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We have performed a spectral-timing analysis on the black hole X-ray binary MAXI J1535--571 during its 2017 outburst, with the aim of exploring the evolution of the inner accretion flow geometry. X-ray reverberation lags are observed in the hard-intermediate state (HIMS) and soft-intermediate state (SIMS) of the outburst. During the HIMS, the characteristic frequency of the reverberation lags $\nu_0$ (the frequency at which the soft lag turns to zero in the lag-frequency spectra) increases when the spectrum softens. This reflects a reduction of the spatial distance between the corona and accretion disc, when assuming the measured time lags are associated with the light travel time. We also find a strong correlation between $\nu_0$ and type-C Quasi Periodic Oscillation (QPO) centroid frequency $\nu_{QPO}$, which can be well explained by the Lense-Thirring (L-T) precession model under a truncated disk geometry. Despite the degeneracy in the spectral modellings, our results suggest that the accretion disc is largely truncated in the low hard state (LHS), and moves inward as the spectrum softens. Combine the spectral modelling results with the $\nu_0$ - $\nu_{QPO}$ evolution, we are inclined to believe that this source probably have a truncated disk geometry in the hard state.

Generated in high-Q optical microresonators, dissipative Kerr soliton microcombs constitute broadband optical frequency combs with chip sizes and repetition rates in the microwave to millimeter-wave range. For frequency metrology applications such as spectroscopy, optical atomic clocks and frequency synthesizers, octave-spanning soliton microcombs generated in dispersion optimized microresonator are required, which allow self-referencing for full frequency stabilization. In addition, field-deployable applications require the generation of such soliton microcombs simple, deterministic, and reproducible. Here, we demonstrate a novel scheme to generate self-emerging solitons in integrated lithium niobate microresonators. The single soliton features a broadband spectral bandwidth with dual dispersive waves, allowing 2f-3f self-referencing. Via harnessing the photorefractive effect of lithium niobate to significantly extend the soliton existence range, we observe a spontaneous yet deterministic single-soliton formation. The soliton is immune to external perturbation and can operate continuously over 13 hours without active feedback control. Finally, via integration with a pre-programed DFB laser, we demonstrate turnkey soliton generation. With further improvement of microresonator Q and hybrid integration with chip-scale laser chips, compact soliton microcomb devices with electronic actuation can be created, which can become central elements for future LiDAR, microwave photonics and optical telecommunications., Comment: 9 pages, 4 figures

Deep-learning-based super-resolution photoacoustic angiography (PAA) is a powerful tool that restores blood vessel images from under-sampled images to facilitate disease diagnosis. Nonetheless, due to the scarcity of training samples, PAA super-resolution models often exhibit inadequate generalization capabilities, particularly in the context of continuous monitoring tasks. To address this challenge, we propose a novel approach that employs a super-resolution PAA method trained with forged PAA images. We start by generating realistic PAA images of human lips from hand-drawn curves using a diffusion-based image generation model. Subsequently, we train a self-similarity-based super-resolution model with these forged PAA images. Experimental results show that our method outperforms the super-resolution model trained with authentic PAA images in both original-domain and cross-domain tests. Specially, our approach boosts the quality of super-resolution reconstruction using the images forged by the deep learning model, indicating that the collaboration between deep learning models can facilitate generalization, despite limited initial dataset. This approach shows promising potential for exploring zero-shot learning neural networks for vision tasks., Comment: 12 pages, 6 figures, journal

Different from the quasi-static network in the traditional power system, the dynamic network in the power-electronic-dominated power system should be considered due to rapid response of converters' controls. In this paper, a nonlinear differential-algebraic model framework is established with algebraic equations for dynamic electrical networks and differential equations for the (source) nodes, by generalizing the Kron reduction. The internal and terminal voltages of source nodes including converters are chosen as ports of nodes and networks. Correspondingly, the impact of dynamic network becomes clear, namely, it serves as a voltage divider and generates the terminal voltage based on the internal voltage of the sources instantaneously, even when the dynamics of inductance are included. With this simplest model, the roles of both nodes and the network become apparent.Simulations verify the proposed model framework in the modified 9-bus system., Comment: 4 pages, 6 figures

Multifold observations are common for different data modalities, e.g., a 3D shape can be represented by multi-view images and an image can be described with different captions. Existing cross-modal contrastive representation learning (XM-CLR) methods such as CLIP are not fully suitable for multifold data as they only consider one positive pair and treat other pairs as negative when computing the contrastive loss. In this paper, we propose MXM-CLR, a unified framework for contrastive learning of multifold cross-modal representations. MXM-CLR explicitly models and learns the relationships between multifold observations of instances from different modalities for more comprehensive representation learning. The key of MXM-CLR is a novel multifold-aware hybrid loss which considers multiple positive observations when computing the hard and soft relationships for the cross-modal data pairs. We conduct quantitative and qualitative comparisons with SOTA baselines for cross-modal retrieval tasks on the Text2Shape and Flickr30K datasets. We also perform extensive evaluations on the adaptability and generalizability of MXM-CLR, as well as ablation studies on the loss design and effects of batch sizes. The results show the superiority of MXM-CLR in learning better representations for the multifold data. The code is available at https://github.com/JLU-ICL/MXM-CLR., Comment: 16 pages, 14 figures

This paper constructs the first mixed finite element for the linear elasticity problem in 3D using $P_3$ polynomials for the stress and discontinuous $P_2$ polynomials for the displacement on tetrahedral meshes under some mild mesh conditions. The degrees of freedom of the stress space as well as the corresponding nodal basis are established by characterizing a space of some piecewise constant symmetric matrices on a patch around each edge. Macro-element techniques are used to define a stable interpolation to prove the discrete inf-sup condition. Optimal convergence is obtained theoretically.

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of \textit{Insight}-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first $\sim$1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from $\sim$ 10 keV to $\sim$ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy ($E_{\rm iso}$) of $\bf \sim 1.5 \times 10^{55}$ erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at $\sim950$ s from the afterglow starting time $T_{AG}$, which corresponds to a jet opening angle of $\sim {0.7^\circ} \ (\eta_\gamma n)^{1/8}$, where $n$ is the ambient medium density in units of $\rm cm^{-3}$ and $\eta_\gamma$ is the ratio between $\gamma$-ray energy and afterglow kinetic energy. The beaming-corrected total $\gamma$-ray energy $E_{\gamma}$ is $\sim 1.15 \times10^{51} \ (\eta_\gamma n)^{1/4}$ erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement., Comment: Submitted to National Science Review. This paper is under press embargo, contact the corresponding author for details

Biological membranes are able to exhibit various morphology due to the fluidity of the lipid molecules within the monolayers. The shape transformation of membranes has been well described by the classical Helfrich theory, which consists only a few phenomenological parameters, including the mean and the Gaussian curvature modulus. Though various methods have been proposed to measure the mean curvature modulus, determination of the Gaussian curvature modulus remains difficult both in experiments and in simulation. In this paper we study the buckling process of a rectangular membrane and a circular membrane subject to compressive stresses and under different boundary conditions. We find that the buckling of a rectangular membrane takes place continuously, while the buckling of a circular membrane can be discontinous depending on the boundary conditions. Furthermore, our results show that the stress-strain relationship of a buckled circular membrane can be used to effectively determine the Gaussian curvature modulus.

Lithium niobate (LN) thin film has recently emerged as an important platform for nonlinear optical investigations for its large $\chi^{(2)}$ nonlinear coefficients and ability of light localization. In this paper, we report the first fabrication of LN on insulator (LNOI) ridge waveguides with generalized quasi-periodic poled superlattices using the electric field polarization technique and microfabrication techniques. Benefiting from the abundant reciprocal vectors, we observed efficient second-harmonic and cascaded third-harmonic signals in the same device, with the normalized conversion efficiency 1735% W$^{-1}$cm$^{-2}$ and 0.41% W$^{-2}$cm$^{-4}$, respectively. This work opens a new direction of nonlinear integrated photonics based on LN thin film., Comment: 4 pages, 4 figures

The cellular uptake of self-propelled nanoparticles (NPs) or viruses, usually nonspherical, by cell membrane is crucial in many biological processes. In this study, using Onsager variational principle, we obtain a general wrapping equation for nonspherical self-propelled nanoparticles. Two analytical critical conditions are theoretically derived, one for the continuous full uptake of prolate particles and the other for snapthrough full wrapping of oblate particles. They capture considerably well the full uptake critical boundaries in the phase diagrams constructed in terms of active force, aspect ratio, adhesion energy density, and membrane tension based on numerical calculations. It is found that enhancing activity (active force), reducing effective dynamic viscosity, increasing adhesion energy density, and decreasing membrane tension, can significantly improve the wrapping efficiency for the self-propelled particles. These results elucidate some of the previous specific investigations conclusively and may offer novel possibilities for designing an effective active NP-based vehicle for controlled drug delivery.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

Fine-grained classification and counting of bone marrow erythroid cells are vital for evaluating the health status and formulating therapeutic schedules for leukemia or hematopathy. Due to the subtle visual differences between different types of erythroid cells, it is challenging to apply existing image-based deep learning models for fine-grained erythroid cell classification. Moreover, there is no large open-source datasets on erythroid cells to support the model training. In this paper, we introduce BMEC (Bone Morrow Erythroid Cells), the first large fine-grained image dataset of erythroid cells, to facilitate more deep learning research on erythroid cells. BMEC contains 5,666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells. To distinguish the erythroid cells, one key indicator is the cell shape which is closely related to the cell growth and maturation. Therefore, we design a novel shape-aware image classification network for fine-grained erythroid cell classification. The shape feature is extracted from the shape mask image and aggregated to the raw image feature with a shape attention module. With the shape-attended image feature, our network achieved superior classification performance (81.12\% top-1 accuracy) on the BMEC dataset comparing to the baseline methods. Ablation studies also demonstrate the effectiveness of incorporating the shape information for the fine-grained cell classification. To further verify the generalizability of our method, we tested our network on two additional public white blood cells (WBC) datasets and the results show our shape-aware method can generally outperform recent state-of-the-art works on classifying the WBC. The code and BMEC dataset can be found on https://github.com/wangye8899/BMEC.

Blind watermarking provides powerful evidence for copyright protection, image authentication, and tampering identification. However, it remains a challenge to design a watermarking model with high imperceptibility and robustness against strong noise attacks. To resolve this issue, we present a framework Combining the Invertible and Non-invertible (CIN) mechanisms. The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks. For the invertible part, we develop a diffusion and extraction module (DEM) and a fusion and split module (FSM) to embed and extract watermarks symmetrically in an invertible way. For the non-invertible part, we introduce a non-invertible attention-based module (NIAM) and the noise-specific selection module (NSM) to solve the asymmetric extraction under a strong noise attack. Extensive experiments demonstrate that our framework outperforms the current state-of-the-art methods of imperceptibility and robustness significantly. Our framework can achieve an average of 99.99% accuracy and 67.66 dB PSNR under noise-free conditions, while 96.64% and 39.28 dB combined strong noise attacks. The code will be available in https://github.com/rmpku/CIN., Comment: 9 pages, 9 figures, 5 tables

We propose to realize visual cryptography in an indirect way with the help of computer-generated hologram. At present, the recovery method of visual cryptography is mainly superimposed on transparent film or superimposed by computer equipment, which greatly limits the application range of visual cryptography. In this paper, the shares of the visual cryptography were encoded with computer-generated hologram, and the shares is reproduced by optical means, and then superimposed and decrypted. This method can expand the application range of visual cryptography and further increase the security of visual cryptography., Comment: International Workshop on Holography and related technologies (IWH) 2018

Endocytosis is an essential biological process for the trafficking of macromolecules (cargo) and membrane proteins in cells. In yeast cells, this involves the invagination of a tubular structure on the membrane and the formation of endocytic vesicles. Bin/Amphiphysin/Rvs (BAR) proteins holding a crescent-shape are generally assumed to be the active player to squeeze the tubular structure and pinch off the vesicle by forming a scaffold on the side of the tubular membrane. Here we use the extended Helfrich model to theoretically investigate how BAR proteins help drive the formation of vesicles via generating anisotropic curvatures. Our results show that, within the classical Helfrich model, increasing the spontaneous curvature at the side of a tubular membrane is unable to reduce the tube radius to a critical size to induce membrane fission. However, membranes coated with proteins that generate anisotropic curvatures are prone to experience an hourglass-shaped necking or a tube-shaped necking process, an important step leading to membrane fission and vesicle formation. In addition, our study shows that depending on the type of anisotropic curvatures generated by a protein, the force to maintain the protein coated membrane at a tubular shape exhibits qualitatively different relationship with the spontaneous curvature. This result provides an experimental guidance to determine the type of anisotropic curvatures of a protein.

In nature, active matter, such as worms or dogs, tend to spontaneously form a stable rotational cluster when they flock to the same food source on an unregulated and unconfined surface. {In this paper we present an $n$-node flexible active matter model to study the collective motion due to the flocking of individual agents on a two-dimensional surface, and confirm that there exists a spontaneous stable cluster rotation synchronizing with a chirality produced by the alignment of their bodies under the impetus of the active force.} A prefactor of 1.86 is obtained for the linear relationship between normalized angular velocity and chirality. The angular velocity of such a rotation is found to be dependent on the individual flexibility, the number of nodes in each individual, and the magnitude of the active force. The conclusions well explain the spontaneous stable rotation of clusters that exists in many flexible active matter, like worms or {dogs}, when they flock to the same single source.

Recent years have witnessed the great success of blind image quality assessment (BIQA) in various task-specific scenarios, which present invariable distortion types and evaluation criteria. However, due to the rigid structure and learning framework, they cannot apply to the cross-task BIQA scenario, where the distortion types and evaluation criteria keep changing in practical applications. This paper proposes a scalable incremental learning framework (SILF) that could sequentially conduct BIQA across multiple evaluation tasks with limited memory capacity. More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other. Each parameter subset is temporarily settled to Remember one evaluation preference toward its corresponding task, and the previously settled parameter subsets can be adaptively reused in the following BIQA to achieve better performance based on the task relevance. To suppress the unrestrained expansion of memory capacity in sequential tasks learning, we develop a scalable memory unit by gradually and selectively pruning unimportant neurons from previously settled parameter subsets, which enable us to Forget part of previous experiences and free the limited memory capacity for adapting to the emerging new tasks. Extensive experiments on eleven IQA datasets demonstrate that our proposed method significantly outperforms the other state-of-the-art methods in cross-task BIQA. The source code of the proposed method is available at https://github.com/maruiperfect/SILF.

Traditional recommendation systems mainly focus on modeling user interests. However, the dynamics of recommended items caused by attribute modifications (e.g. changes in prices) are also of great importance in real systems, especially in the fast-growing e-commerce environment, which may cause the users' demands to emerge, shift and disappear. Recent studies that make efforts on dynamic item representations treat the item attributes as side information but ignore its temporal dependency, or model the item evolution with a sequence of related users but do not consider item attributes. In this paper, we propose Core Attribute Evolution Network (CAEN), which partitions the user sequence according to the attribute value and thus models the item evolution over attribute dynamics with these users. Under this framework, we further devise a hierarchical attention mechanism that applies attribute-aware attention for user aggregation under each attribute, as well as personalized attention for activating similar users in assessing the matching degree between target user and item. Results from the extensive experiments over actual e-commerce datasets show that our approach outperforms the state-of-art methods and achieves significant improvements on the items with rapid changes over attributes, therefore helping the item recommendation to adapt to the growth of the e-commerce platform., Comment: 10 pages, Accepted by RecSys 2022

Machine learning is a novel and powerful technology and has been widely used in various science topics. We demonstrate a machine-learning based approach built by a set of general metrics and rules inspired by physics. Taking advantages of physical constraints, such as dimension identity, symmetry and generalization, we succeed to rediscover the GellMann Okubo formula using a technique of symbolic regression. This approach can effectively find explicit solutions among user-defined observable, and easily extend to study on exotic hadron spectrum., Comment: 7 pages, 3 figures

Class activation map (CAM) has been widely studied for visual explanation of the internal working mechanism of convolutional neural networks. The key of existing CAM-based methods is to compute effective weights to combine activation maps in the target convolution layer. Existing gradient and score based weighting schemes have shown superiority in ensuring either the discriminability or faithfulness of the CAM, but they normally cannot excel in both properties. In this paper, we propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CAM-based CNN visual explanation. First, we improve the faithfulness and discriminability of the score-based weights by performing a grouped channel switching operation. Specifically, for each channel, we compute its similarity group and switch the group of channels on or off simultaneously to compute changes in the class prediction score as the weights. Then, we combine the improved score-based weights with the conventional gradient-based weights so that the discriminability of the final CAM can be further improved. We perform extensive comparisons with the state-of-the-art CAM algorithms. The quantitative and qualitative results show our FD-CAM can produce more faithful and more discriminative visual explanations of the CNNs. We also conduct experiments to verify the effectiveness of the proposed grouped channel switching and weight combination scheme on improving the results. Our code is available at https://github.com/crishhh1998/FD-CAM., Comment: Accepted by ICPR 2022 and also accepted by CVPR 2022 Explainable Artificial Intelligence for Computer Vision (XAI4CV) Workshop