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Qi Wang

Publications and source records attributed to Qi Wang.

At least 433 records · Page 24Linked to original sources
arXiv2023

Perspective on Nanoscaled Magnonic Networks

With the rapid development of artificial intelligence in recent years, mankind is facing an unprecedented demand for data processing. Today, almost all data processing is performed using electrons in conventional complementary metal-oxide-semiconductor (CMOS) circuits. Over the past few decades, scientists have been searching for faster and more efficient ways to process data. Now, magnons, the quanta of spin waves, show the potential for higher efficiency and lower energy consumption in solving some specific problems. While magnonics remains predominantly in the realm of academia, significant efforts are being made to explore the scientific and technological challenges of the field. Numerous proof-of-concept prototypes have already been successfully developed and tested in laboratories. In this article, we review the developed magnonic devices and discuss the current challenges in realizing magnonic circuits based on these building blocks. We look at the application of spin waves in neuromorphic networks, stochastic and reservoir computing and discuss the advantages over conventional electronics in these areas. We then introduce a new powerful tool, inverse design magnonics, which has the potential to revolutionize the field by enabling the precise design and optimization of magnonic devices in a short time. Finally, we provide a theoretical prediction of energy consumption and propose benchmarks for universal magnonic circuits.

physics.app-ph↗
arXiv2023

RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization

Landmines remain a threat to war-affected communities for years after conflicts have ended, partly due to the laborious nature of demining tasks. Humanitarian demining operations begin by collecting relevant information from the sites to be cleared, which is then analyzed by human experts to determine the potential risk of remaining landmines. In this paper, we propose RELand system to support these tasks, which consists of three major components. We (1) provide general feature engineering and label assigning guidelines to enhance datasets for landmine risk modeling, which are widely applicable to global demining routines, (2) formulate landmine presence as a classification problem and design a novel interpretable model based on sparse feature masking and invariant risk minimization, and run extensive evaluation under proper protocols that resemble real-world demining operations to show a significant improvement over the state-of-the-art, and (3) build an interactive web interface to suggest priority areas for demining organizations. We are currently collaborating with a humanitarian demining NGO in Colombia that is using our system as part of their field operations in two areas recently prioritized for demining.

cs.CY↗
arXiv2023

Pressure-induced Superconductivity and Structure Phase Transition in SnAs-based Zintl Compound SrSn2As2

Layered SnAs-based Zintl compounds exhibit a distinctive electronic structure, igniting extensive research efforts in areas of superconductivity, topological insulators and quantum magnetism. In this paper, we systematically investigate the crystal structures and electronic properties of the Zintl compound SrSn2As2 under high-pressure. At approximately 20.8 GPa, pressure-induced superconductivity is observed in SrSn2As2 with a characteristic dome-like evolution of Tc. Theoretical calculations together with high pressure synchrotron X-ray diffraction and Raman spectroscopy have identified that SrSn2As2 undergoes a structural transformation from a trigonal to a monoclinic structure. Beyond 28.3 GPa, the superconducting transition temperature is suppressed due to a reduction of the density of state at the Fermi level. The discovery of pressure-induced superconductivity, accompanied by structural transitions in SrSn2As2, greatly expands the physical properties of layered SnAs-based compounds and provides a new ground states upon compression.

cond-mat.supr-con↗
arXiv2023

Effect of physical and chemical pressure on the superconductivity of caged-type quasiskutterudite Lu5Rh6Sn18

Lu5Rh6Sn18 is one of the caged-type quasiskutterudite superconductors with superconducting transition temperature Tc = 4.12 K. Here, we investigate the effect of pressure on the superconductivity in Lu5Rh6Sn18 by combining high pressure electrical transport, synchrotron x-ray diffraction (XRD) and chemical doping. Application of high pressure can enhance both the metallicity and the superconducting transition temperature in Lu5Rh6Sn18. Tc is found to show a continuous increase reaching up to 5.50 K at 11.4 GPa. Our high pressure synchrotron XRD measurements demonstrate the stability of the pristine crystal structure up to 12.0 GPa. In contrast, Tc is suppressed after the substitution of La ions in Lu sites, inducing negative chemical pressure. Our study provides valuable insights into the improvement of superconductivity in caged compounds.

cond-mat.supr-con↗
arXiv2023

Pressure-induced Superconductivity and Topological Quantum Phase Transitions in the Topological Semimetal ZrTe2

Topological transition metal dichalcogenides (TMDCs) have attracted much attention due to its potential applications in spintronics and quantum computations. In this work, we systematically investigate the structural and electronic properties of topological TMDCs candidate ZrTe2 under high pressure. A pressure-induced Lifshitz transition is evidenced by the change of charge carrier type as well as the Fermi surface. Superconductivity was observed at around 8.3 GPa without structural phase transition. A typical dome-shape phase diagram is obtained with the maximum Tc of 5.6 K for ZrTe2. Furthermore, our theoretical calculations suggest the presence of multiple pressure-induced topological quantum phase transitions, which coexists with emergence of superconductivity. The results demonstrate that ZrTe2 with nontrivial topology of electronic states display new ground states upon compression.

cond-mat.supr-con↗
arXiv2023

Representation type of cyclotomic quiver Hecke algebras of type $A_\ell^{(1)}$

We first investigate a connected quiver consisting of all dominant maximal weights for an integrable highest weight module in affine type A. This quiver provides an efficient method to obtain all dominant maximal weights. Then, we completely determine the representation type of cyclotomic Khovanov-Lauda-Rouquier algebras of arbitrary level in affine type A, by using the quiver we construct. This result gives a complete classification for the representation type of blocks of cyclotomic Hecke algebras since cyclotomic KLR algebras of type $A^{(1)}_\ell$ form a one-parameter family and cyclotomic Hecke algebras occur at a special parameter, i.e., $t=-2$ if $\ell=1$ and $t=(-1)^{\ell+1}$ if $\ell\geq2$.

math.RT↗
arXiv2023

A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm

Meta learning is a promising paradigm to enable skill transfer across tasks. Most previous methods employ the empirical risk minimization principle in optimization. However, the resulting worst fast adaptation to a subset of tasks can be catastrophic in risk-sensitive scenarios. To robustify fast adaptation, this paper optimizes meta learning pipelines from a distributionally robust perspective and meta trains models with the measure of expected tail risk. We take the two-stage strategy as heuristics to solve the robust meta learning problem, controlling the worst fast adaptation cases at a certain probabilistic level. Experimental results show that our simple method can improve the robustness of meta learning to task distributions and reduce the conditional expectation of the worst fast adaptation risk.

cs.LG↗
arXiv2023

RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation

The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset RenderIH for interacting hands with accurate and diverse pose annotations. The dataset contains 1M photo-realistic images with varied backgrounds, perspectives, and hand textures. To generate natural and diverse interacting poses, we propose a new pose optimization algorithm. Additionally, for better pose estimation accuracy, we introduce a transformer-based pose estimation network, TransHand, to leverage the correlation between interacting hands and verify the effectiveness of RenderIH in improving results. Our dataset is model-agnostic and can improve more accuracy of any hand pose estimation method in comparison to other real or synthetic datasets. Experiments have shown that pretraining on our synthetic data can significantly decrease the error from 6.76mm to 5.79mm, and our Transhand surpasses contemporary methods. Our dataset and code are available at https://github.com/adwardlee/RenderIH.

cs.CV↗
arXiv2023

SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website:https://github.com/7ohnKeats/SCVCNet.

eess.SP↗
arXiv2023

$τ$-tilting finiteness of two-point algebras II

In this paper, we explain a strategy on $g$-vectors to discover some new minimal $τ$-tilting infinite two-point algebras. Consequently, the $τ$-tilting finiteness of various two-point monomial algebras, including all radical cube zero cases, could be determined. Moreover, we find that the derived equivalence class of the Kronecker algebra contains only itself and its opposite algebra.

math.RT↗
arXiv2023

$L^q$-spectra of box-like graph-directed self-affine measures: closed forms, with rotation

We consider $L^q$-spectra of planar graph-directed self-affine measures generated by diagonal or anti-diagonal matrices. Assuming the directed graph is strongly connected and the system satisfies the rectangular open set condition, we obtain a general closed form expression for the $L^q$-spectra. Consequently, we obtain a closed form expression for box dimensions of associated planar graph-directed box-like self-affine sets. We also provide a precise answer to a question of Fraser in 2016 concerning the $L^q$-spectra of planar self-affine measures generated by diagonal matrices.

math.CA↗
arXiv2023

Effects of Pressure and Doping on Ruddlesden-Popper phases Lan+1NinO3n+1

Recently the discovery of superconductivity with a critical temperature Tc up to 80 K in Ruddlesden-Popper phases Lan+1NinO3n+1 (n = 2) under pressure has garnered considerable attention. Up to now, the superconductivity was only observed in La3Ni2O7 single crystal grown with the optical-image floating zone furnace under oxygen pressure. It remains to be understood the effect of chemical doping on superconducting La3Ni2O7 as well as other Ruddlesden-Popper phases. Here, we systematically investigate the effect of external pressure and chemical doping on polycrystalline Ruddlesden-Popper phases. Our results demonstrate the application of pressure and doping effectively tunes the transport properties of Ruddlesden-Popper phases. We find pressure-induced superconductivity up to 86 K in La3Ni2O7 polycrystalline sample, while no signatures of superconductivity are observed in La2NiO4 and La4Ni3O10 systems under high pressure up to 50 GPa. Our study sheds light on the exploration of high-Tc superconductivity in nickelates.

cond-mat.supr-con↗
arXiv2023

Infrared Nanoimaging of Hydrogenated Perovskite Nickelate Synaptic Devices

Solid-state devices made from correlated oxides such as perovskite nickelates are promising for neuromorphic computing by mimicking biological synaptic function. However, comprehending dopant action at the nanoscale poses a formidable challenge to understanding the elementary mechanisms involved. Here, we perform operando infrared nanoimaging of hydrogen-doped correlated perovskite, neodymium nickel oxide (H-NdNiO3) devices and reveal how an applied field perturbs dopant distribution at the nanoscale. This perturbation leads to stripe phases of varying conductivity perpendicular to the applied field, which define the macroscale electrical characteristics of the devices. Hyperspectral nano-FTIR imaging in conjunction with density functional theory calculations unveil a real-space map of multiple vibrational states of H-NNO associated with OH stretching modes and their dependence on the dopant concentration. Moreover, the localization of excess charges induces an out-of-plane lattice expansion in NNO which was confirmed by in-situ - x-ray diffraction and creates a strain that acts as a barrier against further diffusion. Our results and the techniques presented here hold great potential to the rapidly growing field of memristors and neuromorphic devices wherein nanoscale ion motion is fundamentally responsible for function.

physics.app-ph↗
arXiv2023

A Fast Minimization Algorithm for the Euler Elastica Model Based on a Bilinear Decomposition

The Euler Elastica (EE) model with surface curvature can generate artifact-free results compared with the traditional total variation regularization model in image processing. However, strong nonlinearity and singularity due to the curvature term in the EE model pose a great challenge for one to design fast and stable algorithms for the EE model. In this paper, we propose a new, fast, hybrid alternating minimization (HALM) algorithm for the EE model based on a bilinear decomposition of the gradient of the underlying image and prove the global convergence of the minimizing sequence generated by the algorithm under mild conditions. The HALM algorithm comprises three sub-minimization problems and each is either solved in the closed form or approximated by fast solvers making the new algorithm highly accurate and efficient. We also discuss the extension of the HALM strategy to deal with general curvature-based variational models, especially with a Lipschitz smooth functional of the curvature. A host of numerical experiments are conducted to show that the new algorithm produces good results with much-improved efficiency compared to other state-of-the-art algorithms for the EE model. As one of the benchmarks, we show that the average running time of the HALM algorithm is at most one-quarter of that of the fast operator-splitting-based Deng-Glowinski-Tai algorithm.

math.OC↗
arXiv2023

DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise Cleaning

MRI super-resolution (SR) and denoising tasks are fundamental challenges in the field of deep learning, which have traditionally been treated as distinct tasks with separate paired training data. In this paper, we propose an innovative method that addresses both tasks simultaneously using a single deep learning model, eliminating the need for explicitly paired noisy and clean images during training. Our proposed model is primarily trained for SR, but also exhibits remarkable noise-cleaning capabilities in the super-resolved images. Instead of conventional approaches that introduce frequency-related operations into the generative process, our novel approach involves the use of a GAN model guided by a frequency-informed discriminator. To achieve this, we harness the power of the 3D Discrete Wavelet Transform (DWT) operation as a frequency constraint within the GAN framework for the SR task on magnetic resonance imaging (MRI) data. Specifically, our contributions include: 1) a 3D generator based on residual-in-residual connected blocks; 2) the integration of the 3D DWT with $1\times 1$ convolution into a DWT+conv unit within a 3D Unet for the discriminator; 3) the use of the trained model for high-quality image SR, accompanied by an intrinsic denoising process. We dub the model "Denoising Induced Super-resolution GAN (DISGAN)" due to its dual effects of SR image generation and simultaneous denoising. Departing from the traditional approach of training SR and denoising tasks as separate models, our proposed DISGAN is trained only on the SR task, but also achieves exceptional performance in denoising. The model is trained on 3D MRI data from dozens of subjects from the Human Connectome Project (HCP) and further evaluated on previously unseen MRI data from subjects with brain tumours and epilepsy to assess its denoising and SR performance.

eess.IV↗
arXiv2023

Effect of Grain Coalescence on Dislocation and Stress Evolution of GaN Films Grown on Nanoscale Patterned Sapphire Substrates

Two types of nucleation layers (NLs), including in-situ low-temperature grown GaN (LT-GaN) and ex-situ sputtered physical vapor deposition AlN (PVD-AlN), are applied on cone-shaped nanoscale patterned sapphire substrate (NPSS). The initial growth process of GaN on these two NLs is comparably investigated by a series of growth interruptions. The coalescence process of GaN grains is modulated by adjusting the three-dimensional (3D) temperatures. The results indicate that higher 3D temperatures reduce the edge dislocation density while increasing the residual compressive stress in GaN films. Compared to the LT-GaN NLs, the PVD-AlN NLs effectively resist Ostwald ripening and facilitate the uniform growth of GaN grains on NPSS. Furthermore, GaN films grown on NPSS with PVD-AlN NLs exhibit a reduction of over 50% in both screw and edge dislocation densities compared to those grown on LT-GaN NLs. Additionally, PVD-AlN NLs result in an increase of about 0.5 GPa in the residual compressive stress observed in GaN films.

cond-mat.mtrl-sci↗
arXiv2023

Derivation and Efficient Entropy-Production-Rate-Preserving Algorithms for a Thermodynamically Consistent Nonisothermal Model of Incompressible Binary Fluids

We present a new hydrodynamic model for incompressible binary fluids that is thermodynamically consistent and non-isothermal. This model follows the generalized Onsager principle and Boussinesq approximation and preserves the volume of each fluid phase and the positive entropy production rate under consistent boundary conditions. To solve the governing partial differential equations in the model numerically, we design a set of second-order, volume and entropy-production-rate preserving numerical algorithms. Using an efficient adaptive time-stepping strategy, we conduct several numerical simulations. These simulations accurately simulate the Rayleigh-Bénard convection in binary fluids and the interfacial dynamics between two immiscible fluids under the effects of the temperature gradient, gravity, and interfacial forces. Our numerical results show roll cell patterns and thermally induced mixing of binary fluids in a rectangular computational domain with a set of specific boundary conditions: a zero-velocity boundary condition all around, the insulation boundary condition at the lateral boundaries, and an imposed temperature difference vertically. We also perform long-time simulations of interfacial dynamics, demonstrating the robustness of our new structure-preserving schemes and reveal interesting fluid mixing phenomena.

physics.flu-dyn↗
arXiv2023

Cross-correlated quantum thermometry using diamond containing dual-defect centers

The contactless temperature measurement at micro/nanoscale is vital to a broad range of fields in modern science and technology. The nitrogen vacancy (NV) center, a kind of diamond defect with unique spin-dependent photoluminescence, has been recognized as one of the most promising nanothermometers. However, this quantum thermometry technique has been prone to a number of possible perturbations, which will unavoidably degrade its actual temperature sensitivity. Here, for the first time, we have developed a cross-validated optical thermometry method using a bulk diamond sample containing both NV centers and silicon vacancy (SiV) centers. Particularly, the latter allowing all-optical method has been intrinsically immune to those influencing perturbations for the NV-based quantum thermometry, hence serving as a real-time cross validation system. As a proof-of-concept demonstration, we have shown a trustworthy temperature measurement under the influence of varying magnetic fields. This multi-modality approach allows a synchronized cross-validation of the measured temperature, which is required for micro/nanoscale quantum thermometry in complicated environments such as a living cell.

quant-ph↗