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Ultrahigh-field MRI, such as those operating at 7 Tesla, enhances diagnostic capabilities but also presents unique challenges, including the need for advanced RF coil designs to achieve an optimal signal-to-noise ratio and transmit efficiency, particularly when imaging large samples. In this work, we introduce the coupled planar array, a novel technique for high-frequency, large-size RF coil design with enhanced the RF magnetic field (B1) efficiency and transmit performance for ultrahigh-field spine imaging applications. This array comprises multiple resonators that are electromagnetically coupled to function as a single multimodal resonator. The field distribution of its highest frequency mode is suitable for spine imaging applications. Based on the numerical modeling and calculation, a prototype of the coupled planar array was constructed and its performance was evaluated through comprehensive numerical simulations, rigorous RF measurements, empirical tests, and a comparison against a conventional surface coil with the same size and geometry. The results of this study demonstrate that the proposed coupled planar array exhibits superior performance compared to conventional surface coils in terms of B1 efficiency for both transmit (B1+) and receive (B1-) fields, specific absorption rate (SAR), and the ability to operate at high frequencies. This study suggests a promising and efficient approach to the design of high-frequency, large-size RF coils for spine MR imaging at ultrahigh magnetic fields.

The discovery of high-Tc superconductivity around 80K in bilayer nickelate La3Ni2O7 under high pressure has expanded the family of high-Tc superconductors above the nitrogen boiling temperature. Recent studies have further shown that ambient pressure superconductivity with a Tc exceeding 40K can be achieved in compressively strained La3Ni2O7 thin films, offering a tunable platform for investigating the pairing mechanism in high-Tc nickelates. A comprehensive understanding of the structural and electronic properties of bilayer nickelate under epitaxial strain is essential to advance this active field. In this work, we employ first-principles calculations to systematically explore the entire rare-earth (Re) series of bilayer nickelates Re3Ni2O7 in the realistic orthorhombic Amam phase under various compressive and tensile strain conditions. We highlight the materials properties change when strain is applied, and compare these results with those observed under high pressure. Our findings show that 2.5\% compressive strain increases the apical Ni-O-Ni bond angle toward 180 degree, and causes the Ni $d_{z^2}$ bands to move away from the Fermi level. The tight-binding parameters for the 2.5\% compressively strained La3Ni2O7 are quite similar to those of the unstrained material, except that the on-site energy difference between the Ni $d_{z^2}$ and $d_{x^2-y^2}$ orbitals increases by about 50 percent. Notably, the absence of the $d_{z^2}$ bands at the Fermi energy under compressive strain contrasts sharply with the electronic structure in the high-pressure {\it Fmmm} phase, suggesting that the presence of $d_{z^2}$ bands at the Fermi energy may not be a requisite for superconductivity.

This study presents a novel discretely dielectric material-coated (DDMC) dipole antenna design for ultra-high-field (UHF) MRI applications. This design improves frequency tuning, lowers electric field intensity, and reduces SAR by including discrete high-permittivity dielectric coatings at both ends of the dipole. The DDMC dipole's performance was compared to that of a fractionated dipole design using metrics such as inter-element coupling, B1 field distribution, and SNR. Simulations and experimental results showed that the DDMC dipole provided superior B1 field uniformity with significantly reduced B1 variation along the dipole conductor while reducing SAR, making it a safer and more efficient option for MR signal excitation and reception in UHF MR imaging. Furthermore, with its improved electromagnetic decoupling performance, the multichannel array made from the proposed DDMC dipoles shows promise for improving parallel imaging and imaging quality in UHF MRI, with future work focusing on material optimization and scalability for multi-channel arrays., Comment: 29 pages, 6 figures

Altermagnetism, a recently discovered class of magnetic order characterized by vanishing net magnetization and spin-splitting band structures, has garnered significant research attention. In this work, we introduce a novel two-dimensional system that exhibits $g$-wave altermagnetism and undergoes a strain-induced transition from $g$-wave to $d$-wave altermagnetism. This system can be realized in an unconventional monolayer Cairo pentagonal lattice, for which we present a realistic tight-binding model that incorporates both magnetic and non-magnetic sites. Furthermore, we demonstrate that non-trivial band topology can emerge in this system by breaking the symmetry that protects the spin-polarized nodal points. Finally, \emph{ab initio} calculations on several candidate materials, such as FeS$_2$ and Nb$_2$FeB$_2$, which exhibit symmetry consistent with the proposed tight-binding Hamiltonian, are also presented. These findings open new avenues for exploring spintronic devices based on altermagnetic systems.

The performance of radiofrequency (RF) coils has a significant impact on the quality and speed of magnetic resonance imaging (MRI). Consequently, rigid coils with attached cables are commonly employed to achieve optimal SNR performance and parallel imaging capability. However, since the adoption of MRI in clinical imaging, both patients and doctors have long suffered from the poor examination experience and physical strain caused by the bulky housings and cumbersome cables of traditional coils. This paper presents a new architectural concept, the Near-Field Coupling (NFC) coil system, which integrates a pickup coil array within the magnet with an NFC coil worn by the patient. In contrast to conventional coils, the NFC coil system obviates the necessity for bed-mounted connectors. It provides a lightweight, cost-effective solution that enhances patient comfort and supports disposable, custom designs for the NFC coils. The paper also derives the SNR expression for the NFC coil system, proposes two key design principles, and demonstrates the system's potential in SNR and parallel imaging through an implementation case.

This study incorporates electromagnetic simulations to assess the performance of multi-turn solenoid coils for ultra-low field MR imaging with various conductor materials (superconducting material, low-temperature copper, and room-temperature copper) across different human samples (elbow, knee, and brain). At 70 mT, superconducting materials performed significantly better than both room-temperature and low-temperature copper. The high Q-factor of the superconducting material indicates lower energy loss, which is useful for MR imaging. Furthermore, B1+ field efficiency increased significantly with superconducting materials, indicating superior performance. SNR evaluations revealed that materials with higher conductivity significantly improve SNR, which is critical for producing high-quality MR images. These results show that superconducting and low-temperature copper materials can significantly improve MR imaging quality at ultra-low fields, which has important implications for coil design and optimization., Comment: 26 pages, 7 figures, 3 tables

Ultra-high field MRI (7T+) unlocks a new era of brain research with superior resolution and signal-to-noise. Capturing intricate neural activity and detailed soft tissue pathology, this technology, coupled with advanced RF coil arrays, holds immense potential for clinical diagnosis and discovery. For high-field body imaging, microstrip resonators and dipole arrays for high-field body imaging show promise due to high-frequency operation and improved decoupling. Microstrip arrays excel in compactness, reduced radiation loss, and customizable lengths. Dipole arrays excel in deeper body penetration. Shorter wavelengths at ultrahigh fields can cause image inhomogeneity and elevated SAR. Multichannel transmit arrays address these issues. This research compares three common array types (L/C loop, microstrip, dipole) using CST Studio simulations to evaluate electric/magnetic fields and decoupling. This can guide RF array selection for ultrahigh field MRI.

Leveraging the potential of low-field Magnetic Resonance Imaging (MRI), our study introduces the multimodal surface RF coil, a design tailored to overcome the limitations of conventional coils in this context. The inherent challenges of low-field MRI, notably suboptimal signal-to-noise ratio (SNR) and the need for specialized RF coils, are effectively addressed by our novel design. The multimodal surface coil is characterized by a unique assembly of resonators, optimized for both B1 efficiency and low-frequency tuning capabilities, essential for low-field applications. This paper provides a thorough investigation of the conceptual framework, design intricacies, and bench test validation of the multimodal surface coil. Through detailed simulations and comparative analyses, we demonstrate its superior performance in terms of B1 field efficiency, outperforming conventional surface coils.

