Origin of gamma-ray emission in the shell of Cassiopeia A

Non-thermal X-ray emission from the shell of Cassiopeia A (Cas A) has been an interesting subject of study, as it provides information about relativistic electrons and their acceleration mechanisms in the shocks. Chandra X-ray observatory revealed the detailed spectral and spatial structure of this SNR in X-rays. The spectral analysis of Chandra X-ray data of Cas A shows unequal flux levels for different regions of the shell, which can be attributed to different magnetic fields in those regions. Additionally, the GeV gamma-ray emission observed by Large Area Telescope on board Fermi Gamma Ray Space Telescope showed that the hadronic processes are dominating in Cas A, a clear signature of acceleration of protons. In this paper we aim to explain the GeV-TeV gamma-ray data in the context of both leptonic and hadronic scenario. We modeled the multi-wavelength spectrum of Cas A. We use synchrotron emission process to explain the observed non-thermal X-ray fluxes from different regions of the shell. These result in estimation of the model parameters, which are then used to explain TeV gamma-ray emission spectrum. We also use hadronic scenario to explain both GeV and TeV fluxes simultaneously. We show that a leptonic model alone cannot explain the GeV-TeV data. Therefore, we need to invoke a hadronic model to explain the observed GeV-TeV fluxes. We found that although pure hadronic model is able to explain the GeV-TeV data, a lepto-hadronic model provides the best fit to the data.Comment: Accepted in A&

Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry

The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks; however, its biological plausibility has been strongly criticized, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximization between layer activations as an alternative normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This new framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. The coordinate descent-based optimization of the corresponding objective, combined with the mean square error loss function for fitting labeled supervision data, gives rise to a neural network structure that emulates a more biologically realistic network of multi-compartment pyramidal neurons with dendritic processing and lateral inhibitory neurons. Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm. This is achieved by leveraging two alternative, yet equivalent forms of the correlative mutual information objective. These alternatives intrinsically lead to forward and backward prediction networks without weight symmetry issues, providing a compelling solution to this long-standing challenge

Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders

Extracting insight from the enormous quantity of data generated from molecular simulations requires the identification of a small number of collective variables whose corresponding low-dimensional free-energy landscape retains the essential features of the underlying system. Data-driven techniques provide a systematic route to constructing this landscape, without the need for extensive a priori intuition into the relevant driving forces. In particular, autoencoders are powerful tools for dimensionality reduction, as they naturally force an information bottleneck and, thereby, a low-dimensional embedding of the essential features. While variational autoencoders ensure continuity of the embedding by assuming a unimodal Gaussian prior, this is at odds with the multi-basin free-energy landscapes that typically arise from the identification of meaningful collective variables. In this work, we incorporate this physical intuition into the prior by employing a Gaussian mixture variational autoencoder (GMVAE), which encourages the separation of metastable states within the embedding. The GMVAE performs dimensionality reduction and clustering within a single unified framework, and is capable of identifying the inherent dimensionality of the input data, in terms of the number of Gaussians required to categorize the data. We illustrate our approach on two toy models, alanine dipeptide, and a challenging disordered peptide ensemble, demonstrating the enhanced clustering effect of the GMVAE prior compared to standard VAEs. The resulting embeddings appear to be promising representations for constructing Markov state models, highlighting the transferability of the dimensionality reduction from static equilibrium properties to dynamics

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

Extraction of latent sources of complex stimuli is critical for making sense of the world. While the brain solves this blind source separation (BSS) problem continuously, its algorithms remain unknown. Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistically independent or uncorrelated sources, limiting the domain of applicability of these algorithms. To overcome this limitation, we propose novel biologically-plausible neural networks for the blind separation of potentially dependent/correlated sources. Differing from previous work, we assume some general geometric, not statistical, conditions on the source vectors allowing separation of potentially dependent/correlated sources. Concretely, we assume that the source vectors are sufficiently scattered in their domains which can be described by certain polytopes. Then, we consider recovery of these sources by the Det-Max criterion, which maximizes the determinant of the output correlation matrix to enforce a similar spread for the source estimates. Starting from this normative principle, and using a weighted similarity matching approach that enables arbitrary linear transformations adaptable by local learning rules, we derive two-layer biologically-plausible neural network algorithms that can separate mixtures into sources coming from a variety of source domains. We demonstrate that our algorithms outperform other biologically-plausible BSS algorithms on correlated source separation problems.Comment: NeurIPS 2022, 37 page

Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation

The brain effortlessly extracts latent causes of stimuli, but how it does this at the network level remains unknown. Most prior attempts at this problem proposed neural networks that implement independent component analysis which works under the limitation that latent causes are mutually independent. Here, we relax this limitation and propose a biologically plausible neural network that extracts correlated latent sources by exploiting information about their domains. To derive this network, we choose maximum correlative information transfer from inputs to outputs as the separation objective under the constraint that the outputs are restricted to their presumed sets. The online formulation of this optimization problem naturally leads to neural networks with local learning rules. Our framework incorporates infinitely many source domain choices and flexibly models complex latent structures. Choices of simplex or polytopic source domains result in networks with piecewise-linear activation functions. We provide numerical examples to demonstrate the superior correlated source separation capability for both synthetic and natural sources.Comment: Preprint, 32 page

Photonuclear reactions with Zinc: A case for clinical linacs

The use of bremsstrahlung photons produced by a linac to induce photonuclear reactions is wide spread. However, using a clinical linac to produce the photons is a new concept. We aimed to induce photonuclear reactions on zinc isotopes and measure the subsequent transition energies and half-lives. For this purpose, a bremsstrahlung photon beam of 18 MeV endpoint energy produced by the Philips SLI-25 linac has been used. The subsequent decay has been measured with a well-shielded single HPGe detector. The results obtained for transition energies are in good agreement with the literature data and in many cases surpass these in accuracy. For the half-lives, we are in agreement with the literature data, but do not achieve their precision. The obtained accuracy for the transition energies show what is achievable in an experiment such as ours. We demonstrate the usefulness and benefits of employing clinical linacs for nuclear physics experiments

Complications in children with ventricular assist devices: systematic review and meta-analyses

Heart failure is a significant cause of mortality in children with cardiovascular diseases. Treatment of heart failure depends on patients’ symptoms, age, and severity of their condition, with heart transplantation required when other treatments are unsuccessful. However, due to lack of fitting donor organs, many patients are left untreated, or their transplant is delayed. In these patients, ventricular assist devices (VADs) are used to bridge to heart transplant. However, VAD support presents various complications in patients. The aim of this study was to compile, review, and analyse the studies reporting risk factors and aetiologies of complications of VAD support in children. Random effect risk ratios (RR) with 95% confidence intervals were calculated to analyse relative risk of thrombosis (RR = 3.53 [1.04, 12.06] I2 = 0% P = 0.04), neurological problems (RR = 0.95 [0.29, 3.15] I2 = 53% P = 0.93), infection (RR = 0.31 [0.05, 2.03] I2 = 86% P = 0.22), bleeding (RR = 2.57 [0.76, 8.66] I2 = 0% P = 0.13), and mortality (RR = 2.20 [1.36, 3.55] I2 = 0% P = 0.001) under pulsatile-flow and continuous-flow VAD support, relative risk of mortality (RR = 0.45 [0.15, 1.37] I2 = 36% P = 0.16) under left VAD and biVAD support, relative risk of thrombosis (RR = 1.72 [0.46, 6.44] I2 = 0% P = 0.42), infection (RR = 1.77 [0.10, 32.24] I2 = 46% P = 0.70) and mortality (RR = 0.92 [0.14, 6.28] I2 = 45% P = 0.93) in children with body surface area  1.2 m2 under VAD support, relative risk of mortality in children supported with VAD and diagnosed with cardiomyopathy and congenital heart diseases (RR = 1.31 [0.10, 16.61] I2 = 73% P = 0.84), and cardiomyopathy and myocarditis (RR = 0.91 [0.13, 6.24] I2 = 58% P = 0.92). Meta-analyses results show that further research is necessary to reduce complications under VAD support

Theory and analysis of electrode size optimization for capacitive microfabricated ultrasonic transducers

Cataloged from PDF version of article.Theoretical analysis and computer simulations of capacitive microfabricated ultrasonic transducers indicate that device performance can be optimized through judicious patterning of electrodes. The conceptual basis of the analysis is that electrostatic force should be applied only where it is most effective, such as at the center of a circular membrane. If breakdown mechanisms are ignored, an infinitesimally small electrode with an infinite bias voltage results in the optimal transducer, A more realistic design example compares the 3-dB bandwidths of a fully metalized transducer and a partially metalized transducer, each tuned with a lossless Butterworth network. It is found that the bandwidth of the optimally metalized device is twice that of the fully metalized device