Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem?

This thesis aims to test whether data models can fit the venture capital funding process better, and if they do fit, can they help improve the venture capital funding efficiency? Based on the reported results, venture capitalists can only see returns in 20% of their investments. The thesis argues that it is essential to help venture capital investment as it can help drive economic growth through investments in innovation. The thesis considers four startup scenarios and the related investment factors. The scenarios are a funded artificial intelligence startup seeking follow-on funding, a new startup seeking first funding, the survivability of a sustainability-focused startup, and the importance of patents for exit. Patents are a proxy for innovation in this thesis. Through quantitative analysis using generalized linear models, logit regressions, and t-tests, the thesis can establish that data models can identify the relative significance of funding factors. Once the factor significance is established, it can be deployed in a model. Building the machine learning model has been considered outside the scope of this thesis. A mix of academic and real-world research has been used for the data analysis of this thesis. Accelerators and venture capitalists also used some of the results to improve their own processes. Many of the models have shifted from a prediction to factor significance. This thesis implies that it could help venture capitalists plan for a 10% efficiency improvement. From an academic perspective, this study focuses on the entire life of a startup, from the first funding stage to the exit. It also links the startup ecosystem with economic development. Two additional factors from the study are the regional perspective of funding differences between Asia, Europe, and the US and that this study would include the recent economic sentiment. The impact of the funding slowdown has been measured through a focus on first funding and longitudinal validations of the data decision before the slowdown. Based on the results of the thesis, data models are a credible alternative and show significant correlations between returns and factors. It is advisable for a venture capitalist to consider these

Computing the homology of basic semialgebraic sets in weak exponential time

We describe and analyze an algorithm for computing the homology (Betti numbers and torsion coefficients) of basic semialgebraic sets which works in weak exponential time. That is, out of a set of exponentially small measure in the space of data the cost of the algorithm is exponential in the size of the data. All algorithms previously proposed for this problem have a complexity which is doubly exponential (and this is so for almost all data)

Alternating register automata on finite words and trees

We study alternating register automata on data words and data trees in relation to logics. A data word (resp. data tree) is a word (resp. tree) whose every position carries a label from a finite alphabet and a data value from an infinite domain. We investigate one-way automata with alternating control over data words or trees, with one register for storing data and comparing them for equality. This is a continuation of the study started by Demri, Lazic and Jurdzinski. From the standpoint of register automata models, this work aims at two objectives: (1) simplifying the existent decidability proofs for the emptiness problem for alternating register automata; and (2) exhibiting decidable extensions for these models. From the logical perspective, we show that (a) in the case of data words, satisfiability of LTL with one register and quantification over data values is decidable; and (b) the satisfiability problem for the so-called forward fragment of XPath on XML documents is decidable, even in the presence of DTDs and even of key constraints. The decidability is obtained through a reduction to the automata model introduced. This fragment contains the child, descendant, next-sibling and following-sibling axes, as well as data equality and inequality tests

Establishing a Central Archive for Transit Passenger Data

This report describes the rationale, background, establishing organization, and future steps of CATPAD, the Central Archive for Transit Passenger Data. The Central Archive for Transit Passenger Data is a repository that collects, indexes, archives, and makes available online the transit survey instruments, data, and reports collected across the country. This resource is unique in its focus on the disaggregated information of individual transit users – information that is critical for a range of transportation planning analyses. In addition, where available, CATPAD contains aggregated information, such as station boardings and service and fare schedules, to provide key context for the disaggregate person-level data. The Central Archive for Transit Passenger Data seeks to overcome the current impediments to accessing transit survey data by providing a single, searchable, internet archive to store and disseminate this valuable information. The Central Archive for Transit Passenger Data explicitly aims to expand the public return on the considerable investment made to gather transit passenger data. The resource is designed from the start to serve the needs of a range of use cases from transportation planners and policy makers to researchers and community advocates. The goal of CATPAD is to make useful data available to inform transit decision making at all levels and to foster ongoing refinement of the nation’s transit network

A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a \theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose \axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF

A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a "theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose "axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF