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We present experiments to reproduce the characteristics of core-collapse supernovae with different stellar masses and initial explosion energies in the laboratory. In the experiments, shocks are driven in 1.2 atm and 1.9 atm xenon gas by laser with energy from 1600J to 2800J on the SGIII prototype laser facility. The average shock velocities and shocked densities are obtained from experiments. Experimental results reveal that higher laser energy and lower Xe gas density led to higher shock velocity, and lower Xe gas initial density has a higher compression. Modeling of the experiments using the 2D radiation hydrodynamic codes Icefire shows excellent agreement with the experimental results and gives the temperature. These results will contribute to time-domain astrophysical systems, such as gravitational supernovae, where a strong radiative shock propagates outward from the center of the star after the core collapses., Comment: 7 pages, 2 figures, 1 supplement (8 pages, 3 figures, 2 tables), accepted for publication in Science Bulletin

Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet, MAISI can process organ segmentation, including 127 anatomical structures, as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic, anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data.

With a great potential of improving the service fairness and quality for user equipments (UEs), cell-free massive multiple-input multiple-output (mMIMO) has been regarded as an emerging candidate for 6G network architectures. Under ideal assumptions, the coherent joint transmission (CJT) serving mode has been considered as an optimal option for cell-free mMIMO systems, since it can achieve coherent cooperation gain among the access points. However, when considering the limited fronthaul constraint in practice, the non-coherent joint transmission (NCJT) serving mode is likely to outperform CJT, since the former requires much lower fronthaul resources. In other words, the performance excellence and worseness of single serving mode (CJT or NCJT) depends on the fronthaul capacity, and any single transmission mode cannot perfectly adapt the capacity limited fronthaul. To explore the performance potential of the cell-free mMIMO system with limited fronthauls by harnessing the merits of CJT and NCJT, we propose a CJT-NCJT hybrid serving mode framework, in which UEs are allocated to operate on CJT or NCJT serving mode. To improve the sum-rate of the system with low complexity, we first propose a probability-based random serving mode allocation scheme. With a given serving mode, a successive convex approximation-based power allocation algorithm is proposed to maximize the system's sum-rate. Simulation results demonstrate the superiority of the proposed scheme., Comment: 6 pages, 2 figures, accepted by GLOBECOM 2024

Since the release of Segment Anything 2 (SAM2), the medical imaging community has been actively evaluating its performance for 3D medical image segmentation. However, different studies have employed varying evaluation pipelines, resulting in conflicting outcomes that obscure a clear understanding of SAM2's capabilities and potential applications. We shortly review existing benchmarks and point out that the SAM2 paper clearly outlines a zero-shot evaluation pipeline, which simulates user clicks iteratively for up to eight iterations. We reproduced this interactive annotation simulation on 3D CT datasets and provided the results and code~\url{https://github.com/Project-MONAI/VISTA}. Our findings reveal that directly applying SAM2 on 3D medical imaging in a zero-shot manner is far from satisfactory. It is prone to generating false positives when foreground objects disappear, and annotating more slices cannot fully offset this tendency. For smaller single-connected objects like kidney and aorta, SAM2 performs reasonably well but for most organs it is still far behind state-of-the-art 3D annotation methods. More research and innovation are needed for 3D medical imaging community to use SAM2 correctly.

We update the HI surface density measurements for a subset of 17 THINGS galaxies by dealing with the short-spacing problem of the original VLA HI images. It is the same sample that Bigiel et al. (2010) used to study the relation between HI surface densities and star formation rate surface densities in galaxy outer disks, which are beyond the optical radius r25. For ten galaxies, the update is based on combining original THINGS VLA HI images with HI images taken by the single-dish FAST in the FEASTS program. The median increment of HI surface densities in outer disks is 0.15 to 0.4 dex at a given new HI surface density. Several galaxies change significantly in the shape of radial profiles HI surface densities, and seven galaxies are now more than 1-$\sigma$ below the HI size-mass relation. We update the HI star formation laws in outer disks. The median relation between HI surface densities and star formation rate surface densities based on pixelwise measurements shifts downward by around 0.15 dex because the HI surface density values shift rightward, and the scatter increases significantly. The scatter of the relation, indicating the star forming efficiency, exhibits a much stronger positive correlation with the stellar mass surface density than before. Thus, detecting the previously missed, diffuse HI due to short-spacing problem of the VLA observation is important in revealing the true condition and variation of star formation possibly regulated by stellar feedbacks in localized environment of outer disks., Comment: 27 pages, the second paper of "FEASTS Combined with Interferometry". Data is available on the page http://kavli.pku.edu.cn/~jwang/FEASTS_data.html

We study the reliability function of general classical-quantum channels, which describes the optimal exponent of the decay of decoding error when the communication rate is below the capacity. As main result, we prove a lower bound, in terms of the quantum Renyi information in Petz's form, for the reliability function. This resolves Holevo's conjecture proposed in 2000, a long-standing open problem in quantum information theory. It turns out that the obtained lower bound matches the upper bound derived by Dalai in 2013, when the communication rate is above a critical value. Thus we have determined the reliability function in this high-rate case. Our approach relies on Renes' breakthrough made in 2022, which relates classical-quantum channel coding to that of privacy amplification, as well as our new characterization of the channel Renyi information., Comment: Revised version, 7.2 pages, no figures, references updated. See also independent work arXiv:2407.11118 by Joseph M. Renes

We study silting objects over derived preprojective algebras of acyclic quivers by giving a direct relationship between silting objects, spherical twist functors and mutations. Especially, for a Dynkin quiver, we establish a bijection between the elements of the braid group and the set of isomorphism classes of basic silting objects over the derived preprojective algebra., Comment: Reference to [BDL] and [HW] added

Covering all languages with a multilingual speech recognition model (MASR) is very difficult. Performing language extension on top of an existing MASR is a desirable choice. In this study, the MASR continual learning problem is probabilistically decomposed into language identity prediction (LP) and cross-lingual adaptation (XLA) sub-problems. Based on this, we propose an architecture-based framework for language extension that can fundamentally solve catastrophic forgetting, debudded as PELE. PELE is designed to be parameter-efficient, incrementally incorporating an add-on module to adapt to a new language. Specifically, different parameter-efficient fine-tuning (PEFT) modules and their variants are explored as potential candidates to perform XLA. Experiments are carried out on 5 new languages with a wide range of low-resourced data sizes. The best-performing PEFT candidate can achieve satisfactory performance across all languages and demonstrates superiority in three of five languages over the continual joint learning setting. Notably, PEFT methods focusing on weight parameters or input features are revealed to be limited in performance, showing significantly inferior extension capabilities compared to inserting a lightweight module in between layers such as an Adapter., Comment: Accepted by Interspeech 2024

Medical image segmentation is a core component of precision medicine, and 3D computed tomography (CT) is one of the most important imaging techniques. A highly accurate and clinically applicable segmentation foundation model will greatly facilitate clinicians and researchers using CT images. Although existing foundation models have attracted great interest, none are adequate for 3D CT, either because they lack accurate automatic segmentation for large cohort analysis or the ability to segment novel classes. An ideal segmentation solution should possess two features: accurate out-of-the-box performance covering major organ classes, and effective adaptation or zero-shot ability to novel structures. To achieve this goal, we introduce Versatile Imaging SegmenTation and Annotation model (VISTA3D). VISTA3D is trained systematically on 11454 volumes and provides accurate out-of-the-box segmentation for 127 common types of human anatomical structures and various lesions. Additionally, VISTA3D supports 3D interactive segmentation, allowing convenient editing of automatic results and achieving state-of-the-art annotation results on unseen classes. The novel model design and training recipe represent a promising step toward developing a versatile medical image foundation model and will serve as a valuable foundation for CT image analysis. Code and model weights are available at https://github.com/Project-MONAI/VISTA

This paper is aimed at exploring implications of velocity dispersion scalings on high-mass star formation in molecular clouds, including the scalings of Larson's linewidth--size ($\sigma$--$R$) and ratio--mass surface density ($\cal{L}$--$\Sigma$; here $\cal{L}$$=\sigma/R^{0.5}$). We have systematically analyzed the $\sigma$ parameter of well-selected 221 massive clumps, complemented with published samples of other hierarchical density structures of molecular clouds over spatial scales of 0.01--10 pc. Those massive clumps are classified into four phases: quiescent, protostellar, HII region, and PDR clumps in an evolutionary sequence. The velocity dispersion of clumps increases overall with the evolutionary sequence, reflecting enhanced stellar feedback in more evolved phases. The relations of $\sigma$--$R$ and $\cal{L}$--$\Sigma$ are weak with the clump sample alone, but become evident when combined with others spanning a much wider spatial scales. For $\sigma$--$R$, its tight relation indicates a kinematic connection between hierarchical density structures, supporting theoretical models of multiscale high-mass star formation. From the $\cal{L}$--$\Sigma$ relation, cloud structures can be found to transition from over-virial state ($\alpha_\mathrm{vir} > 2$) to sub-virial state ($\alpha_\mathrm{vir} < 2$) as they become smaller and denser, indicating a possible shift in the governing force from turbulence to gravity. This implies that the multiscale physical process of high-mass star formation hinges on the self-gravity of sub-virial molecular clouds. However, the influence of turbulence may not be dismissed until large-scale clouds attain a sub-virial state. This is pending confirmation from future multiscale kinematic observations of molecular clouds with uniform observing settings., Comment: 18 pages,9 figures, accepted by AJ

