Analysis of a Broad Class of Earth-Mars Cycler Trajectories

2002 ◽  
Author(s):  
Troy McConaghy ◽  
James Longuski ◽  
Dennis Byrnes
Keyword(s):  
Broad Class ◽  
Cycler Trajectories

Contrast From Point Defects in The Infinitesimal Approximation

1980 ◽  
Vol 38 ◽  
pp. 350-351
Author(s):  
L. J. Sykes ◽  
J. J. Hren
Keyword(s):  
Electron Microscope ◽  
Point Defects ◽  
Broad Class ◽  
Stacking Fault ◽  
Crystalline Solids ◽  
Dislocation Loops ◽  
Analytical Expression ◽  
Stacking Fault Tetrahedra

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.

Similarity problems of the theory of unsteady concentration boundary layer

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.

Disabilities and Well-Being

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.

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

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.

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
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.

scholarly journals Searching for elusive dark sectors with terrestrial and celestial observations

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.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
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.

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

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.

scholarly journals Effects of mechanical interventions in the management of knee osteoarthritis: protocol for an OA Trial Bank systematic review and individual participant data meta-analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e043026
Author(s):  
Erin M Macri ◽  
Michael Callaghan ◽  
Marienke van Middelkoop ◽  
Miriam Hattle ◽  
Sita M A Bierma-Zeinstra
Keyword(s):  
Systematic Review ◽  
Knee Osteoarthritis ◽  
Broad Class ◽  
Meta Analysis ◽  
Individual Participant Data ◽  
Knee Oa ◽  
International Regulations ◽  
Individual Participant ◽  
Registration Number ◽  
Mechanical Intervention

IntroductionKnee osteoarthritis (OA) is a prevalent and disabling musculoskeletal condition. Biomechanical factors may play a key role in the aetiology of knee OA, therefore, a broad class of interventions involves the application or wear of devices designed to mechanically support knees with OA. These include gait aids, bracing, taping, orthotics and footwear. The literature regarding efficacy of mechanical interventions has been conflicting or inconclusive, and this may be because certain subgroups with knee OA respond better to mechanical interventions. Our primary aim is to identify subgroups with knee OA who respond favourably to mechanical interventions.Methods and analysisWe will conduct a systematic review to identify randomised clinical trials of any mechanical intervention for the treatment of knee OA. We will invite lead authors of eligible studies to share individual participant data (IPD). We will perform an IPD meta-analysis for each type of mechanical intervention to evaluate efficacy, with our main outcome being pain. Where IPD are not available, this will be achieved using aggregate data. We will then evaluate five potential treatment effect modifiers using a two-stage approach. If data permit, we will also evaluate whether biomechanics mediate the effects of mechanical interventions on pain in knee OA.Ethics and disseminationNo new data will be collected in this study. We will adhere to institutional, national and international regulations regarding the secure and confidential sharing of IPD, addressing ethics as indicated. We will disseminate findings via international conferences, open-source publication in peer-reviewed journals and summaries posted on websites serving the public and clinicians.PROSPERO registration numberCRD42020155466.

scholarly journals Valproic acid Suppresses Breast Cancer Cell Growth Through Triggering Pyruvate Kinase M2 Isoform Mediated Warburg Effect

2021 ◽  
Vol 30 ◽  
pp. 096368972110275
Author(s):  
Zhen Li ◽  
Lina Yang ◽  
Shuai Zhang ◽  
Jiaqi Song ◽  
Huanran Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Valproic Acid ◽  
Cancer Therapy ◽  
Pyruvate Kinase ◽  
Histone Deacetylases ◽  
Warburg Effect ◽  
Broad Class ◽  
Pyruvate Kinase M2 ◽  
Breast Cancer Cell Growth ◽  
The Warburg Effect

Energy metabolism programming is a hallmark of cancer, and serves as a potent target of cancer therapy. Valproic acid (VPA), a broad Class I histone deacetylases (HDACs) inhibitor, has been used as a therapeutic agent for cancer. However, the detail mechanism about the potential role of VPA on the Warburg effect in breast cancer remains unclear. In this study, we highlight that VPA significantly attenuates the Warburg effect by decreasing the expression of pyruvate kinase M2 isoform (PKM2), leading to inhibited cell proliferation and reduced colony formation in breast cancer MCF-7 and MDA-MB-231 cells. Mechanistically, Warburg effect suppression triggered by VPA was mediated by inactivation of ERK1/2 phosphorylation through reduced HDAC1 expression, resulting in suppressing breast cancer growth. In summary, we uncover a novel mechanism of VPA in regulating the Warburg effect which is essential for developing the effective approach in breast cancer therapy.

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