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Hao Wu

Publications and source records attributed to Hao Wu.

At least 559 records · Page 31Linked to original sources
arXiv2023

Hypergeometric SLE with $κ=8$: Convergence of UST and LERW in Topological Rectangles

We consider uniform spanning tree (UST) in topological rectangles with alternating boundary conditions. The Peano curves associated to the UST converge weakly to hypergeometric SLE$_8$, denoted by hSLE$_8$. From the convergence result, we obtain the continuity and reversibility of hSLE$_8$ as well as an interesting connection between SLE$_8$ and hSLE$_8$. The loop-erased random walk (LERW) branch in the UST converges weakly to SLE$_2(-1, -1; -1, -1)$. We also obtain the limiting joint distribution of the two end points of the LERW branch.

math.PR↗
arXiv2023

Direct Sum Theorems From Fortification

We revisit the direct sum questions in communication complexity which asks whether the resource needed to solve $n$ communication problems together is (approximately) the sum of resources needed to solve these problems separately. Our work starts with the observation that Dinur and Meir's fortification lemma can be generalized to a general fortification lemma for a sub-additive measure over set. By applying this lemma to the case of cover number, we obtain a dual form of cover number, called "$δ$-fooling set" which is a generalized fooling set. Any rectangle which contains enough number of elements from a $δ$-fooling set can not be monochromatic. With this fact, we are able to reprove the classic direct sum theorem of cover number with a simple double counting argument. Formally, let $S \subseteq (A\times B) \times O$ and $T \subseteq (P\times Q) \times Z$ be two communication problems, $ \log \mathsf{Cov}\left(S\times T\right) \geq \log \mathsf{Cov}\left(S\right) + \log\mathsf{Cov}(T) -\log\log|P||Q|-4.$ where $\mathsf{Cov}$ denotes the cover number. One issue of current deterministic direct sum theorems about communication complexity is that they provide no information when $n$ is small, especially when $n=2$. In this work, we prove a new direct sum theorem about protocol size which imply a better direct sum theorem for two functions in terms of protocol size. Formally, let $\mathsf{L}$ denotes complexity of the protocol size of a communication problem, given a communication problem $F:A \times B \rightarrow \{0,1\}$, $ \log\mathsf{L}\left(F\times F\right)\geq \log \mathsf{L}\left(F\right) +Ω\left(\sqrt{\log\mathsf{L}\left(F\right)}\right)-\log\log|A||B| -4$. All our results are obtained in a similar way using the $δ$-fooling set to construct a hardcore for the direct sum problem.

cs.CC↗
arXiv2023

Giant 2D Skyrmion Topological Hall Effect with Ultrawide Temperature Window and Low-Current Manipulation in 2D Room-Temperature Ferromagnetic Crystals

The discovery and manipulation of topological Hall effect (THE), an abnormal magnetoelectric response mostly related to the Dzyaloshinskii-Moriya interaction (DMI), are promising for next-generation spintronic devices based on topological spin textures such as magnetic skyrmions. However, most skyrmions and THE are stabilized in a narrow temperature window either below or over room temperature with high critical current manipulation. It is still elusive and challenging to achieve large THE with both wide temperature window till room temperature and low critical current manipulation. Here, by using controllable, naturally-oxidized, sub-20 and sub-10 nm 2D van der Waals room-temperature ferromagnetic Fe3GaTe2-x crystals, robust 2D THE with ultrawide temperature window ranging in three orders of magnitude from 2 to 300 K is reported, combining with giant THE of ~5.4 micro-ohm cm at 10 K and ~0.15 micro-ohm cm at 300 K which is 1-3 orders of magnitude larger than that of all known room-temperature 2D skyrmion systems. Moreover, room-temperature current-controlled THE is also realized with a low critical current density of ~6.2*10^5 A cm^-2. First-principles calculations unveil natural oxidation-induced highly-enhanced 2D interfacial DMI reasonable for robust giant THE. This work paves the way to room-temperature, electrically-controlled 2D THE-based practical spintronic devices.

