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Ding Li

Publications and source records attributed to Ding Li.

At least 55 records · Page 3Linked to original sources
arXiv2024

Quantum oscillations in kagome metals (Ti, Zr, Hf)V6Sn6 at Van Hove filling

Kagome materials have recently drawn great attention due to the interplay between nontrivial band topology, electron correlations, and Van Hove singularities related many-body orders. Here we report three new vanadium-based kagome metals, TiV6Sn6, ZrV6Sn6, and HfV6Sn6, and conduct a comprehensive investigation of their structural, magnetic, and electrical transport properties. All three compounds exhibit large unsaturated magnetoresistances and multiband Hall effects at low temperatures, indicating the existence of multiple highly mobile carriers. Both the diagonal and off-diagonal resistivity show quantum oscillations with nontrivial Berry phases and high quantum mobilities. First-principles calculations together with quantum oscillation analyses suggest the Van Hove singularities at the M point for the three compounds all located in close vicinity of the Fermi level, and there also exist multiple topological nontrivial band crossings, including a nodal ring and a massive Dirac cone. Our work extends the kagome AM6X6 family and paves the way for searching possible Van Hove physics in the V kagome lattice.

cond-mat.str-el↗
arXiv2024

Switchable in-plane anomalous Hall effect by magnetization orientation in monolayer $\mathrm{Mn}_{3}\mathrm{Si}_{2}\mathrm{Te}_{6}$

In-plane anomalous Hall effect (IPAHE) is an unconventional anomalous Hall effect (AHE) with the Hall current flows in the plane spanned by the magnetization or magnetic field and the electric field. Here,we predict a stable two-dimensional ferromagnetic monolayer $\mathrm{Mn}_{3}\mathrm{Si}_{2}\mathrm{Te}_{6}$ with collinear ordering of Mn moments in the basal plane. Moreover, we reveal that the monolayer $\mathrm{Mn}_{3}\mathrm{Si}_{2}\mathrm{Te}_{6}$ possesses a substantial periodic IPAHE due to the threefold rotational symmetry, which can be switched by changing the magnetization orientation by external magnetic fields. In addition, we briefly discuss the impacts of moderate strains on the electronic states and AHE, which lead to a near quantized Hall conductivity. Our work provides a potential platform for realizing a sizable and controllable IPAHE that greatly facilatates the application of energy-efficient spintronic devices.

cond-mat.mtrl-sci↗
arXiv2024

GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving

Modeling complicated interactions among the ego-vehicle, road agents, and map elements has been a crucial part for safety-critical autonomous driving. Previous works on end-to-end autonomous driving rely on the attention mechanism for handling heterogeneous interactions, which fails to capture the geometric priors and is also computationally intensive. In this paper, we propose the Interaction Scene Graph (ISG) as a unified method to model the interactions among the ego-vehicle, road agents, and map elements. With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow. Since a mass of unnecessary interactions are omitted, the more efficient scene-graph-based framework is able to focus on indispensable connections and leads to better performance. We evaluate the proposed method for end-to-end autonomous driving on the nuScenes dataset. Compared with strong baselines, our method significantly outperforms in the full-stack driving tasks, including perception, prediction, and planning. Code will be released at https://github.com/zhangyp15/GraphAD.

cs.CV↗
arXiv2023

Surface skyrmions and dual topological Hall effect in antiferromagnetic topological insulator EuCd$_2$As$_2$

In this work, we synthesized single crystal of EuCd$_2$As$_2$, which exhibits A-type antiferromagnetic (AFM) order with in-plane spin orientation below $T_N$ = 9.5~K.Optical spectroscopy and transport measurements suggest its topological insulator (TI) nature with an insulating gap around 0.1eV. Remarkably, a dual topological Hall resistivity that exhibits same magnitude but opposite signs in the positive to negative and negative to positive magnetic field hysteresis branches emerges below 20~K. With magnetic force microscopy (MFM) images and numerical simulations, we attribute the dual topological Hall effect to the Néel-type skyrmions stabilized by the interactions between topological surface states and magnetism, and the sign reversal in different hysteresis branches indicates potential coexistence of skyrmions and antiskyrmions. Our work uncovers a unique two-dimensional (2D) magnetism on the surface of intrinsic AFM TI, providing a promising platform for novel topological quantum states and AFM spintronic applications.

cond-mat.supr-con↗
arXiv2023

Identifying Vulnerable Third-Party Java Libraries from Textual Descriptions of Vulnerabilities and Libraries

To address security vulnerabilities arising from third-party libraries, security researchers maintain databases monitoring and curating vulnerability reports. Application developers can identify vulnerable libraries by directly querying the databases with their used libraries. However, the querying results of vulnerable libraries are not reliable due to the incompleteness of vulnerability reports. Thus, current approaches model the task of identifying vulnerable libraries as a named-entity-recognition (NER) task or an extreme multi-label learning (XML) task. These approaches suffer from highly inaccurate results in identifying vulnerable libraries with complex and similar names, e.g., Java libraries. To address these limitations, in this paper, we propose VulLibMiner, the first to identify vulnerable libraries from textual descriptions of both vulnerabilities and libraries, together with VulLib, a Java vulnerability dataset with their affected libraries. VulLibMiner consists of a TF-IDF matcher to efficiently screen out a small set of candidate libraries and a BERT-FNN model to identify vulnerable libraries from these candidates effectively. We evaluate VulLibMiner using four state-of-the-art/practice approaches of identifying vulnerable libraries on both their dataset named VeraJava and our VulLib dataset. Our evaluation results show that VulLibMiner can effectively identify vulnerable libraries with an average F1 score of 0.657 while the state-of-the-art/practice approaches achieve only 0.521.

cs.CR↗
arXiv2023

NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation

Advanced Persistent Threats (APT) attacks have plagued modern enterprises, causing significant financial losses. To counter these attacks, researchers propose techniques that capture the complex and stealthy scenarios of APT attacks by using provenance graphs to model system entities and their dependencies. Particularly, to accelerate attack detection and reduce financial losses, online provenance-based detection systems that detect and investigate APT attacks under the constraints of timeliness and limited resources are in dire need. Unfortunately, existing online systems usually sacrifice detection granularity to reduce computational complexity and produce provenance graphs with more than 100,000 nodes, posing challenges for security admins to interpret the detection results. In this paper, we design and implement NodLink, the first online detection system that maintains high detection accuracy without sacrificing detection granularity. Our insight is that the APT attack detection process in online provenance-based detection systems can be modeled as a Steiner Tree Problem (STP), which has efficient online approximation algorithms that recover concise attack-related provenance graphs with a theoretically bounded error. To utilize STP approximation algorithm frameworks for APT attack detection, we propose a novel design of in-memory cache, an efficient attack screening method, and a new STP approximation algorithm that is more efficient than the conventional one in APT attack detection while maintaining the same complexity. We evaluate NodLink in a production environment. The open-world experiment shows that NodLink outperforms two state-of-the-art (SOTA) online provenance analysis systems by achieving magnitudes higher detection and investigation accuracy while having the same or higher throughput.

cs.CR↗
arXiv2023

Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition

Self-supervised skeleton-based action recognition enjoys a rapid growth along with the development of contrastive learning. The existing methods rely on imposing invariance to augmentations of 3D skeleton within a single data stream, which merely leverages the easy positive pairs and limits the ability to explore the complicated movement patterns. In this paper, we advocate that the defect of single-stream contrast and the lack of necessary feature transformation are responsible for easy positives, and therefore propose a Cross-Stream Contrastive Learning framework for skeleton-based action Representation learning (CSCLR). Specifically, the proposed CSCLR not only utilizes intra-stream contrast pairs, but introduces inter-stream contrast pairs as hard samples to formulate a better representation learning. Besides, to further exploit the potential of positive pairs and increase the robustness of self-supervised representation learning, we propose a Positive Feature Transformation (PFT) strategy which adopts feature-level manipulation to increase the variance of positive pairs. To validate the effectiveness of our method, we conduct extensive experiments on three benchmark datasets NTU-RGB+D 60, NTU-RGB+D 120 and PKU-MMD. Experimental results show that our proposed CSCLR exceeds the state-of-the-art methods on a diverse range of evaluation protocols.

cs.CV↗
arXiv2023

No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML

On-device ML introduces new security challenges: DNN models become white-box accessible to device users. Based on white-box information, adversaries can conduct effective model stealing (MS) and membership inference attack (MIA). Using Trusted Execution Environments (TEEs) to shield on-device DNN models aims to downgrade (easy) white-box attacks to (harder) black-box attacks. However, one major shortcoming is the sharply increased latency (up to 50X). To accelerate TEE-shield DNN computation with GPUs, researchers proposed several model partition techniques. These solutions, referred to as TEE-Shielded DNN Partition (TSDP), partition a DNN model into two parts, offloading the privacy-insensitive part to the GPU while shielding the privacy-sensitive part within the TEE. This paper benchmarks existing TSDP solutions using both MS and MIA across a variety of DNN models, datasets, and metrics. We show important findings that existing TSDP solutions are vulnerable to privacy-stealing attacks and are not as safe as commonly believed. We also unveil the inherent difficulty in deciding optimal DNN partition configurations (i.e., the highest security with minimal utility cost) for present TSDP solutions. The experiments show that such ``sweet spot'' configurations vary across datasets and models. Based on lessons harvested from the experiments, we present TEESlice, a novel TSDP method that defends against MS and MIA during DNN inference. TEESlice follows a partition-before-training strategy, which allows for accurate separation between privacy-related weights from public weights. TEESlice delivers the same security protection as shielding the entire DNN model inside TEE (the ``upper-bound'' security guarantees) with over 10X less overhead (in both experimental and real-world environments) than prior TSDP solutions and no accuracy loss.

cs.CR↗
arXiv2023

Auditing Frameworks Need Resource Isolation: A Systematic Study on the Super Producer Threat to System Auditing and Its Mitigation

System auditing is a crucial technique for detecting APT attacks. However, attackers may try to compromise the system auditing frameworks to conceal their malicious activities. In this paper, we present a comprehensive and systematic study of the super producer threat in auditing frameworks, which enables attackers to either corrupt the auditing framework or paralyze the entire system. We analyze that the main cause of the super producer threat is the lack of data isolation in the centralized architecture of existing solutions. To address this threat, we propose a novel auditing framework, NODROP, which isolates provenance data generated by different processes with a threadlet-based architecture design. Our evaluation demonstrates that NODROP can ensure the integrity of the auditing frameworks while achieving an average 6.58% higher application overhead compared to vanilla Linux and 6.30% lower application overhead compared to a state-of-the-art commercial auditing framework, Sysdig across eight different hardware configurations.

cs.CR↗
arXiv2023

Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools

Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry: (1) overlooking client-side overhead; (2) imbalanced alarm triage cost and interpretation cost; and (3) excessive server-side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry.

cs.CR↗
arXiv2023

Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries

Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by applying various searching strategies carefully. As existing approaches mainly support only coarse-grained global searching strategies, they cannot efficiently traverse through complex code structures. In this paper, we propose Eunomia, a symbolic execution technique that allows users to specify local domain knowledge to enable fine-grained search. In Eunomia, we design an expressive DSL, Aes, that lets users precisely pinpoint local searching strategies to different parts of the target program. To further optimize local searching strategies, we design an interval-based algorithm that automatically isolates the context of variables for different local searching strategies, avoiding conflicts between local searching strategies for the same variable. We implement Eunomia as a symbolic execution platform targeting WebAssembly, which enables us to analyze applications written in various languages (like C and Go) but can be compiled into WebAssembly. To the best of our knowledge, Eunomia is the first symbolic execution engine that supports the full features of the WebAssembly runtime. We evaluate Eunomia with a dedicated microbenchmark suite for symbolic execution and six real-world applications. Our evaluation shows that Eunomia accelerates bug detection in real-world applications by up to three orders of magnitude. According to the results of a comprehensive user study, users can significantly improve the efficiency and effectiveness of symbolic execution by writing a simple and intuitive Aes script. Besides verifying six known real-world bugs, Eunomia also detected two new zero-day bugs in a popular open-source project, Collections-C.

cs.SE↗
arXiv2023

Global simulations of kinetic-magnetohydrodynamic processes with energetic electrons in tokamak plasmas

The energetic electrons (EEs) generated through auxiliary heating have been found to destabilize various Alfven eigenmodes (AEs) in recent experiments, which in turn lead to the EE transport and degrade the plasma energy confinement. In this work, we propose a global fluid-kinetic hybrid model for studying corresponding kinetic-magnetohydrodynamic (MHD) processes by coupling the drift-kinetic EEs to the Landau-fluid model of bulk plasmas in a non-perturbative manner. The numerical capability of Landau-fluid bulk plasmas is obtained based on a well-benchmarked eigenvalue code MAS [Multiscale Analysis of plasma Stabilities, J. Bao et al. Nucl. Fusion accepted 2023], and the EE responses to the electromagnetic fluctuations are analytically derived, which not only contribute to the MHD interchange drive and parallel current but also lead to the newly kinetic particle compression with the precessional drift resonance in the leading order. The hybrid model is casted into a nonlinear eigenvalue matrix equation and solved iteratively using Newton's method. By calibrating the EE precession frequency against the particle equation of motion in general geometry and applying more realistic trapped particle distribution in the poloidal plane, MAS simulations of EE-driven beta-induced Alfven eigenmodes (e-BAE) show excellent agreements with gyrokinetic particle-in-cell simulations, and the non-perturbative effects of EEs on e-BAE mode structure, growth rate and damping rate are demonstrated. With these efforts, the upgraded MAS greatly improves the computation efficiency for plasma problems related to deeply-trapped EEs, which is superior than initial-value simulations restricted by the stringent electron Courant condition regarding to the practical application of fast linear analysis.

physics.plasm-ph↗
arXiv2023

Planning-inspired Hierarchical Trajectory Prediction for Autonomous Driving

Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal coupled space. However, such methods typically neglect a deeper semantic interpretation of path intents and suffer from inferior performance under the imperfect High-Definition (HD) map. To address this challenge, we propose a novel Planning-inspired Hierarchical (PiH) trajectory prediction framework that selects path and speed intents through a hierarchical lateral and longitudinal decomposition. Especially, a hybrid lateral predictor is presented to select a set of fixed-distance lateral paths from map-based road-following and cluster-based free-move path candidates. {Then, the subsequent longitudinal predictor selects plausible goals sampled from a set of lateral paths as speed intents.} Finally, a trajectory decoder is given to generate future trajectories conditioned on a categorical distribution over lateral-longitudinal intents. Experiments demonstrate that PiH achieves competitive and more balanced results against state-of-the-art methods on the Argoverse motion forecasting benchmark and has the strongest robustness under the imperfect HD map.

cs.RO↗
arXiv2023

MAS: A versatile Landau-fluid eigenvalue code for plasma stability analysis in general geometry

We have developed a new global eigenvalue code, Multiscale Analysis for plasma Stabilities (MAS), for studying plasma problems with wave toroidal mode number n and frequency omega in a broad range of interest in general tokamak geometry, based on a five-field Landau-fluid description of thermal plasmas. Beyond keeping the necessary plasma fluid response, we further retain the important kinetic effects including diamagnetic drift, ion finite Larmor radius, finite parallel electric field, ion and electron Landau resonances in a self-consistent and non-perturbative manner without sacrificing the attractive efficiency in computation. The physical capabilities of the code are evaluated and examined in the aspects of both theory and simulation. In theory, the comprehensive Landau-fluid model implemented in MAS can be reduced to the well-known ideal MHD model, electrostatic ion-fluid model, and drift-kinetic model in various limits, which clearly delineates the physics validity regime. In simulation, MAS has been well benchmarked with theory and other gyrokinetic and kinetic-MHD hybrid codes in a manner of adopting the unified physical and numerical framework, which covers the kinetic Alfven wave, ion sound wave, low-n kink, high-n ion temperature gradient mode and kinetic ballooning mode. Moreover, MAS is successfully applied to model the Alfven eigenmode (AE) activities in DIII-D discharge #159243, which faithfully captures the frequency sweeping of RSAE, the tunneling damping of TAE, as well as the polarization characteristics of KBAE and BAAE being consistent with former gyrokinetic theory and simulation. With respect to the key progress contributed to the community, MAS has the advantage of combining rich physics ingredients, realistic global geometry and high computation efficiency together for plasma stability analysis in linear regime.

physics.plasm-ph↗
arXiv2023

Fuzzing the Latest NTFS in Linux with Papora: An Empirical Study

Recently, the first feature-rich NTFS implementation, NTFS3, has been upstreamed to Linux. Although ensuring the security of NTFS3 is essential for the future of Linux, it remains unclear, however, whether the most recent version of NTFS for Linux contains 0-day vulnerabilities. To this end, we implemented Papora, the first effective fuzzer for NTFS3. We have identified and reported 3 CVE-assigned 0-day vulnerabilities and 9 severe bugs in NTFS3. Furthermore, we have investigated the underlying causes as well as types of these vulnerabilities and bugs. We have conducted an empirical study on the identified bugs while the results of our study have offered practical insights regarding the security of NTFS3 in Linux.

cs.CR↗
arXiv2023

FaaSLight: General Application-Level Cold-Start Latency Optimization for Function-as-a-Service in Serverless Computing

Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverless computing, representing applications as event-driven and stateless functions. However, existing studies report that functions of FaaS applications severely suffer from cold-start latency. In this paper, we propose an approach namely FaaSLight to accelerating the cold start for FaaS applications through application-level optimization. We first conduct a measurement study to investigate the possible root cause of the cold start problem of FaaS. The result shows that application code loading latency is a significant overhead. Therefore, loading only indispensable code from FaaS applications can be an adequate solution. Based on this insight, we identify code related to application functionalities by constructing the function-level call graph, and separate other code (i.e., optional code) from FaaS applications. The separated optional code can be loaded on demand to avoid the inaccurate identification of indispensable code causing application failure. In particular, a key principle guiding the design of FaaSLight is inherently general, i.e., platform- and language-agnostic. The evaluation results on real-world FaaS applications show that FaaSLight can significantly reduce the code loading latency (up to 78.95%, 28.78% on average), thereby reducing the cold-start latency. As a result, the total response latency of functions can be decreased by up to 42.05% (19.21% on average). Compared with the state-of-the-art, FaaSLight achieves a 21.25X improvement in reducing the average total response latency.

cs.DC↗
arXiv2022

Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization

Temporal Action Localization (TAL) aims to predict both action category and temporal boundary of action instances in untrimmed videos, i.e., start and end time. Fully-supervised solutions are usually adopted in most existing works, and proven to be effective. One of the practical bottlenecks in these solutions is the large amount of labeled training data required. To reduce expensive human label cost, this paper focuses on a rarely investigated yet practical task named semi-supervised TAL and proposes an effective active learning method, named AL-STAL. We leverage four steps for actively selecting video samples with high informativeness and training the localization model, named \emph{Train, Query, Annotate, Append}. Two scoring functions that consider the uncertainty of localization model are equipped in AL-STAL, thus facilitating the video sample rank and selection. One takes entropy of predicted label distribution as measure of uncertainty, named Temporal Proposal Entropy (TPE). And the other introduces a new metric based on mutual information between adjacent action proposals and evaluates the informativeness of video samples, named Temporal Context Inconsistency (TCI). To validate the effectiveness of proposed method, we conduct extensive experiments on two benchmark datasets THUMOS'14 and ActivityNet 1.3. Experiment results show that AL-STAL outperforms the existing competitors and achieves satisfying performance compared with fully-supervised learning.

cs.CV↗
arXiv2022

Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation

This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed to extract the interactive features between agent-to-agent and agent-to-goals using a graph-based vectorized representation. Further, the Conditional Goal Prediction Network (CGPNet) focuses on goal interactive prediction via a combined form of marginal and conditional goal predictors. Finally, the Goaloriented Trajectory Forecasting Network (GTFNet) is proposed to implement trajectory interactive prediction via the conditional goal-oriented predictors, with the predicted future states of the other interacting agent taken as inputs. In addition, a new goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents. In the end, the proposed method is conducted on Argoverse motion forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset. The comparative results demonstrate the superior performance of our proposed CGTP model than the mainstream prediction methods.

cs.RO↗