Plasmonic octamer objects: reversal of near-field optical binding force without the aid of backgrounds.

In recent years, the near-field optical binding force has gained a lot of interest in the field of optical manipulation. The reversal of the near-field binding force, a new, to the best of our knowledge, kind of optical manipulation, has so far been investigated mostly between dimers and in a very few cases among tetramers by utilizing the help of suitable substrates or backgrounds. Until now, no known way to control the near-field optical binding force among octamer configurations has been found, to our knowledge. In this paper, we propose a plasmonic (silver) octamer configuration where we demonstrate the control and reversal (attraction and repulsion) of the near-field optical binding force of octamers by illuminating the system with a TM polarized Bessel beam. The control of the binding force and its reversal is explained based on the polarization and gradient forces created by the Bessel beam. As the aid of a background or substrate is not required, our proposed simplified approach has the potential to open up novel ways of manipulating multiple particles. Our investigation also implicitly suggests that for future research on controlling the reversal of the near-field optical binding force of multiple particles, Bessel beams can be the appropriate choice instead of plane waves.

Solving visual pollution with deep learning: A new nexus in environmental management.

Visual pollution is a relatively new concern amidst the existing plethora of mainstream environmental pollution, recommending the necessity for research to conceptualize, formalize, quantify and assess it from different dimensions. The purpose of this study is to create a new field of automated visual pollutant classification, harnessing the technological prowess of the 21st century for applications in environmental management. From the wide range of visual pollutants, four categories have been considered viz. (i) billboards and signage, (ii) telephone and communication wires, (iii) network and communication towers and (iv) street litter. The deep learning model used in this study simulates the human learning experience in the context of image recognition for visual pollutant classification by training and testing a convolutional neural network with several layers of artificial neurons. Data augmentation using image processing techniques and a train-test split ratio of 80:20 have been used. Training accuracy of 95% and validation accuracy of 85% have been achieved by the deep learning model. The results indicate that the upper limit of accuracy i.e. the asymptote, depends on the dataset size for this type of task. This study has several applications in environmental management. For example, the deployment of the trained model for processing of video/live footage from smartphone applications, closed-circuit television and drones/unmanned aerial vehicles can be applied for both the removal and management of visual pollutants in the natural and built environment. Furthermore, generating the 'visual pollution score/index' of urban regions such as towns and cities will create a new 'metric/indicator' in the field of urban environmental management.

Optical pulling using evanescent mode in sub-wavelength channels.

Optical evanescent wave in total internal reflection has been widely used in efficient optical manipulation, where the object is trapped by the intrinsic intensity gradient of the evanescent wave while transported by the scattering force along the orthogonal direction. Here, we propose a distinct optical manipulation scheme using the attenuated modes in subwavelength optical channels, where both the trapping and transportation forces are along the channel direction. We create such a mode in a sub-wavelength photonic crystal waveguide and quantitatively obtain the net pushing and pulling forces, which can overcome the Brownian motion within a critical length. Due to the presence of the physical channel, subwavelength trapping on the transverse direction is natural, and manipulation along bend trajectories is also possible without the assistance of the self-acceleration beams provided a channel is adopted. This optical manipulation method can be extended to any other channels that support attenuation mode, and may provide an alternate way for flexible optical manipulation.

Quantum mechanical lateral force on an atom due to matter wave.

The area of trapping the atoms or molecules using light has advanced tremendously in the last few decades. In contrast, the idea of controlling (not only trapping) the movement of atomic-sized particles using matter waves is a completely new emerging area of particle manipulation. Though a single previous report has suggested the pulling of atoms based on matter-wave tractor beams, an attempt is yet to be made to produce a lateral force using this technique. This article demonstrates an asymmetric setup that engenders reversible lateral force on an atom due to the interaction energy of the matter wave in the presence of a metal surface. Several full-wave simulations and analytical calculations were performed on a particular set-up of Xenon scatterers placed near a Copper surface, with two counter-propagating plane matter waves of Helium impinging in the direction parallel to the surface. By solving the time-independent Schrödinger equation and using the solution, quantum mechanical stress tensor formalism is applied to compute the force acting on the particle. The simulation results are in excellent agreement with the analytical calculations. The results for the adsorbed scatterer case find this technique to be an efficient cleaning procedure similar to electron-stimulated desorption for futuristic applications.

