Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis.

Cervical cancer is a prevalent malignant tumor within the female reproductive system and is regarded as a prominent cause of female mortality on a global scale. Timely and precise detection of various phases of cervical cancer holds the potential to substantially enhance both the rate of successful treatment and the duration of patient survival. Fluorescence spectroscopy is a highly sensitive method for detecting the biochemical changes that arise during cancer progression. In our study, fluorescence spectral data is collected from a diverse group of 110 subjects. The potential of the scattering transform technique for the purpose of cancer detection is explored. The processed signal undergoes an initial decomposition into scattering coefficients using the wavelet scattering transform (WST). Subsequently, the scattering coefficients are subjected to computation for fuzzy entropy, dispersion entropy, phase entropy, and spectral entropy, for effectively characterizing the fluorescence spectral signals. These combined features generated through the proposed approach are then fed to 1D convolutional neural network (CNN) classifier to classify them into normal, pre-cancerous, and cancerous categories, thereby evaluating the effectiveness of the proposed methodology. We obtained mean classification accuracy of 97% using 5-fold cross-validation. This demonstrates the potential of combining WST and entropic features for analyzing fluorescence spectroscopy signals using 1D CNN classifier that enables early cancer detection in contrast to prevailing diagnostic methods.

Quantum scissor from exact generalized photon number statistics.

We report the close form expressions of the photon number statistics for a generalized coherent state and a generalized photon-added coherent state, which are shown to be crucial for proposing a variety of quantum scissor operations. The analytically obtained distributions are also capable of predicting the precise laser intensity windows for realizing a variety of quantum scissors. Truncating a photon added state overcomes the selection rule of obtaining the lower order Fock states. Photon addition also enables us to obtain a higher order Fock state in a lower order superposition. The importance of circular geometry is also demonstrated for engineering such quantum scissors.

A smartphone-based standalone fluorescence spectroscopy tool for cervical precancer diagnosis in clinical conditions.

Real-time prediction about the severity of noncommunicable diseases like cancers is a boon for early diagnosis and timely cure. Optical techniques due to their minimally invasive nature provide better alternatives in this context than the conventional techniques. The present study talks about a standalone, field portable smartphone-based device which can classify different grades of cervical cancer on the basis of the spectral differences captured in their intrinsic fluorescence spectra with the help of AI/ML technique. In this study, a total number of 75 patients and volunteers, from hospitals at different geographical locations of India, have been tested and classified with this device. A classification approach employing a hybrid mutual information long short-term memory model has been applied to categorize various subject groups, resulting in an average accuracy, specificity, and sensitivity of 96.56%, 96.76%, and 94.37%, respectively using 10-fold cross-validation. This exploratory study demonstrates the potential of combining smartphone-based technology with fluorescence spectroscopy and artificial intelligence as a diagnostic screening approach which could enhance the detection and screening of cervical cancer.

Stronger EPR-steering criterion based on inferred Schrödinger-Robertson uncertainty relation.

Steering is one of the three in-equivalent forms of nonlocal correlations intermediate between Bell nonlocality and entanglement. Schrödinger-Robertson uncertainty relation (SRUR), has been widely used to detect entanglement and steering. However, the steering criterion in earlier works, based on SRUR, did not involve complete inferred-variance uncertainty relation. In this paper, by considering the local hidden state model and Reid's formalism, we derive a complete inferred-variance EPR-steering criterion based on SRUR in the bipartite scenario. Furthermore, we check the effectiveness of our steering criterion with discrete variable bipartite two-qubit and two-qutrit isotropic states.

Cervical pre-cancer classification using entropic features and CNN: In vivo validation with a handheld fluorescence probe.

Cervical cancer is one of the most prevalent forms of cancer, with a lengthy latent period and a gradual onset phase. Conventional techniques are found to be severely lacking in real time detection of disease progression which can greatly enhance the cure rate. Due to their high sensitivity and specificity, optical techniques are emerging as reliable tools, particularly in case of cancer. It has been seen that biochemical changes are better highlighted through intrinsic fluorescence devoid of interference from absorption and scattering. Its effectiveness in in-vivo conditions is affected by the fact that the intrinsic spectral signatures vary from patient to patient, as well as in different population groups. Here, we overcome this limitation by collectively enumerating the subtle changes in the spectral profiles and correlations through an information theory based entropic approach, which significantly amplifies the minute spectral variations. In conjunction with artificial intelligence (AI)/machine learning (ML) tools, it yields high specificity and sensitivity with a small dataset from patients in clinical conditions, without artificial augmentation. We have used an in-house developed handheld probe (i-HHP) for extracting intrinsic fluorescence spectra of human cervix from 110 different subjects drawn from diverse population groups. The average classification accuracy of the proposed methodology using 10-fold cross validation is 93.17%. A combination of polarised fluorescence spectra from i-HHP and the proposed classifier is proven to be minimally invasive with the ability to diagnose patients in real time. This paves the way for effective use of relatively smaller sized sensitive fluorescence data with advanced AI/ML tools for early cervical cancer detection in clinics.

GPTFX: A Novel GPT-3 Based Framework for Mental Health Detection and Explanations.

This paper introduces a novel approach GPTFX, an AI-based mental detection with GPT frameworks. This approach leverages GPT embeddings and the fine-tuning of GPT-3. This approach exhibits superior performance in both classifying mental health disorders and generating explanations with accuracy of around 87% in classification and Rouge-L of around 0.75. We utilized GPT embeddings with machine learning models for the classification of mental health disorders. Additionally, GPT-3 was fine-tuned for generating explanations related to the predictions made by these machine learning models. Notably, the proposed algorithm proves well-suited for real-time monitoring of mental health by deploying in AI-IoMT devices, as it has demonstrated greater reliability when compared to traditional algorithms.

Exact envelope solitons in topological Floquet insulators.

The existence of new types of four-wave mixing Floquet solitons were recently realized numerically through a resonant phase matching in a photonic lattice of type-I Dirac cones; specifically, a honeycomb lattice of helical array waveguides imprinted on a weakly birefringent medium. We present a wide class of exact solutions in this system for the envelope solitons in dark-bright pairs and a "molecular" form of bright-dark combinations. Some of the solutions, red or blue detuned, are mode-locked in their momenta, while the others offer a spectrum of allowed momenta subject to constraints amongst the system and solution parameters. We show that the characteristically different solutions exist at and away from the band edge, with the exact band edge possessing a periodic pair of sinusoidal excitations akin to that of two-level systems apart from localized solitons. These could have possible applications for designing quantum devices.

Inner composition alignment networks reveal financial impacts of COVID-19.

We show that inner composition alignment networks derived for financial time-series data, studied in response to worldwide lockdown imposed in response to COVID-19 situation, show distinct patterns before, during and after lockdown phase. It is observed that significant couplings between companies as captured by inner composition alignment between time series, reduced considerably across the globe during lockdown and post-lockdown recovery period. The study of global community structure of the networks show that factions of companies emerge during recovery phase, with strong coupling within the members of the faction group, a trend which was absent before lockdown period. The study of strongly connected components of the networks further show that market has fragmented in response to COVID-19 situation. We find that most central firms as characterized by in-degree, out-degree and betweenness centralities belong to Chinese and Japanese economies, indicating a role played by these organizations in financial information propagation across the globe. We further observe that recovery phase of the lockdown period is strongly influenced by financial sector, which is one of the main result of this study. It is also observed that two different group of companies, which may not be co-moving, emerge across economies during COVID-19. We further notice that many companies in US and European economy tend to shield themselves from local influences.

Variation of self-imaging length in multimode waveguides beyond the paraxial approximation.

We demonstrate the precise variation of self-imaging distance with width of a Gaussian input, centrally fed into a symmetric dielectric slab waveguide of width ∼20λ0. The width of the Gaussian is varied from the paraxial to completely nonparaxial domain. Unlike the paraxial case, the self-imaging distance is found to depend on the beam width and change with the number of excited modes in the waveguide. These features should be useful in designing devices that exploit self-imaging for improved efficiency, especially in nanophotonic circuits.

Photon added cat state: phase space structure and statistics.

The phase space structure and statistics of the photon added cat state are studied in the state's general form. Photon addition leads to a π phase shift at the origin in the observed phase space interference of the Wigner function, which may serve as an error syndrome detector. The maxima and minima of the sub-Planck tiles in the phase space of the kitten state are interchanged after photon addition, leading to their orthogonality. Interestingly, photon addition to the Yurke-Stoler state characterized by Poissonian statistics leads to a sub-Poissonian distribution, which may find potential use in quantum noise reduction.

Studying the effect of lockdown using epidemiological modelling of COVID-19 and a quantum computational approach using the Ising spin interaction.

COVID-19 is a respiratory tract infection that can range from being mild to fatal. In India, the countrywide lockdown has been imposed since 24th march 2020, and has got multiple extensions with different guidelines for each phase. Among various models of epidemiology, we use the SIR(D) model to analyze the extent to which this multi-phased lockdown has been active in 'flattening the curve' and lower the threat. Analyzing the effect of lockdown on the infection may provide a better insight into the evolution of epidemic while implementing the quarantine procedures as well as improving the healthcare facilities. For accurate modelling, incorporating various parameters along with sophisticated computational facilities are required. Parallel to SIRD modelling, we tend to compare it with the Ising model and derive a quantum circuit that incorporates the rate of infection and rate of recovery, etc as its parameters. The probabilistic plots obtained from the circuit qualitatively resemble the shape of the curve for the spread of Coronavirus. We also demonstrate how the curve flattens when the lockdown is imposed. This kind of quantum computational approach can be useful in reducing space and time complexities of a huge amount of information related to the epidemic.

Survival probability of a lazy prey on lattices and complex networks.

