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Drawing on social exchange theory, our study aims to examine how age-inclusive human resource (HR) practices affect work engagement by shaping the age-diversity climate and perceived organizational support (POS). We hypothesize that diversity beliefs play a moderating role in the relationship between age-inclusive HR practices and POS. Our analysis of a sample of 983 employees from 48 organizations in China highlights the direct impact of age-inclusive HR practices on work engagement. Moreover, age-diversity climate and POS mediate the association between age-inclusive HR practices and work engagement. We further demonstrate that diversity beliefs play a moderating role in the association between age-inclusive HR practices and POS. Our findings not only contribute to the literature but also provide practical implications for managing an aging workforce.

Capturing long-range dependencies during feature extraction is crucial for video-based person re-identification (re-id) since it would help to tackle many challenging problems such as occlusion and dramatic pose variation. Moreover, capturing subtle differences, such as bags and glasses, is indispensable to distinguish similar pedestrians. In this paper, we propose a novel and efficacious Statistic Attention (SA) block which can capture both the long-range dependencies and subtle differences. SA block leverages high-order statistics of feature maps, which contain both long-range and high-order information. By modeling relations with these statistics, SA block can explicitly capture long-range dependencies with less time complexity. In addition, high-order statistics usually concentrate on details of feature maps and can perceive the subtle differences between pedestrians. In this way, SA block is capable of discriminating pedestrians with subtle differences. Furthermore, this lightweight block can be conveniently inserted into existing deep neural networks at any depth to form Statistic Attention Network (SANet). To evaluate its performance, we conduct extensive experiments on two challenging video re-id datasets, showing that our SANet outperforms the state-of-the-art methods. Furthermore, to show the generalizability of SANet, we evaluate it on three image re-id datasets and two more general image classification datasets, including ImageNet. The source code is available at http://vipl.ict.ac.cn/resources/codes/code/SANet code.zip.

TGFβ pretreatment decreases NK cell cytotoxicity against o-HSV infected glioblastoma cell lines and patient-derived glioblastoma stem-like cells (S1); TGFβ mediated inhibition of NK cell and macrophages/microglia intracranial infiltration persists 2 days after oHSV infection (S2); TGFβ pretreatment decreases spleen NK cell activity in 4C8 B6D2F1 mouse model in the presence of oHSV (S3); Virus titer, macrophages/microglia infiltration levels remain similar after NK cell depletion with an asialo-GM1 antibody (S4); A single dose TGFβ treatment in the absence of oHSV is not oncogenic (S5).

Description of additional methods and procedures used in the study. Also includes Supplementary References.

Background A significant roadblock in surgical education research has been the inability to compare trainee performance to the outcomes of those surgeons after they enter independent practice. We describe the feasibility of an innovative method to link trainee performance data with patient outcomes. Methods We extracted surgeon NPI numbers from Medicare claims data for common general surgery procedures between 2007 and 2017. Next, American Board of Surgery (ABS) trainee performance data was cross-referenced with additional resources to supplement NPI data. The patient and trainee datasets were linked using NPI number and a linkage rate was calculated. Results We identified 12,952 unique surgeons in the Medicare file. Medicare surgeons were matched with ABS records by NPI number, with 96.2% (n = 12,460) of surgeons linked successfully. Conclusions We demonstrated a novel process to link patient outcomes to trainee performance. This innovation can enable future research investigating the relationship between surgical trainee performance and patient outcomes in independent practice.

Person reidentification (reID) by convolutional neural network (CNN)-based networks has achieved favorable performance in recent years. However, most of existing CNN-based methods do not take full advantage of spatial–temporal context modeling. In fact, the global spatial–temporal context can greatly clarify local distractions to enhance the target feature representation. To comprehensively leverage the spatial–temporal context information, in this work, we present a novel block, interaction–aggregation-update (IAU), for high-performance person reID. First, the spatial–temporal IAU (STIAU) module is introduced. STIAU jointly incorporates two types of contextual interactions into a CNN framework for target feature learning. Here, the spatial interactions learn to compute the contextual dependencies between different body parts of a single frame, while the temporal interactions are used to capture the contextual dependencies between the same body parts across all frames. Furthermore, a channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts. Therefore, the IAU block enables the feature to incorporate the globally spatial, temporal, and channel context. It is lightweight, end-to-end trainable, and can be easily plugged into existing CNNs to form IAUnet. The experiments show that IAUnet performs favorably against state of the art on both image and video reID tasks and achieves compelling results on a general object categorization task. The source code is available at https://github.com/blue-blue272/ImgReID-IAnet .