On chip all-optical distinguishing of independently placed distinct types of single Rayleigh particle.

In order to determine whether a particle is plasmonic, dielectric, or chiral, different complex processes and chemicals are applied in lab setups and pharmaceutical industries. Sorting or categorizing a particle based on distinct optical forces can be a novel technique. When a beam of light interacts with a particle, it usually pushes the particle in the direction of the light's propagation. Counterintuitively, it can also pull the particle toward the light beam or move it toward a lateral direction. As far as we know, to date, no comprehensive report exists regarding a single optical arrangement capable of inducing entirely distinct behaviors of force for three disparate types of independently placed single Rayleigh particle. This study introduces an all-optical technique aimed at effectively sorting nanoscale Rayleigh-sized objects employing a plasmonic substrate, when each distinct type of single particle is placed over the substrate independently. Unfortunately, this proposed technique does not work for the cluster or mixture of distinct particles. In our proposed configuration, a simple linearly polarized plane wave is incident onto the plasmonic substrate, thereby engendering completely different responses from three different types of nanoparticles: Gold (plasmonic), SiO2 (dielectric), and Chiral particles. We conducted individual tests for our setup using linearly polarized plane waves at angles of 30-degree, 45-degree, and 60-degree individually. Consistent results were obtained across all angles. In each of the three distinct setups involving the aforementioned particle, a dielectric Rayleigh particle experiences an optical pulling force, a plasmonic Rayleigh particle experiences an optical pushing force, and a chiral Rayleigh particle encounters an optical lateral force. These distinctive force behaviors manifest as a result of the intricate interplay between the material properties of the nanoparticles and the characteristics of the plane-polarized beam, encompassing aspects such as plasmonic response, chirality, and refractive index. Moreover, this technique presents an environmentally sustainable and economically viable alternative to the utilization of expensive and potentially hazardous chemicals in nanoparticle sorting processes within industrial domains.

Significant improvement of fidelity for encoded quantum bell pairs at long and short-distance communication along with generalized circuit.

Quantum entanglement is a unique criterion of the quantum realm and an essential tool to secure quantum communication. Ensuring high-fidelity entanglement has always been a challenging task owing to interaction with the hostile channel environment created due to quantum noise and decoherence. Though several methods have been proposed, correcting almost all arbitrary errors is still a gigantic task. As one of the main contributions of this work, a new model for 'large distance communication' has been proposed, which may correct all bit flip errors or other errors quite extensively if proper encoding and subspace measurements are used. To achieve this purpose, at the very first step, the idea of differentiating the 'long-distance communication' and 'short-distance applications' has been introduced. Short-distance is determined by the maximum range of applying unitary control gates by the qubit technology. How the error correcting ability of Quantum codes change for short and long-distance application is investigated in this work, which was not explored in previous literatures as far as we know. At the beginning, we have applied stabilizer formalism and Repetition Code for decoding to distinguish the error correcting ability in long and short distance communication. Particularly for short-distance communication, it has been demonstrated that a 'properly encoded' bell state can identify all the bit flip, or phase flip errors with 100% accuracy theoretically. In contrast, if the bell states are used in long-distance communication, the error-detecting and correcting ability reduces at huge amounts. To increase the fidelity significantly and correct the errors quite extensively for long-distance communication, a new model based on classical communication protocol has been suggested. All the required circuits in these processes have been generalized for arbitrary (even) numbers of ancilla qubits during encoding. Proposed analytical results have also been verified with the Simulation results of IBM QISKIT QASM.

Forecasting the United State Dollar(USD)/Bangladeshi Taka (BDT) exchange rate with deep learning models: Inclusion of macroeconomic factors influencing the currency exchange rates.

Forecasting a currency exchange rate is one of the most challenging tasks nowadays. Due to government monetary policy and some uncertain factors, such as political stability, it becomes difficult to correctly forecast the currency exchange rate. Previously, many investigations have been done to forecast the exchange rate of the United State Dollar(USD)/Bangladeshi Taka(BDT) using statistical time series models, machine learning models, and neural network models. But none of the previous methods considered the underlying macroeconomic factors of the two countries, such as GDP, import/export, government revenue, etc., for forecasting the USD/BDT exchange rate. We have included various time-sensitive macroeconomic features directly impacting the USD/BDT exchange rate to address this issue. These features will create a new dimension for researchers to predict and forecast the USD/BDT exchange rate. We have used various types of models for predicting and forecasting the USD/BDT exchange rate and found that Among all our models, Time Distributed MLP provides the best performance with an RMSE of 0.1984. Finally, we have proposed a pipeline for forecasting the USD/BDT exchange rate, which reduced the RMSE of Time Distributed MLP to 0.1900 and has proven effective in reducing the error of all our models.

Controlling the counterintuitive optical repulsive thrust of nano dimers with counter propagating type waves and background medium.

This work focuses on the utilization of counter-propagating plane waves for optical manipulation, which provides a unique approach to control the behavior of Rayleigh and Dipolar nanoparticles immersed in a homogeneous or heterogeneous medium. Our study presents an interesting finding of a repulsive force between plasmonic-chiral heterodimers where the particles move away from each other in both near and far field regions. Interestingly, this repulsive thrust supports the wave like nature of light for the case of homogeneous background but particle type nature of light for heterogenous background. At first, we have investigated the theory underlying the optical trapping of the chiral particle and the impact of this phenomenon on the overall repulsive behavior of the heterodimers placed in air (homogeneous) background. After that, our proposed set-up has further been investigated putting in air-water interface (heterogenous background) and by varying light angle only a little bit. Our observation for this interface case is suggesting the transfer of Minkowski momentum of photon to each optically pulled Rayleigh or dipolar particle of the dimer set, which ultimately causes a broad-band giant repulsive thrust of the dimers. However, in absence of the other particle in the cluster, a single half-immersed particle does not experience the pulling force for the broad-band spectrum. The 'common' reason of the observed repulsive thrust of the dimers for both the aforementioned cases has been attributed to "modified" longitudinal Optical Binding Force (OBF). Technically, this work may open a new way to control the repulsion and attraction between the nanoparticles both in near and far field regions by utilizing the background and the counter-propagating waves. We also believe that this work manifests a possible simple set-up, which will support to observe a background dependent wave 'or' particle nature of light experimentally.

Quantifying imbalanced classification methods for leukemia detection.

Uncontrolled proliferation of B-lymphoblast cells is a common characterization of Acute Lymphoblastic Leukemia (ALL). B-lymphoblasts are found in large numbers in peripheral blood in malignant cases. Early detection of the cell in bone marrow is essential as the disease progresses rapidly if left untreated. However, automated classification of the cell is challenging, owing to its fine-grained variability with B-lymphoid precursor cells and imbalanced data points. Deep learning algorithms demonstrate potential for such fine-grained classification as well as suffer from the imbalanced class problem. In this paper, we explore different deep learning-based State-Of-The-Art (SOTA) approaches to tackle imbalanced classification problems. Our experiment includes input, GAN (Generative Adversarial Networks), and loss-based methods to mitigate the issue of imbalanced class on the challenging C-NMC and ALLIDB-2 dataset for leukemia detection. We have shown empirical evidence that loss-based methods outperform GAN-based and input-based methods in imbalanced classification scenarios.

Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models.

Invasive ductal carcinoma (IDC) breast cancer is a significant health concern for women all around the world and early detection of the disease may increase the survival rate in patients. Therefore, Computer-Aided Diagnosis (CAD) based systems can assist pathologists to detect the disease early. In this study, we present an ensemble model to detect IDC using DenseNet-121 and DenseNet-169 followed by test time augmentation (TTA). The model achieved a balanced accuracy of 92.70% and an F1-score of 95.70% outperforming the current state-of-the-art. Comparative analysis against various pre-trained deep learning models and preprocessing methods have been carried out. Qualitative analysis has also been conducted on the test dataset. After the detection of IDC breast cancer, it is important to grade it for further treatment. In our study, we also propose an ensemble model for the grading of IDC using the pre-trained DenseNet-121, DenseNet-201, ResNet-101v2, and ResNet-50 architectures. The model is inferred from two validation cohorts. For the patch-level classification, the model yielded an overall accuracy of 69.31%, 75.07%, 61.85%, and 60.50% on one validation cohort and 62.44%, 79.14%, 76.62%, and 71.05% on the second validation cohort for 4×, 10×, 20×, and 40× magnified images respectively. The same architecture is further validated using a different IDC dataset where it achieved an overall accuracy of 90.07%. The performance of the models on the detection and grading of IDC shows that they can be useful to help pathologists detect and grade the disease.

3C-GAN: class-consistent CycleGAN for malaria domain adaptation model.

Unpaired domain translation models with distribution matching loss such as CycleGAN are now widely being used to shift domain in medical images. However, synthesizing medical images using CycleGAN can lead to misdiagnosis of a medical condition as it might hallucinate unwanted features, especially if theres a data bias. This can potentially change the original class of the input image, which is a very serious problem. In this paper, we have introduced a modified distribution matching loss for CycleGAN to eliminate feature hallucination on the malaria dataset. In the context of the malaria dataset, unintentional feature hallucination may introduce a facet that resembles a parasite or remove the parasite after the translation. Our proposed approach has enabled us to shift the domain of the malaria dataset without the risk of changing their corresponding class. We have presented experimental evidence that our modified loss significantly reduced feature hallucination by preserving original class labels. The experimental results are better in comparison to the baseline (classic CycleGAN) that targets the translating domain. We believe that our approach will expedite the process of developing unsupervised unpaired GAN that is safe for clinical use.

A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset.

Malaria, one of the leading causes of death in underdeveloped countries, is primarily diagnosed using microscopy. Computer-aided diagnosis of malaria is a challenging task owing to the fine-grained variability in the appearance of some uninfected and infected class. In this paper, we transform a malaria parasite object detection dataset into a classification dataset, making it the largest malaria classification dataset (63,645 cells), and evaluate the performance of several state-of-the-art deep neural network architectures pretrained on both natural and medical images on this new dataset. We provide detailed insights into the variation of the dataset and qualitative analysis of the results produced by the best models. We also evaluate the models using an independent test set to demonstrate the model's ability to generalize in different domains. Finally, we demonstrate the effect of conditional image synthesis on malaria parasite detection. We provide detailed insights into the influence of synthetic images for the class imbalance problem in the malaria diagnosis context.

Automatic segmentation of blood cells from microscopic slides: A comparative analysis.

With the recent developments in deep learning, automatic cell segmentation from images of microscopic examination slides seems to be a solved problem as recent methods have achieved comparable results on existing benchmark datasets. However, most of the existing cell segmentation benchmark datasets either contain a single cell type, few instances of the cells, not publicly available. Therefore, it is unclear whether the performance improvements can generalize on more diverse datasets. In this paper, we present a large and diverse cell segmentation dataset BBBC041Seg1, which consists both of uninfected cells (i.e., red blood cells/RBCs, leukocytes) and infected cells (i.e., gametocytes, rings, trophozoites, and schizonts). Additionally, all cell types do not have equal instances, which encourages researchers to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. Furthermore, we conduct a comparative study using both classical rule-based and recent deep learning state-of-the-art (SOTA) methods for automatic cell segmentation and provide them as strong baselines. We believe the introduction of BBBC041Seg will promote future research towards clinically applicable cell segmentation methods from microscopic examinations, which can be later used for downstream tasks such as detecting hematological diseases (i.e., malaria).

Plasmonic Spherical Heterodimers: Reversal of Optical Binding Force Based on the Forced Breaking of Symmetry.

The stimulating connection between the reversal of near-field plasmonic binding force and the role of symmetry-breaking has not been investigated comprehensively in the literature. In this work, the symmetry of spherical plasmonic heterodimer-setup is broken forcefully by shining the light from a specific side of the set-up instead of impinging it from the top. We demonstrate that for the forced symmetry-broken spherical heterodimer-configurations: reversal of lateral and longitudinal near-field binding force follow completely distinct mechanisms. Interestingly, the reversal of longitudinal binding force can be easily controlled either by changing the direction of light propagation or by varying their relative orientation. This simple process of controlling binding force may open a novel generic way of optical manipulation even with the heterodimers of other shapes. Though it is commonly believed that the reversal of near-field plasmonic binding force should naturally occur for the presence of bonding and anti-bonding modes or at least for the Fano resonance (and plasmonic forces mostly arise from the surface force), our study based on Lorentz-force dynamics suggests notably opposite proposals for the aforementioned cases. Observations in this article can be very useful for improved sensors, particle clustering and aggregation.

Substrate and Fano Resonance Effects on the Reversal of Optical Binding Force between Plasmonic Cube Dimers.

The behavior of Fano resonance and the reversal of near field optical binding force of dimers over different substrates have not been studied so far. Notably, for particle clustering and aggregation, controlling the near filed binding force can be a key factor. In this work, we observe that if the closely located plasmonic cube homodimers over glass or high permittivity dielectric substrate are illuminated with plane wave, no reversal of lateral optical binding force occurs. But if we apply the same set-up over a plasmonic substrate, stable Fano resonance occurs along with the reversal of near field lateral binding force. It is observed that during such Fano resonance, stronger coupling occurs between the dimers and plasmonic substrate along with the strong enhancement of the substrate current. Such binding force reversals of plasmonic cube dimers have been explained based on the observed unusual behavior of optical Lorentz force during the induced stronger Fano resonance and the dipole-dipole resonance. Although previously reported reversals of near field optical binding forces were highly sensitive to particle size/shape (i.e. for heterodimers) and inter-particle distance, our configuration provides much relaxation of those parameters and hence could be verified experimentally with simpler experimental set-ups.

Negative Refraction with Superior Transmission in Graphene-Hexagonal Boron Nitride (hBN) Multilayer Hyper Crystal.

In this article, we have theoretically investigated the performance of graphene-hexagonal Boron Nitride (hBN) multilayer structure (hyper crystal) to demonstrate all angle negative refraction along with superior transmission. hBN, one of the latest natural hyperbolic materials, can be a very strong contender to form a hyper crystal with graphene due to its excellence as a graphene-compatible substrate. Although bare hBN can exhibit negative refraction, the transmission is generally low due to its high reflectivity. Whereas due to graphene's 2D nature and metallic characteristics in the frequency range where hBN behaves as a type-I hyperbolic material, we have found graphene-hBN hyper-crystals to exhibit all angle negative refraction with superior transmission. Interestingly, superior transmission from the whole structure can be fully controlled by the tunability of graphene without hampering the negative refraction originated mainly from hBN. We have also presented an effective medium description of the hyper crystal in the low-k limit and validated the proposed theory analytically and with full wave simulations. Along with the current extensive research on hybridization of graphene plasmon polaritons with (hyperbolic) hBN phonon polaritons, this work might have some substantial impact on this field of research and can be very useful in applications such as hyper-lensing.