arXiv subjects

Yao Yin

Publications and source records attributed to Yao Yin.

7 recordsLinked to original sources

arXiv2026

DRAMPyML: A Formal Description of DRAM Protocols with Timed Petri Nets

The JEDEC committee defines various domain-specific DRAM standards. These standards feature increasingly complex and evolving protocol specifications, which are detailed in timing diagrams and command tables. Understanding these protocols is becoming progressively challenging as new features and complex device hierarchies are difficult to comprehend without an expressive model. While each JEDEC standard features a simplified state machine, this state machine fails to reflect the parallel operation of memory banks. In this paper, we present an evolved modeling approach based on timed Petri nets and Python. This model provides a more accurate representation of DRAM protocols, making them easier to understand and directly executable, which enables the evaluation of interesting metrics and the verification of controller RTL models, DRAM logic and memory simulators.

cs.AR↗

arXiv2026

MMTS-BENCH: A Comprehensive Benchmark for Time Series Understanding and Reasoning

Time series data are central to domains such as finance, healthcare, and cloud computing, yet existing benchmarks for evaluating various large language models (LLMs) on temporal tasks remain scattered and unsystematic. To bridge this gap, we introduce MMTS-BENCH, a comprehensive multimodal benchmark built upon a hierarchical taxonomy of time-series tasks, spanning structural awareness, feature analysis, temporal reasoning, sequence matching and cross-modal alignment. MMTS-BENCH comprises 2,424 time series question answering (TSQA) pairs across 4 subsets: Base, InWild, Match, and Align, generated through a progressive real-world QA framework and modular synthetic data construction. We conduct extensive evaluations on closed-source, open-source LLMs and existing time series adapted large language models (TS-LLMs), revealing that: (1) TS-LLMs significantly lag behind general-purpose LLMs in cross-domain generalization, (2) LLMs show weaknesses in local tasks compared to global tasks, (3) chain-of-thought (CoT) reasoning and multimodal integration substantially improve performance, and (4) the dominant factor in existing TS-LLMs remains the backbone network capability rather than the time series encoder design. MMTS-BENCH not only provides a rigorous evaluation framework but also offers clear directions for advancing LLMs toward robust, interpretable, and generalizable time-series reasoning.

cs.DB↗

arXiv2022

The First Two Years of FLEET: an Active Search for Superluminous Supernovae

In November 2019 we began operating FLEET (Finding Luminous and Exotic Extragalactic Transients), a machine learning algorithm designed to photometrically identify Type I superluminous supernovae (SLSNe) in transient alert streams. Using FLEET, we spectroscopically classified 21 of the 50 SLSNe identified worldwide between November 2019 and January 2022. Based on our original algorithm, we anticipated that FLEET would achieve a purity of about 50\% for transients with a probability of being a SLSN, \pslsn$>0.5$; the true on-sky purity we obtained is closer to 80\%. Similarly, we anticipated FLEET could reach a completeness of about 30\%, and we indeed measure an upper limit on the completeness of $\approx 33$\%. Here, we present FLEET 2.0, an updated version of FLEET trained on 4,780 transients (almost 3 times more than in FLEET 1.0). FLEET 2.0 has a similar predicted purity to FLEET 1.0, but outperforms FLEET 1.0 in terms of completeness, which is now closer to $\approx 40$\% for transients with \pslsn$>0.5$. Additionally, we explore possible systematics that might arise from the use of FLEET for target selection. We find that the population of SLSNe recovered by FLEET is mostly indistinguishable from the overall SLSN population, in terms of physical and most observational parameters. We provide FLEET as an open source package on GitHub https://github.com/gmzsebastian/FLEET

astro-ph.HE↗

arXiv2022

The Renovated Thacher Observatory and First Science Results

Located on the campus of the Thacher School in Southern California, the Thacher Observatory has a legacy of astronomy research and education that dates back to the late 1950's. In 2016, the observatory was fully renovated with upgrades including a new 0.7-m telescope, a research grade camera, and a slit dome with full automation capabilities. The low-elevation site is bordered by the Los Padres National Forest and therefore affords dark to very dark skies allowing for accurate and precise photometric observations. We present a characterization of the site including sky brightness, weather, and seeing, and we demonstrate the on-sky performance of the facility. Our primary research programs are based around our multi-band photometric capabilities and include photometric monitoring of variable sources, a nearby supernova search and followup program, a quick response transient followup effort, and exoplanet and eclipsing binary light curves. Select results from these programs are included in this work which highlight the broad range of science available to an automated observatory with a moderately sized telescope.

astro-ph.IM↗

arXiv2021

Optical Observations and Modeling of the Superluminous Supernova 2018lfe

We present optical imaging and spectroscopy of SN\,2018lfe, which we classify as a Type I superluminous supernova (SLSN-I) at a redshift of $z = 0.3501$ with a peak absolute magnitude of $M_r\approx -22.1$ mag, one of the brightest SLSNe discovered. SN\,2018lfe was identified for follow-up using our FLEET machine learning pipeline. Both the light curve and the spectra of SN\,2018lfe are consistent with the broad population of SLSNe. We fit the light curve with a magnetar central engine model and find an ejecta mass of $M_{\rm ej}\approx 3.8$ M$_\odot$, a magnetar spin period of $P\approx 2.9$ ms and a magnetic field strength of $B_{\perp}\approx 2.8\times 10^{14}$ G. The magnetic field strength is near the top of the distribution for SLSNe, while the spin period and ejecta mass are near the median values of the distribution for SLSNe. From late-time imaging and spectroscopy we find that the host galaxy of SN\,2018lfe has an absolute magnitude of $M_r\approx -17.85$, ($L_B \approx 0.029$ $L^*$), and an inferred metallicity of $Z\approx 0.3$ Z$_\odot$, and star formation rate of $\approx 0.8$ M$_\odot$ yr$^{-1}$.

astro-ph.HE↗

arXiv2020

FLEET: A Redshift-Agnostic Machine Learning Pipeline to Rapidly Identify Hydrogen-Poor Superluminous Supernovae

Over the past decade wide-field optical time-domain surveys have increased the discovery rate of transients to the point that $\lesssim 10\%$ are being spectroscopically classified. Despite this, these surveys have enabled the discovery of new and rare types of transients, most notably the class of hydrogen-poor superluminous supernovae (SLSN-I), with about 150 events confirmed to date. Here we present a machine-learning classification algorithm targeted at rapid identification of a pure sample of SLSN-I to enable spectroscopic and multi-wavelength follow-up. This algorithm is part of the FLEET (Finding Luminous and Exotic Extragalactic Transients) observational strategy. It utilizes both light curve and contextual information, but without the need for a redshift, to assign each newly-discovered transient a probability of being a SLSN-I. This classifier can achieve a maximum purity of about 85\% (with 20\% completeness) when observing a selection of SLSN-I candidates. Additionally, we present two alternative classifiers that use either redshifts or complete light curves and can achieve an even higher purity and completeness. At the current discovery rate, the FLEET algorithm can provide about $20$ SLSN-I candidates per year for spectroscopic follow-up with 85\% purity; with the Legacy Survey of Space and Time we anticipate this will rise to more than $\sim 10^3$ events per year.

astro-ph.HE↗

arXiv2011

First-principles calculations on temperature- dependent elastic constants of rare-earth intermetallic compounds:YAg and YCu

we present the temperature-dependent elastic constants of two ductile rare-earth intermetallic compounds YAg and YCu with CsCl-type B2 structure by using a first-principles approach. The elastic moduli as a function of temperature are predicted from the combination of static volumedependent elastic constants obtained by the first-principles total-energy method with density functional theory and the thermal expansion obtained by the first-principles phonon calculations with density-functional perturbation theory. The comparison between our calculated results and the available experimental data for Ag and Cu provides good agreements. In the calculated temperature $0-1000K$, the elastic constants of YAg and YCu follow a normal behavior with temperature that those decrease with increasing temperature, and satisfy the stability conditions for B2 structures. The Cauchy pressure for YAg and YCu as a function of temperature is also discussed, and our results mean that YAg and YCu become more ductile while increasing temperature.

cond-mat.mtrl-sci↗