Searcharxiv⌕ Search

arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 325 records · Page 18Linked to original sources
arXiv2022

Strongly-overdoped La$_{2-x}$Sr$_x$CuO$_4$: Evidence for Josephson-coupled grains of strongly-correlated superconductor

The interpretation of how superconductivity disappears in cuprates at large hole doping has been controversial. To address this issue, we present an experimental study of single-crystal and thin film samples of La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) with $x\ge0.25$. In particular, measurements of bulk susceptibility on LSCO crystals with $x=0.25$ indicate an onset of diamagnetism at $T_{c1}=38.5$ K, with a sharp transition to a phase with full bulk shielding at $T_{c2}=18$ K, independent of field direction. Strikingly, the in-plane resistivity only goes to zero at $T_{c2}$. Inelastic neutron scattering on $x=0.25$ crystals confirms the presence of low-energy incommensurate magnetic excitations with reduced strength compared to lower doping levels. The ratio of the spin gap to $T_{c2}$ is anomalously large. Our results are consistent with a theoretical prediction for strongly overdoped cuprates by Spivak, Oreto, and Kivelson, in which superconductivity initially develops within disconnected self-organized grains characterized by a reduced hole concentration, with bulk superconductivity occurring only after superconductivity is induced by proximity effect in the surrounding medium of higher hole concentration. Beyond the superconducting-to-metal transition, local differential conductance measurements on an LSCO thin film suggest that regions with pairing correlations survive, but are too dilute to support superconducting order. Future experiments will be needed to test the degree to which these results apply to overdoped cuprates in general.

cond-mat.supr-con↗
arXiv2022

Strongly correlated doped hole carriers in the superconducting nickelates: Their location, local many-body state, and low-energy effective Hamiltonian

The families of high-temperature superconductors recently welcomed a new member: hole doped nickelate Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ with a $\sim$15K transition temperature. To understand its emergent low-energy behaviors and experimental properties, an immediate key question is whether the superconducting hole carriers reside in oxygen as in the cuprates, or in nickel as in most nickelates. We answer this crucial question via a ``(LDA+$U$)+ED'' scheme: deriving an effective interacting Hamiltonian of the hole carriers from density functional LDA+$U$ calculation, and studying its local many-body states via exact diagonalization. Surprisingly, distinct from the expected Ni$^{2+}$ spin-triplet state found in most nickelates, the local ground state of two holes is actually a Ni-O spin-singlet state with second hole greatly residing in oxygen. The emerged eV-scale model therefore resembles that of the cuprates, advocating further systematic experimental comparisons. Tracing the microscopic origin of this unexpected result to the lack of apical oxygen in this material, we proposed a route to increase superconducting temperature, and a possible quantum phase transition absent in the cuprates.

cond-mat.supr-con↗
arXiv2022

Multi-scale frequency separation network for image deblurring

Image deblurring aims to restore the detailed texture information or structures from blurry images, which has become an indispensable step in many computer vision tasks. Although various methods have been proposed to deal with the image deblurring problem, most of them treated the blurry image as a whole and neglected the characteristics of different image frequencies. In this paper, we present a new method called multi-scale frequency separation network (MSFS-Net) for image deblurring. MSFS-Net introduces the frequency separation module (FSM) into an encoder-decoder network architecture to capture the low- and high-frequency information of image at multiple scales. Then, a cycle-consistency strategy and a contrastive learning module (CLM) are respectively designed to retain the low-frequency information and recover the high-frequency information during deblurring. At last, the features of different scales are fused by a cross-scale feature fusion module (CSFFM). Extensive experiments on benchmark datasets show that the proposed network achieves state-of-the-art performance.

cs.CV↗
arXiv2022

Magnetic domain structure and domain wall bound states of topological semimetal EuB6

Electrically manipulating domain wall (DW) is at central in the field of spintronics. Magnetic Weyl semimetal (WSM) offers an additional knob for electric manipulation of DW by utilizing the topological DW bound states. Using magnetic WSM material EuB6 as a prototype system, we investigate its DW bound states combining first principles calculations and domain imaging via magnetic force microscopy (MFM). The MFM measurements reveal that domains with magnetization aligned along three [100] directions dominate over others under a small magnetic field along [001] direction. Accordingly, first principles calculations are performed based on experimentally determined DW type, which show a robust DW bound state featuring a spectral distribution over a large portion of the DW Brillouin zone. Such DW bound states should result in significant amount of localized charges which can be used to drive DW motion via the electrostatic forces exerted on the localized DW charges.

