In this section, we introduce more detailed background knowledge. A.1 Normalizing Flows Let x be a high-dimensional continuous variable.

In this section, we introduce more detailed background knowledge. A.1 Normalizing Flows Let x be a high-dimensional continuous variable.

Due to the varying intensity of pavement cracks, the complexity of topological structure, and the noise of texture background, image classification for asphalt pavement cracking has proven to be a challenging problem. Fatigue cracking, also known as alligator cracking, is one of the common distresses of asphalt pavement. It is thus important to detect and monitor the condition of alligator cracking on roadway pavements. Most research in this area has typically focused on pixel-level detection of cracking using limited datasets. A novel deep convolutional neural network that can achieve two objectives is proposed. The first objective of the proposed neural network is to classify presence of fatigue cracking based on pavement surface images. The second objective is to classify the fatigue cracking severity level based on the Distress Identification Manual (DIM) standard. In this paper, a databank of 4484 high-resolution pavement surface images is established in which images are taken locally in the Town of Blacksburg, Virginia, USA. In the data pre-preparation, over 4000 images are labeled into 4 categories manually according to DIM standards. A four-layer convolutional neural network model is then built to achieve the goal of classification of images by pavement crack severity category. The trained model reached the highest accuracy among all existing methods. After only 30 epochs of training, the model achieved a crack existence classification accuracy of 96.23% and a severity level classification accuracy of 96.74%. After 20 epochs of training, the model achieved a pavement marking presence classification accuracy of 97.64%.

Deep neural networks (DNN) have found wide applicability in numerous fields due to their ability to accurately learn very complex input-output relations. Despite their accuracy and extensive use, DNNs are highly susceptible to adversarial attacks due to limited generalizability. For future progress in the field, it is essential to build DNNs that are robust to any kind of perturbations to the data points. In the past, many techniques have been proposed to robustify DNNs using first-order derivative information of the network. This paper proposes a new robustification approach based on control theory. A neural network architecture that incorporates feedback control, named Feedback Neural Networks, is proposed. The controller is itself a neural network, which is trained using regular and adversarial data such as to stabilize the system outputs. The novel adversarial training approach based on the feedback control architecture is called Feedback Looped Adversarial Training (FLAT). Numerical results on standard test problems empirically show that our FLAT method is more effective than the state-of-the-art to guard against adversarial attacks.

Torc Robotics and Daimler Truck AG enter their fourth year of partnership, with a focus on customers, industry collaboration, and commercializing Level 4 autonomous trucks in the U.S. for long-haul applications. The powerhouse team continues to develop safe, sustained innovation in the freight industry as they combine Daimler Truck's extensive experience in manufacturing and relationships in the freight industry with Torc's experience in developing autonomous vehicle solutions. Since Daimler Truck's majority share investment in Torc in 2019, the two have worked hand-in-hand to be the first to commercialize a profitable autonomous truck solution at scale. Torc continues to operate as an independent subsidiary and serves as the lead for autonomous driving system development, innovation, and fleet testing. "Bringing a safe Level 4 autonomous truck to market is by no means a simple task. Over the past three years, we have benefited from the strong collaboration with Daimler Truck, bringing us significantly closer to our goal of developing a highly optimized self-driving truck that will meet the fleets' needs for cost, safety, and performance. The teamwork shown has been outstanding so far and we're entering our fourth year of partnership with a clear roadmap – focusing on one manufacturer and one initial use case in one geographic area," said Peter Vaughan Schmidt, Torc CEO.

The deployment of future intelligent transportation systems is contingent upon seamless and reliable operation of connected and autonomous vehicles (CAVs). One key challenge in developing CAVs is the design of an autonomous controller that can accurately execute near real-time control decisions, such as a quick acceleration when merging to a highway and frequent speed changes in a stop-and-go traffic. However, the use of conventional feedback controllers or traditional learning-based controllers, solely trained by each CAV's local data, cannot guarantee a robust controller performance over a wide range of road conditions and traffic dynamics. In this paper, a new federated learning (FL) framework enabled by large-scale wireless connectivity is proposed for designing the autonomous controller of CAVs. In this framework, the learning models used by the controllers are collaboratively trained among a group of CAVs. To capture the varying CAV participation in the FL training process and the diverse local data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, as well as the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal autonomous controller. In particular, the impacts of varying CAV participation in the FL process and diverse CAV data quality on the convergence of the proposed DFP algorithm are explicitly analyzed. A preliminary version of this work has been submitted to the proceeding of IEEE Conference on Decision and Control (CDC), 2021 [1]. This research was supported by the U.S. National Science Foundation under Grants CNS-1739642, CNS-1941348, and CNS-2008646, and by the Academy of Finland Project CARMA, by the Academy of Finland Project MISSION, by the Academy of Finland Project SMARTER, as well as by the INFOTECH Project NOOR. T. Zeng and W. Saad are with Wireless@VT, Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061 USA.

This paper focuses on the problem of unsupervised relation extraction. Existing probabilistic generative model-based relation extraction methods work by extracting sentence features and using these features as inputs to train a generative model. This model is then used to cluster similar relations. However, these methods do not consider correlations between sentences with the same entity pair during training, which can negatively impact model performance. To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information. Given multiple sentences with the same entity pair as inputs, self-supervised learning is deployed by predicting the shortest path between entity pairs on the dependency graph of one of the sentences. After that, we extract the relation information using the well-trained encoder. Then, entity pairs that share the same relation are clustered based on their corresponding relation information. Each cluster is labeled with a few words based on the words in the shortest paths corresponding to the entity pairs in each cluster. These cluster labels also describe the meaning of these relation clusters. We compare the triplets extracted by our proposed framework (CURE) and baseline methods with a ground-truth Knowledge Base. Experimental results show that our model performs better than state-of-the-art models on both New York Times (NYT) and United Nations Parallel Corpus (UNPC) standard datasets.

This paper develops a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. This paper also enhances classification accuracy by extracting new frequency domain features. Many researchers have obtained these features from global positioning system data; however, this data was excluded in this paper, as the system use might deplete the smartphone's battery and signals may be lost in some areas. Our proposed two-layer framework differs from previous classification attempts in three distinct ways: 1) the outputs of the two layers are combined using Bayes' rule to choose the transportation mode with the largest posterior probability; 2) the proposed framework combines the new extracted features with traditionally used time domain features to create a pool of features; and 3) a different subset of extracted features is used in each layer based on the classified modes. Several machine learning techniques were used, including k-nearest neighbor, classification and regression tree, support vector machine, random forest, and a heterogeneous framework of random forest and support vector machine. Results show that the classification accuracy of the proposed framework outperforms traditional approaches. Transforming the time domain features to the frequency domain also adds new features in a new space and provides more control on the loss of information. Consequently, combining the time domain and the frequency domain features in a large pool and then choosing the best subset results in higher accuracy than using either domain alone. The proposed two-layer classifier obtained a maximum classification accuracy of 97.02%.

Federal regulators have announced plans to allow drone operators to fly their unmanned aerial vehicles over populated areas and at night. A Wing Hummingbird drone from Project Wing arrives and sets down its package at a delivery location in Blacksburg, Va., last year. Federal regulators have announced plans to allow drone operators to fly their unmanned aerial vehicles over populated areas and at night. A Wing Hummingbird drone from Project Wing arrives and sets down its package at a delivery location in Blacksburg, Va., last year. Package delivery by drone is one small step closer to reality today.

Autonomous-driving company Torc Robotics may not be as well known as, say, Waymo, but that may change soon as Torc looks to expand. The company is looking to nearly double its number of employees in order to continue developing tech for self-driving cars. Torc unveiled its Asimov (named after science-fiction writer Isaac Asimov) autonomous-driving system last year, and gave public demonstrations at CES 2018. The company is headquartered in Blacksburg, Virginia, and continues to test self-driving cars there and in Las Vegas. Last year, it sent one of its modified Lexus RX SUVs on a cross-country trip.