Genome-wide association studies (GWAS) allow researchers to scan the entire human genome to find genetic variations associated with particular diseases. The document describes a multi-stage GWAS that identified genetic variants associated with type 2 diabetes. It involved initial genome-wide scans followed by focused validation stages with increasing sample sizes. Several loci were confirmed, including regions near TCF7L2, SLC30A8, and HHEX genes. A variant near IRS1 was also identified. Association results were assessed in additional populations and metabolic trait analyses.
The document provides an overview of OpenCV including:
- It is an open source computer vision and machine learning software library with over 500 algorithms for image processing, video analysis, and more.
- It has been used in applications like Google Maps, robotics, and factory inspection systems.
- The library contains modules for features, calibration, stereo processing, object detection and more.
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.
4. 个亳仍 舒弍仂舒
x ' = x cos(慮 ) + y sin(慮 )
y ' = x sin(慮 ) + y cos(慮 )
i ( (
慮 - 仂亳亠仆舒亳
了 - 亟仍亳仆舒 于仂仍仆
- 亳亞仄舒 亞舒亳舒仆舒
粒 - 仂仂仆仂亠仆亳亠 舒亰仄亠仂于 (aspect
ratio), 束仍仍亳仗亳仆仂 亳仍舒損
- 亟于亳亞 舒亰
2D 亳仍 舒弍仂舒 磲仂 亞舒亳仆舒, 亟仂仄仆仂亢亠仆仆仂亠 仆舒 亳仆仂亳亟
亠亟仍仂亢亠仆 于 1947 亠仆亳仂仄 舒弍仂仂仄 (仆仂弍亠仍亠于从亳仄 仍舒亠舒仂仄),
仆亠亰舒于亳亳仄仂 仗亠亠仂从 于 1980 亞仂亟
5. 弌于磶 仂 亰亠仆亳亠仄 亠仍仂于亠从舒
仂仂亢亳 仆舒 仂仄 亠亠仗亳于仆 仗仂仍亠亶 仗仂
从仍亠仂从 (simple cells) 于 于亳亰舒仍仆仂亶 从仂亠 仄仂亰亞舒
亠仍仂于亠从舒
J. G. Daugman, Two-dimensional spectral analysis of cortical receptive field profiles.,
Vision research, vol. 20, no. 10, pp. 847856, 1980.
J. G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and
orientation optimized by two-dimensional visual cortical filters, J. Opt. Soc. Am. A, vol. 2,
no. 7, pp. 11601169, 1985.
11. 亠从亳仗仂 亳亰仂弍舒亢亠仆亳
TORRALBA, A., MURPHY, K. P., FREEMAN, W. T., AND RUBIN. Context-based
vision system for place and object recognition. In ICCV 2003
E. P. Simoncelli and W. T. Freeman. The steerable pyramid: 舒 flexible architecture
for multi-scale derivative computation. IEEE Intl. Conf. on Image Processing, 1995.