arXiv subjects

Yi Liu

Publications and source records attributed to Yi Liu.

At least 631 records · Page 35Linked to original sources

arXiv2021

Superconductivity modulated by structural phase transitions in pressurized vanadium-based kagome metals

The interplay of superconductivity with electronic and structural instabilities on the kagome lattice provides a fertile ground for the emergence of unusual phenomena. The vanadium-based kagome metals $A$V$_3$Sb$_5$ ($A=$ K, Rb, Cs) exhibit superconductivity on an almost ideal kagome lattice, with the superconducting transition temperature $T_{\rm c}$ forming two domes upon pressure-tuning. The first dome arises from the competition between superconductivity and a charge-density-wave, whereas the origin for the second dome remains unclear. Herein, we show that the appearance of the second superconducting dome in KV$_3$Sb$_5$ and RbV$_3$Sb$_5$ is associated with transitions from hexagonal $P6$/$mmm$ to monoclinic $P2$/$m$ structures, evidenced by splitting of structural peaks from synchrotron powder X-ray diffraction experiments and imaginary phonon frequencies in first-principles calculations. In KV$_3$Sb$_5$, transition to an orthorhombic $Pmmm$ structure is further observed for pressure $p\gtrsim20$ GPa, and is correlated with the strong suppression of $T_{\rm c}$ in the second superconducting dome. Our findings indicate distortions of the crystal structure modulates superconductivity in $A$V$_3$Sb$_5$ under pressure, providing a platform to study the emergence of superconductivity in the presence of multiple structural instabilities.

cond-mat.supr-con↗

arXiv2021

Locally Adaptive Structure and Texture Similarity for Image Quality Assessment

The latest advances in full-reference image quality assessment (IQA) involve unifying structure and texture similarity based on deep representations. The resulting Deep Image Structure and Texture Similarity (DISTS) metric, however, makes rather global quality measurements, ignoring the fact that natural photographic images are locally structured and textured across space and scale. In this paper, we describe a locally adaptive structure and texture similarity index for full-reference IQA, which we term A-DISTS. Specifically, we rely on a single statistical feature, namely the dispersion index, to localize texture regions at different scales. The estimated probability (of one patch being texture) is in turn used to adaptively pool local structure and texture measurements. The resulting A-DISTS is adapted to local image content, and is free of expensive human perceptual scores for supervised training. We demonstrate the advantages of A-DISTS in terms of correlation with human data on ten IQA databases and optimization of single image super-resolution methods.

eess.IV↗

arXiv2021

Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis

Efficient collaboration between collaborative machine learning and wireless communication technology, forming a Federated Edge Learning (FEEL), has spawned a series of next-generation intelligent applications. However, due to the openness of network connections, the FEEL framework generally involves hundreds of remote devices (or clients), resulting in expensive communication costs, which is not friendly to resource-constrained FEEL. To address this issue, we propose a distributed approximate Newton-type algorithm with fast convergence speed to alleviate the problem of FEEL resource (in terms of communication resources) constraints. Specifically, the proposed algorithm is improved based on distributed L-BFGS algorithm and allows each client to approximate the high-cost Hessian matrix by computing the low-cost Fisher matrix in a distributed manner to find a "better" descent direction, thereby speeding up convergence. Second, we prove that the proposed algorithm has linear convergence in strongly convex and non-convex cases and analyze its computational and communication complexity. Similarly, due to the heterogeneity of the connected remote devices, FEEL faces the challenge of heterogeneous data and non-IID (Independent and Identically Distributed) data. To this end, we design a simple but elegant training scheme, namely FedOVA, to solve the heterogeneous statistical challenge brought by heterogeneous data. In this way, FedOVA first decomposes a multi-class classification problem into more straightforward binary classification problems and then combines their respective outputs using ensemble learning. In particular, the scheme can be well integrated with our communication efficient algorithm to serve FEEL. Numerical results verify the effectiveness and superiority of the proposed algorithm.

cs.LG↗

arXiv2021

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementing and benchmarking various advanced tasks are still painful and time-consuming with existing libraries. To facilitate graph deep learning research, we introduce DIG: Dive into Graphs, a turnkey library that provides a unified testbed for higher level, research-oriented graph deep learning tasks. Currently, we consider graph generation, self-supervised learning on graphs, explainability of graph neural networks, and deep learning on 3D graphs. For each direction, we provide unified implementations of data interfaces, common algorithms, and evaluation metrics. Altogether, DIG is an extensible, open-source, and turnkey library for researchers to develop new methods and effortlessly compare with common baselines using widely used datasets and evaluation metrics. Source code is available at https://github.com/divelab/DIG.

