**Local Expansion via Minimum One Norm (LEMON)**

We propose a novel approach for finding overlapping communities called LEMON (Local Expansion via Minimum One Norm). The algorithm finds the community by seeking a sparse vector in the span of the local spectra such that the seeds are in its support. We show that LEMON can achieve the highest detection accuracy among state-of-the-art proposals. The running time depends on the size of the community rather than that of the entire graph. The algorithm is easy to implement, and is highly parallelizable. … **Classification Without Labels**

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available. … **Tuatara GS1**

The Tuatara GS1 algorithm relies on the more advanced Tuatara GS2 algorithm which generates relationships between objects based on principles in congnition related to Computational Theory of the Mind (CTM) (Pinker, S. 1997) and auto-association (Xijin Ge , Shuichi Iwata, 2002) and reinforced learning (Wenhuan, X., Nandi, A. K., Zhang, J., Evans, K. G. 2005) with exponential decays that follow the Golden Ratio F (Dunlap, Richard A. 1997). …

# If you did not already know

**22**
*Tuesday*
Aug 2017

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