Search

Reflectionless potentials following the prescription of Kay and Moses allow for total transmission of incoming waves of any kinetic energy. The optical analogue of such potentials occur as dielectric stratified media that can offer null reflectivity and near total transmission over a large range of incidence angles and wavelengths. In a previous work (S. Dutta Gupta and G. S. Agarwal, Opt. Express 15, 9614-9624, 2007), this was demonstrated for linearly polarized plane waves. We extend the earlier work valid for plane waves to structured beams to show near-total transmission of beams across the reflectionless dielectric profile. The analysis is based on the angular spectrum decomposition treating the beam as a collection of plane waves. Gaussian and Laguerre-Gaussian beams are shown to be transmitted through the film with <1% reflection in most scenarios. We also discuss the superlative performance of our proposed profile in preserving the beam shape during transmission comparing these results to a conventional lambda/2 antireflection coating.

In the realm of quantum information and sensing, there has been substantial interest in the single-photon emission associated with defects in hexagonal boron nitride (hBN). With the goal of producing deterministic emission centers, in this work, we present a platform for engineering emission in hBN integrated with gold truncated nanocone structures. Our findings highlights that, the activation of emission is due to the truncated gold nanocones. Furthermore, we measure the quantum characteristics of this emission and find that while our system demonstrates support for single-photon emission, the origin of this emission remains ambiguous. Specifically, it is unclear whether the emission arises from defects generated by the induced strain or from alternative defect mechanisms. This uncertainty stems from the fluorescence properties inherent to gold, complicating our definitive attribution of the quantum emission source. To provide a rigorous theoretical foundation, we elucidate the effects of strain via the Kirchhoff-Love theory. Additionally, the enhancements observed due to plasmonic effects are comprehensively explained through the resolution of Maxwell's equations. This study will be useful for the development of deterministic and tunable single photonic sources in two dimensional materials and their integration with plasmonic platforms.

Monolithic integration of solid-state color centers with photonic elements of the same material is a promising approach to overcome the constraints of fabrication complexity and coupling losses in traditional hybrid integration approaches. A wide band-gap, low-loss silicon nitride (SiN) platform is a mature technology, having CMOS compatibility, widely used in hybrid integrated photonics and optoelectronics. However, it has been shown that certain growth conditions enable the SiN material to host color centers, whose origin is currently under investigation. In this work, we have engineered a novel technique for the efficient coupling of these intrinsic emitters into the whispering gallery modes (WGMs) of the SiN microring cavity -- which has not been explored previously. We have engineered a subwavelength-sized notch into the rim of the SiN microring structure, to optimize the collection efficiency of the cavity-coupled enhanced photoluminescence (PL) spectra at room temperature. The platform presented in this work will enable the development of monolithic integration of color centers with nanophotonic elements for application to quantum photonic technologies.

Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%. A quick and accurate diagnosis is crucial to increase the chance of survival. However, in medical analysis, the manual annotation and segmentation of a brain tumor can be a complicated task. Multiple MRI modalities are typically analyzed as they provide unique information regarding the tumor regions. Although these MRI modalities are helpful for segmenting gliomas, they tend to increase overfitting and computation. This paper proposes a region of interest detection algorithm that is implemented during data preprocessing to locate salient features and remove extraneous MRI data. This decreases the input size, allowing for more aggressive data augmentations and deeper neural networks. Following the preprocessing of the MRI modalities, a fully convolutional autoencoder with soft attention segments the different brain MRIs. When these deep learning algorithms are implemented in practice, analysts and physicians cannot differentiate between accurate and inaccurate predictions. Subsequently, test time augmentations and an energy-based model were used for voxel-based uncertainty predictions. Experimentation was conducted on the BraTS benchmarks and achieved state-of-the-art segmentation performance. Additionally, qualitative results were used to assess the segmentation models and uncertainty predictions., Comment: 11 pages, 6 figures, code available at https://github.com/WeToTheMoon/BrainTumorSegmentation, submitted to Computers in Biology and Medicine

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signal-leak bias is particularly significant when models are tuned to a specific style, causing sub-optimal style matching. Recent research tries to avoid the signal leakage during training. We instead show how we can exploit this signal-leak bias in existing diffusion models to allow more control over the generated images. This enables us to generate images with more varied brightness, and images that better match a desired style or color. By modeling the distribution of the signal leak in the spatial frequency and pixel domains, and including a signal leak in the initial latent, we generate images that better match expected results without any additional training., Comment: corrected the author names in reference [24]

The interaction of light beams with resonant structures has led to the development of various optical platforms for sensing, particle manipulation, and strong light-matter interaction. In the current study, we investigate the manifestations of the bound states in continuum (BIC) on the in plane and out of plane shifts (referred to as Goos-Hanchen (GH) and Imbert-Fedorov (IF) shifts, respectively) of a finite beam with specific polarization incident at an arbitrary angle. Based on the angular spectrum decomposition, we develop a generic formalism for understanding the interaction of the finite beam with an arbitrary stratified medium with isotropic and homogeneous components. it is applied to the case of a Gaussian beam with p and circularly polarized light incident on a symmetric structure containing two polar dielectric layers separated by a spacer layer. For p-polarized plane wave incidence one of the coupled Berreman modes of the structure was recently shown to evolve to the bound state with infinite localization and diverging quality factor coexisting with the other mode with large radiation leakage (Remesh et al. Optics Communications, 498:127223, 2021). A small deviation from the ideal BIC resonance still offers resonances with very high quality factors and these are exploited in this study to report giant GH shifts. A notable enhancement in the IF shift for circularly polarized light is also shown. Moreover, the reflected beam is shown to undergo distortion leading to a satellite spot. The origin of such a splitting of the reflected beam is traced to a destructive interference due to the left and right halves of the corresponding spectra.

An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, doing this accurately is a difficult task due to the black-box nature of such networks. Most existing approaches find such attributions either using activations and gradients or by repeatedly perturbing the input. We instead address this challenge by training a second deep network, the Explainer, to predict attributions for a pre-trained black-box classifier, the Explanandum. These attributions are provided in the form of masks that only show the classifier-relevant parts of an image, masking out the rest. Our approach produces sharper and more boundary-precise masks when compared to the saliency maps generated by other methods. Moreover, unlike most existing approaches, ours is capable of directly generating very distinct class-specific masks in a single forward pass. This makes the proposed method very efficient during inference. We show that our attributions are superior to established methods both visually and quantitatively with respect to the PASCAL VOC-2007 and Microsoft COCO-2014 datasets.

Although the field of 2D materials has democratized materials science by making high quality samples accessible cheaply, due to the atomically thin nature of these systems, an integration with nanostructures is almost always required to obtain a significant optical response. Traditionally, these nanostructures are fabricated via electron beam lithography or focused ion beam milling, which are expensive and large area fabrication can be further time consuming. In order to overcome this problem, we report the integration of 2D semiconductors on a cost-effective and large area fabricated nanocone platform. We show that the plasmon modes of our nanocone structures lead to photoluminescence (PL) enhancement of monolayer WSe$_2$ by about eight to ten times compared to the non-plasmonic case, consistent with finite-difference time-domain simulations. Excitation power-dependent measurements reveal that our nanocone platform enables a versatile route to engineering the relative exciton trion contributions to the emission.

Interlayer excitons in two dimensional semiconductor heterostructures show suppressed electron-hole overlap resulting in longer radiative lifetimes as compared to intralyer excitons. Such tightly bound interlayer excitons are relevant for important optoelectronic applications including light storage and quantum communication. Their optical accessibility is, however, limited due to their out-of-plane transition dipole moment. In this work, we design a CMOS compatible photonic integrated chip platform for enhanced near field coupling of these interlayer excitons with the whispering gallery modes of a microresonator, exploiting the high confinement of light in a small modal volume and high quality factor of the system. Our platform allows for highly selective emission routing via engineering an asymmetric light transmission which facilitates efficient readout and channeling of the excitonic valley state from such systems.

Recent advances in text-to-speech (TTS) synthesis, such as Tacotron and WaveRNN, have made it possible to construct a fully neural network based TTS system, by coupling the two components together. Such a system is conceptually simple as it only takes grapheme or phoneme input, uses Mel-spectrogram as an intermediate feature, and directly generates speech samples. The system achieves quality equal or close to natural speech. However, the high computational cost of the system and issues with robustness have limited their usage in real-world speech synthesis applications and products. In this paper, we present key modeling improvements and optimization strategies that enable deploying these models, not only on GPU servers, but also on mobile devices. The proposed system can generate high-quality 24 kHz speech at 5x faster than real time on server and 3x faster than real time on mobile devices., Comment: 7 pages 2 figures, accepted to ASRU 2021

We study coupling of leaky Berreman modes in polar dielectric films (SiO2) through a thin metallic layer (gold) and show the familiar signatures of normal mode splitting. Due to very large negative real part of the dielectric function of gold, the splitting shows up only for extremely thin coupling layers. In contrast, coupling of Berreman modes through a dielectric spacer layer reveals novel possibilities of having bound states in continuum, albeit in the limit of vanishing losses. It is shown that the corresponding dispersion branches of the symmetric and antisymmetric modes can cross. BIC is shown to occur on one of these branches which is characterized by lower loss. In fact the BIC corresponds to the point where the radiative losses are minimized. For thicker layers (both spacer and the polar dielectric) BIC is shown to occur on the higher order dispersion branches. The origin of BIC is traced to the Fabry-Perot type mechanism due to the excitation of the leaky guided modes in the central layer.

In this paper we introduce a novel way of estimating prediction uncertainty in deep networks through the use of uncertainty surrogates. These surrogates are features of the penultimate layer of a deep network that are forced to match predefined patterns. The patterns themselves can be, among other possibilities, a known visual symbol. We show how our approach can be used for estimating uncertainty in prediction and out-of-distribution detection. Additionally, the surrogates allow for interpretability of the ability of the deep network to learn and at the same time lend robustness against adversarial attacks. Despite its simplicity, our approach is superior to the state-of-the-art approaches on standard metrics as well as computational efficiency and ease of implementation. A wide range of experiments are performed on standard datasets to prove the efficacy of our approach.

Heat generated by spin currents in spintronics-based devices is typically much less than that generated by charge current flows in conventional electronic devices. However, the conventional approaches for excitation of spin currents based on spin-pumping and spin Hall effect are limited in efficiency which restricts their application for viable spintronic devices. We propose a novel type of photonic-crystal (PC) based structures for efficient and tunable optically-induced spin current generation via the Spin Seebeck and inverse spin Hall effects. It is experimentally demonstrated that optical surface modes localized at the PC surface covered by ferromagnetic layer and materials with giant spin-orbit coupling (SOC) notably increase the efficiency of the optically-induced spin current generation and provides its tunability by modifying light wavelength or angle of incidence. Up to 100% of the incident light power can be transferred to heat within the SOC layer and, therefore, to spin current. Importantly, high efficiency becomes accessible even for ultra-thin SOC layers. Moreover, surface patterning of the PC-based spintronic nanostructure allows local generation of spin currents at the pattern scales rather than diameter of the laser beam.

Privacy becomes a crucial issue when outsourcing the training of machine learning (ML) models to cloud-based platforms offering machine-learning services. While solutions based on cryptographic primitives have been developed, they incur a significant loss in accuracy or training efficiency, and require modifications to the backend architecture. A key challenge we tackle in this paper is the design of image obfuscation schemes that provide enough privacy without significantly degrading the accuracy of the ML model and the efficiency of the training process. In this endeavor, we address another challenge that has persisted so far: quantifying the degree of privacy provided by visual obfuscation mechanisms. We compare the ability of state-of-the-art full-reference quality metrics to concur with human subjects in terms of the degree of obfuscation introduced by a range of techniques. By relying on user surveys and two image datasets, we show that two existing image quality metrics are also well suited to measure the level of privacy in accordance with human subjects as well as AI-based recognition, and can therefore be used for quantifying privacy resulting from obfuscation. With the ability to quantify privacy, we show that we can provide adequate privacy protection to the training image set at the cost of only a few percentage points loss in accuracy.

This paper presents NANCY, a system that generates adaptive bit rates (ABR) for video and adaptive network coding rates (ANCR) using reinforcement learning (RL) for video distribution over wireless networks. NANCY trains a neural network model with rewards formulated as quality of experience (QoE) metrics. It performs joint optimization in order to select: (i) adaptive bit rates for future video chunks to counter variations in available bandwidth and (ii) adaptive network coding rates to encode the video chunk slices to counter packet losses in wireless networks. We present the design and implementation of NANCY, and evaluate its performance compared to state-of-the-art video rate adaptation algorithms including Pensieve and robustMPC. Our results show that NANCY provides 29.91% and 60.34% higher average QoE than Pensieve and robustMPC, respectively., Comment: Accepted in Globecom, 2020

Hot carrier dynamics in a dispersionless plasmonic structures over a broad wavelength are studied by pump-probe measurements with 45 fs time resolution. The role of direct excited as well as plasmon generated hot carriers on low energy probe plasmons are studied by simultaneous measurement of differential transmittance and reflectance. While the pump fluence dependence on the decay times is linear for hot electrons and plasmon generated hot electrons, when pump is near resonant with the X- symmetry point, decay time varied as square of pump fluence. Decay times of 800 nm degenerate pump-probe measurements highlight the difference in surface (reflection) and the bulk (transmission) mechanisms. Decay time corresponding to the hot carrier relaxation is in the 1 -3 ps range for different excitation energies. Rise time, governed by the plasmon to hot carrier conversion and electron - electron scattering processes, is about 200 fs for the hot carrier and hot plasmon excitation cases which increased to about 485 fs for when pump is resonant with interband transition at X- symmetry point., Comment: 15 pages, 7 figures, 3 tables

A Superoscillatory lens (SOL) is known to produce a sub-diffraction hotspot which is useful for high-resolution imaging. However, high-energy rings called sidelobes coexist with the central hotspot. Additionally, SOLs have not yet been directly used to image reflective objects due to low efficiency and poor imaging properties. We propose a novel reflection confocal nanoscope which mitigates these issues by relaying the SOL intensity pattern onto the object and use conventional optics for detection. We experimentally demonstrate super-resolution by imaging double bars with 330 nm separation using a 632.8 nm excitation and a 0.95 NA objective. We also discuss the enhanced contrast properties of the SOL nanoscope against a laser confocal microscope, and the degradation of performance while imaging large objects., Comment: 17 pages, 15 figures, supplementary included

We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary. To obtain similar binary networks, existing methods rely on the sign activation function. This function, however, has no gradients for non-zero values, which makes standard backpropagation impossible. To circumvent the difficulty of training a network relying on the sign activation function, these methods alternate between floating-point and binary representations of the network during training, which is sub-optimal and inefficient. We approach the binarization task by training on a unique representation involving a smooth activation function, which is iteratively sharpened during training until it becomes a binary representation equivalent to the sign activation function. Additionally, we introduce a new technique to perform binary batch normalization that simplifies the conventional batch normalization by transforming it into a simple comparison operation. This is unlike existing methods, which are forced to the retain the conventional floating-point-based batch normalization. Our binary networks, apart from displaying advantages of lower memory and computation as compared to conventional floating-point and binary networks, also show higher classification accuracy than existing state-of-the-art methods on multiple benchmark datasets., Comment: 9 pages, 5 figures

Ultracold atom-traps on a chip enhances the practical application of atom traps in quantum information processing, sensing, and metrology. Plasmon mediated near-field optical potentials are promising for trapping atoms. The combination of plasmonic nanostructures and ultracold atoms has the potential to create a two dimensional array of neutral atoms with lattice spacing smaller than that of lattices created from interfering light fields -- the optical lattices. We report the design, fabrication and characterization of a nano-scale array of near-field optical traps for neutral atoms using plasmonic nanostructures. The building block of the array is a metallic nano-disc fabricated on the surface of an ITO-coated glass substrate. We numerically simulate the electromagnetic field-distribution using Finite Difference Time Domain method around the nanodisc, and calculate the intensity, optical potential and the dipole force for $^{87}$Rb atoms. The optical near-field generated from the fabricated nanostructures is experimentally characterized by using Near-field Scanning Optical Microscopy. We find that the optical potential and dipole force has all the desired characteristics to trap cold atoms when a blue-detuned light-field is used to excite the nanostructures. This trap can be used for effective trapping and manipulation of isolated atoms and also for creating a lattice of neutral atoms having sub-optical wavelength lattice spacing. Near-field measurements are affected by the influence of tip on the sub-wavelength structure. We present a deconvolution method to extract the actual near-field profile from the measured data., Comment: 9 pages, 8 figures

Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among others. With current image sizes in the order of megabytes, it is extremely expensive to run conventional algorithms such as gradient descent, making them unfavorable especially when closed-form solutions can be derived and computed efficiently. This paper explains in detail the framework for solving convex image optimization and deconvolution in the Fourier domain. We begin by explaining the mathematical background and motivating why the presented setups can be transformed and solved very efficiently in the Fourier domain. We also show how to practically use these solutions, by providing the corresponding implementations. The explanations are aimed at a broad audience with minimal knowledge of convolution and image optimization. The eager reader can jump to Section 3 for a footprint of how to solve and implement a sample optimization function, and Section 5 for the more complex cases.

Titanium dioxide is one of the most widely used wide bandgap materials. However, the TiO2 deposited on a substrate is not always transparent leading to a loss in efficiency of the device, especially, the photo response. Herein, we show that atomic layer deposition (ALD) and sputtered TiO2 thin films can be highly absorbing in the visible region. While in ALD, the mechanism is purported to be due to oxygen deficiency, intriguingly, in sputtered films it has been observed that in fact oxygen rich atmosphere leads to visible absorption. We show that the oxygen content during deposition, the resistivity of the film could be controlled and also the photocatalysis response has been evaluated for both the ALD and sputtered films. High resolution TEM and STEM studies show that the origin of visible absorption could be due to the presence of nanoparticles with surface defects inside the amorphous film.

We consider the problem of signal reconstruction for a system under sparse signal corruption by a malicious agent. The reconstruction problem follows the standard error coding problem that has been studied extensively in the literature. We include a new challenge of robust estimation of the attack support. The problem is then cast as a constrained optimization problem merging promising techniques in the area of deep learning and estimation theory. A pruning algorithm is developed to reduce the ``false positive" uncertainty of data-driven attack localization results, thereby improving the probability of correct signal reconstruction. Sufficient conditions for the correct reconstruction and the associated reconstruction error bounds are obtained for both exact and inexact attack support estimation. Moreover, a simulation of a water distribution system is presented to validate the proposed techniques., Comment: 7 pages

The CMS hadron Calorimeter is made of alternating layers of scintillating tiles and metals, such as brass or iron. The original photo detectors were hybrid units with a single accelerating gap called Hybrid Photo Diodes (HPD). Scintillating light was transmitted to the HPDs by means of optical fibers. During data taking at the Large Hadron Collider (LHC), the signal strength of scintillator tiles of detector units in the forward region degraded significantly due to the damage related to the amount of radiation to which the scintillator was exposed to. Scintillators suffer damage when exposed to radiation, however, the amount of damage observed was more than originally estimated. Several HPDs were removed during a detector shut down period. Microscopic scans of relative quantum efficiencies for few of these HPDs were made. The damage of the photocathode was determined to vary with the amount of optical signal transmitted by optical fibers to the HPD. Imprints of each fiber (1 mm) on the photocathode with varying damage within the same pixel were observed. Most of the observed reduction of the calorimeter signal can be attributed to localised damage of the photocathode., Comment: Article to be submitted to Review of Scientific Instruments

We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical cluster structures in feature space. This is visualized as heat maps, which highlight semantically matching regions across a set of images, revealing what the network `perceives' as similar. DFF can also be used to perform co-segmentation and co-localization, and we report state-of-the-art results on these tasks., Comment: The European Conference on Computer Vision (ECCV), 2018

Fano resonance in plasmonic oligomers originating from the interference of a spectrally broad superradiant mode and a discrete subradiant mode is under intensive recent investigations due to numerous potential applications. In this regard, development of experimental means to understand and control the complex Fano interference process and to modulate the resulting asymmetric Fano spectral line shape is highly sought after. Here we present a polarization Mueller matrix measurement and inverse analysis approach for quantitative understanding and interpretation of the complex interference process that lead to Fano resonance in symmetry broken plasmonic oligomers. The spectral Mueller matrices of the plasmonic oligomers were recorded using a custom designed dark-field Mueller matrix spectroscopy system. These were subsequently analyzed using differential Mueller matrix decomposition technique to yield the quantitative sample polarimetry characteristics, namely, polarization diattenuation (d) and linear retardance ({\delta}) parameters. The unique signature of the interference of the superradiant dipolar plasmon mode and the subradiant quadrupolar mode of the symmetry broken plasmonic oligomers manifested as rapid spectral variation of the diattenuation and the linear retardance parameters across the Fano spectral dip. The polarization information contained in the Mueller matrix was further utilized to desirably control the Fano spectral line shape. The experimental Mueller matrix analysis was complemented with finite element based numerical simulations, which enabled quantitative understanding of the interference of the superradiant and the subradiant plasmon modes and its link with the polarization diattenuation and retardance parameters.

Metasurface based super absorbers exhibit near unity absorbance. While the absorption peak can be tuned by the geometry/size of the sub-wavelength resonator, broadband absorption can be obtained by placing multiple resonators of various size or shapes in a unit cell. Metal dispersion hinders high performance broadband absorption at optical frequencies and careful designing is essential to achieve good structures. We propose a novel analytical framework for designing a broadband super absorber which is much faster than the time consuming full wave simulations that are employed so far. Analytical expressions are derived for the wavelength dependency of the design parameters, which are then used in the optimization of broadband absorption. Numerical simulations report an average polarization-independent absorption of ~97 in the 450 to 950 nm spectral region with a near unity absorption (99.36) in the 500 to 850 nm region. Experimentally, we demonstrate an average absorption over 98 in the 450 to 950 nm spectral region at 20 degree incident angle The designed super absorber is polarization insensitive and has a weak launch angle dependency. The proposed framework simplifies the design process and provides a quicker optimal solution for high performance broadband super absorbers.

In digital photography, two image restoration tasks have been studied extensively and resolved independently: demosaicing and super-resolution. Both these tasks are related to resolution limitations of the camera. Performing super-resolution on a demosaiced images simply exacerbates the artifacts introduced by demosaicing. In this paper, we show that such accumulation of errors can be easily averted by jointly performing demosaicing and super-resolution. To this end, we propose a deep residual network for learning an end-to-end mapping between Bayer images and high-resolution images. By training on high-quality samples, our deep residual demosaicing and super-resolution network is able to recover high-quality super-resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately. We perform extensive experiments to show that our deep residual network achieves demosaiced and super-resolved images that are superior to the state-of-the-art both qualitatively and in terms of PSNR and SSIM metrics.

Nanostructured magneto-optical materials sustaining optical resonances open very efficient way for light control via magnetic field, which is of prime importance for telecommunication and sensing applications. However, usually their response is narrowband due to its resonance character. Here we demonstrate and investigate a novel type of the magnetoplasmonic structure, the magnetoplasmonic quasicrystal, which demonstrates unique magneto-optical response. It consists of the magnetic dielectric film covered by a thin gold layer perforated by slits forming a Fibonacci-like binary sequence. The transverse magneto-optical Kerr effect (TMOKE) acquires controllable multiple plasmon-related resonances resulting in a magneto-optical response in a wide frequency range. The broadband TMOKE is valuable for numerous nanophotonics applications including optical sensing, control of light, all-optical control of magnetization etc. On the other hand, TMOKE spectroscopy is an efficient tool for investigation of the peculiarities of plasmonic quasicrystals., Comment: 6 pages, 3 figures

The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scale with an increased number of classes? Under the assumptions that the classes are sampled identically and independently from a population, and that the classifier is based on independently learned scoring functions, we show that the expected accuracy when the classifier is trained on k classes is the (k-1)st moment of a certain distribution that can be estimated from data. We present an unbiased estimation method based on the theory, and demonstrate its application on a facial recognition example., Comment: Submitted to JMLR

Central focus of spintronics is concentrated on generation of pure spin current and associated spin torque. Pure spin current can be generated by spin Hall effect in heavy metals by passing charge current. By spin Seebeck effect pure spin current can also be generated in ferromagnet. In this work we experimentally demonstrate that if heavy metals like Platinum with high spin orbit coupling carry heat current it can convert it into spin current due to relativistic spin orbit interaction. This conversion of heat current into spin current in non magnet is equivalent of thermally driven spin Hall effect or it is known as spin Nernst effect. We observed spin Nernst effect in Ni/Pt bi-layer experimentally and we confirm that when Pt is replaced by low spin orbit material like Al spin Nernst effect significantly reduces. So we have detected spin Nernst effect unambiguously and compare its strength with electrical spin Hall effect.

Size uniformity is one of the main criteria of superpixel methods. But size uniformity rarely conforms to the varying content of an image. The chosen size of the superpixels therefore represents a compromise - how to obtain the fewest superpixels without losing too much important detail. We propose that a more appropriate criterion for creating image segments is information uniformity. We introduce a novel method for segmenting an image based on this criterion. Since information is a natural way of measuring image complexity, our proposed algorithm leads to image segments that are smaller and denser in areas of high complexity and larger in homogeneous regions, thus simplifying the image while preserving its details. Our algorithm is simple and requires just one input parameter - a threshold on the information content. On segmentation comparison benchmarks it proves to be superior to the state-of-the-art. In addition, our method is computationally very efficient, approaching real-time performance, and is easily extensible to three-dimensional image stacks and video volumes., Comment: 9 pages

We study phase conjugation in a nonlinear composite slab when the counter propagating pump waves are completely absorbed by means of coherent perfect absorption. Under the undepleted pump approximation the coupling constant and the phase conjugated reflectivity are shown to undergo a substantial increase and multivalued response. The effect can be used for efficient switching of the phase conjugated reflectivity in photonic circuits.