Perturbations - PowerPoint PPT Presentation

Explore how to improve interpretability of AI models through generating counterfactual explanations with minimal perturbations and realistic, actionable suggestions. Addressing limitations of current methods and the need for a more flexible generative framework to ensure explanations are clear, conc

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Earth-GRAM is a global reference atmospheric model providing monthly mean and standard deviation data for various atmospheric variables. It is used in engineering for dispersion simulations but is not a forecasting model. Updates to Earth-GRAM include the Modern Era Retrospective Analysis, a global

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Network-based interpretation and integration play a crucial role in understanding genetic perturbations in biological systems. Perturbations in networks can affect nodes or edges, leading to valuable insights into gene function and phenotypic outcomes. Various algorithms, such as graph diffusion and

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Investigating f(R) gravity models by extending the Einstein-Hilbert action with an arbitrary function f(R). Conditions for viable models include positive gravitational constants, stable cosmological perturbations, asymptotic behavior towards the ΛCDM model, stability of late-time de Sitter point, a

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This research project conducted at Texas Tech University delves into the effects of chaos seeding within perturbation experiments on atmospheric conditions, with a focus on local and nonlocal modifications resulting from factors such as irrigation, wind farms, and urban development. By analyzing tin

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Explore quantum mechanics concepts related to atoms, energy levels, perturbations, transition probabilities, and emission rates. Understand the behavior of two-level systems under various perturbations and the dynamics of transitions in hydrogen atoms. Dive into calculations involving transition rat

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Deep Learning has shown great performance in various machine learning tasks, especially classification. However, adversarial samples can manipulate neural networks into misclassifying inputs, posing serious risks such as autonomous vehicle accidents. Distillation, a training technique, is proposed a

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Explore the vulnerability of machine learning models to adversarial examples, including speculative explanations and the importance of linear behavior. Learn about fast gradient sign methods, adversarial training of deep networks, and overcoming vulnerabilities. Discover how linear perturbations imp

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COTEKINO Priority Project involved developing and testing perturbation methodologies for convection-permitting ensembles across various operational weather services. Tasks included deriving perturbations, testing models, and coordinating with different agencies. The project aimed to enhance forecast

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Exploring axion cosmology with post-Newtonian corrections, this study delves into linear density perturbations for dust, the role of axion as a cold dark matter candidate, and fully nonlinear perturbation formulations. It addresses continuity, momentum conservation, and quantum stress to identify ke

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Create a model grid with 2D arrays for prognostic variables, set initial dry and adiabatic environmental conditions, introduce thermal and pressure perturbations, and visualize using GrADS. Define variables like velocities, perturbation temperature, and pressure fields, along with base state variabl

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Dive into the formalism and applications of Density Functional Theory (DFT) through a comprehensive review of basic theory, equations, and numerical implementations. Explore concepts like Time-Dependent DFT (TDDFT) and Linear Response formalism, and discover its applications in studying plasmons, co

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This study utilizes wavelet-based scaleograms and a convolutional neural network (CNN) for anomaly detection in nuclear reactors. By analyzing neutron flux signals from in-core and ex-core sensors, the proposed methodology aims to identify perturbations such as fuel assembly vibrations, synchronized

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Hybrid Variational/Ensemble Data Assimilation combines features from the Ensemble Kalman Filter and Variational assimilation methods to improve the NCEP Global Forecast System. It incorporates ensemble perturbations into the variational cost function, leading to more accurate forecasts. The approach

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This content delves into the concept of continuity analysis in programs, focusing on aspects like uncertainty, robustness, and infinitesimal changes in inputs and outputs. It explores the implications of handling uncertainty robustly, the significance of correctness in uncertain environments, and th

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Pretrained large-scale vision-language models like CLIP show strong generalization on unseen tasks but are vulnerable to imperceptible adversarial perturbations. This work delves into adapting these models for zero-shot transferability in adversarial robustness, even without specific training on unk

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The Ocean Surface Topography Science Team Meeting delved into the precise orbit determination of the Jason-3 satellite, focusing on miscentering observations and the benefits of yaw flips. The meeting discussed previous approaches, the context of the satellite's orientation, goals for monitoring per

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Explore the interconnected nature of Earth's atmosphere, hydrosphere, and biosphere through a study of global carbon cycling and the relationships between these systems. Gain insight into systems thinking, create diagrams illustrating system interactions, and learn how perturbations can impact Earth

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Hugues Goosse's Chapter 4 delves into the response of the climate system to perturbations, emphasizing radiative forcing, greenhouse gases, aerosols, and their impacts on Earth's radiative budget. The chapter intricately describes physical feedbacks, interactions involving energy balance, hydrologic

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Research conducted in the area of the Odessa magnetic anomaly investigates ionospheric perturbations caused by solar and geomagnetic activity. The study utilizes radio astronomical and magnetometric observations from various observatories, highlighting the significance of the geomagnetic anomaly nea

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Design and upgrade of wavefront sensors for diffraction-limited beamlines at various national laboratories, focusing on advanced light sources and soft X-ray applications. The sensors aim to monitor wavefront perturbations and optimize beam quality for enhanced performance. Various sensor designs an

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Explore the application of interpolants in nonlinear theories over the real numbers, delving into topics such as reasoning about continuous formulae, Craig interpolation, and branch-and-prune strategies. Discover how nonlinear theories can be both undecidable and decidable with perturbations, captur

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Delve into the world of microlensing planets exploration through hands-on sessions led by Jennifer Yee & Etienne Bachelet. The sessions aim to identify planetary perturbations in light curves, calculate microlensing parameters, perform numerical fits, and analyze the physical properties of planets.

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This study explores a method to defend against adversarial images by approximating their projection onto the image manifold through nearest-neighbor search. The approach involves finding the nearest neighbors in a web-scale image database to classify and mitigate the impact of adversarial perturbati

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The study discusses the evolution of perturbations and cosmological constraints in decaying dark matter models, focusing on the impact on ClTT, large-scale structure, and small-scale discrepancies. Mechanisms to address these issues, such as decaying dark matter, are explored in detail. The model co

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Inflation in the very early Universe and the goal to detect the large-scale B-mode pattern originating from primordial gravitational waves, a key signature of inflation. Single-field slow-roll inflation produces nearly scale-invariant perturbations. Explore the landscape of super-Planckian scenarios

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This study investigates the effects of Resonant Magnetic Perturbations (RMPs) on pressure-driven modes in the Large Helical Device (LHD) through three-dimensional equilibrium and dynamics analysis. The research explores coil configurations, equilibrium with RMP, and the impact of RMPs on pressure pr

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Adversarial examples in neural networks refer to inputs intentionally modified to cause misclassification. This phenomenon occurs due to the sensitivity of deep neural networks to perturbations, making them vulnerable to attacks. By understanding the generation and impact of adversarial examples, re

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