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

Fin Rudder Roll Stabilisation of Ships: a Gain Scheduling Control Methodology

International audienceActive control of ship roll is necessary for operability of an important number of ships. As such it has been strongly developed in the past twenty years. Taking into account the variations of the environment is a means of improving performances. The ship behaviour is modeled as a MIMO LPV system; a methodology is presented which leads to a gain-scheduled control law. Synthesis is based on multi-objective optimisation and polytopic representation of the standard system, which depends on ship speed and on a stabilisation quality factor. Simulation results are given

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

MULTI-OBJECTIVE OPTIMISATION OF PID AND H∞ FIN/RUDDER ROLL CONTROLLERS

International audienceActive control of ship roll is necessary for operability of an important number of ships. As such it has been strongly developed in the past twenty years. A way of improving performances is to use and control rudders as well as fins. A MIMO control law synthesis methodology is presented in this paper, which is based on multi-objective optimisation. The optimisation is realised with a genetic algorithm. It is applied to a PID and a H∞ controller, both MIMO. Simulation results with various speeds are given

Interpolated versus Polytopic Gain Scheduling Control Laws for Fin/Rudder Roll Stabilisation of Ships

International audienceTaking into account the variations of the environment of ships is a means of improving performances of roll stabilisation systems; this can be done through the use of gain-scheduling (GS) control law. In this study, a GS-control law is obtained by interpolation of fixed HP controllers which have been synthetized for different sailing conditions of the ship represented by linear models. The GS controller depends on the ship speed and on a stabilisation quality factor. It is compared to a previously synthetized HP LPV controller (Linear Parameters Varying). Simulation results are given