Character of frustration on magnetic correlation in doped Hubbard model

The magnetic correlation in the Hubbard model on a two-dimensional anisotropic triangular lattice is studied by using the determinant quantum Monte Carlo method. Around half filling, it is found that the increasing frustration $t'/t$ could change the wave vector of maximum spin correlation along ($\pi,\pi$)$\rightarrow$($\pi,\frac{5\pi}{6}$)$\rightarrow$($\frac{5\pi}{6},\frac{5\pi}{6}$)$\rightarrow$ ($\frac{2\pi}{3},\frac{2\pi}{3}$), indicating the frustration's remarkable effect on the magnetism. In the studied filling region =1.0-1.3, the doping behaves like some kinds of {\it{frustration}}, which destroys the $(\pi,\pi)$ AFM correlation quickly and push the magnetic order to a wide range of the $(\frac{2\pi}{3},\frac{2\pi}{3})$ $120^{\circ}$ order when the $t'/t$ is large enough. Our non-perturbative calculations reveal a rich magnetic phase diagram over both the frustration and electron doping.Comment: 6 pages, 7 figure

Non-Hermitian Chiral Skin Effect

The interplay between non-Hermitian effects and topological insulators has become a frontier of research in non-Hermitian physics. However, the existence of a non-Hermitian skin effect for topological-protected edge states remains controversial. In this paper, we discover an alternative form of the non-Hermitian skin effect called the non-Hermitian chiral skin effect (NHCSE). NHCSE is a non-Hermitian skin effect under periodic boundary condition rather than open boundary condition. Specifically, the chiral modes of the NHCSE localize around \textquotedblleft topological defects\textquotedblright characterized by global dissipation rather than being confined to the system boundaries. We show its detailed physical properties by taking the non-Hermitian Haldane model as an example. As a result, the intrinsic mechanism of the hybrid skin-topological effect in Chern insulators is fully understood via NHCSE. Therefore, this progress will be helpful for solving the controversial topic of hybrid skin-topological effect and thus benefit the research on both non-Hermitian physics and topological quantum states

Isolation and Characteristics of a Bacterial Strain for Deodorization of Dimethyl Sulfide

AbstractThe removal characteristics of dimethyl sulfide (DMS) with a peat packed tower were studied. The peat itself did not remove DMS. The peat inoculated with activated sludge as a source of microorganisms showed an efficient removal of DMS. Dominant microorganisms for degradation of DMS in the peat packed tower were some chemolithotrophic and non-acidophilic sulfur-oxidizing microorganisms originating from sludge. A dominant DMS-oxidizing strain Au7 was isolated and identified as chemolithotrophic Thiobacilli. Product of DMS oxidation by strain Au7 was sulfate. The optimum pH of DMS removal by strain Au7 was 7-5.45

Does learning different script systems affect configural visual processing? ERP evidence from early readers of Chinese and German

Reading is a complex cultural skill requiring considerable training, apparently affecting also the processing of non-linguistic visual stimuli. We examined whether the different visual demands involved in reading different script systems—alphabetic German versus logographic Chinese script—would differentially influence configural visual processing. Our main dependent measure was the N170 component of the ERP, which is considered as a signature of configural processing. In the present study, German and Chinese children (N = 28 vs. 27) who had received about one year of formal instruction in their native script system, worked on a series of one-back tasks with naturalistic faces, two-tone Mooney faces and doodles, and on an adaptation task with pairs of faces were either identical or differed in their second-order relations. Chinese children showed larger N170 amplitudes than German children for naturalistic and Mooney faces, specifically indicating superior holistic processing in Chinese children. In contrast, there was no superiority in Chinese children on the second-order adaptation effect at the N170, providing no evidence for differences in second-order relations processing of facial configurations between the groups. Given the sensitivity of the visual system to reading acquisition, these findings suggest that these group differences in holistic processing might be due to the extensive training with the highly complex logographic script system learned by Chinese children, imposing high demands on higher-order visual perception.China Scholarship Council http://dx.doi.org/10.13039/501100004543National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809Peer Reviewe

Zeeman effect in centrosymmetric antiferromagnets controlled by an electric field

