arXiv subjects

Ying Liu

Publications and source records attributed to Ying Liu.

At least 181 records · Page 10Linked to original sources

arXiv2023

Magnetic Electrides: High-Throughput Material Screening, Intriguing Properties, and Applications

Electrides are a unique class of electron-rich materials where excess electrons are localized in interstitial lattice sites as anions, leading to a range of unique properties and applications. While hundreds of electrides have been discovered in recent years, magnetic electrides have received limited attention, with few investigations into their fundamental physics and practical applications. In this work, 51 magnetic electrides (12 antiferromagnetic, 13 ferromagnetic, and 26 interstitial-magnetic) were identified using high-throughput computational screening methods and the latest Material Project database. Based on their compositions, these magnetic electrides can be classified as magnetic semiconductors, metals, or half-metals, each with unique topological states and excellent catalytic performance for N2 fixation due to their low work functions and excess electrons. The novel properties of magnetic electrides suggest potential applications in spintronics, topological electronics, electron emission, and as high-performance catalysts. This work marks the beginning of a new era in the identification, investigation, and practical applications of magnetic electrides.

cond-mat.mtrl-sci↗

arXiv2023

Excellent catalytic performance towards the hydrogen evolution reaction in topological semimetals

Topological states of matter and their corresponding properties are classical research topics in condensed matter physics. Quite recently, the application of materials that feature these states has been extended to the field of electrochemical catalysis and become an emerging research topic that is receiving increasing interest. In particular, several recent experimental studies performed on topological semimetals have already revealed high catalytic performance towards hydrogen evolution reaction (HER), strongly promoting acceptance of the fresh concept of the topological catalyst. This emerging concept has experienced rapid developments in the last few years, but these developments have been rarely summarized. Herein, we offer a comprehensive review on the state-of-the-art progress in developing topological catalysts for the HER process through topological semimetals such as Weyl semimetals, Dirac semimetals, nodal line semimetals, etc. The course of development, the general research routes, and the fundamental mechanisms in topological catalysts are also systematically analyzed in this review.

cond-mat.mtrl-sci↗

arXiv2022

HL-nets: Physics-informed neural networks for hydrodynamic lubrication with cavitation

Recently, physics-informed neural networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs). In this study, we establish a deep learning computational framework, HL-nets, for computing the flow field of hydrodynamic lubrication involving cavitation effects. Two classical cavitation conditions, i.e., the Swift-Stieber (SS) condition and the Jakobsson-Floberg-Olsson (JFO) condition, are implemented in the PINNs to solve the Reynolds equation. For the non-negativity constraint of the SS cavitation condition, a penalizing scheme with a residual of the non-negativity and an imposing scheme with a continuous differentiable non-negative function are proposed. For the complementarity constraint of the JFO cavitation condition, the pressure and cavitation fraction are taken as the neural network outputs, and the residual of the Fischer-Burmeister (FB) equation constrains their complementary relationships. Multi-task learning (MTL) methods are applied to balance the newly introduced loss terms described above. To estimate the accuracy of HL-nets, we present a numerical solution of the Reynolds equation for oil-lubricated bearings involving cavitation. The results indicate that the proposed HL-nets can highly accurately simulate hydrodynamic lubrication involving cavitation phenomena. The imposing scheme can effectively improve the accuracy of the training results of PINNs, and it is expected to have great potential to be applied to different fields where the non-negativity constraint is needed.

physics.flu-dyn↗

arXiv2022

Translational boundaries as incipient ferrielectric domains in antiferroelectric PbZrO3

In the archetypal antiferroelectric PbZrO3, antiparallel electric dipoles cancel each other, resulting in zero spontaneous polarisation at the macroscopic level. Yet in actual hysteresis loops, the cancellation is rarely perfect and some remnant polarization is often observed, suggesting the metastability of polar phases in this material. In this work, using aberration-corrected scanning transmission electron microscopy methods on a PbZrO3 single crystal, we uncover the coexistence of the common antiferroelectric phase and a ferrielectric phase featuring an electric dipole pattern of "up down up". This dipole arrangement, predicted by Aramberri et al. (2021) to be the ground state of PbZrO3 at 0K, appears at room temperature in the form of translational boundaries that aggregate to form wider stripe domains of the polar phase embedded within the antiferroelectric matrix.

cond-mat.mtrl-sci↗

arXiv2022

Topological bulk solitons in a nonlinear photonic Chern insulator

Nonlinearities in lattices with topological band structures can induce topological interfaces in the bulk of structures and give rise to bulk solitons in the topological bandgaps. Here we study a photonic Chern insulator with saturable nonlinearity and show the existence of topological bulk solitons. The fundamental bulk solitons exhibit as semi-vortex solitons, where only one pseudospin component has a nonzero vorticity. The bulk solitons have equal angular momentum at different valleys. This phenomenon is a direct outcome of the topology of the linear host lattice and the angular momentum can be changed by switching the sign of the nonlinearity. The bulk solitons bifurcate from the linear bulk band edge and terminate when their powers saturate. We find that these bulk solitons are stable within the whole spectrum range. Moreover, these bulk solitons are robust against lattice disorders both from on-site energies and hopping amplitudes. Our work extends the study of Chern insulators into the nonlinear regime and highlights the interplay between topology and nonlinearity.

physics.optics↗

arXiv2022

Interrelated Thermalization and Quantum Criticality in a Lattice Gauge Simulator

Gauge theory and thermalization are both foundations of physics and nowadays are both topics of essential importance for modern quantum science and technology. Simulating lattice gauge theories (LGTs) realized recently with ultracold atoms provides a unique opportunity for carrying out a correlated study of gauge theory and thermalization in the same setting. Theoretical studies have shown that an Ising quantum phase transition exists in this implemented LGT, and quantum thermalization can also signal this phase transition. Nevertheless, it remains an experimental challenge to accurately determine the critical point and controllably explore the thermalization dynamics in the quantum critical regime due to the lack of techniques for locally manipulating and detecting matter and gauge fields. Here, we report an experimental investigation of the quantum criticality in the LGT from both equilibrium and non-equilibrium thermalization perspectives by equipping the single-site addressing and atom-number-resolved detection into our LGT simulator. We accurately determine the quantum critical point agreed with the predicted value. We prepare a $|Z_{2}\rangle$ state deterministically and study its thermalization dynamics across the critical point, leading to the observation that this $|Z_{2}\rangle$ state thermalizes only in the critical regime. This result manifests the interplay between quantum many-body scars, quantum criticality, and symmetry breaking.

cond-mat.quant-gas↗

arXiv2022

Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE

The recently proposed identifiable variational autoencoder (iVAE) framework provides a promising approach for learning latent independent components (ICs). iVAEs use auxiliary covariates to build an identifiable generation structure from covariates to ICs to observations, and the posterior network approximates ICs given observations and covariates. Though the identifiability is appealing, we show that iVAEs could have local minimum solution where observations and the approximated ICs are independent given covariates.-a phenomenon we referred to as the posterior collapse problem of iVAEs. To overcome this problem, we develop a new approach, covariate-informed iVAE (CI-iVAE) by considering a mixture of encoder and posterior distributions in the objective function. In doing so, the objective function prevents the posterior collapse, resulting latent representations that contain more information of the observations. Furthermore, CI-iVAEs extend the original iVAE objective function to a larger class and finds the optimal one among them, thus having tighter evidence lower bounds than the original iVAE. Experiments on simulation datasets, EMNIST, Fashion-MNIST, and a large-scale brain imaging dataset demonstrate the effectiveness of our new method.

stat.ML↗

arXiv2022

Functional building blocks for scalable multipartite entanglement in optical lattices

Featuring excellent coherence and operated parallelly, ultracold atoms in optical lattices form a competitive candidate for quantum computation. For this, a massive number of parallel entangled atom pairs have been realized in superlattices. However, the more formidable challenge is to scale-up and detect multipartite entanglement due to the lack of manipulations over local atomic spins in retro-reflected bichromatic superlattices. Here we developed a new architecture based on a cross-angle spin-dependent superlattice for implementing layers of quantum gates over moderately-separated atoms incorporated with a quantum gas microscope for single-atom manipulation. We created and verified functional building blocks for scalable multipartite entanglement by connecting Bell pairs to one-dimensional 10-atom chains and two-dimensional plaquettes of $2\times4$ atoms. This offers a new platform towards scalable quantum computation and simulation.

cond-mat.quant-gas↗

arXiv2022

Scaling Blockchains with Error Correction Codes: A Survey on Coded Blockchains

This paper reviews and highlights how coding schemes have been used to solve various problems in blockchain systems. Specifically, these problems relate to scaling blockchains in terms of their data storage, computation and communication cost, as well as security. To this end, this paper considers the use of coded blocks or shards that allows participants to store only a fraction of the total blockchain, protect against malicious nodes or erasures due to nodes leaving a blockchain system, ensure data availability in order to promote transparency, and scale the security of sharded blockchains. Further, it helps reduce communication cost when disseminating blocks, which is critical to bootstrapping new nodes and helps speed up consensus of blocks. For each category of solutions, we highlight problems and issues that motivated their designs and use of coding. Moreover, we provide a qualitative analysis of their storage, communication and computation cost.

cs.DC↗

arXiv2022

RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment

Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL backbone (termed as T-MIL) to aggregate the multiple instances along the coronary centerline into the final quality. However, not all instances are informative for final quality. There are some quality-irrelevant/negative instances intervening the exact quality assessment(e.g., instances covering only background or the coronary in instances is not identifiable). Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based on the above two modules, we propose a Reinforced Transformer Network (RTN) for automatic CCTA VIQA based on end-to-end optimization. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the real-world CCTA dataset, exceeding previous MIL methods by a large margin.

cs.MM↗

arXiv2022

Physically Explainable CNN for SAR Image Classification

Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in deep neural networks is essential in order to enhance the explainability and physics awareness of deep learning. In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL). It comprises three parts: (1) explainable models (XM) to provide prior physics knowledge, (2) physics guided network (PGN) to encode the knowledge into physics-aware features, and (3) physics injected network (PIN) to adaptively introduce the physics-aware features into classification pipeline for label prediction. A hybrid Image-Physics SAR dataset format is proposed for evaluation, with both Sentinel-1 and Gaofen-3 SAR data being experimented. The results show that the proposed PGIL substantially improve the classification performance in case of limited labeled data compared with the counterpart data-driven CNN and other pre-training methods. Additionally, the physics explanations are discussed to indicate the interpretability and the physical consistency preserved in the predictions. We deem the proposed method would promote the development of physically explainable deep learning in SAR image interpretation field.

eess.IV↗

arXiv2022

Self-localized topological states in three dimensions

Three-dimensional (3D) topological materials exhibit much richer phenomena than their lower-dimensional counterparts. Here, we propose self-localized topological states (i.e., topological solitons) in a 3D nonlinear photonic Chern insulator. Despite being in the bulk and self-localized in all 3D, the topological solitons at high-symmetry points K and K' rotate in the same direction, due to the underlying topology. Specifically, under the saturable nonlinearity the solitons are stable over a broad frequency range. Our results highlight how topology and nonlinearity interact with each other and can be extended to other 3D topological systems.

physics.optics↗

arXiv2022

RCMNet: A deep learning model assists CAR-T therapy for leukemia

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is higher than other SOTA models. The study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications.

eess.IV↗

arXiv2022

Recent Progress of Heterostructures Based on Two Dimensional Materials and Wide Bandgap Semiconductors

Recent progress in the synthesis and assembly of two-dimensional (2D) materials has laid the foundation for various applications of atomically thin layer films. These 2D materials possess rich and diverse properties such as layer-dependent band gaps, interesting spin degrees of freedom, and variable crystal structures. They exhibit broad application prospects in micro-nano devices. In the meantime, the wide bandgap semiconductors (WBS) with an elevated breakdown voltage, high mobility, and high thermal conductivity have shown important applications in high-frequency microwave devices, high-temperature and high-power electronic devices. Beyond the study on single 2D materials or WBS materials, the multi-functional 2D/WBS heterostructures can promote the carrier transport at the interface, potentially providing novel physical phenomena and applications, and improving the performance of electronic and optoelectronic devices. In this review, we overview the advantages of the heterostructures of 2D materials and WBS materials, and introduce the construction methods of 2D/WBS heterostructures. Then, we present the diversity and recent progress in the applications of 2D/WBS heterostructures, including photodetectors, photocatalysis, sensors, and energy related devices. Finally, we put forward the current challenges of 2D/WBS heterostructures and propose the promising research directions in the future.

physics.app-ph↗

arXiv2022

Remiod: Reference-based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with non-ignorable missingness

Missing data on response variables are common in clinical studies. Corresponding to the uncertainty of missing mechanism, theoretical frameworks on controlled imputation have been developed. In practice, it is recommended to conduct a statistically valid analysis under the primary assumptions on missing data, followed by sensitivity analysis under alternative assumptions to assess the robustness of results. Due to the availability of software, controlled multiple imputation (MI) procedures, including delta-based and reference-based approaches, have become popular for analyzing continuous variables under missing-not-at-random assumptions. Similar tools, however, still limit application of these methods to categorical data. In this paper, we introduce the R package \textbf{remiod}, which utilizes the Bayesian framework to perform imputation in regression models on binary and ordinal outcomes. Following outlining theoretical backgrounds, usage and features of \textbf{remiod} are described and illustrated using examples.

stat.ME↗

arXiv2022

Universal and Efficient p-Doping of Organic Semiconductors by Electrophilic Attack of Cations

Doping is of great importance to tailor the electrical properties of semiconductors. However, the present doping methodologies for organic semiconductors (OSCs) are either inefficient or can only apply to a small number of OSCs, seriously limiting their general application. Herein, we reveal a novel p-doping mechanism by investigating the interactions between the dopant trityl cation and poly(3-hexylthiophene) (P3HT). It is found that electrophilic attack of the trityl cations on thiophenes results in the formation of alkylated ions that induce electron transfer from neighboring P3HT chains, resulting in p-doping. This unique p-doping mechanism can be employed to dope various OSCs including those with high ionization energy (IE=5.8 eV). Moreover, this doping mechanism endows trityl cation with strong doping ability, leading to polaron yielding efficiency of 100 % and doping efficiency of over 80 % in P3HT. The discovery and elucidation of this novel doping mechanism not only points out that strong electrophiles are a class of efficient p-dopants for OSCs, but also provides new opportunities towards highly efficient doping of OSCs.

cond-mat.mtrl-sci↗

arXiv2022

PACSan: Enforcing Memory Safety Based on ARM PA

Memory safety is a key security property that stops memory corruption vulnerabilities. Existing sanitizers enforce checks and catch such bugs during development and testing. However, they either provide partial memory safety or have overwhelmingly high performance overheads. Our novel sanitizer PACSan enforces spatial and temporal memory safety with no false positives at low performance overheads. PACSan removes the majority of the overheads involved in pointer tracking by sealing metadata in pointers through ARM PA (Pointer Authentication), and performing the memory safety checks when pointers are dereferenced. We have developed a prototype of PACSan and systematically evaluated its security and performance on the Magma, Juliet, Nginx, and SPEC CPU2017 test suites, respectively. In our evaluation, PACSan shows no false positives together with negligible false negatives, while introducing stronger security guarantees and lower performance overheads than state-of-the-art sanitizers, including HWASan, ASan, SoftBound+CETS, Memcheck, LowFat, and PTAuth. Specifically, PACSan has 0.84x runtime overhead and 1.92x memory overhead on average. Compared to the widely deployed ASan, PACSan has no false positives and much fewer false negatives and reduces 7.172% runtime overheads and 89.063%memory overheads.

cs.CR↗

arXiv2022

The Exponential-Time Complexity of the complex weighted #CSP

In this paper, I consider a fine-grained dichotomy of Boolean counting constraint satisfaction problem (#CSP), under the exponential time hypothesis of counting version (#ETH). Suppose $\mathscr{F}$ is a finite set of algebraic complex-valued functions defined on Boolean domain. When $\mathscr{F}$ is a subset of either two special function sets, I prove that #CSP($\mathscr{F}$) is polynomial-time solvable, otherwise it can not be computed in sub-exponential time unless #ETH fails. I also improve the result by proving the same dichotomy holds for #CSP with bounded degree (every variable appears at most constant constraints), even for #R$_3$-CSP. An important preparation before proving the result is to argue that pinning (two special unary functions $[1,0]$ and $[0,1]$ are used to reduce arity) can also keep the sub-exponential lower bound of a Boolean #CSP problem. I discuss this issue by utilizing some common methods in proving #P-hardness of counting problems. The proof illustrates the internal correlation among these commonly used methods.

cs.CC↗