Black Shadow

Poetry by Shauna Sartoris

Phonetic Shock

Poetry by Shauna Sartoris. First place in the 2019 Manuscripts Poetry Contest

Anomaly Detection in Lidar Data by Combining Supervised and Self-Supervised Methods

To enable safe autonomous driving, a reliable and redundant perception of the environment is required. In the context of autonomous vehicles, the perception is mainly based on machine learning models that analyze data from various sensors such as camera, Radio Detection and Ranging (radar), and Light Detection and Ranging (lidar). Since the performance of the models depends significantly on the training data used, it is necessary to ensure perception even in situations that are difficult to analyze and deviate from the training dataset. These situations are called corner cases or anomalies. Motivated by the need to detect such situations, this thesis presents a new approach for detecting anomalies in lidar data by combining Supervised (SV) and Self-Supervised (SSV) models. In particular, inconsistent point-wise predictions between a SV and a SSV part serve as an indication of anomalies arising from the models used themselves, e.g., due to lack of knowledge. The SV part is composed of a SV semantic segmentation model and a SV moving object segmentation model, which together assign a semantic motion class to each point of the point cloud. Based on the definition of semantic motion classes, a first motion label, denoting whether the point is static or dynamic, is predicted for each point. The SSV part mainly consists of a SSV scene flow model and a SSV odometry model and predicts a second motion label for each point. Thereby, the scene flow model estimates a displacement vector for each point, which, using the odometry information of the odometry model, represents only a point’s own induced motion. A separate quantitative analysis of the two parts and a qualitative analysis of the anomaly detection capabilities by combining the two parts are performed. In the qualitative analysis, the frames are classified into four main categories, namely correctly consistent, incorrectly consistent, anomalies detected by the SSV part, and anomalies detected by the SV part. In addition, weaknesses were identified in both the SV part and the SSV part

Neural Network Reconstruction via Graph Locality-Driven Machine Learning

Senior Project submitted to The Division of Science, Mathematics and Computing of Bard College

Combining clustering and abundances of galaxy clusters to test cosmology and primordial non-Gaussianity

We present the clustering of galaxy clusters as a useful addition to the common set of cosmological observables. The clustering of clusters probes the large-scale structure of the Universe, extending galaxy clustering analysis to the high-peak, high-bias regime. Clustering of galaxy clusters complements the traditional cluster number counts and observable-mass relation analyses, significantly improving their constraining power by breaking existing calibration degeneracies. We use the maxBCG galaxy clusters catalogue to constrain cosmological parameters and cross-calibrate the mass-observable relation, using cluster abundances in richness bins and weak-lensing mass estimates. We then add the redshift-space power spectrum of the sample, including an effective modelling of the weakly non-linear contribution and allowing for an arbitrary photometric redshift smoothing. The inclusion of the power spectrum data allows for an improved self-calibration of the scaling relation. We find that the inclusion of the power spectrum typically brings a $\sim 50$ per cent improvement in the errors on the fluctuation amplitude $\sigma_8$ and the matter density $\Omega_{\mathrm{m}}$. Finally, we apply this method to constrain models of the early universe through the amount of primordial non-Gaussianity of the local type, using both the variation in the halo mass function and the variation in the cluster bias. We find a constraint on the amount of skewness $f_{\mathrm{NL}} = 12 \pm 157$ ($1\sigma$) from the cluster data alone.Comment: 12 pages, 10 figures, 2 tables. Minor changes to match published version on MNRA