arXiv subjects

Liang Li

Publications and source records attributed to Liang Li.

At least 253 records · Page 14Linked to original sources

arXiv2022

Nanoscale three-dimensional magnetic sensing with a probabilistic nanomagnet driven by spin-orbit torque

Detection of vector magnetic fields at nanoscale dimensions is critical in applications ranging from basic material science, to medical diagnostic. Meanwhile, an all-electric operation is of great significance for achieving a simple and compact sensing system. Here, we propose and experimentally demonstrate a simple approach to sensing a vector magnetic field at nanoscale dimensions, by monitoring a probabilistic nanomagnet's transition probability from a metastable state, excited by a driving current due to SOT, to a settled state. We achieve sensitivities for Hx, Hy, and Hz of 1.02%/Oe, 1.09%/Oe and 3.43%/Oe, respectively, with a 200 x 200 nm^2 nanomagnet. The minimum detectable field is dependent on the driving pulse events N, and is expected to be as low as 1 uT if N = 3 x 10^6.

cond-mat.mtrl-sci↗

arXiv2022

Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.

cs.LG↗

arXiv2022

Impact of tensor force on quantum shell effects in quasifission reactions

Quantum shell effects drive many aspects of many-body quantal systems and their interactions. Among these are the quasifission reactions that impede the formation of a compound nucleus in superheavy element (SHE) searches. Fragment production in quasifission is influenced by shell effects as a nontrivial manifestation of microscopic dynamics hindering the full equilibration of the composite system to form the compound nucleus. In this Letter, we use the microscopic time-dependent Hartree-Fock (TDHF) theory to study 48Ca+249Bk collisions to investigate the influence of the tensor component of the effective nucleon-nucleon interaction. The results show that the inclusion of the tensor force causes the spherical shell effect to become more prominent, particularly for the neutron number yield whose peak is exactly at magic number N = 126. This suggests that the tensor force plays a compelling role in the evolution of dynamical shell effects in nuclear reactions, influencing the competition between spherical and deformed shell gaps.

nucl-th↗

arXiv2022

Relativistic Effects and GRB Polarization in Power-Law Evolution

Despite decades of polarization observations and high-significance polarized $γ$-ray, X-ray, optical, and radio emissions in gamma-ray bursts (GRBs) have been accumulating in dozens of cases, people have yet to find a consistent scenario for understanding the globally observed timing properties of GRB polarization to date. Here, we report that the observed properties of GRB polarization exhibit a four-segment timing evolution at the cosmological distance: (I) an initial hump early on (within the first few seconds); (II) a later on power-law decay (from $\sim$10$^{1}$ to $\sim$10$^{4}$ s), which takes the form of $π_{\rm obs} \propto t^{-0.50 \pm 0.02}$; (III) afterwards a late-time rebrightening hump (from $\sim$10$^{4}$ to $\sim$10$^{5}$ s); and (IV) finally a flatting power-law decay (from $\sim$ 10$^{5}$ to $\sim$ 10$^{7}$ s), with the the form of $π_{\rm obs} \propto t^{-0.21 \pm 0.08}$. These findings may present a challenge to the mainstream of polarization models that assume the polarization time evolution change in different emission regions. We show that these results can be explained by relativistic and geometric effects of a highly relativistic and magnetized jet generated by the central engine, and "magnetic patches" distributed as a globally random but locally coherent form. Our analysis suggests that there is a single dominant mechanism that might account for the global observational properties of GRB polarization, and other emission mechanisms and effects might play a role in spatially local and temporally short effects on GRB polarization.

astro-ph.HE↗

arXiv2022

Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between target and expression. Two main problems exist in weakly supervised REG. First, the lack of region-level annotations introduces ambiguities between proposals and queries. Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects. To address the above challenges, we design an entity-enhanced adaptive reconstruction network (EARN). Specifically, EARN includes three modules: entity enhancement, adaptive grounding, and collaborative reconstruction. In entity enhancement, we calculate semantic similarity as supervision to select the candidate proposals. Adaptive grounding calculates the ranking score of candidate proposals upon subject, location and context with hierarchical attention. Collaborative reconstruction measures the ranking result from three perspectives: adaptive reconstruction, language reconstruction and attribute classification. The adaptive mechanism helps to alleviate the variance of different referring expressions. Experiments on five datasets show EARN outperforms existing state-of-the-art methods. Qualitative results demonstrate that the proposed EARN can better handle the situation where multiple objects of a particular category are situated together.

