arXiv subjects

Scott Adams

Publications and source records attributed to Scott Adams.

12 recordsLinked to original sources

arXiv2025

Voice-guided Orchestrated Intelligence for Clinical Evaluation (VOICE): A Voice AI Agent System for Prehospital Stroke Assessment

We developed a voice-driven artificial intelligence (AI) system that guides anyone - from paramedics to family members - through expert-level stroke evaluations using natural conversation, while also enabling smartphone video capture of key examination components for documentation and potential expert review. This addresses a critical gap in emergency care: current stroke recognition by first responders is inconsistent and often inaccurate, with sensitivity for stroke detection as low as 58%, causing life-threatening delays in treatment. Three non-medical volunteers used our AI system to assess ten simulated stroke patients, including cases with likely large vessel occlusion (LVO) strokes and stroke-like conditions, while we measured diagnostic accuracy, completion times, user confidence, and expert physician review of the AI-generated reports. The AI system correctly identified 84% of individual stroke signs and detected 75% of likely LVOs, completing evaluations in just over 6 minutes. Users reported high confidence (median 4.5/5) and ease of use (mean 4.67/5). The system successfully identified 86% of actual strokes but also incorrectly flagged 2 of 3 non-stroke cases as strokes. When an expert physician reviewed the AI reports with videos, they identified the correct diagnosis in 100% of cases, but felt confident enough to make preliminary treatment decisions in only 40% of cases due to observed AI errors including incorrect scoring and false information. While the current system's limitations necessitate human oversight, ongoing rapid advancements in speech-to-speech AI models suggest that future versions are poised to enable highly accurate assessments. Achieving human-level voice interaction could transform emergency medical care, putting expert-informed assessment capabilities in everyone's hands.

cs.HC↗

arXiv2025

Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach

Manipulation of local training data and local updates, i.e., the poisoning attack, is the main threat arising from the collaborative nature of the federated learning (FL) paradigm. Most existing poisoning attacks aim to manipulate local data/models in a way that causes denial-of-service (DoS) issues. In this paper, we introduce a novel attack method, named Federated Learning Sliding Attack (FedSA) scheme, aiming at precisely introducing the extent of poisoning in a subtle controlled manner. It operates with a predefined objective, such as reducing global model's prediction accuracy by 10%. FedSA integrates robust nonlinear control-Sliding Mode Control (SMC) theory with model poisoning attacks. It can manipulate the updates from malicious clients to drive the global model towards a compromised state, achieving this at a controlled and inconspicuous rate. Additionally, leveraging the robust control properties of FedSA allows precise control over the convergence bounds, enabling the attacker to set the global accuracy of the poisoned model to any desired level. Experimental results demonstrate that FedSA can accurately achieve a predefined global accuracy with fewer malicious clients while maintaining a high level of stealth and adjustable learning rates.

cs.LG↗

arXiv2025

Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. I: Luminosity Function, Volumetric Rate

We present the luminosity function and volumetric rate of a sample of Type IIP supernovae (SNe) from the Zwicky Transient Facility Census of the Local Universe survey (CLU). This is the largest sample of Type IIP SNe from a systematic volume-limited survey to-date. The final sample includes 330 Type IIP SNe and 36 low-luminosity Type II (LLIIP) SNe with $M_{\textrm{r,peak}}>-16$ mag, which triples the literature sample of LLIIP SNe. The fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the total CLU Type IIP SNe population ($8^{+1}_{-2}\%$ of all core-collapse SNe). This implies that while LLIIP SNe likely represent the fate of core-collapse SNe of $8-12$ \Msun\ progenitors, they alone cannot account for the fate of all massive stars in this mass range. To derive an absolute rate, we estimate the ZTF pipeline efficiency as a function of the apparent magnitude and the local surface brightness. We derive a volumetric rate of $(3.9_{-0.4}^{+0.4}) \times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for Type IIP SNe and $(7.3_{-0.6}^{+0.6}) \times 10^{3}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for LLIIP SNe. Now that the rate of LLIIP SNe is robustly derived, the unresolved discrepancy between core-collapse SN rates and star-formation rates cannot be explained by LLIIP SNe alone.

astro-ph.HE↗

arXiv2023

Long-rising Type II Supernovae in the Zwicky Transient Facility Census of the Local Universe

SN 1987A was an unusual hydrogen-rich core-collapse supernova originating from a blue supergiant star. Similar blue supergiant explosions remain a small family of events, and are broadly characterized by their long rises to peak. The Zwicky Transient Facility (ZTF) Census of the Local Universe (CLU) experiment aims to construct a spectroscopically complete sample of transients occurring in galaxies from the CLU galaxy catalog. We identify 13 long-rising (>40 days) Type II supernovae from the volume-limited CLU experiment during a 3.5 year period from June 2018 to December 2021, approximately doubling the previously known number of these events. We present photometric and spectroscopic data of these 13 events, finding peak r-band absolute magnitudes ranging from -15.6 to -17.5 mag and the tentative detection of Ba II lines in 9 events. Using our CLU sample of events, we derive a long-rising Type II supernova rate of $1.37^{+0.26}_{-0.30}\times10^{-6}$ Mpc$^{-3}$ yr$^{-1}$, $\approx$1.4% of the total core-collapse supernova rate. This is the first volumetric rate of these events estimated from a large, systematic, volume-limited experiment.

astro-ph.HE↗

arXiv2022

Self-Supervised Leaf Segmentation under Complex Lighting Conditions

As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the semantically similar pixels by iteratively referring to the self-contained information, allowing the pixels of the same semantic object to be jointly considered by the color-based leaf segmentation algorithm for identifying the leaf regions. Additionally, we propose to use a self-supervised color correction model for images taken under complex illumination conditions. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework in achieving effective and generalizable leaf segmentation.

cs.CV↗

arXiv2021

A Search for Extragalactic Fast Blue Optical Transients in ZTF and the Rate of AT2018cow-like Transients

We present a search for extragalactic fast blue optical transients (FBOTs) during Phase I of the Zwicky Transient Facility (ZTF). We identify 38 candidates with durations above half-maximum light 1 d < t1/2 < 12 d, of which 28 have blue (g-r<-0.2 mag) colors at peak light. Of the 38 transients (28 FBOTs), 19 (13) can be spectroscopically classified as core-collapse supernovae (SNe): 11 (8) H- or He-rich (Type II/IIb/Ib) SNe, 6 (4) interacting (Type IIn/Ibn) SNe, and 2 (1) H&He-poor (Type Ic/Ic-BL) SNe. Two FBOTs (published previously) had high-S/N predominantly featureless spectra and luminous radio emission: AT2018lug and AT2020xnd. Seven (five) did not have a definitive classification: AT 2020bdh showed tentative broad H$\alpha$ in emission, and AT 2020bot showed unidentified broad features and was 10 kpc offset from the center of an early-type galaxy. Ten (six) have no spectroscopic observations or redshift measurements. We present multiwavelength (radio, millimeter, and/or X-ray) observations for five FBOTs (three Type Ibn, one Type IIn/Ibn, one Type IIb). Additionally, we search radio-survey (VLA and ASKAP) data to set limits on the presence of radio emission for 22 of the transients. All X-ray and radio observations resulted in non-detections; we rule out AT2018cow-like X-ray and radio behavior for five FBOTs and more luminous emission (such as that seen in the Camel) for four additional FBOTs. We conclude that exotic transients similar to AT2018cow, the Koala, and the Camel represent a rare subset of FBOTs, and use ZTF's SN classification experiments to measure the rate to be at most 0.1% of the local core-collapse SN rate.

astro-ph.HE↗

arXiv2020

The Zwicky Transient Facility Census of the Local Universe I: Systematic search for Calcium rich gap transients reveal three related spectroscopic sub-classes

(Abridged) Using the Zwicky Transient Facility alert stream, we are conducting a large campaign to spectroscopically classify all transients occurring in galaxies in the Census of the Local Universe (CLU) catalog. The aim of the experiment is to construct a spectroscopically complete, volume-limited sample of transients coincident within 100" of CLU galaxies out to 200 Mpc, and to a depth of 20 mag. We describe the survey design and spectroscopic completeness from the first 16 months of operations. We present results from a systematic search for Calcium rich gap transients in the sample of 22 low luminosity (peak absolute magnitude $M > -17$), hydrogen poor events found in the experiment (out of 754 spectroscopically classified SNe). We report the detection of eight Calcium rich gap transients, and constrain their volumetric rate to be at least $\approx 15\pm5$% of the SN Ia rate. Combining this sample with ten events from the literature, we find a likely continuum of spectroscopic properties ranging from events with SN Ia-like features (Ca-Ia objects) to SN Ib/c-like features (Ca-Ib/c objects) at peak light. Within the Ca-Ib/c events, we find two populations of events distinguished by their red ($g - r \approx 1.5$ mag) or green ($g - r \approx 0.5$ mag) spectral colors at $r$-band peak, wherein redder events show strong line blanketing signatures, slower light curves, weaker He lines and lower [Ca II]/[O I] in the nebular phase. Together, we find that the spectroscopic continuum, volumetric rates and striking old environments are consistent with the explosive burning of He shells on low mass white dwarfs. We posit that Ca-Ia and red Ca-Ib/c objects are consistent with the double detonation of He shells with high He burning efficiency, while green Ca-Ib/c objects could arise from less efficient He burning scenarios such as detonations in low density He shells or He shell deflagrations.

astro-ph.HE↗

arXiv2019

The Broad-lined Ic Supernova ZTF18aaqjovh (SN 2018bvw): An Optically-discovered Engine-driven Supernova Candidate with Luminous Radio Emission

We present ZTF18aaqjovh (SN 2018bvw), a high-velocity ("broad-lined") stripped-envelope (Type Ic) supernova (Ic-BL SN) discovered in the Zwicky Transient Facility one-day cadence survey. ZTF18aaqjovh shares a number of features in common with engine-driven explosions: the photospheric velocity and the shape of the optical light curve are very similar to that of the Type Ic-BL SN 1998bw, which was associated with a low-luminosity gamma-ray burst (LLGRB) and had relativistic ejecta. However, the radio luminosity of ZTF18aaqjovh is almost two orders of magnitude fainter than that of SN 1998bw at the same velocity phase, and the shock velocity is at most mildly relativistic (v=0.06-0.4c). A search of high-energy catalogs reveals no compelling GRB counterpart to ZTF18aaqjovh, and the limit on the prompt GRB luminosity of $L_{\gamma,\mathrm{iso}} \approx 1.6 \times 10^{48}$ erg/sec excludes a classical GRB but not an LLGRB. Altogether, ZTF18aaqjovh represents another transition event between engine-driven SNe associated with GRBs and "ordinary" Ic-BL SNe.

astro-ph.HE↗

arXiv2019

Missing Red Supergiants and Carbon Burning

Recent studies on direct imaging of Type II core-collapse supernova progenitors indicate a possible threshold around $M_{\rm ZAMS}\sim 16-20$ M$_\odot$, where red supergiants with larger birth masses do not appear to result in supernova explosions and instead implode directly into a black hole. In this study we argue that it is not a coincidence that this threshold closely matches the critical transition of central Carbon burning in massive stars from the convective to radiative regime. In lighter stars, Carbon burns convectively in the center and result in compact final presupernova cores that are likely to result in explosions, while in heavier stars after the transition, it burns as a radiative flame and the stellar cores become significantly harder to explode. Using the KEPLER code we demonstrate the sensitivity of this transition to the rate of $^{12}$C$(\alpha,\gamma)^{16}$O reaction and the overshoot mixing efficiency, and we argue that the upper mass limit of exploding red supergiants could be employed to constrain uncertain input physics of massive stellar evolution calculations. The initial mass corresponding to the central Carbon burning transition range from 14 to 26 M$_\odot$ in recently published models from various groups and codes, and only a few are in agreement with the estimates inferred from direct imaging studies.

astro-ph.HE↗

arXiv2019

The Dynamic Infrared Sky

Opening up the dynamic infrared sky for systematic time-domain exploration would yield many scientific advances. Multi-messenger pursuits such as localizing gravitational waves from neutron star mergers and quantifying the nucleosynthetic yields require the infrared. Another multi-messenger endeavor that needs infrared surveyors is the study of the much-awaited supernova in our own Milky Way. Understanding shocks in novae, true rates of supernovae and stellar mergers are some other examples of stellar evolution and high energy physics wherein the answers are buried in the infrared. We discuss some of the challenges in the infrared and pathfinders to overcome them. We conclude with recommendations on both infrared discovery engines and infrared follow-up machines that would enable this field to flourish in the next decade.

astro-ph.HE↗

arXiv2019

Machine Learning for the Zwicky Transient Facility

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.

astro-ph.IM↗

arXiv2018

Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data

Catheters are commonly inserted life supporting devices. X-ray images are used to assess the position of a catheter immediately after placement as serious complications can arise from malpositioned catheters. Previous computer vision approaches to detect catheters on X-ray images either relied on low-level cues that are not sufficiently robust or only capable of processing a limited number or type of catheters. With the resurgence of deep learning, supervised training approaches are begining to showing promising results. However, dense annotation maps are required, and the work of a human annotator is hard to scale. In this work, we proposed a simple way of synthesizing catheters on X-ray images and a scale recurrent network for catheter detection. By training on adult chest X-rays, the proposed network exhibits promising detection results on pediatric chest/abdomen X-rays in terms of both precision and recall.

cs.CV↗