Reviewerly/exHarmony huggingface dataset:
접지그리드의 과도전위상승의 뇌전류응답

A type method of evaluating the performance of grounding electrodes for lightning protection system is to analyze transient potential rises of the grounding electrode by flowing lightning currents. Recently, many studies related to potential rises of grounding electrode systems have been carried out, but more detailed and in-depth studies under various design parameters are required. This paper aims to analyze the transient potential rises response to lightning currents entering into grounding electrodes. The transient potential rises due to the lightning currents injected into the grounding grid with 4 mesh 10m10m size according to the incident location as a parameter of the earth resistivity is simulated by using CDEGS program. As a result, the potential rise of grounding grid was different depending on the wave front time and incident point of lightning currents, and the soil resistivity where the grounding grid is buried. The potential rises of grounding grid conductors was highest when the lightning current was injected at a corner of the grounding grid, but on the other hand it is relatively low by injecting the lightning current at the center of grounding grid. Also when 0.25/100㎲ impulse current corresponding to the subsequent negative stroke lightning current was injected, the potential rise was maximum value. The lower the earth resistivity where the grounding gird was buried, the lower the potential rise of grounding grid was. It was confirmed that the potential rise of grounding grid buried in a place with low earth resistivity was dominantly influenced by the wave front time and the incident location of lightning currents.
THE MORPHOLOGICAL AND AUDIATIVE INTERCONNECTEDNESS OF SOUND: EQUIVALENCE IN A MULTIDIMENSIONAL SOUNDSCAPE

Article is dedicated to the morphological and audiative interconnectedness of sound: equivalence in a multidimensional soundscape. To underline my goal of aiding guitarists, performers, composers, and researchers, the underlying purpose of this text is the development of pedagogical tools. Drawing on personal recent theoretical and practical research into the morphology of sound and audiation we will explore the notion of ‘equivalence in a multidimensional soundscape’. Correlations between the interconnectedness of sound-based morphologies emanating from extended guitar techniques, and comprehending internal realisation of the senses to hear and feel when sound is not physically present will be assessed. By outlining recent relevant texts that have made forward strides in guitar and music theory, pedagogy, and analysis we gain useful knowledge on all sounds, and listening strategies. To express an all-encompassing mental and visual image of apprehending the value of sound from a morphological and audiative perspective, three-dimensional topological diagrams will be evaluated; a development of previous two-dimensional visualisations from prior work. In regard to morphologies, topics of interest are spectromorphology, spatiomorphology, spectral quality, performance space, and performance aspects. Studying these aspects will help in the understanding of morphological value. Learning to comprehend morphologies in relation to the listening experience will deepen all round musical abilities. We will therefore investigate audiation through encompassing deep listening, reduced listening, inherent and external qualities, psychological experience, imagination, and improvisation. The perception of spectral motions in space involve dynamic thinking and action, and as more mutual inclusivity is discovered we can start to contemplate more adventurous pedagogical tools from which future.
Deep Learning Techniques to make Gravitational Wave Detections from Weak Time-Series Data

Gravitational waves are ripples in the space time fabric when high energy events such as black hole mergers or neutron star collisions take place. The first Gravitational Wave (GW) detection (GW150914) was made by the Laser Interferometer Gravitational-wave Observatory (LIGO) and Virgo Collaboration on September 14, 2015. Furthermore, the proof of the existence of GWs had countless implications from Stellar Evolution to General Relativity. Gravitational waves detection requires multiple filters and the filtered data has to be studied intensively to come to conclusions on whether the data is a just a glitch or an actual gravitational wave detection. However, with the use of Deep Learning the process is simplified heavily, as it reduces the level of filtering greatly, and the output is more definitive, even though the model produces a probabilistic result. Our technique, Deep Learning, utilizes a different implementation of a one-dimensional convolutional neural network (CNN). The model is trained by a composite of real LIGO noise, and injections of GW waveform templates. The CNN effectively uses classification to differentiate weak GW time series from non-gaussian noise from glitches in the LIGO data stream. In addition, we are the first study to utilize fine-tuning as a means to train the model with a second pass of data, while maintaining all the learned features from the initial training iteration. This enables our model to have a sensitivity of 100%, higher than all prior studies in this field, when making real-time detections of GWs at an extremely low Signal-to-noise ratios (SNR), while still being less computationally expensive. This sensitivity, in part, is also achieved through the use of deep signal manifolds from both the Hanford and Livingston detectors, which enable the neural network to be responsive to false positives.
Multi-Sensor Fall Detection for Smartphones

