Anisotropic hybrid excitation modes in monolayer and double-layer phosphorene on polar substrates

We investigate the anisotropic hybrid plasmon-SO phonon dispersion relations in monolayer and double-layer phosphorene systems located on the polar substrates, such as SiO2, h-BN and Al2O3. We calculate these hybrid modes with using the dynamical dielectric function in the RPA by considering the electron-electron interaction and long-range electric field generated by the substrate SO phonons via Frohlich interaction. In the long-wavelength limit, we obtain some analytical expressions for the hybrid plasmon-SO phonon dispersion relations which represent the behavior of these modes akin to the modes obtaining from the loss function. Our results indicate a strong anisotropy in plasmon-SO phonon modes, whereas they are stronger along the light-mass direction in our heterostructures. Furthermore, we find that the type of substrate has a significant effect on the dispersion relations of the coupled modes. Also, by tuning the misalignment and separation between layers in double-layer phosphorene on polar substrates, we can engineer the hybrid modes.Comment: 10 pages, 7 figure

Designing Precoding and Receive Matrices for Interference Alignment in MIMO Interference Channels

Interference is a key bottleneck in wireless communication systems. Interference alignment is a management technique that align interference from other transmitters in the least possibly dimension subspace at each receiver and provides the remaining dimensions for free interference signal. An uncoordinated interference is an example of interference which cannot be aligned coordinately with interference from coordinated part; consequently, the performance of interference alignment approaches are degraded. In this paper, we propose a rank minimization method to enhance the performance of interference alignment in the presence of uncoordinated interference sources. Firstly, to obtain higher multiplexing gain, a new rank minimization based optimization problem is proposed; then, a new class of convex relaxation is introduced which can reduce the optimal value of the problem and obtain lower rank solutions by expanding the feasibility set. Simulation results show that our proposed method can obtain considerably higher multiplexing gain and sum rate than other approaches in the interference alignment framework

Budgeted Online Model Selection and Fine-Tuning via Federated Learning

Online model selection involves selecting a model from a set of candidate models 'on the fly' to perform prediction on a stream of data. The choice of candidate models henceforth has a crucial impact on the performance. Although employing a larger set of candidate models naturally leads to more flexibility in model selection, this may be infeasible in cases where prediction tasks are performed on edge devices with limited memory. Faced with this challenge, the present paper proposes an online federated model selection framework where a group of learners (clients) interacts with a server with sufficient memory such that the server stores all candidate models. However, each client only chooses to store a subset of models that can be fit into its memory and performs its own prediction task using one of the stored models. Furthermore, employing the proposed algorithm, clients and the server collaborate to fine-tune models to adapt them to a non-stationary environment. Theoretical analysis proves that the proposed algorithm enjoys sub-linear regret with respect to the best model in hindsight. Experiments on real datasets demonstrate the effectiveness of the proposed algorithm.Comment: Accepted by Transactions on Machine Learning Research (TMLR

Commentary: The History of Neurosurgery at Albany Medical College and Albany Medical Center Hospital, Albany, New York.

The origins of the Department of Neurosurgery at Albany Medical College closely parallel the development of early America and the establishment of modern health care.The tales of Washington Irving, the works of the Hudson River School of painters, and summers in the Catskill Mountains or Adirondacks are the stories that color the history of Upstate New York (Figure1). As a social, industrial, and political hub of the American colonies, New England’s need for centers providing structured medicine led to the creation of Albany Medical College in1839, one of the earliest such institutions in the young nation.1 Rapid progress in nearly every other realm of life required medical advancements as well, prompting subspecialization and the development of neurosurgery in the region

How Human Resource and Information Systems Practices Amplify the Returns on Information Technology Investments

This study examines the important roles that human resources (HR) for information technology (IT) professionals and information systems (IS) practices for all workers in an organization play in shaping returns on firms’ IT investments. In particular, we consider how incentives, autonomy, and training for IT professionals can enable a firm to better leverage the value of its IT investments. We argue that well-trained, motivated, and empowered IT professionals can help firms make better strategic choices in allocating IT investments and implementing IT projects. We also demonstrate how this moderating relationship depends upon collaborative IS and autonomy-enhancing IS practices that affect other knowledge workers in the firm. We leverage archival data for 228 firms with 736 firm-year observations and document two key findings. We find (1) that empowering HR practices for IT professionals positively moderate the effect of IT investments on firm performance, and (2) that the alignment between empowering HR practices for IT professionals and firm-wide collaborative IS practices enhances the value that firms derive from IT investments. Our results suggest that the business value of IT investments is linked to the rewards and opportunities offered to IT professionals, who have a pivotal role in the effective deployment of IT in organizations