Search

Recently the Belle II Collaboration has reported a measurement of the $B^+\to K^+\nu\bar\nu$ rate that is higher than the standard-model expectation. Since the emitted neutrinos are unobserved, the excess could be due to the $B^+$ decaying into a $K^+$ and a dark-matter pair. We entertain this possibility in a two-Higgs-doublet model supplemented with a real singlet scalar boson acting as the dark matter. This model also accommodates strangeness-changing interactions providing new sources of $CP$ violation which can affect hyperon and kaon nonleptonic transitions. We find that the resulting $CP$ violation in the hyperon sector can be significant, reaching the current empirical bounds, after taking into account constraints from kaon mixing and decay and from dark-matter relic-density data and direct searches including the Migdal effect. We demonstrate that the hyperon and kaon processes are complementary probes of this new-physics scenario. Its prediction for sizable hyperon $CP$ violation is potentially testable in ongoing experiments, such as BESIII, Belle II, and LHCb, and in next-generation ones like PANDA and at the Super Tau Charm Facility., Comment: 23 pages, 7 figures

In the realm of microservices architecture, the occurrence of frequent incidents necessitates the employment of Root Cause Analysis (RCA) for swift issue resolution. It is common that a serious incident can take several domain experts hours to identify the root cause. Consequently, a contemporary trend involves harnessing Large Language Models (LLMs) as automated agents for RCA. Though the recent ReAct framework aligns well with the Site Reliability Engineers (SREs) for its thought-action-observation paradigm, its hallucinations often lead to irrelevant actions and directly affect subsequent results. Additionally, the complex and variable clues of the incident can overwhelm the model one step further. To confront these challenges, we propose Flow-of-Action, a pioneering Standard Operation Procedure (SOP) enhanced LLM-based multi-agent system. By explicitly summarizing the diagnosis steps of SREs, SOP imposes constraints on LLMs at crucial junctures, guiding the RCA process towards the correct trajectory. To facilitate the rational and effective utilization of SOPs, we design an SOP-centric framework called SOP flow. SOP flow contains a series of tools, including one for finding relevant SOPs for incidents, another for automatically generating SOPs for incidents without relevant ones, and a tool for converting SOPs into code. This significantly alleviates the hallucination issues of ReAct in RCA tasks. We also design multiple auxiliary agents to assist the main agent by removing useless noise, narrowing the search space, and informing the main agent whether the RCA procedure can stop. Compared to the ReAct method's 35.50% accuracy, our Flow-of-Action method achieves 64.01%, meeting the accuracy requirements for RCA in real-world systems., Comment: Accepted by WWW'25 Industry Track

Charge-parity ($C\!P$) violation in the tau-charm energy region is a promising area for sensitive tests of Standard Model (SM) predictions and searches for new, beyond the SM physics. A future Tau-Charm Facility that operates at center-of-mass energies between 2.0 and 7.0 GeV, with a peak luminosity of $0.5\times10^{35}$~cm$^{-2}$s$^{-1}$, would provide huge numbers of hadrons and tau ($\tau$) leptons that are produced in low-background environments and with well understood kinematic properties. In this report, prospects for unique studies of $C\!P$ violation in the decay of charmed hadrons, and in the production and decay of hyperons and $\tau$ leptons at a next-generation tau-charm facility are discussed. In addition, opportunities for improved tests of $CPT$ invariance test in $K^{0}-\bar{K}^{0}$ mixing are presented.

Multi-view clustering (MvC) utilizes information from multiple views to uncover the underlying structures of data. Despite significant advancements in MvC, mitigating the impact of missing samples in specific views on the integration of knowledge from different views remains a critical challenge. This paper proposes a novel Mask-informed Deep Contrastive Incomplete Multi-view Clustering (Mask-IMvC) method, which elegantly identifies a view-common representation for clustering. Specifically, we introduce a mask-informed fusion network that aggregates incomplete multi-view information while considering the observation status of samples across various views as a mask, thereby reducing the adverse effects of missing values. Additionally, we design a prior knowledge-assisted contrastive learning loss that boosts the representation capability of the aggregated view-common representation by injecting neighborhood information of samples from different views. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed Mask-IMvC method over state-of-the-art approaches across multiple MvC datasets, both in complete and incomplete scenarios.

