arXiv subjects

Luyao Zou

Publications and source records attributed to Luyao Zou.

8 recordsLinked to original sources

arXiv2026

Molecule-dependent Abundance Behavior of Oxygen-bearing Complex Organics in High-Mass Star-Forming Regions: A Uniform 50-source Survey

We present a uniform IRAM-30\,m survey analysis of four oxygen-bearing complex organic molecules (COMs), methanol (CH$_3$OH), acetaldehyde (CH$_3$CHO), methyl formate (CH$_3$OCHO), and dimethyl ether (CH$_3$OCH$_3$), toward 50 high-mass star-forming regions (HMSFRs) associated with 6.7\,GHz methanol masers. Column densities were derived through a homogeneous rotation-diagram approach, with CH$_3$CN used as a proxy excitation-temperature reference when needed. In CH$_3$OH-normalized abundance-ratio space, CH$_3$OCHO/CH$_3$OH and CH$_3$OCH$_3$/CH$_3$OH show the strongest pairwise correlation, whereas the correlations involving CH$_3$CHO are weaker. No clear monotonic trends are found with Galactocentric distance or beam-averaged H$_2$ column density. Comparison with previous observations places the CH$_3$OCHO--CH$_3$OCH$_3$ behavior within the range of earlier abundance-ratio measurements, while CH$_3$CHO shows larger inter-study variation. A representative warm-up chemical model is used only for qualitative comparison with the observed abundance ranges, which are most closely matched during the decline from the post-desorption abundance peaks in the model. These results provide homogeneous beam-averaged abundance-ratio constraints for common O-bearing COMs in high-mass star-forming regions and show that their source-to-source behavior is molecule-dependent rather than fully described by a single common abundance pattern.

astro-ph.GA↗

arXiv2026

A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System

With the development of wireless network, Multi-Access Edge Computing (MEC) and Artificial Intelligence (AI)-native Radio Access Network (RAN) have attracted significant attention. Particularly, the integration of AI-RAN and MEC is envisioned to transform network efficiency and responsiveness. Therefore, it is valuable to investigate AI-RAN enabled MEC system. Federated learning (FL) nowadays is emerging as a promising approach for AI-RAN enabled MEC system, in which edge devices are enabled to train a global model cooperatively without revealing their raw data. However, conventional FL encounters the challenge in processing the non-independent and identically distributed (non-IID) data. Single prototype obtained by averaging the embedding vectors per class can be employed in FL to handle the data heterogeneity issue. Nevertheless, this may result in the loss of useful information owing to the average operation. Therefore, in this paper, a multi-prototype-guided federated knowledge distillation (MP-FedKD) approach is proposed. Particularly, self-knowledge distillation is integrated into FL to deal with the non-IID issue. To cope with the problem of information loss caused by single prototype-based strategy, multi-prototype strategy is adopted, where we present a conditional hierarchical agglomerative clustering (CHAC) approach and a prototype alignment scheme. Additionally, we design a novel loss function (called LEMGP loss) for each local client, where the relationship between global prototypes and local embedding will be focused. Extensive experiments over multiple datasets with various non-IID settings showcase that the proposed MP-FedKD approach outperforms the considered state-of-the-art baselines regarding accuracy, average accuracy and errors (RMSE and MAE).

cs.LG↗

arXiv2026

Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagery

Federated learning (FL) has recently become a promising solution for analyzing remote sensing satellite imagery (RSSI). However, the large scale and inherent data heterogeneity of images collected from multiple satellites, where the local data distribution of each satellite differs from the global one, present significant challenges to effective model training. To address this issue, we propose a Geometric Knowledge-Guided Federated Dual Knowledge Distillation (GK-FedDKD) framework for RSSI analysis. In our approach, each local client first distills a teacher encoder (TE) from multiple student encoders (SEs) trained with unlabeled augmented data. The TE is then connected with a shared classifier to form a teacher network (TN) that supervises the training of a new student network (SN). The intermediate representations of the TN are used to compute local covariance matrices, which are aggregated at the server to generate global geometric knowledge (GGK). This GGK is subsequently employed for local embedding augmentation to further guide SN training. We also design a novel loss function and a multi-prototype generation pipeline to stabilize the training process. Evaluation over multiple datasets showcases that the proposed GK-FedDKD approach is superior to the considered state-of-the-art baselines, e.g., the proposed approach with the Swin-T backbone surpasses previous SOTA approaches by an average 68.89% on the EuroSAT dataset.

cs.CV↗

arXiv2025

Evidence for electron spin-torsion coupling in the rotational spectrum of the CH$_3$CO radical

Open-shell non-rigid molecular systems exhibiting an internal rotation are likely candidates for a coupling between the spin angular momentum of the unpaired electron and the torsional motion. This electron spin-torsion coupling lacked both an experimental validation and a theoretical modeling. Here, the first experimental observation of the electron spin-torsion coupling is reported analyzing the pure rotational spectrum at millimeter wavelengths of the CH$_3$CO radical, a $^2\Sigma$ open-shell molecule displaying an internal rotation of its methyl group. To account for this coupling, a specific Hamiltonian incorporating the rotational, torsional, and electronic degrees of freedom is developed and allows us to reproduce the experimental spectrum. The present demonstration of the electron spin-torsion coupling will undoubtedly be key to future investigations of large open-shell molecules exhibiting a complex internal dynamics.

physics.chem-ph↗

arXiv2024

Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach

Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an effective EOD processing mechanism, it is imperative to investigate: 1) the challenge of processing the observed data without transmitting those large-size data to the ground because the connection between the satellites and the ground stations is intermittent, and 2) the challenge of processing the non-independent and identically distributed (non-IID) satellite data. In this paper, to cope with those challenges, we propose an orbit-based spectral clustering-assisted clustered federated self-knowledge distillation (OSC-FSKD) approach for each orbit of an LEO satellite constellation, which retains the advantage of FL that the observed data does not need to be sent to the ground. Specifically, we introduce normalized Laplacian-based spectral clustering (NLSC) into federated learning (FL) to create clustered FL in each round to address the challenge resulting from non-IID data. Particularly, NLSC is adopted to dynamically group clients into several clusters based on cosine similarities calculated by model updates. In addition, self-knowledge distillation is utilized to construct each local client, where the most recent updated local model is used to guide current local model training. Experiments demonstrate that the observation accuracy obtained by the proposed method is separately 1.01x, 2.15x, 1.10x, and 1.03x higher than that of pFedSD, FedProx, FedAU, and FedALA approaches using the SAT4 dataset. The proposed method also shows superiority when using other datasets.

cs.NI↗

arXiv2024

Cyber Attacks Prevention Towards Prosumer-based EV Charging Stations: An Edge-assisted Federated Prototype Knowledge Distillation Approach

In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection on prosumers' network traffic (NT) data, and 2) cyber-attack intervention. To establish an effective prevention mechanism, several challenges need to be tackled, for instance, the NT data per prosumer may be non-independent and identically distributed (non-IID), and the boundary between benign and malicious traffic becomes blurred. To this end, we propose an edge-assisted federated prototype knowledge distillation (E-FPKD) approach, where each client is deployed on a dedicated local edge server (DLES) and can report its availability for joining the federated learning (FL) process. Prior to the E-FPKD approach, to enhance accuracy, the Pearson Correlation Coefficient is adopted for feature selection. Regarding the proposed E-FPKD approach, we integrate the knowledge distillation and prototype aggregation technique into FL to deal with the non-IID challenge. To address the boundary issue, instead of directly calculating the distance between benign and malicious traffic, we consider maximizing the overall detection correctness of all prosumers (ODC), which can mitigate the computational cost compared with the former way. After detection, a rule-based method will be triggered at each DLES for cyber-attack intervention. Experimental analysis demonstrates that the proposed E-FPKD can achieve the largest ODC on NSL-KDD, UNSW-NB15, and IoTID20 datasets in both binary and multi-class classification, compared with baselines. For instance, the ODC for IoTID20 obtained via the proposed method is separately 0.3782% and 4.4471% greater than FedProto and FedAU in multi-class classification.

cs.CR↗

arXiv2023

Millimeter-wave spectrum of 2-propanimine

Up to date, only 6 imines have been detected in the interstellar medium. The 3-carbon imine, 2-propanimine ((CH$_3$)$_2$C=NH), is predicted to be the structural isomer with the lowest energy in the C$_3$H$_7$N group, and appears to be a good candidate for astronomical searches. Unexpectedly, no microwave or millimeter wave spectrum is available for 2-propanimine. In this work, we provide the first high resolution millimeter wave spectrum of 2-propanimine and its analysis. With the guide of this laboratory measurement, we aim to search for 2-propanimine in two molecule-rich sources Sgr B2(N) and IRAS 16293-2422 using observations from the Atacama Large Millimeter/submillimeter Array (ALMA). Starting from a synthesized sample, we measured the spectrum of 2-propanimine from 50 to 500 GHz, and the ground state lines are successfully assigned and fitted using XIAM and ERHAM programs with the aid of theoretical calculations. The barriers to internal rotation of the two CH$_3$ tops are determined to be 531.956(64) cm$^{-1}$ and 465.013(26) cm$^{-1}$. These data are able to provide reliable prediction of transition frequencies for astronomical search. Although a few line matches exist, no confirmed detection of 2-propanimine has been found in the hot molecular core Sgr B2(N1S) and the Class 0 protostar IRAS 16293B. Upper-limits of its column density have been derived, and indicate that 2-propanimine is at least 18 times less abundant than methanimine in Sgr B2(N1S), and is at most 50-83 % of methanimine in IRAS 16293B.

astro-ph.GA↗

arXiv2021

Window Function for Chirped Pulse Spectroscopy with Enhanced Signal-to-noise Ratio and Lineshape Correction

In chirped pulse experiments, magnitude Fourier transform is used to generate frequency domain spectra. The application of window function as a tool for lineshape correction and signal-to-noise ratio (SnR) enhancement is rarely discussed in chirped spectroscopy, with the only exception of using Kaiser-Bessel window and trivial rectangular window. We present a specific window function, called "Voigt-1D" window, designed for chirped pulse spectroscopy. The window function corrects the magnitude Fourier-transform spectra to Voigt lineshape, and offers wide tunability to control the SnR and lineshape of the final spectral lines. We derived the mathematical properties of the window function, and evaluated the performance of the window function in comparison to the Kaiser-Bessel window on experimental and simulated data sets. Our result shows that, compared with un-windowed spectra, the Voigt-1D window is able to produce 100 % SnR enhancement on average.

physics.chem-ph↗