arXiv subjects

Jie Yu

Publications and source records attributed to Jie Yu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks

Structure-Based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO), the gradient-based SBMO framework that facilitates joint guidance signals across different modalities while preserving SE(3)-equivariance. We introduce a novel backward correction strategy that optimizes within a sliding window of the past histories, allowing for a seamless trade-off between explore-and-exploit during optimization. MolJO achieves state-of-the-art performance on CrossDocked2020 benchmark (Success Rate 51.3%, Vina Dock -9.05 and SA 0.78), more than 4x improvement in Success Rate compared to the gradient-based counterpart, and 2x "Me-Better" Ratio as much as 3D baselines. Furthermore, we extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug design such as R-group optimization and scaffold hopping, further underscoring its versatility. Code is available at https://github.com/AlgoMole/MolCRAFT.

q-bio.BM↗

arXiv2025

Optical spectroscopic signatures of the red giant evolutionary state

Modern spectroscopic surveys output large data volumes. Theoretical models provide a means to transform the information encoded in these data to measurements of physical stellar properties. However, in detail the models are incomplete and simplified, and prohibit interpretation of the fine details in spectra. Instead, the available data provide an opportunity to use data-driven, differential analysis techniques, as a means towards understanding spectral signatures. We deploy such an analysis to examine core helium-fusing red clump (RC) and shell hydrogen-fusing red giant branch (RGB) stars, to uncover signatures of evolutionary state imprinted in optical stellar spectra. We exploit 786 pairs of RC and RGB stars from the GALAH survey, chosen to minimise spectral differences, with evolutionary state classifications from TESS and K2 asteroseismology. We report sub-percent residual, systematic spectral differences between the two classes of stars, and show that these residuals are significant compared to a reference sample of RC$-$RC and RGB$-$RGB pairs selected using the same criteria. First, we report systematic differences in the Swan ($\rm{C}_2$) band and CN bands caused by stellar evolution and a difference in mass, where RGB stars at similar stellar parameters have higher masses than RC stars. Secondly, we observe systematic differences in the line-width of the H$_α$ and H$_β$ lines caused by a difference in microturbulence, as measured by GALAH, where we measure higher microturbulence in RC stars than RGB stars. This work demonstrates the ability of large surveys to uncover the subtle spectroscopic signatures of stellar evolution using model-free, data-driven methods.

astro-ph.SR↗

arXiv2025

Identifying Knowledge Editing Types in Large Language Models

Knowledge editing has emerged as an efficient technique for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there is a lack of effective measures to prevent the malicious misuse of this technique, which could lead to harmful edits in LLMs. These malicious modifications could cause LLMs to generate toxic content, misleading users into inappropriate actions. In front of this risk, we introduce a new task, $\textbf{K}$nowledge $\textbf{E}$diting $\textbf{T}$ype $\textbf{I}$dentification (KETI), aimed at identifying different types of edits in LLMs, thereby providing timely alerts to users when encountering illicit edits. As part of this task, we propose KETIBench, which includes five types of harmful edits covering the most popular toxic types, as well as one benign factual edit. We develop five classical classification models and three BERT-based models as baseline identifiers for both open-source and closed-source LLMs. Our experimental results, across 92 trials involving four models and three knowledge editing methods, demonstrate that all eight baseline identifiers achieve decent identification performance, highlighting the feasibility of identifying malicious edits in LLMs. Additional analyses reveal that the performance of the identifiers is independent of the reliability of the knowledge editing methods and exhibits cross-domain generalization, enabling the identification of edits from unknown sources. All data and code are available in https://github.com/xpq-tech/KETI.

cs.CL↗

arXiv2025

Enhanced magnetic activity in rapidly rotating binary stars

Stellar activity is fundamental to stellar evolution and the formation and habitability of exoplanets. The interaction between convective motions and rotation in cool stars results in a dynamo process that drives magnetic surface activity. In single stars, activity increases with rotation rate until it saturates for stars with rotation periods Prot < 3 - 10 d. However, the mechanism responsible for saturation remains unclear. Observations indicate that red giants in binary systems that are in spin-orbit resonance exhibit stronger chromospheric activity than single stars with similar rotation rates, suggesting that tidal flows can influence surface activity. Here, we investigate the chromospheric activity of main-sequence binary stars to understand the impact of tidal forces on saturation phenomena. For binaries with 0.5 < Prot/d < 1, mainly contact binaries that share a common thermal envelope, we find enhanced activity rather than saturation. This result supports theoretical predictions that a large-scale $α$ - $ω$ dynamo during common-envelope evolution can generate strong magnetic fields. We also observe supersaturation in chromospheric activity, a phenomenon tentatively noted previously in coronal activity, where activity levels fall below saturation and decrease with shorter rotation periods. Our findings emphasise the importance of studying stellar activity in stars with extreme properties compared to the Sun's.

astro-ph.SR↗

arXiv2025

LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment

As Large Language Models (LLMs) demonstrate exceptional performance across various domains, deploying LLMs on edge devices has emerged as a new trend. Quantization techniques, which reduce the size and memory requirements of LLMs, are effective for deploying LLMs on resource-limited edge devices. However, existing one-size-fits-all quantization methods often fail to dynamically adjust the memory requirements of LLMs, limiting their applications to practical edge devices with various computation resources. To tackle this issue, we propose Layer-Specific Adaptive Quantization (LSAQ), a system for adaptive quantization and dynamic deployment of LLMs based on layer importance. Specifically, LSAQ evaluates the importance of LLMs' neural layers by constructing top-k token sets from the inputs and outputs of each layer and calculating their Jaccard similarity. Based on layer importance, our system adaptively adjusts quantization strategies in real time according to the computation resource of edge devices, which applies higher quantization precision to layers with higher importance, and vice versa. {Experimental results show that LSAQ consistently outperforms the selected quantization baselines in terms of perplexity and zero-shot tasks. Additionally, it can devise appropriate quantization schemes for different usage scenarios to facilitate the deployment of LLMs.

cs.CL↗

arXiv2025

Asteroseismology of Long-Period Variables with OGLE-IV data: Using Global Seismic Parameters as Luminosity Indicators

Long-period variables (LPVs) are high-luminosity red giants or supergiants with pulsation periods ranging from days to years. Many LPVs in the Large Magellanic Cloud (LMC) and Galactic Bulge (BLG) have been continuously observed over a time span of 26 years by the Optical Gravitational Lensing Experiment (OGLE) survey. Using OGLE-IV data, we applied Gaussian Processes with kernels tailored for solar-like oscillations to extract two global asteroseismic parameters: the frequency of maximum power (numax) and the large frequency separation (Dnu), for LPVs with primary mode periods (P1) between 10 and 100 days in the LMC and BLG. We found that the numax-Dnu relation for LPVs in this work aligns with that of lower-luminosity Kepler red giants, confirming that the pulsations of these LPVs are likely solar-like. We found that numax and Dnu can serve as luminosity indicators. Compared to P1, numax and Dnu exhibit significantly tighter correlations with the absolute magnitude in the 2MASS K_s band (M_{K}), with corresponding scatter of 0.27 mag and 0.21 mag, respectively. Using the calibrated numax-mk and Dnu-mk relations for LPVs in the LMC, we determined the M_{K} values for individual stars in the BLG. By accounting for extinction, we further calculated the distances to 4,948 BLG stars. The peak of the resulting distance distribution corresponds to an estimated distance to the Galactic center of approximately 9.1 kpc, which appears to be overestimated, suggesting that the seismic luminosity relation calibrated from the LMC may not be directly applicable to BLG stars.

astro-ph.SR↗

arXiv2025

C3PO IV: co-natal stars depleted in refractories are magnetically more active -- possible imprints of planets

Chemical abundance anomalies in twin stars have recently been considered tell-tale signs of interactions between stars and planets. While such signals are prevalent, their nature remains a subject of debate. On one hand, exoplanet formation may induce chemical depletion in host stars by locking up refractory elements. On the other hand, exoplanet engulfment can result in chemical enrichment, both processes potentially producing similar differential signals. In this study, we aim to observationally disentangle these processes by using the Ca II infrared triplet to measure the magnetic activity of 125 co-moving star pairs with high SNR, high-resolution spectra from the Magellan, Keck, and VLT telescopes. We find that co-natal star pairs in which the two stars exhibit significant chemical abundance differences also show differences in their magnetic activity, with stars depleted in refractories being magnetically more active. Furthermore, the strength of this correlation between differential chemical abundances and differential magnetic activity increases with condensation temperature. One possible explanation is that the chemical anomaly signature may be linked to planet formation, wherein refractory elements are locked into planets, and the host stars become more active due to more efficient contraction during the pre-main-sequence phase or star-planet tidal and magnetic interactions.

astro-ph.EP↗

arXiv2025

SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering

The general capabilities of large language models (LLMs) make them the infrastructure for various AI applications, but updating their inner knowledge requires significant resources. Recent model editing is a promising technique for efficiently updating a small amount of knowledge of LLMs and has attracted much attention. In particular, local editing methods, which directly update model parameters, are proven suitable for updating small amounts of knowledge. Local editing methods update weights by computing least squares closed-form solutions and identify edited knowledge by vector-level matching in inference, which achieve promising results. However, these methods still require a lot of time and resources to complete the computation. Moreover, vector-level matching lacks reliability, and such updates disrupt the original organization of the model's parameters. To address these issues, we propose a detachable and expandable Subject Word Embedding Altering (SWEA) framework, which finds the editing embeddings through token-level matching and adds them to the subject word embeddings in Transformer input. To get these editing embeddings, we propose optimizing then suppressing fusion method, which first optimizes learnable embedding vectors for the editing target and then suppresses the Knowledge Embedding Dimensions (KEDs) to obtain final editing embeddings. We thus propose SWEA$\oplus$OS method for editing factual knowledge in LLMs. We demonstrate the overall state-of-the-art (SOTA) performance of SWEA$\oplus$OS on the CounterFact and zsRE datasets. To further validate the reasoning ability of SWEA$\oplus$OS in editing knowledge, we evaluate it on the more complex RippleEdits benchmark. The results demonstrate that SWEA$\oplus$OS possesses SOTA reasoning ability.

cs.CL↗

arXiv2025

How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization

Large Language Models (LLMs) exhibit strong general language capabilities. However, fine-tuning these models on domain-specific tasks often leads to catastrophic forgetting, where the model overwrites or loses essential knowledge acquired during pretraining. This phenomenon significantly limits the broader applicability of LLMs. To address this challenge, we propose a novel approach to compute the element-wise importance of model parameters crucial for preserving general knowledge during fine-tuning. Our method utilizes a dual-objective optimization strategy: (1) regularization loss based on element-wise parameter importance, which constrains the updates to parameters crucial for general knowledge; (2) cross-entropy loss to adapt to domain-specific tasks. Additionally, we introduce layer-wise coefficients to account for the varying contributions of different layers, dynamically balancing the dual-objective optimization. Extensive experiments on scientific, medical, and physical tasks using GPT-J and LLaMA-3 demonstrate that our approach mitigates catastrophic forgetting while enhancing model adaptability. Compared to previous methods, our solution is approximately 20 times faster and requires only 10-15% of the storage, highlighting the practical efficiency. The code will be released.

cs.CL↗

arXiv2025

How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Model

We explore Multimodal Large Language Models (MLLMs), which integrate LLMs like GPT-4 to handle multimodal data, including text, images, audio, and more. MLLMs demonstrate capabilities such as generating image captions and answering image-based questions, bridging the gap towards real-world human-computer interactions and hinting at a potential pathway to artificial general intelligence. However, MLLMs still face challenges in addressing the semantic gap in multimodal data, which may lead to erroneous outputs, posing potential risks to society. Selecting the appropriate modality alignment method is crucial, as improper methods might require more parameters without significant performance improvements. This paper aims to explore modality alignment methods for LLMs and their current capabilities. Implementing effective modality alignment can help LLMs address environmental issues and enhance accessibility. The study surveys existing modality alignment methods for MLLMs, categorizing them into four groups: (1) Multimodal Converter, which transforms data into a format that LLMs can understand; (2) Multimodal Perceiver, which improves how LLMs percieve different types of data; (3) Tool Learning, which leverages external tools to convert data into a common format, usually text; and (4) Data-Driven Method, which teaches LLMs to understand specific data types within datasets.

