The document introduces basic concepts of linear algebra including:
1) Common number sets such as real numbers, integers, and rational numbers.
2) Matrix operations including addition, scalar multiplication, and matrix multiplication.
3) Derivatives of functions with multiple variables are represented using partial derivatives.
4) Concepts of vectors, matrices, determinants, transpose and inner products are defined.
Документ обсуждает сети SlowFast для распознавания видео, которые используют два потока: медленный для пространственной информации и быстрый для временной. Влагается в исследования, связанные с классификацией ввода видео, а также проводятся абляционные испытания, чтобы изучить роль каждого потока в модели. Выявляются вычислительные затраты, связанные с медленным и быстрым потоками, при этом подчеркивается эффективность сети в задаче распознавания.
- The document discusses making a presentation at the UIST conference on HCI topics. It provides background on HCI conferences like CHI, UbiComp, and UIST.
- It lists upcoming HCI conferences in Asia and encourages developing a research question, story, and discussing best papers for a potential UIST presentation.
- It also provides context on HCI research in Japan and different laboratories and companies doing HCI work in areas like IoT, VR, and more.
This document summarizes research on implementing autopoietic systems over the past 50 years. It reviews developments in the theory of autopoiesis and computational models, including early work by Maturana and Varela, Beer's model using Conway's Game of Life, sensorimotor Lenia which allows learning autopoietic behaviors, and Friston's model of a Markov blanket. It also discusses open issues around evolution/development, self-referentiality, and collective behavior of autopoietic systems interacting with humans. Areas of ongoing work aim to address how autopoietic systems develop cognitive complexity and express internal states.
- The document discusses making a presentation at the UIST conference on HCI topics. It provides background on HCI conferences like CHI, UbiComp, and UIST.
- It lists upcoming HCI conferences in Asia and encourages developing a research question, story, and discussing best papers for a potential UIST presentation.
- It also provides context on HCI research in Japan and different laboratories and companies doing HCI work in areas like IoT, VR, and more.
This document summarizes research on implementing autopoietic systems over the past 50 years. It reviews developments in the theory of autopoiesis and computational models, including early work by Maturana and Varela, Beer's model using Conway's Game of Life, sensorimotor Lenia which allows learning autopoietic behaviors, and Friston's model of a Markov blanket. It also discusses open issues around evolution/development, self-referentiality, and collective behavior of autopoietic systems interacting with humans. Areas of ongoing work aim to address how autopoietic systems develop cognitive complexity and express internal states.
Core ML 3 was announced at WWDC with new features for the Core ML API. The Core ML framework allows importing machine learning models in the .mlmodel format, including updates to supported model types and operations defined in protobuf files. Core ML Tools was also updated with over 3500 new models and operations supported for conversion between Core ML and other frameworks like TensorFlow and PyTorch.
The document discusses the technical aspects of depth sensing in iOS using ARKit and TrueDepth technology. It covers how depth data is captured, including the methods for using AVCaptureDevice and AVDepthData, while mentioning associated features such as disparity mapping and portrait matte effects. The content is tailored for developers, emphasizing practical implementation within Apple's frameworks and tools.
The document discusses the performance comparison between UIImageView and Metal for rendering images in iOS applications. It highlights that Metal can be 10x to 20x faster than UIImageView, emphasizing the importance of GPU optimization and processing flow between CPU and GPU. Key points include considerations for resource management and efficient command buffer handling when using Metal for image rendering.
1. The presenter compared the graphics rendering performance of Metal to UIImageView to learn about GPU usage.
2. Metal was initially 10-20x faster than UIImageView for rendering images but was found to be slower after further analysis and optimization of the measurement code.
3. Two key problems were identified with the Metal implementation: processing on the CPU was blocking the GPU, and texture loading was a bottleneck.
4. Optimizations including combining operations, caching textures, and ensuring resources were in GPU memory improved the Metal performance.
The document discusses the use of Apple's Metal framework for graphics rendering and machine learning applications on iOS. It includes code snippets demonstrating the creation and management of the Metal command queue, blit command encoders, and texture loading. Additionally, it highlights the comparison between Metal and OpenGL regarding GPU functionality and performance.
This document discusses implementing deep learning on iOS using various frameworks. It provides an overview of Metal Performance Shaders (MPSCNN), Accelerate (BNNS), Core ML, and Vision. It then details the 3 step process to implement a deep learning model with MPSCNN: 1) create the model, 2) implement the network, and 3) perform inference. Examples of logo detection and increased performance are shown. Core ML and Vision provide easier implementations compared to needing Metal knowledge for MPSCNN. BNNS may be better for small networks due to reduced CPU-GPU communication costs.
The document discusses client-side deep learning and introduces MPSCNN, a library that allows running convolutional neural networks on iOS devices using Metal Performance Shaders. MPSCNN can import trained models from frameworks like TensorFlow and run them to perform tasks like object detection on images at 60 times per second. Client-side deep learning could enable new mobile applications for areas like self-driving cars, AI assistants, and cancer detection by taking advantage of on-device processing power.
The document provides an overview of building iOS apps using Bluetooth Low Energy (BLE), highlighting its advantages like low energy consumption and no need for infrastructure. It discusses the Core Bluetooth API, practical tips for app development, and various hardware products that utilize BLE. Additionally, it covers the definition and usage of GATT attributes, limitations of background operations, and recommendations for app testing without hardware prototypes.
Practical Core Bluetooth in IoT & Wearable projects @ AltConf 2016Shuichi Tsutsumi
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This document provides an overview of practical applications of Core Bluetooth in IoT and wearable projects, emphasizing Bluetooth Low Energy (BLE) technology and its advantages for iOS developers. It details the process of connecting to BLE devices, handling sensor data, and defining GATT for data communication. The document also covers challenges, background functionality, and limitations associated with BLE in iOS development.
Practical Core Bluetooth in IoT & Wearable projects @ UIKonf 2016Shuichi Tsutsumi
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The document provides an overview of practical applications of Core Bluetooth in IoT and wearable projects, highlighting the capabilities of Bluetooth Low Energy (BLE) for iOS developers. It explains key concepts such as BLE connections, sensor data transmission, and background functionalities in an app context, emphasizing the importance of defining GATT (Generic Attribute Profile) for sensor interactions. Additionally, it discusses hardware projects involved with BLE and offers insight into app development and limitations faced during testing and implementation.
Protect Your IoT Data with UbiBot's Private Platform.pptxユビボット 株式会社
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Our on-premise IoT platform offers a secure and scalable solution for businesses, with features such as real-time monitoring, customizable alerts and open API support, and can be deployed on your own servers to ensure complete data privacy and control.