Secret Key Agreement by Soft-decision of Signals in Gaussian Maurer's Model

We consider the problem of secret key agreement in Gaussian Maurer's Model. In Gaussian Maurer's model, legitimate receivers, Alice and Bob, and a wire-tapper, Eve, receive signals randomly generated by a satellite through three independent memoryless Gaussian channels respectively. Then Alice and Bob generate a common secret key from their received signals. In this model, we propose a protocol for generating a common secret key by using the result of soft-decision of Alice and Bob's received signals. Then, we calculate a lower bound on the secret key rate in our proposed protocol. As a result of comparison with the protocol that only uses hard-decision, we found that the higher rate is obtained by using our protocol.Comment: 10 pages, 4 figures, to be appear in Proc. of 2008 IEEE International Symposium on Information Theory in Toronto, Canad

Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

Knowledge base completion (KBC) aims to predict missing information in a knowledge base.In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC:how to answer queries concerning test entities not observed at training time. Existing embedding-based KBC models assume that all test entities are available at training time, making it unclear how to obtain embeddings for new entities without costly retraining. To solve the OOKB entity problem without retraining, we use graph neural networks (Graph-NNs) to compute the embeddings of OOKB entities, exploiting the limited auxiliary knowledge provided at test time.The experimental results show the effectiveness of our proposed model in the OOKB setting.Additionally, in the standard KBC setting in which OOKB entities are not involved, our model achieves state-of-the-art performance on the WordNet dataset. The code and dataset are available at https://github.com/takuo-h/GNN-for-OOKBComment: This paper has been accepted by IJCAI1

Ridge Regression, Hubness, and Zero-Shot Learning

This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example space is desirable to suppress the emergence of hubs in the subsequent nearest neighbor search step. Assuming a simple data model, we prove that the proposed approach indeed reduces hubness. This was verified empirically on the tasks of bilingual lexicon extraction and image labeling: hubness was reduced with both of these tasks and the accuracy was improved accordingly.Comment: To be presented at ECML/PKDD 201

Extracting discriminative features using task-oriented gaze maps measured from observers for personal attribute classification

We discuss how to reveal and use the gaze locations of observers who view pedestrian images for personal attribute classification. Observers look at informative regions when attempting to classify the attributes of pedestrians in images. Thus, we hypothesize that the regions in which observers’ gaze locations are clustered will contain discriminative features for the classifiers of personal attributes. Our method acquires the distribution of gaze locations from several observers while they perform the task of manually classifying each personal attribute. We term this distribution a task-oriented gaze map. To extract discriminative features, we assign large weights to the region with a cluster of gaze locations in the task-oriented gaze map. In our experiments, observers mainly looked at different regions of body parts when classifying each personal attribute. Furthermore, our experiments show that the gaze-based feature extraction method significantly improved the performance of personal attribute classification when combined with a convolutional neural network or metric learning technique

Functional bracing for delayed union of a femur fracture associated with Paget's disease of the bone in an Asian patient: a case report

Paget's disease of the bone is a common metabolic bone disease in most European countries, Australia, New Zealand, and North America. Conversely, this disease is rare in Scandinavia, Asia, and Africa. In Japan, it is extremely rare, with a prevalence of 0.15/100000. Paget's disease is a localized disorder of bone remodeling. Excessive bone resorption and abnormal bone formation result in biomechanically weakened bone and predispose patients to fracture. Delayed union and non-union of fractures have been reported in patients with Paget's disease. Therefore, open reduction and internal fixation of fractures has been recommended to prevent such complications. Here we report an unusual case of a 63-year-old Asian woman with delayed union of a femur fracture secondary to Paget's disease, which was treated successfully by functional bracing