arXiv subjects

Yong Du

Publications and source records attributed to Yong Du.

At least 55 records · Page 3Linked to original sources

arXiv2024

Fundamental Tests of P and CP Symmetries Using Octet Baryons at the $J/ψ$ Threshold

We systematically investigate tests of the parity P and the combined parity and charge-conjugate CP symmetries from differential angular distributions of $J/ψ$ decaying into the lowest-lying baryon pairs at BESIII and the next-generation super tau-charm facilities (STCFs). Large corrections from $Z$ and $W$ exchange induced parity violating effects are found for $J/ψ$ decays with large logarithms resummed up to $\mathcal{O}(α_s)$. The parity-violating asymmetries on the production and the decay sides of $J/ψ$ are both estimated to be of $\mathcal{O}(10^{-4})$, thus barely observable with the 10 billion $J/ψ$ events currently collected at BESIII. Nevertheless, these asymmetries utilizing the current BESIII data already permit a measurement of the weak mixing angle with an absolute uncertainty $δs_w^2\approx{0.09}$, corresponding to the first determination of $s_w^2$ at the $J/ψ$ threshold. In the future, STCFs are estimated to improve this bound by a factor of $\sim\,20$ to $δs_w^2\approx{0.005}$ within one year based on luminosity rescaling. We also obtain the 95% confidence level upper bounds on the electric dipole moments of the octet baryons, which are of $\mathcal{O}(10^{-18})\,e{\rm\,cm}$ for BESIII and $\mathcal{O}(10^{-19})\,e{\rm\,cm}$ for STCFs. These bounds are improved by two to three orders of magnitude in comparison with the only existing one on $Λ$ from Fermilab. The method discussed in this work also paves a way for a first and direct measurement of the $Ξ$ and $Σ$ electric dipole moments.

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arXiv2024

Impact of parity violation on quantum entanglement and Bell nonlocality

Quantum entanglement (QE), evidenced by Bell inequality (BI) violations, reveals the nonlocality of nature. Fundamental interactions manifest in various forms, each with distinct effects on QE and BI, but have not yet been studied in depth. We investigate in detail the relationship between QE, Bell nonlocality, and parity-violating interactions in spin-1/2 bipartite systems arising from the decays of spin-0 and spin-1 particles within the quantum field theory (QFT). Our findings reveal that parity (P) violation can completely disentangle particle pairs, rendering Bell tests ineffective in distinguishing between classical and quantum theories. In the spin-0 case, complete disentanglement occurs at maximal P violation, which is similarly true for spin-1 decays. Without restrictions from the QFT, the predicted relation between entanglement and the Bell nonlocality may no longer be valid and we propose promising methods for testing it. Additionally, we emphasize the previously overlooked influence of magnetic fields within detectors, which alters predictions for QE and Bell nonlocality. This environmental effect induces spurious P and charge-parity (CP) violations and thus has to be subtracted for genuine P, CP, and Bell tests.

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arXiv2024

Unsupervised Learning of Multi-modal Affine Registration for PET/CT

Affine registration plays a crucial role in PET/CT imaging, where aligning PET with CT images is challenging due to their respective functional and anatomical representations. Despite the significant promise shown by recent deep learning (DL)-based methods in various medical imaging applications, their application to multi-modal PET/CT affine registration remains relatively unexplored. This study investigates a DL-based approach for PET/CT affine registration. We introduce a novel method using Parzen windowing to approximate the correlation ratio, which acts as the image similarity measure for training DNNs in multi-modal registration. Additionally, we propose a multi-scale, instance-specific optimization scheme that iteratively refines the DNN-generated affine parameters across multiple image resolutions. Our method was evaluated against the widely used mutual information metric and a popular optimization-based technique from the ANTs package, using a large public FDG-PET/CT dataset with synthetic affine transformations. Our approach achieved a mean Dice Similarity Coefficient (DSC) of 0.870, outperforming the compared methods and demonstrating its effectiveness in multi-modal PET/CT image registration.

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arXiv2024

Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing

Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities, yet balancing reconstruction fidelity and editability for real images remains a significant challenge. In this work, we introduce \textbf{T}ask-\textbf{O}riented \textbf{D}iffusion \textbf{I}nversion (\textbf{TODInv}), a novel framework that inverts and edits real images tailored to specific editing tasks by optimizing prompt embeddings within the extended $\mathcal{P}^*$ space. By leveraging distinct embeddings across different U-Net layers and time steps, TODInv seamlessly integrates inversion and editing through reciprocal optimization, ensuring both high fidelity and precise editability. This hierarchical editing mechanism categorizes tasks into structure, appearance, and global edits, optimizing only those embeddings unaffected by the current editing task. Extensive experiments on benchmark dataset reveal TODInv's superior performance over existing methods, delivering both quantitative and qualitative enhancements while showcasing its versatility with few-step diffusion model.

cs.CV↗

arXiv2024

D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On

In this paper, we introduce D$^4$-VTON, an innovative solution for image-based virtual try-on. We address challenges from previous studies, such as semantic inconsistencies before and after garment warping, and reliance on static, annotation-driven clothing parsers. Additionally, we tackle the complexities in diffusion-based VTON models when handling simultaneous tasks like inpainting and denoising. Our approach utilizes two key technologies: Firstly, Dynamic Semantics Disentangling Modules (DSDMs) extract abstract semantic information from garments to create distinct local flows, improving precise garment warping in a self-discovered manner. Secondly, by integrating a Differential Information Tracking Path (DITP), we establish a novel diffusion-based VTON paradigm. This path captures differential information between incomplete try-on inputs and their complete versions, enabling the network to handle multiple degradations independently, thereby minimizing learning ambiguities and achieving realistic results with minimal overhead. Extensive experiments demonstrate that D$^4$-VTON significantly outperforms existing methods in both quantitative metrics and qualitative evaluations, demonstrating its capability in generating realistic images and ensuring semantic consistency.

cs.CV↗

arXiv2024

Global SMEFT Fits at Future Colliders

Based on the framework of Standard Model Effective Field Theory, we performed a few global fits, each containing a subset of dimension-6 operators, for the measurements that are expected at future colliders. The fit for the Higgs and electroweak sector improves what has been done for the European Strategy Update in 2020 on both EFT treatments and experimental inputs. A new comprehensive fit is performed focusing on 4-fermion interactions at future colliders. Top-quark sector is studied in a dedicated fit which restricts the operators and measurements to be directly related to top-quark. A small subset of CP-violating operators involving bosonic fields alone are also investigated. Various running scenarios for future e+e- and Muon Colliders that are suggested in the Snowmass 2021 discussion are considered in the global fits. The outcomes from each fit are expressed in terms of either direct constraint on Wilson Coefficients or precision on Higgs and electroweak effective couplings.

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arXiv2024

High-order Neighborhoods Know More: HyperGraph Learning Meets Source-free Unsupervised Domain Adaptation

Source-free Unsupervised Domain Adaptation (SFDA) aims to classify target samples by only accessing a pre-trained source model and unlabelled target samples. Since no source data is available, transferring the knowledge from the source domain to the target domain is challenging. Existing methods normally exploit the pair-wise relation among target samples and attempt to discover their correlations by clustering these samples based on semantic features. The drawback of these methods includes: 1) the pair-wise relation is limited to exposing the underlying correlations of two more samples, hindering the exploration of the structural information embedded in the target domain; 2) the clustering process only relies on the semantic feature, while overlooking the critical effect of domain shift, i.e., the distribution differences between the source and target domains. To address these issues, we propose a new SFDA method that exploits the high-order neighborhood relation and explicitly takes the domain shift effect into account. Specifically, we formulate the SFDA as a Hypergraph learning problem and construct hyperedges to explore the local group and context information among multiple samples. Moreover, we integrate a self-loop strategy into the constructed hypergraph to elegantly introduce the domain uncertainty of each sample. By clustering these samples based on hyperedges, both the semantic feature and domain shift effects are considered. We then describe an adaptive relation-based objective to tune the model with soft attention levels for all samples. Extensive experiments are conducted on Office-31, Office-Home, VisDA, and PointDA-10 datasets. The results demonstrate the superiority of our method over state-of-the-art counterparts.

cs.CV↗

arXiv2024

From Registration Uncertainty to Segmentation Uncertainty

Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both transformation and appearance uncertainties related to the registration process, elucidating areas where the model may exhibit ambiguity regarding the generated deformation. However, our study reveals that neither uncertainty effectively estimates the potential errors when the registration model is used for label propagation. Here, we propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration. To this end, we implement a compact deep neural network (DNN) designed to transform the appearance discrepancy in the warping into aleatoric segmentation uncertainty by minimizing a negative log-likelihood loss function. Furthermore, we present epistemic segmentation uncertainty within the label propagation process as the entropy of the propagated labels. By introducing segmentation uncertainty along with existing methods for estimating registration uncertainty, we offer vital insights into the potential uncertainties at different stages of image registration. We validated our proposed framework using publicly available datasets, and the results prove that the segmentation uncertainties estimated with the proposed method correlate well with errors in label propagation, all while achieving superior registration performance.

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arXiv2023

Curricular Contrastive Regularization for Physics-aware Single Image Dehazing

Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are nonconsensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy image, and 2) corresponding restorations by other existing methods. Further, due to the different similarities between the embeddings of the clear image and negatives, the learning difficulty of the multiple components is intrinsically imbalanced. To tackle this issue, we customize a curriculum learning strategy to reweight the importance of different negatives. In addition, to improve the interpretability in the feature space, we build a physics-aware dual-branch unit according to the atmospheric scattering model. With the unit, as well as curricular contrastive regularization, we establish our dehazing network, named C2PNet. Extensive experiments demonstrate that our C2PNet significantly outperforms state-of-the-art methods, with extreme PSNR boosts of 3.94dB and 1.50dB, respectively, on SOTS-indoor and SOTS-outdoor datasets.

cs.CV↗

arXiv2023

Type-II seesaw Complex Triplet Model: Phase Transition and A Global Fit Analysis

The type-II seesaw model can explain neutrino masses and address the baryon asymmetry problem of the Universe simultaneously. In this letter, we explore its phase transition and the resulting gravitational wave signals. We find a strong first-order electroweak phase transition generically prefers a relatively light triplet in the $300\sim500$GeV range, which is ideal for collider searches and can generate gravitational waves within the sensitivity reach of BBO and Ultimate-DECIGO. While above $\sim$1TeV where a future 100TeV $pp$ collider will play a key role in model discovery, we integrate out the triplet and perform a global fit analysis of this model at various future colliders. A lower bound in the $10^{-3}\sim10^{-2}$eV range on the triplet vacuum expectation value is obtained, which is comparable to or even better than that from current $μ\to eγ$ experiments depending on the lightest neutrino mass.

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arXiv2023

Spatially-varying Regularization with Conditional Transformer for Unsupervised Image Registration

In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions. However, deep learning-based registration models have mostly relied on spatially-invariant regularization. Here, we introduce an end-to-end framework that uses neural networks to learn a spatially-varying deformation regularizer directly from data. The hyperparameter of the proposed regularizer is conditioned into the network, enabling easy tuning of the regularization strength. The proposed method is built upon a Transformer-based model, but it can be readily adapted to any network architecture. We thoroughly evaluated the proposed approach using publicly available datasets and observed a significant performance improvement while maintaining smooth deformation. The source code of this work will be made available after publication.

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arXiv2023

Deformable Cross-Attention Transformer for Medical Image Registration

Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration. Recent advancements in designing registration Transformers have focused on using cross-attention (CA) to enable a more precise understanding of spatial correspondences between moving and fixed images. Here, we propose a novel CA mechanism that computes windowed attention using deformable windows. In contrast to existing CA mechanisms that require intensive computational complexity by either computing CA globally or locally with a fixed and expanded search window, the proposed deformable CA can selectively sample a diverse set of features over a large search window while maintaining low computational complexity. The proposed model was extensively evaluated on multi-modal, mono-modal, and atlas-to-patient registration tasks, demonstrating promising performance against state-of-the-art methods and indicating its effectiveness for medical image registration. The source code for this work will be available after publication.

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arXiv2022

Report of the Topical Group on Electroweak Precision Physics and Constraining New Physics for Snowmass 2021

The precise measurement of physics observables and the test of their consistency within the standard model (SM) are an invaluable approach, complemented by direct searches for new particles, to determine the existence of physics beyond the standard model (BSM). Studies of massive electroweak gauge bosons (W and Z bosons) are a promising target for indirect BSM searches, since the interactions of photons and gluons are strongly constrained by the unbroken gauge symmetries. They can be divided into two categories: (a) Fermion scattering processes mediated by s- or t-channel W/Z bosons, also known as electroweak precision measurements; and (b) multi-boson processes, which include production of two or more vector bosons in fermion-antifermion annihilation, as well as vector boson scattering (VBS) processes. The latter categories can test modifications of gauge-boson self-interactions, and the sensitivity is typically improved with increased collision energy. This report evaluates the achievable precision of a range of future experiments, which depend on the statistics of the collected data sample, the experimental and theoretical systematic uncertainties, and their correlations. In addition it presents a combined interpretation of these results, together with similar studies in the Higgs and top sector, in the Standard Model effective field theory (SMEFT) framework. This framework provides a model-independent prescription to put generic constraints on new physics and to study and combine large sets of experimental observables, assuming that the new physics scales are significantly higher than the EW scale.

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arXiv2022

Report of the Topical Group on Top quark physics and heavy flavor production for Snowmass 2021

This report summarizes the work of the Energy Frontier Topical Group on EW Physics: Heavy flavor and top quark physics (EF03) of the 2021 Community Summer Study (Snowmass). It aims to highlight the physics potential of top-quark studies and heavy-flavor production processes (bottom and charm) at the HL-LHC and possible future hadron and lepton colliders and running scenarios.

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arXiv2022

TransMorph: Transformer for unsupervised medical image registration

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.

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arXiv2022

Editing Out-of-domain GAN Inversion via Differential Activations

Despite the demonstrated editing capacity in the latent space of a pretrained GAN model, inverting real-world images is stuck in a dilemma that the reconstruction cannot be faithful to the original input. The main reason for this is that the distributions between training and real-world data are misaligned, and because of that, it is unstable of GAN inversion for real image editing. In this paper, we propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. In particular, during the phase of composition, we introduce a differential activation module for detecting semantic changes from a global perspective, \ie, the relative gap between the features of edited and unedited images. With the aid of the generated Diff-CAM mask, a coarse reconstruction can intuitively be composited by the paired original and edited images. In this way, the attribute-irrelevant regions can be survived in almost whole, while the quality of such an intermediate result is still limited by an unavoidable ghosting effect. Consequently, in the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction. Extensive experiments exhibit superiorities over the state-of-the-art methods, in terms of qualitative and quantitative evaluations. The robustness and flexibility of our method is also validated on both scenarios of single attribute and multi-attribute manipulations.

cs.CV↗

arXiv2022

Neutrino seesaw models at one-loop matching: Discrimination by effective operators

Using the functional method, one-loop matching of the type-I, -II and -III seesaw models are investigated and the results are presented in both the Green's and the Warsaw bases. Although these models generate the same dimension-5 Weinberg operator, they could induce quite different types of dimension-6 effective operators that can be utilized for model discrimination. We also find the threshold effects from one-loop matching could be significant, which turn out to allow triggering electroweak symmetry breaking radiatively in type-II seesaw while forbid that in type-I/-III models. An analytical criterion for such radiative symmetry breaking is also derived in type-II seesaw. Finally, we investigate the indirect signatures from different types of dimension-6 operators at high-energy colliders, low-energy precision experiments and forward physics facilities for model discrimination.

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arXiv2022

The Forward Physics Facility: Sites, Experiments, and Physics Potential

The Forward Physics Facility (FPF) is a proposal to create a cavern with the space and infrastructure to support a suite of far-forward experiments at the Large Hadron Collider during the High Luminosity era. Located along the beam collision axis and shielded from the interaction point by at least 100 m of concrete and rock, the FPF will house experiments that will detect particles outside the acceptance of the existing large LHC experiments and will observe rare and exotic processes in an extremely low-background environment. In this work, we summarize the current status of plans for the FPF, including recent progress in civil engineering in identifying promising sites for the FPF and the experiments currently envisioned to realize the FPF's physics potential. We then review the many Standard Model and new physics topics that will be advanced by the FPF, including searches for long-lived particles, probes of dark matter and dark sectors, high-statistics studies of TeV neutrinos of all three flavors, aspects of perturbative and non-perturbative QCD, and high-energy astroparticle physics.

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