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Autism Spectrum Disorder (ASD) significantly affects the social and communication abilities of children, and eye-tracking is commonly used as a diagnostic tool by identifying associated atypical gaze patterns. Traditional methods demand manual identification of Areas of Interest in gaze patterns, lowering the performance of gaze behavior analysis in ASD subjects. To tackle this limitation, we propose a novel method to automatically analyze gaze behaviors in ASD children with superior accuracy. To be specific, we first apply and optimize seven clustering algorithms to automatically group gaze points to compare ASD subjects with typically developing peers. Subsequently, we extract 63 significant features to fully describe the patterns. These features can describe correlations between ASD diagnosis and gaze patterns. Lastly, using these features as prior knowledge, we train multiple predictive machine learning models to predict and diagnose ASD based on their gaze behaviors. To evaluate our method, we apply our method to three ASD datasets. The experimental and visualization results demonstrate the improvements of clustering algorithms in the analysis of unique gaze patterns in ASD children. Additionally, these predictive machine learning models achieved state-of-the-art prediction performance ($81\%$ AUC) in the field of automatically constructed gaze point features for ASD diagnosis. Our code is available at \url{https://github.com/username/projectname}.

Convolutional neural networks are widely used in various segmentation tasks in medical images. However, they are challenged to learn global features adaptively due to the inherent locality of convolutional operations. In contrast, MLP Mixers are proposed as a backbone to learn global information across channels with low complexity. However, they cannot capture spatial features efficiently. Additionally, they lack effective mechanisms to fuse and mix features adaptively. To tackle these limitations, we propose a novel Dynamic Decomposed Mixer module. It is designed to employ novel Mixers to extract features and aggregate information across different spatial locations and channels. Additionally, it employs novel dynamic mixing mechanisms to model inter-dependencies between channel and spatial feature representations and to fuse them adaptively. Subsequently, we incorporate it into a U-shaped Transformer-based architecture to generate a novel network, termed the Dynamic Decomposed MLP Mixer. We evaluated it for medical image segmentation on two datasets, and it achieved superior segmentation performance than other state-of-the-art methods., Comment: 5 pages, 2 figures

Electrical manipulation of spin-polarized current is highly desirable yet tremendously challenging in developing ultracompact spintronic device technology. Here we propose a scheme to realize the all-electrical manipulation of spin-polarized current in an altermagnetic bilayer. Such a bilayer system can host layer-spin locking, in which one layer hosts a spin-polarized current while the other layer hosts a current with opposite spin polarization. An out-of-plane electric field breaks the layer degeneracy, leading to a gate-tunable spin-polarized current whose polarization can be fully reversed upon flipping the polarity of the electric field. Using first-principles calculations, we show that CrS bilayer with C-type antiferromagnetic exchange interaction exhibits a hidden layer-spin locking mechanism that enables the spin polarization of the transport current to be electrically manipulated via the layer degree of freedom. We demonstrate that sign-reversible spin polarization as high as 87% can be achieved at room temperature. This work presents the pioneering concept of layer-spintronics which synergizes altermagnetism and bilayer stacking to achieve efficient electrical control of spin., Comment: 20 pages, 5 figures

A light (GeV mass) long-lived ($c\tau$ around dozens of millimeters) CP-odd scalar can be readily predicted in new physics models. In this work we investigate the Higgs decay into such a light scalar plus a $Z$-boson and take the aligned two-Higgs-doublet model (2HDM) as an example. This light long-lived scalar, with the dominant decay to tau leptons, will fly over a distance from the production point and present a displaced vertex in an Inner Detector of a generally purposed experiment like ATLAS or CMS. In our study we focus on the LHC experiment and perform Monte Carlo simulations for the signal and backgrounds. We demonstrate some benchmark points for the aligned 2HDM and find the signal to be detectable when the luminosity is accumulated to 300 fb$^{-1}$. So our study suggests an experimental search for this process in the ongoing LHC., Comment: 18 pages, 6 figures

Numerous approaches employing various strategies have been developed to test the graphical user interfaces (GUIs) of mobile apps. However, traditional GUI testing techniques, such as random and model-based testing, primarily focus on generating test sequences that excel in achieving high code coverage but often fail to act as effective test oracles for non-crash functional (NCF) bug detection. To tackle these limitations, this study empirically investigates the capability of leveraging large language models (LLMs) to be test oracles to detect NCF bugs in Android apps. Our intuition is that the training corpora of LLMs, encompassing extensive mobile app usage and bug report descriptions, enable them with the domain knowledge relevant to NCF bug detection. We conducted a comprehensive empirical study to explore the effectiveness of LLMs as test oracles for detecting NCF bugs in Android apps on 71 well-documented NCF bugs. The results demonstrated that LLMs achieve a 49% bug detection rate, outperforming existing tools for detecting NCF bugs in Android apps. Additionally, by leveraging LLMs to be test oracles, we successfully detected 24 previously unknown NCF bugs in 64 Android apps, with four of these bugs being confirmed or fixed. However, we also identified limitations of LLMs, primarily related to performance degradation, inherent randomness, and false positives. Our study highlights the potential of leveraging LLMs as test oracles for Android NCF bug detection and suggests directions for future research.

