Startups and Stanford University

Startups have become in less than 50 years a major component of innovation and economic growth. Silicon Valley has been the place where the startup phenomenon was the most obvious and Stanford University was a major component of that success. Companies such as Google, Yahoo, Sun Microsystems, Cisco, Hewlett Packard had very strong links with Stanford but even these vary famous success stories cannot fully describe the richness and diversity of the Stanford entrepreneurial activity. This report explores the dynamics of more than 5000 companies founded by Stanford University alumni and staff, through their value creation, their field of activities, their growth patterns and more. The report also explores some features of the founders of these companies such as their academic background or the number of years between their Stanford experience and their company creation

280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification

We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach leverages the interlanguage links of Wikipedia followed by character-level classifiers to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages

Phrase-based Image Captioning

Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO

Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning

Privacy policies are the primary channel through which companies inform users about their data collection and sharing practices. These policies are often long and difficult to comprehend. Short notices based on information extracted from privacy policies have been shown to be useful but face a significant scalability hurdle, given the number of policies and their evolution over time. Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies. To address these hurdles, we propose an automated framework for privacy policy analysis (Polisis). It enables scalable, dynamic, and multi-dimensional queries on natural language privacy policies. At the core of Polisis is a privacy-centric language model, built with 130K privacy policies, and a novel hierarchy of neural-network classifiers that accounts for both high-level aspects and fine-grained details of privacy practices. We demonstrate Polisis' modularity and utility with two applications supporting structured and free-form querying. The structured querying application is the automated assignment of privacy icons from privacy policies. With Polisis, we can achieve an accuracy of 88.4% on this task. The second application, PriBot, is the first freeform question-answering system for privacy policies. We show that PriBot can produce a correct answer among its top-3 results for 82% of the test questions. Using an MTurk user study with 700 participants, we show that at least one of PriBot's top-3 answers is relevant to users for 89% of the test questions.Comment: Published at USENIX Security 2018; associated website: https://pribot.or

Generating Video Descriptions with Topic Guidance

Generating video descriptions in natural language (a.k.a. video captioning) is a more challenging task than image captioning as the videos are intrinsically more complicated than images in two aspects. First, videos cover a broader range of topics, such as news, music, sports and so on. Second, multiple topics could coexist in the same video. In this paper, we propose a novel caption model, topic-guided model (TGM), to generate topic-oriented descriptions for videos in the wild via exploiting topic information. In addition to predefined topics, i.e., category tags crawled from the web, we also mine topics in a data-driven way based on training captions by an unsupervised topic mining model. We show that data-driven topics reflect a better topic schema than the predefined topics. As for testing video topic prediction, we treat the topic mining model as teacher to train the student, the topic prediction model, by utilizing the full multi-modalities in the video especially the speech modality. We propose a series of caption models to exploit topic guidance, including implicitly using the topics as input features to generate words related to the topic and explicitly modifying the weights in the decoder with topics to function as an ensemble of topic-aware language decoders. Our comprehensive experimental results on the current largest video caption dataset MSR-VTT prove the effectiveness of our topic-guided model, which significantly surpasses the winning performance in the 2016 MSR video to language challenge.Comment: Appeared at ICMR 201

Rôle des médias audiovisuels, de la téléphonie mobile, de la publicité et des documents imprimés dans la gestion des PME agroalimentaires

Cette communication est organisée sous deux angles : 1. l’utilisation des médias audiovisuels, de la téléphonie mobile, de la publicité et des documents imprimés pour véhiculer de l’information vers les entreprises et les aider à améliorer leurs performances, 2. leur utilisation par les entreprises pour promouvoir leurs produits et améliorer leur efficacité commerciale.Cette communication met l'accent sur l’utilisation des médias et autres supports de communication pour véhiculer de l’information vers les entreprises et les aider à améliorer leurs performances et leur efficacité commerciale..

A GAIN SCHEDULED CONTROL LAW FOR FIN/RUDDER ROLL STABILISATION OF SHIPS

International audienceTaking into account the variations of the environment is a means of improving performances of roll stabilisation systems. The ship behaviour is modelled as a MIMO LPV system. A methodology is presented which leads to a gain-scheduled control law. The synthesis is based on multi objective optimisation, and on the representation of the standard system as a polytopical system, which depends on ship speed and on a stabilisation quality factor. Simulation results are given