arXiv subjects

Peng Xie

Publications and source records attributed to Peng Xie.

At least 55 records · Page 3Linked to original sources

arXiv2025

Developing a Multilingual Dataset and Evaluation Metrics for Code-Switching: A Focus on Hong Kong's Polylingual Dynamics

The existing audio datasets are predominantly tailored towards single languages, overlooking the complex linguistic behaviors of multilingual communities that engage in code-switching. This practice, where individuals frequently mix two or more languages in their daily interactions, is particularly prevalent in multilingual regions such as Hong Kong, China. To bridge this gap, we have developed a 34.8-hour dataset of Mixed Cantonese and English (MCE) audio using our Multi-Agent Data Generation Framework (MADGF). We fine-tuned the open-source multilingual Automatic Speech Recognition (ASR) model, Whisper, with the MCE dataset, leading to impressive zero-shot performance. The traditional metrics overlook important factors such as latency in real-world applications and code-switching scenarios. We have introduced a novel evaluation metric called Fidelity to the Original Audio, Accuracy, and Latency (FAL). This metric aims to overcome the limitations of traditional metrics used to assess ASR systems.

cs.SD↗

arXiv2024

Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks

Pre-trained vision-language models (VLMs) have showcased remarkable performance in image and natural language understanding, such as image captioning and response generation. As the practical applications of vision-language models become increasingly widespread, their potential safety and robustness issues raise concerns that adversaries may evade the system and cause these models to generate toxic content through malicious attacks. Therefore, evaluating the robustness of open-source VLMs against adversarial attacks has garnered growing attention, with transfer-based attacks as a representative black-box attacking strategy. However, most existing transfer-based attacks neglect the importance of the semantic correlations between vision and text modalities, leading to sub-optimal adversarial example generation and attack performance. To address this issue, we present Chain of Attack (CoA), which iteratively enhances the generation of adversarial examples based on the multi-modal semantic update using a series of intermediate attacking steps, achieving superior adversarial transferability and efficiency. A unified attack success rate computing method is further proposed for automatic evasion evaluation. Extensive experiments conducted under the most realistic and high-stakes scenario, demonstrate that our attacking strategy can effectively mislead models to generate targeted responses using only black-box attacks without any knowledge of the victim models. The comprehensive robustness evaluation in our paper provides insight into the vulnerabilities of VLMs and offers a reference for the safety considerations of future model developments.

cs.CV↗

arXiv2024

The Mitochondrial Genome of Cathaya argyrophylla Reaches 18.99 Mb: Analysis of Super-Large Mitochondrial Genomes in Pinaceae

Mitochondrial genomes in the Pinaceae family are notable for their large size and structural complexity. In this study, we sequenced and analyzed the mitochondrial genome of Cathaya argyrophylla, an endangered and endemic Pinaceae species, uncovering a genome size of 18.99 Mb, meaning the largest mitochondrial genome reported to date. To investigate the mechanisms behind this exceptional size, we conducted comparative analyses with other Pinaceae species possessing both large and small mitochondrial genomes, as well as with other gymnosperms. We focused on repeat sequences, transposable element activity, RNA editing events, chloroplast-derived sequence transfers (mtpts), and sequence homology with nuclear genomes. Our findings indicate that while Cathaya argyrophylla and other extremely large Pinaceae mitochondrial genomes contain substantial amounts of repeat sequences and show increased activity of LINEs and LTR retrotransposons, these factors alone do not fully account for the genome expansion. Notably, we observed a significant incorporation of chloroplast-derived sequences in Cathaya argyrophylla and other large mitochondrial genomes, suggesting that extensive plastid-to-mitochondrial DNA transfer may play a crucial role in genome enlargement. Additionally, large mitochondrial genomes exhibited distinct patterns of RNA editing and limited similarity with nuclear genomes compared to smaller genomes. These results suggest that the massive mitochondrial genomes in Pinaceae are likely the result of multiple contributing factors, including repeat sequences, transposon activity, and extensive plastid sequence incorporation. Our study enhances the understanding of mitochondrial genome evolution in plants and provides valuable genetic information for the conservation and study of Cathaya argyrophylla.

