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Gravitational waves (GWs) from core-collapse supernovae (CCSNe) have been proposed as a means to probe the internal physical properties of supernovae. However, due to their complex time-frequency structure, effectively searching for and extracting GW signals from CCSNe remains an unsolved challenge. In this paper, we apply the improved multisynchrosqueezing transform (IMSST) method to reconstruct simulated GW data based on the advanced LIGO (aLIGO) and Einstein Telescope (ET) detectors. These data are generated by the magnetorotational and neutrino-driven mechanisms, and we use the match score as the criterion for evaluating the quality of the reconstruction. To assess whether the reconstructed waveforms correspond to true GW signals, we calculate the false alarm probability of reconstruction (FAPR). For GW sources located at 10 kpc and datasets where the waveform amplitudes are normalized to $5 \times 10^{-21}$ observed by aLIGO, FAPR are $2.1 \times 10^{-2}$ and $6.2 \times 10^{-3}$, respectively. For GW sources at 100 kpc and with waveform amplitudes normalized to $5 \times 10^{-21}$ observed by ET, FAPR are $1.3 \times 10^{-1}$ and $1.5 \times 10^{-2}$, respectively. When the gravitational wave strain reaches $7 \times 10^{-21}$ and the match score threshold is set to 0.75, the IMSST method achieves maximum reconstruction distances of approximately 37 kpc and 317 kpc for aLIGO and ET, respectively. Finally, we compared the performance of IMSST and STFT in waveform reconstruction based on the ET. The results show that the maximum reconstructable distance using STFT is 186 kpc.

Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies. The core of this task involves distinguishing subtle morphological variations within the same broad category of gigapixel-resolution images, which presents a significant challenge. While the multi-instance learning (MIL) paradigm alleviates the computational burden of WSIs, existing MIL methods often overlook hierarchical label correlations, treating fine-grained classification as a flat multi-class classification task. To overcome these limitations, we introduce a novel hierarchical multi-instance learning (HMIL) framework. By facilitating on the hierarchical alignment of inherent relationships between different hierarchy of labels at instance and bag level, our approach provides a more structured and informative learning process. Specifically, HMIL incorporates a class-wise attention mechanism that aligns hierarchical information at both the instance and bag levels. Furthermore, we introduce supervised contrastive learning to enhance the discriminative capability for fine-grained classification and a curriculum-based dynamic weighting module to adaptively balance the hierarchical feature during training. Extensive experiments on our large-scale cytology cervical cancer (CCC) dataset and two public histology datasets, BRACS and PANDA, demonstrate the state-of-the-art class-wise and overall performance of our HMIL framework. Our source code is available at https://github.com/ChengJin-git/HMIL., Comment: Under Review

Long-duration gamma-ray bursts (GRBs) are thought to be from core collapse of massive stars, and a rapidly spinning magnetar or black hole may be formed as the central engine. The extended emission in the prompt emission, flares and plateaus in X-ray afterglow, are proposed to be as the signature of central engine re-activity. However, the directly evidence from observations of identifying the central engines remain an open question. In this paper, we systemically search for long-duration GRBs that consist of bumps in X-ray afterglow detected by Swift/XRT, and find that the peak time of the X-ray bumps exhibit bimodal distribution (defined as early and late bumps) with division line at $t=7190$ s. Although we cannot rule out that such a bimodality arises from selection effects. We proposed that the long-duration GRBs with an early (or late) bumps may be originated from the fall-back accretion onto a new-born magnetar (or black hole). By adopting MCMC method to fit the early (or late) bumps of X-ray afterglow with the fall-back accretion of magnetar (or black hole), it is found that the initial surface magnetic filed and period of magnetars for most early bumps are clustered around $5.88\times10^{13}$ G and $1.04$ ms, respectively. Meanwhile, the derived accretion mass of black hole for late bumps is range of $[4\times10^{-4}, 1.8\times10^{-2}]~M_{\odot}$, and the typical fall-back radius is distributed range of $[1.04, 4.23]\times 10^{11}$ cm which is consistent with the typical radius of a Wolf-Rayet star. However, we also find that the fall-back accretion magnetar model is disfavored by the late bumps, but the fall-back accretion of black hole model can not be ruled out to interpret the early bumps of X-ray afterglow., Comment: 14 pages, 5 figures, accepted for publication in MNRAS

The prompt emission mechanism of gamma-ray bursts (GRBs) is a long-standing open question, and GRBs have been considered as potential sources of high-energy neutrinos. Despite many years of search for the neutrino events associated with GRBs from IceCube, there were no results. However, the absence of search results for neutrinos provides a unique opportunity to constrain the parameter space of GRB jet models. In this paper, we chose four peculiar GRBs with two different types of jet composition to investigate neutrino emission. It is found that only GRB 211211A could be well constrained within the dissipative photosphere model. By adopting the specific parameters of the photosphere, one can obtain $\varepsilon _{\rm p}/\varepsilon_{\rm e}<8$ for $f_{\rm p}>0.2$ from GRB 211211A. For the Internal-collision-induced Magnetic Reconnection and Turbulence (ICMART) model, we can effectively constrain neither GRB 230307A nor GRB 080916C. Moreover, we also investigate the detection prospects of high-energy neutrinos from GRBs and find that it is difficult to detect at least one high-energy neutrino associated with GRBs from the ICMART model even during the IceCube-Gen2 operation. For the GRB 211211A-like events, it is possible to detect at least one neutrino coincident with the gravitational wave during the IceCube-Gen2 operation, if such an event originated from mergers of compact stars within the photosphere dissipation., Comment: 15 pages, 10 figures, matched with the published verison

Short gamma-ray bursts (SGRBs) with extended emission (EE) are composed of initial main emission (ME) with a short-hard spike, followed by a long-lasting EE. Whether the ME and EE originated from the same origin or not, as well as the jet composition, remains an open question. In this paper, we present a systematic analysis of 36 gamma ray bursts (GRBs) in our sample, which are identified as the category of SGRBs with EE as observed by Fermi/Gamma-ray Burst Monitor. By extracting time-integrated spectra of ME and EE with cutoff power-law or Band models for our sample, we find that 20 out of 36 SGRBs have $\alpha$ values that exceed the death line (e.g., -2/3) of synchrotron emission within either ME or EE phases, and we suggest that the quasi-thermal component should exist in the prompt emission. Then, we extract the time-resolved spectra of our samples, but only four GRBs are bright enough to extract the time-resolved spectra. We find that both thermal and nonthermal emissions do exist in the prompt emission of those four bright GRBs, which suggests that a hybrid jet (e.g., matter and Poynting-flux outflow) in GRBs should exist. Moreover, strong positive correlations (e.g., $F_{\rm tot}-\Gamma$ and $F_{\rm tot}-kT$) in the time-resolved spectra of ME and EE for those four GRBs have been discovered. This indicates that the spectral evolution of both ME and EE seem to share similar behavior, possibly from the same physical origin., Comment: 30 pages, 9 figures and 11 tables, matched with the published verison

The observation of high-$T_c$ superconductivity (HTSC) in concomitant with pressure-induced orthorhombic-tetragonal structural transition in the bilayer La$_{3}$Ni$_2$O$_7$ has sparked hopes of achieving HTSC by stabilizing the tetragonal phase at ambient pressure. To mimic the effect of external physical pressures, the application of chemical pressure via replacing La$^3$$^+$ with smaller rare-earth R$^3$$^+$ has been considered as a potential route. Here we clarify the distinct effects of chemical and physical pressures on the structural transition of bilayer nickelates through a combined experimental and theoretical investigation. Contrary to general expectations, we find that substitutions of smaller R$^3$$^+$ for La$^3$$^+$ in La$_{3-x}$R$_x$Ni$_2$O$_{7-\delta}$, despite of an overall lattice contraction, produce stronger orthorhombic structural distortions and thus require higher pressures to induce the structural transition. We established a quantitative relationship between the critical pressure $P_c$ for structural transition and the average size of $A$-site cations. A linear extrapolation of $P_c$ versus <$r_A$> yields a putative critical value of <$r_A$>$_c$ ~ 1.23 angstrom for $P_c$ ~ 1 bar. The negative correlation between $P_c$ and <$r_A$> indicates that it is unlikely to reduce $P_c$ to ambient by replacing La$^3$$^+$ with smaller R$^3$$^+$ ions. Instead, partial substitution of La$^3$$^+$ with larger cations such as alkaline-earth Sr$^2$$^+$ or Ba$^2$$^+$ might be a feasible approach. Our results provide valuable guidelines in the quest of ambient-pressure HTSC in bilayer nickelates., Comment: 17 pages and 5 figures

The Ruddlesden-Popper (R-P) bilayer nickelate, La3Ni2O7, was recently found to show signatures of high-temperature superconductivity (HTSC) at pressures above 14 GPa. Subsequent investigations achieved zero resistance in single- and poly-crystalline samples under hydrostatic pressure conditions. Yet, obvious diamagnetic signals, the other hallmark of superconductors, are still lacking owing to the filamentary nature with low superconducting volume fraction. The presence of a novel "1313" polymorph and competing R-P phases obscured proper identification of the phase for HTSC. Thus, achieving bulk HTSC and identifying the phase at play are the most prominent tasks at present. Here, we address these issues in the praseodymium (Pr)-doped La2PrNi2O7 polycrystalline samples. We find that the substitutions of Pr for La effectively inhibits the intergrowth of different R-P phases, resulting in nearly pure bilayer structure. For La2PrNi2O7, pressure-induced orthorhombic-to-tetragonal structural transition takes place at Pc ~ 11 GPa, above which HTSC emerges gradually upon further compression. The superconducting transition temperatures at 18-20 GPa reach Tconset = 82.5 K and Tczero = 60 K, which are the highest values among known nickelate superconductors. More importantly, bulk HTSC was testified by detecting clear diamagnetic signals below ~75 K corresponding to an estimated superconducting volume fraction ~ 57(5)% at 20 GPa. Our results not only resolve the existing controversies but also illuminate directions for exploring bulk HTSC in the bilayer nickelates.

We present optical photometry for the afterglow of GRB 221009A, in some respects the most extraordinary gamma-ray burst (GRB) ever observed. Good quality in the R-band light curve is obtained, covering 0.32-19.57 days since the Fermi-GBM trigger. We find that a weak bump emerges fromthe declining afterglow at $t \approx 11$ days; a supernova (SN) may be responsible. We use a smooth broken power-law and $^{56}\mathrm{Ni}$ model to fit the light curve. The best-fitting results reveal that the SN ejected a total mass of $M_\mathrm{ej} = 3.70 M_\odot$, a $^{56}\mathrm{Ni}$ mass of $M_\mathrm{Ni} = 0.23 M_\odot$, and a kinetic energy of $E_\mathrm{SN,K} = 2.35 \times 10^{52} \mathrm{erg}$. We also compare GRB 221009A with other GRB-SN events based on a GRB-associated SN sample, and find that only SN 2003lw and SN 2011kl can be obviously revealed in the afterglow of GRB 221009A by setting these objects at its distance. This suggests that a supernova (SN 2022xiw) is possibly obscured by the brighter afterglow emission from GRB 221009A.

Positron Emission Tomography (PET) is an important clinical imaging tool but inevitably introduces radiation hazards to patients and healthcare providers. Reducing the tracer injection dose and eliminating the CT acquisition for attenuation correction can reduce the overall radiation dose, but often results in PET with high noise and bias. Thus, it is desirable to develop 3D methods to translate the non-attenuation-corrected low-dose PET (NAC-LDPET) into attenuation-corrected standard-dose PET (AC-SDPET). Recently, diffusion models have emerged as a new state-of-the-art deep learning method for image-to-image translation, better than traditional CNN-based methods. However, due to the high computation cost and memory burden, it is largely limited to 2D applications. To address these challenges, we developed a novel 2.5D Multi-view Averaging Diffusion Model (MADM) for 3D image-to-image translation with application on NAC-LDPET to AC-SDPET translation. Specifically, MADM employs separate diffusion models for axial, coronal, and sagittal views, whose outputs are averaged in each sampling step to ensure the 3D generation quality from multiple views. To accelerate the 3D sampling process, we also proposed a strategy to use the CNN-based 3D generation as a prior for the diffusion model. Our experimental results on human patient studies suggested that MADM can generate high-quality 3D translation images, outperforming previous CNN-based and Diffusion-based baseline methods., Comment: 15 pages, 7 figures

Magnetars are potential energy sources or central engines for numerous transient phenomena in the Universe. How newborn magnetars evolve in different environments remains an open question. Based on both observed and candidate magnetars, it is found that the periods of all magnetars or candidates appear as a bimodal distribution, and are defined as the ``long-P'' and ``short-P'' magnetar subclasses, respectively. We find that for the ``short-P'' subclass of magnetars, the $\dot{P}$ values also appear as a bimodal distribution, and therefore can be classified as ``high-$\dot{P}$ short-P'' and ``low-$\dot{P}$ short-P'' magnetar subclasses. In this paper, we use Monte Carlo simulations to generate synthetic magnetar populations and investigate the evolution of the ``high-$\dot{P}$ short-P'' and ``low-$\dot{P}$ short-P'' magnetar subclasses by considering both the magnetar spin and inclination, as well as the decay of their magnetic field within their evolution in both vacuum and plasma-filled magnetospheres. We find that the magnetar evolution is dependent on both spin and magnetic field, but seems to be insensitive to inclination evolution and magnetospheric environment for the ``high-$\dot{P}$ short-P'' sub-class. In comparison for the case of ``high-$\dot{P}$ short-P'', the magnetar evolution is dependent on spin, magnetic field, and inclination evolution, as well as the magnetospheric environment. The best evolution model should be the case of inclination evolution in vacuum with a small value of $\overline{\mathrm{FOM}}$. The differences in the best-fit parameters also suggest that the ``high-$\dot{P}$ short-P'' and ``low-$\dot{P}$ short-P'' magnetar subclasses may be tracking with different evolution channels., Comment: 18 pages, 2 Tables, and 9 Figures. PRD in press, and matched with the published verison

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error., Comment: Accepted at Medical Image Analysis Journal

Developing comprehensive explicit world models is crucial for understanding and simulating real-world scenarios. Recently, Procedural Controllable Generation (PCG) has gained significant attention in large-scale scene generation by enabling the creation of scalable, high-quality assets. However, PCG faces challenges such as limited modular diversity, high expertise requirements, and challenges in managing the diverse elements and structures in complex scenes. In this paper, we introduce a large-scale scene generation framework, SceneX, which can automatically produce high-quality procedural models according to designers' textual descriptions. Specifically, the proposed method comprises two components, PCGHub and PCGPlanner. The former encompasses an extensive collection of accessible procedural assets and thousands of hand-craft API documents to perform as a standard protocol for PCG controller. The latter aims to generate executable actions for Blender to produce controllable and precise 3D assets guided by the user's instructions. Extensive experiments demonstrated the capability of our method in controllable large-scale scene generation, including nature scenes and unbounded cities, as well as scene editing such as asset placement and season translation.

Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains). However, most existing FL methods assume that domain labels are provided during training, and their evaluation imposes explicit constraints on the number of domains, which must strictly match the number of clients. Because of the underutilization of numerous edge devices and additional cross-client domain annotations in the real world, such restrictions may be impractical and involve potential privacy leaks. In this paper, we propose an efficient and novel approach, called Disentangled Prompt Tuning (DiPrompT), a method that tackles the above restrictions by learning adaptive prompts for domain generalization in a distributed manner. Specifically, we first design two types of prompts, i.e., global prompt to capture general knowledge across all clients and domain prompts to capture domain-specific knowledge. They eliminate the restriction on the one-to-one mapping between source domains and local clients. Furthermore, a dynamic query metric is introduced to automatically search the suitable domain label for each sample, which includes two-substep text-image alignments based on prompt tuning without labor-intensive annotation. Extensive experiments on multiple datasets demonstrate that our DiPrompT achieves superior domain generalization performance over state-of-the-art FL methods when domain labels are not provided, and even outperforms many centralized learning methods using domain labels.

Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps (u-map) for PET attenuation correction significantly elevates radiation doses. To address this concern and further mitigate radiation exposure in low-dose PET exams, we propose POUR-Net - an innovative population-prior-aided over-under-representation network that aims for high-quality attenuation map generation from low-dose PET. First, POUR-Net incorporates an over-under-representation network (OUR-Net) to facilitate efficient feature extraction, encompassing both low-resolution abstracted and fine-detail features, for assisting deep generation on the full-resolution level. Second, complementing OUR-Net, a population prior generation machine (PPGM) utilizing a comprehensive CT-derived u-map dataset, provides additional prior information to aid OUR-Net generation. The integration of OUR-Net and PPGM within a cascade framework enables iterative refinement of $\mu$-map generation, resulting in the production of high-quality $\mu$-maps. Experimental results underscore the effectiveness of POUR-Net, showing it as a promising solution for accurate CT-free low-count PET attenuation correction, which also surpasses the performance of previous baseline methods., Comment: 10 pages, 5 figures

The nearby long-duration GRB 191019A recently detected by Swift lacks an associated supernova and belongs to a host galaxy with little star formation activity, suggesting that the origin of this burst is the result of a merger of two compact objects with dynamical interactions in a high-density medium of an active galactic nucleus. Given the potential motivation of this event, and given that it occurs in such a high-density environment, the ejecta-circumstellar medium (CSM) interaction cannot be ignored as possibly contributing to the kilonova emission. Here, we theoretically calculate the kilonova emission by considering the contribution of the ejecta-CSM interaction in a high-density environment. We find that the contribution to the kilonova emission from the ejecta-CSM interaction will dominate at a later time, and a smaller ejecta mass will have a stronger kilonova emission from the ejecta-CSM interaction. Moreover, we try to apply it to GRB 191019A, but we find that it is difficult to identify the possible kilonova emission from the observations, due to the contribution of the bright host galaxy. On the other hand, less injected mass (less than $M_{\rm ej}=2\times10^{-5}M_{\odot}$) will be required if one can detect the kilonova emission associated with a GRB 191019A-like event in the future. The {\em r}-process-powered and spin energy contributions from the magnetar are also discussed., Comment: 10 pages, 6 figures, updated the rederence list. ApJ in press, and matched with the published verison

The prompt emission, X-ray plateau, and X-ray flares of Gamma-ray bursts (GRB) are thought to be from internal dissipation, and the magnetar as the central engine with propeller fallback accretion is proposed to interpret the observed phenomena of GRBs. In this paper, by systematically searching for X-ray emission observed by Swift/Xry Telescope, we find that seven robust GRBs include both X-ray flares and plateau emissions with measured redshift. More interestingly, the X-ray flares/bumps for those seven GRBs are simultaneously observed in the gamma-ray band. By adopting the propeller fallback accretion model to fit the observed data, it is found that the free parameters of two GRBs (140512A and 180329B) can be constrained very well, while in the other five cases, more or less, they are not all sufficiently constrained. On the other hand, this requires that the conversion efficiency of the propeller is to be two or three times higher than that of the spindown dipole radiation of the magnetar. If this is the case, it is contradictory to the expectation from the propeller model: namely, a dirtier ejecta should be less efficient in producing gamma-ray emissions. Our results hint that at least the magnetar central engine with propeller fallback accretion model cannot interpret very well both the GRB X-ray flares simultaneously observed in the gamma-ray band and the X-ray flares of GRBs with a high Lorentz factor., Comment: 13 pages, 5 figures, 1 table, updated references, match with published version