Phase diagram of CeFeAs1−x_{1-x}Px_{x}O obtained from electric resistivity, magnetization, and specific heat measurements

We performed a systematic study on the properties of CeFeAs$_{1-x}$P$_{x}$O ($0\leq x\leq 1$) 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 $x \approx 0.4$. Meanwhile, the Ce-4f electrons develop a ferromagnetic order near the same doping level. The ferromagnetic order is vanishingly small around $x=0.9$. The data suggest a heavy-fermion-like behavior as $x\geq 0.95$. 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 MnBi2Te4\rm{MnBi_2Te_4}

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 $\rm{MnBi_2Te_4}$ with tailored domain structure. This makes $\rm{MnBi_2Te_4}$ 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 $T$ 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