arXiv subjects

Yao Li

Publications and source records attributed to Yao Li.

At least 91 records · Page 5Linked to original sources

arXiv2025

Enhancing Android Malware Detection: The Influence of ChatGPT on Decision-centric Task

With the rise of large language models, such as ChatGPT, non-decisional models have been applied to various tasks. Moreover, ChatGPT has drawn attention to the traditional decision-centric task of Android malware detection. Despite effective detection methods proposed by scholars, they face low interpretability issues. Specifically, while these methods excel in classifying applications as benign or malicious and can detect malicious behavior, they often fail to provide detailed explanations for the decisions they make. This challenge raises concerns about the reliability of existing detection schemes and questions their true ability to understand complex data. In this study, we investigate the influence of the non-decisional model, ChatGPT, on the traditional decision-centric task of Android malware detection. We choose three state-of-the-art solutions, Drebin, XMAL, and MaMaDroid, conduct a series of experiments on publicly available datasets, and carry out a comprehensive comparison and analysis. Our findings indicate that these decision-driven solutions primarily rely on statistical patterns within datasets to make decisions, rather than genuinely understanding the underlying data. In contrast, ChatGPT, as a non-decisional model, excels in providing comprehensive analysis reports, substantially enhancing interpretability. Furthermore, we conduct surveys among experienced developers. The result highlights developers' preference for ChatGPT, as it offers in-depth insights and enhances efficiency and understanding of challenges. Meanwhile, these studies and analyses offer profound insights, presenting developers with a novel perspective on Android malware detection--enhancing the reliability of detection results from a non-decisional perspective.

cs.CR↗

arXiv2025

Probing the mass effect of heavy quark jets in high-energy nuclear collisions

The production of heavy quark (HQ) jets provides a new arena to address the mass effect of jet quenching in heavy-ion physics. This paper presents a theoretical study of HQ jet yield suppression in Pb+Pb collisions at the LHC and focuses on the energy loss of HQ jets produced by different mechanisms. The p+p baseline is carried out by the SHERPA generator, and the jet-medium interactions are described by the SHELL transport model, which considers the elastic and inelastic partonic energy loss in the quark-gluon plasma (QGP). In p+p collisions, our numerical results indicate that the HQ jets from gluon splitting ($g \rightarrow Q$-jet) give the dominant contribution at high $p_T$, and it shows more dispersive structures than the HQ-initiated one ($Q \rightarrow Q$-jet). In nucleus-nucleus collisions, our calculations are consistent with the inclusive and b-jet $R_{AA}$ recently measured by the ATLAS collaboration, which suggests a remarkable manifestation of the mass effect of jet energy loss. As a result of the dispersive substructure, the $g \rightarrow Q$-jet will lose more energy than the $Q \rightarrow Q$-jet in the QGP. Due to the significant contribution of $g \rightarrow c$-jet, the $R_{AA}$ of c-jet will be comparable or even smaller than that of inclusive jet. To experimentally distinguish the $g \rightarrow Q$-jet and $Q \rightarrow Q$-jet, we propose the event selection strategies based on their topological features and test the performances. By isolating the $c \rightarrow c$-jet and $b \rightarrow b$-jet, the jets initiated by heavy quarks, we predict that the order of their $R_{AA}$ are in line with the mass hierarchy of energy loss. Future measurements on the $R_{AA}$ of $Q \rightarrow Q$-jet and $g \rightarrow Q$-jet will provide a unique chance to test the flavor/mass dependence of energy loss at the jet level.

hep-ph↗

arXiv2025

Competing Hexagonal and Square Lattices on a Spherical Surface

The structural properties of packed soft-core particles provide a platform to understand the cross-pollinated physical concepts in solid-state- and soft-matter physics. Confined on spherical surface, the traditional differential geometry also dictates the overall defect properties in otherwise regular crystal lattices. Using molecular dynamics simulation of the Hertzian model as a tool, we report here the emergence of new types of disclination patterns: domain and counter-domain defects, when hexagonal and square patterns coexist. A new angle is presented to understand the incompatibility between tiling lattice shapes and the available spherical areal shapes, which is common in nature -- from molecular systems in biology to backbone construction in architectures.

cond-mat.soft↗

arXiv2024

Medium modifications of heavy-flavor jet angularities in high-energy nuclear collisions