cs.CV↗

arXiv2022

Model order reduction for parameterized electromagnetic problems using matrix decomposition and deep neural networks

A non-intrusive model order reduction (MOR) method for solving parameterized electromagnetic scattering problems is proposed in this paper. A database collecting snapshots of high-fidelity solutions is built by solving the parameterized time-domain Maxwell equations for some values of the material parameters using a fullwave solver based on a high order discontinuous Galerkin time-domain (DGTD) method. To perform a prior dimensionality reduction, a set of reduced basis (RB) functions are extracted from the database via a two-step proper orthogonal decomposition (POD) method. Projection coefficients of the reduced basis functions are further compressed through a convolutional autoencoder (CAE) network. Singular value decomposition (SVD) is then used to extract the principal components of the reduced-order matrices generated by CAE, and a cubic spline interpolation-based (CSI) approach is employed for approximating the dominating time- and parameter-modes of the reduced-order matrices. The generation of the reduced basis and the training of the CAE and CSI are accomplished in the offline stage, thus the RB solution for given time/parameter values can be quickly recovered via outputs of the interpolation model and decoder network. In particular, the offline and online stages of the proposed RB method are completely decoupled, which ensures the validity of the method. The performance of the proposed CAE-CSI ROM is illustrated with numerical experiments for scattering of a plane wave by a 2-D dielectric disk and a multi-layer heterogeneous medium.

math.NA↗

arXiv2022

DSPNet: Towards Slimmable Pretrained Networks based on Discriminative Self-supervised Learning

Self-supervised learning (SSL) has achieved promising downstream performance. However, when facing various resource budgets in real-world applications, it costs a huge computation burden to pretrain multiple networks of various sizes one by one. In this paper, we propose Discriminative-SSL-based Slimmable Pretrained Networks (DSPNet), which can be trained at once and then slimmed to multiple sub-networks of various sizes, each of which faithfully learns good representation and can serve as good initialization for downstream tasks with various resource budgets. Specifically, we extend the idea of slimmable networks to a discriminative SSL paradigm, by integrating SSL and knowledge distillation gracefully. We show comparable or improved performance of DSPNet on ImageNet to the networks individually pretrained one by one under the linear evaluation and semi-supervised evaluation protocols, while reducing large training cost. The pretrained models also generalize well on downstream detection and segmentation tasks. Code will be made public.

cs.CV↗

arXiv2022

Multiple Kernel Clustering with Dual Noise Minimization

Clustering is a representative unsupervised method widely applied in multi-modal and multi-view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating complementary information from base kernels. As a representative, late fusion MKC first decomposes the kernels into orthogonal partition matrices, then learns a consensus one from them, achieving promising performance recently. However, these methods fail to consider the noise inside the partition matrix, preventing further improvement of clustering performance. We discover that the noise can be disassembled into separable dual parts, i.e. N-noise and C-noise (Null space noise and Column space noise). In this paper, we rigorously define dual noise and propose a novel parameter-free MKC algorithm by minimizing them. To solve the resultant optimization problem, we design an efficient two-step iterative strategy. To our best knowledge, it is the first time to investigate dual noise within the partition in the kernel space. We observe that dual noise will pollute the block diagonal structures and incur the degeneration of clustering performance, and C-noise exhibits stronger destruction than N-noise. Owing to our efficient mechanism to minimize dual noise, the proposed algorithm surpasses the recent methods by large margins.

cs.LG↗

arXiv2022

GRB 190829A -- A Showcase of Binary Late Evolution

GRB 190829A is the fourth closest gamma-ray burst (GRB) to date ($z=0.0785$). Owing to its wide range of radio, optical, X-ray, and the very-high-energy (VHE) observations by H.E.S.S., it has become an essential new source examined by various models with complementary approaches. We here show in GRB 190829A the double-prompt pulses and the three-multiwavelength afterglows are consistent with the type II binary-driven hypernova (BdHN II) model. The progenitor is a binary composed of a carbon-oxygen (CO) star and a neutron star (NS) companion. The gravitational collapse of the iron core of the CO star produces a supernova (SN) explosion and leaves behind a new neutron star ($ν$NS) at its center. The accretion of the SN ejecta onto the NS companion and onto the $ν$NS via matter fallback spins up the NSs and produces the double-peak prompt emission. The synchrotron emission from the expanding SN ejecta with the energy injection from the rapidly spinning $ν$NS and its subsequently spin-down leads to the afterglow in the radio, optical, and X-ray. We model the sequence of physical and related radiation processes in BdHNe and focus on individuating the binary properties that play the relevant roles.

astro-ph.HE↗

arXiv2022

Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing

Cross-modal hashing is an important approach for multimodal data management and application. Existing unsupervised cross-modal hashing algorithms mainly rely on data features in pre-trained models to mine their similarity relationships. However, their optimization objectives are based on the static metric between the original uni-modal features, without further exploring data correlations during the training. In addition, most of them mainly focus on association mining and alignment among pairwise instances in continuous space but ignore the latent structural correlations contained in the semantic hashing space. In this paper, we propose an unsupervised hash learning framework, namely Adaptive Structural Similarity Preservation Hashing (ASSPH), to solve the above problems. Firstly, we propose an adaptive learning scheme, with limited data and training batches, to enrich semantic correlations of unlabeled instances during the training process and meanwhile to ensure a smooth convergence of the training process. Secondly, we present an asymmetric structural semantic representation learning scheme. We introduce structural semantic metrics based on graph adjacency relations during the semantic reconstruction and correlation mining stage and meanwhile align the structure semantics in the hash space with an asymmetric binary optimization process. Finally, we conduct extensive experiments to validate the enhancements of our work in comparison with existing works.

cs.IR↗

arXiv2022

Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proved to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering per-formance. Although existing localized MKC algorithms exhibit improved performance compared to globally-designed competi-tors, most of them widely adopt KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this paper proposes a novel local sample-weighted multiple kernel clustering (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Further, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC im-plicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algo-rithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.

cs.LG↗

arXiv2022

An inequality regarding non-radiative linear waves via a geometric method

In this work we consider the operator \[ (\mathbf{T} G) (x)= \int_{\mathbb{S}^2} G(x\cdot ω, ω) dω, \quad x\in \mathbb{R}^3, \; G\in L^2(\mathbb{R}\times \mathbb{S}^2). \] This is the adjoint operator of the Radon transform. We manage to give an optimal $L^6$ decay estimate of $\mathbf{T} G$ near the infinity by a geometric method, if the function $G$ is compactly supported. As an application we give decay estimate of non-radiative solutions to the 3D linear wave equation in the exterior region $\{(x,t)\in \mathbb{R}^3 \times \mathbb{R}: |x|>R+|t|\}$. This kind of decay estimate is useful in the channel of energy method for wave equations

math.AP↗

arXiv2022

Radiation fields and non-radiative solutions to the energy sub-critical wave equations

Radiation field and channel of energy method have become important tools in the study of nonlinear wave equations in recent years. In this work we give basic theory of radiation fields of free waves in the energy sub-critical case. We also show that the asymptotic behaviours of non-radiative solutions to a wide range of non-linear wave equations resemble those of non-radiative free waves. Our theory is completely given in the critical Sobolev spaces of the corresponding nonlinear wave equation and avoids any assumption on the energy of the solutions.

math.AP↗

arXiv2022

Automatic Relation-aware Graph Network Proliferation

Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information. Moreover, due to diverse mechanisms in the message passing, the graph search space is much larger than that of CNNs. This hinders the straightforward application of classical search strategies for exploring complicated graph search space. We propose Automatic Relation-aware Graph Network Proliferation (ARGNP) for efficiently searching GNNs with a relation-guided message passing mechanism. Specifically, we first devise a novel dual relation-aware graph search space that comprises both node and relation learning operations. These operations can extract hierarchical node/relational information and provide anisotropic guidance for message passing on a graph. Second, analogous to cell proliferation, we design a network proliferation search paradigm to progressively determine the GNN architectures by iteratively performing network division and differentiation. The experiments on six datasets for four graph learning tasks demonstrate that GNNs produced by our method are superior to the current state-of-the-art hand-crafted and search-based GNNs. Codes are available at https://github.com/phython96/ARGNP.

cs.LG↗

arXiv2022

FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset

We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed to take better advantage of our hybrid dataset. In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc. Then in the fine module, a conditional StyleGAN architecture trained with high-fidelity scan models is introduced to enrich elaborate facial geometric and texture details. Note that different from previous methods, our base and detailed modules are both changeable, which enables an innovative application of adjusting both the basic attributes and the facial details of 3D face models. Furthermore, we propose a single-image fitting framework based on differentiable rendering. Rich experiments show that our method outperforms the state-of-the-art methods.

cs.CV↗

arXiv2022

Ultrahigh ion diffusion in oxide crystal by engineering the interfacial transporter channels

The mass storage and removal in solid conductors always played vital role on the technological applications such as modern batteries, permeation membranes and neuronal computations, which were seriously lying on the ion diffusion and kinetics in bulk lattice. However, the ions transport was kinetically limited by the low diffusional process, which made it a challenge to fabricate applicable conductors with high electronic and ionic conductivities at room temperature. It was known that at essentially all interfaces, the existed space charge layers could modify the charge transport, storage and transfer properties. Thus, in the current study, we proposed an acid solution/WO3/ITO structure and achieved an ultrafast hydrogen transport in WO3 layer by interfacial job-sharing diffusion. In this sandwich structure, the transport pathways of the protons and electrons were spatially separated in acid solution and ITO layer respectively, resulting the pronounced increasing of effective hydrogen diffusion coefficient (Deff) up to 106 times. The experiment and theory simulations also revealed that this accelerated hydrogen transport based on the interfacial job-sharing diffusion was universal and could be extended to other ions and oxide materials as well, which would potentially stimulate systematic studies on ultrafast mixed conductors or faster solid-state electrochemical switching devices in the future.

cond-mat.mtrl-sci↗

arXiv2022

Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission

Federated learning (FL) is a popular collaborative distributed machine learning paradigm across mobile devices. However, practical FL over resource constrained mobile devices confronts multiple challenges, e.g., the local on-device training and model updates in FL are power hungry and radio resource intensive for mobile devices. To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices. Specifically, we develop flexible weight quantization schemes to facilitate on-device local training over heterogeneous mobile devices. Based on the observation that the energy consumption of local computing is comparable to that of model updates, we formulate the energy efficient FL problem into a mixed-integer programming problem where the quantization and spectrum resource allocation strategies are jointly determined for heterogeneous mobile devices to minimize the overall FL energy consumption (computation + transmissions) while guaranteeing model performance and training latency. Since the optimization variables of the problem are strongly coupled, an efficient iterative algorithm is proposed, where the bandwidth allocation and weight quantization levels are derived. Extensive simulations are conducted to verify the effectiveness of the proposed scheme.

cs.NI↗

arXiv2022

Adaptive actuation of magnetic soft robots using deep reinforcement learning

Magnetic soft robots have attracted growing interest due to their unique advantages in terms of untethered actuation and excellent controllability. However, finding the required magnetization patterns or magnetic fields to achieve the desired functions of these robots is quite challenging in many cases. No unified framework for design has been proposed yet, and existing methods mainly rely on manual heuristics, which are hard to satisfy the high complexity level of the desired robotic motion. Here, we develop an intelligent method to solve the related inverse-design problems, implemented by introducing a novel simulation platform for magnetic soft robots based on Cosserat rod models and a deep reinforcement learning framework based on TD3. We demonstrate that magnetic soft robots with different magnetization patterns can learn to move without human guidance in simulations, and effective magnetic fields can be autonomously generated that can then be applied directly to real magnetic soft robots in an open-loop way.

cs.RO↗