The TNMSSM is an attractive extension of the Standard Model. It combines the advantages of the NMSSM and the TMSSM to give three tiny Majorana neutrinos masses via a type I+II seesaw mechanism. With the on-shell renormalization scheme, we consider the neutrino masses up to one loop approximation. Applying the effective Lagrangian method, we study the transition magnetic moments of Majorana neutrinos and consider the normal hierarchy (NH) and inverse hierarchy (IH) neutrino mass spectra within the constraints of experimental data on neutrino oscillations. The solar neutrino transition magnetic moment is further deduced, and compared with the XENONnT experiment limit., Comment: 33 pages, 8 figures

Recent advancements in large language models (LLMs), such as GPT-4 and GPT-4o, have shown exceptional performance, especially in languages with abundant resources like English, thanks to extensive datasets that ensure robust training. Conversely, these models exhibit limitations when processing under-resourced languages such as Chinese and Korean, where issues including hallucinatory responses remain prevalent. This paper traces the roots of these disparities to the tokenization process inherent to these models. Specifically, it explores how the tokenizer vocabulary, often used to speed up the tokenization process and reduce tokens but constructed independently of the actual model training data, inadequately represents non-English languages. This misrepresentation results in the propagation of 'under-trained' or 'untrained' tokens, which perpetuate biases and pose serious concerns related to data security and ethical standards. We aim to dissect the tokenization mechanics of GPT-4o, illustrating how its simplified token-handling methods amplify these risks and offer strategic solutions to mitigate associated security and ethical issues. Through this study, we emphasize the critical need to rethink tokenization frameworks to foster more equitable and secure AI technologies., Comment: 6 pages, 3 figures, and 5 tables

Lepton flavor violation (LFV) represents a clear new physics (NP) signal beyond the standard model (SM). In this paper, we study LFV decays $Z\rightarrow l^{\pm}_{i}l^{\mp}_{j}$ in the B-L Supersymmetric Standard Model(B-LSSM). We calculate these processes separately in the mass eigenstate basis and the electroweak interaction basis, and the latter adopt the mass insertion approximation (MIA) method. The MIA clearly shows the effect of parameters on the LFV decays $Z\rightarrow l^{\pm}_{i}l^{\mp}_{j}$ in the analytic level, which provides a new way for us to analyze the LFV processes. At the same time, the corresponding constraints from the LFV decays $l^{-}_{j} \rightarrow l^{-}_{i} \gamma$ and $(g-2)_{\mu}$ are considered to analyze the numerical results.

This study investigates the excesses observed in the diphoton and $b\bar b$ data around $95\;{\rm GeV}$ within the framework of the $B-L$ supersymmetric model (B-LSSM). Comparing with the minimal supersymmetric standard model, the B-LSSM incorporates two singlet chiral Higgs bosons which mix with the SM-like Higgs boson due to the gauge kinetic mixing effect. The richer Higgs sector indicates that designating the B-LSSM specific CP-even Higgs state as the lightest Higgs boson has great potential to explain the excesses at around $95\;{\rm GeV}$. Considering the two-loop effective potential corrections to the squared Higgs mass matrix, it is found that the signal strengthes $\mu(h_{95})_{\gamma\gamma}$, $\mu(h_{95})_{b b}$ can be described simultaneously in the experimental $1\sigma$ interval. And the B-LSSM specific parameters $\tan\beta'$, $B_\eta$, $g_{YB}$, $M_{Z'}$ affect the theoretical predictions on the light Higgs boson masses and signal strengthes $\mu(h_{95})_{\gamma\gamma}$, $\mu(h_{95})_{bb}$ significantly., Comment: 28 pages, 5 figures. arXiv admin note: text overlap with arXiv:2405.07243

Motivated by the null results of current dark matter searches and the small-scale problems, we study a dark sector charged by a spontaneous broken gauge $U(1)'$. To explore the parameter space of this model, in addition to the consideration of the small-scale data, we also consider the theoretical bounds on the dark Higgs mass, with the upper bound coming from the tree-level perturbative unitarity and the lower bound from the one-loop Linde-Weinberg bound. We deeply examine the dependence of the Linde-Weinberg bound on gauge choice and energy scale, and present a Linde-Weinberg bound that is gauge and scale independent. Combining the theoretical and observational constraints, we obtain the following ranges for the parameter space: the dark matter mass is 10-500 GeV, the mediator (dark photon) mass is 0.5-5 MeV, the dark Higgs mass is 0.05-50 MeV, and the dark fine-structure constant is 0.001-0.4. We conclude that the dark Higgs in this model cannot be ignored in the phenomenological study of the dark sector., Comment: 45 pages, 7 figures

A flavor-dependent model (FDM) is proposed in this work. The model extends the Standard Model by an extra $U(1)_F$ local gauge group, two scalar doublets, one scalar singlet and two right-handed neutrinos, where the additional $U(1)_F$ charges are related to the particles' flavor. The new fermion sector in the FDM can explain the flavor mixings puzzle and the mass hierarchy puzzle simultaneously, and the nonzero Majorana neutrino masses can be obtained naturally by the Type I see-saw mechanism. In addition, the $B$ meson rare decay processes $\bar B \to X_s\gamma$, $B_s^0 \to \mu^+\mu^-$, the top quark rare decay processes $t\to ch$, $t\to uh$ and the $\tau$ lepton flavor violation processes $\tau\to 3e$, $\tau\to 3\mu$, $\mu\to 3e$ predicted in the FDM are analyzed., Comment: 30pages, 9 figures

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, but 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 exclude 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 575 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) The simulated false vacuum fraction will approach the theoretical one with increasing simulated volume. When the side length of the cubic simulation volume becomes larger than 14.5 $\beta_{th}^{-1}$ , the simulated results do not change significantly; (2) The theoretical expected total number of bubbles do not agree with the simulated ones, which may be caused by the inconsistent use of the false vacuum fraction formula; (3) The different nucleation rate prefactors do not affect the bubble kinetics much; (4) The lifetime distribution in the sound shell model does not obey an exponential distribution, in such a way as to cause a suppression in the gravitational wave spectra., Comment: PRD accepted version, 12 pages, 9 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.