Presentation slides for the following two papers (mainly (1)):
(1) Masuda. Proceedings of the Royal Society B: Biological Sciences, 274, 1815-1821 (2007).
(2) Masuda and Aihara. Physics Letters A, 313, 55-61 (2003).
Localized methods in graph mining exploit the local structures in a graph instead attempting to find global structures. These are widely successful at all sorts of problems including community detection, label propagation, and a few others.
Higher-order organization of complex networksDavid Gleich
A talk I gave at the Park City Institute of Mathematics about our recent work on using motifs to analyze and cluster networks. This involves a higher-order cheeger inequality in terms of motifs.
This document introduces the concept of average sensitivity of algorithms and summarizes results for several graph algorithms. It defines average sensitivity as the average change in an algorithm's output when a single input element is changed. The document presents algorithms for minimum spanning tree, minimum cuts, and matching problems that have low average sensitivity. It argues that average sensitivity is an important dimension for understanding the stability of algorithms and their practical use with noisy real-world data.
Spacey random walks and higher order Markov chainsDavid Gleich
My talk at SIAM NetSci workshop (2015) on our new spacey random walk and spacey random surfer models and how we derived them. There many potential extensions and opportunities to use this for analyzing big data as tensors.
This document discusses a technique called "data smashing" for zero-knowledge feature-free anomaly detection in data. Data smashing involves quantizing, inverting, and comparing signals to detect anomalies without extracting or using features from the data. It allows detection of deviations from normal patterns in an unlabeled dataset in a way that preserves privacy and limits assumptions about the data's features or generation process.
Return times of random walk on generalized random graphsNaoki Masuda
1) The document summarizes a study on return times of random walks on generalized random graphs.
2) The authors derive an explicit formula for the return probability of a random walk using generating functions and Lagrange's inversion formula.
3) They apply the formula to examples like Cayley trees, Erdos-Renyi random graphs, and scale-free networks, showing slower return times for more heterogeneous networks.
PageRank Centrality of dynamic graph structuresDavid Gleich
A talk I gave at the SIAM Annual Meeting Mini-symposium on the mathematics of the power grid organized by Mahantesh Halappanavar. I discuss a few ideas on how our dynamic centrality could help analyze such situations.
Localized methods in graph mining exploit the local structures in a graph instead attempting to find global structures. These are widely successful at all sorts of problems including community detection, label propagation, and a few others.
Higher-order organization of complex networksDavid Gleich
A talk I gave at the Park City Institute of Mathematics about our recent work on using motifs to analyze and cluster networks. This involves a higher-order cheeger inequality in terms of motifs.
This document introduces the concept of average sensitivity of algorithms and summarizes results for several graph algorithms. It defines average sensitivity as the average change in an algorithm's output when a single input element is changed. The document presents algorithms for minimum spanning tree, minimum cuts, and matching problems that have low average sensitivity. It argues that average sensitivity is an important dimension for understanding the stability of algorithms and their practical use with noisy real-world data.
Spacey random walks and higher order Markov chainsDavid Gleich
My talk at SIAM NetSci workshop (2015) on our new spacey random walk and spacey random surfer models and how we derived them. There many potential extensions and opportunities to use this for analyzing big data as tensors.
This document discusses a technique called "data smashing" for zero-knowledge feature-free anomaly detection in data. Data smashing involves quantizing, inverting, and comparing signals to detect anomalies without extracting or using features from the data. It allows detection of deviations from normal patterns in an unlabeled dataset in a way that preserves privacy and limits assumptions about the data's features or generation process.
Return times of random walk on generalized random graphsNaoki Masuda
1) The document summarizes a study on return times of random walks on generalized random graphs.
2) The authors derive an explicit formula for the return probability of a random walk using generating functions and Lagrange's inversion formula.
3) They apply the formula to examples like Cayley trees, Erdos-Renyi random graphs, and scale-free networks, showing slower return times for more heterogeneous networks.
PageRank Centrality of dynamic graph structuresDavid Gleich
A talk I gave at the SIAM Annual Meeting Mini-symposium on the mathematics of the power grid organized by Mahantesh Halappanavar. I discuss a few ideas on how our dynamic centrality could help analyze such situations.
The document discusses measuring sample quality using kernels. It introduces the kernel Stein discrepancy (KSD) as a new quality measure for comparing samples approximating a target distribution. The KSD is based on Stein's method and uses reproducing kernels. It can detect when a sample sequence is converging to the target distribution or not. Computing the KSD reduces to pairwise evaluations of kernel functions and is feasible. The KSD converges to zero if and only if the sample sequence converges to the target distribution for certain choices of kernels like the inverse multiquadric kernel with parameter between -1 and 0.
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
1) Start with a small learning rate and large batch size to find a flat minimum with good generalization. 2) Gradually increase the learning rate and decrease the batch size to find sharper minima that may improve training accuracy. 3) Monitor both training and validation/test accuracy - similar accuracy suggests good generalization while different accuracy indicates overfitting.
In categorical data analysis, the odds ratio is an important approach to quantify the strength of association between two variables in a contingency table. Here, we present a novel Bayesian approach to analyze an unrestricted 2x2 table along with several constructed nuisance parameters using objective Bayesian methods. The prior for the odds ratio has many desirable properties such as propriety, symmetry and finite moments on log scale, and others. Simulation results indicate that the proposed approach to this problem is far superior to the straightforward and widely used frequentist approaches that dominate this area as well as other objective candidates. Real data examples also typically yield more sensible results, especially for small sample sizes or for tables that contain zeros.
Delayed acceptance for Metropolis-Hastings algorithmsChristian Robert
The document proposes a delayed acceptance method for accelerating Metropolis-Hastings algorithms. It begins with a motivating example of non-informative inference for mixture models where computing the prior density is costly. It then introduces the delayed acceptance approach which splits the acceptance probability into pieces that are evaluated sequentially, avoiding computing the full acceptance ratio each time. It validates that the delayed acceptance chain is reversible and provides bounds on its spectral gap and asymptotic variance compared to the original chain. Finally, it discusses optimizing the delayed acceptance approach by considering the expected square jump distance and cost per iteration to maximize efficiency.
1. The document provides examples and concepts related to limits, including definitions, evaluating limits of various functions as the variable approaches a number, and evaluating one-sided and two-sided limits.
2. Worked examples evaluate limits of rational functions, radical functions, and trigonometric functions as the variable approaches numbers or infinity.
3. Key concepts such as left-hand and right-hand limits, and limits at infinity, are demonstrated through multiple examples.
The document describes a non-local neural network approach for image processing and computer vision tasks. It summarizes the non-local means approach which averages similar pixel values while not averaging dissimilar pixels. It then describes how the non-local neural network extends this idea by using a similarity function to compute a weighted sum of all pixels, allowing it to capture long-range dependencies. The document outlines various implementations of the non-local operation and how it can be integrated into common network architectures like ResNet.
The document discusses distributed online convex optimization algorithms for coordinating multiple agents. It presents a coordination algorithm where each agent performs proportional-integral feedback to minimize local objectives while sharing information with neighbors over noisy communication channels. The algorithm is proven to achieve exponential convergence of second moments to the optimal solution and an ultimate bound on the error that depends on the noise level. Simulation results on a medical diagnosis example are also presented to illustrate the algorithm's behavior.
1. The document discusses various machine learning algorithms for classification and regression including logistic regression, neural networks, decision trees, and ensemble methods.
2. It explains key concepts like overfitting, regularization, kernel methods, and different types of neural network architectures like convolutional neural networks.
3. Decision trees are described as intuitive algorithms for classification and regression but are unstable and use greedy optimization. Techniques like pre-pruning and post-pruning are used to improve decision trees.
This document provides errata for the second edition, first printing of a book. It lists over 30 corrections needed for page numbers, equations, figures, problems and other elements. Corrections include fixing page numbers, changing values in equations and problems, replacing words and phrases, and updating figures. The errata cover issues throughout the book to improve accuracy.
1. The document is a tutorial on inferential statistics, statistical modeling, and survey methods.
2. It includes an example of calculating Pearson's correlation coefficient between two variables and testing for a significant positive correlation.
3. It also demonstrates linear regression analysis to model the relationship between two variables and test whether the slope of the regression line is significantly different from a given value.
Global network structure of dominance hierarchy of ant workersAntnet slides-s...Naoki Masuda
Presentation slides for the following paper:
Hiroyuki Shimoji, Masato S. Abe, Kazuki Tsuji, Naoki Masuda.
Global network structure of dominance hierarchy of ant workers.
Journal of the Royal Society Interface, in press (2014).
The document summarizes 10 common misunderstandings about feng shui and provides clarification on each one based on the author's practical experience. Some misconceptions are only partially true if certain additional conditions are met, while others are outright false. The author aims to offer clearer understanding of feng shui and dispel confusion caused by incomplete or incorrect information spread through various sources. Feng shui is presented as one factor among others that can create beneficial conditions but not solve all problems or guarantee certain outcomes like wealth or love on its own.
Preparing Students for Collaborative Leadership: Lowering the walls and cross...Lyle Birkey
Preparing Students for Collaborative Leadership: Lowering the walls and crossing boundaries using business-based professional assessments to develop interdisciplinary teams
Pengujian mutu fisik benih dilakukan untuk menentukan komposisi benih murni, benih lain, dan kotoran pada contoh benih. Contoh dibagi menjadi tiga komponen berdasarkan kriteria dan ditimbang. Persentase masing-masing dihitung dari berat awal dan akhir. Hasil menunjukkan kadar benih murni, benih lain, dan kotoran pada benih kedelai, jagung, dan padi.
Maximizing the spectral gap of networks produced by node removalNaoki Masuda
Presentation slides for the following two papers (currently available in the pdf format only).
(1) T. Watanabe, N. Masuda.
Enhancing the spectral gap of networks by node removal.
Physical Review E, 82, 046102 (2010).
(2) N. Masuda, T. Fujie, K. Murota.
Semidefinite programming for maximizing the spectral gap.
In: Complex Networks IV, Studies in Computational Intelligence, 476, 155-163 (2013).
Slides from our PacificVis 2015 presentation.
The paper tackles the problems of the “giant hairballs”, the dense and tangled structures often resulting from visualiza- tion of large social graphs. Proposed is a high-dimensional rotation technique called AGI3D, combined with an ability to filter elements based on social centrality values. AGI3D is targeted for a high-dimensional embedding of a social graph and its projection onto 3D space. It allows the user to ro- tate the social graph layout in the high-dimensional space by mouse dragging of a vertex. Its high-dimensional rotation effects give the user an illusion that he/she is destructively reshaping the social graph layout but in reality, it assists the user to find a preferred positioning and direction in the high- dimensional space to look at the internal structure of the social graph layout, keeping it unmodified. A prototype im- plementation of the proposal called Social Viewpoint Finder is tested with about 70 social graphs and this paper reports four of the analysis results.
Complex systems are characterized by constituents -- from neurons in the brain to individuals in a social network -- which exhibit special structural organization and nonlinear dynamics. As a consequence, a complex system cannot be understood by studying its units separately because their interactions lead to unexpected emerging phenomena, from collective behavior to phase transitions.
Recently, we have discovered that a new level of complexity characterizes a variety of natural and artificial systems, where units interact, simultaneously, in distinct ways. For instance, this is the case of multimodal transportation systems (e.g., metro, bus and train networks) or of biological molecules, whose interactions might be of different type (e.g. physical, chemical, genetic) or functionality (e.g., regulatory, inhibitory, etc.). The unprecedented newfound wealth of multivariate data allows to categorize system's interdependency by defining distinct "layers", each one encoding a different network representation of the system. The result is a multilayer network model.
Analyzing data from different domains -- including molecular biology, neuroscience, urban transport, telecommunications -- we will show that neglecting or disregarding multivariate information might lead to poor results. Conversely, multilayer models provide a suitable framework for complex data analytics, allowing to quantify the resilience of a system to perturbations (e.g., localized failures or targeted attacks), improving forecasting of spreading processes and accuracy in classification problems.
The document discusses measuring sample quality using kernels. It introduces the kernel Stein discrepancy (KSD) as a new quality measure for comparing samples approximating a target distribution. The KSD is based on Stein's method and uses reproducing kernels. It can detect when a sample sequence is converging to the target distribution or not. Computing the KSD reduces to pairwise evaluations of kernel functions and is feasible. The KSD converges to zero if and only if the sample sequence converges to the target distribution for certain choices of kernels like the inverse multiquadric kernel with parameter between -1 and 0.
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
1) Start with a small learning rate and large batch size to find a flat minimum with good generalization. 2) Gradually increase the learning rate and decrease the batch size to find sharper minima that may improve training accuracy. 3) Monitor both training and validation/test accuracy - similar accuracy suggests good generalization while different accuracy indicates overfitting.
In categorical data analysis, the odds ratio is an important approach to quantify the strength of association between two variables in a contingency table. Here, we present a novel Bayesian approach to analyze an unrestricted 2x2 table along with several constructed nuisance parameters using objective Bayesian methods. The prior for the odds ratio has many desirable properties such as propriety, symmetry and finite moments on log scale, and others. Simulation results indicate that the proposed approach to this problem is far superior to the straightforward and widely used frequentist approaches that dominate this area as well as other objective candidates. Real data examples also typically yield more sensible results, especially for small sample sizes or for tables that contain zeros.
