Foreign competition and innovation: The mediating role of imitation

This study examines the extent to which foreign competition affects the innovation performance of domestic firms through imitation, given firms’ absorptive capacity. In analyzing longitudinal firm-level data from the U.K., we find a mediating effect of imitation on the relationship between foreign competition and local firms’ innovation performance, and an inverted U-shaped relationship between imitation and the innovation performance of local firms. Our findings further reveal that absorptive capacity moderates the mediating effect of imitation, diminishing innovation gains at moderate levels of imitation and mitigating the diminishing innovation performance at high levels of imitation

Research on Image Retrieval Optimization Based on Eye Movement Experiment Data

Satisfying a user's actual underlying needs in the image retrieval process is a difficult challenge facing image retrieval technology. The aim of this study is to improve the performance of a retrieval system and provide users with optimized search results using the feedback of eye movement. We analyzed the eye movement signals of the user’s image retrieval process from cognitive and mathematical perspectives. Data collected for 25 designers in eye tracking experiments were used to train and evaluate the model. In statistical analysis, eight eye movement features were statistically significantly different between selected and unselected groups of images (p < 0.05). An optimal selection of input features resulted in overall accuracy of the support vector machine prediction model of 87.16%. Judging the user’s requirements in the image retrieval process through eye movement behaviors was shown to be effective

ERA: Expert Retrieval and Assembly for Early Action Prediction

Early action prediction aims to successfully predict the class label of an action before it is completely performed. This is a challenging task because the beginning stages of different actions can be very similar, with only minor subtle differences for discrimination. In this paper, we propose a novel Expert Retrieval and Assembly (ERA) module that retrieves and assembles a set of experts most specialized at using discriminative subtle differences, to distinguish an input sample from other highly similar samples. To encourage our model to effectively use subtle differences for early action prediction, we push experts to discriminate exclusively between samples that are highly similar, forcing these experts to learn to use subtle differences that exist between those samples. Additionally, we design an effective Expert Learning Rate Optimization method that balances the experts' optimization and leads to better performance. We evaluate our ERA module on four public action datasets and achieve state-of-the-art performance.Comment: Accepted to ECCV 202

MARVAND: Mobile Application for Relief Volunteering Activity after Natural Disaster

poster abstractModern technologies play significant roles in the natural disaster domain. Current services focus mostly on providing information, recruiting volunteers, and donating money and goods, butless on supporting the activities of on-site volunteers. Our preliminary interviews showed that there are not enough experts on hand to help support on-site volunteers, and it is difficult to keep track of whether help requests have been met. To fill this gap, we proposed a MARVAND, utilizing LBS, with three main features: ‘Instant Crowd Knowledge’ providing access to remote experts using crowdsourcing; ‘Volunteer Radar’ providing awareness of other volunteers nearby; and ‘Reunite Missing Family Members’ helping reunite families who have been separated as a result of the disaster. The results of the evaluations with twelve participants who had experience in disaster relief volunteering activities demonstrated that the MARVAND could support activities of onsite volunteers after the natural disaster, and serve as an additional communication channel between volunteers and experts

One-step Multi-view Clustering with Diverse Representation

Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-view clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, which limits the expressiveness of the model. Moreover, a range of methods suffer from a two-step process, i.e., multimodal learning and the subsequent $k$-means, inevitably causing a sub-optimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm to solve the resultant problem with proven convergence. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method

EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones

The superior performance of modern deep networks usually comes with a costly training procedure. This paper presents a new curriculum learning approach for the efficient training of visual backbones (e.g., vision Transformers). Our work is inspired by the inherent learning dynamics of deep networks: we experimentally show that at an earlier training stage, the model mainly learns to recognize some 'easier-to-learn' discriminative patterns within each example, e.g., the lower-frequency components of images and the original information before data augmentation. Driven by this phenomenon, we propose a curriculum where the model always leverages all the training data at each epoch, while the curriculum starts with only exposing the 'easier-to-learn' patterns of each example, and introduces gradually more difficult patterns. To implement this idea, we 1) introduce a cropping operation in the Fourier spectrum of the inputs, which enables the model to learn from only the lower-frequency components efficiently, 2) demonstrate that exposing the features of original images amounts to adopting weaker data augmentation, and 3) integrate 1) and 2) and design a curriculum learning schedule with a greedy-search algorithm. The resulting approach, EfficientTrain, is simple, general, yet surprisingly effective. As an off-the-shelf method, it reduces the wall-time training cost of a wide variety of popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, and CSWin) by >1.5x on ImageNet-1K/22K without sacrificing accuracy. It is also effective for self-supervised learning (e.g., MAE). Code is available at https://github.com/LeapLabTHU/EfficientTrain.Comment: ICCV 202

Combating Disinformation or Reinforcing Cognitive Bias: Effect of Weibo Poster’s Location Disclosure

This study conducted a controlled experiment to examine the impact of posters’ IP disclosure on the perceptions of Weibo users with different habits and information preferences and explore whether such disclosure facilitates the fight against disinformation or deepens cognitive biases. Results showed that the IP location of the information poster does influence users’ judgments of the authenticity of the information and that the consistency between users’ long-term residence and poster IP is not important for users to make judgments about the credibility of information. The high level of usage of Weibo also has no effect on users’ judgment of the credibility of the information, and this may be related to the small difference in college students’ overall use of Weibo. The results also showed that users’ perceptions of information’s accuracy, logical coherence, absence of bias, alignment with their own views, consistency with the majority opinion, and trustworthiness of its source are all statistically positively correlated with the overall credibility of information

E2F1 Suppresses Oxidative Metabolism and Endothelial Differentiation of Bone Marrow Progenitor Cells

RATIONALE: The majority of current cardiovascular cell therapy trials use bone marrow progenitor cells (BM PCs) and achieve only modest efficacy; the limited potential of these cells to differentiate into endothelial-lineage cells is one of the major barriers to the success of this promising therapy. We have previously reported that the E2F transcription factor 1 (E2F1) is a repressor of revascularization after ischemic injury. OBJECTIVE: We sought to define the role of E2F1 in the regulation of BM PC function. METHODS AND RESULTS: Ablation of E2F1 (E2F1 deficient) in mouse BM PCs increases oxidative metabolism and reduces lactate production, resulting in enhanced endothelial differentiation. The metabolic switch in E2F1-deficient BM PCs is mediated by a reduction in the expression of pyruvate dehydrogenase kinase 4 and pyruvate dehydrogenase kinase 2; overexpression of pyruvate dehydrogenase kinase 4 reverses the enhancement of oxidative metabolism and endothelial differentiation. Deletion of E2F1 in the BM increases the amount of PC-derived endothelial cells in the ischemic myocardium, enhances vascular growth, reduces infarct size, and improves cardiac function after myocardial infarction. CONCLUSION: Our results suggest a novel mechanism by which E2F1 mediates the metabolic control of BM PC differentiation, and strategies that inhibit E2F1 or enhance oxidative metabolism in BM PCs may improve the effectiveness of cell therapy