Wigner distribution transformations in high-order systems

By combining the definition of the Wigner distribution function (WDF) and the matrix method of optical system modeling, we can evaluate the transformation of the former in centered systems with great complexity. The effect of stops and lens diameter are also considered and are shown to be responsible for non-linear clipping of the resulting WDF in the case of coherent illumination and non-linear modulation of the WDF when the illumination is incoherent. As an example, the study of a single lens imaging systems illustrates the applicability of the method.Comment: 16 pages, 7 figures. To appear in J. of Comp. and Appl. Mat

Programming matrix optics into Mathematica

The various non-linear transformations incurred by the rays in an optical system can be modelled by matrix products up to any desired order of approximation. Mathematica software has been used to find the appropriate matrix coefficients for the straight path transformation and for the transformations induced by conical surfaces, both direction change and position offset. The same software package was programmed to model optical systems in seventh-order. A Petzval lens was used to exemplify the modelling power of the program.Comment: 15 page

Decision trees and forests: a probabilistic perspective

Decision trees and ensembles of decision trees are very popular in machine learning and often achieve state-of-the-art performance on black-box prediction tasks. However, popular variants such as C4.5, CART, boosted trees and random forests lack a probabilistic interpretation since they usually just specify an algorithm for training a model. We take a probabilistic approach where we cast the decision tree structures and the parameters associated with the nodes of a decision tree as a probabilistic model; given labeled examples, we can train the probabilistic model using a variety of approaches (Bayesian learning, maximum likelihood, etc). The probabilistic approach allows us to encode prior assumptions about tree structures and share statistical strength between node parameters; furthermore, it offers a principled mechanism to obtain probabilistic predictions which is crucial for applications where uncertainty quantification is important. Existing work on Bayesian decision trees relies on Markov chain Monte Carlo which can be computationally slow and suffer from poor mixing. We propose a novel sequential Monte Carlo algorithm that computes a particle approximation to the posterior over trees in a top-down fashion. We also propose a novel sampler for Bayesian additive regression trees by combining the above top-down particle filtering algorithm with the Particle Gibbs (Andrieu et al., 2010) framework. Finally, we propose Mondrian forests (MFs), a computationally efficient hybrid solution that is competitive with non-probabilistic counterparts in terms of speed and accuracy, but additionally produces well-calibrated uncertainty estimates. MFs use the Mondrian process (Roy and Teh, 2009) as the randomization mechanism and hierarchically smooth the node parameters within each tree (using a hierarchical probabilistic model and approximate Bayesian updates), but combine the trees in a non-Bayesian fashion. MFs can be grown in an incremental/online fashion and remarkably, the distribution of online MFs is the same as that of batch MFs

Non-negative matrix factorization for parameter estimation in hidden Markov models

Hidden Markov models are well-known in analysis of random processes, which exhibit temporal or spatial structure and have been successfully applied to a wide variety of applications such as but not limited to speech recognition, musical scores, handwriting, and bio-informatics. We present a novel algorithm for estimating the parameters of a hidden Markov model through the application of a non-negative matrix factorization to the joint probability distribution of two consecutive observations. We start with the discrete observation model and extend the results to the continuous observation model through a non-parametric approach of kernel density estimation. For both the cases, we present results on a toy example and compare the performance with the Baum-Welch algorithm. ©2010 IEEE

A Syllable-Level Probabilistic Framework for Bird Species Identification

In this paper, we present new probabilistic models for identifying bird species from audio recordings. We introduce the independent syllable model and consider two ways of aggregating frame level features within a syllable. We characterize each syllable as a probability distribution of its frame level features. The independent frame independent syllable (IFIS) model allows us to distinguish syllables whose feature distributions are different from one another. The Markov chain frame independent syllable (MCFIS) model is introduced for scenarios where the temporal structure within the syllable provides significant amount of discriminative information. We derive the Bayes risk minimizing classifier for each model and show that it can be approximated as a nearest neighbour classifier. Our experiments indicate that the IFIS and MCFIS models achieve 88.26% and 90.61% correct classification rates, respectively, while the equivalent SVM implementation achieves 86.15%. © 2009 IEEE

Wide angle near-field diffraction and Wigner distribution

Free-space propagation can be described as a shearing of the Wigner distribution function in the spatial coordinate; this shearing is linear in paraxial approximation but assumes a more complex shape for wide-angle propagation. Integration in the frequency domain allows the determination of near-field diffraction, leading to the well known Fresnel diffraction when small angles are considered and allowing exact prediction of wide-angle diffraction. The authors use this technique to demonstrate evanescent wave formation and diffraction elimination for very small apertures