cond-mat.mtrl-sci↗
arXiv2022

Continual learning on deployment pipelines for Machine Learning Systems

Following the development of digitization, a growing number of large Original Equipment Manufacturers (OEMs) are adapting computer vision or natural language processing in a wide range of applications such as anomaly detection and quality inspection in plants. Deployment of such a system is becoming an extremely important topic. Our work starts with the least-automated deployment technologies of machine learning systems includes several iterations of updates, and ends with a comparison of automated deployment techniques. The objective is, on the one hand, to compare the advantages and disadvantages of various technologies in theory and practice, so as to facilitate later adopters to avoid making the generalized mistakes when implementing actual use cases, and thereby choose a better strategy for their own enterprises. On the other hand, to raise awareness of the evaluation framework for the deployment of machine learning systems, to have more comprehensive and useful evaluation metrics (e.g. table 2), rather than only focusing on a single factor (e.g. company cost). This is especially important for decision-makers in the industry.

cs.LG↗
arXiv2022

Engineering a pure Dirac regime in ZrTe$_5$

Real-world topological semimetals typically exhibit Dirac and Weyl nodes that coexist with trivial Fermi pockets. This tends to mask the physics of the relativistic quasiparticles. Using the example of ZrTe5, we show that strain provides a powerful tool for in-situ tuning of the band structure such that all trivial pockets are pushed far away from the Fermi energy, but only for a certain range of Van der Waals gaps. Our results naturally reconcile contradicting reports on the presence or absence of additional pockets in ZrTe$_5$, and provide a clear map of where to find a pure three-dimensional Dirac semimetallic phase in the structural parameter space of the material.

cond-mat.mtrl-sci↗
arXiv2022

Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation

One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for a better warping in the next iteration. However, there is a key weakness in such dual-model iteration that the spatial misalignment inevitably caused by the reg-model could misguide the seg-model, which makes it converge on an inferior segmentation performance eventually. In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues. Specifically, we first utilize the reg-model to warp the atlas onto an unlabeled image, and then employ the Fourier-based amplitude exchange with perturbation to transplant the style of the unlabeled image into the aligned atlas. This allows the subsequent seg-model to learn on the aligned and style-transferred copies of the atlas instead of unlabeled images, which naturally guarantees the correct spatial correspondence of an image-mask training pair, without sacrificing the diversity of intensity patterns carried by the unlabeled images. Furthermore, we introduce a feature-aware content consistency in addition to the image-level similarity to constrain the reg-model for a promising initialization, which avoids the collapse of image-aligned style transformation in the first iteration. Experimental results on two public datasets demonstrate 1) a competitive segmentation performance of our method compared to the fully-supervised method, and 2) a superior performance over other state-of-the-art with an increase of average Dice by up to 4.67%. The source code is available at: https://github.com/JinxLv/One-shot-segmentation-via-IST.

cs.CV↗
arXiv2022

Latency Minimization for Multiuser Computation Offloading in Fog-Radio Access Networks

This paper considers computation offloading in fog-radio access networks (F-RAN), where multiple user equipments (UEs) offload their computation tasks to the F-RAN through a number of fog nodes. Each UE can choose one of the fog nodes to offload its task, and each fog node may serve multiple UEs. Depending on the computation burden at the fog nodes, the tasks may be computed by the fog nodes or further offloaded to the cloud via capacity-limited fronthaul links. To compute all UEs' tasks as fast as possible, joint optimization of UE-Fog association, radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs. This min-max problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem, and then employ the majorization minimization (MM) approach to find a solution. The MM approach that we develop is unconventional in that -- each MM subproblem can be solved inexactly with the same provable convergence guarantee as the conventional exact MM, thereby reducing the complexity of each MM iteration. In addition, we also consider a cooperative offloading model, where the fog nodes compress-and-forward their received signals to the cloud. Under this model, a similar min-max latency optimization problem is formulated and tackled again by the inexact MM approach. Simulation results show that the proposed algorithms outperform some heuristic offloading strategies, and that the cooperative offloading can better exploit the transmission diversity to attain better latency performance than the non-cooperative one.

eess.SP↗
arXiv2022

Joint Secure Communication and Radar Beamforming: A Secrecy-Estimation Rate-Based Design

This paper considers transmit beamforming in dual-function radar-communication (DFRC) system, where a DFRC transmitter simultaneously communicates with a communication user and detects a malicious target with the same waveform. Since the waveform is embedded with information, the information is risked to be intercepted by the target. To address this problem, physical-layer security technique is exploited. By using secrecy rate and estimation rate as performance measure for communication and radar, respectively, three secrecy rate maximization (SRM) problems are formulated, including the SRM with and without artificial noise (AN), and robust SRM. For the SRM beamforming, we prove that the optimal beamformer can be computed in closed form. For the AN-aided SRM, by leveraging alternating optimization similar closed-form solution is obtained for the beamformer and the AN covariance matrix. Finally, the imperfect CSI of the target is also considered under the premise of a moment-based random phase-error model on the direction of arrival at the target. Simulation results demonstrate the efficacy and robustness of the proposed designs.

eess.SP↗
arXiv2022

Colossal Nernst power factor in topological semimetal NbSb$_2$

Today solid-state cooling technologies below liquid nitrogen boiling temperature (77 K), crucial to quantum information technology and probing quantum state of matter, are greatly limited due to the lack of good thermoelectric and/or thermomagnetic materials. Here, we report the discovery of colossal Nernst power factor of 3800$\times$10$^{-4}$ W m$^{-1}$ K$^{-2}$ under 5 T at 25 K and high Nernst figure-of-merit of 71$\times$10$^{-4}$ K$^{-1}$ under 5 T at 20 K in topological semimetal NbSb$_2$ single crystals. The observed high thermomagnetic performance is attributed to large Nernst thermopower and longitudinal electrical conductivity, and relatively low transverse thermal conductivity. The large and unsaturated Nernst thermopower is the result of the combination of highly desirable electronic structures of NbSb$_2$ having compensated high mobility electrons and holes near Fermi level and strong phonon-drag effect. This discovery opens an avenue for exploring material option for the solid-state heat pumping below liquid nitrogen temperature.

cond-mat.mtrl-sci↗
arXiv2022

The solution to the `$1/2$ vs $3/2$' puzzle

Using an almost complete relativistic method based on the Bethe-Salpeter equation, we study the mixing angle $θ$, the mass splitting $\bigtriangleup M$, the strong decay widths $Γ(D^{({\prime})}_1)$ and the weak production rates $Br(B\to D^{({\prime})}_1\ellν_{\ell})$ of the $D_1(2420)$ and $D_1^{\prime}(2430)$. We find there is the strong cancellation between the $^1P_1$ and $^3P_1$ partial waves in $D_1^{\prime}(2430)$ with $θ\sim-35.3^{\circ}$, which leads to the `$1/2$ vs $3/2$' puzzle. The puzzle can not be overcome by adding only relativistic corrections since in a large parameter range where $\bigtriangleup M$ is linear varying and not small, the $θ$, $Γ(D^{({\prime})}_1)$ and $Br(B\to D^{({\prime})}_1\ellν_{\ell})$ remain almost unchanged but conflict with data. While in a special range around the mass inverse point where $\bigtriangleup M=0$ and $θ=\pm 90^{\circ}$, they change rapidly but we find the windows where $\bigtriangleup M$, $Γ(D^{({\prime})}_1)$ and $Br(B\to D^{({\prime})}_1\ellν_{\ell})$ are all consistent with data. The small $\bigtriangleup M$ confirmed by experiment, is crucial to solve the `$1/2$ vs $3/2$' puzzle.

