arXiv subjects

Rui Xu

Publications and source records attributed to Rui Xu.

At least 199 records · Page 11Linked to original sources

arXiv2022

Experiments and simulations demonstrating the rapid ultrasonic rewarming of frozen beef cryovials

The development of methods to safely rewarm large volume cryopreserved biological samples remains a barrier to the widespread adoption of cryopreservation. Here, experiments and simulations were performed to demonstrate that ultrasound can increase rewarming rates relative to thermal conduction alone. An ultrasonic rewarming setup based on a custom 444 kHz tubular piezoelectric transducer was designed, characterized, and tested with 2 mL cryovials filled with frozen ground beef. Rewarming rates were characterized in the -20$^{\circ}$C to 5$^{\circ}$C range. Thermal conduction-based rewarming was compared to thermal conduction plus ultrasonic rewarming, demonstrating a ten-fold increase in rewarming rate when ultrasound was applied. The maximum recorded rewarming rate with ultrasound was 57$^{\circ}$C per minute, approximately 2.5 times faster than with thermal conduction alone. Coupled acoustic and thermal simulations were developed and showed good agreement with the heating rates demonstrated experimentally and were also used to demonstrate spatial heating distributions with small ($<$3$^{\circ}$C) temperature differentials throughout the sample when the sample was below 0$^{\circ}$C. The experiments and simulations performed in this work demonstrate the potential for ultrasound as a rewarming method for cryopreserved tissues, as faster rewarming rates may improve the viability of cryopreserved tissues and reduce the time needed for cells to regain normal function.

physics.med-ph↗

arXiv2022

An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022

Stereo-matching is a fundamental problem in computer vision. Despite recent progress by deep learning, improving the robustness is ineluctable when deploying stereo-matching models to real-world applications. Different from the common practices, i.e., developing an elaborate model to achieve robustness, we argue that collecting multiple available datasets for training is a cheaper way to increase generalization ability. Specifically, this report presents an improved RaftStereo trained with a mixed dataset of seven public datasets for the robust vision challenge (denoted as iRaftStereo_RVC). When evaluated on the training sets of Middlebury, KITTI-2015, and ETH3D, the model outperforms its counterparts trained with only one dataset, such as the popular Sceneflow. After fine-tuning the pre-trained model on the three datasets of the challenge, it ranks at 2nd place on the stereo leaderboard, demonstrating the benefits of mixed dataset pre-training.

cs.CV↗

arXiv2022

Millionfold improvement in multivibration-feedback optomechanical refrigeration via auxiliary mechanical coupling

The simultaneous ground-state refrigeration of multiple vibrational modes is a prerequisite for observing significant quantum effects of multiple-vibration systems. Here we propose how to realize a large amplification in the net-refrigeration rates based on cavity optomechanics and to largely improve the cooling performance of multivibration modes beyond the resolved-sideband regime. By employing an auxiliary mechanical coupling (AMC) between two mechanical vibrations, the dark mode, which is induced by the coupling of these vibrational modes to a common optical mode and cuts off cooling channels, can be fully removed. We use fully analytical treatments for the effective mechanical susceptibilities and net-cooling rates and find that when the AMC is turned on, the amplification of the net-refrigeration rates by more than six orders of magnitude can be observed. In particular, we reveal that the simultaneous ground-state cooling beyond the resolved-sideband regime arises from the introduced AMC, without which it vanishes. Our work paves the way for quantum control of multiple vibrational modes in the bad-cavity regime.

quant-ph↗

arXiv2022

Rapid Recovery of Program Execution Under Power Failures for Embedded Systems with NVM

After power is switched on, recovering the interrupted program from the initial state can cause negative impact. Some programs are even unrecoverable. To rapid recovery of program execution under power failures, the execution states of checkpoints are backed up by NVM under power failures for embedded systems with NVM. However, frequent checkpoints will shorten the lifetime of the NVM and incur significant write overhead. In this paper, the technique of checkpoint setting triggered by function calls is proposed to reduce the write on NVM. The evaluation results show an average of 99.8% and 80.5$% reduction on NVM backup size for stack backup, compared to the log-based method and step-based method. In order to better achieve this, we also propose pseudo-function calls to increase backup points to reduce recovery costs, and exponential incremental call-based backup methods to reduce backup costs in the loop. To further avoid the content on NVM is cluttered and out of NVM, a method to clean the contents on the NVM that are useless for restoration is proposed. Based on aforementioned problems and techniques, the recovery technology is proposed, and the case is used to analyze how to recover rapidly under different power failures.

cs.OS↗

arXiv2022

Oscillation Modes and Gravitational Waves from Strangeon Stars

The strong interaction at low energy scales determines the equation of state (EOS) of supranuclear matters in neutron stars (NSs). It is conjectured that the bulk dense matter may be composed of strangeons, which are quark clusters with nearly equal numbers of $u$, $d$, and $s$ quarks. To characterize the strong-repulsive interaction at short distance and the nonrelativistic nature of strangeons, a phenomenological Lennard-Jones model with two parameters is used to describe the EOS of strangeon stars (SSs). For the first time, we investigate the oscillation modes of non-rotating SSs and obtain their frequencies for various parameterizations of the EOS. We find that the properties of radial oscillations of SSs are different from those of NSs, especially for stars with relatively low central energy densities. Moreover, we calculate the $f$-mode frequency of nonradial oscillations of SSs within the relativistic Cowling approximation. The frequencies of the $f$-mode of SSs are found to be in the range from $6.7\,$kHz to $ 8.7\,\rm{kHz}$. Finally, we study the universal relations between the $f$-mode frequency and global properties of SSs, such as the compactness and the tidal deformability. The results we obtained are relevant to pulsar timing and gravitational waves, and will help to probe NSs' EOSs and infer nonperturbative behaviours in quantum chromodynamics.

gr-qc↗

arXiv2022

Properties and device performance of BN thin films grown on GaN by pulsed laser deposition

Wide and ultrawide-bandgap semiconductors lie at the heart of next-generation high-power, high-frequency electronics. Here, we report the growth of ultrawide-bandgap boron nitride (BN) thin films on wide-bandgap gallium nitride (GaN) by pulsed laser deposition. Comprehensive spectroscopic (core level and valence band XPS, FTIR, Raman) and microscopic (AFM and STEM) characterizations confirm the growth of BN thin films on GaN. Optically, we observed that BN/GaN heterostructure is second-harmonic generation active. Moreover, we fabricated the BN/GaN heterostructure-based Schottky diode that demonstrates rectifying characteristics, lower turn-on voltage, and an improved breakdown capability (234 V) as compared to GaN (168 V), owing to the higher breakdown electrical field of BN. Our approach is an early step towards bridging the gap between wide and ultrawide-bandgap materials for potential optoelectronics as well as next-generation high-power electronics.

physics.app-ph↗

arXiv2022

Global Well-posedness of a Prandtl Model from MHD in Gevrey Function Spaces

We consider a Prandtl model derived from MHD in the Prandtl-Hartmann regime that has a damping term due to the effect of the Hartmann boundary layer. A global-in-time well-posedness is obtained in the Gevrey function space with the optimal index $2$. The proof is based on a cancellation mechanism through some auxiliary functions from the study of the Prandtl equation and an observation about the structure of the loss of one order tangential derivatives through twice operations of the Prandtl operator

math.AP↗

arXiv2022

LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vital for global information extraction. In computer vision tasks, non-local operations have been widely utilized, but the computational cost could be very high for 3D computed tomography (CT) images. To address this issue, we propose a long short slice-aware network (LSSANet) for the detection of pulmonary nodules. In particular, we develop a new non-local mechanism termed long short slice grouping (LSSG), which splits the compact non-local embeddings into a short-distance slice grouped one and a long-distance slice grouped counterpart. This not only reduces the computational burden, but also keeps long-range dependencies among any elements across slices and in the whole feature map. The proposed LSSG is easy-to-use and can be plugged into many pulmonary nodule detection networks. To verify the performance of LSSANet, we compare with several recently proposed and competitive detection approaches based on 2D/3D CNN. Promising evaluation results on the large-scale PN9 dataset demonstrate the effectiveness of our method. Code is at https://github.com/Ruixxxx/LSSANet.

eess.IV↗

arXiv2022

Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval

Sketch-based 3D shape retrieval is a challenging task due to the large domain discrepancy between sketches and 3D shapes. Since existing methods are trained and evaluated on the same categories, they cannot effectively recognize the categories that have not been used during training. In this paper, we propose a novel domain disentangled generative adversarial network (DD-GAN) for zero-shot sketch-based 3D retrieval, which can retrieve the unseen categories that are not accessed during training. Specifically, we first generate domain-invariant features and domain-specific features by disentangling the learned features of sketches and 3D shapes, where the domain-invariant features are used to align with the corresponding word embeddings. Then, we develop a generative adversarial network that combines the domain-specific features of the seen categories with the aligned domain-invariant features to synthesize samples, where the synthesized samples of the unseen categories are generated by using the corresponding word embeddings. Finally, we use the synthesized samples of the unseen categories combined with the real samples of the seen categories to train the network for retrieval, so that the unseen categories can be recognized. In order to reduce the domain shift problem, we utilized unlabeled unseen samples to enhance the discrimination ability of the discriminator. With the discriminator distinguishing the generated samples from the unlabeled unseen samples, the generator can generate more realistic unseen samples. Extensive experiments on the SHREC'13 and SHREC'14 datasets show that our method significantly improves the retrieval performance of the unseen categories.

cs.CV↗

arXiv2022

An Efficient Methodology to Identify Missing Tags in Large-Scale RFID Systems

Radio frequency identification (RFID) has been widely has broad applications. One such application is to use RFID to track inventory in warehouses and retail stores. In this application, timely identifying the missing items is an ongoing engineering problem. A feasible solution to this problem is to map each tag to a time slot and verify the presence of a tag by comparing the status of the predicted time slot and the actual time slot. However, existing works are time inefficient because they only verify tags one by one in singleton slots but ignore the collision slots mapped by multiple tags. To accelerate the identification process, we use bit tracking to verify tags in collision slots and design two protocols accordingly. We first propose the Sequential String based Missing Tag Identification (SSMTI) protocol, which converts all time slots to collision slots and enables tags in each slot to reply to a designed string simultaneously. By using bit tracking to decode the combined string, the reader can verify multiple tags together. To improve the performance of SSMTI when most tags are missing, we further propose the Interactive String based Missing Tag Identification (ISMTI) protocol. ISMTI improves the strategies of designing strings for each collided tag so that the reader can verify more tags using shorter strings than SSMTI.Besides, ISMTI can dynamically adjust the verification mechanism according to the proportion of missing tags to maintain time efficiency. We also provide theoretical analysis for proposed protocols to minimize execution time and evaluate their performance through extensive simulations. Compared with state-of-the-art solutions, the proposed SSMTI and ISMTI can reduce the time cost by as much as 39.74% and 68.87%.

cs.NI↗

arXiv2022

Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints

However, current autoregressive approaches suffer from high latency. In this paper, we focus on non-autoregressive translation (NAT) for this problem for its efficiency advantage. We identify that current constrained NAT models, which are based on iterative editing, do not handle low-frequency constraints well. To this end, we propose a plug-in algorithm for this line of work, i.e., Aligned Constrained Training (ACT), which alleviates this problem by familiarizing the model with the source-side context of the constraints. Experiments on the general and domain datasets show that our model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints.

cs.CL↗

arXiv2022

Testing gravitational redshift based on microwave frequency links onboard China Space Station

In 2022 China Space Station (CSS) will be equipped with atomic clocks and optical clocks with stabilities of $2 \times 10^{-16}$ and $8 \times 10^{-18}$, respectively, which provides an excellent opportunity to test gravitational redshift (GR) with higher accuracy than previous results. Based on high-precise frequency links between CSS and a ground station, we formulated a model and provided simulation experiments to test GR. Simulation results suggest that this method could test the GR at the accuracy level of $(0.27 \pm 2.15) \times10^{-7}$, more than two orders in magnitude higher than the result of the experiment of a hydrogen clock on board a flying rocket more than 40 years ago.

astro-ph.IM↗

arXiv2022

Twisted Angle-Dependent Work Functions in CVD-Grown Twisted Bilayer Graphene by Kelvin Probe Force Microscopy

Tailoring the interlayer twist angle of bilayer graphene (BLG) has a significant influence on its electronic properties, including superconductivity, topological transitions, ferromagnetic states and correlated insulating states. These exotic electronic properties are sensitively dependent on the work functions of bilayer graphene samples. Here, the twisted angle-dependent work functions of CVD-grown twisted bilayer graphene (tBLG) are detailed investigated by Kelvin Probe Force Microscopy (KPFM) in combination with Raman spectra. The thickness-dependent surface potentials of Bernal-stacked multilayer graphene were measured. The AB-BLG and tBLG are directly determined by KPFM due to their twist angle-specific surface potentials. The detailed relationship of twist angles and surface potentials are further obtained by the in-situ combination investigation of KPFM and Raman spectra measurements. The thermal stability of tBLG was further explored through controlled annealing process. Our work provides the twisted angle-dependent surface potentials of tBLG and lays the foundation for further exploring their twist-angle-dependent novel electronic properties.

cond-mat.mtrl-sci↗

arXiv2022

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning

The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in English) problems sourced from the Civil Service Exams, which require intensive background knowledge to solve. More importantly, we design a free-text explanation scheme to explain whether an analogy should be drawn, and manually annotate them for each and every question and candidate answer. Empirical results suggest that this benchmark is very challenging for some state-of-the-art models for both explanation generation and analogical question answering tasks, which invites further research in this area.

cs.CL↗

arXiv2022

Coverage Axis: Inner Point Selection for 3D Shape Skeletonization

In this paper, we present a simple yet effective formulation called Coverage Axis for 3D shape skeletonization. Inspired by the set cover problem, our key idea is to cover all the surface points using as few inside medial balls as possible. This formulation inherently induces a compact and expressive approximation of the Medial Axis Transform (MAT) of a given shape. Different from previous methods that rely on local approximation error, our method allows a global consideration of the overall shape structure, leading to an efficient high-level abstraction and superior robustness to noise. Another appealing aspect of our method is its capability to handle more generalized input such as point clouds and poor-quality meshes. Extensive comparisons and evaluations demonstrate the remarkable effectiveness of our method for generating compact and expressive skeletal representation to approximate the MAT.

cs.GR↗

arXiv2021

Gevrey well-posedness of the hyperbolic Prandtl equations

We study the 2D and 3D Prandtl equations of degenerate hyperbolic type, and establish without any structural assumption the Gevrey well-posedness with Gevrey index $\leq 2$. Compared with the classical parabolic Prandtl equations, the loss of the derivatives, caused by the hyperbolic feature coupled with the degeneracy, can't be overcame by virtue of the classical cancellation mechanism that developed for the parabolic counterpart. Inspired by the abstract Cauchy-Kowalewski theorem and by virtue of the hyperbolic feature, we give in this text a straightforward proof, basing on an elementary $L^2$ energy estimate. In particular our argument does not involve the cancellation mechanism used efficiently for the classical Prandtl equations.

math.AP↗

arXiv2021

Neutron stars in massive scalar-Gauss-Bonnet gravity: Spherical structure and time-independent perturbations

The class of scalar-tensor theories with the scalar field coupling to the Gauss-Bonnet invariant has drawn great interest since solutions of spontaneous scalarization were found for black holes in these theories. We contribute to the existing literature a detailed study of the spontaneously scalarized neutron stars (NSs) in a typical theory where the coupling function of the scalar field takes the quadratic form and the scalar field is massive. The investigation here includes the spherical solutions of the NSs as well as their perturbative properties, namely the tidal deformability and the moment of inertia, treated in a unified and extendable way under the framework of spherical decomposition. We find that while the mass, the radius, and the moment of inertia of the spontaneously scalarized NSs show very moderate deviations from those of the NSs in general relativity (GR), the tidal deformability exhibits significant differences between the solutions in GR and the solutions of spontaneous scalarization for certain values of the parameters in the scalar-Gauss-Bonnet theory. As a result, the celebrated universal relation between the moment of inertia and the tidal deformability of neutron stars breaks down. With the mass and the tidal deformability of NSs attainable in the gravitational waves from binary NS mergers, the radius measurable using the X-ray satellites, and the moment of inertia accessible via the high-precision pulsar timing techniques, future multi-messenger observations can be contrasted with the theoretical results and provide us necessary information for building up theories beyond GR.

gr-qc↗

arXiv2021

MDFM: Multi-Decision Fusing Model for Few-Shot Learning

In recent years, researchers pay growing attention to the few-shot learning (FSL) task to address the data-scarce problem. A standard FSL framework is composed of two components: i) Pre-train. Employ the base data to generate a CNN-based feature extraction model (FEM). ii) Meta-test. Apply the trained FEM to the novel data (category is different from base data) to acquire the feature embeddings and recognize them. Although researchers have made remarkable breakthroughs in FSL, there still exists a fundamental problem. Since the trained FEM with base data usually cannot adapt to the novel class flawlessly, the novel data's feature may lead to the distribution shift problem. To address this challenge, we hypothesize that even if most of the decisions based on different FEMs are viewed as weak decisions, which are not available for all classes, they still perform decently in some specific categories. Inspired by this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM), which comprehensively considers the decisions based on multiple FEMs to enhance the efficacy and robustness of the model. MDFM is a simple, flexible, non-parametric method that can directly apply to the existing FEMs. Besides, we extend the proposed MDFM to two FSL settings (i.e., supervised and semi-supervised settings). We evaluate the proposed method on five benchmark datasets and achieve significant improvements of 3.4%-7.3% compared with state-of-the-arts.

cs.CV↗