Repurposing cancer drugs, batimastat and marimastat, to inhibit the activity of a group I metalloprotease from the venom of the Western Diamondback rattlesnake, Crotalus atrox

Snakebite envenomation causes over 140,000 deaths every year predominantly in developing countries. As a result, it is one of the most lethal neglected tropical diseases. It is associated with an incredibly complex pathophysiology due to the vast number of unique toxins/proteins found in the venoms of diverse snake species found worldwide. Here, we report the purification and functional characteristics of a group I metalloprotease (CAMP-2) from the venom of the western diamondback rattlesnake, Crotalus atrox. Its sensitivity to matrix metalloprotease inhibitors (batimastat and marimastat) was established using specific in vitro experiments and in silico molecular docking analysis. CAMP-2 shows high sequence homology to atroxase from the venom of Crotalus atrox and exhibits collagenolytic, fibrinogenolytic and mild haemolytic activities. It exerts a mild inhibitory effect on agonist-induced platelet aggregation in the absence of plasma proteins. Its collagenolytic activity was completely inhibited by batimastat and marimastat. Zinc chloride also inhibits the collagenolytic activity of CAMP-2 by around 75% at 50 M, while it is partially potentiated by calcium chloride. Molecular docking studies demonstrate that batimastat and marimastat are able to bind strongly to the active site residues of CAMP-2. This study demonstrates the impact of matrix metalloprotease inhibitors in the modulation of a purified, group I metalloprotease activities in comparison to the whole venom. By improving our understanding of snake venom metalloproteases and their sensitivity to small molecule inhibitors, we can begin to develop novel and improved treatment strategies for snakebites

Validating secure and reliable IP/MPLS communications for current differential protection

Current differential protection has stringent real-time communications requirements and it is critical that protection traffic is transmitted securely, i.e., by using appropriate data authentication and encryption methods. This paper demonstrates that real-time encryption of protection traffic in IP/MPLS-based communications networks is possible with negligible impact on performance and system operation. It is also shown how the impact of jitter and asymmetrical delay in real communications networks can be eliminated. These results will provide confidence to power utilities that modern IP/MPLS infrastructure can securely and reliably cater for even the most demanding applications

AFMB-Net: DeepFake Detection Network Using Heart Rate Analysis

With advances in deepfake generating technology, it is getting increasingly difficult to detect deepfakes. Deepfakes can be used for many malpractices such as blackmail, politics, social media, etc. These can lead to widespread misinformation and can be harmful to an individual or an institution’s reputation. It has become important to be able to identify deepfakes effectively, while there exist many machine learning techniques to identify them, these methods are not able to cope up with the rapidly improving GAN technology which is used to generate deepfakes. Our project aims to identify deepfakes successfully using machine learning along with Heart Rate Analysis. The heart rate identified by our model is unique to each individual and cannot be spoofed or imitated by a GAN and is thus susceptible to improving GAN technology. To solve the deepfake detection problem we employ various machine learning models along with heart rate analysis to detect deepfakes

Impact of COVID-19 pandemic on postpartum contraception services in women delivering at a tertiary care centre in South India

Background: Contraception and sexual health are a fundamental human right and an integral part of women’s health services. Postpartum period is the unique opportunity for counselling and availing contraception. COVID-19 pandemic hindered routine and special services like postpartum clinics has negative impact on family planning services. Objectives were to assess the effect of the COVID-19 pandemic on postpartum contraceptive care services received and to describe the challenges faced in availing these services among pregnant women delivering at a tertiary care centre in South India.Methods: A descriptive study carried out in 422 women who had delivered at our hospital from July 2020 to October 2020. Data was collected in Epicollect version 5 and analysed using Stata version 14.2.Results: A total of 422 women were interviewed. Only one-third of the participants received contraceptive counselling in the antenatal period compared to 90% postpartum. 39% (primiparous-34%/multiparous-5%) had adopted barrier methods followed by post-partum intrauterine uterine contraceptive devices in 33% (primiparous-19.6%/multiparous-13.4%) and 5% had sterilisation concurrent with caesarean section. Around 30-40% of women faced challenges in accessing the family planning methods due to closure of elective services like postpartum clinics, operation theatres, nationwide lockdown, and non-availability of field health workers.Conclusions: Contraceptive choices for postpartum women appear to be largely restricted to temporary methods with additional challenges of availing these services during the pandemic. With the ongoing COVID-19 crisis and continuous need for contraception, there is a need to refocus and motivate eligible couples for long-acting reversible contraceptive methods (LARC) with significantly lower failure rates