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Background: Hypoxia/reoxygenation (H/R)-mediated apoptosis and inflammation are major causes of tissue injury in acute myocardial infarction (AMI). Exploring the underlying mechanisms of cardiomyocyte injury induced by H/R is important for AMI treatment. Circular RNAs have been demonstrated to paly vital roles in the pathogenesis of AMI. Our study aimed to explore the function of circular RNA UBXN7 (circUBXN7) in regulating H/R-induced cardiomyocyte injury. Methods: H/R-treated H9c2 cells and a mouse model of AMI were used to investigate the function of circUBXN7 in H/R damage and AMI. The expressions of circUNXN7, miR-622 and MCL1 were analyzed by RT-qPCR. CCK-8 was used for examining cell viability. Cell apoptosis was evaluated with caspase 3 activity and Annexin V/PI staining. MCL1, Bax, Bcl-2 and cleaved-caspase 3 were examined with western blot. ELISA was used to examine the secretion of IL-6, TNF-α and IL-1β. Results: CircUBXN7 was downregulated in patients and mice with AMI, as well as in H/R-treated cells. Overexpression of circUBXN7 mitigated H/R-mediated apoptosis and secretion of inflammatory factors including IL-6, TNF-α and IL-1β. CircUBXN7 suppressed cell apoptosis and inflammatory reaction induced by H/R via targeting miR-622. MiR-622 targeted MCL1 to restrain its expression in H9c2 cells. Knockdown of MCL1 abrogated circUBXN7-mediated alleviation of apoptosis and inflammation after H/R treatment. Conclusion: CircUBXN7 mitigates cardiomyocyte apoptosis and inflammatory reaction in H/R injury by targeting miR-622 and maintaining MCL1 expression. Our study provides novel potential therapeutic targets for AMI treatment.

A new non-fullerene small molecule acceptor IDT2ST-4F bearing a bisalkylthiothiophene unit as the π-bridge was designed and synthesized, which exhibited a low optical bandgap of 1.43 eV. The optimized organic solar cells based on PBDB-T:IDT2ST-4F gave a high power conversion efficiency (PCE) of 11.43% with a relatively low energy loss of 0.58 eV.

A series of non-fullerene acceptors based on the indeno[1,2-b]fluorene central moiety, with thiophene or 3-octylthiophene as π-bridge and either non-fluorinated or fluorinated 2-(3-oxo-2,3-dihydro-1H-inden-1-ylidene) malononitrile as end-capping groups (namely ICBF-O, ICBF, FICBF-O and FICBF), have been designed and synthesized. The effect of alkylation of the π-bridge and fluorination of the end-capping groups on the absorption spectra, energy levels, active layer morphology and photovoltaic performance were systematically investigated. Alkylation upshifts the molecular LUMO levels and thereby a high open circuit voltage (Voc) of 1.06 V was obtained. However, the larger band gap induced by alkylation led to lower short circuit current (Jsc). Fluorinated acceptors display lower Voc but higher Jsc and FF compared with non-fluorinated acceptors, coinciding with their lower LUMO levels, narrower band gaps and favourable morphology. As a result, the non-fullerene solar cells (NFSCs) based on FICBF showed the highest PCE of 7.41% among these four acceptors. The ICBF based device delivered a comparatively high Voc of 0.99 V and a PCE of 6.07%. The results demonstrated that indeno[1,2-b]fluorene is a promising building block for efficient NFSCs.

A new A2–π–A1–π–A2-type small molecule denoted as BDD–IN was synthesized employing a strong electron-withdrawing unit, 3-bis(4-(2-ethylhexyl)-thiophen-2-yl)-5,7-bis(2-ethylhexyl)benzo[1,2:4,5-c′]-dithiophene-4,8-dione (BDD) as the central acceptor unit (A1), thiophene-alkoxy benzene-thiophene as the π-bridge, and indenedione (IN) as the end-capped unit (A2). An optimal power conversion efficiency (PCE) of 8.70% with a high open-circuit voltage (Voc) of 0.965 V and a high fill factor (FF) of 72.3% was achieved.

In this paper we develop a novel recommendation model that explicitly incorporates time information. The model relies on an embedding layer and TSL attention-like mechanism with inner products in different vector spaces, that can be thought of as a modification of multi-headed attention. This mechanism allows the model to efficiently treat sequences of user behavior of different length. We study the properties of our state-of-the-art model on statistically designed data set. Also, we show that it outperforms more complex models with longer sequence length on the Taobao User Behavior dataset., 17 pages, 7 figures

Remarkable progress has been achieved in non-fullerene polymer organic solar cells. However, most all-small-molecule solar cells (All-SMSCs) exhibit poor performance. In this work, two small molecule (SM) donors using thieno[3,2-b]thiophene (TT) or thiophene as π-bridge to link dithienosilole (DTS) and the terminal group in conjunction with the SM acceptor IDT-C8 were employed for the fabrication of All-SMSCs. The effect of different π-bridges of the donors on the photovoltaic performance was systematically investigated. DINTTDTS which consists of TT unit presents red shift and stronger crystallinity compared with DINDTS containing a thiophene linker. However, DINDTS film shows a better complementary absorption to that of IDT-C8. All-SMSCs based on DINTTDTS give power conversion efficiency (PCE) of 2.79%. However, All-SMSCs made with DINDTS achieve a higher PCE of 4.52%, which are mainly due to the better complementary absorption between DINDTS and IDT-C8 and its better morphology of active layers.

