The document describes the Vedic method for multiplying multi-digit numbers using a technique called "Vedic Mathematics". It breaks down multiplication into place-value groups and uses dots and lines to represent the digits being multiplied. It then provides examples of multiplying several pairs of numbers step-by-step using this method. The numbers are converted back to normal format after each calculation.
The document describes the Vedic method for multiplying multi-digit numbers using a technique called "Vedic Mathematics". It breaks down multiplication into place-value groups and uses dots and lines to represent the digits being multiplied. It then provides examples of multiplying several pairs of numbers step-by-step using this method. The numbers are converted back to normal format after each calculation.
RFID et le NFC differences et points communs.
Plus de détails sur: http://www.mobilitygeeks.fr/introduction-a-la-technologie-nfc/
Etude réalisée dans le cadre du cours
Architecture des réseaux mobiles du MBA eBusiness de l’ESG .
This document provides an overview of graph edit distance, including its definition, history, and algorithms. It begins by defining an edit path as a sequence of node/edge insertions, deletions, and substitutions that transforms one graph into another. The graph edit distance is the cost of the lowest cost edit path. It describes tree search algorithms used to explore the space of possible edit paths efficiently. It also explains how edit paths can be modeled as assignment problems that are solved using techniques like the Hungarian algorithm to find approximations of the graph edit distance.
Recurrent and Recursive Networks (Part 1)sohaib_alam
1. Recurrent neural networks (RNNs) can be represented as computational graphs that are unfolded through time. This unfolding allows the input size to remain fixed and for shared parameters to be used at each time step.
2. Common RNN architectures include those with recurrence between hidden units and those with recurrence from output to hidden units. Training RNNs involves backpropagation through time, which has linear time and memory costs with respect to the sequence length.
3. RNNs can be viewed as directed graphical models that represent the joint probability distribution over an output sequence conditioned on inputs. Introducing hidden units allows parameter sharing across time steps.
CHAPITRE VIII : Systèmes linéaires Modélisation & SimulationMohammed TAMALI
Dans la réalité des choses, les systèmes qui composent et forment notre univers sont parfaitement non-linéaires. Pour des raisons de prise en considération et d’étude, nous considérons, pour des systèmes donnés, que la région où celui-ci se comporte d’une manière continu et linéaire.
Le caractère de proportionnalité et de superposition est alors significatif pour ce genre de systèmes.
Le système, c’est la composition au sens Bertallanfy. Nous sous-entendons par cette remarque que SYSTÈME LINEAIRE est non totalement compatible à SYSTÈME D’EQUATIONS LINEAIRES.
Une équation est dite linéaire si les variables font apparaître des évolutions indépendantes proportionnelles. Un système d’équations linéaires, est une compositions de telles équations.
Le système est dit LINÉAIRE si la fonction de transfert qui décrit son comportement fonctionnel est elle-même linéaire. Cette dernière vérifie alors les principes de la proportionnalité et de la superposition.
The document describes a module called fft_16 that implements a 16 point fast Fourier transform (FFT). It takes in input signals x0 to x15 and parameters w0 to w7. It performs the FFT in 3 stages using butterfly operations defined in submodules bfly1 to bfly4. The results y0 to y15 are output based on a select signal.
The document discusses strategies for hiring employees over time in an environment of uncertainty. It begins by introducing the secretary problem, where the goal is to maximize the probability of choosing the best candidate among a pool of applicants. It then discusses different hiring strategies such as setting a quality threshold and only hiring candidates above it, only hiring candidates better than current employees (maximum hiring), and Lake Wobegon strategies of hiring candidates above the mean or median quality. It analyzes these strategies, finding that threshold hiring results in stagnating quality, maximum hiring leads to extremely slow hiring, and Lake Wobegon strategies do not allow for tight concentration of quality and result in a log-normal distribution of hiring qualities. The goal is to explore the
3.6 & 7. pumping lemma for cfl & problems based on plSampath Kumar S
The document discusses the pumping lemma for context-free grammars (CFGs). It defines the pumping lemma, which states that for any CFG there is a pumping length n such that any string in the language of length >= n can be broken into five parts that satisfy certain properties related to pumping. The document gives examples of using the pumping lemma to determine whether languages are context-free or not, such as the language L={0n1n2n | n >= 1}. It concludes by listing additional problems involving using the pumping lemma to prove languages are not context-free.
Advanced Comuter Architecture Ch6 Problem SolutionsJoe Christensen
This document contains problems and solutions related to pipelining and superscalar techniques in computer architecture. It discusses speedup factors, efficiency, throughput, and latency for a pipelined processor. It also analyzes the DEC Alpha architecture in terms of scalability and addresses a multiprocessor implementation. Several problems are solved related to reservation tables, collision vectors, state transition diagrams, and determining minimum average latency for pipeline scheduling.
Lecture 3 - Introduction to InterpolationEric Cochran
The document discusses polynomial interpolation. It begins by defining polynomials and interpolating polynomials. Interpolating polynomials are polynomials of order n that precisely fit n data points. The document then discusses how Matlab can be used to generate, evaluate, and find properties of polynomials. Specifically, it describes how the polyfit, polyval, and roots functions work. Finally, it discusses two methods for generating interpolating polynomials: Newton and Lagrange polynomials. The key application of interpolating polynomials is estimating values within tabulated data points.
This document summarizes orthogonal matching pursuit (OMP) and K-SVD, which are algorithms for sparse encoding of signals using dictionaries. OMP is a greedy algorithm that selects atoms from an overcomplete dictionary to sparsely represent a signal. It uses an orthogonal projection to the residual to ensure selected atoms are not reselected. K-SVD learns an optimized dictionary for sparse encoding by iteratively sparse encoding training data and updating dictionary atoms to minimize representation error.
This document discusses sparse representations and dictionary learning. It introduces the concepts of sparsity, redundant dictionaries, and sparse coding. The goal of sparse coding is to find the sparsest representation of signals using an overcomplete dictionary. Dictionary learning aims to learn an optimized dictionary from exemplar data by alternately solving sparse coding subproblems and dictionary update steps. Patch-based dictionary learning has applications in image denoising and texture synthesis. In contrast to PCA, learned dictionaries contain non-linear atoms adapted to the data.
RFID et le NFC differences et points communs.
Plus de détails sur: http://www.mobilitygeeks.fr/introduction-a-la-technologie-nfc/
Etude réalisée dans le cadre du cours
Architecture des réseaux mobiles du MBA eBusiness de l’ESG .
This document provides an overview of graph edit distance, including its definition, history, and algorithms. It begins by defining an edit path as a sequence of node/edge insertions, deletions, and substitutions that transforms one graph into another. The graph edit distance is the cost of the lowest cost edit path. It describes tree search algorithms used to explore the space of possible edit paths efficiently. It also explains how edit paths can be modeled as assignment problems that are solved using techniques like the Hungarian algorithm to find approximations of the graph edit distance.
Recurrent and Recursive Networks (Part 1)sohaib_alam
1. Recurrent neural networks (RNNs) can be represented as computational graphs that are unfolded through time. This unfolding allows the input size to remain fixed and for shared parameters to be used at each time step.
2. Common RNN architectures include those with recurrence between hidden units and those with recurrence from output to hidden units. Training RNNs involves backpropagation through time, which has linear time and memory costs with respect to the sequence length.
3. RNNs can be viewed as directed graphical models that represent the joint probability distribution over an output sequence conditioned on inputs. Introducing hidden units allows parameter sharing across time steps.
CHAPITRE VIII : Systèmes linéaires Modélisation & SimulationMohammed TAMALI
Dans la réalité des choses, les systèmes qui composent et forment notre univers sont parfaitement non-linéaires. Pour des raisons de prise en considération et d’étude, nous considérons, pour des systèmes donnés, que la région où celui-ci se comporte d’une manière continu et linéaire.
Le caractère de proportionnalité et de superposition est alors significatif pour ce genre de systèmes.
Le système, c’est la composition au sens Bertallanfy. Nous sous-entendons par cette remarque que SYSTÈME LINEAIRE est non totalement compatible à SYSTÈME D’EQUATIONS LINEAIRES.
Une équation est dite linéaire si les variables font apparaître des évolutions indépendantes proportionnelles. Un système d’équations linéaires, est une compositions de telles équations.
Le système est dit LINÉAIRE si la fonction de transfert qui décrit son comportement fonctionnel est elle-même linéaire. Cette dernière vérifie alors les principes de la proportionnalité et de la superposition.
The document describes a module called fft_16 that implements a 16 point fast Fourier transform (FFT). It takes in input signals x0 to x15 and parameters w0 to w7. It performs the FFT in 3 stages using butterfly operations defined in submodules bfly1 to bfly4. The results y0 to y15 are output based on a select signal.
The document discusses strategies for hiring employees over time in an environment of uncertainty. It begins by introducing the secretary problem, where the goal is to maximize the probability of choosing the best candidate among a pool of applicants. It then discusses different hiring strategies such as setting a quality threshold and only hiring candidates above it, only hiring candidates better than current employees (maximum hiring), and Lake Wobegon strategies of hiring candidates above the mean or median quality. It analyzes these strategies, finding that threshold hiring results in stagnating quality, maximum hiring leads to extremely slow hiring, and Lake Wobegon strategies do not allow for tight concentration of quality and result in a log-normal distribution of hiring qualities. The goal is to explore the
3.6 & 7. pumping lemma for cfl & problems based on plSampath Kumar S
The document discusses the pumping lemma for context-free grammars (CFGs). It defines the pumping lemma, which states that for any CFG there is a pumping length n such that any string in the language of length >= n can be broken into five parts that satisfy certain properties related to pumping. The document gives examples of using the pumping lemma to determine whether languages are context-free or not, such as the language L={0n1n2n | n >= 1}. It concludes by listing additional problems involving using the pumping lemma to prove languages are not context-free.
Advanced Comuter Architecture Ch6 Problem SolutionsJoe Christensen
This document contains problems and solutions related to pipelining and superscalar techniques in computer architecture. It discusses speedup factors, efficiency, throughput, and latency for a pipelined processor. It also analyzes the DEC Alpha architecture in terms of scalability and addresses a multiprocessor implementation. Several problems are solved related to reservation tables, collision vectors, state transition diagrams, and determining minimum average latency for pipeline scheduling.
Lecture 3 - Introduction to InterpolationEric Cochran
The document discusses polynomial interpolation. It begins by defining polynomials and interpolating polynomials. Interpolating polynomials are polynomials of order n that precisely fit n data points. The document then discusses how Matlab can be used to generate, evaluate, and find properties of polynomials. Specifically, it describes how the polyfit, polyval, and roots functions work. Finally, it discusses two methods for generating interpolating polynomials: Newton and Lagrange polynomials. The key application of interpolating polynomials is estimating values within tabulated data points.
This document summarizes orthogonal matching pursuit (OMP) and K-SVD, which are algorithms for sparse encoding of signals using dictionaries. OMP is a greedy algorithm that selects atoms from an overcomplete dictionary to sparsely represent a signal. It uses an orthogonal projection to the residual to ensure selected atoms are not reselected. K-SVD learns an optimized dictionary for sparse encoding by iteratively sparse encoding training data and updating dictionary atoms to minimize representation error.
This document discusses sparse representations and dictionary learning. It introduces the concepts of sparsity, redundant dictionaries, and sparse coding. The goal of sparse coding is to find the sparsest representation of signals using an overcomplete dictionary. Dictionary learning aims to learn an optimized dictionary from exemplar data by alternately solving sparse coding subproblems and dictionary update steps. Patch-based dictionary learning has applications in image denoising and texture synthesis. In contrast to PCA, learned dictionaries contain non-linear atoms adapted to the data.
Generating random numbers in a highly parallel program is surprising non-trivial. A lot of good generators have lots of state and is purely serial. Simple generators like LCG can leapfrog ahead but of limited quality and depends on #cores. We want our code to be independent of the degree of parallelism.
Gdc2012 frames, sparsity and global illumination Manchor Ko
This document discusses the need for a new spherical basis for representing signals on the sphere that addresses deficiencies in spherical harmonics. It introduces spherical needlets, which are a tight frame constructed from spherical quadrature and Littlewood-Paley weights localized in frequency bands. Spherical needlets provide spatially compact representations with good localization properties compared to spherical harmonics. The document outlines key properties a good spherical basis should have and areas for future work in developing improved spherical representations.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Dictionary Learning for Massive Matrix FactorizationArthur Mensch
This document proposes a method for scaling up dictionary learning for massive matrix factorization. It presents an online algorithm that can handle large datasets in both dimensions (many samples and many features) by introducing subsampling. The key steps are:
1) Computing codes on random subsets of samples instead of full samples to reduce complexity from O(p) to O(s) where s is the subsample size.
2) Partially updating the surrogate functions used for dictionary updates instead of full updates to also achieve O(s) complexity.
3) Performing cautious dictionary updates, leaving values unchanged for unseen features, to minimize in O(s) time.
Validation on fMRI and collaborative filtering datasets shows the method
This document discusses random number generators and reviews Intel's random number generator. It begins with an introduction to random number generation and common pseudorandom number generators like linear congruential generators. It then describes Intel's true random number generator which uses thermal noise from resistors to modulate the frequency of an oscillator. The random bits generated from the clock drift are then processed digitally before being made available through Intel's software library. Empirical and theoretical tests for evaluating random number generators are also summarized.
CVPR2010: Sparse Coding and Dictionary Learning for Image Analysis: Part 1: S...zukun
1. The document outlines sparse methods for machine learning, beginning with an introduction to sparse linear estimation using the l1-norm, such as with the Lasso.
2. It then discusses recent theoretical results showing when the Lasso can correctly identify the support of sparse weights vectors.
3. Finally, it compares the Lasso to other sparse methods like ridge regression and forward selection on simulated data, showing the Lasso achieves better performance in the sparse case.
Blind Source Separation using Dictionary LearningDavide Nardone
The sparse decomposition of images and signals found great use in the field of: Compression, Noise removal and also in the Sources separation. This implies the decomposition of signals in the form of linear combinations with some elements of a redundant dictionary. The dictionary may be either a fixed dictionary (Fourier, Wavelet, etc) or may be learned from a set of samples. The algorithms based on learning the dictionary can be applied to a broad class of signals and have a better compression performance than methods based on fixed dictionary. Here we present a Compressed Sensing (CS) approach with an adaptive dictionary for solving a Determined Blind Source Separation (DBSS). The proposed method has been developed by reformulating a DBSS as Sparse Coding (SC) problem. The algorithm consist of few steps: Mixing matrix estimation, Sparse source separation and Source reconstruction. A sparse mixture of the original source signals has been used for the estimating the mixing matrix which have been used for the reconstruction of the of the source signals. A 'block signal representation' is used for representing the mixture in order to greatly improve the computation efficiency of the 'mixing matrix estimation' and the 'signal recovery' processes without particularly lose separation accuracy. Some experimental results are provided to compare the computation and separation performance of the method by varying the type of the dictionary used, be it fixed or an adaptive one. Finally a real case of study in the field of the Wireless Sensor Network (WSN) is illustrated in which a set of sensor nodes relay data to a multi-receiver node. Since more nodes transmits messages simultaneously it's necessary to separate the mixture of information at the receiver, thus solving a BSS problem.
This document is the 15th edition of The Student's Practical Dictionary containing English words with English and Hindi meanings and pronunciations. It provides over 3,000 English entries with definitions and pronunciations in Devanagari script. The dictionary is a thorough revision and expansion of previous editions and is intended to be a practical reference for students.
This document provides an overview of sparse coding presented by Shao-Chuan Wang from Academia Sinica. It first reviews principal component analysis (PCA) and then introduces the concept of sparsity regularization. It discusses how to solve the optimization problem through algorithms like matching pursuit and orthogonal matching pursuit. It also covers dictionary learning methods like K-SVD. Finally, it lists applications of sparse coding like image denoising and edge detection.
This document discusses tests for random number generation, including the autocorrelation test, gap test, and poker test. The autocorrelation test examines dependence between numbers in a sequence. The gap test analyzes the length of gaps between numbers that fall within a given range. The poker test categorizes groups of five consecutive numbers based on arrangements like pairs, three of a kind, etc. and applies a chi-squared test to assess randomness.
Dictionary Learning for Massive Matrix Factorizationrecsysfr
The document presents a new algorithm called Subsampled Online Dictionary Learning (SODL) for solving very large matrix factorization problems with missing values efficiently. SODL adapts an existing online dictionary learning algorithm to handle missing values by only using the known ratings for each user, allowing it to process large datasets with billions of ratings in linear time with respect to the number of known ratings. Experiments on movie rating datasets show that SODL achieves similar prediction accuracy as the fastest existing solver but with a speed up of up to 6.8 times on the largest Netflix dataset tested.
Problems and Prospects of Human Right InstrumentsTheola Bonsi
The document discusses how human rights instruments have both prospects and problems when applied to women's rights in Afghan communities in light of universal human rights. It uses the example of the Pashtun practice of "Ghagh" (forced marriage) and its tension with laws against violence against women. While instruments provide prospects like global collaboration, they also face issues like cultural imposition and homogenization. HR instruments challenge moral relativism but cultural practices like Ghagh are justified by some. The implementation and enforcement of universal rights is an ongoing process that both progresses women's rights but also faces obstacles.
Brain reading, compressive sensing, fMRI and statistical learning in PythonGael Varoquaux
This document discusses techniques for predictive modeling of brain imaging data using statistical learning methods. It presents an approach that combines sparse recovery, randomized clustering, and total variation regularization to predict stimuli from fMRI data with over 50,000 voxels and around 100 samples. The key steps are clustering spatially correlated voxels, running sparse models on the reduced feature set, and accumulating selected features over multiple runs. Simulations show this approach outperforms other methods at recovering brain patches. The document also discusses disseminating research through open source Python libraries like scikit-learn, which has helped popularize machine learning techniques.
This document provides a template for planning programming Content and Language Integrated Learning (CLIL) units. The template includes sections for defining the learning outcomes, subject content, language content and skills, cognitive processes, activities, methodology, and evaluation. The language content section specifies vocabulary, grammar structures, language skills, and discourse types to be practiced. The methodology section addresses class organization, timing, resources, and basic competences. Examples of CLIL units and lessons are also provided.
Dictionaries can be used effectively in the ESL classroom set up to facilitate language learning and acquisition. It enlists certain dictionary activities to be carried out in the classroom.
Practicum iii elena ramirez_garcia_16-04-2016ELe Na
This document provides a CLIL (Content and Language Integrated Learning) lesson plan about animals for English language learners in year 2 of primary education. The 3-part, 3-session lesson aims to develop students' language skills through activities where they identify, describe, compare and classify animals. Students will learn vocabulary about animal names, features and abilities. They will complete tasks such as a mind map of animal descriptions, a game about diurnal and nocturnal animals, writing and solving animal riddles, and a detectives game involving assembling sentences to describe animals. The plan provides details on learning outcomes, contents, language points, methodology and assessment.
The latest LUMA Display Ad Tech Landscape is a living document. While it is impossible to categorize companies across an industry into discrete categories, this is at least an attempt to organize the landscape. If you have constructive suggestions, please email them to me at tkawaja@lumapartners.com.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017StampedeCon
Words are no longer sufficient in delivering the search results users are looking for, particularly in relation to image search. Text and languages pose many challenges in describing visual details and providing the necessary context for optimal results. Machine Learning technology opens a new world of search innovation that has yet to be applied by businesses.
In this session, Mike Ranzinger of Shutterstock will share a technical presentation detailing his research on composition aware search. He will also demonstrate how the research led to the launch of AI technology allowing users to more precisely find the image they need within Shutterstock’s collection of more than 150 million images. While the company released a number of AI search enabled tools in 2016, this new technology allows users to search for items in an image and specify where they should be located within the image. The research identifies the networks that localize and describe regions of an image as well as the relationships between things. The goal of this research was to improve the future of search using visual data, contextual search functions, and AI. A combination of multiple machine learning technologies led to this breakthrough.
Exploring Simple Siamese Representation LearningSungchul Kim
This document discusses an unsupervised representation learning method called SimSiam. It proposes that SimSiam can be interpreted as an expectation-maximization algorithm that alternates between updating the encoder parameters and assigning representations to images. Key aspects discussed include how the stop-gradient operation prevents collapsed representations, the role of the predictor network, effects of batch size and batch normalization, and alternatives to the cosine similarity measure. Empirical results show that SimSiam learns meaningful representations without collapsing, and the various design choices affect performance but not the ability to prevent collapsed representations.
GDC 2012: Advanced Procedural Rendering in DX11smashflt
The document discusses procedural rendering techniques in DirectX 11. It introduces signed distance fields (SDFs) as a useful tool for procedural geometry creation. SDFs define the distance to surfaces and can be used to create complex shapes by combining simple primitives and applying operations like unions and differences. The document shows how SDFs can be generated from triangle meshes or particle systems and used for effects like cutting holes and repeating patterns. It also covers techniques for visualizing SDFs through raycasting and polygonization via marching cubes. The document concludes by discussing how these techniques can be applied to smoothed particle hydrodynamics simulations.
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.
This document summarizes the performance of an algebraic multigrid solver on leading multicore architectures. It describes how the multigrid solver works by repeating pre-smoothing, coarse-grid correction, and post-smoothing steps until convergence. It also discusses the SPE10 oil reservoir modeling benchmark problem being solved, the Cray XC30 and Intel Xeon Phi machines studied, and optimizations that improved the performance of the PCG solver. Charts are included showing runtimes, where time is spent in the AMG cycle, and how parameters affect performance.
Practical spherical harmonics based PRT methods.ppsxMannyK4
This document discusses practical spherical harmonics based precomputed radiance transfer (PRT) methods. It outlines background on ambient occlusion and HL2 basis, goals of diffuse self-shadowing and generalizing to interreflections. It describes using spherical harmonics to project visibility functions and environment maps to generate PRT coefficients, and reconstructing lighting in vertex shaders. It also discusses compressing PRT data from 36 bytes to 4-9 bytes per sample using quantization, and demonstrates the methods in game scenes at 30+ fps.
This document provides an outline for a course on neural networks and fuzzy systems. The course is divided into two parts, with the first 11 weeks covering neural networks topics like multi-layer feedforward networks, backpropagation, and gradient descent. The document explains that multi-layer networks are needed to solve nonlinear problems by dividing the problem space into smaller linear regions. It also provides notation for multi-layer networks and shows how backpropagation works to calculate weight updates for each layer.
This document provides an overview of deep learning including definitions, prerequisites, and examples of techniques like linear regression, multi-layer perceptrons, backpropagation, convolutional neural networks, and frameworks like PyTorch. It defines deep learning as being driven by very deep neural networks, explains why large networks are necessary to handle non-well-defined and ambiguous problems, and discusses how frameworks make deep learning models easy to implement and generalize.
Modern enterprise data—tracking key performance indicators like conversions or click-throughs—exhibits a pathologically high dimensionality, which requires re-thinking data representation to make analysis tractable.
1. The document discusses barriers to scaling electronic structure methods to large systems, such as the inability of sparse matrix multiplication kernels to access strong parallel scaling and entrenched data structures that limit innovation.
2. It proposes a fast, generic, and data local N-body solver approach using new mathematics that is not constrained by row-column data structures and allows a single programming model.
3. Key aspects of this approach include exploiting locality in higher dimensional product volumes through techniques like occlusion-culling, resolving identity iteratively to compress matrices by orders of magnitude, and developing optimized sparse matrix multiplication kernels.
Show, Attend and Tell: Neural Image Caption Generation with Visual AttentionEun Ji Lee
1. The document summarizes a research paper on neural image caption generation using visual attention mechanisms. It introduces attention models that allow an image captioning model to focus on salient regions of the image dynamically.
2. It describes the image captioning model which uses an LSTM decoder conditioned on an encoded image representation and a context vector. The context vector is generated by taking a weighted sum of image features, with the weights determined by an attention model.
3. It discusses two types of attention mechanisms - "hard" or stochastic attention which selects a single image location at each time step, and "soft" or deterministic attention which blends all locations with learned weights. The model is trained end-to-end to maximize
The document describes a simple approach for text-to-image generation using a transformer that models text and image tokens as a single stream. It involves training the transformer in two stages: (1) Pretraining a VQ-VAE to encode images into discrete tokens, and (2) Training the transformer to autoregressively model the joint distribution of image tokens and BPE-encoded text tokens. With sufficient data and scale, this approach is competitive with previous domain-specific models for text-to-image generation.
This document summarizes research on using algebraic multigrid (AMG) methods to solve equations modeling porous media flow. The key points are:
1) A spectral element-based AMG method is used to build a coarse level that represents important components in the problem's near-nullspace added by high contrast in properties.
2) Directly applying standard AMG to the resulting coarse problem is ineffective since it has a different structure than assumed.
3) A "three-level" approach is taken where the coarse problem is transformed to match assumptions of a standard AMG, which enables accurate and scalable solution of problems with millions of unknowns.
Scaling out logistic regression with SparkBarak Gitsis
This document discusses scaling out logistic regression with Apache Spark. It describes the need to classify a large number of websites using machine learning. Several approaches to logistic regression were tried, including a single machine Java implementation and moving to Spark for better scalability. Spark's L-BFGS algorithm was chosen for its out of the box distributed logistic regression solution. Challenges implementing logistic regression at large scale are discussed, such as overfitting and regularization. Methods used to address these challenges include L2 regularization, cross-validation to select the regularization parameter, and extensions made to Spark's LBFGS implementation.
Similar to Dictionary Learning in Games - GDC 2014 (20)
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
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.
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
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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).
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.
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
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
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.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Full-RAG: A modern architecture for hyper-personalization
Dictionary Learning in Games - GDC 2014
1. Dictionary Learning for Games
Manny Ko
Principal Engineer, Activision R&D
Graphics Research and Development
2. Outline
● K-SVD and dictionary learning
● Linear Blend Skinning
● Brief survey on automatic skinning and compression
● Dictionary learning for LBS
● Two-layer sparse compression of Le & Deng.
● This talk is about compressing skinned animations.
