A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic sector in en route airspace. Currently the sector capacity is limited by human air traffic controller's cognitive limitation. In order to scale up to a high-density airspace, in this work we investigate the feasibility of a new concept (autonomous separation assurance) and a new approach (multi-agent reinforcement learning) to push the sector capacity above human cognitive limitation. We propose the concept of using distributed vehicle autonomy to ensure separation, instead of a centralized sector air traffic controller. Our proposed framework utilizes an actor-critic model, Proximal Policy Optimization (PPO) that we customize to incorporate an attention network. By using the attention network, we are able to encode the information from a variable number of intruder aircraft into a fixed length vector and allow the agents to learn which intruder aircraft's information is critical to achieve the optimal performance. This allows the agents to have access to variable aircraft information in the sector in a scalable, efficient approach to achieve high traffic throughput under uncertainty. The agents are trained using a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents in the environment. To validate the proposed framework, we designed three challenging case studies in the BlueSky air traffic control environment. Numerical results show the proposed framework significantly reduces the offline training time without sacrificing performance.

Human Activity Recognition from body-worn sensor data poses an inherent challenge in capturing spatial and temporal dependencies of time-series signals. In this regard, the existing recurrent or convolutional or their hybrid models for activity recognition struggle to capture spatio-temporal context from the feature space of sensor reading sequence. To address this complex problem, we propose a self-attention based neural network model that foregoes recurrent architectures and utilizes different types of attention mechanisms to generate higher dimensional feature representation used for classification. We performed extensive experiments on four popular publicly available HAR datasets: PAMAP2, Opportunity, Skoda and USC-HAD. Our model achieve significant performance improvement over recent state-of-the-art models in both benchmark test subjects and Leave-one-subject-out evaluation. We also observe that the sensor attention maps produced by our model is able capture the importance of the modality and placement of the sensors in predicting the different activity classes.

Recently, generative adversarial networks (GANs), which learn data distributions through adversarial training, have gained special attention owing to their high image reproduction ability. However, one limitation of standard GANs is that they recreate training images faithfully despite image degradation characteristics such as blur, noise, and compression. To remedy this, we address the problem of blur, noise, and compression robust image generation. Our objective is to learn a non-degraded image generator directly from degraded images without prior knowledge of image degradation. The recently proposed noise robust GAN (NR-GAN) already provides a solution to the problem of noise degradation. Therefore, we first focus on blur and compression degradations. We propose blur robust GAN (BR-GAN) and compression robust GAN (CR-GAN), which learn a kernel generator and quality factor generator, respectively, with non-degraded image generators. Owing to the irreversible blur and compression characteristics, adjusting their strengths is non-trivial. Therefore, we incorporate switching architectures that can adapt the strengths in a data-driven manner. Based on BR-GAN, NR-GAN, and CR-GAN, we further propose blur, noise, and compression robust GAN (BNCR-GAN), which unifies these three models into a single model with additionally introduced adaptive consistency losses that suppress the uncertainty caused by the combination. We provide benchmark scores through large-scale comparative studies on CIFAR-10 and a generality analysis on FFHQ dataset.

Predicting the behavior of road agents is a difficult and crucial task for both advanced driver assistance and autonomous driving systems. Traditional confidence measures for this important task often ignore the way predicted trajectories affect downstream decisions and their utilities. In this paper we devise a novel neural network regressor to estimate the utility distribution given the predictions. Based on reasonable assumptions on the utility function, we establish a decision criterion that takes into account the role of prediction in decision making. We train our real-time regressor along with a human driver intent predictor and use it in shared autonomy scenarios where decisions depend on the prediction confidence. We test our system on a realistic urban driving dataset, present the advantage of the resulting system in terms of recall and fall-out performance compared to baseline methods, and demonstrate its effectiveness in intervention and warning use cases.

Online forums and social media platforms provide noisy but valuable data every day. In this paper, we propose a novel end-to-end neural network-based user embedding system, Author2Vec. The model incorporates sentence representations generated by BERT (Bidirectional Encoder Representations from Transformers) with a novel unsupervised pre-training objective, authorship classification, to produce better user embedding that encodes useful user-intrinsic properties. This user embedding system was pre-trained on post data of 10k Reddit users and was analyzed and evaluated on two user classification benchmarks: depression detection and personality classification, in which the model proved to outperform traditional count-based and prediction-based methods. We substantiate that Author2Vec successfully encoded useful user attributes and the generated user embedding performs well in downstream classification tasks without further finetuning.

This paper proposes a convolutional neural network (CNN) model which utilizes the spectral correlation function (SCF) for wireless radio access technology identification without any prior information about bandwidth and/or the center frequency. The sensing and classification methods are applied to the baseband equivalent signals. Two different approaches are elaborated. The proposed method is implemented in two different settings; in the first setting, signals are jointly sensed and classified. Sensing and classification are conducted in a sequential manner in the second setting. The performance of both approaches is discussed in detail. The proposed method eliminates the threshold estimation processes of classical estimators. It also eliminates the need to know the distinct features of signals beforehand. Over-the-air real-world measurements are used to show the robustness and the validity of the proposed method and various wireless signals are successfully distinguished from each other without any a priori knowledge. The over-the-air real-world measurements are also shared in the format of SCF. The performance of SCF-based identification is compared with the cases when fast Fourier transform and amplitude-phase representation are used as the training inputs for CNN. The comparative performance of the proposed method is quantified by precision, recall, and F1-score metrics. Moreover, a setup to compare the performance of the proposed approach with classical cyclostationary features detection (CFD) is prepared. Measurement results indicate the superiority of the proposed method against CFD, especially at the low signal-to-noise ratio regime.

Physiological signals, such as the electrocardiogram and the phonocardiogram are very often corrupted by noisy sources. Usually, artificial intelligent algorithms analyze the signal regardless of its quality. On the other hand, physicians use a completely orthogonal strategy. They do not assess the entire recording, instead they search for a segment where the fundamental and abnormal waves are easily detected, and only then a prognostic is attempted. Inspired by this fact, a new algorithm that automatically selects an optimal segment for a post-processing stage, according to a criteria defined by the user is proposed. In the process, a Neural Network is used to compute the output state probability distribution for each sample. Using the aforementioned quantities, a graph is designed, whereas state transition constraints are physically imposed into the graph and a set of constraints are used to retrieve a subset of the recording that maximizes the likelihood function, proposed by the user. The developed framework is tested and validated in two applications. In both cases, the system performance is boosted significantly, e.g in heart sound segmentation, sensitivity increases 2.4% when compared to the standard approaches in the literature.

In semi-supervised learning, information from unlabeled examples is used to improve the model learned from labeled examples. But in some learning problems, partial label information can be inferred from otherwise unlabeled examples and used to further improve the model. In particular, partial label information exists when subsets of training examples are known to have the same label, even though the label itself is missing. By encouraging a model to give the same label to all such examples, we can potentially improve its performance. We call this encouragement \emph{Nullspace Tuning} because the difference vector between any pair of examples with the same label should lie in the nullspace of a linear model. In this paper, we investigate the benefit of using partial label information using a careful comparison framework over well-characterized public datasets. We show that the additional information provided by partial labels reduces test error over good semi-supervised methods usually by a factor of 2, up to a factor of 5.5 in the best case. We also show that adding Nullspace Tuning to the newer and state-of-the-art MixMatch method decreases its test error by up to a factor of 1.8.

Autonomous Vehicles (AVs) with Deep Reinforcement Learning (DRL)-based controllers are used for reducing traffic jams. AVs trained with such deep neural networks render them vulnerable to machine learning-based attacks. In this work, we explore the backdooring of a DRL-based AV controller in a standard traffic scenario. The AV exhibits intended operation of reducing congestion during genuine observations, but when a particular set of observations appears, the AV can be triggered to either decelerate to cause congestion (congestion attack) or to accelerate and crash into the vehicle in front (insurance attack). These backdoors in AVs may be engineered to pose serious threats to human lives.

Recently, deep learning approaches have been extensively investigated to reconstruct images from accelerated magnetic resonance image (MRI) acquisition. Although these approaches provide significant performance gain compared to compressed sensing MRI (CS-MRI), it is not clear how to choose a suitable network architecture to balance the trade-off between network complexity and performance. Recently, it was shown that an encoder-decoder convolutional neural network (CNN) can be interpreted as a piecewise linear basis-like representation, whose specific representation is determined by the ReLU activation patterns for a given input image. Thus, the expressivity or the representation power is determined by the number of piecewise linear regions. As an extension of this geometric understanding, this paper proposes a systematic geometric approach using bootstrapping and subnetwork aggregation using an attention module to increase the expressivity of the underlying neural network. Our method can be implemented in both k-space domain and image domain that can be trained in an end-to-end manner. Experimental results show that the proposed schemes significantly improve reconstruction performance with negligible complexity increases.