检索结果-内蒙古大学图书馆

Scheduling Space Expander: An Extension of Concurrency Control for data Ingestion Queries

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Nakazono, Sho Uchiyama, Hiroyuki Fujiwara, Yasuhiro Kawashima, Hideyuki NTT Computer and Data Science Laboratories Tokyo Japan Recruit Co. Ltd. Tokyo Japan NTT Communication Science Laboratories Kanagawa Japan Faculty of Environment and Information Studies Keio University Kanagawa Japan

With the continuing advances of sensing devices and IoT applications, database systems needs to process data ingestion queries that update the sensor data frequently. To process data ingestion queries with transactional correctness, we propose a novel protocol extension method, scheduling space expander (SSE). The key idea of SSE is that we can safely omit an update if the update becomes outdated and unnecessary. SSE adds another control flow to conventional protocols to test the transactional correctness of an erasing version order, which assumes that a transactions' updates are all outdated and unnecessary. In addition, we present an optimization of SSE called epoch-based SSE (ESSE), which generates, tests, and maintains the erasing version order more efficiently than SSE. Our approach makes the performance of data ingestion queries more efficient. Experimental results demonstrate that our ESSE extensions of Silo and MVTO improve 2.7× and 2.5× performance on the TATP benchmark on a 144-core machine, and the extensions achieved performance comparable to that of the original protocol for the TPC-C benchmark. © 2023, CC BY.

关键词： Concurrency control

Few-shot learning for unsupervised feature selection

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Kumagai, Atsutoshi Iwata, Tomoharu Fujiwara, Yasuhiro NTT Computer and Data Science Laboratories NTT Communication Science Laboratories

We propose a few-shot learning method for unsupervised feature selection, which is a task to select a subset of relevant features in unlabeled data. Existing methods usually require many instances for feature selection. However, sufficient instances are often unavailable in practice. The proposed method can select a subset of relevant features in a target task given a few unlabeled target instances by training with unlabeled instances in multiple source tasks. Our model consists of a feature selector and decoder. The feature selector outputs a subset of relevant features taking a few unlabeled instances as input such that the decoder can reconstruct the original features of unseen instances from the selected ones. The feature selector uses the Concrete random variables to select features via gradient descent. To encode task-specific properties from a few unlabeled instances to the model, the Concrete random variables and decoder are modeled using permutation-invariant neural networks that take a few unlabeled instances as input. Our model is trained by minimizing the expected test reconstruction error given a few unlabeled instances that is calculated with datasets in source tasks. We experimentally demonstrate that the proposed method outperforms existing feature selection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Random variables

Meta-learning for relative density-ratio estimation 21

学校读者我要写书评

暂无评论

Meta-learning for relative density-ratio estimation

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Atsutoshi Kumagai Tomoharu Iwata Yasuhiro Fujiwara NTT Computer and Data Science Laboratories NTT Communication Science Laboratories

ISBN: (纸本)9781713845393

The ratio of two probability densities, called a density-ratio, is a vital quantity in machine learning. In particular, a relative density-ratio, which is a bounded extension of the density-ratio, has received much attention due to its stability and has been used in various applications such as outlier detection and dataset comparison. Existing methods for (relative) density-ratio estimation (DRE) require many instances from both densities. However, sufficient instances are often unavailable in practice. In this paper, we propose a meta-learning method for relative DRE, which estimates the relative density-ratio from a few instances by using knowledge in related datasets. Specifically, given two datasets that consist of a few instances, our model extracts the datasets' information by using neural networks and uses it to obtain instance embeddings appropriate for the relative DRE. We model the relative density-ratio by a linear model on the embedded space, whose global optimum solution can be obtained as a closed-form solution. The closed-form solution enables fast and effective adaptation to a few instances, and its differentiability enables us to train our model such that the expected test error for relative DRE can be explicitly minimized after adapting to a few instances. We empirically demonstrate the effectiveness of the proposed method by using three problems: relative DRE, dataset comparison, and outlier detection.

关键词：

Meta-learning for relative density-ratio estimation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Kumagai, Atsutoshi Iwata, Tomoharu Fujiwara, Yasuhiro NTT Computer and Data Science Laboratories NTT Communication Science Laboratories

关键词： Anomaly detection

Edit and Alphabet-Ordering Sensitivity of Lex-parse

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Nakashima, Yuto Köppl, Dominik Funakoshi, Mitsuru Inenaga, Shunsuke Bannai, Hideo Department of Informatics Kyushu University Japan Department of Computer Science and Engineering University of Yamanashi Japan NTT Communication Science Laboratories M&D Data Science Center Tokyo Medical and Dental University Japan

We investigate the compression sensitivity [Akagi et al., 2023] of lex-parse [Navarro et al., 2021] for two operations: (1) single character edit and (2) modification of the alphabet ordering, and give tight upper and lower bounds for both operations. For both lower bounds, we use the family of Fibonacci words. For the bounds on edit operations, our analysis makes heavy use of properties of the Lyndon factorization of Fibonacci words to characterize the structure of lex-parse. © 2024, CC BY.

关键词：

Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation

学校读者我要写书评

暂无评论

Recurrent Neural Networks for Learning Long-term Temporal De...

IEEE International Conference on Big Knowledge (ICBK)

作者： Kentaro Ohno Atsutoshi Kumagai NTT Computer and Data Science Laboratories

ISBN: (纸本)9781665438599

Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to control information flow in a hidden state in the RNN, has recently been re-interpreted as a representative of the time scale of the state, i.e., a measure how long the RNN retains information on inputs. On the basis of this interpretation, several parameter initialization methods to exploit prior knowledge on temporal dependencies in data have been proposed to improve learn-ability. However, the interpretation relies on various unrealistic assumptions, such as that there are no inputs after a certain time point. In this work, we reconsider this interpretation of the forget gate in a more realistic setting. We first generalize the existing theory on gated RNNs so that we can consider the case where inputs are successively given. We then argue that the interpretation of a forget gate as a temporal representation is valid when the gradient of loss with respect to the state decreases exponentially as time goes back. We empirically demonstrate that existing RNNs satisfy this gradient condition at the initial training phase on several tasks, which is in good agreement with previous initialization methods. On the basis of this finding, we propose an approach to construct new RNNs that can represent a longer time scale than conventional models, which will improve the learnability for long-term sequential data. We verify the effectiveness of our method by experiments with real-world datasets.

关键词： Training Analytical models Recurrent neural networks Conferences Logic gates data models Time measurement

Recurrent neural networks for learning long-term temporal dependencies with reanalysis of time scale representation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Ohno, Kentaro Kumagai, Atsutoshi NTT Computer and Data Science Laboratories

Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to control information flow in a hidden state in the RNN, has recently been re-interpreted as a representative of the time scale of the state, i.e., a measure how long the RNN retains information on inputs. On the basis of this interpretation, several parameter initialization methods to exploit prior knowledge on temporal dependencies in data have been proposed to improve learnability. However, the interpretation relies on various unrealistic assumptions, such as that there are no inputs after a certain time point. In this work, we reconsider this interpretation of the forget gate in a more realistic setting. We first generalize the existing theory on gated RNNs so that we can consider the case where inputs are successively given. We then argue that the interpretation of a forget gate as a temporal representation is valid when the gradient of loss with respect to the state decreases exponentially as time goes back. We empirically demonstrate that existing RNNs satisfy this gradient condition at the initial training phase on several tasks, which is in good agreement with previous initialization methods. On the basis of this finding, we propose an approach to construct new RNNs that can represent a longer time scale than conventional models, which will improve the learnability for long-term sequential data. We verify the effectiveness of our method by experiments with real-world datasets. © 2021, CC BY.

关键词： Time measurement

Designing Secure Sparse Coding via Multiple Random Unitary Transforms

学校读者我要写书评

暂无评论

Designing Secure Sparse Coding via Multiple Random Unitary T...

2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Nakachi, Takayuki Bandoh, Yukihiro University of The Ryukyus Information Technology Center Okinawa Japan Nippon Telegraph and Telephone Corporation NTT Computer and Data Science Laboratories Kanagawa Japan

ISBN: (纸本)9781665419512

In this paper, we propose a design method of secure sparse coding via multiple random unitary transforms (RUTs). The proposed method operates as an Encryption-then-Compression (EtC) system. The multiple RUTs will increase the security strength of the secure sparse coding. Despite the improvement in security strength, the rate-distortion performance does not deteriorate. It provides the same rate-distortion performance as that of sparse coding without encryption. We demonstrate its performance via simulation. © 2021 IEEE.

关键词： Cryptography

Utilizing resource-rich language datasets for end-to-end scene text recognition in resource-poor languages

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Orihashi, Shota Yamazaki, Yoshihiro Makishima, Naoki Ihori, Mana Takashima, Akihiko Tanaka, Tomohiro Masumura, Ryo NTT Computer and Data Science Laboratories NTT Corporation Japan

This paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the encoder-decoder model based on Transformer. To train a highly accurate end-to-end model, we need to prepare a large image-to-text paired dataset for the target language. However, it is difficult to collect this data, especially for resource-poor languages. To overcome this difficulty, our proposed method utilizes well-prepared large datasets in resource-rich languages such as English, to train the resource-poor encoder-decoder model. Our key idea is to build a model in which the encoder reflects knowledge of multiple languages while the decoder specializes in knowledge of just the resource-poor language. To this end, the proposed method pre-trains the encoder by using a multilingual dataset that combines the resource-poor language's dataset and the resource-rich language's dataset to learn language-invariant knowledge for scene text recognition. The proposed method also pre-trains the decoder by using the resource-poor language's dataset to make the decoder better suited to the resource-poor language. Experiments on Japanese scene text recognition using a small, publicly available dataset demonstrate the effectiveness of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding