arXiv subjects

Zhiqiang Wang

Publications and source records attributed to Zhiqiang Wang.

At least 109 records · Page 6Linked to original sources

arXiv2022

Higher angular momentum pairing states in Sr$_2$RuO$_4$ in the presence of longer-range interactions

The superconducting symmetry of Sr$_2$RuO$_4$ remains a puzzle. Time-reversal symmetry breaking $d_{x^2-y^2} + ig_{xy(x^2-y^2)}$ pairing has been proposed for reconciling multiple key experiments. However, its stability remains unclear. In this work, we theoretically study the superconducting instabilities in Sr$_2$RuO$_4$, including the effects of spin-orbit coupling (SOC), in the presence of both local and longer-range interactions within a random phase approximation. We show that the inclusion of second nearest neighbor repulsions, together with non-local SOC in the $B_{2g}$ channel or orbital-anisotropy of the non-local interactions, can have a significant impact on the stability of both $d_{x^2-y^2}$- and $g$-wave pairing channels. We analyze the properties, such as Knight shift and spontaneous edge current, of the realized $d_{x^2-y^2} + ig$, $s^{\prime}+id_{xy}$ and mixed helical pairings in different parameter spaces and find that the $d_{x^2-y^2} + ig$ solution is in better agreement with the experimental data.

cond-mat.supr-con↗

arXiv2022

120-fs single-pulse generation from stretched-pulse fiber Kerr resonators

Fiber Kerr resonators are simple driven resonators with desirable wavelength and repetition rate flexibility for generating ultrashort pulses for applications including telecommunications, biomedicine, and materials processing. However, fiber Kerr resonators to date often generate longer pulses and require more complicated techniques for generating single pulses than would be desirable for applications. Here we address these limits by demonstrating robust single-pulse performance with 120-fs pulse durations in fiber Kerr resonators based on stretched-pulse solitons. Through matching numerical and experimental studies, stretched-pulse soliton performance is found to strongly depend on the total cavity length, and the optimum length is found to depend on the drive, Raman scattering, and the total pulse stretching. By designing the cavity for this optimum with the described setup, stable stretched-pulse solitons with 120-fs duration are experimentally observed. In addition, soliton trapping is demonstrated with a pulsed drive source despite large intracavity breathing and single-pulse performance is observed. Robust with high performance single-pulse generation is a critical step toward useful femtosecond pulse generation.

physics.optics↗

arXiv2022

Weak Convergence and Spectrality of Infinite Convolutions

Let $\{ A_k\}_{k=1}^\infty$ be a sequence of finite subsets of $\mathbb{R}^d$ satisfying that $\# A_k \ge 2$ for all integers $k \ge 1$. In this paper, we first give a sufficient and necessary condition for the existence of the infinite convolution $$ν=δ_{A_1}*δ_{A_2} * \cdots *δ_{A_n}*\cdots, $$ where all sets $A_k \subseteq \mathbb{R}_+^d$ and $δ_A = \frac{1}{\# A} \sum_{a \in A} δ_a$. Then we study the spectrality of a class of infinite convolutions generated by Hadamard triples in $\mathbb{R}$ and construct a class of singular spectral measures without compact support. Finally we show that such measures are abundant, and the dimension of their supports has the intermediate-value property.

math.CA↗

arXiv2022

Spectral pulsations of dissipative solitons in ultrafast fiber lasers: period doubling and beyond

Period doubling is a universal bifurcation of central importance in all disciplines of nonlinear science, which generally signals the existence of chaotic dynamics in the vicinity of the system parameters. Although observed in diverse ultrafast laser configurations, there is still no consensus on its physical origin. The observations also include other types of pulsating dissipative solitons, with either short or long periods. Real time spectral characterization allows to investigate optical spectral oscillations, whose features reveal the intracavity dynamics leading to instabilities. Following a contextual review, this article presents a variety of period doubling dynamics manifesting in the spectral domain of dissipative solitons. These dynamics are obtained with ultrafast fiber lasers featuring either anomalous or normal dispersion. It reveals a sequence of period doubling bifurcations and instabilities within transient dynamics, unveiling intertwined bifurcations and the entrainment of new pulsating frequencies. The oscillating frequencies tend to lock to the integral of roundtrip numbers as well as coexist with period doubling, demonstrating new combinations of the period doubling bifurcation with other bifurcations. These experimental findings are confirmed by numerical simulations, emphasizing both the universality of the period doubling bifurcations and their potentially highly complicated manifestations within ultrafast laser systems.

physics.optics↗

arXiv2022

Benchmarks for Corruption Invariant Person Re-identification

When deploying person re-identification (ReID) model in safety-critical applications, it is pivotal to understanding the robustness of the model against a diverse array of image corruptions. However, current evaluations of person ReID only consider the performance on clean datasets and ignore images in various corrupted scenarios. In this work, we comprehensively establish six ReID benchmarks for learning corruption invariant representation. In the field of ReID, we are the first to conduct an exhaustive study on corruption invariant learning in single- and cross-modality datasets, including Market-1501, CUHK03, MSMT17, RegDB, SYSU-MM01. After reproducing and examining the robustness performance of 21 recent ReID methods, we have some observations: 1) transformer-based models are more robust towards corrupted images, compared with CNN-based models, 2) increasing the probability of random erasing (a commonly used augmentation method) hurts model corruption robustness, 3) cross-dataset generalization improves with corruption robustness increases. By analyzing the above observations, we propose a strong baseline on both single- and cross-modality ReID datasets which achieves improved robustness against diverse corruptions. Our codes are available on https://github.com/MinghuiChen43/CIL-ReID.

cs.CV↗

arXiv2022

Heat-bath approach to anomalous thermal transport: effects of inelastic scattering

We present results for the entire set of anomalous charge and heat transport coefficients for metallic systems in the presence of a finite-temperature heat bath. In realistic physical systems this necessitates the inclusion of inelastic dissipation mechanisms; relatively little is known theoretically about their effects on anomalous transport. Here we demonstrate how these dissipative processes are strongly intertwined with Berry-curvature physics. Our calculations are made possible by the introduction of a Caldeira-Leggett reservoir which allows us to avoid the sometimes-problematic device of the pseudogravitational potential. Using our formulas, we focus on the finite-temperature behavior of the important anomalous Wiedemann-Franz ratio. Despite previous expectations, this ratio is found to be non-universal as it can exhibit either an upturn or a downturn as temperature increases away from zero. We emphasize that this derives from a \textit{competition} between Berry curvatures having different signs in different regions of the Brillouin zone. We point to experimental support for these observations and for the behavior of an alternative ratio involving a thermoelectric response which, by contrast, appears to be more universal at low temperatures. Our work paves the way for future theory and experiment, demonstrating how inelastic scattering at non-zero temperature affects the behavior of all anomalous transport coefficients.

cond-mat.mes-hall↗

arXiv2022

GLSD: The Global Large-Scale Ship Database and Baseline Evaluations

In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks. The designed GLSD database includes a total of 212,357 annotated instances from 152,576 images. Based on the collected images, we propose 13 ship categories that widely exist in international routes. These categories include Sailing boat, Fishing boat, Passenger ship, Warship, General cargo ship, Container ship, Bulk cargo carrier, Barge, Ore carrier, Speed boat, Canoe, Oil carrier, and Tug. The motivations of developing GLSD include the following: 1) providing a refine and extensive ship detection database that benefits the object detection community, 2) establishing a database with exhaustive labels (bounding boxes and ship class categories) in a uniform classification scheme, and 3) providing a large-scale ship database with geographic information (covering more than 3000 ports and 33 routes) that benefits multi-modal analysis. In addition, we discuss the evaluation protocols corresponding to image characteristics in GLSD and analyze the performance of selected state-of-the-art object detection algorithms on GSLD, aiming to establish baselines for future studies. More information regarding the designed GLSD can be found at https://github.com/jiaming-wang/GLSD.

cs.CV↗

arXiv2022

Symmetry and Asymmetry in the 1+N Coorbital Problem

The relative equilibria of planar Newtonian $N$-body problem become coorbital around a central mass in the limit when all but one of the masses becomes zero. We prove a variety of results about the coorbital relative equilibria, with an emphasis on the relation between symmetries of the configurations and symmetries in the masses, or lack thereof. We prove that in the $N=4$, $N=6$, and $N=8$ Newtonian coorbital problems there exist symmetric relative equilibria with asymmetric positive masses. This result can be generalized to other homogeneous potentials, and we conjecture similar results hold for larger even numbers of infinitesimal masses. We prove that some equalities of the masses in the $1+4$ and $1+5$ coorbital problems imply symmetry of a class of convex relative equilibria. We also prove there is at most one convex central configuration of the symmetric $1+5$ problem.

math.DS↗

arXiv2021

Reducibility of Quantum Harmonic Oscillator on $\mathbb{R}^d$ Perturbed by a Quasi-periodic Potential with Logarithmic Decay

We prove the reducibility of quantum harmonic oscillators in $\mathbb R^d$ perturbed by a quasi-periodic in time potential $V(x,ωt)$ with $\mathit{logarithmic~decay}$. By a new estimate built for solving the homological equation we improve the reducibility result by Grébert-Paturel(Annales de la Faculté des sciences de Toulouse : Mathématiques. $\mathbf{28}$, 2019).

math-ph↗

arXiv2021

Reducibility of 1-D Quantum Harmonic Oscillator with Decaying Conditions on the Derivative of Perturbation Potentials

We prove the reducibility of 1-D quantum harmonic oscillators in $\mathbb R$ perturbed by a quasi-periodic in time potential $V(x,ωt)$ under the following conditions, namely there is a $C>0$ such that \begin{equation*} |V(x,θ)|\le C,\quad|x\partial_xV(x,θ)|\le C,\quad\forall~(x,θ)\in\mathbb R\times\mathbb T_σ^n. \end{equation*} The corresponding perturbation matrix $(P_i^j(θ))$ is proved to satisfy $(1+|i-j|)| P_i^j(θ)|\le C$ and $\sqrt{ij}|P_{i+1}^{j+1}(θ)-P_i^j(θ)|\le C$ for any $θ\in\mathbb T_σ^n$ and $i,j\geq 1$. A new reducibility theorem is set up under this kind of decay in the perturbation matrix element $P_{i}^j(θ)$ as well as the discrete difference matrix element $P_{i+1}^{j+1}(θ)-P_i^j(θ)$. For the proof the novelty is that we use the decay in the discrete difference matrix element to control the measure estimates for the thrown parameter sets.

math-ph↗

arXiv2021

Possibility of mixed helical p-wave pairings in Sr$_2$RuO$_4$

The exact nature of unconventional superconductivity in Sr$_2$RuO$_4$ remains a mystery. At the phenomenological level, no superconducting order parameter proposed thus far seems able to coherently account for all essential experimental signatures. Among the latter is the prominent polar Kerr effect, which implies a nonzero ac anomalous Hall conductivity. Assuming the Kerr effect is intrinsic, it can be accounted for by a bulk chiral Cooper pairing with nonzero orbital angular momentum, such as $p+ip$ or $d+id$, which, however, has difficulties in being reconciled with other experimental results. Given the situation, in this paper, we propose alternative possibilities with complex mixtures of distinct helical p-wave order parameters, namely, $A_{1u}+iA_{2u}$ and $B_{1u}+iB_{2u}$ in the group theory nomenclature. These states essentially consist of two copies of chiral p-wave pairings with opposite chirality and different pairing amplitudes, and therefore support intrinsic Hall and Kerr effects. We further show that these states exhibit salient features that may explain several other key observations in this material, including the absence of spontaneous edge current, a substantial Knight shift drop, and possibly signatures in uniaxial strain and ultrasound measurements.

cond-mat.supr-con↗

arXiv2021

Unified approach to electrical and thermal transport in high-$T_c$ superconductors

In this paper we present a consolidated equation for all low-field transport coefficients, based on a reservoir approach developed for non-interacting quasiparticles. This formalism allows us to treat the two distinct types of charged (fermionic and bosonic) quasiparticles that can be simultaneously present, as for example in superconductors. Indeed, in the underdoped cuprate superconductors these two types of carriers result in two onset temperatures with distinct features in transport: $T^*$, where the fermions first experience an excitation (pseudo)gap, and $T_c$, where bosonic conduction processes are dominant and often divergent. This provides the central goal of this paper, which is to address the challenges in thermoelectric transport that stem from having two characteristic temperatures as well as two types of charge carriers whose contributions can in some instances enhance each other and in others compete. We show how essential features of the cuprates (their bad-metal character and the presence of Fermi arcs) provide an explanation for the classic pseudogap onset signatures at $T^*$ in the longitudinal resistivity, $ρ_{xx}$. Based on the fits to the temperature-dependent $ρ_{xx}$, we present the implications for all of the other thermoelectric transport properties.

cond-mat.supr-con↗

arXiv2021

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and it's important for ranking models to effectively capture complex high-order features. Shallow feed-forward network is widely used in many state-of-the-art DNN models such as FNN, DeepFM and xDeepFM to implicitly capture high-order feature interactions. However, some research has proved that addictive feature interaction, particular feed-forward neural networks, is inefficient in capturing common feature interaction. To resolve this problem, we introduce specific multiplicative operation into DNN ranking system by proposing instance-guided mask which performs element-wise product both on the feature embedding and feed-forward layers guided by input instance. We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper. MaskBlock combines the layer normalization, instance-guided mask, and feed-forward layer and it is a basic building block to be used to design new ranking model under various configurations. The model consisting of MaskBlock is called MaskNet in this paper and two new MaskNet models are proposed to show the effectiveness of MaskBlock as basic building block for composing high performance ranking systems. The experiment results on three real-world datasets demonstrate that our proposed MaskNet models outperform state-of-the-art models such as DeepFM and xDeepFM significantly, which implies MaskBlock is an effective basic building unit for composing new high performance ranking systems.

cs.IR↗

arXiv2021

Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction

Click-through rate (CTR) prediction plays important role in personalized advertising and recommender systems. Though many models have been proposed such as FM, FFM and DeepFM in recent years, feature engineering is still a very important way to improve the model performance in many applications because using raw features can rarely lead to optimal results. For example, the continuous features are usually transformed to the power forms by adding a new feature to allow it to easily form non-linear functions of the feature. However, this kind of feature engineering heavily relies on peoples experience and it is both time consuming and labor consuming. On the other side, concise CTR model with both fast online serving speed and good model performance is critical for many real life applications. In this paper, we propose LeafFM model based on FM to generate new features from the original feature embedding by learning the transformation functions automatically. We also design three concrete Leaf-FM models according to the different strategies of combing the original and the generated features. Extensive experiments are conducted on three real-world datasets and the results show Leaf-FM model outperforms standard FMs by a large margin. Compared with FFMs, Leaf-FM can achieve significantly better performance with much less parameters. In Avazu and Malware dataset, add version Leaf-FM achieves comparable performance with some deep learning based models such as DNN and AutoInt. As an improved FM model, Leaf-FM has the same computation complexity with FM in online serving phase and it means Leaf-FM is applicable in many industry applications because of its better performance and high computation efficiency.

cs.IR↗

arXiv2021

ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding

Click-through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems and it's important for ranking models to effectively capture complex high-order features.Inspired by the success of ELMO and Bert in NLP field, which dynamically refine word embedding according to the context sentence information where the word appears, we think it's also important to dynamically refine each feature's embedding layer by layer according to the context information contained in input instance in CTR estimation tasks. We can effectively capture the useful feature interactions for each feature in this way. In this paper, We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions by dynamically refining each feature's embedding according to the input context. Specifically, ContextNet consists of two key components: contextual embedding module and ContextNet block. Contextual embedding module aggregates contextual information for each feature from input instance and ContextNet block maintains each feature's embedding layer by layer and dynamically refines its representation by merging contextual high-order interaction information into feature embedding. To make the framework specific, we also propose two models(ContextNet-PFFN and ContextNet-SFFN) under this framework by introducing linear contextual embedding network and two non-linear mapping sub-network in ContextNet block. We conduct extensive experiments on four real-world datasets and the experiment results demonstrate that our proposed ContextNet-PFFN and ContextNet-SFFN model outperform state-of-the-art models such as DeepFM and xDeepFM significantly.

cs.IR↗

arXiv2021

SSCAN: A Spatial-spectral Cross Attention Network for Hyperspectral Image Denoising

Hyperspectral images (HSIs) have been widely used in a variety of applications thanks to the rich spectral information they are able to provide. Among all HSI processing tasks, HSI denoising is a crucial step. Recently, deep learning-based image denoising methods have made great progress and achieved great performance. However, existing methods tend to ignore the correlations between adjacent spectral bands, leading to problems such as spectral distortion and blurred edges in denoised results. In this study, we propose a novel HSI denoising network, termed SSCAN, that combines group convolutions and attention modules. Specifically, we use a group convolution with a spatial attention module to facilitate feature extraction by directing models' attention to band-wise important features. We propose a spectral-spatial attention block (SSAB) to exploit the spatial and spectral information in hyperspectral images in an effective manner. In addition, we adopt residual learning operations with skip connections to ensure training stability. The experimental results indicate that the proposed SSCAN outperforms several state-of-the-art HSI denoising algorithms.

eess.IV↗

arXiv2020

On the asymptotic stability of wave equations coupled by velocities of anti-symmetric type

In this paper, we study the asymptotic stability of two wave equations coupled by velocities of anti-symmetric type via only one damping. We adopt the frequency domain method to prove that the system with smooth initial data is logarithmically stable, provided that the coupling domain and the damping domain intersect each other. Moreover, we show, by an example, that this geometric assumption of the intersection is necessary for 1-D case.

math.AP↗

arXiv2020

High voltage transition metal-free cathode material LiBC3F4 for Li ion batteries

The structural stability and electrochemical performance of boron substituted fluorinated graphite as a Li ion batteries cathode material are studied by first principles calculations. The results show that boron substituted fluorinated graphite BC3F4 possesses excellent structural stability, good electrical and ionic conductivities. Unexpectedly, the average Li intercalation voltage of LiBC3F4 is up to 4.44 V, which is much larger than that of LiBCF2. The average voltage of LiBC3F4 is even larger than that of common commercial transition metal oxides cathodes, indicating that LiBC3F4 is a breakthrough of transition metal-free high voltage cathode materials. By comparing the Fermi level, we found the Fermi level of LiBC3F4 is 1.38 eV lower than that of LiBCF2, leading to the decrease of the electron filling energy for the Li intercalation and forming much higher voltage. Moreover, LiBC3F4 shows small volume expansion and high energy density. LiBC3F4 is a promising high voltage cathode material for Li ion batteries. Finally, by calculating the evolution of Li intercalation voltage and Fermi level during the discharging process, a linear correlation between the Fermi level and Li intercalation voltage has been found.

cond-mat.mtrl-sci↗