Reviewerly/exHarmony huggingface dataset:
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Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 59, Issue 2 p. I-I Full Issue Full Issue First published: 01 April 2022 https://doi.org/10.1111/j.1467-6346.2022.10456.xAboutPDF 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 Volume59, Issue2April 2022Pages I-I RelatedInformation
Using seismograms to investigate earthquake determinism

&amp;lt;p&amp;gt;It is relatively simple to calculate the magnitude of an earthquake after it has happened.&amp;amp;#160; However, it is unclear if an earthquake 'knows' its final magnitude before rupture ends.&amp;amp;#160; We are interested in whether earthquakes are deterministic: whether features of the initial stages of an earthquake make accurate predictions about the earthquakes' final size.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;A major piece in the puzzle of determinism was proposed around 15 years ago by Olson and Allen (2005), who found a relationship between the predominant period of the early stages of an earthquake and its final magnitude. However, the results remain controversial, partly because Olsen and Allen (2005) analysed only 71 events. Here we aim to test their prediction in a statistically robust way using many more earthquakes, from a variety of settings.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We calculate the predominant and average periods for several thousand earthquakes from around the world. Our preliminary results find a deterministic relationship, where both parameters increase with earthquake magnitude, but with a large scatter. They highlight the importance of filtering, and the parameters used to filter, as these have a significant effect on your final result. We are therefore now analysing the spectra of these earthquakes to look for patterns amongst them, and to better understand the physical basis of the predominant and average period calculations.&amp;lt;/p&amp;gt;
A method of data export based on ASP.NET

Under B/S structure, the data of the server is displayed on the web page. Aiming at the problem that some data displayed on the web page need to be exported to the local area for processing and storage, this paper introduces a method of using ASP.NET technology to export data to Excel with examples. In this method, the data of web page is exported by the way of Datagrid control, the arrangement of the exported data is basically the same as that of the web page, and corresponding reports can be made according to the site requirements. The concrete realization process of data export is introduced emphatically.
Interactions

After a general discussion of string amplitudes we compute the 4-scalar amplitude and discuss properties of the 4-graviton amplitude, such as resonances and Regge trajectories. Then we introduce background fields and briefly discuss world-sheet beta functions and marginal deformations. We explain how effective actions can be obtained from either scattering amplitudes or beta functions. The string frame and Einstein frame are introduced and the universal part of the string effective action for graviton, Kalb–Ramond field, and dilaton is given. We also discuss background independence.
ℤ4R-additive cyclic and constacyclic codes and MDSS codes

In this paper, we will study the structure of [Formula: see text]-additive codes where [Formula: see text] is the well-known ring of 4 elements and [Formula: see text] is the ring given by [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text]. We will classify all maximum distance separable codes with respect to the Singleton bound (MDSS) over [Formula: see text] Then we will focus on [Formula: see text]-additive cyclic and constacyclic codes. We will find a unique set of generator polynomials for these codes and determine minimum spanning sets for them. We will also find the generator polynomials for the dual of any [Formula: see text]-additive cyclic or constacyclic code.
DECODING M-ARY LINEAR CODES WITH THE QUANTUM APPROXIMATE OPTIMIZATION ALGORITHM

Abstract The NP-hardness of the Minimum Distance Decoding Problem (MDDP) is the core of the McEliece cryptosystem. The difficulty of decoding a received word to the closest codeword in a given arbitrary code is key to its security. Related to the MDDP is the Coset Leader Problem (CLP), which consists in finding a word of a given syndrome and minimum Hamming weight. Both can be modelled as optimization problems, and solved using the Quantum Approximate Optimization Algorithm (QAOA), a well-known hybrid quantum- classical algorithm. In this paper, we model both the MDDP and CLP for linear codes over arbitrary m−ary alphabets, we make the theoretical analysis of the first level for the binary CLP problem, and introduce some experiments to test its performance. The experiments were carried out on both quantum computer simulators and real quantum devices, and use codes of different lengths and different depths of the QAOA.
Recovery of Te, Bi and W from mine tailings and concentrates, using environmentally-benign Deep Eutectic Solvents at Bj&amp;#246;rkdal Gold Mine, Sweden&amp;#160;

&amp;lt;p&amp;gt;The mining and minerals sector faces a dilemma: the transition away from hydrocarbons and race to net-zero requires enormous quantities of mineral resources for low-carbon technologies. However, mining and processing can be hugely energy-intensive and sometimes environmentally harmful, thus there is a need for novel solutions in extraction and processing. Gold ore processing in particular often features cyanide, which is highly toxic and has a large carbon footprint [1], and the often low Au concentrations can result in relatively large volumes of waste rock.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In addition, gold deposits are often also enriched in other metals, and the Bj&amp;amp;#246;rkdal mine produces gold concentrates and tailings that also contain Te, Bi and W. Initial data indicates a high degree of liberation for bismuth-tellurides which is promising for extraction. Currently, there is little incentive and few options by which to process and extract these additional metals for Mandalay Resources, the mine operator [2]. Developing a way in which these metals could be recovered rather than sent to tailings would be ideal for meeting some of the demand for these metals, without opening any new mines.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Deep Eutectic Solvents (DES), developed at the University of Leicester, are a promising advancement for both gold and by-product extraction [3][4]. DES are eutectic mixtures of an organic salt and a hydrogen-bond donor, which are powerful solvents and liquid at room temperature. The components are cheap, readily available and environmentally-benign compounds, such as choline chloride (Vitamin B4) and urea, and can be recycled and reused. Previous work on DES has demonstrated rapid leaching of Au, Ag, Sb, Te and other metals [1], including success with dissolution of tellurides and bismuth tellurides [5].&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This project aims to understand the flows of Te, Bi and W through the Bj&amp;amp;#246;rkdal processing plant, and mineralogically characterise tailings and concentrates. This will be used to test the feasibility and impacts of inserting DES leaching stages, and/or altering the processing procedure or reprocessing tailings, in order to maximise by-product recovery in an environmentally-friendly and low energy process.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;em&amp;gt;References:&amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[1] Norgate T and Haque N (2012) J Clean Prod 29-30:53-63&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[2] Jenkin G et al. (2019) 15th SGA Biennial Meeting 4:1512-1515&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[3] Abbott A et al. (2004) J Am Chem Soc 126 (29):9142-9147&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[4] Smith E et al. (2014) Chem Rev 224 (21):11060-11082&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[5] Jenkin G et al. (2016) Min Eng 87:18-24&amp;lt;/p&amp;gt;
Deep Volumetric Descriptor Learning for Dense Correspondence of Cone-Beam Computed Tomography via Spectral Maps

The deep neural network has achieved great success in 3D volumetric correspondence. These methods infer the dense displacement or velocity fields directly from the extracted volumetric features without addressing the intrinsic structure correspondence, being prone to shape and pose variations. On the other hand, the spectral maps address the intrinsic structure matching in the low dimensional embedding space, remain less involved in volumetric image correspondence. This paper presents an unsupervised deep volumetric descriptor learning neural network via the low dimensional spectral maps to address the dense volumetric correspondence. The neural network is optimized by a novel criterion on descriptor alignments in the spectral domain regarding the supervoxel graph. Aside from the deep convolved multi-scale features, we explicitly address the supervoxel-wise spatial and cross-channel dependencies to enrich deep descriptors. The dense volumetric correspondence is formulated as the low-dimensional spectral mapping. The proposed approach has been applied to both synthetic and clinically obtained cone-beam computed tomography images to establish dense supervoxel-wise and up-scaled voxel-wise correspondences. Extensive series of experimental results demonstrate the contribution of the proposed approach in volumetric descriptor extraction and consistent correspondence, facilitating attribute transfer for segmentation and landmark location. The proposed approach performs favorably against the state-of-the-art volumetric descriptors and the deep registration models, being resilient to pose or shape variations and independent of the prior transformations.
Necessary Knowledge Sharing for Cooperative Learning Among Agent

This chapter discusses the problems of knowledge exchange, transfer, and sharing in cooperative learning among agents. Each learning agent is specialized to interact with the different learning environment and encapsulates a specific set of knowledge independently obtained from his own experiences. The chapter discusses two types of cooperation, the cooperative process model and cooperative learning. In the cooperative model, learning agents put forward their decisions based on their own learnt knowledge, and the negotiation among agents is made in order to reach a group or global decision at the cooperative stage. In cooperative learning, learning agents put forward their learnt knowledge. The chapter explores the concept of learning agent. for the individual agents, each agent, endowed with his own learning capability, is specialized to interact with a specific problem domain, and it learns to improve his problem-solving skills based on his observation and experiences.
CHANGES AND CHALLENGES FACING BY MALAYSIA’S TAMIL SCHOOLS IN TEACHING AND LEARNING DURING MOVEMENT CONTROL IN PANDEMIC OUTBREAK

