295 research outputs found

    Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification

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    Recent work on scene classification still makes use of generic CNN features in a rudimentary manner. In this ICCV 2015 paper, we present a novel pipeline built upon deep CNN features to harvest discriminative visual objects and parts for scene classification. We first use a region proposal technique to generate a set of high-quality patches potentially containing objects, and apply a pre-trained CNN to extract generic deep features from these patches. Then we perform both unsupervised and weakly supervised learning to screen these patches and discover discriminative ones representing category-specific objects and parts. We further apply discriminative clustering enhanced with local CNN fine-tuning to aggregate similar objects and parts into groups, called meta objects. A scene image representation is constructed by pooling the feature response maps of all the learned meta objects at multiple spatial scales. We have confirmed that the scene image representation obtained using this new pipeline is capable of delivering state-of-the-art performance on two popular scene benchmark datasets, MIT Indoor 67~\cite{MITIndoor67} and Sun397~\cite{Sun397}Comment: To Appear in ICCV 201

    Impact of investment behaviour on financial markets during COVID-19: a case of UK

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    This study aims to determine the impact of investment behavior on financial markets during COVID-19 with respect to the UK. This study is quantitative, where the data has been gathered from the primary sources of information, i.e., through a survey questionnaire. The researcher adopted the non-probability convenience sampling through which 337 responses were gathered. The questionnaire was self-administered, which was based on 7 points Likert scale. Concerning the analysis, the SEM technique has been adopted in which CFA and path analysis were carried out to determine the impact of variables. The study’s analysis determined significant moderation of COVID-19 uncertainty over the relationship of risk perception and general risk to tolerance. Similarly, the moderation of COVID-19 uncertainty over the relationship of risk perception and financial risk to tolerance was also determined. Additionally, the profitability rate’s effect was determined by the financial risk tolerance and general risk tolerance. Moreover, the effect of risk perception was also determined over the financial risk to tolerance. Lastly, the effect of satisfaction was determined to be significant over the general risk to tolerance

    Learning the heterogeneous representation of brain's structure from serial SEM images using a masked autoencoder

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    IntroductionThe exorbitant cost of accurately annotating the large-scale serial scanning electron microscope (SEM) images as the ground truth for training has always been a great challenge for brain map reconstruction by deep learning methods in neural connectome studies. The representation ability of the model is strongly correlated with the number of such high-quality labels. Recently, the masked autoencoder (MAE) has been shown to effectively pre-train Vision Transformers (ViT) to improve their representational capabilities.MethodsIn this paper, we investigated a self-pre-training paradigm for serial SEM images with MAE to implement downstream segmentation tasks. We randomly masked voxels in three-dimensional brain image patches and trained an autoencoder to reconstruct the neuronal structures.Results and discussionWe tested different pre-training and fine-tuning configurations on three different serial SEM datasets of mouse brains, including two public ones, SNEMI3D and MitoEM-R, and one acquired in our lab. A series of masking ratios were examined and the optimal ratio for pre-training efficiency was spotted for 3D segmentation. The MAE pre-training strategy significantly outperformed the supervised learning from scratch. Our work shows that the general framework of can be a unified approach for effective learning of the representation of heterogeneous neural structural features in serial SEM images to greatly facilitate brain connectome reconstruction

    Using the Extended Theory of Planned Behavior to Predict Privacy-Protection Behavioral Intentions in the Big Data Era: The Role of Privacy Concern

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    This research extends the theory of planned behavior to explore how privacy concerns influence privacy-protection behavioral intentions via attitudes, subjective norms, and perceived behavioral control. Using snowball sampling, 637 participants were recruited in China to complete this online survey. After eliminating the invalid questionnaire, this study received valid samples of 599 respondents. The finding demonstrated that privacy concerns were positively associated with individuals’ attitudes, subjective norms, and perceived behavioral control. And these factors also were found positively influence privacy-protection behavioral intentions. Theoretical and practical contributions are discussed as well

    Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

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    Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective --- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.Comment: This paper is accepted by WSDM201

    Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations

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    Inverse reinforcement learning (IRL) aims to explicitly infer an underlying reward function based on collected expert demonstrations. Considering that obtaining expert demonstrations can be costly, the focus of current IRL techniques is on learning a better-than-demonstrator policy using a reward function derived from sub-optimal demonstrations. However, existing IRL algorithms primarily tackle the challenge of trajectory ranking ambiguity when learning the reward function. They overlook the crucial role of considering the degree of difference between trajectories in terms of their returns, which is essential for further removing reward ambiguity. Additionally, it is important to note that the reward of a single transition is heavily influenced by the context information within the trajectory. To address these issues, we introduce the Distance-rank Aware Sequential Reward Learning (DRASRL) framework. Unlike existing approaches, DRASRL takes into account both the ranking of trajectories and the degrees of dissimilarity between them to collaboratively eliminate reward ambiguity when learning a sequence of contextually informed reward signals. Specifically, we leverage the distance between policies, from which the trajectories are generated, as a measure to quantify the degree of differences between traces. This distance-aware information is then used to infer embeddings in the representation space for reward learning, employing the contrastive learning technique. Meanwhile, we integrate the pairwise ranking loss function to incorporate ranking information into the latent features. Moreover, we resort to the Transformer architecture to capture the contextual dependencies within the trajectories in the latent space, leading to more accurate reward estimation. Through extensive experimentation, our DRASRL framework demonstrates significant performance improvements over previous SOTA methods
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