Materials with exceptional magnetism and superconductivity usually conceive emergent physical phenomena. Here, we investigate the physical properties of the (Eu,La)FeAs2 system with double magnetic sublattices. The parent EuFeAs2 shows anisotropy-ass
ociated magnetic behaviors, such as Eu-related moment canting and exchange bias. Through La doping, the magnetic anisotropy is enhanced with ferromagnetism of Eu2+ realized in the overdoped region, and a special exchange bias of the superposed ferromagnetic/superconducting loop revealed in Eu0.8La0.2FeAs2. Meanwhile, the Fe-related antiferromagnetism shows unusual robustness against La doping. Theoretical calculation and 57Fe Mossbauer spectroscopy investigation reveal a doping-tunable dual itinerant/localized nature of the Fe-related antiferromagnetism. Coexistence of the Eu-related ferromagnetism, Fe-related robust antiferromagnetism, and superconductivity is further revealed in Eu0.8La0.2FeAs2, providing a platform for further exploration of potential applications and emergent physics. Finally, an electronic phase diagram is established for (Eu,La)FeAs2 with the whole superconducting dome adjacent to the Fe-related antiferromagnetic phase, which is of benefit for seeking underlying clues to high-temperature superconductivity.
The discovery of EuFeAs2, currently the only charge-neutral parent phase of the 112-type iron-pnictide system, provides a new platform for the study of elemental doping effects on magnetism and superconductivity (SC). In this study, a series of polyc
rystalline EuFe1-yCoyAs2 and Eu0.9Pr0.1Fe1-yCoyAs2 samples are synthesized through solid-state reaction, and the evolutions of SC and magnetism with Co doping in EuFeAs2 and Eu0.9Pr0.1FeAs2 are investigated by electrical transport and magnetic susceptibility measurements. For EuFe1-yCoyAs2, the Eu-related antiferromagnetic (AFM) transition around 40 K is barely affected by Co doping, while the Fe-related spin density wave (SDW) transition temperature drops rapidly. Meanwhile, SC is induced by a trace amount of Co doping, with a highest transition temperature Tc ~ 28 K found in EuFe0.9Co0.1As2. For the Eu0.9Pr0.1Fe1-yCoyAs2 series, the magnetism and superconductivity show similar evolutions upon Co doping, and the highest Tc is enhanced to 30.6 K with an optimum doping level y ~ 0.07. Our results shed light on the competition between SC and SDW with Co doping in the 112-type EuFeAs2 system.
In this paper, we present a robust classification-autoencoder (CAE) which has strong ability to recognize outliers and defend adversaries. The basic idea is to change the autoencoder from an unsupervised learning method into a classifier. The CAE is
a modified autoencoder, where the encoder is used to compress samples with different labels into disjoint compression spaces and the decoder is used to recover a sample with a given label from the corresponding compression space. The encoder is used as a classifier and the decoder is used to decide whether the classification given by the encoder is correct by comparing the input sample with the output. Since adversary samples are seeming inevitable for the current DNN framework, we introduce the list classification based on CAE to defend adversaries, which outputs several labels and the corresponding samples recovered by the CAE. The CAE is evaluated using the MNIST dataset in great detail. It is shown that the CAE network can recognize almost all outliers and the list classification contains the correct label for almost all adversaries.
In a federated setting, agents coordinate with a central agent or a server to solve an optimization problem in which agents do not share their information with each other. Wirth and his co-authors, in a recent paper, describe how the basic additive-i
ncrease multiplicative-decrease (AIMD) algorithm can be modified in a straightforward manner to solve a class of optimization problems for federated settings for a single shared resource with no inter-agent communication. The AIMD algorithm is one of the most successful distributed resource allocation algorithms currently deployed in practice. It is best known as the backbone of the Internet and is also widely explored in other application areas. We extend the single-resource algorithm to multiple heterogeneous shared resources that emerge in smart cities, sharing economy, and many other applications. Our main results show the convergence of the average allocations to the optimal values. We model the system as a non-homogeneous Markov chain with place-dependent probabilities. Furthermore, simulation results are presented to demonstrate the efficacy of the algorithms and to highlight the main features of our analysis.
Here we report a systematic investigation on the evolution of the structural and physical properties, including the charge density wave and superconductivity of the polycrystalline CuIr2Te4-xIx. X-ray diffraction results indicate that both of a and c
lattice parameters increase linearly. The resistivity measurements indicate that the charge density wave is destabilized with slight x but reappears when x is large than 0.9. Meanwhile, the superconducting transition temperature enhances as x raises and reaches a maximum value of around 2.95 K for the optimal composition CuIr2Te3.9I0.1 followed by a slight decrease with higher iodine doping content. The specific heat jump for the optimal composition CuIr2Te3.9I0.1 is approximately 1.46, which is close to the Bardeen Cooper Schrieffer value which is 1.43, indicating it is a bulk superconductor. The results of thermodynamic heat capacity measurements under different magnetic fields, magnetization and magneto-transport measurements further suggest that CuIr2Te4-xIx bulks are type II superconductors. Finally, an electronic phase diagram for this CuIr2Te4-xIx system has been constructed. The present study provides a suitable material platform for further investigation of the interplay of the CDW and superconductivity.
We analyse a Singapore-based COVID-19 Telegram group with more than 10,000 participants. First, we study the groups opinion over time, focusing on four dimensions: participation, sentiment, topics, and psychological features. We find that engagement
peaked when the Ministry of Health raised the disease alert level, but this engagement was not sustained. Second, we search for government-identified misinformation in the group. We find that government-identified misinformation is rare, and that messages discussing these pieces of misinformation express skepticism.
We report experimental studies of a series of BaFe$_2$S$_{3-x}$Se$_x$ ($0leq xleq3$) single crystals and powder specimens using x-ray diffraction, neutron diffraction, muon spin relaxation, and electrical transport measurements. A structural transfor
mation from Cmcm (BaFe$_2$S$_3$) to Pnma (BaFe$_2$Se$_3$) was identified around $x = 0.7sim 1$. Neutron diffraction measurements on the samples with $x$ = 0.2, 0.4, and 0.7 reveal that the N${e}$el temperature of the stripe antiferromagnetic order is gradually suppressed from $sim$120 to 85 K, while the magnitude of the ordered Fe$^{2+}$ moments shows very little variation. Similarly, the block antiferromagnetic order in BaFe$_2$Se$_3$ remains robust for $1.5leq xleq3$ with negligible variation in the ordered moment and a slight decrease of the N${e}$el temperature from 250 K ($x=3$) to 225 K ($x=1.5$). The sample with $x=1$ near the Cmcm and Pnma border shows coexisting, two-dimensional, short-range stripe- and block-type antiferromagnetic correlations. The system remains insulating for all $x$, but the thermal activation gap shows an abrupt increase when traversing the boundary from the Cmcm stripe phase to the Pnma block phase. The results demonstrate that the crystal structure, magnetic order, and electronic properties are strongly coupled in the BaFe$_2$S$_{3-x}$Se$_x$ system.
ChangE 4 is the first mission to the far side of the Moon and consists of a lander, a rover, and a relay spacecraft. Lander and rover were launched at 18:23 UTC on December 7, 2018 and landed in the von Karman crater at 02:26 UTC on January 3, 2019.
Here we describe the Lunar Lander Neutron & Dosimetry experiment (LND) which is part of the ChangE 4 Lander scientific payload. Its chief scientific goal is to obtain first active dosimetric measurements on the surface of the Moon. LND also provides observations of fast neutrons which are a result of the interaction of high-energy particle radiation with the lunar regolith and of their thermalized counterpart, thermal neutrons, which are a sensitive indicator of subsurface water content.
In this paper, we consider a dynamic asset pricing model in an approximate fractional economy to address empirical regularities related to both investor protection and past information. Our newly developed model features not only in terms with a cont
rolling shareholder who diverts a fraction of the output, but also good (or bad) memory in his budget dynamics which can be well-calibrated by a pathwise way from the historical data. We find that poorer investor protection leads to higher stock holdings of controlling holders, lower gross stock returns, lower interest rates, and lower modified stock volatilities if the ownership concentration is sufficiently high. More importantly, by establishing an approximation scheme for good (bad) memory of investors on the historical market information, we conclude that good (bad) memory would increase (decrease) aforementioned dynamics and reveal that good (bad) memory strengthens (weakens) investor protection for minority shareholder when the ownership concentration is sufficiently high, while good (bad) memory inversely weakens (strengthens) investor protection for minority shareholder when the ownership concentration is sufficiently low. Our models implications are consistent with a number of interesting facts documented in the recent literature.
Cognitive and metacognitive strategy had demonstrated a significant role in self-regulated learning (SRL), and an appropriate use of strategies is beneficial to effective learning or question-solving tasks during a human-computer interaction process.
This paper proposes a novel method combining Knowledge Map (KM) based data mining technique with Thinking Map (TM) to detect learners cognitive and metacognitive strategy in the question-solving scenario. In particular, a graph-based mining algorithm is designed to facilitate our proposed method, which can automatically map cognitive strategy to metacognitive strategy with raising abstraction level, and make the cognitive and metacognitive process viewable, which acts like a reverse engineering engine to explain how a learner thinks when solving a question. Additionally, we develop an online learning environment system for participants to learn and record their behaviors. To corroborate the effectiveness of our approach and algorithm, we conduct experiments recruiting 173 postgraduate and undergraduate students, and they were asked to complete a question-solving task, such as What are similarities and differences between array and pointer? from The C Programming Language course and What are similarities and differences between packet switching and circuit switching? from Computer Network Principle course. The mined strategies patterns results are encouraging and supported well our proposed method.