Two new acceptor-donor-acceptor small molecules DINDTS and DINCNDTS, with dithienosilole as a core unit and 1,3-indanedione (IN) or malononitrile derivative 1,3-indanedione (INCN) units as end-capped groups, respectively, have been designed and synthesized for solution-processable bulk-heterojunction (BHJ) solar cells. The impact of these two end-capped groups on their optical, electrochemical properties and photovoltaic performance was systematically studied. The optimal DINDTS:PC 71 BM based solar cells showed a short-circuit current density ( J sc ) of 13.50 mA cm −2 and power conversion efficiency (PCE) of 6.60%. However, DINCNDTS:PC 71 BM based devices exhibited a poor PCE of 0.58% with a very low J sc of 1.82 mA cm −2 , which are mainly due to its poor morphology of active layers.

With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their need to handle categorical features and are not well studied or understood. In this paper, we develop a state-of-the-art deep learning recommendation model (DLRM) and provide its implementation in both PyTorch and Caffe2 frameworks. In addition, we design a specialized parallelization scheme utilizing model parallelism on the embedding tables to mitigate memory constraints while exploiting data parallelism to scale-out compute from the fully-connected layers. We compare DLRM against existing recommendation models and characterize its performance on the Big Basin AI platform, demonstrating its usefulness as a benchmark for future algorithmic experimentation and system co-design., Comment: 10 pages, 6 figures

Detecting and describing objects is one of the fundamental challenges in computer vision. Teaching computers to find and parse objects in the images is an interesting artificial intelligence problem in its own right, and a working technique also has enormous potential to benefit a lot of other computer vision tasks. In this thesis, we focus on three highly correlated tasks for detecting and describing objects, i.e., object detection, object parsing and human pose estimation, and propose a series of novel methods for these tasks.The first step to recognize an object is arguably to localize it. We start from studying the role of context for object detection and semantic segmentation in the wild. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category, and propose a novel deformable part-based contextual reasoning method. We show that this method significantly helps in detecting objects. Parsing objects into semantic body parts is important for understanding them further. We propose novel graphical model based approaches to describe objects in terms of the semantic body parts. In order to study different representation learning methods, we also study an end-to-end Deep Convolutional Neural Networks (DCNNs) based method to model the relationship between object and body parts in a holistic manner. For training and evaluating our methods, we provide fully annotated object parts for PASCAL VOC 2010.Human is one of the most important objects. It is crucial to teach computers to understand different poses of human. We present a method for estimating human pose based on a graphical model with novel pairwise relations that make adaptive use of local image measurements. We make novel use of the DCNNs and combine their statistical power with the representational flexibility of graphical models. To parse humans when there is significant occlusion. We further propose a novel method for learning occlusion cues, and exploit the fact that occlusions often occur in regular patterns. We evaluate these models on popular benchmark datasets and show significant performance improvements over the state of the arts.

In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization. For this purpose, we annotate semantic parts for all 20 object categories on the PASCAL VOC 2012 dataset, which provides information on object pose, occlusion, viewpoint and functionality. DeePM is a latent graphical model based on the state-of-the-art R-CNN framework, which learns an explicit representation of the object-part configuration with flexible type sharing (e.g., a sideview horse head can be shared by a fully-visible sideview horse and a highly truncated sideview horse with head and neck only). For comparison, we also present an end-to-end Object-Part (OP) R-CNN which learns an implicit feature representation for jointly mapping an image ROI to the object and part bounding boxes. We evaluate the proposed methods for both the object and part detection performance on PASCAL VOC 2012, and show that DeePM consistently outperforms OP R-CNN in detecting objects and parts. In addition, it obtains superior performance to Fast and Faster R-CNNs in object detection., the final revision to ICLR 2016, in which some color errors in the figures are fixed

Base-detail separation is a fundamental computer vision problem consisting of modeling a smooth base layer with the coarse structures, and a detail layer containing the texture-like structures. One of the challenges of estimating the base is to preserve sharp boundaries between objects or parts to avoid halo artifacts. Many methods have been proposed to address this problem, but there is no ground-truth dataset of real images for quantitative evaluation. We proposed a procedure to construct such a dataset, and provide two datasets: Pascal Base-Detail and Fashionista Base-Detail, containing 1000 and 250 images, respectively. Our assumption is that the base is piecewise smooth and we label the appearance of each piece by a polynomial model. The pieces are objects and parts of objects, obtained from human annotations. Finally, we proposed a way to evaluate methods with our base-detail ground-truth and we compared the performances of seven state-of-the-art algorithms., Comment: This paper has been withdrawn by the author due to some un-proper examples

This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior that, even in presence of occlusion, the visible nodes form a connected subtree of the graphical model. We call each connected subtree a flexible composition of object parts. This involves a novel method for learning occlusion cues. During inference we need to search over a mixture of different flexible models. By exploiting part sharing, we show that this inference can be done extremely efficiently requiring only twice as many computations as searching for the entire object (i.e., not modeling occlusion). We evaluate our model on the standard benchmarked "We Are Family" Stickmen dataset and obtain significant performance improvements over the best alternative algorithms., CVPR 15 Camera Ready

We present a method for estimating articulated human pose from a single static image based on a graphical model with novel pairwise relations that make adaptive use of local image measurements. More precisely, we specify a graphical model for human pose which exploits the fact the local image measurements can be used both to detect parts (or joints) and also to predict the spatial relationships between them (Image Dependent Pairwise Relations). These spatial relationships are represented by a mixture model. We use Deep Convolutional Neural Networks (DCNNs) to learn conditional probabilities for the presence of parts and their spatial relationships within image patches. Hence our model combines the representational flexibility of graphical models with the efficiency and statistical power of DCNNs. Our method significantly outperforms the state of the art methods on the LSP and FLIC datasets and also performs very well on the Buffy dataset without any training., Comment: NIPS 2014 Camera Ready