Search

In this work, we study the properties of neutron stars using the linear Relativistic Mean-Field (RMF) theory and consider multiple degrees of freedom inside neutron stars, including hyperons and $\Delta$ resonances. We investigate different coupling parameters $x_{\sigma \Delta}$ between $\Delta$ resonances and nucleons and compare the differences between neutron stars with and without strange mesons $\sigma^*$ and $\phi$. These effects include particle number distributions, equations of state (EOS), mass-radius relations, and tidal deformabilities. To overcome the "hyperon puzzle," we employ the $\sigma-cut$ scheme to obtain neutron stars with masses up to $2M_{\odot}$. We find that strange mesons appear at around 3$\rho_0$ and reduce the critical density of baryons in the high-density region. With increasing coupling parameter $x_{\sigma \Delta}$, the $\Delta$ resonances suppress hyperons, leading to a shift of the critical density towards lower values. The early appearance of $\Delta$ resonances may play a crucial role in the stability of neutron stars. Strange mesons soften the EOS slightly, while $\Delta$ resonances predominantly soften the EOS in the low-density region. By calculating tidal deformabilities and comparing with astronomical observation GW170817, we find that the inclusion of $\Delta$ resonances decreases the radius of neutron stars., Comment: 10 pages, 9 figures. arXiv admin note: text overlap with arXiv:2211.11498

Buildings are the basic carrier of social production and human life; roads are the links that interconnect social networks. Building and road information has important application value in the frontier fields of regional coordinated development, disaster prevention, auto-driving, etc. Mapping buildings and roads from very high-resolution (VHR) remote sensing images have become a hot research topic. However, the existing methods often ignore the strong spatial correlation between roads and buildings and extract them in isolation. To fully utilize the complementary advantages between buildings and roads, we propose a building-road collaborative extraction method based on multi-task and cross-scale feature interaction to improve the accuracy of both tasks in a complementary way. A multi-task interaction module is proposed to interact information across tasks and preserve the unique information of each task, which tackle the seesaw phenomenon in multitask learning. By considering the variation in appearance and structure between buildings and roads, a cross-scale interaction module is designed to automatically learn the optimal reception field for different tasks. Compared with many existing methods that train each task individually, the proposed collaborative extraction method can utilize the complementary advantages between buildings and roads by the proposed inter-task and inter-scale feature interactions, and automatically select the optimal reception field for different tasks. Experiments on a wide range of urban and rural scenarios show that the proposed algorithm can achieve building-road extraction with outstanding performance and efficiency., Comment: IEEE Transactions on Geoscience and Remote Sensing

Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently propelled automatic CD into a technological revolution. Nevertheless, deep learning-based CD methods are still plagued by two primary issues: 1) insufficient temporal relationship modeling and 2) pseudo-change misclassification. To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD. Firstly, we designed a hard sample-aware contrastive loss, which reweights the importance of hard and simple samples. This loss allows for explicit modeling of the temporal correlation between bi-temporal remote sensing images. Furthermore, the modeled temporal relations are utilized as knowledge prior to guide the segmentation process for detecting change regions. The DeepCL framework is thoroughly evaluated both theoretically and experimentally, demonstrating its superior feature discriminability, resilience against pseudo changes, and adaptability to a variety of CD algorithms. Extensive comparative experiments substantiate the quantitative and qualitative superiority of DeepCL over state-of-the-art CD approaches., Comment: 12 pages,7 figures, submitted to IEEE Transactions on Image Processing

The efficacy of building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness. Many existing building segmentation methods were developed upon the encoder-decoder architecture of U-Net, in which the encoder is finetuned from the newly developed backbone networks that are pre-trained on ImageNet. However, the heavy computational burden of the existing decoder designs hampers the successful transfer of these modern encoder networks to remote sensing tasks. Even the widely-adopted deep supervision strategy fails to mitigate these challenges due to its invalid loss in hybrid regions where foreground and background pixels are intermixed. In this paper, we conduct a comprehensive evaluation of existing decoder network designs for building footprint segmentation and propose an efficient framework denoted as BFSeg to enhance learning efficiency and effectiveness. Specifically, a densely-connected coarse-to-fine feature fusion decoder network that facilitates easy and fast feature fusion across scales is proposed. Moreover, considering the invalidity of hybrid regions in the down-sampled ground truth during the deep supervision process, we present a lenient deep supervision and distillation strategy that enables the network to learn proper knowledge from deep supervision. Building upon these advancements, we have developed a new family of building segmentation networks, which consistently surpass prior works with outstanding performance and efficiency across a wide range of newly developed encoder networks.

Weakly-supervised change detection (WSCD) aims to detect pixel-level changes with only image-level annotations. Owing to its label efficiency, WSCD is drawing increasing attention recently. However, current WSCD methods often encounter the challenge of change missing and fabricating, i.e., the inconsistency between image-level annotations and pixel-level predictions. Specifically, change missing refer to the situation that the WSCD model fails to predict any changed pixels, even though the image-level label indicates changed, and vice versa for change fabricating. To address this challenge, in this work, we leverage global-scale and local-scale priors in WSCD and propose two components: a Dilated Prior (DP) decoder and a Label Gated (LG) constraint. The DP decoder decodes samples with the changed image-level label, skips samples with the unchanged label, and replaces them with an all-unchanged pixel-level label. The LG constraint is derived from the correspondence between changed representations and image-level labels, penalizing the model when it mispredicts the change status. Additionally, we develop TransWCD, a simple yet powerful transformer-based model, showcasing the potential of weakly-supervised learning in change detection. By integrating the DP decoder and LG constraint into TransWCD, we form TransWCD-DL. Our proposed TransWCD and TransWCD-DL achieve significant +6.33% and +9.55% F1 score improvements over the state-of-the-art methods on the WHU-CD dataset, respectively. Some performance metrics even exceed several fully-supervised change detection (FSCD) competitors. Code will be available at https://github.com/zhenghuizhao/TransWCD.

The electronic band structure is an intrinsic property of solid-state materials that is intimately connected to the crystalline arrangement of atoms. Moir\'e crystals, which emerge in twisted stacks of atomic layers, feature a band structure that can be continuously tuned by changing the twist angle between adjacent layers. This class of artificial materials blends the discrete nature of the moir\'e superlattice with intrinsic symmetries of the constituent materials, providing a versatile platform for investigation of correlated phenomena whose origins are rooted in the geometry of the superlattice, from insulating states at "magic angles" to flat bands in quasicrystals. Here we present a route to mechanically tune the twist angle of individual atomic layers with a precision of a fraction of a degree inside a scanning probe microscope, which enables continuous control of the electronic band structure in-situ. Using nanostructured rotor devices, we achieve the collective rotation of a single layer of atoms with minimal deformation of the crystalline lattice. In twisted bilayer graphene, we demonstrate nanoscale control of the moir\'e superlattice period via external rotations, as revealed using piezoresponse force microscopy. We also extend this methodology to create twistable boron nitride devices, which could enable dynamic control of the domain structure of moir\'e ferroelectrics. This approach provides a route for real-time manipulation of moir\'e materials, allowing for systematic exploration of the phase diagrams at multiple twist angles in a single device.

In a generic random system, the coexistence of extended and localized states can be evidenced by the subextensive width of energy distribution of a physical initial state in, for example, the quantum quenches which involving the local Hamiltonian. The robust thermalization is also evidenced in terms of the microscopic canonical ensemble average in the thermalization limit\cite{Shiraishi}, which satisfies the weak eigenstate thermalization hypothesis (ETH). In this article, we study the method of two-particle self-consistency for a system without condensation, i.e., without the inaccessible localizations that violating the ETH. We provide another pespective that considering the local conservation of an nonintegrable system the stubborn correlations between the three kinds of decompositions for the four-point functions, which can be regarded as the elements in a product of the self-energy and Green function matrices (i.e., the two-particle correlations in Kadanoff and Baym notation\cite{Vilk Y M}).

Oscillation modes of compact stars, in general, can serve as a fingerprint in determining the equation of state (EOS) of dense matter. In this study, we examine the impact of symmetry energy slope ($L$) on the oscillation frequencies of neutron stars (NSs) with nucleon-nucleon short range correlation (SRC) and admixed dark matter (DM) for the first time within the relativistic mean-field theory. By adjusting the $L$, we revise the EOS and coupling parameters in light of the SRC and DM effects, and construct the new sets. The results reveal that NSs containing SRC and DM inside are more likely to satisfy the observational constraints, and we find that smaller $L$ exhibits larger fundamental non-radial and radial frequencies, and that the effect on Large Separation (LG) is also mainly concentrated in the low-mass region. Moreover, we update the linear relationship between the non-radial frequency and mean density, and we further give empirical relations between non-radial and radial frequencies and tidal deformability at different $L$ for 1.4$M_{\odot}$ and 2$M_{\odot}$. These findings will enable us to more effectively confine the NS EOSs, in turn, also provide a strategy to place constraints on the $L$., Comment: 18 pages, 13 figures

Microwave impedance microscopy (MIM) is a near-field imaging technique that has been used to visualize the local conductivity of materials with nanoscale resolution across the GHz regime. In recent years, MIM has shown great promise for the investigation of topological states of matter, correlated electronic states and emergent phenomena in quantum materials. To explore these low-energy phenomena, many of which are only detectable in the milliKelvin regime, we have developed a novel low-temperature MIM incorporated into a dilution refrigerator. This setup which consists of a tuning-fork-based atomic force microscope with microwave reflectometry capabilities, is capable of reaching temperatures down to 70 mK during imaging and magnetic fields up to 9 T. To test the performance of this microscope, we demonstrate microwave imaging of the conductivity contrast between graphite and silicon dioxide at cryogenic temperatures and discuss the resolution and noise observed in these results. We extend this methodology to visualize edge conduction in Dirac semimetal cadmium arsenide in the quantum Hall regime, Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Review of Scientific Instruments and may be found at doi.org/10.1063/5.0159548

Photoplethysmogram (PPG) signals are easily contaminated by motion artifacts in real-world settings, despite their widespread use in Internet-of-Things (IoT) based wearable and smart health devices for cardiovascular health monitoring. This study proposed a lightweight deep neural network, called Tiny-PPG, for accurate and real-time PPG artifact segmentation on IoT edge devices. The model was trained and tested on a public dataset, PPG DaLiA, which featured complex artifacts with diverse lengths and morphologies during various daily activities of 15 subjects using a watch-type device (Empatica E4). The model structure, training method and loss function were specifically designed to balance detection accuracy and speed for real-time PPG artifact detection in resource-constrained embedded devices. To optimize the model size and capability in multi-scale feature representation, the model employed depth-wise separable convolution and atrous spatial pyramid pooling modules, respectively. Additionally, the contrastive loss was also utilized to further optimize the feature embeddings. With additional model pruning, Tiny-PPG achieved state-of-the-art detection accuracy of 87.4% while only having 19,726 model parameters (0.15 megabytes), and was successfully deployed on an STM32 embedded system for real-time PPG artifact detection. Therefore, this study provides an effective solution for resource-constraint IoT smart health devices in PPG artifact detection.

We investigate the eigenstate thermalization in terms of a Hermitian operator and the complex eigenkets that follows Gaussian ensemble distribution. With the non-Hermitian open bipartite system, there are, however, some global restrictions such that the elements share some of the properties of Gaussian orthogonal ensemble in diagonal and off-diagonal perspective. Such global restrictions enforce that one of the subsystem contains a nullspace with non-defective degeneracies (the primary subsystem), and the another full-ranked subsystem (the secondary subsystem). For the primary subsystem, the mixed densities in Hermitian and non-Hermitian basis exhibits global fluctuation and unidirectional (non-Hermitian skin effect) fluctuation, respectively. The former is due to the global restrictions of the whole system which plays teh role of environmental disorder, while the latter is due to the nonlocal symmetries which is allowed in the restricted Hilbert space. We also investigate the integrablity-chaos transition with independent perturbations in terms of the Berry autocorrelation in semicalssical limit, where there is a phase space spanned by the momentum-like projection and the range of local wave function.

Wearing face mask is an effective measure to reduce the risk of COVID-19 infections and control its transmission, thus its usage survey is important for better policy decision to mitigate the epidemic spread. Current existing worldwide surveys are mostly self-reported, whose accuracies are hard to guaranteed, and may exaggerate the percentage of face mask wearing. Therefore, we collected busking videos with a large amount on YouTube from December 2019 to December 2020, mainly from South Korea, and reported an objective investigation of face mask use in the crowds outdoor. It is found that the face mask wearing rate has an obvious positive correlation with effective reproductive number (Rt) in the South Korea, which indicates that the people in South Korea kept sensitive to the COVID-19 epidemic. The face mask wearing rate in South Korea is higher than some other countries, and two rate droppings in June and September also corresponds to the temporary remission in 2020. This study shows significant potentials to utilize public big video data to make an accurate worldwide survey of face mask use with the support of deep learning technology.

Microwave impedance microscopy (MIM) has been utilized to directly visualize topological edge states in many quantum materials, from quantum Hall systems to topological insulators, across the GHz regime. While the microwave response for conventional metals and insulators can be accurately quantified using simple lumped-element circuits, the applicability of these classical models to more exotic quantum systems remains limited. In this work, we present a general theoretical framework of the MIM response of arbitrary quantum materials within linear response theory. As a special case, we model the microwave response of topological edge states in a Chern insulator and predict an enhanced MIM response at the crystal boundaries due to collective edge magnetoplasmon (EMP) excitations. The resonance frequency of these plasmonic modes should depend quantitatively on the topological invariant of the Chern insulator state and on the sample's circumference, which highlights their non-local, topological nature. To benchmark our analytical predictions, we experimentally probe the MIM response of quantum anomalous Hall edge states in a Cr-doped (Bi,Sb)2Te3 topological insulator and perform numerical simulations using a classical formulation of the EMP modes based on this realistic tip-sample geometry, both of which yield results consistent with our theoretical picture. We also show how the technique of MIM can be used to quantitatively extract the topological invariant of a Chern insulator, disentangle the signatures of topological versus trivial edge states, and shed light on the microscopic nature of dissipation along the crystal boundaries., Comment: 8 pages, 4 figures

High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, defense, and emergency response. However, most current algorithms are still confined to describing local features and fail to incorporate a global perspective, which limits their ability to capture interactions between global features, thus usually resulting in incomplete change regions. In this paper, we propose a Global Multi-head INteractive self-attention change Detection network (GlobalMind) to explore the implicit correlation between different surface objects and variant land cover transformations, acquiring a comprehensive understanding of the data and accurate change detection result. Firstly, a simple but effective Global Axial Segmentation (GAS) strategy is designed to expand the self-attention computation along the row space or column space of hyperspectral images, allowing the global connection with high efficiency. Secondly, with GAS, the global spatial multi-head interactive self-attention (Global-M) module is crafted to mine the abundant spatial-spectral feature involving potential correlations between the ground objects from the entire rich and complex hyperspectral space. Moreover, to acquire the accurate and complete cross-temporal changes, we devise a global temporal interactive multi-head self-attention (GlobalD) module which incorporates the relevance and variation of bi-temporal spatial-spectral features, deriving the integrate potential same kind of changes in the local and global range with the combination of GAS. We perform extensive experiments on five mostly used hyperspectral datasets, and our method outperforms the state-of-the-art algorithms with high accuracy and efficiency., Comment: 14 page, 18 figures

In this paper, we calculated the quasinormal modes (QNMs) of a charged non-commutative black hole in scalar, electromagnetic and gravitational fields by three methods. We gave the influence of non-commutative parameter $\theta$ and charge $Q$ on QNMs in different fields. Thereafter, we calculated the shadow radius of the black hole and provided the valid range of $\theta$ and $Q$ using the constraints on the shadow radius of $\text{M87}^{\ast}$ and $\text{Sgr A}^{\ast}$ from the Event Horizon Telescope (EHT). In addition, we estimated the ``relative deviation'' of the shadow radius ($\delta_{R_{s}}$) between non-commutative spacetime and commutative spacetime. We found that the maximum values of $\delta_{R_{s}}$ decreases with the increase of charge $Q$. In other words, the non-commutativity of spacetime becomes harder to distinguish as the charge of the black hole increases., Comment: 25 pages, 12 figures

Generative models such as StyleGAN2 and Stable Diffusion have achieved state-of-the-art performance in computer vision tasks such as image synthesis, inpainting, and de-noising. However, current generative models for face inpainting often fail to preserve fine facial details and the identity of the person, despite creating aesthetically convincing image structures and textures. In this work, we propose Person Aware Tuning (PAT) of Mask-Aware Transformer (MAT) for face inpainting, which addresses this issue. Our proposed method, PATMAT, effectively preserves identity by incorporating reference images of a subject and fine-tuning a MAT architecture trained on faces. By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity. Moreover, PATMAT's use of multiple images per anchor during training allows the model to use fewer reference images than competing methods. We demonstrate that PATMAT outperforms state-of-the-art models in terms of image quality, the preservation of person-specific details, and the identity of the subject. Our results suggest that PATMAT can be a promising approach for improving the quality of personalized face inpainting.

When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set., Comment: Accepted in CVPR 2023

Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration. In this paper, we have proposed an original Efficient Multi-temporal Self-attention Network (EMS-Net) for hyperspectral change detection. The designed EMS module cuts redundancy of those similar and containing-no-changes feature maps, computing efficient multi-temporal change information for precise binary change map. Besides, to explore the clustering characteristics of the change detection, a novel supervised contrastive loss is provided to enhance the compactness of the unchanged. Experiments implemented on two hyperspectral change detection datasets manifests the out-standing performance and validity of proposed method., Comment: 4 pages, 5 figures, submitted to IGARSS2023

In practice, metric analysis on a specific train and test dataset does not guarantee reliable or fair ML models. This is partially due to the fact that obtaining a balanced, diverse, and perfectly labeled dataset is typically expensive, time-consuming, and error-prone. Rather than relying on a carefully designed test set to assess ML models' failures, fairness, or robustness, this paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images to allow model diagnosis, interpretability, and robustness. Traditional adversarial training is a popular methodology for robustifying ML models against attacks. However, existing adversarial methods do not combine the two aspects that enable the interpretation and analysis of the model's flaws: semantic traceability and perceptual quality. SIA combines the two features via iterative gradient ascent on a predefined semantic attribute space and the image space. We illustrate the validity of our approach in three scenarios for keypoint detection and classification. (1) Model diagnosis: SIA generates a histogram of attributes that highlights the semantic vulnerability of the ML model (i.e., attributes that make the model fail). (2) Stronger attacks: SIA generates adversarial examples with visually interpretable attributes that lead to higher attack success rates than baseline methods. The adversarial training on SIA improves the transferable robustness across different gradient-based attacks. (3) Robustness to imbalanced datasets: we use SIA to augment the underrepresented classes, which outperforms strong augmentation and re-balancing baselines., Comment: Initial version submitted to NeurIPS 2022

In a federated learning (FL) system, malicious participants can easily embed backdoors into the aggregated model while maintaining the model's performance on the main task. To this end, various defenses, including training stage aggregation-based defenses and post-training mitigation defenses, have been proposed recently. While these defenses obtain reasonable performance against existing backdoor attacks, which are mainly heuristics based, we show that they are insufficient in the face of more advanced attacks. In particular, we propose a general reinforcement learning-based backdoor attack framework where the attacker first trains a (non-myopic) attack policy using a simulator built upon its local data and common knowledge on the FL system, which is then applied during actual FL training. Our attack framework is both adaptive and flexible and achieves strong attack performance and durability even under state-of-the-art defenses.

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.

Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem. In this paper, we have proposed a hierarchical change guiding map network (HCGMNet) for change detection. The model uses hierarchical convolution operations to extract multiscale features, continuously merges multi-scale features layer by layer to improve the expression of global and local information, and guides the model to gradually refine edge features and comprehensive performance by a change guide module (CGM), which is a self-attention with changing guide map. Extensive experiments on two CD datasets show that the proposed HCGMNet architecture achieves better CD performance than existing state-of-the-art (SOTA) CD methods.

In this work, we investigate the scattering of massless plane scalar waves by the high dimensional noncommutative Schwarzschild-Tangherlini black hole. We use the partial wave approach to determine the scattering and absorption cross sections in the incident wavelength range. Our numerical results demonstrate that the bigger the noncommutative parameter, the smaller the maximum value of the related partial absorption cross section, however the tendency is slightly. We also discovered that when the noncommutative parameter is weak, the absorption cross section of the high dimensional black hole oscillates in the low frequency zone. The total absorption cross section oscillates around the geometrical optical limit in the high frequency range, and the scattering characteristics of black holes with various parameters are visibly different. The influence on the differential scattering cross section is particularly pronounced at large angles., Comment: 10 pages and 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.

In this work, we numerically investigate the scattering and absorption cross section of the massless scalar field from some well-known regular black holes by using the partial wave approach. Our computational results indicate that the larger the parameters, the lower the associated total absorption cross section maxima. When compared to the Schwarzschild black hole, the scattering cross section is enhanced in some regular black hole spacetimes, meanwhile the scattering width is narrow in the forward orientation. Moreover, it is found that the null geodesics of the critical impact parameter and the geometrical optical value in the high frequency regime have similar changing behavior., Comment: 15 pages, 5 figures

We study the equation of state of dense baryon matter within the relativistic mean-field model, and we include ${\Delta}$(1232) isobars into IUFSU model with hyperons and consider the possibility of kaon meson condensation. We find that it is necessary to consider the $\Delta$ resonance state inside the massive neutron star. The critical density of Kaon mesons and hyperons is shifted to a higher density region, in this respect an early appearance of $\Delta$ resonances is crucial to guarantee the stability of the branch of hyperonized star with the difference of the coupling parameter $x_{\sigma \Delta}$ constrained based on the QCD rules in nuclear matter. The $\Delta$ resonance produces a softer equation of state in the low density region, which makes the tidal deformability and radius consistent with the observation of GW170817. As the addition of new degrees of freedom will lead to a softening of the equation of state, the ${\sigma}$-cut scheme, which states the decrease of neutron star mass can be lowered if one assumes a limited decrease of the ${\sigma}$-meson strength at ${\rho_B}$($\rho_B > \rho_0$), finally we get a maximum mass neutron star with $\Delta$ resonance heavier than 2$M_{\odot}$., Comment: 10 pages, 9 figures

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.

Multimodal headline utilizes both video frames and transcripts to generate the natural language title of the videos. Due to a lack of large-scale, manually annotated data, the task of annotating grounded headlines for video is labor intensive and impractical. Previous researches on pre-trained language models and video-language models have achieved significant progress in related downstream tasks. However, none of them can be directly applied to multimodal headline architecture where we need both multimodal encoder and sentence decoder. A major challenge in simply gluing language model and video-language model is the modality balance, which is aimed at combining visual-language complementary abilities. In this paper, we propose a novel approach to graft the video encoder from the pre-trained video-language model on the generative pre-trained language model. We also present a consensus fusion mechanism for the integration of different components, via inter/intra modality relation. Empirically, experiments show that the grafted model achieves strong results on a brand-new dataset collected from real-world applications., Comment: Accepted by EMNLP 2022

The Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite has been making observations of the mesosphere and lower thermosphere (MLT) region for two decades. The TIMED Doppler Interferometer (TIDI) measures the neutral winds using four orthogonal telescopes. In this study, the line of sight (LOS) winds from individual telescopes are compared to the measurements from the Ionospheric Connection Explorer's (ICON's) Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) instrument from 90-100 km altitude during 2020. With the MIGHTI vector winds projected onto the LOS direction of each TIDI telescope, coincidences of the two datasets are found. The four telescopes perform differently and the performance depends on the satellite configuration and local solar zenith angle. Measurements from the coldside telescopes, Telescope 1 (Tel1) and Telescope 2 (Tel2), are better correlated with the MIGHTI winds in general with Tel2 having higher correlation coefficients across all conditions. The performance of Tel1 is comparable to that of Tel2 during backward flight while showing systematic errors larger than the average wind speeds during forward flight. The warmside LOS winds from Telescope 3 (Tel3) and Telescope 4 (Tel4) vary widely in magnitude, especially on the nightside. Compared with MIGHTI winds, the Tel4 measurements have the weakest correlation, while the Tel3 performance is comparable to that of the coldside telescopes during the ascending phase but deteriorates during the descending phase. Based on the TIDI/MIGHTI comparisons, figures of merit are generated to quantify the quality of measurements from individual telescopes in different configurations., Comment: 27 pages, 15 figures