Superconductivity near Itinerant Ferromagnetic Quantum Criticality

Superconductivity mediated by spin fluctuations in weak and nearly ferromagnetic metals is studied close to the zero-temperature magnetic transition. We solve analytically the Eliashberg equations for p-wave pairing and obtain the normal state quasiparticle self-energy and the superconducting transition temperature $T_c$ as a function of the distance to the quantum critical point. We show that the reduction of quasiparticle coherence and life-time due to scattering by quasistatic spin fluctuations is the dominant pair-breaking process, which leads to a rapid suppression of $T_c$ to a nonzero value near the quantum critical point. We point out the differences and the similarities of the problem to that of the theory of superconductivity in the presence of paramagnetic impurities.Comment: 4 pages, 1 figure, revised version to appear in Phys. Rev. Let

Measurement of spin-exchange rate constants between 129Xe and alkali metals

By measuring the relaxation rates of the nuclear spin polarization of Xe-129 in the presence of alkali-metal vapor at different densities, we have extracted the spin-exchange rates between Xe-129 and the three alkali metals K, Rb, and Cs. By studying the alkali-metal-Xe-129 spin-exchange rates as functions of the cell number density from 0.2 to 0.7 amagat, the binary collision and van der Waals molecular terms are separated, and constants governing both mechanisms are determined. The results from our work can be used to optimize the parameter space for polarizing Xe-129, a promising agent for magnetic resonance imaging and other applications

Remember me: how we can modify the home for people with dementia

More than 50 million people live with dementia worldwide. For reasons of familiarity, affordability, and psychological comfort, the home is uniquely preferred by people with dementia (PwD) and their caregivers for aging in place. Ample studies show that built environmental features (e.g., furnishing, lighting, layout) influence the daily lives of PwD. These features can be modified easily and with fewer disruptions to daily life at home. However, most PwD and their caregivers usually have little knowledge of what can be achieved through simple interventions to environmental features. There is a great need for an exhibition to explain the dementia-friendly home environment to the general public. Therefore, a series of annual exhibitions, distributed around the world and adapted to the characteristics of the local home environment, is introduced. These regularly-held and updatable exhibitions can meet the constant new cases and build long-term relationships with people for multiple study visits. This laboratory immersion exhibition will break through the traditional format of communicating information. During the continuous exploration and interactive activities in the experiment areas, visitors can learn about dementia-friendly home environments to facilitate and customize evidence-based home modifications by themselves. Meanwhile, the simulation of the real home environment brings visitors through a more immersive experience. The exhibition will also help promote further research in this field through bringing together PwD, caregivers, researchers, and designers to communicate the best practice. In the end, the exhibition should provide support for creating dementia-friendly environments and help improve PwD’s quality of life

Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission with Statistical CSIT

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.Comment: Typos corrected. 14 pages, 7 figures. Accepted for publication on IEEE Transactions on Signal Processing. arXiv admin note: text overlap with arXiv:2002.0488

Independent Asymmetric Embedding for Cascade Prediction on Social Networks

The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control. Cascade prediction aims to predict the individuals who will potentially repost the message on the social network. One kind of methods either exploit demographical, structural, and temporal features for prediction, or explicitly rely on particular information diffusion models. The other kind of models are fully data-driven and do not require a global network structure. Thus massive diffusion prediction models based on network embedding are proposed. These models embed the users into the latent space using their cascade information, but are lack of consideration for the intervene among users when embedding. In this paper, we propose an independent asymmetric embedding method to learn social embedding for cascade prediction. Different from existing methods, our method embeds each individual into one latent influence space and multiple latent susceptibility spaces. Furthermore, our method captures the co-occurrence regulation of user combination in cascades to improve the calculating effectiveness. The results of extensive experiments conducted on real-world datasets verify both the predictive accuracy and cost-effectiveness of our approach