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Jing Zhou

Publications and source records attributed to Jing Zhou.

At least 73 records · Page 4Linked to original sources
arXiv2024

Holographic Schwinger Effect in Anisotropic Media

According to gauge/gravity correspondence, we study the holographic Schwinger effect within an anisotropic background. Firstly, the separate length of the particle-antiparticle pairs is computed within the context of an anisotropic background which is parameterized by dynamical exponent $ν$. It is found that the maximum separate length $x$ increases with the increase of dynamical exponent $ν$. By analyzing the potential energy, we find that the potential barrier increases with the dynamical exponent $ν$ at a small separate distance. This observation implies that the Schwinger effect within an anisotropic background is comparatively weaker when contrasted with its manifestation in an isotropic background.Finally, we also find that the Schwinger effect in the transverse direction is weakened compared to the parallel direction in the anisotropic background, which is consistent with the top-down model.

hep-th↗
arXiv2024

Inferring Intentions to Speak Using Accelerometer Data In-the-Wild

Humans have good natural intuition to recognize when another person has something to say. It would be interesting if an AI can also recognize intentions to speak. Especially in scenarios when an AI is guiding a group discussion, this can be a useful skill. This work studies the inference of successful and unsuccessful intentions to speak from accelerometer data. This is chosen because it is privacy-preserving and feasible for in-the-wild settings since it can be placed in a smart badge. Data from a real-life social networking event is used to train a machine-learning model that aims to infer intentions to speak. A subset of unsuccessful intention-to-speak cases in the data is annotated. The model is trained on the successful intentions to speak and evaluated on both the successful and unsuccessful cases. In conclusion, there is useful information in accelerometer data, but not enough to reliably capture intentions to speak. For example, posture shifts are correlated with intentions to speak, but people also often shift posture without having an intention to speak, or have an intention to speak without shifting their posture. More modalities are likely needed to reliably infer intentions to speak.

cs.LG↗
arXiv2023

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning

Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications, such as social networks and e-commerce, involve temporal graphs where nodes and edges are dynamically evolving. Temporal graph neural networks (TGNNs) have progressively emerged as an extension of GNNs to address time-evolving graphs and have gradually become a trending research topic in both academics and industry. Advancing research and application in such an emerging field necessitates the development of new tools to compose TGNN models and unify their different schemes for dealing with temporal graphs. In this work, we introduce LasTGL, an industrial framework that integrates unified and extensible implementations of common temporal graph learning algorithms for various advanced tasks. The purpose of LasTGL is to provide the essential building blocks for solving temporal graph learning tasks, focusing on the guiding principles of user-friendliness and quick prototyping on which PyTorch is based. In particular, LasTGL provides comprehensive temporal graph datasets, TGNN models and utilities along with well-documented tutorials, making it suitable for both absolute beginners and expert deep learning practitioners alike.

cs.LG↗
arXiv2023

On RIS-Aided SIMO Gaussian Channels: Towards A Single-RF MIMO Transceiver Architecture

In this paper, for a single-input multiple-output (SIMO) system aided by a passive reconfigurable intelligent surface (RIS), the joint transmission accomplished by the single transmit antenna and the RIS with multiple controllable reflective elements is considered. Relying on a general capacity upper bound derived by a maximum-trace argument, we respectively characterize the capacity of such \rev{a} channel in the low-SNR or the rank-one regimes, in which the optimal configuration of the RIS is proved to be beamforming with carefully-chosen phase shifts. To exploit the potential of modulating extra information on the RIS, based on the QR decomposition, successive interference cancellation, and a strategy named \textit{partially beamforming and partially information-carrying}, we propose a novel transceiver architecture with only a single RF front end at the transmitter, by which the considered channel can be regarded as a concatenation of a vector Gaussian channel and several phase-modulated channels. Especially, we investigate a class of vector Gaussian channels with a hypersphere input support constraint, and not only generalize the existing result to arbitrary-dimensional real spaces but also present its high-order capacity asymptotics, by which both capacities of hypersphere-constrained channels and achievable rates of the proposed transceiver with two different signaling schemes can be well-approximated. Information-theoretic analyses show that the transceiver architecture designed for the SIMO channel has a boosted multiplexing gain, rather than one for the conventionally-used optimized beamforming scheme.Numerical results verify our derived asymptotics and show notable superiority of the proposed transceiver.

cs.IT↗
arXiv2023

Charge 4e superconductivity and chiral metal in the $45^\circ$-twisted bilayer cuprates and similar bilayers

