Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is application of regression based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily automated. This paper introduces a new machine learning (ML) based approach as an alternative to the traditional techniques. The proposed ML model is called regularized deep belief network, which is a deep neural network with two training steps: it is first trained using an unsupervised learning algorithm and then fine-tuned by initializing a Bayesian neural network with the trained weights from the first step. The resulting model is expected to have improved prediction power and reduced need for the time-consuming human intervention. In this paper, we attempt to demonstrate the potential of this new model for crash prediction through two case studies including a collision data set from 800 km stretch of Highway 401 and other highways in Ontario, Canada. Our intention is to show the performance of this ML approach in comparison to various traditional models including negative binomial (NB) model, kernel regression (KR), and Bayesian neural network (Bayesian NN). We also attempt to address other related issues such as effect of training data size and training parameters.

Deformable registration of clinical scans is a fundamental task for many applications, such as population studies or the monitoring of long-term disease progression in individual patients. This task is challenging because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85% of the slices in comparison. Furthermore, the anatomy in the acquired slices is not consistent across scans because of variations in patient orientation with respect to the scanner. In this work, we introduce Sparse VoxelMorph (SparseVM), which adapts a state-of-the-art learning-based registration method to improve the registration of sparse clinical images. SparseVM is a fast, unsupervised method that weights voxel contributions to registration in proportion to confidence in the voxels. This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans. SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy. Because of its short runtimes and accurate behavior, SparseVM can enable clinical analyses not previously possible. The code is publicly available at voxelmorph.mit.edu.

The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way. We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder. The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen. Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.

As deep learning advances, algorithms of music composition increase in performance. However, most of the successful models are designed for specific musical structures. Here, we present BachProp, an algorithmic composer that can generate music scores in many styles given sufficient training data. To adapt BachProp to a broad range of musical styles, we propose a novel representation of music and train a deep network to predict the note transition probabilities of a given music corpus. In this paper, new music scores generated by BachProp are compared with the original corpora as well as with different network architectures and other related models. We show that BachProp captures important features of the original datasets better than other models and invite the reader to a qualitative comparison on a large collection of generated songs.

Orthogonal frequency division multiplexing (OFDM) is one of the key technologies that are widely applied in current communication systems. Recently, artificial intelligence (AI)-aided OFDM receivers have been brought to the forefront to break the bottleneck of the traditional OFDM systems. In this paper, we investigate two AIaided OFDM receivers, data-driven fully connected-deep neural network (FC-DNN) receiver and model-driven ComNet receiver, respectively. We first study their performance under different channel models through simulation and then establish a real-time video transmission system using a 5G rapid prototyping (RaPro) system for over-the-air (OTA) test. To address the performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and real environments, we develop a novel online training strategy, called SwitchNet receiver. The SwitchNet receiver is with a flexible and extendable architecture and can adapts to real channel by training one parameter online. The OTA test verifies its feasibility and robustness to real environments and indicates its potential for future communications systems. At the end of this paper, we discuss some challenges to inspire future research. P. Jiang, T. Wang, B. Han, X. Gao, J. Zhang and S. Jin are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (email: wangtianqi@seu.edu.cn; C.-K. Wen is with the Institute of Communications Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan (email: ckwen@ieee.org). G. Y. Li is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (email: liye@ece.gatech.edu). I. INTRODUCTION By introducing artificial intelligence (AI), intelligent communications can potentially address manychallenging issues in traditional communication systems. There have been many achievements in intelligent communications recently [1], [2], [3], including using AI for signal classification [4], multiple-input multiple-output (MIMO) detection [5], channel state information (CSI) feedback [6], [7], novel autoencoder-based end-to-end communication systems [8] and [9]. Orthogonal frequency division multiplexing (OFDM) has been proved to be an effective technique to deal with delay spread of wireless channels [10], [11]. OFDM receivers can be classified into two categories: linear and nonlinear receivers.

Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNNs that can meet a stringent energy budget is becoming increasingly important. This paper proposes ECC, a framework that compresses DNNs to meet a given energy constraint while minimizing accuracy loss. The key idea of ECC is to model the DNN energy consumption via a novel bilinear regression function. The energy estimate model allows us to formulate DNN compression as a constrained optimization that minimizes the DNN loss function over the energy constraint. The optimization problem, however, has nontrivial constraints. Therefore, existing deep learning solvers do not apply directly. We propose an optimization algorithm that combines the essence of the Alternating Direction Method of Multipliers (ADMM) framework with gradient-based learning algorithms. The algorithm decomposes the original constrained optimization into several subproblems that are solved iteratively and efficiently. ECC is also portable across different hardware platforms without requiring hardware knowledge. Experiments show that ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art resource-constrained DNN compression techniques.

The plain network design is generally kept as a first step of proposed approaches application ([28], [42]) given that it leads to simple networks and allows to focus on the method itself before switching to more complex structures with modular design ([29], [49]). A third option used in design approaches at a lower scale is the prediction of explored architectures rewards before full training the most promising ones ([25], [29]). This training acceleration technique is implemented for performance improvement purpose and requires further attention to control possible bias impact on the models behavior. The success of current reinforcement-learning-based approaches to design CNN architectures is widely proven especially for image classification tasks. However, it is achieved at the cost of high computational resources despite the acceleration attempts of most of recent models. Such fact is preventing individual researchersand small research entities (companies and laboratories) from fully access to this innovative technology [42]. Hence, deeper and more revolutionary optimizing methods are required to practically operate CNN automatic design. Transformation approaches based on extended network morphisms [49] are among the first attempts in this direction that achieved drastic decrease in computational cost and demonstrated generalization capacity. Additionalfuture directions to control automatic design complexity is to develop methods for multi-task problems [58] and weights sharing [59] in order to benefit from knowledge transfer contributions.

As the number of various positioning sensors and location-based devices increase, a huge amount of spatial and temporal information data is collected and accumulated. These data are expressed as trajectory data by connecting the data points in chronological sequence, and thses data contain movement information of any moving object. Particularly, in this study, urban vehicle trajectory prediction is studied using trajectory data of vehicles in urban traffic network. In the previous work, Recurrent Neural Network model for urban vehicle trajectory prediction is proposed. For the further improvement of the model, in this study, we propose Attention-based Recurrent Neural Network model for urban vehicle trajectory prediction. In this proposed model, we use attention mechanism to incorporate network traffic state data into urban vehicle trajectory prediction. The model is evaluated by using the Bluetooth data collected in Brisbane, Australia, which contains the movement information of private vehicles. The performance of the model is evaluated with 5 metrics, which are BLEU-1, BLEU-2, BLEU-3, BLEU-4, and METEOR. The result shows that ARNN model have better performance compared to RNN model.

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings.

This work develops a deep learning power control framework for energy efficiency maximization in wireless interference networks. Rather than relying on suboptimal power allocation policies, the training of the deep neural network is based on the globally optimal power allocation rule, leveraging a newly proposed branch-and-bound procedure with a complexity affordable for the offline generation of large training sets. In addition, no initial power vector is required as input of the proposed neural network architecture, which further reduces the overall complexity. As a benchmark, we also develop a first-order optimal power allocation algorithm. Numerical results show that the neural network solution is virtually optimal, outperforming the more complex first-order optimal method, while requiring an extremely small online complexity.