arXiv subjects

Jun Li

Publications and source records attributed to Jun Li.

At least 559 records · Page 31Linked to original sources

arXiv2022

Novel Valence Transition in Elemental Metal Europium around 80 GPa

Valence transition could induce structural, insulator-metal, nonmagnetic-magnetic and superconducting transitions in rare-earth metals and compounds, while the underlying physics remains unclear due to the complex interaction of localized 4f electrons as well as their coupling with itinerant electrons. The valence transition in the elemental metal europium (Eu) still has remained as a matter of debate. Using resonant x-ray emission scattering and x-ray diffraction, we pressurize the states of 4f electrons in Eu and study its valence and structure transitions up to 160 GPa. We provide compelling evidence for a valence transition around 80 GPa, which coincides with a structural transition from a monoclinic (C2/c) to an orthorhombic phase (Pnma). We show that the valence transition occurs when the pressure-dependent energy gap between 4f and 5d electrons approaches the Coulomb interaction. Our discovery is critical for understanding the electrodynamics of Eu, including magnetism and high-pressure superconductivity.

cond-mat.str-el↗

arXiv2022

Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

Local representation learning has been a key challenge to promote the performance of the histopathological whole slide images analysis. The previous representation learning methods followed the supervised learning paradigm. However, manual annotation for large-scale WSIs is time-consuming and labor-intensive. Hence, the self-supervised contrastive learning has recently attracted intensive attention. The present contrastive learning methods treat each sample as a single class, which suffers from class collision problems, especially in the domain of histopathology image analysis. In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis. We built a lesion queue based on the memory bank structure to store the representations of different classes of WSIs, which allowed the contrastive model to selectively define the negative pairs during the training. Moreover, We designed a queue refinement strategy to purify the representations stored in the lesion queue. The experimental results demonstrate that LACL achieves the best performance in histopathology image representation learning on different datasets, and outperforms state-of-the-art methods under different WSI classification benchmarks. The code is available at https://github.com/junl21/lacl.

cs.CV↗

arXiv2022

Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification

Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits the effectiveness and efficiency in the application to gigapixel histopathology images. In this paper, we propose a kernel attention Transformer (KAT) for histopathology WSI classification. The information transmission of the tokens is achieved by cross-attention between the tokens and a set of kernels related to a set of positional anchors on the WSI. Compared to the common Transformer structure, the proposed KAT can better describe the hierarchical context information of the local regions of the WSI and meanwhile maintains a lower computational complexity. The proposed method was evaluated on a gastric dataset with 2040 WSIs and an endometrial dataset with 2560 WSIs, and was compared with 6 state-of-the-art methods. The experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI classification and is superior to the state-of-the-art methods. The code is available at https://github.com/zhengyushan/kat.

cs.CV↗

arXiv2022

Enhanced low-energy magnetic excitations evidencing the Cu-induced localization in an Fe-based superconductor Fe$_{0.98}$Te$_{0.5}$Se$_{0.5}$

We have performed inelastic neutron scattering measurements on optimally-doped Fe$_{0.98}$Te$_{0.5}$Se$_{0.5}$ and 10% Cu-doped Fe$_{0.88}$Cu$_{0.1}$Te$_{0.5}$Se$_{0.5}$ to investigate the substitution effects on the spin excitations in the whole energy range up to 300 meV. It is found that substitution of Cu for Fe enhances the low-energy spin excitations ($\le$ 100 meV), especially around the (0.5, 0.5) point, and leaves the high-energy magnetic excitations intact. In contrast to the expectation that Cu with spin 1/2 will dilute the magnetic moments contributed by Fe with a larger spin, we find that the 10% Cu doping enlarges the effective fluctuating moment from 2.85 to 3.13 $μ_{\rm B}$/Fe, although there is no long- or short-range magnetic order around (0.5, 0.5) and (0.5, 0). The presence of enhanced magnetic excitations in the 10% Cu doped sample which is in the insulating state indicates that the magnetic excitations must have some contributions from the local moments, reflecting the dual nature of the magnetism in iron-based superconductors. We attribute the substitution effects to the localization of the itinerant electrons induced by Cu dopants. These results also indicate that the Cu doping does not act as electron donor as in a rigid-band shift model, but more as scattering centers that localize the system.

