This document provides a tutorial on spectral clustering. It discusses the history and foundations of spectral clustering including graph partitioning, ratio cut, normalized cut and minmax cut objectives. It also covers properties of the graph Laplacian matrix and how to recover partitions from eigenvectors. The document compares different clustering objectives and shows their performance on example datasets. Finally, it discusses extensions of spectral clustering to bipartite graphs.
Spectral clustering is a technique for clustering data points into groups using the spectrum (eigenvalues and eigenvectors) of the similarity matrix of the points. It works by constructing a graph from the pairwise similarities of points, calculating the Laplacian of the graph, and using the k eigenvectors of the Laplacian corresponding to the smallest eigenvalues to embed the points into a k-dimensional space. K-means clustering is then applied to the embedded points to obtain the final clustering. The document discusses two basic spectral clustering algorithms that differ in whether they use the normalized or unnormalized Laplacian.
This document provides an overview of spectral clustering. It begins with a review of clustering and introduces the similarity graph and graph Laplacian. It then describes the spectral clustering algorithm and interpretations from the perspectives of graph cuts, random walks, and perturbation theory. Practical details like constructing the similarity graph, computing eigenvectors, choosing the number of clusters, and which graph Laplacian to use are also discussed. The document aims to explain the mathematical foundations and intuitions behind spectral clustering.
Principal component analysis and matrix factorizations for learning (part 2) ...zukun
1) Spectral clustering is a technique for clustering data based on the eigenvectors of the similarity matrix of the data. 2) It works by computing the generalized eigenvectors of the normalized graph Laplacian matrix, which leads to a low-dimensional embedding of the data that can then be clustered using k-means. 3) Spectral clustering is related to other graph clustering techniques like normalized cut that aim to minimize similarities between clusters while balancing cluster sizes.
Distributed Parallel Process Particle Swarm Optimization on Fixed Charge Netw...Corey Clark, Ph.D.
The document presents a dynamically distributed binary particle swarm optimization (BPSO) approach for solving fixed-charge network flow problems. The approach distributes the BPSO algorithm across a cluster of devices using a distributed accelerated analytics platform. Testing showed the distributed BPSO approach found better solutions faster than serial BPSO and optimization approaches for various problem sizes, demonstrating the benefits of dynamic distributed computing for difficult mixed integer programs.
This document provides an overview of higher dimensional theories and their connection to lower dimensional theories through renormalization group flows and fixed points. It discusses the Banks-Zaks fixed point of QCD specifically. Perturbative fixed points in higher dimensions are connected to non-perturbative fixed points in lower dimensions, allowing access to these non-perturbative regimes. As an example, it examines the O(N)×O(m) Landau-Ginzburg-Wilson model in 6-2 dimensions and calculates critical exponents to show universality between this theory and the 4D model. It also shows that critical exponents in QCD are renormalization scheme independent by calculating them to high loops in different schemes.
This document summarizes quantization design techniques including Lloyd-Max quantizers and variable rate optimum quantizers. It discusses the problem setup for scalar quantization and outlines the local optimality conditions, alternating optimization approach, and dynamic programming approach for designing Lloyd-Max quantizers. It also covers the problem setup for variable rate optimum quantizer design subject to an entropy constraint, and describes analyzing this using a generalized Lloyd-Max algorithm.
Spectral clustering is a technique for clustering data points into groups using the spectrum (eigenvalues and eigenvectors) of the similarity matrix of the points. It works by constructing a graph from the pairwise similarities of points, calculating the Laplacian of the graph, and using the k eigenvectors of the Laplacian corresponding to the smallest eigenvalues to embed the points into a k-dimensional space. K-means clustering is then applied to the embedded points to obtain the final clustering. The document discusses two basic spectral clustering algorithms that differ in whether they use the normalized or unnormalized Laplacian.
This document provides an overview of spectral clustering. It begins with a review of clustering and introduces the similarity graph and graph Laplacian. It then describes the spectral clustering algorithm and interpretations from the perspectives of graph cuts, random walks, and perturbation theory. Practical details like constructing the similarity graph, computing eigenvectors, choosing the number of clusters, and which graph Laplacian to use are also discussed. The document aims to explain the mathematical foundations and intuitions behind spectral clustering.
Principal component analysis and matrix factorizations for learning (part 2) ...zukun
1) Spectral clustering is a technique for clustering data based on the eigenvectors of the similarity matrix of the data. 2) It works by computing the generalized eigenvectors of the normalized graph Laplacian matrix, which leads to a low-dimensional embedding of the data that can then be clustered using k-means. 3) Spectral clustering is related to other graph clustering techniques like normalized cut that aim to minimize similarities between clusters while balancing cluster sizes.
Distributed Parallel Process Particle Swarm Optimization on Fixed Charge Netw...Corey Clark, Ph.D.
The document presents a dynamically distributed binary particle swarm optimization (BPSO) approach for solving fixed-charge network flow problems. The approach distributes the BPSO algorithm across a cluster of devices using a distributed accelerated analytics platform. Testing showed the distributed BPSO approach found better solutions faster than serial BPSO and optimization approaches for various problem sizes, demonstrating the benefits of dynamic distributed computing for difficult mixed integer programs.
This document provides an overview of higher dimensional theories and their connection to lower dimensional theories through renormalization group flows and fixed points. It discusses the Banks-Zaks fixed point of QCD specifically. Perturbative fixed points in higher dimensions are connected to non-perturbative fixed points in lower dimensions, allowing access to these non-perturbative regimes. As an example, it examines the O(N)×O(m) Landau-Ginzburg-Wilson model in 6-2 dimensions and calculates critical exponents to show universality between this theory and the 4D model. It also shows that critical exponents in QCD are renormalization scheme independent by calculating them to high loops in different schemes.
