Understanding metallic behaviour is still one of the central tasks in Condensed Matter Physics. Recent developments have energized the interest in several modern concepts, such as strange metal, bad metal, and Planckian metal. However, a unified desc
ription of metallic resistivity applicable to the existing diversity of materials is still missing. Herein we present an empirical analysis of a large variety of metals, from normal metals to strongly correlated metals, using the same phenomenological approach. The electrical resistivity in all the cases follows a parallel resistor formalism, which takes both T-linear and T-quadratic dependence of the scattering rates into account. The results reveal the significance of the model by showing that the different metallic classes are determined by the relative magnitude of these two components. Importantly, our analysis shows that the T-linear term arises from the Planckian dissipation limit and it is present in all considered systems. This formalism extends previous reports on strange and normal metals, facilitating the classification of materials with non-linear resistivity curves, an important step towards the experimental confirmation of the universal character of the Planckian dissipation bound.
The convolutional neural network (CNN) based approaches have shown great success for speaker verification (SV) tasks, where modeling long temporal context and reducing information loss of speaker characteristics are two important challenges significa
ntly affecting the verification performance. Previous works have introduced dilated convolution and multi-scale aggregation methods to address above challenges. However, such methods are also hard to make full use of some valuable information, which make it difficult to substantially improve the verification performance. To address above issues, we construct a novel CNN-based architecture for SV, called RSKNet-MTSP, where a residual selective kernel block (RSKBlock) and a multiple time-scale statistics pooling (MTSP) module are first proposed. The RSKNet-MTSP can capture both long temporal context and neighbouring information, and gather more speaker-discriminative information from multi-scale features. In order to design a portable model for real applications with limited resources, we then present a lightweight version of RSKNet-MTSP, namely RSKNet-MTSP-L, which employs a combination technique associating the depthwise separable convolutions with low-rank factorization of weight matrices. Extensive experiments are conducted on two public SV datasets, VoxCeleb and Speaker in the Wild (SITW). The results demonstrate that 1) RSKNet-MTSP outperforms the state-of-the-art deep embedding architectures by at least 9%-26% in all test sets. 2) RSKNet-MTSP-L achieves competitive performance compared with baseline models with 17%-39% less network parameters. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods.
Heavily oxygen deficient NdNiO$_3$ (NNO) films, which are insulating due to electron localization, contain pristine regions that undergo a hidden metal-insulator transition. Increasing oxygen content increases the connectivity of the metallic regions
and the metal-insulator transition is first revealed, upon reaching the percolation threshold, by the presence of hysteresis. Only upon further oxygenation is the global metallic state (with a change in the resistivity slope) eventually achieved. It is shown that sufficient oxygenation leads to linear temperature dependence of resistivity in the metallic state, with a scattering rate directly proportional to temperature. Despite the known difficulties to establish the proportionality constant, the experiments are consistent with a relationship 1/$tau$= $alpha k_B T/hbar$, with $alpha$ not far from unity. These results could provide experimental support for recent theoretical predictions of disorder in a two-fluid model as a possible origin of Planckian dissipation.
Magneto-optical effect refers to a rotation of polarization plane, which has been widely studied in traditional ferromagnetic metal and insulator films and scarcely in two-dimensional layered materials. Here we uncover a new nonreciprocal magneto-ine
lastic light scattering effect in ferromagnetic few-layer CrI3. We observed a rotation of the polarization plane of inelastic light scattering between -20o and +60o that are tunable by an out-of-plane magnetic field from -2.5 to 2.5 T. It is experimentally observed that the degree of polarization can be magnetically manipulated between -20% and 85%. This work raises a new magneto-optical phenomenon and could create opportunities of applying 2D ferromagnetic materials in Raman lasing, topological photonics, and magneto-optical modulator for information transport and storage.
Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. There is no specific drug for COVID-19, highlighting the urgent need for the development of effective therapeutics. T
o identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating the gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from mild and severe COVID-19 patients. We identified 281 FDA-approved drugs that have the potential to be effective against SARS-CoV-2 infection, 16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. We experimentally tested the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a on the replication of the single-stranded ribonucleic acid (ssRNA) virus influenza A virus. In conclusion, we have identified a list of repurposable anti-SARS-CoV-2 drugs using a systems biology approach.
We report a detailed analysis of the electrical resistivity exponent of thin films of NdNiO3 as a function of epitaxial strain. Strain-free thin-films show a linear dependence of the resistivity vs temperature, consistent with a classical Fermi gas r
uled by electron-phonon interactions. In addition, the apparent temperature exponent, n, can be tuned with the epitaxial strain between n= 1 and n= 3. We discuss the critical role played by quenched random disorder in the value of n. Our work shows that the assignment of Fermi/Non-Fermi liquid behaviour based on experimentally obtained resistivity exponents requires an in-depth analysis of the degree of disorder in the material.
Long-range magnetic orders in atomically thin ferromagnetic CrI3 give rise to new fascinating physics and application perspectives. The physical properties of two-dimensional (2D) ferromagnetism CrI3 are significantly influenced by interlayer spacing
and stacking order, which are sensitive to the hydrostatic pressure and external environments. However, there remains debate on the stacking order at low temperature. Here, we study the interlayer coupling and stacking order of non-encapsulated 2-5 layer and bulk CrI3 at 10 K by Raman spectroscopy; demonstrate a rhombohedral stacking in both antiferromagnetic and ferromagnetic CrI3. The opposite helicity dependence of Ag and Eg modes arising from phonon symmetry further validate the rhombohedral stacking. An anomalous temperature-dependent behavior is observed due to spin-phonon coupling below 60 K. Our work provides insights into the interlayer coupling and stacking orders of 2D ferromagnetic materials.