Hereditary angioedema (HAE) is a rare and potentially life-threatening disease that affects an estimated 1 in 50 000 individuals worldwide. Until recently, prophylactic HAE treatment options were limited to injectables, a burdensome administration route that has driven the need for an oral treatment. A substantial body of evidence has shown that potent and selective plasma kallikrein inhibitors that block the generation of bradykinin represent a promising approach for the treatment of HAE. Berotralstat (BCX7353, discovered by BioCryst Pharmaceuticals using a structure-guided drug design strategy) is a synthetic plasma kallikrein inhibitor that is potent and highly selective over other structurally related serine proteases. This once-daily, small-molecule drug is the first orally bioavailable prophylactic treatment for HAE attacks, having successfully completed a Phase III clinical trial (meeting its primary end point) and recently receiving the U.S. Food and Drug Administration's approval for the prophylactic treatment of HAE attacks in patients 12 years and older.

In this paper, we address the semantic segmentation task with a new context aggregation scheme named object context, which focuses on enhancing the role of object information. Motivated by the fact that the category of each pixel is inherited from the object it belongs to, we define the object context for each pixel as the set of pixels that belong to the same category as the given pixel in the image. We use a binary relation matrix to represent the relationship between all pixels, where the value one indicates the two selected pixels belong to the same category and zero otherwise. We propose to use a dense relation matrix to serve as a surrogate for the binary relation matrix. The dense relation matrix is capable to emphasize the contribution of object information as the relation scores tend to be larger on the object pixels than the other pixels. Considering that the dense relation matrix estimation requires quadratic computation overhead and memory consumption w.r.t. the input size, we propose an efficient interlaced sparse self-attention scheme to model the dense relations between any two of all pixels via the combination of two sparse relation matrices. To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling (Zhao et al. 2017) and atrous spatial pyramid pooling (Chen et al. 2018). We empirically show the advantages of our approach with competitive performances on five challenging benchmarks including: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff.

Objective Surgical trainees are subject to pressure from variety of stakeholders to secure board certification from the American Board of Surgery (ABS). To meet these expectations, trainees must pass a written qualifying exam (QE) and an oral certifying exam (CE) within 7 years of completing general surgery residency. Board certification outcomes for candidates who fail either the QE or CE examination are not well characterized, but this information could help candidates, policymakers, and other stakeholders make informed decisions about how to respond to examination failure. Methods We retrospectively examined ABS records for all surgeons who completed general surgery residency from 2000 to 2013 and attempted general surgery board certification. Results Among 14,483 surgeons who attempted general surgery certification, 13,566 (94%) passed both the QE and CE within the 7-year certification window. Of those who did ultimately obtain certification, 97% passed the QE within 2 attempts and 97% passed the CE within 2 attempts. For those who failed either the QE or the CE twice, 67% ultimately obtained certification. Conclusions Most surgeons who obtained ABS general surgery board certification did so within 2 attempts at each board examination. Candidates who fail either examination twice are less likely to achieve board certification.

State-of-the-art classification models are usually considered as black boxes since their decision processes are implicit to humans. On the contrary, human experts classify objects according to a set of explicit hierarchical criteria. For example, “ tabby is a domestic cat with stripes, dots, or lines”, where tabby is defined by combining its superordinate category ( domestic cat ) and some certain attributes (e.g., has stripes). Inspired by this mechanism, we propose an interpretable Hierarchical Criteria Network (HCN) by additionally learning such criteria. To achieve this goal, images and semantic entities (e.g., taxonomies and attributes) are embedded into a common space, where each category can be represented by the linear combination of its superordinate category and a set of learned discriminative attributes. Specifically, a two-stream convolutional neural network (CNN) is elaborately devised, which embeds images and taxonomies with the two streams respectively. The model is trained by minimizing the prediction error of hierarchy labels on both streams. Extensive experiments on two widely studied datasets (CIFAR-100 and ILSVRC) demonstrate that HCN can learn meaningful attributes as well as reasonable and interpretable classification criteria. Therefore, the proposed method enables further human feedback for model correction as an additional benefit.

