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Lei Xu

Publications and source records attributed to Lei Xu.

At least 181 records · Page 10Linked to original sources
arXiv2022

Towards Explainable Meta-Learning for DDoS Detection

The Internet is the most complex machine humankind has ever built, and how to defense it from intrusions is even more complex. With the ever increasing of new intrusions, intrusion detection task rely on Artificial Intelligence more and more. Interpretability and transparency of the machine learning model is the foundation of trust in AI-driven intrusion detection results. Current interpretation Artificial Intelligence technologies in intrusion detection are heuristic, which is neither accurate nor sufficient. This paper proposed a rigorous interpretable Artificial Intelligence driven intrusion detection approach, based on artificial immune system. Details of rigorous interpretation calculation process for a decision tree model is presented. Prime implicant explanation for benign traffic flow are given in detail as rule for negative selection of the cyber immune system. Experiments are carried out in real-life traffic.

cs.AI↗
arXiv2022

Electronic excitations and spin interactions in chromium trihalides from embedded many-body wavefunctions

Although chromium trihalides are widely regarded as a promising class of two-dimensional magnets for next-generation devices, an accurate description of their electronic structure and magnetic interactions has proven challenging to achieve. Here, we quantify electronic excitations and spin interactions in Cr$X_3$ ($X=$~Cl, Br, I) using embedded many-body wavefunction calculations and fully generalized spin Hamiltonians. We find that the three trihalides feature comparable $d$-shell excitations, consisting of a high-spin $^4A_2$ $(t^3_{2g}e^0_{g})$ ground state lying 1.5$-$1.7 eV below the first excited state $^4T_2$ ($t^2_{2g}e^1_{g}$). CrCl$_3$ exhibits a single-ion anisotropy $A_{\rm sia} = -0.02$ meV, while the Cr spin-3/2 moments are ferromagnetically coupled through bilinear and biquadratic exchange interactions of $J_1 = -0.97$ meV and $J_2 = -0.05$ meV, respectively. The corresponding values for CrBr$_3$ and CrI$_3$ increase to $A_{\rm sia} = -0.08$ meV and $A_{\rm sia} = -0.12$ meV for the single-ion anisotropy, $J_1 = -1.21$ meV, $J_2 = -0.05$ meV and $J_1 = -1.38$ meV, $J_2 = -0.06$ meV for the exchange couplings, respectively. We find that the overall magnetic anisotropy is defined by the interplay between $A_{\rm sia}$ and $A_{\rm dip}$ due to magnetic dipole-dipole interaction that favors in-plane orientation of magnetic moments in ferromagnetic monolayers and bulk layered magnets. The competition between the two contributions sets CrCl$_3$ and CrI$_3$ as the easy-plane ($A_{\rm sia}+ A_{\rm dip} > 0$) and easy-axis ($A_{\rm sia}+ A_{\rm dip} < 0$) ferromagnets, respectively. The differences between the magnets trace back to the atomic radii of the halogen ligands and the magnitude of spin-orbit coupling. Our findings are in excellent agreement with recent experiments, thus providing reference values for the fundamental interactions in chromium trihalides.

cond-mat.mes-hall↗
arXiv2022

Boosted second-harmonic generation in the LiNbO$\mathrm{_3}$ metasurface governed by high-Q guided resonances and bound states in the continuum

To date, second-harmonic generation (SHG) at nanoscale has been concentrated on employing high-refractive-index nanostructures, owing to the strong field confinement at deep subwavelength scales based on optically resonant effects. However, low-index nanostructures generally exhibit weaker resonant effects and lower field confinement. To address this issue, by harnessing the large nonlinearity of LiNbO$\mathrm{_3}$, we propose a novel approach to employ guided resonances and bound states in the continuum (BICs) with a LiNbO$\mathrm{_3}$ metasurface consisting of a LiNbO$\mathrm{_3}$ disk array sitting on a LiNbO3 thin film. Such a system can transform the guided modes supported by LiNbO$\mathrm{_3}$ thin film into high-quality guided resonances which can be excited directly under plane-wave illumination. Importantly, we further demonstrate strong field confinement inside LiNbO3 thin film with tailorable Q-factor by realising a Friedrich-Wintgen BIC. Such a unique mode engineering enables a record-high SHG efficiency of 5\% under a pump intensity as low as 0.4 $\mathrm{MW/cm^{2}}$. Moreover, we reveal the influence of nonlinear resonances and cross-coupling on the SHG by showing the anomalous SHG and efficiency tuning with the rotation of the crystal axis. Our work offers a new route to constructing enhanced SHG based on high-Q guided resonances and BICs, including low-index and high-index nonlinear materials.

physics.optics↗
arXiv2022

Proposal for stable atom trapping on a GaN-on-Sapphire chip

The hybrid photon-atom integrated circuits, which include photonic microcavities and trapped single neutral atom in their evanescent field, are of great potential for quantum information processing. In this platform, the atoms provide the single-photon nonlinearity and long-lived memory, which are complementary to the excellent passive photonics devices in conventional quantum photonic circuits. In this work, we propose a stable platform for realizing the hybrid photon-atom circuits based on an unsuspended photonic chip. By introducing high-order modes in the microring, a feasible evanescent-field trap potential well $\sim0.3\,\mathrm{mK}$ could be obtained by only $10\,\mathrm{mW}$-level power in the cavity, compared with $100\,\mathrm{mW}$-level power required in the scheme based on fundamental modes. Based on our scheme, stable single atom trapping with relatively low laser power is feasible for future studies on high-fidelity quantum gates, single-photon sources, as well as many-body quantum physics based on a controllable atom array in a microcavity.

physics.optics↗
arXiv2022

Local Differential Privacy Meets Computational Social Choice -- Resilience under Voter Deletion

The resilience of a voting system has been a central topic in computational social choice. Many voting rules, like plurality, are shown to be vulnerable as the attacker can target specific voters to manipulate the result. What if a local differential privacy (LDP) mechanism is adopted such that the true preference of a voter is never revealed in pre-election polls? In this case, the attacker can only infer stochastic information about a voter's true preference, and this may cause the manipulation of the electoral result significantly harder. The goal of this paper is to provide a quantitative study on the effect of adopting LDP mechanisms on a voting system. We introduce the metric PoLDP (power of LDP) that quantitatively measures the difference between the attacker's manipulation cost under LDP mechanisms and that without LDP mechanisms. The larger PoLDP is, the more robustness LDP mechanisms can add to a voting system. We give a full characterization of PoLDP for the voting system with plurality rule and provide general guidance towards the application of LDP mechanisms.

cs.CY↗
arXiv2022

HatCUP: Hybrid Analysis and Attention based Just-In-Time Comment Updating

When changing code, developers sometimes neglect updating the related comments, bringing inconsistent or outdated comments. These comments increase the cost of program understanding and greatly reduce software maintainability. Researchers have put forward some solutions, such as CUP and HEBCUP, which update comments efficiently for simple code changes (i.e. modifying of a single token), but not good enough for complex ones. In this paper, we propose an approach, named HatCUP (Hybrid Analysis and Attention based Comment UPdater), to provide a new mechanism for comment updating task. HatCUP pays attention to hybrid analysis and information. First, HatCUP considers the code structure change information and introduces a structure-guided attention mechanism combined with code change graph analysis and optimistic data flow dependency analysis. With a generally popular RNN-based encoder-decoder architecture, HatCUP takes the action of the code edits, the syntax, semantics and structure code changes, and old comments as inputs and generates a structural representation of the changes in the current code snippet. Furthermore, instead of directly generating new comments, HatCUP proposes a new edit or non-edit mechanism to mimic human editing behavior, by generating a sequence of edit actions and constructing a modified RNN model to integrate newly developed components. Evaluation on a popular dataset demonstrates that HatCUP outperforms the state-of-the-art deep learning-based approaches (CUP) by 53.8% for accuracy, 31.3% for recall and 14.3% for METEOR of the original metrics. Compared with the heuristic-based approach (HEBCUP), HatCUP also shows better overall performance.

cs.SE↗
arXiv2022

Exploring the Universal Vulnerability of Prompt-based Learning Paradigm

Prompt-based learning paradigm bridges the gap between pre-training and fine-tuning, and works effectively under the few-shot setting. However, we find that this learning paradigm inherits the vulnerability from the pre-training stage, where model predictions can be misled by inserting certain triggers into the text. In this paper, we explore this universal vulnerability by either injecting backdoor triggers or searching for adversarial triggers on pre-trained language models using only plain text. In both scenarios, we demonstrate that our triggers can totally control or severely decrease the performance of prompt-based models fine-tuned on arbitrary downstream tasks, reflecting the universal vulnerability of the prompt-based learning paradigm. Further experiments show that adversarial triggers have good transferability among language models. We also find conventional fine-tuning models are not vulnerable to adversarial triggers constructed from pre-trained language models. We conclude by proposing a potential solution to mitigate our attack methods. Code and data are publicly available at https://github.com/leix28/prompt-universal-vulnerability

cs.CL↗
arXiv2022

Spatially entangled photon-pairs from lithium niobate nonlocal metasurfaces

Metasurfaces consisting of nano-scale structures are underpinning new physical principles for the creation and shaping of quantum states of light. Multi-photon states that are entangled in spatial or angular domains are an essential resource for quantum imaging and sensing applications, however their production traditionally relies on bulky nonlinear crystals. We predict and demonstrate experimentally the generation of spatially entangled photon pairs through spontaneous parametric down-conversion from a metasurface incorporating a nonlinear thin film of lithium niobate. This is achieved through nonlocal resonances with tailored angular dispersion mediated by an integrated silica meta-grating, enabling control of the emission pattern and associated quantum states of photon pairs by designing the grating profile and tuning the pump frequency. We measure the correlations of photon positions and identify their spatial anti-bunching through violation of the classical Cauchy-Schwartz inequality, witnessing the presence of multi-mode entanglement. Simultaneously, the photon-pair rate is strongly enhanced by 450 times as compared to unpatterned films due to high-quality-factor metasurface resonances, and the coincidence to accidental ratio reaches 5000. These results pave the way to miniaturization of various quantum devices by incorporating ultra-thin metasurfaces functioning as room-temperature sources of quantum-entangled photons.

physics.optics↗
arXiv2022

IA-FaceS: A Bidirectional Method for Semantic Face Editing

Semantic face editing has achieved substantial progress in recent years. Known as a growingly popular method, latent space manipulation performs face editing by changing the latent code of an input face to liberate users from painting skills. However, previous latent space manipulation methods usually encode an entire face into a single low-dimensional embedding, which constrains the reconstruction capacity and the control flexibility of facial components, such as eyes and nose. This paper proposes IA-FaceS as a bidirectional method for disentangled face attribute manipulation as well as flexible, controllable component editing without the need for segmentation masks or sketches in the original image. To strike a balance between the reconstruction capacity and the control flexibility, the encoder is designed as a multi-head structure to yield embeddings for reconstruction and control, respectively: a high-dimensional tensor with spatial properties for consistent reconstruction and four low-dimensional facial component embeddings for semantic face editing. Manipulating the separate component embeddings can help achieve disentangled attribute manipulation and flexible control of facial components. To further disentangle the highly-correlated components, a component adaptive modulation (CAM) module is proposed for the decoder. The semantic single-eye editing is developed for the first time without any input visual guidance, such as segmentation masks or sketches. According to the experimental results, IA-FaceS establishes a good balance between maintaining image details and performing flexible face manipulation. Both quantitative and qualitative results indicate that the proposed method outperforms the other techniques in reconstruction, face attribute manipulation, and component transfer.

