Potential Readings of Deep Learning We Plan to Finish in 2017-Fall
Foundations
- DeepLearningSummerSchool17
- Andrew Ng - Nuts and Bolts of Applying Deep Learning : https://www.youtube.com/watch?v=F1ka6a13S9I :
- Ganguli - Theoretical Neuroscience and Deep Learning DLSS16 http://videolectures.net/deeplearning2016_ganguli_theoretical_neuroscience/
- Ganguli - Theoretical Neuroscience and Deep Learning.pdf DLSS17 https://drive.google.com/file/d/0B6NHiPcsmak1dkZMbzc2YWRuaGM/view
- Sharp Minima Can Generalize For Deep Nets, Laurent Dinh (Univ. Montreal), Razvan Pascanu, Samy Bengio (Google Brain), Yoshua Bengio (Univ. Montreal)
- Automated Curriculum Learning for Neural Networks, Alex Graves, Marc G. Bellemare, Jacob Menick, Koray Kavukcuoglu, Remi Munos
- Learning to learn without gradient descent by gradient descent, Yutian Chen, Matthew Hoffman, Sergio Gomez, Misha Denil, Timothy Lillicrap, Matthew Botvinick , Nando de Freitas
- Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study, Samuel Ritter, David Barrett, Adam Santoro, Matt Botvinick
- Geometry of Neural Network Loss Surfaces via Random Matrix Theory, Jeffrey Pennington, Yasaman Bahri
- On the Expressive Power of Deep Neural Networks, Maithra Raghu, Ben Poole, Surya Ganguli, Jon Kleinberg, Jascha Sohl-Dickstein
- Neuroscience-Inspired Artificial Intelligence, http://www.cell.com/neuron/fulltext/S0896-6273(17)30509-3
- Understanding deep learning requires rethinking generalization, ICLR17
- On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, ICLR17
- Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes, ICLR17
- Capacity and Trainability in Recurrent Neural Networks, ICLR17
- Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations, ICLR17
- Frustratingly Short Attention Spans in Neural Language Modeling, ICLR17
- Topology and Geometry of Half-Rectified Network Optimization, ICLR17
- Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning, ICLR17
- Adversarial Feature Learning, ICLR17
- Do Deep Convolutional Nets Really Need to be Deep and Convolutional?, ICLR17
- Why Deep Neural Networks for Function Approximation?, ICLR17
- Bengio - Recurrent Neural Networks - DLSS 2017.pdf: https://drive.google.com/file/d/0ByUKRdiCDK7-LXZkM3hVSzFGTkE/view
- On the Expressive Power of Deep Neural Networks, Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein ; PMLR 70:2847-2854
- Equivariance Through Parameter-Sharing, Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos ; PMLR 70:2892-2901
- Large-Scale Evolution of Image Classifiers, Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, Alexey Kurakin ; PMLR 70:2902-2911
- Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks, Itay Safran, Ohad Shamir ; PMLR 70:2979-2987
- A Closer Look at Memorization in Deep Networks, ICML17
- Dynamic Word Embeddings, ICML17
- Combining Low-Density Separators with CNNs, Yu-Xiong Wang*, Carnegie Mellon University; Martial Hebert, Carnegie Mellon University, NIPS16
- CNNpack: Packing Convolutional Neural Networks in the Frequency Domain, NIPS16
- Residual Networks are Exponential Ensembles of Relatively Shallow Networks, NIPS16
- Dense Associative Memory for Pattern Recognition, NIPS16
- Learning Kernels with Random Features, Aman Sinha*, Stanford University; John Duchi,
- Simple and Efficient Weighted Minwise Hashing, NIPS16
- Reward Augmented Maximum Likelihood for Neural Structured Prediction
- Unimodal Probability Distributions for Deep Ordinal Classification, ICML17
- End-to-End Learning for Structured Prediction Energy Networks, ICML17
- Orthogonal Random Features, NIPS16
- Learning Structured Sparsity in Deep Neural Networks, NIPS16
- Learning the Number of Neurons in Deep Networks, NIPS16
- Quantized Random Projections and Non-Linear Estimation of Cosine Similarity, NIPS16
- An equivalence between high dimensional Bayes optimal inference and M-estimation, NIPS16
- High Dimensional Structured Superposition Models, NIPS16
- Learning Deep Embeddings with Histogram Loss, NIPS16
- Learning values across many orders of magnitude, NIPS16
- Learning Deep Parsimonious Representations, NIPS16
- Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information, NIPS16
- A Bayesian method for reducing bias in neural representational similarity analysis, NIPS16
- Richards - Deep_Learning_in_the_Brain.pd https://drive.google.com/file/d/0B2A1tnmq5zQdcFNkWU1vdDJiT00/view and
https://drive.google.com/file/d/0B2A1tnmq5zQdQWU0Skd6TVVQYUE/view?usp=drive_web
DNN with Varying Structures
- SCAN: Learning Abstract Hierarchical Compositional Visual Concepts, https://arxiv.org/pdf/1707.03389.pdf
- Krueger - Bayesian Hypernetworks.pdf https://drive.google.com/file/d/0B6NHiPcsmak1RUlucW1RN29oS3M/view?usp=drive_web
- Leblond and Alayrac - SeaRNN.pdf https://drive.google.com/file/d/0B6NHiPcsmak1SDVEaWc0OWtaV0k/view?usp=drive_web
- Sharir - Overlapping Architectures.pdf https://drive.google.com/file/d/0B6NHiPcsmak1ZzVkci1EdVN2YkU/view?usp=drive_web
- Ullrich - Bayesian Compression.pd https://drive.google.com/file/d/0B6NHiPcsmak1WlRUeHFpSW5OZGc/view?usp=drive_web
- Understanding Synthetic Gradients and Decoupled Neural Interfaces, Wojtek Czarnecki, Grzegorz Świrszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu, ICML17
- Video Pixel Networks, Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu
- AdaNet: Adaptive Structural Learning of Artificial Neural Networks, Corinna Cortes, Xavi Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang
- Learning to Generate Long-term Future via Hierarchical Prediction, Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee
- Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning, Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli
- Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data, Manzil Zaheer, Amr Ahmed, Alex Smola
- Large-Scale Evolution of Image Classifiers, Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alexey Kurakin
- Sequence Modeling via Segmentations, Chong Wang (Microsoft Research) · Yining Wang (CMU) · Po-Sen Huang (Microsoft Research) · Abdelrahman Mohammad (Microsoft) · Dengyong Zhou (Microsoft Research) · Li Deng (Citadel)
- ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
- Adaptive Neural Networks for Fast Test-Time Prediction
- Making Neural Programming Architectures Generalize via Recursion, ICLR17
- Optimization as a Model for Few-Shot Learning, ICLR17
- Learning End-to-End Goal-Oriented Dialog, ICLR17
- Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer, ICLR17
- Nonparametric Neural Networks, ICLR17
- An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax, ICLR17
- Improving Neural Language Models with a Continuous Cache, ICLR17
- Variational Recurrent Adversarial Deep Domain Adaptation, ICLR17
- Soft Weight-Sharing for Neural Network Compression, ICLR17
- Tracking the World State with Recurrent Entity Networks, (Lecun), ICLR17
- Deep Biaffine Attention for Neural Dependency Parsing, ICLR17
- Learning to Remember Rare Events, ICLR17
- Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks, ICLR17
- Deep Learning with Dynamic Computation Graphs, ICLR17
- Query-Reduction Networks for Question Answering, ICLR17
- Bidirectional Attention Flow for Machine Comprehension, ICLR17
- Dynamic Coattention Networks For Question Answering, ICLR17
- Structured Attention Networks, ICLR17
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, (Dean), ICLR17
- Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain, ICLR17
- Mollifying Networks, Bengio, ICLR17
- Automatic Rule Extraction from Long Short Term Memory Networks, ICLR17
- Loss-aware Binarization of Deep Networks, ICLR17
- Deep Multi-task Representation Learning: A Tensor Factorisation Approach, ICLR17
- Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music, ICLR17
- Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17
- Semi-Supervised Classification with Graph Convolutional Networks, ICLR17
- Hierarchical Multiscale Recurrent Neural Networks, ICLR17
- AdaNet: Adaptive Structural Learning of Artificial Neural Networks, ICML17
- Language Modeling with Gated Convolutional Networks, ICML17
- Image-to-Markup Generation with Coarse-to-Fine Attention, ICML17
- Input Switched Affine Networks: An RNN Architecture Designed for Interpretability, ICML17
- Differentiable Programs with Neural Libraries, ICML17
- Convolutional Sequence to Sequence Learning, ICML17
- State-Frequency Memory Recurrent Neural Networks, ICML17
- SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization, Juyong Kim, Yookoon Park, Gunhee Kim, Sung Ju Hwang ; PMLR 70:1866-1874
- Deriving Neural Architectures from Sequence and Graph Kernels
Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola ; PMLR 70:2024-2033
- Delta Networks for Optimized Recurrent Network Computation, Daniel Neil, Jun Haeng Lee, Tobi Delbruck, Shih-Chii Liu ; PMLR 70:2584-2593
- Recurrent Highway Networks, Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutnı́k, Jürgen Schmidhuber ; PMLR 70:4189-4198
- Ask Me Anything: Dynamic Memory Networks for Natural Language Processing, ICML17
- OptNet: Differentiable Optimization as a Layer in Neural Networks, ICML17
- Swapout: Learning an ensemble of deep architectures, Saurabh Singh*, UIUC; Derek Hoiem, UIUC; David Forsyth, UIUC, NIPS16
- Natural-Parameter Networks: A Class of Probabilistic Neural Networks, Hao Wang*, HKUST; Xingjian Shi, ; Dit-Yan Yeung, NIPS16
- Learning What and Where to Draw, NIPS16
- Hierarchical Question-Image Co-Attention for Visual Question Answering, NIPS16
- Proximal Deep Structured Models, NIPS16
- Direct Feedback Alignment Provides Learning In Deep Neural Networks, NIPS16
- Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes, NIPS16
- Matching Networks for One Shot Learning, NIPS16
- Can Active Memory Replace Attention? Łukasz Kaiser*, ; Samy Bengio, NIPS16
- Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences, NIPS16
- Binarized Neural Networks, NIPS16
- Interaction Networks for Learning about Objects, Relations and Physics, NIPS16
- Optimal Architectures in a Solvable Model of Deep Networks, NIPS16
Reliable and Benchmarking and Applications
- Conditional Image Generation with Pixel CNN Decoders, NIPS16
- Dhruv - Visual Dialog - RLSS 2017 https://drive.google.com/file/d/0BzUSSMdMszk6RndSbkEzcnRFMGs/view and
https://drive.google.com/file/d/0BzUSSMdMszk6cDVBMlRqLUs3TFk/view
- Input Switched Affine Networks: An RNN Architecture Designed for Interpretability, Jakob Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo
- Axiomatic Attribution for Deep Networks, Ankur Taly, Qiqi Yan,,Mukund Sundararajan
- Differentiable Programs with Neural Libraries, Alex L Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
- Neural Optimizer Search with Reinforcement Learning, Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc Le
- Measuring Sample Quality with Kernels, Jackson Gorham (STANFORD) · Lester Mackey (Microsoft Research)
- Learning Continuous Semantic Representations of Symbolic Expressions, ICML17
- Recovery Guarantees for One-hidden-layer Neural Networks, ICML17
- On the State of the Art of Evaluation in Neural Language Models, https://arxiv.org/abs/1707.05589
- End-to-end Optimized Image Compression, ICLR17
- Multi-Agent Cooperation and the Emergence of (Natural) Language, ICLR17
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data, ICLR17
- Deep Learning with Differential Privacy,
- Privacy-Preserving Deep Learning, CCS15
- Learning to Query, Reason, and Answer Questions On Ambiguous Texts, ICLR17
- Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy, ICLR17
- Data Noising as Smoothing in Neural Network Language Models (Ng), ICLR17
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks, ICLR17
- Visualizing Deep Neural Network Decisions: Prediction Difference Analysis, ICLR17
- On Detecting Adversarial Perturbations, ICLR17
- Delving into Transferable Adversarial Examples and Black-box Attacks, ICLR17
- Parseval Networks: Improving Robustness to Adversarial Examples, ICML17
- iSurvive: An Interpretable, Event-time Prediction Model for mHealth, ICML17
- Being Robust (in High Dimensions) Can Be Practical, ICML17
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, ICML17
- On Calibration of Modern Neural Networks, ICML17
- Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs, ICML17
- Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation, ICML17
- Analogical Inference for Multi-relational Embeddings, Hanxiao Liu, Yuexin Wu, Yiming Yang ; PMLR 70:2168-2178
- Deep Transfer Learning with Joint Adaptation Networks, Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan ; PMLR 70:2208-2217
- Sequence to Better Sequence: Continuous Revision of Combinatorial Structures, Jonas Mueller, David Gifford, Tommi Jaakkola ; PMLR 70:2536-2544
- Meta Networks, Tsendsuren Munkhdalai, Hong Yu ; PMLR 70:2554-2563
- Geometry of Neural Network Loss Surfaces via Random Matrix Theory, Jeffrey Pennington, Yasaman Bahri ; PMLR 70:2798-2806
- Asymmetric Tri-training for Unsupervised Domain Adaptation, Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada ; PMLR 70:2988-2997
- Developing Bug-Free Machine Learning Systems With Formal Mathematics, Daniel Selsam, Percy Liang, David L. Dill ; PMLR 70:3047-3056
- Learning Important Features Through Propagating Activation Differences, Avanti Shrikumar, Peyton Greenside, Anshul Kundaje ; PMLR 70:3145-3153
- High-Dimensional Structured Quantile Regression, ICML17
- Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs, Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song ; PMLR 70:3462-3471
- Learning to Generate Long-term Future via Hierarchical Prediction, Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, Honglak Lee ; PMLR 70:3560-3569
- Sequence Modeling via Segmentations, Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng ; PMLR 70:3674-3683
- A Unified View of Multi-Label Performance Measures, Xi-Zhu Wu, Zhi-Hua Zhou ; PMLR 70:3780-3788
- Convexified Convolutional Neural Networks, Yuchen Zhang, Percy Liang, Martin J. Wainwright ; PMLR 70:4044-4053
- Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin, ICML17
- Learning Transferrable Representations for Unsupervised Domain Adaptation, NIPS16
- Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity, NIPS16
- Unsupervised Domain Adaptation with Residual Transfer Networks, Mingsheng Long*, Tsinghua University; Han Zhu, Tsinghua University; Jianmin Wang, Tsinghua University; Michael Jordan, NIPS16
- Interpretable Distribution Features with Maximum Testing Power, Wittawat Jitkrittum*, Gatsby Unit, UCL; Zoltan Szabo, ; Kacper Chwialkowski, Gatsby Unit, UCL; Arthur Gretton, NIPS16
- Domain Separation Networks, NIPS16
- Multimodal Residual Learning for Visual QA, NIPS16
- Learning feed-forward one-shot learners, NIPS16
- Adversarial Multiclass Classification: A Risk Minimization Perspective, NIPS16
- Generating Images with Perceptual Similarity Metrics based on Deep Networks, NIPS16
- Dialog-based Language Learning, Jason Weston*, NIPS16
- The Robustness of Estimator Composition, NIPS16
- Large Margin Discriminant Dimensionality Reduction in Prediction Space, NIPS16
- Robustness of classifiers: from adversarial to random noise, NIPS16
- Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning, NIPS16
- Blind Attacks on Machine Learners, Alex Beatson*, Princeton University; Zhaoran Wang, Princeton University; Han Liu, NIPS16
- Composing graphical models with neural networks for structured representations and fast inference, NIPS16
- Spatiotemporal Residual Networks for Video Action Recognition, NIPS16
Optimization
- Johnson - Automatic Differentiation.p https://drive.google.com/file/d/0B6NHiPcsmak1ckYxR2hmRGdzdFk/view
- Osborne - Probabilistic numerics for deep learning - DLSS 2017.pdf https://drive.google.com/file/d/0B2A1tnmq5zQdWHBYOFctNi1KdVU/view
- Learned Optimizers that Scale and Generalize, Olga Wichrowska, Niru Maheswaranathan, Matthew Hoffman, Sergio Gomez, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein
- Learning to learn by gradient descent by gradient descent
- Asynchronous Stochastic Gradient Descent with Delay Compensation,
- How to Escape Saddle Points Efficiently, Chi Jin (UC Berkeley) · Rong Ge (Duke University) · Praneeth Netrapalli (Microsoft Research) · Sham M. Kakade (University of Washington) · Michael Jordan (UC Berkeley)
- Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter
- Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
- Towards Principled Methods for Training Generative Adversarial Networks, ICLR17
- Optimization as a Model for Few-Shot Learning, ICLR17
- Amortised MAP Inference for Image Super-resolution, ICLR17
- Neural Architecture Search with Reinforcement Learning, ICLR17
- Distributed Second-Order Optimization using Kronecker-Factored Approximations, ICLR17
- Mode Regularized Generative Adversarial Networks, ICLR17
- Highway and Residual Networks learn Unrolled Iterative Estimation, ICLR17
- Snapshot Ensembles: Train 1, Get M for Free, ICLR17
- Learning to Optimize, ICLR17
- Recurrent Batch Normalization, ICLR17
- Adversarially Learned Inference, ICLR17
- Reasoning with Memory Augmented Neural Networks for Language Comprehension, ICLR17
- Deep ADMM-Net for Compressive Sensing MRI, NIPS16
- Sharp Minima Can Generalize For Deep Nets, ICML17
- Forward and Reverse Gradient-Based Hyperparameter Optimization, ICML17
- Automated Curriculum Learning for Neural Networks, ICML17
- How to Escape Saddle Points Efficiently, ICML17
- Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs, ICML17
- An overview of gradient optimization algorithms, (https://arxiv.org/abs/1609.04747)
- Learning Deep Architectures via Generalized Whitened Neural Networks, Ping Luo ; PMLR 70:2238-2246
- The Loss Surface of Deep and Wide Neural Networks, Quynh Nguyen, Matthias Hein ; PMLR 70:2603-2612
- Relative Fisher Information and Natural Gradient for Learning Large Modular Models, Ke Sun, Frank Nielsen ; PMLR 70:3289-3298
- meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting, Xu Sun, Xuancheng Ren, Shuming Ma, Houfeng Wang ; PMLR 70:3299-3308
- Axiomatic Attribution for Deep Networks, Mukund Sundararajan, Ankur Taly, Qiqi Yan ; PMLR 70:3319-3328
- Follow the Moving Leader in Deep Learning, Shuai Zheng, James T. Kwok ; PMLR 70:4110-4119
- Oracle Complexity of Second-Order Methods for Finite-Sum Problems, ICML17
- The Shattered Gradients Problem: If resnets are the answer, then what is the question?, ICML17
- Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks, ICML17
- End-to-End Differentiable Adversarial Imitation Learning, ICML17
- Neural Optimizer Search with Reinforcement Learning, ICML17
- Adaptive Neural Networks for Efficient Inference, ICML17
- Practical Gauss-Newton Optimisation for Deep Learning, ICML17
- Deep Tensor Convolution on Multicores, ICML17
- The Generalized Reparameterization Gradient, Francisco Ruiz*, Columbia University; Michalis K. Titsias, ; David Blei, NIPS16
- Attend, Infer, Repeat: Fast Scene Understanding with Generative Models, NIPS16
- Memory-Efficient Backpropagation Through Time, NIPS16
- Professor Forcing: A New Algorithm for Training Recurrent Networks, NIPS16
- Understanding the Effective Receptive Field in Deep Convolutional Neural Networks, NIPS16
Generative
- GAN tutorial by Ian Goodfellow (NIPS 2016): https://arxiv.org/abs/1701.00160 https://www.youtube.com/watch?v=AJVyzd0rqdc
- Goodfellow - Generative Models I - DLSS 2017 https://drive.google.com/file/d/0ByUKRdiCDK7-bTgxTGoxYjQ4NW8/view
- Courville - Generative Models II - DLSS 2017. https://drive.google.com/file/d/0B_wzP_JlVFcKQ21udGpTSkh0aVk/view
- Makhzani and Frey - PixelGAN Autoencoders.pdf https://drive.google.com/file/d/0B6NHiPcsmak1SFdRN2lmS3FnekE/view
- Welling - Graphical Models and Deep Learning.pd https://drive.google.com/file/d/0B6NHiPcsmak1NHJHdzEySzNNQ0U/view
- Parallel Multiscale Autoregressive Density Estimation, Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Ziyu Wang, Dan Belov, Nando de Freitas
- Count-Based Exploration with Neural Density Models, Georg Ostrovski, Marc Bellemare, Aaron van den Oord, Remi Munos
- Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo, Maithra Raghu, Ben Poole, Surya Ganguli, Jon Kleinberg, Jascha Sohl-Dickstein
- Johnson - Graphical Models and Deep Learning https://drive.google.com/file/d/0B6NHiPcsmak1RmZ3bmtFWUd5bjA/view?usp=drive_web
- Variational Boosting: Iteratively Refining Posterior Approximations, Andrew Miller, Nicholas J Foti, Ryan Adams
- Stochastic Generative Hashing, Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song, ICML17
- Robust Structured Estimation with Single-Index Models, ICML17
- Learning to Act by Predicting the Future, ICLR17
- Improving Generative Adversarial Networks with Denoising Feature Matching, ICLR17
- Boosted Generative Models, ICLR17
- The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, ICLR17
- Robust Probabilistic Modeling with Bayesian Data Reweighting, ICML17
- Deep Generative Models for Relational Data with Side Information, ICML17
- Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim ; PMLR 70:1857-1865
- Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks, Lars Mescheder, Sebastian Nowozin, Andreas Geiger ; PMLR 70:2391-2400
- McGan: Mean and Covariance Feature Matching GAN, Youssef Mroueh, Tom Sercu, Vaibhava Goel ; PMLR 70:2527-2535
- Parallel Multiscale Autoregressive Density Estimation, Scott Reed, Aäron Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, Nando Freitas ; PMLR 70:2912-2921
- Adversarial Feature Matching for Text Generation, Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin ; PMLR 70:4006-4015
- Learning Hierarchical Features from Deep Generative Models, Shengjia Zhao, Jiaming Song, Stefano Ermon ; PMLR 70:4091-4099
- Wasserstein Generative Adversarial Networks, ICML17
- Generalization and Equilibrium in Generative Adversarial Nets (GANs), ICML17
- Exponential Family Embeddings, NIPS16
Reinforcement
- Hasselt - Deep Reinforcement Learning - RLSS 2017.pdf https://drive.google.com/file/d/0BzUSSMdMszk6UE5TbWdZekFXSE0/view?usp=drive_web
- Pineau - RL Basic Concepts - RLSS 2017.pdf https://drive.google.com/file/d/0BzUSSMdMszk6bjl3eU5CVmU0cWs/view
http://videolectures.net/deeplearning2016_pineau_reinforcement_learning/
and http://videolectures.net/deeplearning2016_pineau_advanced_topics/
- Roux - RL in the Industry - RLSS 2017.pdf https://drive.google.com/file/d/0BzUSSMdMszk6bEprTUpCaHRrQ28/view
- Singh - Steps Towards Continual Learning.pdf https://drive.google.com/file/d/0BzUSSMdMszk6YVhFUUNLZnZLSWs/view?usp=drive_web
- Sutton - Temporal-Difference Learning- RLSS 2017.pd https://drive.google.com/file/d/0BzUSSMdMszk6VE9kMkY2SzQzSW8/view?usp=drive_web
- Szepesvari - Theory of RL - RLSS 2017.pdf https://drive.google.com/file/d/0BzUSSMdMszk6U194Ym5jSnZQbGM/view?usp=drive_web
- Thomas - Safe Reinforcement Learning - RLSS 2017.pdf https://drive.google.com/file/d/0BzUSSMdMszk6TDRMRGRaM0dBcHM/view?usp=drive_web
- Minimax Regret Bounds for Reinforcement Learning, Mohammad Gheshlaghi Azar, Ian Osband, Remi Munos
- Why is Posterior Sampling Better than Optimism for Reinforcement Learning? Ian Osband, Benjamin Van Roy
- DARLA: Improving Zero-Shot Transfer in Reinforcement Learning, Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Chris Burgess, Alexander Pritzel, Matt Botvinick, Charles Blundell, Alexander Lerchner
- A Distributional Perspective on Reinforcement Learning, Marc G. Bellemare, Will Dabney, Remi Munos
- A Laplacian Framework for Option Discovery in Reinforcement Learning, Marlos Machado (Univ. Alberta), Marc G. Bellemare, Michael Bowling
- The Predictron: End-to-End Learning and Planning, David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris
- FeUdal Networks for Hierarchical Reinforcement Learning, Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Hees, Max Jaderberg, David Silver, Koray Kavukcuoglu
- Neural Episodic Control, Alex Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomenech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell
- Robust Adversarial Reinforcement Learning, Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta
- Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs, Michael Gygli, Mohammad Norouzi, Anelia Angelova
- Distral: Robust Multitask Reinforcement Learning, https://arxiv.org/pdf/1707.04175.pdf
- Reinforcement Learning with Unsupervised Auxiliary Tasks, ICLR17
- Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic, ICLR17
- DARLA: Improving Zero-Shot Transfer in Reinforcement Learning, ICML17
- Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning, Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli ; PMLR 70:2661-2670
- Count-Based Exploration with Neural Density Models, Georg Ostrovski, Marc G. Bellemare, Aäron Oord, Rémi Munos ; PMLR 70:2721-2730
- Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction, Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell ; PMLR 70:3309-3318
More:
- ICLR 2017 Papers
- ICML 2017 Papers
- NIPS 2017 papers
- Yann Lecun
- Y. Bengio
- G. Hinton
- Juergen Schmidhuber


geomesa-compute¶
Run distributed spatial computations and analytics on your geospatial
data using Apache Spark. To instantiate a RDD[SimpleFeature]
, call
GeoMesaSpark.init(sparkConf, ds)
on one of your data stores. Then,
request a RDD
using a CQL filter as follows:
geomesa.compute.spark.GeoMesaSpark.rdd(hadoopConf, sparkContext, ds, query)
The following example demonstrates computing a time series across geospatial data within the constraints of a CQL query.
object CountByDay {
def main(args: Array[String]) {
// Get a handle to the data store
val params = Map(
"instanceId" -> "instance",
"zookeepers" -> "zoo1,zoo2,zoo3",
"user" -> "user",
"password" -> "*****",
"auths" -> "USER,ADMIN",
"tableName" -> "geomesa_catalog")
val ds = DataStoreFinder.getDataStore(params).asInstanceOf[AccumuloDataStore]
// Construct a CQL query to filter by bounding box
val ff = CommonFactoryFinder.getFilterFactory2
val f = ff.bbox("geom", -80, 35, -79, 36, "EPSG:4326")
val q = new Query("myFeatureType", f)
// Configure Spark
val conf = new Configuration
val sconf = init(new SparkConf(true), ds)
val sc = new SparkContext(sconf)
// Create an RDD from a query
val queryRDD = geomesa.compute.spark.GeoMesaSpark.rdd(conf, sc, ds, query)
// Convert RDD[SimpleFeature] to RDD[(String, SimpleFeature)] where the first
// element of the tuple is the date to the day resolution
val dayAndFeature = queryRDD.mapPartitions { iter =>
val df = new SimpleDateFormat("yyyyMMdd")
val ff = CommonFactoryFinder.getFilterFactory2
val exp = ff.property("dtg")
iter.map { f => (df.format(exp.evaluate(f).asInstanceOf[java.util.Date]), f) }
}
// Group the results by day
val groupedByDay = dayAndFeature.groupBy { case (date, _) => date }
// Count the number of features in each day
val countByDay = groupedByDay.map { case (date, iter) => (date, iter.size) }
// Collect the results and print
countByDay.collect.foreach(println)
}
}
To run the example, execute the following command:
$ /opt/spark/bin/spark-submit --master yarn-client --num-executors 40 --executor-cores 4 countbyday.jar --deploy-mode client --class com.mycompany.example.CountByDay
Spark Shell Execution¶
To run the spark shell (for spark version 1.1.0) compile and run:
bin/spark-shell --driver-class-path /path/to/geomesa-compute-accumulo1.5-1.0.0-rc.3-SNAPSHOT-shaded.jar


WeWork’s Quarterly Loss Doubled to $1.3 Billion as IPO Faltered
by Bloomberg(Nov 14): WeWork reported a net loss of $1.25 billion in the third quarter, eclipsing its sales and more than doubling its loss from the same period last year. The quarter coincided with a spending spree in anticipation of the initial public offering, which failed.
Revenue in the quarter was $934 million, up from $482 million a year earlier, according to a financial document that was presented to bondholders Wednesday and reviewed by Bloomberg. A spokeswoman for WeWork parent company We Co. declined to comment on the report. - Bloomberg


This demo application showcases some of the features of the OpenFin Runtime. You can find production-ready applications built on OpenFin's Runtime in our Demos.
This is an HTML5/Javascript application that leverages the OpenFin API to access native functionality.
The code is open-sourced and can be found on GitHub. A walk through can be found in the OpenFin API Docs.


Striving for perfection and a keen eye for detail,
I promise that I won't disappoint or fail.
A passion for creativity and design,
I ensure my work will be divine.
An aspiring full stack web developer with strong interests in design and Asian media.
Passionate about video games and Japanese culture with a desire to one day move to Japan and work for closely
with Nintendo.
RPG styled games where customization is plentiful are what I enjoy the most but my favorite series is Fire
Emblem.
I love comedies and fantasy, prefer cute and fluffy, and am an absolute coward when it comes to horror.
Truffle Shuffle
The Truffle-Shuffle project is a lightweight application that helps an indecisive user(s) generate ideas on what movie to watch and what cocktail to drink. The user is first given the option to choose a cocktail based on their alcohol preference or just a random drink from the CocktailDB database. After getting their drink, the user is presented with a wheel that lets them spin for a random genre and decade. If need be, the user can respin the wheel to grab a different genre/decade. Based on these results a movie is obtained from the MovieDB database and presented to the user.
Weather Dashboard
This project is a practice on using api and ajax. It is weather checking application where the user can enter in a city and find the its current weather, temperature, 5 day update and more. The application also keeps track of past cities that the user has searched for and can quickly bring up the latest information for that city. On a page load, the last city to be manually searched for is brought up. A clear button is provided to clear the search history.
Art Stack
The Art Stack project is a full-stack application made to help connect local artists and interested parties. It creates a space where artists can post their work and onlookers can view the assortment of artwork available.
Beach Girl Illustration
A drawing made to practice grayscale to color shading technique.
Obio
Obio is a mobile puzzle game made for the iOS App store and Android Playstore. It was a college project where I was called in to help design and create various UI elements and game icons.
Reach out to me!


DTTextAttachment Class Reference
| Inherits from | NSObject |
| Declared in | DTTextAttachment.h |
Tasks
-
originalSize
property -
displaySize
property -
contents
property -
contentType
property -
contentURL
property -
hyperLinkURL
property -
hyperLinkGUID
property -
attributes
property -
verticalAlignment
property -
+ textAttachmentWithElement:options:
Initialize and return a DTTextAttachment with the specified DTHTMLElement and options. Convenience initializer.
The element must have a valid tagName. The size of the returned text attachment is determined by the element, constrained by the option's key for DTMaxImageSize. Any valid image resource included in the element (denoted by the method attributeForKey: "src") is loaded and determines the text attachment size if it was not known before. If a size is too large the image is downsampled with sizeThatFitsKeepingAspectRatio() which preserves the aspect ratio.
-
– dataURLRepresentation
Retrieves a string which is in the format “data:image/png;base64,%@” with this DTTextAttachment’s content’s data representation encoded in Base64 string encoding. For image contents only.
-
– adjustVerticalAlignmentForFont:
-
– ascentForLayout
-
– descentForLayout
Class Methods
textAttachmentWithElement:options:
Initialize and return a DTTextAttachment with the specified DTHTMLElement and options. Convenience initializer.
The element must have a valid tagName. The size of the returned text attachment is determined by the element, constrained by the option's key for DTMaxImageSize. Any valid image resource included in the element (denoted by the method attributeForKey: "src") is loaded and determines the text attachment size if it was not known before. If a size is too large the image is downsampled with sizeThatFitsKeepingAspectRatio() which preserves the aspect ratio.
+ (DTTextAttachment *)textAttachmentWithElement:(DTHTMLElement *)element options:(NSDictionary *)options
Parameters
- element
A DTHTMLElement that must have a valid tag name and should have a size. Any element attributes are copied to the text attachment’s elements.
- options
An NSDictionary of options. Used to specify the max image size with the key DTMaxImageSize.
Return Value
Returns an initialized DTTextAttachment built using the element and options parameters.
Declared In
DTTextAttachment.h
Instance Methods
dataURLRepresentation
Retrieves a string which is in the format “data:image/png;base64,%@” with this DTTextAttachment’s content’s data representation encoded in Base64 string encoding. For image contents only.
- (NSString *)dataURLRepresentation
Return Value
A Base64 encoded string of the png data representation of this text attachment’s image contents.
Declared In
DTTextAttachment.h


Nourishing Neighbors
My first project allowed people to share a meal, food, clothes, or greeting with a neighbor in need during the holiday season.
Technology Used: Ruby, Active Record, Sinatra, BCrypt, Heroku
See the site See the codeWanderlust
My group created a website that takes a person's location and finds (and provides driving directions to) the nearest trails.
Technology Used: Ruby, Rails, JQuery, Bootstrap, Several APIs
See the site See the codeDC Tunez
I used the Ticketfly API to find local concerts. I then passed the artist info to the Spotify API to generate streamable playlists.
Technology Used: Ruby, Rails, JQuery, Ticketfly and Spotify APIs
See the site See the code


--- layout: post title: Fire truck out window! How exciting world-class science comes to a standstill. date: '2012-04-20T12:54:00.001-04:00' author: Bob Sica tags: modified_time: '2013-08-02T15:55:02.101-04:00' blogger_id: tag:blogger.com,1999:blog-736339229338318027.post-5043985195684052589 blogger_orig_url: http://blog.purplecrowlidar.ca/2012/04/fire-truck-out-window-how-exciting.html ---


{% for location in locations %} {# Sanity Check in case of a None and skipping certain placeholder locations #} {% if location and not (location.id == 3 or location.id == 38) %}


SN
I am a graduate of Hotel and Restaurant Management and because I enjoy exploring, I started to explore the world of programming. I enjoy working with HTML and CSS for making website, BLENDER for making 3D model and WORDPRESS for my blog.
I'm currently looking for Web Design projects. If your interested send me an mail to shieramae.nogra@gmail.com


Thank you for purchasing MerryCafe. Welcome to this documentation that explains briefly how this theme works. We constantly work on improving this theme and update this documentation as well. Here are few things we want you to know first:
Support
After reading the documentation, if there is still something you need to ask, please go to our Support Site and create a thread in the forum.
Our support covers all queries related to this theme except customization requests and any problems related to third party plugins.
We hope you will enjoy going through this documentation.
Once you go back to the dashboard after installing the plugin, you will be prompted to install the required plugins. Theses plugins are required to make the theme work similar to the demo.
Install plugins prompt
Click Begin Installing Plugins and follow the instructions to install the plugins. Once you install them, you need to active them. Follow instructions in the following screenshots:
Installing Child Theme
We will recommend you to install the Child Theme before you start creating your website with our theme. Read Why use a child theme to know more about Child Themes
Child Theme is inside with your downloaded folder from Theme Forest. Look for MerryCafe-Child.zip and install/activate it.
Importing Demo
Once you install the theme, you can see there is no content to experience the awesomeness. A good way to start modifying your theme is by importing one of the Demo Sites in your theme installation. Here is how you can do that:
Demo import files are inside the "Demos" folder in your downloaded Theme Forest folder (Make sure you downloaded "All Files" from your Theme Forest download section). Open a demo folder of your choice and you will find two files in each folder. One is a .xml file and other is a .txt file. Both contain data that is required to make the import work.
In your WordPress dashboard, Go to Tools > Import and follow the instructions in the screenshow below to first import the contents via .xml file.
Once you are done with importing the contents via .xml file, you need to import the customizer content. Open the customizer.txt file and copy the content inside it.
Go to Customizer Sync from your WordPress dashboard and replace the content of the text area with your copied content. Check the Check this to approve the update. checkbox and click "Update Customizer Options".
Now, you need to set the homepage. Go to Settings > Reading and under Front Page Displays, choose A static page (select below) and choose the Home page from the dropdown. Click Save Changes
There you go. Check your site and enjoy the shining look.
Header Setup
You can access the header options through customizer by going to Appearance > Customizer and the going to Header
Logo
You can add the logo through the customizer by going to Appearance > Customizer and then clicking the Logo section. You can just upload a regular logo in the Logo option or you can add a logo for high resolution displays in the Retina display logo option. Make sure to define your logo width in the Retina logo width in px option if you add a retina logo or else your logo will look oddly sized.
Favicon
You can add a favicon for your site by opening the customizer through Appearance > Customizer and then clicking the Favicon section. You can upload a favicon (Recommended size is 128px X 128px) with the favicon option.
Top Header
You can access the options for Top Header section by opening the customizer through Appearance > Customizer and then clicking the Top Header section. There you can see various customization options for your site's top header.
Primary Header
You can access the options for Primary Header section by opening the customizer through Appearance > Customizer and then clicking the Header > Primary Header section. There you can see various customization options for your site's primary header.
Navigation Menu
You can access the options for Menu section by opening the customizer through Appearance > Customizer and then clicking the Menu section. There you can see various customization options for your site's navigation menu.
Home Page Template
Home Page Template is a template you can use to create the home page layout as you can see in the screenshot below:
To add home template to any page, follow the instructions in the screenshot below:
Follow the instructions below to customize the home page template sections.
Above The Fold
Above the fold section is the top section on your site's home page. You can access the options for Above The fold section by opening the customizer through Appearance > Customizer and then clicking on the Above The Fold section. There you can see various customization options for your site's above the fold section
Screenshot of Above the fold section
Home Slider
Home Slider is a slider bundled with the theme to show slides on the Home Page Template
To add slides to the Home Slider, go to Home Slider from your WordPress dashboard as you can see in the screenshot below:
Open customizer by going to Appearance > Customizer and then go to Home > Above The Fold to see the options for enabling the slider.
Featured Section
You can access the options for Featured section by opening the customizer through Appearance > Customizer and then clicking on Featured Section
Recommended Section
You can access the options for Recommended section by opening the customizer through Appearance > Customizer and then clicking on Recommended Section
Content Boxes Section
You can access the options for Content Boxes section by opening the customizer through Appearance > Customizer and then clicking on Content Boxes Section
Event Section
You can access the options for Event section by opening the customizer through Appearance > Customizer and then clicking on Event Section
Page Content
If you want to add more content on the homepage, you can add it through the WordPress page editor. You can enable/disable this section from the customizer. You can access the options for Page Content section by opening the customizer through Appearance > Customizer and then clicking on Page content
Food Menu
MerryCafe offers a beautiful template and post type to show your cafe's/restaurant's menu.
Adding food menu items
You can add food menu items by going to Menu Items from your WordPress Dashboard. The menu is shown category wise. Before adding any menu items, go to Menu Items > Menu Categories and add your menu categories
Once you add menu categories, Add menu items by clicking Menu Items > Add New. Make sure you add the already created Menu Category to the menu item.
It is important that you add atleast one Menu Image to any of the Menu Item falling under a specific Menu Category to comply the standards of menu template design.
Creating the menu page
To show menu on any page, edit that page and choose the Menu template as you can see in the screenshot below:
Styling the menu
You can access the styling options for Food Menu by opening the customizer through Appearance > Customizer and then clicking on Food Menu section.
Blog
Blog is visible through the default index page or you can configure it to show on any page by choosing the Blog Template. To show Blog on any page, edit that page and choose the Blog template as you can see in the screenshot below:
Styling the blog
You can access the styling options for Blog by opening the customizer through Appearance > Customizer and then clicking on Posts section.
Pages
You can access the styling options for Pages by opening the customizer through Appearance > Customizer and then clicking on Pages section.
Page Title Bar Image
When creating a page, you can choose it's title image by choosing a Page Title Featured Image
Contact Page
Contact Page template offers a two column layout to show a contact form and some other content besides it.
To enable Contact Page Layout on any page, edit that page and choose the Contact template as you can see in the screenshot below:
Embedding the contact form
We use Contact Form 7 plugin to embed the contact forms. To add contact form in your Contact Page, create a form by going to Contact from your WordPress Dashboard and follow the instructions as stated in these screenshots:
Once you create a contact form, copy it's shorcode as pointed out in this screenshot:
Open the customizer through Appearance > Customizer and then clicking on Contact and paste your shortcode in the Contact Form 7 Shortcode option. Click Save & Publish
Reservation Form
You can embed a reservation form on any page via Contact Form 7 plugin.
To add a reservation form on any page, create a form by going to Contact from your WordPress Dashboard and follow the instructions as stated in these screenshots:
Once you create a contact form, copy it's shorcode as pointed out in this screenshot:
Edit the page where you want to insert this form and paste the shortcode. Save and publish your page
Typography
MerryCafe offers 100s of Google Fonts that you can use for your sites. To choose fonts for your site, open customizer by going to Appearance > Customizer and then go to Typography section.
Form Styles
You can stylize your form fields with MerryCafe's customizer options. Open customizer by going to Appearance > Customizer and then go to Form Styles section.
Defaults
You can set default styles for your theme with this section. Open customizer by going to Appearance > Customizer and then go to Defaults section to modify the default options.
Default Colors
The Primary and Secondry colors you set in the Defaults section of Appearance > Customizer do not replace all the colors in customizer imediately. Instead, they only change the 'default' values of those colors. See the following screenshot for reference:
Footer
Footer styles and options can be modified inside the Footer section of Appearance > Customizer
Footer Widgets
The Footer 1, Footer 2 and Footer 3 sidebars in Appearance > Widgets are the three columns in the footer area. You can drag/drop widgets here.


