A Pressure-Stabilized Lagrange-Galerkin Method in a Parallel Domain Decomposition System

A pressure-stabilized Lagrange-Galerkin method is implemented in a parallel domain decomposition system in this work, and the new stabilization strategy is proved to be effective for large Reynolds number and Rayleigh number simulations. The symmetry of the stiffness matrix enables the interface problems of the linear system to be solved by the preconditioned conjugate method, and an incomplete balanced domain preconditioner is applied to the flow-thermal coupled problems. The methodology shows good parallel efficiency and high numerical scalability, and the new solver is validated by comparing with exact solutions and available benchmark results. It occupies less memory than classical product-type solvers; furthermore, it is capable of solving problems of over 30 million degrees of freedom within one day on a PC cluster of 80 cores

Two Modes Near-zero Dispersion Flattened Photonic-crystal Fiber

Through massive computer simulation, a photonic-crystal fiber with seven air-hole defects as fiber core is proposed by using the software CUDOS based on the multipole method. In the given fiber parameters, the photonic-crystal fiber’s fundamental and second modes are dispersion flattened simultaneously in the communication O wave band, S wave band and C wave band. It is important in the relative application of multi-mode dispersion flattened photonic-crystal fiber

Deep unfolding as iterative regularization for imaging inverse problems

Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems. Unlike general end-to-end DNNs, unfolding methods have better interpretability and performance. However, to our knowledge, their accuracy and stability in solving inverse problems cannot be fully guaranteed. To bridge this gap, we modified the training procedure and proved that the unfolding method is an iterative regularization method. More precisely, we jointly learn a convex penalty function adversarially by an input-convex neural network (ICNN) to characterize the distance to a real data manifold and train a DNN unfolded from the proximal gradient descent algorithm with this learned penalty. Suppose the real data manifold intersects the inverse problem solutions with only the unique real solution. We prove that the unfolded DNN will converge to it stably. Furthermore, we demonstrate with an example of MRI reconstruction that the proposed method outperforms conventional unfolding methods and traditional regularization methods in terms of reconstruction quality, stability and convergence speed

PVP: Pre-trained Visual Parameter-Efficient Tuning

Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Visual Prompt Tuning (VPT) and Low-Rank Adaptation (LoRA), have significantly reduced the computation and storage cost by inserting lightweight prompt modules into the pre-trained models and tuning these prompt modules with a small number of trainable parameters, while keeping the transformer backbone frozen. Although only a few parameters need to be adjusted, most PETuning methods still require a significant amount of downstream task training data to achieve good results. The performance is inadequate on low-data regimes, especially when there are only one or two examples per class. To this end, we first empirically identify the poor performance is mainly due to the inappropriate way of initializing prompt modules, which has also been verified in the pre-trained language models. Next, we propose a Pre-trained Visual Parameter-efficient (PVP) Tuning framework, which pre-trains the parameter-efficient tuning modules first and then leverages the pre-trained modules along with the pre-trained transformer backbone to perform parameter-efficient tuning on downstream tasks. Experiment results on five Fine-Grained Visual Classification (FGVC) and VTAB-1k datasets demonstrate that our proposed method significantly outperforms state-of-the-art PETuning methods

Accelerating Magnetic Resonance Parametric Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)

Quantitative magnetic resonance (MR) parametric mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinders its widespread applications. Recently, low-rank tensor has been employed and demonstrated good performance in accelerating MR parametricmapping. In this study, we propose a novel method that uses spatial patch-based and parametric group-based low rank tensors simultaneously (SMART) to reconstruct images from highly undersampled k-space data. The spatial patch-based low-rank tensor exploits the high local and nonlocal redundancies and similarities between the contrast images in parametric mapping. The parametric group based low-rank tensor, which integrates similar exponential behavior of the image signals, is jointly used to enforce the multidimensional low-rankness in the reconstruction process. In vivo brain datasets were used to demonstrate the validity of the proposed method. Experimental results have demonstrated that the proposed method achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and three-dimensional acquisitions, respectively, with more accurate reconstructed images and maps than several state-of-the-art methods. Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR quantitative imaging.Comment: 15 pages, 12 figure

Establishing the carrier scattering phase diagram for ZrNiSn-based half-Heusler thermoelectric materials

Chemical doping is one of the most important strategies for tuning electrical properties of semiconductors, particularly thermoelectric materials. Generally, the main role of chemical doping lies in optimizing the carrier concentration, but there can potentially be other important effects. Here, we show that chemical doping plays multiple roles for both electron and phonon transport properties in half-Heusler thermoelectric materials. With ZrNiSn-based half-Heusler materials as an example, we use high-quality single and polycrystalline crystals, various probes, including electrical transport measurements, inelastic neutron scattering measurement, and first-principles calculations, to investigate the underlying electron-phonon interaction. We find that chemical doping brings strong screening effects to ionized impurities, grain boundary, and polar optical phonon scattering, but has negligible influence on lattice thermal conductivity. Furthermore, it is possible to establish a carrier scattering phase diagram, which can be used to select reasonable strategies for optimization of the thermoelectric performance.Comment: 21 pages, 5 figure

Genetic diversity and phylogenetic analyses of 11 cohorts of captive rhesus macaques from Chinese zoos

Rhesus macaques are raised in almost every Chinese zoo due to their likeability and ease in feeding; however, little is yet known about the genetic diversity of rhesus macaques in captivity. In this study, a 475-base pair nucleotide sequence of the mitochondrial DNA control region was obtained from the fecal DNA of 210 rhesus macaque individuals in captivity. A total of 69 haplotypes were defined, 51 of which (73.9%) were newly identified. Of all haplotypes, seven were shared between two zoos, and 62 haplotypes (89.8%) appeared only in a specific zoo, indicating a low rate of animal exchange between Chinese zoos. Moreover, there was a relatively high level of genetic diversity among the rhesus macaques (Hd = 0.0623 ± 0.0009, Pi = 0.979 ± 0.003, K = 28.974). Phylogenetic analysis demonstrated that all haplotypes were clearly clustered into two major haplogroups—Clade A (southeastern China) and Clade B (southwestern China)—and each major clade contained several small sub-haplogroups. The haplotypes of rhesus macaques from the same zoo were not clustered together for the most part, but scattered among several subclades on the phylogenetic tree. This indicates that the rhesus macaques in most Chinese zoos may originat from a diverse collection of geographical areas. Our results demonstrate that zoos play an important role in the conservation of the genetic diversity of rhesus macaques, as well as provide useful information on the genetic management of captive rhesus macaques

Wenxin-Keli Regulates the Calcium/Calmodulin-Dependent Protein Kinase II Signal Transduction Pathway and Inhibits Cardiac Arrhythmia in Rats with Myocardial Infarction

Wenxin-Keli (WXKL) is a Chinese herbal compound reported to be of benefit in the treatment of cardiac arrhythmia, cardiac inflammation, and heart failure. Amiodarone is a noncompetitive inhibitor of the α- and β-adrenergic receptors and prevents calcium influx in the slow-response cells of the sinoatrial and atrioventricular nodes. Overexpression of Ca2+/calmodulin-dependent protein kinase II (CaMKII) in transgenic mice results in heart failure and arrhythmias. We hypothesised that administration of WXKL and amiodarone can reduce the incidence of arrhythmias by regulating CaMKII signal transduction. A total of 100 healthy Sprague Dawley rats were used in the study. The rats were randomly divided into four groups (a sham group, a myocardial infarction (MI) group, a WXKL-treated group, and an amiodarone-treated group). A myocardial infarction model was established in these rats by ligating the left anterior descending coronary artery for 4 weeks. Western blotting was used to assess CaMKII, p-CaMKII (Thr-286), PLB, p-PLB (Thr-17), RYR2, and FK binding protein 12.6 (FKBP12.6) levels. The Ca2+ content in the sarcoplasmic reticulum (SR) and the calcium transient amplitude were studied by confocal imaging using the fluorescent indicator Fura-4. In conclusion, WXKL may inhibit heart failure and cardiac arrhythmias by regulating the CaMKII signal transduction pathway similar to amiodarone

Spin-orbit-coupled triangular-lattice spin liquid in rare-earth chalcogenides

Spin-orbit coupling is an important ingredient in many spin liquid candidate materials, especially among the rare-earth magnets and Kitaev materials. We explore the rare-earth chalcogenides NaYbS$_2$ where the Yb$^{3+}$ ions form a perfect triangular lattice. Unlike its isostructural counterpart YbMgGaO$_4$ and the kagom\'{e} lattice herbertsmithite, this material does not have any site disorders both in magnetic and non-magnetic sites. We carried out the thermodynamic and inelastic neutron scattering measurements. The magnetic dynamics could be observed with a broad gapless excitation band up to 1.0 meV at 50 mK and 0 T, no static long-range magnetic ordering is detected down to 50 mK. We discuss the possibility of Dirac spin liquid for NaYbS$_2$. We identify the experimental signatures of field-induced transitions from the disordered spin liquid to an ordered antiferromagnet with an excitation gap at finite magnetic fields and discuss this result with our Monte Carlo calculation of the proposed spin model. Our findings could inspire further interests in the spin-orbit-coupled spin liquids and the magnetic ordering transition from them