cond-mat.mtrl-sci↗
arXiv2023

Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems

In this paper, we introduce an innovative approach for addressing Bayesian inverse problems through the utilization of physics-informed invertible neural networks (PI-INN). The PI-INN framework encompasses two sub-networks: an invertible neural network (INN) and a neural basis network (NB-Net). The primary role of the NB-Net lies in modeling the spatial basis functions characterizing the solution to the forward problem dictated by the underlying partial differential equation. Simultaneously, the INN is designed to partition the parameter vector linked to the input physical field into two distinct components: the expansion coefficients representing the forward problem solution and the Gaussian latent noise. If the forward mapping is precisely estimated, and the statistical independence between expansion coefficients and latent noise is well-maintained, the PI-INN offers a precise and efficient generative model for Bayesian inverse problems, yielding tractable posterior density estimates. As a particular physics-informed deep learning model, the primary training challenge for PI-INN centers on enforcing the independence constraint, which we tackle by introducing a novel independence loss based on estimated density. We support the efficacy and precision of the proposed PI-INN through a series of numerical experiments, including inverse kinematics, 1-dimensional and 2-dimensional diffusion equations, and seismic traveltime tomography. Specifically, our experimental results showcase the superior performance of the proposed independence loss in comparison to the commonly used but computationally demanding kernel-based maximum mean discrepancy loss.

math.NA↗
arXiv2023

Reaction coordinate flows for model reduction of molecular kinetics

In this work, we introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems. The RC flow utilizes a normalizing flow to design the coordinate transformation and a Brownian dynamics model to approximate the kinetics of RC, where all model parameters can be estimated in a data-driven manner. In contrast to existing model reduction methods for molecular kinetics, RC flow offers a trainable and tractable model of reduced kinetics in continuous time and space due to the invertibility of the normalizing flow. Furthermore, the Brownian dynamics-based reduced kinetic model investigated in this work yields a readily discernible representation of metastable states within the phase space of the molecular system. Numerical experiments demonstrate how effectively the proposed method discovers interpretable and accurate low-dimensional representations of given full-state kinetics from simulations.

cs.LG↗
arXiv2023

Weyl Fermions and Broken Symmetry Phases of Laterally Confined $^3$He Films

Broken symmetries in topological condensed matter systems have implications for the spectrum of Fermionic excitations confined on surfaces or topological defects. The Fermionic spectrum of confined (quasi-2D) $^3$He-A consists of branches of chiral edge states. The negative energy states are related to the ground-state angular momentum, $L_z = (N/2) \hbar$, for $N/2$ Cooper pairs. The power law suppression of the angular momentum, $L_z(T) \simeq (N/2)\,\hbar\,[1 - \frac{2}{3}(πT/Δ)^2 ]$ for $0 \le T \ll T_c$, in the fully gapped 2D chiral A-phase reflects the thermal excitation of the chiral edge Fermions. We discuss the effects of wave function overlap, and hybridization between edge states confined near opposing edge boundaries on the edge currents, ground-state angular momentum and ground-state order parameter of superfluid $^3$He thin films. Under strong lateral confinement, the chiral A phase undergoes a sequence of phase transitions, first to a pair density wave (PDW) phase with broken translational symmetry at $D_{c2} \sim 16 ξ_0$. The PDW phase is described by a periodic array of chiral domains with alternating chirality, separated by domain walls. The period of PDW phase diverges as the confinement length $D\rightarrow D_{c_2}$. The PDW phase breaks time-reversal symmetry, translation invariance, but is invariant under the combination of time-reversal and translation by a one-half period of the PDW. The mass current distribution of the PDW phase reflects this combined symmetry, and originates from the spectra of edge Fermions and the chiral branches bound to the domain walls. Under sufficiently strong confinement a second-order transition occurs to the non-chiral ``polar phase'' at $D_{c1} \sim 9ξ_0$, in which a single p-wave orbital state of Cooper pairs is aligned along the channel.

cond-mat.supr-con↗
arXiv2023

Room-Temperature Highly-Tunable Coercivity and Highly-Efficient Nonvolatile Multi-States Magnetization Switching by Small Current in Single 2D Ferromagnet Fe$_3$GaTe$_2$

Room-temperature electrically-tuned coercivity and nonvolatile multi-states magnetization switching is crucial for next-generation low-power 2D spintronics. However, most methods have limited ability to adjust the coercivity of ferromagnetic systems, and room-temperature electrically-driven magnetization switching shows high critical current density and high power dissipation. Here, highly-tunable coercivity and highly-efficient nonvolatile multi-states magnetization switching are achieved at room temperature in single-material based devices by 2D van der Waals itinerant ferromagnet Fe$_3$GaTe$_2$. The coercivity can be readily tuned up to ~98.06% at 300 K by a tiny in-plane electric field that is 2-5 orders of magnitude smaller than that of other ferromagnetic systems. Moreover, the critical current density and power dissipation for room-temperature magnetization switching in 2D Fe$_3$GaTe$_2$ are down to ~1.7E5 A cm$^{-2}$ and ~4E12 W m$^{-3}$, respectively. Such switching power dissipation is 2-6 orders of magnitude lower than that of other 2D ferromagnetic systems. Meanwhile, multi-states magnetization switching are presented by continuously controlling the current, which can dramatically enhance the information storage capacity and develop new computing methodology. This work opens the avenue for room-temperature electrical control of ferromagnetism and potential applications for vdW-integrated 2D spintronics.

cond-mat.mtrl-sci↗
arXiv2023

Enhancing cardiovascular risk prediction through AI-enabled calcium-omics

Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease. Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction. Methods. We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with elastic-net regularization on 2457 CT calcium score (CTCS) enriched for MACE events obtained from a large no-cost CLARIFY program (ClinicalTri-als.gov Identifier: NCT04075162). We employed sampling techniques to enhance model training. We also investigated Cox models with selected features to identify explainable high-risk characteristics. Results. Our proposed calcium-omics model with modified synthetic down sampling and up sampling gave C-index (80.5%/71.6%) and two-year AUC (82.4%/74.8%) for (80:20, training/testing), respectively (sampling was applied to the training set only). Results compared favorably to Agatston which gave C-index (71.3%/70.3%) and AUC (71.8%/68.8%), respectively. Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were important determinants of increased risk, with dense calcification (>1000HU) associated with lower risk. The calcium-omics model reclassified 63% of MACE patients to the high risk group in a held-out test. The categorical net-reclassification index was NRI=0.153. Conclusions. AI analysis of coronary calcification can lead to improved results as compared to Agatston scoring. Our findings suggest the utility of calcium-omics in improved prediction of risk.

q-bio.QM↗
arXiv2023

Information Bottleneck Revisited: Posterior Probability Perspective with Optimal Transport

Information bottleneck (IB) is a paradigm to extract information in one target random variable from another relevant random variable, which has aroused great interest due to its potential to explain deep neural networks in terms of information compression and prediction. Despite its great importance, finding the optimal bottleneck variable involves a difficult nonconvex optimization problem due to the nonconvexity of mutual information constraint. The Blahut-Arimoto algorithm and its variants provide an approach by considering its Lagrangian with fixed Lagrange multiplier. However, only the strictly concave IB curve can be fully obtained by the BA algorithm, which strongly limits its application in machine learning and related fields, as strict concavity cannot be guaranteed in those problems. To overcome the above difficulty, we derive an entropy regularized optimal transport (OT) model for IB problem from a posterior probability perspective. Correspondingly, we use the alternating optimization procedure and generalize the Sinkhorn algorithm to solve the above OT model. The effectiveness and efficiency of our approach are demonstrated via numerical experiments.

cs.IT↗
arXiv2023

RIS-assisted High-Speed Railway Integrated Sensing and Communication System

One technology that has the potential to improve wireless communications in years to come is integrated sensing and communication (ISAC). In this study, we take advantage of reconfigurable intelligent surface's (RIS) potential advantages to achieve ISAC while using the same frequency and resources. Specifically, by using the reflecting elements, the RIS dynamically modifies the radio waves' strength or phase in order to change the environment for radio transmission and increase the ISAC systems' transmission rate. We investigate a single cell downlink communication situation with RIS assistance. Combining the ISAC base station's (BS) beamforming with RIS's discrete phase shift optimization, while guaranteeing the sensing signal, The aim of optimizing the sum rate is specified. We take advantage of alternating maximization to find practical solutions with dividing the challenge into two minor issues. The first power allocation subproblem is non-convex that CVX solves by converting it to convex. A local search strategy is used to solve the second subproblem of phase shift optimization. According to the results of the simulation, using RIS with adjusted phase shifts can significantly enhance the ISAC system's performance.

eess.SP↗
arXiv2023

AI-GOMS: Large AI-Driven Global Ocean Modeling System

Ocean modeling is a powerful tool for simulating the physical, chemical, and biological processes of the ocean, which is the foundation for marine science research and operational oceanography. Modern numerical ocean modeling mainly consists of governing equations and numerical algorithms. Nonlinear instability, computational expense, low reusability efficiency and high coupling costs have gradually become the main bottlenecks for the further development of numerical ocean modeling. Recently, artificial intelligence-based modeling in scientific computing has shown revolutionary potential for digital twins and scientific simulations, but the bottlenecks of numerical ocean modeling have not been further solved. Here, we present AI-GOMS, a large AI-driven global ocean modeling system, for accurate and efficient global ocean daily prediction. AI-GOMS consists of a backbone model with the Fourier-based Masked Autoencoder structure for basic ocean variable prediction and lightweight fine-tuning models incorporating regional downscaling, wave decoding, and biochemistry coupling modules. AI-GOMS has achieved the best performance in 30 days of prediction for the global ocean basic variables with 15 depth layers at 1/4° spatial resolution. Beyond the good performance in statistical metrics, AI-GOMS realizes the simulation of mesoscale eddies in the Kuroshio region at 1/12° spatial resolution and ocean stratification in the tropical Pacific Ocean. AI-GOMS provides a new backbone-downstream paradigm for Earth system modeling, which makes the system transferable, scalable and reusable.

physics.ao-ph↗
arXiv2023

Real-time FPGA Implementation of CNN-based Distributed Fiber Optic Vibration Event Recognition Method

Utilizing optical fibers to detect and pinpoint vibrations, Distributed Optical Fiber Vibration Sensing (DVS) technology provides real-time monitoring and surveillance of wide-reaching areas. This field has been leveraging Convolutional Neural Networks (CNN). Recently, a study has accomplished end-to-end vibration event recognition, enabling utilization of CNN-based DVS algorithms as real-time embedded system for edge computing in practical application situations. Considering the power consumption of central processing unit (CPU) and graphics processing unit (GPU), and the inflexibility of application-specific integrated circuit (ASIC), field-Programmable gate array (FPGA) is the optimal computing platform for the system. This paper proposes to compress pre-trained network and adopt a novel hardware structure, to design a fully on-chip, pipelined inference accelerator for CNN-based DVS algorithm, without fine tuning or re-training. This design allows for real-time processing with low power consumption and system requirement.An examination has been executed on an existing DVS algorithm based on a 40-layer CNN model comprising 2.7 million parameters. It is completely implemented on-chip, pipelined, with no reduction in accuracy.

eess.SP↗
arXiv2023

Observation of abnormal resistance-temperature behavior along with diamagnetic transition in Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O-based composite

Recently, Sukbae Lee et al.reported that material Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O (LK-99) has a series of characteristics of room temperature superconductors, including diamagnetic transition, resistance jump, nearly zero-resistance, magnetic field-dependent IV characteristics and so on (10.6111/JKCGCT.2023.33.2.061, arXiv:2307.12008, arXiv:2307.12037). However, whether LK-99 is really a room temperature superconductor is still controversial. On the one hand, some people think that the relatively weak diamagnetism of LK-99 reported by Sukbae Lee et al. is not the Meissner effect. On the other hand, there are doubts about the authenticity of its zero-resistance test results. Global replication studies have shown that LK-99 does have a large diamagnetic (arXiv:2308.01516), and also found a zero-resistance behavior at a low temperature of 110 $^\circ$K (arXiv:2308.01192). However, up to now, there is still no direct reproducible evidence to support Sukbae Lee et al.'s conclusion that LK-99 is a room temperature superconductor. Here, a distinct resistance jump was observed at about 387 $^\circ$K under ambient pressure in our experiment for unclear reason including possible impurity's contribution. The overall resistance of the test LK-99 sample still shows semiconductivity, and the resistance cannot really drop to zero. Our findings indicate that to identify the true potential of LK-99, high quality crystals without impurity are very important.

cond-mat.supr-con↗
arXiv2023

Successful growth and room temperature ambient-pressure magnetic levitation of LK-99

Recently, Sukbae Lee et al. reported inspiring experimental findings on the atmospheric superconductivity of a modified lead apatite crystal (LK-99) at room temperature (10.6111/JKCGCT.2023.33.2.061, arXiv: 2307.12008, arXiv: 2307.12037). They claimed that the synthesized LK-99 materials exhibit the Meissner levitation phenomenon of superconductors and have a superconducting transition temperature (Tc) higher than 400 K. Here, for the first time, we successfully verify and synthesize the LK-99 crystals which can be magnetically levitated with larger levitated angle than Sukbae Lee's sample at room temperature. It is expected to realize the true potential of room temperature, non-contact superconducting magnetic levitation in near future.

cond-mat.supr-con↗
arXiv2023

On a Navier-Stokes-Cahn-Hilliard System for Viscous Incompressible Two-phase Flows with Chemotaxis, Active Transport and Reaction