hep-ph↗
arXiv2022

Searching for pulsars with phase characteristics

We present a method by using the phase characteristics of radio observation data for pulsar search and candidate identification. The phase characteristics are relations between the pulsar signal and the phase correction in the frequency-domain, and we regard it as a new search diagnostic characteristic. Based on the phase characteristics, a search method is presented: calculating DM (dispersion measure) -- frequency data to select candidate frequencies, and then confirming of candidates by using the broadband characteristics of pulsar signals. Based on this method, we performed a search test on short observation data of M15 and M71, which were observed by Five-hundred-meter Aperture spherical radio Telescope (FAST), and some of the Galactic Plane Pulsar Snapshot survey (GPPS) data. Results show that it can get similar search results to PRESTO (PulsaR Exploration and Search TOolkit) while having a faster processing speed.

astro-ph.IM↗
arXiv2022

Functional Connectome of the Human Brain with Total Correlation

Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such as correlation or mutual information. In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of total correlation and clustering results are trustable compared to ground truth values. Second, the inferred large scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies but, interestingly, can estimate additional relations beyond pair-wise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.

q-bio.NC↗
arXiv2022

Electronically phase separated nano-network in antiferromagnetic insulating LaMnO3/PrMnO3/CaMnO3 tricolor superlattice

Strongly correlated materials often exhibit an electronic phase separation (EPS) phenomena whose domain pattern is random in nature. The ability to control the spatial arrangement of the electronic phases at microscopic scales is highly desirable for tailoring their macroscopic properties and/or designing novel electronic devices. Here we report the formation of EPS nanoscale network in a mono-atomically stacked LaMnO3/CaMnO3/PrMnO3 superlattice grown on SrTiO3 (STO) (001) substrate, which is known to have an antiferromagnetic (AFM) insulating ground state. The EPS nano-network is a consequence of an internal strain relaxation triggered by the structural domain formation of the underlying STO substrate at low temperatures. The same nanoscale network pattern can be reproduced upon temperature cycling allowing us to employ different local imaging techniques to directly compare the magnetic and transport state of a single EPS domain. Our results confirm the one-to-one correspondence between ferromagnetic (AFM) to metallic (insulating) state in manganite. It also represents a significant step in a paradigm shift from passively characterizing EPS in strongly correlated systems to actively engaging in its manipulation.

cond-mat.str-el↗
arXiv2022

Richness out of smallness: a Possible Staged Blueprint on Future Colliders

Novel collision methods and rich phenomena are crucial to keeping high-energy collision physics more robust and attractive. In this document, we present a staged blueprint for future high-energy colliders: from neutrino-neutrino collision, neutrino-lepton collision to electron-muon and muon-muon collisions. Neutrino beam from TeV scale muons is a good candidate to enrich high-energy collision programs and can serve as a practical step toward a high-energy muon collider, which still requires tens of years of R&D. Neutrinos-neutrinos collision provides a promising way to probe heavy Majorana neutrinos and effective neutrino mass; neutrino and antineutrino annihilation into Z boson has a huge cross-section at 10K pb level; leptons-neutrinos collision benefits W boson mass precision measurements. With only a minimal amount of integrated luminosity, one can envision the ``Richness out of smallness''. This document summarizes the current status and the roadmap towards the muon-muon collider with less challenging techniques required through intermediate facilities, where a wide variety of physics goals could be achieved. A (preparatory) laboratory on novel colliders could attract vast international interests and collaborations.

hep-ph↗
arXiv2022

Bridging CLIP and StyleGAN through Latent Alignment for Image Editing

Text-driven image manipulation is developed since the vision-language model (CLIP) has been proposed. Previous work has adopted CLIP to design a text-image consistency-based objective to address this issue. However, these methods require either test-time optimization or image feature cluster analysis for single-mode manipulation direction. In this paper, we manage to achieve inference-time optimization-free diverse manipulation direction mining by bridging CLIP and StyleGAN through Latent Alignment (CSLA). More specifically, our efforts consist of three parts: 1) a data-free training strategy to train latent mappers to bridge the latent space of CLIP and StyleGAN; 2) for more precise mapping, temporal relative consistency is proposed to address the knowledge distribution bias problem among different latent spaces; 3) to refine the mapped latent in s space, adaptive style mixing is also proposed. With this mapping scheme, we can achieve GAN inversion, text-to-image generation and text-driven image manipulation. Qualitative and quantitative comparisons are made to demonstrate the effectiveness of our method.

cs.CV↗
arXiv2022

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

eess.IV↗