Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, which may lead to privacy concerns and hazards. Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data. However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients. In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation. Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients. More specifically, the clients request the user model and news representations from the server, and send their locally computed gradients to the server for aggregation. The server updates its global user model with the aggregated gradients, and further updates its news model to infer updated news representations. Since the local gradients may contain private information, we propose a secure aggregation method to aggregate gradients in a privacy-preserving way. Experiments on two real-world datasets show that our method can reduce the computation and communication cost on clients while keep promising model performance

Metabolomics and Transcriptomics Reveal the Response Mechanisms of Mikania micrantha to Puccinia spegazzinii Infection

Mikania micrantha is one of the worst invasive species globally and can cause significant negative impacts on agricultural and forestry economics, particularly in Asia and the Pacific region. The rust Puccinia spegazzinii has been used successfully as a biological control agent in several countries to help manage M. micrantha. However, the response mechanisms of M. micrantha to P. spegazzinii infection have never been studied. To investigate the response of M. micrantha to infection by P. spegazzinii, an integrated analysis of metabolomics and transcriptomics was performed. The levels of 74 metabolites, including organic acids, amino acids, and secondary metabolites in M. micrantha infected with P. spegazzinii, were significantly different compared to those in plants that were not infected. After P. spegazzinii infection, the expression of the TCA cycle gene was significantly induced to participate in energy biosynthesis and produce more ATP. The content of most amino acids, such as L-isoleucine, L-tryptophan and L-citrulline, increased. In addition, phytoalexins, such as maackiain, nobiletin, vasicin, arachidonic acid, and JA-Ile, accumulated in M. micrantha. A total of 4978 differentially expressed genes were identified in M. micrantha infected by P. spegazzinii. Many key genes of M. micrantha in the PTI (pattern-triggered immunity) and ETI (effector-triggered immunity) pathways showed significantly higher expression under P. spegazzinii infection. Through these reactions, M. micrantha is able to resist the infection of P. spegazzinii and maintain its growth. These results are helpful for us to understand the changes in metabolites and gene expression in M. micrantha after being infected by P. spegazzinii. Our results can provide a theoretical basis for weakening the defense response of M. micrantha to P. spegazzinii, and for P. spegazzinii as a long-term biological control agent of M. micrantha

Analgesic Effects of Triterpenoid Saponins From Stauntonia chinensis via Selective Increase in Inhibitory Synaptic Response in Mouse Cortical Neurons

Triterpenoid saponins from Stauntonia chinensis (TSS) are potential therapeutic agents because of its analgesic properties. However, the underlying mechanisms of the anti-nociceptive activity of TSS are largely unclear, especially in CNS. The present study confirmed the analgesic effect of TSS using four models of acute pain based on thermal or chemical stimuli. TSS treatment specifically impaired the threshold of thermal- and chemical-stimulated acute pain. Naloxone did not block the anti-nociceptive effects of TSS, which showed no participation of the opioid system. We investigated the electrical signal in cultured cortical neurons to explore whether TSS treatment directly affected synaptic transmission. TSS treatment selectively increased spontaneous inhibitory synaptic release and GABA induced charge transfer in mouse cortical neurons. The effects of TSS were maintained for at least 8 h in cultured neurons and in injected mice. Taken together, our results suggest that the analgesic role of TSS in cortex occurs via a particular increase in the inhibitory synaptic response at resting state, which supports TSS as a potential candidate for inflammatory pain relief

Cortex phellodendri

Cortex phellodendri is used to reduce fever and remove dampness and toxin. Berberine is an active ingredient of C. phellodendri. Berberine from Argemone ochroleuca can relax airway smooth muscle (ASM); however, whether the nonberberine component of C. phellodendri has similar relaxant action was unclear. An n-butyl alcohol extract of C. phellodendri (NBAECP, nonberberine component) was prepared, which completely inhibits high K+- and acetylcholine- (ACH-) induced precontraction of airway smooth muscle in tracheal rings and lung slices from control and asthmatic mice, respectively. The contraction induced by high K+ was also blocked by nifedipine, a selective blocker of L-type Ca2+ channels. The ACH-induced contraction was partially inhibited by nifedipine and pyrazole 3, an inhibitor of TRPC3 and STIM/Orai channels. Taken together, our data demonstrate that NBAECP can relax ASM by inhibiting L-type Ca2+ channels and TRPC3 and/or STIM/Orai channels, suggesting that NBAECP could be developed to a new drug for relieving bronchospasm

Performance characterization and optimization of pruning patterns for sparse DNN inference

Deep neural networks are suffering from over parameterized high storage and high consumption problems. Pruning can effectively reduce storage and computation costs of deep neural networks by eliminating their redundant parameters. In existing pruning methods, filter pruning achieves more efficient inference, while element-wise pruning maintains better accuracy. To make a trade-off between the two endpoints, a variety of pruning patterns has been proposed. This study analyzes the performance characteristics of sparse DNNs pruned by different patterns, including element-wise, vector-wise, block-wise, and group-wise. Based on the analysis, we propose an efficient implementation of group-wise sparse DNN inference, which can make better use of GPUs. Experimental results on VGG, ResNet, BERT and ViT show that our optimized group-wise pruning pattern achieves much lower inference latency on GPU than other sparse patterns and the existing group-wise pattern implementation

Convergence Investigation of XFEM Enrichment Schemes for Modeling Cohesive Cracks

When simulating cohesive cracks in the XFEM framework, specific enrichment schemes are designed for the non-singular near-tip field and an iteration procedure is used to solve the nonlinearity problem. This paper focuses on convergence and accuracy analysis of XFEM enrichment schemes for cohesive cracks. Four different kinds of enrichment schemes were manufactured based on the development of XFEM. A double-cantilever beam specimen under an opening load was simulated by Matlab programming, assuming both linear and exponential constitutive models. The displacement and load factors were solved simultaneously by the Newton–Raphson iterative procedure. Finally, based on a linear or an exponential constitutive law, the influences of variations in these enrichment schemes, including (i) specialized tip branch functions and (ii) corrected approximations for blending elements, were determined and some conclusions were drawn

An Energy-Efficient Routing Algorithm for Underwater Wireless Sensor Networks Inspired by Ultrasonic Frogs

The area of three-dimensional (3D) underwater wireless sensor networks (UWSNs) has attracted significant attention recently due to its applications in detecting and observing phenomena that cannot be adequately observed by means of two-dimensional UWSNs. However, designing routing protocols for 3D UWSNs is a challenging task due to stringent constraints imposed by acoustic communications and high energy consumption in acoustic modems. In this paper, we present an ultrasonic frog calling algorithm (UFCA) that aims to achieve energy-efficient routing under harsh underwater conditions of UWSNs. In UFCA, the process of selecting relay nodes to forward the data packet is similar to that of calling behavior of ultrasonic frog for mating. We define the gravity function to represent the attractiveness from one sensor node to another. In order to save energy, different sensor nodes adopt different transmission radius and the values can be tuned dynamically according to their residual energy. Moreover, the sensor nodes that own less energy or locate in worse places choose to enter sleep mode for the purpose of saving energy. Simulation results show the performance improvement in metrics of packet delivery ratio, energy consumption, throughput, and end-to-end delay as compared to existing state-of-the-art routing protocols