arXiv subjects

Yiwen Li

Publications and source records attributed to Yiwen Li.

24 records · Page 2Linked to original sources

arXiv2022

Comparative studies on unconventional superconductivity in Cr$_3$Ru compounds with bcc and A15 structures

Chromium (Cr) is a transition metal element with 3$d$ orbital electrons. In most compounds containing Cr, due to the correlation effect, twofold features of localization and itinerancy are expected. The localization gives rise to a magnetic moment, while the latter exhibits as the effective coherent weight for conductivity. Here we report the physical properties of Cr$_3$Ru compounds with bcc or A15 phases by using multiple experimental tools. The resistivity measurements show sharp superconducting transitions at $T_{\rm c}$ = 2.77 K and $T_{\rm c}$ = 3.37 K for the bcc and A15 structures. A high residual resistivity exists for both samples with the mean-free-path about 2 nm. Magnetization measurements also show rather narrow transitions, with a clear hump structure at high temperatures around 150 K, which may be ascribed to the remaining antiferromagnetic spin fluctuations. The specific heat coefficient reveals a clear jump at $T_{\rm c}$. We find that s-wave gaps can be adopted to fit the low temperature specific heat data yielding ratios of about $2Δ/k_{\scriptscriptstyle B}T_{\rm c}$ $\approx$ 3.6, indicating a moderate pairing strength. Interestingly, the Wilson ratios are 3.81 and 3.62 for the bcc and A15 phases, suggesting a moderate correlation effect of conducting electron in the normal state. Besides, for samples with A15 structure, another specific heat anomaly occurs at about 0.85 K and is sensitive to magnetic fields. By applying high pressures, both system exhibit an enhancement of $T_{\rm c}$ with a rate of about 0.019 K/GPa and 0.013 K/GPa for the bcc and A15 phases. We also conduct tunneling spectrum measurements and found that the coherence peaks are strongly smeared out. The strong suppression to the coherence peaks may be ascribed to the strong scattering. Our combinatory results point to an unconventional superconductivity in these Cr based compounds.

cond-mat.supr-con↗

arXiv2022

Superconductivity arising from pressure induced Lifshitz transition in Rb$_2$Pd$_3$Se$_4$ with kagome lattice

According to the Bardeen-Cooper-Schrieffer (BCS) theory, superconductivity usually needs well defined Fermi surface(s) with strong electron-phonon coupling and moderate quasiparticle density of states (DOS). A kagome lattice can host flat bands and topological Dirac bands; meanwhile, due to the parallel Fermi surfaces and the saddle points, many interesting orders are expected. Here, we report the observation of superconductivity by pressurizing a kagome compound Rb$_2$Pd$_3$Se$_4$ using a DAC anvil. The parent compound shows an insulating behavior; however, it gradually becomes metallic and turns to a superconducting state when a high pressure is applied. High pressure synchrotron measurements show that there is no structural transition occurring during this transition. The density-functional-theory (DFT) calculations illustrate that the insulating behavior of the parent phase is due to the crystalline field splitting of the partial Pd-4d $t_{2g}$ bands and the Se-derivative 4$p$-band. However, the threshold of metallicity and superconductivity are reached when the Lifshitz transition occurs, leading to the emergence of tiny Fermi surface at $Γ$ point. Our results point to an unconventional superconductivity and shed new light on understanding the electronic evolution of a kagome material.

cond-mat.supr-con↗

arXiv2022

Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. We propose an image alignment module registering the predicted segmentation of both query and support data, in a standard prototypical learning algorithm, to a reference atlas space. The built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects, regardless whether they are from the same institution or not. Experimental results demonstrated that the proposed registration-assisted prototypical learning significantly improved segmentation accuracy (p-values<0.01) on query data from a holdout institution, with varying availability of support data from multiple institutions. We also report the additional benefits of the proposed 3D networks with 75% fewer parameters and an arguably simpler implementation, compared with existing 2D few-shot approaches that segment 2D slices of volumetric medical images.

eess.IV↗

arXiv2021

Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes

Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentation remains a challenging task due to the limited training data and the generalisation requirement for unseen classes. While recent progress has been particularly encouraging, we discover that existing methods tend to have poor performance in terms of meanIoU when query images contain other semantic classes besides the target class. To address this issue, we propose a novel self-supervised task that generates random pseudo-classes in the background of the query images, providing extra training data that would otherwise be unavailable when predicting individual target classes. To that end, we adopted superpixel segmentation for generating the pseudo-classes. With this extra supervision, we improved the meanIoU performance of the state-of-the-art method by 2.5% and 5.1% on the one-shot tasks, as well as 6.7% and 4.4% on the five-shot tasks, on the PASCAL-5i and COCO benchmarks, respectively.

cs.CV↗

arXiv2021

1550 nm compatible ultrafast photoconductive material based on a GaAs/ErAs/GaAs heterostructure

The sub-bandgap absorption and ultrafast relaxation in a GaAs/ErAs/GaAs heterostructure are reported. The infrared absorption and 1550 nm-excited ultrafast photo-response are studied by Fourier transform infrared (FTIR) spectrometry and time-domain pump-probe technique. The two absorption peaks located at 2.0 um (0.62 eV) and 2.7 um (0.45 eV) are originated from the ErAs/GaAs interfacial Schottky states and sub-bandgap transition within GaAs, respectively. The photo-induced carrier lifetime, excited using 1550 nm light, is measured to be as low as 190 fs for the GaAs/ErAs/GaAs heterostructure, making it a promising material for 1550-nm-technology-compatible, high critical-breakdown-field THz devices. The relaxation mechanism is proposed and the functionality of ErAs is revealed.

cond-mat.mtrl-sci↗

arXiv2020

GroSS: Group-Size Series Decomposition for Grouped Architecture Search

We present a novel approach which is able to explore the configuration of grouped convolutions within neural networks. Group-size Series (GroSS) decomposition is a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms. GroSS allows for dynamic and differentiable selection of factorisation rank, which is analogous to a grouped convolution. Therefore, to the best of our knowledge, GroSS is the first method to enable simultaneous training of differing numbers of groups within a single layer, as well as all possible combinations between layers. In doing so, GroSS is able to train an entire grouped convolution architecture search-space concurrently. We demonstrate this through architecture searches with performance objectives on multiple datasets and networks. GroSS enables more effective and efficient search for grouped convolutional architectures.

cs.LG↗