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Understanding the extent of urban flooding is crucial for assessing building damage, casualties and economic losses. Synthetic Aperture Radar (SAR) technology offers significant advantages for mapping flooded urban areas due to its ability to collect data regardless weather and solar illumination conditions. However, the wide range of existing methods makes it difficult to choose the best approach for a specific situation and to identify future research directions. Therefore, this study provides a comprehensive review of current research on urban flood mapping using SAR data, summarizing key characteristics of floodwater in SAR images and outlining various approaches from scientific articles. Additionally, we provide a brief overview of the advantages and disadvantages of each method category, along with guidance on selecting the most suitable approach for different scenarios. This study focuses on the challenges and advancements in SAR-based urban flood mapping. It specifically addresses the limitations of spatial and temporal resolution in SAR data and discusses the essential pre-processing steps. Moreover, the article explores the potential benefits of Polarimetric SAR (PolSAR) techniques and uncertainty analysis for future research. Furthermore, it highlights a lack of open-access SAR datasets for urban flood mapping, hindering development in advanced deep learning-based methods. Besides, we evaluated the Technology Readiness Levels (TRLs) of urban flood mapping techniques to identify challenges and future research areas. Finally, the study explores the practical applications of SAR-based urban flood mapping in both the private and public sectors and provides a comprehensive overview of the benefits and potential impact of these methods., Comment: Accepted by IEEE Geoscience and Remote Sensing Magazine

Precise phase measurements by interferometers are crucial in science for detecting subtle changes, such as gravitational waves. However, phase sensitivity is typically limited by the standard quantum limit (SQL) with uncorrelated particles N. This limit can be surpassed using quantum correlations, but achieving high-quality correlations in large systems is challenging. Here, we propose and demonstrate an atom-light hybrid quantum interferometry whose sensitivity is enhanced beyond the SQL with atom-light quantum correlation and newly developed phase comb superposition via atomic-memory-assisted multiple quantum amplification. Finally, a phase sensitivity beyond the SQL of up to $8.3\pm 0.2$ dB is achieved, especially at $N=4 \times10^{13}/s$, resulting in both atomic and optical phase sensitivities of $6\times10^{-8} rad/\sqrt{Hz}$. This technique can advance sensitive quantum measurements in various fields., Comment: 11 pages, 3 figures

Trajectory modeling, which includes research on trajectory data pattern mining and future prediction, has widespread applications in areas such as life services, urban transportation, and public administration. Numerous methods have been proposed to address specific problems within trajectory modelling. However, due to the heterogeneity of data and the diversity of trajectory tasks, achieving unified trajectory modelling remains an important yet challenging task. In this paper, we propose TrajAgent, a large language model-based agentic framework, to unify various trajectory modelling tasks. In TrajAgent, we first develop UniEnv, an execution environment with a unified data and model interface, to support the execution and training of various models. Building on UniEnv, we introduce TAgent, an agentic workflow designed for automatic trajectory modelling across various trajectory tasks. Specifically, we design AutOpt, a systematic optimization module within TAgent, to further improve the performance of the integrated model. With diverse trajectory tasks input in natural language, TrajAgent automatically generates competitive results via training and executing appropriate models. Extensive experiments on four tasks using four real-world datasets demonstrate the effectiveness of TrajAgent in unified trajectory modelling, achieving an average performance improvement of 15.43% over baseline methods., Comment: 12 pages; the code will be openly accessible at: https://github.com/tsinghua-fib-lab/TrajAgent

In recent years, the issue of radon emanation and diffusion has become a critical concern for rare decay experiments, such as JUNO and PandaX-4T. This paper introduces a detector design featuring a symmetric radon detector cavity for the quantitative assessment of membrane materials' radon blocking capabilities. The performance of this design is evaluated through the application of Fick's Law and the diffusion equation considering material solubility. Our detector has completed measurements of radon diffusion coefficients for four types of membrane materials currently used in experiments, which also confirms the rationality of this detector design. The findings are instrumental in guiding the selection and evaluation of optimal materials for radon shielding to reduce radon background, contributing to boost sensitivities of rare event research., Comment: 7 pages, 10 figures and 2 tables

