arXiv subjects

Jie Li

Publications and source records attributed to Jie Li.

At least 505 records · Page 28Linked to original sources

arXiv2021

Observation of magnon cross-Kerr effect in cavity magnonics

When there is a certain amount of field inhomogeneity, the biased ferrimagnetic crystal will exhibit the higher-order magnetostatic (HMS) mode in addition to the uniform-precession Kittel mode. In cavity magnonics, we show both experimentally and theoretically the cross-Kerr-type interaction between the Kittel mode and HMS mode. When the Kittel mode is driven to generate a certain number of excitations, the HMS mode displays a corresponding frequency shift and vice versa. The cross-Kerr effect is caused by an exchange interaction between these two spin-wave modes. Utilizing the cross-Kerr effect, we realize and integrate a multi-mode cavity magnonic system with only one yttrium iron garnet (YIG) sphere. Our results will bring new methods to magnetization dynamics studies and pave a way for novel cavity magnonic devices by including the magnetostatic mode-mode interaction as an operational degree of freedom.

quant-ph↗

arXiv2021

Input-Specific Robustness Certification for Randomized Smoothing

Although randomized smoothing has demonstrated high certified robustness and superior scalability to other certified defenses, the high computational overhead of the robustness certification bottlenecks the practical applicability, as it depends heavily on the large sample approximation for estimating the confidence interval. In existing works, the sample size for the confidence interval is universally set and agnostic to the input for prediction. This Input-Agnostic Sampling (IAS) scheme may yield a poor Average Certified Radius (ACR)-runtime trade-off which calls for improvement. In this paper, we propose Input-Specific Sampling (ISS) acceleration to achieve the cost-effectiveness for robustness certification, in an adaptive way of reducing the sampling size based on the input characteristic. Furthermore, our method universally controls the certified radius decline from the ISS sample size reduction. The empirical results on CIFAR-10 and ImageNet show that ISS can speed up the certification by more than three times at a limited cost of 0.05 certified radius. Meanwhile, ISS surpasses IAS on the average certified radius across the extensive hyperparameter settings. Specifically, ISS achieves ACR=0.958 on ImageNet ($σ=1.0$) in 250 minutes, compared to ACR=0.917 by IAS under the same condition. We release our code in \url{https://github.com/roy-ch/Input-Specific-Certification}.

cs.CV↗

arXiv2021

Learning to Learn Transferable Attack

Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. However, the transferability of perturbations from existing methods is still limited, since the adversarial perturbations are easily overfitting with a single surrogate model and specific data pattern. In this paper, we propose a Learning to Learn Transferable Attack (LLTA) method, which makes the adversarial perturbations more generalized via learning from both data and model augmentation. For data augmentation, we adopt simple random resizing and padding. For model augmentation, we randomly alter the back propagation instead of the forward propagation to eliminate the effect on the model prediction. By treating the attack of both specific data and a modified model as a task, we expect the adversarial perturbations to adopt enough tasks for generalization. To this end, the meta-learning algorithm is further introduced during the iteration of perturbation generation. Empirical results on the widely-used dataset demonstrate the effectiveness of our attack method with a 12.85% higher success rate of transfer attack compared with the state-of-the-art methods. We also evaluate our method on the real-world online system, i.e., Google Cloud Vision API, to further show the practical potentials of our method.

cs.LG↗

arXiv2021

Quantum network with magnonic and mechanical nodes

A quantum network consisting of magnonic and mechanical nodes connected by light is proposed. Recent years have witnessed a significant development in cavity magnonics based on collective spin excitations in ferrimagnetic crystals, such as yttrium iron garnet (YIG). Magnonic systems are considered to be a promising building block for a future quantum network. However, a major limitation of the system is that the coherence time of the magnon excitations is limited by their intrinsic loss (typically in the order of 1 $μ$s for YIG). Here, we show that by coupling the magnonic system to a mechanical system using optical pulses, an arbitrary magnonic state (either classical or quantum) can be transferred to and stored in a distant long-lived mechanical resonator. The fidelity depends on the pulse parameters and the transmission loss. We further show that the magnonic and mechanical nodes can be prepared in a macroscopic entangled state. These demonstrate the quantum state transfer and entanglement distribution in such a novel quantum network of magnonic and mechanical nodes. Our work shows the possibility to connect two separate fields of optomagnonics and optomechanics, and to build a long-distance quantum network based on magnonic and mechanical systems.

quant-ph↗

arXiv2021

Slow Spin Relaxation in Single Endohedral Fullerene Molecules

Well-protected magnetization, tunable quantum states and long coherence time are desired for the use of magnetic molecules in spintronics and quantum information technologies. In this work, endohedral fullerene molecules M@C28 with different transition metal cores were explored through systematic first-principles calculations and spin dynamics analyses. Many of them have stable magnetization, giant magnetic anisotropy energy and bias-tunable structure. In particular, some of them may have coherence time up to several milliseconds for their quantum spin states at high temperature (~10K) after full consideration of spin-vibration couplings. These results suggest that these M@C28 provide a rich pool for the development of single-molecule magnets, magnetic molecular junctions, and molecular qubits.

cond-mat.mtrl-sci↗

arXiv2021

Curvature-based Feature Selection with Application in Classifying Electronic Health Records

Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML) techniques. As a powerful tool, ML has been widely applied in patient-centric healthcare solutions. To further improve the quality of patient care, Electronic Health Records (EHRs) are commonly adopted in healthcare facilities for analysis. It is a crucial task to apply AI and ML to analyse those EHRs for prediction and diagnostics due to their highly unstructured, unbalanced, incomplete, and high-dimensional nature. Dimensionality reduction is a common data preprocessing technique to cope with high-dimensional EHR data, which aims to reduce the number of features of EHR representation while improving the performance of the subsequent data analysis, e.g. classification. In this work, an efficient filter-based feature selection method, namely Curvature-based Feature Selection (CFS), is presented. The proposed CFS applied the concept of Menger Curvature to rank the weights of all features in the given data set. The performance of the proposed CFS has been evaluated in four well-known EHR data sets, including Cervical Cancer Risk Factors (CCRFDS), Breast Cancer Coimbra (BCCDS), Breast Tissue (BTDS), and Diabetic Retinopathy Debrecen (DRDDS). The experimental results show that the proposed CFS achieved state-of-the-art performance on the above data sets against conventional PCA and other most recent approaches. The source code of the proposed approach is publicly available at https://github.com/zhemingzuo/CFS.

cs.LG↗

arXiv2021

WEM: A Node Importance Algorithm in Weighted Networks

In view of the node importance in weighted networks, weighted expected method (WEM), was proposed in this paper, which take an advantages of uncertain graph algorithm. First, a weight processing method is proposed based on the relationship between the weight of edges and the intensity of contact between nodes, and the calculation method of the contribution of the weight of edges to the node importance is defined, even if there are two converse situations in the reality. Then, because of the use of dynamic programming method, which reduces the complexity of computational time to a linear level, WEM will be more suitable for the calculation in large weighted networks. Owning to its features of calculating, WEM can, to the greatest extent, ensure a precise order of node essential scores for each node. The node connectivity experiment and SIR simulation experiment show that the WEM has higher accuracy and relatively low time complexity.

cs.SI↗

arXiv2021

Image-specific Convolutional Kernel Modulation for Single Image Super-resolution

Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples. Yet intuitively, each image has its representation, and is expected to acquire an adaptive model. For this issue, we propose a novel image-specific convolutional kernel modulation (IKM) by exploiting the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels, which outperforms the vanilla convolution and several existing attention mechanisms while embedding into the state-of-the-art architectures without any additional parameters. Particularly, to optimize our IKM in mini-batch training, we introduce an image-specific optimization (IsO) algorithm, which is more effective than the conventional mini-batch SGD optimization. Furthermore, we investigate the effect of IKM on the state-of-the-art architectures and exploit a new backbone with U-style residual learning and hourglass dense block learning, terms U-Hourglass Dense Network (U-HDN), which is an appropriate architecture to utmost improve the effectiveness of IKM theoretically and experimentally. Extensive experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods. Code is available at github.com/YuanfeiHuang/IKM.

eess.IV↗

arXiv2021

A Generic Transformation for Optimal Node Repair in MDS Array Codes over $\mathbf{F}_2$

For high-rate linear systematic maximum distance separable (MDS) codes, most early constructions could initially optimally repair all the systematic nodes but not all the parity nodes. Fortunately, this issue was first solved by Li et al. in (IEEE Trans. Inform. Theory, 64(9), 6257-6267, 2018), where a transformation that can convert any nonbinary MDS array code into another one with desired properties was proposed. However, the transformation does not work for binary MDS array codes. In this paper, we address this issue by proposing another generic transformation that can convert any $[n, k]$ binary MDS array code into a new one, which endows any $r=n-k\ge2$ chosen nodes with optimal repair bandwidth and optimal rebuilding access properties, and at the same time, preserves the normalized repair bandwidth/rebuilding access for the remaining $k$ nodes under some conditions. As two immediate applications, we show that 1) by applying the transformation multiple times, any binary MDS array code can be converted into one with optimal rebuilding access for all nodes, 2) any binary MDS array code with optimal repair bandwidth or optimal rebuilding access for the systematic nodes can be converted into one with the corresponding optimality property for all nodes.

cs.IT↗

arXiv2021

Curvature estimates for spacelike graphic hypersurfaces in Lorentz-Minkowski space $\mathbb{R}^{n+1}_{1}$

In this paper, we can obtain curvature estimates for spacelike admissible graphic hypersurfaces in the $(n+1)$-dimensional Lorentz-Minkowski space $\mathbb{R}^{n+1}_{1}$, and through which the existence of spacelike admissible graphic hypersurfaces, with prescribed $2$-th Weingarten curvature and Dirichlet boundary data, defined over a strictly convex domain in the hyperbolic plane $\mathscr{H}^{n}(1)\subset\mathbb{R}^{n+1}_{1}$ of center at origin and radius $1$, can be proven.

math.DG↗

arXiv2021

Long-Time Memory and Ternary Logic Gate Using a Multistable Cavity Magnonic System

Multistability is an extraordinary nonlinear property of dynamical systems and can be explored to implement memory and switches. Here we experimentally realize the tristability in a three-mode cavity magnonic system with Kerr nonlinearity. The three stable states in the tristable region correspond to the stable solutions of the frequency shift of the cavity magnon polariton under specific driving conditions. We find that the system staying in which stable state depends on the history experienced by the system, and this state can be harnessed to store the history information. In our experiment, the memory time can reach as long as 5.11 s. Moreover, we demonstrate the ternary logic gate with good on-off characteristics using this multistable hybrid system. Our new findings pave a way towards cavity magnonics-based information storage and processing.

physics.app-ph↗

arXiv2021

Nonhomogeneous associative Yang-Baxter equations

We introduce the notion of associative (BiHom-)Yang-Baxter pair of weight $(λ,γ)$ which can provide the solution to the double curved Rota-Baxter (BiHom-)system. Equivalent characterizations of (quasitriangular) covariant BiHom-bialgebra are given. We also prove that associative BiHom-Yang-Baxter equation of weight $-1$ can be obtained by the unitary quasitriangular covariant BiHom-bialgebra. At last, we present two approaches to construct (BiHom-)pre-Lie modules from Rota-Baxter (BiHom-)paired modules.

math.RA↗

arXiv2021

Denoised Non-Local Neural Network for Semantic Segmentation

The non-local network has become a widely used technique for semantic segmentation, which computes an attention map to measure the relationships of each pixel pair. However, most of the current popular non-local models tend to ignore the phenomenon that the calculated attention map appears to be very noisy, containing inter-class and intra-class inconsistencies, which lowers the accuracy and reliability of the non-local methods. In this paper, we figuratively denote these inconsistencies as attention noises and explore the solutions to denoise them. Specifically, we inventively propose a Denoised Non-Local Network (Denoised NL), which consists of two primary modules, i.e., the Global Rectifying (GR) block and the Local Retention (LR) block, to eliminate the inter-class and intra-class noises respectively. First, GR adopts the class-level predictions to capture a binary map to distinguish whether the selected two pixels belong to the same category. Second, LR captures the ignored local dependencies and further uses them to rectify the unwanted hollows in the attention map. The experimental results on two challenging semantic segmentation datasets demonstrate the superior performance of our model. Without any external training data, our proposed Denoised NL can achieve the state-of-the-art performance of 83.5\% and 46.69\% mIoU on Cityscapes and ADE20K, respectively.

cs.CV↗

arXiv2021

Bone Marrow Cell Recognition: Training Deep Object Detection with A New Loss Function

For a long time, bone marrow cell morphology examination has been an essential tool for diagnosing blood diseases. However, it is still mainly dependent on the subjective diagnosis of experienced doctors, and there is no objective quantitative standard. Therefore, it is crucial to study a robust bone marrow cell detection algorithm for a quantitative automatic analysis system. Currently, due to the dense distribution of cells in the bone marrow smear and the diverse cell classes, the detection of bone marrow cells is difficult. The existing bone marrow cell detection algorithms are still insufficient for the automatic analysis system of bone marrow smears. This paper proposes a bone marrow cell detection algorithm based on the YOLOv5 network, trained by minimizing a novel loss function. The classification method of bone marrow cell detection tasks is the basis of the proposed novel loss function. Since bone marrow cells are classified according to series and stages, part of the classes in adjacent stages are similar. The proposed novel loss function considers the similarity between bone marrow cell classes, increases the penalty for prediction errors between dissimilar classes, and reduces the penalty for prediction errors between similar classes. The results show that the proposed loss function effectively improves the algorithm's performance, and the proposed bone marrow cell detection algorithm has achieved better performance than other cell detection algorithms.

cs.CV↗

arXiv2021

Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving

Multi-object tracking is an important ability for an autonomous vehicle to safely navigate a traffic scene. Current state-of-the-art follows the tracking-by-detection paradigm where existing tracks are associated with detected objects through some distance metric. The key challenges to increase tracking accuracy lie in data association and track life cycle management. We propose a probabilistic, multi-modal, multi-object tracking system consisting of different trainable modules to provide robust and data-driven tracking results. First, we learn how to fuse features from 2D images and 3D LiDAR point clouds to capture the appearance and geometric information of an object. Second, we propose to learn a metric that combines the Mahalanobis and feature distances when comparing a track and a new detection in data association. And third, we propose to learn when to initialize a track from an unmatched object detection. Through extensive quantitative and qualitative results, we show that when using the same object detectors our method outperforms state-of-the-art approaches on the NuScenes and KITTI datasets.

cs.CV↗

arXiv2021

Towards the Capacity of Private Information Retrieval from Coded and Colluding Servers

In this work, two practical concepts related to private information retrieval (PIR) are introduced and coined full support-rank PIR and strongly linear PIR. Being of full support-rank is a technical, yet natural condition required to prove a converse result for a capacity expression and satisfied by almost all currently known capacity-achieving schemes, while strong linearity is a practical requirement enabling implementation over small finite fields with low subpacketization degree. Then, the capacity of MDS-coded, linear, full support-rank PIR in the presence of colluding servers is derived, as well as the capacity of symmetric, linear PIR with colluding, adversarial, and nonresponsive servers for the recently introduced concept of matched randomness. This positively settles the capacity conjectures stated by Freij-Hollanti et al. and Tajeddine et al. in the presented cases. It is also shown that, further restricting to strongly-linear PIR schemes with deterministic linear interference cancellation, the so-called star product scheme proposed by Freij-Hollanti et al. is essentially optimal and induces no capacity loss.

cs.IT↗

arXiv2021

Learning to Track with Object Permanence

Tracking by detection, the dominant approach for online multi-object tracking, alternates between localization and association steps. As a result, it strongly depends on the quality of instantaneous observations, often failing when objects are not fully visible. In contrast, tracking in humans is underlined by the notion of object permanence: once an object is recognized, we are aware of its physical existence and can approximately localize it even under full occlusions. In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning. We build on top of the recent CenterTrack architecture, which takes pairs of frames as input, and extend it to videos of arbitrary length. To this end, we augment the model with a spatio-temporal, recurrent memory module, allowing it to reason about object locations and identities in the current frame using all the previous history. It is, however, not obvious how to train such an approach. We study this question on a new, large-scale, synthetic dataset for multi-object tracking, which provides ground truth annotations for invisible objects, and propose several approaches for supervising tracking behind occlusions. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI and MOT17 datasets thanks to its robustness to occlusions.

cs.CV↗

arXiv2021

Entanglement Emerges from Dissipation-Structured Quantum Self-Organization

Entanglement is a holistic property of multipartite quantum systems, which is accompanied by the establishment of nonclassical correlations between subsystems. Most entanglement mechanisms can be described by a coherent interaction Hamiltonian, and entanglement develops over time. In other words, the generation of entanglement has a time arrow. Dissipative structure theory directs the evolving time arrow of a non-equilibrium system. By dissipating energy to the environment, the system establishes order out of randomness. This is also referred to as self-organization. Here, we explore a new mechanism to create entanglement, utilizing the wisdom of dissipative structure theory in quantum systems. The entanglement between subsystems can emerge via the dissipation-structured correlation. This method requires a non-equilibrium initial state and cooperative dissipation, which can be implemented in a variety of waveguide-coupled quantum systems.

quant-ph↗