Optimal Remote Qubit Teleportation Using Node2vec

Much research work is done on implementing quantum teleportation and entanglement swapping for remote entanglement. Due to dynamical topological changes in quantum networks, nodes have to construct the shortest paths every time they want to communicate with a remote neighbour. But due to the entanglement failures remote entanglement establishment is still a challenging task. Also as the nodes know only about their neighbouring nodes computing optimal paths between source and remote nodes is time consuming too. In finding the next best neighbour in the optimal path between a given source and remote nodes so as to decrease the entanglement cost, deep learning techniques can be applied. In this paper we defined throughput of the quantum network as the maximum qubits transmitted with minimum entanglement cost. Much of research work is done to improve the throughput of the quantum network using the deep learning techniques. In this paper we adopted deep learning techniques for implementing remote entanglement between two non-neighbour nodes using remote qubit teleportation and entanglement swapping. The proposed method called Optimal Remote Qubit Teleportation outperforms the throughput obtained by the state of art approach

Text Classification of Mixed Model Based on Deep Learning

At present, deep learning has been widely used many fields, but the research on text classification is still relatively few. This paper makes full use of the good learning characteristics of deep learning, proposes a hybrid model based on deep learning, and designs a text classifier based on the hybrid model. This hybrid model uses two common deep learning models, sparse automatic encoder and deep confidence network, to mix. The hybrid model is mainly composed of three parts, the first two layers are constructed by sparse automatic encoder, the middle layer is a three-layer depth Convolutional Neural Network (CNN), and finally Softmax regression is used as the classification layer. In order to test the classification performance of the classifier based on deep learning hybrid model, relevant experiments were conducted on English data set 20Newsgroup and Chinese data set Fudan University Chinese Corpus. In the English text classification experiment, the classifier based on deep learning hybrid model is used to classify, and a high classification accuracy rate is obtained. In order to further verify the superiority of its performance, a comparative experiment with naive Bayes classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier demonstrates that the classification effect of the classifier based on deep learning hybrid model is better than that of naive Bayes classifier, KNN classifier and support vector machine classifier. In the experiment of Chinese text classification, the Chinese corpus of Fudan University is tested, and a good classification effect is obtained. The influence of different parameter settings on the classification accuracy is discussed

A New Record of Cymatium encausticum (Ranellidae: Tonnoidea: Gastropoda) from Korea

The Cymatium Roding, 1798 is a small- to large-sized marine gastropod genus. Three species has been reported thus far for Cymatium in the Korean waters. In general, Cymatium encausticum (Reeve, 1844) is known to occur in tropical seawaters including the Philippine Islands along with its congeners C. gutturnium, C. springsteeni, and C. exile. A single individual of the species was collected from Jejudo Island by SCUBA diving and morphological features were observed using a stereomicroscope. This is the first study to report the occurrence of Cymatium encausticum (Reeve, 1844) from the Korean waters, providing a detailed description of the species with the illustration for the shell morphology

Query-Efficient Black-Box Red Teaming via Bayesian Optimization

The deployment of large-scale generative models is often restricted by their potential risk of causing harm to users in unpredictable ways. We focus on the problem of black-box red teaming, where a red team generates test cases and interacts with the victim model to discover a diverse set of failures with limited query access. Existing red teaming methods construct test cases based on human supervision or language model (LM) and query all test cases in a brute-force manner without incorporating any information from past evaluations, resulting in a prohibitively large number of queries. To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations. Experimental results on various user input pools demonstrate that our method consistently finds a significantly larger number of diverse positive test cases under the limited query budget than the baseline methods. The source code is available at https://github.com/snu-mllab/Bayesian-Red-Teaming.Comment: ACL 2023 Long Paper - Main Conferenc

Improvement of electrolessly gap-filled Cu using 2,2'-dipyridyl and bis-(3-sulfopropyl)-disulfide (SPS)

The use of bis~3-sulfopropyl! disulfide ~SPS! in Cu electroless deposition resulted in Cu bottom-up filling. However, the high accelerating effect of SPS led to a poor electrical property of the film and generated many voids in the film by increasing the surface roughness and causing unstable deposition behavior. The addition of 2,28-dipyridyl together with SPS substantially improved the film quality of the gap-filled Cu maintaining the bottom-up filling behavior. It lowered the film resistivity by approximately 23% and enhanced the crystallinity. No voids were detected in the as-deposited Cu even after annealing