This document summarizes quantization design techniques including Lloyd-Max quantizers and variable rate optimum quantizers. It discusses the problem setup for scalar quantization and outlines the local optimality conditions, alternating optimization approach, and dynamic programming approach for designing Lloyd-Max quantizers. It also covers the problem setup for variable rate optimum quantizer design subject to an entropy constraint, and describes analyzing this using a generalized Lloyd-Max algorithm.
A New Enhanced Method of Non Parametric power spectrum Estimation.CSCJournals
The spectral analysis of non uniform sampled data sequences using Fourier Periodogram method is the classical approach. In view of data fitting and computational standpoints why the Least squares periodogram(LSP) method is preferable than the “classical” Fourier periodogram and as well as to the frequently-used form of LSP due to Lomb and Scargle is explained. Then a new method of spectral analysis of nonuniform data sequences can be interpreted as an iteratively weighted LSP that makes use of a data-dependent weighting matrix built from the most recent spectral estimate. It is iterative and it makes use of an adaptive (i.e., data-dependent) weighting, we refer to it as the iterative adaptive approach (IAA).LSP and IAA are nonparametric methods that can be used for the spectral analysis of general data sequences with both continuous and discrete spectra. However, they are most suitable for data sequences with discrete spectra (i.e., sinusoidal data), which is the case we emphasize in this paper. Of the existing methods for nonuniform sinusoidal data, Welch, MUSIC and ESPRIT methods appear to be the closest in spirit to the IAA proposed here. Indeed, all these methods make use of the estimated covariance matrix that is computed in the first iteration of IAA from LSP. MUSIC and ESPRIT, on the other hand, are parametric methods that require a guess of the number of sinusoidal components present in the data, otherwise they cannot be used; furthermore.
study Streaming Multigrid For Gradient Domain Operations On Large ImagesChiamin Hsu
The document describes a streaming multigrid solver for solving Poisson's equation on large images. It develops a multigrid method using a B-spline finite element basis that can efficiently process images in a streaming fashion using only a small window of image rows in memory at a time. The method achieves accurate solutions to Poisson's equation on gigapixel images in only 2 V-cycles by leveraging the temporal locality of the multigrid algorithm.
A new transformation into State Transition Algorithm for finding the global m...Michael_Chou
To promote the global search ability of the original state transition algorithm, a new operator called axesion is suggested, which aims to search along the axes and strengthen single dimensional search. Several benchmark minimization
problems are used to illustrate the advantages of the improved algorithm over other random search methods. The results of
numerical experiments show that the new transformation can enhance the performance of the state transition algorithm and the new strategy is effective and reliable.
CVPR2010: Advanced ITinCVPR in a Nutshell: part 6: Mixtureszukun
1. Gaussian mixtures are commonly used in computer vision and pattern recognition tasks like classification, segmentation, and probability density function estimation.
2. The document reviews Gaussian mixtures, which model a probability distribution as a weighted sum of Gaussian distributions. It discusses estimating Gaussian mixture models with the EM algorithm and techniques for model order selection like minimum description length and Gaussian deficiency.
3. Gaussian mixtures can model images and perform color-based segmentation. The EM algorithm is used to estimate the parameters of Gaussian mixtures by alternating between expectation and maximization steps.
Solving connectivity problems via basic Linear Algebracseiitgn
Directed reachability and undirected connectivity are well studied problems in Complexity Theory. Reachability/Connectivity between distinct pairs of vertices through disjoint paths are well known but hard variations. We talk about recent algorithms to solve variants and restrictions of these problems in the static and dynamic settings by reductions to the determinant.
(If visualization is slow, please try downloading the file.)
Part 2 of a tutorial given in the Brazilian Physical Society meeting, ENFMC. Abstract: Density-functional theory (DFT) was developed 50 years ago, connecting fundamental quantum methods from early days of quantum mechanics to our days of computer-powered science. Today DFT is the most widely used method in electronic structure calculations. It helps moving forward materials sciences from a single atom to nanoclusters and biomolecules, connecting solid-state, quantum chemistry, atomic and molecular physics, biophysics and beyond. In this tutorial, I will try to clarify this pathway under a historical view, presenting the DFT pillars and its building blocks, namely, the Hohenberg-Kohn theorem, the Kohn-Sham scheme, the local density approximation (LDA) and generalized gradient approximation (GGA). I would like to open the black box misconception of the method, and present a more pedagogical and solid perspective on DFT.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris
This document proposes methods to incorporate higher-order graph properties into graph neural networks (GNNs). It shows that GNNs are as powerful as the 1-dimensional Weisfeiler-Lehman graph isomorphism test for distinguishing graphs, but cannot capture higher-order properties like triangle counts. The document introduces k-dimensional GNNs and hierarchical k-GNNs to learn representations of subgraphs. Experimental results show these methods improve over 1-GNN baselines on graph classification and regression tasks.
2014-06-20 Multinomial Logistic Regression with Apache SparkDB Tsai
Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs Christopher Morris
This document proposes a new graph kernel called the glocalized Weisfeiler-Lehman graph kernel. It extends the classic Weisfeiler-Lehman graph kernel to consider both local and global graph properties. The kernel maps graphs to feature vectors based on the k-dimensional Weisfeiler-Lehman algorithm. Approximation algorithms using adaptive sampling are introduced to make the kernel scalable to large graphs. Experimental results on graph classification benchmarks demonstrate the kernel achieves high accuracy while having fast running times.
This document proposes a modular beamforming architecture for ultrasound imaging that uses FPGA DSP cells to overcome limitations of previous designs. It interleaves the interpolation and coherent summation processes, reducing hardware resources. This allows implementing a 128-channel beamformer in a single FPGA, achieving flexibility like FPGAs but with lower power consumption like ASICs. The design is scalable, allowing a tradeoff between number of channels, time resolution, and resource usage.
We review our recent progress in the development of graph kernels. We discuss the hash graph kernel framework, which makes the computation of kernels for graphs with vertices and edges annotated with real-valued information feasible for large data sets. Moreover, we summarize our general investigation of the benefits of explicit graph feature maps in comparison to using the kernel trick. Our experimental studies on real-world data sets suggest that explicit feature maps often provide sufficient classification accuracy while being computed more efficiently. Finally, we describe how to construct valid kernels from optimal assignments to obtain new expressive graph kernels. These make use of the kernel trick to establish one-to-one correspondences. We conclude by a discussion of our results and their implication for the future development of graph kernels.
The document summarizes a presentation on minimizing tensor estimation error using alternating minimization. It begins with an introduction to tensor decompositions including CP, Tucker, and tensor train decompositions. It then discusses nonparametric tensor estimation using an alternating minimization method. The method iteratively updates components while holding other components fixed, achieving efficient computation. The analysis shows that after t iterations, the estimation error is bounded by the sum of a statistical error term and an optimization error term decaying exponentially in t. Real data analysis uses the method for multitask learning.
Principal component analysis and matrix factorizations for learning (part 1) ...zukun
This document discusses principal component analysis (PCA) and matrix factorizations for learning. It provides an overview of PCA and singular value decomposition (SVD), their history and applications. PCA and SVD are widely used techniques for dimensionality reduction and data transformation. The document also discusses how PCA relates to other methods like spectral clustering and correspondence analysis.
Ilya Shkredov – Subsets of Z/pZ with small Wiener norm and arithmetic progres...Yandex
It is proved that any subset of Z/pZ, p is a prime number, having small Wiener norm (l_1-norm of its Fourier transform) contains a subset which is close to be an arithmetic progression. We apply the obtained results to get some progress in so-called Littlewood conjecture in Z/pZ as well as in a quantitative version of Beurling-Helson theorem.
Semi-empirical Monte Carlo optical-gain modelling of Nuclear Imaging scintill...Anax Fotopoulos
The document describes a semi-empirical Monte Carlo model to estimate the optical gain (DOG) of single crystal scintillators excited by gamma rays. The model divides the crystal into layers, uses EGSnrc to simulate gamma ray absorption, and combines this with an analytical model of optical photon propagation between layers. The model is validated against experimental data for LSO:Ce, GSO:Ce and YAP:Ce crystals at 140keV and 364keV. Results show the model can predict DOG values and determine an optimum crystal thickness for different gamma ray energies.
Non-interacting and interacting Graphene in a strong uniform magnetic fieldAnkurDas60
We study monolayer graphene in a uniform magnetic field in the absence and presence of interactions. In the non-interacting limit for p/q flux quanta per unit cell, the central two bands have 2q Dirac points in the Brillouin zone in the nearest-neighbor model. These touchings and their locations are guaranteed by chiral symmetry and the lattice symmetries of the honeycomb structure. If we add a staggered potential and a next nearest neighbor hopping we find their competition leads to a topological phase transition. We also study the stability of the Dirac touchings to one-body perturbations that explicitly lowers the symmetry.
In the interacting case, we study the phases in the strong magnetic field limit. We consider on-site Hubbard and nearest-neighbor Heisenberg interactions. In the continuum limit, the theory has been studied before [1]. It has been found that there are four competing phases namely, ferromagnetic, antiferromagnetic, charge density wave, and Kekulé distorted phases. We find phase diagrams for q=3,4,5,6,9,12 where some of the phases found in the continuum limit are co-existent in the lattice limit with some phases not present in the continuum limit.
[1] M. Kharitonov PRB 85, 155439 (2012)
*NSF DMR-1306897
NSF DMR-1611161
US-Israel BSF 2016130
The document summarizes several advanced policy gradient methods for reinforcement learning, including trust region policy optimization (TRPO), proximal policy optimization (PPO), and using the natural policy gradient with the Kronecker-factored approximation (K-FAC). TRPO frames policy optimization as solving a constrained optimization problem to limit policy updates, while PPO uses a clipped objective function as a pessimistic bound. Both methods improve upon vanilla policy gradients. K-FAC provides an efficient way to approximate the natural policy gradient using the Fisher information matrix. The document reviews the theory and algorithms behind these methods.
icml2004 tutorial on spectral clustering part IIzukun
This document provides an overview of advanced and related topics in spectral clustering. It discusses spectral embedding which shows that clusters aggregate to distinct centroids in the embedded space, forming a simplex structure. It also covers perturbation analysis which treats small between-cluster connections as perturbations. Additionally, it shows the equivalence between K-means clustering and PCA in the embedded spectral space.
The document summarizes key concepts in social network analysis including metrics like degree distribution, path lengths, transitivity, and clustering coefficients. It also discusses models of network growth and structure like random graphs, small-world networks, and preferential attachment. Computational aspects of analyzing large networks like calculating shortest paths and the diameter are also covered.
