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Antiferromagnetic insulators are a ubiquitous class of magnetic materials, holding the promise of low-dissipation spin-based computing devices that can display ultra-fast switching and are robust against stray fields. However, their imperviousness to magnetic fields also makes them difficult to control in a reversible and scalable manner. Here we demonstrate a novel proof-of-principle ionic approach to control the spin reorientation (Morin) transition reversibly in the common antiferromagnetic insulator α-Fe2O3 (haematite) – now an emerging spintronic material that hosts topological antiferromagnetic spin-textures and long magnon-diffusion lengths. We use a low-temperature catalytic-spillover process involving the post-growth incorporation or removal of hydrogen from α-Fe2O3 thin films. Hydrogenation drives pronounced changes in its magnetic anisotropy, Néel vector orientation and canted magnetism via electron injection and local distortions. We explain these effects with a detailed magnetic anisotropy model and first-principles calculations. Tailoring our work for future applications, we demonstrate reversible control of the room-temperature spin-state by doping/expelling hydrogen in Rh-substituted α-Fe2O3.

Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. CodedFedL enables coded computing for non-linear federated learning by efficiently exploiting distributed kernel embedding via random Fourier features that transforms the training task into computationally favourable distributed linear regression. Furthermore, clients generate local parity datasets by coding over their local datasets, while the server combines them to obtain the global parity dataset. Gradient from the global parity dataset compensates for straggling gradients during training, and thereby speeds up convergence. For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays. We also characterize the leakage in data privacy when clients share their local parity datasets with the server. We analyze the convergence rate and iteration complexity of CodedFedL under simplifying assumptions, by treating CodedFedL as a stochastic gradient descent algorithm. Furthermore, we conduct numerical experiments using practical network parameters and benchmark datasets, where CodedFedL speeds up the overall training time by up to $15\times$ in comparison to the benchmark schemes., Final version to appear in the first issue of the IEEE JSAC Series on Machine Learning for Communications and Networks

Detection and mitigation of Byzantine behaviors in a decentralized learning setting is a daunting task, especially when the data distribution at the users is heterogeneous. As our main contribution, we propose Basil, a fast and computationally efficient Byzantine robust algorithm for decentralized training systems, which leverages a novel sequential, memory assisted and performance-based criteria for training over a logical ring while filtering the Byzantine users. In the IID dataset distribution setting, we provide the theoretical convergence guarantees of Basil, demonstrating its linear convergence rate. Furthermore, for the IID setting, we experimentally demonstrate that Basil is robust to various Byzantine attacks, including the strong Hidden attack, while providing up to ${\sim}16 \%$ higher test accuracy over the state-of-the-art Byzantine-resilient decentralized learning approach. Additionally, we generalize Basil to the non-IID dataset distribution setting by proposing Anonymous Cyclic Data Sharing (ACDS), a technique that allows each node to anonymously share a random fraction of its local non-sensitive dataset (e.g., landmarks images) with all other nodes. We demonstrate that Basil alongside ACDS with only $5\%$ data sharing provides effective toleration of Byzantine nodes, unlike the state-of-the-art Byzantine robust algorithm that completely fails in the heterogeneous data setting. Finally, to reduce the overall latency of Basil resulting from its sequential implementation over the logical ring, we propose Basil+. In particular, Basil+ provides scalability by enabling Byzantine-robust parallel training across groups of logical rings, and at the same time, it retains the performance gains of Basil due to sequential training within each group. Furthermore, we experimentally demonstrate the scalability gains of Basil+ through different sets of experiments., Comment: Final version was accepted for publication in IEEE JSAC Series on Machine Learning for Communications and Networks. A part of the work was presented at the NeurIPS Workshop on Privacy in Machine Learning, 2021

