arXiv subjects

Jianhua Ma

Publications and source records attributed to Jianhua Ma.

36 records · Page 2Linked to original sources

arXiv2020

Social-Similarity-aware TCP with Collision Avoidance in Ad-hoc Social Networks

Ad-hoc Social Network (ASNET), which explores social connectivity between users of mobile devices, is becoming one of the most important forms of today's internet. In this context, maximum bandwidth utilization of intermediate nodes in resource scarce environments is one of the challenging tasks. Traditional Transport Control Protocol (TCP) uses the round trip time mechanism for sharing bandwidth resources between users. However, it does not explore socially-aware properties between nodes and cannot differentiate effectively between various types of packet losses in wireless networks. In this paper, a socially-aware congestion avoidance protocol, namely TIBIAS, which takes advantage of similarity matching social properties among intermediate nodes, is proposed to improve the resource efficiency of ASNETs. TIBIAS performs efficient data transfer over TCP. During the course of bandwidth resource allocation, it gives high priority for maximally matched interest similarity between different TCP connections on ASNET links. TIBIAS does not require any modification at lower layers or on receiver nodes. Experimental results show that TIBIAS performs better as compared against existing protocols, in terms of link utilization, unnecessary reduction of the congestion window, throughput and retransmission ratio.

cs.NI↗

arXiv2020

Improving Smart Conference Participation through Socially-Aware Recommendation

This research addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm, Socially-Aware Recommendation of Venues and Environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real world dataset. In our experiments, we compare SARVE to two related state-of-the-art methods, namely: Context-Aware Mobile Recommendation Services (CAMRS) and Conference Navigator (Recommender) Model. Our experimental results show that in terms of the utilized evaluation metrics: precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.

cs.SI↗

arXiv2019

Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network

Energy-resolving computed tomography (ErCT) has the ability to acquire energy-dependent measurements simultaneously and quantitative material information with improved contrast-to-noise ratio. Meanwhile, ErCT imaging system is usually equipped with an advanced photon counting detector, which is expensive and technically complex. Therefore, clinical ErCT scanners are not yet commercially available, and they are in various stage of completion. This makes the researchers less accessible to the ErCT images. In this work, we investigate to produce ErCT images directly from existing energy-integrating CT (EiCT) images via deep neural network. Specifically, different from other networks that produce ErCT images at one specific energy, this model employs a unified generative adversarial network (uGAN) to concurrently train EiCT datasets and ErCT datasets with different energies and then performs image-to-image translation from existing EiCT images to multiple ErCT image outputs at various energy bins. In this study, the present uGAN generates ErCT images at 70keV, 90keV, 110keV, and 130keV simultaneously from EiCT images at140kVp. We evaluate the present uGAN model on a set of over 1380 CT image slices and show that the present uGAN model can produce promising ErCT estimation results compared with the ground truth qualitatively and quantitatively.

eess.IV↗

arXiv2019

Computational search for ultrasmall and fast skyrmions in the Inverse Heusler family

Skyrmions are magnetic excitations that are potentially ultrasmall and topologically protected, making them interesting for high-density all-electronic ultrafast storage applications. While recent experiments have confirmed the existence of various types of skyrmions, their typical sizes are much larger than traditional domain walls, except at very low temperature. In this work, we explore the optimal material parameters for hosting ultra-small, fast, and room temperature stable skyrmions. As concrete examples, we explore potential candidates from the inverse Heusler family. Using first-principles calculations of structural and magnetic properties, we identify several promising ferrimagnetic inverse Heusler half-metal/near half-metals and analyze their phase space for size and metastability.

cond-mat.mes-hall↗

arXiv2018

Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features

To predict lung nodule malignancy with a high sensitivity and specificity, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep convolutional neural network (CNN). First, we extracted twenty-nine handcrafted features, including nine intensity features, eight geometric features, and twelve texture features based on grey-level co-occurrence matrix (GLCM) averaged from thirteen directions. We then trained 3D CNNs modified from three state-of-the-art 2D CNN architectures (AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at the output layer. For each 3D CNN, the CNN features combined with the 29 handcrafted features were used as the input for the support vector machine (SVM) coupled with the sequential forward feature selection (SFS) method to select the optimal feature subset and construct the classifiers. The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer. It can overcome the disadvantage of the handcrafted features that may not fully reflect the unique characteristics of a particular lesion by combining the intrinsic CNN features. Meanwhile, it also alleviates the requirement of a large scale annotated dataset for the CNNs based on the complementary of handcrafted features. The patient cohort includes 431 malignant nodules and 795 benign nodules extracted from the LIDC/IDRI database. For each investigated CNN architecture, the proposed fusion algorithm achieved the highest AUC, accuracy, sensitivity, and specificity scores among all competitive classification models.

cs.CV↗

arXiv2018

Machine learning electron correlation in a disordered medium

Learning from data has led to a paradigm shift in computational materials science. In particular, it has been shown that neural networks can learn the potential energy surface and interatomic forces through examples, thus bypassing the computationally expensive density functional theory calculations. Combining many-body techniques with a deep learning approach, we demonstrate that a fully-connected neural network is able to learn the complex collective behavior of electrons in strongly correlated systems. Specifically, we consider the Anderson-Hubbard (AH) model, which is a canonical system for studying the interplay between electron correlation and strong localization. The ground states of the AH model on a square lattice are obtained using the real-space Gutzwiller method. The obtained solutions are used to train a multi-task multi-layer neural network, which subsequently can accurately predict quantities such as the local probability of double occupation and the quasiparticle weight, given the disorder potential in the neighborhood as the input.

cond-mat.str-el↗

arXiv2018

Radon Inversion via Deep Learning

Radon transform is widely used in physical and life sciences and one of its major applications is the X-ray computed tomography (X-ray CT), which is significant in modern health examination. The Radon inversion or image reconstruction is challenging due to the potentially defective radon projections. Conventionally, the reconstruction process contains several ad hoc stages to approximate the corresponding Radon inversion. Each of the stages is highly dependent on the results of the previous stage. In this paper, we propose a novel unified framework for Radon inversion via deep learning (DL). The Radon inversion can be approximated by the proposed framework with an end-to-end fashion instead of processing step-by-step with multiple stages. For simplicity, the proposed framework is short as iRadonMap (inverse Radon transform approximation). Specifically, we implement the iRadonMap as an appropriative neural network, of which the architecture can be divided into two segments. In the first segment, a learnable fully-connected filtering layer is used to filter the radon projections along the view-angle direction, which is followed by a learnable sinusoidal back-projection layer to transfer the filtered radon projections into an image. The second segment is a common neural network architecture to further improve the reconstruction performance in the image domain. The iRadonMap is overall optimized by training a large number of generic images from ImageNet database. To evaluate the performance of the iRadonMap, clinical patient data is used. Qualitative results show promising reconstruction performance of the iRadonMap.

cs.CV↗

arXiv2018

From materials to systems: a multiscale analysis of nanomagnetic switching

With the increasing demand for low-power electronics, nanomagnetic devices have emerged as strong potential candidates to complement present day transistor technology. A variety of novel switching effects such as spin torque and giant spin Hall offer scalable ways to manipulate nano-sized magnets. However, the low intrinsic energy cost of switching spins is often compromised by the energy consumed in the overhead circuitry in creating the necessary switching fields. Scaling brings in added concerns such as the ability to distinguish states (readability) and to write information without spontaneous backflips (reliability). A viable device must ultimately navigate a complex multi-dimensional material and design space defined by volume, energy budget, speed and a target read-write-retention error. In this paper, we review the major challenges facing nanomagnetic devices and present a multi-scale computational framework to explore possible innovations at different levels (material, device, or circuit), along with a holistic understanding of their overall energy-delay-reliability tradeoff.

cond-mat.mes-hall↗

arXiv2017

Computational Investigation of Inverse-Heusler compounds for Spintronics Applications

First-principles calculations of the electronic structure, magnetism and structural stability of inverse-Heusler compounds with the chemical formula \textit{X$_2$YZ} are presented and discussed with a goal of identifying compounds of interest for spintronics. Compounds for which the number of electrons per atom for \textit{Y} exceed that for \textit{X} and for which \textit{X} is one of Sc, Ti, V, Cr, Mn, Fe, Co, Ni, or Cu; \textit{Y} is one of Ti, V, Cr, Mn, Fe, Co, Ni, Cu, or Zn; and \textit{Z} is one of Al, Ga, In, Si, Ge, Sn, P, As or Sb were considered. The formation energy per atom of each compound was calculated. By comparing our calculated formation energies to those calculated for phases in the Inorganic Crystal Structure Database (ICSD) of observed phases, we estimate that inverse-Heuslers with formation energies within 0.052 eV/atom of the calculated convex hull are reasonably likely to be synthesizable in equilibrium. The observed trends in the formation energy and relative structural stability as the \textit{X}, \textit{Y} and \textit{Z} elements vary are described. In addition to the Slater-Pauling gap after 12 states per formula unit in one of the spin channels, inverse-Heusler phases often have gaps after 9 states or 14 states. We describe the origin and occurrence of these gaps. We identify 14 inverse-Heusler semiconductors, 51 half-metals and 50 near half-metals with negative formation energy. In addition, our calculations predict 4 half-metals and 6 near half-metals to lie close to the respective convex hull of stable phases, and thus may be experimentally realized under suitable synthesis conditions, resulting in potential candidates for future spintronics applications.

cond-mat.mtrl-sci↗

arXiv2017

Pseudo Dual Energy CT Imaging using Deep Learning Based Framework: Initial Study

Dual energy computed tomography (DECT) has become of particular interest in clinic recent years. The DECT scan comprises two images, corresponding to two photon attenuation coefficients maps of the objects. Meanwhile, the DECT images are less accessible sometimes, compared to the conventional single energy CT (SECT). This motivates us to simulate pseudo DECT (pDECT) images from the SECT images. Inspired by recent advances in deep learning, we present a deep learning based framework to yield pDECT images from SECT images, utilizing the intrinsic characteristics underlying DECT images, i.e., global correlation and high similarity. To demonstrate the performance of the deep learning based framework, a cascade deep ConvNet (CD-ConvNet) approach is specifically presented in the deep learning framework. In the training step, the CD-ConvNet is designed to learn the non-linear mapping from the measured energy-specific (i.e., low-energy) CT images to the desired energy-specific (i.e., high-energy) CT images. In the testing step, the trained CD-ConvNet can be used to yield desired high-energy CT images from the low-energy CT images, and then produce accurate basic material maps. Clinical patient data were employed to validate and evaluate the presented CD-ConvNet approach performance. Both visual and quantitative results demonstrate the presented CD-ConvNet approach can yield high quality pDECT images and basic material maps.

physics.med-ph↗

arXiv2017

Characterization of the previous normal-dose CT scan induced nonlocal means regularization method for low-dose CT image reconstruction

Repeated computed tomography (CT) scans are required in some clinical applications such as image-guided radiotherapy and follow-up observations over a time period. To optimize the radiation dose utility, a normal-dose (or full-dose) CT scan is often first performed to set up reference, followed by a series of low-dose scans. Using the previous normal-dose scan to improve follow-up low-dose scans reconstruction has drawn great interests recently, such as the previous normal-dose induced nonlocal means (ndiNLM) regularization method. However, one major concern with this method is that whether it would introduce false structures or miss true structures when the previous normal-dose image and current low-dose image have different structures (e.g., a tumor could be present, grow, shrink or absent in either image). This study aims to investigate the performance of the ndiNLM regularization method in the above mentioned situations. A patient with lung nodule for biopsy was recruited to this study. A normal-dose scan was acquired to set up biopsy operation, followed by a few low-dose scans during needle intervention toward the nodule. We used different slices to mimic different possible cases wherein the previous normal-dose image and current low-dose image have different structures. The experimental results characterize performance of our ndiNLM regularization method.

physics.med-ph↗

arXiv2016

Computational Investigation of Half-Heusler Compounds for Spintronics Applications

We present first-principles density functional calculations of the electronic structure, magnetism, and structural stability of 378 $\textit{XYZ}$ half-Heusler compounds (with $X=$ Cr, Mn, Fe, Co, Ni, Ru, Rh, $Y=$ Ti, V, Cr, Mn, Fe, Ni, $Z=$ Al, Ga, In, Si, Ge, Sn, P, As, Sb). We find that a "Slater-Pauling density of states" with a gap or pseudogap at three states per atom below the gap in at least one spin channel is a common feature in half-Heusler compounds. We find that the presence of such a gap at the Fermi energy in one or both spin channels contributes greatly to the stability of a half-Heusler compound. We calculate the formation energy of each compound and systematically investigate its stability against all other phases in the Open Quantum Materials Database (OQMD). We represent the thermodynamic phase stability of each compound as its distance from the convex hull of stable phases in the respective chemical space and show that the hull distance of a compound is a good measure of the likelihood of its experimental synthesis. We identify 26 18-electron semiconductors, 45 half-metals, and 34 near half-metals with negative formation energy, that follow the Slater-Pauling rule of three electrons per atom. Our calculations predict new thermodynamically stable semiconducting phases NiScAs, RhTiP, and RuVAs, which merit further experimental exploration. Further, two interesting zero-moment half-metals, CrMnAs and MnCrAs, are calculated to have negative formation energy. In addition, our calculations predict a number of new, hitherto unreported, semiconducting (e.g., CoVGe, FeVAs), half-metallic (e.g., RhVSb), near half-metallic (e.g., CoFeSb, CoVP) half-Heusler compounds to lie close to the respective convex hull of stable phases, and thus may be experimentally realized under suitable synthesis conditions, resulting in potential candidates for various spintronics applications.

cond-mat.mtrl-sci↗

arXiv2015

Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose X-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method for various clinical tasks. According to the maximum a posterior (MAP) estimation, the SIR methods can be typically formulated by an objective function consisting of two terms: (1) data-fidelity (or equivalently, data-fitting or data-mismatch) term modeling the statistics of projection measurements, and (2) regularization (or equivalently, prior or penalty) term reflecting prior knowledge or expectation on the characteristics of the image to be reconstructed. Existing SIR methods for low-dose CT can be divided into two groups: (1) those that use calibrated transmitted photon counts (before log-transform) with penalized maximum likelihood (pML) criterion, and (2) those that use calibrated line-integrals (after log-transform) with penalized weighted least-squares (PWLS) criterion. Accurate statistical modeling of the projection measurements is a prerequisite for SIR, while the regularization term in the objective function also plays a critical role for successful image reconstruction. This paper reviews several statistical models on CT projection measurements and various regularization strategies incorporating prior knowledge or expected properties of the image to be reconstructed, which together formulate the objective function of the SIR methods for low-dose X-ray CT.

physics.med-ph↗

arXiv2015

Silicene Nanomesh

Similar to graphene, zero band gap limits the application of silicene in nanoelectronics despite of its high carrier mobility. By using first-principles calculations, we reveal that a band gap is opened in silicene nanomesh (SNM) when the width W of the wall between the neighboring holes is even. The size of the band gap increases with the reduced W and has a simple relation with the ratio of the removed Si atom and the total Si atom numbers of silicene. Quantum transport simulation reveals that the sub-10 nm single-gated SNM field effect transistors show excellent performance at zero temperature but such a performance is greatly degraded at room temperature.

cond-mat.mtrl-sci↗

arXiv2013

An Optimal ODAM-Based Broadcast Algorithm for Vehicular Ad-Hoc Networks

Broadcast routing has become an important research field for vehicular ad-hoc networks (VANETs) recently. However, the packet delivery rate is generally low in existing VANET broadcast routing protocols. Therefore, the design of an appropriate broadcast protocol based on the features of VANET has become a crucial part of the development of VANET. This paper analyzes the disadvantage of existing broadcast routing protocols in VANETs, and proposes an improved algorithm (namely ODAM-C) based on the ODAM (Optimized Dissemination of Alarm Messages) protocol. The ODAM-C algorithm improves the packet delivery rate by two mechanisms based on the forwarding features of ODAM. The first distance-based mechanism reduces the possibility of packet loss by considering the angles between source nodes, forwarding nodes and receiving nodes. The second mechanism increases the redundancy of forwarding nodes to guarantee the packet success delivery ratio. We show by analysis and simulations that the proposed algorithm can improve packet delivery rate for vehicular networks compared against two widely-used existing protocols.

cs.NI↗

arXiv2013

Socially-Aware Networking: A Survey

The widespread proliferation of handheld devices enables mobile carriers to be connected at anytime and anywhere. Meanwhile, the mobility patterns of mobile devices strongly depend on the users' movements, which are closely related to their social relationships and behaviors. Consequently, today's mobile networks are becoming increasingly human centric. This leads to the emergence of a new field which we call socially-aware networking (SAN). One of the major features of SAN is that social awareness becomes indispensable information for the design of networking solutions. This emerging paradigm is applicable to various types of networks (e.g. opportunistic networks, mobile social networks, delay tolerant networks, ad hoc networks, etc) where the users have social relationships and interactions. By exploiting social properties of nodes, SAN can provide better networking support to innovative applications and services. In addition, it facilitates the convergence of human society and cyber physical systems. In this paper, for the first time, to the best of our knowledge, we present a survey of this emerging field. Basic concepts of SAN are introduced. We intend to generalize the widely-used social properties in this regard. The state-of-the-art research on SAN is reviewed with focus on three aspects: routing and forwarding, incentive mechanisms and data dissemination. Some important open issues with respect to mobile social sensing and learning, privacy, node selfishness and scalability are discussed.

cs.SI↗

arXiv2013

Tunable band gap in few-layer graphene by surface adsorption

There is a tunable band gap in ABC-stacked few-layer graphene (FLG) via applying a vertical electric field, but the operation of FLG-based field effect transistor (FET) requires two gates to create a band gap and tune channel's conductance individually. Using first principle calculations, we propose an alternative scheme to open a band gap in ABC-stacked FLG namely via single-side adsorption. The band gap is generally proportional to the charge transfer density. The capability to open a band gap of metal adsorption decreases in this order: K/Al > Cu/Ag/Au > Pt. Moreover, we find that even the band gap of ABA-stacked FLG can be opened if the bond symmetry is broken. Finally, a single-gated FET based on Cu-adsorbed ABC-stacked trilayer graphene is simulated. A clear transmission gap is observed, which is comparable with the band gap. This renders metal-adsorbed FLG a promising channel in a single-gated FET device.

cond-mat.mes-hall↗

arXiv2011

Building Smart Communities with Cyber-Physical Systems

There is a growing trend towards the convergence of cyber-physical systems (CPS) and social computing, which will lead to the emergence of smart communities composed of various objects (including both human individuals and physical things) that interact and cooperate with each other. These smart communities promise to enable a number of innovative applications and services that will improve the quality of life. This position paper addresses some opportunities and challenges of building smart communities characterized by cyber-physical and social intelligence.

cs.SI↗