A New Enhanced Method of Non Parametric power spectrum Estimation.CSCJournals
The spectral analysis of non uniform sampled data sequences using Fourier Periodogram method is the classical approach. In view of data fitting and computational standpoints why the Least squares periodogram(LSP) method is preferable than the “classical” Fourier periodogram and as well as to the frequently-used form of LSP due to Lomb and Scargle is explained. Then a new method of spectral analysis of nonuniform data sequences can be interpreted as an iteratively weighted LSP that makes use of a data-dependent weighting matrix built from the most recent spectral estimate. It is iterative and it makes use of an adaptive (i.e., data-dependent) weighting, we refer to it as the iterative adaptive approach (IAA).LSP and IAA are nonparametric methods that can be used for the spectral analysis of general data sequences with both continuous and discrete spectra. However, they are most suitable for data sequences with discrete spectra (i.e., sinusoidal data), which is the case we emphasize in this paper. Of the existing methods for nonuniform sinusoidal data, Welch, MUSIC and ESPRIT methods appear to be the closest in spirit to the IAA proposed here. Indeed, all these methods make use of the estimated covariance matrix that is computed in the first iteration of IAA from LSP. MUSIC and ESPRIT, on the other hand, are parametric methods that require a guess of the number of sinusoidal components present in the data, otherwise they cannot be used; furthermore.
study Streaming Multigrid For Gradient Domain Operations On Large ImagesChiamin Hsu
The document describes a streaming multigrid solver for solving Poisson's equation on large images. It develops a multigrid method using a B-spline finite element basis that can efficiently process images in a streaming fashion using only a small window of image rows in memory at a time. The method achieves accurate solutions to Poisson's equation on gigapixel images in only 2 V-cycles by leveraging the temporal locality of the multigrid algorithm.
A new transformation into State Transition Algorithm for finding the global m...Michael_Chou
To promote the global search ability of the original state transition algorithm, a new operator called axesion is suggested, which aims to search along the axes and strengthen single dimensional search. Several benchmark minimization
problems are used to illustrate the advantages of the improved algorithm over other random search methods. The results of
numerical experiments show that the new transformation can enhance the performance of the state transition algorithm and the new strategy is effective and reliable.
CVPR2010: Advanced ITinCVPR in a Nutshell: part 6: Mixtureszukun
1. Gaussian mixtures are commonly used in computer vision and pattern recognition tasks like classification, segmentation, and probability density function estimation.
2. The document reviews Gaussian mixtures, which model a probability distribution as a weighted sum of Gaussian distributions. It discusses estimating Gaussian mixture models with the EM algorithm and techniques for model order selection like minimum description length and Gaussian deficiency.
3. Gaussian mixtures can model images and perform color-based segmentation. The EM algorithm is used to estimate the parameters of Gaussian mixtures by alternating between expectation and maximization steps.
Solving connectivity problems via basic Linear Algebracseiitgn
Directed reachability and undirected connectivity are well studied problems in Complexity Theory. Reachability/Connectivity between distinct pairs of vertices through disjoint paths are well known but hard variations. We talk about recent algorithms to solve variants and restrictions of these problems in the static and dynamic settings by reductions to the determinant.
(If visualization is slow, please try downloading the file.)
Part 2 of a tutorial given in the Brazilian Physical Society meeting, ENFMC. Abstract: Density-functional theory (DFT) was developed 50 years ago, connecting fundamental quantum methods from early days of quantum mechanics to our days of computer-powered science. Today DFT is the most widely used method in electronic structure calculations. It helps moving forward materials sciences from a single atom to nanoclusters and biomolecules, connecting solid-state, quantum chemistry, atomic and molecular physics, biophysics and beyond. In this tutorial, I will try to clarify this pathway under a historical view, presenting the DFT pillars and its building blocks, namely, the Hohenberg-Kohn theorem, the Kohn-Sham scheme, the local density approximation (LDA) and generalized gradient approximation (GGA). I would like to open the black box misconception of the method, and present a more pedagogical and solid perspective on DFT.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris
This document proposes methods to incorporate higher-order graph properties into graph neural networks (GNNs). It shows that GNNs are as powerful as the 1-dimensional Weisfeiler-Lehman graph isomorphism test for distinguishing graphs, but cannot capture higher-order properties like triangle counts. The document introduces k-dimensional GNNs and hierarchical k-GNNs to learn representations of subgraphs. Experimental results show these methods improve over 1-GNN baselines on graph classification and regression tasks.
2014-06-20 Multinomial Logistic Regression with Apache SparkDB Tsai
Logistic Regression can not only be used for modeling binary outcomes but also multinomial outcome with some extension. In this talk, DB will talk about basic idea of binary logistic regression step by step, and then extend to multinomial one. He will show how easy it's with Spark to parallelize this iterative algorithm by utilizing the in-memory RDD cache to scale horizontally (the numbers of training data.) However, there is mathematical limitation on scaling vertically (the numbers of training features) while many recent applications from document classification and computational linguistics are of this type. He will talk about how to address this problem by L-BFGS optimizer instead of Newton optimizer.
Bio:
DB Tsai is a machine learning engineer working at Alpine Data Labs. He is recently working with Spark MLlib team to add support of L-BFGS optimizer and multinomial logistic regression in the upstream. He also led the Apache Spark development at Alpine Data Labs. Before joining Alpine Data labs, he was working on large-scale optimization of optical quantum circuits at Stanford as a PhD student.
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs Christopher Morris
This document proposes a new graph kernel called the glocalized Weisfeiler-Lehman graph kernel. It extends the classic Weisfeiler-Lehman graph kernel to consider both local and global graph properties. The kernel maps graphs to feature vectors based on the k-dimensional Weisfeiler-Lehman algorithm. Approximation algorithms using adaptive sampling are introduced to make the kernel scalable to large graphs. Experimental results on graph classification benchmarks demonstrate the kernel achieves high accuracy while having fast running times.
This document proposes a modular beamforming architecture for ultrasound imaging that uses FPGA DSP cells to overcome limitations of previous designs. It interleaves the interpolation and coherent summation processes, reducing hardware resources. This allows implementing a 128-channel beamformer in a single FPGA, achieving flexibility like FPGAs but with lower power consumption like ASICs. The design is scalable, allowing a tradeoff between number of channels, time resolution, and resource usage.
We review our recent progress in the development of graph kernels. We discuss the hash graph kernel framework, which makes the computation of kernels for graphs with vertices and edges annotated with real-valued information feasible for large data sets. Moreover, we summarize our general investigation of the benefits of explicit graph feature maps in comparison to using the kernel trick. Our experimental studies on real-world data sets suggest that explicit feature maps often provide sufficient classification accuracy while being computed more efficiently. Finally, we describe how to construct valid kernels from optimal assignments to obtain new expressive graph kernels. These make use of the kernel trick to establish one-to-one correspondences. We conclude by a discussion of our results and their implication for the future development of graph kernels.
The document summarizes a presentation on minimizing tensor estimation error using alternating minimization. It begins with an introduction to tensor decompositions including CP, Tucker, and tensor train decompositions. It then discusses nonparametric tensor estimation using an alternating minimization method. The method iteratively updates components while holding other components fixed, achieving efficient computation. The analysis shows that after t iterations, the estimation error is bounded by the sum of a statistical error term and an optimization error term decaying exponentially in t. Real data analysis uses the method for multitask learning.
Principal component analysis and matrix factorizations for learning (part 1) ...zukun
This document discusses principal component analysis (PCA) and matrix factorizations for learning. It provides an overview of PCA and singular value decomposition (SVD), their history and applications. PCA and SVD are widely used techniques for dimensionality reduction and data transformation. The document also discusses how PCA relates to other methods like spectral clustering and correspondence analysis.
Ilya Shkredov – Subsets of Z/pZ with small Wiener norm and arithmetic progres...Yandex
It is proved that any subset of Z/pZ, p is a prime number, having small Wiener norm (l_1-norm of its Fourier transform) contains a subset which is close to be an arithmetic progression. We apply the obtained results to get some progress in so-called Littlewood conjecture in Z/pZ as well as in a quantitative version of Beurling-Helson theorem.
Semi-empirical Monte Carlo optical-gain modelling of Nuclear Imaging scintill...Anax Fotopoulos
The document describes a semi-empirical Monte Carlo model to estimate the optical gain (DOG) of single crystal scintillators excited by gamma rays. The model divides the crystal into layers, uses EGSnrc to simulate gamma ray absorption, and combines this with an analytical model of optical photon propagation between layers. The model is validated against experimental data for LSO:Ce, GSO:Ce and YAP:Ce crystals at 140keV and 364keV. Results show the model can predict DOG values and determine an optimum crystal thickness for different gamma ray energies.
Non-interacting and interacting Graphene in a strong uniform magnetic fieldAnkurDas60
We study monolayer graphene in a uniform magnetic field in the absence and presence of interactions. In the non-interacting limit for p/q flux quanta per unit cell, the central two bands have 2q Dirac points in the Brillouin zone in the nearest-neighbor model. These touchings and their locations are guaranteed by chiral symmetry and the lattice symmetries of the honeycomb structure. If we add a staggered potential and a next nearest neighbor hopping we find their competition leads to a topological phase transition. We also study the stability of the Dirac touchings to one-body perturbations that explicitly lowers the symmetry.
In the interacting case, we study the phases in the strong magnetic field limit. We consider on-site Hubbard and nearest-neighbor Heisenberg interactions. In the continuum limit, the theory has been studied before [1]. It has been found that there are four competing phases namely, ferromagnetic, antiferromagnetic, charge density wave, and Kekulé distorted phases. We find phase diagrams for q=3,4,5,6,9,12 where some of the phases found in the continuum limit are co-existent in the lattice limit with some phases not present in the continuum limit.
[1] M. Kharitonov PRB 85, 155439 (2012)
*NSF DMR-1306897
NSF DMR-1611161
US-Israel BSF 2016130
The document summarizes several advanced policy gradient methods for reinforcement learning, including trust region policy optimization (TRPO), proximal policy optimization (PPO), and using the natural policy gradient with the Kronecker-factored approximation (K-FAC). TRPO frames policy optimization as solving a constrained optimization problem to limit policy updates, while PPO uses a clipped objective function as a pessimistic bound. Both methods improve upon vanilla policy gradients. K-FAC provides an efficient way to approximate the natural policy gradient using the Fisher information matrix. The document reviews the theory and algorithms behind these methods.
icml2004 tutorial on spectral clustering part IIzukun
This document provides an overview of advanced and related topics in spectral clustering. It discusses spectral embedding which shows that clusters aggregate to distinct centroids in the embedded space, forming a simplex structure. It also covers perturbation analysis which treats small between-cluster connections as perturbations. Additionally, it shows the equivalence between K-means clustering and PCA in the embedded spectral space.
The document summarizes key concepts in social network analysis including metrics like degree distribution, path lengths, transitivity, and clustering coefficients. It also discusses models of network growth and structure like random graphs, small-world networks, and preferential attachment. Computational aspects of analyzing large networks like calculating shortest paths and the diameter are also covered.
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...grssieee
The document describes techniques for segmenting and classifying polarimetric synthetic aperture radar (PolSAR) images using mean-shift clustering and hierarchical clustering. It discusses (1) using mean-shift clustering to group segmented regions based on radiometric and textural attributes, (2) measuring distances between clusters using maximum likelihood estimates, and (3) performing hierarchical clustering by sequentially merging the closest clusters to minimize decreases in maximum log-likelihood. The techniques were able to effectively segment multi-looked PolSAR images into meaningful groups and classes.
The document provides an overview of community detection in networks. It defines what a community and partition are, and describes several algorithms for partitioning networks into communities:
1. Kernighan and Lin's algorithm from 1970 which iteratively swaps nodes between partitions to minimize the cost.
2. Newman and Girvan's algorithm from 2004 which removes the edge with the highest betweenness centrality on each iteration.
3. Bagrow and Bollt's algorithm from 2008 which expands a shell of nodes out from a seed node, looking for a drop in the total emerging degree to identify community boundaries.
The document also discusses different ways to define communities, assess partition quality, and represent partitioning results as a dendrogram
Mapping Ash Tree Colonization in an Agricultural Moutain Landscape_ Investiga...grssieee
This document summarizes a study that used hyperspectral imagery to map ash tree colonization in an agricultural mountain landscape. Researchers were able to accurately differentiate ash trees from other tree species using support vector machines with kernel alignment on very high resolution hyperspectral images. Field data was collected on tree species and biophysical parameters for analysis. Experimental results showed 94% overall accuracy and 89.9% producer accuracy for identifying ash trees. The study concluded that hyperspectral imagery enables accurate ash tree mapping and has potential for estimating biophysical parameters, with perspectives on spatial regularization.
This document discusses resolution limits in community detection and defines resolution-free community detection methods. It finds that methods using local edge weights, like the Constant Potts Model (CPM), are resolution-free as they do not merge communities in subgraphs that are separate in the original graph. The CPM is shown to perform well on directed networks, providing strong evidence that resolution-free methods using local edge weights can effectively detect communities across network scales.
This document summarizes a distributed cloud-based genetic algorithm framework called TunUp for tuning the parameters of data clustering algorithms. TunUp integrates existing machine learning libraries and implements genetic algorithm techniques to tune parameters like K (number of clusters) and distance measures for K-means clustering. It evaluates internal clustering quality metrics on sample datasets and tunes parameters to optimize a chosen metric like AIC. The document outlines TunUp's features, describes how it implements genetic algorithms and parallelization, and concludes it is an open solution for clustering algorithm evaluation, validation and tuning.
Random Matrix Theory and Machine Learning - Part 4Fabian Pedregosa
Deep learning models with millions or billions of parameters should overfit according to classical theory, but they do not. The emerging theory of double descent seeks to explain why larger neural networks can generalize well. Random matrix theory provides a tractable framework to model double descent through random feature models, where the number of random features controls model capacity. In the high-dimensional limit, the test error of random feature regression exhibits a double descent shape that can be computed analytically.
This document summarizes Frank Nielsen's talk on divergence-based center clustering and their applications. Some key points:
- Center-based clustering aims to minimize an objective function that assigns data points to their closest cluster centers. This is an NP-hard problem when the number of dimensions and data points are greater than 1.
- Mixed divergences use dual centroids per cluster to define cluster assignments. Total Jensen divergences are proposed as a way to make divergences more robust by incorporating a conformal factor.
- For clustering when centroids do not have closed-form solutions, initialization methods like k-means++ can be used which randomly select initial seeds without computing centroids. Total Jensen k-means++
This document discusses using Gaussian process models for change point detection in atmospheric dispersion problems. It proposes using multiple kernels in a Gaussian process to model different regimes indicated by change points. A two-stage process is used to first estimate the change point (release time) and then estimate the source location. Simulation results show the approach outperforms existing techniques in estimating change points and source locations from concentration sensor measurements. The approach is applied to model real concentration data to estimate a CBRN release scenario.
This document summarizes research on using deformable models for object recognition. It discusses using deformable part models to detect objects by optimizing part locations. Efficient algorithms like dynamic programming and min-convolutions are used for matching. Non-rigid objects are modeled using triangulated polygons that can deform individual triangles. Hierarchical shape models capture shape variations. The document applies these techniques to the PASCAL visual object recognition challenge, achieving state-of-the-art results on 10 of 20 object categories through discriminatively trained, multiscale deformable part models.
Elementary Landscape Decomposition of Combinatorial Optimization Problemsjfrchicanog
This document discusses elementary landscape decomposition for analyzing combinatorial optimization problems. It begins with definitions of landscapes, elementary landscapes, and landscape decomposition. Elementary landscapes have specific properties, like local maxima and minima. Any landscape can be decomposed into a set of elementary components. This decomposition provides insights into problem structure and can be used to design selection strategies and predict search performance. The document concludes that landscape decomposition is useful for understanding problems but methodology is still needed to decompose general landscapes.
This document summarizes a class lecture on global illumination techniques for computer graphics. It discusses ray tracing and path tracing to solve the rendering equation through Monte Carlo integration. Radiosity for diffuse interreflection using form factors is covered. Participating media and photon mapping are also summarized. The next class will cover acceleration structures to speed up ray tracing computations. Project 4 is assigned, involving implementing a simple ray tracer.
