A Formalization of Robustness for Deep Neural Networks

Deep neural networks have been shown to lack robustness to small input perturbations. The process of generating the perturbations that expose the lack of robustness of neural networks is known as adversarial input generation. This process depends on the goals and capabilities of the adversary, In this paper, we propose a unifying formalization of the adversarial input generation process from a formal methods perspective. We provide a definition of robustness that is general enough to capture different formulations. The expressiveness of our formalization is shown by modeling and comparing a variety of adversarial attack techniques

Il Tesoro di Sanam (Sudan)

In the year 1913 F.Ll. Griffith brought to light a large building which he called "the Treasury". He dated it to the period between Pi (ankh)y and Aspelta, most of all on the basis of seals bearing the names of these sovereigns. The structure was enormous and Griffith excavated only part of it. In, 2001 the site was granted to the University of Cassino. Our first campaigns of excavations aimed to better define the entire plan of the building and try to clarify, if possible, some aspects left unresolved by Griffith. The building is 267 meters long and 68 meters wide and it was destroyed by a violent fire. The rooms seem to be situated around a long courtyard with a portico upheld by 112 sandstone columns 80 cm in diameter. There are 35 rooms in all; 17 on the north side, 17 on the south side of the courtyard and one at the end. This suggests that the entrance is on the west side of the edifice. The rooms are rectangular and measure 14 x 21 m. The floor is composed of slabs of well-smoothened sandstone which are joined in a very precise manner. As many as 76 columns sustained the roof of each room. Most of the ceramics is composed of large storage jars for transporting and storing foodstuffs. Ninety percent of these are of Egyptian origin or imitation of Egyptian pottery. Phoenician amphorae are also present. The two types are dated VII- VI century B.C. The accumulation, transformation and administration of quality goods took place there but an ostentatious and perhaps celebratory use seems also probabl

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data

A Metric for Linear Temporal Logic

We propose a measure and a metric on the sets of infinite traces generated by a set of atomic propositions. To compute these quantities, we first map properties to subsets of the real numbers and then take the Lebesgue measure of the resulting sets. We analyze how this measure is computed for Linear Temporal Logic (LTL) formulas. An implementation for computing the measure of bounded LTL properties is provided and explained. This implementation leverages SAT model counting and effects independence checks on subexpressions to compute the measure and metric compositionally

A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed