arXiv subjects

Peiyuan Liu

Publications and source records attributed to Peiyuan Liu.

24 records · Page 2Linked to original sources

arXiv2024

Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting

In recent years, generative pre-trained paradigms such as Large Language Models (LLMs) and Large Vision Models (LVMs) have achieved revolutionary advancements and widespread real-world applications. Particularly, the emergence of pre-trained LLMs-based temporal works, compared to previous deep model approaches, has demonstrated superior generalization and robustness, showcasing the potential of generative pre-trained paradigms as foundation models for time series. However, those LLMs-based works mainly focus on cross-modal research, i.e., leveraging the language capabilities of LLMs in time series contexts. Although they have achieved impressive performance, there still exist the issues of concept drift caused by differences in data distribution and inflexibility caused by misalignment of dimensions. To this end, inspired by recent work on LVMs, we reconsider the paradigm of time series modeling. In this paper, we comprehensively explore, for the first time, the effectiveness and superiority of the Generative Pre-trained Diffusion (GPD) paradigm in real-world multivariate time series forecasting (TSF). Specifically, to mitigate performance bias introduced by sophisticated networks, we propose a straightforward MLP diffusion network for unconditional modeling of time series. Then we employ a zero-shot and tuning-free method to predict (generate) future data using historical data as prompts. The GPD paradigm is established on the time series modality, effectively preventing the phenomenon of concept drift, and enabling flexible forecasting of arbitrary lengths. We demonstrate that the GPD paradigm achieves comprehensive performance and generalization comparable to current SOTA LLM-based and deep model paradigms on mainstream benchmarks and various TSF tasks. Extensive experiments validate the potential of the GPD paradigm and its assistance in future related research.

cs.CE↗

arXiv2024

WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting

Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourier transform, which cannot capture fine-grained and local frequency structure. In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. WFTNet utilizes both Fourier and wavelet transforms to extract comprehensive temporal-frequency information from the signal, where Fourier transform captures the global periodic patterns and wavelet transform captures the local ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to adaptively balance the importance of global and local frequency patterns. Extensive experiments on various time series datasets show that WFTNet consistently outperforms other state-of-the-art baseline. Code is available at https://github.com/Hank0626/WFTNet.

cs.LG↗

arXiv2023

DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes

Cross-view multi-object tracking aims to link objects between frames and camera views with substantial overlaps. Although cross-view multi-object tracking has received increased attention in recent years, existing datasets still have several issues, including 1) missing real-world scenarios, 2) lacking diverse scenes, 3) owning a limited number of tracks, 4) comprising only static cameras, and 5) lacking standard benchmarks, which hinder the investigation and comparison of cross-view tracking methods. To solve the aforementioned issues, we introduce DIVOTrack: a new cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians in realistic and non-experimental environments. Our DIVOTrack has fifteen distinct scenarios and 953 cross-view tracks, surpassing all cross-view multi-object tracking datasets currently available. Furthermore, we provide a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT, which learns object detection, single-view association, and cross-view matching with an all-in-one embedding model. Finally, we present a summary of current methodologies and a set of standard benchmarks with our DIVOTrack to provide a fair comparison and conduct a comprehensive analysis of current approaches and our proposed CrossMOT. The dataset and code are available at https://github.com/shengyuhao/DIVOTrack.

cs.CV↗

arXiv2023

SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems

Recently proliferated semantic communications (SC) aim at effectively transmitting the semantics conveyed by the source and accurately interpreting the meaning at the destination. While such a paradigm holds the promise of making wireless communications more intelligent, it also suffers from severe semantic security issues, such as eavesdropping, privacy leaking, and spoofing, due to the open nature of wireless channels and the fragility of neural modules. Previous works focus more on the robustness of SC via offline adversarial training of the whole system, while online semantic protection, a more practical setting in the real world, is still largely under-explored. To this end, we present SemProtector, a unified framework that aims to secure an online SC system with three hot-pluggable semantic protection modules. Specifically, these protection modules are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination by a semantic signature generation method. Our framework enables an existing online SC system to dynamically assemble the above three pluggable modules to meet customized semantic protection requirements, facilitating the practical deployment in real-world SC systems. Experiments on two public datasets show the effectiveness of our proposed SemProtector, offering some insights of how we reach the goal of secrecy, privacy and integrity of an SC system. Finally, we discuss some future directions for the semantic protection.

cs.CR↗

arXiv2022

Towards a Rigorous Statistical Analysis of Empirical Password Datasets

A central challenge in password security is to characterize the attacker's guessing curve i.e., what is the probability that the attacker will crack a random user's password within the first $G$ guesses. A key challenge is that the guessing curve depends on the attacker's guessing strategy and the distribution of user passwords both of which are unknown to us. In this work we aim to follow Kerckhoffs' principle and analyze the performance of an optimal attacker who knows the password distribution. Let $λ_G$ denote the probability that such an attacker can crack a random user's password within $G$ guesses. We develop several statistically rigorous techniques to upper and lower bound $λ_G$ given $N$ independent samples from the unknown distribution. We show that our bounds hold with high confidence and apply our techniques to analyze eight password datasets. Our empirical analysis shows that even state-of-the-art password cracking models are often significantly less guess efficient than an attacker who can optimize its attack based on its (partial) knowledge of the password distribution. We also apply our techniques to re-examine the empirical password distribution and Zipf's Law. We find that the empirical distribution closely matches our bounds on $λ_G$ when $G$ is not too large i.e., $G \ll N$. However, for larger values of $G$ our empirical analysis rigorously demonstrates that the empirical distribution (resp. Zipf's Law) overestimates the attacker's success rate. We apply our techniques to upper/lower bound the effectiveness of password throttling mechanisms (key-stretching) which are used to reduce the number of attacker guesses $G$. Finally, if we make an additional assumption about the way users respond to password restrictions, we can use our techniques to evaluate the effectiveness of password composition policies which restrict the passwords users may select.

cs.CR↗

arXiv2018

Cluster-induced deagglomeration in dilute gravity-driven gas-solid flows of cohesive grains

Clustering is often presumed to lead to enhanced agglomeration between cohesive grains due to the reduced relative velocities of particles within a cluster. Our discrete-particle simulations on gravity-driven, gas-solid flows of cohesive grains exhibit the opposite trend, revealing a new mechanism we coin "cluster-induced deagglomeration." Specifically, we examine relatively dilute gas-solid flows and isolate agglomerates of cohesive origin from overall heterogeneities in the system, i.e., agglomerates of cohesive origin and clusters of hydrodynamic origin. We observe enhanced clustering with an increasing system size (as is the norm for noncohesive systems) as well as reduced agglomeration. The reduced agglomeration is traced to the increased collisional impact velocities of particles at the surface of a cluster; i.e., higher levels of clustering lead to larger relative velocities between the clustered and nonclustered regions, thereby serving as an additional source of granular temperature. This physical picture is further evidenced by a theoretical model based on a balance between the generation and breakage rates of agglomerates. Finally, cluster-induced deagglomeration also provides an explanation for a surprising saturation of agglomeration levels in gravity-driven, gas-solid systems with increasing levels of cohesion, as opposed to the monotonically increasing behavior seen in free-evolving or driven granular systems in the absence of gravity. Namely, higher cohesion leads to more energy dissipation, which is associated with competing effects: enhanced agglomeration and enhanced clustering, the latter of which results in more cluster-induced deagglomeration.

cond-mat.soft↗