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Gang Xu

Publications and source records attributed to Gang Xu.

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

Multi-robot Task Allocation and Path Planning with Maximum Range Constraints

This letter presents a novel multi-robot task allocation and path planning method that considers robots' maximum range constraints in large-sized workspaces, enabling robots to complete the assigned tasks within their range limits. Firstly, we developed a fast path planner to solve global paths efficiently. Subsequently, we propose an innovative auction-based approach that integrates our path planner into the auction phase for reward computation while considering the robots' range limits. This method accounts for extra obstacle-avoiding travel distances rather than ideal straight-line distances, resolving the coupling between task allocation and path planning. Additionally, to avoid redundant computations during iterations, we implemented a lazy auction strategy to speed up the convergence of the task allocation. Finally, we validated the proposed method's effectiveness and application potential through extensive simulation and real-world experiments. The implementation code for our method will be available at https://github.com/wuuya1/RangeTAP.

cs.RO↗
arXiv2024

A topological Hund nodal line antiferromagnet

The interplay of topology, magnetism, and correlations gives rise to intriguing phases of matter. In this study, through state-of-the-art angle-resolved photoemission spectroscopy, density functional theory and dynamical mean-field theory calculations, we visualize a fourfold degenerate Dirac nodal line at the boundary of the bulk Brillouin zone in the antiferromagnet YMn2Ge2. We further demonstrate that this gapless, antiferromagnetic Dirac nodal line is enforced by the combination of magnetism, space-time inversion symmetry and nonsymmorphic lattice symmetry. The corresponding drumhead surface states traverse the whole surface Brillouin zone. YMn2Ge2 thus serves as a platform to exhibit the interplay of multiple degenerate nodal physics and antiferromagnetism. Interestingly, the magnetic nodal line displays a d-orbital dependent renormalization along its trajectory in momentum space, thereby manifesting Hund coupling. Our findings offer insights into the effect of electronic correlations on magnetic Dirac nodal lines, leading to an antiferromagnetic Hund nodal line.

cond-mat.str-el↗
arXiv2024

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction

Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However, the data quality of client datasets can not be guaranteed since corresponding annotations of different clients often contain complex label noise of varying degrees, which inevitably causes the performance degradation. Intuitively, the performance degradation is dominated by clients with higher noise rates since their trained models contain more misinformation from data, thus it is necessary to devise an effective optimization scheme to mitigate the negative impacts of these noisy clients. In this work, we propose a two-stage framework FedELC to tackle this complicated label noise issue. The first stage aims to guide the detection of noisy clients with higher label noise, while the second stage aims to correct the labels of noisy clients' data via an end-to-end label correction framework which is achieved by learning possible ground-truth labels of noisy clients' datasets via back propagation. We implement sixteen related methods and evaluate five datasets with three types of complicated label noise scenarios for a comprehensive comparison. Extensive experimental results demonstrate our proposed framework achieves superior performance than its counterparts for different scenarios. Additionally, we effectively improve the data quality of detected noisy clients' local datasets with our label correction framework. The code is available at https://github.com/Sprinter1999/FedELC.

cs.LG↗
arXiv2024

Chiral kagome superconductivity modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5

Superconductivity involving finite momentum pairing can lead to spatial gap and pair density modulations, as well as Bogoliubov Fermi states within the superconducting gap. However, the experimental realization of their intertwined relations has been challenging. Here, we detect chiral kagome superconductivity modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5 by normal and Josephson scanning tunneling microscopy down to 30mK with resolved electronic energy difference at microelectronvolt level. We observe a U-shaped superconducting gap with flat residual in-gap states. This gap exhibits chiral 2 by 2 spatial modulations with magnetic field tunable chirality, which align with the chiral 2 by 2 pair density modulations observed through Josephson tunneling. These findings demonstrate a chiral pair density wave (PDW) that breaks time-reversal symmetry. Quasiparticle interference imaging of the in-gap zero-energy states reveals segmented arcs, with high-temperature data linking them to parts of the reconstructed V d-orbital states within the charge order. The detected residual Fermi arcs can be explained by the partial suppression of these d-orbital states through an interorbital 2 by 2 PDW and thus serve as candidate Bogoliubov Fermi states. Additionally, we differentiate the observed PDW order from impurity-induced gap modulations. Our observations not only uncover a chiral PDW order with orbital-selectivity, but also illuminate the fundamental space-momentum correspondence inherent in finite momentum paired superconductivity.

cond-mat.supr-con↗
arXiv2024

Excitation and manipulation of super cavity solitons in multi-stable passive Kerr resonators

We report on the theoretical analysis as well as the numerical simulations about the nonlinear dynamics of cavity solitons in a passive Kerr resonator operating in the multistable regime under the condition of a sufficiently strong pump. In this regime, the adjacent tilted cavity resonances might overlap, thus leading to the co-existence of combinatory states of temporal cavity solitons and the extended modulation instability patterns. Very interestingly, the cavity in the regime of multistablity may sustain distinct families of cavity solitons, vividly termed as super cavity solitons with much higher intensity and broader spectra if compared with those in the conventional bi-stable regime. The description of such complex cavity dynamics in the multstable regime requires either the infinite-dimensional Ikeda map, or the derived mean-field coupled Lugiato-Lefever equations by involving the contributing cavity resonances. With the latter model, for the first time, we revealed the existence of different orders of super cavity solitons, whose stationary solutions were obtained by using the Newton-Raphson algorithm. Along this line, with the continuation calculation, we have plotted the Hopf / saddle-node bifurcation curves, thus identifying the existing map of the stable and breathing (super) cavity solitons. With this defined parameter space, we have proposed an efficient method to excite and switch the super cavity solitons by adding an appropriate intensity (or phase) perturbation on the pump. Such deterministic cavity soliton manipulation technique is demonstrated to underpin the multi-level coding, which may enable the large capacity all-optical buffering based on the passive fiber ring cavities.

physics.optics↗
arXiv2024

CAMEL2: Enhancing weakly supervised learning for histopathology images by incorporating the significance ratio

Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labour-intensive labelling. In contrast, weakly supervised learning methods, which only require coarse-grained labels at the image level, can significantly reduce the labeling efforts. Unfortunately, while these methods perform reasonably well in slide-level prediction, their ability to locate cancerous regions, which is essential for many clinical applications, remains unsatisfactory. Previously, we proposed CAMEL, which achieves comparable results to those of fully supervised baselines in pixel-level segmentation. However, CAMEL requires 1,280x1,280 image-level binary annotations for positive WSIs. Here, we present CAMEL2, by introducing a threshold of the cancerous ratio for positive bags, it allows us to better utilize the information, consequently enabling us to scale up the image-level setting from 1,280x1,280 to 5,120x5,120 while maintaining the accuracy. Our results with various datasets, demonstrate that CAMEL2, with the help of 5,120x5,120 image-level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance- and slide-level classifications.

cs.CV↗
arXiv2024

Spatial-temporal manipulations of visible nanosecond sub-pulse sequences in an actively Q-switched Pr:YLF laser

Pulsed visible lasers either by Q-switching or mode locking have been attracting intense attentions both in solid-state laser and fiber laser. Here, we report on the simultaneous manipulation of reconfigurable sub-pulse sequences and customizable high-order vortex beams in an actively Q-switched visible laser. On the one hand, pulse sequences with up to 4 sub-pulses could be generated and fully controlled by means of an acoustic-optic modulator driven by an arbitrary waveform generator. Both pulse number and pulse intensity can be manipulated through the programmable step-signal, which is also theoretically simulated through the rate equations. On the other hand, assisted by the off-axis pumping technique and the astigmatic mode conversion, the laser cavity could emit high-quality vortex beams carrying Laguerre-Gaussian modes up to 30th order. To the best of our knowledge, this is the most flexible active manipulations not only on the intensity distribution of the transverse modes but also on the temporal distribution of the pulse sequences in a visible laser. The versatile manipulating techniques in this work could be immediately implemented into all other solid-state lasers to obtain sub-pulse vortex beams, which may provide enhanced functionality and flexibility for a large range of laser systems.

physics.optics↗
arXiv2024

Intersite Coulomb repulsion driven quadrupole instability and magnetic ordering in the orbital frustrated Ba$_2$MgReO$_6$

We develop an unrestricted Hartree-Fock mean-field method including Coulomb repulsion $U$, $V$ and spin-orbital coupling $λ$ self-consistently to investigate the mechanism of structural instability and magnetic ordering in Ba$_2$MgReO$_6$. A comprehensive quadrupole phase diagram versus $U$ and $V$ with $λ$=0.28eV is calculated. Our results demonstrate that the easy-plane anisotropy and the intersite Coulomb repulsion $V$ must be considered to remove the orbital frustration. The increasing of $V$ to $>$20meV would arrange quadrupole $Q_{x^2-y^2}$ antiparallelly, accompanied with small parallel $Q_{3z^2-r^2}$, and stabilize Ba$_2$MgReO$_6$ into the body-centered tetragonal structure. Such antiparallel $Q_{x^2-y^2}$ provides a new mechanism of Dzyaloshinskii-Moriya interaction, and gives rise to the canted antiferromagnetic (CAF) state along [110] axis. Moreover, sizable octupoles such as $O_{21}^{31}$, $O_{21}^{33}$, $O_{21}^{34}$ and $O_{21}^{36}$ are discovered for the first time in CAF state. Our study not only provides a comprehensive understanding of the experiment results in Ba$_2$MgReO$_6$, but also reveals some commonalities of 5d compounds.

cond-mat.str-el↗
arXiv2024

StaPep: an open-source tool for the structure prediction and feature extraction of hydrocarbon-stapled peptides

Many tools exist for extracting structural and physiochemical descriptors from linear peptides to predict their properties, but similar tools for hydrocarbon-stapled peptides are lacking.Here, we present StaPep, a Python-based toolkit designed for generating 2D/3D structures and calculating 21 distinct features for hydrocarbon-stapled peptides.The current version supports hydrocarbon-stapled peptides containing 2 non-standard amino acids (norleucine and 2-aminoisobutyric acid) and 6 nonnatural anchoring residues (S3, S5, S8, R3, R5 and R8).Then we established a hand-curated dataset of 201 hydrocarbon-stapled peptides and 384 linear peptides with sequence information and experimental membrane permeability, to showcase StaPep's application in artificial intelligence projects.A machine learning-based predictor utilizing above calculated features was developed with AUC of 0.85, for identifying cell-penetrating hydrocarbon-stapled peptides.StaPep's pipeline spans data retrieval, cleaning, structure generation, molecular feature calculation, and machine learning model construction for hydrocarbon-stapled peptides.The source codes and dataset are freely available on Github: https://github.com/dahuilangda/stapep_package.

q-bio.BM↗
arXiv2023

Electronic and topological characters of the ideal magnetic topological materials EuAuX with X = P, As, Sb, and Bi

Ideal magnetic topological materials have great significance in both fundamental physics and technical applications, due to their abundant exotic quantum properties and facilitation of control. Using first-principles calculations, we find several ideal magnetic topological materials in EuTX (T = Cu, Ag, and Au; X= P, As, Sb, and Bi) family. Particularly, EuAuP is the ferromagnetic Weyl semimetal, and EuAuX (X=As, Sb, and Bi) in their ground state with in-plane moments are the antiferromagnetic semimetals hosting the topological gap near the Fermi level. By tuning the magnetic moments to z-axis, EuAuX (X=As, Sb, and Bi) could further evolve into triple degenerate nodal points (TDNPs) semimetal states. The main characteristics of antiferromagnetic TDNP semimetal, including the Fermi arcs, and tangent Fermi surfaces with opposite spin winding numbers, are also studied. Our work provides a promising platform to modulate the magnetism, topological electronic structures and emergent quantum states.

cond-mat.mtrl-sci↗
arXiv2023

A Parallel Feature-preserving Mesh Variable Offsetting Method with Dynamic Programming

Mesh offsetting plays an important role in discrete geometric processing. In this paper, we propose a parallel feature-preserving mesh offsetting framework with variable distance. Different from the traditional method based on distance and normal vector, a new calculation of offset position is proposed by using dynamic programming and quadratic programming, and the sharp feature can be preserved after offsetting. Instead of distance implicit field, a spatial coverage region represented by polyhedral for computing offsets is proposed. Our method can generate an offsetting model with smaller mesh size, and also can achieve high quality without gaps, holes, and self-intersections. Moreover, several acceleration techniques are proposed for the efficient mesh offsetting, such as the parallel computing with grid, AABB tree and rays computing. In order to show the efficiency and robustness of the proposed framework, we have tested our method on the quadmesh dataset, which is available at [https://www.quadmesh.cloud]. The source code of the proposed algorithm is available on GitHub at [https://github.com/iGame-Lab/PFPOffset].

cs.GR↗
arXiv2023

Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark

Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images. Recent networks mainly resort to image decomposition techniques with complex multi-branch architectures. However, the fixed decomposition techniques would largely restricts their power on versatile images. To exploit the potential power of decomposition mechanism, in this paper, we generalize it from the image domain to the broader feature domain. To this end, we propose a lightweight Feature Decomposition Aggregation Network (FDAN). In particular, we design a Feature Decomposition Block (FDB) to achieve learnable separation of detail and base feature maps, and develop a Hierarchical Feature Decomposition Group by cascading FDBs for powerful multi-level feature decomposition. Moreover, to better evaluate the comparison methods, we collect a large-scale dataset for joint SR-ITM, i.e., SRITM-4K, which provides versatile scenarios for robust model training and evaluation. Experimental results on two benchmark datasets demonstrate that our FDAN is efficient and outperforms state-of-the-art methods on joint SR-ITM. The code of our FDAN and the SRITM-4K dataset are available at https://github.com/CS-GangXu/FDAN.

cs.CV↗
arXiv2023

Random number generation using spontaneous symmetry breaking in a Kerr resonator

We experimentally demonstrate an all-optical random number generator based on spontaneous symmetry breaking in a coherently-driven Kerr resonator. Random bit sequences are generated by repeatedly tuning a control parameter across a symmetry-breaking bifurcation that enacts random selection between two possible steady-states of the system. Experiments are performed in a fibre ring resonator, where the two symmetry-broken steady-states are associated with orthogonal polarization modes. Detrimental biases due to system asymmetries are completely suppressed by leveraging a recently-discovered self-symmetrization phenomenon that ensures the symmetry breaking acts as an unbiased coin toss, with a genuinely random selection between the two available steady-states. We optically generate bits at a rate of over 3~MHz without post-processing and verify their randomness using the National Institute of Standards and Technology and Dieharder statistical test suites.

physics.optics↗
arXiv2023

Semantic-aware Generation of Multi-view Portrait Drawings

Neural radiance fields (NeRF) based methods have shown amazing performance in synthesizing 3D-consistent photographic images, but fail to generate multi-view portrait drawings. The key is that the basic assumption of these methods -- a surface point is consistent when rendered from different views -- doesn't hold for drawings. In a portrait drawing, the appearance of a facial point may changes when viewed from different angles. Besides, portrait drawings usually present little 3D information and suffer from insufficient training data. To combat this challenge, in this paper, we propose a Semantic-Aware GEnerator (SAGE) for synthesizing multi-view portrait drawings. Our motivation is that facial semantic labels are view-consistent and correlate with drawing techniques. We therefore propose to collaboratively synthesize multi-view semantic maps and the corresponding portrait drawings. To facilitate training, we design a semantic-aware domain translator, which generates portrait drawings based on features of photographic faces. In addition, use data augmentation via synthesis to mitigate collapsed results. We apply SAGE to synthesize multi-view portrait drawings in diverse artistic styles. Experimental results show that SAGE achieves significantly superior or highly competitive performance, compared to existing 3D-aware image synthesis methods. The codes are available at https://github.com/AiArt-HDU/SAGE.

cs.CV↗
arXiv2023

Masked and Adaptive Transformer for Exemplar Based Image Translation

We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as supplementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimental results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. The codes are available at \url{https://github.com/AiArt-HDU/MATEBIT}.

cs.CV↗
arXiv2023

Nonlinear topological symmetry protection in a dissipative system

We report an experimental and theoretical investigation of a system whose dynamics is dominated by an intricate interplay between three key concepts of modern physics: topology, nonlinearity, and spontaneous symmetry breaking. The experiment is based on a two-mode coherently-driven optical resonator in which photons interact through the Kerr nonlinearity. In presence of a phase defect between the modes, a nonlinear attractor develops, which confers a synthetic Möbius topology to the modal structure of the system. That topology is associated with an inherently protected exchange symmetry between the modes, enabling the realization of spontaneous symmetry breaking in ideal, bias-free, conditions without any fine tuning of parameters. The dynamic manifests itself by a periodic alternation of the modes from one resonator roundtrip to the next reminiscent of period-doubling. This extends to a range of localized structures in the form of domain walls, bright solitons, and breathers, which have all been observed with remarkable long term stability. A rigorous testing of the randomness of the symmetry-broken state selection statistics has also confirmed the robustness of the exchange symmetry in our experiment. Our results and conclusions are supported by an effective Hamiltonian model explaining the symmetry protection in our system. The model also shows that our work has relevance to other systems of interacting bosons and to the Floquet engineering of quantum matter. Our work could also be beneficial to the implementation of coherent Ising machines.

physics.optics↗
arXiv2023

Field-Induced Lifshitz Transition in the Magnetic Weyl Semimetal Candidate PrAlSi

Lifshitz transition (LT) refers to an abrupt change in the electronic structure and Fermi surface, and is associated to a variety of emergent quantum phenomena. Amongst the LTs observed in known materials, the field-induced LT has been rare and its origin remains elusive. To understand the origin of field-induced LT, it is important to extend the material basis beyond the usual setting of heavy fermion metals. Here, we report on a field-induced LT in PrAlSi, a magnetic Weyl semimetal candidate with localized 4$f$ electrons, through a study of magnetotransport up to 55 T. The quantum oscillation analysis reveals that across a threshold field $B^*\approx$14.5 T the oscillation frequency ($F_1$ = 43 T) is replaced by two new frequencies ($F_2$ = 62 T and $F_3$ = 103 T). Strikingly, the LT occurs well below quantum limit, with obvious temperature-dependent oscillation frequency and field-dependent cyclotron mass. Our work not only enriches the rare examples of field-induced LTs, but also paves the way for further investigation on the interplay among topology, magnetism and electronic correlation.

cond-mat.str-el↗
arXiv2023

Topological superconductor candidates PdBi$_2$Te$_4$ and PdBi$_2$Te$_5$ from a generic ab initio strategy

Superconducting topological metals (SCTMs) have recently emerged as a promising platform of topological superconductivity (TSC) and Majorana zero modes(MZMs) for quantum computation. Despite their importance in both fundamental research and applications, SCTMs are very rare in nature. In addition, some superconductors with topological electronic structures have been reported recently, but a feasible program to determine their TSC properties is still lacking. Here, we propose a new strategy to design SCTMs by intercalating the superconducting units into the topological insulators. A program that characterizes the superconducting BdG Chern number of 2D BdG Hamiltonian from ab initio calculations is also developed. Following this strategy, PdBi$_2$Te$_5$ and PdBi$_2$Te$_4$ are found to be experimentally synthesizable and ideal SCTMs. Chiral TSC could be realized in such SCTMs by incorporating topological surface states with Zeeman effect, which can be realized by an external magnetic field or in proximity to ferromagnetic (FM) insulator. Our strategy provides a new method for identifying the SCTMs and TSC candidates, and the program makes it possible to design and modulate the TSC candidates from ab initio calculations.

cond-mat.mtrl-sci↗