Superconducting quantum computing emerges as one of leading candidates for achieving quantum advantage. However, a prevailing challenge is the coding overhead due to limited quantum connectivity, constrained by nearest-neighbor coupling among superconducting qubits. Here, we propose a novel multimode coupling scheme using three resonators driven by two microwaves, based on the resonator-induced phase gate, to extend the $ZZ$ interaction distance between qubits. We demonstrate a CZ gate fidelity exceeding 99.9\% within 160 ns at free spectral range (FSR) of 1.4 GHz, and by optimizing driving pulses, we further reduce the residual photon to nearly $10^{-3}$ within 100 ns at FSR of 0.2 GHz. These facilitate the long-range CZ gate over separations reaching sub-meters, thus significantly enhancing qubit connectivity and making a practical step towards the scalable integration and modularization of quantum processors. Specifically, our approach supports the implementation of quantum error correction codes requiring high connectivity, such as low-density parity check codes that paves the way to achieving fault-tolerant quantum computing., Comment: 7 pages, 4 figures

Human mobility prediction plays a crucial role in various real-world applications. Although deep learning based models have shown promising results over the past decade, their reliance on extensive private mobility data for training and their inability to perform zero-shot predictions, have hindered further advancements. Recently, attempts have been made to apply large language models (LLMs) to mobility prediction task. However, their performance has been constrained by the absence of a systematic design of workflow. They directly generate the final output using LLMs, which limits the potential of LLMs to uncover complex mobility patterns and underestimates their extensive reserve of global geospatial knowledge. In this paper, we introduce AgentMove, a systematic agentic prediction framework to achieve generalized mobility prediction for any cities worldwide. In AgentMove, we first decompose the mobility prediction task into three sub-tasks and then design corresponding modules to complete these subtasks, including spatial-temporal memory for individual mobility pattern mining, world knowledge generator for modeling the effects of urban structure and collective knowledge extractor for capturing the shared patterns among population. Finally, we combine the results of three modules and conduct a reasoning step to generate the final predictions. Extensive experiments on mobility data from two sources in 12 cities demonstrate that AgentMove outperforms the best baseline more than 8% in various metrics and it shows robust predictions with various LLMs as base and also less geographical bias across cities. Codes and data can be found in https://github.com/tsinghua-fib-lab/AgentMove., Comment: 13 pages

Volumetric segmentation is important in medical imaging, but current methods face challenges like requiring lots of manual annotations and being tailored to specific tasks, which limits their versatility. General segmentation models used for natural images don't perform well with the unique features of medical images. There's a strong need for an adaptable approach that can effectively handle different 3D medical structures and imaging modalities. In this study, we present PAM (Propagating Anything Model), a segmentation approach that uses a 2D prompt, like a bounding box or sketch, to create a complete 3D segmentation of medical image volumes. PAM works by modeling relationships between slices, maintaining information flow across the 3D structure. It combines a CNN-based UNet for processing within slices and a Transformer-based attention module for propagating information between slices, leading to better generalizability across various imaging modalities. PAM significantly outperformed existing models like MedSAM and SegVol, with an average improvement of over 18.1% in dice similarity coefficient (DSC) across 44 medical datasets and various object types. It also showed stable performance despite prompt deviations and different propagation setups, and faster inference speeds compared to other models. PAM's one-view prompt design made it more efficient, reducing interaction time by about 63.6% compared to two-view prompts. Thanks to its focus on structural relationships, PAM handled unseen and complex objects well, showing a unique ability to generalize to new situations. PAM represents an advancement in medical image segmentation, effectively reducing the need for extensive manual work and specialized training. Its adaptability makes it a promising tool for more automated and reliable analysis in clinical settings., Comment: 28 pages, 6 figures

To enhance the reasoning capabilities of large language models (LLMs), self-consistency has gained significant popularity by combining multiple sampling with majority voting. However, the state-of-the-art self-consistency approaches consume substantial computational resources and lead to significant additional time costs due to the multiple sampling. This prevents its full potential from being realized in scenarios where computational resources are critical. To improve the inference efficiency, this paper introduces \textit{path-consistency}, a method that leverages the confidence of answers generated in earlier branches to identify the prefix of the most promising path. By dynamically guiding the generation of subsequent branches based on this prefix, the \textit{path-consistency} mitigates both the errors and redundancies from random or less useful sampling in self-consistency. As a result, it can significantly accelerate the inference process by reducing the number of tokens generated. Our extensive empirical evaluation shows that the \textit{path-consistency} achieves significant acceleration in inference latency ranging from $7.8\%$ to $40.5\%$, while maintaining or even improving task accuracy across different datasets, including mathematical reasoning, common sense reasoning, symbolic reasoning, and code generation.

Cloud Computing has established itself as an efficient and cost-effective paradigm for the execution of web-based applications, and scientific workloads, that need elasticity and on-demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large-scale data centers. Therefore, Cloud simulators are an essential tool for academic and industrial researchers, to investigate on the effectiveness of novel algorithms and mechanisms in large-scale scenarios. This article unveils CloudSim7G, the seventh generation of CloudSim, one of the first simulators specialized in evaluating resource management techniques for Cloud infrastructures. In particular, CloudSim7G features a re-engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions, which were previously available independently and often had compatibility issues, within the same simulated environment. Such architectural change is coupled with an extensive refactoring and refinement of the codebase, leading to the removal of over 13,000 lines of code without loss of functionality. As a result, CloudSim7G delivers significantly better performance in both run-time and total memory allocated (up to ~20% less heap memory allocated), along with increased flexibility, ease-of-use, and extensibility of the framework. These improvements benefit not only CloudSim developers but also researchers and practitioners using the framework for modeling and simulating next-generation cloud computing environments., Comment: Paper submitted to Wiley Online Software: Practice and Experience

Gaussian channel simulation is an essential paradigm in understanding the evolution of bosonic quantum states. It allows us to investigate how such states are influenced by the environment and how they transmit quantum information. This makes it an essential tool for understanding the properties of Gaussian quantum communication. Quantum teleportation provides an avenue to effectively simulate Gaussian channels such as amplifier channels, loss channels and classically additive noise channels. However, implementations of these channels, particularly quantum amplifier channels and channels capable of performing Gaussian noise suppression are limited by experimental imperfections and non-ideal entanglement resources. In this work, we overcome these difficulties using a heralded quantum teleportation scheme that is empowered by a measurement-based noiseless linear amplifier. The noiseless linear amplification enables us to simulate a range of Gaussian channels that were previously inaccessible. In particular, we demonstrate the simulation of non-physical Gaussian channels otherwise inaccessible using conventional means. We report Gaussian noise suppression, effectively converting an imperfect quantum channel into a near-identity channel. The performance of Gaussian noise suppression is quantified by calculating the transmitted entanglement., Comment: 9 pages, 4 figures

Entangled graph states can be used for quantum sensing and computing applications. Error correction in measurement-based quantum computing schemes will require the construction of cluster states in at least 3 dimensions. Here we generate 1-, 2-, 3-, and 4-dimensional optical frequency-mode cluster states by sending broadband 2-mode vacuum-squeezed light through an electro-optical modulator (EOM) driven with multiple frequencies. We create the squeezed light using 4-wave mixing in Rb atomic vapor and mix the sideband frequencies (qumodes) using an EOM, as proposed by Zhu et al. (1), producing a pattern of entanglement correlations that constitute continuous-variable graph states containing up to several hundred qumodes. We verify the entanglement structure by using homodyne measurements to construct the covariance matrices and evaluate the nullifiers. This technique enables scaling of optical cluster states to multiple dimensions without increasing loss.

The Lyman-alpha Solar Telescope (LST) on board the Advanced Space-based Solar Observatory (ASO-S) is the first payload to image the full solar disk and the solar corona in both white-light (WL) and ultraviolet (UV) H I Lya, extending up to 2.5 solar radii (Rs). Since the launch of the ASO-S on 9 October 2022, LST has captured various significant solar activities including flares, prominences, coronal mass ejections (CMEs). LST covers different passbands of 121.6 nm, 360 nm and 700 nm. The Lya Solar Disk Imager (SDI) has a field of view (FOV) of 38.4 arcmin and a spatial resolution of around 9.5 arcsec, while the White-Light Solar Telescope (WST) has a FOV of 38.43 arcmin and a spatial resolution of around 3.0 arcsec. The FOV of the Lya Solar Corona Imager (SCI) reaches 81.1 arcmin and its spatial resolution is 4.3 arcsec. The stray-light level in the 700 nm waveband is about 7.8e-6 MSB (mean solar brightness) at 1.1 Rs and 7.6e-7 MSB at 2.5 Rs, and in the Lya waveband it is around 4.3e-3 MSB at 1.1 Rs and 4.1e-4 MSB at 2.5 Rs. This article will detail the results from on-orbit tests and calibrations., Comment: Solar Physics (ASO-S mission topical collection), accepted

Human pose estimation remains a multifaceted challenge in computer vision, pivotal across diverse domains such as behavior recognition, human-computer interaction, and pedestrian tracking. This paper proposes an improved method based on the spatial-temporal graph convolution net-work (UGCN) to address the issue of missing human posture skeleton sequences in single-view videos. We present the improved UGCN, which allows the network to process 3D human pose data and improves the 3D human pose skeleton sequence, thereby resolving the occlusion issue., Comment: Proceedings of IEEE AICON2024

Measurement estimation bounds for local quantum multiparameter estimation, which provide lower bounds on possible measurement uncertainties, have so far been formulated in two ways: by extending the classical Cram\'er--Rao bound (e.g., the quantum Cram\'er--Rao bound and the Nagaoka Cram'er--Rao bound) and by incorporating the parameter estimation framework with the uncertainty principle, as in the Lu--Wang uncertainty relation. In this work, we present a general framework that allows a direct comparison between these different types of estimation limits. Specifically, we compare the attainability of the Nagaoka Cram\'er--Rao bound and the Lu--Wang uncertainty relation, using analytical and numerical techniques. We show that these two limits can provide different information about the physically attainable precision. We present an example where both limits provide the same attainable precision and an example where the Lu--Wang uncertainty relation is not attainable even for pure states. We further demonstrate that the unattainability in the latter case arises because the figure of merit underpinning the Lu--Wang uncertainty relation (the difference between the quantum and classical Fisher information matrices) does not necessarily agree with the conventionally used figure of merit (mean squared error). The results offer insights into the general attainability and applicability of the Lu--Wang uncertainty relation. Furthermore, our proposed framework for comparing bounds of different types may prove useful in other settings., Comment: 13 pages, 7 figures

In the paper, we discuss the development of the multi-gap resistive plate chamber Time-of-Flight (TOF) technology and the production of the STAR TOF detector in China at the beginning of the 21st century. Then we review recent experimental results from the first beam energy scan program (BES-I) at the Relativistic Heavy Ion Collider (RHIC). Topics cover measurements of collectivity, chirality, criticality, global polarization, strangeness, heavy-flavor, di-lepton and light nuclei productions., Comment: 31 pages, 33 figures. This review is dedicated to Professor Wenqing Shen on the occasion to celebrate his leadership of the Chinese STAR Collaboration, the development and production of the STAR MRPC TOF detector in China and many physics analyses

The multipole modes of excitation for tetrahedrally deformed neutron-rich Zr isotopes are investigated using the quasiparticle finite amplitude method based on the covariant density functional theories. By employing the density-dependent point-coupling covariant density functional theory with the parameter set DD-PC1 in the particle-hole channel and a separable pairing interaction of finite range, it is observed that a distinct peak emerges at $\omega = 9.0 - 10.0$ MeV in the isoscalar quadrupole $K=0$ strength when $\beta_{32}$ distortion is considered for $^{110,112}$Zr. This peak is absent when the deformation is limited to axially symmetric octuple or spherical case. It also does not appears in neighboring axially quadrople or octupole deformed nuclei, thus can be viewed as an indicator for the tetrahedral shape., Comment: 6 pages, 5 figures, accepted for publication in Phys. Rev. C

Multi-Task Evolutionary Optimization (MTEO), an important field focusing on addressing complex problems through optimizing multiple tasks simultaneously, has attracted much attention. While MTEO has been primarily focusing on task similarity, there remains a hugely untapped potential in harnessing the shared characteristics between different domains to enhance evolutionary optimization. For example, real-world complex systems usually share the same characteristics, such as the power-law rule, small-world property, and community structure, thus making it possible to transfer solutions optimized in one system to another to facilitate the optimization. Drawing inspiration from this observation of shared characteristics within complex systems, we set out to extend MTEO to a novel framework - multi-domain evolutionary optimization (MDEO). To examine the performance of the proposed MDEO, we utilize a challenging combinatorial problem of great security concern - community deception in complex networks as the optimization task. To achieve MDEO, we propose a community-based measurement of graph similarity to manage the knowledge transfer among domains. Furthermore, we develop a graph representation-based network alignment model that serves as the conduit for effectively transferring solutions between different domains. Moreover, we devise a self-adaptive mechanism to determine the number of transferred solutions from different domains and introduce a novel mutation operator based on the learned mapping to facilitate the utilization of knowledge from other domains. Experiments on eight real-world networks of different domains demonstrate MDEO superiority in efficacy compared to classical evolutionary optimization. Simulations of attacks on the community validate the effectiveness of the proposed MDEO in safeguarding community security.

Sympathetic eruptions of solar prominences have been studied for decades, however, it is usually difficult to identify their causal links. Here we present two failed prominence eruptions on 26 October 2022 and explore their connections. Using stereoscopic observations, the south prominence (PRO-S) erupts with untwisting motions, flare ribbons occur underneath, and new connections are formed during the eruption. The north prominence (PRO-N) rises up along with PRO-S, and its upper part disappears due to catastrophic mass draining along an elongated structure after PRO-S failed eruption. We suggest that the eruption of PRO-S initiates due to a kink instability, further rises up, and fails to erupt due to reconnection with surrounding fields. The elongated structure connecting PRO-N overlies PRO-S, which causes the rising up of PRO-N along with PRO-S and mass drainage after PRO-S eruption. This study suggests that a prominence may end its life through mass drainage forced by an eruption underneath., Comment: 15 pages, 7 figures, has been accepted by Solar Physics

Criminal case matching endeavors to determine the relevance between different criminal cases. Conventional methods predict the relevance solely based on instance-level semantic features and neglect the diverse legal factors (LFs), which are associated with diverse court judgments. Consequently, comprehensively representing a criminal case remains a challenge for these approaches. Moreover, extracting and utilizing these LFs for criminal case matching face two challenges: (1) the manual annotations of LFs rely heavily on specialized legal knowledge; (2) overlaps among LFs may potentially harm the model's performance. In this paper, we propose a two-stage framework named Diverse Legal Factor-enhanced Criminal Case Matching (DLF-CCM). Firstly, DLF-CCM employs a multi-task learning framework to pre-train an LF extraction network on a large-scale legal judgment prediction dataset. In stage two, DLF-CCM introduces an LF de-redundancy module to learn shared LF and exclusive LFs. Moreover, an entropy-weighted fusion strategy is introduced to dynamically fuse the multiple relevance generated by all LFs. Experimental results validate the effectiveness of DLF-CCM and show its significant improvements over competitive baselines. Code: https://github.com/jiezhao6/DLF-CCM.

