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  1. This work aims to generate natural and diverse group motions of multiple humans from textual descriptions. While singleperson text-to-motion generation is extensively studied, it remains challenging to synthesize motions for more than one or two subjects from in-the-wild prompts, mainly due to the lack of available datasets. In this work, we curate human pose and motion datasets by estimating pose information from large-scale image and video datasets. Our models use a transformer-based diffusion framework that accommodates multiple datasets with any number of subjects or frames. Experiments explore both generation of multi-person static poses and generation of multiperson motion sequences. To our knowledge, our method is the first to generate multi-subject motion sequences with high diversity and fidelity from a large variety of textual prompts. 
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    Free, publicly-accessible full text available October 4, 2025
  2. Free, publicly-accessible full text available May 13, 2025
  3. As the next-generation battery substitute for IoT system, energy harvesting (EH) technology revolutionizes the IoT industry with environmental friendliness, ubiquitous accessibility, and sustainability, which enables various self-sustaining IoT applications. However, due to the weak and intermittent nature of EH power, the performance of EH-powered IoT systems as well as its collaborative routing mechanism can severely deteriorate, rendering unpleasant data package loss during each power failure. Such a phenomenon makes conventional routing policies and energy allocation strategies impractical. Given the complexity of the problem, reinforcement learning (RL) appears to be one of the most promising and applicable methods to address this challenge. Nevertheless, although the energy allocation and routing policy are jointly optimized by the RL method, due to the energy restriction of EH devices, the inappropriate configuration of multi-hop network topology severely degrades the data collection performance. Therefore, this article first conducts a thorough mathematical discussion and develops the topology design and validation algorithm under energy harvesting scenarios. Then, this article developsDeepIoTRouting, a distributed and scalable deep reinforcement learning (DRL)-based approach, to address the routing and energy allocation jointly for the energy harvesting powered distributed IoT system. The experimental results show that with topology optimization,DeepIoTRoutingachieves at least 38.71% improvement on the amount of data delivery to sink in a 20-device IoT network, which significantly outperforms state-of-the-art methods.

     
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    Free, publicly-accessible full text available March 31, 2025
  4. Homomorphic Encryption (HE) is a promising technology to protect clients’ data privacy for Machine Learning as a Service (MLaaS) on public clouds. However, HE operations can be orders of magnitude slower than their counterparts for plaintexts and thus result in prohibitively high inference latency, seriously hindering the practicality of HE. In this paper, we propose a HE-based fast neural network (NN) inference framework–SpENCNN built upon the co-design of HE operation-aware model sparsity and the single-instruction-multiple-data (SIMD)-friendly data packing, to improve NN inference latency. In particular, we first develop an encryption-aware HE-group convolution technique that can partition channels among different groups based on the data size and ciphertext size, and then encode them into the same ciphertext by novel group-interleaved encoding, so as to dramatically reduce the number of bottlenecked operations in HE convolution. We further tailor a HE-friendly sub-block weight pruning to reduce the costly HE-based convolution operation. Our experiments show that SpENCNN can achieve overall speedups of 8.37×, 12.11×, 19.26×, and 1.87× for LeNet, VGG-5, HEFNet, and ResNet-20 respectively, with negligible accuracy loss. Our code is publicly available at https://github.com/ranran0523/SPECNN. 
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