Multiscale examination of strain effects in Nd-Fe-B permanent magnets

We have performed a combined first-principles and micromagnetic study on the strain effects in Nd-Fe-B magnets. First-principles calculations on Nd2Fe14B reveal that the magnetocrystalline anisotropy (K) is insensitive to the deformation along c axis and the ab in-plane shrinkage is responsible for the K reduction. The predicted K is more sensitive to the lattice deformation than what the previous phenomenological model suggests. The biaxial and triaxial stress states have a greater impact on K. Negative K occurs in a much wider strain range in the ab biaxial stress state. Micromagnetic simulations of Nd-Fe-B magnets using first-principles results show that a 3-4% local strain in a 2-nm-wide region near the interface around the grain boundaries and triple junctions leads to a negative local K and thus decreases the coercivity by ~60%. The local ab biaxial stress state is more likely to induce a large loss of coercivity. In addition to the local stress states and strain levels themselves, the shape of the interfaces and the intergranular phases also makes a difference in determining the coercivity. Smoothing the edge and reducing the sharp angle of the triple regions in Nd-Fe-B magnets would be favorable for a coercivity enhancement.Comment: 9 figure

Alkali burn induced corneal spontaneous pain and activated neuropathic pain matrix in the central nerve system in mice

Purpose: To explore whether alkali burn causes corneal neuropathic pain and activates neuropathic pain matrix in the central nerve system in mice. Methods: A corneal alkali burn mouse model (grade II) was used. Mechanical threshold in the cauterized area was tested using Von Frey hairs. Spontaneous pain behavior was investigated with conditioned place preference (CPP). Phosphor extracellular signal-regulated kinase (ERK), which is a marker for neuronal activation in chronic pain processing, was investigated in several representative areas of the neuropathic pain matrix: the two regions of the spinal trigeminal nucleus (subnucleus interpolaris/caudalis ,Vi/Vc; subnucleus caudalis/upper cervical cord , Vc/C1), insular cortex, anterior cingulated cortex (ACC), and the rostroventral medulla (RVM). Further, pharmacologically blocking pERK activation in ACC of alkali burn mice was performed in a separate study. Results: Corneal alkali burn caused long lasting damage to the corneal subbasal nerve fibers and mice exhibited spontaneous pain behavior. By testing in several representative areas of neuropathic pain matrix in the higher nerve system, phosphor extracellular signal-regulated kinase (ERK) was significantly activated in Vc/C1, but not in Vi/Vc. Also, ERK was activated in the insular cortex, ACC, and RVM. Furthermore, pharmacologically blocking ERK activation in ACC abolished alkali burn induced corneal spontaneous pain. Conclusion: Alkali burn could cause corneal spontaneous pain and activate neuropathic pain matrix in the central nerve system. Furthermore, activation of ERK in ACC is required for alkali burn induced corneal spontaneous pain

On Robustness and Bias Analysis of BERT-based Relation Extraction

Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks. However, the resultant model generalizability remains poorly understood. We do not know, for example, how excellent performance can lead to the perfection of generalization models. In this study, we analyze a fine-tuned BERT model from different perspectives using relation extraction. We also characterize the differences in generalization techniques according to our proposed improvements. From empirical experimentation, we find that BERT suffers a bottleneck in terms of robustness by way of randomizations, adversarial and counterfactual tests, and biases (i.e., selection and semantic). These findings highlight opportunities for future improvements. Our open-sourced testbed DiagnoseRE is available in \url{https://github.com/zjunlp/DiagnoseRE}.Comment: work in progres

Disentangled Contrastive Learning for Learning Robust Textual Representations

Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs. Intuitively, the representations should be similar in the feature space with subtle input permutations, while large variations occur with different meanings. This motivates us to investigate the learning of robust textual representation in a contrastive manner. However, it is non-trivial to obtain opposing semantic instances for textual samples. In this study, we propose a disentangled contrastive learning method that separately optimizes the uniformity and alignment of representations without negative sampling. Specifically, we introduce the concept of momentum representation consistency to align features and leverage power normalization while conforming the uniformity. Our experimental results for the NLP benchmarks demonstrate that our approach can obtain better results compared with the baselines, as well as achieve promising improvements with invariance tests and adversarial attacks. The code is available in https://github.com/zjunlp/DCL.Comment: Work in progres

Identification of Changes in Wheat (Triticum aestivum L.) Seeds Proteome in Response to Anti–trx s Gene

BACKGROUND: Thioredoxin h (trx h) is closely related to germination of cereal seeds. The cDNA sequences of the thioredoxin s (trx s) gene from Phalaris coerulescens and the thioredoxin h (trx h) gene from wheat are highly homologous, and their expression products have similar biological functions. Transgenic wheat had been formed after the antisense trx s was transferred into wheat, and it had been certified that the expression of trx h decreased in transgenic wheat, and transgenic wheat has high resistance to pre-harvest sprouting. METHODOLOGY/PRINCIPAL FINDINGS: Through analyzing the differential proteome of wheat seeds between transgenic wheat and wild type wheat, the mechanism of transgenic wheat seeds having high resistance to pre-harvest sprouting was studied in the present work. There were 36 differential proteins which had been identified by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS). All these differential proteins are involved in regulation of carbohydrates, esters, nucleic acid, proteins and energy metabolism, and biological stress. The quantitative real time PCR results of some differential proteins, such as trx h, heat shock protein 70, α-amylase, β-amylase, glucose-6-phosphate isomerase, 14-3-3 protein, S3-RNase, glyceraldehyde-3-phosphate dehydrogenase, and WRKY transcription factor 6, represented good correlation between transcripts and proteins. The biological functions of many differential proteins are consistent with the proposed role of trx h in wheat seeds. CONCLUSIONS/SIGNIFICANCE: A possible model for the role of trx h in wheat seeds germination was proposed in this paper. These results will not only play an important role in clarifying the mechanism that transgenic wheat has high resistance to pre-harvest sprouting, but also provide further evidence for the role of trx h in germination of wheat seeds

Decomposition of branching volume data by tip detection

We present an approach to decomposing branching volume data into sub-branches. First, a metric is proposed for evaluating local convexities in volumetric data, and it is a criterion for global selection of tip points. Second, a multi-path growing strategy is adopted to segment the volumes based on a DFS transformation starting from the tips. Experiments show that this approach is capable of generating desirable components and reasonable segmentation boundaries of a volume.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000265921401004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artificial IntelligenceEngineering, Electrical & ElectronicImaging Science & Photographic TechnologyCPCI-S(ISTP)

Epidemiology and spatial distribution of bluetongue virus in Xinjiang, China

Bluetongue (BT) is a non-contagious disease affecting domestic and wild ruminants. Outbreaks of BT can cause serious economic losses. To investigate the distribution characteristics of bluetongue virus (BTV), two large-scale censuses of BTV prevalence in Xinjiang, China were collected. Spatial autocorrelation analysis, including global spatial autocorrelation and local spatial autocorrelation, was performed. Risk areas for BTV occurrence in Xinjiang were detected using the presence-only maximum entropy model. The global spatial autocorrelation of BTV distribution in Xinjiang in 2012 showed a random pattern. In contrast, the spatial distribution of BTV from 2014 to 2015 was significantly clustered. The hotspot areas for BTV infection included Balikun County (p < 0.05), Yiwu County (p < 0.05) and Hami City (p < 0.05) in 2012. These three regions were also hotspot areas during 2014 and 2015. Sheep distribution (25.6% contribution), precipitation seasonality (22.1% contribution) and mean diurnal range (16.2% contribution) were identified as the most important predictors for BTV occurrence in Xinjiang. This study demonstrated the presence of high-risk areas for BTV infection in Xinjiang, which can serve as a tool to aid in the development of preventative countermeasures of BT outbreaks

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-frequent, coarse-grained, and time-invariant concepts from formal texts. In real applications, however, it is necessary to extract less-frequent, fine-grained, and time-varying conceptual knowledge and build taxonomy in an evolving manner. In this paper, we introduce an approach to implementing and deploying the conceptual graph at Alibaba. Specifically, We propose a framework called AliCG which is capable of a) extracting fine-grained concepts by a novel bootstrapping with alignment consensus approach, b) mining long-tail concepts with a novel low-resource phrase mining approach, c) updating the graph dynamically via a concept distribution estimation method based on implicit and explicit user behaviors. We have deployed the framework at Alibaba UC Browser. Extensive offline evaluation as well as online A/B testing demonstrate the efficacy of our approach.Comment: Accepted by KDD 2021 (Applied Data Science Track

On the origin of incoherent magnetic exchange coupling in MnBi/Fex_xCo1−x_{1-x} bilayer system

In this study we investigate the exchange coupling between the hard magnetic compound MnBi and the soft magnetic alloy FeCo including the interface structure between the two phases. Exchange spring MnBi-Fe$_x$Co$_{1-x}$ (x = 0.65 and 0.35) bilayers with various thicknesses of the soft magnetic layer were deposited onto quartz glass substrates in a DC magnetron sputtering unit from alloy targets. Magnetic measurements and density functional theory (DFT) calculations reveal that a Co-rich FeCo layer leads to more coherent exchange coupling. The optimum soft layer thickness is about 1 nm. In order to take into account the effect of incoherent interfaces with finite roughness, we have combined a cross-sectional High Resolution Transmission Electron Microscopy (HR-TEM) analysis with DFT calculations and micromagnetic simulations. The experimental results can be consistently described by modeling assuming a polycrystalline FeCo layer consisting of crystalline (110) and amorphous grains as confirmed by HR-TEM. The micromagnetic simulations show in general how the thickness of the FeCo layer and the interface roughness between the hard and soft magnetic phases both control the effectiveness of exchange coupling in an exchange spring system

Penaeid shrimp genome provides insights into benthic adaptation and frequent molting

Crustacea, the subphylum of Arthropoda which dominates the aquatic environment, is of major importance in ecology and fisheries. Here we report the genome sequence of the Pacific white shrimp Litopenaeus vannamei, covering similar to 1.66 Gb (scaffold N50 605.56 Kb) with 25,596 protein-coding genes and a high proportion of simple sequence repeats (>23.93%). The expansion of genes related to vision and locomotion is probably central to its benthic adaptation. Frequent molting of the shrimp may be explained by an intensified ecdysone signal pathway through gene expansion and positive selection. As an important aquaculture organism, L. vannamei has been subjected to high selection pressure during the past 30 years of breeding, and this has had a considerable impact on its genome. Decoding the L. vannamei genome not only provides an insight into the genetic underpinnings of specific biological processes, but also provides valuable information for enhancing crustacean aquaculture