Phase change materials provide unique reconfigurable properties for photonic applications that mainly arise from their exotic characteristic to reversibly switch between the amorphous and crystalline nonvolatile phases. Optical pulse based reversible switching of nonvolatile phases is exploited in various nanophotonic devices. However, large area reversible switching is extremely challenging and has hindered its translation into a technologically significant terahertz spectral domain. Here, this limitation is circumvented by exploiting the semiconducting nature of germanium antimony telluride (GST) to achieve dynamic terahertz control at picosecond timescales. It is also shown that the ultrafast response can be actively altered by changing the crystallographic phase of GST. The ease of fabrication of phase change materials allows for the realization of a variable ultrafast terahertz modulator on a flexible platform. The rich properties of phase change materials combined with the diverse functionalities of metamaterials and all-optical ultrafast control enables an ideal platform for design of efficient terahertz communication devices, terahertz neuromorphic photonics, and smart sensor systems. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) National Research Foundation (NRF) The authors acknowledge research funding support from the Ministry of Education, Singapore (AcRF Tier 1, Grant RG191/17, MOE2017-T2-1-110, MOE2019-T2-1-058, and MOE2016-T3-1-006(S)), Advanced Manufacturing and Engineering (AME) Programmatic grant (A18A5b0056) from Agency for Science, Technology and Research (A*STAR) and NRF CRP on Oxide Electronics on silicon Beyond Moore (NRF-CRP15-2015-01).

In the quest for post-CMOS technologies, ferromagnetic skyrmions and their anti-particles have shown great promise as topologically protected solitonic information carriers in memory-in-logic or neuromorphic devices. However, the presence of dipolar fields in ferromagnets, restricting the formation of ultra-small topological textures, and the deleterious skyrmion Hall effect when driven by spin torques have thus far inhibited their practical implementations. Antiferromagnetic analogues, which are predicted to demonstrate relativistic dynamics, fast deflection-free motion and size scaling have recently come into intense focus, but their experimental realizations in natural antiferromagnetic systems are yet to emerge. Here, we demonstrate a family of topological antiferromagnetic spin-textures in $\alpha$-Fe$_2$O$_3$ - an earth-abundant oxide insulator - capped with a Pt over-layer. By exploiting a first-order analogue of the Kibble-Zurek mechanism, we stabilize exotic merons-antimerons (half-skyrmions), and bimerons, which can be erased by magnetic fields and re-generated by temperature cycling. These structures have characteristic sizes of the order ~100 nm that can be chemically controlled via precise tuning of the exchange and anisotropy, with pathways to further scaling. Driven by current-based spin torques from the heavy-metal over-layer, some of these AFM textures could emerge as prime candidates for low-energy antiferromagnetic spintronics at room temperature., Comment: 18 pages, 4 figures

Antiferromagnetic insulators are a ubiquitous class of magnetic materials, holding the promise of low-dissipation spin-based computing devices that can display ultra-fast switching and are robust against stray fields. However, their imperviousness to magnetic fields also makes them difficult to control in a reversible and scalable manner. Here we demonstrate a novel proof-of-principle ionic approach to control the spin reorientation (Morin) transition reversibly in the common antiferromagnetic insulator α-Fe2O3 (haematite) – now an emerging spintronic material that hosts topological antiferromagnetic spin-textures and long magnon-diffusion lengths. We use a low-temperature catalytic-spillover process involving the post-growth incorporation or removal of hydrogen from α-Fe2O3 thin films. Hydrogenation drives pronounced changes in its magnetic anisotropy, Néel vector orientation and canted magnetism via electron injection and local distortions. We explain these effects with a detailed magnetic anisotropy model and first-principles calculations. Tailoring our work for future applications, we demonstrate reversible control of the room-temperature spin-state by doping/expelling hydrogen in Rh-substituted α-Fe2O3., One major challenge for antiferromagnetic spintronics is how to control the antiferromagnetic state. Here Jani et al. demonstrate the reversible ionic control of the room-temperature magnetic anisotropy and spin reorientation transition in haematite, via the incorporation and removal of hydrogen.

Using interlayer interaction to control functional heterostructures with atomic-scale designs has become one of the most effective interface-engineering strategies nowadays. Here, we demonstrate the effect of a crystalline LaFeO3 buffer layer on amorphous and crystalline LaAlO3/SrTiO3 heterostructures. The LaFeO3 buffer layer acts as an energetically favored electron acceptor in both LaAlO3/SrTiO3 systems, resulting in modulation of interfacial carrier density and hence metal-to-insulator transition. For amorphous and crystalline LaAlO3/SrTiO3 heterostructures, the metal-to-insulator transition is found when the LaFeO3 layer thickness crosses 3 and 6 unit cells, respectively. Such different critical LaFeO3 thicknesses are explained in terms of distinct characteristic lengths of the redox-reaction-mediated and polar-catastrophe-dominated charge transfer, controlled by the interfacial atomic contact and Thomas-Fermi screening effect, respectively. Our results not only shed light on the complex interlayer charge transfer across oxide heterostructures but also provides a new route to precisely tailor the charge-transfer process at a functional interface., 40 Pages, 14 Figures, 1 Table

Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data. The convergence performance of federated learning is severely impacted in heterogeneous computing platforms such as those at the wireless edge, where straggling computations and communication links can significantly limit timely model parameter updates. This paper develops a novel coded computing technique for federated learning to mitigate the impact of stragglers. In the proposed Coded Federated Learning (CFL) scheme, each client device privately generates parity training data and shares it with the central server only once at the start of the training phase. The central server can then preemptively perform redundant gradient computations on the composite parity data to compensate for the erased or delayed parameter updates. Our results show that CFL allows the global model to converge nearly four times faster when compared to an uncoded approach, Presented at the Wireless Edge Intelligence Workshop, IEEE GLOBECOM 2019

Spatiotemporal manipulation of electromagnetic waves has recently enabled a plethora of exotic optical functionalities, such as non-reciprocity, dynamic wavefront control, unidirectional transmission, linear frequency conversion, and electromagnetic Doppler cloak. Here, an additional dimension is introduced for advanced manipulation of terahertz waves in the space-time, and frequency domains through integration of spatially reconfigurable microelectromechanical systems and photoresponsive material into metamaterials. A large and continuous frequency agility is achieved through movable microcantilevers. The ultrafast resonance modulation occurs upon photoexcitation of ion-irradiated silicon substrate that hosts the microcantilever metamaterial. The fabricated metamaterial switches in 400 ps and provides large spectral tunability of 250 GHz with 100% resonance modulation at each frequency. The integration of perfectly complementing technologies of microelectromechanical systems, femtosecond optical control and ion-irradiated silicon provides unprecedented concurrent control over space, time, and frequency response of metamaterial for designing frequency-agile spatiotemporal modulators, active beamforming, and low-power frequency converters for the next generation terahertz wireless communications. National Research Foundation (NRF) The authors acknowledge the research funding support from National Research Foundation (NRF) Singapore and Agence Nationale de la Recherche (ANR), France- NRF2016- ANR004 (M4197003), NRF CRP on Oxide Electronics on silicon Beyond Moore (NRF-CRP15-2015-01) and Advanced Manufacturing and Engineering (AME) Programmatic grant (A18A5b0056) from Agency for Science, Technology and Research (A*STAR).

Herein, we report a systematic study of water contact angle (WCA) of rare-earth oxide thin-films. These ultra-smooth and epitaxial thin-films were grown using pulsed laser deposition and then characterized using X-Ray diffraction (XRD), Rutherford backscattering spectroscopy (RBS), and atomic force microscopy (AFM). Through both the traditional sessile drop and the novel f–d method, we found that the films were intrinsically hydrophilic (WCA < 10°) just after being removed from the growth chamber, but their WCAs evolved with an exposure to the atmosphere with time to reach their eventual saturation values near 90° (but always stay ‘technically’ hydrophilic). X-Ray photoelectron spectroscopy analysis was used to further investigate qualitatively the nature of hydrocarbon contamination on the freshly prepared as well as the environmentally exposed REO thin-film samples as a function of the exposure time after they were removed from the deposition chamber. A clear correlation between the carbon coverage of the surface and the increase in WCA was observed for all of the rare-earth films, indicating the extrinsic nature of the surface wetting properties of these films and having no relation to the electronic configuration of the rare-earth atoms as proposed by Azimi et al.

In wireless mobile edge computing platforms, such as those supported by vehicular networks, location specific machine learning models can be trained by distributing computations using resources available at the edge. The computational and communication resources at the wireless edge are heterogeneous and unreliable, which can lead to straggler effects that significantly slow down recursive learning tasks, such as gradient descent. In this paper we propose a coded computation framework, which utilizes statistical knowledge of resource heterogeneity to determine optimal encoding and load balancing of training data using Random Linear codes, while avoiding an explicit step for decoding gradients. Results show that the proposed coding framework speeds up the training time for linear regression models, by up to ten times, when compared with repetition coding schemes.

The high demand for computational and storage resources severely impedes the deployment of deep convolutional neural networks (CNNs) in limited resource devices. Recent CNN architectures have proposed reduced complexity versions (e.g,. SuffleNet and MobileNet) but at the cost of modest decreases in accuracy. This paper proposes pSConv, a pre-defined sparse 2D kernel based convolution, which promises significant improvements in the trade-off between complexity and accuracy for both CNN training and inference. To explore the potential of this approach, we have experimented with two widely accepted datasets, CIFAR-10 and Tiny ImageNet, in sparse variants of both the ResNet18 and VGG16 architectures. Our approach shows a parameter count reduction of up to 4.24× with modest degradation in classification accuracy relative to that of standard CNNs. Our approach outperforms a popular variant of ShuffleNet using a variant of ResNet18 with pSConv having 3 × 3 kernels with only four of nine elements not fixed at zero. In particular, the parameter count is reduced by 1.7× for CIFAR-10 and 2.29× for Tiny ImageNet with an increased accuracy of ∼ 4%.

Scaling up distributed machine learning systems face two major bottlenecks – delays due to stragglers and limited communication bandwidth. Recently, a number of coding theoretic strategies have been proposed for mitigating these bottlenecks. In particular, the Gradient Coding (GC) scheme was proposed to speed up distributed gradient descent algorithm in a synchronous master-worker setting by providing robustness to stragglers. A major drawback of the master-worker architecture for distributed learning is however, the bandwidth contention at the master, which can significantly deteriorate the performance as the cluster size increases. In this paper, we propose a new framework named Tree Gradient Coding (TGC) for distributed gradient aggregation, which parallelizes communication over a tree topology while providing straggler robustness. As our main contribution, we characterize the minimum computation load for TGC for a given tree topology and straggler resiliency, and design a tree gradient coding algorithm that achieves this optimal computation load. Furthermore, we provide results from experiments over Amazon EC2, where TGC speeds up the training time by up to 18.8× in comparison to GC.

The reduced electrical screening in 2D materials provides an ideal platform for realization of exotic quasiparticles, that are robust and whose functionalities can be exploited for future electronic, optoelectronic, and valleytronic applications. Recent examples include an interlayer exciton, where an electron from one layer binds with a hole from another, and a Holstein polaron, formed by an electron dressed by a sea of phonons. Here, a new quasiparticle is reported, "polaronic trion" in a heterostructure of MoS

We focus on the commonly used synchronous Gradient Descent paradigm for large-scale distributed learning, for which there has been a growing interest to develop efficient and robust gradient aggregation strategies that overcome two key system bottlenecks: communication bandwidth and stragglers' delays. In particular, Ring-AllReduce (RAR) design has been proposed to avoid bandwidth bottleneck at any particular node by allowing each worker to only communicate with its neighbors that are arranged in a logical ring. On the other hand, Gradient Coding (GC) has been recently proposed to mitigate stragglers in a master-worker topology by allowing carefully designed redundant allocation of the data set to the workers. We propose a joint communication topology design and data set allocation strategy, named CodedReduce (CR), that combines the best of both RAR and GC. That is, it parallelizes the communications over a tree topology leading to efficient bandwidth utilization, and carefully designs a redundant data set allocation and coding strategy at the nodes to make the proposed gradient aggregation scheme robust to stragglers. In particular, we quantify the communication parallelization gain and resiliency of the proposed CR scheme, and prove its optimality when the communication topology is a regular tree. Moreover, we characterize the expected run-time of CR and show order-wise speedups compared to the benchmark schemes. Finally, we empirically evaluate the performance of our proposed CR design over Amazon EC2 and demonstrate that it achieves speedups of up to 27.2x and 7.0x, respectively over the benchmarks GC and RAR., Final version to appear in IEEE Transactions on Networking

