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

Beyond slurry-cast supercapacitor electrodes: PAN/MWNT heteromat-mediated ultrahigh capacitance electrode sheets

Supercapacitors (SCs) have garnered considerable attention as an appealing power source for forthcoming smart energy era. An ultimate challenge facing the SCs is the acquisition of higher energy density without impairing their other electrochemical properties. Herein, we demonstrate a new class of polyacrylonitrile (PAN)/multi-walled carbon tube (MWNT) heteromat-mediated ultrahigh capacitance electrode sheets as an unusual electrode architecture strategy to address the aforementioned issue. Vanadium pentoxide (V2O5) is chosen as a model electrode material to explore the feasibility of the suggested concept. The heteromat V2O5 electrode sheets are produced through one-pot fabrication based on concurrent electrospraying (for V2O5 precursor/MWNT) and electrospinning (for PAN nanofiber) followed by calcination, leading to compact packing of V2O5 materials in intimate contact with MWNTs and PAN nanofibers. As a consequence, the heteromat V2O5 electrode sheets offer three-dimensionally bicontinuous electron (arising from MWNT networks)/ion (from spatially reticulated interstitial voids to be filled with liquid electrolytes) conduction pathways, thereby facilitating redox reaction kinetics of V2O5 materials. In addition, elimination of heavy metallic foil current collectors, in combination with the dense packing of V2O5 materials, significantly increases (electrode sheet-based) specific capacitances far beyond those accessible with conventional slurry-cast electrodes.ope