To explore the potential role of gravity, turbulence and magnetic fields in high-mass star formation in molecular clouds, this study revisits the velocity dispersion--size ($\sigma$--$L$) and density--size ($\rho$--$L$) scalings and the associated turbulent energy spectrum using an extensive data sample. The sample includes various hierarchical density structures in high-mass star formation clouds, across scales of 0.01 to 100 pc. We observe $\sigma \propto L^{0.26}$ and $\rho \propto L^{-1.54}$ scalings, converging toward a virial equilibrium state. A nearly flat virial parameter--mass ($\alpha_{\rm vir}-M$) distribution is seen across all density scales, with $\alpha_{\rm vir}$ values centered around unity, suggesting a global equilibrium maintained by the interplay between gravity and turbulence across multiple scales. Our turbulent energy spectrum ($E(k)$) analysis, based on the $\sigma$--$L$ and $\rho$--$L$ scalings, yields a characteristic $E(k) \propto k^{-1.52}$. These findings indicate the potential significance of gravity, turbulence, and possibly magnetic fields all in regulating dynamics of molecular clouds and high-mass star formation therein., Comment: 15 pages,5 figures, Accepted by RAA

We present a statistical study of the properties of diffuse HI in ten nearby galaxies, comparing the HI detected by the single-dish telescope FAST (FEASTS program) and the interferometer VLA (THINGS program), respectively. The THINGS' observation missed HI with a median of 23% due to the short-spacing problem of interferometry and limited sensitivity. We extract the diffuse HI by subtracting the dense HI, which is obtained from the THINGS data with a uniform flux-density threshold, from the total HI detected by FAST. Among the sample, the median diffuse-HI fraction is 34%, and more diffuse HI is found in galaxies exhibiting more prominent tidal-interaction signatures. The diffuse HI we detected seems to be distributed in disk-like layers within a typical thickness of $1\,\text{kpc}$, different from the more halo-like diffuse HI detected around NGC 4631 in a previous study. Most of the diffuse HI is cospatial with the dense HI and has a typical column density of $10^{17.7}$-$10^{20.1}\,\text{cm}^{-2}$. The diffuse and dense HI exhibits a similar rotational motion, but the former lags by a median of 25% in at least the inner disks, and its velocity dispersions are typically twice as high. Based on a simplified estimation of circum-galactic medium properties and assuming pressure equilibrium, the volume density of diffuse HI appears to be constant within each individual galaxy, implying its role as a cooling interface. Comparing with existing models, these results are consistent with a possible link between tidal interactions, the formation of diffuse HI, and gas accretion., Comment: 45 pages, 23 figures. In press at ApJ. Data will be released at the FEASTS site upon publication

Artificial Intelligence-Generated Content (AIGC) is a rapidly evolving field that utilizes advanced AI algorithms to generate content. Through integration with mobile edge networks, mobile AIGC networks have gained significant attention, which can provide real-time customized and personalized AIGC services and products. Since blockchains can facilitate decentralized and transparent data management, AIGC products can be securely managed by blockchain to avoid tampering and plagiarization. However, the evolution of blockchain-empowered mobile AIGC is still in its nascent phase, grappling with challenges such as improving information propagation efficiency to enable blockchain-empowered mobile AIGC. In this paper, we design a Graph Attention Network (GAT)-based information propagation optimization framework for blockchain-empowered mobile AIGC. We first innovatively apply age of information as a data-freshness metric to measure information propagation efficiency in public blockchains. Considering that GATs possess the excellent ability to process graph-structured data, we utilize the GAT to obtain the optimal information propagation trajectory. Numerical results demonstrate that the proposed scheme exhibits the most outstanding information propagation efficiency compared with traditional routing mechanisms., Comment: arXiv admin note: substantial text overlap with arXiv:2403.13237

To achieve the physics goal of precisely measure the Higgs, Z, W bosons and the top quark, future electron-positron colliders require that their detector system has excellent jet energy resolution. One feasible technical option is the high granular calorimetery based on the particle flow algorithm (PFA). A new high-granularity hadronic calorimeter with glass scintillator tiles (GSHCAL) has been proposed, which focus on the significant improvement of hadronic energy resolution with a notable increase of the energy sampling fraction by using high-density glass scintillator tiles. The minimum ionizing particle (MIP) response of a glass scintillator tile is crucial to the hadronic calorimeter, so a dedicated beamtest setup was developed for testing the first batch of large-size glass scintillators. The maximum MIP response of the first batch of glass scintillator tiles can reach up to 107 p.e./MIP, which essentially meets the design requirements of the CEPC GSHCAL. An optical simulation model of a single glass scintillator tile has been established, and the simulation results are consistent with the beamtest results.

The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate. Recently, generative large language models (LLMs) have shown outstanding performance on several tasks such as information extraction and dialog systems. Leveraging them for KGC re-ranking is beneficial for leveraging the extensive pre-trained knowledge and powerful generative capabilities. However, it may encounter new problems when accomplishing the task, namely mismatch, misordering and omission. To this end, we introduce KC-GenRe, a knowledge-constrained generative re-ranking method based on LLMs for KGC. To overcome the mismatch issue, we formulate the KGC re-ranking task as a candidate identifier sorting generation problem implemented by generative LLMs. To tackle the misordering issue, we develop a knowledge-guided interactive training method that enhances the identification and ranking of candidates. To address the omission issue, we design a knowledge-augmented constrained inference method that enables contextual prompting and controlled generation, so as to obtain valid rankings. Experimental results show that KG-GenRe achieves state-of-the-art performance on four datasets, with gains of up to 6.7% and 7.7% in the MRR and Hits@1 metric compared to previous methods, and 9.0% and 11.1% compared to that without re-ranking. Extensive analysis demonstrates the effectiveness of components in KG-GenRe., Comment: This paper has been accepted for publication in the proceedings of LREC-COLING 2024

We present UTDUSS, the UTokyo-SaruLab system submitted to Interspeech2024 Speech Processing Using Discrete Speech Unit Challenge. The challenge focuses on using discrete speech unit learned from large speech corpora for some tasks. We submitted our UTDUSS system to two text-to-speech tracks: Vocoder and Acoustic+Vocoder. Our system incorporates neural audio codec (NAC) pre-trained on only speech corpora, which makes the learned codec represent rich acoustic features that are necessary for high-fidelity speech reconstruction. For the acoustic+vocoder track, we trained an acoustic model based on Transformer encoder-decoder that predicted the pre-trained NAC tokens from text input. We describe our strategies to build these models, such as data selection, downsampling, and hyper-parameter tuning. Our system ranked in second and first for the Vocoder and Acoustic+Vocoder tracks, respectively., Comment: 5 pages, 3 figures

Photonuclear reactions using a laser Compton scattering (LCS) gamma source provide a new method for producing radioisotopes for medical applications. Compared with the conventional method, this method has the advantages of a high specific activity and less heat. Initiated by the Shanghai Laser Electron Gamma Source (SLEGS), we conducted a survey of potential photonuclear reactions, $(\upgamma,n)$, $(\upgamma,p)$, and $(\upgamma,\upgamma')$ whose cross-sections can be measured at SLEGS by summarizing the experimental progress. In general, the data are rare and occasionally inconsistent. Therefore, theoretical calculations are often used to evaluate the production of medical radioisotopes. Subsequently, we verified the model uncertainties of the widely used reaction code TALYS-1.96, using the experimental data of the \ce{^100Mo}$(\upgamma,n)$\ce{^99Mo}, \ce{^65Cu}$(\upgamma,n)$\ce{^64Cu}, and \ce{^68Zn}$(\upgamma,p)$\ce{^67Cu} reactions., Comment: 16 pages, 19 figures

Scattering and attenuation of light in no-homogeneous imaging media or inconsistent light intensity will cause insufficient contrast and color distortion in the collected images, which limits the developments such as vision-driven smart urban, autonomous vehicles, and intelligent robots. In this paper, we propose an all-in-one scene recovery network via multi-knowledge integration (termed AoSRNet) to improve the visibility of imaging devices in typical low-visibility imaging scenes (e.g., haze, sand dust, and low light). It combines gamma correction (GC) and optimized linear stretching (OLS) to create the detail enhancement module (DEM) and color restoration module (CRM). Additionally, we suggest a multi-receptive field extraction module (MEM) to attenuate the loss of image texture details caused by GC nonlinear and OLS linear transformations. Finally, we refine the coarse features generated by DEM, CRM, and MEM through Encoder-Decoder to generate the final restored image. Comprehensive experimental results demonstrate the effectiveness and stability of AoSRNet compared to other state-of-the-art methods. The source code is available at \url{https://github.com/LouisYuxuLu/AoSRNet}.

It is shown that the gentle one-cycle algebra $\Lambda(n-1,1,1)$ has Hall polynomials. The Hall polynomials are explicitly given for all triples of indecomposable modules, and as a consequence, the Ringel--Hall Lie algebra of $\Lambda(n-1,1,1)$ is shown to be isomorphic to its Riedtmann Lie algebra., Comment: 7 pages

Developing Text-to-Speech (TTS) systems that can synthesize natural breath is essential for human-like voice agents but requires extensive manual annotation of breath positions in training data. To this end, we propose a self-training method for training a breath detection model that can automatically detect breath positions in speech. Our method trains the model using a large speech corpus and involves: 1) annotation of limited breath sounds utilizing a rule-based approach, and 2) iterative augmentation of these annotations through pseudo-labeling based on the model's predictions. Our detection model employs Conformer blocks with down-/up-sampling layers, enabling accurate frame-wise breath detection. We investigate its effectiveness in multi-speaker TTS using text transcripts with detected breath marks. The results indicate that using our proposed model for breath detection and breath mark insertion synthesizes breath-contained speech more naturally than a baseline model., Comment: Accepted by INTERSPEECH2024

High-quality imaging is crucial for ensuring safety supervision and intelligent deployment in fields like transportation and industry. It enables precise and detailed monitoring of operations, facilitating timely detection of potential hazards and efficient management. However, adverse weather conditions, such as atmospheric haziness and precipitation, can have a significant impact on image quality. When the atmosphere contains dense haze or water droplets, the incident light scatters, leading to degraded captured images. This degradation is evident in the form of image blur and reduced contrast, increasing the likelihood of incorrect assessments and interpretations by intelligent imaging systems (IIS). To address the challenge of restoring degraded images in hazy and rainy conditions, this paper proposes a novel multi-view knowledge-guided scene recovery network (termed MvKSR). Specifically, guided filtering is performed on the degraded image to separate high/low-frequency components. Subsequently, an en-decoder-based multi-view feature coarse extraction module (MCE) is used to coarsely extract features from different views of the degraded image. The multi-view feature fine fusion module (MFF) will learn and infer the restoration of degraded images through mixed supervision under different views. Additionally, we suggest an atrous residual block to handle global restoration and local repair in hazy/rainy/mixed scenes. Extensive experimental results demonstrate that MvKSR outperforms other state-of-the-art methods in terms of efficiency and stability for restoring degraded scenarios in IIS.

Toward high-performance multilingual automatic speech recognition (ASR), various types of linguistic information and model design have demonstrated their effectiveness independently. They include language identity (LID), phoneme information, language-specific processing modules, and cross-lingual self-supervised speech representation. It is expected that leveraging their benefits synergistically in a unified solution would further improve the overall system performance. This paper presents a novel design of a hierarchical information path, named LUPET, which sequentially encodes, from the shallow layers to deep layers, multiple aspects of linguistic and acoustic information at diverse granularity scales. The path starts from LID prediction, followed by acoustic unit discovery, phoneme sharing, and finally token recognition routed by a mixture-of-expert. ASR experiments are carried out on 10 languages in the Common Voice corpus. The results demonstrate the superior performance of LUPET as compared to the baseline systems. Most importantly, LUPET effectively mitigates the issue of performance compromise of high-resource languages with low-resource ones in the multilingual setting., Comment: Accepted by Interspeech 2024

In this work, we propose a new computing process, named DeepBEVdose, which is essentially distinct to the previous deep learning-based dose calculation methods.We present a novel image-domain dose calculation algorithm to automatically compute dose distributions from the computer tomography images and radiation field fluence maps.