|
JTS Topology Suite version 1.13 | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.vividsolutions.jts.geomgraph.EdgeIntersectionList
public class EdgeIntersectionList
A list of edge intersections along an Edge
.
Implements splitting an edge with intersections
into multiple resultant edges.
| Constructor Summary | |
|---|---|
EdgeIntersectionList(Edge edge)
|
| Method Summary | |
|---|---|
EdgeIntersection |
add(Coordinate intPt,
int segmentIndex,
double dist)
Adds an intersection into the list, if it isn't already there. |
void |
addEndpoints()
Adds entries for the first and last points of the edge to the list |
void |
addSplitEdges(java.util.List edgeList)
Creates new edges for all the edges that the intersections in this list split the parent edge into. |
boolean |
isIntersection(Coordinate pt)
Tests if the given point is an edge intersection |
java.util.Iterator |
iterator()
Returns an iterator of EdgeIntersection s |
void |
print(java.io.PrintStream out)
|
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public EdgeIntersectionList(Edge edge)
| Method Detail |
|---|
public EdgeIntersection add(Coordinate intPt, int segmentIndex, double dist)
public java.util.Iterator iterator()
EdgeIntersection
s
public boolean isIntersection(Coordinate pt)
pt
- the point to test
public void addEndpoints()
public void addSplitEdges(java.util.List edgeList)
edgeList
- a list of EdgeIntersectionspublic void print(java.io.PrintStream out)
|
JTS Topology Suite version 1.13 | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


TOWN & COUNTRY A film review by Steve Rhodes Copyright 2001 Steve Rhodes RATING (0 TO ****): * 1/2
"We've got a problem?" Porter Stoddard asks his wife Ellie. "Which one?" she asks in reply. Although she is asking him which color of swatch he prefers, this interchange could just have easily been said about Peter Chelsom's comedy TOWN & COUNTRY. Before an orchestra starts, it carefully establishes a reference tone. One of the biggest problems with TOWN & COUNTRY is that it never finds any consistent or successful tone. The result is a dreadful comedy that's as flat as a can of soda left open all night.
Wearing her signature nerdy and eclectic suits, Diane Keaton plays Diane Keaton, although the other characters insist on referring to her as Ellie Stoddard. Warren Beatty plays Porter, Ellie's husband of 25 years. When we first meet Porter he is enjoying a personal cello concerto in his hotel bedroom. A naked Nastassja Kinski has her cello in a bear hug. The cello looks particularly happy. Can't say that I blame it. But, I digress.
It seems that this is the first time that Porter has ever cheated on his wife. He swears to himself to never yield to such temptation again, but he, of course, does not keep this vow. Before his wife can catch him, we switch to their equally wealthy Manhattan friends, Mona and Griffin (Goldie Hawn and Garry Shandling), whose marriage is going through its own crisis since Griffin has been caught dallying.
The country part of the story comes from the decrepit mansions and snow cabins that the couples visit after their marriages go awry. While in Sun Valley, the two guys off on their own run into a local (Jenna Elfman) who takes them to a Halloween party. Elfman dresses as Marilyn Monroe on her visit to President Kennedy, Beatty dons a polar bear suit and Shandling wears an Elvis outfit. The visuals of a tipsy Marilyn Monroe frolicking in the snow with a horny polar bear is one of the few memorable or even mildly amusing parts of the production. (The less said about Charlton Heston's embarrassing role as a mad, gun-toting father, the better.)
The film has reportedly been recut several times in order to find some version that works. The editors clearly stopped too soon. They should have kept cutting until all that was left was confetti. They could have used the confetti to celebrate something worthwhile like some better movie's release.
TOWN & COUNTRY runs 1:36. It is rated R for sexuality and language and would be acceptable for most teenagers.
Web: <http://www.InternetReviews.com>
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The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
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Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


A brief explanation of ceramics terms.
I often find that people are interested in ceramics and pottery, but are
confused by the terms used. So, here is a short list of common terms and
methods.
- High Fired - The temperature range of around 2250 to 2400 degrees Fahrenheit.
- Low Fired - The wide temperature range of around 1400 to 2000 degrees Fahrenheit.
- Cone - Potters and ceramists often say that something is fired to cone 10. The cone system is a measurement of both heat and time, and is a small cone that softens and bends at a chemically reliable point. This allows the person firing the kiln to see how much heat work has been done to the ware in the kiln. For more information, please see the Orton Ceramic website: https://www.ortonceramic.com/Resources/Pyrometric_Cones/
- Reduction - The atmosphere of a kiln that is lacking the oxygen to completely burn. This causes the fuel to react with the oxygen in the glaze and clay.
- Oxidation - The atmosphere of a kiln that has ample oxygen to burn the fuel. This is also typical of electric powered kilns. The ware in the kiln is also oxidized and is typically lighter in color than in reduction.
- Porcelain - This type of clay is generally white to grey in color, and develops a strong tight body when fired. Natural porcelain is typically high fired.
- Stoneware - This type of clay is generally tan to brown, with the color coming from the inclusion of natural metals and minerals. Most natural stoneware is high fired.
- Kiln - Pronounced both "kill" and "kilnn", this is a structure that is able to withstand high temperatures for extended periods of time and is used to fire clay, turning it into ceramics.
- Glaze - A glaze is often added to a ceramics piece for durability, aesthetics, or functionality. The glaze layer over the clay is often glassy and very strong.
- Shino - A type of glaze that came from Japan, and is usually white to red. A highly varied glaze, the atmosphere is very important for how the color turns out. Shino glazes are high fired.
- Celadon - A type of glaze that came from China, Korea, and Japan, and typically is a shade of green. There are also Celadons that are shades of blue, and ones that are clear or white. Most Celadons are high fired in reduction.
- Raku - A style of firing that was developed in Japan for the tea ceremony. The ceramic ware is low fired, and historically under-fired to have a softer feel and sound. This generally means that Raku ware is not food safe by modern standards. The current American style of Raku is mostly decorative and uses a post-firing reduction to create lustrous metallic glazes and deep blacks.
- Soda Fired - A type of firing where the ceramic ware is glazed by the introduction of baking soda or soda ash at high fire temperatures. This glazes the whole interior of the kiln with sodium, which is very reactive at high temperatures. The kiln must be specially prepared for soda firing, or the bricks will be eaten away by the sodium. This style of firing came from a search for an alternative to salt firing, which is very similar in methods, but has a different look and behavior.
- Tenmoku or Temmoku - A glaze that is dark brown and black in most cases, named by Japanese Tea Masters for Tea Bowls (Chawan) from the Mountain of Heaven area in China. While most of my Tenmokus are fired in reduction, there are some Tenmoku glazes that only work in oxidation.


I was just wondering if the skill parry is of any use even if you max out the skill? By max out I mean take skill focus and epic skill focus and have your character be dexterity based since that's the modifier for that skill. You would have 56 + your dex modifier + 1d20 to beat the AB of the incoming blow. I think for a counter attack to occur the difference between the parry roll and the AB has to be 10 or greater.
Also it says that you can only parry as many attacks per round as your character has but does this apply to 2 handed fighting as well? So say my char only has 3 attacks per round but with 2 weapons he has 5, so does this mean I can still only parry 3 incoming attacks or does it raise to 5?
Actually, right now you can only block 3 attacks in any given round. One of the buggs out there that hasn't been fixed.
I have never seen parry as worth placing points in.
 Isn't it amazing how we have found all these bugs and even after the last patch they have yet to fix them all. Sheesh.
On a funny note, wouldn't it be interesting to see two melees in parry mode. Wouldn't they both just sit there? LOL.
Its not a bug actually. Its hard coded that way and meant to be otherwise a simple one level in a class that has this will make a melee build almost impossible to hit. Expertise /improved expertise should be used for the same effect instead.
Syrath
Parry is widely acknowledged as the most useless skill ever. It's not even as useful as Craft Armour, since that conceivably has a use,if you don't like the look of your armour, and it's a really, *really* powerful suit, and you have lots of gold.
Even in those limited circumstances, it's still more use than parry


