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

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

The Interactions Between Candida albicans and Mucosal Immunity

Mucosa protects the body against external pathogen invasion. However, pathogen colonies on the mucosa can invade the mucosa when the immunosurveillance is compromised, causing mucosal infection and subsequent diseases. Therefore, it is necessary to timely and effectively monitor and control pathogenic microorganisms through mucosal immunity. Candida albicans is the most prevalent fungi on the mucosa. The C. albicans colonies proliferate and increase their virulence, causing severe infectious diseases and even death, especially in immunocompromised patients. The normal host mucosal immune defense inhibits pathogenic C. albicans through stepwise processes, such as pathogen recognition, cytokine production, and immune cell phagocytosis. Herein, the current advances in the interactions between C. albicans and host mucosal immune defenses have been summarized to improve understanding on the immune mechanisms against fungal infections