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This study investigates the excesses observed in the CMS diphoton and ditau data around 95 GeV within the framework of the flavor-dependent U(1)X model. The model introduces a singlet scalar to explain the nonzero neutrino masses. This newly introduced Higgs interacts directly with the quark sector, motivated by the aim to explain the flavor numbers of the fermion sector. Additionally, it undergoes mixing with the SM-like Higgs boson. The study suggests that designating this singlet Higgs state in this model as the lightest Higgs boson holds great potential for explaining the excesses around 95 GeV. In the calculations, we maintained the masses of the lightest and next-to-lightest Higgs bosons at around 95 GeV and 125 GeV respectively. It was found that the theoretical predictions on the signal strengthes {\mu}(h95)_{\gamma}{\gamma}, {\mu}(h95)_{\tau}{\tau} in the flavor-dependent U(1)X model can be fitted well to the excesses observed at CMS., Comment: 16 pages, 3 figures

Considering the hierarchical structure of fermionic masses and the fermionic flavor mixing puzzles in the Standard Model, we propose to relate them by the see-saw mechanism, i.e. only the third generation of quarks and charged leptons achieve the masses at the tree level, the first two generations achieves masses through the mixings with the third generation, and the neutrinos achieve tiny Majorana masses by the so-called Type-I see-saw mechanism. This new picture at the fermion sector can explain simultaneously the flavor mixing puzzle and mass hierarchy puzzle in the SM. In addition, a flavor-dependent model (FDM) is proposed to realize the new mechanism, and observing the top quark rare decay processes $t\to ch$, $t\to uh$ and the lepton flavor violation processes $\mu\to3e,\;\tau\to3e,\;\mu\to3\mu$ is effective to test the proposed FDM., Comment: 12 pages, 4 figures

Medical image segmentation plays an important role in many image-guided clinical approaches. However, existing segmentation algorithms mostly rely on the availability of fully annotated images with pixel-wise annotations for training, which can be both labor-intensive and expertise-demanding, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. To alleviate this challenge, there has been a growing focus on developing segmentation methods that can train deep models with weak annotations, such as image-level, bounding boxes, scribbles, and points. The emergence of vision foundation models, notably the Segment Anything Model (SAM), has introduced innovative capabilities for segmentation tasks using weak annotations for promptable segmentation enabled by large-scale pre-training. Adopting foundation models together with traditional learning methods has increasingly gained recent interest research community and shown potential for real-world applications. In this paper, we present a comprehensive survey of recent progress on annotation-efficient learning for medical image segmentation utilizing weak annotations before and in the era of foundation models. Furthermore, we analyze and discuss several challenges of existing approaches, which we believe will provide valuable guidance for shaping the trajectory of foundational models to further advance the field of medical image segmentation.

Video moment retrieval and highlight detection are two highly valuable tasks in video understanding, but until recently they have been jointly studied. Although existing studies have made impressive advancement recently, they predominantly follow the data-driven bottom-up paradigm. Such paradigm overlooks task-specific and inter-task effects, resulting in poor model performance. In this paper, we propose a novel task-driven top-down framework TaskWeave for joint moment retrieval and highlight detection. The framework introduces a task-decoupled unit to capture task-specific and common representations. To investigate the interplay between the two tasks, we propose an inter-task feedback mechanism, which transforms the results of one task as guiding masks to assist the other task. Different from existing methods, we present a task-dependent joint loss function to optimize the model. Comprehensive experiments and in-depth ablation studies on QVHighlights, TVSum, and Charades-STA datasets corroborate the effectiveness and flexibility of the proposed framework. Codes are available at https://github.com/EdenGabriel/TaskWeave.

In the past decades, deep neural networks, particularly convolutional neural networks, have achieved state-of-the-art performance in a variety of medical image segmentation tasks. Recently, the introduction of the vision transformer (ViT) has significantly altered the landscape of deep segmentation models. There has been a growing focus on ViTs, driven by their excellent performance and scalability. However, we argue that the current design of the vision transformer-based UNet (ViT-UNet) segmentation models may not effectively handle the heterogeneous appearance (e.g., varying shapes and sizes) of objects of interest in medical image segmentation tasks. To tackle this challenge, we present a structured approach to introduce spatially dynamic components to the ViT-UNet. This adaptation enables the model to effectively capture features of target objects with diverse appearances. This is achieved by three main components: \textbf{(i)} deformable patch embedding; \textbf{(ii)} spatially dynamic multi-head attention; \textbf{(iii)} deformable positional encoding. These components were integrated into a novel architecture, termed AgileFormer. AgileFormer is a spatially agile ViT-UNet designed for medical image segmentation. Experiments in three segmentation tasks using publicly available datasets demonstrated the effectiveness of the proposed method. The code is available at \href{https://github.com/sotiraslab/AgileFormer}{https://github.com/sotiraslab/AgileFormer}.

Hierarchical transformers have achieved significant success in medical image segmentation due to their large receptive field and capabilities of effectively leveraging global long-range contextual information. Convolutional neural networks (CNNs) can also deliver a large receptive field by using large kernels, enabling them to achieve competitive performance with fewer model parameters. However, CNNs incorporated with large convolutional kernels remain constrained in adaptively capturing multi-scale features from organs with large variations in shape and size due to the employment of fixed-sized kernels. Additionally, they are unable to utilize global contextual information efficiently. To address these limitations, we propose Dynamic Large Kernel (DLK) and Dynamic Feature Fusion (DFF) modules. The DLK module employs multiple large kernels with varying kernel sizes and dilation rates to capture multi-scale features. Subsequently, a dynamic selection mechanism is utilized to adaptively highlight the most important spatial features based on global information. Additionally, the DFF module is proposed to adaptively fuse multi-scale local feature maps based on their global information. We integrate DLK and DFF in a hierarchical transformer architecture to develop a novel architecture, termed D-Net. D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information. Extensive experimental results demonstrate that D-Net outperforms other state-of-the-art models in the two volumetric segmentation tasks, including abdominal multi-organ segmentation and multi-modality brain tumor segmentation. Our code is available at https://github.com/sotiraslab/DLK., Comment: 12 pages, 4 figures, 2 tables

We propose a chirped-pulse driving protocol and reveal its exceptional property for quantum coherent control. The nonadiabatic passage generated by the driving protocol, which includes the population inversion and the nonadiabaticity-induced transition as its ingredients, is shown to be robust against pulse truncation. We further demonstrate that the protocol allows for universal manipulation on the qubit system through designing pulse sequences with either properly adjusted sweeping frequency or pulsing intensity., Comment: 6 pages, 3 figures

U-Net has been widely used for segmenting abdominal organs, achieving promising performance. However, when it is used for multi-organ segmentation, first, it may be limited in exploiting global long-range contextual information due to the implementation of standard convolutions. Second, the use of spatial-wise downsampling (e.g., max pooling or strided convolutions) in the encoding path may lead to the loss of deformable or discriminative details. Third, features upsampled from the higher level are concatenated with those that persevered via skip connections. However, repeated downsampling and upsampling operations lead to misalignments between them and their concatenation degrades segmentation performance. To address these limitations, we propose Dynamically Calibrated Convolution (DCC), Dynamically Calibrated Downsampling (DCD), and Dynamically Calibrated Upsampling (DCU) modules, respectively. The DCC module can utilize global inter-dependencies between spatial and channel features to calibrate these features adaptively. The DCD module enables networks to adaptively preserve deformable or discriminative features during downsampling. The DCU module can dynamically align and calibrate upsampled features to eliminate misalignments before concatenations. We integrated the proposed modules into a standard U-Net, resulting in a new architecture, termed Dynamic U-Net. This architectural design enables U-Net to dynamically adjust features for different organs. We evaluated Dynamic U-Net in two abdominal multi-organ segmentation benchmarks. Dynamic U-Net achieved statistically improved segmentation accuracy compared with standard U-Net. Our code is available at https://github.com/sotiraslab/DynamicUNet., Comment: 11 pages, 3 figures, 2 tables

Upgradeable smart contracts (USCs) have been widely adopted to enable modifying deployed smart contracts. While USCs bring great flexibility to developers, improper usage might introduce new security issues, potentially allowing attackers to hijack USCs and their users. In this paper, we conduct a large-scale measurement study to characterize USCs and their security implications in the wild. We summarize six commonly used USC patterns and develop a tool, USCDetector, to identify USCs without needing source code. Particularly, USCDetector collects various information such as bytecode and transaction information to construct upgrade chains for USCs and disclose potentially vulnerable ones. We evaluate USCDetector using verified smart contracts (i.e., with source code) as ground truth and show that USCDetector can achieve high accuracy with a precision of 96.26%. We then use USCDetector to conduct a large-scale study on Ethereum, covering a total of 60,251,064 smart contracts. USCDetecor constructs 10,218 upgrade chains and discloses multiple real-world USCs with potential security issues., Comment: 12 pages, 5 figures

In current studies for testing Bell inequalities at colliders, the reconstruction of spin correlations from scattering cross-sections relies on the bilinear form of the spin correlations, and not all local hidden variable models (LHVMs) have such a property. To demonstrate that a general LHVM cannot be rule out via scattering cross-section data, we propose a specific LHVM, which can exactly duplicate the same scattering cross-section for particle production and decay as the standard quantum theory, making it indistinguishable at colliders in principle. Despite of this, we find that reconstructing spin correlations through scattering cross-sections can still rule out a broad class of LHVMs, e.g., those models employing classical spin correlations as a surrogate for quantum spin correlations., Comment: 14 pages, 2 figures

Type-II seesaw leptogenesis is a model that integrates inflation, baryon number asymmetry, and neutrino mass simultaneously. It employs the Affleck-Dine mechanism to generate lepton asymmetry, with the Higgs bosons serving as the inflaton. Previous studies assumed inflation to occur in a valley of the potential, employing the single-field approximation. In this work, we explore an alternative scenario for the type-II seesaw leptogenesis, where the inflation takes place along a ridge of the potential. Firstly, we conduct a comprehensive numerical calculation in the canonical scenario, where inflation occurs in a valley, confirming the effectiveness of the single-field approximation. Then, we introduce a novel scenario wherein inflation initiates along the potential's ridge and transitions to the valley in the late stages. In this case, the single-field inflation approximation is no longer valid, yet leptogenesis is still successfully achieved. We find that this scenario can generate a significant non-Gaussianity signature, offering testable predictions for future experiments.

In the realm of natural supersymmetric models, higgsinos are typically the lightest electroweakinos. In gauge-mediated supersymmetry breaking models, the lightest higgsino-dominated particles decay into a $Z$-boson or a Higgs boson ($h$), along with an ultra-light gravitino ($\tilde{G}$) serving as the lightest supersymmetric particle (LSP). This scenario suggests a significant non-resonant $hh$ production. Basing on the recent global fitting results of the $\tilde{G}$-EWMSSM (MSSM with light electroweakinos and an eV-scale gravitino as the LSP) performed by the \textsf{GAMBIT} collaboration, which support a higgsino-dominated electroweakino as light as 140 GeV, we develop two simplified models to evaluate their detection potential at the high-luminosity LHC (HL-LHC) with $\sqrt{s} = 14~{\rm TeV}$ and an integrated luminosity of $3000~{\rm fb}^{-1}$. The first model examines the processes where heavier higgsino-dominated states decay into soft $W/Z$ bosons, while the second focuses on direct decays of all three higgsino-dominated electroweakinos into $W/Z/h$ plus a $\tilde{G}$. Our study, incorporating both models and their distinct decay channels, utilizes detailed Monte Carlo simulations for signals and standard model backgrounds. We find that the HL-LHC can probe higgsinos up to above 500 GeV, potentially discovering or excluding the natural SUSY scenario in the context of a gravitino LSP. Further, we reinterpret this discovery potential using the GAMBIT global fit samples, and find that the entire parameter space of $|\mu| \leq 500~{\rm GeV}$ with an electroweak fine-tuning measure ($\Delta_{\rm EW}$) under 100 in $\tilde{G}$-EWMSSM is accessible at the HL-LHC., Comment: 31 pages, 12 figures, and 6 tables

In order to study the validity of analytical formulas used in the calculation of characteristic physical quantities related to vacuum bubbles, we conduct several numerical simulations of bubble kinematics in the context of cosmological first-order phase transitions to determine potentially existing systematic uncertainties. By comparing with the analytical results, we obtain the following observations: (1) For the total number of bubbles, there is a 10% discrepancy between the values from simulations and from analytical prediction; (2) For the false vacuum fraction, the difference between the results from simulations and from analytical prediction is small, which, however, plays a crucial role in explaining the discrepancy observed in the total number of bubbles; (3) The bubble lifetime distribution obtained from the simulations deviates from the exponential distribution and is not obviously influenced by different nucleation rates; (4) These differences propagate into the final gravitational waves spectra, which we calculate with the sound shell model for the usually dominant contribution from sound waves, and find that the bubble number deviation enhances the peak value in the gravitational wave spectra, while the deviation in the lifetime distribution suppresses the peak value., Comment: 12 pages, 13 figures

Accurate determination of high strain rate (> 10^3 1/s) constitutive properties of soft materials remains a formidable challenge. Albeit recent advancements among experimental techniques, in particular inertial microcavitation rheometry (IMR), the intrinsic requirement to visualize the bubble cavitation dynamics has limited its application to nominally transparent materials. Here, in an effort to address this challenge and to expand the experimental capability of IMR to optically opaque materials, we investigated whether one could use the acoustic signature of the time interval between bubble nucleation and collapse, characterized as the bubble collapse time, to infer the viscoelastic material properties without being able to image the bubble directly in the tissue. By introducing a modified Rayleigh collapse time for soft materials, which is strongly dependent on the stiffness of the material at hand, we show that, in principle, one can obtain an order of magnitude or better estimate of the viscoelastic material properties of the soft material under investigation. Using a newly developed energy-based theoretical framework, we show that for materials stiffer than 10 kPa the bubble collapse time during a single bubble cavitation event can provide quantitative and meaningful information about the constitutive properties of the material at hand. For very soft materials (i.e., shear modulus less than 10 kPa) our theory shows that unless the collapse time measurement has very high precision and low uncertainties, the material property estimates based on the bubble collapse time only will not be accurate and require visual resolution of the full cavitation kinematics.

Productions of multiple gauge bosons at the LHC are sensitive to triple or quartic gauge couplings and thus provide a sensitive test for the electroweak sector of the Standard Model and allow for a probe of new physics. In this work we calculate the gluon-gluon initiate state contribution to the productions of three gauge bosons ($Z\gamma\gamma$, $ZZ\gamma$ and $W^+W^-\gamma$) at the LHC, which is formally part of NNLO effects compared to the LO quark-antiquark channels corrections. For each process we present the ratio between the gluon-gluon channels contribution and the quark-antiquark channels contribution. We found that such a ratio for $Z\gamma\gamma$ ($ZZ\gamma$) is of the order of $10^{-3}$ ($10^{-4}$), much smaller than the corresponding ratio for the diboson production due to the decrease of gluon PDF when more particles appear in the final states. These small ratios imply that gluon-gluon fusion contribution is phenomenological negligible for the productions of $Z\gamma\gamma$ and $ZZ\gamma$. However, for $W^+W^-\gamma$ production, the ratio is about 5\%, which is of the same order of magnitude as the ratio for $W^+W^-$ production due to the big cancellation between the amplitudes of quark-antiquark channels. While such an effect can be neglected currently at the LHC, it may be accessible at the HL-LHC., Comment: 1+15 pages, 8 figures, 4 tables. Uncertainties provided, more discussions added, more references added

Electronic band structures underlie the physical properties of crystalline materials, their geometrical exploration renovates the conventional cognition and brings about novel applications. Inspired by geometry phases, we introduce a geometric amplitude named as the geometric density of states (GDOS) dictated by the differential curvature of the constant-energy contour. The GDOS determines the amplitude of the real-space Green's function making it attain the ultimate expression with transparent physics. The local responses of crystalline materials are usually formulated by the real-space Green's function, so the relevant physics should be refreshed by GDOS. As an example of local responses, we suggest using scanning tunneling microscopy (STM) to characterize the surface states of three-dimensional topological insulator under an in-plane magnetic field. The GDOS favors the straightforward simulation of STM measurement without resorting to Fourier transform of the real-space measurement, and also excavates the unexplored potential of STM measurement to extract the phase information of wavefunction through its amplitude, i.e., the spin and curvature textures. Therefore, the proposed GDOS deepens the understanding of electronic band structures and is indispensable in local responses, and it should be universal for any periodic systems., Comment: 6 pages, 2 figures