Dense-and-Implicit-Attention (DIA)
Attention-based deep neural networks (DNNs) that emphasize the informative information in a local receptive field of an input image have successfully boosted the performance of deep learning in various challenging problems. In this paper, we propose a Dense-and-Implicit-Attention (DIA) unit that can be applied universally to different network architectures and enhance their generalization capacity by repeatedly fusing the information throughout different network layers. The communication of information between different layers is carried out via a modified Long Short Term Memory (LSTM) module within the DIA unit that is in parallel with the DNN. The sharing DIA unit links multi-scale features from different depth levels of the network implicitly and densely. Experiments on benchmark datasets show that the DIA unit is capable of emphasizing channel-wise feature interrelation and leads to significant improvement of image classification accuracy. We further empirically show that the DIA unit is a nonlocal normalization tool that enhances the Batch Normalization. The code is released at https://…/DIANet.

Expansive Automata Network
An Automata Network is a map ${f:Q^n\rightarrow Q^n}$ where $Q$ is a finite alphabet. It can be viewed as a network of $n$ entities, each holding a state from $Q$, and evolving according to a deterministic synchronous update rule in such a way that each entity only depends on its neighbors in the network’s graph, called interaction graph. A major trend in automata network theory is to understand how the interaction graph affects dynamical properties of $f$. In this work we introduce the following property called expansivity: the observation of the sequence of states at any given node is sufficient to determine the initial configuration of the whole network. Our main result is a characterization of interaction graphs that allow expansivity. Moreover, we show that this property is generic among linear automata networks over such graphs with large enough alphabet. We show however that the situation is more complex when the alphabet is fixed independently of the size of the interaction graph: no alphabet is sufficient to obtain expansivity on all admissible graphs, and only non-linear solutions exist in some cases. Finally, among other results, we consider a stronger version of expansivity where we ask to determine the initial configuration from any large enough observation of the system. We show that it can be achieved for any number of nodes and naturally gives rise to maximum distance separable codes. …

Deep Feature Synthesis (DFS)
In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically. To achieve this automation, we first propose and develop the Deep Feature Synthesis algorithm for automatically generating features for relational datasets. The algorithm follows relationships in the data to a base field, and then sequentially applies mathematical functions along that path to create the final feature. Second, we implement a generalizable machine learning pipeline and tune it using a novel Gaussian Copula process based approach. We entered the Data Science Machine in 3 data science competitions that featured 906 other data science teams. Our approach beats 615 teams in these data science competitions. In 2 of the 3 competitions we beat a majority of competitors, and in the third, we achieved 94% of the best competitor’s score. In the best case, with an ongoing competition, we beat 85.6% of the teams and achieved 95.7% of the top submissions score.
Deep Feature Synthesis: How Automated Feature Engineering Works

Bitcoin
Bitcoin is a payment system invented by Satoshi Nakamoto in 2008 and introduced as open-source software in 2009. The system is peer-to-peer; all nodes verify transactions in a public distributed ledger called the block chain. The ledger uses its own unit of account, also called bitcoin. The system works without a central repository or single administrator, which has led the US Treasury to categorize it as a decentralized virtual currency. While bitcoin is not the first virtual currency, it is the first decentralized digital currency and cryptocurrency. It is the largest of its kind in terms of total market value. …