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The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Behance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time., Comment: 18 pages, 3 figures, 1 table, SI(4 figures, 3 tables)

The negatively charged boron vacancy ($\mathrm{V}_{\mathrm{B}}^-$) in hexagonal boron nitride (hBN) has garnered significant attention among defects in two-dimensional materials. This owes, in part, to its deterministic generation, well-characterized atomic structure, and optical polarizability at room temperature. We investigate the latter through extensive measurements probing both the ground and excited state polarization dynamics. We develop a semiclassical model based on these measurements that predicts a near-unity degree of spin polarization, surpassing other solid-state spin defects under ambient conditions. Building upon our model, we include the presence of nuclear spin degrees of freedom adjacent to the $\mathrm{V}_{\mathrm{B}}^-$ and perform a comprehensive set of Lindbladian numerics to investigate the hyperfine-induced polarization of the nuclear spins. Our simulations predict a number of important features that emerge as a function of magnetic field which are borne out by experiment.

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names

We present a high-dynamic-range vector atomic magnetometer based on applying a fast-rotating magnetic field to a pulsed $^{87}$Rb scalar atomic magnetometer. This method enables simultaneous measurements of the total magnetic field and two polar angles relative to the plane of magnetic field rotation. Using two channels in a gradiometer mode, it provides simultaneous measurements of the total field gradient with a sensitivity of 50 $\mathrm{fT/\sqrt{Hz}}$ (1 part per billion), as well as two polar angles with resolutions of 8 $\mathrm{nrad/\sqrt{Hz}}$ at 50 $\mu$T Earth field strength. The noise spectrums of these measurements are flat down to 1 Hz and 0.1 Hz, respectively. Crucially, this approach avoids several metrological difficulties associated with vector magnetometers and gradiometers. We detail the fundamental, systematic, and practical limits of such vector magnetometers. Notably, we provide a comprehensive study of the systematic effects of vector atomic magnetometers. We introduce a new concept of dynamic heading error and investigate several other systematic effects. A unique cosine-altering rotating field modulation is proposed to cancel out these systematics. Additionally, we derive fundamental limits on the sensitivity of such sensors and demonstrate that the vector sensitivity of the sensor can approach its scalar sensitivity while retaining the accuracy and metrological advantages of scalar sensors. This high-dynamic-range vector magnetometer, with ultrahigh resolution and inherent calibration, is suitable for a wide array of applications., Comment: 13 pages

COVID-19 has become one of the most widely talked about topics on social media. This research characterizes risk communication patterns by analyzing the public discourse on the novel coronavirus from four Asian countries: South Korea, Iran, Vietnam, and India, which suffered the outbreak to different degrees. The temporal analysis shows that the official epidemic phases issued by governments do not match well with the online attention on COVID-19. This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics. Here, we propose an automatic method to detect topical phase transitions and compare similarities in major topics across these countries over time. We examine the time lag difference between social media attention and confirmed patient counts. For dynamics, we find an inverse relationship between the tweet count and topical diversity., Comment: 10 pages (+ 12 pages of Appendices), 18 figures, 2 tables