This article enlightens about the changes that been took place in the schools during pandemic outbreak. As we know, learning is a life long process and it begins in school which it helps young learner to start his journey with a help of a teacher. Sudden changes during pandemic shook entire education system and it forces schools to jump into the new norm. Teachers are trying to teach students with their own effort and ministry helps in providing a clearer guideline for teachers. However, the lack of digital tools such as computers, tab, smartphone and internet access seemed challenging distance learning. Parents are most affected by this learning style which they need to provide everything for their kids. This article discusses about teaching and learning strategies that have been used and challenges that been faced by students, parents, teachers, school management and the ways they took to overcome it.
CTBTO International Data Centre analysis of the Hunga Tonga&amp;#8211;Hunga Ha&amp;#699;apai eruption

&amp;lt;p&amp;gt;Almost 20 years ago, the first infrasound event built only from infrasound arrivals was reported in the Reviewed Event Bulletin (REB) of the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Over the last 25 years, 53 infrasound stations from the International Monitoring System (IMS) have been installed and are transmitting data to the IDC for the purpose of detecting any nuclear explosions in the atmosphere. The infrasound component of the IMS daily registers infragenic signals originating from various sources such as volcanic eruptions, earthquakes, microbaroms, meteorite entering the atmosphere or explosions. The IDC routinely and automatically processes infrasound data with the objective to detect and locate events then reviewed by interactive analysis.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As the IDC advances its methods and continuously improves its automatic system for the infrasound technology, several events received global interest from the scientific community and the public. On 15 February 2013 the Chelyabinsk meteor entered the atmosphere over Ural region (Russian Federation) and generated infrasound waves that were recorded by 20 of the 42 infrasound IMS stations operating at the time. Almost 9 years later, on 15 January 2022 the Hunga Tonga&amp;amp;#8211;Hunga Ha&amp;amp;#699;apai eruption reached a climax around 04:15 UTC, which generated acoustic waves circumnavigating the Earth for several days. In addition to seismic and hydro-acoustic recordings, all 53 IMS infrasound stations registered signals from this eruption. This event is the largest ever recorded by the infrasound component of the IMS network.&amp;lt;/p&amp;gt;
Secure Your Application with OpenId Connect

I can’t finish this book without talking to you about safety. Security is essential in an application, by which I mean that almost all applications need a mechanism to identify the user attempting to perform actions on your application. This is called authentication, which should not be confused with authorization, a mechanism allowing privileges to be given to an authenticated user—that is, allowing the user to perform specific actions that other users will not achieve. In this chapter, you will learn how to do the following:
First-Order Methods for Constrained Optimization

Here, we describe methods for minimizing a smooth function over a closed convex set, using gradient information. We first state results that characterize optimality of points in a way that can be checked, and describe the vital operation of projection onto the feasible set. We next describe the projected gradient algorithm, which is in a sense the extension of the steepest-descent method to the constrained case, analyze its convergence, and describe several extensions. We next analyze the conditional-gradient method (also known as “Frank-Wolfe”) for the case in which the feasible set is compact and demonstrate sublinear convergence of this approach when the objective function is convex.
Revisiting stylolites as a gage of overburden pressure &amp;#8211; insights from fractal analysis

&amp;lt;p&amp;gt;Stylolites are ubiquitous structures generated by pressure solution primarily found in limestones. They and have been used as indicator for maximum stress a rock has suffered. This is commonly done by characterizing the fractal dimensions of stylolites. The current canon is the expectation from the theory that stylolites form through two physical pressure-driven regimes: low-frequency and higher-energetic - dominated by elastic forces and high-frequency lower-energetic dominated by surface tension. The so-called characteristic length separates both regimes, analytically marked by a kink in the power spectrum, which relates the energy contributions to the frequency.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;However, determining this kink is not straightforward, and requires additional assumptions. We present a data set of stylolites recovered from a drill hole in the Alpine foreland basin. We mapped stylolites from different depths at sub-mm resolution semi-automatically and analyzed them using new methods of fractal analysis.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Excitingly, our preliminary studies did not identify the expected kink&amp;amp;#8217;s position from several different images of probes of drill cores, despite satisfactory reliability of laboratory preparation. Standard methods such as power spectral density, averaging wavelet coefficients, RMS, min/max, and rescaled range-based approaches revealed variations in their results, generally without evidence for a kink in the corresponding graphs. Implementing more recently developed methods such as adaptive fractal analysis could not improve the results. This finding challenges the classic interpretation of fractal characteristics of stylolites.&amp;amp;#160;&amp;lt;/p&amp;gt;
Inequity in Popular Speech Recognition Systems for Accented English Speech

Voice-enabled technology has become increasingly common in homes, businesses, and other parts of everyday life. The benefits of smart speakers, hands-free controllers, and digital assistants should be equally accessible to everyone, yet voice recognition performance can be frustratingly low for speakers with accents.In this work, we examine algorithmic bias in several voice recognition systems by measuring standard accuracy metrics on under-represented English accents.
From “Trustless Trust” to “The Great Chain of Certainty”

To what extent can human and institutional actors place their trust in a technology like distributed ledger technology, including blockchain? With the key role that technology increasingly plays in human trusting relationships, exploring the nature of trust in technology (and thus of trust in distributed ledgers as technical artefacts) is important. This chapter defines and explores three distinct but interrelated forms of trust applicable to blockchain technology: the notion of ‘trustless trust’; the notion of ‘user trust’; and the notion of ‘ledger trust’. In considering these three notions of trust, the chapter argues that trust relies—at least in part—upon evidence of the trustworthiness of parties to be trusted. But where does that evidence come from? One such source is records, including the ledgers that have long been used as evidence of all sorts of human transactions, especially financial ones. Distributed ledgers—which include blockchains—thus theoretically contribute to the formation of grounds for trust through affording a new form of recordkeeping that is said to assure trustworthy records as sources of evidence relating to transacting parties’ trustworthiness.
Tools and incentives for the curation of geosciences data. Experiences from GFZ Data Services

&amp;lt;p&amp;gt;GFZ Data Services, hosted at the GFZ German Research Centre for Geosciences (GFZ), is a domain repository for geosciences data that assigns digital object identifier (DOI) to data and scientific software since 2004 and is Allocating Agent for IGSN, the globally unique persistent identifier for physical samples, providing IGSN minting services for physical samples since 2012. The repository provides DOI minting services for several global monitoring networks/observatories in geodesy and geophysics (e.g. INTERMAGNET; IAG Services ICGEM, IGETS, IGS; GEOFON), collaborative projects (TERENO, EnMAP, GRACE, CHAMP) and the curation of long-tail data by domain specialists.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Provision of (1) comprehensive domain-specific data description via standardised and machine-readable metadata with controlled domain vocabularies, (2) complementing the metadata with comprehensive and standardised technical data descriptions or reports; and (3) embedding the research data in wider context by providing cross-references through Persistent Identifiers (DOI, IGSN, ORCID, Fundref) to related research products (text, data, software) and people or institutions involved are used by GFZ Data Services to increase the interoperability of long-tail data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;For their data and software publication activities, GFZ Data Services provides an XML metadata editor18 that assists scientists to create metadata in different international metadata schemas (ISO19115, DataCite), while being at the same time usable by and understandable for the scientists (Ulbricht et al., 2017, 2020). Together with the new website launch of GFZ Data Services in 2022 user guidance has increased significantly and the website has further developed from a searchable data portal (only) to an information point for data publications and data management. This includes information on metadata, data formats, the data publication workflow, FAQ, links to different versions of our metadata editor and downloadable data description templates.&amp;lt;/p&amp;gt;
Seismic Event Characterization using Manifold Learning Methods

&amp;lt;p&amp;gt;Manifold learning is a branch of machine learning that focuses on compactly representing complex data-sets based on their fundamental intrinsic parameters. One such method is diffusion maps, which reduces the dimension of the data while preserving its geometric structure. In this work, diffusion maps are applied to several seismic event characterization tasks. The first task is automatic earthquake-explosion&amp;amp;#160;discrimination, which is an essential component of nuclear test monitoring. We also use this technique to automatically identify mine explosions and aftershocks following large earthquakes. Identification of such events helps to lighten the analysts&amp;amp;#8217; burden and allow for timely production of reviewed seismic bulletins.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The proposed methods begin with a pre-processing stage in which a time&amp;amp;#8211;frequency representation is extracted from each seismogram while capturing common properties of seismic events and overcoming magnitude differences. Then, diffusion maps are used in order to construct a low-dimensional model of the original data. In this new low-dimensional space, classification analysis is carried out.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The algorithm&amp;amp;#8217;s discrimination performance is demonstrated on several seismic data sets. For instance, using the seismograms from EIL station, we identify arrivals that were caused by explosions at the nearby Eshidiya mine in Jordan. The model provides a visualization of the data, organized by its intrinsic factors. Thus, along with the discrimination results, we provide a compact organization of the data that characterizes the activity patterns in the mine.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our results demonstrate the potential and strength of the manifold learning based approach, which may be suitable to other in other geophysics domains.&amp;lt;/p&amp;gt;