This document provides an overview of distributed information retrieval (DIR) and is presented by Prof. Fabio Crestani. It covers the background of information retrieval (IR) including indexing, querying, IR models and evaluation. The document then outlines topics in DIR including introduction, architectures, broker-based systems, evaluation and applications of DIR.
This document summarizes key concepts related to network flows, including maximum flows, minimum cost flows, and multicommodity flows. It provides an example of modeling internet routing as a maximum flow problem. It then describes the Ford-Fulkerson algorithm for finding maximum flows and some of its properties, including that the maximum flow equals the minimum cut. It discusses how the algorithm works by finding augmenting paths in the residual graph and updating the flow. Finally, it analyzes the running time of Ford-Fulkerson and describes how using shortest augmenting paths leads to a polynomial time algorithm.
The document discusses the benefits of exercise for mental health. It notes that regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise has also been shown to enhance self-esteem and serve as a healthy distraction from daily stressors.
1) Computer vision techniques such as stereo vision and object detection can be used for applications in robotics.
2) Stereo vision involves calculating depth from two images using techniques like block matching and disparity maps. It can be implemented efficiently on GPUs.
3) Object detection methods include feature detection using SIFT or SURF descriptors followed by matching and geometry validation using techniques like RANSAC. These allow objects to be detected and their pose estimated.
This document provides an overview of distributed information retrieval (DIR) and is presented by Prof. Fabio Crestani. It covers the background of information retrieval (IR) including indexing, querying, IR models and evaluation. The document then outlines topics in DIR including introduction, architectures, broker-based systems, evaluation and applications of DIR.
This document summarizes key concepts related to network flows, including maximum flows, minimum cost flows, and multicommodity flows. It provides an example of modeling internet routing as a maximum flow problem. It then describes the Ford-Fulkerson algorithm for finding maximum flows and some of its properties, including that the maximum flow equals the minimum cut. It discusses how the algorithm works by finding augmenting paths in the residual graph and updating the flow. Finally, it analyzes the running time of Ford-Fulkerson and describes how using shortest augmenting paths leads to a polynomial time algorithm.
The document discusses the benefits of exercise for mental health. It notes that regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise has also been shown to enhance self-esteem and serve as a healthy distraction from daily stressors.
1) Computer vision techniques such as stereo vision and object detection can be used for applications in robotics.
2) Stereo vision involves calculating depth from two images using techniques like block matching and disparity maps. It can be implemented efficiently on GPUs.
3) Object detection methods include feature detection using SIFT or SURF descriptors followed by matching and geometry validation using techniques like RANSAC. These allow objects to be detected and their pose estimated.
El documento describe el modelado y verificación de sistemas de transición de estado utilizando el lenguaje de especificación temporal CTL. Explica cómo construir un modelo de estado, definir propiedades temporales y utilizar un verificador para determinar si el modelo cumple o no cumple las propiedades.
El documento describe el modelado y verificación de sistemas de transición de estado utilizando el lenguaje de especificación temporal CTL. Explica cómo construir un modelo de estado, definir propiedades temporales y utilizar un verificador para determinar si el modelo cumple o no cumple las propiedades.
Современные средства NLP в поисковых задач - Стачка 2017Nikita Zhiltsov
Доклад посвящен современным средствам обработки текстов на основе машинного обучения, применяемым для некоторых задач поиска в проектах Rambler&Co (портал, ЖЖ). Докладчик делится опытом разработки решений на основе векторного представления word2vec и нейронных сетей, обучаемых на реальных данных. Будут рассмотрены примеры использования библиотек fastText, Keras и Tensorflow.
Подписывайтесь на мой Telegram-канал: http://t.me/ai_review
The document discusses concurrency and synchronization in distributed computing. It provides an overview of Petr Kuznetsov's research at Telecom ParisTech, which includes algorithms and models for distributed systems. Some key points discussed are:
- Concurrency is important due to multi-core processors and distributed systems being everywhere. However, synchronization between concurrent processes introduces challenges.
- Common synchronization problems include mutual exclusion, readers-writers problems, and producer-consumer problems. Tools for synchronization include semaphores, transactional memory, and non-blocking algorithms.
- Characterizing distributed computing models and determining what problems can be solved in a given model is an important area of research, with implications for distributed system design.
The document discusses weakly supervised learning from video and images using convolutional neural networks. It describes using scripts as weak supervision for learning actions from movies without explicit labeling. Methods are presented for jointly learning actors and actions from scripts, and for action learning with ordering constraints. The use of CNNs for object and action recognition in images is also summarized, including work on training CNNs using only image-level labels without bounding boxes.
This document discusses common C++ bugs and tools to find them. It describes various types of memory access bugs like buffer overflows on the stack, heap, and globals that can lead to crashes or security vulnerabilities. Threading bugs like data races, deadlocks, and race conditions on object destruction are also covered. Other undefined behaviors like initialization order issues, lack of sequence points, and integer overflows are explained. The document provides examples of each type of bug and emphasizes that undefined behavior does not guarantee a predictable result. It concludes with a quiz to find bugs in a code sample and links to additional reading materials.
AddressSanitizer, ThreadSanitizer, and MemorySanitizer are compiler-based tools that detect bugs like buffer overflows, data races, and uninitialized memory reads in C/C++ programs. AddressSanitizer instruments loads and stores to detect out-of-bounds memory accesses. ThreadSanitizer intercepts synchronization calls to detect data races between threads. MemorySanitizer tracks initialized and uninitialized memory using shadow memory to find uses of uninitialized values. The tools have found thousands of bugs with low overhead. Future work includes supporting more platforms and languages and detecting additional bug classes.
This document discusses common C++ bugs and tools to find them. It describes various types of memory access bugs like buffer overflows on the stack, heap, and globals that can lead to crashes or security vulnerabilities. Threading bugs like data races, deadlocks, and race conditions on object destruction are also covered. Other undefined behaviors like initialization order issues, lack of sequence points, and integer overflows are explained. The document provides examples of each type of bug and quizzes the reader to find bugs in a code sample. It recommends resources for further reading on debugging techniques and thread sanitizers that can detect races and data races.
This document provides examples and snippets of code for MapReduce, Pig, Hive, Spark, Shark, and Disco frameworks. It also includes two sections of references for related papers and Disco documentation. The examples demonstrate basic MapReduce jobs with drivers, mappers, and reducers in Java, Pig and Hive queries, Spark and Shark table operations, and a Disco MapReduce job.
3. Биологические причины
• Поиск особенностей и сопоставление
• Веро
Вероятность отклика каждой отдельной клетки
ос о а а ой о е ой е
(переменной) низка
• Вероятность случайного (ложного) повторения того же
самого шаблона тоже низка
• Хранение данных в ассоциативной памяти
• Если данных разрежены (sparcified), то сети позволяют
хранить больше данных, и извлекать их быстрее
4. Формализация
x ∈ ℜn n ×m
y ∈ℜ D ∈ℜ
m
- сигнал - представление
y = Dx
x0<L
• 0-норма – «псевдонорма», равна количеству ненулевых
р д р ,р у у
элементов вектора x
• Поскольку точного равенства достичь нельзя, ищем
минимум нормы ||Dx y|| при условии разреженного
||Dx-y||
представления x
5. Свойства простых клеток
• Рецептивные поля простых клеток в
визуальной коре головного мозга
й
обладают следующими свойствами
• Пространственно локализованные
П
• Ориентированные
• Заданная полоса частот (bandpass)
– Чувствительны к структуре на определенном
пространственном масштабе
• Гипотеза: стратегия кодирования сигнала,
максимизирующая разреженность,
достаточна для объяснения этих свойств
B.A. Olshausen and B.J. Field, Emergence of simple-cell receptive field
simple cell
properties by learning a sparse code for natural images, Nature, 381
(1996), pp. 607–609.
6. Разреженное представление
• Словарь, полученный с помощью обучения без
учителя (кластеризации) с учётом
(кластеризации),
предположения (условия) разреженности
представления
8. Алгоритмы выбора x
y = Dx x0<L
• В общем случае при 0-норме задача NP-
сложная
• Разработан ряд приближенных жадных
методов
• Basis Pursuit
• Matching Pursuit
• Orthogonal M t hi P
O th l Matching Pursuit
it
• LARS-Lasso
20. К-SVD
• Обобщение метода K-средних для
обучения словаря
• Задача:
• Итеративный алгоритм:
• Инициализация словаря
• Повторяем:
– Получаем разреженное представление X по D, Y
у р р р
» Orthogonal matching pursuit
– Для каждого k=1,K
» Обновляем атом dk с помощью SVD разложения
M. Ah
M Aharon, M El d and A M B k t i “Th K SVD an algorithm f designing
M. Elad, d A. M. Bruckstein, “The K-SVD: l ith for d i i
of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal
Process., vol. 54, no. 11, pp. 4311–4322, 2006.
21. Image-Signature Dictionary
M. Aharon and M. Elad, “Sparse and redundant modeling of image
content using an image-signature-dictionary,” SIAM Journal on Imaging
Sciences, vol. 1, no. 3, pp. 228–247, 2008.
22. Применение
• Шумоподавление, реконструкция
изображений,
изображений распознавание
• Схема шумоподавления:
• Построение словаря
– По исходному изображениям (или даже по
зашумленным!)
– Примеры: ISD размером 75x75, патчи 8x8, L=2, 110000
примеров
• Кодирование
– Использование OMP
– Добавляем атомы, пока ошибка не превысит 1.1 * sigma
– Sigma – оценка шума в исходном изображении
27. Использование для классификации
• Идея:
• Обучим по словарю д каждого класса объектов
у р для д
• Новому сигналу назначим класс, реконструкция по
которому наиболее точная
• Детали:
• Построим ROC-кривую по параметру L для
реконструкции по каждому словарю
• Построим словари для разных разрешений и разных
классов
• Набор ROC-кривых составит вектор-признак для
классификации
J. Mairal, M. Leordeanu, F. Bach, M. Hebert,
J Mairal M Leordeanu F Bach M Hebert and J Ponce Discriminative sparse
J. Ponce.
image models for class-specic edge detection and image interpretation. In
Proceedings of the European Conference on Computer Vision (ECCV), 2008b
29. Поиск краёв
• Обучающая выборка – результат C
Об б Canny, выбор хороших/плохих
б /
краёв по отсегментированным пользователем изображениям
• 14 классификаторов, 7 р
ф р , размеров фр
р фрагментов, 2 р р
, разрешения ( (макс
и половина), k=256, по 150000 фрагментов для хороших / плохих
краёв, L=6
33. Аутентификация картин
J. M. Hugues, D. J. Graham, and D. N. Rockmore. Quantication of artistic style through sparse
coding analysis in the drawings of Pieter Bruegel the Elder. Proceedings of the National Academy
of Science, TODO USA, 107(4):1279-1283, 2009