Delayed acceptance for Metropolis-Hastings algorithmsChristian Robert
The document proposes a delayed acceptance method for accelerating Metropolis-Hastings algorithms. It begins with a motivating example of non-informative inference for mixture models where computing the prior density is costly. It then introduces the delayed acceptance approach which splits the acceptance probability into pieces that are evaluated sequentially, avoiding computing the full acceptance ratio each time. It validates that the delayed acceptance chain is reversible and provides bounds on its spectral gap and asymptotic variance compared to the original chain. Finally, it discusses optimizing the delayed acceptance approach by considering the expected square jump distance and cost per iteration to maximize efficiency.
1. The document provides examples and concepts related to limits, including definitions, evaluating limits of various functions as the variable approaches a number, and evaluating one-sided and two-sided limits.
2. Worked examples evaluate limits of rational functions, radical functions, and trigonometric functions as the variable approaches numbers or infinity.
3. Key concepts such as left-hand and right-hand limits, and limits at infinity, are demonstrated through multiple examples.
The document describes a non-local neural network approach for image processing and computer vision tasks. It summarizes the non-local means approach which averages similar pixel values while not averaging dissimilar pixels. It then describes how the non-local neural network extends this idea by using a similarity function to compute a weighted sum of all pixels, allowing it to capture long-range dependencies. The document outlines various implementations of the non-local operation and how it can be integrated into common network architectures like ResNet.
The document discusses distributed online convex optimization algorithms for coordinating multiple agents. It presents a coordination algorithm where each agent performs proportional-integral feedback to minimize local objectives while sharing information with neighbors over noisy communication channels. The algorithm is proven to achieve exponential convergence of second moments to the optimal solution and an ultimate bound on the error that depends on the noise level. Simulation results on a medical diagnosis example are also presented to illustrate the algorithm's behavior.
1. The document discusses various machine learning algorithms for classification and regression including logistic regression, neural networks, decision trees, and ensemble methods.
2. It explains key concepts like overfitting, regularization, kernel methods, and different types of neural network architectures like convolutional neural networks.
3. Decision trees are described as intuitive algorithms for classification and regression but are unstable and use greedy optimization. Techniques like pre-pruning and post-pruning are used to improve decision trees.
This document provides errata for the second edition, first printing of a book. It lists over 30 corrections needed for page numbers, equations, figures, problems and other elements. Corrections include fixing page numbers, changing values in equations and problems, replacing words and phrases, and updating figures. The errata cover issues throughout the book to improve accuracy.
1. The document is a tutorial on inferential statistics, statistical modeling, and survey methods.
2. It includes an example of calculating Pearson's correlation coefficient between two variables and testing for a significant positive correlation.
3. It also demonstrates linear regression analysis to model the relationship between two variables and test whether the slope of the regression line is significantly different from a given value.
Global network structure of dominance hierarchy of ant workersAntnet slides-s...Naoki Masuda
Presentation slides for the following paper:
Hiroyuki Shimoji, Masato S. Abe, Kazuki Tsuji, Naoki Masuda.
Global network structure of dominance hierarchy of ant workers.
Journal of the Royal Society Interface, in press (2014).
The document summarizes 10 common misunderstandings about feng shui and provides clarification on each one based on the author's practical experience. Some misconceptions are only partially true if certain additional conditions are met, while others are outright false. The author aims to offer clearer understanding of feng shui and dispel confusion caused by incomplete or incorrect information spread through various sources. Feng shui is presented as one factor among others that can create beneficial conditions but not solve all problems or guarantee certain outcomes like wealth or love on its own.
Preparing Students for Collaborative Leadership: Lowering the walls and cross...Lyle Birkey
Preparing Students for Collaborative Leadership: Lowering the walls and crossing boundaries using business-based professional assessments to develop interdisciplinary teams
Pengujian mutu fisik benih dilakukan untuk menentukan komposisi benih murni, benih lain, dan kotoran pada contoh benih. Contoh dibagi menjadi tiga komponen berdasarkan kriteria dan ditimbang. Persentase masing-masing dihitung dari berat awal dan akhir. Hasil menunjukkan kadar benih murni, benih lain, dan kotoran pada benih kedelai, jagung, dan padi.
Maximizing the spectral gap of networks produced by node removalNaoki Masuda
Presentation slides for the following two papers (currently available in the pdf format only).
(1) T. Watanabe, N. Masuda.
Enhancing the spectral gap of networks by node removal.
Physical Review E, 82, 046102 (2010).
(2) N. Masuda, T. Fujie, K. Murota.
Semidefinite programming for maximizing the spectral gap.
In: Complex Networks IV, Studies in Computational Intelligence, 476, 155-163 (2013).
Slides from our PacificVis 2015 presentation.
The paper tackles the problems of the “giant hairballs”, the dense and tangled structures often resulting from visualiza- tion of large social graphs. Proposed is a high-dimensional rotation technique called AGI3D, combined with an ability to filter elements based on social centrality values. AGI3D is targeted for a high-dimensional embedding of a social graph and its projection onto 3D space. It allows the user to ro- tate the social graph layout in the high-dimensional space by mouse dragging of a vertex. Its high-dimensional rotation effects give the user an illusion that he/she is destructively reshaping the social graph layout but in reality, it assists the user to find a preferred positioning and direction in the high- dimensional space to look at the internal structure of the social graph layout, keeping it unmodified. A prototype im- plementation of the proposal called Social Viewpoint Finder is tested with about 70 social graphs and this paper reports four of the analysis results.
Complex systems are characterized by constituents -- from neurons in the brain to individuals in a social network -- which exhibit special structural organization and nonlinear dynamics. As a consequence, a complex system cannot be understood by studying its units separately because their interactions lead to unexpected emerging phenomena, from collective behavior to phase transitions.
Recently, we have discovered that a new level of complexity characterizes a variety of natural and artificial systems, where units interact, simultaneously, in distinct ways. For instance, this is the case of multimodal transportation systems (e.g., metro, bus and train networks) or of biological molecules, whose interactions might be of different type (e.g. physical, chemical, genetic) or functionality (e.g., regulatory, inhibitory, etc.). The unprecedented newfound wealth of multivariate data allows to categorize system's interdependency by defining distinct "layers", each one encoding a different network representation of the system. The result is a multilayer network model.
Analyzing data from different domains -- including molecular biology, neuroscience, urban transport, telecommunications -- we will show that neglecting or disregarding multivariate information might lead to poor results. Conversely, multilayer models provide a suitable framework for complex data analytics, allowing to quantify the resilience of a system to perturbations (e.g., localized failures or targeted attacks), improving forecasting of spreading processes and accuracy in classification problems.
Epidemic processes on switching networksNaoki Masuda
Presentation slides for the following two papers:
- Leo Speidel, Konstantin Klemm, Víctor M. Eguíluz, Naoki Masuda.
New Journal of Physics, 18, 073013 (2016).
- Tomokatsu Onaga, James P. Gleeson, Naoki Masuda.
Physical Review Letters, 119, 108301 (2017).
This document summarizes a thesis on numerical methods for stochastic systems subject to generalized Levy noise. It includes:
1) Motivation for studying such systems from both mathematical and applicational perspectives, such as in mathematical finance and chaotic flows.
2) An introduction to Levy processes and the probability collocation method (PCM) for uncertainty quantification (UQ).
3) Details on improving PCM through a multi-element approach and constructing orthogonal polynomials for discrete measures.
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.
Spatial patterns in evolutionary games on scale-free networks and multiplexesKolja Kleineberg
The document discusses evolutionary games on scale-free networks and multiplexes. It finds that cooperation can be sustained in metric clusters that form on scale-free networks. These metric clusters shield cooperators from surrounding defectors similar to spatial selection. The survival of metric clusters is favored when the network is less heterogeneous, has a higher clustering coefficient, and the clusters are larger. Similar clusters are also found for different games played on correlated multiplex networks.
Decomposition and Denoising for moment sequences using convex optimizationBadri Narayan Bhaskar
This document summarizes research on using convex optimization techniques like atomic norm minimization to solve problems involving decomposing signals into sparse representations using atoms from predefined dictionaries. It discusses how atomic norm regularization provides a unified framework for problems like sparse recovery, low-rank matrix recovery, and line spectral estimation. It presents theoretical guarantees on exact recovery and convergence rates for atomic norm denoising and shows how to implement it using alternating direction methods and semidefinite programming. Experimental results demonstrate state-of-the-art performance of atomic norm techniques on line spectral estimation tasks.
This document summarizes techniques for approximating marginal likelihoods and Bayes factors, which are important quantities in Bayesian inference. It discusses Geyer's 1994 logistic regression approach, links to bridge sampling, and how mixtures can be used as importance sampling proposals. Specifically, it shows how optimizing the logistic pseudo-likelihood relates to the bridge sampling optimal estimator. It also discusses non-parametric maximum likelihood estimation based on simulations.
Composition of Clans for Solving Linear Systems on Parallel ArchitecturesDmitryZaitsev5
This document discusses a method for solving linear systems of equations called composition of clans. It involves decomposing the system into subgroups of related equations called clans, solving each clan separately, and then combining the solutions. This approach can provide speed-up when implemented in parallel on multiple computing nodes. The document provides examples and discusses software called ParAd that implements parallel composition of clans. It concludes that speed-ups of up to 180 times have been achieved on problems with this approach.
Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...Gota Morota
The document discusses modeling allele frequency changes over time as stochastic processes. It describes allele frequencies changing as random walks or Brownian motion. It presents the Fokker-Planck equation for describing the probability distribution of allele frequencies over time under various evolutionary forces like genetic drift, selection, and mutation. The steady state distribution of allele frequencies and solutions to the Fokker-Planck equation are discussed for different evolutionary scenarios. Time series analysis methods are introduced for modeling allele frequency change as a discrete time process. An example application to cattle genotype data is shown.
The document summarizes research on threshold network models, which generate scale-free networks without growth by assigning intrinsic weights to nodes based on a given distribution and connecting nodes based on whether their total weight exceeds a threshold. The model has been extended to spatial networks by incorporating distance between nodes and to include homophily. Analytical results show the degree distribution and other properties depend on the weight distribution and thresholding function used. Several open problems are also discussed.
Towards controlling evolutionary dynamics through network geometry: some very...Kolja Kleineberg
The document discusses how network geometry can control evolutionary dynamics through the formation of cooperating clusters. It presents examples showing how the placement of initial cooperators in metric space clusters versus randomly can influence whether cooperation emerges in evolutionary games and navigation processes on networks. The author suggests that network geometry may allow active control of evolutionary dynamics by strategically placing control agents based on the underlying geometry.
This document provides formulas and rules for calculus, including:
- Derivative rules for common functions like sin, cos, ln, and e^x.
- Properties of integrals, such as linearity and the Fundamental Theorem of Calculus.
- Formulas for area, volume, work, force, and other physical applications that use calculus.
- Guidelines for integration by parts and strategies for integrals involving trigonometric functions.
This document provides formulas and rules for calculus, including:
- Derivative rules for common functions like sin, cos, ln, and e^x.
- Properties of integrals, such as linearity and the Fundamental Theorem of Calculus.
- Formulas for area, volume, work, force, and other physical applications that use calculus.
- Guidelines for integration by parts and strategies for integrals involving trigonometric functions.
This document provides formulas and rules for calculus, including:
- Derivative rules for common functions like sin, cos, ln, and e^x.
- Properties of integrals, such as linearity and the Fundamental Theorem of Calculus.
- Formulas for area, volume, work, force, and other physical applications that use calculus.
- Guidelines for integration by parts and strategies for integrals involving trigonometric functions.
Molecular dynamics simulations allow researchers to model biomolecular mechanisms across wide ranges of time and length scales. The simulations integrate Newton's laws of motion over discrete timesteps to generate molecular trajectories. Force fields are used to define potential energies and forces in the system. While all-atom representation and applicability to diverse systems are advantages, the simulations are computationally expensive and limited in system size. The document provides examples of using molecular dynamics and small angle X-ray scattering to study protein folding, gene regulation, and protein structural ensembles.
Similar to Participation costs dismiss the advantage of heterogeneous networks in evolution of cooperation (20)
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
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.
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.
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
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
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Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
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HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
“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.
Participation costs dismiss the advantage of heterogeneous networks in evolution of cooperation
1. ParNcipaNon
costs
dismiss
the
advantage
of
heterogeneous
networks
in
evoluNon
of
cooperaNon
Naoki
Masuda
(University
of
Tokyo)
Ref:
Masuda.
Proc.
R.
Soc.
B,
274,
1815-‐1821
(2007).
Also
see
Masuda
&
Aihara,
Phys.
LeK.
A,
313,
55-‐61
(2003).
3. Mechanisms
for
cooperaNon
•
•
•
•
•
•
•
Kin
selecNon
(Hamilton,
1964)
Direct
reciprocity
(Trivers,
1971;
Axelrod
&
Hamilton
1981)
•
Iterated
Prisoner’s
dilemma
Group
selecNon
(Wilson,
1975;
Traulsen
&
Nowak,
2006)
SpaNal
reciprocity
(Axelrod,
1984;
Nowak
&
May,
1992)
Indirect
reciprocity
(Nowak
&
Sigmund,
1998)
•
Image
scoring
Network
reciprocity
(Lieberman,
Hauert
&
Nowak,
2005;
Santos
et
al.,
2005,
2006;
Ohtsuki
et
al.,
2006)
Others
(punishment?,
voluntary
parNcipaNon
etc.)