Introduction to second gradient theory of elasticity - Arjun NarayananArjun Narayanan
This document introduces higher gradient theories of elasticity. It begins with an overview of how gradients appear in classical field theories like Newtonian gravity and Einsteinian gravity. It then discusses how higher gradients are relevant to continuum mechanics. The remainder of the document outlines the mathematical and variational framework for developing higher gradient elasticity theories. This includes discussions of geometric notions, variational principles, obtaining the strong form of the governing equations, and finite element discretization methods.
1. The document summarizes analysis of Gaussian belief propagation (GaBP) on graphical models, including walk-sum analysis of means and variances and orbit-product analysis of determinants.
2. GaBP provides an approximate inference algorithm that computes marginal distributions by passing messages between nodes. In tree models it is exact, but in loopy graphs it can underestimate variances.
3. The analysis shows that GaBP computes a complete walk-sum for the means but an incomplete walk-sum for the variances, accounting for its inexactness on loopy graphs. It also shows that the GaBP estimate of the partition function is equal to the totally backtracking orbit-product.
The document discusses subspace indexing on Grassmannian manifolds for large scale visual identification. It proposes using local subspace models built on neighborhoods defined by queries, but notes issues with computational complexity and lack of optimality. It then introduces Grassmannian and Stiefel manifolds to characterize subspace similarity and define distances. A model hierarchical tree is proposed to index subspaces through iterative merging based on distances on the Grassmannian manifold.
T. Popov - Drinfeld-Jimbo and Cremmer-Gervais Quantum Lie AlgebrasSEENET-MTP
This document summarizes work on Drinfeld-Jimbo and Cremmer-Gervais quantum Lie algebras. It describes how quantum spaces arise from braided deformations of commutative spaces, and how bicovariant differential calculi on quantum groups lead to quantum Lie algebras. It presents the Drinfeld-Jimbo and Cremmer-Gervais R-matrices, and shows how they give rise to quantum Lie algebra structures through their associated braidings. It also establishes relationships between Drinfeld-Jimbo, Cremmer-Gervais, and "strict RIME" quantum Lie algebras through changes of basis.
Elementary Landscape Decomposition of Combinatorial Optimization Problemsjfrchicanog
This document summarizes research on decomposing optimization problem landscapes into elementary components. It introduces landscape theory and defines elementary landscapes as eigenvectors of the graph Laplacian. While most real landscapes are non-elementary, any landscape can be decomposed into a set of elementary landscapes. The document outlines a general methodology for performing such decompositions which involves representing the objective function as a vector and computing its projections onto the eigenvectors of the Laplacian matrix. Examples of applying this methodology to problems like the traveling salesman and quadratic assignment problems are also discussed.
Similar to icml2004 tutorial on spectral clustering part I (20)
Mylyn helps address information overload and context loss when multi-tasking. It integrates tasks into the IDE workflow and uses a degree-of-interest model to monitor user interaction and provide a task-focused UI with features like view filtering, element decoration, automatic folding and content assist ranking. This creates a single view of all tasks that are centrally managed within the IDE.
This document provides an overview of OpenCV, an open source computer vision and machine learning software library. It discusses OpenCV's core functionality for representing images as matrices and directly accessing pixel data. It also covers topics like camera calibration, feature point extraction and matching, and estimating camera pose through techniques like structure from motion and planar homography. Hints are provided for Android developers on required permissions and for planar homography estimation using additional constraints rather than OpenCV's general homography function.
This document provides information about the Computer Vision Laboratory 2012 course at the Institute of Visual Computing. The course focuses on computer vision on mobile devices and will involve 180 hours of project work per person. Students will work in groups of 1-2 people on topics like 3D reconstruction from silhouettes or stereo images on mobile devices. Key dates are provided for submitting a work plan, mid-term presentation, and final report. Contact information is given for the lecturers and teaching assistant.
This document summarizes a presentation on natural image statistics given by Siwei Lyu at the 2009 CIFAR NCAP Summer School. The presentation covered several key topics:
1) It discussed the motivation for studying natural image statistics, which is to understand representations in the visual system and develop computer vision applications like denoising.
2) It reviewed common statistical properties found in natural images like 1/f power spectra and non-Gaussian distributions.
3) Maximum entropy and Bayesian models were presented as approaches to model these statistics, with Gaussian and independent component analysis discussed as specific examples.
4) Efficient coding principles from information theory were introduced as a framework for understanding neural representations that aim to decorrelate and
Camera calibration involves determining the internal camera parameters like focal length, image center, distortion, and scaling factors that affect the imaging process. These parameters are important for applications like 3D reconstruction and robotics that require understanding the relationship between 3D world points and their 2D projections in an image. The document describes estimating internal parameters by taking images of a calibration target with known geometry and solving the equations that relate the 3D target points to their 2D image locations. Homogeneous coordinates and projection matrices are used to represent the calibration transformations mathematically.
Brunelli 2008: template matching techniques in computer visionzukun
The document discusses template matching techniques in computer vision. It begins with an overview that defines template matching and discusses some common computer vision tasks it can be used for, like object detection. It then covers topics like detection as hypothesis testing, training and testing techniques, and provides a bibliography.
The HARVEST Programme evaluates feature detectors and descriptors through indirect and direct benchmarks. Indirect benchmarks measure repeatability and matching scores on the affine covariant testbed to evaluate how features persist across transformations. Direct benchmarks evaluate features on image retrieval tasks using the Oxford 5k dataset to measure real-world performance. VLBenchmarks provides software for easily running these benchmarks and reproducing published results. It allows comparing features and selecting the best for a given application.
This document summarizes VLFeat, an open source computer vision library. It provides concise summaries of VLFeat's features, including SIFT, MSER, and other covariant detectors. It also compares VLFeat's performance to other libraries like OpenCV. The document highlights how VLFeat achieves state-of-the-art results in tasks like feature detection, description and matching while maintaining a simple MATLAB interface.
This document summarizes and compares local image descriptors. It begins with an introduction to modern descriptors like SIFT, SURF and DAISY. It then discusses efficient descriptors such as binary descriptors like BRIEF, ORB and BRISK which use comparisons of intensity value pairs. The document concludes with an overview section.
This document discusses various feature detectors used in computer vision. It begins by describing classic detectors such as the Harris detector and Hessian detector that search scale space to find distinguished locations. It then discusses detecting features at multiple scales using the Laplacian of Gaussian and determinant of Hessian. The document also covers affine covariant detectors such as maximally stable extremal regions and affine shape adaptation. It discusses approaches for speeding up detection using approximations like those in SURF and learning to emulate detectors. Finally, it outlines new developments in feature detection.
The document discusses modern feature detection techniques. It provides an introduction and agenda for a talk on advances in feature detectors and descriptors, including improvements since a 2005 paper. It also discusses software suites and benchmarks for feature detection. Several application domains are described, such as wide baseline matching, panoramic image stitching, 3D reconstruction, image search, location recognition, and object tracking.
System 1 and System 2 were basic early systems for image matching that used color and texture matching. Descriptor-based approaches like SIFT provided more invariance but not perfect invariance. Patch descriptors like SIFT were improved by making them more invariant to lighting changes like color and illumination shifts. The best performance came from combining descriptors with color invariance. Representing images as histograms of visual word occurrences captured patterns in local image patches and allowed measuring similarity between images. Large vocabularies of visual words provided more discriminative power but were costly to compute and store.
This document summarizes a research paper on internet video search. It discusses several key challenges: [1] the large variation in how the same thing can appear in images/videos due to lighting, viewpoint etc., [2] defining what defines different objects, and [3] the huge number of different things that exist. It also notes gaps in narrative understanding, shared concepts between humans and machines, and addressing diverse query contexts. The document advocates developing powerful yet simple visual features that capture uniqueness with invariance to irrelevant changes.
The document discusses computer vision techniques for object detection and localization. It describes methods like selective search that group image regions hierarchically to propose object locations. Large datasets like ImageNet and LabelMe that provide training examples are also discussed. Performance on object detection benchmarks like PASCAL VOC is shown to improve significantly over time. Evaluation standards for concept detection like those used in TRECVID are presented. The document concludes that results are impressively improving each year but that the number of detectable concepts remains limited. It also discusses making feature extraction more efficient using techniques like SURF that take advantage of integral images.
This document provides an outline and overview of Yoshua Bengio's 2012 tutorial on representation learning. The key points covered include:
1) The tutorial will cover motivations for representation learning, algorithms such as probabilistic models and auto-encoders, and analysis and practical issues.
2) Representation learning aims to automatically learn good representations of data rather than relying on handcrafted features. Learning representations can help address challenges like exploiting unlabeled data and the curse of dimensionality.
3) Deep learning algorithms attempt to learn multiple levels of increasingly complex representations, with the goal of developing more abstract, disentangled representations that generalize beyond local patterns in the data.
Advances in discrete energy minimisation for computer visionzukun
This document discusses string algorithms and data structures. It introduces the Knuth-Morris-Pratt algorithm for finding patterns in strings in O(n+m) time where n is the length of the text and m is the length of the pattern. It also discusses common string data structures like tries, suffix trees, and suffix arrays. Suffix trees and suffix arrays store all suffixes of a string and support efficient pattern matching and other string operations in linear time or O(m+logn) time where m is the pattern length and n is the text length.
This document provides a tutorial on how to use Gephi software to analyze and visualize network graphs. It outlines the basic steps of importing a sample graph file, applying layout algorithms to organize the nodes, calculating metrics, detecting communities, filtering the graph, and exporting/saving the results. The tutorial demonstrates features of Gephi including node ranking, partitioning, and interactive visualization of the graph.
EM algorithm and its application in probabilistic latent semantic analysiszukun
The document discusses the EM algorithm and its application in Probabilistic Latent Semantic Analysis (pLSA). It begins by introducing the parameter estimation problem and comparing frequentist and Bayesian approaches. It then describes the EM algorithm, which iteratively computes lower bounds to the log-likelihood function. Finally, it applies the EM algorithm to pLSA by modeling documents and words as arising from a mixture of latent topics.
This document describes an efficient framework for part-based object recognition using pictorial structures. The framework represents objects as graphs of parts with spatial relationships. It finds the optimal configuration of parts through global minimization using distance transforms, allowing fast computation despite modeling complex spatial relationships between parts. This enables soft detection to handle partial occlusion without early decisions about part locations.
Iccv2011 learning spatiotemporal graphs of human activities zukun
The document presents a new approach for learning spatiotemporal graphs of human activities from weakly supervised video data. The approach uses 2D+t tubes as mid-level features to represent activities as segmentation graphs, with nodes describing tubes and edges describing various relations. A probabilistic graph mixture model is used to model activities, and learning estimates the model parameters and permutation matrices using a structural EM algorithm. The learned models allow recognizing and segmenting activities in new videos through robust least squares inference. Evaluation on benchmark datasets demonstrates the ability to learn characteristic parts of activities and recognize them under weak supervision.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.