This document aims at providing instructions for the annotation of aliases in the Novelties corpus. The corpus itself will be the object of a separate description. It was constituted mainly to fulfill two goals: in the short term, train and test NLP methods able to handle long texts, and in the longer term, be used to develop Renard [2], a pipeline aiming at extracting character networks from literary fiction. This pipeline includes several processing steps besides alias resolution, including named entity recognition and coreference resolution. Character networks can be used to tackle a number of tasks, including the assessment of literary theories, the level of historicity of a narrative, detecting roles in stories, classifying novels, identify subplots, segment a storyline, summarize a story, design recommendation systems, align narratives, etc. See the detailed survey of Labatut and Bost [6] for more information regarding character networks. There are seldom annotation guidelines for alias resolution in the literature, so the one presented here are designed from scratch, taking into account this application's context.

In the modular robot reconfiguration problem, we are given $n$ cube-shaped modules (or robots) as well as two configurations, i.e., placements of the $n$ modules so that their union is face-connected. The goal is to find a sequence of moves that reconfigures the modules from one configuration to the other using "sliding moves," in which a module slides over the face or edge of a neighboring module, maintaining connectivity of the configuration at all times. For many years it has been known that certain module configurations in this model require at least $\Omega(n^2)$ moves to reconfigure between them. In this paper, we introduce the first universal reconfiguration algorithm -- i.e., we show that any $n$-module configuration can reconfigure itself into any specified $n$-module configuration using just sliding moves. Our algorithm achieves reconfiguration in $O(n^2)$ moves, making it asymptotically tight. We also present a variation that reconfigures in-place, it ensures that throughout the reconfiguration process, all modules, except for one, will be contained in the union of the bounding boxes of the start and end configuration.

We present a sequence-to-sequence vision-language model whose parameters are jointly trained on all tasks (all for one) and fully shared among multiple tasks (one for all), resulting in a single model which we named Musketeer. The integration of knowledge across heterogeneous tasks is enabled by a novel feature called Task Explanation Prompt (TEP). TEP reduces interference among tasks, allowing the model to focus on their shared structure. With a single model, Musketeer achieves results comparable to or better than strong baselines trained on single tasks, almost uniformly across multiple tasks.

While classical forms of stochastic gradient descent algorithm treat the different coordinates in the same way, a framework allowing for adaptive (non uniform) coordinate sampling is developed to leverage structure in data. In a non-convex setting and including zeroth order gradient estimate, almost sure convergence as well as non-asymptotic bounds are established. Within the proposed framework, we develop an algorithm, MUSKETEER, based on a reinforcement strategy: after collecting information on the noisy gradients, it samples the most promising coordinate (all for one); then it moves along the one direction yielding an important decrease of the objective (one for all). Numerical experiments on both synthetic and real data examples confirm the effectiveness of MUSKETEER in large scale problems.

I am a Mechanical engineer by education. And I started my career with a core job in the steel industry. But I didn't like it and so I left that. I made it my goal to move into the analytics and data science space somewhere around in 2013. From then on, it has taken me a lot of failures and a lot of efforts to shift.

I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference. To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic.

I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference. To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic.

SoftBank Chairman and CEO, Masayoshi Son (C), Foxconn Chairman and CEO, Terry Gou (R), and Alibaba Group Executive Chairman Jack Ma (L) pose with Pepper, the world's first personal robot that can read emotions in 2015. When billionaire Terry Gou acknowledged eyeing a $7 billion U.S. investment at his New Year's press meeting in Taiwan, it solidified his role as the Third Musketeer in East Asia's emerging involvement in Donald Trump's "America First" push. Gou is the founder chairman of Foxconn Technology (aka Hon Hai Precision Industry), the biggest supplier of Apple iPhones and iPads as well as other brand-name devices. Foxconn does most of its assembly in mainland China. So when the 66-year-old Gou said talks had started on a display-screen plant that could create 30,000-50,000 U.S. jobs, his aims fell into place with earlier pronouncements by Asian tech tycoons Masayoshi Son and Jack Ma as they paid visits to Trump Tower after the new president's election.