Higher frequencies and shorter wavelengths present significant design issues at ultra-high fields, making multi-channel array setup a critical component for ultra-high field MR imaging. The requirement for multi-channel arrays, as well as ongoing efforts to increase the number of channels in an array, are always limited by the major issue known as inter-element coupling. This coupling affects the current and field distribution, noise correlation between channels, and frequency of array elements, lowering imaging quality and performance. To realize the full potential of UHF MRI, we must ensure that the coupling between array elements is kept to a minimum. High-impedance coils allow array systems to completely realize their potential by providing optimal isolation while requiring minimal design modifications. These minor design changes, which demand the use of low capacitance on the conventional loop to induce elevated impedance, result in a significant safety hazard that cannot be overlooked. High electric fields are formed across these low capacitance lumped elements, which may result in higher SAR values in the imaging subject, depositing more power and, ultimately, providing a greater risk of tissue heating-related injury to the human sample. We propose an innovative method of utilizing high-dielectric material to effectively reduce electric fields and SAR values in the imaging sample while preserving the B1 efficiency and inter-element decoupling between the array elements to address this important safety concern with minimal changes to the existing array design comprising high-impedance coils., Comment: 12 pages, 18 figures, 2 tables

The advent of low field open magnetic resonance imaging (MRI) systems has greatly expanded the accessibility of MRI technology to meet a wide range of patient needs. However, the inherent challenges of low-field MRI, such as limited signal-to-noise ratios and limited availability of dedicated RF coil, have prompted the need for innovative coil designs that can improve imaging quality and diagnostic capabilities. In response to these challenges, we introduce the coupled stack-up volume coil, a novel RF coil design that addresses the shortcomings of conventional birdcage in the context of low field open MRI. The proposed coupled stack-up volume coil design utilizes a unique architecture that optimizes both transmit/receive efficiency and RF field homogeneity and offers the advantage of a simple design and construction, making it a practical and feasible solution for low field MRI applications. This paper presents a comprehensive exploration of the theoretical framework, design considerations, and experimental validation of this innovative coil design. Through rigorous analysis and empirical testing, we demonstrate the superior performance of the coupled stack-up volume coil in achieving improved transmit/receive efficiency and more uniform magnetic field distribution compared to traditional birdcage coils., Comment: 23 pages, 9 Figures

$Objective:$ Information on the metabolism of tissues in both healthy and diseased states plays a significant role in the detection and understanding of tumors, neurodegenerative diseases, diabetes, and other metabolic disorders. Hyperpolarized carbon-13 magnetic resonance imaging ($^{13}$C-HPMRI) and deuterium metabolic imaging ($^2$H-DMI) are two emerging X-nuclei used as practical imaging tools to investigate tissue metabolism. However due to their low gyromagnetic ratios ($\gamma_{13C}$ = 10.7 MHz/T; $\gamma_{2H}$ = 6.5 MHz/T) and natural abundance, such method required a sophisticated dual-tuned radiofrequency (RF) coil. $ Methods:$ Here, we report a dual-tuned coaxial transmission line (CTL) RF coil agile for metabolite information operating at 7T with independent tuning capability. The design analysis has demonstrated how both resonant frequencies can be individually controlled by simply varying the constituent of the design parameters. $Results:$ Numerical results have demonstrated a broadband tuning range capability, covering most of the X-nucleus signal, especially the $^{13}$C and $^2$H spectra at 7T. Furthermore, in order to validate the feasibility of the proposed design, both dual-tuned $^1$H/$^{13}$C and $^1$H/$^2$H RF coils are fabricated using a semi-flexible RG-405 .086" coaxial cable and bench test results (scattering parameters and magnetic field efficiency/distribution) are successfully obtained. $Conclusion:$ The proposed dual-tuned RF coils reveal highly effective magnetic field obtained from both proton and heteronuclear signal which is crucial for accurate and detailed imaging. $Significance:$ The successful development of this new dual-tuned RF coil technique would provide a tangible and efficient tool for ultrahigh field metabolic MR imaging., Comment: 10 pages, 9 figures

