Topological superconductivity of spin-3/2 carriers in a three-dimensional doped Luttinger semimetal

We investigate topological Cooper pairing, including gapless Weyl and fully gapped class DIII superconductivity, in a three-dimensional doped Luttinger semimetal. The latter describes effective spin-3/2 carriers near a quadratic band touching and captures the normal-state properties of the 227 pyrochlore iridates and half-Heusler alloys. Electron-electron interactions may favor non-$s$-wave pairing in such systems, including even-parity $d$-wave pairing. We argue that the lowest energy $d$-wave pairings are always of complex (e.g., $d + i d$) type, with nodal Weyl quasiparticles. This implies $\varrho(E) \sim |E|^2$ scaling of the density of states (DoS) at low energies in the clean limit, or $\varrho(E) \sim |E|$ over a wide critical region in the presence of disorder. The latter is consistent with the $T$-dependence of the penetration depth in the half-Heusler compound YPtBi. We enumerate routes for experimental verification, including specific heat, thermal conductivity, NMR relaxation time, and topological Fermi arcs. Nucleation of any $d$-wave pairing also causes a small lattice distortion and induces an $s$-wave component; this gives a route to strain-engineer exotic $s+d$ pairings. We also consider odd-parity, fully gapped $p$-wave superconductivity. For hole doping, a gapless Majorana fluid with cubic dispersion appears at the surface. We invent a generalized surface model with $\nu$-fold dispersion to simulate a bulk with winding number $\nu$. Using exact diagonalization, we show that disorder drives the surface into a critically delocalized phase, with universal DoS and multifractal scaling consistent with the conformal field theory (CFT) SO($n$)${}_\nu$, where $n \rightarrow 0$ counts replicas. This is contrary to the naive expectation of a surface thermal metal, and implies that the topology tunes the surface renormalization group to the CFT in the presence of disorder.Comment: Published Version in PRB (Editors' Suggestion): 49 Pages, 17 Figures, 3 Table

Multiculturalism and Citizenship in the Netherlands

The discourse on multiculturalism in the Netherlands dates to the arrival of the so-called “guest workers” in the late 1950s. By the 1980s, when the Dutch government realized that migration, initially viewed as temporary, had gained a more permanent character, it started to focus on the integration of the immigrants

Agents of change or passive victims: the impact of welfare states (the case of the Netherlands) on refugees

This paper explores the impact of a regulated society such as the Netherlands on the lives of refugees in general and on those of Iranian women refugees in particular. Two periods are distinguished in regard to Dutch asylum policies: the 1980s and post-1990. For the 1980s when refugee reception was less restricted, I use empirical material collected between 1995 and 2000. The women I interviewed during this period were leftist activists involved in the Iranian revolution of 1979 and had to leave Iran because of their political backgrounds. The material used for the post-1990 or more restricted period, is mainly from secondary sources, supplemented by occasional, informal visits to asylum seeker centres. The paper argues that a strict refugee policy - especially the policy that was put in place during the 1990s - has a direct effect on the affected refugees by making them dependents of the state. These restricted policies reinforce the image of refugees as problems in society and have an effect, albeit less direct, on the lives of the refugees who arrived prior to the 1990s and who are now Dutch citizens. © The Author [2005]. Published by Oxford University Press. All rights reserved

Introduction: Scholarly engagement and decolonisation:Views from South Africa, The Netherlands and the United States

Considering that one of the core tasks of academia is to provide social critique and reflection, universities have an undeniable role to formulate the contours of a more inclusive academia in contrast to visible and normalised structures of exclusion. Translating such ambitions into transformative practices seems to be easier said than done. Academics need mutual inspiration and exchange of thoughts and practices to reflect on their actions and their own knowledge productions. The authors in this book mirror the challenges and achievements of academics and practitioners in three national contexts, which could serve as a foundation for academia to move towards dismantling elitist and privileged-based assumptions, and formulating new forms of knowledge production and institutional policies, inside and outside academia. The book aims to help create a more inclusive society in which academics, students and practitioners can engage, learn and transform structures of inequality, exclusion and disconnection where it seems to have the biggest impact

Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach

Human-centered data collection is typically costly and implicates issues of privacy. Various solutions have been proposed in the literature to reduce this cost, such as crowd-sourced data collection, or the use of semisupervised algorithms. However, semisupervised algorithms require a source of unlabeled data, and crowd-sourcing methods require numbers of active participants. An alternative passive data collection modality is fingerprint-based localization. Such methods use received signal strength or channel state information in wireless sensor networks to localize users in indoor/outdoor environments. In this letter, we introduce a novel approach to reduce training data collection costs in fingerprint-based localization by using synthetic data. Generative adversarial networks (GANs) are used to learn the distribution of a limited sample of collected data and, following this, to produce synthetic data that can be used to augment the real collected data in order to increase overall positioning accuracy. Experimental results on a benchmark dataset show that by applying the proposed method and using a combination of 10% collected data and 90% synthetic data, we can obtain essentially similar positioning accuracy to that which would be obtained by using the full set of collected data. This means that by employing GAN-generated synthetic data, we can use 90% less real data, thereby reducing data-collection costs while achieving acceptable accuracy

Throughput Improvement by Mode Selection in Hybrid Duplex Wireless Networks

Hybrid duplex wireless networks, use half duplex (HD) as well as full duplex (FD) modes to utilize the advantages of both technologies. This paper tries to determine the proportion of the network nodes that should be in HD or FD modes in such networks, to maximize the overall throughput of all FD and HD nodes. Here, by assuming imperfect self-interference cancellation (SIC) and using ALOHA protocol, the local optimum densities of FD, HD and idle nodes are obtained in a given time slot, using Karush–Kuhn–Tucker (KKT) conditions as well as stochastic geometry tool. We also obtain the sub-optimal value of the signal-to-interference ratio (SIR) threshold constrained by fixed node densities, using the steepest descent method in order to maximize the network throughput. The results show that in such networks, the proposed hybrid duplex mode selection scheme improves the level of throughput. The results also indicate the effect of imperfect SIC on reducing the throughput. Moreover, it is demonstrated that by choosing an optimal SIR threshold for mode selection process, the achievable throughput in such networks can increase by around 5%