RC-SSFL: Towards Robust and Communication-efficient Semi-supervised Federated Learning System

Federated Learning (FL) is an emerging decentralized artificial intelligence paradigm, which promises to train a shared global model in high-quality while protecting user data privacy. However, the current systems rely heavily on a strong assumption: all clients have a wealth of ground truth labeled data, which may not be always feasible in the real life. In this paper, we present a practical Robust, and Communication-efficient Semi-supervised FL (RC-SSFL) system design that can enable the clients to jointly learn a high-quality model that is comparable to typical FL's performance. In this setting, we assume that the client has only unlabeled data and the server has a limited amount of labeled data. Besides, we consider malicious clients can launch poisoning attacks to harm the performance of the global model. To solve this issue, RC-SSFL employs a minimax optimization-based client selection strategy to select the clients who hold high-quality updates and uses geometric median aggregation to robustly aggregate model updates. Furthermore, RC-SSFL implements a novel symmetric quantization method to greatly improve communication efficiency. Extensive case studies on two real-world datasets demonstrate that RC-SSFL can maintain the performance comparable to typical FL in the presence of poisoning attacks and reduce communication overhead by $2 \times \sim 4 \times$

CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services

Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items. Unlike universal e-commerce platforms such as Taobao and Amazon, user behaviors and interests on the OFD platform are more location and time-sensitive due to limited delivery ranges and regional commodity supplies. However, existing CTR prediction algorithms in OFD scenarios concentrate on capturing interest from historical behavior sequences, which fails to effectively model the complex spatiotemporal information within features, leading to poor performance. To address this challenge, this paper introduces the Contrastive Sres under different search states using three modules: contrastive spatiotemporal representation learning (CSRL), spatiotemporal preference extractor (StPE), and spatiotemporal information filter (StIF). CSRL utilizes a contrastive learning framework to generate a spatiotemporal activation representation (SAR) for the search action. StPE employs SAR to activate users' diverse preferences related to location and time from the historical behavior sequence field, using a multi-head attention mechanism. StIF incorporates SAR into a gating network to automatically capture important features with latent spatiotemporal effects. Extensive experiments conducted on two large-scale industrial datasets demonstrate the state-of-the-art performance of CSPM. Notably, CSPM has been successfully deployed in Alibaba's online OFD platform Ele.me, resulting in a significant 0.88% lift in CTR, which has substantial business implications

Y2O3 nanosheets as slurry abrasives for chemical-mechanical planarization of copper

Abstract Continued reduction in feature dimension in integrated circuits demands high degree of flatness after chemical mechanical polishing. Here we report using new yttrium oxide (Y2O3) nanosheets as slurry abrasives for chemical-mechanical planarization (CMP) of copper. Results showed that the global planarization was improved by 30% using a slurry containing Y2O3 nanosheets in comparison with a standard industrial slurry. During CMP, the two-dimensional square shaped Y2O3 nanosheet is believed to induce the low friction, the better rheological performance, and the laminar flow leading to the decrease in the within-wafer-non-uniformity, surface roughness, as well as dishing. The application of the two-dimensional nanosheets as abrasive in CMP would increase the manufacturing yield of integrated circuits.</jats:p

Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface

Neuroplasticity, also known as brain plasticity, is an inclusive term that covers the permanent changes in the brain during the course of an individual's life, and neuroplasticity can be broadly defined as the changes in function or structure of the brain in response to the external and/or internal influences. Long-term potentiation (LTP), a well-characterized form of functional synaptic plasticity, could be influenced by rapid-frequency stimulation (or “tetanus”) within in vivo human sensory pathways. Also, stochastic resonance (SR) has brought new insight into the field of visual processing for the study of neuroplasticity. In the present study, a brain-computer interface (BCI) intervention based on rapid and repetitive motion-reversal visual stimulation (i.e., a “tetanizing” stimulation) associated with spatiotemporal visual noise was implemented. The goal was to explore the possibility that the induction of LTP-like plasticity in the visual cortex may be enhanced by the SR formalism via changes in the amplitude of visual evoked potentials (VEPs) measured non-invasively from the scalp of healthy subjects. Changes in the absolute amplitude of P1 and N1 components of the transient VEPs during the initial presentation of the steady-state stimulation were used to evaluate the LTP-like plasticity between the non-noise and noise-tagged BCI interventions. We have shown that after adding a moderate visual noise to the rapid-frequency visual stimulation, the degree of the N1 negativity was potentiated following an ~40-min noise-tagged visual tetani. This finding demonstrated that the SR mechanism could enhance the plasticity-like changes in the human visual cortex

A TRPV4-dependent neuroimmune axis in the spinal cord promotes neuropathic pain

Microglia, resident macrophages of the CNS, are essential to brain development, homeostasis, and disease. Microglial activation and proliferation are hallmarks of many CNS diseases, including neuropathic pain. However, molecular mechanisms that govern the spinal neuroimmune axis in the setting of neuropathic pain remain incompletely understood. Here, we show that genetic ablation or pharmacological blockade of transient receptor potential vanilloid type 4 (TRPV4) markedly attenuated neuropathic pain-like behaviors in a mouse model of spared nerve injury. Mechanistically, microglia-expressed TRPV4 mediated microglial activation and proliferation and promoted functional and structural plasticity of excitatory spinal neurons through release of lipocalin-2. Our results suggest that microglial TRPV4 channels reside at the center of the neuroimmune axis in the spinal cord, which transforms peripheral nerve injury into central sensitization and neuropathic pain, thereby identifying TRPV4 as a potential new target for the treatment of chronic pain