We present the first theoretical study of heavy-flavor jet angularities ($λ^κ_α$) in Pb+Pb collisions at $\sqrt{s_{\rm NN}}=$ 5.02 TeV. The initial production of heavy-flavor jets is carried out using the POWHEG+PYTHIA8 prescription, while the jet evolution in the quark-gluon plasma (QGP) is described by the SHELL transport model. In p+p collisions, we observe narrower angularity distributions for D$^0$-tagged jets compared to inclusive jets, consistent with the ALICE preliminary results. We then demonstrate that jet quenching in the QGP slightly widens the angularity distribution of D$^0$-tagged jets in Pb+Pb collisions relative to that in p+p collisions for jet transverse momentum of $10 < p_{\rm T,jet} < 20$ GeV/c, while the angularity distributions of inclusive and D$^0$-tagged jets become narrower in Pb+Pb collisions relative to p+p at $p_{\rm T,jet} > 20$ GeV/c due to the strong influence of the selection bias. Additionally, by comparing the average angularities $\langle λ^κ_α \rangle$ of inclusive, D$^0$-tagged and B$^0$-tagged jets with varying $α$ and $κ$, we show that the larger the quark mass is, the lower the jet's $\langle λ^κ_α \rangle$ values are. As a result of the slenderer initial distribution, we predict that as compared to inclusive jets, the heavy-flavor jets, especially the B$^0$-tagged ones, will suffer stronger modifications of $\langle λ^κ_α \rangle$ in Pb+Pb relative to p+p at $10 < p_{\rm T,jet} < 20$ GeV/c. For a larger jet radius, a more significant broadening of jet angularities is predicted because of the enhanced contributions of the wide-angle particles.

hep-ph↗

arXiv2024

Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures

When planning for autonomous driving, it is crucial to consider essential traffic elements such as lanes, intersections, traffic regulations, and dynamic agents. However, they are often overlooked by the traditional end-to-end planning methods, likely leading to inefficiencies and non-compliance with traffic regulations. In this work, we endeavor to integrate the perception of these elements into the planning task. To this end, we propose Perception Helps Planning (PHP), a novel framework that reconciles lane-level planning with perception. This integration ensures that planning is inherently aligned with traffic constraints, thus facilitating safe and efficient driving. Specifically, PHP focuses on both edges of a lane for planning and perception purposes, taking into consideration the 3D positions of both lane edges and attributes for lane intersections, lane directions, lane occupancy, and planning. In the algorithmic design, the process begins with the transformer encoding multi-camera images to extract the above features and predicting lane-level perception results. Next, the hierarchical feature early fusion module refines the features for predicting planning attributes. Finally, the double-edge interpreter utilizes a late-fusion process specifically designed to integrate lane-level perception and planning information, culminating in the generation of vehicle control signals. Experiments on three Carla benchmarks show significant improvements in driving score of 27.20%, 33.47%, and 15.54% over existing algorithms, respectively, achieving the state-of-the-art performance, with the system operating up to 22.57 FPS.

cs.CV↗

arXiv2024

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling

Deep learning-based image denoising models demonstrate remarkable performance, but their lack of robustness analysis remains a significant concern. A major issue is that these models are susceptible to adversarial attacks, where small, carefully crafted perturbations to input data can cause them to fail. Surprisingly, perturbations specifically crafted for one model can easily transfer across various models, including CNNs, Transformers, unfolding models, and plug-and-play models, leading to failures in those models as well. Such high adversarial transferability is not observed in classification models. We analyze the possible underlying reasons behind the high adversarial transferability through a series of hypotheses and validation experiments. By characterizing the manifolds of Gaussian noise and adversarial perturbations using the concept of typical set and the asymptotic equipartition property, we prove that adversarial samples deviate slightly from the typical set of the original input distribution, causing the models to fail. Based on these insights, we propose a novel adversarial defense method: the Out-of-Distribution Typical Set Sampling Training strategy (TS). TS not only significantly enhances the model's robustness but also marginally improves denoising performance compared to the original model.

cs.CV↗

arXiv2024

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices

Deep learning is reshaping mobile applications, with a growing trend of deploying deep neural networks (DNNs) directly to mobile and embedded devices to address real-time performance and privacy. To accommodate local resource limitations, techniques like weight compression, convolution decomposition, and specialized layer architectures have been developed. However, the \textit{dynamic} and \textit{diverse} deployment contexts of mobile devices pose significant challenges. Adapting deep models to meet varied device-specific requirements for latency, accuracy, memory, and energy is labor-intensive. Additionally, changing processor states, fluctuating memory availability, and competing processes frequently necessitate model re-compression to preserve user experience. To address these issues, we introduce AdaScale, an elastic inference framework that automates the adaptation of deep models to dynamic contexts. AdaScale leverages a self-evolutionary model to streamline network creation, employs diverse compression operator combinations to reduce the search space and improve outcomes, and integrates a resource availability awareness block and performance profilers to establish an automated adaptation loop. Our experiments demonstrate that AdaScale significantly enhances accuracy by 5.09%, reduces training overhead by 66.89%, speeds up inference latency by 1.51 to 6.2 times, and lowers energy costs by 4.69 times.

