Ab initio study of the thermodynamic properties of rare-earthmagnesium intermetallics MgRE (RE=Y, Dy, Pr, Tb)

We have performed an ab initio study of the thermodynamical properties of rare-earth-magnesium intermetallic compounds MgRE (RE=Y, Dy, Pr, Tb) with CsCl-type B2-type structures. The calculations have been carried out the density functional theory and density functional perturbation theory in combination with the quasiharmonic approximation. The phonon-dispersion curves and phonon total and partial density of states have been investigated. Our results show that the contribution of RE atoms is dominant in phonon frequency, and this character agrees with the previous discussion by using atomistic simulations. The temperature dependence of various quantities such as the thermal expansions, bulk modulus, and the heat capacity are obtained. The electronic contributions to the specific heat are discussed, and found to be important for the calculated MgRE intermetallics.Comment: 12 pages, 6 figure

Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks

In this paper, we propose a novel distributed learning scheme, named group-based split federated learning (GSFL), to speed up artificial intelligence (AI) model training. Specifically, the GSFL operates in a split-then-federated manner, which consists of three steps: 1) Model distribution, in which the access point (AP) splits the AI models and distributes the client-side models to clients; 2) Model training, in which each client executes forward propagation and transmit the smashed data to the edge server. The edge server executes forward and backward propagation and then returns the gradient to the clients for updating local client-side models; and 3) Model aggregation, in which edge servers aggregate the server-side and client-side models. Simulation results show that the GSFL outperforms vanilla split learning and federated learning schemes in terms of overall training latency while achieving satisfactory accuracy

Coresets for Clustering with General Assignment Constraints

Designing small-sized \emph{coresets}, which approximately preserve the costs of the solutions for large datasets, has been an important research direction for the past decade. We consider coreset construction for a variety of general constrained clustering problems. We significantly extend and generalize the results of a very recent paper (Braverman et al., FOCS'22), by demonstrating that the idea of hierarchical uniform sampling (Chen, SICOMP'09; Braverman et al., FOCS'22) can be applied to efficiently construct coresets for a very general class of constrained clustering problems with general assignment constraints, including capacity constraints on cluster centers, and assignment structure constraints for data points (modeled by a convex body $\mathcal{B})$. Our main theorem shows that a small-sized $\epsilon$-coreset exists as long as a complexity measure $\mathsf{Lip}(\mathcal{B})$ of the structure constraint, and the \emph{covering exponent} $\Lambda_\epsilon(\mathcal{X})$ for metric space $(\mathcal{X},d)$ are bounded. The complexity measure $\mathsf{Lip}(\mathcal{B})$ for convex body $\mathcal{B}$ is the Lipschitz constant of a certain transportation problem constrained in $\mathcal{B}$, called \emph{optimal assignment transportation problem}. We prove nontrivial upper bounds of $\mathsf{Lip}(\mathcal{B})$ for various polytopes, including the general matroid basis polytopes, and laminar matroid polytopes (with better bound). As an application of our general theorem, we construct the first coreset for the fault-tolerant clustering problem (with or without capacity upper/lower bound) for the above metric spaces, in which the fault-tolerance requirement is captured by a uniform matroid basis polytope

Energy-Efficient Train Control with Onboard Energy Storage Systems considering Stochastic Regenerative Braking Energy

With the rapid development of energy storage technology, onboard energy storage systems(OESS) have been applied in modern railway systems to help reduce energy consumption. In addition, regenerative braking energy utilization is becoming increasingly important to avoid energy waste in the railway systems, undermining the sustainability of urban railway transportation. However, the intelligent energy management of the trains equipped with OESSs considering regenerative braking energy utilization is still rare in the field. This paper considers the stochastic characteristics of the regenerative braking power distributed in railway power networks. It concurrently optimizes the train trajectory with OESS and regenerative braking energy utilization. The expected regenerative braking power distribution can be obtained based on the Monte-Carlo simulation of the train timetable. Then, the integrated optimization using mixed integer linear programming (MILP) can be conducted and combined with the expected available regenerative braking energy. A generic four-station railway system powered by one traction substation is modeled and simulated for the study. The results show that by applying the proposed method, 68.8% of the expected regenerative braking energy in the environment will be further utilized. The expected amount of energy from the traction substation is reduced by 22.0% using the proposed train control method to recover more regenerative braking energy from improved energy interactions between trains and OESSs