cs.CV↗
arXiv2022

Lessons Learned from Blockchain Applications of Trusted Execution Environments and Implications for Future Research

Modern computer systems tend to rely on large trusted computing bases (TCBs) for operations. To address the TCB bloating problem, hardware vendors have developed mechanisms to enable or facilitate the creation of a trusted execution environment (TEE) in which critical software applications can execute securely in an isolated environment. Even under the circumstance that a host OS is compromised by an adversary, key security properties such as confidentiality and integrity of the software inside the TEEs can be guaranteed. The promise of integrity and security has driven developers to adopt it for use cases involving access control, PKS, IoT among other things. Among these applications include blockchain-related use cases. The usage of the TEEs doesn't come without its own implementation challenges and potential pitfalls. In this paper, we examine the assumptions, security models, and operational environments of the proposed TEE use cases of blockchain-based applications. The exercise and analysis help the hardware TEE research community to identify some open challenges and opportunities for research and rethink the design of hardware TEEs in general.

cs.CR↗
arXiv2022

Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking

As robots play an increasingly important role in the industrial, the expectations about their applications for everyday living tasks are getting higher. Robots need to perform long-horizon tasks that consist of several sub-tasks that need to be accomplished. Task and Motion Planning (TAMP) provides a hierarchical framework to handle the sequential nature of manipulation tasks by interleaving a symbolic task planner that generates a possible action sequence, with a motion planner that checks the kinematic feasibility in the geometric world, generating robot trajectories if several constraints are satisfied, e.g., a collision-free trajectory from one state to another. Hence, the reasoning about the task plan's geometric grounding is taken over by the motion planner. However, motion planning is computationally intense and is usability as feasibility checker casts TAMP methods inapplicable to real-world scenarios. In this paper, we introduce neural feasibility classifier (NFC), a simple yet effective visual heuristic for classifying the feasibility of proposed actions in TAMP. Namely, NFC will identify infeasible actions of the task planner without the need for costly motion planning, hence reducing planning time in multi-step manipulation tasks. NFC encodes the image of the robot's workspace into a feature map thanks to convolutional neural network (CNN). We train NFC using simulated data from TAMP problems and label the instances based on IK feasibility checking. Our empirical results in different simulated manipulation tasks show that our NFC generalizes to the entire robot workspace and has high prediction accuracy even in scenes with multiple obstructions. When combined with state-of-the-art integrated TAMP, our NFC enhances its performance while reducing its planning time.

cs.RO↗
arXiv2022

The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining

In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm named \textit{machine unlearning}, which enables data holders to proactively erase their data from a trained model. Existing machine unlearning techniques focus on centralized training, where access to all holders' training data is a must for the server to conduct the unlearning process. It remains largely underexplored about how to achieve unlearning when full access to all training data becomes unavailable. One noteworthy example is Federated Learning (FL), where each participating data holder trains locally, without sharing their training data to the central server. In this paper, we investigate the problem of machine unlearning in FL systems. We start with a formal definition of the unlearning problem in FL and propose a rapid retraining approach to fully erase data samples from a trained FL model. The resulting design allows data holders to jointly conduct the unlearning process efficiently while keeping their training data locally. Our formal convergence and complexity analysis demonstrate that our design can preserve model utility with high efficiency. Extensive evaluations on four real-world datasets illustrate the effectiveness and performance of our proposed realization.

