FILM DEPOSITION AND MICROFABRICATION OF MAGNETIC TUNNEL JUNCTIONS WITH AN MgO BARRIER

Magnetic tunnel junctions (MTJs), which consist of a thin insulation layer sandwiched by two ferromagnetic (FM) layers, are among the key devices of spintronics that have promising technological applications for computer hard disk drives, magnetic random access memory (MRAM) and other future spintronic devices. The work presented here is related to the development of relevant techniques for the preparation and characterization of magnetic films, exchanged biased systems and MTJs. The fabrication and characterization of PtMn/CoFe exchange biased systems and MTJs with Al-O barriers were undertaken when the new Aviza StratIon fxP ion beam deposition tool was developed by the project consortium funded by DTI MNT. After the Nordiko 9550 spintronic deposition tool was installed at Plymouth, the work focused on the development of MTJ multilayer stacks with layer structures of CoFeB/MgO/CoFe/IrMn and IrMn/CoFeB/MgO/CoFeB to achieve coherent tunneling with a crystalline MgO barrier. The film deposition, microfabrication, magnetic field annealing, microstructural and nano-scale characterization, magnetic and magneto-transport measurement for these devices have been systematically studied to achieve smooth interfaces and desired crystallographic textures and magnetic properties of layer stacks. Magnetoresistance (MR) of up to 200% was obtained from MTJs with a layer structure of Ta/CuN/Ta/CoFeB/MgO/CoFe/IrMn/Ta and a CuN bottom electrode. Enhanced exchange anisotropy from the bottom pinned IrMn/CoFeB stacks has been obtained, which demonstrated the possibility of fabricating MTJs with CoFeB as both the top and bottom FM electrodes with strong exchange bias. The origin of the enhanced exchange bias field was studied by employing high resolution transmission electron microscopy (HRTEM) and x-ray magnetic circular dichroism (XMCD) to examine the mmicrostructure properties and element specific magnetic properties of the stacks. Results demonstrate that the enhanced exchange anisotropy in the IrMn/CoFeB system is closely associated with the increased uncompensated interfacial spins. MTJs with layered structures of IrMn/CoFeB/MgO/CoFeB were prepared based on this exchange bias system. However, further work is required for the optimisation of the (001) crystallographic textures of the CoFeB/MgO/CoFeB stack to achieve coherent tunneling

High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning

Edge machine learning involves the deployment of learning algorithms at the wireless network edge so as to leverage massive mobile data for enabling intelligent applications. The mainstream edge learning approach, federated learning, has been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for updating a global AI model. Since each stochastic gradient is typically high-dimensional (with millions to billions of coefficients), communication overhead becomes a bottleneck for edge learning. To address this issue, we propose in this work a novel framework of hierarchical stochastic gradient quantization and study its effect on the learning performance. First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively. Subsequently, the quantized normalized block gradients are scaled and cascaded to yield the quantized normalized stochastic gradient using a so-called hinge vector designed under the criterion of minimum distortion. The hinge vector is also efficiently compressed using another low-dimensional Grassmannian quantizer. The other feature of the framework is a bit-allocation scheme for reducing the quantization error. The scheme determines the resolutions of the low-dimensional quantizers in the proposed framework. The framework is proved to guarantee model convergency by analyzing the convergence rate as a function of the quantization bits. Furthermore, by simulation, our design is shown to substantially reduce the communication overhead compared with the state-of-the-art signSGD scheme, while both achieve similar learning accuracies

Time-varying β\beta-model for dynamic directed networks

We extend the well-known $\beta$-model for directed graphs to dynamic network setting, where we observe snapshots of adjacency matrices at different time points. We propose a kernel-smoothed likelihood approach for estimating $2n$ time-varying parameters in a network with $n$ nodes, from $N$ snapshots. We establish consistency and asymptotic normality properties of our kernel-smoothed estimators as either $n$ or $N$ diverges. Our results contrast their counterparts in single-network analyses, where $n\to\infty$ is invariantly required in asymptotic studies. We conduct comprehensive simulation studies that confirm our theory's prediction and illustrate the performance of our method from various angles. We apply our method to an email data set and obtain meaningful results

Capacity of Remote Classification Over Wireless Channels

Wireless connectivity creates a computing paradigm that merges communication and inference. A basic operation in this paradigm is the one where a device offloads classification tasks to the edge servers. We term this remote classification, with a potential to enable intelligent applications. Remote classification is challenged by the finite and variable data rate of the wireless channel, which affects the capability to transfer high-dimensional features and thus limits the classification resolution. We introduce a set of metrics under the name of classification capacity that are defined as the maximum number of classes that can be discerned over a given communication channel while meeting a target classification error probability. The objective is to choose a subset of classes from a library that offers satisfactory performance over a given channel. We treat two cases of subset selection. First, a device can select the subset by pruning the class library until arriving at a subset that meets the targeted error probability while maximizing the classification capacity. Adopting a subspace data model, we prove the equivalence of classification capacity maximization to Grassmannian packing. The results show that the classification capacity grows exponentially with the instantaneous communication rate, and super-exponentially with the dimensions of each data cluster. This also holds for ergodic and outage capacities with fading if the instantaneous rate is replaced with an average rate and a fixed rate, respectively. In the second case, a device has a preference of class subset for every communication rate, which is modeled as an instance of uniformly sampling the library. Without class selection, the classification capacity and its ergodic and outage counterparts are proved to scale linearly with their corresponding communication rates instead of the exponential growth in the last case.Comment: Submitted to IEEE for possible publicatio

Comparative Transcriptome Analysis of Resistant and Susceptible Tomato Lines in Response to Infection by Xanthomonas perforans Race T3

Bacterial spot, incited by several Xanthomonas sp., is a serious disease in tomato (Solanum lycopersicum L.). Although genetics of resistance has been widely investigated, the interactions between the pathogen and tomato plants remain unclear. In this study, tanscriptomes of X. perforans race T3 infected tomato lines were compared to those of controls. An average of 7 million reads were generated with approximately 21,526 genes mapped in each sample post-inoculation at 6h (6 HPI) and 6d (6 DPI) using RNA-sequencing technology. Overall, the numbers of differentially expressed genes (DEGs) were higher in the resistant tomato line PI 114490 than in the susceptible line OH 88119, and the numbers of DEGs were higher at 6 DPI than at 6 HPI. Fewer genes (78 in PI 114490 and 15 in OH 88119) were up-regulated and most DEGs were down-regulated, suggesting that the inducible defense response might not be fully activated at 6 HPI. Accumulation expression levels of 326 co-up regulated genes in both tomato lines at 6 DPI might be involved in basal defense, while the specific and strongly induced genes at 6 DPI might be correlated with the resistance in PI114490. Most DEGs were involved in plant hormone signal transduction, plant-pathogen interaction and phenylalanine metabolism, and the genes significantly up-regulated in PI114490 at 6 DPI were associated with defense response pathways. DEGs containing NBS-LRR domain or defense-related WRKY transcription factors were also identified. The results will provide a valuable resource for understanding the interactions between X. perforans and tomato plants