A TeX file contains basic instructions for typesetting, and can be rendered to various formats, most frequently PDFs. LaTeX contains a set of convenience macros that make it easy to define and re-use writing essentials, such as titles, headers, equations, sections, and footers. Having documents stored in TeX means that all you need to recreate these pretty, structured, and formatted research papers in their full glory are their source .tex

Slack created a new solution for workplace communication, one copied by many, even Microsoft. But the product, which is meant to help individuals and businesses collaborate, has been critiqued for sending too many notifications, with some claiming it's sabotaged workplace productivity. Quill, a startup led by Ludwig Pettersson, Stripe's former creative director and design aficionado, claims to offer "meaningful conversations, without disturbing your team." The company has raised a $2 million seed round led by Sam Altman with participation from General Catalyst, followed by a $12.5 million Series A at a $62.5 million valuation led by Index Ventures partner and former Slack board observer Sarah Cannon, TechCrunch has learned. Quill and Cannon declined to comment.

DUBAI, UAE – The United States of America and the United Arab Emirates reaffirm their shared commitment to a strong bilateral relationship within the framework of the U.S.-UAE Strategic Energy Dialogue first established in 2010 and reiterated in 2017. U.S. Secretary of Energy Rick Perry and UAE Minister of State for Artificial Intelligence Omar bin Sultan Al-Olama met to exchange views on the responsible use of Artificial Intelligence in improving grid resilience, increasing energy exploration and environmental sustainability, optimizing transportation and enabling smarter cities, improving water resource management, and in the discovery of new materials and compounds. They identified opportunities for DOE's Artificial Intelligence & Technologies Office and the Dubai Futures Foundation to hold further discussions, and agreed to explore the potential to expand the U.S.-UAE Strategic Dialogue to include cooperation on areas of mutual interest in AI. The parties also reiterated the importance of addressing energy security challenges through public and private sector partnerships and investment to support the research, development and deployment of all forms of energy and technologies.

W e argue the case for Gaussian Belief Propagation (GBP) as a strong algorithmic framework for the distributed, generic and incremental probabilistic estimation we need in Spatial AI as we aim at high performance smart robots and devices which operate within the constraints of real products. Processor hardware is changing rapidly, and GBP has the right character to take advantage of highly distributed processing and storage while estimating global quantities, as well as great flexibility. W e present a detailed tutorial on GBP, relating to the standard factor graph formulation used in robotics and computer vision, and give several simulation examples with code which demonstrate its properties.

Thompson sampling is an efficient algorithm for sequential decision making, which exploits the posterior uncertainty to solve the exploration-exploitation dilemma. There has been significant recent interest in integrating Bayesian neural networks into Thompson sampling. Most of these methods rely on global variable uncertainty for exploration. In this paper, we propose a new probabilistic modeling framework for Thompson sampling, where local latent variable uncertainty is used to sample the mean reward. Variational inference is used to approximate the posterior of the local variable, and semi-implicit structure is further introduced to enhance its expressiveness. Our experimental results on eight contextual bandits benchmark datasets show that Thompson sampling guided by local uncertainty achieves state-of-the-arts performance while having low computational complexity.

We have a new document of unknown authorship; we would like to determine its author. We have several corpora of documents, each of homogeneous authorship, and we believe the unknown author of the new document is represented among our corpora. The unprecedented abundance and availability of text data in our age generates manyauthorship attribution problems of this form. Existing approaches for such problems usually construct a set of handcrafted features to discriminate between potential candidate authors [1, 2, 3, 4]. Typically, these features originate from linguistic heuristics, such as rate of usage of certain words and length of sentences, and are often first constructed by trial and error, or based on domain expertise or historical tradition.

Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study reveals the weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, for training processes and for checking a model's quality and stability. Presented results are based on the CoNLL 2003 data set for the English language. A new enriched semantic annotation of errors for this data set and new diagnostic data sets are attached in the supplementary materials.

Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data. Wrong or inaccurate annotations generated by the ensemble may lead to performance degradation of the trained model. To deal with this problem we propose filtering the auto-labeled data using a trained model that predicts the quality of the annotation from the degree of consensus between ensemble models. Using semantic segmentation as an example, we show the advantage of the proposed auto-annotation filtering over training on data contaminated with inaccurate labels. Moreover, our experimental results show that in the case of semantic segmentation, the performance of a state-of-the-art model can be achieved by training it with only a fraction (30$\%$) of the original manually labeled data set, and replacing the rest with the auto-annotated, quality filtered labels.

We investigate rules which allow variable elimination in binary CSP (constraint satisfaction problem) instances while conserving satisfiability. We study variable-elimination rules based on the language of forbidden patterns enriched with counting and quantification over variables and values. We propose new rules and compare them, both theoretically and experimentally. We give optimised algorithms to apply these rules and show that each defines a novel tractable class. Using our variable-elimination rules in preprocessing allowed us to solve more benchmark problems than without.

The difficulty of the problem stems from the lack of reliable, quantitative and publicly available data. In this paper, we contribute to this endeavour by constructing an exit predictor model based on qualitative data, which blends the outcomes of three classifiers, namely, a Logistic Regression model, a Random Forest model, and a Support V ector Machine model. The output of the combined model is selected on the basis of the majority of the output classes of the component models. The models are trained using data extracted from the Thomson Reuters Eikon repository of 54697 US and European companies over the 1996-2011 time span. Experiments have been conducted for predicting whether the company eventually either gets acquired or goes public (IPO), against the complementary event that it remains private or goes bankrupt, in the considered time window. Our model achieves a 63% predictive accuracy, which is quite a valuable figure for Private Equity investors, who typically expect very high returns from successful investments.