Reviewerly/exHarmony huggingface dataset:
Insight on the 2014 MJMA 6.7, northern Nagano earthquake sequence evolution in space and time through high resolved earthquake locations

&amp;lt;p&amp;gt;The estimation of spatial and temporal changes in the host medium physical properties is a quest to improve risk evaluation and hazard forecasting application. The space-time evolution of the seismicity gives information about stress variations, fluid content, and pore-pressure changes inside the medium. Thus, the accuracy of arrival-time measurements is crucial for travel-time-based seismological applications, such as earthquake location and travel(delay)-time tomography, especially when double-difference times are used. Standard monitoring networks and tools implements single-station, STA/LTA-based, automatic event detection/location procedures, which may produce inconsistent arrival-times of the same phase among stations. To overcome this problem, refined cross-correlation (CC) techniques for time picking have been recently developed. Their basic approach is to use CC to refine picks of event pairs with high waveform similarity. Similar events are grouped in families, considering the max CC values, the inter-distance and/or the focal mechanism similarity. Two drawbacks of this common approach are (1) the impact of noise from individual receiver levels on the quality of reference trace (RT) and (2) the inability to adjust the systematic shift of automatic picks.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we propose a new, fully automatic approach to refine the phase time picks. The CC is used to identify family members with a hierarchical clustering procedure. In each family, after the trace alignment, we build the RT by stacking the events weighted by the signal-to-noise ratio and the polarity. We applied this technique to a catalog of 3574 events of the 2014 M&amp;lt;sub&amp;gt;JMA&amp;lt;/sub&amp;gt; 6.7 sequence occurred at the Northern Nagano prefecture. The results indicate that we can improve the precision of phase picks of similar events and to adjust the systematic shift introduced by the automatic picker with mean differences between refined and automatic picks up to 0,5-1 s.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The high consistency of the phase picks allows to increase the accuracy of absolute location by reducing the mean location error from 0,6 km to 0,1 km and the root-mean-square from 0,15 to 0,075. Consequently, we observe an alignment of the seismicity respect to the main fault plane with an 30&amp;amp;#176;-45&amp;amp;#176; east-dipping angle for the shallow part while the deeper part dips at 50&amp;amp;#176;-65&amp;amp;#176;. Then, the double difference location provides highly resolved hypocenter locations and medium parameters by considering events of the same family as events pair. This improvement allows to use fast-tracking methods, as the Vp/Vs in time and the Coda-Wave Interferometry, to get information about the velocity variations before and during the sequence. By using those methods, we expect to get accurate information of the physical properties evolution and especially about the role of fluid in the triggering of the sequence.&amp;lt;/p&amp;gt;
Removing Stripe Noise from Satellite Images using Convolutional Neural Networks in Frequency Domain

&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;Many satellite images are corrupted by stripping; this noise degrades the visual quality of the images and inevitably introduces errors in processing. Thermal and hyperspectral images often suffer from stripping. The frequency distribution characteristic of stripe noise makes it difficult to remove such noise in the spatial domain; contrariwise, this noise can be efficiently detected in the frequency domain. Numerous solutions have been proposed to eliminate such noise using Fourier transform; however, most are subjective and time-consuming approaches.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;The lack of a fast and automated tool in this subject has motivated us to introduce a Convolutional Neural Network-based tool that uses the U-Net architecture in the frequency domain to suppress the anomalies caused by stripe noise. We added synthetic noise to satellite images to train the model. Then, we taught the network how to mask these anomalies in the frequency domain. The input image dataset was down-sampled to a size of 128 x128 pixels for a fast training time. However, our results suggest that the output mask can be up-scaled and applied on the original Fourier transform of the image and still achieve satisfying results; this means that the proposed algorithm is applicable on images regardless of their size. &amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;span&amp;gt;After the training step, the U-Net architecture can confidently find the anomalies and create an acceptable bounding mask; the results show that - with enough training data- the proposed procedure can efficiently remove stripe noise from all sorts of images. At this stage, we are trying to further develop the model to detect and suppress more complex synthetic noise. Next, we will focus on removing real stripe noise on satellite images to present a robust tool.&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
Bubble flower

Computer Graphics ForumVolume 41, Issue 1 p. 507-507 Miscellaneous Bubble flower First published: 24 March 2022 https://doi.org/10.1111/cgf.14450Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume41, Issue1February 2022Pages 507-507 RelatedInformation
Impact of wise leadership in enhancing the virtual teams' learning: An analytical study

This paper aims to identify the effect of adopting wise leadership in enhancing the learning processes of virtual teams.To achieve this, the contemporary literature that supports the relationship between the study variables in the context of the six capabilities of a wise leader and the fiveprocesses of team learning was explored.After adjusting the measurement model, data were collected from the study sample consisting of ( 232) participants in the representations of the Ministry of Education in the Kurdistan Region / Iraq.The target data and information were analyzed using the structural equation modeling method and other statistical methods that were implemented using my software (SPSS, and AMOS v.24).The results of the study model showed that there is a significant effect of adopting the capabilities of the wise leadership to enhance the learning processes of the virtual team in times of crises such as the (COVID-19) pandemic.The research recommends the necessity of adopting wise leadership capabilities as criteria for selecting team leaders in educational institutions.In addition, the need to formulate work training programs on wise leadership, its capabilities, and behaviors related to its activation in those institutions.
Publishing Scholarly Societies: One Library's Approach

University of Minnesota Libraries Publishing started publishing the journals of scholarly societies in 2018. This paper provides background on the publishing context at the University of Minnesota (UMN) Libraries, discusses the service expansion to include scholarly societies, and reflects on working directly with scholarly societies. The paper will also document some of the challenges and workflows involved in publishing society journals in hopes of encouraging more library publishers to enter into publishing partnerships with societies.
The DEnse mulTi-paramEtriC observations and 4D high resoluTion imaging (DETECT) experiment, a new paradigm for near-fault observations

&amp;lt;p&amp;gt;Near-fault observations can provide insights into the physical process interaction between fault slip activation, fluid presence/migration and seismicity production, processes acting at different timescales that generate large earthquakes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The DETECT experiment aims at exploiting very dense seismic networks deployed across a segmented faults system to foster the development of scientific integrated methodologies for monitoring and imaging the faults behavior during the inter-seismic phase. Target of the monitoring is to: detect and track space-time trends of different source parameters that could be related to a preparation process leading to a larger earthquake; investigate the frictional and stress states of the fault segments to anticipate the characteristics of the future large earthquake (e.g., hypocenter but also future large seismic energy release locations); analyze the interactions between the different fault segments to model/anticipate potential cascade effects.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The DETECT experiment is carried out in the Irpinia area (southern Italy), one of the regions in Italy and Europe showing the highest seismic hazard. Since august 2021, a constellation of 20 seismic arrays, for a total of 200 seismic stations (20 broad-band sensors and 180 short-periods), has been installed over the fault segments responsible for the Ms 6.9, 1980 Irpinia earthquake, the strongest and most destructive seismic event of the last half-century in southern Italy.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;DETECT results from a joint effort of local Universities, National and International Research Institutes. A novel and crucial aspect is that, differently from most studies concerning intra-plate earthquakes which are usually carried out after that large magnitude earthquakes have occurred, DETECT aims to put us in advantageous position and to unveil the preparatory process that generate large earthquakes and anticipate the role of the segmentation by studying at one time different fault segments, some of which are in rather late stage of their seismic cycle.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;With this contribution, we aim to present the DETECT experiment, the preliminary results and foster additional cooperation including complementary expertise to further enrich the partnership.&amp;lt;/p&amp;gt;
LayeredCNN: Segmenting Layers with Autoregressive Models

We address a subclass of segmentation problems where the labels of the image are structured in layers. We propose applying autoregressive CNNs which, when given an image and a partial segmentation of layers, complete the segmentation. Initializing the model with a user-provided partial segmentation allows for choosing which layers the model should segment. Alternatively, the model can produce an automatic initialization, albeit with some performance loss. The model is trained exclusively on synthetic data from our data generation algorithm. It yields impressive performance on the synthetic data and generalizes to real data it has never seen.
Concept drift adaptation for audio scene classification using high-level features

Data used in the model training is assumed to have a similar distribution when the model is applied. However, in some applications, the data distributions may change over time. This condition, known as the concept drift, might decrease the model performance because the model is trained and evaluated in different distributions. To solve this problem in the audio scene classification task, we previously proposed the Combine-merge Gaussian mixture model (CMGMM) algorithm, where Mel-frequency cepstral coefficients (MFCCs) are used as the feature vector. In this paper, in the CMGMM algorithm, we propose to use the Pre-trained audio neural networks (PANNs) to model event audio that exists in the scene. A motivation is, instead of acoustic features, to make the best use of high-level features obtained by a model that trained using a large amount of audio data. The experiment result shows that the proposed method using PANNs improves model accuracy. In the active methods with abrupt and gradual concept drift, it is recommended to use PANNs to have significant accuracy improvement and obtain optimal adaptation results.
Measure of Tripartite Quantum Correlation in Multiqubit Symmetric Pure State

We propose a direct measure of tripartite quantum correlation in an arbitrary symmetric pure state of N correlated two-level atoms (qubits). We compute the third-order moments of the collective pseudo-spin operators in terms of the individual atomic operators of the atoms in the assembly and find that all the bipartite quantum correlation terms among the atoms cancel out leaving only the all possible tripartite quantum correlation terms among them. We observe that the third-order moments are made up of solely the tripartite quantum correlations among the atoms. This helps to extract out only the tripartite quantum correlations among the N atoms and propose a measure and quantification of these correlations. We also propose the necessary and sufficient condition for the presence of tripartite quantum correlation in such multi-atomic systems. We conjecture the way of determining the third-order moments of the pseudo-spin operators of the two-level atoms in the assembly, experimentally.
Designing S-Boxes Providing Stronger Security Against Differential Cryptanalysis for Ciphers Using Byte-Wise XOR

In this paper, we develop an S-box designing method by considering an interplay between an S-box and a linear layer, which enhances security against differential cryptanalysis. The basic idea can be found in bitslice-friendly ciphers such as Serpent and bit-permutation ciphers such as PRESENT. In those designs, S-boxes were chosen so that the branch number is not too small, which rapidly diffuses differences. We apply a similar analysis to other constructions. The first target is extended generalized Feistel networks (EGFN) and its instance Lilliput, which has an XOR layer after the standard GFN. We show that security of EGFN can be enhanced by using an S-box that does not allow any difference $$\varDelta $$ to be mapped to the same $$\varDelta $$ with a high probability, say $$2^{-2}$$ for a 4-bit S-box. The second target is AES-like ciphers that use a binary matrix in MixColumns. We focus on the chain of differences $$\varDelta A \rightarrow \varDelta B \rightarrow \varDelta C \rightarrow \cdots $$ over the S-box, where each transition occurs with a high probability. We show that security of such AES-like ciphers can be enhanced if the maximum length of the chains is short. As a proof-of-concept, we evaluate Lilliput, Midori, and SKINNY with the new S-box satisfying the property.
A universally composable bit generation scheme for zero involvement authentication

Context-based zero-involvement authentication and pairing (ZIA and ZIP) is a set of techniques that allow nearby devices to form self-organizing networks using authentication tokens derived from ambient environmental noise like electromagnetic radiation, audio, vibrations, etc. Environmental noise encodes entropy that can be simultaneously measured by a group of nearby devices which extract identical authentication or encryption tokens. Measurement of the same environmental noise signals is evidence that each device in the group is located in the same physical location at the same time and should belong to the same user.
Human-environmental interaction possibilities at the Neanderthals&amp;#8217; northern edge: focus on last interglacial and early last glacial

&amp;lt;p&amp;gt;Neanderthals are believed to have been adapted to cold climate conditions, nevertheless Neanderthal findings in the north and northeast European region are scares, non-existing or at least speculative. In between periods of arctic and subarctic conditions, warm periods like the Eemian interglacial would have offered mild living conditions in present-day southern Scandinavia and have evidently allowed migration of prey species to the region. We therefore suggest that the apparent absence of Neanderthals in the geological record here is partly steered by poor conditions of conservation, lack of &amp;lt;em&amp;gt;in situ&amp;lt;/em&amp;gt; archives and lack of focused investigation, rather than a limit of the Neanderthal habitat &amp;lt;em&amp;gt;per se&amp;lt;/em&amp;gt;. In this paper we seek to identify potential hotspots for Neanderthal activity in the geological record. We do this by mapping the paleolandscape from all available data sources and by 3D paleolandscape modelling, to identify potential Neanderthal living sites such as lake beaches, seashores and floodplains, where there would be easy access to water, prey and open habitat. Among the possible candidate sites, we will investigate by means of coring, whether the shores from Eemian lakes offer good conditions for preservation of potential find layers, as they might have a low degree of disturbance and in rare cases can offer &amp;lt;em&amp;gt;in situ&amp;lt;/em&amp;gt; burial and conservation of interglacial fossils, archaeological material and sedimentary climate archives.&amp;lt;/p&amp;gt;
Findability of laboratory data in the solid Earth sciences: a portal for cross-disciplinary metadata

