30 research outputs found
General relativistic calculation of magnetic field and Power loss for a misaligned pulsar
In this study, we model a pulsar as a general relativistic oblique rotator,
where the oblique rotator is a rotationally deformed neutron star whose
rotation and magnetic axis are inclined at an angle. The oblique rotator spins
down, losing rotational energy through the magnetic poles. The magnetic field
is assumed to be dipolar; however, the star has a non-zero azimuthal component
due to the misalignment. The magnetic field induces an electric field for a
force-free condition. The magnetic field decreases as the misalignment
increases and is minimum along the equatorial plane of the star. In contrast,
the electric field remains almost constant initially but decreases rapidly at a
high misalignment angle. The charge separation at the star surface is
qualitatively similar to that of Newtonian calculation. We find that the power
loss for a general relativistic rotator is minimum for either an aligned or an
orthogonal rotator, which contrasts with Newtonian calculation, where the power
loss increases with an increase in the misalignment angle.Comment: 19 page, 10 figure
Quantum Solutions to the Privacy vs. Utility Tradeoff
In this work, we propose a novel architecture (and several variants thereof)
based on quantum cryptographic primitives with provable privacy and security
guarantees regarding membership inference attacks on generative models. Our
architecture can be used on top of any existing classical or quantum generative
models. We argue that the use of quantum gates associated with unitary
operators provides inherent advantages compared to standard Differential
Privacy based techniques for establishing guaranteed security from all
polynomial-time adversaries
Universal Relations For Generic Family Of Neutron Star Equations Of State
Universal relations are important in testing many theories of physics. In the
case of general relativity, we have the celebrated no-hair theorem for black
holes. Unfortunately, the other compact stars, like neutron stars and white
dwarfs, do not have such universal relation. However, neutron stars (and quark
stars) have recently been found to follow certain universality, the I-Love-Q
relations. These relations can provide a greater understanding of the
structural and macro properties of compact astrophysical objects with knowledge
of any one of the observables. The reason behind this is the lack of
sensitivity to the relations with the equation of state of matter. In our
present work, we have investigated the consistency of universal relations for a
generic family of equations of state, which follows all the recent
astrophysical constraints. Although the spread in the EoS is significant the
universal nature of the trio holds relatively well up to a certain tolerance
limit. The deviation from universality is seen to cross the tolerance limit
with EoS, which is characteristically different from the original set.Comment: 8 pages, 14 figure
Unveiling extra dimensions through the shadow of a dark star
The shadow of a black hole or a collapsing star is of great importance as we
can extract important properties of the object and of the surrounding spacetime
from the shadow profile. It can also be used to distinguish different types of
black holes and ultra compact objects. In this work, we have analytically
calculated the shadow of a higher dimensional collapsing dark star, described
by higher dimensional Vaidya metric, by choosing a slightly generalized version
of Misner Sharp mass function. We have also numerically investigated the
properties of the shadows of the black holes and the collapsing stars for a
slightly more general mass function. Examining the potential influence of extra
spatial dimensions on the shadow, we have explored the possibility of
distinguishing higher dimensions from the standard four dimensional spacetime.Comment: 15 pages, 4 figure
Clustering Network Vertices in Sparse Contextual Multilayer Networks
We consider the problem of learning the latent community structure in a
Multi-Layer Contextual Block Model introduced by Ma and Nandy (2021), where the
average degree for each of the observed networks is of constant order and
establish a sharp detection threshold for the community structure, above which
detection is possible asymptotically, while below the threshold no procedure
can perform better than random guessing. We further establish that the
detection threshold coincides with the threshold for weak recovery of the
common community structure using multiple correlated networks and co-variate
matrices. Finally, we provide a quasi-polynomial time algorithm to estimate the
latent communities in the recovery regime. Our results improve upon the results
of Ma and Nandy (2021), which considered the diverging degree regime and
recovers the results of Lu and Sen (2020) in the special case of a single
network structure
Quantum Boosting using Domain-Partitioning Hypotheses
Boosting is an ensemble learning method that converts a weak learner into a
strong learner in the PAC learning framework. Freund and Schapire gave the
first classical boosting algorithm for binary hypothesis known as AdaBoost, and
this was recently adapted into a quantum boosting algorithm by Arunachalam et
al. Their quantum boosting algorithm (which we refer to as Q-AdaBoost) is
quadratically faster than the classical version in terms of the VC-dimension of
the hypothesis class of the weak learner but polynomially worse in the bias of
the weak learner.
In this work we design a different quantum boosting algorithm that uses
domain partitioning hypotheses that are significantly more flexible than those
used in prior quantum boosting algorithms in terms of margin calculations. Our
algorithm Q-RealBoost is inspired by the "Real AdaBoost" (aka. RealBoost)
extension to the original AdaBoost algorithm. Further, we show that Q-RealBoost
provides a polynomial speedup over Q-AdaBoost in terms of both the bias of the
weak learner and the time taken by the weak learner to learn the target concept
class.Comment: 24 pages, 3 figures, 1 tabl
Computational materials discovery and development for Li and non-Li advanced battery chemistries
Since the discovery of batteries in the 1800s, their fascinating physical and chemical properties have led to much research on their synthesis and manufacturing. Though lithium-ion batteries have been crucial for civilization, they can still not meet all the growing demands for energy storage because of the geographical distribution of lithium resources and the intrinsic limitations in the cell energy density, performance, and reliability issues. As a result, non-Li-ion batteries are becoming increasingly popular alternatives. Designing novel materials with desired properties is crucial for a quicker transition to the green energy ecosystem. Na, K, Mg, Zn, Al ion, etc. batteries are considered the most alluring and promising. This article covers all these Li, non-Li, and metal-air cell chemistries. Recently, computational screening has proven to be an effective tool to accelerate the discovery of active materials for all these cell types. First-principles methods such as density functional theory, molecular dynamics, and Monte Carlo simulations have become established techniques for the preliminary, theoretical analysis of battery systems. These computational methods generate a wealth of data that might be immensely useful in the training and validating of artificial intelligence and machine learning techniques to reduce the time and capital expenditure needed for discovering advanced materials and final product development. This review aims to summarize the application of these techniques and the recent developments in computational methods to discover and develop advanced battery chemistries
Mapping 123 million neonatal, infant and child deaths between 2000 and 2017
Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations
Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017
A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic
Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019
Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd