Tootfinder

SO emission in the dynamically perturbed protoplanetary disks around CQ Tau and MWC 758
Francesco Zagaria, Haochang Jiang, Gianni Cataldi, Stefano Facchini, Myriam Benisty, Yuri Aikawa, Sean Andrews, Jaehan Bae, Marcelo Barraza-Alfaro, Pietro Curone, Ian Czekala, Daniele Fasano, Cassandra Hall, Iain Hammond, Jane Huang, John D. Ilee, Andr\'es F. Izquierdo, Jensen Lawrence, Giuseppe Lodato, Fran\c{c}ois M\'enard, Christophe Pinte, Giovanni P. Rosotti, Jochen Stadler, Richard Tea…

SO emission in the dynamically perturbed protoplanetary disks around CQ Tau and MWC 758
We report the serendipitous detection of the SO $J_N=6_5-5_4$ (219.949 GHz) rotational transition in archival Atacama Large Millimeter/submillimeter Array (ALMA) observations of the spiral hosting protoplanetary disks around CQ Tau (with $\approx4.9σ$ significance) and MWC 758 (with $\approx3.4σ$ significance). In the former, the SO emission comes in the shape of a ring, arises from the edge of the continuum cavity, and is qualitatively consistent, at the currently available spectral resoluti…

Maximal Speed of Glucose Change Significantly Distinguishes Prediabetes from Diabetes
Dandan Wang, Xiaoyan Chen, Jingxiang Lin, Teng Zhang, Lianyi Huang, Dongliang Leng, Xiaohua Douglas Zhang, Gang Li
https://arxiv.org/abs/2506.12567

Maximal Speed of Glucose Change Significantly Distinguishes Prediabetes from Diabetes
Rapid changes in blood glucose levels can have severe and immediate health consequences, leading to the need to develop indices for assessing these rapid changes based on continuous glucose monitoring (CGM) data. We proposed a CGM index, maxSpeed, that represents the maximum of speed of glucose change (SGC) in a subject, respectively, and conducted a clinical study to investigate this index along with SGC mean (meanSpeed) and SGC standard deviation (sdSpeed), coefficient of variation (CV), stan…

Multivariate Time-series Transformer Embeddings for Light Curves
Gabriel Chiong, Ignacio Becker, Pavlos Protopapas
https://arxiv.org/abs/2506.11637 https:/…

Multivariate Time-series Transformer Embeddings for Light Curves
Astronomical surveys produce time-series data by observing stellar objects across multiple wavelength bands. Foundational transformer-based models, such as Astromer, encode each time-series as a sequence of embeddings of uniform dimensions. However, such models operate independently on each band at a single time and do not natively leverage information across telescope filters. We extend this framework by introducing a fusion mechanism that maps the collection of single-band embeddings to a uni…

Choquet rating criteria, risk measures, and risk consistency
Nan Guo, Ruodu Wang, Chenxi Xia, Jingping Yang
https://arxiv.org/abs/2506.13435 https://

Choquet rating criteria, risk measures, and risk consistency
Credit ratings are widely used by investors as a screening device. We introduce and study several natural notions of risk consistency that promote prudent investment decisions in the framework of Choquet rating criteria. Three closely related notions of risk consistency are considered: with respect to risk aversion, the asset pooling effect, and the benefit of portfolio diversification. These notions are formulated either under a single probability measure or multiple probability measures. We s…

DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
A. Ruzza, G. Lodato, G. P. Rosotti, P. J. Armitage
https://arxiv.org/abs/2506.11200

DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
Dust substructures in protoplanetary discs can be signatures of embedded young planets whose detection and characterisation would provide a better understanding of planet formation. Traditional techniques used to link substructures' morphology to the properties of putative embedded planets present several limitations that the use of deep learning methods has partly overcome. In our previous work, we developed DBNets, a tool exploiting an ensemble of Convolutional Neural Networks (CNNs) to estim…

Variations in Dominant Wave Period in the Solar Atmosphere
Pradeep Kayshap, K. Murawski, Z. E. Musielak, Suresh Babu
https://arxiv.org/abs/2506.07493 https…

Variations in Dominant Wave Period in the Solar Atmosphere
Waves are an integral part of the solar atmosphere, and their characteristics (e.g., dominant period, range of periods, power, and phase angle) change on a diverse spatio-temporal scale. It is well well-established observationally that the dominant periods of solar oscillations are 5-min and 3-min in the photosphere and chromosphere, respectively. This shows that the wave spectra and their dominant periods evolve between these two layers. We present observational results that demonstrate variat…

This https://arxiv.org/abs/2505.19379 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Neural nanophotonic object detector with ultra-wide field-of-view
Intelligent object detection, which extracts crucial information like targets categories and locations, plays a vital role in emerging technologies including autonomous driving, the Internet of Things, and next-generation mobile communication systems. With the advancement of intelligent object detectors towards higher integration and miniaturization, their portability and adaptability to a broader range of scenarios have been significantly enhanced. However, this progress comes at the cost of r…

Multi-period Mean-Buffered Probability of Exceedance in Defined Contribution Portfolio Optimization
Duy-Minh Dang, Chang Chen
https://arxiv.org/abs/2505.22121

Multi-period Mean-Buffered Probability of Exceedance in Defined Contribution Portfolio Optimization
We investigate multi-period mean-risk portfolio optimization for long-horizon Defined Contribution plans, focusing on buffered Probability of Exceedance (bPoE), a more intuitive, dollar-based alternative to Conditional Value-at-Risk (CVaR). We formulate both pre-commitment and time-consistent Mean-bPoE and Mean-CVaR portfolio optimization problems under realistic investment constraints (e.g., no leverage, no short selling) and jump-diffusion dynamics. These formulations are naturally framed as …

This https://arxiv.org/abs/2505.19379 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Neural nanophotonic object detector with ultra-wide field-of-view
Intelligent object detection, which extracts crucial information like targets categories and locations, plays a vital role in emerging technologies including autonomous driving, the Internet of Things, and next-generation mobile communication systems. With the advancement of intelligent object detectors towards higher integration and miniaturization, their portability and adaptability to a broader range of scenarios have been significantly enhanced. However, this progress comes at the cost of r…