In contrast to linearly polarized RF coil arrays, quadrature transceiver coil arrays are capable of improving signal-to-noise ratio (SNR), spatial resolution and parallel imaging performance. Owing to a reduced excitation power, low specific absorption rate can be also obtained using quadrature RF coils. However, due to the complex nature of their structure and their electromagnetic proprieties, it is challenging to achieve sufficient electromagnetic decoupling while designing multichannel quadrature RF coil arrays, particularly at ultrahigh fields. In this work, we proposed a double cross magnetic wall decoupling for quadrature transceiver RF arrays and implemented the decoupling method on common-mode differential mode quadrature (CMDM) quadrature transceiver arrays at ultrahigh field of 7T. The proposed magnetic decoupling wall comprised of two intrinsic decoupled loops is used to reduce the mutual coupling between all the multi-mode current present in the quadrature CMDM array. The decoupling network has no physical connection with the CMDMs' coils giving leverage over size adjustable RF arrays. In order to validate the feasibility of the proposed cross magnetic decoupling wall, systematic studies on the decoupling performance based on the impedance of two intrinsic loops are numerically performed. A pair of quadrature transceiver CMDMs is constructed along with the proposed decoupling network and their scattering matrix is characterized using a network analyzer. The measured results show all the current modes coupling are concurrently suppressed using the proposed cross magnetic wall. Moreover, field distribution, and SNR intensity are numerically obtained for a well-decoupled 8-channel quadrature knee-coil array., Comment: 9 pages, 10 Figures

Accurate and reliable prediction of driving intentions and future trajectories contributes to cooperation between human drivers and ADAS in complex traffic environments. This paper proposes a visual AOI (Area of Interest) based multimodal trajectory prediction model for probabilistic risk assessment at intersections. In this study, we find that the visual AOI implies the driving intention and is about 0.6-2.1 s ahead of the operation. Therefore, we designed a trajectory prediction model that integrates the driving intention (DI) and the multimodal trajectory (MT) predictions. The DI model was pre-trained independently to extract the driving intention using features including the visual AOI, historical vehicle states, and environmental context. The intention prediction experiments verify that the visual AOI-based DI model predicts steering intention 0.925 s ahead of the actual steering operation. The trained DI model is then integrated into the trajectory prediction model to filter multimodal trajectories. The trajectory prediction experiments show that the proposed model outperforms the state-of-the-art models. Risk assessment for traffics at intersections verifies that the proposed method achieves high accuracy and a low false alarm rate, and identifies the potential risk about 3 s before a conflict occurs., Comment: 11 pages, 13 figures

UTE (Ultrashort Echo Time) and ZTE (Zero Echo Time) sequences have been developed to detect short T2 relaxation signals coming from regions that are unable to be detected by conventional MRI methods. Due to the high dipole-dipole interactions in solid and semi-solid tissues, the echo time generated is simply not enough to produce a signal using conventional imaging method, often leading to void signal coming from the discussed areas. By the application of these techniques, solid and semi-solid areas can be imaged which can have a profound impact in clinical imaging. High and Ultra-high field strength (UHF) provides a vital advantage in providing better sensitivity and specificity of MR imaging. When coupled with the UTE and ZTE sequences, the image can recover void signals as well as a much-improved signal quality. To further this strategy, secondary data from various research tools was obtained to further validate the research while addressing the drawbacks to this approach. It was found that UTE and ZTE sequences coupled with some techniques such as qualitative imaging and new trajectories are very crucial for accurate image depiction of the areas of the musculoskeletal system, neural system, lung imaging and dental imaging.

It is technically challenging to design efficient transceiver coil arrays for foot and ankle imaging at ultrahigh fields due to the irregular geometry of the anatomy. Shortened wavelength because of the high operation frequency at ultrahigh fields increases phase variation, leading to inhomogeneous B1 distribution in the imaging sample. In this work, we propose a hybrid design with mixed L-C loops and microstrip resonators for multichannel foot/ankle transceiver arrays at the ultrahigh field of 7T. The proposed transceiver array consists of 14 microstrip resonators and 5 L-C loop coils to cover the entire region of interest of the irregular-shaped foot/ankle with a relatively uniform B1 distribution at 7T. The feasibility and field behavior of the proposed design are systematically investigated numerically., Comment: 8 pages,9 figures

High impedance RF coils, such as self-decoupled coils, reduce the electromagnetic coupling between the coil elements and eliminates the use of complex decoupling technologies. Although the high impedance design promises excellent decoupling between the coil elements, it also results in high electric fields across the RF coil, leading to potential safety problems during imaging. It also causes B1 field asymmetry, ultimately leading to difficulties in imaging quantification. In this study, we propose and investigate using a high dielectric sheet to reduce the electric fields across the coil and achieve excellent electromagnetic decoupling among the coil elements, thereby ensuring safer MRI at ultra-high fields and maintaining high imaging performance., Comment: 7 pages, 8 figures , Published in 2021 ISMRM & SMRT Annual Meeting & Exhibition

Microstrip lines are being used in MR applications due to their unique properties, such as reduced radiation loss, high-frequency capability, and reduced perturbation of sample loading to the RF coil compared to conventional coils. Here, we present the design of the 13-channel hybrid array consisting of 12 Microstrips, 1 volume half birdcage coil placed on the foot/ankle phantom, and high permittivity materials to cover the maximum area of the subject at 7T/300MHz. We demonstrate using electromagnetic simulations, magnetic field distribution, SAR performance, and the coupling performance of the array elements. This work provides an ultrahigh field multichannel RF solution to lower extremity MR imaging with excellent imaging coverage and field uniformity., Comment: 12 pages, 7 figures , Published in 2022 Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting. Corresponding author: Xiaoliang Zhang xzhang89@buffalo.edu

Deep learning methods have been successfully used in various computer vision tasks. Inspired by that success, deep learning has been explored in magnetic resonance imaging (MRI) reconstruction. In particular, integrating deep learning and model-based optimization methods has shown considerable advantages. However, a large amount of labeled training data is typically needed for high reconstruction quality, which is challenging for some MRI applications. In this paper, we propose a novel reconstruction method, named DURED-Net, that enables interpretable self-supervised learning for MR image reconstruction by combining a self-supervised denoising network and a plug-and-play method. We aim to boost the reconstruction performance of Noise2Noise in MR reconstruction by adding an explicit prior that utilizes imaging physics. Specifically, the leverage of a denoising network for MRI reconstruction is achieved using Regularization by Denoising (RED). Experiment results demonstrate that the proposed method requires a reduced amount of training data to achieve high reconstruction quality among the state-of-art of MR reconstruction utilizing the Noise2Noise method.

The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential (MTP) for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials. *Author

Electromagnetic decoupling among a close-fitting or high-density transceiver RF array elements is required to maintain the integrity of the magnetic flux density from individual channel for enhanced performance in detection sensitivity and parallel imaging. High-impedance RF coils have demonstrated to be a prominent design method to circumvent these coupling issues. Yet, inherent characteristics of these coils have ramification on the B1 field efficiency and SNR. In this work, we propose a hairpin high impedance RF resonator design for highly decoupled multichannel transceiver arrays at ultrahigh magnetic fields. Due to the high impedance property of the hairpin resonators, the proposed transceiver array can provide high decoupling performance without using any dedicated decoupling circuit among the resonant elements. Because of elimination of lumped inductors in the resonator circuit, higher B1 field efficiency in imaging subjects can be expected. In order to validate the feasibility of the proposed hairpin RF coils, systematical studies on decoupling performance, field distribution, and SNR are performed, and the results are compared with those obtained from existing high-impedance RF coil, e.g., "self-decoupled RF coil". To further investigate its performance, an 8-channel head coil array using the proposed hairpin resonators loaded with a cylindrical phantom is designed, demonstrating a 19 % increase of the B1+ field intensity compared to the "self-decoupled" coils at 7T. Furthermore, the characteristics of the hairpin RF coils are evaluated using a more realistic human head voxel model numerically. The proposed hairpin RF coil provides excellent decoupling performance and superior RF magnetic field efficiency compared to the self-decoupled high impedance coils. Bench test of a pair of fabricated hairpin coils prove to be in good accordance with numerical results., Comment: 10 pages, 12 figures, 2 tables. Second version: Add bench test results and One dimensional profile of the simulated B1+

Multi-nuclear radio-frequency (RF) coils at ultrahigh field strengths are challenging to develop due to the high operating frequency, increased electromagnetic interaction among the coil elements, and increased electromagnetic interaction between the coils and the subject. In this work, a triple-nuclear surface coil for 1H, 13C, and 31P for small animal metabolic imaging at 14.1 T is built by nesting three surface coil geometries: a lumped-element L-C loop coil, a butterfly coil, and a microstrip transmission line (MTL) resonator. The loop coil, butterfly coil, and MTL are tuned to 31P, 13C, and 1H, respectively. The successful bench tests and phantom imaging with this novel triple-nuclear coil demonstrates the feasibility of the design for triple-nuclear MR imaging and spectroscopy studies at the ultrahigh field of 14.1 T. In particular, the microstrip transmission line resonator demonstrates excellent coil performance for proton imaging at 600 MHz, where conventional lumped-element design is limited., Comment: Original research was done in 2016. Y. Chen, S. Sukumar, and X. Zhang have since left UCSF

As the scale of object detection dataset is smaller than that of image recognition dataset ImageNet, transfer learning has become a basic training method for deep learning object detection models, which will pretrain the backbone network of object detection model on ImageNet dataset to extract features for classification and localization subtasks. However, the classification task focuses on the salient region features of object, while the location task focuses on the edge features of object, so there is certain deviation between the features extracted by pretrained backbone network and the features used for localization task. In order to solve this problem, a decoupled self attention(DSA) module is proposed for one stage object detection models in this paper. DSA includes two decoupled self-attention branches, so it can extract appropriate features for different tasks. It is located between FPN and head networks of subtasks, so it is used to extract global features based on FPN fused features for different tasks independently. Although the network of DSA module is simple, but it can effectively improve the performance of object detection, also it can be easily embedded in many detection models. Our experiments are based on the representative one-stage detection model RetinaNet. In COCO dataset, when ResNet50 and ResNet101 are used as backbone networks, the detection performances can be increased by 0.4% AP and 0.5% AP respectively. When DSA module and object confidence task are applied in RetinaNet together, the detection performances based on ResNet50 and ResNet101 can be increased by 1.0% AP and 1.4% AP respectively. The experiment results show the effectiveness of DSA module. Code is at: https://github.com/chenzuge1/DSANet.git., Comment: 15 pages, 5 figures

In this paper a characteristics-based open boundary condition (CBC) is proposed for the magnetohydrodynamic (MHD) system of equations. The algorithm is carefully designed and implemented in the context of a high-order flux reconstruction (FR) scheme under the Generalized Lagrange Multiplier (GLM)-MHD system of equations. It is implemented by adding the contribution of the characteristic equation directly to the corrected flux term in the FR scheme dispensing with solving time-dependent characteristic equations along boundary faces. The CBC method is shown to be more accurate and robust than commonly used zero normal derivative (ZND) and approximate Riemann solver boundary conditions (ARBC) in solving 1D, 2D, and 3D test problems. The CBC method is successfully applied to simulate challenging problems of magnetic reconnection for which other options failed to get stable results over long-period time integration.

Purpose: To propose a deep learning-based reconstruction framework for ultrafast and robust diffusion tensor imaging and fiber tractography. Methods: We propose SuperDTI to learn the nonlinear relationship between diffusion-weighted images (DWIs) and the corresponding tensor-derived quantitative maps as well as the fiber tractography. Super DTI bypasses the tensor fitting procedure, which is well known to be highly susceptible to noise and motion in DWIs. The network is trained and tested using datasets from Human Connectome Project and patients with ischemic stroke. SuperDTI is compared against the state-of-the-art methods for diffusion map reconstruction and fiber tracking. Results: Using training and testing data both from the same protocol and scanner, SuperDTI is shown to generate fractional anisotropy and mean diffusivity maps, as well as fiber tractography, from as few as six raw DWIs. The method achieves a quantification error of less than 5% in all regions of interest in white matter and gray matter structures. We also demonstrate that the trained neural network is robust to noise and motion in the testing data, and the network trained using healthy volunteer data can be directly applied to stroke patient data without compromising the lesion detectability. Conclusion: This paper demonstrates the feasibility of superfast diffusion tensor imaging and fiber tractography using deep learning with as few as six DWIs directly, bypassing tensor fitting. Such a significant reduction in scan time may allow the inclusion of DTI into the clinical routine for many potential applications., Comment: 27 pages, 7 figures, 3 tables, 3 supporting figures

Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks, where each RSP data block has a probability distribution similar to the whole big data set. Under this data model, efficient block level sampling is used to randomly select RSP data blocks, replacing expensive record level sampling to select sample data from a big distributed data set on a computing cluster. We show how RSP data blocks can be employed to estimate statistics of a big data set and build models which are equivalent to those built from the whole big data set. In this approach, analysis of a big data set becomes analysis of few RSP data blocks which have been generated in advance on the computing cluster. Therefore, the new method for data analysis based on RSP data blocks is scalable to big data., Comment: 9 pages, 7 figures

Many physical properties of metals can be understood in terms of the free electron model, as proven by the Wiedemann-Franz law. According to this model, electronic thermal conductivity ($\kappa_{el}$) can be inferred from the Boltzmann transport equation (BTE). However, the BTE does not perform well for some complex metals, such as Cu. Moreover, the BTE cannot clearly describe the origin of the thermal energy carried by electrons or how this energy is transported in metals. The charge distribution of conduction electrons in metals is known to reflect the electrostatic potential (EP) of the ion cores. Based on this premise, we develop a new methodology for evaluating $\kappa_{el}$ by combining the free electron model and non-equilibrium ab initio molecular dynamics (NEAIMD) simulations. We demonstrate that the kinetic energy of thermally excited electrons originates from the energy of the spatial electrostatic potential oscillation (EPO), which is induced by the thermal motion of ion cores. This method directly predicts the $\kappa_{el}$ of pure metals with a high degree of accuracy., Comment: 7 pages, 3 figures, with Supplementary Information of 19 pages, 7 figures and 7 tables

Probing detailed spectral dependence of phonon transport properties in bulk materials is critical to improve the function and performance of structures and devices in a diverse spectrum of technologies. Currently, such information can only be provided by the phonon spectral energy density (SED) or equivalently time domain normal mode analysis (TDNMA) methods in the framework of equilibrium molecular dynamics simulation (EMD), but has not been realized in non-equilibrium molecular dynamics simulations (NEMD) so far. In this paper we generate a new scheme directly based on NEMD and lattice dynamics theory, called time domain direct decomposition method (TDDDM), to predict the phonon mode specific thermal conductivity. Two benchmark cases of Lennard-Jones (LJ) Argon and Stillinger-Weber (SW) Si are studied by TDDDM to characterize contributions of individual phonon modes to overall thermal conductivity and the results are compared with that predicted using SED and TDNMA. Excellent agreements are found for both cases, which confirm the validity of our TDDDM approach. The biggest advantage of TDDDM is that it can be used to investigate the size effect of individual phonon modes in NEMD simulations, which cannot be tackled by SED and TDNMA in EMD simulations currently. We found that the phonon modes with mean free path larger than the system size are truncated in NEMD and contribute little to the overall thermal conductivity. The TDDDM provides direct physical origin for the well-known strong size effects in thermal conductivity prediction by NEMD.