The material realization of the charge-4e/6e superconductivity (SC) is a big challenge. Here we propose realization of the charge-4e SC and chiral metal through stacking a homo-bilayer with the largest twist angle, forming the twist-bilayer quasi-crystal (TB-QC), exampled by the 45$^\circ$-twisted bilayer cuprates and 30$^\circ$-twisted bilayer graphene. When each mononlayer hosts a pairing state with the largest pairing angular momentum, previous studies yield that the second-order interlayer Josephson coupling would drive chiral topological SC (TSC) in the TB-QC. Here we propose that, above the $T_c$ of the chiral TSC, either the total- or relative- pairing phase of the two layers can be unilateral quasi-ordered or ordered, leading to the charge-4e SC or the chiral metal phase. Based on a thorough symmetry analysis to get the low-energy effective Hamiltonian, we conduct a combined renormalization-group and Monte-Carlo study and obtain the phase diagram, which includes the charge-4e SC and chiral metal phases.

cond-mat.supr-con↗
arXiv2023

Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images

This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images. For an effective use of existing large amount of labeled color fundus photo (CFP) data and the relatively small amount of WF and UWF data, we propose a supervised domain adaptation method named Cross-domain Collaborative Learning (CdCL). Inspired by the success of fixed-ratio based mixup in unsupervised domain adaptation, we re-purpose this strategy for the current task. Due to the intrinsic disparity between the field-of-view of CFP and WF/UWF images, a scale bias naturally exists in a mixup sample that the anatomic structure from a CFP image will be considerably larger than its WF/UWF counterpart. The CdCL method resolves the issue by Scale-bias Correction, which employs Transformers for producing scale-invariant features. As demonstrated by extensive experiments on multiple datasets covering both WF and UWF images, the proposed method compares favorably against a number of competitive baselines.

eess.IV↗
arXiv2023

String breaking and running coupling of $\rm Q\bar{Q}$ in a rotating media from holography

Through gravity/gauge duality, the string breaking and running coupling constant of heavy quark-antiquark pair are investigated in the rotating background. For the meson decay mode $\rm Q\bar{Q} \rightarrow Q \bar{q}+\bar{Q} q$, we discuss the string breaking and running coupling in the parallel and transverse case. It is found that the parallel case has a more significant on string breaking and running coupling constant than the transverse case in the confined phase. The string-breaking distance and the maximum value of running coupling will decrease with the increase of angular velocity in the parallel case. Besides, we also investigate the running coupling and screening distance at finite angular velocity in the deconfined phase. It is found that the maximum values of the running coupling and screening distance are decreasing functions of angular velocity. The parallel case has a more significant influence on the running coupling and screening distance than the transverse case in the deconfined phase.

hep-ph↗
arXiv2023

Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs) in handling homogeneous graphs with heterophily, little work has been conducted on investigating the heterophily properties in the context of heterogeneous graphs. To bridge this research gap, we identify the heterophily in heterogeneous graphs using metapaths and propose two practical metrics to quantitatively describe the levels of heterophily. Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily. To address the challenge, we present Hetero$^2$Net, a heterophily-aware HGNN that incorporates both masked metapath prediction and masked label prediction tasks to effectively and flexibly handle both homophilic and heterophilic heterogeneous graphs. We evaluate the performance of Hetero$^2$Net on five real-world heterogeneous graph benchmarks with varying levels of heterophily. The results demonstrate that Hetero$^2$Net outperforms strong baselines in the semi-supervised node classification task, providing valuable insights into effectively handling more complex heterogeneous graphs.

cs.LG↗
arXiv2023

Holographic Schwinger Effect in Anisotropic Media

According to gauge/gravity correspondence, we study the holographic Schwinger effect within an anisotropic background. Firstly, the separate length of the particle-antiparticle pairs is computed within the context of an anisotropic background which is parameterized by dynamical exponent $ν$. It is found that the maximum separate length $x$ increases with the increase of dynamical exponent $ν$. By analyzing the potential energy, we find that the potential barrier increases with the dynamical exponent $ν$ at a small separate distance. This observation implies that the Schwinger effect within an anisotropic background is comparatively weaker when contrasted with its manifestation in an isotropic background. Finally, we also find that the Schwinger effect in the transverse direction is weakened, compared to the parallel direction in the anisotropic background, which is consistent with the top-down model.

hep-th↗
arXiv2023

Recovery of a generic local Hamiltonian from a degenerate steady state

Hamiltonian Learning (HL) is essential for validating quantum systems in quantum computing. Not all Hamiltonians can be uniquely recovered from a steady state. HL success depends on the Hamiltonian model and steady state. Here, we analyze HL for a specific type of steady state composed of eigenstates with degenerate mixing weight, making these Hamiltonian's eigenstates indistinguishable. To overcome this challenge, we utilize the orthogonality relationship between the eigenstate space and its complement space, constructing the orthogonal space equation. By counting the number of linearly independent equations derived from a steady state, we determine the recoverability of a generic local Hamiltonian. Our scheme is applicable for generic local Hamiltonians under various steady state, therefore offering a way of measuring the degree to which a steady state characterizes a Hamiltonian.

quant-ph↗
arXiv2023

High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach

Lung cancer, particularly in its advanced stages, remains a leading cause of death globally. Though early detection via low-dose computed tomography (CT) is promising, the identification of high-risk factors crucial for surgical mode selection remains a challenge. Addressing this, our study introduces an Attention-Enhanced Graph Convolutional Network (AE-GCN) model to classify whether there are high-risk factors in stage I lung cancer based on the preoperative CT images. This will aid surgeons in determining the optimal surgical method before the operation. Unlike previous studies that relied on 3D patch techniques to represent nodule spatial features, our method employs a GCN model to capture the spatial characteristics of pulmonary nodules. Specifically, we regard each slice of the nodule as a graph vertex, and the inherent spatial relationships between slices form the edges. Then, to enhance the expression of nodule features, we integrated both channel and spatial attention mechanisms with a pre-trained VGG model for adaptive feature extraction from pulmonary nodules. Lastly, the effectiveness of the proposed method is demonstrated using real-world data collected from the hospitals, thereby emphasizing its potential utility in the clinical practice.

eess.IV↗
arXiv2023

All about sample-size calculations for A/B testing: Novel extensions and practical guide

While there exists a large amount of literature on the general challenges of and best practices for trustworthy online A/B testing, there are limited studies on sample size estimation, which plays a crucial role in trustworthy and efficient A/B testing that ensures the resulting inference has a sufficient power and type I error control. For example, when sample size is under-estimated, the statistical inference, even with the correct analysis methods, will not be able to detect the true significant improvement leading to misinformed and costly decisions. This paper addresses this fundamental gap by developing new sample size calculation methods for correlated data, as well as absolute vs. relative treatment effects, both ubiquitous in online experiments. Additionally, we address a practical question of the minimal observed difference that will be statistically significant and how it relates to average treatment effect and sample size calculation. All proposed methods are accompanied by mathematical proofs, illustrative examples, and simulations. We end by sharing some best practices on various practical topics on sample size calculation and experimental design.

stat.ME↗
arXiv2023

A Universal Discriminator for Zero-Shot Generalization

Generative modeling has been the dominant approach for large-scale pretraining and zero-shot generalization. In this work, we challenge this convention by showing that discriminative approaches perform substantially better than generative ones on a large number of NLP tasks. Technically, we train a single discriminator to predict whether a text sample comes from the true data distribution, similar to GANs. Since many NLP tasks can be formulated as selecting from a few options, we use this discriminator to predict the concatenation of input and which option has the highest probability of coming from the true data distribution. This simple formulation achieves state-of-the-art zero-shot results on the T0 benchmark, outperforming T0 by 16.0\%, 7.8\%, and 11.5\% respectively on different scales. In the finetuning setting, our approach also achieves new state-of-the-art results on a wide range of NLP tasks, with only 1/4 parameters of previous methods. Meanwhile, our approach requires minimal prompting efforts, which largely improves robustness and is essential for real-world applications. Furthermore, we also jointly train a generalized UD in combination with generative tasks, which maintains its advantage on discriminative tasks and simultaneously works on generative tasks.

cs.CL↗
arXiv2023

Distribution Decomposition and Sum-Capacity Results of Two-User Optical Intensity Multiple Access Channels

This paper investigates the sum-capacity of two-user optical intensity multiple access channels with per-user peak- or/and average-intensity constraints. By leveraging tools from the decomposition of certain maxentropic distributions, we derive several lower bounds on the sum-capacity. In the high signal-to-noise ratio (SNR) regime, some bounds asymptotically match or approach the sum-capacity, thus closing or reducing the existing gaps to the high-SNR asymptotic sum-capacity. At moderate SNR, some bounds are also fairly close to the sum-capacity.

