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Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. On the MNIST and CIFAR10 datasets, when faced with Drift attacks, the proposed algorithm outperforms common existing aggregation algorithms (Krum, Trimmed Mean, Bulyan), with accuracies of 93.3%, 94.06% (MNIST dataset), and 48.66%, 51.55% (CIFAR10 dataset), respectively. This is an improvement of 3.0%, 3.8% (MNIST dataset), and 19.0%, 26.1% (CIFAR10 dataset) over the current state-of-the-art methods, and successfully defended against other attack methods.

Charge density wave (CDW) is a startling quantum phenomenon, distorting a metallic lattice into an insulating state with a periodically modulated charge distribution. Astonishingly, such modulations appear in various patterns even within the same family of materials. Moreover, this phenomenon features a puzzling diversity in its dimensional evolution. Here, we propose a general framework, unifying distinct trends of CDW ordering in an isoelectronic group of materials, 2 H-MX 2 ( M = Nb, Ta and X = S, Se). We show that while NbSe 2 exhibits a strongly enhanced CDW order in two dimensions, TaSe 2 and TaS 2 behave oppositely, with CDW being absent in NbS 2 entirely. Such a disparity is demonstrated to arise from a competition of ionic charge transfer, electron-phonon coupling, and electron correlation. Despite its simplicity, our approach can, in principle, explain dimensional dependence of CDW in any material, thereby shedding new light on this intriguing quantum phenomenon and its underlying mechanisms. The dimensional dependence of charge density wave (CDW) in two-dimensional dichalcogenides remains puzzled. Here, Lin et al. study trends of CDW ordering in an isoelectronic group of materials 2 H - MX 2 and provide a unified understanding involving several microscopic factors.

Compressive data gathering (CDG) is an adequate method to reduce the amount of data transmission, thereby decreasing energy expenditure for wireless sensor networks (WSNs). Sleep scheduling integrated with CDG can further promote energy efficiency. Most of existing sleep scheduling methods for CDG were formulated as centralized optimization problems which introduced many extra control message exchanges. Meanwhile, a few distributed methods usually adopted stochastic decision which could not adapt to variance in residual energy of nodes. A part of nodes were prone to prematurely run out of energy. In this paper, a reinforcement learning-based sleep scheduling algorithm for CDG (RLSSA-CDG) is proposed. Active nodes selection is modeled as a finite Markov decision process. The mode-free Q learning algorithm is used to search optimal decision strategies. Residual energy of nodes and sampling uniformity are considered into the reward function of the Q learning algorithm for load balance of energy consumption and accurate data reconstruction. It is a distributed algorithm that avoids large amounts of control message exchanges. Each node takes part in one step of the decision process. Thus, computation overhead for sensor nodes is affordable. Simulation experiments are carried out on the MATLAB platform to validate the effectiveness of the proposed RLSSA-CDG against the distributed random sleep scheduling algorithm for CDG (DSSA-CDG) and the original sparse-CDG algorithm without sleep scheduling. The simulation results indicate that the proposed RLSSA-CDG outperforms the two contrast algorithms in terms of energy consumption, network lifetime, and data recovery accuracy. The proposed RLSSA-CDG reduces energy consumption by 4.64% and 42.42%, respectively, compared to the DSSA-CDG and the original sparse-CDG, prolongs life span by 57.3%, and promotes data recovery accuracy by 84.7% compared to the DSSA-CDG.

Embedding non-hexagonal rings into sp 2 -hybridized carbon networks is considered a promising strategy to enrich the family of low-dimensional graphenic structures. However, non-hexagonal rings are energetically unstable compared to the hexagonal counterparts, making it challenging to embed non-hexagonal rings into carbon-based nanostructures in a controllable manner. Here, we report an on-surface synthesis of graphene-like nanoribbons with periodically embedded four- and eight-membered rings. The scanning tunnelling microscopy and atomic force microscopy study revealed that four- and eight-membered rings are formed between adjacent perylene backbones with a planar configuration. The non-hexagonal rings as a topological modification markedly change the electronic properties of the nanoribbons. The highest occupied and lowest unoccupied ribbon states are mainly distributed around the eight- and four-membered rings, respectively. The realization of graphene-like nanoribbons comprising non-hexagonal rings demonstrates a controllable route to fabricate non-hexagonal rings in nanoribbons and makes it possible to unveil their unique properties induced by non-hexagonal rings. Graphene nanoribbons consist of carbon atoms arranged in a hexagonal lattice. Despite non-hexagonal rings generally being more unstable, the authors demonstrate the successful synthesis of graphene-like nanoribbons with periodically embedded four- and eight-membered carbon rings, with tailored electronic properties.

Emergency event monitoring is a hot topic in wireless sensor networks (WSNs). Benefiting from the progress of Micro-Electro-Mechanical System (MEMS) technology, it is possible to process emergency events locally by using the computing capacities of redundant nodes in large-scale WSNs. However, it is challenging to design a resource scheduling and computation offloading strategy for a large number of nodes in an event-driven dynamic environment. In this paper, focusing on cooperative computing with a large number of nodes, we propose a set of solutions, including dynamic clustering, inter-cluster task assignment and intra-cluster one-to-multiple cooperative computing. Firstly, an equal-size K-means clustering algorithm is proposed, which activates the nodes around event location and then divides active nodes into several clusters. Then, through inter-cluster task assignment, every computation task of events is alternately assigned to the cluster heads. Next, in order to make each cluster efficiently complete the computation tasks within the deadline, a Deep Deterministic Policy Gradient (DDPG)-based intra-cluster one-to-multiple cooperative computing algorithm is proposed to obtain a computation offloading strategy. Simulation studies show that the performance of the proposed algorithm is close to that of the exhaustive algorithm and better than other classical algorithms and the Deep Q Network (DQN) algorithm.

Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

Topological magnons are emergent quantum spin excitations featured by magnon bands crossing linearly at the points dubbed nodes, analogous to fermions in topological electronic systems. Experimental realisation of topological magnons in three dimensions has not been reported so far. Here, by measuring spin excitations (magnons) of a three-dimensional antiferromagnet Cu 3 TeO 6 with inelastic neutron scattering, we provide direct spectroscopic evidence for the coexistence of symmetry-protected Dirac and triply degenerate nodes, the latter involving three-component magnons beyond the Dirac–Weyl framework. Our theoretical calculations show that the observed topological magnon band structure can be well described by the linear-spin-wave theory based on a Hamiltonian dominated by the nearest-neighbour exchange interaction J 1 . As such, we showcase Cu 3 TeO 6 as an example system where Dirac and triply degenerate magnonic nodal excitations coexist, demonstrate an exotic topological state of matter, and provide a fresh ground to explore the topological properties in quantum materials. Topological magnonic materials display exotic properties which may enable high-efficiency and low-cost spintronic devices. Here the authors demonstrate a three-dimensional antiferromagnet Cu 3 TeO 6 that hosts symmetry-protected Dirac and triply degenerate magnons.

The tumor milieu is a dynamic ecosystem where immune cells, stromal cells, and tumor cells interact to influence tumor progression and anti-tumor immunity. Traditional experimental methods, limited to static in vitro or ex vivo analyses at specific time points, cannot fully capture the complexity and dynamic evolution of the tumor microenvironment (TME) in living organisms. Intravital microscopy (IVM), powered by advanced imaging technologies, precise labeling strategies, and optimized experimental approaches, enables real-time visualization of biological structures and cellular interactions within living animals. This review synthesizes findings from IVM-based research, focusing on the dynamic and transient interactions between tumor cells and other cell types, such as normal epithelial cells, immune cells, and stromal cells. It explores the nature of these interactions, their impact on tumor progression, and the outcomes of therapeutic interventions.Overall, we aim to provide a comprehensive resource that highlights the role of IVM in uncovering the dynamic cellular interplay within the TME and its implications for advancing tumor biological research and improving cancer therapies.

BackgroundThe effect of entrepreneurship on open innovation under challenging environmental dynamics remains an unresolved issue critical for global economic progress, as the environmental dynamics alter businesses’ capacity for entrepreneurship.AimThis study aims to examine how entrepreneurship influences open innovation in Chinese companies by investigating the moderating role of environmental dynamism.SettingA survey questionnaire collected data from 329 middle and senior managers in Shandong, Shanghai, Beijing and other provinces.MethodThis study used hierarchical regression analysis as the data analysis method to test the causal relationships and mediating and moderating effects of each hypothesis.ResultsIt was found that entrepreneurship has a significant positive impact on open innovation in firms. Specifically, entrepreneurial risk-taking and anticipation positively affected open innovation behaviour and strategic orientation of innovation. Moreover, environmental dynamism moderated the relationship between entrepreneurial mechanisms and open innovation.ConclusionThe findings provided new insights into how environmental dynamism shapes the emergence of innovative companies in China. The study suggests entrepreneurs should adjust efforts to promote open innovation behaviour according to the degree of dynamism.ContributionThe study expands and improves the development of the resource-based view and the capability-based view from a theoretical perspective. Furthermore, it broadens the theoretical lens by examining how the strategic orientation of innovation towards entrepreneurship supports open innovation practices to improve the operational performance of organisations. Finally, this research empirically examined the moderating impact of environmental dynamism on the relationship between entrepreneurial strategic orientation and open innovation-based environmental dynamism on company performance.

Capturing images under rainy days degrades image visual quality and affects analysis tasks, such as object detection and classification. Therefore, image de-raining has attracted a lot of attention in recent years. In this paper, an improved generative adversarial network for single image de-raining is proposed. According to the principles of divide-and-conquer, we divide an image de-raining task into rain locating, rain removing, and detail refining sub-tasks. A multi-stream DenseNet, termed as Rain Estimation Network, is proposed to estimate the rain location map. A Generative Adversarial Network is proposed to remove the rain streaks. A Refinement Network is proposed to refine the details. These three models accomplish rain locating, rain removing, and detail refining sub-tasks, respectively. Experiments on two synthetic datasets and real world images demonstrate that the proposed method outperforms state-of-the-art de-raining studies in both objective and subjective measurements.