Task-Aware Network Coding Over Butterfly Network

Network coding allows distributed information sources such as sensors to efficiently compress and transmit data to distributed receivers across a bandwidth-limited network. Classical network coding is largely task-agnostic -- the coding schemes mainly aim to faithfully reconstruct data at the receivers, regardless of what ultimate task the received data is used for. In this paper, we analyze a new task-driven network coding problem, where distributed receivers pass transmitted data through machine learning (ML) tasks, which provides an opportunity to improve efficiency by transmitting salient task-relevant data representations. Specifically, we formulate a task-aware network coding problem over a butterfly network in real-coordinate space, where lossy analog compression through principal component analysis (PCA) can be applied. A lower bound for the total loss function for the formulated problem is given, and necessary and sufficient conditions for achieving this lower bound are also provided. We introduce ML algorithms to solve the problem in the general case, and our evaluation demonstrates the effectiveness of task-aware network coding

Pressure-induced spin reorientation transition in layered ferromagnetic insulator Cr2Ge2Te6

Anisotropic magnetoresistance (AMR) of Cr2Ge2Te6 (CGT), a layered ferromagnetic insulator, is investigated under an applied hydrostatic pressure up to 2 GPa. The easy axis direction of the magnetization is inferred from the AMR saturation feature in the presence and absence of the applied pressure. At zero applied pressure, the easy axis is along the c-direction or perpendicular to the layer. Upon application of a hydrostatic pressure>1 GPa, the uniaxial anisotropy switches to easy-plane anisotropy which drives the equilibrium magnetization from the c-axis to the ab-plane at zero magnetic field, which amounts to a giant magnetic anisotropy energy change (>100%). As the temperature is increased across the Curie temperature, the characteristic AMR effect gradually decreases and disappears. Our first-principles calculations confirm the giant magnetic anisotropy energy change with moderate pressure and assign its origin to the increased off-site spin-orbit interaction of Te atoms due to a shorter Cr-Te distance. Such a pressure-induced spin reorientation transition is very rare in three-dimensional ferromagnets, but it may be common to other layered ferromagnets with similar crystal structures to CGT, and therefore offers a unique way to control magnetic anisotropy

FedTP: Federated Learning by Transformer Personalization

Federated learning is an emerging learning paradigm where multiple clients collaboratively train a machine learning model in a privacy-preserving manner. Personalized federated learning extends this paradigm to overcome heterogeneity across clients by learning personalized models. Recently, there have been some initial attempts to apply Transformers to federated learning. However, the impacts of federated learning algorithms on self-attention have not yet been studied. This paper investigates this relationship and reveals that federated averaging algorithms actually have a negative impact on self-attention where there is data heterogeneity. These impacts limit the capabilities of the Transformer model in federated learning settings. Based on this, we propose FedTP, a novel Transformer-based federated learning framework that learns personalized self-attention for each client while aggregating the other parameters among the clients. Instead of using a vanilla personalization mechanism that maintains personalized self-attention layers of each client locally, we develop a learn-to-personalize mechanism to further encourage the cooperation among clients and to increase the scablability and generalization of FedTP. Specifically, the learn-to-personalize is realized by learning a hypernetwork on the server that outputs the personalized projection matrices of self-attention layers to generate client-wise queries, keys and values. Furthermore, we present the generalization bound for FedTP with the learn-to-personalize mechanism. Notably, FedTP offers a convenient environment for performing a range of image and language tasks using the same federated network architecture - all of which benefit from Transformer personalization. Extensive experiments verify that FedTP with the learn-to-personalize mechanism yields state-of-the-art performance in non-IID scenarios. Our code is available online

Electric Field Effect in Multilayer Cr2Ge2Te6: a Ferromagnetic Two-Dimensional Material

The emergence of two-dimensional (2D) materials has attracted a great deal of attention due to their fascinating physical properties and potential applications for future nanoelectronic devices. Since the first isolation of graphene, a Dirac material, a large family of new functional 2D materials have been discovered and characterized, including insulating 2D boron nitride, semiconducting 2D transition metal dichalcogenides and black phosphorus, and superconducting 2D bismuth strontium calcium copper oxide, molybdenum disulphide and niobium selenide, etc. Here, we report the identification of ferromagnetic thin flakes of Cr2Ge2Te6 (CGT) with thickness down to a few nanometers, which provides a very important piece to the van der Waals structures consisting of various 2D materials. We further demonstrate the giant modulation of the channel resistance of 2D CGT devices via electric field effect. Our results illustrate the gate voltage tunability of 2D CGT and the potential of CGT, a ferromagnetic 2D material, as a new functional quantum material for applications in future nanoelectronics and spintronics.Comment: To appear in 2D Material

Planning distribution network using the multi-agent game and distribution system operators