cond-mat.supr-con↗

arXiv2022

Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent

Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This paper proposes a novel algorithm that encodes the partial derivatives themselves and furthermore optimizes the codes by performing lossy compression on the derivative codewords by maximizing the information contained in the codewords while minimizing the information between the codewords. The utility of this application of coding theory is a geometrical consequence of the observed fact in optimization research that noise is tolerable, sometimes even helpful, in gradient descent based learning algorithms since it helps avoid overfitting and local minima. This stands in contrast with much current conventional work on distributed coded computation which focuses on recovering all of the data from the workers. A second further contribution is that the low-weight nature of the coding scheme allows for asynchronous gradient updates since the code can be iteratively decoded; i.e., a worker's task can immediately be updated into the larger gradient. The directional derivative is always a linear function of the direction vectors; thus, our framework is robust since it can apply linear coding techniques to general machine learning frameworks such as deep neural networks.

cs.LG↗

arXiv2022

A Self-Guided Framework for Radiology Report Generation

Automatic radiology report generation is essential to computer-aided diagnosis. Through the success of image captioning, medical report generation has been achievable. However, the lack of annotated disease labels is still the bottleneck of this area. In addition, the image-text data bias problem and complex sentences make it more difficult to generate accurate reports. To address these gaps, we pre-sent a self-guided framework (SGF), a suite of unsupervised and supervised deep learning methods to mimic the process of human learning and writing. In detail, our framework obtains the domain knowledge from medical reports with-out extra disease labels and guides itself to extract fined-grain visual features as-sociated with the text. Moreover, SGF successfully improves the accuracy and length of medical report generation by incorporating a similarity comparison mechanism that imitates the process of human self-improvement through compar-ative practice. Extensive experiments demonstrate the utility of our SGF in the majority of cases, showing its superior performance over state-of-the-art meth-ods. Our results highlight the capacity of the proposed framework to distinguish fined-grained visual details between words and verify its advantage in generating medical reports.

cs.CL↗

arXiv2022

Design of optical voltage sensor based on electric field regulation and rotating isomerism electrode

Temperature drift, stress birefringence and low frequency vibration lead to the randomness and fluctuation of the output of optical voltage sensor(OVS). In order to solve the problem, this study adopts the lock-in amplifier technology with the aid of a high-speed rotating electrode to realize electric field modulation. This technology could shift the measured signal frequency band from near 50 Hz moved to several kilometer Hz, so as to make the output signal avoid the interference from low-frequency temperature drift, stress birefringence and vibration, leading to higher stability and reliability. The electro-optic coupling wave theory and static electric field finite element method are utilized to investigate the shape of modulation wave. The simulation results proves that lock-in technology is able to prevent the measured voltage signal from the large step signal interference and restore the perfect original signal. While the sample rate is decreased to the value of the modulation frequency.

eess.SP↗

arXiv2022

Coexistence of Quasi-two-dimensional Superconductivity and Tunable Kondo Lattice in a van der Waals Superconductor

Realization of Kondo lattice in superconducting van der Waals materials not only provides a unique opportunity for tuning the Kondo lattice behavior by electrical gating or intercalation, but also is helpful for further understanding the heavy fermion superconductivity. Here we report a low-temperature and vector-magnetic-field scanning tunneling microscopy and spectroscopy study on a superconducting compound (4Hb-TaS2) with alternate stacking of 1T-TaS2 and 1H-TaS2 layers. We observe the quasi-two-dimensional superconductivity in the 1H-TaS2 layer with anisotropic response to the in-plane and out-of-plane magnetic fields. In the 1T-TaS2 layer, we detect the Kondo resonance peak that results from the Kondo screening of the unpaired electrons in the Star-of-David clusters. We also find the intensity of the Kondo resonance peak is sensitive to its relative position with the Fermi level, and it can be significantly enhanced when it's further shifted towards the Fermi level by evaporating Pb atoms onto the 1T-TaS2 surface. Our results are not only important for fully understanding the electronic properties of 4Hb-TaS2, but also pave the way for creating tunable Kondo lattice in the superconducting van der Waals materials.

cond-mat.supr-con↗

arXiv2022

CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers

Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details. In this paper, we propose a novel point cloud completion network, namely CompleteDT. Specifically, features are learned from point clouds with different resolutions, which is sampled from the incomplete input, and are converted to a series of \textit{spots} based on the geometrical structure. Then, the Dense Relation Augment Module (DRA) based on the transformer is proposed to learn features within \textit{spots} and consider the correlation among these \textit{spots}. The DRA consists of Point Local-Attention Module (PLA) and Point Dense Multi-Scale Attention Module (PDMA), where the PLA captures the local information within the local \textit{spots} by adaptively measuring weights of neighbors and the PDMA exploits the global relationship between these \textit{spots} in a multi-scale densely connected manner. Lastly, the complete shape is predicted from \textit{spots} by the Multi-resolution Point Fusion Module (MPF), which gradually generates complete point clouds from \textit{spots}, and updates \textit{spots} based on these generated point clouds. Experimental results show that, because the DRA based on the transformer can learn the expressive features from the incomplete input and the MPF can fully explore these feature to predict the complete input, our method largely outperforms the state-of-the-art methods.