We analyze a Navier-Stokes-Cahn-Hilliard model for viscous incompressible two-phase flows where the mechanisms of chemotaxis, active transport and reaction are taken into account. The evolution system couples the Navier-Stokes equations for the volume-averaged fluid velocity, a convective Cahn-Hilliard equation for the phase-field variable, and an advection-diffusion equation for the density of certain chemical substance. This system is thermodynamically consistent and generalizes the well-known ``Model H'' for viscous incompressible binary fluids. For the initial-boundary value problem with a physically relevant singular potential in a general bounded smooth domain $Ω\subset \mathbb{R}^3$, we first prove the existence and uniqueness of a local strong solution. When the initial velocity is small and the initial phase-field function as well as the initial chemical density are small perturbations of a local minimizer of the free energy, we establish the existence of a unique global strong solution. Afterwards, we show the uniqueness of asymptotic limit for any global strong solution as time goes to infinity and provide an estimate on the convergence rate. The proofs for global well-posedness and long-time behavior are based on the dissipative structure of the system and the Lojasiewicz-Simon approach. Our analysis reveals the effects of chemotaxis, active transport and a long-range interaction of Oono's type on the global dynamics of the coupled system.

math.AP↗
arXiv2023

Well-posedness and Long-time Behavior of a Bulk-surface Coupled Cahn-Hilliard-diffusion System with Singular Potential for Lipid Raft Formation

We study a bulk-surface coupled system that describes the processes of lipid-phase separation and lipid-cholesterol interaction on cell membranes, in which cholesterol exchange between cytosol and cell membrane is also incorporated. The PDE system consists of a surface Cahn-Hilliard equation for the relative concentration of saturated/unsaturated lipids and a surface diffusion-reaction equation for the cholesterol concentration on the membrane, together with a diffusion equation for the cytosolic cholesterol concentration in the bulk. The detailed coupling between bulk and surface evolutions is characterized by a mass exchange term $q$. For the system with a physically relevant singular potential, we first prove the existence, uniqueness and regularity of global weak solutions to the full bulk-surface coupled system under suitable assumptions on the initial data and the mass exchange term $q$. Next, we investigate the large cytosolic diffusion limit that gives a reduction of the full bulk-surface coupled system to a system of surface equations with non-local contributions. Afterwards, we study the long-time behavior of global solutions in two categories, i.e., the equilibrium and non-equilibrium models according to different choices of the mass exchange term $q$. For the full bulk-surface coupled system with a decreasing total free energy, we prove that every global weak solution converges to a single equilibrium as $t\to +\infty$. For the reduced surface system with a mass exchange term of reaction type, we establish the existence of a global attractor.

math.AP↗
arXiv2023

Priors in Deep Image Restoration and Enhancement: A Survey

Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) has recently been applied to image restoration and enhancement. Due to its ill-posed property, plenty of works have been explored priors to facilitate training deep neural networks (DNNs). However, the importance of priors has not been systematically studied and analyzed by far in the research community. Therefore, this paper serves as the first study that provides a comprehensive overview of recent advancements in priors for deep image restoration and enhancement. Our work covers five primary contents: (1) A theoretical analysis of priors for deep image restoration and enhancement; (2) A hierarchical and structural taxonomy of priors commonly used in the DL-based methods; (3) An insightful discussion on each prior regarding its principle, potential, and applications; (4) A summary of crucial problems by highlighting the potential future directions, especially adopting the large-scale foundation models as prior, to spark more research in the community; (5) An open-source repository that provides a taxonomy of all mentioned works and code links.

cs.CV↗
arXiv2023

Searching for new globular clusters in M 31 with Gaia EDR3

We found 50 new globular cluster (GC) candidates around M\,31 with Gaia Early Data Release 3 (EDR3), with the help from Pan-STARRS1 DR1 magnitudes and Pan-Andromeda Archaeological Survey (PAndAS) images. Based on the latest Revised Bologna Catalog and \textit{simbad}, we trained 2 Random Forest (RF) classifiers, the first one to distinguish extended sources from point sources and the second one to further select GCs from extended sources. From 1.85 million sources of $16^m{<}g{<}19.5^m$ and within a large area of $\sim$392\,deg$^2$ around M\,31, we selected 20,658 extended sources and 1,934 initial GC candidates. After visual inspection of the PAndAS images to eliminate the contamination of non-cluster sources, particularly galaxies, we finally got 50 candidates. These candidates are divided into 3 types (\textbf{a}, \textbf{b}, \textbf{c}) according to their projected distance $D$ to the center of M\,31 and their probability to be a true GC, $P_{GC}$, which is calculated by our second RF classifier. Among these candidates, 14 are found to be associated (in projection) with the large-scale structures within the halo of M\,31. We also provided several simple parameter criteria for selecting extended sources effectively from the Gaia EDR3, which can reach a completeness of 92.1\% with a contamination fraction lower than 10\%.

astro-ph.GA↗