Independent tuning of electronic properties and induced ferromagnetism in topological insulators with heterostructure approach

The quantum anomalous Hall effect (QAHE) has been recently demonstrated in Cr- and V-doped three-dimensional topological insulators (TIs) at temperatures below 100 mK. In those materials, the spins of unfilled d-electrons in the transition metal dopants are exchange coupled to develop a long-range ferromagnetic order, which is essential for realizing QAHE. However, the addition of random dopants does not only introduce excess charge carriers that require readjusting the Bi/Sb ratio, but also unavoidably introduces paramagnetic spins that can adversely affect the chiral edge transport in QAHE. In this work, we show a heterostructure approach to independently tune the electronic and magnetic properties of the topological surface states in (BixSb1-x)2Te3 without resorting to random doping of transition metal elements. In heterostructures consisting of a thin (BixSb1-x)2Te3 TI film and yttrium iron garnet (YIG), a high Curie temperature (~ 550 K) magnetic insulator, we find that the TI surface in contact with YIG becomes ferromagnetic via proximity coupling which is revealed by the anomalous Hall effect (AHE). The Curie temperature of the magnetized TI surface ranges from 20 to 150 K but is uncorrelated with the Bi fraction x in (BixSb1-x)2Te3. In contrast, as x is varied, the AHE resistivity scales with the longitudinal resistivity. In this approach, we decouple the electronic properties from the induced ferromagnetism in TI. The independent optimization provides a pathway for realizing QAHE at higher temperatures, which is important for novel spintronic device applications.Comment: Accepted by Nano Letter

A Benchmark for Structured Extractions from Complex Documents

Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry. Although recent multi-modal language models have achieved impressive results, we find that existing benchmarks do not reflect the complexity of real documents seen in industry. In this work, we identify the desiderata for a more comprehensive benchmark and propose one we call Visually Rich Document Understanding (VRDU). VRDU contains two datasets that represent several challenges: rich schema including diverse data types as well as nested entities, complex templates including tables and multi-column layouts, and diversity of different layouts (templates) within a single document type. We design few-shot and conventional experiment settings along with a carefully designed matching algorithm to evaluate extraction results. We report the performance of strong baselines and three observations: (1) generalizing to new document templates is very challenging, (2) few-shot performance has a lot of headroom, and (3) models struggle with nested fields such as line-items in an invoice. We plan to open source the benchmark and the evaluation toolkit. We hope this helps the community make progress on these challenging tasks in extracting structured data from visually rich documents

Research on Community Detection Algorithm Based on the UIR-Q

Aiming at the current problems of community detection algorithm in which user’s property is not used; the community structure is not stable and the efficiency of the algorithm is low, this paper proposes a community detection algorithm based on the user influence and its parallelization method. In terms of the concept of user influence in the subject communication and the PageRank algorithm, this paper uses the properties of nodes of users in social networks to form the user influence factors. Then, the user with the biggest influence is set as the initial node of new community and and the local modularity is introduced into detecting the community structure.  in order to make the result of community detection quick and efficient. Many experiments show that the improved algorithm can efficiently detect the community structure with large scale users and the results are stable. Therefore, this algorithm will have a wide applied prospect

The Magnetic Memory Effect of Ferromagnetic Materials in the Process of Stress-Magnetism Coupling

Ferromagnetic materials can produce the magnetic memory effect under stress. This provides a practical method to measure stress concentration. The relation between stress and magnetic characteristic is analyzed through energy balance theory. Force-magnetism coupling process of Fe-C crystal system is simulated by CASTEP software which is based on first principle. Electron band structure, electron density of states, and atomic magnetic moment in the process of force-magnetism coupling process are calculated. Experimental investigation of the magnetic memory effect of ferromagnetic material under different stresses has been undertaken in X52 pipeline. The results show that the magnetic characteristic of ferromagnetic material weakens under stress, and the magnetic memory signals intensity linearly decreases with the increasing stress. When material yields, the variation character of magnetic memory signals suddenly changes and the inflection points of the stress-B curves emerge. Experimental investigation is in agreement with the theoretical analysis

Associations of serum alkaline phosphatase level with all-cause and cardiovascular mortality in the general population

Background and aimsThere are few population studies on the associations of serum alkaline phosphatase (AlkP) with all-cause and cardiovascular mortality. We aimed to investigate the relevancy of serum AlkP with all-cause and cardiovascular mortality in the general population.Methods and resultsOur research included 34,147 adults in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014. Cox proportional hazards regression models were used to assess the associations of serum AlkP with all-cause and cardiovascular mortality. Mediation analysis was used to analyze mechanisms that might link serum AlkP to all-cause and cardiovascular mortality. After 139.7 ± 57.8 months of follow-up, 5413 participants experienced all-cause death and 1820 participants experienced cardiovascular death. Mortality rates per 1000 person-years from various diseases increased with increasing serum concentrations of AlkP, especially all-cause death, cerebrovascular disease and cardiovascular death. High serum AlkP level significantly increased all-cause and cardiovascular mortality. After multivariate adjustment, the highest AlkP group had the highest risk to experience all-cause (hazard ratio [HR] = 1.30, P < 0.001) and cardiovascular mortality (HR = 1.39, P < 0.001) than the lowest AlkP group. γ-glutamyl transpeptidase (GGT) (13.33% and 15.79%), followed by Vitamin D (8.33% and 7.14%) and C-reactive protein (CRP) (7.69% and 10.35%) were identified as possible major mediators.ConclusionHigher AlkP concentrations were associated with higher all-cause and cardiovascular mortality, largely related to mediated factors such as GGT, Vitamin D, and CRP. These findings suggest that lower serum AlkP level may reduce all-cause and cardiovascular mortality in general population

How to Construct Mutually Orthogonal Complementary Sets With Non-Power-of-Two Lengths?

Mutually orthogonal complementary sets (MOCSs) have received significant research attention in recent years due to their wide applications in communications and radar. Existing MOCSs which are constructed based on generalized Boolean functions (GBFs) mostly have lengths of power-of-two. How to construct MOCSs with non-power-of-two lengths whilst having large set sizes is a largely open problem. With the aid of GBFs, in this paper, we present new constructions of such MOCSs and show that the maximal achievable set size is 1/2 of the flock size of an MOCS