&amp;lt;p&amp;gt;The Thematic Core Service Multi-scale Laboratories (TCS MSL) is a community within the European Plate Observing System (EPOS) that includes a wide range of world-class laboratory infrastructures and that provides a cross-disciplinary, though coherent platform for virtual access to data and physical access to solid Earth science labs. The data produced at the participating laboratories are crucial to serving society&amp;amp;#8217;s need for geo-resources exploration and for protection against geo-hazards. To model resource formation and system behaviour during exploitation, researchers need an understanding from the molecular to the continental scale, based on experimental and analytical data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Data coming from the MSL laboratories provide the backbone for scientific publications, but they are often available only as supplementary information to research articles. Moreover, the vast majority of&amp;amp;#160; the collected data remain unpublished, inaccessible, and often not sustainably preserved for the long term. To allow reuse of these valuable but often neglected data, the TCS MSL developed a full publication chain to support their FAIR dissemination and sustainable accessibility. This chain consists of a community-driven metadata standard that allows multiple discipline-specific detailed descriptions, a publication tool (metadata editor), and an online community portal that gives access to DOI-referenced data publications at multiple research data repositories related to the TCS MSL context (https://epos-msl.uu.nl/). The portal is built on the CKAN repository toolkit and is driven by the richness of the TCS MSL metadata standard. Besides its importance for the TCS MSL community, it also provides a showcase of how to set up the CKAN environment as a cross-disciplinary catalogue for FAIR metadata exchange through a cascade of infrastructures.&amp;lt;/p&amp;gt;
COVID-19 and Emergency Powers in Western European Democracies

This chapter addresses the legal reactions of several Western European democracies to COVID-19. More specifically, the authors highlight some common trends and address several issues arising from the use of emergency powers. The chapter first offers an overview of whether and how the constitutions of selected Western European countries regulate emergency, so as to attempt a categorisation of emergency powers. Secondly, the analysis considers whether (or not) these emergency models have been applied in the fight against the pandemic caused by COVID-19. In doing so, it finds out that resort to emergency powers as written in constitutions has been unusual as a response to the pandemic, since many countries have preferred alternative strategies. The conclusion points out that a sort of ‘escape’ from pre-existing emergency powers can be observed and discusses the reasons that may lie behind this choice. Furthermore, the authors provide recommendations as to changes that might be introduced once COVID-19 is over, to improve emergency frameworks and make them suitable to face global emergencies.
Bounded Hanoi

The classic Tower of Hanoi puzzle involves moving a set of disks on three pegs. The number of moves required for a given number of disks is easy to determine, but when the number of pegs is increased to four or more, this becomes more challenging. After 75 years, the answer for four pegs was resolved only recently, and this time complexity question remains open for five or more pegs. In this article, the space complexity, i.e., how many disks need to be accommodated on the pegs involved in the transfer, is considered for the first time. Suppose m disks are to be transferred from some peg L to another peg R using k intermediate work pegs of heights j1,…,jk, then how large can m be? We denote this value by H(j1,…,jk). If k = 1, as in the classic problem, the answer is easy: H(j)=j+1. We have the exact value for two work pegs, but so far only very partial results for three or more pegs. For example, H(10!,10!)=26336386137601 and H(0!,1!,2!,…,10!)=16304749471397, but we still do not know the value for H(1,3,3).
Hierarchical Attention Networks for Multimodal Machine Learning

Abstract The Visual Question Answering (VQA) task is to infer the correct answer to a free-form question based on the given image. This task is challenging because it requires model handling both visual and textual information. Most successful attempts on VQA task have been achieved by using attention mechanism which can capture inter-modal and intra-modal dependencies. In this paper, we raise a new attention-based model to solve VQA. We use question information to guide model concentrate on special regions and attribute and hierarchically reason the answer. We also propose multi-modal fusion strategy based on co-attention method to fuse both visual and textual information. Under the same experimental conditions, extensive experiments on VQA-v2.0 dataset illustrate our method performance exceeds the performance of some state-of-the-art methods of the same experimental conditions.