1,388 research outputs found
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
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