arXiv subjects

Kun Wang

Publications and source records attributed to Kun Wang.

At least 379 records · Page 21Linked to original sources

arXiv2022

Domain Wall Enabled Steep Slope Switching in MoS$_2$ Transistors Towards Hysteresis-Free Operation

The device concept of ferroelectric-based negative capacitance (NC) transistors offers a promising route for achieving energy-efficient logic applications that can outperform the conventional semiconductor technology, while viable operation mechanisms remain a central topic of debate. In this work, we report steep slope switching in MoS$_2$ transistors back-gated by single-layer polycrystalline PbZr$_{0.35}$Ti$_{0.65}$O$_3$. The devices exhibit current on/off ratios up to 8$\times$10$^6$ within an ultra-low gate voltage window of $V_g$ = $\pm$0.5 V and subthreshold swing (SS) as low as 9.7 mV decade$^{-1}$ at room temperature, transcending the 60 mV decade$^{-1}$ Boltzmann limit without involving additional dielectric layers. Theoretical modeling reveals the dominant role of the metastable polar states within domain walls in enabling the NC mode, which is corroborated by the relation between SS and domain wall density. Our findings shed light on a hysteresis-free mechanism for NC operation, providing a simple yet effective material strategy for developing low-power 2D nanoelectronics.

physics.app-ph↗

arXiv2022

A machine-learning based closed orbit feedback for the SSRF storage ring

In order to improve the stability of synchrotron radiation, we developed a new method of machine learning-based closed orbit feedback and piloted it in the storage ring of the Shanghai Synchrotron Radiation Facility (SSRF). In our experiments, not only can the machine learning-based closed orbit feedback carry out horizontal, vertical and RF frequency feedback simultaneously, but it also has better convergence and convergence speed than the traditional Slow Orbit Feed Back (SOFB) system. What's more, the residual values of the correctors' currents variations after correction can be almost ignored. This machine learning-based new method is expected to establish a new closed orbit feedback system and improve the orbit stability of the storage ring in daily operation.

physics.acc-ph↗

arXiv2022

Generic bond energy formalism within the modified quasichemical model for ternary solutions

The Modified Quasichemical Model in the Pair Approximation (MQMPA) can effectively capture the thermodynamic features of a binary solution with Short-Range Ordering (SRO). If the model is used to treat a ternary solution, a geometric interpolation method must be employed to extend the bond energy expression from binary to ternary formalism. The aim of the present work is to implement such extension by means of a generic geometric interpolation approach. The generic method is unbiased and can be transformed into the widely used Kohler, Toop and Muggianu approaches with special interpolation parameters. The interpolation parameters can be calculated by the integration method as well as be optimized by ternary experimental data. The generic bond energy formalism (GBEF) has thus been derived to provide the MQMPA great flexibility to describe ternary solutions with complex configurations. Moreover, the GBEF is more concise than the formula derived by a combinatorial Kohler-Toop method. The concise GBEF is in the respect more conveniently programmed. Eventually, the Cu-Li-Sn liquid where both SRO and clustering among atoms occur is employed to validate the effectiveness and reliability of the GBEF within the MQMPA.

cond-mat.mtrl-sci↗

arXiv2022

DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion

Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually sparse, it contains valid and real distance information, i.e., scale-consistent absolute depth values. Meanwhile, scale-agnostic counterparts seek to estimate relative depth and have achieved impressive performance. To leverage both the inherent characteristics, we thus suggest to model scale-consistent depth upon unsupervised scale-agnostic frameworks. Specifically, we propose the decomposed scale-consistent learning (DSCL) strategy, which disintegrates the absolute depth into relative depth prediction and global scale estimation, contributing to individual learning benefits. But unfortunately, most existing unsupervised scale-agnostic frameworks heavily suffer from depth holes due to the extremely sparse depth input and weak supervised signal. To tackle this issue, we introduce the global depth guidance (GDG) module, which attentively propagates dense depth reference into the sparse target via novel dense-to-sparse attention. Extensive experiments show the superiority of our method on outdoor KITTI benchmark, ranking 1st and outperforming the best KBNet more than 12% in RMSE. In addition, our approach achieves state-of-the-art performance on indoor NYUv2 dataset.

cs.CV↗

arXiv2022

Event-Triggered Intermittent Prescribed Performance Control for Spacecraft Attitude Reorientation

This paper focuses on the issue of how to realize spacecraft attitude control with guaranteed performance while conspicuously reducing the actuator acting frequency simultaneously. The prescribed performance control (PPC) scheme is often employed for the control with guaranteed performance. However, conventional PPC controllers are designed from the perspective of continuous system, which contradicts the "discrete" control logic in actual spacecraft control system, and such a problem limited the application value of PPC scheme in actual applications. In order to significantly lower the actuator acting frequency while still maintaining the desired performance, a composite event-trigger mechanism is proposed for this issue, turning off the actuator and eliminating unnecessary control output under appropriate conditions. Further, the proposed composite event-trigger mechanism is combined with a prescribed performance control scheme, constructing a complete controller structure for solving the presented issue. Meanwhile, a special performance function is designed to provide mild transition process. Based on the proposed scheme, a specific backstepping controller is further developed as a validation. Finally, numerical simulation results are presented to validate the effectiveness of the proposed scheme.