cs.AI↗

arXiv2024

Constrained Online Recursive Source Separation Framework for Real-time Electrophysiological Signal Processing

Background and Objective: Processing electrophysiological signals often requires blind source separation (BSS) due to the nature of mixing source signals. However, its complex computational demands make real-time BSS challenging. The objective of this work is to develop an advanced real-time BSS method suitable for processing electrophysiological signals. Methods: In this paper, a novel BSS framework termed constrained online recursive source separation (CORSS) was proposed. In the framework, a stepwise recursive unmixing matrix learning rule was adopted to enable real-time updates with minimal computational cost. Moreover, by incorporating prior information of target signals to optimize the cost function, the framework algorithm was more likely to converge to the target sources. To validate its performance, the proposed framework was applied to both downstream tasks, namely real-time surface electromyogram (sEMG) decomposition and real-time respiratory intent monitoring based on diaphragmatic electromyogram (sEMGdi) extraction. Results: The proposed method achieved a matching rate of 96.00 % for the sEMG decomposition task and 98.12 % for the sEMGdi extraction task, exhibiting superior performance over other comparison methods (p < 0.05). Our method also exhibited minimal time delay during computation, with only 12.5 ms delay when the block size was 0.1s, demonstrating its good capabilities in online processing. Conclusions: The proposed method was demonstrated to enable real-time BSS with both improved separation performance and low computational latency. It is of substantial importance for real-time electrophysiological signal processing and applications towards advanced neural-machine interaction and clinical monitoring.

cs.HC↗

arXiv2024

Noninvasive Extraction of Maternal and Fetal Electrocardiograms Using Progressive Periodic Source Peel-off

Abdominal electrocardiogram (AECG) gives a safe and non-invasive way to monitor fetal well-being during pregnancy using surface electrodes. However, it is challenging to extract weak fetal ECG (fECG) from the AECG recordings with larger maternal ECG (mECG) and external noises. In this study, we introduce a novel progressive periodic source peel-off (PPSP) method for extracting periodic ECG sources from multi-channel AECG recordings, including three main modules: 1) A periodic constrained FastICA (PCFICA) module with ECG physiology-informed constraints for extracting precise ECG spike trains, 2) A singular value decomposition module for estimating ECG waveforms, and 3) A peel-off strategy that facilitates to discern weak fECG source by eliminating previously separated sources or noises. The performance of the PPSP method was examined on two public databases, synthetic data and our clinical data. For extracting fECG spike trains, our PPSP method achieved an F1-scores of 99.59% on public data, 99.50% on synthetic data at the highest noise level. It further yielded the lowest RMSE of fetal heart rate of 6.20% on clinical data. It significantly outperformed other state-of-the-art methods on any set of data (p < 0.05). This study demonstrated effectiveness of the PPSP method for extracting and separating mECG and weak fECG signals, with high precision especially at high noise levels. Our study promotes noninvasive measurement and intelligent monitoring of both fetal and maternal heart activities towards advanced healthcare in perinatal medicine.

physics.med-ph↗

arXiv2024

RayFormer: Improving Query-Based Multi-Camera 3D Object Detection via Ray-Centric Strategies

The recent advances in query-based multi-camera 3D object detection are featured by initializing object queries in the 3D space, and then sampling features from perspective-view images to perform multi-round query refinement. In such a framework, query points near the same camera ray are likely to sample similar features from very close pixels, resulting in ambiguous query features and degraded detection accuracy. To this end, we introduce RayFormer, a camera-ray-inspired query-based 3D object detector that aligns the initialization and feature extraction of object queries with the optical characteristics of cameras. Specifically, RayFormer transforms perspective-view image features into bird's eye view (BEV) via the lift-splat-shoot method and segments the BEV map to sectors based on the camera rays. Object queries are uniformly and sparsely initialized along each camera ray, facilitating the projection of different queries onto different areas in the image to extract distinct features. Besides, we leverage the instance information of images to supplement the uniformly initialized object queries by further involving additional queries along the ray from 2D object detection boxes. To extract unique object-level features that cater to distinct queries, we design a ray sampling method that suitably organizes the distribution of feature sampling points on both images and bird's eye view. Extensive experiments are conducted on the nuScenes dataset to validate our proposed ray-inspired model design. The proposed RayFormer achieves superior performance of 55.5% mAP and 63.3% NDS, respectively.

cs.CV↗

arXiv2024

DALD: Improving Logits-based Detector without Logits from Black-box LLMs

The advent of Large Language Models (LLMs) has revolutionized text generation, producing outputs that closely mimic human writing. This blurring of lines between machine- and human-written text presents new challenges in distinguishing one from the other a task further complicated by the frequent updates and closed nature of leading proprietary LLMs. Traditional logits-based detection methods leverage surrogate models for identifying LLM-generated content when the exact logits are unavailable from black-box LLMs. However, these methods grapple with the misalignment between the distributions of the surrogate and the often undisclosed target models, leading to performance degradation, particularly with the introduction of new, closed-source models. Furthermore, while current methodologies are generally effective when the source model is identified, they falter in scenarios where the model version remains unknown, or the test set comprises outputs from various source models. To address these limitations, we present Distribution-Aligned LLMs Detection (DALD), an innovative framework that redefines the state-of-the-art performance in black-box text detection even without logits from source LLMs. DALD is designed to align the surrogate model's distribution with that of unknown target LLMs, ensuring enhanced detection capability and resilience against rapid model iterations with minimal training investment. By leveraging corpus samples from publicly accessible outputs of advanced models such as ChatGPT, GPT-4 and Claude-3, DALD fine-tunes surrogate models to synchronize with unknown source model distributions effectively.

cs.CL↗

arXiv2024

Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer

In the hyperspectral image classification (HSIC) task, the most commonly used model validation paradigm is partitioning the training-test dataset through pixel-wise random sampling. By training on a small amount of data, the deep learning model can achieve almost perfect accuracy. However, in our experiments, we found that the high accuracy was reached because the training and test datasets share a lot of information. On non-overlapping dataset partitions, well-performing models suffer significant performance degradation. To this end, we propose a spectral-spatial axial aggregation transformer model, namely SaaFormer, that preserves generalization across dataset partitions. SaaFormer applies a multi-level spectral extraction structure to segment the spectrum into multiple spectrum clips, such that the wavelength continuity of the spectrum across the channel are preserved. For each spectrum clip, the axial aggregation attention mechanism, which integrates spatial features along multiple spectral axes is applied to mine the spectral characteristic. The multi-level spectral extraction and the axial aggregation attention emphasize spectral characteristic to improve the model generalization. The experimental results on five publicly available datasets demonstrate that our model exhibits comparable performance on the random partition, while significantly outperforming other methods on non-overlapping partitions. Moreover, SaaFormer shows excellent performance on background classification.

cs.CV↗

arXiv2024

On Coprimary Filtrations

The coprimary filtration is a basic construction in commutative algebra. In this article, we prove the existence and uniqueness of coprimary filtration of modules (not necessarily finitely generated) over a Noetherian ring. Moreover, we also prove the existence and uniqueness of coprimary filtrations of coherent sheaves over a locally Noetherian scheme.

math.AC↗

arXiv2024

CELLmap: Enhancing LiDAR SLAM through Elastic and Lightweight Spherical Map Representation

SLAM is a fundamental capability of unmanned systems, with LiDAR-based SLAM gaining widespread adoption due to its high precision. Current SLAM systems can achieve centimeter-level accuracy within a short period. However, there are still several challenges when dealing with largescale mapping tasks including significant storage requirements and difficulty of reusing the constructed maps. To address this, we first design an elastic and lightweight map representation called CELLmap, composed of several CELLs, each representing the local map at the corresponding location. Then, we design a general backend including CELL-based bidirectional registration module and loop closure detection module to improve global map consistency. Our experiments have demonstrated that CELLmap can represent the precise geometric structure of large-scale maps of KITTI dataset using only about 60 MB. Additionally, our general backend achieves up to a 26.88% improvement over various LiDAR odometry methods.

cs.RO↗

arXiv2024

Allium Vegetables Intake and Digestive System Cancer Risk: A Study Based on Mendelian Randomization, Network Pharmacology and Molecular Docking

Background: Allium vegetables (garlic and onion) are one of the flavorings in people's daily diets. Observational studies suggest that intake of allium vegetables may be correlated with a lower incidence of digestive system cancers. However, the existence of a causal relationship is still controversial due to confounding factors and reverse causation. Therefore, we explored the causal relationship between intake of allium vegetables and digestive system cancers using Mendelian randomization approach. Methods: First, we performed Mendelian randomization analyses using inverse variance weighting (IVW), weighted median, and MR-Egger approaches, and demonstrated the reliability of the results in the sensitivity step. Second, Multivariable Mendelian randomization was applied to adjust for smoking and alcohol consumption. Third, we explored the molecular mechanisms behind the positive results through network pharmacology and molecular docking methods. Results: The study suggests that increased intake of garlic reduced gastric cancer risk. However, onion intake was not statistically associated with digestive system cancer. Conclusion: Garlic may have a protective effect against gastric cancer.

q-bio.GN↗

arXiv2024

In-Loop Filtering via Trained Look-Up Tables

In-loop filtering (ILF) is a key technology for removing the artifacts in image/video coding standards. Recently, neural network-based in-loop filtering methods achieve remarkable coding gains beyond the capability of advanced video coding standards, which becomes a powerful coding tool candidate for future video coding standards. However, the utilization of deep neural networks brings heavy time and computational complexity, and high demands of high-performance hardware, which is challenging to apply to the general uses of coding scene. To address this limitation, inspired by explorations in image restoration, we propose an efficient and practical in-loop filtering scheme by adopting the Look-up Table (LUT). We train the DNN of in-loop filtering within a fixed filtering reference range, and cache the output values of the DNN into a LUT via traversing all possible inputs. At testing time in the coding process, the filtered pixel is generated by locating input pixels (to-be-filtered pixel with reference pixels) and interpolating cached filtered pixel values. To further enable the large filtering reference range with the limited storage cost of LUT, we introduce the enhanced indexing mechanism in the filtering process, and clipping/finetuning mechanism in the training. The proposed method is implemented into the Versatile Video Coding (VVC) reference software, VTM-11.0. Experimental results show that the ultrafast, very fast, and fast mode of the proposed method achieves on average 0.13%/0.34%/0.51%, and 0.10%/0.27%/0.39% BD-rate reduction, under the all intra (AI) and random access (RA) configurations. Especially, our method has friendly time and computational complexity, only 101%/102%-104%/108% time increase with 0.13-0.93 kMACs/pixel, and only 164-1148 KB storage cost for a single model. Our solution may shed light on the journey of practical neural network-based coding tool evolution.

eess.IV↗

arXiv2024

Modeling, Inference, and Prediction in Mobility-Based Compartmental Models for Epidemiology

Classical compartmental models in epidemiology often assume a homogeneous population for simplicity, which neglects the inherent heterogeneity among individuals. This assumption frequently leads to inaccurate predictions when applied to real-world data. For example, evidence has shown that classical models overestimate the final pandemic size in the H1N1-2009 and COVID-19 outbreaks. To address this issue, we introduce individual mobility as a key factor in disease transmission and control. We characterize disease dynamics using mobility distribution functions for each compartment and propose a mobility-based compartmental model that incorporates population heterogeneity. Our results demonstrate that, for the same basic reproduction number, our mobility-based model predicts a smaller final pandemic size compared to the classical models, effectively addressing the common overestimation problem. Additionally, we infer mobility distributions from the time series of the infected population. We provide sufficient conditions for uniquely identifying the mobility distribution from a dataset and propose a machine-learning-based approach to learn mobility from both synthesized and real-world data.

q-bio.PE↗

arXiv2024

Efficient Polarization Demosaicking via Low-cost Edge-aware and Inter-channel Correlation

Efficient and high-fidelity polarization demosaicking is critical for industrial applications of the division of focal plane (DoFP) polarization imaging systems. However, existing methods have an unsatisfactory balance of speed, accuracy, and complexity. This study introduces a novel polarization demosaicking algorithm that interpolates within a three-stage basic demosaicking framework to obtain DoFP images. Our method incorporates a DoFP low-cost edge-aware technique (DLE) to guide the interpolation process. Furthermore, the inter-channel correlation is used to calibrate the initial estimate in the polarization difference domain. The proposed algorithm is available in both a lightweight and a full version, tailored to different application requirements. Experiments on simulated and real DoFP images demonstrate that our two methods have the highest interpolation accuracy and speed, respectively, and significantly enhance the visuals. Both versions efficiently process a 1024*1024 image on an AMD Ryzen 5600X CPU in 0.1402s and 0.2693s, respectively. Additionally, since our methods only involve computational processes within a 5*5 window, the potential for parallel acceleration on GPUs or FPGAs is highly feasible.

eess.IV↗