arXiv subjects

Jian Wang

Publications and source records attributed to Jian Wang.

At least 541 records · Page 30Linked to original sources

arXiv2024

Uniform large deviations and metastability of random dynamical systems

In this paper, we first provide a criterion on uniform large deviation principles (ULDP) of stochastic differential equations under Lyapunov conditions on the coefficients, which can be applied to stochastic systems with coefficients of polynomial growth and possible degenerate driving noises. In the second part, using the ULDP criterion we preclude the concentration of limiting measures of invariant measures of stochastic dynamical systems on repellers and acyclic saddle chains and extend Freidlin and Wentzell's asymptotics theorem to stochastic systems with unbounded coefficients. Of particular interest, we determine the limiting measures of the invariant measures of the famous stochastic van der Pol equation and van der Pol Duffing equation whose noises are naturally degenerate. We also construct two examples to match the global phase portraits of Freidlin and Wentzell's unperturbed systems and to explicitly compute their transition difficulty matrices. Other applications include stochastic May-Leonard system and random systems with infinitely many equivalent classes.

math.PR↗

arXiv2024

Photoassociation of multiple cold molecules in a dipole trap

The generation of cold molecules is a core topic in the field of cold atoms and molecules, which has advanced relevant research like ultracold chemistry, quantum computation, and quantum metrology. With high atomic phase space density, optical dipole trap has been widely performed to prepare and trap cold molecules, and can also be further developed for multiple cold molecule formation and dynamics study. In this work, Rb2 molecules are photoassociated in the magneto-optical trap to obtain precise rovibrational spectroscopy, which provides accurate numerical references for multiple photoassociations. By achieving the harsh requirements of photoassociation in the optical dipole trap, the cold molecule photoassociation process is well explored, and different rovibrational cold molecules are formed in the optical dipole trap for the first time. This method can be universally extended to simultaneously photoassociate various molecules with different internal states or atomic species in just one optical dipole trap, and then advance generous cold molecule research such as cold molecule collision dynamics.

physics.atom-ph↗

arXiv2024

Exponential contractivity and propagation of chaos for Langevin dynamics of McKean-Vlasov type with Lévy noises

By the probabilistic coupling approach which combines a new refined basic coupling with the synchronous coupling for Lévy processes, we obtain explicit exponential contraction rates in terms of the standard $L^1$-Wasserstein distance for the following Langevin dynamic $(X_t,Y_t)_{t\ge0}$ of McKean-Vlasov type on $\mathbb{R}^{2d}$: \begin{equation*}\left\{\begin{array}{l} dX_t=Y_tdt,\\ dY_t=\left(b(X_t)+\displaystyle\int_{\mathbb{R}^d}\tilde{b}(X_t,z)μ^X_t(dz)-γY_t\right)dt+dL_t,\quad μ^X_t={\rm Law}(X_t),\end{array}\right. \end{equation*} where $γ>0$, $b:\mathbb{R}^d\rightarrow\mathbb{R}^d$ and $\tilde{b}:\mathbb{R}^{2d}\rightarrow\mathbb{R}^d$ are two globally Lipschitz continuous functions, and $(L_t)_{t\ge0}$ is an $\mathbb{R}^d$-valued pure jump Lévy process. The proof is also based on a novel distance function, which is designed according to the distance of the marginals associated with the constructed coupling process. Furthermore, by applying the coupling technique above with some modifications, we also provide the propagation of chaos uniformly in time for the corresponding mean-field interacting particle systems with Lévy noises in the standard $L^1$-Wasserstein distance as well as with explicit bounds.

math.PR↗

arXiv2024

Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training

Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels. Our method aligns the transverse and longitudinal views of the same nodule, thereby enabling the model to focus more on the nodule area. We designed an adaptive loss function that eliminates the limitations of the paired data. Additionally, we adopted a two-stage pre-training to exploit the pre-training on ImageNet and thyroid ultrasound images. Extensive experiments were conducted on a large-scale dataset collected from multiple centers. The results showed that the proposed method significantly improves nodule classification and segmentation performance with limited manual labels and outperforms state-of-the-art self-supervised methods. The two-stage pre-training also significantly exceeded ImageNet pre-training.

cs.CV↗

arXiv2024

Intrinsic in-plane magnetononlinear Hall effect in tilted Weyl semimetals

Armed with the extended semiclassical theory, we propose a Hall effect at $EB$ order, particularly in Weyl semimetals (WSMs). We dub this effect the in-plane magnetononlinear Hall effect (IMHE) since the Hall current and the driving electric and magnetic fields are confined in the same plane. Similar to the intrinsic anomalous Hall effect, the IMHE features an intrinsic nature because it arises from the field-induced anomalous velocity $\vec{E} \times \vecΩ^B$, where $\vecΩ^B$ is the Berry curvature induced by the magnetic field through both minimal and Zeeman couplings. Employing the low-energy effective Hamiltonian of WSMs, we reveal that the tilt of the Weyl cone is the key to triggering this effect. Notably, we find that the IMHE can survive even when the \textit{chiral anomaly} disappears because $\vectΩ^B$ (as the correction of the conventional Berry curvature) does not contribute to the monopole charge. Furthermore, we elucidate the interplay between minimal and Zeeman couplings for this effect. Finally, the experimental strategy to detect the IMHE is discussed.

cond-mat.mes-hall↗

arXiv2024

Understanding and tuning magnetism in layered Ising-type antiferromagnet FePSe3 for potential 2D magnet

Recent development in two-dimensional (2D) magnetic materials have motivated the search for new van der Waals magnetic materials, especially Ising-type magnets with strong magnetic anisotropy. Fe-based MPX3 (M = transition metal, X = chalcogen) compounds such as FePS3 and FePSe3 both exhibit an Ising-type magnetic order, but FePSe3 receives much less attention compared to FePS3. This work focuses on establishing the strategy to engineer magnetic anisotropy and exchange interactions in this less-explored compound. Through chalcogen and metal substitutions, the magnetic anisotropy is found to be immune against S substitution for Se whereas tunable only with heavy Mn substitution for Fe. In particular, Mn substitution leads to a continuous rotation of magnetic moments from the out-of-plane direction towards in-plane. Furthermore, the magnetic ordering temperature displays non-monotonic doping dependence for both chalcogen and metal substitutions but due to different mechanisms. These findings provide deeper insight into the Ising-type magnetism in this important van der Waals material, shedding light on the study of other Ising-type magnetic systems as well as discovering novel 2D magnets for potential applications in spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Linear displacement current solely driven by the quantum metric

Quantum metric and Berry curvature are the real part and imaginary part of the quantum geometric tensor, respectively. The T-odd (T: time-reversal) nonlinear Hall effect driven by the quantum metric dipole, recently confirmed in Science 381, 181 (2023) and Nature 621, 487 (2023), established the geometric duality to the T-even nonlinear Hall effect that driven by the Berry curvature dipole. Interestingly, a similar geometric duality between the quantum metric and the Berry curvature, particularly for the linear response of Bloch electrons, has not been established, although the T-odd linear intrinsic anomalous Hall effect (IAHE) solely driven by the Berry curvature has been known for a long time. Herein, we develop the quantum theory for displacement current under an AC electric field. Particularly, we show that the T-even component of the linear displacement current conductivity (LDCC) is solely determined by the quantum metric, by both the response theory and the semiclassical theory. Notably, with symmetry analysis we find that the T-even LDCC can contribute a Hall current in T-invariant systems but with low symmetry, while its longitudinal component is immune to symmetry. Furthermore, employing the Dirac Hamiltonian, we arrive at a $1/μ$ ($μ$: chemical potential) experimental observable enhancement of the displacement current owing to the divergent behavior of quantum metric near Dirac point, similar to the IAHE at Weyl point. Our work reveals the band geometric origin of the linear displacement current and establishes, together with the IAHE, the geometric duality for the linear response of Bloch electrons. Additionally, our work offers the very first intrinsic Hall effect in T-invariant materials, which can not be envisioned in DC transport in both linear and nonlinear regimes.

cond-mat.mes-hall↗

arXiv2024

Longevity Studies of CSC Prototypes Operating with Ar+CO$_{2}$ Gas Mixture and Different Fractions of CF$_{4}$

Studies of Cathode Strip Chamber longevity, comparing Ar+CO2 gas mixtures with fractions of 5%, 2%, and 0% CF4, were performed using several small cathode strip prototype chambers. In each trial, a localized source of radiation was used to irradiate up to an accumulated charge of about 300 mC/cm. Additionally, longevity of a uniformly irradiated prototype operating with 2% CF4 was studied at the CERN Gamma Irradiation Facility GIF++. Post-hoc analysis of the chamber electrodes using spectroscopy techniques was also done.

physics.ins-det↗

arXiv2024

M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining

Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence. Nevertheless, VLM models supporting multi-language, e.g., in both Chinese and English, have lagged due to the relative scarcity of large-scale pretraining datasets. Toward this end, we introduce a comprehensive bilingual (Chinese-English) dataset BM-6B with over 6 billion image-text pairs, aimed at enhancing multimodal foundation models to well understand images in both languages. To handle such a scale of dataset, we propose a novel grouped aggregation approach for image-text contrastive loss computation, which reduces the communication overhead and GPU memory demands significantly, facilitating a 60% increase in training speed. We pretrain a series of bilingual image-text foundation models with an enhanced fine-grained understanding ability on BM-6B, the resulting models, dubbed as $M^2$-Encoders (pronounced "M-Square"), set new benchmarks in both languages for multimodal retrieval and classification tasks. Notably, Our largest $M^2$-Encoder-10B model has achieved top-1 accuracies of 88.5% on ImageNet and 80.7% on ImageNet-CN under a zero-shot classification setting, surpassing previously reported SoTA methods by 2.2% and 21.1%, respectively. The $M^2$-Encoder series represents one of the most comprehensive bilingual image-text foundation models to date, so we are making it available to the research community for further exploration and development.

cs.CV↗

arXiv2024

Global Flows of Foliated Gravity-Matter Systems

Asymptotic safety is a promising mechanism for obtaining a consistent and predictive quantum theory for gravity. The ADM formalism allows to introduce a (Euclidean) time-direction in this framework. It equips spacetime with a foliation structure by encoding the gravitational degrees of freedom in a lapse function, shift vector, and a metric measuring distances on the spatial slices. We use the Wetterich equation to study the renormalization group flow of the graviton 2-point function extracted from the spatial metric. The flow is driven by the 3- and 4-point vertices generated by the foliated Einstein-Hilbert action supplemented by minimally coupled scalar and vector fields. We derive bounds on the number of matter fields cast by asymptotic safety. Moreover, we show that the phase diagram obtained in the pure gravity case is qualitatively stable within these bounds. An intriguing feature is the presence of an IR-fixed point for the graviton mass which prevents the squared mass taking negative values. This feature persists for any number of matter fields and, in particular, also in situations where there is no suitable interacting fixed point rendering the theory asymptotically safe. Our work complements earlier studies of the subject by taking contributions from the matter fields into account.

hep-th↗

arXiv2024

Exponential Ergodicity of CBIRE-Processes with Competition and Catastrophes

We establish the exponential ergodic property in a weighted total variation distance of continuous-state branching processes with immigration in random environments with competition and catastrophes, under a Lyapunov-type condition and other mild assumptions. The proof is based on a Markov coupling process along with some delicate estimates for the associated coupling generator. In particular, the main result indicates whether and how the competition mechanism, the environment and the catastrophe could balance the branching mechanism respectively to guarantee the exponential ergodicity of the process.

math.PR↗

arXiv2024

OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy

We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novel multi-adapter method, OrchMoE, which capitalizes on modular skill architecture for enhanced forward transfer in neural networks. Unlike prior models that depend on explicit task identification inputs, OrchMoE automatically discerns task categories, streamlining the learning process. This is achieved through an integrated mechanism comprising an Automatic Task Classification module and a Task-Skill Allocation module, which collectively deduce task-specific classifications and tailor skill allocation matrices. Our extensive evaluations on the 'Super Natural Instructions' dataset, featuring 1,600 diverse instructional tasks, indicate that OrchMoE substantially outperforms comparable multi-adapter baselines in terms of both performance and sample utilization efficiency, all while operating within the same parameter constraints. These findings suggest that OrchMoE offers a significant leap forward in multi-task learning efficiency.

cs.LG↗

arXiv2024

Code Reviewer Recommendation Based on a Hypergraph with Multiplex Relationships

Code review is an essential component of software development, playing a vital role in ensuring a comprehensive check of code changes. However, the continuous influx of pull requests and the limited pool of available reviewer candidates pose a significant challenge to the review process, making the task of assigning suitable reviewers to each review request increasingly difficult. To tackle this issue, we present MIRRec, a novel code reviewer recommendation method that leverages a hypergraph with multiplex relationships. MIRRec encodes high-order correlations that go beyond traditional pairwise connections using degree-free hyperedges among pull requests and developers. This way, it can capture high-order implicit connectivity and identify potential reviewers. To validate the effectiveness of MIRRec, we conducted experiments using a dataset comprising 48,374 pull requests from ten popular open-source software projects hosted on GitHub. The experiment results demonstrate that MIRRec, especially without PR-Review Commenters relationship, outperforms existing stateof-the-art code reviewer recommendation methods in terms of ACC and MRR, highlighting its significance in improving the code review process.

cs.SE↗

arXiv2024

Sharp two-sided heat kernel estimates for Schrödinger operators with decaying potentials

We establish global two-sided heat kernel estimates (for full time and space) of the Schrödinger operator $-\frac{1}{2}Δ+V$ on $\R^d$, where the potential $V(x)$ is locally bounded and behaves like $c|x|^{-α}$ near infinity with $α\in (0,2)$ and $c> 0$, or with $α>0$ and $c<0$.Our results improve all known results in the literature, and it seems that the current paper is the first one where consistent two-sided heat kernel bounds for the long range potentials are established.

math.AP↗

arXiv2024

Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis

Medical dialogue systems have attracted growing research attention as they have the potential to provide rapid diagnoses, treatment plans, and health consultations. In medical dialogues, a proper diagnosis is crucial as it establishes the foundation for future consultations. Clinicians typically employ both intuitive and analytic reasoning to formulate a differential diagnosis. This reasoning process hypothesizes and verifies a variety of possible diseases and strives to generate a comprehensive and rigorous diagnosis. However, recent studies on medical dialogue generation have overlooked the significance of modeling a differential diagnosis, which hinders the practical application of these systems. To address the above issue, we propose a medical dialogue generation framework with the Intuitive-then-Analytic Differential Diagnosis (IADDx). Our method starts with a differential diagnosis via retrieval-based intuitive association and subsequently refines it through a graph-enhanced analytic procedure. The resulting differential diagnosis is then used to retrieve medical knowledge and guide response generation. Experimental results on two datasets validate the efficacy of our method. Besides, we demonstrate how our framework assists both clinicians and patients in understanding the diagnostic process, for instance, by producing intermediate results and graph-based diagnosis paths.

cs.CL↗

arXiv2024

Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction

Automatic Modulation Recognition (AMR) plays a crucial role in wireless communication systems. Deep learning AMR strategies have achieved tremendous success in recent years. Modulated signals exhibit long temporal dependencies, and extracting global features is crucial in identifying modulation schemes. Traditionally, human experts analyze patterns in constellation diagrams to classify modulation schemes. Classical convolutional-based networks, due to their limited receptive fields, excel at extracting local features but struggle to capture global relationships. To address this limitation, we introduce a novel hybrid deep framework named TLDNN, which incorporates the architectures of the transformer and long short-term memory (LSTM). We utilize the self-attention mechanism of the transformer to model the global correlations in signal sequences while employing LSTM to enhance the capture of temporal dependencies. To mitigate the impact like RF fingerprint features and channel characteristics on model generalization, we propose data augmentation strategies known as segment substitution (SS) to enhance the model's robustness to modulation-related features. Experimental results on widely-used datasets demonstrate that our method achieves state-of-the-art performance and exhibits significant advantages in terms of complexity. Our proposed framework serves as a foundational backbone that can be extended to different datasets. We have verified the effectiveness of our augmentation approach in enhancing the generalization of the models, particularly in few-shot scenarios. Code is available at \url{https://github.com/AMR-Master/TLDNN}.

eess.SP↗

arXiv2023

Lateral quantum confinement effect on monolayer high-Tc superconductors

Despite decades of research in spatially confined superconducting systems to understand the modification of superconductivity from reduced length scales, the investigation of the quantum confinement effect on high-temperature superconductors remains an outstanding challenge. Here, we report scanning tunneling spectroscopy measurements on laterally confined FeSe monolayers on SrTiO3 substrates, which are formed by epitaxially growing FeSe films with a coverage less than one unit cell. Comparing to the uniform regions of FeSe monolayers, the peninsula regions at the monolayer boundary exhibit reduced Fermi energy and undiminished superconductivity, leading to a putative crossover from a Bardeen-Cooper-Schrieffer state to a Bose-Einstein condensate state. In isolated FeSe monolayer islands, superconductivity is shown to exist in samples of smaller volume in contrast to conventional superconductors, while the validity of Anderson's criterion remains fulfilled. Our work reveals lateral quantum confinement effects in unconventional superconductors, to enrich the understanding of high-temperature superconductivity in low-dimensional systems.

cond-mat.supr-con↗

arXiv2023

Personalized Restoration via Dual-Pivot Tuning

Generative diffusion models can serve as a prior which ensures that solutions of image restoration systems adhere to the manifold of natural images. However, for restoring facial images, a personalized prior is necessary to accurately represent and reconstruct unique facial features of a given individual. In this paper, we propose a simple, yet effective, method for personalized restoration, called Dual-Pivot Tuning - a two-stage approach that personalize a blind restoration system while maintaining the integrity of the general prior and the distinct role of each component. Our key observation is that for optimal personalization, the generative model should be tuned around a fixed text pivot, while the guiding network should be tuned in a generic (non-personalized) manner, using the personalized generative model as a fixed ``pivot". This approach ensures that personalization does not interfere with the restoration process, resulting in a natural appearance with high fidelity to the person's identity and the attributes of the degraded image. We evaluated our approach both qualitatively and quantitatively through extensive experiments with images of widely recognized individuals, comparing it against relevant baselines. Surprisingly, we found that our personalized prior not only achieves higher fidelity to identity with respect to the person's identity, but also outperforms state-of-the-art generic priors in terms of general image quality. Project webpage: https://personalized-restoration.github.io

cs.CV↗