Annotated Reconstruction of 3D Spaces Using Drones

As the fields of robotics and drone technologies are continually advancing, the challenge of teaching these agents to learn and maneuver in the real world becomes increasingly important. A critical component of this is the ability for a robot to map and understand its surrounding unknown environment, both in terms of physical structure and object classification. In this project we tackle the challenge of mapping a 3D space with annotations using only 2D images acquired from a Parrot Drone. In order to make such a system operate efficiently in close to real time, we address a number challenges including (1) creating a optimized version of Faster RCNN that can operate on drone hardware while still being accurate, (2) developing a method to reconstruct 3D spaces from 2D images annotated with bounding boxes, and (3) using generated 3D annotations to complete drone motion planning for unknown space exploration

Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning

In Earthquake Early Warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere nuisance signals. One of the most fundamental - and difficult - tasks in EEW is to rapidly and reliably discriminate real local earthquake signals from all other signals. This discrimination is necessarily based on very little information, typically a few seconds worth of seismic waveforms from a small number of stations. As a result, current EEW systems struggle to avoid discrimination errors, and suffer from false and missed alerts. In this study we show how modern machine learning classifiers can strongly improve real-time signal/noise discrimination. We develop and compare a series of non-linear classifiers with variable architecture depths, including fully connected, convolutional (CNN) and recurrent neural networks, and a model that combines a generative adversarial network with a random forest (GAN+RF). We train all classifiers on the same data set, which includes 374k local earthquake records (M3.0-9.1) and 946k impulsive noise signals. We find that all classifiers outperform existing simple linear classifiers, and that complex models trained directly on the raw signals yield the greatest degree of improvement. Using 3s long waveform snippets, the CNN and the GAN+RF classifiers both reach 99.5% precision and 99.3% recall on an independent validation data set. Most misclassifications stem from impulsive teleseismic records, and from incorrectly labeled records in the data set. Our results suggest that machine learning classifiers can strongly improve the reliability and speed of EEW alerts

Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning

In earthquake early warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere nuisance signals. One of the most fundamental—and difficult—tasks in EEW is to rapidly and reliably discriminate real local earthquake signals from all other signals. This discrimination is necessarily based on very little information, typically a few seconds worth of seismic waveforms from a small number of stations. As a result, current EEW systems struggle to avoid discrimination errors and suffer from false and missed alerts. In this study we show how modern machine learning classifiers can strongly improve real‐time signal/noise discrimination. We develop and compare a series of nonlinear classifiers with variable architecture depths, including fully connected, convolutional and recurrent neural networks, and a model that combines a generative adversarial network with a random forest. We train all classifiers on the same data set, which includes 374 k local earthquake records (M3.0–9.1) and 946 k impulsive noise signals. We find that all classifiers outperform existing simple linear classifiers and that complex models trained directly on the raw signals yield the greatest degree of improvement. Using 3‐s‐long waveform snippets, the convolutional neural network and the generative adversarial network with a random forest classifiers both reach 99.5% precision and 99.3% recall on an independent validation data set. Most misclassifications stem from impulsive teleseismic records, and from incorrectly labeled records in the data set. Our results suggest that machine learning classifiers can strongly improve the reliability and speed of EEW alerts

Franciskanism in the Literary Work of Stanisław Witkiewicz

It was Stanisław Witkiewicz’s interests in Franciscanism that made him take up a very original work, that is, translating broad passages of „Little Flowers of St. Francis” into the language of the Polish mountaineers, into Podhale dialect (From the Tatras, 1907). The author of „On the Saddle” was, as it is well-known, a great lover of the mountaineers’ language, creator of the so-called Zakopiane style. He saw in the mountaineers’ language a source of the regeneration of Polish culture, „a living national sacrum”. In this context „Little Flowers” have taken on a particular meaning. It was rendered in the folk’s speech with whom Witkiewicz set his great hopes. In the volume „From the Tatras” apart from „Fioretti” the writer placed stories based on the Franciscan conception of reality. The mountaineers, „people of the future”, put into practice in their life the idea of the Saint of Assisi (Zośka Galicka, Wojtek Gandara, Jędrek Cajka). Simple, modest and often made light of people grow above mediocrityx and become „heroes of ethics”. Witkiewicz expressly meant to link the world of values of the mountaineers with Franciscan spirituality which he held in high estime. One may then talk here about Tatra culture subjected to Franciscan sacralization, but also about Franciscanism which bears the explicit Tatra signs. The problem of the changes in man takes on a great significance in the collection entitled „From the Tatras”. This change constitutes the most essential Witkiewicz’s conception of „man of the future”. It is to be man internally regenerated by love and suffering who „in the development of his inner essence reaches the point which the whole humanity has to cross in order to live a higher life [...] who reaches this point of development which St. Francis has reached”. („Zośka Galicka”). The considerations on „man of the future” allow us to state that Witkiewicz understood the essence of Franciscanism in a very specific manner, somewhat remote from authenticity. The writer exposes too much the sphere of feelings in living religious values, perceiving in the Saint of Assisi above all his spontneity, independence and inner unabrasiveness