eess.SY↗

arXiv2022

ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations

Deep learning models rely on highly optimized tensor libraries for efficient inference on heterogeneous hardware. Current deep compilers typically predetermine layouts of tensors and then optimize loops of operators. However, such unidirectional and one-off workflow strictly separates graph-level optimization and operator-level optimization into different system layers, missing opportunities for unified tuning. This paper proposes ALT, a compiler that performs joint graph- and operator-level optimizations for deep models. ALT provides a generic transformation module to manipulate layouts and loops with easy-to-use primitive functions. ALT further integrates an auto-tuning module that jointly optimizes graph-level data layouts and operator-level loops while guaranteeing efficiency. Experimental results show that ALT significantly outperforms state-of-the-art compilers (e.g., Ansor) in terms of both single operator performance (e.g., 1.5x speedup on average) and end-to-end inference performance (e.g., 1.4x speedup on average).

cs.LG↗

arXiv2022

R$^2$F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference

Document-level natural language inference (DOCNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents. Current datasets and baselines largely follow sentence-level settings, but fail to address the issues raised by longer documents. In this paper, we establish a general solution, named Retrieval, Reading and Fusion (R2F) framework, and a new setting, by analyzing the main challenges of DOCNLI: interpretability, long-range dependency, and cross-sentence inference. The basic idea of the framework is to simplify document-level task into a set of sentence-level tasks, and improve both performance and interpretability with the power of evidence. For each hypothesis sentence, the framework retrieves evidence sentences from the premise, and reads to estimate its credibility. Then the sentence-level results are fused to judge the relationship between the documents. For the setting, we contribute complementary evidence and entailment label annotation on hypothesis sentences, for interpretability study. Our experimental results show that R2F framework can obtain state-of-the-art performance and is robust for diverse evidence retrieval methods. Moreover, it can give more interpretable prediction results. Our model and code are released at https://github.com/phoenixsecularbird/R2F.

cs.CL↗

arXiv2022

6N-DoF Pose Tracking for Tensegrity Robots

Tensegrity robots, which are composed of compressive elements (rods) and flexible tensile elements (e.g., cables), have a variety of advantages, including flexibility, low weight, and resistance to mechanical impact. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and track their state. This work aims to address what has been recognized as a grand challenge in this domain, i.e., the state estimation of tensegrity robots through a markerless, vision-based method, as well as novel, onboard sensors that can measure the length of the robot's cables. In particular, an iterative optimization process is proposed to track the 6-DoF pose of each rigid element of a tensegrity robot from an RGB-D video as well as endcap distance measurements from the cable sensors. To ensure that the pose estimates of rigid elements are physically feasible, i.e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization. Real-world experiments are performed with a 3-bar tensegrity robot, which performs locomotion gaits. Given ground truth data from a motion capture system, the proposed method achieves less than 1~cm translation error and 3 degrees rotation error, which significantly outperforms alternatives. At the same time, the approach can provide accurate pose estimation throughout the robot's motion, while motion capture often fails due to occlusions.

cs.RO↗

arXiv2022

Unveiling hidden stellar aggregates in the Milky Way: 1656 new star clusters found in Gaia EDR3

We report 1,656 new star clusters found in the Galactic disk (|b|<20 degrees) beyond 1.2 kpc, using Gaia EDR3 data. Based on an unsupervised machine learning algorithm, DBSCAN, and followed our previous studies, we utilized a unique method to do the data preparation and obtained the clustering coefficients, which proved to be an effective way to search blindly for star clusters. We tabulated the physical parameters and member stars of the new clusters, and presented some interesting examples, including a globular cluster candidate. The cluster parameters and member stars are available at CDS via anonymous ftp to https://cdsarc.cds.unistra.fr/ftp/vizier.submit//he22c. We examined the new discoveries and discussed their statistical properties. The proper motion dispersions and radii of the new clusters were the same as the previously reported ones. The new star clusters beyond 1.2 kpc were older than those in the solar neighborhood, and the new objects found in the third Galactic quadrant presented the lowest line-of-sight extinctions. Combined with our previous results, the total population of new clusters detected through our method was 2,541, corresponding to 55% of all newly published clusters in the Gaia era. The number of cataloged Gaia star clusters was also increased to nearly six thousand. In the near future, it is necessary to make a unified confirmation and member star determination for all reported clusters.

astro-ph.GA↗

arXiv2022

Topological K-homology for non-proper actions and assembly maps

For a countable discrete group $G$, we construct a new and concrete model for the equivariant topological $K$-homology theory of $G$, which is defined for all $G$-actions, not just for proper $G$-actions. The construction of our model combines some of the ideas from the localization algebra approach to the coarse Baum-Connes conjecture and the controlled algebra approach to the Farrell-Jones conjecture. It brings new ideas into the study of the Baum-Connes conjecture. First, as the model is defined for all $G$-actions, we are able to define relative assembly maps. We prove, by using an induction structure of our theory, a transitivity principle that can be used to verify when a relative assembly map is an isomorphism. This promotes the study of the original assembly map relative to the family of finite subgroups to assembly maps relative to any family of subgroups of a group. Second, our new model is suitable for the use of the method of controlled topology and controlled algebra, which plays an important role in proving the coarse Baum-Connes conjecture and the Farrell-Jones conjecture for a large class of groups.

math.KT↗

arXiv2022

Experimental optimal verification of three-dimensional entanglement on a silicon chip

High-dimensional entanglement is significant for the fundamental studies of quantum physics and offers unique advantages in various quantum information processing (QIP) tasks. Integrated quantum devices have recently emerged as a promising platform for creating, processing, and detecting complex high-dimensional entangled states. A crucial step towards practical quantum technologies is to verify that these devices work reliably with an optimal strategy. In this work, we experimentally implement an optimal quantum verification strategy on a three-dimensional maximally entangled state using local projective measurements on a silicon photonic chip. A 95% confidence is achieved from 1190 copies to verify the target quantum state. The obtained scaling of infidelity as a function of the number of copies is -0.5497+-0.0002, exceeding the standard quantum limit of -0.5 with 248 standard deviations. Our results indicate that quantum state verification could serve as an efficient tool for complex quantum measurement tasks.

quant-ph↗

arXiv2022

Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy

Large-scale datasets play a vital role in computer vision. But current datasets are annotated blindly without differentiation to samples, making the data collection inefficient and unscalable. The open question is how to build a mega-scale dataset actively. Although advanced active learning algorithms might be the answer, we experimentally found that they are lame in the realistic annotation scenario where out-of-distribution data is extensive. This work thus proposes a novel active learning framework for realistic dataset annotation. Equipped with this framework, we build a high-quality vision dataset -- Bamboo, which consists of 69M image classification annotations with 119K categories and 28M object bounding box annotations with 809 categories. We organize these categories by a hierarchical taxonomy integrated from several knowledge bases. The classification annotations are four times larger than ImageNet22K, and that of detection is three times larger than Object365. Compared to ImageNet22K and Objects365, models pre-trained on Bamboo achieve superior performance among various downstream tasks (6.2% gains on classification and 2.1% gains on detection). We believe our active learning framework and Bamboo are essential for future work.

cs.CV↗

arXiv2022

Full-Waveform Modeling for Time-of-Flight Measurements based on Arrival Time of Photons

Modern LiDAR sensors find increasing use in safety-critical applications. Therefore, highly accurate modeling of the system's behavior under demanding environmental conditions is necessary. In this paper, we present a modular structure to accurately simulate the amplified raw detector signal of a direct time-of-flight LiDAR system for coaxial transmitter-receiver optics. Our model describes, a measurement system based on standard optical components and a detector able of converting single photons to an electrical signal. To verify the model's predictions, single-point measurements for targets of different reflectivity at defined distances were performed. Statistical analysis shows an R-squared value greater than 0.990 for simulated and measured signal amplitude levels. Noise modeling shows good accordance with the performed measurements for different target irradiance levels. The presented results have a guiding significance in the modeling of the complex signal processing chain of LiDAR systems, as it enables the prediction of key parameters of the system early in the development process. Hence, unnecessary costs by design flaws can be mitigated. The modular structure allows easy adaption for arbitrary LiDAR systems.

physics.ins-det↗

arXiv2022

Extremely low-mass white dwarf stars observed in Gaia DR2 and LAMOST DR8

We present the first results from our ongoing project to study extremely low mass (ELM) white dwarfs (WDs) ($M$ $\leq$ 0.3$M_{\sun}$) with the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) spectra. Based on the LAMOST DR8 spectral database, we analyzed 136 ELM WD candidates selected from $\it Gaia$ DR2 data and 12 known objects previously identified by the ELM Survey. The atmospheric parameters and radial velocities of these stars were obtained by fitting the LAMOST low-resolution spectra. After comparing the atmospheric parameters of the 12 known objects from this work to the results reported by the ELM Survey, we demonstrated the potential of LAMOST spectra in probing into the nature of ELM WDs. Based on the atmospheric parameters and $\it Gaia$ EDR3 data, we identified 21 new high-probability ELM WDs with masses $M$ $\leq$ 0.3$M_{\sun}$ and parallax estimates that agree to within a factor of 3. Two of them, J0338+4134 and J1129+4715, show significant radial velocity variability and are very likely to be binary systems containing at least one ELM WD.

astro-ph.SR↗

arXiv2022

RigNet: Repetitive Image Guided Network for Depth Completion

Depth completion deals with the problem of recovering dense depth maps from sparse ones, where color images are often used to facilitate this task. Recent approaches mainly focus on image guided learning frameworks to predict dense depth. However, blurry guidance in the image and unclear structure in the depth still impede the performance of the image guided frameworks. To tackle these problems, we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values. Specifically, the repetition is embodied in both the image guidance branch and depth generation branch. In the former branch, we design a repetitive hourglass network to extract discriminative image features of complex environments, which can provide powerful contextual instruction for depth prediction. In the latter branch, we introduce a repetitive guidance module based on dynamic convolution, in which an efficient convolution factorization is proposed to simultaneously reduce its complexity and progressively model high-frequency structures. Extensive experiments show that our method achieves superior or competitive results on KITTI benchmark and NYUv2 dataset.

cs.CV↗

arXiv2022

Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion

In this paper, we formulate a potentially valuable panoramic depth completion (PDC) task as panoramic 3D cameras often produce 360° depth with missing data in complex scenes. Its goal is to recover dense panoramic depths from raw sparse ones and panoramic RGB images. To deal with the PDC task, we train a deep network that takes both depth and image as inputs for the dense panoramic depth recovery. However, it needs to face a challenging optimization problem of the network parameters due to its non-convex objective function. To address this problem, we propose a simple yet effective approach termed M{^3}PT: multi-modal masked pre-training. Specifically, during pre-training, we simultaneously cover up patches of the panoramic RGB image and sparse depth by shared random mask, then reconstruct the sparse depth in the masked regions. To our best knowledge, it is the first time that we show the effectiveness of masked pre-training in a multi-modal vision task, instead of the single-modal task resolved by masked autoencoders (MAE). Different from MAE where fine-tuning completely discards the decoder part of pre-training, there is no architectural difference between the pre-training and fine-tuning stages in our M$^{3}$PT as they only differ in the prediction density, which potentially makes the transfer learning more convenient and effective. Extensive experiments verify the effectiveness of M{^3}PT on three panoramic datasets. Notably, we improve the state-of-the-art baselines by averagely 26.2% in RMSE, 51.7% in MRE, 49.7% in MAE, and 37.5% in RMSElog on three benchmark datasets.

cs.CV↗

arXiv2022

A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data

Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex robotic systems. These engines are often executed at a high-frequency to achieve accurate simulation. Ground truth trajectories for training differentiable engines, however, are not typically available at such high frequencies due to limitations of real-world sensors. The present work focuses on this frequency mismatch, which impacts the modeling accuracy. We proposed a recurrent structure for a differentiable physics engine of tensegrity robots, which can be trained effectively even with low-frequency trajectories. To train this new recurrent engine in a robust way, this work introduces relative to prior work: (i) a new implicit integration scheme, (ii) a progressive training pipeline, and (iii) a differentiable collision checker. A model of NASA's icosahedron SUPERballBot on MuJoCo is used as the ground truth system to collect training data. Simulated experiments show that once the recurrent differentiable engine has been trained given the low-frequency trajectories from MuJoCo, it is able to match the behavior of MuJoCo's system. The criterion for success is whether a locomotion strategy learned using the differentiable engine can be transferred back to the ground-truth system and result in a similar motion. Notably, the amount of ground truth data needed to train the differentiable engine, such that the policy is transferable to the ground truth system, is 1% of the data needed to train the policy directly on the ground-truth system.

cs.RO↗

arXiv2022

A Blind All-sky Search for Star Clusters in Gaia EDR3: 886 Clusters within 1.2 kpc of the Sun

Although previous searches for star clusters have been very successful, many clusters are likely still omitted, especially at high Galactic latitude regions. In this work, based on the astrometry of Gaia EDR3, we searched nearby (parallax > 0.8 mas) all-sky regions, obtaining 886 star clusters, of which 270 candidates have not been cataloged before. At the same time, we have presented the physical parameters of the clusters by fitting theoretical isochrones to their optical magnitudes. More halo members and expanding structures in many star clusters were also found. Most of the new objects are young clusters that are less than 100 million years old. Our work greatly increased the sample size and physical parameters of star clusters in the solar neighborhood, in particular, 46 clusters are newly found with |b| > 20 deg, which represents an increase of nearly three fold of cluster numbers at high Galactic latitude regions. The cluster parameters and member stars are available at CDS via https://cdsarc.u-strasbg.fr/ftp/vizier.submit//hezh22b/, and the cluster figure sets are available via https://doi.org/10.12149/101133.

astro-ph.GA↗