arXiv subjects

Zi Zhuang

Publications and source records attributed to Zi Zhuang.

4 recordsLinked to original sources

arXiv2024

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting

Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. However, these tasks are usually solved separately without ensuring coherence, which is crucial for aligning downstream decisions. Previous works mainly focus on ensuring coherence with some straightforward methods, e.g., aggregation from the forecasts of fine granularity to the coarse ones, and allocation from the coarse granularity to the fine ones. These methods merely take the temporal hierarchical structure to maintain coherence without improving the forecasting accuracy. In this paper, we propose a novel granularity message-passing mechanism (GMP) that leverages temporal hierarchy information to improve forecasting performance and also utilizes an adaptive reconciliation (AR) strategy to maintain coherence without performance loss. Furthermore, we introduce an optimization module to achieve task-based targets while adhering to more real-world constraints. Experiments on real-world datasets demonstrate that our framework (GMP-AR) achieves superior performances on temporal hierarchical forecasting tasks compared to state-of-the-art methods. In addition, our framework has been successfully applied to a real-world task of payment traffic management in Alipay by integrating with the task-based optimization module.

cs.LG↗

arXiv2024

Distribution and Properties of Molecular Gas Toward the Monoceros OB1 Region

We perform a comprehensive CO study toward the Monoceros OB1 (Mon OB1) region based on the MWISP survey at an angular resolution of about $50''$. The high-sensitivity data, together with the high dynamic range, shows that molecular gas in the $\rm 8^{\circ}\times4^{\circ}$ region displays complicated hierarchical structures and various morphology (e.g., filamentary, cavity-like, shell-like, and other irregular structures). Based on Gaussian decomposition and clustering for $\mathrm{^{13}CO}$ data, a total of 263 $\mathrm{^{13}CO}$ structures are identified in the whole region, and 88% of raw data flux is recovered. The dense gas with relatively high column density from the integrated CO emission is mainly concentrated in the region where multiple $\rm ^{13}CO$ structures are overlapped. Combining the results of 32 large $\mathrm{^{13}CO}$ structures with distances from Gaia DR3, we estimate an average distance of $\rm 729^{+45}_{-45}~pc$ for the GMC complex. The total mass of the GMC Complex traced by $\mathrm{^{12}CO}$, $\mathrm{^{13}CO}$, and $\mathrm{C^{18}O}$ are $1.1\times10^5~M_\odot$, $4.3\times10^4~M_\odot$, and $8.4\times10^3~M_\odot$, respectively. The dense gas fraction shows a clear difference between Mon OB1 GMC East (12.4%) and Mon OB1 GMC West (3.3%). Our results show that the dense gas environment is closely linked to the nearby star-forming regions. On the other hand, star-forming activities have a great influence on the physical properties of the surrounding molecular gas (e.g., greater velocity dispersion, higher temperatures, and more complex velocity structures, etc.). We also discuss the distribution/kinematics of molecular gas associated with nearby star-forming activities.

astro-ph.GA↗

arXiv2024

The Multilayer Nature of Molecular Gas toward the Cygnus Region

We study the physical properties and 3D distribution of molecular clouds (MCs) toward the Cygnus region using the MWISP CO survey and Gaia DR3 data. Based on Gaussian decomposition and clustering for $\rm ^{13}CO$ lines, over 70% of the fluxes are recovered. With the identification result of $\rm ^{13}CO$ structures, two models are designed to measure the distances of the molecular gas in velocity crowding regions. The distances of more than 200 large $\rm ^{13}CO$ structures are obtained toward the 150 square degree region. Additionally, tens of the identified MC structures coincide well with masers and/or intense mid-IR emission. We find multiple gas layers toward the region: (1) the extensive gas structures composing the Cygnus Rift from 700 pc to 1 kpc across the whole region; (2) the $\sim$ 1.3 kpc gas layer mainly in the Cygnus X South region; and (3) the 1.5 kpc dense filament at the Cygnus X North region and many cometary clouds shaped by Cygnus OB2. We also note that the spatial distribution of YSO candidates is generally consistent with the molecular gas structures. The total molecular mass of the Cygnus region is estimated to be $\sim 2.7\times10^{6}~M_{\odot}$ assuming an X-factor ratio $X_{\rm CO} = 2 \times 10^{20} \rm cm^{-2} (K\cdot km\cdot s^{-1})^{-1}$. The foreground Cygnus Rift contributes $\sim$25% of the molecular mass in the whole region. Our work presents a new 3D view of the MCs' distribution toward the Cygnus X region, as well as the exact molecular gas mass distribution in the foreground Cygnus Rift.

astro-ph.GA↗

arXiv2023

Deep Optimal Timing Strategies for Time Series

Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data. This solution has plenty of valuable applications to reduce the operation costs. In this paper, we propose a mechanism that combines a probabilistic time series forecasting task and an optimal timing decision task as a first systematic attempt to tackle these practical problems with both solid theoretical foundation and real-world flexibility. Specifically, it generates the future paths of the underlying time series via probabilistic forecasting algorithms, which does not need a sophisticated mathematical dynamic model relying on strong prior knowledge as most other common practices. In order to find the optimal execution time, we formulate the decision task as an optimal stopping problem, and employ a recurrent neural network structure (RNN) to approximate the optimal times. Github repository: \url{github.com/ChenPopper/optimal_timing_TSF}.

cs.AI↗