Energy
Technical Perspective: NeuroRadar: Can Radar Systems Be Reimagined Using Computational Principles?
Interest in miniature radar systems has grown dramatically in recent years as they enable rich interaction and health monitoring in everyday settings. By 2025, industrial radar applications are anticipated to encompass 10 million devices, whereas the consumer market will reach a substantial 250 million. The applications are diverse--for example, Google's Pixel phones incorporated radar for gesture control, while small radar sensors are being deployed in homes to monitor elderly residents' movements and detect falls, offering more privacy than camera-based solutions. However, conventional radar architectures rely on complex RF front ends with power amplifiers, low-noise amplifiers, and phase-locked loops, collectively consuming hundreds of milliwatts of power. This makes radar sensing impractical for battery-powered or self-powered Internet of Things (IoT) devices and wearables.
Anytime-Competitive Reinforcement Learning with Policy Prior
In contrast, the goal of A-CMDP is to optimize the expected reward while guaranteeing a bounded cost in each round of any episode against a policy prior.
Anytime-Competitive Reinforcement Learning with Policy Prior
In contrast, the goal of A-CMDP is to optimize the expected reward while guaranteeing a bounded cost in each round of any episode against a policy prior.
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data
In recent years, machine learning (ML) has offered new modeling approaches for natural and engineering sciences.
Supplementary Material for: Convolutional Neural Operators for Robust and Accurate Learning of PDEs. T able of Contents AT echnical Details for Section 2 of main text. 15 A.1
We obtain this result by discarding the high-frequency components, e.g. higher than frequency This proves the statement of the lemma. Appendix A.2 can be adapted to bandlimited Note that the single-channel versions were defined in the main text. We will describe these filters later in the text (see C.1.4) We use the same notation as in Section 2 and Appendix A.2. Hence, they consist of three elementary mappings between spaces of bandlimited functions, i.e., We recall that the convolutional operator appearing in (A.8) takes the form K As in Appendix A.2, the above proofs can be readily adapted We present the proof of a generalization of the universality result of Theorem 3.1. In addition, we will use the following notation in the proof.