Face presentation attack detection (PAD) is essential for securing the widely used face recognition systems. Most of the existing PAD methods do not generalize well to unseen scenarios because labeled training data of the new domain is usually not available. In light of this, we propose an unsupervised domain adaptation with disentangled representation (DR-UDA) approach to improve the generalization capability of PAD into new scenarios. DR-UDA consists of three modules, i.e., ML-Net, UDA-Net and DR-Net. ML-Net aims to learn a discriminative feature representation using the labeled source domain face images via metric learning. UDA-Net performs unsupervised adversarial domain adaptation in order to optimize the source domain and target domain encoders jointly, and obtain a common feature space shared by both domains. As a result, the source domain PAD model can be effectively transferred to the unlabeled target domain for PAD. DR-Net further disentangles the features irrelevant to specific domains by reconstructing the source and target domain face images from the common feature space. Therefore, DR-UDA can learn a disentangled representation space which is generative for face images in both domains and discriminative for live vs. spoof classification. The proposed approach shows promising generalization capability in several public-domain face PAD databases.

Location is an important distinguishing information for instance segmentation. In this paper, we propose a novel model, called Location Sensitive Network (LSNet), for human instance segmentation. LSNet integrates instance-specific location information into one-stage segmentation framework. Specifically, in the segmentation branch, Pose Attention Module (PAM) encodes the location information into the attention regions through coordinates encoding. Based on the location information provided by PAM, the segmentation branch is able to effectively distinguish instances in feature-level. Moreover, we propose a combination operation named Keypoints Sensitive Combination (KSCom) to utilize the location information from multiple sampling points. These sampling points construct the points representation for instances via human keypoints and random points. Human keypoints provide the spatial locations and semantic information of the instances, and random points expand the receptive fields. Based on the points representation for each instance, KSCom effectively reduces the mis-classified pixels. Our method is validated by the experiments on public datasets. LSNet-5 achieves 56.2 mAP at 18.5 FPS on COCOPersons. Besides, the proposed method is significantly superior to its peers in the case of severe occlusion.

OBJECTIVE To determine if initial American Board of Surgery certification in general surgery is associated with better risk-adjusted patient outcomes for Medicare patients undergoing partial colectomy by an early career surgeon. BACKGROUND Board certification is a voluntary commitment to professionalism, continued learning, and delivery of high-quality patient care. Not all surgeons are certified, and some have questioned the value of certification due to limited evidence that board-certified surgeons have better patient outcomes. In response, we examined the outcomes of certified versus noncertified early career general surgeons. METHODS We identified Medicare patients who underwent a partial colectomy between 2008 and 2016 and were operated on by a non-subspecialty trained surgeon within their first 5 years of practice. Surgeon certification status was determined using the American Board of Surgery data. Generalized linear mixed models were used to control for patient-, procedure-, and hospital-level effects. Primary outcomes were the occurrence of severe complications and occurrence of death within 30 days. RESULTS We identified 69,325 patients who underwent a partial colectomy by an early career general surgeon. The adjusted rate of severe complications after partial colectomy by certified (n = 4239) versus noncertified (n = 191) early-career general surgeons was 9.1% versus 10.7% (odds ratio 0.83, P = 0.03). Adjusted mortality rate for certified versus noncertified early-career general surgeons was 4.9% versus 6.1% (odds ratio 0.79, P = 0.01). CONCLUSION Patients undergoing partial colectomy by an early career general surgeon have decreased odds of severe complications and death when their surgeon is board certified.