4. Iterated
Prisoner’s
Dilemma
• Players
randomly
interact
with
others
• Discount
factor
w
(0
≤
w
≤
1)
to
specify
the
prob.
that
the
next
game
is
played
in
a
round
A
plays
C
D
D
C
D
C
B
plays
C
C
D
C
D
D
A
gets
3
5
1
3
1
0
A’s
accumulated
payoff
=
3
+
5w
+
1w2
+
3w3
+
1w4
+
0w5
+
…
acNon
C
D
C
(3,
3)
(0,
5)
D
(5,
0)
(1,
1)
5. • SelecNon
based
on
accumulated
payoff
aher
each
round
• Replicator
dynamics
• Best-‐response
dynamics
• Nice,
retalitatory,
and
forgiving
strategies
(e.g.
Tit-‐for-‐Tat)
are
generally
strong
(but
not
the
strongest).
6. SpaNal
Prisoner’s
Dilemma
(Axelrod,
1984;
Nowak
&
May,
1992)
• e.g.
square
laice
• Either
cooperator
or
C:24 C:18 D:24 D:12
defector
on
each
vertex
• Each
player
plays
against
all
(4
or
8)
neighbors.
C:24
C:21
C:12
D:16
C:24
C:21
C:12
D:16
C:21
C:15
D:20
D:16
7. •
•
•
Successful
strategies
propagate
aher
one
generaNon.
Result:
Cs
form
“clusters”
to
resist
invasion
by
Ds.
Note:
2-‐neighbor
CA.
More
complex
than
1-‐neighbor
dynamics
such
as
spin
(opinion)
dynamics
and
disease
dynamics
8. PD
on
the
WaKs-‐Strogatz
small-‐world
network
(Masuda
and
Aihara,
Phys.
LeK.
A,
2003)
acNon
C
D
C
(1,
1)
(0,
T)
D
(T,
0)
(0,
0)
p
=
0
p:
small
p
≈
1
1
0.8
0.6
%C
0.4
Note:
The
degrees
of
all
the
nodes
are
the
same
regardless
of
the
rewiring.
0.2
p=0
p=0.01
p=0.9
0
1
1.5
T
2
2.5
9. large
p
1
•
1-‐dim
ring
•
QualitaNvely
the
0.8
0.6
%C
0.4
same
results
for
2-‐dim
networks
small
p
1
small
p
0.2
0
0
100
200
generation
T=1.1
300
400
1
0.8
0.8
0.6
%C
0.4
0.6
%C
0.4
0.2
0.2
0
large
p
0
100
200
generation
T=1.7
300
400
large
p
0
small
p
T=3.0
0
100
200
generation
300
400
14. AssumpNon
1:
addiNve
payoff
scheme
• AddiNve:
add
the
payoffs
gained
via
all
the
neighbors
• Nowak,
Bonhoeffer
&
May
1994;
Abramson
&
Kuperman
2001;
Ebel
&
Bornholdt,
2002;
Ihi,
Killingback
&
Doebeli
2004;
Durán
&
Mulet,
2005;
Santos
et
al.,
2005;
2006;
Ohtsuki
et
al.,
2006
• Average:
divide
the
summed
payoffs
by
the
number
of
neighbors
• Kim
et
al.,
2002;
Holme
et
al.,
2003;
Vukov
&
Szabó,
2005;
Taylor
&
Nowak,
2006
16. • Average
payoff
diminishes
cooperaNon
in
heterogeneous
networks
(Santos
&
Pacheco,
J.
Evol.
Biol.,
2006).
17. AssumpNon
2:
PosiNvely
biased
payoffs
•
C
D
C
a
b
D
c
d
(
Originate
from
the
translaNon
invariance
of
replicator
dynamics
!
!
(
⇡C =
⇡D =
axC + bxD
cxC + dxD
h⇡i = ⇡C xC + ⇡D xD
xC =
˙
xD =
˙
xC (⇡C
xD (⇡D
✓
a b
c d
◆
✓
◆
h b h →
parNcipaNon
h d h
cost
✓
◆ ✓
◆
a b
ka kb
!
→
Nme
rescaling
c d
kc kd
◆
◆ ✓
✓
a b
a+k b+k
!
c d
c
d
!
a
c
h⇡i)
h⇡i)
acNon
C
D
acNon
C
D
C
(1,
1)
(0,
T)
C
(1,
1)
(S,
T)
D
(T,
0)
(0,
0)
D
(T,
S)
(0,
0)
18. Reason
for
enhanced
cooperaNon
•
Hubs
earn
more
than
leaves.
•
C
on
hubs
(with
at
least
some
C
neighbors)
are
stable.
•
C
spreads
from
hubs
to
leaves.
10
acNon
C
D
C
(1,
1)
(0,
T)
D
(T,
0)
(0,
0)
90
v1
k 1 = 100
v2
k2 = 2
=C
=D
19. Payoff
matrix
is
not
invariant
on
heterogeneous
networks
C
D
C
a
b
D
c
d
(
!
!
(
⇡C =
⇡D =
✓
h⇡i = ⇡C xC + ⇡D xD
xC =
˙
xD =
˙
xC (⇡C
xD (⇡D
✓
!
h⇡i)
h⇡i)
acNon
C
D
C
(1,
1)
(0,
T)
D
(T,
0)
(0,
0)
a
c
◆
h b h →
parNcipaNon
h d NG
h
cost
✓
◆ ✓
◆
a b
ka kb
!
✔ →
Nme
rescaling
c d
kc kd
◆
◆ ✓
✓
a b
a+k b+k
!
NG
c d
c
d
axC + bxD
cxC + dxD
a b
c d
◆
20. Our
assumpNons
• AddiNve
payoff
scheme
• Introduce
the
parNcipaNon
cost
• Do
numerics
• N
=
5000
players
• Each
player
plays
against
all
the
neighbors.
• Replicator-‐type
update
rule:
player
i
copies
player
j’s
strategy
with
prob
(πj-‐πi)/[max(ki,kj)
*
(max
possible
payoff
–
min
possible
payoff)]
21. Simplified
prisoner’s
dilemma
regular
random
net
(a)
3
1
cf
0.75
2
0.5
1
0.25
0
acNon
C
D
C
(1-h,
1-h) (-h,
T-h)
D
(T-h,
-h)
0
h
0.7
1 T 1.3
scale-‐free
net
1.6
(-h,
-h)
3
(roughly
separated)
regimes
Strong
influence
of
iniNal
cnds
due
1
to
long
transients
(a)
3
2
Somewhat
reduced
cooperaNon
h
1
2
Enhanced
cooperaNon
(prev
results) 3
0
0.7
1 T 1.3
1.6
22. T
=
1.5
(b)
h
=
0
100
50
h
=
0.2
h
=
0.23
0
h
=
0.24
h
=
0.25
-50
h
=
0.5
0
50
h
=
0.3
100
150
# neighbors
200
h
=
0.2 h
=
0
# flips
generation payoff
(a)
h
=
0.5
50
h
=
0.23
h
=
0.3
h
=
0.24
h
=
0.25
0
10
100
# neighbors
Strategy
spreads
from
stubborn
leaves
to
hubs.
h
1
0
0.7
1 T 1.3
1.6
2
From
hub
cooperators
to
leaves.
2
1
From
leaves
to
hubs.
PD
payoff
structure
is
most
relevant.
(a)
3
3
23. General
matrix
game
• Homogeneous
(in
degree)
→
2
parameters
(S
and
T)
• e.g.
well-‐mixed,
square
laice,
regular
random
graph
• Heterogeneous
→
3
parameters
(S,
T,
and
h)
• e.g.
ER
random
graph,
scale-‐free
• PD,
snowdrih
game,
hawk-‐dove
game
included
acNon
C
D
C
(1-h,
1-h) (S-h,
T-h)
D
(T-h,
S-h)
(-h,
-h)
24. consistent
with
Santos
et
al.,
PNAS
(2006)
Regular
RG
(h
=
0)
1
(a)
SF
(h
=
0.5)
snowdrih
0
1
(b)
1
(c)
S
no
dilemma
S
S
0
stag
hunt
-1
SF
(h
=
0)
0
PD
1
T
1
2
-1
0
1
0
SF
(h
=
1)
1
T
2
SF
(h
=
2)
1
(d)
-1
2
0
1
1
cf
1
(e)
0.75
S
S
0
-1
0.5
0
2
0
1
T
2
-1
3
0
1
T
2
0.25
0
T
2
25. Thoughts
about
the
payoff
bias
• Naturally
understood
as
the
parNcipaNon
cost
• Payoffs
may
be
negaNve
in
many
pracNcal
situaNons.
• Environmental
problems?
• InternaNonal
relaNons?
• When
one
is
‘forced’
to
play
games
26. Conclusions
• Games
with
parNcipaNon
costs
on
networks
• More
C
for
small
parNcipaNon
cost
h
(previous
work).
• Networks
determine
dynamics
for
small
and
large
h.
• Payoff
matrix
is
most
relevant
for
intermediate
h.
• Think
twice
about
the
use
of
simplified
PD
payoff
matrices.