3. Frames, Sparsity and Global Illumination:
New Math for Games
GDC 2012
Robin Green – Microsoft Corp
Manny Ko – PDI/Dreamworks
4. Orthogonal Matching Pursuit
and K-SVD for Sparse Encoding
Manny Ko
Senior Software Engineer, Imaginations Technologies
Robin Green
SSDE, Microsoft Xbox ATG
5. Representing Signals
● We represent signals as linear combinations of things we already know –
the ‘basis’
× 𝛼1 +
× 𝛼2 +
× 𝛼3 + ⋯
=
× 𝛼0 +
6. Orthonormal Bases (ONBs)
● The simplest way to represent signals is using a set of orthonormal bases
𝑏𝑖 𝑡 𝑏𝑗(𝑡)
+∞
−∞
𝑑𝑡 =
0 𝑖 ≠ 𝑗
1 𝑖 = 𝑗
8. Benefits of ONB
● Analytic formulations
● Well understood mathematical properties
● Fast and simple algorithms for projection
9. Problems with ONB
● One-size-fits all – not data adaptive
● Global support cannot adapt to data locally
● Fourier support is infinite, SH support spans the sphere
● Try using Fourier to represent a step-function
● Not sparse – very few zero coefficients
● Not additive - relies on destructive cancellation.
11. What is Overcomplete Dictionary?
● Overcomplete means the dictionary has more atoms
(columns) than the minimum required for the
dimension of the signal
● In 3D, an ONB only needs 3 basis
● A 3D dictionary can have dozens or hundreds
12. The Sparse Signal Model
𝐃
A fixed dictionary
𝛼
=
𝑥
𝑁 𝑁
𝐾
resulting
signal
Sparse
vector of
coefficients
13. Why so many atoms?
● More atoms give our algorithm a better chance to
find a small subset that matches a given signal
● Let’s look at some patches from Barbara
17. Project onto Dictionaries
● Overcomplete and non-orthogonal
● interactions among atoms cannot be ignored
● How do we project?
● Sparse Coding problem
18. Matching Pursuit
1. Set the residual 𝑟 = 𝑥
2. Find an unselected atom
that best matches the
residual 𝐃𝛼 − 𝑟
3. Re-calculate the residual
from matched atoms
𝑟 = 𝑥 − 𝐃𝛼
4. Repeat until 𝑟 ≤ 𝜖
Greedy Methods
𝐃
𝛼
=
𝑥
19. Orthogonal Matching Pursuit (OMP)
● Add an Orthogonal Projection to the residual calculation
1. set 𝐼 ∶= ∅ , 𝑟 ≔ 𝑥, 𝛾 ≔ 0
2. while (𝑠𝑡𝑜𝑝𝑝𝑖𝑛𝑔 𝑡𝑒𝑠𝑡 𝑓𝑎𝑙𝑠𝑒) do
3. 𝑘 ≔ argmax
𝑘
𝑑 𝑘
𝑇
𝑟
4. 𝐼 ≔ 𝐼, 𝑘
5. 𝛾𝐼 ≔ 𝐃𝐼
+ 𝑥
6. 𝑟 ≔ 𝑥 − 𝐃𝐼 𝛾𝐼
7. end while
20. What is Dictionary Learning?
● select a few atoms for each signal – e.g. OMP
● Adjust the atoms to better fit those signals
● Repeat
21. K-SVD
● Is one of the well known dictionary learning
methods
● Check out our GDC2013 talk
● our GDC13 slides "OMP and K-SVD for Sparse Coding“
● See Jim’s talk just before this session
● Miral’s Online Learning is the other.
22. Overcomplete Dictionary Recap
● Importance of overcomplete dictionaries
● OMP for efficient projection onto dictionaries
● K-SVD for learning a better dictionary using samples
from the real data
24. Linear Blend Skinning
● 𝑣𝑖 = 𝑤𝑖𝑗(𝑅𝑗
|𝐵|
𝑗=1 𝑝𝑗 + 𝑇𝑗)
● 𝑝𝑖 is the position for the 𝑖th vertex of the rest pose
● 𝑤𝑖𝑗 ≥ 0 𝑎𝑛𝑑 𝑠𝑢𝑚𝑠 𝑡𝑜 𝑜𝑛𝑒(affinity). The non-negative
constraint makes the blend additive. The affinity
constraint prevents over-fitting and artifacts.
● 𝑅𝑗 usually is orthogonal to avoid shearing or scaling
● |𝐵| is the number of weights (usually <= 6)
28. LBS on GPUs
● 𝑤𝑖𝑗 typically very sparse – 4-6 weights or less per-
vertex
● Ideally a group of vertices all have the same weights
to avoid thread divergence or splitting drawcalls
● These are fairly serious constraints
a) Some vertices might need more weights – e.g. very
smooth meshes or complex topology (hand)
29. WeightsReduction
Poisson-based Weight Reduction of Animated Meshes [Landreneau and Schaefer 2010]
Discrete optimization:
– Impossible to find optimum solution
– Very high cost for non-optimum solution
• Fracture
• Significant increase of computing cost: nK non-zero n(K+1) non-zero
33. Magic 4
● why 4 weights is too few to generate smooth
weights
● 4 vertices specifies an affine transform exactly.
