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Object localization is a hot issue in computer vision area, which aims to identify and determine the precise location of specific objects from image or video. Most existing object localization methods heavily rely on extensive labeled data, which are costly to annotate and constrain their applicability. Therefore, we propose a new Zero-Shot Object Localization (ZSOL) framework for addressing the aforementioned challenges. In the proposed framework, we introduce the Contrastive Language Image Pre-training (CLIP) module which could integrate visual and linguistic information effectively. Furthermore, we design a Text Self-Similarity Matching (TSSM) module, which could improve the localization accuracy by enhancing the representation of text features extracted by CLIP module. Hence, the proposed framework can be guided by prompt words to identify and locate specific objects in an image in the absence of labeled samples. The results of extensive experiments demonstrate that the proposed method could improve the localization performance significantly and establishes an effective benchmark for further research.

While thin film lithium niobate (TFLN) is known for efficient signal generation, on-chip signal amplification remains challenging from fully integrated optical communication circuits. Here we demonstrate the first continuous-wave-pump optical parametric amplification (OPA) using an x-cut domain-engineered TFLN waveguide, with high gain over the telecom band up to 13.9 dB, and test it for high signal-to-noise ratio signal amplification using a commercial optical communication module pair. Fabricated in wafer scale using common process as devices including modulators, this OPA device marks an important step in TFLN photonic integration.

Circumstellar OH maser lines are useful for studying the dynamics of the circumstellar envelope (CSE) around evolved stars. This study aims to identify CSEs around cold stars bf, which exhibit deviations from the spherical expansion, by comparing the velocity ranges of the OH main lines (1665/1667 MHz) with those of the satellite line (1612 MHz), using a database of circumstellar OH maser sources. We performed this comparison for 377 circumstellar OH maser sources. In addition, using infrared two-color diagrams, we examined the evolutionary stages and infrared properties of objects showing velocity excess (velocity excess means the detection of the main lines outside the velocity range of the satellite line). A periodicity analysis of the WISE light curves was also carried out. As a result of the velocity range comparison, eight circumstellar OH maser sources were found to exhibit velocity excess. The infrared colors of these objects match those of post-AGB stars. Periodic variations were observed in the WISE light curves of five of these eight objects. The results suggest that examining velocity excess of the main lines relative to the satellite line is scientifically significant because mainline masers probe the CSE dynamics over a broader range of evolutionary stages compared to the 22.235 GHz H${_2}$O maser line. Additionally, during the post-AGB phase, the emission regions of the mainline and 22.235 GHz H${_2}$O masers may overlap in a CSE, whereas they originate from different regions during the AGB phase., Comment: 48 pages, 24 figures, Accepted for publication in ApJ

The concept of skyrmions was introduced as early as the 1960s by Tony Skyrme. The topologically protected configuration embedded in skyrmions has prompted some investigations into their fundamental properties and versatile applications, sparking interest and guiding ongoing development. The topological protection associated with skyrmions was initially observed in systems with interactions. It is widely believed that skyrmions are stable yet relevant confirmation and empirical research remains limited. A pertinent question is whether skyrmion configurations formed by single-particle wave functions also exhibit topological stability. In this study, we affirm this hypothesis by investigating the effects of local decoherence. We analytically and numerically demonstrate the topological resilience of skyrmions and occurrence of transition points of skyrmion numbers in local decoherence of three typical decoherence channels. On the other hand, we show that these qualities are independent of the initial state. From the numerical results, we verify that inhomogeneous but continuous decoherence channels also adhere to the same behaviors and hold topological stability of skyrmions as homogeneous decoherence channels. These properties of skyrmions contribute to further applications in various areas including communication and imaging., Comment: 19 pages

Spontaneous imbibition (SI) is a process by which liquid is drawn into partially saturated porous media by capillary forces, relevant for subsurface processes like underground fluid storage and withdrawal. Accurate modeling and scaling of counter-current SI have long been challenging. In this study, we proposed a generalized fractional flow theory (GFFT) using the Hausdorff fractal derivative, combined with non-Boltzmann scaling. The model links imbibition distance to time through the power law exponent alpha/2, where alpha is the fractal index (0 < alpha < 2 in this study). We applied the GFFT to various experimental and stimulated datasets of both porous and fractured media, finding that alpha varied with factors such as contact angle (of the imbibing fluid), dynamic viscosity, pore structure, and fracture properties. By analyzing SI data from sandstones, diatomite, carbonate, and synthetic porous media, we demonstrated that the non-Boltzmann scaling provided a better collapse of the SI data than the traditional Boltzmann approach alpha = 1), with alpha values ranging from 0.88 to 1.54. These deviations illustrate the model's adaptability to different porous materials. Using the GFFT, we expect to better predict fluid imbibition rates when properties like porosity, permeability, initial and maximum saturations, viscosity, and wettability are known, offering a more accurate alternative to traditional models., Comment: 7 figures and 1 table

This work considers an uplink asynchronous massive random access scenario in which a large number of users asynchronously access a base station equipped with multiple receive antennas. The objective is to alleviate the problem of massive collision due to the limited number of orthogonal preambles of an access scheme in which user activity detection is performed. We propose a user activity detection with delay-calibration (UAD-DC) algorithm and investigate the benefits of oversampling for the estimation of continuous time delays at the receiver. The proposed algorithm iteratively estimates time delays and detects active users by noting that the collided users can be identified through accurate estimation of time delays. Due to the sporadic user activity patterns, the user activity detection problem can be formulated as a compressive sensing (CS) problem, which can be solved by a modified Turbo-CS algorithm under the consideration of correlated noise samples resulting from oversampling. A sliding-window technique is applied in the proposed algorithm to reduce the overall computational complexity. Moreover, we propose a new design of the pulse shaping filter by minimizing the Bayesian Cram\'er-Rao bound of the detection problem under the constraint of limited spectral bandwidth. Numerical results demonstrate the efficacy of the proposed algorithm in terms of the normalized mean squared error of the estimated channel, the probability of misdetection and the successful detection ratio., Comment: Submitted to IEEE Transactions on Vehicular Technology

A very robust and efficient hybrid analytic-numerical Fock build, aMECP+aCOSx, has been developed for accelerating HF/DFT calculations. The essential idea is to extract those portions of the Fock matrix that can readily be evaluated analytically, so as to minimize numerical noises arising from the semi-numerical and numerical integrations. As a result, the combination of aMECP with a medium grid and aCOSx with a coarse grid is already sufficient to achieve an accuracy of less than 1{\mu}Eh/atom in total energies. The acceleration of aMECP+aCOSx over the analytic Fock build is already seen in calculations of small molecular systems and is more enhanced in calculations of large molecules with extended basis sets., Comment: 34 pages, 6 figures

The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98\% duty cycle. In this work, we report the detection of two outbursts of the Fanaroff-Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between November 2022 and January 2023 with statistical significance of 5.2~$\sigma$ and 8.3~$\sigma$. The observed spectral energy distribution in the range from 500 GeV to 3 TeV is fitted by a power-law with a best-fit spectral index of $\alpha=-3.37\pm0.52$ and $-3.35\pm0.29$, respectively. The outburst flux above 0.5~TeV was ($4.55\pm 4.21)\times~10^{-11}~\rm cm^{-2}~s^{-1}$ and ($3.45\pm 1.78)\times~10^{-11}~\rm cm^{-2}~s^{-1}$, corresponding to 60\%, 45\% of Crab Nebula flux. Variation analysis reveals the variability time-scale of days at the TeV energy band. A simple test by one-zone synchrotron self-Compton model reproduces the data in the gamma-ray band well., Comment: 11 pages, 8 figures, 3 tables

Physics-Informed Neural Operators provide efficient, high-fidelity simulations for systems governed by partial differential equations (PDEs). However, most existing studies focus only on multi-scale, multi-physics systems within a single spatial region, neglecting the case with multiple interconnected sub-regions, such as gas and thermal systems. To address this, this paper proposes a Physics-Informed Partitioned Coupled Neural Operator (PCNO) to enhance the simulation performance of such networks. Compared to the existing Fourier Neural Operator (FNO), this method designs a joint convolution operator within the Fourier layer, enabling global integration capturing all sub-regions. Additionally, grid alignment layers are introduced outside the Fourier layer to help the joint convolution operator accurately learn the coupling relationship between sub-regions in the frequency domain. Experiments on gas networks demonstrate that the proposed operator not only accurately simulates complex systems but also shows good generalization and low model complexity.

It has been shown that dipolar Bose-Einstein condensates that are tightly trapped along the polarization direction can feature a rich phase diagram. In this paper we show that finite temperature can assist in accessing parts of the phase diagram that otherwise appear hard to realise due to excessively large densities and number of atoms being required. These include honeycomb and stripe phases both in the thermodynamic limit as well as with a finite extent using both variational and numerical calculations. We account for the effect of thermal fluctuations by means of Bogoliubov theory employing the local density approximation. Furthermore, we exhibit real-time evolution simulations leading to such states. We find that finite temperatures can lead to a significant decrease in the necessary particle number and density that might ultimately pave a route for future experimental realisations., Comment: 7 pages, 6 figures

Fragmentation and evolution for the molecular shells of the compact HII regions are less explored compared to their evolved counterparts. We map nine compact HII regions with a typical diameter of 0.4 pc that are surrounded by molecular shells traced by CCH. Several to a dozen dense gas fragments probed by H13CO+ are embedded in these molecular shells. These gas fragments, strongly affected by the HII region, have a higher surface density, mass, and turbulence than those outside the shells but within the same pc-scale natal clump. These features suggest that the shells swept up by the early HII regions can enhance the formation of massive dense structures that may host the birth of higher-mass stars. We examine the formation of fragments and find that fragmentation of the swept-up shell is unlikely to occur in these early HII regions, by comparing the expected time scale of shell fragmentation with the age of HII region. We propose that the appearance of gas fragments in these shells is probably the result of sweeping up pre-existing fragments into the molecular shell that has not yet fragmented. Taken together, this work provides a basis for understanding the interplay of star-forming sites with an intricate environment containing ionization feedback such as those observed in starburst regions., Comment: Accepted by MNRAS, 24 pages, 14 figures

Domain generalization (DG) methods aim to maintain good performance in an unseen target domain by using training data from multiple source domains. While success on certain occasions are observed, enhancing the baseline across most scenarios remains challenging. This work introduces a simple yet effective framework, dubbed learning from multiple experts (LFME), that aims to make the target model an expert in all source domains to improve DG. Specifically, besides learning the target model used in inference, LFME will also train multiple experts specialized in different domains, whose output probabilities provide professional guidance by simply regularizing the logit of the target model. Delving deep into the framework, we reveal that the introduced logit regularization term implicitly provides effects of enabling the target model to harness more information, and mining hard samples from the experts during training. Extensive experiments on benchmarks from different DG tasks demonstrate that LFME is consistently beneficial to the baseline and can achieve comparable performance to existing arts. Code is available at~\url{https://github.com/liangchen527/LFME}., Comment: Accepted by NeurIPS 2024

Self-supervised contrastive learning heavily relies on the view variance brought by data augmentation, so that it can learn a view-invariant pre-trained representation. Beyond increasing the view variance for contrast, this work focuses on improving the diversity of training data, to improve the generalization and robustness of the pre-trained models. To this end, we propose a unified framework to conduct data augmentation in the feature space, known as feature augmentation. This strategy is domain-agnostic, which augments similar features to the original ones and thus improves the data diversity. We perform a systematic investigation of various feature augmentation architectures, the gradient-flow skill, and the relationship between feature augmentation and traditional data augmentation. Our study reveals some practical principles for feature augmentation in self-contrastive learning. By integrating feature augmentation on the instance discrimination or the instance similarity paradigm, we consistently improve the performance of pre-trained feature learning and gain better generalization over the downstream image classification and object detection task., Comment: IJCNN 2024