When planning the distribution network, the income of each market entity is calculated by a fixed price. How to take the price of power into account while developing the planning strategy for each organization in the actual power market is an urgent issue that needs to be addressed imminently. To address this problem, considering the continuous change in the market price due to the change in the supply–demand relationship in the actual power market, this article proposes a distribution network planning method which considers the distribution system operator (DSO) and multi-agent game. First, the planning decision model of distribution network companies and power users with different interest subjects is constructed with grid planning and DG operation as decision variables. Second, DSO is introduced to the game framework. Based on the distribution locational marginal pricing (DLMP), a price accounting model is being developed. Then, the transfer relationships and game behavior among the distribution company, power users, and DSO are analyzed. Finally, an iterative search algorithm is used to solve a multi-agent game planning model of a distribution network that takes into account price signals in the power market. Examples based on IEEE 33-bus systems validate the suggested method’s validity and efficacy

The Origin of Catalytic Benzylic C−H Oxidation over a Redox‐Active Metal–Organic Framework

From Wiley via Jisc Publications RouterHistory: received 2021-02-15, rev-recd 2021-03-27, pub-electronic 2021-06-04Article version: VoRPublication status: PublishedFunder: Engineering and Physical Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/I011870Funder: H2020 European Research Council; Id: http://dx.doi.org/10.13039/100010663; Grant(s): 742401Abstract: Selective oxidation of benzylic C−H compounds to ketones is important for the production of a wide range of fine chemicals, and is often achieved using toxic or precious metal catalysts. Herein, we report the efficient oxidation of benzylic C−H groups in a broad range of substrates under mild conditions over a robust metal–organic framework material, MFM‐170, incorporating redox‐active [Cu2II(O2CR)4] paddlewheel nodes. A comprehensive investigation employing electron paramagnetic resonance (EPR) spectroscopy and synchrotron X‐ray diffraction has identified the critical role of the paddlewheel moiety in activating the oxidant tBuOOH (tert‐butyl hydroperoxide) via partial reduction to [CuIICuI(O2CR)4] species

Design and Parameter Optimization of Transverse-Feed Ramie Decorticator

In view of the elevated labor intensity and low efficiency of ramie fiber decortication, we designed a simple automatic ramie decortication machine in line with the characteristics of the ramie fiber decortication process, design requirements and market demand through an innovative design and theoretical analysis of key components such as the clamping and conveying device and the fiber detecting device, and identified the key factors and parameters affecting the quality of ramie decortication. We develop a mathematical model of the fiber percentage of fresh stalks and the ramie fiber impurity rate by considering decortication clearance, the drum speed, and the conveyance speed as factors, and determine the effect of operating parameters on ramie decortication and the optimal combination of parameters. Finally, a multi-objective optimization test was performed using the Box–Behnken test. In this paper, based on the results of the multi-objective parameter optimization analysis, the optimal parameters for ramie peeling were determined, namely, a decortication clearance of 3.7 mm, and a conveyance speed of 340 rpm. According to the optimized parameters, the ramie peeling process was experimentally validated. Using the optimized parameters, a validation test of the ramie direction in this study was performed. As indicated by the results, the percentage of fiber in the fresh stalk reached 5.05%, and the impurity rate in the ramie fiber was 1.24%. The relative errors of all metrics and model predictions were less than 5%, thus validating the accuracy of the prediction model. The machine achieved a production efficiency of 78.5 kg·h−1, which is in line with the design specifications. The raw fiber had a gum content of 23.45 percent, and the stripped fiber met the national standard for secondary ramekin fiber. This study lays a theoretical basis while providing technical support for fully automatic ramie decorticators

Classifying transformer winding deformation fault types and degrees using FRA based on support vector machine

As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer's failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool

Sulfonated reduced graphene oxide modification layers to improve monovalent anions selectivity and controllable resistance of anion exchange membrane

Graphene oxide with lamellar structure has attracted research interest in various fields. In this study, sulfonated reduced graphene oxide (S-rGO) nanosheets with negatively charged sulfonic acid groups were synthesized via a facile distillation-precipitation polymerization, followed by hydrazine reduction. The sulfanilic acid was grafted on the graphene oxide sheets to separate GO nanosheets each other and provide anion channels for anions selectivity. These nanosheets were used to modify anion exchange membranes (AEMs), and to enhance the membrane monovalent anions selectivity and the modification layer conductivity, in order to meet industrial requirements. The permselectivity and separation efficiency were used to evaluate selectivities of the modified membranes. The results show that the unmodified AEM has no monovalent selectivity, while the permselectivity and separation efficiency of S-rGO modified AEMs (reduced by hydrazine hydrate steam in 10 min) increases from 0.65 to 1.80 and from -0.13 to 0.31 (in 40 min), respectively; and from 0.72 to 2.30 and from -0.07 to 0.28 (in 80 min), respectively