cs.CV↗

arXiv2022

A Method for Self-Service Rehabilitation Training of Human Lower Limbs

In recent years, the research of rehabilitation robot technology has become a hotspot in the field of rehabilitation medicine engineering and robotics. To assist active rehabilitation in patients with unilateral lower extremity injury, we propose a new self-service rehabilitation training method to control the injured lower extremity through its contralateral healthy upper limbs. Firstly, the movement data of upper limbs and lower limbs of healthy people in normal walking state are obtained by gait measurement experiment. Secondly, the eigenvectors of upper limb and lower limb movements in a single movement cycle are extracted respectively. Thirdly, the linear mapping relationship between the upper limbs movement and the lower limbs movement is identified using the least squares method. Finally, the simulation experiment of self-service rehabilitation training is implemented on MATLAB/Simulink. The results indicate that the identified linear mapping model can achieve good accuracy and adaptability. The self-service rehabilitation training method is effective for helping patients with unilateral limb injury to make rehabilitation training on themselves.

cs.RO↗

arXiv2022

Accelerating Grasp Learning via Pretraining with Coarse Affordance Maps of Objects

Self-supervised grasp learning, i.e., learning to grasp by trial and error, has made great progress. However, it is still time-consuming to train such a model and also a challenge to apply it in practice. This work presents an accelerating method of robotic grasp learning via pretraining with coarse affordance maps of objects to be grasped based on a quite small dataset. A model generated through pre-training is harnessed as an initialization policy to warmly start grasp learning so as to guide a robot to capture more effective rewards at the beginning of training. An object in its coarse affordance map is annotated with a single key point and thereby, the burden of labeling is greatly alleviated. Extensive experiments in simulation and on a real robot are conducted to evaluate the proposed method. The simulation results show that it can significantly accelerate grasp learning by nearly three times over a vanilla Deep Q-Network -based method. Its test on a real UR3 robot shows that it reaches a grasp success rate of 89.5% via only 500 times of grasp tries within about two hours, which is four times faster than its competitor. In addition, it enjoys an outstanding generalization ability to grasp prior-unseen novel objects. It outperforms some existing methods and has the potential to directly apply to a robot for real-world grasp learning tasks.

cs.RO↗

arXiv2022

A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model

This paper studies the cooperative learning of two generative flow models, in which the two models are iteratively updated based on the jointly synthesized examples. The first flow model is a normalizing flow that transforms an initial simple density to a target density by applying a sequence of invertible transformations. The second flow model is a Langevin flow that runs finite steps of gradient-based MCMC toward an energy-based model. We start from proposing a generative framework that trains an energy-based model with a normalizing flow as an amortized sampler to initialize the MCMC chains of the energy-based model. In each learning iteration, we generate synthesized examples by using a normalizing flow initialization followed by a short-run Langevin flow revision toward the current energy-based model. Then we treat the synthesized examples as fair samples from the energy-based model and update the model parameters with the maximum likelihood learning gradient, while the normalizing flow directly learns from the synthesized examples by maximizing the tractable likelihood. Under the short-run non-mixing MCMC scenario, the estimation of the energy-based model is shown to follow the perturbation of maximum likelihood, and the short-run Langevin flow and the normalizing flow form a two-flow generator that we call CoopFlow. We provide an understating of the CoopFlow algorithm by information geometry and show that it is a valid generator as it converges to a moment matching estimator. We demonstrate that the trained CoopFlow is capable of synthesizing realistic images, reconstructing images, and interpolating between images.

stat.ML↗

arXiv2022

Squeezed light generated with hyperradiance without nonlinearity

We propose that the squeezed light accompanied by hyperradiance is induced by quantum interference in a linear system consisting of a high quality optical cavity and two coherently driven two-level qubits. When two qubits are placed at the crest and trough of the standing wave in the cavity respectively (i.e., they have the opposite coupling coefficient to the cavity), we show that squeezed light is generated in the hyperradiance regime under the conditions of strong coupling and weak driving. Simultaneously, the Klyshko's criterion alternates up and down at unity when the photon number is even or odd. Moreover, the orthogonal angles of the squeezed light can be controlled by adjusting the frequency detuning pressure between the driving field and the qubits. It can be implemented in a variety of quantum systems, including but not limited to two-level systems such as atoms, quantum dots in single-mode cavities.

quant-ph↗

arXiv2022

CoDo: Contrastive Learning with Downstream Background Invariance for Detection

The prior self-supervised learning researches mainly select image-level instance discrimination as pretext task. It achieves a fantastic classification performance that is comparable to supervised learning methods. However, with degraded transfer performance on downstream tasks such as object detection. To bridge the performance gap, we propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo). The pretext task is converted to focus on instance location modeling for various backgrounds, especially for downstream datasets. The ability of background invariance is considered vital for object detection. Firstly, a data augmentation strategy is proposed to paste the instances onto background images, and then jitter the bounding box to involve background information. Secondly, we implement architecture alignment between our pretraining network and the mainstream detection pipelines. Thirdly, hierarchical and multi views contrastive learning is designed to improve performance of visual representation learning. Experiments on MSCOCO demonstrate that the proposed CoDo with common backbones, ResNet50-FPN, yields strong transfer learning results for object detection.

cs.CV↗

arXiv2022

Towards a Larger Molecular Simulation on the Quantum Computer: Up to 28 Qubits Systems Accelerated by Point Group Symmetry

The exact evaluation of the molecular ground state in quantum chemistry requires an exponentially increasing computational cost. Quantum computation is a promising way to overcome the exponential problem using polynomial-time quantum algorithms. A quantum-classical hybrid optimization scheme known as the variational quantum eigensolver(VQE) is preferred for noisy intermediate-scale quantum devices. However, the circuit depth becomes one of the bottlenecks of its application to large molecules of more than 20 qubits. In this work, we employ the point group symmetry to reduce the number of operators in constructing ansatz so as to achieve a more compact quantum circuit. We illustrate this methodology with a series of molecules ranging from LiH(12 qubits) to C2H4(28 qubits). A significant reduction of up to 82% of the operator numbers is reached on C2H4, which enables the largest molecule ever numerically simulated by VQE-UCC to the best of our knowledge. This also shed light into the further work of this direction to construct even shallower ansatz with enough expressive power and simulate even larger scale system.

quant-ph↗

arXiv2022

Robust quantum control for the manipulation of solid-state spins

Robust and high-fidelity control of electron spins in solids is the cornerstone for facilitating applications of solid-state spins in quantum information processing and quantum sensing. However, precise control of spin systems is always challenging due to the presence of a variety of noises originating from the environment and control fields. Herein, noise-resilient quantum gates, designed with robust optimal control (ROC) algorithms, are demonstrated experimentally with nitrogen-vacancy (NV) centers in diamond to realize tailored robustness against detunings and Rabi errors simultaneously. In the presence of both 10% off-resonant detuning and deviation of a Rabi frequency, we achieve an average single-qubit gate fidelity of up to 99.97%. Our experiments also show that, ROCbased multipulse quantum sensing sequences can suppress spurious responses resulting from finite widths and imperfections of microwave pulses, which provides an efficient strategy for enhancing the performance of existing multipulse quantum sensing sequences.

quant-ph↗

arXiv2022

Quantum Control for Time-dependent Noise by Inverse Geometric Optimization

Quantum systems are exceedingly difficult to engineer because they are sensitive to various types of noises. In particular, time-dependent noises are frequently encountered in experiments but how to overcome them remains a challenging problem. In this work, we extend and apply the recently proposed robust control technique of inverse geometric optimization to time-dependent noises by working it in the filter-function formalism. The basic idea is to parameterize the control filter function geometrically and minimize its overlap with the noise spectral density. This then effectively reduces the noise susceptibility of the controlled system evolution. We show that the proposed method can produce high-quality robust pulses for realizing desired quantum evolutions under realistic noise models, and thus will find practical applications for current physical platforms.

quant-ph↗

arXiv2022

Dynamic Registration: Joint Ego Motion Estimation and 3D Moving Object Detection in Dynamic Environment

Localization in a dynamic environment suffers from moving objects. Removing dynamic object is crucial in this situation but become tricky when ego-motion is coupled. In this paper, instead of proposing a new slam framework, we aim at a more general strategy for a localization scenario. In that case, Dynamic Registration is available for integrating with any lidar slam system. We utilize 3D object detection to obtain potential moving objects and remove them temporarily. Then we proposed Dynamic Registration, to iteratively estimate ego-motion and segment moving objects until no static object generates. Static objects are merged with the environment. Finally, we successfully segment dynamic objects, static environments with static objects, and ego-motion estimation in a dynamic environment. We evaluate the performance of our proposed method on KITTI Tracking datasets. Results show stable and consistent improvements based on other classical registration algorithms.

cs.RO↗