Multipeer Connectivity
Written by Mattt Thompson on
As consumer web technologies and enterprises race towards cloud infrastructure, there is a curious and significant counter-movement towards connected devices.
In this age of mobile computing, the possibilities of collaboration, whether in work or play, have never been greater. In this age of privacy concerns and mass surveillance, the need for secure, ad hoc communications has never been more prescient. In this age of connected devices, the promise of mastery over the everyday objects of our lives has never been closer at hand.
The Multipeer Connectivity APIs, introduced in iOS 7, therefore may well be the most significant for the platform. It allows developers to completely reimagine how mobile apps are built, and to redefine what is possible. And we're not just talking about successors to the lame bump-to-send-contact-information genre, either: multi-peer connectivity has implications on everything from collaborative editing and file sharing to multiplayer gaming and sensor aggregation.
Multipeer Connectivity is a framework that enables nearby devices to communicate over infrastructure Wi-Fi networks, peer-to-peer Wi-Fi, and Bluetooth personal area networks. Connected peers are able securely transmit messages, streams, or file resources to other devices without going through an intermediary web service.
Advertising & Discovering
The first step in communication is to make peers aware of one another. This is accomplished by advertising and discovering services.
Advertising makes a service known to other peers, while discovery is the inverse process of the client being made aware of services advertised by other peers. In many cases, clients both discover and advertise for the same service, which can lead to some initial confusion—especially to anyone rooted in the client-server paradigm.
Each service is identified by a type, which is a
short text string of ASCII letters, numbers, and
dashes, up to 15 characters in length. By convention,
a service name should begin with the app name,
followed by a dash and a unique descriptor for that
service (think of it as simplified
com.apple.*
-esque reverse-DNS notation):
static NSString * const XXServiceType = @"xx-service";
Peers are uniquely identified by an
MCPeerID
object, which are initialized
with a display name. This could be a user-specified
nickname, or simply the current device name:
MCPeerID *localPeerID = [[MCPeerID alloc] initWithDisplayName:[[UIDevice currentDevice] name]];
Peers can be also be advertised or discovered manually using
NSNetService
or the Bonjour C APIs, but this is a rather advanced and specific concern. Additional information about manual peer management can be found in theMCSession
documentation.
Advertising
Services are advertised by the
MCNearbyServiceAdvertiser
, which is
initialized with a local peer, service type, and any
optional information to be communicated to peers that
discover the service.
Discovery information is sent as Bonjour
TXT
records encoded according to RFC 6763.
MCNearbyServiceAdvertiser *advertiser =
[[MCNearbyServiceAdvertiser alloc] initWithPeer:localPeerID
discoveryInfo:nil
serviceType:XXServiceType];
advertiser.delegate = self;
[advertiser startAdvertisingPeer];
Events are handled by the advertiser's
delegate
, conforming to the
MCNearbyServiceAdvertiserDelegate
protocol.
As an example implementation, consider a client that allows the user to choose whether to accept or reject incoming connection requests, with the option to reject and block any subsequent requests from that peer:
#pragma mark - MCNearbyServiceAdvertiserDelegate
- (void)advertiser:(MCNearbyServiceAdvertiser *)advertiser
didReceiveInvitationFromPeer:(MCPeerID *)peerID
withContext:(NSData *)context
invitationHandler:(void(^)(BOOL accept, MCSession *session))invitationHandler
{
if ([self.mutableBlockedPeers containsObject:peerID]) {
invitationHandler(NO, nil);
return;
}
[[UIActionSheet actionSheetWithTitle:[NSString stringWithFormat:NSLocalizedString(@"Received Invitation from %@", @"Received Invitation from {Peer}"), peerID.displayName]
cancelButtonTitle:NSLocalizedString(@"Reject", nil)
destructiveButtonTitle:NSLocalizedString(@"Block", nil)
otherButtonTitles:@[NSLocalizedString(@"Accept", nil)]
block:^(UIActionSheet *actionSheet, NSInteger buttonIndex)
{
BOOL acceptedInvitation = (buttonIndex == [actionSheet firstOtherButtonIndex]);
if (buttonIndex == [actionSheet destructiveButtonIndex]) {
[self.mutableBlockedPeers addObject:peerID];
}
MCSession *session = [[MCSession alloc] initWithPeer:localPeerID
securityIdentity:nil
encryptionPreference:MCEncryptionNone];
session.delegate = self;
invitationHandler(acceptedInvitation, (acceptedInvitation ? session : nil));
}] showInView:self.view];
}
For sake of simplicity, this example contrives a block-based initializer for
UIActionSheet
, which allows for theinvitationHandler
to be passed directly into the action sheet responder in order to avoid the messy business of creating and managing a custom delegate object. This method can be implemented in a category, or adapted from any of the implementations available on CocoaPods
Creating a Session
As in the example above, sessions are created by
advertisers, and passed to peers when accepting an
invitation to connect. An MCSession
object is initialized with the local peer identifier,
as well as securityIdentity
and
encryptionPreference
parameters.
MCSession *session = [[MCSession alloc] initWithPeer:localPeerID
securityIdentity:nil
encryptionPreference:MCEncryptionNone];
session.delegate = self;
securityIdentity
is an optional
parameter that allows peers to securely identify
peers by X.509 certificates. When specified, the
first object should be an SecIdentityRef
identifying the client, followed by one or more
SecCertificateRef
objects than can be
used to verify the local peer’s identity.
The encryptionPreference
parameter
specifies whether to encrypt communication between
peers. Three possible values are provided by the
MCEncryptionPreference
enum:
-
MCEncryptionOptional
: The session prefers to use encryption, but will accept unencrypted connections. -
MCEncryptionRequired
: The session requires encryption. -
MCEncryptionNone
: The session should not be encrypted.
Enabling encryption can significantly reduce transfer rates, so unless your application specifically deals with user-sensitive information,
MCEncryptionNone
is recommended.
The MCSessionDelegate
protocol will be
covered in the section on sending and receiving
information.
Discovering
Clients can discover advertised services using
MCNearbyServiceBrowser
, which is
initialized with the local peer identifier and the
service type, much like for
MCNearbyServiceAdvertiser
.
MCNearbyServiceBrowser *browser = [[MCNearbyServiceBrowser alloc] initWithPeer:localPeerID serviceType:XXServiceType];
browser.delegate = self;
There may be many peers advertising a particular
service, so as a convenience to the user (and the
developer), the MCBrowserViewController
offers a built-in, standard way to present and
connect to advertising peers:
MCBrowserViewController *browserViewController =
[[MCBrowserViewController alloc] initWithBrowser:browser
session:session];
browserViewController.delegate = self;
[self presentViewController:browserViewController
animated:YES
completion:
^{
[browser startBrowsingForPeers];
}];
When a browser has finished connecting to peers, it
calls -browserViewControllerDidFinish:
on its delegate, to notify the presenting view
controller that it should update its UI to
accommodate the newly-connected clients.
Sending & Receiving Information
Once peers are connected to one another, information can be sent between them. The Multipeer Connectivity framework distinguishes between three different forms of data transfer:
- Messages are information with well-defined boundaries, such as short text or small serialized objects.
- Streams are open channels of information used to continuously transfer data like audio, video, or real-time sensor events.
- Resources are files like images, movies, or documents.
Messages
Messages are sent with
-sendData:toPeers:withMode:error:
:
NSString *message = @"Hello, World!";
NSData *data = [message dataUsingEncoding:NSUTF8StringEncoding];
NSError *error = nil;
if (![self.session sendData:data
toPeers:peers
withMode:MCSessionSendDataReliable
error:&error]) {
NSLog(@"[Error] %@", error);
}
Messages are received through the
MCSessionDelegate
method
-sessionDidReceiveData:fromPeer:
. Here's
how one would decode the message sent in the previous
code example:
#pragma mark - MCSessionDelegate
- (void)session:(MCSession *)session
didReceiveData:(NSData *)data
fromPeer:(MCPeerID *)peerID
{
NSString *message =
[[NSString alloc] initWithData:data
encoding:NSUTF8StringEncoding];
NSLog(@"%@", message);
}
Another approach would be to send
NSKeyedArchiver
-encoded objects:
id <NSSecureCoding> object = // ...;
NSData *data = [NSKeyedArchiver archivedDataWithRootObject:object];
NSError *error = nil;
if (![self.session sendData:data
toPeers:peers
withMode:MCSessionSendDataReliable
error:&error]) {
NSLog(@"[Error] %@", error);
}
#pragma mark - MCSessionDelegate
- (void)session:(MCSession *)session
didReceiveData:(NSData *)data
fromPeer:(MCPeerID *)peerID
{
NSKeyedUnarchiver *unarchiver = [[NSKeyedUnarchiver alloc] initForReadingWithData:data];
unarchiver.requiresSecureCoding = YES;
id object = [unarchiver decodeObject];
[unarchiver finishDecoding];
NSLog(@"%@", object);
}
In order to guard against object substitution attacks, it is important to set
requiresSecureCoding
toYES
, such that an exception is thrown if the root object class does not conform to<NSSecureCoding>
. For more information, see the [NSHipster article on NSSecureCoding.
Streams
Streams are created with
-startStreamWithName:toPeer:
:
NSOutputStream *outputStream =
[session startStreamWithName:name
toPeer:peer];
stream.delegate = self;
[stream scheduleInRunLoop:[NSRunLoop mainRunLoop]
forMode:NSDefaultRunLoopMode];
[stream open];
// ...
Streams are received by the
MCSessionDelegate
with
-session:didReceiveStream:withName:fromPeer:
:
#pragma mark - MCSessionDelegate
- (void)session:(MCSession *)session
didReceiveStream:(NSInputStream *)stream
withName:(NSString *)streamName
fromPeer:(MCPeerID *)peerID
{
stream.delegate = self;
[stream scheduleInRunLoop:[NSRunLoop mainRunLoop]
forMode:NSDefaultRunLoopMode];
[stream open];
}
Both the input and output streams must be scheduled and opened before they can be used. Once that's done, streams can be read from and written to just like any other bound pair.
Resources
Resources are sent with
sendResourceAtURL:withName:toPeer:withCompletionHandler:
:
NSURL *fileURL = [NSURL fileURLWithPath:@"path/to/resource"];
NSProgress *progress =
[self.session sendResourceAtURL:fileURL
withName:[fileURL lastPathComponent]
toPeer:peer
withCompletionHandler:^(NSError *error)
{
NSLog(@"[Error] %@", error);
}];
The returned NSProgress
object can be
Key-Value
Observed to monitor progress of the file
transfer, as well as provide a cancellation handler,
through the -cancel
method.
Receiving resources happens across two methods in
MCSessionDelegate
:
-session:didStartReceivingResourceWithName:fromPeer:withProgress:
&
-session:didFinishReceivingResourceWithName:fromPeer:atURL:withError:
:
#pragma mark - MCSessionDelegate
- (void)session:(MCSession *)session
didStartReceivingResourceWithName:(NSString *)resourceName
fromPeer:(MCPeerID *)peerID
withProgress:(NSProgress *)progress
{
// ...
}
- (void)session:(MCSession *)session
didFinishReceivingResourceWithName:(NSString *)resourceName
fromPeer:(MCPeerID *)peerID
atURL:(NSURL *)localURL
withError:(NSError *)error
{
NSURL *destinationURL = [NSURL fileURLWithPath:@"/path/to/destination"];
NSError *error = nil;
if (![[NSFileManager defaultManager] moveItemAtURL:localURL
toURL:destinationURL
error:&error]) {
NSLog(@"[Error] %@", error);
}
}
Again, the NSProgress
parameter in
-session:didStartReceivingResourceWithName:fromPeer:withProgress:
allows the receiving peer to monitor the file
transfer progress. In
-session:didFinishReceivingResourceWithName:fromPeer:atURL:withError:
,
it is the responsibility of the delegate to move the
file at the temporary localURL
to a
permanent location.
Multipeer Connectivity is a ground-breaking API, whose value is only just starting to be fully understood. Although full support for features like AirDrop are currently limited to latest-gen devices, you should expect to see this kind of functionality become expected behavior.
As you look forward to the possibilities of the new year ahead, get your head out of the cloud, and start to consider the incredible possibilities around you.


Anitas kitchen
Languages Used:
- Html
- Css
About Me
I'm kiprotich Bett, a 23 year old, living in Nairobi. I'm currently a student at Moringa School pursuing a course in Software Development, perfecting my skills in Web development while specializing in full-stack development and user experience.
Theres nothing more exiciting than finding a new idea or solutions, that would have a positive impact on people. I've always been passionate about technology since i was in school hence choose to major in Software Engineering in college. A couple of years later its still what am most excited about and pursuing it as a carreer. Helping people and making a difference everyday is something i enjoy doing and this career allows me to do that. When i heard of Moringa School i knew it would be great opportunity for me to more and explore new concepts and ideas as in the tech world you can never learn enough.
My Journey to being a Full Stack Developer
Education Background
Bachelor of Science in Information Technology
Second Class Honours
(Lower Division)
Karatina University
Kenya Certificate of Secondary Education(KCSE),
Chebunyo Boys High School
Mean Grade: B+(Plus)
Work Experience
Networking and ICT Technical User Support (Integrated Supplies & Consultancy Ltd.
WESTLANDS, NAIROBI).
Role –Networking, Fiber Cable Management, Local Area Network Design
Installation, and troubleshooting.
Independent Contract – Responsive Web-cloudonearth.co.keRole – Participated as support developer in Designing and implementing Responsive website for a company offering air conditioning and refrigeration services.
Hobbies and Interest
My interests apart from programming includes:


Audi parked in Dundee driveway vandalised using ‘corrosive substance’
Police are hunting a vandal who caused over £1000 worth of damage to a parked vehicle in Dundee.
by Jake KeithThe blue Audi, which was parked in a driveway on Loftus Road, was severely damaged by a “corrosive substance” between 7pm on Wednesday November 20 and 11am the next day.
It did not belong to the residents of the house and no other nearby cars were damaged.
Anyone with information that could assist police should call 101 or speak to any officer.
Information can be passed anonymously through Crimestoppers on 0800 555 111. The reference is incident 1054 of November 21.


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---
layout: post
title: ToxoDB 11.0 Released
category: [ToxoDB]
---
We
are pleased to announce the release of ToxoDB 11.0
New
data in this release:
- Toxoplasma
gondii ME49 transcriptomes of cat enteroepithelial stages:
This data was funded by the EuPathDB driving biological project
mechanism and constitutes the first whole genome transcriptomic data
from cat stages. This data has been provided prepublication by
Dr. Adrian Hehl and collaborators. View
experimental details and available searches in the dataset
section.
- Toxoplasma
gondii VEG updated genome sequence and annotation:
This data was generated as part of
the Toxoplasma sequencing white paper project.
View experimental details and available searches in the dataset
section.
- RNA
sequence data from T. gondii GT1 and ME49 strains: This
data was generated as part of the Toxoplasma sequencing white paper
project and was used in
improving and updating gene models. View
experimental details and available searches in the dataset
section.
- Updated
Toxoplasma isolate data based on RFLP typing:
This isolate data is an updates the previously available data from
Dr. Chunlei Su. View
experimental details and available searches in the dataset
section.
- Toxoplasma
isolate data based on RFLP typing: This data has
been provided prepublication (Casartelli-Alves et al.,
unpublished).
View experimental details and
available searches in the dataset
section.
- Updated
transcriptome data from Toxoplasma strains during murine
macrophage infection (Minot
et al.): Additional strains were
integrated into this dataset. View
experimental details and available searches in the dataset
section.
- Eimeria
genomes and annotation: A number of Eimeria genomes
and annotations are now available in ToxoDB. These genomes were
provided prepublication by Drs. Arnab Pain and Adam Reid. The
genomes include:
- E.
acervulina Houghton
- E.
brunetti Houghton
- E. maxima
Weybridge
- E. mitis
Houghton
- E.
necatrix Houghton
- E.
praecox Houghton
- E.
tenella Houghton.
- Eimeria
falciformis Bayer Haberkorn 1970 genome and
annotation: This genome sequence and annotation has been
provided prepublication by Dr. Christoph Dieterich. View
experimental details and links to searches in the dataset
section.
New
features:
- Search
genes based on polymorphism data: The
high throughput sequencing SNP searches have been upgraded to
increase their speed and provide additional search functionality.
Meta data associated with samples is now more intuitively
organized. Search for genes based on SNP
characteristics or identify SNPs based on multiple criteria
including within one
group of isolates or between two
groups of isolates.
- Filter
samples based on metadata: It
is easier now to tailor
your searches (such as SNP searches) by defining samples based on
their metadata characteristics.
New
Tutorials:
Our
YouTube channel is updated regularly with new tutorials. The
latest tutorials include:
Note:
Our bimonthly database releases incorporate new data and correct old
data when necessary. Changes in annotation and new
experimental data may slightly alter your search results by
increasing or decreasing the number of hits. When search parameters
change with a new release, we invalidate (Ø)
the search and ask you to rerun it. When IDs are updated or removed,
we map the old IDs to the new ones, remove the old IDs from your
Basket, and leave your Favorites page alone.
Follow
EuPathDB
on FaceBook,
Twitter, and YouTube


Take the examples written in your documentation of functions and use them to create shells (skeletons which must be manually completed by the user) of test files to be tested with the 'testthat' package.
| Version: | 1.4.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | filesstrings (≥ 3.0.0), magrittr, stringr, styler (≥ 1.0.2), purrr, roxygen2, checkmate, readr, utils, ore (≥ 1.4.0), rprojroot, usethis, clisymbols, crayon, fs, glue, rlang |
| Suggests: | testthat, covr, knitr, rmarkdown, spelling, pacman |
| Published: | 2018-11-07 |
| Author: | Rory Nolan [aut, cre], Sergi Padilla-Parra [ths], Thomas Quinn [rev], Laurent Gatto [rev] |
| Maintainer: | Rory Nolan <rorynoolan at gmail.com> |
| BugReports: | https://www.github.com/rorynolan/exampletestr/issues |
| License: | GPL-3 |
| URL: | https://rorynolan.github.io/exampletestr, https://github.com/rorynolan/exampletestr#readme |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | exampletestr citation info |
| Materials: | NEWS |
| CRAN checks: | exampletestr results |
| Reference manual: | exampletestr.pdf |
| Vignettes: |
One .R file at a time One function at a time A whole package |
| Package source: | exampletestr_1.4.1.tar.gz |
| Windows binaries: | r-devel: exampletestr_1.4.1.zip, r-release: exampletestr_1.4.1.zip, r-oldrel: exampletestr_1.4.0.zip |
| OS X binaries: | r-release: exampletestr_1.3.1.tgz, r-oldrel: exampletestr_1.3.1.tgz |
| Old sources: | exampletestr archive |
Please use the canonical form https://CRAN.R-project.org/package=exampletestr to link to this page.


Upon your arrival at Kilimanjaro airport, our representative will meet you and drive you to your hotel for a first evening in Tanzania. Arusha is a major international diplomatic hub. The city hosts and is regarded as the de facto capital of the East African Community. Arusha is surrounded by some of Africa's most famous landscapes and national parks. Situated below Mount Meru on the eastern edge of the eastern branch of the Great Rift Valley, it has a mild climate
Night at Villa Luna Half Board.
We offer morning and afternoon rides, 2-hour ride (depending on pace). The morning rides departs at 8:00AM and the afternoon rides departs at 14.30PM. We are flexible with time however these are the best hours of the day to be out in the bush on horseback.
The ride takes place on nearby Dolly Estate and whether you are a complete beginner or an advanced rider, there is an opportunity and experience for everyone. Those with little riding experience can enjoy a gentle hack along dusty tracks where wildebeest and antelopes graze contentedly. Led by experienced guides, one can explore the meandering paths, spotting a rich array of wildlife along your way. Regularly seen on Dolly estate is one of the most elegant mammals found in Tanzania, the Gerenuk.
The park is home to a variety of snakes common in Eastern and Southern Africa from the Black Mamba to the Black and Red spitting Cobras and the African Python. There are approximately 48 snakes you can view.
Each Tours is led by qualified and professionally trained guides, always pushing for a new and exhilarating adventure. Whether you would like to unfold Africa’s beauty by horseback or by foot, Kaskazi will share its enthusiasm and tell stories that will travel generations. The routes are off the beaten track and with a vast knowledge about the countries fauna and flora, we go deep into the bush and together enjoy the wonders that Africa has to offer, the magnificent wildlife, the scenery and the magical interaction with the local tribes who inhabit these areas. All this viewed from what back in the days also used to roam wild, the horse.
Night at Villa Luna Half Board.
Early morning you will be picked up from your hotel and drive to Tarangire National Park for a full day of game viewing and photographing. Named after the river that flows through the park, Tarangire National Park is one of the lesser known Tanzanian National Parks, giving it a real air of undiscovered Africa.
Famous for its tree climbing pythons, massive baobabs and large herds of elephant, Tarangire National Park is small but still home to a significant wildlife population, with a density of wildlife matched only by Ngorongoro. Birding is particularly good here; the swamps, tinged green year round, are the focus for 550 bird varieties; the most breeding species in one habitat
Today is committed to a full day game drive Tours in Tarangire National Park. Imagine hundreds of zebra stripes reflecting in still waters. Imagine the looming silhouette of elephants burnt into the crimson sunset. Imagine the sight of ghostly baobab trees scattered across shimmering grasslands. These features are not figments of imagination – these features are just a taste of what Tarangire is all about!
The most spectacular feature of Tarangire is that it serves as a place of refuge for the largest elephant population in northern Tanzania.Elephant viewing in Tarangire is outstanding and it is likely that you will see hundreds of elephants in a single day. Set against stunning scenery, elephant viewing will undoubtedly prove to be one of the biggest highlights for a Tours in Tarangire. Tarangire is an incredible park that will reserve a special place in your heart and memories. Rest assured - no matter what time of year you choose to visit Tarangire, you’ll certainly be glad you did!
Night at Ngorongoro Farm House Suite Rooms
Early morning you will be picked up from your hotel and drive to Lake Manyara National Park for a full day of game viewing and photographing. Stretching for 50km along the base of the rusty-gold 600-metre high Rift Valley escarpment, Lake Manyara is a scenic gem, with a setting extolled by Ernest Hemingway as “the loveliest I had seen in Africa”.
From the entrance gate, the road winds through an expanse of lush jungle-like groundwater forest where hundred-strong baboon troops lounge nonchalantly along the roadside, blue monkeys scamper nimbly between the ancient mahogany trees, dainty bushbuck tread warily through the shadows, and outsized forest hornbills honk cacophonously in the high canopy. Manyara provides the perfect introduction to Tanzania’s birdlife. More than 400 species have been recorded, and even a first-time visitor to Africa might reasonably expect to observe 100 of these in one day. Highlights include thousands of pink-hued flamingos on their perpetual migration, as well as other large waterbirds such as pelicans, cormorants and storks.
Manyara provides the perfect introduction to Tanzania’s birdlife. More than 400 species have been recorded, and even a first-time visitor to Africa might reasonably expect to observe 100 of these in one day. Highlights include thousands of pink-hued flamingos on their perpetual migration, as well as other large waterbirds such as pelicans, cormorants and storks.
Night at Ngorongoro Farm House Suite Rooms
Today, you will visit the Ngorongoro crater, the main feature is a large, unbroken, unfolded volcanic caldera. The crater, which formed when a giant volcano exploded and collapsed on itself some two to three million years ago, is 610m (2,000 ft.) deep and its floor covers 260 km² (100 sq. mi). The crater floor is mostly open grassland with two small wooded areas dominated by Acacia xanthophloea.
Game drives through the Ngorongoro Crater begin early in the morning in closed Tours vehicles with pop – up roofs. Accompanied by an expert driver guide, a magnificent experience is sure to be had! The descent into the crater is one of scenic beauty, as you make your way through lush forests, home to numerous elephants and buffalo. As you reach the inside of the Ngorongoro Crater, marvelling at the incredible landscape around you, your guide will take you through the grassy crater floor, the unbelievable fever tree forest, the banks of Lake Magadi and the surrounding marshes. Lunch is served picnic style, surrounded by the natural wonders around you as well as the exceptional wildlife, roaming freely, with which you share this extraordinary paradise.
Night at Ngorongoro Sopa Lodge on a Full Board Basis
Today we will leave Ngorongoro and head to the vast plains of the Serengeti! We will see wildlife along the way, and of course when we reach Serengeti National Park you will have the chance to see the many animals that make the park so famous. The best vantage points and worthwhile areas are chosen to give you the best possible chance to observe animals and to capture your perfect photo. Depending upon the time of year, you have the chance to see the great migration with a hundred thousand wildebeests and zebras. During the small rainy season in October and November, you will typically see these herds moving from the Kenyan Masai Mara through the northwest Serengeti to the plains in the south. After the long rainy season in April through June, the herds travel slowly back through the west and northwest Serengeti. With fluctuations in rainfall, this timetable can change the animals’ migration times and routes. Your guide will know where and when to find the animals and makes sure that you can see the animals from the best spots in the national park
Night at Kati Kati tented Camp
Spend a full day exploring the vast Serengeti plains. Serengeti National Park quite simply offers the finest game viewing in all of Africa. Unrivaled concentrations of wildlife, stunningly beautiful landscapes and vast pristine areas make this region our number one pick.
You will rise early to enjoy an early morning game drive. At sunrise you may encounter animals grazing in the beautiful light of sunrise and predators may still be busy hunting before retiring to rest for the day. Sunrise on the Serengeti is not to be missed and extraordinary photographic opportunities will abound at this magical time of day. You have the option to return to your accommodation for a hot lunch or to opt for a packed lunch which can be enjoyed at one of the various picnic sites found in the Serengeti. After lunch you will continue on a game drive. Around the time of dusk be on the lookout for lion. Cheetah can also be spotted on the plains in the early morning and late afternoon when temperatures are not too high. They rely on daylight and speed to catch their prey. Hippo pods can be seen in the bigger pools and rivers while crocodiles can be seen sunning themselves on the riverbanks. You will return to Camp in time to enjoy a sundowner before your evening meal
Night at Kati Kati tented Camp
Morning departure for a Tours through the famous Serengeti. The Serengeti Plain is a natural savannah straddling Tanzania and Kenya. The most common species found in the "endless plains" are black wildebeest, impala, zebra and buffalo. It also includes jackals, hyenas, hippos, crocodiles, giraffes, elephants, almost all types of antelopes and a rich bird life where birds of prey, big and small, and a various species of vultures are well represented. The Serengeti is probably the most famous game reserve in Tanzania. The park covers 14,763 km square, but the ecosystem is much larger, extending over the Masai Mara, Kenya and the Ngorongoro Conservation Area
Today we will drive to Karatu. You will enjoy a last minute game drive in Serengeti National Park and Ngorongoro Conservation Area on the way. Enjoy this last chance to view the amazing wildlife and landscapes of this beautiful area! It is also a day to recuperate, relax at the hotel and savor a delicious dinner.
Night at Kudu Lodge & Camp Karatu
This morning we would leave to spend the day in the native village of Maasai families, discovering their culture. The Maasai are a Nilotic ethnic group of semi-nomadic people located in Kenya and northern Tanzania. They are among the most well known of African ethnic groups, due to their distinctive customs and dress and residence near the many game parks of East Africa. Visit a Maasai village and get an authentic look at the daily life in this traditional culture! The Maasai are some of Africa’s most iconic people and have maintained the pastoral way of life of their ancestors. In Laiboni, you will see how the people live in harmony with their animals and visit a Maasai home to learn about domestic life. Speak with village inhabitants about their way of life and sample the colorful beaded wares of the Maasai women. This is your chance to get an inside look at this unique culture.
After spending time sharing in the native village of Maasai families, discovering their culture, you will then return to Arusha. You will be driven back to Arusha stopping for souvenir shopping along the way arriving in Arusha for dinner and overnight.
You will be served a delicious hot meal for lunch at Lake Duluti Lodge and the possibility to use the pool and the shower in the pool, changing room to refresh yourself before leaving to the airport.
You will be ready to be driven to Kilimanjaro International Airport. You will enjoy a panoramic drive through the city's sub-urbans and it's environs. Your driver guide will see you off at the Kilimanjaro International Airport for your departure flight back home.
Afri-raha Tanzania Tours wish you a nice flight back home
Do not forget to request information about our group Tours to maximized your stay in Tanzania.


Elevator Finite State Machine
- Doors on an elevator can go from opened to closed, and vice versa
- If your hands are in the door, it won't close.
- Once the doors are closed the elevator may move.
- When the elevator reaches its destination, it may stop moving.
Current State:


--- layout: post title: C++ hello world categories: - C++ - Programming tags: - C++ - HOWTO - Note - Programming published: true comments: true ---
write in file "hello.cpp":
``` cpp hello.cpp // my first program in C++ #includecompile:
g++ -o hello hello.cpp


The Blockchain
You Really Needed
Don't you need fast and scalable transaction processing, not sacrificing its tamper-evidentness?
Are you satisfied by the
existing Blockchain platforms?
BURN is made for eliminating these concerns of existing Blockchain platforms.
Is your blockchain fast enough?
BURN achieves more than 1,000 tps with less than 1 sec confirmation time. BURN is a so-called "centralized pile of trash" alone, but working as a 2nd layer of the public blockchain, it can guarantee tamperproof characteristics at the same time with the scalability.
Is your blockchain compatible with the existing tools?
BURN implements fully compatible interface with Ethereum. You can use existing Ethereum tools such as Metamask connecting with BURN network.
Is your blockchain publicly verifiable?
Not only that BURN's transaction and block data are cryptographically verifiable locally, the bundled block data proof can be also verifiable on the public blockchain such as the Ethereum Mainnet.


This monitoring is for the development instance of the Task
Server used for testing purposes.
Average request bytes is an average size of all incoming
messages from clients.
These messages are usually small for a sync with no data, but
can be arbitrarily large.
Idle is the percentage of time the Taskserver stands doing
nothing.
Service time represents the average time to process a request
from a client, in seconds. High load can push this time
upwards.
Total bytes in and out in the form of requests and responses.
This is a cumulative quantity that is reset on server restart.
Transactions per second.
Transaction count is a cumulative quantity that is reset on
server restart.
Uptime is a count of the seconds of uptime that is reset on
server restart. This is a measure of server stability.
The number of organizations supported by the dev server.
This is not expected to change much.


About Me
My name is Jude Clark. I am a 28 year old full-stack web developer living in Duluth, GA. My coworkers nicknamed me "the human computer". My attention to detail, efficiency, and ability to conceptualize complex systems are features that help me to excel at building and maintaining websites.
I started my education as a musician, completing a Bachelor's of Music Composition from the University of South Carolina in 2014. After university, I worked in the classical music industry for four years, after which I had the urge to try something new. In 2018 I returned to my hometown of Duluth, GA with the goal of building a life for myself and my husband in the city I love (Atlanta!).
When the world changed because of the pandemic, I, like so many others, have turned to the tech industry in search of our next career opportunity. As a teenager, my second choice for education (after classical music) was to study computer science at Georgia Tech. So, it felt very natural for me to enroll in the full time coding bootcamp with Georgia Tech in August of 2020.
Since then, I have had a blast learning about the coding world!
My other interests include: makeup, the Olympics, Nederlands spreken.


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
Utility to compute an approximation plane for a set of points. More...
#include <plane_approximation_utility.h>
Public Member Functions | |
Type Definitions | |
| KRATOS_CLASS_POINTER_DEFINITION (PlaneApproximationUtility) | |
| Pointer definition of PlaneApproximationUtility. More... | |
Operators | |
| PlaneApproximationUtility () | |
| Default constructor. More... | |
| virtual | ~PlaneApproximationUtility () |
| Destructor. More... | |
Static Public Member Functions | |
Operations | |
| static void | ComputePlaneApproximation (const std::vector< array_1d< double, 3 > > &rPointsCoords, array_1d< double, 3 > &rPlaneBasePointCoords, array_1d< double, 3 > &rPlaneNormal) |
| Provided a list of point coordinates, computes the best plane approximation in a least squares sense. More... | |
Utility to compute an approximation plane for a set of points.
Provided a set of points, this utility computes the plane that best approximate them in a least square sense. The base point of the plane is the set of points average coordinates while the plane normal is taken as the eigenvector associated to the minimum eigenvalue of the matrix A. Matrix A is trans(P)*P, being P the matrix that stores in each row the difference between the base point and each one of the points to approximate.
|
inline |
Default constructor.
|
inlinevirtual |
Destructor.
| Kratos::PlaneApproximationUtility< TDim >::KRATOS_CLASS_POINTER_DEFINITION | ( | PlaneApproximationUtility< TDim > | ) |
Pointer definition of PlaneApproximationUtility.


Troopers, driver narrowly escape out of control truck
by Ben HooperNov. 20 (UPI) -- An Illinois State Police dashboard camera was recording when two troopers and a stranded driver had a near-miss with a box truck that lost control on an icy highway.
State Police District 19 said Troopers Mark Zimmerman and Jonathan Pflaum were helping a driver change a tire at the side of Interstate 64 when a two axle box truck lost control on the ice.
Dashboard camera footage shows the truck topple onto its side and crash into the woman's car. The woman and troopers were able to get out of the way in time.
Police said there were no injuries in the "miracle on ice." The truck driver was issued a ticket for failure to slow down.
(0) Leave a comment
upi.com/6960788
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|
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
| Interface Summary | |
|---|---|
| Action | Prototype Action that execute for handle http request. |
| ExceptionResolver | Interface of exception resolver to handle exception. |
| Interceptor | Intercept action's execution like servlet Filter, but interceptors are configured and managed by IoC container. |
| InterceptorChain | Holds all interceptors as a chain. |
| ServletContextAware | Module implements this interface allowing to get a ServletContext object. |
| ViewResolver | To resolve and render a view. |
| Class Summary | |
|---|---|
| ActionContext | Wrap all servlet's objects and can be retrieved via thread local. |
| DispatcherServlet | Core dispatcher servlet. |
| ModelAndView | A ModelAndView object contains a model and a view. |
| Annotation Types Summary | |
|---|---|
| Order | Specify orders. |
|
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| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


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