = 0.26 +/- 0.01. The density was rho = 1.54 +/- 0.04 g cm-3. The surface gravity was 0.251 +/- 0.006 m s-2 and the escape velocity 0.541 +/- 0.006 km s-1 . Upper limits of 13 and 72 nbar (at 1 sigma and 3 sigma levels, respectively) were obtained for the surface pressure of a putative isothermal CO2 atmosphere at T = 70 K. A milliarcsecond precision position was derived: RA = 02h 30m 28.84556s +/- 0.1 mas, DE = 14o 19' 36.5836" +/- 0.2 mas. A large limb parameter of 4.2 km was obtained, in striking agreement with opposite southern hemisphere measurements by Voyager II in 1986. Occultation and Voyager results indicate that the same strong topography variation in the surface of Umbriel is present on both hemispheres.

Python and Prolog express different programming paradigms, with different strengths. Python is wildly popular because it is well-structured, easy to use, and mixes well with thousands of scientific and machine learning programs written in C. Prolog's logic-based approach provides powerful reasoning capabilities, especially when combined with constraint evaluation, probabilistic reasoning, well-founded negation, and other advances. Both languages have commonalities as well: both are usually written in C, both are dynamically typed, and both use data structures based on a small number of recursive types. This paper describes the design and implementation of Janus, a system that tightly combines Prolog and Python into a single process. Janus bi-translates data structures and offers performance of many hundreds of thousands of round-trip inter-language calls per second. Although Janus is still new, it has been used in commercial applications including natural language processing, visual query answering and robotic automation. Janus was developed for XSB, but porting Janus code to a second Prolog has been straightforward, indicating that Janus is a tool that other Prologs may easily adopt., Comment: In Proceedings ICLP 2023, arXiv:2308.14898

We present the photometric and spectroscopic evolution of SN 2022oqm, a nearby multi-peaked hydrogen- and helium-weak calcium-rich transient (CaRT). SN 2022oqm was detected 13.1 kpc from its host galaxy, the face-on spiral galaxy NGC 5875. Extensive spectroscopic coverage reveals an early hot (T >= 40,000 K) continuum and carbon features observed $\sim$1~day after discovery, SN Ic-like photospheric-phase spectra, and strong forbidden calcium emission starting 38 days after discovery. SN 2022oqm has a relatively high peak luminosity (MB = -17 mag) for (CaRTs), making it an outlier in the population. We determine that three power sources are necessary to explain the light curve (LC), with each corresponding to a distinct peak. The first peak is powered by an expanding blackbody with a power law luminosity, suggesting shock cooling by circumstellar material (CSM). Subsequent LC evolution is powered by a double radioactive decay model, consistent with two sources of photons diffusing through optically thick ejecta. From the LC, we derive an ejecta mass and 56Ni mass of ~0.6 solar masses and ~0.09 solar masses. Spectroscopic modeling suggests 0.6 solar masses of ejecta, and with well-mixed Fe-peak elements throughout. We discuss several physical origins for SN 2022oqm and find either a surprisingly massive white dwarf progenitor or a peculiar stripped envelope model could explain SN 2022oqm. A stripped envelope explosion inside a dense, hydrogen- and helium-poor CSM, akin to SNe Icn, but with a large 56Ni mass and small CSM mass could explain SN 2022oqm. Alternatively, helium detonation on an unexpectedly massive white dwarf could also explain SN 2022oqm., Comment: 35 pages, 17 figures, 7 tables, Accepted for Publication in ApJ

On September 26, 2022, NASA's DART spacecraft impacted Dimorphos, the secondary asteroid in the (65803) Didymos system, so that the efficiency with which a satellite could divert an asteroid could be measured from the change in the system's period. We present new data from the Thacher Observatory and measure a change in period, $\Delta P = -34.2 \pm 0.1$ min, which deviates from previous measurements by $3.5\,\sigma$. This suggests that the system period may have decreased by $\sim 1$ minute in the 20 to 30 days between previous measurements and our measurements. We find that no mechanism previously presented for this system can account for this large of a period change, and drag from impact ejecta is an unlikely explanation. Further observations of the (65803) Didymos system are needed to both confirm our result and to further understand this system post impact., Comment: 3 pages, 1 figure. Accepted for publication as a Research Note of the American Astronomical Society

The Bifurcation from a Simple Eigenvalue (BSE) Theorem is the foundation of steady-state bifurcation theory for one-parameter families of functions. When eigenvalues of multiplicity greater than one are caused by symmetry, the Equivariant Branching Lemma (EBL) can often be applied to predict the branching of solutions. The EBL can be interpreted as the application of the BSE Theorem to a fixed point subspace. There are functions which have invariant linear subspaces that are not caused by symmetry. For example, networks of identical coupled cells often have such invariant subspaces. We present a generalization of the EBL, where the BSE Theorem is applied to nested invariant subspaces. We call this the Bifurcation Lemma for Invariant Subspaces (BLIS). We give several examples of bifurcations and determine if BSE, EBL, or BLIS apply. We extend our previous automated bifurcation analysis algorithms to use the BLIS to simplify and improve the detection of branches created at bifurcations., Comment: 24 pages, 7 figures

In this comment on "Origin of correlated isolated flat bands in copper-substituted lead phosphate apatite" (arXiv:2307.16892 [cond-mat.supr-con]), we discuss the flat half-occupied two-band manifold that appears in $\mathrm{Pb}_9\mathrm{Cu}(\mathrm{PO}_4)_6(\mathrm{OH})_2$ when using a semilocal DFT functional. We argue that the flat band is an artifact of the functional's overestimation of the energy of the oxygen p states in the valence band. When using the HSE hybrid functional, the energy of the oxygen p states is reduced, and the copper-derived manifold splits into one fully occupied and one empty band. While these results do not rule out the possibility of superconductivity in doped LK-99, they do predict that stoichiometric $\mathrm{Pb}_9\mathrm{Cu}(\mathrm{PO}_4)_6(\mathrm{OH})_2$ is an insulator, not a superconductor. Furthermore, we have shown that future first-principles studies of these materials should employ hybrid functionals or other advanced methods to ensure that the oxygen-derived valence-band energies are correctly described., Comment: 2 pages, 1 figure

The lone-pair s states of germanium, tin, and lead underlie many of the unconventional properties of the inorganic metal halide perovskites. Dynamic stereochemical expression of the lone pairs is well established for perovskites based on all three metals, but previously only the germanium perovskites were thought to express the lone pair crystallographically. In this work, we use advanced first-principles calculations with a hybrid functional and spin-orbit coupling to predict stable monoclinic polar phases of $\mathrm{CsSnI}_3$ and $\mathrm{CsSnBr}_3$, which exhibit a ferroelectric distortion driven by stereochemical expression of the tin lone pair. We also predict similar metastable ferroelectric phases of $\mathrm{CsPbI}_3$ and $\mathrm{CsPbBr}_3$. In addition to ferroelectricity, these phases exhibit the Rashba effect. Spin splitting in both the conduction and valence bands suggests that nanostructures based on these phases could host bright ground-state excitons. Finally, we discuss paths toward experimental realization of these phases via electric fields and tensile strain., Comment: 21 pages, 4 figures

We consider the second-order cone function (SOCF) $f: {\mathbb R}^n \to \mathbb R$ defined by $f(x)= c^T x + d -\|A x + b \|$. Every SOCF is concave. We give necessary and sufficient conditions for strict concavity of $f$. The parameters $A \in {\mathbb R}^{m \times n}$ and $b \in {\mathbb R}^m$ are not uniquely determined. We show that every SOCF can be written in the form $f(x) = c^T x + d -\sqrt{\delta^2 + (x-x_*)^TM(x-x_*)}$. We give necessary and sufficient conditions for the parameters $c$, $d$, $\delta$, $M = A^T A$, and $x_*$ to be uniquely determined. We also give necessary and sufficient conditions for $f$ to be bounded above., Comment: 21 pages, 5 figures

Accurate on-chip temperature sensing is critical for the optimal performance of modern CMOS integrated circuits (ICs), to understand and monitor localized heating around the chip during operation. The development of quantum computers has stimulated much interest in ICs operating a deep cryogenic temperatures (typically 0.01 - 4 K), in which the reduced thermal conductivity of silicon and silicon oxide, and the limited cooling power budgets make local on-chip temperature sensing even more important. Here, we report four different methods for on-chip temperature measurements native to complementary metal-oxide-semiconductor (CMOS) industrial fabrication processes. These include secondary and primary thermometry methods and cover conventional thermometry structures used at room temperature as well as methods exploiting phenomena which emerge at cryogenic temperatures, such as superconductivity and Coulomb blockade. We benchmark the sensitivity of the methods as a function of temperature and use them to measure local excess temperature produced by on-chip heating elements. Our results demonstrate thermometry methods that may be readily integrated in CMOS chips with operation from the milliKelivin range to room temperature.

We present optical, ultraviolet, and infrared data of the type II supernova (SN II) 2020jfo at 14.5 Mpc. This wealth of multiwavelength data allows to compare different metrics commonly used to estimate progenitor masses of SN II for the same object. Using its early light curve, we infer SN 2020jfo had a progenitor radius of $\approx$700 $R_{\odot}$, consistent with red supergiants of initial mass $M_{\rm ZAMS}=$11-13 $M_{\odot}$. The decline in its late-time light curve is best fit by a ${}^{56}$Ni mass of 0.018$\pm$0.007 $M_{\odot}$ consistent with that ejected from SN II-P with $\approx$13 $M_{\odot}$ initial mass stars. Early spectra and photometry do not exhibit signs of interaction with circumstellar matter, implying that SN 2020jfo experienced weak mass loss within the final years prior to explosion. Our spectra at $>$250 days are best fit by models from 12 $M_{\odot}$ initial mass stars. We analyzed integral field unit spectroscopy of the stellar population near SN 2020jfo, finding its massive star population had a zero age main sequence mass of 9.7$\substack{+2.5\\-1.3} M_{\odot}$. We identify a single counterpart in pre-explosion imaging and find it has an initial mass of at most $7.2\substack{+1.2\\-0.6} M_{\odot}$. We conclude that the inconsistency between this mass and indirect mass indicators from SN 2020jfo itself is most likely caused by extinction with $A_{V}=2$-3 mag due to matter around the progenitor star, which lowered its observed optical luminosity. As SN 2020jfo did not exhibit extinction at this level or evidence for interaction with circumstellar matter between 1.6-450 days from explosion, we conclude that this material was likely confined within $\approx$3000 $R_{\odot}$ from the progenitor star., Comment: 31 pages, 12 figures, accepted to MNRAS