We develop a framework to analyse the survival probability of a prey following a minimal effort evasion strategy, that is being chased by N predators on finite lattices or complex networks. The predators independently perform random walks if the prey is not within their sighting radius, whereas, the prey only moves when a predator moves onto a node within its sighting radius. We verify the proposed framework on three different finite lattices with periodic boundaries through numerical simulations and find that the survival probability (Psur) decays exponentially with a decay rate proportional to P(N, k) (number of permutations), where k is the minimum number of predators required to capture a prey. We then extend the framework onto complex networks and verify its robustness on the networks generated by the Watts-Strogatz (W-S), Barabási-Albert (B-A) models and a few real-world networks. Our analysis predicts that, for the considered lattices, Psur reduces as the degree of the nodes of the lattice is increased. However, for networks, Psur initially increases with the average degree of the nodes, reaches a maximum, and then decreases. Furthermore, we analyse the effect of the long-range connections in networks on Psur in W-S networks. The proposed framework enables one to study the survival probability of such a prey being hunted by multiple predators on any given structure.

Experimental realization of controlled quantum teleportation of arbitrary qubit states via cluster states.

Controlled quantum teleportation involves a third party as a controller for the teleportation of state. Here, we present the novel protocols for controlling teleportation of the arbitrary two-qubit and three-qubit states through five-qubit and seven-qubit cluster states respectively. In these schemes, Alice sends the arbitrary qubit states to the remote receiver Bob through the cluster states as quantum channels under the control of Charlie. Bob can recover the mentioned states by making appropriate unitary operations, and we point out that the efficiency in our schemes is 100%. In the process of our analysis, we find the classical communication cost in our protocols is remarkably reduced when compared to the previous protocols. We perform the experimental realization of the above protocols on "IBM 16 Melbourne" quantum computer and "IBM quantum simulator" and we calculate the fidelity. We also examine the security analysis against Charlie, and these schemes which we considered here are secure against Charlie's attacks.

Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection.

We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.

Duality and quantum state engineering in cavity arrays.

A system of two coupled cavities with N - 1 photons is shown to be dynamically equivalent to an array of N coupled cavities containing one photon. Every transition in the two cavity system has a dual phenomenon in terms of photon transport in the cavity array. This duality is employed to arrive at the required coupling strengths and nonlinearities in the cavity array so that controlled photon transfer is possible between any two cavities. This transfer of photons between two of the cavities in the array is effected without populating the other cavities. The condition for perfect transport enables perfect state transfer between any two cavities in the array. Further, possibility of high fidelity generation of generalized NOON states in two coupled cavities, which are dual to the Bell states of the photon in the cavity array, is established.

Timelike curves can increase entanglement with LOCC.

We study the nature of entanglement in presence of Deutschian closed timelike curves (D-CTCs) and open timelike curves (OTCs) and find that existence of such physical systems in nature would allow us to increase entanglement using local operations and classical communication (LOCC). This is otherwise in direct contradiction with the fundamental definition of entanglement. We study this problem from the perspective of Bell state discrimination, and show how D-CTCs and OTCs can unambiguously distinguish between four Bell states with LOCC, that is otherwise known to be impossible.

On the origin of the coherence of sunlight on the earth.

It is shown that the observed far-field behavior of sunlight on the earth's surface, located in the near-field region, is due to the small angular width it subtends at the center of the sun. The investigation of the angular behavior of the cross-spectral density function explicitly leads to Bessel-like far-zone behavior for a small angle without any restriction on the value of l. Our general analysis for the spherical source can be easily extended to other geometries.

Detecting cervical cancer progression through extracted intrinsic fluorescence and principal component analysis.

Intrinsic fluorescence spectra of the human normal, cervical intraepithelial neoplasia 1 (CIN1), CIN2, and cervical cancer tissue have been extracted by effectively combining the measured polarized fluorescence and polarized elastic scattering spectra. The efficacy of principal component analysis (PCA) to disentangle the collective behavior from smaller correlated clusters in a dimensionally reduced space in conjunction with the intrinsic fluorescence is examined. This combination unambiguously reveals the biochemical changes occurring with the progression of the disease. The differing activities of the dominant fluorophores, collagen, nicotinamide adenine dinucleotide, flavins, and porphyrin of different grades of precancers are clearly identified through a careful examination of the sectorial behavior of the dominant eigenvectors of PCA. To further classify the different grades, the Mahalanobis distance has been calculated using the scores of selected principal components.

Non-stationary dynamics in the bouncing ball: a wavelet perspective.

The non-stationary dynamics of a bouncing ball, comprising both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signatures of self-similarity, complex scaling behavior, and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding to neutral turbulence, viscous dissipation regions, and different time varying periodic modulations.

Multi-scale dynamics of glow discharge plasma through wavelets: self-similar behavior to neutral turbulence and dissipation.

The multiscale dynamics of glow discharge plasma is analysed through wavelet transform, whose scale dependent variable window size aptly captures both transients and non-stationary periodic behavior. The optimal time-frequency localization ability of the continuous Morlet wavelet is found to identify the scale dependent periodic modulations efficiently, as also the emergence of neutral turbulence and dissipation, whereas the discrete Daubechies basis set has been used for detrending the temporal behavior to reveal the multi-fractality of the underlying dynamics. The scaling exponents and the Hurst exponent have been estimated through wavelet based detrended fluctuation analysis, and also Fourier methods and rescale range analysis.