2,982 research outputs found
Phase diagram of CeFeAsPO obtained from electric resistivity, magnetization, and specific heat measurements
We performed a systematic study on the properties of CeFeAsPO
() by electrical resistivity, magnetization and specific heat
measurements. The c-axis lattice constant decreases significantly with
increasing P content, suggesting a remarkable chemical pressure. The Fe-3d
electrons show the enhanced metallic behavior upon P-doping and undergo a
magnetic quantum phase transition around . Meanwhile, the Ce-4f
electrons develop a ferromagnetic order near the same doping level. The
ferromagnetic order is vanishingly small around . The data suggest a
heavy-fermion-like behavior as . No superconductivity is observed
down to 2 K. Our results show the ferromagnetic ordered state as an
intermediate phase intruding between the antiferromagnetic bad metal and the
nonmagnetic heavy fermion metal and support the cerium-containing iron
pnictides as a unique layered Kondo lattice system.Comment: 7 pages, 6 figures, text and figures revised, references added
Critical exponents of the two-layer Ising model
The symmetric two-layer Ising model (TLIM) is studied by the corner transfer
matrix renormalisation group method. The critical points and critical exponents
are calculated. It is found that the TLIM belongs to the same universality
class as the Ising model. The shift exponent is calculated to be 1.773, which
is consistent with the theoretical prediction 1.75 with 1.3% deviation.Comment: 7 pages, with 10 figures include
Dissipationless Layertronics in Axion Insulator
Surface electrons in axion insulators are endowed with a topological layer
degree of freedom followed by exotic transport phenomena, e.g., the layer Hall
effect [Gao et al., Nature 595, 521 (2021)]. Here, we propose that such a layer
degree of freedom can be manipulated in a dissipationless way based on the
antiferromagnetic with tailored domain structure. This makes
a versatile platform to exploit the "layertronics" to encode,
process, and store information. Importantly, the layer filter, layer valve, and
layer reverser devices can be achieved using the layer-locked chiral domain
wall modes. The dissipationless nature of the domain wall modes makes the
performance of the layertronic-devices superior to those in spintronics and
valleytronics. Specifically, the layer reverser, a layer version of Datta-Das
transistor, also fills up the blank in designing the valley reverser in
valleytronics. Our work sheds light on constructing new generation electronic
devices with high performance and low energy consumption in the framework of
layertronics.Comment: 7 pages, 4 figures (+Supplementary Materials: 5 pages, 6 figures
Bidirectional Association between Major Depressive Disorder and Gastroesophageal Reflux Disease:Mendelian Randomization Study
Background: Observational research has found a bidirectional relationship between major depressive disorder and gastroesophageal reflux disease; however, the causal association of this relationship is undetermined. Aims: A bidirectional Mendelian randomization study was performed to explore the causal relationships between major depressive disorder and gastroesophageal reflux disease. Methods: For the instrumental variables of major depressive disorder and gastroesophageal reflux disease, 31 and 24 single-nucleotide polymorphisms without linkage disequilibrium (r(2) <= 0.001) were selected from relevant genome-wide association studies, respectively, at the genome-wide significance level (p <= 5 x 10(-8)). We sorted summary-level genetic data for major depressive disorder, gastroesophageal reflux disease, gastroesophageal reflux disease without esophagitis, and reflux esophagitis from meta-analysis study of genome-wide association studies involving 173,005 individuals (59,851 cases and 113,154 non-cases), 385,276 individuals (80,265 cases and 305,011 non-cases), 463,010 individuals (4360 cases and 458,650 non-cases), and 383,916 individuals (12,567 cases and 371,349 non-cases), respectively. Results: Genetic liability to major depressive disorder was positively associated with gastroesophageal reflux disease and its subtypes. Per one-unit increase in log-transformed odds ratio of major depressive disorder, the odds ratio was 1.31 (95% confidence interval [CI], 1.19-1.43; p = 1.64 x 10(-8)) for gastroesophageal reflux disease, 1.51 (95% CI, 1.15-1.98; p = 0.003) for gastroesophageal reflux disease without esophagitis, and 1.21 (95% CI, 1.05-1.40; p = 0.010) for reflux esophagitis. Reverse-direction analysis suggested that genetic liability to gastroesophageal reflux disease was causally related to increasing risk of major depressive disorder. Per one-unit increase in log-transformed odds ratio of gastroesophageal reflux disease, the odds ratio of major depressive disorder was 1.28 (95% confidence interval, 1.11-1.47; p = 1.0 x 10(-3)). Conclusions: This Mendelian randomization study suggests a bidirectional causal relationship between major depressive disorder and gastroesophageal reflux disease
Online Corrupted User Detection and Regret Minimization
In real-world online web systems, multiple users usually arrive sequentially
into the system. For applications like click fraud and fake reviews, some users
can maliciously perform corrupted (disrupted) behaviors to trick the system.
Therefore, it is crucial to design efficient online learning algorithms to
robustly learn from potentially corrupted user behaviors and accurately
identify the corrupted users in an online manner. Existing works propose bandit
algorithms robust to adversarial corruption. However, these algorithms are
designed for a single user, and cannot leverage the implicit social relations
among multiple users for more efficient learning. Moreover, none of them
consider how to detect corrupted users online in the multiple-user scenario. In
this paper, we present an important online learning problem named LOCUD to
learn and utilize unknown user relations from disrupted behaviors to speed up
learning, and identify the corrupted users in an online setting. To robustly
learn and utilize the unknown relations among potentially corrupted users, we
propose a novel bandit algorithm RCLUB-WCU. To detect the corrupted users, we
devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred
user relations. We prove a regret upper bound for RCLUB-WCU, which
asymptotically matches the lower bound with respect to up to logarithmic
factors, and matches the state-of-the-art results in degenerate cases. We also
give a theoretical guarantee for the detection accuracy of OCCUD. With
extensive experiments, our methods achieve superior performance over previous
bandit algorithms and high corrupted user detection accuracy
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