cs.LG↗

arXiv2021

Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction

Dynamic link prediction (DLP) makes graph prediction based on historical information. Since most DLP methods are highly dependent on the training data to achieve satisfying prediction performance, the quality of the training data is crucial. Backdoor attacks induce the DLP methods to make wrong prediction by the malicious training data, i.e., generating a subgraph sequence as the trigger and embedding it to the training data. However, the vulnerability of DLP toward backdoor attacks has not been studied yet. To address the issue, we propose a novel backdoor attack framework on DLP, denoted as Dyn-Backdoor. Specifically, Dyn-Backdoor generates diverse initial-triggers by a generative adversarial network (GAN). Then partial links of the initial-triggers are selected to form a trigger set, according to the gradient information of the attack discriminator in the GAN, so as to reduce the size of triggers and improve the concealment of the attack. Experimental results show that Dyn-Backdoor launches successful backdoor attacks on the state-of-the-art DLP models with success rate more than 90%. Additionally, we conduct a possible defense against Dyn-Backdoor to testify its resistance in defensive settings, highlighting the needs of defenses for backdoor attacks on DLP.

cs.AI↗

arXiv2021

BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models

Copy mechanisms explicitly obtain unchanged tokens from the source (input) sequence to generate the target (output) sequence under the neural seq2seq framework. However, most of the existing copy mechanisms only consider single word copying from the source sentences, which results in losing essential tokens while copying long spans. In this work, we propose a plug-and-play architecture, namely BioCopy, to alleviate the problem aforementioned. Specifically, in the training stage, we construct a BIO tag for each token and train the original model with BIO tags jointly. In the inference stage, the model will firstly predict the BIO tag at each time step, then conduct different mask strategies based on the predicted BIO label to diminish the scope of the probability distributions over the vocabulary list. Experimental results on two separate generative tasks show that they all outperform the baseline models by adding our BioCopy to the original model structure.

cs.CL↗

arXiv2021

Experimental Evidence of Nonlinear Avalanche Dynamics of Energetic Particle Modes

Recent observations in HL-2A tokamak give new experimental evidences of energetic particle mode (EPM) avalanche. In a strong EPM burst, the mode structure propagates radially outward within two hundred Alfvén time, while the frequency of the dominant mode changes self-consistently to maximize wave-particle power exchange and mode growth. This suggests that significant energetic particle transport occurs in this avalanche phase, in agreement with theoretical framework of EPM convective amplification. A simplified relay runner model yields satisfactory interpretations of the measurements. The results can help understanding the nonlinear dynamics of energetic particle driven modes in future burning plasmas, such as ITER.

physics.plasm-ph↗

arXiv2021

Extrinsic and Intrinsic Anomalous Metallic States in Transition Metal Dichalcogenide Ising Superconductors

The metallic ground state in two-dimensional (2D) superconductors has attracted much attention but is still under intense scrutiny. Especially, the measurements in ultralow temperature region are challenging for 2D superconductors due to the sensitivity to external perturbations. In this work, the resistance saturation induced by external noise, named as "extrinsic anomalous metallic state", is observed in 2D transition metal dichalcogenide (TMD) superconductor 4Ha-TaSe2 nanodevices. However, with further decreasing temperature, credible evidence of intrinsic anomalous metallic state is obtained by adequately filtering external radiation. Our work indicates that at ultralow temperatures the anomalous metallic state can be experimentally revealed as the quantum ground state in 2D crystalline TMD superconductors. Besides, Ising superconductivity revealed by ultrahigh in-plane critical field (Bc2//) going beyond the Pauli paramagnetic limit (Bp) is detected in 4Ha-TaSe2, from one-unit-cell device to bulk situation, which might be due to the weak coupling between the TaSe2 sub-monolayers.

cond-mat.supr-con↗

arXiv2021

The observation of in-plane quantum Griffiths singularity in two-dimensional crystalline superconductors

Quantum Griffiths singularity (QGS) reveals the profound influence of quenched disorder on the quantum phase transitions, characterized by the divergence of the dynamical critical exponent at the boundary of the vortex glass-like phase, named as quantum Griffiths phase. However, in the absence of vortices, whether the QGS can exist under parallel magnetic field remains a puzzle. Here we study the magnetic field induced superconductor-metal transition in ultrathin crystalline PdTe2 films grown by molecular beam epitaxy. Remarkably, the QGS emerges under both perpendicular and parallel magnetic field in 4-monolayer PdTe2 films. The direct activated scaling analysis with a new irrelevant correction has been proposed, providing important evidence of QGS. With increasing film thickness to 6 monolayers, the QGS disappears under perpendicular field but persists under parallel field, and this discordance may originate from the differences in microscopic processes. Our work demonstrates the universality of parallel field induced QGS and can stimulate further investigations on novel quantum phase transitions under parallel magnetic field.

cond-mat.supr-con↗

arXiv2021

Optical vortex coronagraph imaging of a laser-induced plasma filament

A high contrast imaging technique based on an optical vortex coronagraph (OVC) is used to measure the spatial phase profile induced by an air plasma generated by a femtosecond laser pulse. The sensitivity of the OVC method significantly surpassed both in-line holographic and direct imaging methods based on air plasma fluorescence. The estimated phase sensitivity of 0.046 waves provides opportunities for OVC applications in areas such as bioimaging, material characterization, as well as plasma diagnostics.

physics.optics↗

arXiv2021

BoA-PTA, A Bayesian Optimization Accelerated Error-Free SPICE Solver

One of the greatest challenges in IC design is the repeated executions of computationally expensive SPICE simulations, particularly when highly complex chip testing/verification is involved. Recently, pseudo transient analysis (PTA) has shown to be one of the most promising continuation SPICE solver. However, the PTA efficiency is highly influenced by the inserted pseudo-parameters. In this work, we proposed BoA-PTA, a Bayesian optimization accelerated PTA that can substantially accelerate simulations and improve convergence performance without introducing extra errors. Furthermore, our method does not require any pre-computation data or offline training. The acceleration framework can either be implemented to speed up ongoing repeated simulations immediately or to improve new simulations of completely different circuits. BoA-PTA is equipped with cutting-edge machine learning techniques, e.g., deep learning, Gaussian process, Bayesian optimization, non-stationary monotonic transformation, and variational inference via parameterization. We assess BoA-PTA in 43 benchmark circuits against other SOTA SPICE solvers and demonstrate an average 2.3x (maximum 3.5x) speed-up over the original CEPTA.

cs.LG↗

arXiv2021

Swap-Free Fat-Water Separation in Dixon MRI using Conditional Generative Adversarial Networks

Dixon MRI is widely used for body composition studies. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed on the scanner, making the data difficult to analyse. The most common artifact are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which can be wasteful and lead to unintended biases. The UK Biobank is acquiring Dixon MRI for over 100,000 participants, and thousands of swaps will occur. If those go undetected, errors will propagate into processes such as abdominal organ segmentation and dilute the results in population-based analyses. There is a clear need for a fast and robust method to accurately separate fat and water channels. In this work we propose such a method based on style transfer using a conditional generative adversarial network. We also introduce a new Dixon loss function for the generator model. Using data from the UK Biobank Dixon MRI, our model is able to predict highly accurate fat and water channels that are free from artifacts. We show that the model separates fat and water channels using either single input (in-phase) or dual input (in-phase and opposed-phase), with the latter producing improved results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.

eess.IV↗

arXiv2021

A Generic Transformation to Generate MDS Storage Codes with $δ$-Optimal Access Property

For high-rate maximum distance separable (MDS) codes, most of them are designed to optimally repair a single failed node by connecting all the surviving nodes. However, in practical systems, sometimes not all the surviving nodes are available. To facilitate the practical storage system, a few constructions of $(n,k)$ MDS codes with the property that any single failed node can be optimally repaired by accessing any $d$ surviving nodes have been proposed, where $d\in [k+1:n-1)$. However, all of them either have large sub-packetization levels or are not explicit for all the parameters. To address these issues, we propose a generic transformation that can convert any $(n',k')$ MDS code to another $(n,k)$ MDS code with the optimal repair property and optimal access property for an arbitrary set of two nodes, while the repair efficiency of the remaining $n-2$ nodes can be kept. By recursively applying the generic transformation to a scalar MDS code multiple times, we get an MDS code with the optimal repair property and the optimal access property for all nodes, which outperforms previous known MDS codes in terms of either the sub-packetization level or the flexibility of the parameters.

cs.IT↗

arXiv2021

Magnetic and superconducting phase diagram of Eu(Fe$_{1-x}$Ni$_{x}$)As$_{2}$

We report the Ni-doping effect on magnetism and superconductivity (SC) in an Eu-containing 112-type system Eu(Fe$_{1-x}$Ni$_{x})$As$_{2}$ ($0\leq x\leq 0.15$) by the measurements of resistivity, magnetization, and specific heat. The undoped EuFeAs$_2$ undergoes a spin-density-wave (SDW) transition at $T_\mathrm{SDW}\sim$ 105 K in the Fe sublattice and a magnetic ordering at $T_\mathrm{m}\sim$ 40 K in the Eu sublattice. Complex Eu-spin magnetism is manifested by a spin-glass reentrance at $T_\mathrm{SG}\sim$ 15 K and an additional spin reorientation at $T_\mathrm{SR}\sim$ 7 K. With Ni doping, the SDW order is rapidly suppressed, and SC emerges in the Ni-doping range of 0.01 $\leq x\leq$ 0.1 where a maximum of the superconducting transition temperature $T_\mathrm{c}^{\mathrm{max}}=$ 17.6 K shows up at $x$ = 0.04. On the other hand, $T_\mathrm{m}$ decreases very slowly, yet $T_\mathrm{SG}$ and $T_\mathrm{SR}$ hardly change with the Ni doping. The phase diagram has been established, which suggests a very weak coupling between SC and Eu spins. The complex Eu-spin magnetism is discussed in terms of the Ruderman-Kittel-Kasuya-Yosida interactions mediated by the conduction electrons from both layers of FeAs and As surrounding Eu$^{2+}$ ions.

cond-mat.supr-con↗

arXiv2021

First-principles study of the crystal structure, electronic structure, and transport properties of NiTe$_2$ under pressure

Recent experiments showed the distinct observations on the transition metal ditelluride NiTe$_2$ under pressure: one reported a superconducting phase transition at 12 GPa, whereas another observed a sign reversal of Hall resistivity at 16 GPa without the appearance of superconductivity. To clarify the controversial experimental phenomena, we have carried out first-principles electronic structure calculations on the compressed NiTe$_2$ with structure searching and optimization. Our calculations show that the pressure can transform NiTe$_2$ from a layered P-3m1 phase to a cubic Pa-3 phase at $\sim$10 GPa. Meanwhile, both the P-3m1 and Pa-3 phases possess nontrivial topological properties. The calculated superconducting $T_c$'s for these two phases based on the electron-phonon coupling theory both approach 0 K. Further magnetic transport calculations reveal that the sign of Hall resistance for the Pa-3 phase is sensitive to the pressure and the charge doping, in contrast to the case of the P-3m1 phase. Our theoretical predictions on the compressed NiTe$_2$ wait for careful experimental examinations.

cond-mat.supr-con↗

arXiv2021

A Map of Bandits for E-commerce

The rich body of Bandit literature not only offers a diverse toolbox of algorithms, but also makes it hard for a practitioner to find the right solution to solve the problem at hand. Typical textbooks on Bandits focus on designing and analyzing algorithms, and surveys on applications often present a list of individual applications. While these are valuable resources, there exists a gap in mapping applications to appropriate Bandit algorithms. In this paper, we aim to reduce this gap with a structured map of Bandits to help practitioners navigate to find relevant and practical Bandit algorithms. Instead of providing a comprehensive overview, we focus on a small number of key decision points related to reward, action, and features, which often affect how Bandit algorithms are chosen in practice.

cs.LG↗

arXiv2021

A Flexible Joint Model for Multiple Longitudinal Biomarkers and A Time-to-Event Outcome: With Applications to Dynamic Prediction Using Highly Correlated Biomarkers

In biomedical studies it is common to collect data on multiple biomarkers during study follow-up for dynamic prediction of a time-to-event clinical outcome. The biomarkers are typically intermittently measured, missing at some event times, and may be subject to high biological variations, which cannot be readily used as time-dependent covariates in a standard time-to-event model. Moreover, they can be highly correlated if they are from in the same biological pathway. To address these issues, we propose a flexible joint model framework that models the multiple biomarkers with a shared latent reduced rank longitudinal principal component model and correlates the latent process to the event time by the Cox model for dynamic prediction of the event time. The proposed joint model for highly correlated biomarkers is more flexible than some existing methods since the latent trajectory shared by the multiple biomarkers does not require specification of a priori parametric time trend and is determined by data. We derive an Expectation-Maximization (EM) algorithm for parameter estimation, study large sample properties of the estimators, and adapt the developed method to make dynamic prediction of the time-to-event outcome. Bootstrap is used for standard error estimation and inference. The proposed method is evaluated using simulations and illustrated on a lung transplant data to predict chronic lung allograft dysfunction (CLAD) using chemokines measured in bronchoalveolar lavage fluid of the patients.

stat.ME↗

arXiv2021

Optical Mouse: 3D Mouse Pose From Single-View Video

We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos. Many human clinical conditions and their corresponding animal models result in abnormal motion, and accurately measuring 3D motion at scale offers insights into health. The 3D poses improve classification of health-related attributes over 2D representations. The inferred poses are accurate enough to estimate stride length even when the feet are mostly occluded. This method could be applied as part of a continuous monitoring system to non-invasively measure animal health.

cs.CV↗