q-bio.GN↗

arXiv2024

UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method

Accurate prediction of metro Origin-Destination (OD) flow is essential for the development of intelligent transportation systems and effective urban traffic management. Existing approaches typically either predict passenger outflow of departure stations or inflow of destination stations. However, we argue that travelers generally have clearly defined departure and arrival stations, making these OD pairs inherently interconnected. Consequently, considering OD pairs as a unified entity more accurately reflects actual metro travel patterns and allows for analyzing potential spatio-temporal correlations between different OD pairs. To address these challenges, we propose a novel and effective urban metro OD flow prediction method (UMOD), comprising three core modules: a data embedding module, a temporal relation module, and a spatial relation module. The data embedding module projects raw OD pair inputs into hidden space representations, which are subsequently processed by the temporal and spatial relation modules to capture both inter-pair and intra-pair spatio-temporal dependencies. Experimental results on two real-world urban metro OD flow datasets demonstrate that adopting the OD pairs perspective is critical for accurate metro OD flow prediction. Our method outperforms existing approaches, delivering superior predictive performance.

cs.LG↗

arXiv2024

AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy

We propose Adjustable Molecular Representation (AdaMR), a new large-scale uniform pre-training strategy for small-molecule drugs, as a novel unified pre-training strategy. AdaMR utilizes a granularity-adjustable molecular encoding strategy, which is accomplished through a pre-training job termed molecular canonicalization, setting it apart from recent large-scale molecular models. This adaptability in granularity enriches the model's learning capability at multiple levels and improves its performance in multi-task scenarios. Specifically, the substructure-level molecular representation preserves information about specific atom groups or arrangements, influencing chemical properties and functionalities. This proves advantageous for tasks such as property prediction. Simultaneously, the atomic-level representation, combined with generative molecular canonicalization pre-training tasks, enhances validity, novelty, and uniqueness in generative tasks. All of these features work together to give AdaMR outstanding performance on a range of downstream tasks. We fine-tuned our proposed pre-trained model on six molecular property prediction tasks (MoleculeNet datasets) and two generative tasks (ZINC250K datasets), achieving state-of-the-art (SOTA) results on five out of eight tasks.

q-bio.BM↗

arXiv2023

Density Functional Theory Calculations on the Interstellar Formation of Biomolecules

The density functional theory (DFT) is the most versatile electronic structure method used in quantum chemical calculations, and is increasingly applied in astrochemical research. This mini-review provides an overview of the applications of DFT calculations in understanding the chemistry that occurs in star-forming regions. We survey investigations into the formation of biologically-relevant compounds such as nucleobases in the interstellar medium, and also covers the formation of both achiral and chiral amino acids, as well as biologically-relevant molecules such as sugars, and nitrogen-containing polycyclic aromatic hydrocarbons. Additionally, DFT calculations are used to estimate the potential barriers for chemical reactions in astronomical environments. We conclude by noting several areas that require more research, such as the formation pathways of chiral amino acids, complex sugars and other biologically-important molecules, and the role of environmental factors in the formation of interstellar biomolecules.

astro-ph.GA↗

arXiv2023

Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer

Gene mining is an important topic in the field of life sciences, but traditional machine learning methods cannot consider the regulatory relationships between genes. Deep learning methods perform poorly in small sample sizes. This study proposed a deep learning method, called TransGeneSelector, that can mine critical regulatory genes involved in certain life processes using a small-sample transcriptome dataset. The method combines a WGAN-GP data augmentation network, a sample filtering network, and a Transformer classifier network, which successfully classified the state (germinating or dry seeds) of Arabidopsis thaliana seed in a dataset of 79 samples, showing performance comparable to that of Random Forests. Further, through the use of SHapley Additive exPlanations method, TransGeneSelector successfully mined genes involved in seed germination. Through the construction of gene regulatory networks and the enrichment analysis of KEGG, as well as RT-qPCR quantitative analysis, it was confirmed that these genes are at a more upstream regulatory level than those Random Forests mined, and the top 11 genes that were uniquely mined by TransGeneSelector were found to be related to the KAI2 signaling pathway, which is of great regulatory importance for germination-related genes. This study provides a practical tool for life science researchers to mine key genes from transcriptome data.

q-bio.QM↗

arXiv2023

Catalytic role of HI in the interstellar synthesis of complex organic molecule

Using quantum chemical calculations, we model the pathways for synthesizing two purine nucleobases, adenine and guanine, in the gas-phase interstellar environment, surrounded by neutral atomic hydrogen (HI). HI is found active in facilitating a series of fundamental proton transfer processes of organic synthesis, including bond formation, cyclization, dehydrogenation, and H migration. The reactive potential barriers were significantly reduced in the alternative pathways created by HI, leading to a remarkable increase in the reaction rate. The presence of HI also lowered the reactive activation temperature from 757.8 K to 131.5-147.0 K, indicating the thermodynamic feasibility of these pathways in star-forming regions where some of the reactants have been astronomically detected. Our findings suggest that HI may serve as an effective catalyst for interstellar organic synthesis.

astro-ph.GA↗

arXiv2023

Mathematical theory for the interface mode in a waveguide bifurcated from a Dirac point

In this paper, we prove the existence of a bound state in a waveguide that consists of two semi-infinite periodic structures separated by an interface. The two periodic structures are perturbed from the same periodic medium with a Dirac point and they possess a common band gap enclosing the Dirac point. The bound state, which is called interface mode here, decays exponentially away from the interface with a frequency located in the common band gap and can be viewed as a bifurcation from the Dirac point. Using the layer potential technique and asymptotic analysis, we first characterize the band gap opening for the two perturbed periodic media and derive the asymptotics of the Bloch modes near the band gap edges. By formulating the eigenvalue problem for the waveguide with two semi-infinite structures using a boundary integral equation over the interface and analyzing the characteristic values of the associated boundary integral operator, we prove the existence of the interface mode for the waveguide when the perturbation of the periodic medium is small.

math-ph↗

arXiv2022

HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting

Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches, especially deep neural networks. Recently, the Graph Neural Networks (GNNs) based methods have achieved excellent performance for spatio-temporal forecasting. However, the canonical GNNs-based methods only individually model the local graph of meteorological variables per station or the global graph of whole stations, lacking information interaction between meteorological variables in different stations. In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations. An adaptive graph learning layer and spatial graph convolution are employed to construct self-learning graph and study hidden dependency among nodes of variable-level and station-level graph. For capturing temporal pattern, the dilated inception as the backbone of gate temporal convolution is designed to model long and various meteorological trends. Moreover, a dynamic interaction learning is proposed to build bidirectional information passing in hierarchical graph. Experimental results on three real-world meteorological datasets demonstrate the superior performance of HiSTGNN beyond 7 baselines and it reduces the errors by 4.2% to 11.6% especially compared to state-of-the-art weather forecasting method.

cs.LG↗

arXiv2022

Traveling edge states in massive Dirac equations along slowly varying edges

Topologically protected wave motion has attracted considerable interest due to its novel properties and potential applications in many different fields. In this work, we study edge modes and traveling edge states via the linear Dirac equations with so-called domain wall masses. The unidirectional edge state provides a heuristic approach to more general traveling edge states through the localized behavior along slowly varying edges. We show the leading asymptotic solutions of two typical edge states that follow the circular and curved edges with small curvature by analytic and quantitative arguments.

math.AP↗

arXiv2022

Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction

Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, it faces two main challenges. First, different metro stations, e.g. transfer stations and non-transfer stations, have unique traffic patterns. Second, it is challenging to model complex spatio-temporal dynamic relation of metro stations. To address these challenges, we develop a spatio-temporal dynamic graph relational learning model (STDGRL) to predict urban metro station flow. First, we propose a spatio-temporal node embedding representation module to capture the traffic patterns of different stations. Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix. Finally, we provide a transformer-based long-term relationship prediction module for long-term metro flow prediction. Extensive experiments are conducted based on metro data in Beijing, Shanghai, Chongqing and Hangzhou. Experimental results show the advantages of our method beyond 11 baselines for urban metro flow prediction.

cs.LG↗

arXiv2021

Exploring Multi-dimensional Data via Subset Embedding

Multi-dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly-formatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully-connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3-step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.

cs.LG↗

arXiv2020

Formation of interstellar complex polycyclic aromatic hydrocarbons: Insights from molecular dynamics simulations of dehydrogenated benzene

Small organic molecules are thought to provide building blocks for the formation of complex interstellar polycyclic aromatic hydrocarbons (PAHs). However, the underlying chemical mechanisms remain unclear, particularly concerning the role of interstellar dust. Using molecular dynamics, we simulate the chemical reaction between dehydrogenated benzene molecules in the gas phase or on the surface of an onion-like carbon nanoparticle (NP). The reaction leads to the formation of PAHs of complex structures. The size of the formed molecules is found to roughly increase with increasing temperature up to 800 K, and to be correlated with the level of dehydrogenation. Morphology analysis features the formation of large rings that contain up to 32 carbon atom at high temperature. Density functional theory (DFT) calculations are performed to search the fundamental energetic reaction pathways. The DFT results quantitatively confirm the correlation between the reactivity and the dehydrogenation level, and the formation of stable C-8 rings. Moreover, the nanostructures formed on the NP surface point to a possible layer-by-layer formation mechanism for interstellar fullerene and carbon onions.

astro-ph.GA↗

arXiv2020

Wave packets in the fractional nonlinear Schrödinger equation with a honeycomb potential

In this article, we study wave dynamics in the fractional nonlinear Schrödinger equation with a modulated honeycomb potential. This problem arises from recent research interests in the interplay between topological materials and nonlocal governing equations. Both are current focuses in scientific research fields. We first develop the Floquet-Bloch spectral theory of the linear fractional Schrödinger operator with a honeycomb potential. Especially, we prove the existence of conical degenerate points, i.e., Dirac points, at which two dispersion band functions intersect. We then investigate the dynamics of wave packets spectrally localized at a Dirac point and derive the leading effective envelope equation. It turns out the envelope can be described by a nonlinear Dirac equation with a varying mass. With rigorous error estimates, we demonstrate that the asymptotic solution based on the effective envelope equation approximates the true solution well in the weighted-$H^s$ space.

math.AP↗

arXiv2020

Quality Control of Neuron Reconstruction Based on Deep Learning

Neuron reconstruction is essential to generate exquisite neuron connectivity map for understanding brain function. Despite the significant amount of effect that has been made on automatic reconstruction methods, manual tracing by well-trained human annotators is still necessary. To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper. By formulating the quality control problem into a binary classification task regarding each single point, the proposed approach overcomes the technical difficulties resulting from the large image size and complex neuron morphology. Not only it provides the evaluation of reconstruction quality, but also can locate exactly where the wrong tracing begins. This work presents one of the first comprehensive studies for whole-brain scale quality control of neuron reconstructions. Experiments on five-fold cross validation with a large dataset demonstrate that the proposed approach can detect 74.7% errors with only 1.4% false alerts.

cs.CV↗

arXiv2019

Urban flows prediction from spatial-temporal data using machine learning: A survey

Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems.

cs.LG↗

arXiv2019

Wave packet dynamics in slowly modulated photonic graphene

Mathematical analysis on electromagnetic waves in photonic graphene, a photonic topological material which has a honeycomb structure, is one of the most important current research topics. By modulating the honeycomb structure, numerous topological phenomena have been observed recently. The electromagnetic waves in such a media are generally described by the 2-dimensional wave equation. It has been shown that the corresponding elliptic operator with a honeycomb material weight has Dirac points in its dispersion surfaces. In this paper, we study the time evolution of the wave packets spectrally concentrated at such Dirac points in a modulated honeycomb material weight. We prove that such wave packet dynamics is governed by the Dirac equation with a varying mass in a large but finite time. Our analysis provides mathematical insights to those topological phenomena in photonic graphene.

math.AP↗