Academy
Academy
Academy

Conference Papers

Spectrum for Intelligent Transportation Systems: Allocation and Sharing

Ligo A.K., Peha J.P.
IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
2018
Abstract:
This paper investigates how much spectrum should be available for Intelligent Transportation Systems (ITS), and. whether part of that spectrum should be shared with unlicensed devices, as has been considered by the U.S. Federal Communications Commission (FCC). We found that the ITS bandwidth that maximizes social welfare could be either much more or much less than what has already been allocated, because optimal bandwidth is sensitive to uncertain factors such as device penetration, future data rates, and spectrum opportunity cost. That uncertainty is offset if ITS spectrum is shared. We also found that the bandwidth required to obtain given throughputs on shared spectrum can be considerably less than the bandwidth to obtain the same throughputs in separate bands. We conclude that the spectrum available for ITS should be maintained or increased, but much of ITS spectrum should be shared with non-ITS devices.

Spectrum for V2X: Allocation and Sharing

Ligo A., Peha J.
IEEE Transactions on Cognitive Communications and Networking
2019
Abstract:
This paper investigates how much spectrum should be available for Intelligent Transportation Systems (ITS), and whether part of that spectrum should be shared with unlicensed devices, as has been considered by the U.S. Federal Communications Commission (FCC), and if so, what sharing scheme should be adopted. We found that the ITS bandwidth that maximizes social welfare could be either much more or much less than what has already been allocated, because optimal bandwidth is sensitive to uncertain factors such as device penetration, future data rates, and spectrum opportunity cost. That uncertainty is offset if ITS spectrum is shared under a scheme of coexistence among equals. We also found that the bandwidth required to obtain given throughputs on shared spectrum can be considerably less than the bandwidth to obtain the same throughputs in separate bands. We conclude that the spectrum available for ITS should be maintained or increased, but much of ITS spectrum should be shared with non-ITS devices.

Speech Analytics for Medical Applications

Trancoso I., Correia J., Teixeira F., Raj B., Abad A.
International Conference on Text, Speech, and Dialogue - TSD 2018: Text, Speech, and Dialogue
2018
Abstract:
Speech has the potential to provide a rich bio-marker for health, allowing a non-invasive route to early diagnosis and monitoring of a range of conditions related to human physiology and cognition. With the rise of speech related machine learning applications over the last decade, there has been a growing interest in developing speech based tools that perform non-invasive diagnosis. This talk covers two aspects related to this growing trend. One is the collection of large in-the-wild multimodal datasets in which the speech of the subject is affected by certain medical conditions. Our mining effort has been focused on video blogs (vlogs), and explores audio, video, text and metadata cues, in order to retrieve vlogs that include a single speaker which, at some point, admits that he/she is currently affected by a given disease. The second aspect is patient privacy. In this context, we explore recent developments in cryptography and, in particular in Fully Homomorphic Encryption, to develop an encrypted version of a neural network trained with unencrypted data, in order to produce encrypted predictions of health-related labels. As a proof-of-concept, we have selected two target diseases: Cold and Depression, to show our results and discuss these two aspects.

Spillovers from wiring schools with broadband: The critical role of children

Belo R., Ferreira P., Telang R.
Management Science
2016
Abstract:
Providing broadband to schools can be an effective way to foster household Internet adoption in neighboring areas. On the one hand, the infrastructure put into place to meet schools’ needs can also serve households. On the other hand, students get acquainted with the Internet at school and signal its usefulness to adults at home who, consequently, can be more likely to adopt it. In this paper, we model the roles that broadband use at school and Internet adoption in neighboring households play in the decision to adopt the Internet at home and measure their effects empirically. We use data from Portugal between 2006 and 2009 on household Internet penetration and on how much schools use broadband. We use two different sets of instruments for the schools’ broadband use to alleviate endogeneity concerns. Both approaches yield similar results. We find that broadband use at school leads to higher levels of Internet penetration in neighboring households. Broadband use in schools was responsible for a year-over-year increase of 3.5 percentage points on Internet penetration in households with children. Across our data set this effect accounts for about 17% of the increase in home Internet adoption. We also find evidence of regional spillovers in Internet adoption across households. These were roughly responsible for an increase of 2.1 percentage points in Internet penetration or 38% of the total increase in household Internet penetration between 2006 and 2009. These results show that wiring schools with broadband is an effective policy to lower the barriers for Internet adoption at home and as such contributes to accelerating the pace of broadband diffusion.

Spinoffs and the ascension of Silicon Valley

Cheyre C., Kowalski J., Veloso F.M.
Industrial and Corporate Change
2015
Abstract:
This article analyzes firm entry and performance in the semiconductor industry from the introduction of the integrated circuit (IC) in 1965 until 1987. During this period the industry, which was initially concentrated on the east coast of the United States, became increasingly clustered in Silicon Valley (SV), California. This location shift was accompanied by a change in the technology used to produce semiconductor devices. By studying how diversifiers and new firms enter into IC production, and the technology they choose to produce ICs, we document the process of growth of SV. In particular, we analyze how agglomeration economies, as well as the heritage of new firms’ founders affect the opportunities pursued by entrants and their long-term likelihood of success. We find that IC entrants in SV were more likely to enter at the technological frontier than IC entrants in other clusters. However, we find no evidence suggesting that being located in a cluster helped existing IC producers that were not initially at the technological frontier to catch up with the rest of the industry. Examining long-term performance, we find that firms that become top producers are disproportionately spinoffs of leading firms or diversifiers with a transistor background. While most of these firms were located in SV, after controlling for heritage and background, location has no significance on the likelihood of becoming a top producer.

Spinoffs and the mobility of U.S. merchant semiconductor inventors

Cheyre C., Klepper S., Veloso F.
Management Science
2015
Abstract:
Data on inventors and assignees of patents are used to analyze the mobility of semiconductor inventors. Exploiting data on the origins of semiconductor producers with larger sales, we argue that the higher mobility of semiconductor inventors in Silicon Valley is in great part due to the entry of spinoffs there. Our empirical evidence suggests that spinoff entry promoted mobility in Silicon Valley even before the industry was clustered there. Agglomeration economies and the ban on noncompete covenants may influence spinoff entry, but spinoffs promote mobility even in the absence of those conditions. Because most of the greater inventor mobility in Silicon Valley corresponds to inventors moving from incumbents to recent entrants, the benefits that arise from greater mobility rates will be disproportionately reaped by new firms.

SQUARES : A SQL Synthesizer Using Query Reverse Engineering

Orvalho P., Terra-Neves M., Ventura M., Martins R., Manquinho V.
Proceedings of the VLDB Endowment
2020
Abstract:
Nowadays, many data analysts are domain experts, but they lack programming skills. As a result, many of them can provide examples of data transformations but are unable to produce the desired query. Hence, there is an increasing need for systems capable of solving the problem of Query Reverse Engineering (QRE). Given a database and output table, these systems have to find the query that generated this table. We present SQUARES, a program synthesis tool based on input-output examples that can help data analysts to extract and transform data by synthesizing SQL queries, and table manipulation programs using the R language.

SSTS: A syntactic tool for pattern search on time series

Rodrigues J., Folgado D., Belo D., Gamboa H.
Information Processing & Management
2019
Abstract:
Nowadays, data scientists are capable of manipulating and extracting complex information from time series data, given the current diversity of tools at their disposal. However, the plethora of tools that target data exploration and pattern search may require an extensive amount of time to develop methods that correspond to the data scientist’s reasoning, in order to solve their queries. The development of new methods, tightly related with the reasoning and visual analysis of time series data, is of great relevance to improving complexity and productivity of pattern and query search tasks. In this work, we propose a novel tool, capable of exploring time series data for pattern and query search tasks in a set of 3 symbolic steps: Pre-Processing, Symbolic Connotation and Search. The framework is called SSTS (Symbolic Search in Time Series) and uses regular expression queries to search the desired patterns in a symbolic representation of the signal. By adopting a set of symbolic methods, this approach has the purpose of increasing the expressiveness in solving standard pattern and query tasks, enabling the creation of queries more closely related to the reasoning and visual analysis of the signal. We demonstrate the tool’s effectiveness by presenting 9 examples with several types of queries on time series. The SSTS queries were compared with standard code developed in Python, in terms of cognitive effort, vocabulary required, code length, volume, interpretation and difficulty metrics based on the Halstead complexity measures. The results demonstrate that this methodology is a valid approach and delivers a new abstraction layer on data analysis of time series.