FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning

Recently, the success of large models has demonstrated the importance of scaling up model size. This has spurred interest in exploring collaborative training of large-scale models from federated learning perspective. Due to computational constraints, many institutions struggle to train a large-scale model locally. Thus, training a larger global model using only smaller local models has become an important scenario (i.e., the \textbf{small-to-large scenario}). Although recent device-heterogeneity federated learning approaches have started to explore this area, they face limitations in fully covering the parameter space of the global model. In this paper, we propose a method called \textbf{FedBRB} (\underline{B}lock-wise \underline{R}olling and weighted \underline{B}roadcast) based on the block concept. FedBRB can uses small local models to train all blocks of the large global model, and broadcasts the trained parameters to the entire space for faster information interaction. Experiments demonstrate FedBRB yields substantial performance gains, achieving state-of-the-art results in this scenario. Moreover, FedBRB using only minimal local models can even surpass baselines using larger local models

Allele-specific induction of IL-1beta expression by C/EBPbeta and PU.1 contributes to increased tuberculosis susceptibility

Mycobacterium tuberculosis infection is associated with a spectrum of clinical outcomes, from long-term latent infection to different manifestations of progressive disease. Pro-inflammatory pathways, such as those controlled by IL-1beta, have the contrasting potential both to prevent disease by restricting bacterial replication, and to promote disease by inflicting tissue damage. Thus, the ultimate contribution of individual inflammatory pathways to the outcome of M. tuberculosis infection remains ambiguous. In this study, we identified a naturally-occurring polymorphism in the human IL1B promoter region, which alters the association of the C/EBPbeta and PU.1 transcription factors and controls Mtb-induced IL-1beta production. The high-IL-1beta expressing genotype was associated with the development of active tuberculosis, the severity of pulmonary disease and poor treatment outcome in TB patients. Higher IL-1beta expression did not suppress the activity of IFN-gamma-producing T cells, but instead correlated with neutrophil accumulation in the lung. These observations support a specific role for IL-1beta and granulocytic inflammation as a driver of TB disease progression in humans, and suggest novel strategies for the prevention and treatment of tuberculosis

Precipitation Simulation and Dynamic Response of a Transmission Line Subject to Wind-Driven Rain during Super Typhoon Lekima

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rainwater. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan Island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima in 2019. The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increase approximately 17–18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions

Nonlinear AVA Inversion Based on a Novel Quadratic Approximation for Fluid Discrimination

Fluid discrimination plays an important role in reservoir exploration and development. At present, the fluid factors used for fluid discrimination are estimated by linear AVA inversion methods based on the linear approximations of the Zoeppritz equations. However, the Zoeppritz equations show that the relationship between prestack AVA reflection coefficients and reservoir parameters is highly nonlinear. Therefore, inversion methods based on linear approximations will seriously influence the nonuniqueness and uncertainty of inversion results. In this paper, a nonlinear inversion based on the quadratic approximation is carried out to reduce the nonuniqueness and uncertainty of fluid factor. Firstly, in order to directly invert the fluid factor, a novel quadratic approximation in terms of the fluid factor (ρf), shear modulus, and density on both sides of the reflection interface is derived based on poroelasticity theory. Then, a nonlinear inversion objective function is constructed using the novel quadratic approximation in a Bayesian framework, and the Gauss-Newton method is adopted to minimize this objective function. The synthetic data example shows that the new method can obtain reasonable fluid factor inversion results even in low SNR (signal-to-noise ratio) case. Finally, the proposed method is also applied to field data which shows that it can effectively discriminate reservoir fluids

Deep reinforcement learning for UAV routing in the presence of multiple charging stations

Deploying Unmanned Aerial Vehicles (UAVs) for traffic monitoring has been a hotspot given their flexibility and broader view. However, a UAV is usually constrained by battery capacity due to limited payload. On the other hand, the development of wireless charging technology has allowed UAVs to replenish energy from charging stations.In this paper, we study a UAV routing problem in the presence of multiple charging stations (URPMCS) with the objective of minimizing the total distance traveled by the UAV during traffic monitoring. We present a deep reinforcement learning based method, where a multi-head heterogeneous attention mechanism is designed to facilitate learning a policy that automatically and sequentially constructs the route, while taking the energy consumption into account. In our method, two types of attentions are leveraged to learn the relations between monitoring targets and charging station nodes, adopting an encoder-decoder-like policy network. Moreover, we also employ a curriculum learning strategy to enhance generalization to different numbers of charging stations. Computational results show that our method outperforms conventional algorithms with higher solution quality (except for exact methods such as Gurobi) and shorter runtime in general, and also exhibits strong generalized performance on problem instances with different distributions and sizes.This work was supported in part by the National Natural Science Foundation of China under Grant 62073341 and in part by the Fundamental Research Funds for the Central Universities of Central South University under Grant 2022ZZTS0191

Combination of Propylene Glycol Alginate and Lauric Acid on Water Retention and Mechanical Properties of Soy Protein Isolate-Based Films

本研究では，教師が生徒の心理的問題をどのように発見し対応しているかについてのインタビュー調査において，結果的に教師の未整理の困難体験が語られた，高等学校教員Ａ先生の事例を取り上げ，未整理の困難体験を抱え続けることの影響と，教師が改めてその体験をふり返り語ることの意味について事例的に分析した。面接では，新任時に関わった生徒Ｂとの体験が語られた。Ａ先生は，Ｂからの相談に繰り返し乗っていたが，Ｂは死にたいとよく言っており，Ａ先生は「脅かされているような感覚」を抱き続け，Ｂと適切な距離を見つけることができず，「共倒れ」しそうな状態にまで「追い詰められ」た。事例的に分析した結果，その体験は，一人で抱えてしまいがちな新任心性も相まって生じ，Ｂを傷つけたという罪悪感と恐怖心をＡ先生に植えつけ，現在にも影響を及ぼし続ける体験になったことが示唆された。一方で，その困難体験を語り直すことで，客観的に捉えられるようになり，その影響は残りつつも，それを引き受けた上で今後の教師人生を生きていこうとする姿勢が生まれたと考えられた。以上より，教師が教師としての未整理の困難体験をふり返り語ることの意味が示された。In this study, the influence of difficulties in teacher’s careers and the significance of personal reflections and descriptions regarding them were examined. We analyzed a case in which a teacher at a senior high school unintentionally related the difficulties that she encountered during her first year as a teacher during an interview about how teachers find and attend to students with psychological problems. She had listened to a particular student’s worries many times. She expressed that she felt “threatened” because he said he wanted to be dead numerous times. Because she could not keep an appropriate distance from him, she experienced nervous breakdown. It was possible that the experience was derived from being a first-year teacher (with its own set of problems), and that it marked a sense of guilt and fear about possibly hurting the student and precautions to protect herself from harmful influences with her mind. It was indicated that she took an objective view of the impact of her difficulties, and accepted them by looking back and talking about them. Accordingly, she began to move forward as a teacher and grow from her experiences. From this study, it was shown that it was significant for teachers to reflect upon and discuss the challenges encountered in their careers

Meta-optics empowered vector visual cryptography for high security and rapid decryption

Usual methods for optical encryption suffer from a tradeoff between the level of security and the complexity of operation given by multiple optical measurements or digital postprocessing. Here, the authors show a multi-d.o.f. metasurface-based vector optical manipulation protocol enabling secure decryption in real time