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Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments.

The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot’s pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environment.

The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima.

By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.

Belonging to the financial technologies’ companies, equity-based crowdfunding platforms offer investors the opportunity to become shareholders through the purchase of small equity stakes of new innovative ventures. This paper aims to investigate gender-related differences in the behaviour of investors in firms seeking equity financing in Latin America.

Using a unique database, with combined information from different equity crowdfunding platforms in Brazil, Chile and Mexico, the authors study the population of 492 projects between 2013 and 2017. To analyse the relationship between investors’ gender-related differences and equity crowdfunding investment, this paper applies Poisson regression.

Results suggest that the probability that an investor finances a firm is based on gender bias. Investors prefer firms led by entrepreneurs that are similar to them in terms of gender. Furthermore, the authors find evidence that both female and male investors are risk-averse and are more likely to invest in the equity of firms that are older and offer a higher percentage of equity. However, female investors are associated with firms that are on average older and offer 0.02% more equity.

These findings have implications for crowdfunding platforms managers when selecting their target companies and policymakers when defining political actions to promote greater use of equity crowdfunding among female entrepreneurs and decrease barriers hindering women’s access to investment.

Unique in its proposition and data usage, this study sheds light on the relationship between investors and entrepreneurs in the Latin American equity crowdfunding market.

Women’s entrepreneurial activity can significantly impact economic and social development globally, particularly in developing countries. The significant challenges entrepreneurial women face draw the attention of researchers and policymakers. This paper aims to analyse the impact of gender disparity on the likelihood of obtaining equity financing through crowdfunding. The equity crowdfunding industry was selected because it is a non-traditional financial market where gender bias may act differently for women.

To investigate the relationship between gender and equity financing through crowdfunding, this paper applies ordinary least squares regression. The analysis is based on a unique data set of 492 equity crowdfunding campaigns launched between 2013 and 2017 on all existing platforms in Brazil, Chile and Mexico.

The analysis reveals that the involvement of at least one woman on the board of firms seeking equity financing increases campaign success rates in terms of the investors’ average pledge, the target amount reached at the end of the campaign and the percentage raised at the end of the campaign exceeding the initial fundraising goal. Altogether, this suggests that equity crowdfunding campaigns should be based on gender equality in the firms’ boards. The research finds evidence that there is no gender disparity in the likelihood of a campaign being financed by a greater number of investors.

These findings have implications for Latin American female entrepreneurs when selecting funding sources and policymakers when defining political actions to remove the barriers at the root of this historic inequality in female entrepreneurs’ access to finance.

To the best of the authors’ knowledge, this document analyses the gender disparity in the Latin American equity crowdfunding market, shedding light on women’s access to crowdfunding financing for the first time.