cs.IT↗
arXiv2023

All-Electrical Skyrmionic Bits in a Chiral Magnetic Tunnel Junction

Topological spin textures such as magnetic skyrmions hold considerable promise as robust, nanometre-scale, mobile bits for sustainable computing. A longstanding roadblock to unleashing their potential is the absence of a device enabling deterministic electrical readout of individual spin textures. Here we present the wafer-scale realization of a nanoscale chiral magnetic tunnel junction (MTJ) hosting a single, ambient skyrmion. Using a suite of electrical and multi-modal imaging techniques, we show that the MTJ nucleates skyrmions of fixed polarity, whose large readout signal - 20-70% relative to uniform states - corresponds directly to skyrmion size. Further, the MTJ exploits complementary mechanisms to stabilize distinctly sized skyrmions at zero field, thereby realizing three nonvolatile electrical states. Crucially, it can write and delete skyrmions using current densities 1,000 times lower than state-of-the-art. These results provide a platform to incorporate readout and manipulation of skyrmionic bits across myriad device architectures, and a springboard to harness chiral spin textures for multi-bit memory and unconventional computing.

cond-mat.mes-hall↗
arXiv2023

Making Chiral Topological Superconductivities from Non-topological Superconductivities Through the Twist

In this paper, we propose a general scheme to realize chiral TSCs through the "twistronics". Suppose we have a $D_n$-symmetric monolayer superconductor, which carries non-topological SC with pairing angular momentum $L=n/2$. Here we propose that we can obtain chiral TSC with the same $L$, by stacking two such monolayers with the largest twist angle $π/n$, forming a Moireless quasi-crystal (QC) structure, dubbed as the twist-bilayer QC (TB-QC) here. The chiral TSC in the TB-QC is driven by the interlay Josephson coupling between the pairing order parameters of the two layers. An argument based on the universal Ginzburg-Landau theory is provided to understand this proposal. One known example which fits our proposal is the $d+id$-chiral TSC in the 45$^\circ$-twisted bilayer cuprates. Here, based on the microscopic framework developed previously to treat with the electron-electron interactions in the TB-QC, we demonstrate the application of our proposal to a new example, i.e., the $f+if$-chiral TSC obtained by twisting two properly-doped honeycomb-Hubbard-model monolayers by the angle 30$^\circ$. This example is related to the newly synthesized 30$^\circ$-twisted bilayer graphene.

cond-mat.supr-con↗
arXiv2022

Quantized control of non-Lipschitz nonlinear systems: a novel control framework with prescribed transient performance and lower design complexity

A novel control design framework is proposed for a class of non-Lipschitz nonlinear systems with quantized states, meanwhile prescribed transient performance and lower control design complexity could be guaranteed. Firstly, different from all existing control methods for systems with state quantization, global stability of strict-feedback nonlinear systems is achieved without requiring the condition that the nonlinearities of the system model satisfy global Lipschitz continuity. Secondly, a novel barrier function-free prescribed performance control (BFPPC) method is proposed, which can guarantee prescribed transient performance under quantized states. Thirdly, a new \textit{W}-function-based control scheme is designed such that virtual control signals are not required to be differentiated repeatedly and the controller could be designed in a simple way, which guarantees global stability and lower design complexity compared with traditional dynamic surface control (DSC). Simulation results demonstrate the effectiveness of our method.

eess.SY↗
arXiv2022

BCS-BEC crossover in a quasi-two-dimensional Fermi superfluid

We study the crossover from the Bardeen-Cooper-Shrieffer (BCS) regime to the Bose-Einstein-condensation (BEC) regime in a quasi-two-dimensional quantum gas of ultracold fermionic atoms. Using an effective two-dimensional Hamiltonian with renormalized interactions between atoms and dressed molecules within a Gaussian pair fluctuation theory, we investigate how Fermi superfluidity is affected by reduced dimensionality at zero temperature in a wide range of crossover. We observe that the order parameter and pair size show universal relations with the chemical potential on the BCS side, irrespective of dimensionality. However, such universal dependences break down towards the BEC limit with increasing interaction strength. This results reveal the notable effect of reduced dimenionality on pairing physics, which can also be observed in the sound velocity and convexity parameter of the Goldstone mode. We compare our results with the latest experiments in both ${}^{6}$Li atomic gases and layered nitrides LixZrNCl and find good agreements.

cond-mat.quant-gas↗