Unsupervised Learning of Style-sensitive Word Vectors

This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) model (Mikolov et al., 2013) to learn style-sensitive word vectors using a wider context window under the assumption that the style of all the words in an utterance is consistent. In addition, we introduce a novel task to predict lexical stylistic similarity and to create a benchmark dataset for this task. Our experiment with this dataset supports our assumption and demonstrates that the proposed extensions contribute to the acquisition of style-sensitive word embeddings.Comment: 7 pages, Accepted at The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018

Nitrogen isotope effects on boron vacancy quantum sensors in hexagonal boron nitride

Recently, there has been growing interest in researching the use of hexagonal boron nitride (hBN) for quantum technologies. Here we investigate nitrogen isotope effects on boron vacancy (V$_\text{B}$) defects, one of the candidates for quantum sensors, in $^{15}$N isotopically enriched hBN synthesized using metathesis reaction. The Raman shifts are scaled with the reduced mass, consistent with previous work on boron isotope enrichment. We obtain nitrogen isotopic composition dependent optically detected magnetic resonance spectra of V$_\text{B}$ defects and determine the hyperfine interaction parameter of $^{15}$N spin to be -64 MHz. Our investigation provides a design policy for hBNs for quantum technologies

Demonstration of geometric diabatic control of quantum states

Geometric effects can play a pivotal role in streamlining quantum manipulation. We demonstrate a geometric diabatic control, that is, perfect tunneling between spin states in a diamond by a quadratic sweep of a driving field. The field sweep speed for the perfect tunneling is determined by the geometric amplitude factor and can be tuned arbitrarily. Our results are obtained by testing a quadratic version of Berry's twisted Landau-Zener model. This geometric tuning is robust over a wide parameter range. Our work provides a basis for quantum control in various systems, including condensed matter physics, quantum computation, and nuclear magnetic resonance

Wide-field quantitative magnetic imaging of superconducting vortices using perfectly aligned quantum sensors

Various techniques have been applied to visualize superconducting vortices, providing clues to their electromagnetic response. Here, we present a wide-field, quantitative imaging of the stray field of the vortices in a superconducting thin film using perfectly aligned diamond quantum sensors. Our analysis, which mitigates the influence of the sensor inhomogeneities, visualizes the magnetic flux of single vortices in YBa$_2$Cu$_3$O$_{7-\delta}$ with an accuracy of $\pm10~\%$. The obtained vortex shape is consistent with the theoretical model, and penetration depth and its temperature dependence agree with previous studies, proving our technique's accuracy and broad applicability. This wide-field imaging, which in principle works even under extreme conditions, allows the characterization of various superconductors

Optical-power-dependent splitting of magnetic resonance in nitrogen-vacancy centers in diamond

Nitrogen-vacancy (NV) centers in diamonds are a powerful tool for accurate magnetic field measurements. The key is precisely estimating the field-dependent splitting width of the optically detected magnetic resonance (ODMR) spectra of the NV centers. In this study, we investigate the optical power dependence of the ODMR spectra using NV ensemble in nanodiamonds (NDs) and a single-crystal bulk diamond. We find that the splitting width exponentially decays and is saturated as the optical power increases. Comparison between NDs and a bulk sample shows that while the decay amplitude is sample-dependent, the optical power at which the decay saturates is almost sample-independent. We propose that this unexpected phenomenon is an intrinsic property of the NV center due to non-axisymmetry deformation or impurities. Our finding indicates that diamonds with less deformation are advantageous for accurate magnetic field measurements.Comment: 9 pages, 7 figure

Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbation vector, a user can interpret it as an "action" for obtaining one's desired decision result. In practice, however, showing only a perturbation vector is often insufficient for users to execute the action. The reason is that if there is an asymmetric interaction among features, such as causality, the total cost of the action is expected to depend on the order of changing features. Therefore, practical CE methods are required to provide an appropriate order of changing features in addition to a perturbation vector. For this purpose, we propose a new framework called Ordered Counterfactual Explanation (OrdCE). We introduce a new objective function that evaluates a pair of an action and an order based on feature interaction. To extract an optimal pair, we propose a mixed-integer linear optimization approach with our objective function. Numerical experiments on real datasets demonstrated the effectiveness of our OrdCE in comparison with unordered CE methods.Comment: 20 pages, 5 figures, to appear in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021