Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration. (arXiv:1911.09837v1 [cs.LG])

The ability to model and predict ego-vehicle’s surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems. Acceleration prediction is important as one of the major components of traffic prediction. This paper proposes novel approaches to the acceleration prediction problem. By representing spatial relationships between vehicles with a graph model, we build a generalized…

Real-time Ultrasound-enhanced Multimodal Imaging of Tongue using 3D Printable Stabilizer System: A Deep Learning Approach. (arXiv:1911.09840v1 [cs.CV])

Despite renewed awareness of the importance of articulation, it remains a challenge for instructors to handle the pronunciation needs of language learners. There are relatively scarce pedagogical tools for pronunciation teaching and learning. Unlike inefficient, traditional pronunciation instructions like listening and repeating, electronic visual feedback (EVF) systems such as ultrasound technology have been employed in…

A Fully Convolutional Network for MR Fingerprinting. (arXiv:1911.09846v1 [eess.IV])

Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. These methods suffer from heavy storage and computation requirements as the dictionary size grows. To address these issues, we proposed an end to end fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employ…

Time-Domain Multi-modal Bone/air Conducted Speech Enhancement. (arXiv:1911.09847v1 [eess.AS])

Integrating modalities, such as video signals with speech, has been shown to provide a standard quality and intelligibility for speech enhancement (SE). However, video clips usually contain large amounts of data and pose a high cost in terms of computational resources, which may complicate the respective SE. By contrast, a bone-conducted speech signal has a…

On comparison of estimators for proportional error nonlinear regression models in the limit of small measurement error. (arXiv:1911.09680v1 [math.ST])

In this paper, we compare maximum likelihood (ML), quasi likelihood (QL) and weighted least squares (WLS) estimators for proportional error nonlinear regression models. Literature on thermoluminescence sedimentary dating revealed another estimator similar to weighted least squares but observed responses used as weights. This estimator that we refer to as data weighted least squares (DWLS) is…

Fast Power System Cascading Failure Path Searching with High Wind Power Penetration. (arXiv:1911.09848v1 [eess.SY])

Cascading failures have become a severe threat to interconnected modern power systems. The ultrahigh complexity of the interconnected networks is the main challenge toward the understanding and management of cascading failures. In addition, high penetration of wind power integration introduces large uncertainties and further complicates the problem into a massive scenario simulation problem. This paper…

Dual Learning-based Video Coding with Inception Dense Blocks. (arXiv:1911.09857v1 [eess.IV])

In this paper, a dual learning-based method in intra coding is introduced for PCS Grand Challenge. This method is mainly composed of two parts: intra prediction and reconstruction filtering. They use different network structures, the neural network-based intra prediction uses the full-connected network to predict the block while the neural network-based reconstruction filtering utilizes the…

Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning. (arXiv:1911.09682v1 [quant-ph])

We present a classical control mechanism for Quantum devices using Reinforcement Learning. Our strategy is applied to the Quantum Approximate Optimization Algorithm (QAOA) in order to optimize an objective function that encodes a solution to a hard combinatorial problem. This method provides optimal control of the Quantum device following a reformulation of QAOA as an…

A Unified Framework for Lifelong Learning in Deep Neural Networks. (arXiv:1911.09704v1 [cs.LG])

Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting an array of desirable properties, such as non-forgetting, concept rehearsal, forward transfer and backward transfer of knowledge, and so on. Previous approaches to lifelong learning (LLL) have demonstrated subsets of these properties, often with multiple mechanisms. In…