Structural changes often occur in economics and finance due to preferences, technologies, institutional arrangements, policies, crises, etc. Improving forecast accuracy of economic time series with structural is a long-standing problem. Model averaging aims at providing an insurance against selecting poor model. All existing model approaches the literature are designed constant (non-time-varyin...

Known coherent metasurfaces control the interference of waves a given frequency with other at same frequency, either illuminating from different direction or created as intermodulation products. In this paper, we introduce class that are modulated in time coherently radiation. Such modulation opens possibility to reflection, absorption, and transmission multiple frequencies, including illuminat...

Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data problems with the use of internal memory states. However, neuron units weights are shared at each time step to reduce computational costs, limiting their ability learn time-varying relationships between model inputs outputs. In this context, paper proposes two methods characterize dynamic in real-world...

We study time-varying semidefinite programs (TV-SDPs), which are whose data (and solutions) functions of time. Our focus is on the setting where vary polynomially with show that under a strict feasibility assumption, restricting solutions to also be polynomial time does not change optimal value TV-SDP. Moreover, by using Positivstellensatz (positive locus theorem) univariate matrices, we best s...