cs.CR↗
arXiv2022

HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis

Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality. Unfortunately, algorithms for cardiac data synthesis have been so far scarcely studied in the literature. An important imaging modality in the cardiac examination is three-directional CINE multi-slice myocardial velocity mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion in three orthogonal directions of the left ventricle. The long acquisition time and complex acquisition produce make it more urgent to produce synthetic digital twins of this imaging modality. In this study, we propose a hybrid deep learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm is featured by a hybrid UNet and a Generative Adversarial Network with a foreground-background generation scheme. The experimental results show that from temporally down-sampled magnitude CINE images (six times), our proposed algorithm can still successfully synthesise high temporal resolution 3Dir MVM CMR data (PSNR=42.32) with precise left ventricle segmentation (DICE=0.92). These performance scores indicate that our proposed HDL algorithm can be implemented in real-world digital twins for myocardial velocity mapping data simulation. To the best of our knowledge, this work is the first one in the literature investigating digital twins of the 3Dir MVM CMR, which has shown great potential for improving the efficiency of clinical studies via synthesised cardiac data.

eess.IV↗
arXiv2022

D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention

DETR is the first fully end-to-end detector that predicts a final set of predictions without post-processing. However, it suffers from problems such as low performance and slow convergence. A series of works aim to tackle these issues in different ways, but the computational cost is yet expensive due to the sophisticated encoder-decoder architecture. To alleviate this issue, we propose a decoder-only detector called D^2ETR. In the absence of encoder, the decoder directly attends to the fine-fused feature maps generated by the Transformer backbone with a novel computationally efficient cross-scale attention module. D^2ETR demonstrates low computational complexity and high detection accuracy in evaluations on the COCO benchmark, outperforming DETR and its variants.

cs.CV↗
arXiv2021

Revealing the three-component structure of water with principal component analysis (PCA) on X-ray spectrum

Combining the principal component analysis (PCA) of X-ray spectrum with MD simulations, we experimentally reveal the existence of three basic components in water. These components exhibit distinct structures, densities, and temperature dependencies. Among the three, two major components correspond to the low-density liquid (LDL) and the high-density liquid (HDL) predicted by the two-component model, and the third component exhibits a unique 5-hydrogen-bond configuration with an ultra-high local density. As the temperature increases, the LDL component decreases and the HDL component increases, while the third component varies non-monotonically with a peak around 20 $^{\circ}$C to 30 $^{\circ}$C. The 3D structure of the third component is further illustrated as the uniform distribution of five hydrogen-bonded neighbors on a spherical surface. Our study reveals experimental evidence for water's unique three-component structure, which provides a fundamental basis for understanding water's special properties and anomalies.

cond-mat.soft↗
arXiv2021

DeceFL: A Principled Decentralized Federated Learning Framework

Traditional machine learning relies on a centralized data pipeline, i.e., data are provided to a central server for model training. In many applications, however, data are inherently fragmented. Such a decentralized nature of these databases presents the biggest challenge for collaboration: sending all decentralized datasets to a central server raises serious privacy concerns. Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client. Here we propose a principled decentralized federated learning algorithm (DeceFL), which does not require a central client and relies only on local information transmission between clients and their neighbors, representing a fully decentralized learning framework. It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate $O(1/T)$ (where $T$ is the number of iterations in gradient descent) as centralized federated learning when the loss function is smooth and strongly convex. Finally, the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions, demonstrating its applicability to a wide range of real-world medical and industrial applications.

cs.LG↗
arXiv2021

Achieving adjustable elasticity with non-affine to affine transition

For various engineering and industrial applications it is desirable to realize mechanical systems with broadly adjustable elasticity to respond flexibly to the external environment. Here we discover a topology-correlated transition between affine and non-affine regimes in elasticity in both two- and three-dimensional packing-derived networks. Based on this transition, we numerically design and experimentally realize multifunctional systems with adjustable elasticity. Within one system, we achieve solid-like affine response, liquid-like non-affine response and a continuous tunability in between. Moreover, the system also exhibits a broadly tunable Poisson's ratio from positive to negative values, which is of practical interest for energy absorption and for fracture-resistant materials. Our study reveals a fundamental connection between elasticity and network topology, and demonstrates its practical potential for designing mechanical systems and metamaterials.

cond-mat.mtrl-sci↗