Noemi Biancone, Chiara Bicchielli*, Fernando Ferri and Patrizia Grifoni Author Affiliations IRPPS-CNR, Via Palestro 32, Italy Received:February 25, 2021 | Published: March 22, 2021 Corresponding author: Chiara Bicchielli, IRPPS-CNR, Via Palestro 32, Rome, Italy DOI: 10.26717/BJSTR.2021.34.005588
The ‘Principle of absolute doubt’ in Mark Aldanov’s oeuvre

The reviewed monograph of T. I. Dronova presents a multi-layered analysis of M. A. Aldanov’s oeuvre: from the principles of philosophical and historical cognition to narrative strategies. The approach to the legacy of the philosopher and writer suggested by the author allows to reveal the type of his artistic thought and the nature of his dialogue with the predecessors in a very convincing and in many ways novel way.
Geoinformation in support of sustainable soils&amp;#8217; management to strengthen resilience under the pressure of climate change

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Abstract&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The National Map of Saline &amp;amp;#8211; Alkaline Soils of Greece was recently developed within the initiative of the European Soil Partnership (ESP) of FAO. The technique combines between other MODIS satellite imagery, spatial interpolation methods and ground surveying to derive at 1 km spatial resolution maps of soil&amp;amp;#8217;s salinity (SS) and soil organic carbon (SOC).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The present study investigates for the first time the development of higher resolution maps of these soil properties adopting the aforementioned methodology. Furthermore, this study attempted to estimate the Carbon sequestration (SOC) using Remote Sensing and geostatistic methods of spatial analysis, a concern that is eminent today due to its effect on climate change mitigation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As a case study the island of Mytilene in Greece is used, for which detailed information on soil properties as well as climatic, geomorphological, geological and soil data was available from previous studies. An MCDA (Multiple Criteria Decision Analysis) method was applied in a GIS environment using Landsat satellite imagery for the composition of a Saline - Alkaline map. Between the key soil parameters estimated spatially included the Electrical Conductivity (EC), Exchangeable Sodium Percentage (ESP) and pH. Geospatial data analysis methods were implemented to visualize all the derived parameters related for the study area and to analyze the final products in the spatial domain.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Finding suggests that climate change and soil directly affect one another. The impact of environmental and climate change in addition to unsustainable agricultural practices seems to be linked to salinity increase, soil erosion and loss of organic matter.&amp;amp;#160; In addition, when land degradation as well as erosion and loss of vegetation occur, SOC emissions increase. Under these conditions, soil cannot absorb enough amounts of CO2, especially when soil salinization and sodicity exists; inputs are further limited due to declines in vegetation health. The role of geoinformation technologies in support of sustainable agricultural production under the pressure of both climate change and anthropogenic activities is also discussed within the present study framework. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;KEYWORDS:&amp;lt;/strong&amp;gt; geoinformation, soil, pH, salinity, soil organic carbon, geostatistics, earth observation, GIS, Greece&amp;lt;/p&amp;gt;
Neural Extractive Search

Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called ``extractive search'', in which a search query is enriched with capture-slots, to allow for such rapid extraction. Such an extractive search system can be built around syntactic structures, resulting in high-precision, low-recall results. We show how the recall can be improved using neural retrieval and alignment. The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Our prototype is available at \url{this https URL} and a video demonstration is available at \url{this https URL}.
Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning

In order to apply Optical Character Recognition (OCR) to historical printings of Latin script fully automatically, we report on our efforts to construct a widely-applicable polyfont recognition model yielding text with a Character Error Rate (CER) around 2% when applied out-of-the-box. Moreover, we show how this model can be further finetuned to specific classes of printings with little manual and computational effort. The mixed or polyfont model is trained on a wide variety of materials, in terms of age (from the 15th to the 19th century), typography (various types of Fraktur and Antiqua), and languages (among others, German, Latin, and French). To optimize the results we combined established techniques of OCR training like pretraining, data augmentation, and voting. In addition, we used various preprocessing methods to enrich the training data and obtain more robust models. We also implemented a two-stage approach which first trains on all available, considerably unbalanced data and then refines the output by training on a selected more balanced subset. Evaluations on 29 previously unseen books resulted in a CER of 1.73%, outperforming a widely used standard model with a CER of 2.84% by almost 40%. Training a more specialized model for some unseen Early Modern Latin books starting from our mixed model led to a CER of 1.47%, an improvement of up to 50% compared to training from scratch and up to 30% compared to training from the aforementioned standard model. Our new mixed model is made openly available to the community.
Characterizing the Gap Between Actor-Critic and Policy Gradient

Actor-critic (AC) methods are ubiquitous in reinforcement learning. Although it is understood that AC methods are closely related to policy gradient (PG), their precise connection has not been fully characterized previously. In this paper, we explain the gap between AC and PG methods by identifying the exact adjustment to the AC objective/gradient that recovers the true policy gradient of the cumulative reward objective (PG). Furthermore, by viewing the AC method as a two-player Stackelberg game between the actor and critic, we show that the Stackelberg policy gradient can be recovered as a special case of our more general analysis. Based on these results, we develop practical algorithms, Residual Actor-Critic and Stackelberg Actor-Critic, for estimating the correction between AC and PG and use these to modify the standard AC algorithm. Experiments on popular tabular and continuous environments show the proposed corrections can improve both the sample efficiency and final performance of existing AC methods.
Detection and classification of three-qubit states using l_1 norm of coherence

Entanglement is a purely quantum mechanical phenomenon and thus it has no classical analog. On the other hand, coherence is a well-known phenomenon in classical optics and in quantum mechanics. Recent research shows that quantum coherence may act as a useful resource in quantum information theory. We will employ here quantum coherence to detect and classify the entanglement property of three-qubit states. Moreover, we have shown that if any three-qubit state violates another necessary condition for the detection of a general biseparable state then the given three-qubit state cannot be a biseparable state. Since there are only three categories of states for the three-qubit system so if we detect that the state under probe is neither a separable nor a biseparable state then we can definitely conclude that the given three-qubit state is a genuine entangled state. We have illustrated our results with a few examples.
로스역 누가복음의 번역 수정 과정 연구 - 1882년, 1883년 누가복음을 중심으로 -

The purpose of this study is to discuss the Characteristics of the Korean language revealed in John Ross’s translated Bible in pre-modern Korea. Rev. John Ross (1842-1915) was a Scottish missionary dispatched to China in the late 19th Century. He had translated the Bible into Korean from 1882 to 1887. During this period of time he had revised his work for number of times with the help of Korean language assistants from the Northern area of Korea. In this study, we will discuss the following issues: what language characteristics are revealed in Ross’s Versions of Luke; how the vocabulary in the editions of Ross’s Book of Luke (1882 and 1883) were revised; and what was the revision principles between each edition are. When 예수셩교 누가복음젼셔 was published in 1882, it was criticized by Horace G. Underwood (1859-1617) the American missionary. He criticized its provincial dialects which were different from the form of central dialects. However, when we investigate the vocabulary in the versions, the results were quite different from the criticisms at that time. The provincialism was revised and reduced in the subsequent published versions as the Northern dialects were replaced by central dialects. For the analysis we have investigate the ‘t-palatalization’ diffusion rate and ‘vowel-rounding’ in each edition.
문학관광 활성화를 위한 이육사문학관 공간콘텐츠 기획

The purpose of this study is to plan the literature tourism contents of Yi Yuk-sa Literary Museum. To this end, we examined the current status of Yi Yuk-sa and Yi Yuk-sa Literature Museum and examined operational problems. Considering that the essence of tourism lies in the pursuit of sincerity, contents were planned from sightseeing to experience tourism, and from sightseeing for relaxation to tourism for spiritual enhancement, and Yi Yuk-sa Utopia was presented. This was derived from the life and poetry of Yi Yuk-sa, noting that the paradigm of tourism pursues self-renewal through reason and reflection. We examined what the pursuit of authenticity of literature, literature tourism, and literature tourism was, and explored what the local literature museum was aiming for through case analysis. Based on the theoretical discussion and analysis, it was intended to make visitors feel the Yi Yuk History Literature Museum as a romantic and paradise Utopia. It also allows visitors to embody their old dreams through festivals, regulars and event programs that can bring them to life. Visitors' direct participation and experience are expected to serve as an opportunity to enhance their intimacy with the history of Yi Yuk and satisfaction with the Yi Yuk History Literature Museum, as well as enhance their positive image and brand value.
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions

Monitoring complex systems results in massive multivariate time series data, and anomaly detection of these data is very important to maintain the normal operation of the systems. Despite the recent emergence of a large number of anomaly detection algorithms for multivariate time series, most of them ignore the correlation modeling among multivariate, which can often lead to poor anomaly detection results. In this work, we propose a novel anomaly detection model for multivariate time series with \underline{HI}gh-order \underline{F}eature \underline{I}nteractions (HIFI). More specifically, HIFI builds multivariate feature interaction graph automatically and uses the graph convolutional neural network to achieve high-order feature interactions, in which the long-term temporal dependencies are modeled by attention mechanisms and a variational encoding technique is utilized to improve the model performance and robustness. Extensive experiments on three publicly available datasets demonstrate the superiority of our framework compared with state-of-the-art approaches.
Realistic Evaluation Principles for Cross-document Coreference Resolution