The subsurface convective zones (CZs) of massive stars significantly influences many of their key characteristics. Previous studies have paid little attention to the impact of rotation on the subsurface convective zone (CZ), so we aim to investigate the evolution of this zone in rapidly rotating massive stars. We use the Modules for Experiments in Stellar Astrophysics (MESA) to simulate the subsurface CZs of massive stars during the main sequence phase. We establish stellar models with initial masses ranging from 5 $M_{\odot}$ to 120 $M_{\odot}$, incorporating four metallicities (Z = 0.02, 0.006, 0.002, and 0.0001) and three rotational velocities ($\mit\omega/\omega_{\text {crit}}$ = 0, $\mit\omega/\omega_{\text {crit}}$ = 0.50, and $\mit\omega/\omega_{\text {crit}}$ = 0.75). We find that rapid rotation leads to an expansion of the subsurface CZ, increases convective velocities, and promotes the development of this zone. Additionally, subsurface CZs can also emerge in stars with lower metallicities. Comparing our models with observations of massive stars in the Galaxy, the Large Magellanic Cloud, and the Small Magellanic Cloud, we find that rotating models better encompass the observed samples. Rotation significantly influences the evolution of the subsurface CZ in massive stars. By comparing with the observed microturbulence on the surfaces of OB stars, we propose that the subsurface CZs may be one of the sources of microturbulence., Comment: 19 pages, 10 figures.Accepted for publication in RAA

A nonzero electric dipole moment of a tauon, $d_\tau$, signals CP violation and provides an important probe for new physics. We study methods to measure $d_\tau$ at low energy $e^+ e^-$ colliders through the processes $e^+e^- \to \gamma^*, \psi(2S) \to \tau^+\tau^-$ with $\tau^\pm$ decays into a charged hadron and a tau neutrino. We point out that, with measuring energies of the charged hadron, Im$(d_\tau)$ can be measured. On the other hand, selecting events of $\tau$ decays after traveling more than the detector resolution distance, Re$(d_\tau)$ can also be determined. We find that the precision at Super Tau-Charm Facility (STCF) running at the center energy of $m_{\psi (2S)}$ for 10 year data accumulation, the precision of Im$(d_\tau)$ and Re$(d_\tau)$ are found to be 3.5 and 11 in unit of $10^{-18}~e\,\text{cm}$, respectively. The sensitivity for $d_\tau$ measurement precision at the STCF can be reached its optimum at a central energy of $6.3~\text{GeV}$, achieving a precision of $1.3$ for Im$(d_\tau)$ and $2.9$ for Re$(d_\tau)$ in unit of $ 10^{-18}~e\,\text{cm}$., Comment: 4 pages, 2 figures

Defect-free single atom array in optical tweezers is a promising platform for scalable quantum computing, quantum simulation, and quantum metrology. Extending single-species array to mixed-species one promise to offer new possibilities. In our recent proof of principle realization of defect-free two-dimensional assembly of mixed-species $^{85}$Rb ($^{87}$Rb) atom arrays [C. Sheng et al.\href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.128.083202}{{\color{blue} Phys. Rev. Lett. 128, 083202(2022)}}], the filling fractions were limited by the imperfect transfer of atoms and the occurrence of logjams during the atom rearrangement. In order to scale up the size of defect-free mixed-species atom array, we scale up the tweezer array and improve the atom transfer, and upgrade the heuristic heteronuclear algorithm so as to facilitate multiple rearrangement cycles. Consequently, we successfully create defect-free atom arrays with 120 mixed-species single atoms. The corresponding filling fraction and defect-free probability are improved to be 98.6(1)\% and 14(2)\%, respectively. It is anticipated that the enhanced algorithm can be extended to other combinations of atomic species, and this mixed-species atom array is readily for studies of many-body physics, quantum error correction, and quantum metrology., Comment: 8 pages, 5 figures

With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently arise during production. The multi-condition characteristics pose significant challenges to traditional fault diagnosis methods. In this context, multi-condition fault diagnosis has gradually become a key area of research, attracting extensive attention from both academia and industry. This paper aims to provide a systematic and comprehensive review of existing research in the field. Firstly, the mathematical definition of the problem is presented, followed by an overview of the current research status. Subsequently, the existing literature is reviewed and categorized from the perspectives of single-model and multi-model approaches. In addition, standard evaluation metrics and typical real-world application scenarios are summarized and analyzed. Finally, the key challenges and prospects in the field are thoroughly discussed., Comment: 17 pages, 14 figures

Understanding time series is crucial for its application in real-world scenarios. Recently, large language models (LLMs) have been increasingly applied to time series tasks, leveraging their strong language capabilities to enhance various applications. However, research on multimodal LLMs (MLLMs) for time series understanding and reasoning remains limited, primarily due to the scarcity of high-quality datasets that align time series with textual information. This paper introduces ChatTS, a novel MLLM designed for time series analysis. ChatTS treats time series as a modality, similar to how vision MLLMs process images, enabling it to perform both understanding and reasoning with time series. To address the scarcity of training data, we propose an attribute-based method for generating synthetic time series with detailed attribute descriptions. We further introduce Time Series Evol-Instruct, a novel approach that generates diverse time series Q&As, enhancing the model's reasoning capabilities. To the best of our knowledge, ChatTS is the first TS-MLLM that takes multivariate time series as input for understanding and reasoning, which is fine-tuned exclusively on synthetic datasets. We evaluate its performance using benchmark datasets with real-world data, including six alignment tasks and four reasoning tasks. Our results show that ChatTS significantly outperforms existing vision-based MLLMs (e.g., GPT-4o) and text/agent-based LLMs, achieving a 46.0% improvement in alignment tasks and a 25.8% improvement in reasoning tasks.

Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving systems. While end-to-end models trained on large-scale datasets excel in common driving scenarios, they often struggle with rare, long-tail events. Recent progress in large language models (LLMs) has introduced enhanced reasoning capabilities, but their computational demands pose challenges for real-time decision-making and precise planning. This paper presents FASIONAD, a novel dual-system framework inspired by the cognitive model "Thinking, Fast and Slow." The fast system handles routine navigation tasks using rapid, data-driven path planning, while the slow system focuses on complex reasoning and decision-making in challenging or unfamiliar situations. A dynamic switching mechanism based on score distribution and feedback allows seamless transitions between the two systems. Visual prompts generated by the fast system enable human-like reasoning in the slow system, which provides high-quality feedback to enhance the fast system's decision-making. To evaluate FASIONAD, we introduce a new benchmark derived from the nuScenes dataset, specifically designed to differentiate fast and slow scenarios. FASIONAD achieves state-of-the-art performance on this benchmark, establishing a new standard for frameworks integrating fast and slow cognitive processes in autonomous driving. This approach paves the way for more adaptive, human-like autonomous driving systems.

Arrays of single atoms trapped in optical tweezers are increasingly recognized as a promising platform for scalable quantum computing. In both the fault-tolerant and NISQ eras, the ability to individually control qubits is essential for the efficient execution of quantum circuits. Time-division multiplexed control schemes based on atom shuttling or beam scanning have been employed to build programmable neutral atom quantum processors, but achieving high-rate, highly parallel gate operations remains a challenge. Here, we propose a fiber array architecture for atom quantum computing capable of fully independent control of individual atoms. The trapping and addressing lasers for each individual atom are emitted from the same optical waveguide, enabling robust control through common-mode suppression of beam pointing noise. Using a fiber array, we experimentally demonstrate the trapping and independent control of ten single atoms in two-dimensional optical tweezers, achieving individually addressed single-qubit gate with an average fidelity of 0.9966(3). Moreover, we perform simultaneous arbitrary single-qubit gate on four randomly selected qubits, resulting in an average fidelity of 0.9961(4). Our work paves the way for time-efficient execution of quantum algorithms on neutral atom quantum computers., Comment: 12 pages

The $K^{\pi}=8^{-}$ isomeric states and rotational bands in the even-even $N = 150$ isotones with $94 \leqslant Z \leqslant 104$ are investigated by the cranked shell model (CSM) with pairing correlations treated by the particle-number-conserving (PNC) method. The experimental bandhead energies and kinematic moments of inertia (MOIs) are reproduced quite well by the PNC-CSM calculation. The two-neutron state with configuration $\nu 9/2^{-}[734] \otimes \nu 7/2^{+}[624]$ is the lowest $8^{-}$ state for these isomers. This is a demonstration of the deformed neutron shell at $N=152$. Low-lying two proton $\pi^{2}8^{-}$($\pi 9/2^{+}[624] \otimes \pi 7/2^{-}[514]$) configuration state is predicted only for $^{252}$No and $^{254}$Rf due to the deformed proton shell at $Z=100$. A distinct upbending is observed for the $\nu^{2}8^{-}$ bands in the lighter isotones while it is absent for bands in the heavier ones. The upbending of the $\nu^{2}8^{-}$ band at frequency $\hbar\omega\approx 0.20$ MeV in $^{244}$Pu attributes to the sudden proton alignment of the interference term $j_x(\pi5/2^{+}[642]\otimes\pi7/2^{+}[633])$. The irregularity of MOI observed in the $K^{\pi}=8^{-}$ band of $^{252}$No can be explained by the mixing of the $\nu^{2}8^{-}$($\nu 9/2^{-}[734] \otimes \nu 7/2^{+}[624]$) and $\pi^{2}8^{-}$($\pi 9/2^{+}[624] \otimes \pi 7/2^{-}[514]$) configurations. The $20\%-30\%$ increase of the bandhead $J^{(1)}$ for the $8^{-}$ bands comparing to the ground-state band is attributed to the $\sim 5\%$ pairing gap reduction of the two-neutron $\nu 7/2^{+}[624] \otimes \nu 9/2^{-}[734]$ configuration state comparing to the ground-state band.

Domain modeling, a crucial part of model-driven engineering, demands extensive domain knowledge and experience from engineers. When the system description is highly complicated, the modeling task can become particularly challenging and time-consuming. Large language Models(LLMs) can assist by automatically generating an initial object model from the system description. Although LLMs have demonstrated remarkable code-generation ability, they still struggle with model-generation using a single prompt. In real-world domain modeling, engineers usually decompose complex tasks into easily solvable sub-tasks, significantly controlling complexity and enhancing model quality. Inspired by this, we propose an LLM-based domain modeling approach via question decomposition, similar to developer's modeling process. Following conventional modeling guidelines, we divide the model generation task into several sub-tasks, i.e., class generation, association and aggregation generation, and inheritance generation. For each sub-task, we carefully design the prompt by choosing more efficient query words and providing essential modeling knowledge to unlock the modeling potential of LLMs. To sum up all the sub-tasks solutions, we implemente a proof-of-object tool integrated into the standard Ecore editor that asks LLMs to generate an object model from the system description. We evaluate our approach with 20 systems from different application domains. The preliminary results show that our approach outperforms the single-prompt-based prompt by improving recall values and F1 scores in most systems for modeling the classes, attributes, and relationships.

Recent advances indicate that diffusion models hold great promise in image super-resolution. While the latest methods are primarily based on latent diffusion models with convolutional neural networks, there are few attempts to explore transformers, which have demonstrated remarkable performance in image generation. In this work, we design an effective diffusion transformer for image super-resolution (DiT-SR) that achieves the visual quality of prior-based methods, but through a training-from-scratch manner. In practice, DiT-SR leverages an overall U-shaped architecture, and adopts a uniform isotropic design for all the transformer blocks across different stages. The former facilitates multi-scale hierarchical feature extraction, while the latter reallocates the computational resources to critical layers to further enhance performance. Moreover, we thoroughly analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module, enhancing the model's capacity to process distinct frequency information at different time steps. Extensive experiments demonstrate that DiT-SR outperforms the existing training-from-scratch diffusion-based SR methods significantly, and even beats some of the prior-based methods on pretrained Stable Diffusion, proving the superiority of diffusion transformer in image super-resolution., Comment: Code is available at https://github.com/kunncheng/DiT-SR

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., Comment: 5 pages, 1 table, 4 figures

In order to explore how the ubiquitous stochastic low-frequency (SLF) variability of O-type stars is related to various stellar characteristics, we compiled a sample of 150 O-type stars observed via ground-based spectroscopic surveys, alongside photometric data obtained from the Transiting Exoplanet Survey Satellite (TESS). We analyzed 298 light curves obtained from TESS sectors 1-65 for the stars in our sample. Leveraging the spectroscopic parameters, we used Bonnsai to determine masses, radii, fractional main sequence ages, and mass-loss rates for stars of our sample. Subsequently, we identified possible correlations between the fitted parameters of SLF variability and stellar properties. Our analysis unveiled four significant correlations between the amplitude and stellar parameters, including mass, radius, fractional main sequence ages, and mass-loss rate. For stars with $M \gtrsim 30 M_{\odot}$, we observed a decrease in characteristic frequency and steepness with increasing radius. Finally, we compared various physical processes that may account for the SLF variability with our results. The observed SLF variability may arise from the combined effects of FeCZ and IGWs, with IGWs potentially more dominant in the early stages of stellar evolution, and the contribution of FeCZ becoming more significant as stars evolve. Meanwhile, our results indicate that the SLF variability of O-type stars bears certain signatures of the line-driven wind instability and granulation., Comment: 40 pages, 12 figures,accepted by Astrophysical Journal Supplement Series

Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. However, these approaches typically require tens or even hundreds of iterative samplings, resulting in significant latency. Recently, techniques have been devised to enhance the sampling efficiency of diffusion-based SR models via knowledge distillation. Nonetheless, when aligning the knowledge of student and teacher models, these solutions either solely rely on pixel-level loss constraints or neglect the fact that diffusion models prioritize varying levels of information at different time steps. To accomplish effective and efficient image super-resolution, we propose a time-aware diffusion distillation method, named TAD-SR. Specifically, we introduce a novel score distillation strategy to align the data distribution between the outputs of the student and teacher models after minor noise perturbation. This distillation strategy enables the student network to concentrate more on the high-frequency details. Furthermore, to mitigate performance limitations stemming from distillation, we integrate a latent adversarial loss and devise a time-aware discriminator that leverages diffusion priors to effectively distinguish between real images and generated images. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method achieves comparable or even superior performance compared to both previous state-of-the-art (SOTA) methods and the teacher model in just one sampling step. Codes are available at https://github.com/LearningHx/TAD-SR., Comment: 18 pages

In GPS-denied scenarios, a robust environmental perception and localization system becomes crucial for autonomous driving. In this paper, a LiDAR-based online localization system is developed, incorporating road marking detection and registration on a high-definition (HD) map. Within our system, a road marking detection approach is proposed with real-time performance, in which an adaptive segmentation technique is first introduced to isolate high-reflectance points correlated with road markings, enhancing real-time efficiency. Then, a spatio-temporal probabilistic local map is formed by aggregating historical LiDAR scans, providing a dense point cloud. Finally, a LiDAR bird's-eye view (LiBEV) image is generated, and an instance segmentation network is applied to accurately label the road markings. For road marking registration, a semantic generalized iterative closest point (SG-ICP) algorithm is designed. Linear road markings are modeled as 1-manifolds embedded in 2D space, mitigating the influence of constraints along the linear direction, addressing the under-constrained problem and achieving a higher localization accuracy on HD maps than ICP. Extensive experiments are conducted in real-world scenarios, demonstrating the effectiveness and robustness of our system.

We systematically investigate tests of the parity P and the combined parity and charge-conjugate CP symmetries from differential angular distributions of $J/\psi$ 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 $J/\psi$ decays with large logarithms resummed up to $\mathcal{O}(\alpha_s)$. The parity-violating asymmetries on the production and the decay sides of $J/\psi$ are both estimated to be of $\mathcal{O}(10^{-4})$, thus barely observable with the 10 billion $J/\psi$ 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 $\delta s_w^2\approx{0.09}$, corresponding to the first determination of $s_w^2$ at the $J/\psi$ threshold. In the future, STCFs are estimated to improve this bound by a factor of $\sim\,20$ to $\delta s_w^2\approx{0.005}$ 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 $\Lambda$ from Fermilab. The method discussed in this work also paves a way for a first and direct measurement of the $\Xi$ and $\Sigma$ electric dipole moments., Comment: v2: added appendix A for a general spin-1 spin decomposition; minor changes to match the published version

In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sensor malfunctions, and environmental unpredictability can severely impact the performance of autonomous systems. The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies that can withstand and adapt to these real-world variabilities. Focusing on four pivotal tasks -- BEV detection, map segmentation, semantic occupancy prediction, and multi-view depth estimation -- the competition laid down a gauntlet to innovate and enhance system resilience against typical and atypical disturbances. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries, resulting in nearly one thousand submissions evaluated through our servers. The competition culminated in 15 top-performing solutions, which introduced a range of innovative approaches including advanced data augmentation, multi-sensor fusion, self-supervised learning for error correction, and new algorithmic strategies to enhance sensor robustness. These contributions significantly advanced the state of the art, particularly in handling sensor inconsistencies and environmental variability. Participants, through collaborative efforts, pushed the boundaries of current technologies, showcasing their potential in real-world scenarios. Extensive evaluations and analyses provided insights into the effectiveness of these solutions, highlighting key trends and successful strategies for improving the resilience of driving perception systems. This challenge has set a new benchmark in the field, providing a rich repository of techniques expected to guide future research in this field., Comment: ICRA 2024; 32 pages, 24 figures, 5 tables; Code at https://robodrive-24.github.io/

3D occupancy, an advanced perception technology for driving scenarios, represents the entire scene without distinguishing between foreground and background by quantifying the physical space into a grid map. The widely adopted projection-first deformable attention, efficient in transforming image features into 3D representations, encounters challenges in aggregating multi-view features due to sensor deployment constraints. To address this issue, we propose our learning-first view attention mechanism for effective multi-view feature aggregation. Moreover, we showcase the scalability of our view attention across diverse multi-view 3D tasks, including map construction and 3D object detection. Leveraging the proposed view attention as well as an additional multi-frame streaming temporal attention, we introduce ViewFormer, a vision-centric transformer-based framework for spatiotemporal feature aggregation. To further explore occupancy-level flow representation, we present FlowOcc3D, a benchmark built on top of existing high-quality datasets. Qualitative and quantitative analyses on this benchmark reveal the potential to represent fine-grained dynamic scenes. Extensive experiments show that our approach significantly outperforms prior state-of-the-art methods. The codes are available at \url{https://github.com/ViewFormerOcc/ViewFormer-Occ}.

The predictive power of the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with density functional PC-PK1 is demonstrated for superheavy region ($101 \leqslant Z \leqslant 120$) by comparing with available experimental and evaluated data in the AME2020. The DRHBc theory predicts 93 bound nuclei beyond the drip line $N = 258$ in the region of $106 \leqslant Z \leqslant 112$, which form a stability peninsula. The odd-even differences between odd-$N$ and even-$N$ nuclei are remarkable in the stability peninsula; the number of bound odd-$N$ nuclei is less than that of bound even-$N$ nuclei, and the one-neutron separation energy of an odd-$N$ nucleus is smaller than those of its neighboring even-$N$ nuclei due to the blocking effect. The deformation effect is indispensable for the reentrant stability beyond the drip line by significantly affecting the structure of single-particle levels around the Fermi energy. The interplay between deformation and pairing effects affects the position where the odd-$N$ nucleus becomes bound in the stability peninsula. By examining the deformation effect at different orders, it is found that quadrupole deformation makes leading contribution to the appearance of stability peninsula and the effects of hexadecapole and hexacontatetrapole deformations are nonnegligible., Comment: 6 figures

We present novel and comprehensive numerical predictions of CP violation in antitriplet charmed baryon decays, which are the first of their kind. Larger-than-expected CP violations are found in the framework of final state re-scattering, similar in magnitude to those in $D^0 \to \pi^+\pi^- ,K^+K^-$. This makes it promising to observe CP violation for the first time in baryon decays and provides valuable tests for the Standard Model. Utilizing the $SU(3)_F$ symmetry and final state re-scattering, we relate the CKM-suppressed amplitudes to the leading ones, which are proportional to $\lambda_b$ and $\lambda_d$, respectively, with $\lambda_q = V_{uq}V^*_{cq}$. We find that re-scattering effects generate previously undetermined CKM-suppressed amplitudes and enhance CP violation by an order of magnitude, with $A_{CP} (\Xi_c^0 \to p K^-) - A_{CP} (\Xi_c^0 \to \Sigma^+ \pi^-)$ reaching a value as large as $-3.68 \cdot 10^{-3}$. This makes it promising to observe CP violation for the first time in baryon decays., Comment: 5 pages, 3 figures; references and appendices added

The gearbox is a critical component of electromechanical systems. The occurrence of multiple faults can significantly impact system accuracy and service life. The vibration signal of the gearbox is an effective indicator of its operational status and fault information. However, gearboxes in real industrial settings often operate under variable working conditions, such as varying speeds and loads. It is a significant and challenging research area to complete the gearbox fault diagnosis procedure under varying operating conditions using vibration signals. This data article presents vibration datasets collected from a gearbox exhibiting various fault degrees of severity and fault types, operating under diverse speed and load conditions. These faults are manually implanted into the gears or bearings through precise machining processes, which include health, missing teeth, wear, pitting, root cracks, and broken teeth. Several kinds of actual compound faults are also encompassed. The development of these datasets facilitates testing the effectiveness and reliability of newly developed fault diagnosis methods., Comment: 10 pages, 12 figures

Recently Belle II reported the first measurement of $B^+\to K^++{\rm invisible (inv)}$, which is $2.7\sigma$ above the standard model (SM) prediction. If confirmed, this calls for new physics beyond SM. In the SM, the invisible particles are neutrino-anti-neutrino pairs. There are more possibilities when going beyond the SM. In this work, we focus on decays to dark matter (DM) and show that the $B\to K +\mathrm{inv}$ excess from Belle II and DM relic density can be simultaneously explained in a simple extension of the SM. The model introduces a real scalar singlet $\phi$ acting as a DM candidate, and two heavy vector-like quarks $Q,D$ with the same quantum numbers as the SM left-handed quark doublet and right-handed down-type quark singlet, respectively. All these new particles are odd under a $\mathbb{Z}_2$ symmetry while the SM particles are even. The model can successfully explain the Belle II anomaly and DM relic density for TeV-scale heavy quarks with hierarchical Yukawa couplings involving $b$ and $s$ quarks. At the same time, it can easily satisfy other flavour physics constraints. Direct detection searches utilizing the Migdal effect constrain some of the parameter space., Comment: v2: add the dark matter direct detection constraints due to the Migdal effect. To appear in JHEP

Within the supersymmetry scheme, which includes many-body interactions and a perturbation possessing the SO(5) (or SU(5)) symmetry, the rotational bands of the $A\sim 250$ mass region are studied systematically. A novel modification is introduced, extending the Arima coefficient to the third order. This study is dedicated to the quantitative analysis of evolving trends in intraband $\gamma$-transition energy, kinematic, and the dynamic moment of inertia within the rotational bands of $^{244}$Pu and $^{248}$Cm. The computed outcomes exhibit an exceptional degree of agreement with experimental observations across various conditions. The significance of including a higher-order Arima coefficient is further examined by contrasting it with the previously suggested model. The calculated results demonstrate the significance of both the anti-pairing and pairing effects in the evolution of the dynamic moment of inertia. Additionally, these insights reveal the importance of a newly introduced parameter in accurately depicting complex nuclear behaviors such as back-bending, up-bending, and the downturn in the moment of inertia.

Current state-of-the-art open-vocabulary segmentation methods typically rely on image-mask-text triplet annotations for supervision. However, acquiring such detailed annotations is labour-intensive and poses scalability challenges in complex real-world scenarios. While existing weakly-supervised approaches leverage image-text pairs to reduce the expansive annotation cost, the lack of mask supervision makes it difficult for the model to locate multiple instances and accurately group pixels with similar semantics, significantly hampering versatility and performance. In this paper, we introduce Unpair-Seg, a novel weakly-supervised open-vocabulary segmentation framework that learns from unpaired image-mask and image-text pairs, which can be independently and efficiently collected. Unpair-Seg initially predicts a set of binary masks and generates pseudo labels by identifying confident pairs of masks and text entities. We then train a feature adapter to align region embeddings with text embeddings based on these pseudo labels, achieving open-vocabulary segmentation. However, the inherent noise in the mask-entity correspondence poses a challenge to obtaining reliable pairs. To address this, we employ a vision-language large model to re-caption the input images and extract precise entities, and we design a multi-scale matching strategy to reduce noisy mask-entity pairs. Our Unpair-Seg framework demonstrates impressive performance, achieving 14.6\% and 19.5\% mIoU on the ADE-847 and PASCAL Context-459 datasets, significantly narrowing the gap between fully-supervised and weakly-supervised methods., Comment: 27 pages, 18 figures, 10 tables

The recent measurements of $h \to Z \gamma$ from ATLAS and CMS show an excess of the signal strength $\mu_Z = (\sigma\cdot{\cal B})_{\mathrm{obs}}/(\sigma\cdot{\cal B})_{\mathrm{SM}}=2.2\pm 0.7$, normalized as 1 in the standard model~(SM). If confirmed, it would be a signal of new physics (NP) beyond the SM. We study NP explanation for this excess. In general, for a given model, it also affects the process $h \to \gamma \gamma$. Since the measured branching ratio for this process agrees well with the SM prediction, the model is severely constrained. We find that a minimally fermion singlets and doublet extended NP model can explain simultaneously the current data for $h \to Z \gamma$ and $h\to \gamma\gamma$. There are two solutions. Although both solutions enhance the amplitude of $h \to Z \gamma$ to the observed one, in one of the solutions the amplitude of $h \to \gamma \gamma$ flips sign to give the observ ed branching ratio. This seems to be a contrived solution although cannot be ruled out simply using branching ratio measurements alone. However, we find another solution that naturally enhances $h \to Z \gamma$ to the measured value, but keeps the amplitude of $h \to \gamma\gamma$ close to its SM prediction. We also comment on the phenomenology associated with these new fermions., Comment: 8 pages, 7 figures

The inherent polarization gradients in tight optical tweezers can be used to couple the atomic spins to the two-body motion under the action of a microwave spin-flip transition, so that such a spin-motion coupling offers an important control knob on the motional states of optically trapped two colliding atoms. Here, after preparing two elastically scattering $^{85}$Rb atoms in the three-dimensional ground-state in the optical tweezer, we employed this control in order to probe the colliding energies of elastic and inelastic channels. The combination of microwave spectra and corresponding s-wave pseudopotential model allows us to infer the effect of the state-dependent trapping potentials on the elastic colliding energies, as well as to reveal how the presence of inelastic interactions affects elastic part of the relative potential. Our work shows that the spin-motion coupling in a tight optical tweezer expand the experimental toolbox for fundamental studies of ultracold collisions in the two body systems with reactive collisions, and potentially for that of more complex interactions, such as optically trapped atom-molecule and molecule-molecule interactions., Comment: 8 pages, 5 figures

Reducing test inputs that trigger bugs is crucial for efficient debugging. Delta debugging is the most popular approach for this purpose. When test inputs need to conform to certain specifications, existing delta debugging practice encounters a validity problem: it blindly applies reduction rules, producing a large number of invalid test inputs that do not satisfy the required specifications. This overall diminishing effectiveness and efficiency becomes even more pronounced when the specifications extend beyond syntactical structures. Our key insight is that we should leverage input generators, which are aware of these specifications, to generate valid reduced inputs, rather than straightforwardly performing reduction on test inputs. In this paper, we propose a generator-based delta debugging method, namely GReduce, which derives validity-preserving reducers. Specifically, given a generator and its execution, demonstrating how the bug-inducing test input is generated, GReduce searches for other executions on the generator that yield reduced, valid test inputs. The evaluation results on five benchmarks (i.e., graphs, DL models, JavaScript programs, SymPy, and algebraic data types) show that GReduce substantially outperforms state-of-the-art syntax-based reducers including Perses and T-PDD, and also outperforms QuickCheck, SmartCheck, as well as the state-of-the-art choice-sequence-based reducer Hypothesis, demonstrating the effectiveness, efficiency, and versatility of GReduce.