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Yu Liu

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preprint2026arXiv

Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation

Generative recommender systems have recently emerged as a promising paradigm by formulating next-item prediction as an auto-regressive semantic IDs generation, such as OneRec series works. However, with the next-item-agnostic prediction paradigm, its could beam out some next potential items via Semantic IDs but hard to estimate which items are better from them, e.g., select the top-10 from beam-256 items, leading to a gap between generation and ranking performance. To fulfill this gap, we propose RecoChain, a unified generative retrieval and ranking framework that integrates candidate generation and ranking within a single Transformer backbone. Specifically, in inference, the model first generates candidate items via hierarchical semantic ID prediction, then performs the SIM-based ranking process to estimate the click possibility of corresponding item candidate continuously. Extensive experiments on large-scale real-world datasets demonstrate that our approach effectively bridges the gap between generative retrieval and ranking, achieving improved Top-K recommendation performance while maintaining strong generative capability.

preprint2025arXiv

FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI

Due to the diversity of brain anatomy and the scarcity of annotated data, supervised anomaly detection for brain MRI remains challenging, driving the development of unsupervised anomaly detection (UAD) approaches. Current UAD methods typically utilize artificially generated noise perturbations on healthy MRIs to train generative models for normal anatomy reconstruction, enabling anomaly detection via residual maps. However, such simulated anomalies lack the biophysical fidelity and morphological complexity characteristic of true clinical lesions. To advance UAD in brain MRI, we conduct the first systematic frequency-domain analysis of pathological signatures, revealing two key properties: (1) anomalies exhibit unique frequency patterns distinguishable from normal anatomy, and (2) low-frequency signals maintain consistent representations across healthy scans. These insights motivate our Frequency-Decomposition Preprocessing (FDP) framework, the first UAD method to leverage frequency-domain reconstruction for simultaneous pathology suppression and anatomical preservation. FDP can integrate seamlessly with existing anomaly simulation techniques, consistently enhancing detection performance across diverse architectures while maintaining diagnostic fidelity. Experimental results demonstrate that FDP consistently improves anomaly detection performance when integrated with existing methods. Notably, FDP achieves a 17.63% increase in DICE score with LDM while maintaining robust improvements across multiple baselines. The code is available at https://github.com/ls1rius/MRI_FDP.

preprint2025arXiv

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Black holes (BH), one of the most intriguing objects in the universe, can manifest themselves through electromagnetic radiation initiated by the accretion flow. Some stellar-mass BHs drive relativistic jets when accreting matter from their companion stars, forming microquasars. Non-thermal emission from the radio to tera-electronvolt (TeV) gamma-ray band has been observed from microquasars, indicating the acceleration of relativistic particles. Here we report detection of four microquasars (SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070) of spectrum extending to the ultrahigh-energy (UHE; photon energy $E>100$ TeV) band and one microquasar (Cygnus X-1) of spectrum approaching 100 TeV, using the Large High Altitude Air Shower Observatory (LHAASO). Notably, the total emission associated with SS 433 cannot be interpreted with a single leptonic component. In the UHE band, its emission is in spatial coincidence with a giant atomic cloud, which is consistent with a hadronic origin. An elongated source is discovered from V4641 Sgr with the spectrum continuing up to 800 TeV. The detection of UHE gamma rays demonstrates that accreting BHs and their environments can operate as extremely efficient accelerators of particles out of 1 peta-electronvolt (PeV), suggesting microquasars to be important contributors to Galactic cosmic rays especially around the `knee' region.

preprint2024arXiv

Geometric topics related to Besov type spaces on the Grushin setting

The Grushin spaces, as one of the most important models in the Carnot-Carathéodory space, are a class of locally compact and geodesic metric spaces which admit a dilation. Function spaces on Grushin spaces and some related geometric problems are always the research hotspots in this field. Firstly, we investigate two classes of Besov type spaces based on the Grushin semigroup and the fractional Grushin semigroup, respectively, and prove some important properties of these two Besov type spaces. Moreover, we also reveal the relationship between them. Secondly, we establish the isoperimetric inequality for the fractional perimeter, which is defined by the Grushin-Laplace operator on Grushin spaces. Finally, we combine the semigroup theory with a nonlocal calculus for the Grushin-Laplace operator to obtain the Sobolev type inequality. As a corollary, we also obtain the embedding theorem for Besov type spaces.

preprint2024arXiv

Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling

The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services (only one passenger can be assigned to a car at a time like UberX and Lyft). Although on-demand ride pooling services can bring so many benefits, ride pooling services need a well-defined matching strategy to maximize the benefits for all parties (passengers, drivers, aggregation companies and environment), in which the regional dispatching of vehicles has a significant impact on the matching and revenue. Existing algorithms often only consider revenue maximization, which makes it difficult for requests with unusual distribution to get a ride. How to increase revenue while ensuring a reasonable assignment of requests brings a challenge to ride pooling service companies (aggregation companies). In this paper, we propose a framework for vehicle dispatching for ride pooling tasks, which splits the city into discrete dispatching regions and uses the reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We also consider the mutual information (MI) between vehicle and order distribution as the intrinsic reward of the RL algorithm to improve the correlation between their distributions, thus ensuring the possibility of getting a ride for unusually distributed requests. In experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly increase revenue up to an average of 3\% over the existing best on-demand ride pooling method.

preprint2023arXiv

Centralized Cooperative Exploration Policy for Continuous Control Tasks

The deep reinforcement learning (DRL) algorithm works brilliantly on solving various complex control tasks. This phenomenal success can be partly attributed to DRL encouraging intelligent agents to sufficiently explore the environment and collect diverse experiences during the agent training process. Therefore, exploration plays a significant role in accessing an optimal policy for DRL. Despite recent works making great progress in continuous control tasks, exploration in these tasks has remained insufficiently investigated. To explicitly encourage exploration in continuous control tasks, we propose CCEP (Centralized Cooperative Exploration Policy), which utilizes underestimation and overestimation of value functions to maintain the capacity of exploration. CCEP first keeps two value functions initialized with different parameters, and generates diverse policies with multiple exploration styles from a pair of value functions. In addition, a centralized policy framework ensures that CCEP achieves message delivery between multiple policies, furthermore contributing to exploring the environment cooperatively. Extensive experimental results demonstrate that CCEP achieves higher exploration capacity. Empirical analysis shows diverse exploration styles in the learned policies by CCEP, reaping benefits in more exploration regions. And this exploration capacity of CCEP ensures it outperforms the current state-of-the-art methods across multiple continuous control tasks shown in experiments.

preprint2023arXiv

Epitaxially Driven Phase Selectivity of Sn in Hybrid Quantum Nanowires

Hybrid semiconductor/superconductor nanowires constitute a pervasive platform for studying gate-tunable superconductivity and the emergence of topological behavior. Their low-dimensionality and crystal structure flexibility facilitate novel heterostructure growth and efficient material optimization; crucial prerequisites for accurately constructing complex multi-component quantum materials. Here, we present an extensive optimization of Sn growth on InSb, InAsSb and InAs nanowires. We demonstrate how the growth conditions and the crystal structure/symmetry of the semiconductor drive the formation of either semi-metallic $\mathrm{α-Sn}$ or superconducting $\mathrm{β-Sn}$. For InAs nanowires, we obtain phase-pure, superconducting $\mathrm{β-Sn}$ shells. However, for InSb and InAsSb nanowires, an initial epitaxial $\mathrm{α-Sn}$ phase evolves into a polycrystalline shell of coexisting $\mathrmα$ and $\mathrmβ$ phases, where the $β/α$ volume ratio increases with Sn shell thickness. Whether these nanowires exhibit superconductivity or not critically relies on the $\mathrm{β-Sn}$ content. Therefore, this work provides key insights into Sn phase control on a variety of semiconductors, with consequences for the yield of superconducting hybrids suitable for generating topological systems.

preprint2023arXiv

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.

preprint2023arXiv

Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e.g., 4x and 8x). The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Dense Block (RIDB). Moreover, we propose a joint discriminator which discriminates both image data and embedded semantics. The joint discriminator learns the joint probability distribution of the image space and latent space. We also use a Relativistic average Least Squares loss (RaLS) as the adversarial loss to alleviate the gradient vanishing problem and enhance the stability of the training procedure. Extensive experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons.

preprint2022arXiv

A deep domain decomposition method based on Fourier features

In this paper we present a Fourier feature based deep domain decomposition method (F-D3M) for partial differential equations (PDEs). Currently, deep neural network based methods are actively developed for solving PDEs, but their efficiency can degenerate for problems with high frequency modes. In this new F-D3M strategy, overlapping domain decomposition is conducted for the spatial domain, such that high frequency modes can be reduced to relatively low frequency ones. In each local subdomain, multi Fourier feature networks (MFFNets) are constructed, where efficient boundary and interface treatments are applied for the corresponding loss functions. We present a general mathematical framework of F-D3M, validate its accuracy and demonstrate its efficiency with numerical experiments.

preprint2022arXiv

A Novel Generative Convolutional Neural Network for Robot Grasp Detection on Gaussian Guidance

The vision-based grasp detection method is an important research direction in the field of robotics. However, due to the rectangle metric of the grasp detection rectangle's limitation, a false-positive grasp occurs, resulting in the failure of the real-world robot grasp task. In this paper, we propose a novel generative convolutional neural network model to improve the accuracy and robustness of robot grasp detection in real-world scenes. First, a Gaussian-based guided training method is used to encode the quality of the grasp point and grasp angle in the grasp pose, highlighting the highest-quality grasp point position and grasp angle and reducing the generation of false-positive grasps. Simultaneously, deformable convolution is used to obtain the shape features of the object in order to guide the subsequent network to the position. Furthermore, a global-local feature fusion method is introduced in order to efficiently obtain finer features during the feature reconstruction stage, allowing the network to focus on the features of the grasped objects. On the Cornell Grasping Datasets and Jacquard Datasets, our method achieves excellent performance of 99.0$\%$ and 95.9$\%$, respectively. Finally, the proposed method is put to the test in a real-world robot grasping scenario.

preprint2022arXiv

Ancient and recent collisions revealed by phosphate minerals in the Chelyabinsk meteorite

The collision history of asteroids is an important archive of inner Solar System evolution. Evidence for these collisions is brought to Earth by meteorites, which can preserve impact-reset radioisotope mineral ages. However, as meteorites often preserve numerous mineral ages, their interpretation is controversial. Here, we combine analysis of phosphate U-Pb ages and mineral microtextures to construct a collision history for the highly shocked Chelyabinsk meteorite. We show that phosphate U-Pb ages in the meteorite are independent of thermal history at macro-to-microscales, correlating instead with phosphate microtexture. Isotopic data from pristine phosphate domains is largely concordant, whereas fracture-damaged domains universally display discordance. Combining both populations best constrains upper (4,473 +/- 11 Ma) and lower intercept (-9 +/- 55 Ma, i.e., within error of the present day) U-Pb ages for Chelyabinsk phosphates. We conclude that all phosphate U-Pb ages were completely reset during an ancient high energy collision. Fracture-damaged phosphate domains experienced further Pb-loss during mild collisional heating in the geologically recent past, and must be targeted to properly constrain a lower intercept age. Targeting textural sub-populations of phosphate grains can significantly improve the calculation and interpretation of U-Pb ages, permitting more robust reconstruction of both ancient and recent asteroidal collision histories.

preprint2022arXiv

Better automation of beamline control at HEPS

At the High Energy Photon Source (HEPS) where up to 90 beamlines can be provided in the future, minimisation of workload for individual beamlines and maximisation of knowledge about one beamline that can be applied to other beamlines is essential to minimise the total complexity in beamline control. Presented in this paper are our efforts to achieve these goals by composing relatively simple utilities and mechanisms to automate tasks, and always remembering to keep our automation solutions simple and clear. After an introduction to our choice of basic software in EPICS-based beamline control, the issues encountered in introducing package management to EPICS modules, as well as our solutions to them, are presented; then the design and implementation of our packaging system is concisely discussed. After a presentation of our efforts to reduce the need for self-built multi-device EPICS IOC applications by providing reusable modular IOC executables, our implementation of easily maintainable multi-IOC setups through the separation and minimisation of each user's IOC configurations is given. Finally, the ongoing project of comprehensive beamline services at HEPS to further simplify configuration management on multiple scales, ranging from individual beamline devices to all beamlines at HEPS, is introduced.

preprint2022arXiv

BV spaces and the perimeters related to Schrodinger operators with inverse-square potentials and applications to the rank-one theorem

For $a \ge - {( \frac{d}{2}- 1)^2} $ and $2σ= {d - 2}-( {{{(d - 2)}^2} + 4a})^{1/2}$, let $$\begin{cases}\mathcal{H}_{a}= - Δ+ \frac{a} {{{{ | x |}^2}}},\\ \mathcal{\widetilde{H}}_σ= 2\big( { - Δ+ \frac{σ^2} {{{{ | x |}^2}}}}\big)\end{cases}$$ be two Schrödinger operators with inverse-square potentials. In this paper, on the domain $Ω\subset {\mathbb {R}^d}\backslash \{ 0\}, d\geq 2,$ %apart from the origin, the ${\mathcal{H} _a}$-BV space $\mathcal{B} {\mathcal{V} _{{\mathcal{H} _a}}}(Ω)$ and the ${\mathcal{\widetilde{H}}_σ}$-BV space $\mathcal{B} {\mathcal{V} _{{\mathcal{\widetilde H} _σ}}}(Ω)$ related to $\mathcal{H}_{a}$ and $\mathcal{\widetilde{H}}_σ$ are introduced, respectively. We investigate a series of basic properties of $\mathcal{B} {\mathcal{V} _{{\mathcal{H} _a}}}(Ω)$ and $\mathcal{B} {\mathcal{V} _{{\mathcal{\widetilde H} _σ}}}(Ω)$. Furthermore, we prove that ${\mathcal{\widetilde{H}}_σ}$-restricted BV functions can be characterized equivalently via their subgraphs. As applications, we derive the rank-one theorem for ${\mathcal{\widetilde{H}}_σ}$-restricted BV functions.

preprint2022arXiv

Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an important work in MAS. However, many value decomposition methods ignore the coordination among different agents, leading to the notorious "lazy agents" problem. To enhance the coordination in MAS, this paper proposes HyperGraph CoNvolution MIX (HGCN-MIX), a method that incorporates hypergraph convolution with value decomposition. HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nodes and hyperedges among nodes indicate that the corresponding agents can coordinate to achieve larger rewards. Then, it trains a hypergraph that can capture the collaborative relationships among agents. Leveraging the learned hypergraph to consider how other agents' observations and actions affect their decisions, the agents in a MAS can better coordinate. We evaluate HGCN-MIX in the StarCraft II multi-agent challenge benchmark. The experimental results demonstrate that HGCN-MIX can train joint policies that outperform or achieve a similar level of performance as the current state-of-the-art techniques. We also observe that HGCN-MIX has an even more significant improvement of performance in the scenarios with a large amount of agents. Besides, we conduct additional analysis to emphasize that when the hypergraph learns more relationships, HGCN-MIX can train stronger joint policies.

preprint2022arXiv

Deep Learning-based Inertial Odometry for Pedestrian Tracking using Attention Mechanism and Res2Net Module

Pedestrian dead reckoning is a challenging task due to the low-cost inertial sensor error accumulation. Recent research has shown that deep learning methods can achieve impressive performance in handling this issue. In this letter, we propose inertial odometry using a deep learning-based velocity estimation method. The deep neural network based on Res2Net modules and two convolutional block attention modules is leveraged to restore the potential connection between the horizontal velocity vector and raw inertial data from a smartphone. Our network is trained using only fifty percent of the public inertial odometry dataset (RoNIN) data. Then, it is validated on the RoNIN testing dataset and another public inertial odometry dataset (OXIOD). Compared with the traditional step-length and heading system-based algorithm, our approach decreases the absolute translation error (ATE) by 76%-86%. In addition, compared with the state-of-the-art deep learning method (RoNIN), our method improves its ATE by 6%-31.4%.

preprint2022arXiv

DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy

Light-sheet fluorescence microscopy (LSFM) is a cutting-edge volumetric imaging technique that allows for three-dimensional imaging of mesoscopic samples with decoupled illumination and detection paths. Although the selective excitation scheme of such a microscope provides intrinsic optical sectioning that minimizes out-of-focus fluorescence background and sample photodamage, it is prone to light absorption and scattering effects, which results in uneven illumination and striping artifacts in the images adversely. To tackle this issue, in this paper, we propose a blind stripe artifact removal algorithm in LSFM, called DeStripe, which combines a self-supervised spatio-spectral graph neural network with unfolded Hessian prior. Specifically, inspired by the desirable properties of Fourier transform in condensing striping information into isolated values in the frequency domain, DeStripe firstly localizes the potentially corrupted Fourier coefficients by exploiting the structural difference between unidirectional stripe artifacts and more isotropic foreground images. Affected Fourier coefficients can then be fed into a graph neural network for recovery, with a Hessian regularization unrolled to further ensure structures in the standard image space are well preserved. Since in realistic, stripe-free LSFM barely exists with a standard image acquisition protocol, DeStripe is equipped with a Self2Self denoising loss term, enabling artifact elimination without access to stripe-free ground truth images. Competitive experimental results demonstrate the efficacy of DeStripe in recovering corrupted biomarkers in LSFM with both synthetic and real stripe artifacts.

preprint2022arXiv

Direct manipulation of a superconducting spin qubit strongly coupled to a transmon qubit

Spin qubits in semiconductors are currently one of the most promising architectures for quantum computing. However, they face challenges in realizing multi-qubit interactions over extended distances. Superconducting spin qubits provide a promising alternative by encoding a qubit in the spin degree of freedom of an Andreev level. Such an Andreev spin qubit could leverage the advantages of circuit quantum electrodynamic, enabled by an intrinsic spin-supercurrent coupling. The first realization of an Andreev spin qubit encoded the qubit in the excited states of a semiconducting weak-link, leading to frequent decay out of the computational subspace. Additionally, rapid qubit manipulation was hindered by the need for indirect Raman transitions. Here, we exploit a different qubit subspace, using the spin-split doublet ground state of an electrostatically-defined quantum dot Josephson junction with large charging energy. Additionally, we use a magnetic field to enable direct spin manipulation over a frequency range of 10 GHz. Using an all-electric microwave drive we achieve Rabi frequencies exceeding 200 MHz. We furthermore embed the Andreev spin qubit in a superconducting transmon qubit, demonstrating strong coherent qubit-qubit coupling. These results are a crucial step towards a hybrid architecture that combines the beneficial aspects of both superconducting and semiconductor qubits.

preprint2022arXiv

Disordered Hyperuniform Quasi-1D Materials

Carbon nanotubes are quasi-one-dimensional systems that possess superior transport, mechanical, optical, and chemical properties. In this work, we generalize the notion of disorder hyperuniformity, a recently discovered exotic state of matter with hidden long-range order, to quasi-one-dimensional materials. As a proof of concept, we then apply the generalized framework to quantify the density fluctuations in amorphous carbon nanotubes containing randomly distributed Stone-Wales defects. We demonstrate that all of these amorphous nanotubes are hyperuniform, i.e., the infinite-wavelength density fluctuations of these systems are completely suppressed, regardless of the diameter, rolling axis, number of rolling sheets, and defect fraction of the nanotubes. We find that these amorphous nanotubes are energetically more stable than nanotubes with periodically distributed Stone-Wales defects. Moreover, certain semiconducting defect-free carbon nanotubes become metallic as sufficiently large amounts of defects are randomly introduced. This structural study of amorphous nanotubes strengthens our fundamental understanding of these systems, and suggests possible exotic physical properties, as endowed by their disordered hyperuniformity. Our findings also shed light on the effect of dimensionality reduction on the hyperuniformity property of materials.

preprint2022arXiv

Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI

Purpose: To introduce a dual-domain reconstruction network with V-Net and K-Net for accurate MR image reconstruction from undersampled k-space data. Methods: Most state-of-the-art reconstruction methods apply U-Net or cascaded U-Nets in image domain and/or k-space domain. Nevertheless, these methods have following problems: (1) Directly applying U-Net in k-space domain is not optimal for extracting features in k-space domain; (2) Classical image-domain oriented U-Net is heavy-weight and hence is inefficient to be cascaded many times for yielding good reconstruction accuracy; (3) Classical image-domain oriented U-Net does not fully make use information of encoder network for extracting features in decoder network; and (4) Existing methods are ineffective in simultaneously extracting and fusing features in image domain and its dual k-space domain. To tackle these problems, we propose in this paper (1) an image-domain encoder-decoder sub-network called V-Net which is more light-weight for cascading and effective in fully utilizing features in the encoder for decoding, (2) a k-space domain sub-network called K-Net which is more suitable for extracting hierarchical features in k-space domain, and (3) a dual-domain reconstruction network where V-Nets and K-Nets are parallelly and effectively combined and cascaded. Results: Extensive experimental results on the challenging fastMRI dataset demonstrate that the proposed KV-Net can reconstruct high-quality images and outperform current state-of-the-art approaches with fewer parameters. Conclusions: To reconstruct images effectively and efficiently from incomplete k-space data, we have presented a parallel dual-domain KV-Net to combine K-Nets and V-Nets. The KV-Net is more lightweight than state-of-the-art methods but achieves better reconstruction performance.

preprint2022arXiv

Electrical and thermal transport in van der Waals magnets 2H-M$_x$TaS$_2$ (M = Mn, Co)

We report a detailed study of electrical and thermal transport properties in 2H-M$_x$TaS$_2$ (M = Mn, Co) magnets where M atoms are intercalated in the van der Waals gap. The intercalation induces ferromagentism with an easy-plane anisotropy in 2H-Mn$_x$TaS$_2$, but ferromagnetism with a strong uniaxial anisotropy in 2H-Co$_{0.22}$TaS$_2$, which finally evolves into a three-dimensional antiferromagnetism in 2H-Co$_{0.34}$TaS$_2$. Temperature-dependent electrical resistivity shows metallic behavior for all samples. Thermopower is negative in the whole temperature range for 2H-Co$_x$TaS$_2$, whereas the sign changes from negative to positive with increasing Mn for 2H-Mn$_x$TaS$_2$. The diffusive thermoelectric response dominates in both high- and low-temperature ranges for all samples. A clear kink in electrical resistivity, a weak anomaly in thermal conductivity, as well as a slope change in thermopower were observed at the magnetic transitions for 2H-Mn$_{0.28}$TaS$_2$ ($T_\textrm{c}$ $\approx$ 82 K) and 2H-Co$_{0.34}$TaS$_2$ ($T_\textrm{N}$ $\approx$ 36 K), respectively, albeit weaker for lower $x$ crystals. Co-intercalation promoted ferromagnetic to antiferromagnetic transition is further confirmed by the Hall resistivity; the sign change of the ordinary Hall coefficient indicates a multi-band behavior in 2H-Co$_x$TaS$_2$.

preprint2022arXiv

Ferroelectric superdomain controlled graphene plasmon for tunable mid-infrared photodetector with dual-band spectral selectivity

Dual-band infrared photodetectors (DBIPs) can discriminate desired signals from complex scenes and thus are highly expected for threat-warning, remote sensing, and astronomy applications. Conventional DBIPs with high-performances are, however, typically based on semiconductor thin films, but remain the challenges of complex spatial align, expensive growth and cooling requirement. Here, we report a tunable graphene plasmonic photodetector with dual-band infrared spectral selectivity driven by ferroelectric superdomain. The periodic ferroelectric polarization array with nanoscale ring shapes provides ultrahigh electrostatic field for spatially doping of monolayer graphene to desired patterns, and is further used to excite and confine intrinsic graphene plasmons. Our devices exhibit tunable resonance photoresponse in both two bands of 3.7-16.3 um and 15.1-52.1 um. The numerical calculations show that our devices own ultrahigh responsivities of 667-1080 A W-1 at room temperature in range of 5-50 um. Our devices make possible the applications of infrared imaging system and both stationary and motion state of objects detection. These investigations provide a novel approach for advanced infrared system construction by employing simple, low-cost, uncooling multispectral detectors array.

preprint2022arXiv

Giant thermoelectric power factor anisotropy in PtSb$_{1.4}$Sn$_{0.6}$

We report giant anisotropy in thermoelectric power factor ($S^2σ$) of marcasite structure-type PtSb$_{1.4}$Sn$_{0.6}$. PtSb$_{1.4}$Sn$_{0.6}$, synthesized using ambient pressure flux growth method upon mixing Sb and Sn on the same atomic site, is a new phase different from both PtSb$_2$ and PtSn$_2$ that crystallize in the cubic $Pa\bar{3}$ pyrite and $Fm\bar{3}m$ fluorite unit cell symmetry, respectively. Giant difference in $S^2σ$ for heat flow applied along different principal directions of the orthorhombic unit cell mostly stems from anisotropic Seebeck coefficients.

preprint2022arXiv

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given.

preprint2022arXiv

Inline Tests

Unit tests are widely used to check source code quality, but they can be too coarse-grained or ill-suited for testing individual program statements. We introduce inline tests to make it easier to check for faults in statements. We motivate inline tests through several language features and a common testing scenario in which inline tests could be beneficial. For example, inline tests can allow a developer to test a regular expression in place. We also define language-agnostic requirements for inline testing frameworks. Lastly, we implement I-Test, the first inline testing framework. I-Test works for Python and Java, and it satisfies most of the requirements. We evaluate I-Test on open-source projects by using it to test 144 statements in 31 Python programs and 37 Java programs. We also perform a user study. All nine user study participants say that inline tests are easy to write and that inline testing is beneficial. The cost of running inline tests is negligible, at 0.007x--0.014x, and our inline tests helped find two faults that have been fixed by the developers.

preprint2022arXiv

INTERN: A New Learning Paradigm Towards General Vision

Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society. However, down the road, a key challenge awaits us, that is, our capability of meeting rapidly-growing scenario-specific demands is severely limited by the cost of acquiring a commensurate amount of training data. This difficult situation is in essence due to limitations of the mainstream learning paradigm: we need to train a new model for each new scenario, based on a large quantity of well-annotated data and commonly from scratch. In tackling this fundamental problem, we move beyond and develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. We evaluate our model on 26 well-known datasets that cover four categories of tasks in computer vision. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data, often by a significant margin. This is an important step towards a promising prospect where such a model with general vision capability can dramatically reduce our reliance on data, thus expediting the adoption of AI technologies. Furthermore, revolving around our new paradigm, we also introduce a new data system, a new architecture, and a new benchmark, which, together, form a general vision ecosystem to support its future development in an open and inclusive manner. See project website at https://opengvlab.shlab.org.cn .

preprint2022arXiv

Iterative Self-consistent Parallel Magnetic Resonance Imaging Reconstruction based on Nonlocal Low-Rank Regularization

Iterative self-consistent parallel imaging reconstruction (SPIRiT) is an effective self-calibrated reconstruction model for parallel magnetic resonance imaging (PMRI). The joint L1 norm of wavelet coefficients and joint total variation (TV) regularization terms are incorporated into the SPIRiT model to improve the reconstruction performance. The simultaneous two-directional low-rankness (STDLR) in k-space data is incorporated into SPIRiT to realize improved reconstruction. Recent methods have exploited the nonlocal self-similarity (NSS) of images by imposing nonlocal low-rankness of similar patches to achieve a superior performance. To fully utilize both the NSS in Magnetic resonance (MR) images and calibration consistency in the k-space domain, we propose a nonlocal low-rank (NLR)-SPIRiT model by incorporating NLR regularization into the SPIRiT model. We apply the weighted nuclear norm (WNN) as a surrogate of the rank and employ the Nash equilibrium (NE) formulation and alternating direction method of multipliers (ADMM) to efficiently solve the NLR-SPIRiT model. The experimental results demonstrate the superior performance of NLR-SPIRiT over the state-of-the-art methods via three objective metrics and visual comparison.

preprint2022arXiv

Laguerre BV spaces, Laguerre perimeter and their applications

In this paper, we introduce the Laguerre bounded variation space and the Laguerre perimeter, thereby investigating their properties. Moreover, we prove the isoperimetric inequality and the Sobolev inequality in the Laguerre setting. As applications, we derive the mean curvature for the Laguerre perimeter.

preprint2022arXiv

LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders

Variational autoencoders (VAEs), as an important aspect of generative models, have received a lot of research interests and reached many successful applications. However, it is always a challenge to achieve the consistency between the learned latent distribution and the prior latent distribution when optimizing the evidence lower bound (ELBO), and finally leads to an unsatisfactory performance in data generation. In this paper, we propose a latent distribution consistency approach to avoid such substantial inconsistency between the posterior and prior latent distributions in ELBO optimizing. We name our method as latent distribution consistency VAE (LDC-VAE). We achieve this purpose by assuming the real posterior distribution in latent space as a Gibbs form, and approximating it by using our encoder. However, there is no analytical solution for such Gibbs posterior in approximation, and traditional approximation ways are time consuming, such as using the iterative sampling-based MCMC. To address this problem, we use the Stein Variational Gradient Descent (SVGD) to approximate the Gibbs posterior. Meanwhile, we use the SVGD to train a sampler net which can obtain efficient samples from the Gibbs posterior. Comparative studies on the popular image generation datasets show that our method has achieved comparable or even better performance than several powerful improvements of VAEs.

preprint2022arXiv

Mamba: a systematic software solution for beamline experiments at HEPS

To cater for the diverse experiment requirements at the High Energy Photon Source (HEPS) with often limited human resources, Bluesky is chosen as the basis for our software framework, Mamba. In our attempt to address Bluesky's lack of integrated GUIs, command injection with feedback is chosen as the main way for the GUIs to cooperate with the CLI; a RPC service is provided, which also covers functionalities unsuitable for command injection, as well as pushing of status updates. In order to fully support high-frequency applications like fly scans, Bluesky's support for asynchronous control is being improved; to support high-throughput experiments, Mamba Data Worker (MDW) is being developed to cover the complexity in asynchronous online data processing for these experiments. To systematically simplify the specification of metadata, scan parameters and data-processing graphs for each type of experiments, an experiment parameter generator (EPG) will be developed; experiment-specific modules to automate preparation steps will also be made. The integration of off-the-shelf code in Mamba for domain-specific needs is under investigation, and Mamba GUI Studio (MGS) is being developed to simplify the implementation and integration of GUIs.

preprint2022arXiv

Mapping evolving population geography in China

China's demographic changes have important global economic and geopolitical implications. Yet, our understanding of such transitions at the micro-spatial scale remains limited due to spatial inconsistency of the census data caused by administrative boundary adjustments. To fill this gap, we manually collected and built a population census panel from 2010 to 2020 at both the county and prefectural-city levels. We show that the massive internal migration drives China's increasing population concentration and regional disparity, resulting in severe population aging in shrinking cities and increasing gender imbalance in growing cities.

preprint2022arXiv

Meta Knowledge Distillation

Recent studies pointed out that knowledge distillation (KD) suffers from two degradation problems, the teacher-student gap and the incompatibility with strong data augmentations, making it not applicable to training state-of-the-art models, which are trained with advanced augmentations. However, we observe that a key factor, i.e., the temperatures in the softmax functions for generating probabilities of both the teacher and student models, was mostly overlooked in previous methods. With properly tuned temperatures, such degradation problems of KD can be much mitigated. However, instead of relying on a naive grid search, which shows poor transferability, we propose Meta Knowledge Distillation (MKD) to meta-learn the distillation with learnable meta temperature parameters. The meta parameters are adaptively adjusted during training according to the gradients of the learning objective. We validate that MKD is robust to different dataset scales, different teacher/student architectures, and different types of data augmentation. With MKD, we achieve the best performance with popular ViT architectures among compared methods that use only ImageNet-1K as training data, ranging from tiny to large models. With ViT-L, we achieve 86.5% with 600 epochs of training, 0.6% better than MAE that trains for 1,650 epochs.

preprint2022arXiv

MetroGAN: Simulating Urban Morphology with Generative Adversarial Network

Simulating urban morphology with location attributes is a challenging task in urban science. Recent studies have shown that Generative Adversarial Networks (GANs) have the potential to shed light on this task. However, existing GAN-based models are limited by the sparsity of urban data and instability in model training, hampering their applications. Here, we propose a GAN framework with geographical knowledge, namely Metropolitan GAN (MetroGAN), for urban morphology simulation. We incorporate a progressive growing structure to learn hierarchical features and design a geographical loss to impose the constraints of water areas. Besides, we propose a comprehensive evaluation framework for the complex structure of urban systems. Results show that MetroGAN outperforms the state-of-the-art urban simulation methods by over 20% in all metrics. Inspiringly, using physical geography features singly, MetroGAN can still generate shapes of the cities. These results demonstrate that MetroGAN solves the instability problem of previous urban simulation GANs and is generalizable to deal with various urban attributes.

preprint2022arXiv

Nebula Graph: An open source distributed graph database

This paper introduces the recent work of Nebula Graph, an open-source, distributed, scalable, and native graph database. We present a system design trade-off and a comprehensive overview of Nebula Graph internals, including graph data models, partitioning strategies, secondary indexes, optimizer rules, storage-side transactions, graph query languages, observability, graph processing frameworks, and visualization tool-kits. In addition, three sets of large-scale graph b

preprint2022arXiv

Optimal quantum optical control of spin in diamond

The nitrogen-vacancy (NV) center spin represents an appealing candidate for quantum information processing. Besides the widely used microwave control, its coherent manipulation may also be achieved using laser as mediated by the excited energy levels. Nevertheless, the multiple levels of the excited state of NV center spin make the coherent transition process become complex and may affect the fidelity of coherent manipulation. Here, we adopt the strategy of optimal quantum control to accelerate coherent state transfer in the ground state manifold of NV center spin using laser. The results demonstrate improved performance in both the speed and the fidelity of coherent state transfer which will be useful for optical control of NV center spin in diamond.

preprint2022arXiv

Pedestrian Dead Reckoning System using Quasi-static Magnetic Field Detection

Kalman filter-based Inertial Navigation System (INS) is a reliable and efficient method to estimate the position of a pedestrian indoors. Classical INS-based methodology which is called IEZ (INS-EKF-ZUPT) makes use of an Extended Kalman Filter (EKF), a Zero velocity UPdaTing (ZUPT) to calculate the position and attitude of a person. However, heading error which is a key factor of the whole Pedestrian Dead Reckoning (PDR) system is unobservable for IEZ-based PDR system. To minimize the error, Electronic Com-pass (EC) algorithm becomes a valid method. But magnetic disturbance may have a big negative effect on it. In this paper, the Quasi-static Magnetic field Detection (QMD) method is proposed to detect the pure magnetic field and then selects EC algorithm or Heuristic heading Drift Reduction algorithm (HDR) according to the detection result, which implements the complementation of the two methods. Meanwhile, the QMD, EC, and HDR algorithms are integrated into the IEZ framework to form a new PDR solution which is named Advanced IEZ (AIEZ).

preprint2022arXiv

Polaronic Conductivity in Cr$_2$Ge$_2$Te$_6$ Single Crystals

Intrinsic, two-dimensional (2D) ferromagnetic semiconductors are an important class of materials for spin-charge conversion applications. Cr$_2$Ge$_2$Te$_6$ retains long-range magnetic order in bilayer at cryogenic temperatures and shows complex magnetic interactions with considerable magnetic anisotropy. Here, we performed a series of structural, magnetic, X-ray scattering, electronic, thermal transport and first-principles calculation studies which reveal that localized electronic charge carriers in Cr$_2$Ge$_2$Te$_6$ are dressed by surrounding lattice and are involved in polaronic transport via hopping that is sensitive on details of magnetocrystalline anisotropy. This opens possibility for manipulation of charge transport in Cr$_2$Ge$_2$Te$_6$ - based devices by electron-phonon- and spin-orbit coupling-based tailoring of polaron properties.

preprint2022arXiv

Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning Approach

Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedded sensors to monitor their conditions or achieve personalized fitness goals. In this paper, we propose a novel privacy-aware personalized fitness recommender system. We introduce a multi-level deep learning framework that learns important features from a large-scale real fitness dataset that is collected from wearable IoT devices to derive intelligent fitness recommendations. Unlike most existing approaches, our approach achieves personalization by inferring the fitness characteristics of users from sensory data and thus minimizing the need for explicitly collecting user identity or biometric information, such as name, age, height, weight. In particular, our proposed models and algorithms predict (a) personalized exercise distance recommendations to help users to achieve target calories, (b) personalized speed sequence recommendations to adjust exercise speed given the nature of the exercise and the chosen route, and (c) personalized heart rate sequence to guide the user of the potential health status for future exercises. Our experimental evaluation on a real-world Fitbit dataset demonstrated high accuracy in predicting exercise distance, speed sequence, and heart rate sequence compared to similar studies. Furthermore, our approach is novel compared to existing studies as it does not require collecting and using users sensitive information, and thus it preserves the users privacy.

preprint2022arXiv

Quantifying navigation complexity in transportation networks

The complexity of navigation in cities has increased with the expansion of urban areas, creating challenging transportation problems that drive many studies on the navigability of networks. However, due to the lack of individual mobility data, large-scale empirical analysis of the wayfinder's real-world navigation is rare. Here, using 225 million subway trips from three major cities in China, we quantify navigation difficulty from an information perspective. Our results reveal that 1) people conserve a small number of repeatedly used routes, and 2) the navigation information in the subnetworks formed by those routes is much smaller than the theoretical value in the global network, suggesting that the decision cost for actual trips is significantly smaller than the theoretical upper limit found in previous studies. By modeling routing behaviors in growing networks, we show that while the global network becomes difficult to navigate, navigability can be improved in subnetworks. We further present a universal linear relationship between the empirical and theoretical search information, which allows the two metrics to predict each other. Our findings demonstrate how large-scale observations can quantify real-world navigation behaviors and aid in evaluating transportation planning.

preprint2022arXiv

Quantum Fisher information measurement and verification of the quantum Cramér-Rao bound in a solid-state qubit

The quantum Cramér-Rao bound sets a fundamental limit on the accuracy of unbiased parameter estimation in quantum systems, relating the uncertainty in determining a parameter to the inverse of the quantum Fisher information. We experimentally demonstrate near saturation of the quantum Cramér-Rao bound in the phase estimation of a solid-state spin system, provided by a nitrogen-vacancy center in diamond. This is achieved by comparing the experimental uncertainty in phase estimation with an independent measurement of the related quantum Fisher information. The latter is independently extracted from coherent dynamical responses of the system under weak parametric modulations, without performing any quantum-state tomography. While optimal parameter estimation has already been observed for quantum devices involving a limited number of degrees of freedom, our method offers a versatile and powerful experimental tool to explore the Cramér-Rao bound and the quantum Fisher information in systems of higher complexity, as relevant for quantum technologies.

preprint2022arXiv

Quantum phase transition in magnetic nanographenes on a lead superconductor

Quantum spins, referred to the spin operator preserved by full SU(2) symmetry in the absence of the magnetic anistropy, have been proposed to host exotic interactions with superconductivity4. However, spin orbit coupling and crystal field splitting normally cause a significant magnetic anisotropy for d/f-shell spins on surfaces6,9, breaking SU(2) symmetry and fabricating the spins with Ising properties10. Recently, magnetic nanographenes have been proven to host intrinsic quantum magnetism due to their negligible spin orbital coupling and crystal field splitting. Here, we fabricate three atomically precise nanographenes with the same magnetic ground state of spin S=1/2 on Pb(111) through engineering sublattice imbalance in graphene honeycomb lattice. Scanning tunneling spectroscopy reveals the coexistence of magnetic bound states and Kondo screening in such hybridized system. Through engineering the magnetic exchange strength between the unpaired spin in nanographenes and cooper pairs, quantum phase transition from the singlet to the doublet state has been observed, in consistent with quantum models of spins on superconductors. Our work demonstrates delocalized graphene magnetism host highly tunable magnetic bound states with cooper pairs, which can be further developed to study the Majorana bound states and other rich quantum physics of low-dimensional quantum spins on superconductors.

preprint2022arXiv

Rethinking Robust Representation Learning Under Fine-grained Noisy Faces

Learning robust feature representation from large-scale noisy faces stands out as one of the key challenges in high-performance face recognition. Recent attempts have been made to cope with this challenge by alleviating the intra-class conflict and inter-class conflict. However, the unconstrained noise type in each conflict still makes it difficult for these algorithms to perform well. To better understand this, we reformulate the noise type of each class in a more fine-grained manner as N-identities|K^C-clusters. Different types of noisy faces can be generated by adjusting the values of \nkc. Based on this unified formulation, we found that the main barrier behind the noise-robust representation learning is the flexibility of the algorithm under different N, K, and C. For this potential problem, we propose a new method, named Evolving Sub-centers Learning~(ESL), to find optimal hyperplanes to accurately describe the latent space of massive noisy faces. More specifically, we initialize M sub-centers for each class and ESL encourages it to be automatically aligned to N-identities|K^C-clusters faces via producing, merging, and dropping operations. Images belonging to the same identity in noisy faces can effectively converge to the same sub-center and samples with different identities will be pushed away. We inspect its effectiveness with an elaborate ablation study on the synthetic noisy dataset with different N, K, and C. Without any bells and whistles, ESL can achieve significant performance gains over state-of-the-art methods on large-scale noisy faces

preprint2022arXiv

Role of Local Ru Hexamers in Superconductivity of Ruthenium Phosphide

Superconductivity in binary ruthenium pnictides occurs proximal to and upon suppression of a mysterious non-magnetic ground state, preceded by a pseudogap phase associated with Fermi surface instability, and its critical temperature, T$_{c}$, is maximized around the pseudogap quantum critical point. By analogy with isoelectronic iron based counterparts, antiferromagnetic fluctuations became "usual suspects" as putative mediators of superconducting pairing. Here we report on a high temperature local symmetry breaking in RuP, the parent of the maximum-Tc branch of these novel superconductors, revealed by combined nanostructure-sensitive powder and single crystal X-ray total scattering experiments. Large local Ru$_{6}$ hexamer distortions associated with orbital-charge trimerization form above the two-stage electronic transition in RuP. While hexamer ordering enables the nonmagnetic ground state and presumed complex oligomerization, the relevance of pseudogap fluctuations for superconductivity emerges as a distinct prospect. As a transition metal system in which partial d-manifold filling combined with high crystal symmetry promotes electronic instabilities, this represents a further example of local electronic precursors underpinning the macroscopic collective behavior of quantum materials.

preprint2022arXiv

Self-slimmed Vision Transformer

Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bring a huge computation burden, because of the exhausting token-to-token comparison. The previous works focus on dropping insignificant tokens to reduce the computational cost of ViTs. But when the dropping ratio increases, this hard manner will inevitably discard the vital tokens, which limits its efficiency. To solve the issue, we propose a generic self-slimmed learning approach for vanilla ViTs, namely SiT. Specifically, we first design a novel Token Slimming Module (TSM), which can boost the inference efficiency of ViTs by dynamic token aggregation. As a general method of token hard dropping, our TSM softly integrates redundant tokens into fewer informative ones. It can dynamically zoom visual attention without cutting off discriminative token relations in the images, even with a high slimming ratio. Furthermore, we introduce a concise Feature Recalibration Distillation (FRD) framework, wherein we design a reverse version of TSM (RTSM) to recalibrate the unstructured token in a flexible auto-encoder manner. Due to the similar structure between teacher and student, our FRD can effectively leverage structure knowledge for better convergence. Finally, we conduct extensive experiments to evaluate our SiT. It demonstrates that our method can speed up ViTs by 1.7x with negligible accuracy drop, and even speed up ViTs by 3.6x while maintaining 97% of their performance. Surprisingly, by simply arming LV-ViT with our SiT, we achieve new state-of-the-art performance on ImageNet. Code is available at https://github.com/Sense-X/SiT.

preprint2022arXiv

Self-supervised graph neural networks for accurate prediction of Néel temperature

Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications. It is highly demanded of the accurate and efficient theoretical method for determining the critical transition temperatures, Néel temperatures, of antiferromagnetic materials. The powerful graph neural networks (GNN) that succeed in predicting material properties lose their advantage in predicting magnetic properties due to the small dataset of magnetic materials, while conventional machine learning models heavily depend on the quality of material descriptors. We propose a new strategy to extract high-level material representations by utilizing self-supervised training of GNN on large-scale unlabeled datasets. According to the dimensional reduction analysis, we find that the learned knowledge about elements and magnetism transfers to the generated atomic vector representations. Compared with popular manually constructed descriptors and crystal graph convolutional neural networks, self-supervised material representations can help us obtain a more accurate and efficient model for Néel temperatures, and the trained model can successfully predict high Néel temperature antiferromagnetic materials. Our self-supervised GNN may serve as a universal pre-training framework for various material properties.

preprint2022arXiv

Sensitivity tests of cosmic velocity fields to massive neutrinos

We investigate impacts of massive neutrinos on the cosmic velocity fields, employing high-resolution cosmological $N$-body simulations provided by the information-optimized CUBE code, where cosmic neutrinos are evolved using collisionless hydrodynamics and their perturbations can be accurately resolved. In this study we focus, for the first time, on the analysis of massive-neutrino induced suppression effects in various cosmic velocity field components of velocity magnitude, divergence, vorticity and dispersion. By varying the neutrino mass sum $M_ν$ from 0 -- 0.4 eV, the simulations show that, the power spectra of vorticity -- exclusively sourced by non-linear structure formation that is affected by massive neutrinos significantly -- is very sensitive to the mass sum, which potentially provide novel signatures in detecting massive neutrinos. Furthermore, using the chi-square statistic, we quantitatively test the sensitivity of the density and velocity power spectra to the neutrino mass sum. Indeed, we find that, the vorticity spectrum has the highest sensitivity, and the null hypothesis of massless neutrinos is incompatible with both vorticity and divergence spectra from $M_ν=0.1$ eV at high significance ($p$-value $= 0.03$ and $0.07$, respectively). These results demonstrate clearly the importance of peculiar velocity field measurements, in particular of vorticity and divergence components, in determination of neutrino mass and mass hierarchy.

preprint2022arXiv

Short-range Crystalline Order-Tuned Conductivity in Cr$_2$Si$_2$Te$_6$ van der Waals Magnetic Crystals

Two-dimensional magnetic materials (2DMM) are significant for studies on the nature of 2D long range magnetic order but also for future spintronic devices. Of particular interest are 2DMM where spins can be manipulated by electrical conduction. Whereas Cr$_2$Si$_2$Te$_6$ exhibits magnetic order in few-layer crystals, its large band gap inhibits electronic conduction. Here we show that the defect-induced short-range crystal order in Cr$_2$Si$_2$Te$_6$ on the length scale below 0.6 nm induces substantially reduced band gap and robust semiconducting behavior down to 2 K that turns to metallic above 10 GPa. Our results will be helpful to design conducting state in 2DMM and call for spin-resolved measurement of the electronic structure in exfoliated ultrathin crystals.

preprint2022arXiv

Spectroscopy of spin-split Andreev levels in a quantum dot with superconducting leads

We use a hybrid superconductor-semiconductor transmon device to perform spectroscopy of a quantum dot Josephson junction tuned to be in a spin-1/2 ground state with an unpaired quasiparticle. Due to spin-orbit coupling, we resolve two flux-sensitive branches in the transmon spectrum, depending on the spin of the quasi-particle. A finite magnetic field shifts the two branches in energy, favoring one spin state and resulting in the anomalous Josephson effect. We demonstrate the excitation of the direct spin-flip transition using all-electrical control. Manipulation and control of the spin-flip transition enable the future implementation of charging energy protected Andreev spin qubits.

preprint2022arXiv

Spin-polarized imaging of strongly interacting fermions in the ferrimagnetic state of Weyl candidate CeBi

CeBi has an intricate magnetic phase diagram whose fully-polarized state has recently been suggested as a Weyl semimetal, though the role of $f$ states in promoting strong interactions has remained elusive. Here we focus on the less-studied, but also time-reversal symmetry-breaking ferrimagnetic phase of CeBi, where our density functional theory (DFT) calculations predict additional Weyl nodes near the Fermi level $E_\mathrm{F}$. We use spin-polarized scanning tunneling microscopy and spectroscopy to image the surface ferrimagnetic order on the itinerant Bi $p$ states, indicating their orbital hybridization with localized Ce $f$ states. We observe suppression of this spin-polarized signature at $E_\mathrm{F}$, coincident with a Fano line shape in the conductance spectra, suggesting the Bi $p$ states partially Kondo screen the $f$ magnetic moments, and this $p-f$ hybridization causes strong Fermi-level band renormalization. The $p$ band flattening is supported by our quasiparticle interference (QPI) measurements, which also show band splitting in agreement with DFT, painting a consistent picture of a strongly interacting magnetic Weyl semimetal.

preprint2022arXiv

Suppression of Superconductivity and Nematic Order in Fe$_{1-y}$Se$_{1-x}$S$_x$ (0$\leq$$x$$\leq$1, $y$$\leq$0.1) Crystals by Anion Height Disorder

Connections between crystal chemistry and critical temperature $T_c$ have been in the focus of superconductivity, one of the most widely studied phenomena in physics, chemistry and materials science alike. In most Fe-based superconductors, materials chemistry and physics conspire so that $T_c$ correlates with the average anion height above the Fe plane, i. e. with the geometry of the FeAs4 or FeCh4 (Ch = Te, Se, or S) tetrahedron. By synthesizing Fe$_{1-y}$Se$_{1-x}$S$_x$ (0$\leq$$x$$\leq$1, $y$$\leq$0.1), we find that in alloyed crystals $T_c$ is not correlated with the anion height like it is for most other Fe superconductors. Instead, changes in $T_c$($x$) and tetragonal-to-orthorombic (nematic) transition $T_s$($x$) upon cooling are correlated with disorder in Fe vibrations in the direction orthogonal to Fe planes, along the crystallographic c-axis. The disorder stems from the random nature of S substitution, causing deformed Fe(Se,S)4 tetrahedra with different Fe-Se and Fe-S bond distances. Our results provide evidence of $T_c$ and $T_s$ suppression by disorder in anion height. The connection to local crystal chemistry may be exploited in computational prediction of new superconducting materials with Fe/S building blocks.

preprint2022arXiv

Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

Knowledge graph embedding (KGE) has shown great potential in automatic knowledge graph (KG) completion and knowledge-driven tasks. However, recent KGE models suffer from high training cost and large storage space, thus limiting their practicality in real-world applications. To address this challenge, based on the latest findings in the field of Contrastive Learning, we propose a novel KGE training framework called Hardness-aware Low-dimensional Embedding (HaLE). Instead of the traditional Negative Sampling, we design a new loss function based on query sampling that can balance two important training targets, Alignment and Uniformity. Furthermore, we analyze the hardness-aware ability of recent low-dimensional hyperbolic models and propose a lightweight hardness-aware activation mechanism. The experimental results show that in the limited training time, HaLE can effectively improve the performance and training speed of KGE models on five commonly-used datasets. After training just a few minutes, the HaLE-trained models are competitive compared to the state-of-the-art models in both low- and high-dimensional conditions.

preprint2022arXiv

The effect of $f$-$c$ hybridization on the $γ\rightarrowα$ phase transition of cerium studied by lanthanum doping

The hybridization between the localized 4$f$ level ($f$) with conduction ($c$) states in $γ$-Ce upon cooling has been previously revealed in single crystalline thin films experimentally and theoretically, whereas its influence on the $γ\rightarrowα$ phase transition was not explicitly verified, due to the fact that the phase transition happened in the bulk-layer, leaving the surface in the $γ$ phase. Here in our work, we circumvent this issue by investigating the effect of alloying addition of La on Ce, by means of crystal structure, electronic transport and ARPES measurements, together with a phenomenological periodic Anderson model and a modified Anderson impurity model. Our current researches indicate that the weakening of $f$-$c$ hybridization is the major factor in the suppression of $γ\rightarrowα$ phase transition by La doping. The consistency of our results with the effects of other rare earth and actinide alloying additions on the $γ\rightarrowα$ phase transition of Ce is also discussed. Our work demonstrates the importance of the interaction of $f$ and $c$ electrons in understanding the unconventional phase transition in Ce, which is intuitive for further researches on other rare earth and actinide metals and alloys with similar phase transition behaviors.

preprint2022arXiv

The underestimation of high pressure in DFT+$U$ simulation for the wide range cold-pressure of lanthanide metals

Density functional theory plus $U$ (DFT+$U$) is one of the most efficient first-principles methods to simulate the cold pressure properties of strongly-correlated materials. However, the applicability of DFT+$U$ at ultra-high pressure is not sufficiently studied, especially in the widely-used augmented schemes [such as projector augmented wave (PAW) and linearized augmented plane wave (LAPW)]. This work has systematically investigated the performance of DFT+$U$ in PAW and LAPW at the pressure up to several hundred GPa for the lanthanide metals, which is a typical strongly-correlated series. We found DFT+$U$ simulation in PAW exhibits an unphysical underestimating of force at high pressure. By delicate analysis and comparison with local-orbital-independent hybrid functional results, we have demonstrated that this unphysical behavior is related to a normalization problem on the local density matrix caused by the overlap of local orbitals in PAW under high pressure. Additionally, we observed a slight softening of force in DFT+$U$ in heavy lanthanides (Tm, Yb and Lu) at high pressure comparing with the DFT results without the influence of local orbital overlap, and it might be related to the enhancement of bonding effect in correlation correction methods at high pressure. Our work reveals the underestimating of high pressure in DFT+$U$ simulation, analyses two sources of this unusual behavior and proposes their mechanism. Most importantly, our investigation highlights the breakdown of DFT+$U$ for high pressure simulation in VASP package based on PAW framework.

preprint2022arXiv

The universality in urban commuting across and within cities

Commuting is a key mechanism that governs the dynamics of cities. Despite its importance, very little is known of the properties and mechanisms underlying this crucial urban process. Here, we capitalize on $\sim$ 50 million individuals' smartphone data from 234 Chinese cities to show that urban commuting obeys remarkable regularities. These regularities can be generalized as two laws: (i) the scale-invariance of the average commuting distance across cities, which is a long-awaited validation of Marchetti's constant conjecture, and (ii) a universal inverted U-shape of the commuting distance as a function of the distance from the city centre within cities, indicating that the city centre's attraction is bounded. Motivated by such empirical findings, we develop a simple urban growth model that connects individual-level mobility choices with macroscopic urban spatial structure and faithfully explains both commuting laws. Our results further show that the scale-invariants of human mobility will ultimately lead to the polycentric transition in cities, which could be used to better inform urban development strategies.

preprint2022arXiv

Thermal transport and mixed valence in ZrTe$_3$ doped with Hf and Se

Two-dimensional transition metal trichalcogenides (TMTC's) feature covalently bonded metal-chalcogen layers separated by the van der Waals (vdW) gap. Similar to transition metal dichalcogenides (TMDCs), TMTCs often host charge density waves (CDWs) and superconductivity but unlike TMDCs atomic chains in the crystal structure give rise to quasi one-dimensional (quasi 1D) conduction. ZrTe$_3$ features CDW below $T_{\textrm{CDW}}$ = 63 K and filamentary superconductivity below 2 K that can be enhanced by pressure or chemical substitution. Here we report the presence of mixed valent Zr$^{2+}$ and Zr$^{4+}$ atoms in ZrTe$_3$ crystals that is reduced by doping in ZrTe$_{3-x}$Se$_x$ and Zr$_{1-y}$Hf$_y$Te$_3$. Superconductivity is enhanced via disorder in Te2-Te3 atomic chains that are associated with CDW formation. Hf substitution on Zr atomic site enhances $T_{\textrm{CDW}}$ due to unperturbed Te2-Te3 chain periodicity and enhanced electron-phonon coupling. Weak electronic correlations in ZrTe$_{3-x}$Se$_x$ are likely governed by the lattice contraction effects.

preprint2022arXiv

Thermoelectricity and electronic correlations enhancement in FeS by slight Se substitution

We report thermoelectric studies of FeS$_{1-x}$Se$_x$ ($x$ = 0, 0.06) superconducting single crystals that feature high irreversibility fields and critical current density $J_c$ comparable to materials with much higher superconducting critical temperatures ($T_c$'s). The ratio of $T_c$ to the Fermi temperature $T_F$ is very small indicating weak electronic correlations. With a slight selenium substitution on sulfur site in FeS both $T_c$/$T_F$ and the effective mass $m^*$ rise considerably, implying increase in electronic correlation of the bulk conducting states. The first-principle calculations show rise of the density of states at the Fermi level in FeS$_{0.94}$Se$_{0.06}$ when compared to FeS which is related not only to Fe but also to chalcogen-derived electronic states.

preprint2022arXiv

Topological Defects Induced High-Spin Quartet State in Truxene-Based Molecular Graphenoids

Topological defects in graphene materials introduce exotic properties which are absent in their defect-free counterparts with both fundamental importance and technological implications. Although individual topological defects have been widely studied, collective magnetic behaviors originating from well-organized multiple topological defects remain a great challenge. Here, we studied the collective magnetic properties originating from three pentagon topological defects in truxene-based molecular graphenoids by using scanning tunneling microscopy and non-contact atomic force microscopy. Unpaired $π$ electrons are introduced into the aromatic topology of truxene molecular graphenoids one by one by dissociating hydrogen atoms at the pentagon defects via atom manipulation. Scanning tunneling spectroscopy measurements together with density functional theory calculations suggest that the unpaired electrons are ferromagnetically coupled, forming a collective high-spin quartet state of S=3/2. Our work demonstrates that the collective spin ordering can be realized through engineering regular patterned topological defects in molecular graphenoids, providing a new platform for designer one-dimensional ferromagnetic spin chains and two-dimensional ferromagnetic networks.

preprint2022arXiv

Towards Robust Face Recognition with Comprehensive Search

Data cleaning, architecture, and loss function design are important factors contributing to high-performance face recognition. Previously, the research community tries to improve the performance of each single aspect but failed to present a unified solution on the joint search of the optimal designs for all three aspects. In this paper, we for the first time identify that these aspects are tightly coupled to each other. Optimizing the design of each aspect actually greatly limits the performance and biases the algorithmic design. Specifically, we find that the optimal model architecture or loss function is closely coupled with the data cleaning. To eliminate the bias of single-aspect research and provide an overall understanding of the face recognition model design, we first carefully design the search space for each aspect, then a comprehensive search method is introduced to jointly search optimal data cleaning, architecture, and loss function design. In our framework, we make the proposed comprehensive search as flexible as possible, by using an innovative reinforcement learning based approach. Extensive experiments on million-level face recognition benchmarks demonstrate the effectiveness of our newly-designed search space for each aspect and the comprehensive search. We outperform expert algorithms developed for each single research track by large margins. More importantly, we analyze the difference between our searched optimal design and the independent design of the single factors. We point out that strong models tend to optimize with more difficult training datasets and loss functions. Our empirical study can provide guidance in future research towards more robust face recognition systems.

preprint2022arXiv

UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning

It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. The recent advances in this research have been mainly driven by 3D convolutional neural networks and vision transformers. Although 3D convolution can efficiently aggregate local context to suppress local redundancy from a small 3D neighborhood, it lacks the capability to capture global dependency because of the limited receptive field. Alternatively, vision transformers can effectively capture long-range dependency by self-attention mechanism, while having the limitation on reducing local redundancy with blind similarity comparison among all the tokens in each layer. Based on these observations, we propose a novel Unified transFormer (UniFormer) which seamlessly integrates merits of 3D convolution and spatiotemporal self-attention in a concise transformer format, and achieves a preferable balance between computation and accuracy. Different from traditional transformers, our relation aggregator can tackle both spatiotemporal redundancy and dependency, by learning local and global token affinity respectively in shallow and deep layers. We conduct extensive experiments on the popular video benchmarks, e.g., Kinetics-400, Kinetics-600, and Something-Something V1&V2. With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than other state-of-the-art methods. For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1 accuracy respectively. Code is available at https://github.com/Sense-X/UniFormer.

preprint2022arXiv

Unifying Visual Perception by Dispersible Points Learning

We present a conceptually simple, flexible, and universal visual perception head for variant visual tasks, e.g., classification, object detection, instance segmentation and pose estimation, and different frameworks, such as one-stage or two-stage pipelines. Our approach effectively identifies an object in an image while simultaneously generating a high-quality bounding box or contour-based segmentation mask or set of keypoints. The method, called UniHead, views different visual perception tasks as the dispersible points learning via the transformer encoder architecture. Given a fixed spatial coordinate, UniHead adaptively scatters it to different spatial points and reasons about their relations by transformer encoder. It directly outputs the final set of predictions in the form of multiple points, allowing us to perform different visual tasks in different frameworks with the same head design. We show extensive evaluations on ImageNet classification and all three tracks of the COCO suite of challenges, including object detection, instance segmentation and pose estimation. Without bells and whistles, UniHead can unify these visual tasks via a single visual head design and achieve comparable performance compared to expert models developed for each task.We hope our simple and universal UniHead will serve as a solid baseline and help promote universal visual perception research. Code and models are available at https://github.com/Sense-X/UniHead.

preprint2022arXiv

UniNet: Unified Architecture Search with Convolution, Transformer, and MLP

Recently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks. However, how to effectively combine those operators to form high-performance hybrid visual architectures still remains a challenge. In this work, we study the learnable combination of convolution, transformer, and MLP by proposing a novel unified architecture search approach. Our approach contains two key designs to achieve the search for high-performance networks. First, we model the very different searchable operators in a unified form, and thus enable the operators to be characterized with the same set of configuration parameters. In this way, the overall search space size is significantly reduced, and the total search cost becomes affordable. Second, we propose context-aware downsampling modules (DSMs) to mitigate the gap between the different types of operators. Our proposed DSMs are able to better adapt features from different types of operators, which is important for identifying high-performance hybrid architectures. Finally, we integrate configurable operators and DSMs into a unified search space and search with a Reinforcement Learning-based search algorithm to fully explore the optimal combination of the operators. To this end, we search a baseline network and scale it up to obtain a family of models, named UniNets, which achieve much better accuracy and efficiency than previous ConvNets and Transformers. In particular, our UniNet-B5 achieves 84.9% top-1 accuracy on ImageNet, outperforming EfficientNet-B7 and BoTNet-T7 with 44% and 55% fewer FLOPs respectively. By pretraining on the ImageNet-21K, our UniNet-B6 achieves 87.4%, outperforming Swin-L with 51% fewer FLOPs and 41% fewer parameters. Code is available at https://github.com/Sense-X/UniNet.

preprint2021arXiv

ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation

Interpreting human actions requires understanding the spatial and temporal context of the scenes. State-of-the-art action detectors based on Convolutional Neural Network (CNN) have demonstrated remarkable results by adopting two-stream or 3D CNN architectures. However, these methods typically operate in a non-real-time, ofline fashion due to system complexity to reason spatio-temporal information. Consequently, their high computational cost is not compliant with emerging real-world scenarios such as service robots or public surveillance where detection needs to take place at resource-limited edge devices. In this paper, we propose ACDnet, a compact action detection network targeting real-time edge computing which addresses both efficiency and accuracy. It intelligently exploits the temporal coherence between successive video frames to approximate their CNN features rather than naively extracting them. It also integrates memory feature aggregation from past video frames to enhance current detection stability, implicitly modeling long temporal cues over time. Experiments conducted on the public benchmark datasets UCF-24 and JHMDB-21 demonstrate that ACDnet, when integrated with the SSD detector, can robustly achieve detection well above real-time (75 FPS). At the same time, it retains reasonable accuracy (70.92 and 49.53 frame mAP) compared to other top-performing methods using far heavier configurations. Codes will be available at https://github.com/dginhac/ACDnet.

preprint2021arXiv

Anomalous Hall effect in weak-itinerant ferrimagnet FeCr$_2$Te$_4$

We carried out a comprehensive study of electronic transport, thermal and thermodynamic properties in FeCr$_2$Te$_4$ single crystals. It exhibits bad-metallic behavior and anomalous Hall effect (AHE) below a weak-itinerant paramagentic-to-ferrimagnetic transition $T_c$ $\sim$ 123 K. The linear scaling between the anomalous Hall resistivity $ρ_{xy}$ and the longitudinal resistivity $ρ_{xx}$ implies that the AHE in FeCr$_2$Te$_4$ is most likely dominated by extrinsic skew-scattering mechanism rather than intrinsic KL or extrinsic side-jump mechanism, which is supported by our Berry phase calculations.

preprint2021arXiv

Design and characterization of a low-vibration laboratory with cylindrical inertia block geometry

Many modern nanofabrication and imaging techniques require an ultra-quiet environment to reach optimal resolution. Isolation from ambient vibrations is often achieved by placing the sensitive instrument atop a massive block that floats on air springs and is surrounded by acoustic barriers. Because typical building noise drops off above 120 Hz, it is advantageous to raise the flexural resonance frequencies of the inertia block and instrument far above 120 Hz. However, it can be challenging to obtain a high fundamental frequency of the floating block using a simple rectangular design. Here we design, construct, and characterize a vibration isolation system with a cylindrical inertia block, whose lowest resonance frequency of 249 Hz shows good agreement between finite element analysis simulation and directly measured modes. Our simulations show that a cylindrical design can achieve higher fundamental resonance frequency than a rectangular design of the same mass.

preprint2021arXiv

Prediction of Spin Polarized Fermi Arcs in Quasiparticle Interference of CeBi

We predict that CeBi in the ferromagnetic state is a Weyl semimetal. Our calculations within density functional theory show the existence of two pairs of Weyl nodes on the momentum path $(0, 0, k_z)$ at $15$ meV} above and $100$ meV below the Fermi level. Two corresponding Fermi arcs are obtained on surfaces of mirror-symmetric (010)-oriented slabs at $E=15$ meV and both arcs are interrupted into three segments due to hybridization with a set of trivial surface bands. By studying the spin texture of surface states, we find the two Fermi arcs are strongly spin-polarized but in opposite directions, which can be detected by spin-polarized ARPES measurements. Our theoretical study of quasiparticle interference (QPI) for a nonmagnetic impurity at the Bi site also reveals several features related to the Fermi arcs. Specifically, we predict that the spin polarization of the Fermi arcs leads to a bifurcation-shaped feature only in the spin-dependent QPI spectrum, serving as a fingerprint of the Weyl nodes.

preprint2021arXiv

The Sariçiçek howardite fall in Turkey: Source crater of HED meteorites on Vesta and impact risk of Vestoids

The Sariçiçek howardite meteorite shower consisting of 343 documented stones occurred on 2 September 2015 in Turkey and is the first documented howardite fall. Cosmogenic isotopes show that Sariçiçek experienced a complex cosmic ray exposure history, exposed during ~12-14 Ma in a regolith near the surface of a parent asteroid, and that an ca.1 m sized meteoroid was launched by an impact 22 +/- 2 Ma ago to Earth (as did one third of all HED meteorites). SIMS dating of zircon and baddeleyite yielded 4550.4 +/- 2.5 Ma and 4553 +/- 8.8 Ma crystallization ages for the basaltic magma clasts. The apatite U-Pb age of 4525 +/- 17 Ma, K-Ar age of ~3.9 Ga, and the U,Th-He ages of 1.8 +/- 0.7 and 2.6 +/- 0.3 Ga are interpreted to represent thermal metamorphic and impact-related resetting ages, respectively. Petrographic, geochemical and O-, Cr- and Ti- isotopic studies confirm that Sariçiçek belongs to the normal clan of HED meteorites. Petrographic observations and analysis of organic material indicate a small portion of carbonaceous chondrite material in the Sariçiçek regolith and organic contamination of the meteorite after a few days on soil. Video observations of the fall show an atmospheric entry at 17.3 +/- 0.8 kms-1 from NW, fragmentations at 37, 33, 31 and 27 km altitude, and provide a pre-atmospheric orbit that is the first dynamical link between the normal HED meteorite clan and the inner Main Belt. Spectral data indicate the similarity of Sariçiçek with the Vesta asteroid family spectra, a group of asteroids stretching to delivery resonances, which includes (4) Vesta. Dynamical modeling of meteoroid delivery to Earth shows that the disruption of a ca.1 km sized Vesta family asteroid or a ~10 km sized impact crater on Vesta is required to provide sufficient meteoroids <4 m in size to account for the influx of meteorites from this HED clan.

preprint2021arXiv

Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning

This paper presents a simple unsupervised visual representation learning method with a pretext task of discriminating all images in a dataset using a parametric, instance-level classifier. The overall framework is a replica of a supervised classification model, where semantic classes (e.g., dog, bird, and ship) are replaced by instance IDs. However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy. This work presents several novel techniques to handle these difficulties. First, we introduce a hybrid parallel training framework to make large-scale training feasible. Second, we present a raw-feature initialization mechanism for classification weights, which we assume offers a contrastive prior for instance discrimination and can clearly speed up converge in our experiments. Finally, we propose to smooth the labels of a few hardest classes to avoid optimizing over very similar negative pairs. While being conceptually simple, our framework achieves competitive or superior performance compared to state-of-the-art unsupervised approaches, i.e., SimCLR, MoCoV2, and PIC under ImageNet linear evaluation protocol and on several downstream visual tasks, verifying that full instance classification is a strong pretraining technique for many semantic visual tasks.

preprint2021arXiv

Two Quasi-periodic Fast-propagating Magnetosonic Wave Events Observed In Active Region NOAA 11167

We report a detailed observational study of two quasi-periodic fast-propagating (QFP) magnetosonic wave events occurred on 2011 March 09 and 10, respectively. Interestingly, both the two events have two wave trains (WTs): one main and strong (WT-1) whereas the second appears small and weak (WT-2). Peculiar and common characteristics of the two events are observed, namely: 1) the two QFP waves are accompanied with brightenings during the whole stage of the eruptions; 2) both the two main wave trains are nearly propagating along the same direction; 3) EUV waves are found to be associated with the two events. Investigating various aspects of the target events, we argue that: 1) the second event is accompanied with a flux rope eruption during the whole stage; 2) the second event eruption produces a new filament-like (FL) dark feature; 3) the ripples of the two WT-2 QFP waves seem to result from different triggering mechanisms. Based on the obtained observational results, we propose that the funnel-like coronal loop system is indeed playing an important role in the two WT-1 QFP waves. The development of the second WT-2 QFP wave can be explained as due to the dispersion of the main EUV front. The co-existence of the two events offer thereby a significant opportunity to reveal what driving mechanisms and structures are tightly related to the waves.

Yu Liu

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191 published item(s)

Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation

FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Geometric topics related to Besov type spaces on the Grushin setting

Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling

Centralized Cooperative Exploration Policy for Continuous Control Tasks

Epitaxially Driven Phase Selectivity of Sn in Hybrid Quantum Nanowires

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

A deep domain decomposition method based on Fourier features

A Novel Generative Convolutional Neural Network for Robot Grasp Detection on Gaussian Guidance

Ancient and recent collisions revealed by phosphate minerals in the Chelyabinsk meteorite

Better automation of beamline control at HEPS

BV spaces and the perimeters related to Schrodinger operators with inverse-square potentials and applications to the rank-one theorem

Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

Deep Learning-based Inertial Odometry for Pedestrian Tracking using Attention Mechanism and Res2Net Module

DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy

Direct manipulation of a superconducting spin qubit strongly coupled to a transmon qubit

Disordered Hyperuniform Quasi-1D Materials

Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI

Electrical and thermal transport in van der Waals magnets 2H-M$_x$TaS$_2$ (M = Mn, Co)

Ferroelectric superdomain controlled graphene plasmon for tunable mid-infrared photodetector with dual-band spectral selectivity

Giant thermoelectric power factor anisotropy in PtSb$_{1.4}$Sn$_{0.6}$

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Inline Tests

INTERN: A New Learning Paradigm Towards General Vision

Iterative Self-consistent Parallel Magnetic Resonance Imaging Reconstruction based on Nonlocal Low-Rank Regularization

Laguerre BV spaces, Laguerre perimeter and their applications

LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders

Mamba: a systematic software solution for beamline experiments at HEPS

Mapping evolving population geography in China

Meta Knowledge Distillation

MetroGAN: Simulating Urban Morphology with Generative Adversarial Network

Nebula Graph: An open source distributed graph database

Optimal quantum optical control of spin in diamond

Pedestrian Dead Reckoning System using Quasi-static Magnetic Field Detection

Polaronic Conductivity in Cr$_2$Ge$_2$Te$_6$ Single Crystals

Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning Approach

Quantifying navigation complexity in transportation networks

Quantum Fisher information measurement and verification of the quantum Cramér-Rao bound in a solid-state qubit

Quantum phase transition in magnetic nanographenes on a lead superconductor

Rethinking Robust Representation Learning Under Fine-grained Noisy Faces

Role of Local Ru Hexamers in Superconductivity of Ruthenium Phosphide

Self-slimmed Vision Transformer

Self-supervised graph neural networks for accurate prediction of Néel temperature

Sensitivity tests of cosmic velocity fields to massive neutrinos

Short-range Crystalline Order-Tuned Conductivity in Cr$_2$Si$_2$Te$_6$ van der Waals Magnetic Crystals

Spectroscopy of spin-split Andreev levels in a quantum dot with superconducting leads

Spin-polarized imaging of strongly interacting fermions in the ferrimagnetic state of Weyl candidate CeBi

Suppression of Superconductivity and Nematic Order in Fe$_{1-y}$Se$_{1-x}$S$_x$ (0$\leq$$x$$\leq$1, $y$$\leq$0.1) Crystals by Anion Height Disorder

Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

The effect of $f$-$c$ hybridization on the $γ\rightarrowα$ phase transition of cerium studied by lanthanum doping

The underestimation of high pressure in DFT+$U$ simulation for the wide range cold-pressure of lanthanide metals

The universality in urban commuting across and within cities

Thermal transport and mixed valence in ZrTe$_3$ doped with Hf and Se

Thermoelectricity and electronic correlations enhancement in FeS by slight Se substitution

Topological Defects Induced High-Spin Quartet State in Truxene-Based Molecular Graphenoids

Towards Robust Face Recognition with Comprehensive Search

UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning

Unifying Visual Perception by Dispersible Points Learning

UniNet: Unified Architecture Search with Convolution, Transformer, and MLP

ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation

Anomalous Hall effect in weak-itinerant ferrimagnet FeCr$_2$Te$_4$

Design and characterization of a low-vibration laboratory with cylindrical inertia block geometry

Prediction of Spin Polarized Fermi Arcs in Quasiparticle Interference of CeBi

The Sariçiçek howardite fall in Turkey: Source crater of HED meteorites on Vesta and impact risk of Vestoids

Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning

Two Quasi-periodic Fast-propagating Magnetosonic Wave Events Observed In Active Region NOAA 11167

1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020

1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation

3D Gated Recurrent Fusion for Semantic Scene Completion

A First Look at Commercial 5G Performance on Smartphones

A gridded establishment dataset as a proxy for economic activity in China

A novel multimodal approach for hybrid brain-computer interface