MaxSAT Resolution and Subcube Sums

We study the MaxRes rule in the context of certifying unsatisfiability. We show that it can be exponentially more powerful than tree-like resolution, and when augmented with weakening (the system MaxResW), p-simulates tree-like resolution. In devising a lower bound technique specific to MaxRes (and not merely inheriting lower bounds from Res), we define a new proof system called the SubCubeSums proof system. This system, which p-simulates MaxResW, can be viewed as a special case of the semialgebraic Sherali-Adams proof system. In expressivity, it is the integral restriction of conical juntas studied in the contexts of communication complexity and extension complexity. We show that it is not simulated by Res. Using a proof technique qualitatively different from the lower bounds that MaxResW inherits from Res, we show that Tseitin contradictions on expander graphs are hard to refute in SubCubeSums. We also establish a lower bound technique via lifting: for formulas requiring large degree in SubCubeSums, their XOR-ification requires large size in SubCubeSums

Multi Stream Video Display With Automatic Prominence Switching

Viewing or managing multiple video streams, e.g., live streams from a video game, on a mobile device with a relatively small display can lead to fatigue. This disclosure describes a multi-screen mode for live streaming applications that include multiple video streams in which one of the streams is automatically selected and made prominent, e.g., displayed at a larger size relative to the other streams. Automatic stream selection can be based, e.g., on the relative amounts of video or audio activity in each stream

Predicting Race and Ethnicity From the Sequence of Characters in a Name

To answer questions about racial inequality, we often need a way to infer race and ethnicity from a name. Until now, a bulk of the focus has been on optimally exploiting the last names list provided by the Census Bureau. But there is more information in the first names, especially for African Americans. To estimate the relationship between full names and race, we exploit the Florida voter registration data and the Wikipedia data. In particular, we model the relationship between the sequence of characters in a name, and race and ethnicity using Long Short Term Memory Networks. Our out of sample (OOS) precision and recall for the full name model estimated on the Florida Voter Registration data is .83 and .84 respectively. This compares to OOS precision and recall of .79 and .81 for the last name only model. Commensurate numbers for Wikipedia data are .73 and .73 for the full name model and .66 and .67 for the last name model. To illustrate the use of this method, we apply our method to the campaign finance data to estimate the share of donations made by people of various racial groups

Scaling ML Products At Startups: A Practitioner's Guide

How do you scale a machine learning product at a startup? In particular, how do you serve a greater volume, velocity, and variety of queries cost-effectively? We break down costs into variable costs-the cost of serving the model and performant-and fixed costs-the cost of developing and training new models. We propose a framework for conceptualizing these costs, breaking them into finer categories, and limn ways to reduce costs. Lastly, since in our experience, the most expensive fixed cost of a machine learning system is the cost of identifying the root causes of failures and driving continuous improvement, we present a way to conceptualize the issues and share our methodology for the same

Architecture for Extracting Data from Vehicular Sensors

In this thesis we investigate an alternate source of vehicular information for collision avoidance systems and driver assistance applications, which is more accurate, reliable in all conditions and has minimum time lag. We have designed and developed an architecture, which enables us to read, analyze, decode and store the real-time vehicular data from the vehicle’s electric sensors. We have designed two algorithms for decoding the raw data read from the vehicle’s Controller Area Network (CAN) bus, to which various electric components of the vehicle are connected to communicate the real-time data. We have shown that the vehicular speed which is a very important parameter in the calculation of ‘Time to Collision (TTC)’ by collision avoidance algorithms is more accurate, reliable and has higher polling rate, when calculated from the vehicle’s CAN bus as compare to the other source of information i.e. GPS

Scheduling Automatic Pickup by Self-driving Cars

This disclosure includes techniques to automatically schedule pickups by self-driving cars. The techniques utilize data about a user such as the user’s location, payments, calendar, to-do list etc. An example technique uses data from commercial establishments (e.g., payments systems), and self-driving cars to determine a location and a time at which the user needs to be picked up. A self-driving car system schedules a self-driving car to pick up the user at the specific location and time. The techniques described may be implemented in selfdriving cars, a central server system, user mobile devices, client devices, or different combinations of these systems and devices