● simplices in 3D contains 4 vertices for barycentric
coordinates.
35. Two-Layer Sparse Compression, Le & Deng 2013
● Use dictionary learning to compute a two-level
compression using bones
● Work with the weights of the bind-pose directly
36. Why Dictionary for LBS?
● Why dictionary learning?
● limitations of Orthonormal-basis e.g. eigen/PCA
● Not adaptive
● Not purely additive – i.e. negative weights (relies on cancellation)
● No intuitive meaning – bones extracted cannot be used to tweak the
model
45. Analysis of Two-Layer Scheme
● Use 100’s of virtual bones means we are not limited to a
sparse approximation to the original animation.
● virtual bones act as a ‘common subexpression’
● e.g. think compute shader that writes to LDS.
● Still enforce sparsity on VBs to control runtime cost and
LDS usage – but k can be 100’s.
● Per-vertex weights are
● very sparse (2 per vertex) and the same for all vertices
● good for GPU.
46. Learning Virtual Bones
● Virtual bones are learned from the dense vertex weights
by block-coordinate-descent (BCD):
Sparse coding: search for a few good atoms among the
input columns. Use that to project all the rest of the inputs.
● Atom update: given the sparse weights from above we
seek to adjust the atoms to make them fit the inputs that
needs them better – a series of small LS problems.
● Similar to EM/Lloyd-Max
47. Sparse Coding
Sparse coding:
● insert the vertex with the largest L2 norm
● add a few more vertex which has the smallest dot-
product with the 1st atom
● solve the basis-pursuit with OMP (see K-SVD) or LARS.
● solve 2x2 least-square prob. for 𝑤𝑖𝑗 to blend masters
bones
48. Weight Map – matrix A
● Weights and indices for each vertex to blend virtual
bones
● solving a small 2x2 linear system to minimize MSE:
● arg 𝑚𝑖𝑛 𝑥 𝐷𝑥 − 𝑤𝑖 ^2
● runtime per-vertex cost is just 2 dotp
● no bone hierarchy to worry about
● no warp divergence even for high valence vertices
49. Atom Updates
Atom update:
foreach vertex
● update each atom to minimize error for the set of vertices that
reference it (this is like K-SVD)
● Miral’s Online Dictionary Learning [Miral09]
50. Atom Updates
● Precompute A and B
● 𝐴 = 𝛼𝑖
𝑡
𝑖=1 𝛼 𝑇
● B = 𝑥𝑖 𝛼 𝑇𝑡
𝑖=1
● For all atoms
● 𝑢𝑗
1
𝐴 𝑗,𝑗
𝑏𝑗 − 𝐷𝑎𝑗 + 𝑑𝑗 − eq(5)
● 𝑑𝑗
1
max 𝑢 𝑗 2,1
𝑢𝑗. − eq 6
● 𝑢𝑗is thresholded to make sure # of non −
zero is below the # of master bones
55. Recap
● The two-level scheme can work with dense (hand painted)
weights or example poses (blend shape?)
● Only the vertex positions are needed
● a fixed memory footprint and uniform per-vertex cost - GPU
friendly
● Combines the quality of dense skinning and the efficiencies of
sparse-LBS. Animators can use blend-shapes or FFD more.
56. Recap 2
● Besides it uses dictionary learning and modern
sparsity methods – how cool is that?
● Last year we show how good dictionary learning is
for compressing 2d images and 3d volumes
● Now we see what it can do for animation.
● Thank you!
57. Recap 3
● Non-negative LS and Active-set Method (ASM)
● Block-coordinate descent
● Sparsity constraints
● L1 relaxation and L0-norm constraints
● Direct solving
● These are all very useful tools.
58. Acknowledgements
● Binh Huy Le & Zhigang Deng kindly provided the demo and their Siggraph
materials.
● Robin Green for being my collaborator for many years.
● Igor Carron inspired me to learn sparsity methods and matrix factorization
and for his spirit of broad exploration and sharing.
● Julien Mairal for the online learning math
● Peter-Pike who inspired me to apply modern math to graphics and games.
● Carlos Gonzalez Ochoa for sharing his insight in animation.