We point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results. We propose addressing this issue via two evaluation methodology principles. First, as in other tasks, models should be evaluated on predicted mentions rather than on gold mentions. Doing this raises a subtle issue regarding singleton coreference clusters, which we address by decoupling the evaluation of mention detection from that of coreference linking. Second, we argue that models should not exploit the synthetic topic structure of the standard ECB+ dataset, forcing models to confront the lexical ambiguity challenge, as intended by the dataset creators. We demonstrate empirically the drastic impact of our more realistic evaluation principles on a competitive model, yielding a score which is 33 F1 lower compared to evaluating by prior lenient practices.
Modeling morphology with Linear Discriminative Learning: considerations and design choices

This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning. Taking the semi-productive German noun system as example, we illustrate how decisions made about the representation of form and meaning influence model performance. We clarify that for modeling frequency effects in learning, it is essential to make use of incremental learning rather than the endstate of learning. We also discuss how the model can be set up to approximate the learning of inflected words in context. In addition, we illustrate how in this approach the wug task can be modeled in considerable detail. In general, the model provides an excellent memory for known words, but appropriately shows more limited performance for unseen data, in line with the semi-productivity of German noun inflection and generalization performance of native German speakers.
РЕГУЛЯРНАЯ ДИАГНОСТИЧЕСКАЯ СИСТЕМА И ЕЁ КОМПОНЕНТЫ

The reorientation of the lifelong education system towards planning and monitoring the educational process with constant monitoring of the tasks and prospects for the development of the student's personality requires the training of a teacher who is able to assess the results of his own work and the work of students at the proper level and using modern means. This means that modern education needs teachers who have a progressive arsenal of pedagogical diagnostics, who are able to use instrumental methods of quantitative assessment of results as a means of finding reserves for improving teaching and educational work.
Graph-based Trajectory Visualization for Text Mining of COVID-19 Biomedical Literature

Since the emergence of the worldwide pandemic of COVID-19, relevant research has been published at a dazzling pace, which makes it hard to follow the research in this area without dedicated efforts. It is practically impossible to implement this task manually due to the high volume of the relevant literature. Text mining has been considered to be a powerful approach to address this challenge, especially the topic modeling, a well-known unsupervised method that aims to reveal latent topics from the literature. However, in spite of its potential utility, the results generated from this approach are often investigated manually. Hence, its application to the COVID-19 literature is not straightforward and expert knowledge is needed to make meaningful interpretations. In order to address these challenges, we propose a novel analytical framework for effective visualization and mining of topic modeling results. Here we assumed that topics constituting a paper can be positioned on an interaction map, which belongs to a high-dimensional Euclidean space. Based on this assumption, after summarizing topics with their topic-word distributions using the biterm topic model, we mapped these latent topics on networks to visualize relationships among the topics. Moreover, in the proposed approach, the change of relationships among topics can be traced using a trajectory plot generated with different levels of word richness. These results together provide a deeply mined and intuitive representation of relationships among topics related to a specific research area. The application of this proposed framework to the PubMed literature shows that our approach facilitates understanding of the topics constituting the COVID-19 knowledge.
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling

With a better understanding of the loss surfaces for multilayer networks, we can build more robust and accurate training procedures. Recently it was discovered that independently trained SGD solutions can be connected along one-dimensional paths of near-constant training loss. In this paper, we show that there are mode-connecting simplicial complexes that form multi-dimensional manifolds of low loss, connecting many independently trained models. Inspired by this discovery, we show how to efficiently build simplicial complexes for fast ensembling, outperforming independently trained deep ensembles in accuracy, calibration, and robustness to dataset shift. Notably, our approach only requires a few training epochs to discover a low-loss simplex, starting from a pre-trained solution. Code is available at this https URL.
LibGuides: ISU Library: The Bridge Home | G5 Read-aloud

This guide is designed to share information about books in the ISU Library, books we love, books we've read and books we look forward to reading soon.
Conformal prediction interval for dynamic time-series

We develop a method to construct distribution-free prediction intervals for dynamic time-series, called \Verb|EnbPI| that wraps around any bootstrap ensemble estimator to construct sequential prediction intervals. \Verb|EnbPI| is closely related to the conformal prediction (CP) framework but does not require data exchangeability. Theoretically, these intervals attain finite-sample, \textit{approximately valid} marginal coverage for broad classes of regression functions and time-series with strongly mixing stochastic errors. Computationally, \Verb|EnbPI| avoids overfitting and requires neither data-splitting nor training multiple ensemble estimators; it efficiently aggregates bootstrap estimators that have been trained. In general, \Verb|EnbPI| is easy to implement, scalable to producing arbitrarily many prediction intervals sequentially, and well-suited to a wide range of regression functions. We perform extensive real-data analyses to demonstrate its effectiveness.