Learning the conjunction of multiple visual concepts shows practical significance in various real world applications (e.g. multi-attribute image retrieval and visual relationship detection). In this paper, we propose Concept Conjunction Recurrent Neural Network (C2RNN) to tackle this problem. With our model, visual concepts involved in a conjunction are mapped into the hidden units and combined in a recurrent way to generate the representation of the concept conjunction, which is then used to compute a concept conjunction classifier as the output. We also present an order invariant version of the proposed method based on attention mechanism to learn the tasks without pre-defined concept order. To tackle concept conjunction learning from multiple semantic domains, we introduce a multiplicative framework to learn the joint representation. Experimental results on multi-attribute image retrieval and visual relationship detection show that our method achieves significantly better performance than other related methods on various datasets.

In this work, we study the problem of deep representation learning on a large face dataset with long-tail distribution. Training convolutional neural networks on such dataset with conventional strategy suffers from imbalance problem which results in biased classification boundary, and the few-shot classes lying in tail parts further make the model prone to overfitting. Aiming to learn more discriminative CNN model from long-tail data, we propose a novel aggregate-and-disperse training schema. Firstly, our proposed method aggregates similar classes in tail part to avoid imbalance problem. Based on the aggregated super classes and those original head classes, a model is pre-trained to capture accurate discrimination in head classes as well as coarse discrinimation in tail classes. Secondly, we selectively disperses those aggregated super classes to learn precise inter-class variations and refine the representation for better generalization. We perform extensive experiments on MS-Celeb-1M, BLUFR and MegaFace. Compared with baselines and existing methods, our method achieves better performance of face recognition, demonstrating its effectiveness of handling long-tail distribution.

Functionality is a fundamental attribute of an object which indicates the capability to be used to perform specific actions. It is critical to empower robots the functionality knowledge in discovering appropriate objects for a task e.g. cut cake using knife. Existing research works have focused on understanding object functionality through human-object-interaction from extensively annotated image or video data and are hard to scale up. In this paper, we (1) mine object-functionality knowledge through pattern-based and model-based methods from text, (2) introduce a novel task on physical object-functionality prediction, which consumes an image and an action query to predict whether the object in the image can perform the action, and (3) propose a method to leverage the mined functionality knowledge for the new task. Our experimental results show the effectiveness of our methods.

Due to the highly complex semantic information of images, even with the same query image, the expected content-based image retrieval results could be very different and personalized in different scenarios. However, most existing hashing methods only preserve one single type of semantic similarity, making them incapable of addressing such realistic retrieval tasks. To deal with this problem, we propose a unified hashing framework to encode multiple types of information into the binary codes by exploiting convolutional networks (CNNs). Specifically, we assume that typical retrieval tasks are generally defined in two aspects, i.e. high-level semantics (e.g. object categories) and visual attributes (e.g. object shape and color). To this end, our Dual Purpose Hashing model is trained to jointly preserve two kinds of similarities characterizing the two aspects respectively. Moreover, since images with both category and attribute labels are scarce, our model is carefully designed to leverage the abundant partially labelled data as training inputs to alleviate the risk of overfitting. With such a framework, the binary codes of new-coming images can be readily obtained by quantizing the outputs of a specific CNN layer, and different retrieval tasks can be achieved by using the binary codes in different ways. Experiments on two large-scale datasets show that our method achieves comparable or even better performance than those state-of-the-art methods specifically designed for each individual retrieval task while being more compact than the compared methods.