The strikingly contrasting optical properties of various phases of chalcogenide phase change materials (PCM) has recently led to the development of novel photonic devices such as all-optical non-von Neumann memory, nanopixel displays, color rendering, and reconfigurable nanoplasmonics. However, the exploration of chalcogenide photonics is currently limited to optical and infrared frequencies. Here, a phase change material integrated terahertz metamaterial for multilevel nonvolatile resonance switching with spatial and temporal selectivity is demonstrated. By controlling the crystalline proportion of the PCM film, multilevel, non-volatile, terahertz resonance switching states with long retention time at zero hold power are realized. Spatially selective reconfiguration at sub-metamaterial scale is shown by delivering electrical stimulus locally through designer interconnect architecture. The PCM metamaterial also features ultrafast optical modulation of terahertz resonances with tunable switching speed based on the crystalline order of the PCM film. The multilevel nonvolatile, spatially selective, and temporally tunable PCM metamaterial will provide a pathway toward development of novel and disruptive terahertz technologies including spatio-temporal terahertz modulators for high speed wireless communication, neuromorphic photonics, and machine-learning metamaterials.

In this manuscript, we demonstrate a method based on atomic force microscopy which enables local probing of surface wettability. The maximum pull-off force, obtained from force spectroscopy shows a remarkable correlation with the macroscopically observed water contact angle, measured over a wide variety of surfaces starting from hydrophilic, all the way through to hydrophobic ones. This relationship, consequently, facilitates the establishment of a universal behaviour. The adhesion forces scale with the polar component of surface energy. However, no such relation could be established with the dispersive component. Hence, we postulate that the force(s) which enable us to correlate the force spectroscopy data measured on the nanoscale to the macroscopic contact angle are primarily arising from electrostatic-dipole-dipole interactions at the solid-liquid interface. London forces play less of a role. This effect in is line with density functional theory (DFT) calculations suggesting a higher degree of hydroxylation of hydrophilic surfaces. This result shows that molecular simulations and measurements on an atomic scale can be extrapolated to macroscopic surface wetting problems.

Understanding the structural, physical and chemical properties of the surface and interfaces of different metal-oxides and their possible applications in photo-catalysis and biology is a very important emerging research field. Motivated in this direction, this article would enable understanding of how different fluids, particularly water, interact with oxide surfaces. We have studied the water contact angle of 3d transition metal oxide thin films of SrTiO3, and of 4f rare-earth oxide thin films of Lu2O3. These metal oxides were grown using pulsed laser deposition and they are atomically flat and with known orientation and explicitly characterized for their structure and composition. Further study was done on the effects of oxygen vacancies on the water contact angle of the 3d and 4f oxides. For 3d SrTiO3 oxide with oxygen vacancies, we have observed an increase in hydroxylation with consequent increase of wettability which is in line with the previous reports whereas an interesting opposite trend was seen in the case of rare-earth Lu2O3 oxide. Density functional theory simulations of water interaction on the above mentioned systems have also been presented to further substantiate our experimental findings.

Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.

Performance of distributed graph processing systems significantly suffers from 'communication bottleneck' as a large number of messages are exchanged among servers at each step of the computation. Motivated by graph based MapReduce, we propose a coded computing framework that leverages computation redundancy to alleviate the communication bottleneck in distributed graph processing. We develop a novel 'coding' scheme that systematically injects structured redundancy in computation phase to enable 'coded' multicasting opportunities during message exchange between servers, reducing communication load substantially in large-scale graph processing. For theoretical analysis, we consider random graph models, and prove that our proposed scheme enables an (asymptotically) inverse-linear trade-off between 'computation load' and 'average communication load' for two popular random graph models -- Erdos-Renyi model, and power law model. Particularly, for a given computation load r, (i.e. when each graph vertex is carefully stored at r servers), the proposed scheme slashes the average communication load by (nearly) a multiplicative factor of r. For the Erdos-Renyi model, our proposed scheme is optimal asymptotically as the graph size increases by providing an information-theoretic converse. To illustrate the benefits of our scheme in practice, we implement PageRank over Amazon EC2, using artificial as well as real-world datasets, demonstrating significant gains over conventional PageRank. We also specialize our scheme and extend our theoretical results to two other random graph models -- random bi-partite model, and stochastic block model. They asymptotically enable inverse-linear trade-offs between computation and communication loads in distributed graph processing for these popular random graph models as well. We complement the achievability results with converse bounds for both of these models., Comment: Accepted for publication in the IEEE Transactions on Information Theory

In large-scale distributed computing clusters, such as Amazon EC2, there are several types of "system noise" that can result in major degradation of performance: bottlenecks due to limited communication bandwidth, latency due to straggler nodes, etc. On the other hand, these systems enjoy abundance of redundancy - a vast number of computing nodes and large storage capacity. There have been recent results that demonstrate the impact of coding for efficient utilization of computation and storage redundancy to alleviate the effect of stragglers and communication bottlenecks in homogeneous clusters. In this paper, we focus on general heterogeneous distributed computing clusters consisting of a variety of computing machines with different capabilities. We propose a coding framework for speeding up distributed computing in heterogeneous clusters by trading redundancy for reducing the latency of computation. In particular, we propose Heterogeneous Coded Matrix Multiplication (HCMM) algorithm for performing distributed matrix multiplication over heterogeneous clusters that is provably asymptotically optimal for a broad class of processing time distributions. Moreover, we show that HCMM is unboundedly faster than any uncoded scheme. To demonstrate practicality of HCMM, we carry out experiments over Amazon EC2 clusters where HCMM is found to be up to $61\%$, $46\%$ and $36\%$ respectively faster than three benchmark load allocation schemes - Uniform Uncoded, Load-balanced Uncoded, and Uniform Coded. Additionally, we provide a generalization to the problem of optimal load allocation in heterogeneous settings, where we take into account the monetary costs associated with the clusters. We argue that HCMM is asymptotically optimal for budget-constrained scenarios as well, and we develop a heuristic algorithm for (HCMM) load allocation for budget-limited computation tasks., Comment: This work is published in IEEE Transaction on Information Theory (2019). A preliminary version of this work was published in IEEE International Symposium on Information Theory (ISIT) 2017

The reduced electrical screening in 2D materials provides an ideal platform for realization of exotic quasiparticles, that are robust and whose functionalities can be exploited for future electronic, optoelectronic, and valleytronic applications. Recent examples include an interlayer exciton, where an electron from one layer binds with a hole from another, and a Holstein polaron, formed by an electron dressed by a sea of phonons. Here, a new quasiparticle is reported, "polaronic trion" in a heterostructure of MoS2 /SrTiO3 (STO). This emerges as the Frohlich bound state of the trion in the atomically thin monolayer of MoS2 and the very unique low energy soft phonon mode (≤7 meV, which is temperature and field tunable) in the quantum paraelectric substrate STO, arising below its structural antiferrodistortive (AFD) phase transition temperature. This dressing of the trion with soft phonons manifests in an anomalous temperature dependence of photoluminescence emission leading to a huge enhancement of the trion binding energy (≈70 meV). The soft phonons in STO are sensitive to electric field, which enables field control of the interfacial trion-phonon coupling and resultant polaronic trion binding energy. Polaronic trions could provide a platform to realize quasiparticle-based tunable optoelectronic applications driven by many body effects.

In this study we report the existence of novel ultraviolet (UV) and blue emission in rare-earth based perovskite NdGaO3 (NGO) and the systematic quench of the NGO photoluminescence (PL) by Ce doping. Study of room temperature PL was performed in both single-crystal and polycrystalline NGO (substrates and pellets) respectively. Several NGO pellets were prepared with varying Ce concentration and their room temperature PL was studied using 325 nm laser. It was found that the PL intensity shows a systematic quench with increasing Ce concentration. XPS measurements indicated that nearly 50% of Ce atoms are in the 4+ state. The PL quench was attributed to the novel concept of super hydrogenic dopant (SHD)”, where each Ce4+ ion contributes an electron which forms a super hydrogenic atom with an enhanced Bohr radius, due to the large dielectric constant of the host. Based on the critical Ce concentration for complete quenching this SHD radius was estimated to be within a range of 0.85 nm and 1.15 nm whereas the predicted theoretical value of SHD radius for NdGaO3 is ~1.01 nm.

Strongly correlated electronic systems such as Transition Metal Oxides often possess various mid-gap states originating from intrinsic defects in these materials. In this paper, we investigate an extremely sharp Photoluminescence (PL) transition originating from such defect states in two widely used perovskites, LaAlO3 and SrTiO3. A detailed study of the PL as a function of temperature and magnetic field has been conducted to understand the behavior and origin of the transition involved. The temperature dependence of the PL peak position for SrTiO3 is observed to be opposite to that in LaAlO3. Our results reveal the presence of a spin/orbital character in these transitions which is evident from the splitting of these defect energy levels under a high magnetic field. These PL transitions have the potential for enabling non-contact thermal and field sensors.

This chapter gives the opportunity to get an idea of recent trends in image denoising and restoration. It relates to the present research scenario in the field of image restoration. As much as possible the newest break-through regarding the methods of denoising as well as the performance metrics of evaluation has been dealt. The assessments done by the researchers have been included first so as to know how much analysis they propose to be done with respect to the application point of view of the denoising methods. The concept behind the metric selection for the assessment and evaluation has been introduced along with the need for shifting the dependence of the research community towards the newly proposed metrics than the old ones. The new trends in image denoising have been referred duly so that the readers can directly refer to the main algorithms and techniques from the papers proposed by their authors.

Ultrasonography of 56 women with adenexal masses who were admitted for laparotomy were done mainly by transvaginal route. Though Gray scale morphologic evaluations of masses were done routinely, only colour Doppler imaging criteria were taken into consideration in this study. These are pulsatility index (PI), resistance index (RI), peak systolic velocity (PSV), timed average maximal velocity (TAMXV), vessels localisation and dicrotic notch. Histopathological examination was done and considered as gold standard. In 83.78% cases of benign tumour PI is equal or greater than 1, whereas it was less than 1 in 84.21% in malignant ovarian tumour. The sensitivity, specificity, PPV and NPV were respectively 84.21%, 83.78%, 72.72% and 91.11%. RI in benign tumours were equal to or more than 0.4 in 81.08% and less than 0.4 in 68.42% in case of malignant tumours. The sensitivity, specificity, PPV and NPV were respectively 68.42%, 81.08%, 65% and 83.33%. Considering the TAMXV12 cm/second as a criterion for malignancy the sensitivity, specificity, PPV and NPV were respectively 89.45%, 89.19%, 80.95% and 94.28%, and also considering septal/central localisation of vessels as a criterion for malignancy, it was found the sensitivity, specificity, PPV and NPV were 89.47%, 62.16%, 54.84% and 92% respectively. Considering absence of dicrotic notch for malignant tumours we found sensitivity, specificity, PPV and NPV were 89.47%, 81.08%, 70.83% and 93.75% respectively. The above findings of the PI, TAMXV and dicrotic notch evaluation show most useful was colour Doppler parameters for pre-operative screening for ovarian malignancy in this study.