Text style transfer (TST) aims to vary the style polarity of text while preserving the semantic content. Although recent advancements have demonstrated remarkable progress in short TST, it remains a relatively straightforward task with limited practical applications. The more comprehensive long TST task presents two challenges: (1) existing methods encounter difficulties in accurately evaluating content attributes in multiple words, leading to content degradation; (2) the conventional vanilla style classifier loss encounters obstacles in maintaining consistent style across multiple generated sentences. In this paper, we propose a novel method SC2, where a multilayer Joint Style-Content Weighed (JSCW) module and a Style Consistency loss are designed to address the two issues. The JSCW simultaneously assesses the amounts of style and content attributes within a token, aiming to acquire a lossless content representation and thereby enhancing content preservation. The multiple JSCW layers further progressively refine content representations. We design a style consistency loss to ensure the generated multiple sentences consistently reflect the target style polarity. Moreover, we incorporate a denoising non-autoregressive decoder to accelerate the training. We conduct plentiful experiments and the results show significant improvements of SC2 over competitive baselines. Our code: https://github.com/jiezhao6/SC2.

Due to its cloud-penetrating capability and independence from solar illumination, satellite Synthetic Aperture Radar (SAR) is the preferred data source for large-scale flood mapping, providing global coverage and including various land cover classes. However, most studies on large-scale SAR-derived flood mapping using deep learning algorithms have primarily focused on flooded open areas, utilizing available open-access datasets (e.g., Sen1Floods11) and with limited attention to urban floods. To address this gap, we introduce \textbf{UrbanSARFloods}, a floodwater dataset featuring pre-processed Sentinel-1 intensity data and interferometric coherence imagery acquired before and during flood events. It contains 8,879 $512\times 512$ chips covering 807,500 $km^2$ across 20 land cover classes and 5 continents, spanning 18 flood events. We used UrbanSARFloods to benchmark existing state-of-the-art convolutional neural networks (CNNs) for segmenting open and urban flood areas. Our findings indicate that prevalent approaches, including the Weighted Cross-Entropy (WCE) loss and the application of transfer learning with pretrained models, fall short in overcoming the obstacles posed by imbalanced data and the constraints of a small training dataset. Urban flood detection remains challenging. Future research should explore strategies for addressing imbalanced data challenges and investigate transfer learning's potential for SAR-based large-scale flood mapping. Besides, expanding this dataset to include additional flood events holds promise for enhancing its utility and contributing to advancements in flood mapping techniques., Comment: Accepted by CVPR 2024 EarthVision Workshop

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables

Relativistic heavy ion collisions provide a unique opportunity to study the shape of colliding nuclei, even up to higher-order multiple deformations. In this work, several observables that are sensitive to quadrupole and hexadecapole deformations of Uranium-238 in relativistic U+U collisions have been systematically investigated with A Multi-Phase Transport model. We find that the flow harmonic $v_{2}$, the $v_{2}$ and mean transverse momentum correlation, and the three-particle asymmetry cumulant ${\rm ac}_{2}\{3\}$ are sensitive to nuclear quadrupole deformation, while ${\rm ac}_{2}\{3\}$ and nonlinear response coefficient $\chi_{4,22}$ are sensitive to nuclear hexadecapole deformation. Our results from transport model studies are in qualitative agreement with previous hydrodynamic studies. The results indicate that the uncertainties of the hexadecapole deformation of Uranium on the quadrupole deformation determination can be reduced by the abundance of correlation observables provided by the relativistic heavy ion collisions., Comment: 8 pages, 4 figures, version accepted for publication

Liquid scintillator (LS) is commonly utilized in experiments seeking rare events due to its high light yield, transparency, and radiopurity. The concentration of $^{238}$U and $^{232}$Th in LS consistently remains below 1 ppq (10$^{-15}$ g/g), and the current screening result is based on a minimum 20-ton detector. Inductively coupled plasma mass (ICP-MS) spectroscopy is well-regarded for its high sensitivity to trace $^{238}$U and $^{232}$Th. This study outlines a method for detecting $^{238}$U and $^{232}$Th in LS at the sub-ppq level using ICP-MS, involving the enrichment of $^{238}$U/$^{232}$Th from the LS through acid extraction. With meticulous cleanliness control, $^{238}$U/$^{232}$Th in approximately 2 kg of LS is concentrated by acid extraction with 0.4 (0.3) pg $^{238}$U ($^{232}$Th) contamination. Three standard adding methods are employed to assess recovery efficiency, including radon daughter, 2,5-diphenyloxazole (PPO), and natural non-existent $^{233}$U/$^{229}$Th. The method detection limit at a 99% confidence level of this approach can reach approximately 0.2-0.3 ppq for $^{238}$U/$^{232}$Th with nearly 100% recovery efficiency.

Solar white-light flares are characterized by an enhancement in the optical continuum, which are usually large flares (say X- and M-class flares). Here we report a small C2.3 white-light flare (SOL2022-12-20T04:10) observed by the \emph{Advanced Space-based Solar Observatory} and the \emph{Chinese H$\alpha$ Solar Explorer}. This flare exhibits an increase of $\approx$6.4\% in the photospheric Fe \textsc{i} line at 6569.2\,\AA\ and {$\approx$3.2\%} in the nearby continuum. The continuum at 3600\,\AA\ also shows an enhancement of $\approx$4.7\%. The white-light brightening kernels are mainly located at the flare ribbons and co-spatial with nonthermal hard X-ray sources, which implies that the enhanced white-light emissions are related to nonthermal electron-beam heating. At the brightening kernels, the Fe \textsc{i} line displays an absorption profile that has a good Gaussian shape, with a redshift up to $\approx$1.7 km s$^{-1}$, while the H$\alpha$ line shows an emission profile though having a central reversal. The H$\alpha$ line profile also shows a red or blue asymmetry caused by plasma flows with a velocity of several to tens of km s$^{-1}$. It is interesting to find that the H$\alpha$ asymmetry is opposite at the conjugate footpoints. It is also found that the CHASE continuum increase seems to be related to the change of photospheric magnetic field. Our study provides comprehensive characteristics of a small white-light flare that help understand the energy release process of white-light flares., Comment: 23 pages, 6 figures, accepted by Solar Physics

Most existing data preprocessing is done at the CPU. Although some studies use techniques such as multi-processing and double buffering to accelerate CPU preprocessing, CPU computational speed and storage bandwidth still limit the processing speed. Other studies try to use intelligent data storage devices, such as computational storage devices, to complete data preprocessing instead of CPUs. The current studies use only one device to complete data preprocessing operations, which cannot fully overlap data preprocessing and accelerator computation time. To fully exploit the independence and high bandwidth of the novel CSD, this paper proposes an advanced, highly parallel dual-pronged data preprocessing algorithm (DDLP) that significantly improves the execution efficiency and computational overlap between heterogeneous devices. DDLP enables the CPU and CSD to start data preprocessing operations from both ends of the dataset separately. Meanwhile, we propose two adaptive dynamic selection strategies to make DDLP control the GPU to automatically read data from different sources. We achieve sufficient computational overlap between CSD data preprocessing and CPU preprocessing, GPU computation, and GPU data reading. In addition, DDLP leverages GPU Direct Storage technology to enable efficient SSD-to-GPU data transfer. DDLP reduces the usage of expensive CPU and DRAM resources, reduces the number of SSD-to-GPU data transfers, and improves the energy efficiency of preprocessing while reducing the overall preprocessing and training time. Extensive experimental results show that DDLP can improve learning speed by up to 23.5% on ImageNet Dataset while reducing energy consumption by 19.7% and CPU and DRAM usage by 37.6%. DDLP also improve learning speed by up to 27.6% on Cifar-10 Dataset.

Deep learning has recently demonstrated its excellent performance on the task of multi-view stereo (MVS). However, loss functions applied for deep MVS are rarely studied. In this paper, we first analyze existing loss functions' properties for deep depth based MVS approaches. Regression based loss leads to inaccurate continuous results by computing mathematical expectation, while classification based loss outputs discretized depth values. To this end, we then propose a novel loss function, named adaptive Wasserstein loss, which is able to narrow down the difference between the true and predicted probability distributions of depth. Besides, a simple but effective offset module is introduced to better achieve sub-pixel prediction accuracy. Extensive experiments on different benchmarks, including DTU, Tanks and Temples and BlendedMVS, show that the proposed method with the adaptive Wasserstein loss and the offset module achieves state-of-the-art performance.

Model compression has gained significant popularity as a means to alleviate the computational and memory demands of machine learning models. Each compression technique leverages unique features to reduce the size of neural networks. Although intuitively combining different techniques may enhance compression effectiveness, we find that the order in which they are combined significantly influences performance. To identify the optimal sequence for compressing neural networks, we propose the Order of Compression, a systematic and optimal sequence to apply multiple compression techniques in the most effective order. We start by building the foundations of the orders between any two compression approaches and then demonstrate inserting additional compression between any two compressions will not break the order of the two compression approaches. Based on the foundations, an optimal order is obtained with topological sorting. Validated on image-based regression and classification networks across different datasets, our proposed Order of Compression significantly reduces computational costs by up to 859 times on ResNet34, with negligible accuracy loss (-0.09% for CIFAR10) compared to the baseline model. We believe our simple yet effective exploration of the order of compression will shed light on the practice of model compression., Comment: 9 pages, 15 figures

The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas. However, domain-specific applications of such foundation models (e.g., in medicine) remain untouched or often at their very early stages. It will require an individual set of transfer learning and model adaptation techniques by further expanding and injecting these models with domain knowledge and data. The development of such technologies could be largely accelerated if the bundle of data, algorithms, and pre-trained foundation models were gathered together and open-sourced in an organized manner. In this work, we present OpenMEDLab, an open-source platform for multi-modality foundation models. It encapsulates not only solutions of pioneering attempts in prompting and fine-tuning large language and vision models for frontline clinical and bioinformatic applications but also building domain-specific foundation models with large-scale multi-modal medical data. Importantly, it opens access to a group of pre-trained foundation models for various medical image modalities, clinical text, protein engineering, etc. Inspiring and competitive results are also demonstrated for each collected approach and model in a variety of benchmarks for downstream tasks. We welcome researchers in the field of medical artificial intelligence to continuously contribute cutting-edge methods and models to OpenMEDLab, which can be accessed via https://github.com/openmedlab., Comment: Technical Report. Visit https://github.com/openmedlab for more details

The hexadecapole deformation ($\beta_4$) of the $^{238}$U nucleus has not been determined because its effect is overwhelmed by those from the nucleus' large quadrupole deformation ($\beta_2$) in nuclear electric transition measurements. In this Letter, we identify the nonlinear response of the hexadecapole anisotropy to ellipticity in relativistic $^{238}$U+ $^{238}$U collisions that is solely sensitive to $\beta_4$ and insensitive to $\beta_2$. We demonstrate this by state-of-the-art hydrodynamic calculations and discuss the prospects of discovering the $\beta_4$ of $^{238}$U in heavy-ion data at the Relativistic Heavy Ion Collider., Comment: 7 pages, 3 figures, published version