Solving visual question answering (VQA) task requires recognizing many diverse visual concepts as the answer. These visual concepts contain rich structural semantic meanings, e.g., some concepts in VQA are highly related (e.g., red & blue), some of them are less relevant (e.g., red & standing). It is very natural for humans to efficiently learn concepts by utilizing their semantic meanings to concentrate on distinguishing relevant concepts and eliminate the disturbance of irrelevant concepts. However, previous works usually use a simple MLP to output visual concept as the answer in a flat label space that treats all labels equally, causing limitations in representing and using the semantic meanings of labels. To address this issue, we propose a novel visual recognition module named Dynamic Concept Recognizer (DCR), which is easy to be plugged in an attention-based VQA model, to utilize the semantics of the labels in answer prediction. Concretely, we introduce two key features in DCR: 1) a novel structural label space to depict the difference of semantics between concepts, where the labels in new label space are assigned to different groups according to their meanings. This type of semantic information helps decompose the visual recognizer in VQA into multiple specialized sub-recognizers to improve the capacity and efficiency of the recognizer. 2) A feature attention mechanism to capture the similarity between relevant groups of concepts, e.g., human-related group “chef, waiter” is more related to “swimming, running, etc.” than scene related group “sunny, rainy, etc.”. This type of semantic information helps sub-recognizers for relevant groups to adaptively share part of modules and to share the knowledge between relevant sub-recognizers to facilitate the learning procedure. Extensive experiments on several datasets have shown that the proposed structural label space and DCR module can efficiently learn the visual concept recognition and benefit the performance of the VQA model.

Broadway, as one of the leading commercial show districts in the West, has a mature commercial experience and operation model. The theatre industry on Broadway is different from other places, and its production and marketing methods have also proven to be highly successful. This paper analyses the business model, artistic concept, communication strategy, and user research of Western theatre based on Broadway at the academic level. At the practical level, the paper investigates the cross-cultural management and communication model of the Chinese theatre industry. The Broadway theatre management that operates separately for theatres and productions, together with the art form that focuses on scenery, stage art, and sound effects, is applied to the management and communication of the traditional Chinese theatre industry. The paper aims at promoting traditional culture and making traditional theatre more suitable for modern aesthetic needs and market demands. The contribution of the paper is to improve traditional Chinese theatre into a creator-centered theatre performance consisting of music, song, dance, and dialogue in a cross-cultural context, based on the Broadway operation model. A modern marketing campaign is used to promote exposure and sustainability. As a result, a framework for Chinese traditional theatre products based on cross-culture is constructed.

We introduce a new efficient framework, the Unified Context Network (UniCon), for robust active speaker detection (ASD). Traditional methods for ASD usually operate on each candidate's pre-cropped face track separately and do not sufficiently consider the relationships among the candidates. This potentially limits performance, especially in challenging scenarios with low-resolution faces, multiple candidates, etc. Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information: spatial context to indicate the position and scale of each candidate's face, relational context to capture the visual relationships among the candidates and contrast audio-visual affinities with each other, and temporal context to aggregate long-term information and smooth out local uncertainties. Based on such information, our model optimizes all candidates in a unified process for robust and reliable ASD. A thorough ablation study is performed on several challenging ASD benchmarks under different settings. In particular, our method outperforms the state-of-the-art by a large margin of about 15% mean Average Precision (mAP) absolute on two challenging subsets: one with three candidate speakers, and the other with faces smaller than 64 pixels. Together, our UniCon achieves 92.0% mAP on the AVA-ActiveSpeaker validation set, surpassing 90% for the first time on this challenging dataset at the time of submission. Project website: https://unicon-asd.github.io/., Comment: 10 pages, 6 figures; to appear at ACM Multimedia 2021

If an image tells a story, the image caption is the briefest narrator. Generally, a scene graph prefers to be an omniscient generalist, while the image caption is more willing to be a specialist, which outlines the gist. Lots of previous studies have found that a scene graph is not as practical as expected unless it can reduce the trivial contents and noises. In this respect, the image caption is a good tutor. To this end, we let the scene graph borrow the ability from the image caption so that it can be a specialist on the basis of remaining all-around, resulting in the so-called Topic Scene Graph. What an image caption pays attention to is distilled and passed to the scene graph for estimating the importance of partial objects, relationships, and events. Specifically, during the caption generation, the attention about individual objects in each time step is collected, pooled, and assembled to obtain the attention about relationships, which serves as weak supervision for regularizing the estimated importance scores of relationships. In addition, as this attention distillation process provides an opportunity for combining the generation of image caption and scene graph together, we further transform the scene graph into linguistic form with rich and free-form expressions by sharing a single generation model with image caption. Experiments show that attention distillation brings significant improvements in mining important relationships without strong supervision, and the topic scene graph shows great potential in subsequent applications., Comment: published in ICCV 2021