This paper presents Stereo-Fi, the first system for high-fidelity 3D reconstruction of free-form objects using radio frequency (RF) signals from an unconstrained, handheld sensor. Conventional RF methods rely on simplified physics and constrained data acquisition, which degrades geometric fidelity and generality. Removing these constraints reframes reconstruction as an unconstrained, yet fundamentally ill-posed, optimization problem. To this end, Stereo-Fi establishes a new framework that solves this ill-posed problem by iteratively co-training a physics-based and a learned generative model. To this end, Stereo-Fi integrates three innovations. First, it introduces a novel hybrid representation, rfMold, that combines the benefits of explicit and implicit models for a robust optimization foundation. Second, it employs an inverse rendering optimizer, rfChisel, to jointly refine scene geometry and sensor trajectory while effectively navigating the non-convex loss landscape. Finally, it incorporates rfAlign, a generative diffusion model to restore geometric details while ensuring semantic consistency. Comprehensive evaluation demonstrates that Stereo-Fi achieves quasi-vision reconstruction accuracy across diverse targets and environments, significantly outperforming state-of-the-art RF-based 3D reconstruction methods.

Online action detection and anticipation aim to understand current or upcoming actions in video streams. In industry, current artificial neural network (ANN)-based methods suffer from prohibitive energy consumption, fundamentally limiting their deployment on resource-constrained industrial devices. To bridge the gap between theory and application practice of informatics in industrial environments, we propose a novel knowledge distillation-based spiking neural network (KDSNN), which synergistically integrates bioinspired spike-driven processing with knowledge distillation, significantly reducing the energy consumption. Specifically, KDSNN includes a pioneering spiking neural network (SNN) architecture for online action detection and anticipation, which combines well-designed hierarchical spike convolutional neural network (CNN) block and spike Transformer block to capture spike-driven information. To further improve the performance of our SNN while maintaining low energy consumption, we introduce the knowledge distillation paradigm, which aims to utilize an expert-level ANN as a teacher to guide our SNN. Based on this, we propose a novel distillation loss, which consists of feature distillation and logit distillation. Notably, to address the cross-domain feature alignment in feature distillation, the optimal transport theory is employed to realize cross-domain knowledge transfer for the first time by minimizing the Wasserstein distance between continuous features (ANNs) and discrete features (SNNs). Through extensive evaluations on THUMOS14 and EPIC-Kitchen-100 datasets, the energy consumption of our KDSNN is only 27.1% and 10.0% of the state-of-the-art ANN-based method MAT. Equally importantly, the parameter count of our KDSNN is only 37.0% and 27.7% of MAT on THUMOS14 and EPIC-Kitchen-100, respectively.

As a cornerstone for numerous sensing applications, wireless indoor localization has been a pivotal area of research over the last two decades. While techniques such as jamming, spoofing, and adversarial perturbation have been exploited to compromise wireless indoor localization, existing attacks face challenges in accessibility to wireless systems and stealthiness. To address these limitations, we introduceLoki, a novel physical-world attack on wireless indoor localization via differentiable object placement. Specifically, we develop a differentiable wireless ray-tracing technique that allows us to optimize object placement in the scene. By repositioning an existing object in the scene by just a few centimeters,Lokifools existing wireless indoor localization systems into generating erroneous localization results. We also show via experiments that the object placement generated byLokialigns with wireless sensing theory (e.g., the forward scattering region and Fresnel zone), confirming its explainability. Additionally,Lokiproves effective across various localization models and scenarios, highlighting its generalizability.

Multipath packet scheduler is responsible for deliv-ering each packet to an appropriate path. However, rules-based schedulers struggle to adapt to varying network conditions and diverse Quality of Service (QoS) requirements. Despite learning- based scheduler can adapt to various network conditions, reacting quickly to network changes is still challenging. Moreover, for diverse QoS requirements, learning-based schedulers often neces-sitates training from scratch. To solve the above challenges, we propose a multi-head mapping model that selects an optimal sub- scheduler based on the current state. It works with a shared state encoder, a multi-head Q-value decoder, and QoS-oriented reward decomposition. Furthermore, we propose Aquilas, an adaptive multi path packet scheduler with hierarchical intelligence for the Quick UDP Internet Connection (QUIC) protocol. Aquilas adopts a learning-based scheduler selector, thereby enabling selection of an optimal policy from a pool of sub-schedulers. This sub- scheduler pool encompasses a wide range of knowledge for handling various network conditions. In coarse time intervals, the learning-based scheduler selector operates, while during fine- grained time intervals, the selected sub-scheduler delivers each packet to the corresponding path. Aquilas has been evaluated in both controlled emulation and real-world networks. Compared with the state-of-the-art schedulers, Aquilas improves transmis-sion performance in various network conditions and diverse traffic types.

The popularization of video streaming brings challenges in satisfying diverse Quality of Service (QoS) requirements. The multipath extension of the Quick UDP Internet Connection (QUIC) protocol, also called MPQUIC, has the potential to improve video streaming performance with multiple simultaneously transmitting paths. The multipath scheduler of MPQUIC determines how to distribute the packets onto different paths. However, while applying current multipath schedulers into MPQUIC, our experimental results show that they fail to adapt to various receive buffer sizes of different devices and comprehensive QoS requirements of video streaming. These problems are especially severe under heterogeneous and dynamic network environments. To tackle these problems, we propose MARS, a Multi-agent deep Reinforcement learning (MADRL)-based Multipath QUIC Scheduler, which is able to promptly adapt to dynamic network environments. It exploits the MADRL method to learn a neural network for each path and generate scheduling policy. Besides, it introduces a novel multi-objective reward function that takes out-of-order queue size and different QoS metrics into consideration to realize adaptive scheduling optimization. We implement MARS in an MPQUIC prototype and deploy in Dynamic Adaptive Streaming over HTTP system. Then, we compare it with the state-of-the-art multipath schedulers in both emulated and real-world networks. Experimental results show that MARS outperforms the other schedulers with better adaptive capability regarding the receive buffer sizes and QoS.

The multipath extension of the Quick UDP Internet Connection (QUIC) protocol, also called MPQUIC, is currently attracting increasing attention from both industry and academia. The multipath scheduler of MPQUIC determines how to distribute the packets onto different paths. However, our experimental results show that they fail to adapt to various receive buffer sizes and Quality of Service (QoS) requirements while applying current multipath schedulers into MPQUIC due to the diversity of devices and applications. These problems are especially severe under heterogeneous and dynamic network environments. To tackle these problems, we propose MARS, a Multi-Agent deep Reinforcement learning (MADRL) based Multipath QUIC Scheduler, which is able to promptly adapt to dynamic network environments. It exploits the MADRL method to learn a neural network for each path and generate scheduling policy. Besides, it introduces a novel multi-objective reward function that takes out-of-order (OFO) queue size and different QoS metrics into consideration to realize adaptive scheduling optimization. We implement MARS in an MPQUIC prototype and compare it with the state-of-the-art multipath schedulers in both emulated and real-world networks. Experimental results show that MARS outperforms the other schedulers with better adaptive capability regarding the receive buffer sizes and QoS.