Search for: All records

The long-standing one-to-many problem of gold standard responses in open-domain dialogue systems presents challenges for automatic evaluation metrics. Though prior works have demonstrated some success by applying powerful Large Language Models (LLMs), existing approaches still struggle with the one-to-many problem, and exhibit subpar performance in domain-specific scenarios. We assume the commonsense reasoning biases within LLMs may hinder their performance in domain-specific evaluations. To address both issues, we propose a novel framework SLIDE (Small and Large Integrated for Dialogue Evaluation), that leverages both a small, specialized model (SLM), and LLMs for the evaluation of open-domain dialogues. Our approach introduces several techniques: (1) Contrastive learning to differentiate between robust and non-robust response embeddings; (2) A novel metric for semantic sensitivity that combines embedding cosine distances with similarity learned through neural networks, and (3) A strategy for incorporating the evaluation results from both the SLM and LLMs. Our empirical results demonstrate that our approach achieves state-of-the-art performance in both the classification and evaluation tasks, and additionally, the SLIDE evaluator exhibits a better correlation with human judgments. 

Volcanic eruptions provide broad spectral forcing to the atmosphere and understanding the primary mechanisms that are relevant to explain the variety in waveform characteristics in the Ionosphere‐Thermosphere (IT) is still an important open question for the community. In this study, Global Navigation Satellite System (GNSS) Total Electron Content (TEC) data are analyzed and compared to simulations performed by the Global Ionosphere‐Thermosphere Model with Local Mesh Refinement (GITM‐R) for the first phase of the 2015 Calbuco eruption that occurred on 22 April. A simplified source representation and spectral acoustic‐gravity wave (AGW) propagation model are used to specify the perturbation at the lower boundary of GITM‐R at 100 km altitude. Two assumptions on the propagation structure, Direct Spherical (DS) and Ground Coupled (GC), are compared to the GNSS data and these modeling specifications show good agreement with different aspects of the observations for some waveform characteristics. Most notably, GITM‐R is able to reproduce the relative wave amplitude of AGWs as a function of radial distance from the vent, showing acoustic dominant forcing in the near field (<500 km) and gravity dominant forcing in the far‐field (>500 km). The estimated apparent phase speeds from GITM‐R simulations are consistent with observations with ∼10% difference from observation for both acoustic wave packets and a trailing gravity mode. The relevance of the simplifications made in the lower atmosphere to the simulated IT response is then discussed.

 

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 (≈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 ∼6000 km s⁻¹. For a steady, wind mass-loss regime, light-curve modeling results in an ejecta mass of ∼8M_⊙and circumstellar material (CSM) mass below 1M_⊙, and an ejecta velocity consistent with the FWHM of the broad helium lines. We obtain a mass-loss rate of ≈2M_⊙yr⁻¹. This mass-loss rate is 3 orders of magnitude larger than derived for normal Type II supernovae. We estimate that the bulk of the CSM of SN 2021foa must have been expelled within half a year, about 12 yr ago. Our analysis suggests that SN 2021foa had a helium-rich ejecta that 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 redshifted emission of the hydrogen and helium lines, which results in an observed blueshift (∼−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.

 

We assess the Value of Information (VoI) for inspecting components in systems managed by multiple agents, using game theory and Nash equilibrium analysis. We focus on binary systems made up by binary components which can be either intact or damaged. Agents taking maintenance actions are responsible for the repair costs of their own components, and the penalty for system failure is shared among all agents. The precision of inspection is also considered, and we identify the prior and posterior Nash equilibrium with perfect or imperfect inspections. The VoI is assessed for the individual agents as well as for the whole set of agents, and the analysis consider series, parallel and general systems. A negative VoI can trigger the phenomenon of Information Avoidance (IA), where rational agents prefer not to collect free information. We discuss whether it is possible that the VoI is negative for one or for all agents, for the agents with inspected or uninspected components, and for the total sum of VoIs. 

We present multiwavelength data of SN 2020acct, a double-peaked stripped-envelope supernova (SN) in NGC 2981 at ∼150 Mpc. The two peaks are temporally distinct, with maxima separated by 58 rest-frame days and a factor of 20 reduction in flux between. The first is luminous (M_r = −18.00 ± 0.02 mag) and blue (g − r = 0.27 ± 0.03 mag) and displays spectroscopic signatures of interaction with hydrogen-free circumstellar material. The second peak is fainter (M_r = −17.29 ± 0.03 mag) and has some spectroscopic similarities to an evolved stripped-envelope SN, with strong forbidden [Ca ii] and [O ii] features. No other known double-peaked SN exhibits a light curve similar to that of SN 2020acct. We find the likelihood of two individual SNe occurring in the same star-forming region within that time to be highly improbable, while an implausibly fine-tuned configuration would be required to produce two SNe from a single binary system. We find that the peculiar properties of SN 2020acct match models of pulsational pair instability (PPI), in which the initial peak is produced by collisions of shells of ejected material, shortly followed by core collapse. Pulsations from a star with a 72M_⊙helium core provide an excellent match to the double-peaked light curve. The local galactic environment has a metallicity of 0.4Z_⊙, a level where massive single stars are not expected to retain enough mass to encounter the PPI. However, late binary mergers or a low-metallicity pocket may allow the required core mass. We measure the rate of SN 2020acct–like events to be <3.3 × 10⁻⁸Mpc⁻³yr⁻¹atz= 0.07, or <0.1% of the total core-collapse SN rate.

 

We present Lightcurve Anomaly Identification and Similarity Search (LAISS), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes (spectroscopicanomalies), of uncommon host galaxy environments (contextualanomalies), and of peculiar or interaction-powered phenomena (behavioralanomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker,LAISSis built to detect exciting transients in Rubin data.

 

Quasi-periodic eruptions (QPEs) are luminous bursts of soft X-rays from the nuclei of galaxies, repeating on timescales of hours to weeks^1–5. The mechanism behind these rare systems is uncertain, but most theories involve accretion disks around supermassive black holes (SMBHs) undergoing instabilities^6–8or interacting with a stellar object in a close orbit^9–11. It has been suggested that this disk could be created when the SMBH disrupts a passing star^8,11, implying that many QPEs should be preceded by observable tidal disruption events (TDEs). Two known QPE sources show long-term decays in quiescent luminosity consistent with TDEs^4,12and two observed TDEs have exhibited X-ray flares consistent with individual eruptions^13,14. TDEs and QPEs also occur preferentially in similar galaxies¹⁵. However, no confirmed repeating QPEs have been associated with a spectroscopically confirmed TDE or an optical TDE observed at peak brightness. Here we report the detection of nine X-ray QPEs with a mean recurrence time of approximately 48 h from AT2019qiz, a nearby and extensively studied optically selected TDE¹⁶. We detect and model the X-ray, ultraviolet (UV) and optical emission from the accretion disk and show that an orbiting body colliding with this disk provides a plausible explanation for the QPEs.

 

Vehicles can utilize their sensors or receive messages from other vehicles to acquire information about the surrounding environments. However, the information may be inaccurate, faulty, or maliciously compromised due to sensor failures, communication faults, or security attacks. The goal of this work is to detect if a lane-changing decision and the sensed or received information are anomalous. We develop three anomaly detection approaches based on deep learning: a classifier approach, a predictor approach, and a hybrid approach combining the classifier and the predictor. All of them do not need anomalous data nor lateral features so that they can generally consider lane-changing decisions before the vehicles start moving along the lateral axis. They achieve at least 82% and up to 93% F1 scores against anomaly on data from Simulation of Urban MObility (SUMO) and HighD. We also examine system properties and verify that the detected anomaly includes more dangerous scenarios.