The search for a giant Nernst effect beyond conventional mechanisms offers advantages for developing advanced thermoelectric devices and understanding charge-entropy conversion. Here, we study the Seebeck and Nernst effects of HfTe5 over a wide range of magnetic fields. By tracking the unusual magneto-thermoelectric responses, we reveal two magnetic-field-driven phase transitions proposed for weak topological insulators: the gap-closing transition of the zeroth Landau bands and the topological Lifshitz transition. After the magnetic fields exceed approximately ten times the quantum limit, we observe that the Nernst signal no longer varies with the fields, forming a plateau with a remarkably large value, reaching up to 50 {\mu}V/K at 2 K. We theoretically explain the giant Nernst plateau as a unique signature of the ideal 1D Weyl phase formed in such high fields. Our findings expand the understanding of ideal Weyl physics and open new avenues for realizing novel thermoelectric effects without fundamental constraints.

Fast radio bursts (FRBs) are transient radio bursts of extragalactic origin characterized by millisecond durations and high luminosities. We report on observations of FRB 20240114A conducted with the Robert C. Byrd Green Bank Telescope (GBT) at frequencies ranging from 720 to 920 MHz. A total of 429 bursts were detected, with a single observation recording 359 bursts over 1.38 hours, corresponding to a burst rate of 260 bursts per hour. The average rotation measures (RMs) were $349.2 \pm 1.0$ rad m$^{-2}$ on February 23, 2024, and $360.4 \pm 0.4$ rad m$^{-2}$ on March 1, 2024. Of the 297 bursts with detected RMs, 72% have a linear polarization fraction greater than 90%, and 14% exhibit circular polarization with a signal-to-noise ratio $> 5$. Our sample also displayed polarization angle swings. We compare the linear polarization of FRB 20240114A with that of FRB 20201124A, FRB 20220912A, and non-repeating FRBs. The mean linear polarization fraction for non-repeating FRBs is 58%. In contrast, the mean linear polarization fraction for the three repeating FRBs is 94%, which is significantly higher than that of the non-repeating FRBs. Under the T-test, the three repeating FRBs have similar linear polarization distributions, but these distributions differ from those of the non-repeating FRBs. This suggests that non-repeating FRBs may have different emission mechanisms or are subject to depolarization., Comment: 42 pages, 5 figures. arXiv admin note: text overlap with arXiv:2304.14671

We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the locations of middle-aged (62.4~\rm kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM2A data of live 1216 days. A significant excess of \gray induced showers is observed both by WCDA in energy bands of 1-25~\rm TeV and KM2A in energy bands of $>$ 25~\rm TeV with 7.3 $\sigma$ and 13.5 $\sigma$, respectively. The best-fit position derived through WCDA data is R.A. = 42.06$^\circ \pm$ 0.12$^\circ$ and Dec. = 60.24$^\circ \pm $ 0.13$^\circ$ with an extension of 0.69$^\circ\pm$0.15$^\circ$ and that of the KM2A data is R.A.= 42.29$^\circ \pm $ 0.13$^\circ$ and Dec. = 60.38$^\circ \pm$ 0.07$^\circ$ with an extension of 0.37$^\circ\pm$0.07$^\circ$. No clear extended multiwavelength counterpart of this LHAASO source has been found from the radio band to the GeV band. The most plausible explanation of the VHE \gray emission is the inverse Compton process of highly relativistic electrons and positrons injected by the pulsar. These electrons/positrons are hypothesized to be either confined within the pulsar wind nebula or to have already escaped into the interstellar medium, forming a pulsar halo., Comment: 12 pages, 10 figures, Accepted by Sci. China-Phys. Mech. Astron

In this paper, we propose Img2CAD, the first approach to our knowledge that uses 2D image inputs to generate CAD models with editable parameters. Unlike existing AI methods for 3D model generation using text or image inputs often rely on mesh-based representations, which are incompatible with CAD tools and lack editability and fine control, Img2CAD enables seamless integration between AI-based 3D reconstruction and CAD software. We have identified an innovative intermediate representation called Structured Visual Geometry (SVG), characterized by vectorized wireframes extracted from objects. This representation significantly enhances the performance of generating conditioned CAD models. Additionally, we introduce two new datasets to further support research in this area: ABC-mono, the largest known dataset comprising over 200,000 3D CAD models with rendered images, and KOCAD, the first dataset featuring real-world captured objects alongside their ground truth CAD models, supporting further research in conditioned CAD model generation.

The detection of fast radio bursts (FRBs) in radio astronomy is a complex task due to the challenges posed by radio frequency interference (RFI) and signal dispersion in the interstellar medium. Traditional search algorithms are often inefficient, time-consuming, and generate a high number of false positives. In this paper, we present DRAFTS, a deep learning-based radio fast transient search pipeline. DRAFTS integrates object detection and binary classification techniques to accurately identify FRBs in radio data. We developed a large, real-world dataset of FRBs for training deep learning models. The search test on FAST real observation data demonstrates that DRAFTS performs exceptionally in terms of accuracy, completeness, and search speed. In the re-search of FRB 20190520B observation data, DRAFTS detected more than three times the number of bursts compared to Heimdall, highlighting the potential for future FRB detection and analysis., Comment: 20 pages, 10 figures, submitted

The operation and management of the metro system in urban areas rely on accurate predictions of future passenger flow. While using all the available information can potentially improve on the accuracy of the flow prediction, there has been little attention to the hierarchical relationship between the type of tickets collected from the passengers entering/exiting a station and its resulting passenger flow. To this end, we propose a novel Integrative Prediction Framework with the Hierarchical Message-Passing Graph Neural Network (IPF-HMGNN). The proposed framework consists of three components: initial prediction, task judgment and hierarchical coordination modules. Using the Wuxi, China metro network as an example, we study two prediction approaches (i) traditional prediction approach where the model directly predicts passenger flow at the station, and (ii) hierarchical prediction approach where the prediction of ticket type and station passenger flow are performed simultaneously considering the hierarchical constraints (i.e., the sum of predicted passenger flow per ticket type equals the predicted station aggregated passenger flow). Experimental results indicate that in the traditional prediction approach, our IPF-HMGNN can significantly reduce the mean absolute error (MAE) and root mean square error (RMSE) of the GNN prediction model by 49.56% and 53.88%, respectively. In the hierarchical prediction approach, IPF-HMGNN can achieve a maximum reduction of 35.32% in MAE and 36.18% in RMSE, while satisfying the hierarchical constraint.

We investigate quantum-enhanced metrology in a triple point criticality and discover that quantum criticality can not always enhance measuring precision. We have developed suitable adiabatic evolution protocols approaching a final point around the triple point to effectively restrain excitations, which could accelerate the adiabatic evolutions and lead to an exponential super-Heisenberg scaling. This scaling behavior is quite valuable in practical parameter estimating experiments with limited coherence time. The super-Heisenberg scaling will degrade into a sub-Heisenberg scaling if the adiabatic parameter modulations adopted can not reduce excitations and weaken the slowing down effect. Additionally, a feasible experimental scheme is also suggested to achieve the anticipated exponential super-Heisenberg scaling. Our findings strongly indicate that criticality-enhanced metrology can indeed significantly enhance measuring precision to a super-Heisenberg scaling when combining a triple point and beneficial parameter modulations in the adiabatic evolution, which will be conducive to the exploration of other super-Heisenberg scaling and their applications.

Over 700 bright millisecond-duration radio transients, known as Fast Radio Bursts (FRBs), have been identified to date. Nevertheless, the origin of FRBs remains unknown. The two repeating FRBs (FRB 20121102A and FRB 20190520B) have been verified to be associated with persistent radio sources (PRSs), making them the best candidates to study the nature of FRBs. Monitoring the variability in PRSs is essential for understanding their physical nature. We conducted 22 observations of the PRSs linked to FRB 20121102A and FRB 20190520B using the Karl G. Jansky Very Large Array (VLA), to study their variability. We have observed significant flux variability for the PRSs of FRB 20121102A and FRB 20190520B, with a confidence level exceeding 99.99%, based on the observations covering the longest timescale recorded to date. The observed variability of the two PRSs exhibits no significant difference in amplitude across both short and long timescales. We found that the radio-derived star formation rates of the two FRB hosts are significantly higher than those measured by the optical $H_{\alpha}$ emissions, indicating that their host galaxies are highly obscured or most radio emissions are not from star formation processes. The observed timescale of PRS flux evolution constrained the magnetic field of FRB 20121102A with $B_\parallel\gtrsim1~{\rm mG}$ and FRB 20190520B with $B_\parallel\gtrsim0.1~{\rm mG}$., Comment: 16 pages, 7 figures, accepted by ApJ

We explore near-field thermal radiation transport in nanoparticles embedded within a multilayer slab structure, focusing on dynamic modulation of heat flux via cavity interactions. Our findings reveal that by tuning the distance between reflectors and nanoparticles, thermal transport can be significantly suppressed or enhanced, driven by selective excitation of surface modes within the cavity. By precisely adjusting inter-slab gaps, we achieve multi-order control over thermal flux while maintaining stability across a broad range of configurations. Notably, internal slab arrangement plays a pivotal role, with compact designs yielding the most pronounced effects. This work unveils a novel mechanism for manipulating near-field heat transfer, with exciting potential for nanoscale thermal management and thermal sensing technologies.

This paper presents a novel framework for converting 2D videos to immersive stereoscopic 3D, addressing the growing demand for 3D content in immersive experience. Leveraging foundation models as priors, our approach overcomes the limitations of traditional methods and boosts the performance to ensure the high-fidelity generation required by the display devices. The proposed system consists of two main steps: depth-based video splatting for warping and extracting occlusion mask, and stereo video inpainting. We utilize pre-trained stable video diffusion as the backbone and introduce a fine-tuning protocol for the stereo video inpainting task. To handle input video with varying lengths and resolutions, we explore auto-regressive strategies and tiled processing. Finally, a sophisticated data processing pipeline has been developed to reconstruct a large-scale and high-quality dataset to support our training. Our framework demonstrates significant improvements in 2D-to-3D video conversion, offering a practical solution for creating immersive content for 3D devices like Apple Vision Pro and 3D displays. In summary, this work contributes to the field by presenting an effective method for generating high-quality stereoscopic videos from monocular input, potentially transforming how we experience digital media., Comment: 11 pages, 10 figures

We present $\textit{Learn2Aggregate}$, a machine learning (ML) framework for optimizing the generation of Chv\'atal-Gomory (CG) cuts in mixed integer linear programming (MILP). The framework trains a graph neural network to classify useful constraints for aggregation in CG cut generation. The ML-driven CG separator selectively focuses on a small set of impactful constraints, improving runtimes without compromising the strength of the generated cuts. Key to our approach is the formulation of a constraint classification task which favours sparse aggregation of constraints, consistent with empirical findings. This, in conjunction with a careful constraint labeling scheme and a hybrid of deep learning and feature engineering, results in enhanced CG cut generation across five diverse MILP benchmarks. On the largest test sets, our method closes roughly $\textit{twice}$ as much of the integrality gap as the standard CG method while running 40$% faster. This performance improvement is due to our method eliminating 75% of the constraints prior to aggregation., Comment: 12 pages, 8 figures

The DArk Matter Particle Explorer (DAMPE) is a satellite-borne particle detector for measurements of high-energy cosmic rays and {\gamma}-rays. DAMPE has been operating smoothly in space for more than 8 years since launch on December 17, 2015. The trigger logic of DAMPE is designed according to the deposited energy information recorded by the calorimeter. The precise calibration of the trigger thresholds and their long-term evolutions are very important for the scientific analysis of DAMPE. In this work, we develop a new method for the threshold calibration, considering the influence from the electronic noise, and obtain the long-term evolutions of the trigger thresholds. The average increase rate of the trigger thresholds for the first 4 layers of the calorimeter is found to be about 0.9% per year, resulting in variations of the high-energy trigger efficiency of cosmic ray electrons by about -5% per year at 2 GeV and less than about -0.05% above 30 GeV., Comment: 11 pages, 11 figures

Despite significant advancements in monocular depth estimation for static images, estimating video depth in the open world remains challenging, since open-world videos are extremely diverse in content, motion, camera movement, and length. We present DepthCrafter, an innovative method for generating temporally consistent long depth sequences with intricate details for open-world videos, without requiring any supplementary information such as camera poses or optical flow. DepthCrafter achieves generalization ability to open-world videos by training a video-to-depth model from a pre-trained image-to-video diffusion model, through our meticulously designed three-stage training strategy with the compiled paired video-depth datasets. Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets. We also propose an inference strategy that processes extremely long videos through segment-wise estimation and seamless stitching. Comprehensive evaluations on multiple datasets reveal that DepthCrafter achieves state-of-the-art performance in open-world video depth estimation under zero-shot settings. Furthermore, DepthCrafter facilitates various downstream applications, including depth-based visual effects and conditional video generation., Comment: Project webpage: https://depthcrafter.github.io

Metro operation management relies on accurate predictions of passenger flow in the future. This study begins by integrating cross-city (including source and target city) knowledge and developing a short-term passenger flow prediction framework (METcross) for the metro. Firstly, we propose a basic framework for modeling cross-city metro passenger flow prediction from the perspectives of data fusion and transfer learning. Secondly, METcross framework is designed to use both static and dynamic covariates as inputs, including economy and weather, that help characterize station passenger flow features. This framework consists of two steps: pre-training on the source city and fine-tuning on the target city. During pre-training, data from the source city trains the feature extraction and passenger flow prediction models. Fine-tuning on the target city involves using the source city's trained model as the initial parameter and fusing the feature embeddings of both cities to obtain the passenger flow prediction results. Finally, we tested the basic prediction framework and METcross framework on the metro networks of Wuxi and Chongqing to experimentally analyze their efficacy. Results indicate that the METcross framework performs better than the basic framework and can reduce the Mean Absolute Error and Root Mean Squared Error by 22.35% and 26.18%, respectively, compared to single-city prediction models.

3D models, particularly AI-generated ones, have witnessed a recent surge across various industries such as entertainment. Hence, there is an alarming need to protect the intellectual property and avoid the misuse of these valuable assets. As a viable solution to address these concerns, we rigorously define the novel task of automated 3D visible watermarking in terms of two competing aspects: watermark quality and asset utility. Moreover, we propose a method of embedding visible watermarks that automatically determines the right location, orientation, and number of watermarks to be placed on arbitrary 3D assets for high watermark quality and asset utility. Our method is based on a novel rigid-body optimization that uses back-propagation to automatically learn transforms for ideal watermark placement. In addition, we propose a novel curvature-matching method for fusing the watermark into the 3D model that further improves readability and security. Finally, we provide a detailed experimental analysis on two benchmark 3D datasets validating the superior performance of our approach in comparison to baselines. Code and demo are available., Comment: Accepted to WACV2025

Multimodal data has become a crucial element in the realm of big data analytics, driving advancements in data exploration, data mining, and empowering artificial intelligence applications. To support high-quality retrieval for these cutting-edge applications, a robust data retrieval platform should meet the requirements for transparent data storage, rich hybrid queries, effective feature representation, and high query efficiency. However, among the existing platforms, traditional schema-on-write systems, multi-model databases, vector databases, and data lakes, which are the primary options for multimodal data retrieval, are difficult to fulfill these requirements simultaneously. Therefore, there is an urgent need to develop a more versatile multimodal data retrieval platform to address these issues. In this paper, we introduce a Multimodal Data Retrieval Platform with Query-aware Feature Representation and Learned Index based on Data Lake (MQRLD). It leverages the transparent storage capabilities of data lakes, integrates the multimodal open API to provide a unified interface that supports rich hybrid queries, introduces a query-aware multimodal data feature representation strategy to obtain effective features, and offers high-dimensional learned indexes to optimize data query. We conduct a comparative analysis of the query performance of MQRLD against other methods for rich hybrid queries. Our results underscore the superior efficiency of MQRLD in handling multimodal data retrieval tasks, demonstrating its potential to significantly improve retrieval performance in complex environments. We also clarify some potential concerns in the discussion., Comment: 36 pages, 28 figures

Customized video generation aims to generate high-quality videos guided by text prompts and subject's reference images. However, since it is only trained on static images, the fine-tuning process of subject learning disrupts abilities of video diffusion models (VDMs) to combine concepts and generate motions. To restore these abilities, some methods use additional video similar to the prompt to fine-tune or guide the model. This requires frequent changes of guiding videos and even re-tuning of the model when generating different motions, which is very inconvenient for users. In this paper, we propose CustomCrafter, a novel framework that preserves the model's motion generation and conceptual combination abilities without additional video and fine-tuning to recovery. For preserving conceptual combination ability, we design a plug-and-play module to update few parameters in VDMs, enhancing the model's ability to capture the appearance details and the ability of concept combinations for new subjects. For motion generation, we observed that VDMs tend to restore the motion of video in the early stage of denoising, while focusing on the recovery of subject details in the later stage. Therefore, we propose Dynamic Weighted Video Sampling Strategy. Using the pluggability of our subject learning modules, we reduce the impact of this module on motion generation in the early stage of denoising, preserving the ability to generate motion of VDMs. In the later stage of denoising, we restore this module to repair the appearance details of the specified subject, thereby ensuring the fidelity of the subject's appearance. Experimental results show that our method has a significant improvement compared to previous methods., Comment: project page: https://customcrafter.github.io/

The classic house allocation problem involves assigning $m$ houses to $n$ agents based on their utility functions, ensuring each agent receives exactly one house. A key criterion in these problems is satisfying fairness constraints such as envy-freeness. We extend this problem by considering agents with arbitrary weights, focusing on the concept of weighted envy-freeness, which has been extensively studied in fair division. We present a polynomial-time algorithm to determine whether weighted envy-free allocations exist and, if so, to compute one. Since weighted envy-free allocations do not always exist, we also investigate the potential of achieving such allocations through the use of subsidies. We provide several characterizations for weighted envy-freeable allocations (allocations that can be turned weighted envy-free by introducing subsidies) and show that they do not always exist, which is different from the unweighted setting. Furthermore, we explore the existence of weighted envy-freeable allocations in specific scenarios and outline the conditions under which they exist., Comment: 17 pages, 4 figures

This work evaluates the passivation effectiveness of ultrathin nitride layers (SiNx, AlN, TiN) deposited via atomic layer deposition on CdTe/MgCdTe double heterostructures for solar cell applications. Time-resolved photoluminescence and photoluminescence measurements revealed enhanced carrier lifetimes and reduced surface recombination, indicating improved passivation effectiveness. These results underscore the potential of SiNx as a promising passivation material to improve the efficiency of CdTe solar cells., Comment: 16 pages, 4 figures

With generative models producing high quality images that are indistinguishable from real ones, there is growing concern regarding the malicious usage of AI-generated images. Imperceptible image watermarking is one viable solution towards such concerns. Prior watermarking methods map the image to a latent space for adding the watermark. Moreover, Latent Diffusion Models (LDM) generate the image in the latent space of a pre-trained autoencoder. We argue that this latent space can be used to integrate watermarking into the generation process. To this end, we present LaWa, an in-generation image watermarking method designed for LDMs. By using coarse-to-fine watermark embedding modules, LaWa modifies the latent space of pre-trained autoencoders and achieves high robustness against a wide range of image transformations while preserving perceptual quality of the image. We show that LaWa can also be used as a general image watermarking method. Through extensive experiments, we demonstrate that LaWa outperforms previous works in perceptual quality, robustness against attacks, and computational complexity, while having very low false positive rate. Code is available here., Comment: Accepted to ECCV 2024

We apply the method of QCD sum rules to study the $q c \bar q \bar c$ tetraquark states with the exotic quantum number $J^{PC} = 3^{-+}$, and extract the mass of the lowest-lying state to be ${4.49^{+0.45}_{-0.41}}$ GeV. To construct the relevant tetraquark currents we need to explicitly add the covariant derivative operator. Our systematic analysis of these interpolating currents indicates that: a) this state readily decays into the $P$-wave $[\rho J/\psi] / [\omega J/\psi ]$ channel but not into the $ [\rho \chi_{c2}]/[\omega \chi_{c2}]/[J/\psi f_2(1270)]$ channels, and b) it readily decays into the $[D^* \bar D_2^*]$ channel but not into the $P$-wave $[D^* \bar D^*]$ channel., Comment: 13 pages, 4 figures, revised version to be published in PRD

Charge-order states of broken symmetry, such as charge density wave (CDW), are able to induce exceptional physical properties, however, the precise understanding of the underlying physics is still elusive. Here, we combine fluctuational electrodynamics and density functional theory to reveal an unconventional thermophotonic effect in CDW-bearing TiSe$_2$, referred to as thermophotonic-CDW ($tp$-CDW). The interplay of plasmon polariton and CDW electron excitations give rise to an anomalous negative temperature dependency in thermal photons transport, offering an intuitive fingerprint for a transformation of the electron order. Additionally, the demonstrated nontrivial features of $tp$-CDW transition hold promise for a controllable manipulation of heat flow, which could be extensively utilized in various fields such as thermal science and electron dynamics, as well as in next-generation energy devices., Comment: 7 pages, 4 figures

We present the first detection of the ground-state OH emission line at 1612 MHz toward the prototypical carbon-rich planetary nebula (PN) NGC 7027, utilizing the newly installed ultra-wideband (UWB) receiver of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). This emission is likely to originate from the interface of the neutral shell and the ionized region. The other three ground-state OH lines at 1665, 1667, and 1721 MHz are observed in absorption and have velocities well matched with that of HCO$^+$ absorption. We infer that the OH absorption is from the outer shell of NGC 7027, although the possibility that they are associated with a foreground cloud cannot be completely ruled out. All the OH lines exhibit a single blue-shifted component with respect to the central star. The formation of OH in carbon-rich environments might be via photodissociation-induced chemical processes. Our observations offer significant constraints for chemical simulations, and they underscore the potent capability of the UWB receiver of FAST to search for nascent PNe., Comment: 17 pages, 3 figures, accepted for publication in ApJ

Creating crystal bilayers twisted with respect to each other would lead to large periodic supercell structures, which can support a wide range of novel electron correlated phenomena, where the full understanding is still under debate. Here, we propose a new scheme to realize a nonlinearity-induced dynamical self-organized twisted-bilayer lattice in an atomic Bose-Einstein condensate (BEC). The key idea here is to utilize the nonlinear effect from the intrinsic atomic interactions to couple different layers and induce a dynamical self-organized supercell structure, dramatically distinct from the conventional wisdom to achieve the static twisted-bilayer lattices. To illustrate that, we study the dynamics of a two-component BEC and show that the nonlinear interaction effect naturally emerged in the Gross-Pitaevskii equation of interacting bosonic ultracold atoms can dynamically induce both periodic (commensurable) and aperiodic (incommensurable) moir\'{e} structures. One of the interesting moir\'{e} phenomena, i.e., the flat-band physics, is shown through investigating the dynamics of the wave packet of BEC. Our proposal can be implemented using available state-of-the-art experimental techniques and reveal a profound connection between the nonlinearity and twistronics in cold atom quantum simulators., Comment: 6 pages, 5 figures

Orbital angular momentum (OAM), due to its theoretically orthogonal and unbounded helical phase index, has been utilized as an independent physical degree of freedom for ultrahigh-capacity information encryption. However, the imaging distance of an OAM hologram is typically inflexible and determined by the focal length of the Fourier transform lens placed behind the hologram. Here, 3D orbital angular momentum holography is proposed and implemented. The Fourier transform between the holographic plane and imaging plane is performed by superimposing Fresnel zone plates (FZP) onto the computer-generated holograms (CGH). The CGH is binarized and fabricated on the birefringence lithium niobate crystal by femtosecond laser micromachining. Experimental verification demonstrates the feasibility of the encoding method. Moreover, by superimposing FZPs with different focal lengths into various OAM channels, OAM-multiplexing holograms are constructed. Target images are separately projected to different planes, thereby enabling 3D multi-plane holographic imaging with low crosstalk. The interval between adjacent imaging planes can be uniform and minimal, free from depth of field constraints, thus achieving high longitudinal resolution. This work achieves OAM holography in a more compact manner and further expands its applicability.

With the advancement of artificial intelligence technology, the automation of network management, also known as Autonomous Driving Networks (ADN), is gaining widespread attention. The network management has shifted from traditional homogeneity and centralization to heterogeneity and decentralization. Multi-agent deep reinforcement learning (MADRL) allows agents to make decisions based on local observations independently. This approach is in line with the needs of automation and has garnered significant attention from academia and industry. In a distributed environment, information interaction between agents can effectively address the non-stationarity problem of multiple agents and promote cooperation. Therefore, in this survey, we first examined the application of MADRL in network management, including specific application fields such as traffic engineering, wireless network access, power control, and network security. Then, we conducted a detailed analysis of communication behavior between agents, including communication schemes, communication content construction, communication object selection, message processing, and communication constraints. Finally, we discussed the open issues and future research directions of agent communication in MADRL for future network management and ADN applications.

In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement. Despite the significant training costs, maintaining consistency of content between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose a novel formulation that considers both visual quality and consistency of content. Consistency of content is ensured by a proposed loss function that maintains the structure of the input, while visual quality is improved by utilizing the denoising process of pretrained diffusion models. To address the formulated optimization problem, we have developed a plug-and-play noise optimization strategy, referred to as Noise Calibration. By refining the initial random noise through a few iterations, the content of original video can be largely preserved, and the enhancement effect demonstrates a notable improvement. Extensive experiments have demonstrated the effectiveness of the proposed method., Comment: ECCV 2024, Project Page: https://yangqy1110.github.io/NC-SDEdit/, Code Repo: https://github.com/yangqy1110/NC-SDEdit/

In this paper, we exhibit non-uniqueness of Leray weak solutions of the forced magnetohydrodynamic (MHD for short) equations. Similar to the solutions constructed in \cite{ABC2}, we first find a special steady solution of ideal MHD equations whose linear unstability was proved in \cite{Lin}. It is possible to perturb the unstable scenario of ideal MHD to 3D viscous and resistive MHD equations, which can be regarded as the first unstable "background" solution. Our perturbation argument is based on the spectral theoretic approach \cite{Kato}. The second solution we would construct is a trajectory on the unstable manifold associated to the unstable steady solution. It is worth noting that these solutions live precisely on the borderline of the known well-posedness theory., Comment: 23pp. arXiv admin note: text overlap with arXiv:2310.10075, arXiv:2112.03116 by other authors

The dispatch optimization of coal mine integrated energy system is challenging due to high dimensionality, strong coupling constraints, and multiobjective. Existing constrained multiobjective evolutionary algorithms struggle with locating multiple small and irregular feasible regions, making them inaplicable to this problem. To address this issue, we here develop a multitask evolutionary algorithm framework that incorporates the dispatch correlated domain knowledge to effectively deal with strong constraints and multiobjective optimization. Possible evolutionary multitask construction strategy based on complex constraint relationship analysis and handling, i.e., constraint coupled spatial decomposition, constraint strength classification and constraint handling technique, is first explored. Within the multitask evolutionary optimization framework, two strategies, i.e., an elite guided knowledge transfer by designing a special crowding distance mechanism to select dominant individuals from each task, and an adaptive neighborhood technology based mutation to effectively balance the diversity and convergence of each optimized task for the differential evolution algorithm, are further developed. The performance of the proposed algorithm in feasibility, convergence, and diversity is demonstrated in a case study of a coal mine integrated energy system by comparing with CPLEX solver and seven constrained multiobjective evolutionary algorithms.

The beyond mean-field physics due to quantum fluctuations is often described by the Lee-Huang-Yang (LHY) correction, which can be approximately written as a simple analytical expression in terms of the mean-field employing local density approximation. This model has proven to be very successful in predicting the dynamics in dipolar Bose-Einstein condensates both qualitatively and quantitatively. Yet, a small deviation between experimental results and the theoretical prediction has been observed when comparing experiment and theory of the phase boundary of a free-space quantum droplet. For this reason, we revisit the theoretical description of quantum fluctuations in dipolar quantum gases. We study alternative cutoffs, compare them to experimental results and discuss limitations., Comment: 10 pages, 4 figures

This paper introduces a novel privacy-preservation framework named PFID for LLMs that addresses critical privacy concerns by localizing user data through model sharding and singular value decomposition. When users are interacting with LLM systems, their prompts could be subject to being exposed to eavesdroppers within or outside LLM system providers who are interested in collecting users' input. In this work, we proposed a framework to camouflage user input, so as to alleviate privacy issues. Our framework proposes to place model shards on the client and the public server, we sent compressed hidden states instead of prompts to and from servers. Clients have held back information that can re-privatized the hidden states so that overall system performance is comparable to traditional LLMs services. Our framework was designed to be communication efficient, computation can be delegated to the local client so that the server's computation burden can be lightened. We conduct extensive experiments on machine translation tasks to verify our framework's performance., Comment: Submitted to EMNLP2024

GPUs, whose performance has gone through a huge leap over the past decade, have proved their ability to accelerate Online Analytical Processing (OLAP) operations. On the other hand, there is still a huge gap in the field of GPU-accelerated Online Transaction Processing (OLTP) operations since it was generally believed that GPUswere not suitable for OLTP in the past. However, the massive parallelism and high memory bandwidth give GPUs the potential to process thousands of transactions concurrently. Among the components of OLTP systems, Concurrency Control (CC) schemes have a great impact on the performance of transaction processing and they may behave differently on GPUs because of the different hardware architectures between GPUs and CPUs. In this paper, we design and build the first test-bed gCCTB for CCschemes on GPUsandimplement eight CC schemes for gCCTB. These schemes include six common schemes previously designed for CPUs and two schemes designed for GPUs. Then we make a comprehensive evaluation of these CC schemes with YCSB and TPC-C benchmarks and a number of launch parameters on GPUs. The experience accumulated on our test-bed can assist researchers andengineers to design andimplementnewGPU-acceleratedOLTP systems. Furthermore, the results of our evaluation cast light on research directions of high performance CC schemes on GPUs.

We propose a new factorization pattern for tree-level Yang-Mills (YM) amplitudes, where they decompose into a sum of products of two lower-point amplitudes by setting specific two-point non-planar Mandelstam variables within a rectangular configuration to zero. This approach manifests the hidden zeros of YM amplitudes recently identified. Furthermore, by setting specific Lorentz products involving polarization vectors to zero, the amplitudes further reduce to a sum of products of three currents. These novel factorizations provide a fresh perspective on the structure of YM amplitudes, potentially enhancing our understanding and calculation of these fundamental quantities., Comment: 5+2 pages

In this work we try to search for signals generated by ultra-heavy dark matter at the Large High Altitude Air Shower Observatory (LHAASO) data. We look for possible gamma-ray by dark matter annihilation or decay from 16 dwarf spheroidal galaxies in the field of view of LHAASO. Dwarf spheroidal galaxies are among the most promising targets for indirect detection of dark matter which have low fluxes of astrophysical $\gamma$-ray background while large amount of dark matter. By analyzing more than 700 days observational data at LHAASO, no significant dark matter signal from 1 TeV to 1 EeV is detected. Accordingly we derive the most stringent constraints on the ultra-heavy dark matter annihilation cross-section up to EeV. The constraints on the lifetime of dark matter in decay mode are also derived., Comment: 17 pages, 12 figures, accepted by PRL

While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in translating natural language questions into Structured Query Language (NL2SQL), the question of how to select the most beneficial demonstration examples remains an open research problem. While prior works often adapted off-the-shelf encoders to retrieve examples dynamically, an inherent discrepancy exists in the representational capacities between the external retrievers and the LLMs. Further, optimizing the selection of examples is a non-trivial task, since there are no straightforward methods to assess the relative benefits of examples without performing pairwise inference. To address these shortcomings, we propose DeTriever, a novel demonstration retrieval framework that learns a weighted combination of LLM hidden states, where rich semantic information is encoded. To train the model, we propose a proxy score that estimates the relative benefits of examples based on the similarities between output queries. Experiments on two popular NL2SQL benchmarks demonstrate that our method significantly outperforms the state-of-the-art baselines on one-shot NL2SQL tasks.

Recent years have seen massive time-series data generated in many areas. This different scenario brings new challenges, particularly in terms of data ingestion, where existing technologies struggle to handle such massive time-series data, leading to low loading speed and poor timeliness. To address these challenges, this paper presents MatrixGate, a new and efficient data ingestion approach for massive time-series data. MatrixGate implements both single-instance and multi-instance parallel procedures, which is based on its unique ingestion strategies. First, MatrixGate uses policies to tune the slots that are synchronized with segments to ingest data, which eliminates the cost of starting transactions and enhance the efficiency. Second, multi-coroutines are responsible for transfer data, which can increase the degree of parallelism significantly. Third, lock-free queues are used to enable direct data transfer without the need for disk storage or lodging in the master instance. Experiment results on multiple datasets show that MatrixGate outperforms state-of-the-art methods by 3 to 100 times in loading speed, and cuts down about 80% query latency. Furthermore, MatrixGate scales out efficiently under distributed architecture, achieving scalability of 86%.

In this paper, we focus on the finite difference approximation of nonlinear degenerate parabolic equations, a special class of parabolic equations where the viscous term vanishes in certain regions. This vanishing gives rise to additional challenges in capturing sharp fronts, beyond the restrictive CFL conditions commonly encountered with explicit time discretization in parabolic equations. To resolve the sharp front, we adopt the high-order multi-resolution alternative finite difference WENO (A-WENO) methods for the spatial discretization. To alleviate the time step restriction from the nonlinear stiff diffusion terms, we employ the exponential time differencing Runge-Kutta (ETD-RK) methods, a class of efficient and accurate exponential integrators, for the time discretization. However, for highly nonlinear spatial discretizations such as high-order WENO schemes, it is a challenging problem how to efficiently form the linear stiff part in applying the exponential integrators, since direct computation of a Jacobian matrix for high-order WENO discretizations of the nonlinear diffusion terms is very complicated and expensive. Here we propose a novel and effective approach of replacing the exact Jacobian of high-order multi-resolution A-WENO scheme with that of the corresponding high-order linear scheme in the ETD-RK time marching, based on the fact that in smooth regions the nonlinear weights closely approximate the optimal linear weights, while in non-smooth regions the stiff diffusion degenerates. The algorithm is described in detail, and numerous numerical experiments are conducted to demonstrate the effectiveness of such a treatment and the good performance of our method. The stiffness of the nonlinear parabolic partial differential equations (PDEs) is resolved well, and large time-step size computations are achieved.