Hydration influences on the phase heterogeneity of segmented copolymers

In electron microscope studies of crystalline solids there is a broad class of very small objects which are imaged primarily by strain contrast. Typical examples include: dislocation loops, precipitates, stacking fault tetrahedra and voids. Such objects are very difficult to identify and measure because of the sensitivity of their image to a host of variables and a similarity in their images. A number of attempts have been made to publish contrast rules to help the microscopist sort out certain subclasses of such defects. For example, Ashby and Brown (1963) described semi-quantitative rules to understand small precipitates. Eyre et al. (1979) published a catalog of images for BCC dislocation loops. Katerbau (1976) described an analytical expression to help understand contrast from small defects. There are other publications as well.

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Similarity problems of the theory of unsteady concentration boundary layer

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19832751 ◽

1983 ◽

Vol 48 (10) ◽

pp. 2751-2766

Author(s):

Ondřej Wein ◽

N. D. Kovalevskaya

Keyword(s):

Boundary Layer ◽

Approximate Method ◽

Steady Flow ◽

Broad Class ◽

Concentration Boundary Layer ◽

Concentration Change ◽

Concentration Boundary ◽

Flow Problems

Using a new approximate method, transient course of the local and mean diffusion fluxes following a step concentration change on the wall has been obtained for a broad class of steady flow problems.

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Disabilities and Well-Being

The Oxford Handbook of Philosophy and Disability ◽

10.1093/oxfordhb/9780190622879.013.11 ◽

2019 ◽

pp. 121-137

Author(s):

Joshua Shepherd

Keyword(s):

Broad Class ◽

Minimally Conscious State ◽

Well Being ◽

Conscious State ◽

Minimally Conscious

This chapter argues for a normative distinction between disabilities that are inherently negative with respect to well-being and disabilities that are inherently neutral. After clarifying terms, the author discusses recent arguments according to which possession of a disability is inherently neutral with respect to well-being. He notes that although these arguments are compelling, they are only intended to cover certain disabilities and, in fact, that there exists a broad class regarding which they do not apply. He then discusses two problem cases: locked-in syndrome and the minimally conscious state, and explains why these are cases in which possession of these disabilities makes one worse off overall. He argues that disabilities that significantly impair control over one’s situation tend to be inherently negative with respect to well-being; other disabilities do not. The upshot is that we must draw an important normative distinction between disabilities that undermine this kind of control and disabilities that do not.

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Nearly Exact Bayesian Estimation of Non-linear No-Arbitrage Term-Structure Models*

Journal of Financial Econometrics ◽

10.1093/jjfinec/nbaa037 ◽

2021 ◽

Author(s):

Marcello Pericoli ◽

Marco Taboga

Keyword(s):

Bayesian Estimation ◽

Term Structure ◽

Broad Class ◽

Model Parameters ◽

State Variables ◽

Computationally Efficient ◽

Macroeconomic Factors ◽

No Arbitrage ◽

Term Structure Models ◽

Non Linear

Abstract We propose a general method for the Bayesian estimation of a very broad class of non-linear no-arbitrage term-structure models. The main innovation we introduce is a computationally efficient method, based on deep learning techniques, for approximating no-arbitrage model-implied bond yields to any desired degree of accuracy. Once the pricing function is approximated, the posterior distribution of model parameters and unobservable state variables can be estimated by standard Markov Chain Monte Carlo methods. As an illustrative example, we apply the proposed techniques to the estimation of a shadow-rate model with a time-varying lower bound and unspanned macroeconomic factors.

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Intra-phase heterogeneity of stresses in micro-macro models: link with X-ray diffraction measurements

Revue de Métallurgie ◽

10.1051/metal:2003107 ◽

2003 ◽

Vol 100 (12) ◽

pp. 1173-1177 ◽

Cited By ~ 1

Author(s):

R. Brenner ◽

N. Letouzé ◽

J. L. Béchade ◽

O. Castelnau

Keyword(s):

X Ray Diffraction ◽

X Ray ◽

Phase Heterogeneity

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Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽

10.3390/e23010126 ◽

2021 ◽

Vol 23 (1) ◽

pp. 126

Author(s):

Sharu Theresa Jose ◽

Osvaldo Simeone

Keyword(s):

Learning Algorithm ◽

Broad Class ◽

Performance Measure ◽

Training Data ◽

Learning To Learn ◽

Data Set ◽

Information Theoretic ◽

Meta Learning ◽

Task Training ◽

Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

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Searching for elusive dark sectors with terrestrial and celestial observations

Journal of High Energy Physics ◽

10.1007/jhep06(2021)127 ◽

2021 ◽

Vol 2021 (6) ◽

Author(s):

Roberto Contino ◽

Kevin Max ◽

Rashmish K. Mishra

Keyword(s):

Field Theory ◽

Broad Class ◽

High Energy ◽

Effective Field ◽

Dark Sector ◽

Minimum Requirement ◽

Conformal Field ◽

Starting Point ◽

The Standard Model ◽

Visible Sector

Abstract We consider the possible existence of a SM-neutral and light dark sector coupled to the visible sector through irrelevant portal interactions. Scenarios of this kind are motivated by dark matter and arise in various extensions of the Standard Model. We characterize the dark dynamics in terms of one ultraviolet scale Λuv, at which the exchange of heavy mediator fields generates the portal operators, and by one infrared scale ΛIR, setting the mass gap. At energies ΛIR « E « Λuv the dark sector behaves like a conformal field theory and its phenomenology can be studied model independently. We derive the constraints set on this scenario by high- and low-energy laboratory experiments and by astrophysical observations. Our results are conservative and serve as a minimum requirement that must be fulfilled by the broad class of models satisfying our assumptions, of which we give several examples. The experimental constraints are derived in a manner consistent with the validity of the effective field theory used to define the portal interactions. We find that high-energy colliders give the strongest bounds and exclude UV scales up to a few TeV, but only in specific ranges of the IR scale. The picture emerging from current searches can be taken as a starting point to design a future experimental strategy with broader sensitivity.

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Using Sibling Designs to Understand Neurodevelopmental Disorders: From Genes and Environments to Prevention Programming

BioMed Research International ◽

10.1155/2015/672784 ◽

2015 ◽

Vol 2015 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Mark Wade ◽

Heather Prime ◽

Sheri Madigan

Keyword(s):

Neurodevelopmental Disorders ◽

Broad Class ◽

Psychosocial Health ◽

Recurrence Risk ◽

Autism Spectrum ◽

Functional Neuroanatomy ◽

Early Screening ◽

Environmental Liabilities ◽

Hyperactivity Disorder ◽

Significant Bearing

Neurodevelopmental disorders represent a broad class of childhood neurological conditions that have a significant bearing on the wellbeing of children, families, and communities. In this review, we draw on evidence from two common and widely studied neurodevelopmental disorders—autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD)—to demonstrate the utility of genetically informed sibling designs in uncovering the nature and pathogenesis of these conditions. Specifically, we examine how twin, recurrence risk, and infant prospective tracking studies have contributed to our understanding of genetic and environmental liabilities towards neurodevelopmental morbidity through their impact on neurocognitive processes and structural/functional neuroanatomy. It is suggested that the siblings of children with ASD and ADHD are at risk not only of clinically elevated problems in these areas, but also of subthreshold symptoms and/or subtle impairments in various neurocognitive skills and other domains of psychosocial health. Finally, we close with a discussion on the practical relevance of sibling designs and how these might be used in the service of early screening, prevention, and intervention efforts that aim to alleviate the negative downstream consequences associated with disorders of neurodevelopment.

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Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

Machine Learning ◽

10.1007/s10994-021-06006-6 ◽

2021 ◽

Author(s):

Srivatsan Krishnan ◽

Behzad Boroujerdian ◽

William Fu ◽

Aleksandra Faust ◽

Vijay Janapa Reddi

Keyword(s):

Reinforcement Learning ◽

Embedded System ◽

Broad Class ◽

Visual Navigation ◽

Raspberry Pi ◽

Latency Distribution ◽

Hardware In The Loop ◽

Resource Constrained ◽

Aerial Robot ◽

Policy Optimization

AbstractWe introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies’ performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $$40\%$$ 40 % longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute’s choice affects the aerial robot’s performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at: https://github.com/harvard-edge/AirLearning.

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