Centrosymmetric antiferromagnetic semiconductors, although abundant in nature, seem less promising than ferromagnets and ferroelectrics for practical applications in semiconductor spintronics. As a matter of fact, the lack of spontaneous polarization and magnetization hinders the efficient utilization of electronic spin in these materials. Here, we propose a paradigm to harness electronic spin in centrosymmetric antiferromagnets via Zeeman spin splittings of electronic energy levels -- termed as spin Zeeman effect -- which is controlled by electric field.By symmetry analysis, we identify twenty-one centrosymmetric antiferromagnetic point groups that accommodate such a spin Zeeman effect. We further predict by first-principles that two antiferromagnetic semiconductors, Fe$_2$TeO$_6$ and SrFe$_2$S$_2$O, are excellent candidates showcasing Zeeman splittings as large as $\sim$55 and $\sim$30 meV, respectively, induced by an electric field of 6 MV/cm. Moreover, the electronic spin magnetization associated to the splitting energy levels can be switched by reversing the electric field. Our work thus sheds light on the electric-field control of electronic spin in antiferromagnets, which broadens the scope of application of centrosymmetric antiferromagnetic semiconductors

Chinese Medicine in the Battle Against Obesity and Metabolic Diseases

Obesity is a multi-factor chronic disease caused by the mixed influence of genetics, environments and an imbalance of energy intake and expenditure. Due to lifestyle changes, modern society sees a rapid increase in obesity occurrence along with an aggravated risk of metabolic syndromes in the general population, including diabetes, hepatic steatosis, cardiovascular diseases and certain types of cancer. Although obesity has become a serious worldwide public health hazard, effective and safe drugs treating obesity are still missing. Traditional Chinese medicine (TCM) has been implicated in practical use in China for thousands of years and has accumulated substantial front line experience in treating various diseases. Compared to western medicine that features defined composition and clear molecular mechanisms, TCM is consisted with complex ingredients from plants and animals and prescribed based on overall symptoms and collective experience. Because of their fundamental differences, TCM and western medicine were once considered irreconcilable. However, nowadays, sophisticated isolation technologies and deepened molecular understanding of the active ingredients of TCM are gradually bridging the gap between the two, enabling the identification of active TCM components for drug development under the western-style paradigms. Thus, studies on TCM open a new therapeutic avenue and show great potential in the combat against obesity, though challenges exist. In this review, we highlight six key candidate substances derived from TCM, including artemisinin, curcumin, celastrol, capsaicin, berberine and ginsenosides, to review their recent discoveries in the metabolic field, with special focus on their therapeutic efficacy and molecular mechanisms in treating obesity and metabolic diseases. In addition, we discuss the translational challenges and perspectives in implementing modern Chinese medicine into the western pharmaceutical industry

Dense X Retrieval: What Retrieval Granularity Should We Use?

Dense retrieval has become a prominent method to obtain relevant context or world knowledge in open-domain NLP tasks. When we use a learned dense retriever on a retrieval corpus at inference time, an often-overlooked design choice is the retrieval unit in which the corpus is indexed, e.g. document, passage, or sentence. We discover that the retrieval unit choice significantly impacts the performance of both retrieval and downstream tasks. Distinct from the typical approach of using passages or sentences, we introduce a novel retrieval unit, proposition, for dense retrieval. Propositions are defined as atomic expressions within text, each encapsulating a distinct factoid and presented in a concise, self-contained natural language format. We conduct an empirical comparison of different retrieval granularity. Our results reveal that proposition-based retrieval significantly outperforms traditional passage or sentence-based methods in dense retrieval. Moreover, retrieval by proposition also enhances the performance of downstream QA tasks, since the retrieved texts are more condensed with question-relevant information, reducing the need for lengthy input tokens and minimizing the inclusion of extraneous, irrelevant information

Vision-language Assisted Attribute Learning

Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization. Existing attribute learning methods often treat the missing labels as negative or simply ignore them all during training, either of which could hamper the model performance to a great extent. To overcome these limitations, in this paper we leverage the available vision-language knowledge to explicitly disclose the missing labels for enhancing model learning. Given an image, we predict the likelihood of each missing attribute label assisted by an off-the-shelf vision-language model, and randomly select to ignore those with high scores in training. Our strategy strikes a good balance between fully ignoring and negatifying the missing labels, as these high scores are found to be informative on revealing label ambiguity. Extensive experiments show that our proposed vision-language assisted loss can achieve state-of-the-art performance on the newly cleaned VAW dataset. Qualitative evaluation demonstrates the ability of the proposed method in predicting more complete attributes.Comment: Accepted by IEEE IC-NIDC 202