cs.CL↗

arXiv2024

Planets Around Solar Twins/Analogs (PASTA) I.: High precision stellar chemical abundance for 17 planet-hosting stars and the condensation temperature trend

The Sun is depleted in refractory elements compared to nearby solar twins, which may be linked to the formation of giant or terrestrial planets. Here we present high-resolution, high signal-to-noise spectroscopic data for 17 solar-like stars hosting planets, obtained with Magellan II/MIKE, to investigate whether this depletion is related to planet formation. We derive stellar parameters, including stellar atmosphere, age, radius, mass, and chemical abundances for 22 elements from carbon to europium through line-by-line differential analysis. Our uncertainties range from 0.01 dex for Fe and Si to 0.08 dex for Sr, Y, and Eu. By comparing the solar abundances to those of the 17 stars, we investigate the differential abundance ([X/Fe]$_{\rm solar}$ - [X/Fe]$_{\rm star}$) versus condensation temperature ($T_c$) trend. In particular, we apply Galactic chemical evolution corrections to five solar twins within the full sample. Our results conform to previous studies that the Sun is relatively depleted in refractory compared to volatile elements. For both five solar twins and the rest of solar-like stars, we find that all stars hosting known gas giant planets exhibit negative $T_c$ trend slopes, suggesting that the Sun is relatively depleted in refractory elements compared to similar giant-planet-host stars. Additionally, we find no correlation between $T_c$ trend slopes and the total mass of detected terrestrial planets in each system, suggesting that terrestrial planet formation may not be the cause of refractory element depletion in the Sun.

astro-ph.SR↗

arXiv2024

Model Editing for LLMs4Code: How Far are We?

Large Language Models for Code (LLMs4Code) have been found to exhibit outstanding performance in the software engineering domain, especially the remarkable performance in coding tasks. However, even the most advanced LLMs4Code can inevitably contain incorrect or outdated code knowledge. Due to the high cost of training LLMs4Code, it is impractical to re-train the models for fixing these problematic code knowledge. Model editing is a new technical field for effectively and efficiently correcting erroneous knowledge in LLMs, where various model editing techniques and benchmarks have been proposed recently. Despite that, a comprehensive study that thoroughly compares and analyzes the performance of the state-of-the-art model editing techniques for adapting the knowledge within LLMs4Code across various code-related tasks is notably absent. To bridge this gap, we perform the first systematic study on applying state-of-the-art model editing approaches to repair the inaccuracy of LLMs4Code. To that end, we introduce a benchmark named CLMEEval, which consists of two datasets, i.e., CoNaLa-Edit (CNLE) with 21K+ code generation samples and CodeSearchNet-Edit (CSNE) with 16K+ code summarization samples. With the help of CLMEEval, we evaluate six advanced model editing techniques on three LLMs4Code: CodeLlama (7B), CodeQwen1.5 (7B), and Stable-Code (3B). Our findings include that the external memorization-based GRACE approach achieves the best knowledge editing effectiveness and specificity (the editing does not influence untargeted knowledge), while generalization (whether the editing can generalize to other semantically-identical inputs) is a universal challenge for existing techniques. Furthermore, building on in-depth case analysis, we introduce an enhanced version of GRACE called A-GRACE, which incorporates contrastive learning to better capture the semantics of the inputs.

cs.SE↗

arXiv2024

On the distribution of the Cantor-integers

For any positive integer $p\geq 3$, let $A$ be a proper subset of $\{0,1,\ldots, p-1\}$ with $\sharp A=s\geq 2$. Suppose $h: \{0,1,\ldots,s-1\}\to A$ is a one-to-one map which is strictly increasing with $A=\{h(0),h(1),\ldots,h(s-1)\}$. We focus on so-called Cantor-integers $\{a_n\}_{n\geq 1}$, which consist of these positive integers $n$ such that all the digits in the $p$-ary expansion of $n$ belong to $A$. Let $\mathfrak{C}=\left\{\sum\limits_{n\geq 1}\frac{\varepsilon_n}{p^n}: \varepsilon_n\in A \text{ for any positive integer } n\right\}$ be the appropriate Cantor set, and denote the classic self-similar measure supported on $\mathfrak{C}$ by $μ_{\mathfrak{C}}$. Now that $n^{\log_s p}$ is the growth order of $a_n$ and $\left\{\frac{a_n}{n^{\log_s p}}:~n\geq 1\right\}'$ is precisely the set $\left\{\frac{x}{(μ_{\mathfrak{C}}([0,x]))^{\log_s p}}: x\in\mathfrak{C}\cap[\frac{h(1)}{p},1]\right\}$, where $E'$ is the set of limit points of $E$, we show that $\left\{\frac{a_n}{n^{\log_s p}}:~n\geq 1\right\}'$ is just an interval $[m,M]$ with $m:=\inf\left\{\frac{a_n}{n^{\log_s p}}:n\geq 1\right\}$ and $M:=\sup\left\{\frac{a_n}{n^{\log_s p}}:n\geq 1\right\}$. In particular, $\left\{\frac{x}{(μ_{\mathfrak{C}}([0,x]))^{\log_s p}}: x\in\mathfrak{C}\backslash\{0\}\right\}=[m,M]$ if $0\in A$, and $m=\frac{q(s-1)+r}{p-1}, M=\frac{q(p-1)+pr}{p-1}$ if the set $A$ consists of all the integers in $\{0,1,\ldots, p-1\}$ which have the same remainder $r\in\{0,1,\ldots,q-1\}$ modulus $q$ for some positive integer $q \geq 2$ (i.e. $h(x)=qx+r$). We further show that the sequence $\left\{\frac{a_n}{n^{\log_s p}}\right\}_{n\geq 1}$ is not uniformly distributed modulo 1, and it does not have the cumulative distribution function, but has the logarithmic distribution function (give by a specific Lebesgue integral).

math.NT↗

arXiv2024

KAOS: Large Model Multi-Agent Operating System

The intelligent interaction model based on large models reduces the differences in user experience across various system platforms but faces challenges in multi-agent collaboration and resource sharing. To demonstrate a uniform user experience across different foundational software platforms and address resource coordination management challenges, this paper proposes KAOS, a multi-agent operating system based on the open-source Kylin. The research method involves empowering agents with large models to serve applications. First, by introducing management role agents and vertical multi-agent collaboration to construct or replace typical application software. Second, by studying system-level shared resource scheduling strategies to enhance user experience and optimize resource utilization. And finally, by validating the efficiency and superiority of the large model multi-agent operating system through real applications and scoring intelligence. The feasibility of this system is demonstrated, providing a new perspective for the development of multi-agent operating systems. Experimental results show significant advantages of multi-agent collaboration in various application scenarios.

cs.MA↗

arXiv2024

Above-room-temperature intrinsic ferromagnetism in ultrathin van der Waals crystal Fe$_{3+x}$GaTe$_2$

Two-dimensional (2D) van der Waals (vdW) magnets are crucial for ultra-compact spintronics. However, so far, no vdW crystal has exhibited tunable above-room-temperature intrinsic ferromagnetism in the 2D ultrathin regime. Here, we report the tunable above-room-temperature intrinsic ferromagnetism in ultrathin vdW crystal Fe$_{3+x}$GaTe$_2$ ($x$ = 0 and 0.3). By increasing the Fe content, the Curie temperature (TC) and room-temperature saturation magnetization of bulk Fe$_{3+x}$GaTe$_2$ crystals are enhanced from 354 to 376 K and 43.9 to 50.4 emu/g, respectively. Remarkably, the robust anomalous Hall effect in 3-nm Fe$_{3.3}$GaTe$_2$ indicate a record-high TC of 340 K and a large room-temperature perpendicular magnetic anisotropy energy of 6.6 * 10^5 J/m$^3$, superior to other ultrathin vdW ferromagnets. First-principles calculations reveal the asymmetric density of states and an additional large spin exchange interaction in ultrathin Fe$_{3+x}$GaTe$_2$ responsible for robust intrinsic ferromagnetism and higher Tc. This work opens a window for above-room-temperature ultrathin 2D magnets in vdW-integrated spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Genetics-based deperturbation analysis for the spin-orbit coupled ${\rm A}^1Σ^+$ and ${\rm b}^3Π_{0^+}$ states of LiRb

We present a deperturbation analysis of the spin-orbit coupled $\rm A^1Σ^+$ and $\rm b^3Π_{0^+}$ states of LiRb based on the rovibrational energy levels observed previously by photoassociation spectroscopy in bosonic $^7$Li$^{85}$Rb molecule. Using the genetic algorithm, we fit the potential energy curves of the $\rm A^1Σ^+$ state and the $\rm b^3Π$ state into point-wise form. We then fit these point-wise potentials along with the spin-orbit coupling into expanded Morse oscillator functional form and optimise analytical parameters based on the experimental data. From the fitted results, we calculate the transition dipole moment matrix elements for transitions from the rovibrational levels of the coupled $\rm A^1Σ^+$-$\rm b^3Π_{0^+}$ state to the Feshbach state and the absolute rovibrational ground state for fermionic $^6$Li$^{87}$Rb molecule. Based on the calculated transition dipole moment matrix elements, several levels of the coupled $\rm A^1Σ^+$-$\rm b^3Π_{0^+}$ state are predicted to be suitable as the intermediate state for stimulated Raman adiabatic passage transfer from the Feshbach state to the absolute rovibrational ground state. In addition, we also provide a similar estimation for ${\rm B}^1Π$-${\rm c}^3Σ_1^+$-${\rm b}^3Π_1$ state based on available $ab\ initio$ interaction potentials.

physics.atom-ph↗

arXiv2024

DIM: Dynamic Integration of Multimodal Entity Linking with Large Language Model

Our study delves into Multimodal Entity Linking, aligning the mention in multimodal information with entities in knowledge base. Existing methods are still facing challenges like ambiguous entity representations and limited image information utilization. Thus, we propose dynamic entity extraction using ChatGPT, which dynamically extracts entities and enhances datasets. We also propose a method: Dynamically Integrate Multimodal information with knowledge base (DIM), employing the capability of the Large Language Model (LLM) for visual understanding. The LLM, such as BLIP-2, extracts information relevant to entities in the image, which can facilitate improved extraction of entity features and linking them with the dynamic entity representations provided by ChatGPT. The experiments demonstrate that our proposed DIM method outperforms the majority of existing methods on the three original datasets, and achieves state-of-the-art (SOTA) on the dynamically enhanced datasets (Wiki+, Rich+, Diverse+). For reproducibility, our code and collected datasets are released on \url{https://github.com/season1blue/DIM}.

cs.CL↗

arXiv2024

Survey of extended Main Sequence Turn-offs in Galactic Open Clusters: Stellar rotations from Gaia RVS spectra

The origin of extended main-sequence turn-offs (eMSTO) in star clusters younger than 2 Gyr still challenges our current understanding of stellar evolution. Exploiting data from Gaia Data Release 3 (DR3), we investigate eMSTOs in a large sample of 32 Galactic open clusters younger than 2.4 Gyr. We first validate Gaia rotational velocities from Radial Velocity Spectrometer (RVS) spectra by comparing them with literature values and assessing their correlation with magnetic activity measurements from LAMOST spectra. We detect a general positive correlation between turn-off color and projected stellar rotation, with slow-rotating stars predominantly found on the bluer side of the turn-off. Comparing our observations with theoretical models, we find that the eMSTO morphology is well-reproduced by a single population formed with a high rotation rate, and observed with rotation axis inclination ranging between 0$^\circ$ (pole-on) and 90$^\circ$ (edge-on). This contrasts with observations of Magellanic Clouds clusters, where a population of non-rotating stars appears to be ubiquitous in clusters younger than 700 Myr. However, we note that our interpretation, while successfully explaining the overall eMSTO morphology, cannot fully explain the observed projected rotational velocities. Additionally, two young clusters, NGC 3532 and NGC 2287, exhibit moderate evidence of a split main sequence in color and rotation, suggesting a possible small spread in the initial rotation rate. Finally, we advise caution in determining the ages of young clusters from non-rotating isochrones, as neglecting the effects of stellar rotation can impact the isochrone dating by up to factors of 5-20%.

astro-ph.GA↗