Ferroelectricity and related effects on carrier transport in type-II Weyl semimetal WTe2_{2} thin film

We investigate ferroelectric polariation as well as the formation of long-range order and the carrier density distribution in type-II Weyl semimetal WTe$_{2}$ in $T_{d}$ phase. It is been found that the metallicity and ferroelectricity can coexist in bulk WTe$_{2}$ which has a significant impact on the electrical transport\cite{Sharma}, despite its large conductance. Also, our theoretical calculation and numerical simulation provide a deeper insight to the electrical structure-dependent dynamics of WTe$_{2}$. Base on the two-level approximation verify that the polarization stems from uncompensated out-of-plane interband transition of the electrons, which is base on the calculations of the dipole transition moment (in both the momentum space and frequency domain), and we found that the topological character of type-II Weyl system is closely related to the electronic behaviors (like the carrier compensation) and the excitations near the Weyl cone. The anisotropy and the topologically protected spin-polarized bulk (Weyl orbit) and surface states in WTe$_{2}$ induce hysteresis, which exhibitspotential in applications of non-volatile energy-efficient data-storage devices. Part of the properties of WTe$_{2}$ are also shares shared by the thermoelectric properties with other two-dimensional transition-metal dichalcogenides, like the WSe$_{2}$ and MoTe$_{2}$

Improving Outfit Recommendation with Co-supervision of Fashion Generation

The task of fashion recommendation includes two main challenges: visual understanding and visual matching. Visual understanding aims to extract effective visual features. Visual matching aims to model a human notion of compatibility to compute a match between fashion items. Most previous studies rely on recommendation loss alone to guide visual understanding and matching. Although the features captured by these methods describe basic characteristics (e.g., color, texture, shape) of the input items, they are not directly related to the visual signals of the output items (to be recommended). This is problematic because the aesthetic characteristics (e.g., style, design), based on which we can directly infer the output items, are lacking. Features are learned under the recommendation loss alone, where the supervision signal is simply whether the given two items are matched or not. To address this problem, we propose a neural co-supervision learning framework, called the FAshion Recommendation Machine (FARM). FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information. FARM enhances visual matching by introducing a novel layer-to-layer matching mechanism to fuse aesthetic information more effectively, and meanwhile avoiding paying too much attention to the generation quality and ignoring the recommendation performance. Extensive experiments on two publicly available datasets show that FARM outperforms state-of-the-art models on outfit recommendation, in terms of AUC and MRR. Detailed analyses of generated and recommended items demonstrate that FARM can encode better features and generate high quality images as references to improve recommendation performance

Current Status of the Open Abdomen Treatment for Intra-Abdominal Infection

The open abdomen has become an important approach for critically ill patients who require emergent abdominal surgical interventions. This treatment, originating from the concept of damage control surgery, was first applied in severe traumatic patients. The ultimate goal is to achieve formal abdominal fascial closure by several attempts and adjuvant therapies (fluid management, nutritional support, skin grafting, etc.). Up to the present, open abdomen therapy becomes matured and is multistage-approached in the management of patients with severe trauma. However, its application in patients with intra-abdominal infection still presents great challenges due to critical complications and poor clinical outcomes. This review focuses on the specific use of the open abdomen in such populations and detailedly introduces current concerns and advanced progress about this therapy

Three complete chloroplast genomes from two north American Rhus species and phylogenomics of Anacardiaceae

Background: The suamc genus Rhus (sensu stricto) includes two subgenera, Lobadium (ca. 25 spp.) and Rhus (ca. 10 spp.). Their members, R. glabra and R. typhina (Rosanae: Sapindales: Anacardiaceae), are two economic important species. Chloroplast genome information is of great significance for the study of plant phylogeny and taxonomy. Results: The three complete chloroplast genomes from two Rhus glabra and one R. typhina accessions were obtained with a total of each about 159k bp in length including a large single-copy region (LSC, about 88k bp), a small single-copy regions (SSC, about 19k bp) and a pair of inverted repeats regions (IRa/IRb, about 26k bp), to form a canonical quadripartite structure. Each genome contained 88 protein-coding genes, 37 transfer RNA genes, eight ribosomal RNA genes and two pseudogenes. The overall GC content of the three genomes all were same (37.8%), and RSCU values showed that they all had the same codon prefers, i.e., to use codon ended with A/U (93%) except termination codon. Three variable hotspots, i.e., ycf4-cemA, ndhF-rpl32-trnL and ccsA-ndhD, and a total of 152–156 simple sequence repeats (SSR) were identified. The nonsynonymous (Ka)/synonymous (Ks) ratio was calculated, and cemA and ycf2 genes are important indicators of gene evolution. The phylogenetic analyses of the family Anacardiaceae showed that the eight genera were grouped into three clusters, and supported the monophyly of the subfamilies and all the genera. The accessions of five Rhus species formed four clusters, while, one individual of R. typhina grouped with the R. glabra accessions instead of clustering into the two other individuals of R. typhina in the subgenus Rhus, which showed a paraphyletic relationship. Conclusions: Comparing the complete chloroplast genomes of the Rhus species, it was found that most SSRs were A/T rich and located in the intergenic spacer, and the nucleotide divergence exhibited higher levels in the non-coding region than in the coding region. The Ka/Ks ratio of cemA gene was > 1 for species collected in America, while it was < 1 for other species in China, which dedicated that the Rhus species from North America and East Asia have different evolutionary pressure. The phylogenetic analysis of the complete chloroplast genome clarified the Rhus placement and relationship. The results obtained in this study are expected to provide valuable genetic resources to perform species identification, molecular breeding, and intraspecific diversity of the Rhus speciesThis work was supported by the National Natural Science Foundation of China (31870366), Shanxi International Science and Technology Cooperation Project (201803D421051), Research Project Supported by Shanxi Scholarship Council of China (2020-018), the National High Technology Research and Development “863” Program (2014AA021802

A Self-Correcting Sequential Recommender

Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction.We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences