Slides from the talk on recurrent networks and LSTMs at SV AI and Big Data Association meetup. A full video of the talk—https://www.youtube.com/watch?v=TiHpdp4QC6k.
This document presents research on generalized Bernstein polynomials. It defines a new polynomial operator Unr that approximates Lebesgue integrable functions on the interval [0,1+r/n] in the L1 norm. The operator is a modification of the Bernstein operator to work on a finite interval. The document proves a generalization of Voronowskaja's theorem for this new operator, showing that the difference between the function value and the polynomial approximation converges to 1/2 times the second derivative of the function as n+r goes to infinity, under certain conditions. It also presents three technical lemmas used in the proof.
The document discusses numeric partitions and graphs involving "Eugênio Numbers" (EN), "Krishna Numbers" (KN), and functions related to the number e and other constants. It defines EN and KN sets and describes partitioning them into "generations" based on digit counts. Equations are given for EN functions involving operations like addition, multiplication and logarithms. Graphs are proposed plotting the ratios of "Aleph Red" to "Aleph Omega" for different values of variables like ∆.
1. The open loop transfer function is G(s)H(s) = KKbb/(JJJJ + CC).
2. The feedforward transfer function is G(s) = KKbb/(JJJJ + CC).
3. The control ratio is C(s)/R(s) = G(s)/(1 + G(s)H(s)) = KKbb/(KKbbss + JJJJ + CC).
4. The feedback ratio is H(s) = 1.
5. The error ratio is E(s)/R(s) = 1/(1 + G(s)H(s)) = (KKbbss + JJJ
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.
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.
This document contains calculations and equations related to diode circuits. It defines terms like reverse saturation current (IS), thermal voltage (VT), and determines values like junction voltage (VZ) and terminal voltages (VS) for various diode circuits. Key results include:
- The terminal voltage VS is equal to the junction voltage VZ when the applied voltage VE is less than VZ.
- When VE is greater than VZ, VS takes on a value between 0V and VE depending on the resistor ratios.
- For a circuit with VE = 25V, VZ = 12V, and resistor values of 2kΩ and 1.2kΩ, the calculated terminal voltage is VS = 15.
PROBABILITY DISTRIBUTION OF SUM OF TWO CONTINUOUS VARIABLES AND CONVOLUTIONJournal For Research
All physical subjects, involving random phenomena, something depending upon chance, naturally find their own way to theory of Statistics. Hence there arise relations between the results derived for hose random phenomena in different physical subjects and the concepts of Statistics. Convolution theorem has a variety of applications in field of Fourier transforms and many other situations, but it bears beautiful applications in field of statistics also .Here in this paper authors want to discuss some notions of Electrical Engineering in terms of convolution of some probability distributions.
This document discusses numerical differentiation and integration using Newton's forward and backward difference formulas. It provides examples of using these formulas to calculate derivatives from tables of ordered data pairs. Specifically, it shows how to calculate derivatives at interior points using central difference formulas, and at endpoints using forward or backward formulas depending on if the point is near the start or end of the data range. Formulas are derived for calculating the first and second derivatives, and examples are worked through to find acceleration and rates of cooling from given temperature-time tables.
This document presents research on generalized Bernstein polynomials. It defines a new polynomial operator Unr that approximates Lebesgue integrable functions on the interval [0,1+r/n] in the L1 norm. The operator is a modification of the Bernstein operator to work on a finite interval. The document proves a generalization of Voronowskaja's theorem for this new operator, showing that the difference between the function value and the polynomial approximation converges to 1/2 times the second derivative of the function as n+r goes to infinity, under certain conditions. It also presents three technical lemmas used in the proof.
The document discusses numeric partitions and graphs involving "Eugênio Numbers" (EN), "Krishna Numbers" (KN), and functions related to the number e and other constants. It defines EN and KN sets and describes partitioning them into "generations" based on digit counts. Equations are given for EN functions involving operations like addition, multiplication and logarithms. Graphs are proposed plotting the ratios of "Aleph Red" to "Aleph Omega" for different values of variables like ∆.
1. The open loop transfer function is G(s)H(s) = KKbb/(JJJJ + CC).
2. The feedforward transfer function is G(s) = KKbb/(JJJJ + CC).
3. The control ratio is C(s)/R(s) = G(s)/(1 + G(s)H(s)) = KKbb/(KKbbss + JJJJ + CC).
4. The feedback ratio is H(s) = 1.
5. The error ratio is E(s)/R(s) = 1/(1 + G(s)H(s)) = (KKbbss + JJJ
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.
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.
This document contains calculations and equations related to diode circuits. It defines terms like reverse saturation current (IS), thermal voltage (VT), and determines values like junction voltage (VZ) and terminal voltages (VS) for various diode circuits. Key results include:
- The terminal voltage VS is equal to the junction voltage VZ when the applied voltage VE is less than VZ.
- When VE is greater than VZ, VS takes on a value between 0V and VE depending on the resistor ratios.
- For a circuit with VE = 25V, VZ = 12V, and resistor values of 2kΩ and 1.2kΩ, the calculated terminal voltage is VS = 15.
PROBABILITY DISTRIBUTION OF SUM OF TWO CONTINUOUS VARIABLES AND CONVOLUTIONJournal For Research
All physical subjects, involving random phenomena, something depending upon chance, naturally find their own way to theory of Statistics. Hence there arise relations between the results derived for hose random phenomena in different physical subjects and the concepts of Statistics. Convolution theorem has a variety of applications in field of Fourier transforms and many other situations, but it bears beautiful applications in field of statistics also .Here in this paper authors want to discuss some notions of Electrical Engineering in terms of convolution of some probability distributions.
This document discusses numerical differentiation and integration using Newton's forward and backward difference formulas. It provides examples of using these formulas to calculate derivatives from tables of ordered data pairs. Specifically, it shows how to calculate derivatives at interior points using central difference formulas, and at endpoints using forward or backward formulas depending on if the point is near the start or end of the data range. Formulas are derived for calculating the first and second derivatives, and examples are worked through to find acceleration and rates of cooling from given temperature-time tables.
1) The document discusses periodic solutions for nonlinear systems of integro-differential equations with impulsive action of operators.
2) It presents a numerical-analytic method for approximating periodic solutions using uniformly convergent sequences of periodic functions.
3) The method is proved to construct a unique periodic solution that converges uniformly as m approaches infinity.
1) The document describes a lab experiment using MATLAB and Simulink to model differential equations and a mechanical spring-mass damper system.
2) Two differential equations and one spring-mass system were modeled to analyze the transient and steady-state response.
3) The results showed that the solutions from MATLAB and Simulink matched the expected behaviors and verified the initial and final values as well as time constants of the systems.
A Course in Fuzzy Systems and Control Matlab Chapter ThreeChung Hua Universit
This document contains a project report on a fuzzy control system. It includes 6 exercises related to fuzzy sets and fuzzy logic. Exercise 3.1 discusses properties of fuzzy complement functions. Exercise 3.2 analyzes the limit behavior of a fuzzy union operator as the parameter approaches certain values. Exercise 3.3 defines fuzzy intersection, union, and complement operations between two fuzzy sets. Exercise 3.5 proves that the Sugeno fuzzy complement is involutive.
This document proposes a new method for constructing sets of ternary zero correlation zone (ZCZ) sequences. The construction is based on binary mutually orthogonal complementary sets (MOCS). It involves three steps: 1) Generating a MOCS matrix, 2) Constructing an initial sequence set from the MOCS by concatenation and interleaving, 3) Recursively generating new sequence sets from the initial set by further interleaving. Examples are provided. The properties of the proposed ZCZ sequence sets are shown to achieve the theoretical upper bound, making them almost optimal ZCZ sequence sets.
The document discusses connecting best approximation theory to least squares approximation through a motivating example involving weighing fiddler crabs submerged in saline over time. It explores using least squares to fit a line or curve of best fit to the crab weight data. The document then provides mathematical proofs showing that the orthogonal projection of a vector onto a subspace is the best approximation within that subspace, and that the least squares solution minimizes the residual vector.
Unit IV UNCERTAINITY AND STATISTICAL REASONING in AI K.Sundar,AP/CSE,VECsundarKanagaraj1
This document discusses uncertainty and statistical reasoning in artificial intelligence. It covers probability theory, Bayesian networks, and certainty factors. Key topics include probability distributions, Bayes' rule, building Bayesian networks, different types of probabilistic inferences using Bayesian networks, and defining and combining certainty factors. Case studies are provided to illustrate each algorithm.
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.
Study Material Numerical Solution of Odinary Differential EquationsMeenakshisundaram N
1. The document provides information about a numerical methods course for physics majors at Vivekananda College in Tiruvedakam West, including the reference textbook and details about Unit V on numerical solutions of ordinary differential equations.
2. It introduces the concept of using Taylor series approximations to find numerical solutions to differential equations, providing the general Taylor series expansion formula and explaining how to derive the terms needed to solve specific differential equations.
3. It gives examples of using the Taylor series method to solve sample ordinary differential equations, finding approximate values of y at increasing values of x to several decimal places.
Periodic Function, Dirichlet's Condition, Fourier series, Even & Odd functions, Euler's Formula for Fourier Coefficients, Change of Interval, Fourier series in the intervals (0,2l), (-l,l) , (-pi, pi), (0, 2pi), Half Range Cosine & Sine series Root mean square, Complex Form of Fourier series, Parseval's Identity
Basic concepts of integration, definite and indefinite integrals,properties of definite integral, problem based on properties,method of integration, substitution, partial fraction, rational , irrational function integration, integration by parts, reduction formula, improper integral, convergent and divergent of integration
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.
1. Fourier transforms represent a function as a sum of sinusoidal functions using integral transforms. The Fourier transform of a function f(x) is defined as an integral transform using a kernel function, with examples including the Laplace, Fourier, Hankel, and Mellin transforms.
2. The Fourier integral theorem states that if a function f(x) is piecewise continuous and differentiable, its Fourier transform represents the function as an integral using sinusoidal functions.
3. The Fourier transform and its inverse are defined by integrals using the function and a complex exponential kernel. Properties of Fourier transforms include linearity, shifting, scaling, and relationships between a function and its derivative or integral transforms.
The purpose of this work is to formulate and investigate a boundary integral method for the solution of the internal waves/Rayleigh-Taylor problem. This problem describes the evolution of the interface between two immiscible, inviscid, incompressible, irrotational fluids of different density in three dimensions. The motion of the interface and fluids is driven by the action of a gravity force, surface tension at the interface, elastic bending and/or a prescribed far-field pressure gradient. The interface is a generalized vortex sheet, and dipole density is interpreted as the (unnormalized) vortex sheet strength. Presence of the surface tension or elastic bending effects introduces high order derivatives into the evolution equations. This makes the considered problem stiff and the application of the standard explicit time-integration methods suffers strong time-step stability constraints.
The proposed numerical method employs a special interface parameterization that enables the use of an efficient implicit time-integration method via a small-scale decomposition. This approach allows one to capture the nonlinear growth of normal modes for the case of Rayleigh-Taylor instability with the heavier fluid on top.
Validation of the results is done by comparison of numeric solution to the analytic solution of the linearized problem for a short time. We check the energy and the interface mean height preservation. The developed model and numerical method can be efficiently applied to study the motion of internal waves for doubly periodic interfacial flows with surface tension and elastic bending stress at the interface.
Mx/G(a,b)/1 With Modified Vacation, Variant Arrival Rate With Restricted Admi...IJRES Journal
In this paper, a bulk arrival general bulk service queuing system with modified M-vacation policy, variant arrival rate under a restricted admissibility policy of arriving batches and close down time is considered. During the server is in non- vacation, the arrivals are admitted with probability with ' α ' whereas, with probability 'β' they are admitted when the server is in vacation. The server starts the service only if at least ‘a’ customers are waiting in the queue, and renders the service according to the general bulk service rule with minimum of ‘a’ customers and maximum of ‘b’ customers. At the completion of service, if the number of waiting customers in the queue is less than ‘𝑎’ then the server performs closedown work , then the server will avail of multiple vacations till the queue length reaches a consecutively avail of M number of vacations, After completing the Mth vacation, if the queue length is still less than a then the server remains idle till it reaches a. The server starts the service only if the queue length b ≥ a. It is considered that the variant arrival rate dependent on the state of the server.
Second part of Matrices at undergraduate in science (math, physics, engineering) level.
Please send comments and suggestions to solo.hermelin@gmail.com.
For more presentations visit my website at
http://www.solohermelin.com.
To find the complete solution to the second order PDE
(i.e) To find the Complementary Function & Particular Integral for a second order (Higher Order) PDE
1) The document discusses probit transformation for nonparametric kernel estimation of copulas. It introduces a standard kernel estimator for copulas that is inconsistent on boundaries.
2) It then presents a "naive" probit transformation kernel copula density estimator that transforms data to standard normal using the probit function to address boundary issues.
3) It further improves upon this by introducing local log-linear and log-quadratic approximations for the transformed density, yielding two new estimators with better asymptotic properties.
1. The document provides solutions to integrals using substitution methods. It solves integrals of the form ∫f(x)dx by making substitutions to transform the integrals into forms that can be easily evaluated.
2. Various techniques are used, including substituting trigonometric functions, logarithmic functions, and rationalizing denominators.
3. The solutions provide the step-by-step workings and resulting anti-derivatives for each integral presented.
The document discusses various copula families that can model multivariate distributions, including elliptical and Archimedean copulas. It specifically focuses on introducing Archimax copulas, which allow for more flexible modeling of tail dependence than Archimedean copulas. The document outlines key properties of copulas and defines standard copula families like the independent and comonotonic copulas. It also discusses elliptical distributions and their associated elliptical copulas before introducing Archimax copulas and their properties in higher dimensions.
1. Ryan White presented a dissertation defense on random walks on random lattices and their applications.
2. The presentation included models of stochastic cumulative loss processes with delayed observation, where losses arrive randomly over time and the process is observed at random observation times.
3. A time-insensitive analysis was performed to derive a joint functional of the process at successive observation times, allowing properties like the distribution of the first observed threshold crossing to be determined.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
1) Machine learning and predictive analytics can be used to analyze large datasets and build models to find useful insights, predict outcomes, and provide competitive advantages.
2) WSO2 Machine Learner is a product that allows users to upload data, train machine learning models using various algorithms, compare results, and iterate on models.
3) Example use cases demonstrated by WSO2 Machine Learner include predicting airport wait times, tracking people via Bluetooth, predicting the Super Bowl winner, detecting defective manufacturing equipment, and identifying promising customers.
1) The document discusses periodic solutions for nonlinear systems of integro-differential equations with impulsive action of operators.
2) It presents a numerical-analytic method for approximating periodic solutions using uniformly convergent sequences of periodic functions.
3) The method is proved to construct a unique periodic solution that converges uniformly as m approaches infinity.
1) The document describes a lab experiment using MATLAB and Simulink to model differential equations and a mechanical spring-mass damper system.
2) Two differential equations and one spring-mass system were modeled to analyze the transient and steady-state response.
3) The results showed that the solutions from MATLAB and Simulink matched the expected behaviors and verified the initial and final values as well as time constants of the systems.
A Course in Fuzzy Systems and Control Matlab Chapter ThreeChung Hua Universit
This document contains a project report on a fuzzy control system. It includes 6 exercises related to fuzzy sets and fuzzy logic. Exercise 3.1 discusses properties of fuzzy complement functions. Exercise 3.2 analyzes the limit behavior of a fuzzy union operator as the parameter approaches certain values. Exercise 3.3 defines fuzzy intersection, union, and complement operations between two fuzzy sets. Exercise 3.5 proves that the Sugeno fuzzy complement is involutive.
This document proposes a new method for constructing sets of ternary zero correlation zone (ZCZ) sequences. The construction is based on binary mutually orthogonal complementary sets (MOCS). It involves three steps: 1) Generating a MOCS matrix, 2) Constructing an initial sequence set from the MOCS by concatenation and interleaving, 3) Recursively generating new sequence sets from the initial set by further interleaving. Examples are provided. The properties of the proposed ZCZ sequence sets are shown to achieve the theoretical upper bound, making them almost optimal ZCZ sequence sets.
The document discusses connecting best approximation theory to least squares approximation through a motivating example involving weighing fiddler crabs submerged in saline over time. It explores using least squares to fit a line or curve of best fit to the crab weight data. The document then provides mathematical proofs showing that the orthogonal projection of a vector onto a subspace is the best approximation within that subspace, and that the least squares solution minimizes the residual vector.
Unit IV UNCERTAINITY AND STATISTICAL REASONING in AI K.Sundar,AP/CSE,VECsundarKanagaraj1
This document discusses uncertainty and statistical reasoning in artificial intelligence. It covers probability theory, Bayesian networks, and certainty factors. Key topics include probability distributions, Bayes' rule, building Bayesian networks, different types of probabilistic inferences using Bayesian networks, and defining and combining certainty factors. Case studies are provided to illustrate each algorithm.
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.
Study Material Numerical Solution of Odinary Differential EquationsMeenakshisundaram N
1. The document provides information about a numerical methods course for physics majors at Vivekananda College in Tiruvedakam West, including the reference textbook and details about Unit V on numerical solutions of ordinary differential equations.
2. It introduces the concept of using Taylor series approximations to find numerical solutions to differential equations, providing the general Taylor series expansion formula and explaining how to derive the terms needed to solve specific differential equations.
3. It gives examples of using the Taylor series method to solve sample ordinary differential equations, finding approximate values of y at increasing values of x to several decimal places.
Periodic Function, Dirichlet's Condition, Fourier series, Even & Odd functions, Euler's Formula for Fourier Coefficients, Change of Interval, Fourier series in the intervals (0,2l), (-l,l) , (-pi, pi), (0, 2pi), Half Range Cosine & Sine series Root mean square, Complex Form of Fourier series, Parseval's Identity
Basic concepts of integration, definite and indefinite integrals,properties of definite integral, problem based on properties,method of integration, substitution, partial fraction, rational , irrational function integration, integration by parts, reduction formula, improper integral, convergent and divergent of integration
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.
1. Fourier transforms represent a function as a sum of sinusoidal functions using integral transforms. The Fourier transform of a function f(x) is defined as an integral transform using a kernel function, with examples including the Laplace, Fourier, Hankel, and Mellin transforms.
2. The Fourier integral theorem states that if a function f(x) is piecewise continuous and differentiable, its Fourier transform represents the function as an integral using sinusoidal functions.
3. The Fourier transform and its inverse are defined by integrals using the function and a complex exponential kernel. Properties of Fourier transforms include linearity, shifting, scaling, and relationships between a function and its derivative or integral transforms.
The purpose of this work is to formulate and investigate a boundary integral method for the solution of the internal waves/Rayleigh-Taylor problem. This problem describes the evolution of the interface between two immiscible, inviscid, incompressible, irrotational fluids of different density in three dimensions. The motion of the interface and fluids is driven by the action of a gravity force, surface tension at the interface, elastic bending and/or a prescribed far-field pressure gradient. The interface is a generalized vortex sheet, and dipole density is interpreted as the (unnormalized) vortex sheet strength. Presence of the surface tension or elastic bending effects introduces high order derivatives into the evolution equations. This makes the considered problem stiff and the application of the standard explicit time-integration methods suffers strong time-step stability constraints.
The proposed numerical method employs a special interface parameterization that enables the use of an efficient implicit time-integration method via a small-scale decomposition. This approach allows one to capture the nonlinear growth of normal modes for the case of Rayleigh-Taylor instability with the heavier fluid on top.
Validation of the results is done by comparison of numeric solution to the analytic solution of the linearized problem for a short time. We check the energy and the interface mean height preservation. The developed model and numerical method can be efficiently applied to study the motion of internal waves for doubly periodic interfacial flows with surface tension and elastic bending stress at the interface.
Mx/G(a,b)/1 With Modified Vacation, Variant Arrival Rate With Restricted Admi...IJRES Journal
In this paper, a bulk arrival general bulk service queuing system with modified M-vacation policy, variant arrival rate under a restricted admissibility policy of arriving batches and close down time is considered. During the server is in non- vacation, the arrivals are admitted with probability with ' α ' whereas, with probability 'β' they are admitted when the server is in vacation. The server starts the service only if at least ‘a’ customers are waiting in the queue, and renders the service according to the general bulk service rule with minimum of ‘a’ customers and maximum of ‘b’ customers. At the completion of service, if the number of waiting customers in the queue is less than ‘𝑎’ then the server performs closedown work , then the server will avail of multiple vacations till the queue length reaches a consecutively avail of M number of vacations, After completing the Mth vacation, if the queue length is still less than a then the server remains idle till it reaches a. The server starts the service only if the queue length b ≥ a. It is considered that the variant arrival rate dependent on the state of the server.
Second part of Matrices at undergraduate in science (math, physics, engineering) level.
Please send comments and suggestions to solo.hermelin@gmail.com.
For more presentations visit my website at
http://www.solohermelin.com.
To find the complete solution to the second order PDE
(i.e) To find the Complementary Function & Particular Integral for a second order (Higher Order) PDE
1) The document discusses probit transformation for nonparametric kernel estimation of copulas. It introduces a standard kernel estimator for copulas that is inconsistent on boundaries.
2) It then presents a "naive" probit transformation kernel copula density estimator that transforms data to standard normal using the probit function to address boundary issues.
3) It further improves upon this by introducing local log-linear and log-quadratic approximations for the transformed density, yielding two new estimators with better asymptotic properties.
1. The document provides solutions to integrals using substitution methods. It solves integrals of the form ∫f(x)dx by making substitutions to transform the integrals into forms that can be easily evaluated.
2. Various techniques are used, including substituting trigonometric functions, logarithmic functions, and rationalizing denominators.
3. The solutions provide the step-by-step workings and resulting anti-derivatives for each integral presented.
The document discusses various copula families that can model multivariate distributions, including elliptical and Archimedean copulas. It specifically focuses on introducing Archimax copulas, which allow for more flexible modeling of tail dependence than Archimedean copulas. The document outlines key properties of copulas and defines standard copula families like the independent and comonotonic copulas. It also discusses elliptical distributions and their associated elliptical copulas before introducing Archimax copulas and their properties in higher dimensions.
1. Ryan White presented a dissertation defense on random walks on random lattices and their applications.
2. The presentation included models of stochastic cumulative loss processes with delayed observation, where losses arrive randomly over time and the process is observed at random observation times.
3. A time-insensitive analysis was performed to derive a joint functional of the process at successive observation times, allowing properties like the distribution of the first observed threshold crossing to be determined.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
1) Machine learning and predictive analytics can be used to analyze large datasets and build models to find useful insights, predict outcomes, and provide competitive advantages.
2) WSO2 Machine Learner is a product that allows users to upload data, train machine learning models using various algorithms, compare results, and iterate on models.
3) Example use cases demonstrated by WSO2 Machine Learner include predicting airport wait times, tracking people via Bluetooth, predicting the Super Bowl winner, detecting defective manufacturing equipment, and identifying promising customers.
This document provides an overview of backpropagation through time (BPTT) for long short-term memory (LSTM) language models. It describes the forward and backward passes for LSTM, including equations for calculating the input, forget, output and cell gates, as well as the cell state and hidden state. In the backward pass, it derives the equations for calculating the gradients with respect to the weights and biases at each time step to update the model parameters during training.
This document explores RNN, LSTM, and GRU cells and hyperparameters through experiments. It discusses three recurrent cell types, hyperparameters like hidden size and learning rate, and compares results using sliding window and variable length sequences on three datasets. The experiments show GRU generally converges faster than LSTM, and both outperform vanilla RNN. Larger hidden sizes and batch sizes improve performance while additional layers do not.
Anjuli Kannan, Software Engineer, Google at MLconf SF 2016MLconf
Smart Reply: Learning a Model of Conversation from Data: Smart Reply is a text assistance feature that was recently introduced to Inbox by Gmail. Given an incoming email message, the Smartreply system analyzes its contents and suggests complete responses that the recipient can send with just one tap. This talk will cover how we built Smartreply using a combination of deep learning and semantic clustering, as well as what we learned along the way and why we think it shows promise for the future of dialogue models.
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
Revised presentation slide for NLP-DL, 2016/6/22.
Recent Progress (from 2014) in Recurrent Neural Networks and Natural Language Processing.
Profile http://www.cl.ecei.tohoku.ac.jp/~sosuke.k/
Japanese ver. https://www.slideshare.net/hytae/rnn-63761483
This document provides an overview of recurrent neural network (RNN) models including long short-term memory (LSTM) networks and sequence-to-sequence (seq-2-seq) models. RNNs maintain information about previous computations through feedback connections, making them well-suited for sequence processing tasks. LSTMs address the gradient vanishing problem of standard RNNs through gated cell states. Seq-2-seq models consist of an encoder RNN that encodes the input sequence into a vector, and a decoder RNN that generates the output sequence from the vector. The document includes a TensorFlow code example of an RNN trained to predict the next character in a sequence.
This document discusses different types of recurrent neural networks (RNNs) including vanilla RNNs, LSTMs, and GRUs. It notes that while vanilla RNNs are theoretically capable of handling long-term dependencies, they often fail to do so in practice due to the gradient vanishing problem. LSTMs address this issue through their use of cell states and gates. The document provides a step-by-step explanation of how LSTMs work and compares their architecture to GRUs, which combine the forget and input gates of LSTMs.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Recurrent Neural Networks. Part 1: TheoryAndrii Gakhov
The document provides an overview of recurrent neural networks (RNNs) and their advantages over feedforward neural networks. It describes the basic structure and training of RNNs using backpropagation through time. RNNs can process sequential data of variable lengths, unlike feedforward networks. However, RNNs are difficult to train due to vanishing and exploding gradients. More advanced RNN architectures like LSTMs and GRUs address this by introducing gating mechanisms that allow the network to better control the flow of information.
Electricity price forecasting with Recurrent Neural NetworksTaegyun Jeon
This document discusses using recurrent neural networks (RNNs) for electricity price forecasting with TensorFlow. It begins with an introduction to the speaker, Taegyun Jeon from GIST. The document then provides an overview of RNNs and their implementation in TensorFlow. It describes two case studies - using an RNN to predict a sine function and using one to forecast electricity prices. The document concludes with information on running and evaluating the RNN graph and a question and answer section.
The binomial theorem describes the expansion of binomial expressions of the form (x + y)n into a sum of terms involving integers and powers of x and y. It expresses the coefficient of each term using binomial coefficients. The general form of the binomial expansion is (x + y)n = xn + n xn-1 y + n(n-1)/2! xn-2 y2 + ... + yn. Fourier series can be used to represent periodic functions as an infinite sum of sines and cosines of integer multiples of the fundamental frequency. The coefficients of the Fourier series capture the frequency content of the original function.
Partial differentiation, total differentiation, Jacobian, Taylor's expansion, stationary points,maxima & minima (Extreme values),constraint maxima & minima ( Lagrangian multiplier), differentiation of implicit functions.
FOURIER SERIES Presentation of given functions.pptxjyotidighole2
This document discusses periodic functions and their Fourier series representations. It defines periodic functions as those where f(x+T)=f(x) for some period T. Examples given are sin(x), cos(x), and tan(x) with periods of 2π, 2π, and π respectively. It also defines piecewise continuous functions and gives Dirichlet's conditions for the existence of a Fourier series representation over an interval. Specific examples are worked out, including finding the Fourier series of |sin(x)| from -π to π and a piecewise defined function from -π to π. The document derives results for Fourier series over various intervals.
The document contains several equations related to linear equations of the form ax + by + c = 0. It explores solving systems of linear equations and examines properties of lines and planes in a coordinate system, including normal vectors.
This document discusses linear time-invariant (LTI) systems and their representation using Laplace transforms. It provides the definitions of the Laplace transform and inverse Laplace transform. It also defines the transfer function as the ratio of the Laplace transform of the output to the Laplace transform of the input. Properties of poles and zeros are discussed for characterizing an LTI system.
Mpc 006 - 02-01 product moment coefficient of correlationVasant Kothari
1.2 Correlation: Meaning and Interpretation
1.2.1 Scatter Diagram: Graphical Presentation of Relationship
1.2.2 Correlation: Linear and Non-Linear Relationship
1.2.3 Direction of Correlation: Positive and Negative
1.2.4 Correlation: The Strength of Relationship
1.2.5 Measurements of Correlation
1.2.6 Correlation and Causality
1.3 Pearson’s Product Moment Coefficient of Correlation
1.3.1 Variance and Covariance: Building Blocks of Correlations
1.3.2 Equations for Pearson’s Product Moment Coefficient of Correlation
1.3.3 Numerical Example
1.3.4 Significance Testing of Pearson’s Correlation Coefficient
1.3.5 Adjusted r
1.3.6 Assumptions for Significance Testing
1.3.7 Ramifications in the Interpretation of Pearson’s r
1.3.8 Restricted Range
1.4 Unreliability of Measurement
1.4.1 Outliers
1.4.2 Curvilinearity
1.5 Using Raw Score Method for Calculating r
1.5.1 Formulas for Raw Score
1.5.2 Solved Numerical for Raw Score Formula
Numerical Methods and Analysis discusses various root-finding methods including bisection, false position, and Newton-Raphson. Bisection uses interval halving to find a root between two values with opposite signs. False position uses the slope of a line between two points to estimate the next root. Newton-Raphson approximates the root using Taylor series expansion neglecting higher order terms. Interpolation uses forward difference tables to construct a polynomial approximation of a function.
numericai matmatic matlab uygulamalar ali abdullahAli Abdullah
The document discusses various interpolation methods including Newton's forward and backward interpolation methods. Newton's forward interpolation method uses forward difference operators to calculate interpolated values near the beginning of a data set. Newton's backward interpolation method uses backward difference operators to calculate interpolated values near the end of a data set. The document provides examples of applying Newton's forward and backward interpolation methods to calculate interpolated values using given data tables. It also discusses writing a MATLAB program to calculate interpolated values using a third degree polynomial interpolation.
The document defines the trapezoidal rule for approximating definite integrals. It provides the trapezoidal formula, explains the geometric interpretation of dividing the region into trapezoids, and outlines an algorithm and flowchart for implementing the trapezoidal rule in Python. Sample problems applying the trapezoidal rule are included to evaluate definite integrals numerically.
1) This document discusses kinematics equations for motion in one and two dimensions. It presents the equations for position, velocity, and acceleration as vectors along the x and y axes.
2) Equations are developed for the velocity and position of an object experiencing constant acceleration due to gravity along the y-axis of a projectile motion.
3) The equations derived allow calculating the velocity, position, and acceleration of an object along each axis over time given the initial position and velocity.
The document discusses convolution, which is a mathematical operation used to describe the output of a linear time-invariant system when its input is another function. Convolution combines two sequences to produce a third sequence. It has properties like commutativity and associativity. To perform a convolution, the sequences can be directly evaluated as a summation, plotted graphically and multiplied/summed, or calculated using a slide rule method by shifting and multiplying corresponding terms.
The document discusses linear transformations. It provides examples of determining if functions define linear transformations by checking if they satisfy the property that T(αu + βv) = αT(u) + βT(v). It then gives an example of using a system of equations to determine the output of a linear transformation T for a given input, when the outputs of T for two other example inputs are given.
This document discusses three exercises involving linear transformations. The exercises ask the reader to determine if given functions define linear transformations and to determine the output of linear transformations given their behavior on sample inputs. The document provides the definitions, inputs, and step-by-step workings to solve each exercise. It concludes that exercises 1 and 3 define linear transformations while exercise 2 does not and determines the output of two other linear transformations.
The document discusses derivatives and some rules for finding derivatives:
- The derivative of a function f(x) is defined as the limit of the difference quotient as h approaches 0.
- The derivative of a constant c is 0.
- Important formulas are given for finding the derivatives of xn, x, ex, loge x, and other functions.
- Rules are provided for finding the derivative of sums, differences, products, quotients, and composite functions using the chain rule.
- Examples are worked out for finding the derivatives of various polynomial functions.
The document analyzes linear transformations in R2 and R3. It determines whether three given functions define linear transformations based on whether they satisfy the property T(αu + βv) = αT(u) + βT(v).
The first function f(x,y) = (3x - y, x + y) is determined to define a linear transformation in R2 since it satisfies the property.
The second function f(x,y,z) = (x,y,z2) is determined not to define a linear transformation in R3 since z2 is not a linear term.
The third function f(x,y,z) = (x +
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1. Graeffe's root squaring method is used to find all the roots of a polynomial equation by repeatedly squaring the equation. This separates the roots so they can be easily determined.
2. The method was applied to find the roots of x3-8x2 + 17x - 10 = 0. After repeating the process, the roots were determined to be 5, 2, and 1.
3. The same method was used to find the roots of x3-2x2-5x+6=0, resulting in roots of 3, -2, and 1.
4. The method can also determine complex roots, using properties of how the coefficients fluctuate under squ
1. Graeffe's root squaring method is used to find all the roots of a polynomial equation by repeatedly squaring the equation. This separates the roots so they can be easily determined.
2. The method was applied to find the roots of x3-8x2 + 17x - 10 = 0. After repeating the process, the roots were determined to be 5, 2, and 1.
3. The same method was used to find the roots of x3-2x2-5x+6=0, resulting in roots of 3, -2, and 1.
4. The method can also determine complex roots, using properties of how the coefficients fluctuate under squ
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
What is an RPA CoE? Session 2 – CoE RolesDianaGray10
In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
33. hX Y P
“hello” “hello”
“hello ben” “hello ben”
“hello world” “hello world”
34. hX Y P
“it was” “it was”
“it was the” “it was the”
“it was the best” “it was the best”
“It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness… “, A Tale of Two Cities, Charles Dickens
50,000
300,000 (loss = 1.6066)
1,000,000 (loss = 1.8197)
“it was the best of” “it wes the best of” 2,000,000 (loss = 4.0844)
88. Long Short-Term Memory (LSTM)
Source: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
89. Reference
1. Long Term-Short Memory (Hochreiter, 1997),
http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
2. Learning Long Term Dependencies With Gradient Descent is Difficult (Yoshua Bengio, 1994),
http://www.dsi.unifi.it/~paolo/ps/tnn-94-gradient.pdf
3. http://neuralnetworksanddeeplearning.com/chap5.html
4. Deep Learning, Ian Goodfellow et al., The MIT Press
5. Recurrent Neural Networks, LSTM, Andrej Karpathy, Stanford Lectures,
https://www.youtube.com/watch?v=iX5V1WpxxkY
Alex Kalinin alex@alexkalinin.com
Editor's Notes
First, we calculate new hidden state. We use both the previous hidden state and the input. Using the previous hidden states provides “memory”.
Then, we use new hidden state to calculate new output, y. This is a forward pass.
All operations are differentiable, so we can use vanilla back-propagation to train our network.
First, we calculate new hidden state. We use both the previous hidden state and the input. Using the previous hidden states provides “memory”.
Then, we use new hidden state to calculate new output, y. This is a forward pass.
All operations are differentiable, so we can use vanilla back-propagation to train our network.
We need to train our network. Calculating L is a forward pass. Updating weights is a back-propagation.