- Hiroaki Shiokawa's research interests include graph mining, network analysis, and efficient algorithms. He was previously employed at NTT from 2011 to 2015.
- His current research focuses on developing clustering algorithms for large-scale networks and evaluating their performance on real-world network datasets.
- He has published highly cited papers in top data mining and network science conferences such as KDD, CIKM, and WSDM.
This document presents an overview of optimization algorithms on Riemannian manifolds. It begins by introducing concepts such as vector transport and retraction mappings that are used to generalize algorithms from Euclidean spaces to manifolds. It then summarizes several classical optimization methods including gradient descent, conjugate gradient, and variants of quasi-Newton methods adapted to the Riemannian setting using these geometric concepts. The convergence of the Fletcher-Reeves method is analyzed under standard assumptions on the objective function. Overall, the document provides a conceptual and mathematical foundation for optimization on manifolds.
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
1) Start with a small learning rate and large batch size to find a flat minimum with good generalization. 2) Gradually increase the learning rate and decrease the batch size to find sharper minima that may improve training accuracy. 3) Monitor both training and validation/test accuracy - similar accuracy suggests good generalization while different accuracy indicates overfitting.
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
The document discusses modeling stochastic gradient descent (SGD) using stochastic differential equations (SDEs). It outlines SGD, random walks, Wiener processes, and SDEs. It then covers continuous-time SGD and controlled SGD, modeling SGD as an SDE. It provides an example of modeling quadratic loss functions with SGD as an SDE. Finally, it discusses the effects of learning rate and batch size on generalization when modeling SGD as an SDE.
Model Based Fault Detection, Identification and Accommodation in Antilock Bra...Behzad Samadi
1) The document presents a model-based fault detection and identification approach for antilock braking systems (ABS). It develops nonlinear dynamic models for vehicle motion and tire forces.
2) Kalman filtering techniques are used to estimate vehicle states and tire forces based on sensor measurements, and residuals between measurements and estimates are monitored to detect and identify faults.
3) The approach extends previous work by including additional tire force states and their dynamics in the estimation model to improve fault detection performance.
- The document discusses interval valued intuitionistic fuzzy subrings of a ring. It begins with definitions of interval valued fuzzy sets, interval valued fuzzy subrings, and interval valued intuitionistic fuzzy subsets.
- It then defines an interval valued intuitionistic fuzzy subring and establishes some properties, including that the intersection of two interval valued intuitionistic fuzzy subrings is also an interval valued intuitionistic fuzzy subring, and the intersection of a family of such subrings is also a subring.
- Proofs are provided to show that the intersection operations preserve the necessary conditions to be considered an interval valued intuitionistic fuzzy subring.
This document discusses several semi-supervised deep generative models for multimodal data, including the Semi-Supervised Multimodal Variational AutoEncoder (SS-MVAE), Semi-Supervised Hierarchical Multimodal Variational AutoEncoder (SS-HMVAE), and their training procedures. The SS-MVAE extends the Joint Multimodal Variational Autoencoder (JMVAE) to semi-supervised learning. The SS-HMVAE introduces auxiliary variables to model dependencies between modalities more flexibly. Both models maximize a variational lower bound with supervised and unsupervised objectives. The document provides technical details of the generative processes, variational approximations, and optimization of these semi-supervised deep generative models.
- Hiroaki Shiokawa's research interests include graph mining, network analysis, and efficient algorithms. He was previously employed at NTT from 2011 to 2015.
- His current research focuses on developing clustering algorithms for large-scale networks and evaluating their performance on real-world network datasets.
- He has published highly cited papers in top data mining and network science conferences such as KDD, CIKM, and WSDM.
This document presents an overview of optimization algorithms on Riemannian manifolds. It begins by introducing concepts such as vector transport and retraction mappings that are used to generalize algorithms from Euclidean spaces to manifolds. It then summarizes several classical optimization methods including gradient descent, conjugate gradient, and variants of quasi-Newton methods adapted to the Riemannian setting using these geometric concepts. The convergence of the Fletcher-Reeves method is analyzed under standard assumptions on the objective function. Overall, the document provides a conceptual and mathematical foundation for optimization on manifolds.
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
1) Start with a small learning rate and large batch size to find a flat minimum with good generalization. 2) Gradually increase the learning rate and decrease the batch size to find sharper minima that may improve training accuracy. 3) Monitor both training and validation/test accuracy - similar accuracy suggests good generalization while different accuracy indicates overfitting.
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
The document discusses modeling stochastic gradient descent (SGD) using stochastic differential equations (SDEs). It outlines SGD, random walks, Wiener processes, and SDEs. It then covers continuous-time SGD and controlled SGD, modeling SGD as an SDE. It provides an example of modeling quadratic loss functions with SGD as an SDE. Finally, it discusses the effects of learning rate and batch size on generalization when modeling SGD as an SDE.
Model Based Fault Detection, Identification and Accommodation in Antilock Bra...Behzad Samadi
1) The document presents a model-based fault detection and identification approach for antilock braking systems (ABS). It develops nonlinear dynamic models for vehicle motion and tire forces.
2) Kalman filtering techniques are used to estimate vehicle states and tire forces based on sensor measurements, and residuals between measurements and estimates are monitored to detect and identify faults.
3) The approach extends previous work by including additional tire force states and their dynamics in the estimation model to improve fault detection performance.
- The document discusses interval valued intuitionistic fuzzy subrings of a ring. It begins with definitions of interval valued fuzzy sets, interval valued fuzzy subrings, and interval valued intuitionistic fuzzy subsets.
- It then defines an interval valued intuitionistic fuzzy subring and establishes some properties, including that the intersection of two interval valued intuitionistic fuzzy subrings is also an interval valued intuitionistic fuzzy subring, and the intersection of a family of such subrings is also a subring.
- Proofs are provided to show that the intersection operations preserve the necessary conditions to be considered an interval valued intuitionistic fuzzy subring.
This document discusses several semi-supervised deep generative models for multimodal data, including the Semi-Supervised Multimodal Variational AutoEncoder (SS-MVAE), Semi-Supervised Hierarchical Multimodal Variational AutoEncoder (SS-HMVAE), and their training procedures. The SS-MVAE extends the Joint Multimodal Variational Autoencoder (JMVAE) to semi-supervised learning. The SS-HMVAE introduces auxiliary variables to model dependencies between modalities more flexibly. Both models maximize a variational lower bound with supervised and unsupervised objectives. The document provides technical details of the generative processes, variational approximations, and optimization of these semi-supervised deep generative models.
This document discusses the relationship between control as inference, reinforcement learning, and active inference. It provides an overview of key concepts such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), optimality variables, the evidence lower bound (ELBO), variational inference, and the free energy principle as applied to active inference. Control as inference frames reinforcement learning as probabilistic inference by defining a generative process and performing variational inference to find an optimal policy. Active inference uses the free energy principle and minimizes expected free energy to select actions that resolve uncertainty.
1. This document provides an overview of key probability and statistics concepts covered on actuarial exams P and FM.
2. It covers topics like probability spaces, random variables, expectations, distributions, and functions including CDFs, PDFs, moments, and transformations.
3. Formulas and properties are presented for concepts like independence, conditional probability, multivariate distributions, the central limit theorem, and more.
The document discusses control as inference in Markov decision processes (MDPs) and partially observable MDPs (POMDPs). It introduces optimality variables that represent whether a state-action pair is optimal or not. It formulates the optimal action-value function Q* and optimal value function V* in terms of these optimality variables and the reward and transition distributions. Q* is defined as the log probability of a state-action pair being optimal, and V* is defined as the log probability of a state being optimal. Bellman equations are derived relating Q* and V* to the reward and next state value.
This document summarizes the proximal alternating direction method of multipliers (ADMM) algorithm for solving convex optimization problems. It introduces the optimization problem, associated Lagrangian, and equivalent formulation. It then presents the proximal ADMM algorithm, which involves iteratively minimizing the augmented Lagrangian with respect to x, z, and y. It discusses when the sequence of iterates (xk) is uniquely defined. It also compares the proximal ADMM algorithm to the original non-proximal ADMM algorithm.
