Category-Specific CNN for Visual-aware CTR Prediction at JD.com

As one of the largest B2C e-commerce platforms in China, JD com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images.This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms

BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis

Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing them from mono audio has attracted increasing attention. This synthesis process involves not only the basic physical warping of the mono audio, but also room reverberations and head/ear related filtrations, which, however, are difficult to accurately simulate in traditional digital signal processing. In this paper, we formulate the synthesis process from a different perspective by decomposing the binaural audio into a common part that shared by the left and right channels as well as a specific part that differs in each channel. Accordingly, we propose BinauralGrad, a novel two-stage framework equipped with diffusion models to synthesize them respectively. Specifically, in the first stage, the common information of the binaural audio is generated with a single-channel diffusion model conditioned on the mono audio, based on which the binaural audio is generated by a two-channel diffusion model in the second stage. Combining this novel perspective of two-stage synthesis with advanced generative models (i.e., the diffusion models),the proposed BinauralGrad is able to generate accurate and high-fidelity binaural audio samples. Experiment results show that on a benchmark dataset, BinauralGrad outperforms the existing baselines by a large margin in terms of both object and subject evaluation metrics (Wave L2: 0.128 vs. 0.157, MOS: 3.80 vs. 3.61). The generated audio samples (https://speechresearch.github.io/binauralgrad) and code (https://github.com/microsoft/NeuralSpeech/tree/master/BinauralGrad) are available online.Comment: NeurIPS 2022 camera versio

Autologous Skin Fibroblast-Based PLGA Nanoparticles for Treating Multiorgan Fibrosis

Fibrotic diseases remain a substantial health burden with few therapeutic approaches. A hallmark of fibrosis is the aberrant activation and accumulation of myofibroblasts, which is caused by excessive profibrotic cytokines. Conventional anticytokine therapies fail to undergo clinical trials, as simply blocking a single or several antifibrotic cytokines cannot abrogate the profibrotic microenvironment. Here, biomimetic nanoparticles based on autologous skin fibroblasts are customized as decoys to neutralize multiple fibroblast-targeted cytokines. By fusing the skin fibroblast membrane onto poly(lactic-co-glycolic) acid cores, these nanoparticles, termed fibroblast membrane-camouflaged nanoparticles (FNPs), are shown to effectively scavenge various profibrotic cytokines, including transforming growth factor-beta, interleukin (IL)-11, IL-13, and IL-17, thereby modulating the profibrotic microenvironment. FNPs are sequentially prepared into multiple formulations for different administration routines. As a proof-of-concept, in three independent animal models with various organ fibrosis (lung fibrosis, liver fibrosis, and heart fibrosis), FNPs effectively reduce the accumulation of myofibroblasts, and the formation of fibrotic tissue, concomitantly restoring organ function and indicating that FNPs are a potential broad-spectrum therapy for fibrosis management.Peer reviewe

High sulfur loading and shuttle inhibition of advanced sulfur cathode enabled by graphene network skin and N, P, F-doped mesoporous carbon interfaces for ultra-stable lithium sulfur battery

Achieving high loading of active sulfur yet rational regulating the shuttle effect of lithium polysulfide (LiPS) is of great significance in pursuit of high-performance lithium-sulfur (Li-S) battery. Herein, we develop a free-standing graphene-nitrogen (N), phosphorus (P) and fluorine (F) co-doped mesoporous carbon-sulfur (G-NPFMC-S) film, which was used as a binder-free cathode in Li-S battery. The developed mesoporous carbon (MC) achieved a high specific surface area of 921 m2·g–1 with a uniform pore size distribution of 15 nm. The inserted graphene network inside G-NPFMC-S cathode can effectively improve its electrical conductivity and simultaneously restrict the shuttle of LiPS. A high sulfur loading of 86% was achieved due to the excellent porous structures of graphene-NPFMC (G-NPFMC) composite. When implemented as a freestanding cathode in Li-S battery, this G-NPFMC-S achieved a high specific capacity (1,356 mAh·g–1), favorable rate capability, and long-term cycling stability up to 500 cycles with a minimum capacity fading rate of 0.025% per cycle, outperforming the corresponding performances of NPFMC-sulfur (NPFMC-S) and MC-sulfur (MC-S). These promising results can be ascribed to the featured structures that formed inside G-NPFMC-S film, as that highly porous NPFMC can provide sufficient storage space for the loading of sulfur, while, the N, P, F-doped carbonic interface and the inserted graphene network help hinder the shuttle of LiPS via chemical adsorption and physical barrier effect. This proposed unique structure can provide a bright prospect in that high mass loading of active sulfur and restriction the shuttle of LiPS can be simultaneously achieved for Li-S battery

Multifunctional Biomimetic Nanovaccines Based on Photothermal and Weak-Immunostimulatory Nanoparticulate Cores for the Immunotherapy of Solid Tumors

An alternative strategy of choosing photothermal and weak-immunostimulatory porous silicon@Au nanocomposites as particulate cores to prepare a biomimetic nanovaccine is reported to improve its biosafety and immunotherapeutic efficacy for solid tumors. A quantitative analysis method is used to calculate the loading amount of cancer cell membranes onto porous silicon@Au nanocomposites. Assisted with foreign-body responses, these exogenous nanoparticulate cores with weak immunostimulatory effect can still efficiently deliver cancer cell membranes into dendritic cells to activate them and the downstream antitumor immunity, resulting in no occurrence of solid tumors and the survival of all immunized mice during 55 day observation. In addition, this nanovaccine, as a photothermal therapeutic agent, synergized with additional immunotherapies can significantly inhibit the growth and metastasis of established solid tumors, via the initiation of the antitumor immune responses in the body and the reversion of their immunosuppressive microenvironments. Considering the versatile surface engineering of porous silicon nanoparticles, the strategy developed here is beneficial to construct multifunctional nanovaccines with better biosafety and more diagnosis or therapeutic modalities against the occurrence, recurrence, or metastasis of solid tumors in future clinical practice.Peer reviewe

Multifunctional Biomimetic Nanovaccines Based on Photothermal and Weak-Immunostimulatory Nanoparticulate Cores for the Immunotherapy of Solid Tumors

An alternative strategy of choosing photothermal and weak-immunostimulatory porous silicon@Au nanocomposites as particulate cores to prepare a biomimetic nanovaccine is reported to improve its biosafety and immunotherapeutic efficacy for solid tumors. A quantitative analysis method is used to calculate the loading amount of cancer cell membranes onto porous silicon@Au nanocomposites. Assisted with foreign-body responses, these exogenous nanoparticulate cores with weak immunostimulatory effect can still efficiently deliver cancer cell membranes into dendritic cells to activate them and the downstream antitumor immunity, resulting in no occurrence of solid tumors and the survival of all immunized mice during 55 day observation. In addition, this nanovaccine, as a photothermal therapeutic agent, synergized with additional immunotherapies can significantly inhibit the growth and metastasis of established solid tumors, via the initiation of the antitumor immune responses in the body and the reversion of their immunosuppressive microenvironments. Considering the versatile surface engineering of porous silicon nanoparticles, the strategy developed here is beneficial to construct multifunctional nanovaccines with better biosafety and more diagnosis or therapeutic modalities against the occurrence, recurrence, or metastasis of solid tumors in future clinical practice