Evolution of superconductivity in K2-xFe4+ySe5: Spectroscopic studies of X-ray absorption and emission.

This study investigates the evolution of superconductivity in K2-xFe4+ySe5 using temperature-dependent X-ray absorption and resonant inelastic X-ray scattering techniques. Magnetization measurements show that polycrystalline superconducting (SC) K1.9Fe4.2Se5 has a critical temperature (T c) of ∼31 K with a varying superconducting volume fraction, which strongly depends on its synthesis temperature. An increase in Fe-structural/vacancy disorder in SC samples with more Fe atoms occupying vacant 4d sites is found to be closely related to the decrease in the spin magnetic moment of Fe. Moreover, the nearest-neighbor Fe-Se bond length in SC samples exceeds that in the non-SC (NS) sample, K2Fe4Se5, which indicates a weaker hybridization between the Fe 3d and Se 4p states in SC samples. These results clearly demonstrate the correlations among the local electronic and atomic structures and the magnetic properties of K2-xFe4+ySe5 superconductors, providing deeper insight into the electron pairing mechanisms of superconductivity

Enhancing Visual Domain Adaptation with Source Preparation

Robotic Perception in diverse domains such as low-light scenarios, where new modalities like thermal imaging and specialized night-vision sensors are increasingly employed, remains a challenge. Largely, this is due to the limited availability of labeled data. Existing Domain Adaptation (DA) techniques, while promising to leverage labels from existing well-lit RGB images, fail to consider the characteristics of the source domain itself. We holistically account for this factor by proposing Source Preparation (SP), a method to mitigate source domain biases. Our Almost Unsupervised Domain Adaptation (AUDA) framework, a label-efficient semi-supervised approach for robotic scenarios -- employs Source Preparation (SP), Unsupervised Domain Adaptation (UDA) and Supervised Alignment (SA) from limited labeled data. We introduce CityIntensified, a novel dataset comprising temporally aligned image pairs captured from a high-sensitivity camera and an intensifier camera for semantic segmentation and object detection in low-light settings. We demonstrate the effectiveness of our method in semantic segmentation, with experiments showing that SP enhances UDA across a range of visual domains, with improvements up to 40.64% in mIoU over baseline, while making target models more robust to real-world shifts within the target domain. We show that AUDA is a label-efficient framework for effective DA, significantly improving target domain performance with only tens of labeled samples from the target domain

A survey on deep transfer learning and edge computing for mitigating the COVID-19 pandemic

This is an accepted manuscript of an article published by Elsevier in Journal of Systems Architecture on 30/06/2020, available online: https://doi.org/10.1016/j.sysarc.2020.101830 The accepted version of the publication may differ from the final published version.Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected within six months from its first report of appearance and it keeps spreading. The required systems are not ready up to some stages for any pandemic; therefore, mitigation with existing capacity becomes necessary. On the other hand, modern-era largely depends on Artificial Intelligence(AI) including Data Science; and Deep Learning(DL) is one of the current flag-bearer of these techniques. It could use to mitigate COVID-19 like pandemics in terms of stop spread, diagnosis of the disease, drug & vaccine discovery, treatment, patient care, and many more. But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(ED) such as IoT, Webcam, Drone, Intelligent Medical Equipment, Robot, etc. are very useful in a pandemic situation. These types of equipment make the infrastructures sophisticated and automated which helps to cope with an outbreak. But these are equipped with low computing resources, so, applying DL is also a bit challenging; therefore, DTL also would be effective there. This article scholarly studies the potentiality and challenges of these issues. It has described relevant technical backgrounds and reviews of the related recent state-of-the-art. This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic

Intrinsic Photoconductivity of Few-layered ZrS2 Phototransistors via Multiterminal Measurements

We report intrinsic photoconductivity studies on one of the least examinedlayered compounds, ZrS2.Few-atomic layer ZrS2 field-effect transistorswere fabricated on the Si/SiO2 substrate and photoconductivity measurements were performed using both two- and four-terminal configurationsunder the illumination of 532 nm laser source. We measured photocurrentas a function of the incident optical power at several source-drain (bias)voltages. We observe a significantly large photoconductivity when measured in the multiterminal (four-terminal) configuration compared to thatin the two-terminal configuration. For an incident optical power of 90nW, the estimated photosensitivity and the external quantum efficiency(EQE) measured in two-terminal configuration are 0.5 A/W and 120%,respectively, under a bias voltage of 650 mV. Under the same conditions,the four-terminal measurements result in much higher values for both thephotoresponsivity (R) and EQE to 6 A/W and 1400%, respectively. Thissignificant improvement in photoresponsivity and EQE in the four-terminal configuration may have been influenced by the reduction of contactresistance at the metal-semiconductor interface, which greatly impacts thecarrier mobility of low conducting materials. This suggests that photoconductivity measurements performed through the two-terminal configurationin previous studies on ZrS2 and other 2D materials have severely underestimated the true intrinsic properties of transition metal dichalcogenides andtheir remarkable potential for optoelectronic applications

InSb nanoparticles dispersion in Yb-filled Co4Sb12 improves the thermoelectric performance

Out of several methods, one of the most explored strategies to decrease the lattice thermal conductivity of Co4Sb12-based materials are either filling suitable electropositive elements into the voids or the formation of nanocomposites. These two approaches were combined in this work by filling Yb into the void of Co4Sb12 and preparing nanocomposites of Yb0.2Co4Sb12 and InSb according to the formula (InSb)x + Yb0.2Co4Sb12 (where x = 0.1, 0.2, 0.3, 0.4), via ball-milling and spark plasma sintering. Yb2O3 and CoSb2 as impurity phases were found at the grain boundaries. EBSD and TEM micrographs showed nanocrystalline InSb phase (20–200 nm) dispersed in the matrix grains. The charge transfer from Yb filler with an oxidation state of +3 to Co4Sb12 yielded a low electrical resistivity (ρ) of the matrix. An increase in ρ and Seebeck coefficient (S) in the composites with x = 0.1 and 0.3 occurred due to the higher amount of oxide impurities in these two samples and the scattering of charge carriers at the interfaces induced by the secondary phases. The other two composites with x = 0.2 and 0.4 exhibited ρ(T) and S(T) similar to the Yb0.2Co4Sb12 matrix. The dispersion of the InSb and Yb2O3 phases at the grain boundaries combined with the anharmonicity introduced by the fillers (Yb) in the voids enhanced the scattering of phonons within a broad wavelength range and reduced the lattice thermal conductivity significantly. Hence, a highest zT of ~1.2 at 773 K with a thermoelectric efficiency of 8.89% and 8.28% (423–773 K) were obtained for (InSb)0.1 + Yb0.2Co4Sb12 and (InSb)0.2 + Yb0.2Co4Sb12 nanocomposites, respectively. © 2021 Elsevier B.V

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe