arXiv subjects

Jie Jiang

Publications and source records attributed to Jie Jiang.

At least 181 records · Page 10Linked to original sources

arXiv2024

Suppression of Blowup by Slightly Superlinear Degradation in a Parabolic-Elliptic Keller--Segel System with Signal-dependent Motility

In this paper, we consider an initial-Neumann boundary value problem for a parabolic-elliptic Keller-Segel system with signal-dependent motility and a source term. Previous research has rigorously shown that the source-free version of this system exhibits an infinite-time blowup phenomenon when dimension $N \geq 2$. In the current work, when $N \leq 3$, we establish uniform boundedness of global classical solutions with an additional source term that involves slightly super-linear degradation effect on the density, of a maximum growth order $s\log s$, unveiling a sufficient blowup suppression mechanism. The motility function considered in our work takes a rather general form compared with recent works \cite{FuJi2020, LyWa2023} which were restricted to the monotone non-increasing case. The cornerstone of our proof lies in deriving an upper bound for the second component of the system and an entropy-like estimate, which are achieved through tricky comparison skills and energy methods, respectively.

math.AP↗

arXiv2024

Emulating Complex Synapses Using Interlinked Proton Conductors

In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.

cs.NE↗

arXiv2024

Ultrasensitive piezoelectric sensor based on two-dimensional Na2Cl crystals with periodic atom vacancies

Pursuing ultrasensitivity of pressure sensors has been a long-standing goal. Here, we report a piezoelectric sensor that exhibits supreme pressure-sensing performance, including a peak sensitivity up to 3.5*10^6 kPa^-1 in the pressure range of 1-100 mPa and a detection limit of less than 1 mPa, superior to the current state-of-the-art pressure sensors. These properties are attributed to the high percentage of periodic atom vacancies in the two-dimensional Na2Cl crystals formed within multilayered graphene oxide membrane in the sensor, which provides giant polarization with high stability. The sensor can even clearly detect the airflow fluctuations surrounding a flapping butterfly, which have long been the elusive tiny signals in the famous "butterfly effect". The finding represents a step towards next-generation pressure sensors for various precision applications.

physics.app-ph↗

arXiv2024

Deep Neural Network Solutions for Oscillatory Fredholm Integral Equations

We studied the use of deep neural networks (DNNs) in the numerical solution of the oscillatory Fredholm integral equation of the second kind. It is known that the solution of the equation exhibits certain oscillatory behaviors due to the oscillation of the kernel. It was pointed out recently that standard DNNs favour low frequency functions, and as a result, they often produce poor approximation for functions containing high frequency components. We addressed this issue in this study. We first developed a numerical method for solving the equation with DNNs as an approximate solution by designing a numerical quadrature that tailors to computing oscillatory integrals involving DNNs. We proved that the error of the DNN approximate solution of the equation is bounded by the training loss and the quadrature error. We then proposed a multi-grade deep learning (MGDL) model to overcome the spectral bias issue of neural networks. Numerical experiments demonstrate that the MGDL model is effective in extracting multiscale information of the oscillatory solution and overcoming the spectral bias issue from which a standard DNN model suffers.

math.NA↗

arXiv2024

Prevention of Infinite-time Blowup by Slightly Super-linear Degradation in a Keller--Segel System with Density-suppressed Motility

An initial-Neumann boundary value problem for a Keller--Segel system with density-suppressed motility and source terms is considered. Infinite-time blowup of the classical solution was previously observed for its source-free version when dimension $N\geq2$. In this work, we prove that with any source term involving a slightly super-linear degradation effect on the density, of a growth order of $s\log s$ at most, the classical solution is uniformly-in-time bounded when $N\leq3$, thus preventing the infinite-time explosion detected in the source-free counter-part. The cornerstone of our proof lies in an improved comparison argument and a construction of an entropy inequality.

math.AP↗

arXiv2024

STEM: Unleashing the Power of Embeddings for Multi-task Recommendation

Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negative transfer, but existing studies explored negative transfer on all samples, overlooking the inherent complexities within them. We split the samples according to the relative amount of positive feedback among tasks. Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks. Existing work commonly employs a shared-embedding paradigm, limiting the ability of modeling diverse user preferences on different tasks. In this paper, we introduce a novel Shared and Task-specific EMbeddings (STEM) paradigm that aims to incorporate both shared and task-specific embeddings to effectively capture task-specific user preferences. Under this paradigm, we propose a simple model STEM-Net, which is equipped with an All Forward Task-specific Backward gating network to facilitate the learning of task-specific embeddings and direct knowledge transfer across tasks. Remarkably, STEM-Net demonstrates exceptional performance on comparable samples, achieving positive transfer. Comprehensive evaluation on three public MTL recommendation datasets demonstrates that STEM-Net outperforms state-of-the-art models by a substantial margin. Our code is released at https://github.com/LiangcaiSu/STEM.

cs.IR↗

arXiv2023

Overcharging an accelerating Reissner-Nordström-Anti-de Sitter black hole with test field and particle

Accelerating black holes have been widely studied in the context of black hole thermodynamics, holographic gravity theories, and in the description of black holes at the center of galaxies. As a fundamental assumption to ensure spacetime causality, we investigated the weak cosmic censorship conjecture (WCCC) in the accelerating Reissner-Nordström-Anti-de Sitter (RN-AdS) spacetime through the scattering of a charged field and the absorption of a charged particle. For the scattering of a charged scalar field, both near-extremal and extremal accelerating RN-AdS black holes cannot be overcharged, thereby upholding the validity of the WCCC. In the case of the absorption of a test charged particle, the results demonstrate that the event horizon of the extremal accelerating RN-AdS black hole cannot be destroyed, while the event horizon of the near-extremal black hole can be overcharged if the test particle satisfies certain conditions. The above results suggest that, in the case of test particles, second-order effects like self-force and self-energy should be further considered.

gr-qc↗

arXiv2023

Global and Local Semantic Completion Learning for Vision-Language Pre-training

Cross-modal alignment plays a crucial role in vision-language pre-training (VLP) models, enabling them to capture meaningful associations across different modalities. For this purpose, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in a limited cross-modal alignment ability of global representations to local features of the other modality. Therefore, in this paper, we propose a novel Global and Local Semantic Completion Learning (GLSCL) task to facilitate global-local alignment and local-local alignment simultaneously. Specifically, the GLSCL task complements the missing semantics of masked data and recovers global and local features by cross-modal interactions. Our GLSCL consists of masked global semantic completion (MGSC) and masked local token completion (MLTC). MGSC promotes learning more representative global features, which have a great impact on the performance of downstream tasks, while MLTC reconstructs modal-fusion local tokens, further enhancing accurate comprehension of multimodal data. To evaluate the proposed approaches on cross-modal alignment, we develop a validation benchmark called ALIGN-BENCH. Moreover, we present a flexible vision encoder, enabling our model to simultaneously perform image-text and video-text multimodal tasks. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval.

cs.CV↗

arXiv2023

LGFCTR: Local and Global Feature Convolutional Transformer for Image Matching

Image matching that finding robust and accurate correspondences across images is a challenging task under extreme conditions. Capturing local and global features simultaneously is an important way to mitigate such an issue but recent transformer-based decoders were still stuck in the issues that CNN-based encoders only extract local features and the transformers lack locality. Inspired by the locality and implicit positional encoding of convolutions, a novel convolutional transformer is proposed to capture both local contexts and global structures more sufficiently for detector-free matching. Firstly, a universal FPN-like framework captures global structures in self-encoder as well as cross-decoder by transformers and compensates local contexts as well as implicit positional encoding by convolutions. Secondly, a novel convolutional transformer module explores multi-scale long range dependencies by a novel multi-scale attention and further aggregates local information inside dependencies for enhancing locality. Finally, a novel regression-based sub-pixel refinement module exploits the whole fine-grained window features for fine-level positional deviation regression. The proposed method achieves superior performances on a wide range of benchmarks. The code will be available on https://github.com/zwh0527/LGFCTR.

cs.CV↗

arXiv2023

ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning

Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks. Occasionally, learning multiple tasks simultaneously results in lower accuracy than learning only the target task, which is known as negative transfer. This problem is often attributed to the gradient conflicts among tasks, and is frequently tackled by coordinating the task gradients in previous works. However, these optimization-based methods largely overlook the auxiliary-target generalization capability. To better understand the root cause of negative transfer, we experimentally investigate it from both optimization and generalization perspectives. Based on our findings, we introduce ForkMerge, a novel approach that periodically forks the model into multiple branches, automatically searches the varying task weights by minimizing target validation errors, and dynamically merges all branches to filter out detrimental task-parameter updates. On a series of auxiliary-task learning benchmarks, ForkMerge outperforms existing methods and effectively mitigates negative transfer.

cs.LG↗

arXiv2023

Generalized Covariant Entropy Bound in Einstein Gravity with Quadratic Curvature Corrections

We explore the generalized covariant entropy bound in the theory where Einstein gravity is perturbed by quadratic curvature terms, which can be viewed as the first-order quantum correction to Einstein gravity. By replacing the Bekenstein-Hawking entropy with the holographic entanglement entropy of this theory and introducing two reasonable physical assumptions, we demonstrate that the corresponding Generalized Covariant Entropy Bound is satisfied under a first-order approximation of the perturbation from the quadratic curvature terms. Our findings suggest that the entropy bound and the Generalized Second Law of black holes are satisfied in the Einstein gravity under the first-order perturbation from the quadratic curvature corrections, and they also imply that the generalized covariant entropy bound may still hold even after considering the quantum correction of gravity, but in this case, we may need to use holographic entanglement entropy as the formula for gravitational entropy.

gr-qc↗

arXiv2023

Twofold Symmetry Observed in Bi$_{2}$Te$_{3}$/FeTe Interfacial Superconductor

Superconducting pairing symmetry are crucial in understanding the microscopic superconducting mechanism of a superconductor. Here we report the observation of a twofold superconducting gap symmetry in an interfacial superconductor Bi$_{2}$Te$_{3}$/FeTe, by employing quasiparticle interference (QPI) technique in scanning tunneling microscopy and macroscopic magnetoresistance measurements. The QPI patterns corresponding to energies inside and outside the gap reveal a clear anisotropic superconducting gap. Furthermore, both the in-plane angle-dependent magnetoresistance and in-plane upper critical field exhibit a clear twofold symmetry. This twofold symmetry align with the Te-Te direction in FeTe, which weakens the possible generation by bi-collinear antiferromagnetism order. Our finding provides key information in further understanding of the topological properties in Bi$_{2}$Te$_{3}$/FeTe superconducting system and propels further theoretical interests in the paring mechanism in the system.

cond-mat.supr-con↗

arXiv2023

Kinematic topologies of black holes

We investigate the kinematic topologies of light rings (LRs) and massive particle rings (PRs) encircling spherical and axisymmetric black holes. Our results demonstrate that the global topology number of LRs is consistently -1 for asymptotically flat and (Anti-)de Sitter spacetime. Additionally, we show that the global topology of PRs varies, with a value of 0 in asymptotically flat and Anti-de Sitter spacetime but -1 in asymptotic de Sitter spacetime.

gr-qc↗

arXiv2023

The Sun's Magnetic Power Spectra Over Two Solar Cycles. I. Calibration Between SDO/HMI And SOHO/MDI Magnetograms

The Sun's magnetic field is strongly structured over a broad range of scales. The magnetic spatial power spectral analysis provides a powerful tool to understand the various scales of magnetic fields and their interaction with plasma motion. We aim to investigate the power spectra using spherical harmonic decomposition of high-resolution SOHO/MDI and SDO/HMI synoptic magnetograms covering three consecutive solar cycle minima in a series of papers. As the first of the series, we calibrate and analyze the power spectra based on co-temporal SDO/HMI and SOHO/MDI data in this paper. For the first time, we find that the calibration factor $r$ between SOHO/MDI and SDO/HMI varies with the spatial scale $l$ of the magnetic field, where $l$ is the degree of a spherical harmonics. The calibration factor satisfies $r(l)=\sqrt{-0.021 l^{0.64}+2} \quad(5<\mathrm{l}\leq539)$. With the calibration function, most contemporaneous SOHO/MDI and SDO/HMI magnetograms show consistent power spectra from about 8 Mm to the global scales over about 3 orders of magnitudes. Moreover, magnetic power spectra from SOHO/MDI and SDO/HMI maps show peaks/knees at $l\approx120$ corresponding to the typical supergranular scale (about 35 Mm) constrained from direct velocimetric measurements. This study paves the way for investigating the solar-cycle dependence of supergranulation and magnetic power spectra in subsequent studies.

astro-ph.SR↗

arXiv2023

Towards a live homogeneous database of solar active regions based on SOHO/MDI and SDO/HMI synoptic magnetograms. I. Automatic detection and calibration

Recent studies indicate that a small number of rogue solar active regions (ARs) may have a significant impact on the end-of-cycle polar field and the long-term behavior of solar activity. The impact of individual ARs can be qualified based on their magnetic field distribution. This motivates us to build a live homogeneous AR database in a series of papers. As the first of the series, we develop a method to automatically detect ARs from 1996 onwards based on SOHO/MDI and SDO/HMI synoptic magnetograms. The method shows its advantages in excluding decayed ARs and unipolar regions and being compatible with any available synoptic magnetograms. The identified AR flux and area are calibrated based on the co-temporal SDO/HMI and SOHO/MDI data. The homogeneity and reliability of the database are further verified by comparing it with other relevant databases. We find that ARs with weaker flux have a weaker cycle dependence. Stronger ARs show the weaker cycle 24 compared with cycle 23. Several basic parameters, namely, location, area, and flux of negative and positive polarities of identified ARs are provided in the paper. This paves the way for AR's new parameters quantifying the impact on the long-term behavior of solar activity to be presented in the subsequent paper of the series. The constantly updated database covering more than two full solar cycles will be beneficial for the understanding and prediction of the solar cycle. The database and the detection codes are accessible online.

astro-ph.SR↗

arXiv2023

Collisional Penrose process of extended test particles near an extremal Kerr black hole

We investigate the collisional Penrose process of extended test particles near extremal Kerr black holes using the pole-dipole-quadrupole approximation. We analyze the motion of the test particles and examine the dynamics and maximum efficiency of energy extraction in this process. Our results demonstrate that the maximum extracted energy in the collisional Penrose process is influenced by the spin s and quadrupolar parameter CES2 of the test particles. Specifically, we observe that, at a fixed collisional position, the energy extraction efficiency decreases as the spin increases for either the pole-dipole or the pole-dipole-quadrupole approximation case. Furthermore, for a fixed spin, the energy extraction efficiency is higher in the pole-dipole-quadrupole approximation compared to the pole-dipole approximation. These findings provide insight into the role of the internal structures of the test particles in the collisional Penrose process.

gr-qc↗

arXiv2023

Strong Cosmic Censorship in accelerating spacetime

It has been established that Strong Cosmic Censorship Conjecture (SCCC) is respected by an asymptotically flat black hole with charge, but violated by a charged asymptotically de Sitter black hole. We studied the instability of the Cauchy horizon for an accelerating black hole in Einstein theory conformally coupled with a scalar field. The black hole is uncharged while having inner, outer, and acceleration horizons. In the limit of vanishing acceleration, denoted by $A \to 0$, it becomes asymptotically flat and extremal, with its acceleration horizon vanishing as well. By exploring the perturbation of the massless scalar field upon the accelerating black hole, whose decay rate is governed by the quasinormal mode spectra, we show that the SCCC is violated in the near-extremal regime and also in the $A\to 0^+$ limit. Our result is the first observation of a black hole violating the SCCC in an almost asymptotically Minkowskian flat regime, as well as the first example of a black hole violating the SCCC with neither charge nor rotation.

gr-qc↗

arXiv2023

Holographic thermodynamics of rotating black holes

We provide mass/energy formulas for the extended thermodynamics, mixed thermodynamics, and holographic conformal field theory (CFT) thermodynamics for the charged and rotating Kerr-Newman Anti-de Sitter black holes. Then for the CFT thermal states dual to the black hole, we find the first-order phase transitions and criticality phenomena in the canonical ensemble with fixed angular momentum, volume, and central charge. We observe that the CFT states cannot be analogous to the Van der Waals fluids, despite the critical exponents falling into the universality class predicted by the mean field theory. Additionally, we examine the (de)confinement phase transitions within the grand canonical ensemble with fixed angular velocity, volume, and central charge of the CFT. Our findings suggest that the near zero temperature (de)confinement phase transitions can occur with the angular velocity of the CFT that solely depends on the CFT volume.

hep-th↗