Topological Machine Learning for Multivariate Time Series. (arXiv:1911.12082v1 [math.AT])

We develop a framework for analyzing multivariate time series using topological data analysis (TDA) methods. The proposed methodology involves converting the multivariate time series to point cloud data, calculating Wasserstein distances between the persistence diagrams and using the $k$-nearest neighbors algorithm ($k$-NN) for supervised machine learning. Two methods (symmetry-breaking and anchor points) are also introduced…

Demand Side Management for Homes in Smart Grids. (arXiv:1911.12137v1 [eess.SY])

Electricity usage is a major portion of utility bills and the best place to start lowering them. An effective home energy management approach is introduced to decrease customers’ electricity bills by determining the optimal appliance scheduling under hourly pricing based demand response (DR) strategies. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral…

A Simple Distortion Calibration method for Wide-Angle Lenses Based on Fringe-pattern Phase Analysis. (arXiv:1911.12141v1 [eess.IV])

A distortion calibration method for wide-angle lens is proposed based on fringe-pattern phase analysis. Firstly, according to the experimental result of the radial distortion of the image not related to the recording depth of field, but depending on the field of view angle of the wide-angle lens imaging system, two-dimensional image distortion calibration is need…

Data-Driven Wide-Area Control. (arXiv:1911.12151v1 [eess.SY])

We employ a novel data-enabled predictive control (DeePC) in voltage source converter (VSC) based high-voltage DC (HVDC) stations to perform safe and optimal wide-area control. Conventional optimal wide-area control is model-based. However, in practice detailed and accurate parametric power system models are rarely available. In contrast, the DeePC algorithm uses only input/output data measured from…

Self-Enhanced Convolutional Network for Facial Video Hallucination. (arXiv:1911.11136v1 [cs.CV])

As a domain-specific super-resolution problem, facial image hallucination has enjoyed a series of breakthroughs thanks to the advances of deep convolutional neural networks. However, the direct migration of existing methods to video is still difficult to achieve good performance due to its lack of alignment and consistency modelling in temporal domain. Taking advantage of high…

Learning to Determine the Quality of News Headlines. (arXiv:1911.11139v1 [cs.IR])

Today, most newsreaders read the online version of news articles rather than traditional paper-based newspapers. Also, news media publishers rely heavily on the income generated from subscriptions and website visits made by newsreaders. Thus, online user engagement is a very important issue for online newspapers. Much effort has been spent on writing interesting headlines to…

Emotional Neural Language Generation Grounded in Situational Contexts. (arXiv:1911.11161v1 [cs.CL])

Emotional language generation is one of the keys to human-like artificial intelligence. Humans use different type of emotions depending on the situation of the conversation. Emotions also play an important role in mediating the engagement level with conversational partners. However, current conversational agents do not effectively account for emotional content in the language generation process.…

Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently. (arXiv:1911.11167v1 [stat.ML])

Multi-channel sparse blind deconvolution, or convolutional sparse coding, refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple input signals that are sparse. This problem finds numerous applications in signal processing, computer vision, and inverse problems. However, it is challenging to learn the filter efficiently due to the bilinear…

Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning. (arXiv:1911.11170v1 [cs.CV])

We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning. Given an object tracker, our framework learns to fine-tune its model parameters in only a few iterations of gradient-descent during tracking while pruning its network channels using the target ground-truth at the first frame. Such a learning problem…

DeepJSCC-f: Deep Joint-Source Channel Coding of Images with Feedback. (arXiv:1911.11174v1 [cs.IT])

We consider wireless transmission of images in the presence of channel output feedback. From a Shannon theoretic perspective feedback does not improve the asymptotic end-to-end performance, and separate source coding followed by capacity achieving channel coding achieves the optimal performance. Although it is well known that separation is not optimal in the practical finite blocklength…