This document provides an overview of an upcoming course on inverse problems and regularization. The course will cover three topics: inverse problems, compressed sensing, and sparsity and L1 regularization. Inverse problems involve recovering an unknown signal x0 from noisy observations. Regularization is used to incorporate prior information and make the problem well-posed. Compressed sensing allows signals to be sampled below the Nyquist rate if they are sparse. The L1 norm is used as a convex relaxation of the sparsity prior, allowing sparse recovery problems to be solved as convex programs.
Signal Processing Course : Convex OptimizationGabriel Peyré
This document discusses convex optimization and proximal operators. It begins by introducing convex optimization problems with objective functions G mapping from a Hilbert space H to the real numbers. It then discusses properties of convex, lower semi-continuous, and proper functions. Examples are given of regularization problems and total variation denoising. The document covers subdifferentials, proximal operators, proximal calculus including separability and compositions, and relationships between proximal operators and subdifferentials. Gradient descent and subgradient descent algorithms are also briefly discussed.
The document discusses the ABA problem that can occur in non-blocking concurrent queue algorithms. It shows an example of how the ABA problem can allow a thread to incorrectly retrieve another thread's data from the queue. It then describes two common solutions to the ABA problem - adding version numbers to data structures or using load-linked/store-conditional instructions. Finally, it shows pseudocode for an enqueue operation of a non-blocking concurrent queue that avoids the ABA problem by using a double-compare-and-swap with version numbers.
1. The document discusses vector optimization problems and presents definitions and concepts related to nondominated solutions.
2. It introduces the concept of θ-ordering between solutions and defines what it means for one solution to be better than another based on their θ-ordering.
3. Formulas and properties are presented for calculating the θ-value of solutions based on the objective function values.
This document summarizes a lecture on linear support vector machines (SVMs) in the dual formulation. It begins with an overview of linear SVMs and their optimization as a quadratic program with inequality constraints. It then derives the dual formulation of the linear SVM problem, which involves maximizing an objective function over Lagrange multipliers while satisfying constraints. The Karush-Kuhn-Tucker conditions, which are necessary for optimality, are presented for the dual problem. Finally, the document expresses the dual problem and KKT conditions in matrix form to solve for the optimal weights and bias of the linear SVM classifier.
The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.
1. The document describes utility functions and lottery preferences in decision theory.
2. It introduces concepts like utility functions, lotteries, and preference relations between lotteries.
3. Formulas are provided for calculating the utility of lotteries that are a convex combination of other lotteries.
Linear Bayesian update surrogate for updating PCE coefficientsAlexander Litvinenko
This is our joint work with colleagues from TU Braunschweig. Prof. H. G. Matthies had an excellent idea to develop a Bayesian surrogate formula for updating not probability densities (like in classical Bayesian formula), but PCE coefficients of the given random variable. Bojana Rosic implemented the linear case. I (with help of Elmar Zander) implemented non-linear case. Later on Elmar significantly simplified the algorithm.
The document appears to discuss Bayesian statistical modeling and inference. It includes definitions of terms like the correlation coefficient (ρ), bivariate normal distributions, and binomial distributions. It shows the setup of a Bayesian hierarchical model with multivariate normal outcomes and estimates of the model parameters, including the correlations (ρA and ρB) between two groups of bivariate data.
Model Selection with Piecewise Regular GaugesGabriel Peyré
Talk given at Sampta 2013.
The corresponding paper is :
Model Selection with Piecewise Regular Gauges (S. Vaiter, M. Golbabaee, J. Fadili, G. Peyré), Technical report, Preprint hal-00842603, 2013.
http://hal.archives-ouvertes.fr/hal-00842603/
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.
Signal Processing Course : Inverse Problems RegularizationGabriel Peyré
This document discusses regularization techniques for inverse problems. It introduces variational priors like Sobolev and total variation to regularize inverse problems. Gradient descent and proximal gradient methods are presented to minimize regularization functionals for problems like denoising. Conjugate gradient and projected gradient descent are discussed for solving the regularized inverse problems. Total variation priors are shown to better recover edges compared to Sobolev priors. Non-smooth optimization methods may be needed to handle non-differentiable total variation functionals.
The document describes a method for summarizing the essential information of a document in 3 sentences or less. It begins by providing definitions for key terms used in the method such as sets, functions, and ordering relationships. It then provides an example application of the method to a specific problem instance, calculating an ordering relationship over subsets of a set based on a given valuation function.
The document discusses Bayesian networks and how they can be used to concisely represent probability distributions over many variables by specifying conditional independence relationships between variables. It provides examples of how to construct Bayesian networks from probability distributions, how to perform inference by eliminating variables, and concepts like d-separation that characterize conditional independence in Bayesian networks.
jhkl,l.มือครูคณิตศาสตร์พื้นฐาน ม.4 สสวท เล่ม 2fuyhfgTonn Za
This document summarizes a book titled "The Development of the Thai Language Teaching Materials for Grade 3-4 Students" by Dr. Somchai Srisa-an.
The book was published in 2001 to provide Thai language teaching materials for grades 3-4. It includes 4 chapters, with each chapter focusing on a different grade level (grade 3, chapter 1 and grade 4, chapter 4).
The summary highlights that the book aims to develop Thai language skills for students in grades 3-4 and provides teaching materials tailored to each grade level. It also seeks to appropriately introduce students to the Thai language in order to enhance their language abilities and prepare them for further study.
This document discusses the relationship between control as inference, reinforcement learning, and active inference. It provides an overview of key concepts such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), optimality variables, the evidence lower bound (ELBO), variational inference, and the free energy principle as applied to active inference. Control as inference frames reinforcement learning as probabilistic inference by defining a generative process and performing variational inference to find an optimal policy. Active inference uses the free energy principle and minimizes expected free energy to select actions that resolve uncertainty.
1. This document provides an overview of key probability and statistics concepts covered on actuarial exams P and FM.
2. It covers topics like probability spaces, random variables, expectations, distributions, and functions including CDFs, PDFs, moments, and transformations.
3. Formulas and properties are presented for concepts like independence, conditional probability, multivariate distributions, the central limit theorem, and more.
The document discusses control as inference in Markov decision processes (MDPs) and partially observable MDPs (POMDPs). It introduces optimality variables that represent whether a state-action pair is optimal or not. It formulates the optimal action-value function Q* and optimal value function V* in terms of these optimality variables and the reward and transition distributions. Q* is defined as the log probability of a state-action pair being optimal, and V* is defined as the log probability of a state being optimal. Bellman equations are derived relating Q* and V* to the reward and next state value.
This document summarizes the proximal alternating direction method of multipliers (ADMM) algorithm for solving convex optimization problems. It introduces the optimization problem, associated Lagrangian, and equivalent formulation. It then presents the proximal ADMM algorithm, which involves iteratively minimizing the augmented Lagrangian with respect to x, z, and y. It discusses when the sequence of iterates (xk) is uniquely defined. It also compares the proximal ADMM algorithm to the original non-proximal ADMM algorithm.
This document provides an overview of an upcoming course on inverse problems and regularization. The course will cover three topics: inverse problems, compressed sensing, and sparsity and L1 regularization. Inverse problems involve recovering an unknown signal x0 from noisy observations. Regularization is used to incorporate prior information and make the problem well-posed. Compressed sensing allows signals to be sampled below the Nyquist rate if they are sparse. The L1 norm is used as a convex relaxation of the sparsity prior, allowing sparse recovery problems to be solved as convex programs.
Signal Processing Course : Convex OptimizationGabriel Peyré
This document discusses convex optimization and proximal operators. It begins by introducing convex optimization problems with objective functions G mapping from a Hilbert space H to the real numbers. It then discusses properties of convex, lower semi-continuous, and proper functions. Examples are given of regularization problems and total variation denoising. The document covers subdifferentials, proximal operators, proximal calculus including separability and compositions, and relationships between proximal operators and subdifferentials. Gradient descent and subgradient descent algorithms are also briefly discussed.
The document discusses the ABA problem that can occur in non-blocking concurrent queue algorithms. It shows an example of how the ABA problem can allow a thread to incorrectly retrieve another thread's data from the queue. It then describes two common solutions to the ABA problem - adding version numbers to data structures or using load-linked/store-conditional instructions. Finally, it shows pseudocode for an enqueue operation of a non-blocking concurrent queue that avoids the ABA problem by using a double-compare-and-swap with version numbers.
1. The document discusses vector optimization problems and presents definitions and concepts related to nondominated solutions.
2. It introduces the concept of θ-ordering between solutions and defines what it means for one solution to be better than another based on their θ-ordering.
3. Formulas and properties are presented for calculating the θ-value of solutions based on the objective function values.
This document summarizes a lecture on linear support vector machines (SVMs) in the dual formulation. It begins with an overview of linear SVMs and their optimization as a quadratic program with inequality constraints. It then derives the dual formulation of the linear SVM problem, which involves maximizing an objective function over Lagrange multipliers while satisfying constraints. The Karush-Kuhn-Tucker conditions, which are necessary for optimality, are presented for the dual problem. Finally, the document expresses the dual problem and KKT conditions in matrix form to solve for the optimal weights and bias of the linear SVM classifier.
The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.
1. The document describes utility functions and lottery preferences in decision theory.
2. It introduces concepts like utility functions, lotteries, and preference relations between lotteries.
3. Formulas are provided for calculating the utility of lotteries that are a convex combination of other lotteries.
Linear Bayesian update surrogate for updating PCE coefficientsAlexander Litvinenko
This is our joint work with colleagues from TU Braunschweig. Prof. H. G. Matthies had an excellent idea to develop a Bayesian surrogate formula for updating not probability densities (like in classical Bayesian formula), but PCE coefficients of the given random variable. Bojana Rosic implemented the linear case. I (with help of Elmar Zander) implemented non-linear case. Later on Elmar significantly simplified the algorithm.
The document appears to discuss Bayesian statistical modeling and inference. It includes definitions of terms like the correlation coefficient (ρ), bivariate normal distributions, and binomial distributions. It shows the setup of a Bayesian hierarchical model with multivariate normal outcomes and estimates of the model parameters, including the correlations (ρA and ρB) between two groups of bivariate data.
Model Selection with Piecewise Regular GaugesGabriel Peyré
Talk given at Sampta 2013.
The corresponding paper is :
Model Selection with Piecewise Regular Gauges (S. Vaiter, M. Golbabaee, J. Fadili, G. Peyré), Technical report, Preprint hal-00842603, 2013.
http://hal.archives-ouvertes.fr/hal-00842603/
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.
Signal Processing Course : Inverse Problems RegularizationGabriel Peyré
This document discusses regularization techniques for inverse problems. It introduces variational priors like Sobolev and total variation to regularize inverse problems. Gradient descent and proximal gradient methods are presented to minimize regularization functionals for problems like denoising. Conjugate gradient and projected gradient descent are discussed for solving the regularized inverse problems. Total variation priors are shown to better recover edges compared to Sobolev priors. Non-smooth optimization methods may be needed to handle non-differentiable total variation functionals.
The document describes a method for summarizing the essential information of a document in 3 sentences or less. It begins by providing definitions for key terms used in the method such as sets, functions, and ordering relationships. It then provides an example application of the method to a specific problem instance, calculating an ordering relationship over subsets of a set based on a given valuation function.
The document discusses Bayesian networks and how they can be used to concisely represent probability distributions over many variables by specifying conditional independence relationships between variables. It provides examples of how to construct Bayesian networks from probability distributions, how to perform inference by eliminating variables, and concepts like d-separation that characterize conditional independence in Bayesian networks.
jhkl,l.มือครูคณิตศาสตร์พื้นฐาน ม.4 สสวท เล่ม 2fuyhfgTonn Za
This document summarizes a book titled "The Development of the Thai Language Teaching Materials for Grade 3-4 Students" by Dr. Somchai Srisa-an.
The book was published in 2001 to provide Thai language teaching materials for grades 3-4. It includes 4 chapters, with each chapter focusing on a different grade level (grade 3, chapter 1 and grade 4, chapter 4).
The summary highlights that the book aims to develop Thai language skills for students in grades 3-4 and provides teaching materials tailored to each grade level. It also seeks to appropriately introduce students to the Thai language in order to enhance their language abilities and prepare them for further study.
The document discusses resource allocation among users. It first defines the utility function for each user i as πi, which depends on resource allocation qi and other users' allocations. It shows πi is concave in qi. It then proves the existence of a Nash equilibrium allocation that maximizes each user's utility given others' allocations. The allocation satisfies the first order condition that the derivative of each user's utility with respect to its own allocation is non-positive.
El text.life science6.matsubayashi191120RCCSRENKEI
This document discusses molecular dynamics (MD) simulations. It provides equations for modeling interactions in MD, such as bonds, angles, torsions, and nonbonded interactions. It describes algorithms like Verlet integration that are used to solve the equations of motion in MD. It also discusses ensembles like NVE, NVT, and NPT that are commonly used, and methods like Langevin dynamics and barostats that are applied to control temperature and pressure.
This document provides an outline and definitions for fundamental concepts in set theory and discrete mathematics, including:
1. Definitions of sets, operations on sets like union and intersection, and relations.
2. Functions, relations, and properties like domains, ranges, and composition.
3. Partial orders, trees, groups, and other algebraic structures.
1. The document defines various functions and relations using set-builder and function notation.
2. Examples of linear, quadratic, and polynomial functions are provided with their domain and range restrictions.
3. Common transformations of basic quadratic functions like y=x^2 are demonstrated, such as shifting the graph left or right and changing the sign of coefficients.
The document describes calculating the change of basis matrices between two bases B1 and B2.
It gives the bases B1 and B2. It then calculates the change of basis matrix from B1 to B2 as Q, and the inverse change of basis matrix from B2 to B1 as P.
It verifies that P is indeed the inverse of Q.
This document contains mathematical formula tables from the University of Manchester. It provides formulas for topics including trigonometric identities, derivatives, integrals, Laplace transforms, and more. The tables are identical to version 2.0 tables from UMIST with the exception of the front cover. The tables contain over 30 pages of formulas organized by topic.
Randomized smoothing is a method to make a classifier robust against adversarial attacks. I introduce two papers to improve the performance of a method using randomized smoothing technique.
The document defines and provides examples of random variables. A random variable is a function that maps outcomes from a sample space to real numbers. It must satisfy the property that the inverse image of any real number set is an event. Random variables allow probabilities to be represented as real numbers. The cumulative distribution function of a random variable gives the probability that the random variable is less than or equal to each real number.
University of manchester mathematical formula tablesGaurav Vasani
This document contains mathematical formula tables covering a wide range of topics including:
- Greek alphabet
- Indices and logarithms
- Trigonometric, complex number, and hyperbolic identities
- Power series expansions
- Derivatives of common functions
- Integrals of common functions
- Laplace transforms
- And more advanced topics such as vector calculus, mechanics, and statistical distributions.
The document provides solutions to problems from an IIT-JEE 2004 mathematics exam. Problem 1 asks the student to find the center and radius of a circle defined by a complex number relation. The solution shows that the center is the midpoint of points dividing the join of the constants in the ratio k:1, and gives the radius. Problem 2 asks the student to prove an inequality relating dot products of four vectors satisfying certain conditions. The solution shows that the vectors must be parallel or antiparallel.
The document contains mathematical equations related to optimization problems. It begins with a quote about finding happiness even in dark times by remembering to turn on the light. The rest of the document consists of optimization equations with variables like Q, P, q1, q2, and πi being defined and solved for in different scenarios.
This document provides an overview of key concepts in multivariable calculus including:
- Three-dimensional coordinate systems and vectors in space. Operations on vectors such as addition, scalar multiplication, dot products, and cross products.
- Lines, planes, and quadric surfaces in space. Multiple integrals, integration in vector fields including line integrals, work, and flux.
- Coordinate transformations between rectangular and cylindrical coordinates. Green's theorem and its application to calculating line integrals and surface areas.
The document solves the Bessel equation with order v = 1/3. It finds two partial solutions by expanding the general solution as a power series and solving the characteristic equation. The first partial solution is a power series with terms of x^(2k+1/3) and the second partial solution is a power series with terms of x^(2k-1/3). The general solution is the linear combination of these two partial solutions.
This document contains an exercise set with 46 problems involving real numbers, intervals, and inequalities. The problems cover topics such as determining whether numbers are rational or irrational, solving equations, graphing inequalities on number lines, factoring polynomials, and solving compound inequalities.
Hidden Markov models can be used to model sequential data and detect patterns. The document describes an HMM to detect CpG islands in DNA sequences. It has two states, "CpG island" and "not CpG island". Transition and emission probabilities are estimated from training data. The Viterbi, forward-backward, and Baum-Welch algorithms are used to find the most likely state sequence and re-estimate parameters when the true state sequence is unknown. The model can be extended to higher-order HMMs and different state duration distributions.
This document provides information about lambda calculus and combinators. It includes definitions and examples of:
- Beta reduction and how it works with functions
- Church numerals for representing numbers
- Defining basic operations like addition and multiplication
- Boolean logic using true, false, and, or, not, cond
- Pairs and accessing elements
- Moses Schönfinkel who invented combinators
- The three basic combinators: I, K, S and what they represent
【DL輪読会】Unbiased Gradient Estimation for Marginal Log-likelihoodDeep Learning JP
1. The document proposes methods for estimating the marginal log-likelihood of latent variable models in an unbiased manner.
2. It discusses using Monte Carlo methods like MCMC and importance sampling to estimate the intractable integral in the marginal log-likelihood. Multilevel Monte Carlo can provide an unbiased estimate with fewer samples than standard Monte Carlo.
3. Stochastically Unbiased Marginalization Objective (SUMO) is introduced to provide an unbiased estimate of the marginal log-likelihood using a single sample. This involves weighting the importance weighted bound with a geometric distribution.
1. The document presents mathematical formulas and analysis involving probability distributions with parameters μ and ν.
2. Constraints on μ and ν are derived such that μ is less than 1/2 but greater than 1/3, while ν can be either less than or greater than 1/2.
3. The analysis examines optimal probability distributions for strategies in a game theoretical setting involving players with outcomes ma, mb, and parameters μ and ν.
1. The document discusses two organizations: the India-UN organization and the India-UN Society.
2. It provides details on their goals and activities, which include promoting cooperation between India and the UN, and education on global issues.
3. It also mentions the roles of the India-UN Society in collaborating with the India-UN organization and universities.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
Re:ゲーム理論入門 - ナッシュ均衡の存在証明
1.
2. “No one should be ashamed to admit they are wrong,
which is but saying, in other words,
that they are wiser today than they were yesterday”
— Alexander Pope —
10.
a1
, a2
, ⋯, ak
∈ Rn
∀λ1, λ2, ⋯, λk ∈ R, λ1a1
+ λ2a2
+ ⋯ + λkak
= 0 ⇒ λ1 = λ2 = ⋯ = λk = 0
Rn
a
b
c
a, b, c
b − a, c − a
a, b, c a = b or c = t(b − a) + a
λ1(b − a) + λ2(c − a) = λ2(c − a)
a = b
λ2 = 0 λ1
b ≠ a, c = t(b − a) + a
λ1(b − a) + λ2(c − a) = λ1(b − a) + λ2t(b − a) = (λ1 + λ2t)(b − a)
λ1 = − tλ2 λ1, λ2
11.
a0
, a1
, a2
, ⋯, ak
∈ Rn
∀λ1, λ2, ⋯, λk ∈ R, λ1a1
+ λ2a2
+ ⋯ + λkak
= 0 ⇒ λ1 = λ2 = ⋯ = λk = 0
Rn
a
b
c
a, b, c
b − a, c − a
λ1(b − a) + λ2(c − a) = 0
b − a, c − a
(λ1, λ2) ≠ (0, 0)
λ2 = 0 (λ1 ≠ 0) λ1(b − a) = 0 ⇔ b = a
λ2 ≠ 0, λ1 ≠ 0
λ1(b − a) + λ2(c − a) = 0 ⇔ c =
λ1
λ2
(b − a) + a
12.
a0
, a1
, a2
, ⋯, ak
∈ Rn
∀λ1, λ2, ⋯, λk ∈ R, λ1a1
+ λ2a2
+ ⋯ + λkak
= 0 ⇒ λ1 = λ2 = ⋯ = λk = 0
Rn a, b, c
b − a, c − a
b − a, c − a
a, b, c
Rn a0
, a1
, a2
, ⋯, ar
{
r
∑
i=0
λi
ai
|0 ≤ λi
≤ 1,
r
∑
i=0
λi
= 1} a1
− a0
, a2
− a0
, ⋯, ar
− a0
24.
F : X → Y x ∈ X F(x) ⊂ Y
X = {1,2,3}, Y = {5,6,7}
F(1) = {5}, F(2) = {5,7}, F(3) = ϕ
X = [0, 1], Y = [0, 1]
F(x) =
1 x < 1/2
[0, 1] x = 1/2
0 x > 1/2
F(x) F(x)
25.
F : X → Y x ∈ X F(x) ⊂ Y
F* : X → 2Y
Y = {5,6,7} → 2Y
= {ϕ, {5}, {6}, {7}, {5,6}, {5,7}, {6,7}, {5,6,7}}
X = {1,2,3}
F*(1) = {5}, F*(2) = {5,7}, F*(3) = ϕ
26.
F : X → Y x ∈ X F(x) ⊂ Y
F : X → Y
x ∈ X F(x) ⊆ Y F
x ∈ X F(x) ⊆ Y F
x ∈ X F(x) ⊆ Y F
x ∈ X F(x) ⊆ Y F
x ∈ X F(x) ⊆ Y F
F(x) =
1 x < 1/2
[0, 1] x = 1/2
0 x > 1/2
F(x) =
1 x < 1/2
[0, 1) x = 1/2
0 x > 1/2
27.
28.
F : X → Y x F(x) ⊆ V V ⊆ Y
x O ⊆ X x′ ∈ O F(x′) ⊆ V
F : X → Y x F(x) ∩ V ≠ ϕ V ⊆ Y
x O ⊆ X x′ ∈ O F(x′) ∩ V ≠ ϕ
x ∈ X
F : X → Y
29.
F : X → Y x F(x) ⊆ V V ⊆ Y
x O ⊆ X x′ ∈ O F(x′) ⊆ V
x
F(x)
x
V
O
x′ x′
F(x′) F(x′)
O
F(x′) ⊈ V
V
30.
F : X → Y x
x O ⊆ X x′ ∈ O
F(x) ∩ V ≠ ϕ V ⊆ Y
F(x′) ∩ V ≠ ϕ
x
F(x)
x
F(x)
V
O
F(x′)
x′
F(x′) ∩ V ≠ ϕ
O
V
x′
F(x) ∩ V ≠ ϕ
F(x′) ∩ V ≠ ϕ
31.
x1
y1 ∈ F(x1)
xx2 xv
F(x)
yv ∈ F(xv)
y ∈ F(x)
y2 ∈ F(x2)
{xv}∞
v=1, {yv}∞
v=1
yv ∈ F(xv), v = 1,2,… xv → x, yv → y (v → ∞)
y ∈ F(x)
x1
y1 ∈ F(x1)
x x2xv
F(x)
yv ∈ F(xv)
y ∉ F(x)
y2 ∈ F(x2)
yν = y
F : X → Y
xν = x + 1/ν
x
32.
x1
y1 ∈ F(x1)
xx2 xv
F(x)
yv ∈ F(xv)
y ∈ F(x)
y2 ∈ F(x2)
x1
y1 ∈ F(x1)
x x2xv
F(x)
yv ∈ F(xv)
y ∉ F(x)
y2 ∈ F(x2)
yν = y
F : X → Y
xν = x + 1/ν
{(x, y) ∈ X × Y|y ∈ F(x)}
33.
{xv}∞
v=1, xv → x (v → ∞)
y ∈ F(x) yv → y (v → ∞) yv ∈ F(xv)
x1
y1 ∈ F(x1)
x x2xv
F(x)
yv ∈ F(xv)
y ∈ F(x)
y2 ∈ F(x2)
yν = y
F : X → Y
xν = x + 1/ν
{yv}∞
v=1
x1
y1 ∈ F(x1)
xx2 xv
F(x)
yv ∈ F(xv)
y ∈ F(x)
y2 ∈ F(x2)
yν = y′( ≠ y)
xν = x − 1/ν
x
34.
G = (N, {Qi}i∈N, {Fi}i∈N)
N
Fi
Qi
q−i = (q1, ⋯, qi−1, qi+1, ⋯, qn)
Bi(q−i) = {qi ∈ Qi |Fi(qi, q−i) = max
ri∈Qi
Fi(ri, q−i)}
Bi : Q1 × … × Qi−1 × Qi+1 × … × Qn → Qi