Reviewerly/exHarmony huggingface dataset:
Operational interpretation and estimation of quantum trace-norm asymmetry based on weak-value measurement and some bounds

The asymmetry of a quantum state relative to a translational group is a central concept in many areas of quantum science and technology. An important and geometrically intuitive measure of translational asymmetry of a state is given by the trace-norm asymmetry, which is defined as the trace norm of the commutator between the state and the generator of the translation group. While trace-norm asymmetry satisfies all the requirements for a bona fide measure of translational asymmetry of a state within the quantum resource theoretical framework, its meaning in terms of laboratory operations is still missing. Here, we first show that the trace-norm asymmetry is equal to the average absolute imaginary part of the weak value of the generator of the translation group optimized over all possible orthonormal bases of the Hilbert space. Hence, it can be estimated via the measurement of weak value combined with a classical optimization in the fashion of quantum variational circuit which may be implemented using the near-term quantum hardware. We then use the link between the trace-norm asymmetry and the nonreal weak value to derive the relation between the trace-norm asymmetry with other basic concepts in quantum statistics. We further obtain trade-off relations for the trace-norm asymmetry and quantum Fisher information, having analogous forms to the Kennard-Weyl-Robertson uncertainty relation.
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected.Once initialized, the connection strengths remain unchanged.Such a simple structure turns RC into a non-linear dynamical system that maps lowdimensional inputs into a high-dimensional space.The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications.RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices.This yields greater flexibility and shorter computation time.Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes.While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience.We first review the early RC models, and then survey the state-of-the-art models and their applications.We further introduce studies on modeling the brain's mechanisms by RC.Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.
An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C)

Abstract Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.
Virtual Machine Migration Techniques for Optimizing Energy Consumption in Cloud Data Centers

The energy used by cloud data centers (CDCs) to support large volumes of data storage and computation is dramatically increasing as the scope of cloud services continues to expand. This puts a greater burden on the environment and results in higher expenses for cloud providers. Virtualization migration and consolidation have been widely used in current CDCs to achieve ser-vice consolidation and reduce energy consumption (EC). This study divides the fundamental tasks of virtual machine (VM) migration into three portions: determining migration timing, choosing the VMs to migrate out, and selecting the migration destination hosts. An EC levels-based adaptive dynamic threshold method for determining migration timing was proposed, as well as a correlation and utilization-based strategy for selecting the VMs to migrate out and an improved EC-aware best-fit algorithm for selecting the migration destination hosts. The pro-posed algorithms were evaluated using the CloudSim toolbox, and the real VM workload traces from PlanetLab were used as experimental data. According to the experiments, the proposed algorithms reduce EC, service level agreement violation (SLAV), and the number of VM migrations by an average of 15.49%, 7.85%, and 83.32% in comparison to the related state-of-the-art methods and benchmark algorithms. This suggests that the proposed methods outperform other techniques for VM migration, even when the workload necessitates a significant number of VMs or a greater amount of host resources, and improve the quality of service while optimizing energy consumption. However, the experiments were conducted in a simulation platform, which has some drawbacks, leading to the experimental results varying slightly from the actual environment.
Digital cities and villages: African writers and a sense of place in short online fiction

This article analyses how young African writers challenge stereotypes about the continent through their imagination of places in online short stories. These stories appear on the literary websites Brittle Paper, Jalada, Saraba, Flash Fiction Ghana, Adda and African Writer Magazine with a focus on cities and villages. Authored by ten writers from Ghana, Nigeria, Kenya, Malawi and Egypt, the stories contain elements of fiction that risk perpetuating negative stereotypes about Africa as they imagine their respective settings. However, textual analysis supported by an appreciation of context reveals how the writers use these stereotypes as basis to craft strong African narratives. By doing so, the writers emphasize the effect that places have on characters, theme, setting and the image of Africa. Ultimately, the roles that urban and rural spaces play in online fiction are multifaceted and enhance the African narrative in complex ways.
Dynamic Keypoint Detection Network for Image Matching

Establishing effective correspondences between a pair of images is difficult due to real-world challenges such as illumination, viewpoint and scale variations. Modern detector-based methods typically learn fixed detectors from a given dataset, which is hard to extract repeatable and reliable keypoints for various images with extreme appearance changes and weakly textured scenes. To deal with this problem, we propose a novel Dynamic Keypoint Detection Network (DKDNet) for robust image matching via a dynamic keypoint feature learning module and a guided heatmap activator. The proposed DKDNet enjoys several merits. First, the proposed dynamic keypoint feature learning module can generate adaptive keypoint features via the attention mechanism, which is flexibly updated with the current input image and can capture keypoints with different patterns. Second, the guided heatmap activator can effectively fuse multi-group keypoint heatmaps by fully considering the importance of different feature channels, which can realize more robust keypoint detection. Extensive experimental results on four standard benchmarks demonstrate that our DKDNet outperforms state-of-the-art image-matching methods by a large margin. Specifically, our DKDNet can outperform the best image-matching method by 2.1% in AUC@ 3px on HPatches, 3.74% in AUC@ <inline-formula><tex-math notation="LaTeX">$5^\circ$</tex-math></inline-formula> on ScanNet, 7.14% in AUC@ <inline-formula><tex-math notation="LaTeX">$5^\circ$</tex-math></inline-formula> on MegaDepth and 12.32% in AUC@ <inline-formula><tex-math notation="LaTeX">$5^\circ$</tex-math></inline-formula> on YFCC100M.
The Development of Decentralized Governance Models for Web 3 Ecosystems

This chapter delves into the development of decentralized governance models for Web 3 ecosystems. It analyzes the shortcomings of traditional governance models and how decentralized models can overcome them. The chapter then investigates several decentralized governance models, such as reputation-based systems, liquid democracy, and decentralized autonomous organizations (DAOs), as well as their benefits and drawbacks. The challenges associated with establishing decentralized governance models are also explored. Lastly, the chapter explores potential future advancements as well as best practices for creating and implementing decentralized governance models in Web 3 ecosystems. All in all, the importance of decentralized governance models in encouraging openness, participation, and accountability in Web 3 ecosystems is highlighted in this chapter.
Inhibitory tagging in the superior colliculus during visual search

Searching a cluttered scene for an object of interest is a ubiquitous task in everyday life, which we often perform relatively quickly and efficiently. It has been suggested that to achieve such speed and efficiency an inhibitory-tagging mechanism inhibits saccades to objects in the scene once they have been searched and rejected. Here, we demonstrate that the superior colliculus represents this type of inhibition during search, consistent with its role in saccade target selection.
Semantic Parsing for Conversational Question Answering over Knowledge Graphs

In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.
The Mediating Role of the Participatory Leadership Style on the Effect of Job Stress on Job Performance

The aim of the current study is to shed light on the impact of workplace stress on job performance in Iraqi public sector universities through the mediating role of leadership style. Where stresses in the workplace were measured through three types: role conflict, role burden and work conditions, job performance was determined as a dependent variable, the participatory leadership style was tested as a mediating variable for the relationship, the structural equation modeling method was applied to test the study model. A sample of professors of public universities in Iraq-Baghdad was selected, and 120 answers were collected that were valid for analysis. The study concluded that conflicting roles and working conditions are the factors that have an impact on academic performance. In contrast to the workload, which did not have a statistically significant effect on the performance of university professors. As for the mediation role of participatory management, it was contrary to expectations. It was not a contributing factor in reducing the effects of work stresses on the performance of university professors. These results are attributed to the fact that the university work environment affects its leaders and professors and is governed by the instructions and regulations of higher authorities.
Essential requirements for the governance and management of data trusts, data repositories, and other data collaborations

Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation while ensuring that data are secure and privacy is protected. As a contribution to broader efforts to improve data governance and management, in 2020 members of our team published 12 minimum specification essential requirements (min specs) to provide practical guidance for organisations establishing or operating data trusts and other forms of data infrastructure.We convened an international team, consisting mostly of participants from Canada and the United States of America, to test and refine the original 12 min specs. Twenty-three (23) data-focused organisations and initiatives recorded the various ways they address the min specs. Sub-teams analysed the results, used the findings to make improvements to the min specs, and identified materials to support organisations/initiatives in addressing the min specs.Analyses and discussion led to an updated set of 15 min specs covering five categories: one min spec for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. Multiple changes were made to make the min specs language more technically complete and precise. The updated set of 15 min specs has been integrated into a Canadian national standard that, to our knowledge, is the first to include requirements for public engagement and Indigenous Data Sovereignty.The testing and refinement of the min specs led to significant additions and improvements. The min specs helped the 23 organisations/initiatives involved in this project communicate and compare how they achieve responsible and trustworthy data governance and management. By extension, the min specs, and the Canadian national standard based on them, are likely to be useful for other data-focused organisations and initiatives.
BlockAuth: A blockchain-based framework for secure vehicle authentication and authorization

Intelligent Transport System (ITS) offers inter-vehicle communication, safe driving, road condition updates, and intelligent traffic management. This research intends to propose a novel decentralized “BlockAuth” architecture for vehicles, authentication, and authorization, traveling across the border. It is required because the existing architects rely on a single Trusted Authority (TA) for issuing certifications, which can jeopardize privacy and system integrity. Similarly, the centralized TA, if failed, can cause the whole system to collapse. Furthermore, a unique “Proof of Authenticity and Integrity” process is proposed, redirecting drivers/vehicles to their home country for authentication, ensuring the security of their credentials. Implemented with Hyperledger Fabric, BlockAuth ensures secure vehicle authentication and authorization with minimal computational overhead, under 2%. Furthermore, it opens up global access, enforces the principles of separation of duty and least privilege, and reinforces resilience via decentralization and automation.
Effect of entanglement in the generalized photon subtraction scheme

Abstract The paper considers the possibility of generating different non-Gaussian states using the entangled state photon measurement scheme. In the paper, we have proposed a way to explicitly find the wave function and the Wigner function of the output state of this scheme. Moreover, the solutions found are not restricted to any particular case, but have maximum generality (depend on the number of measured photons and on all parameters of the scheme). Such a notation allowed us to carry out a complete analysis of the output states, depending on the scheme parameters. Using explicit expressions, we have analyzed the magnitude of non-Gaussianity of the output states, and we have revealed which particular states can be obtained in the proposed scheme. We have considered in detail a particular case of measurement (single photon measurement) and have shown that using explicit expressions for the output state wave function one can find scheme parameters to obtain states suitable for quantum error correction codes with a large fidelity value and high probability. The Schrodinger’s cat state with amplitude α = 2 can be obtained with fidelity F ≈ 0.88 and probability 18 percent, and the squeezed Schrodinger’s cat state ( α = 0.5, R = 1) with fidelity F ≈ 0.98 and probability 22%.
The Asymmetric Effects of Foreign Direct Investment Inflow on Economic Growth: Fresh Evidence from Somalia

Purposes: Although there has been some progress since the 2012 establishment of the Federal Government Somalia, Foreign direct investment is the one of critical engines in economic growth. The latest value of foreign direct investment for 2021 was 5.97% of GDP, comparison of previous years to 2020 was 6.74% of GDP in Somalia. This paper aims to investigate the asymmetric effects of foreign direct investment (FDI) inflow on economic growth in Somalia from 1977-2021. In addition to FDI, inflation, gross capital formation, and trade openness are used as control variables to examine their influence on economic growth. Design/Methodology/approach: This research employs the nonlinear autoregressive distributed lag (NARDL) technique. Finding: The findings reveal that a positive shock in FDI positively influences Somalia's economic growth, while a negative FDI has a negative impact. The Wald test establishes the asymmetric effect of FDI on economic growth in both the short and long run. Moreover, trade openness and inflation rate significantly reduce the pace of economic growth in both the long and short run. However, gross capital formation only boosts economic growth in the long run. Research limitations/ implications: although Somali faced a lot of challenges including civil war, terrorists, macroeconomics instability, poor infrastructure, and corruptions are not considered owing to data availability. Policy implications: firstly, increase revenue through tax reforms and anti-corruption measures. Secondly, initiatives policies that help public finance investments and reduce trade barriers. Finally, maintain macroeconomic stability of inflation, and exchange rates, can help reduce the negative impact on economic growth improve the investment climate and Somalia’s image abroad.
Digital competence of the university student. A systematic and bibliographic update

Society has experienced an essential technological revolution in recent decades, generating significant repercussions in the educational field, especially in higher education. This technological evolution demands the development of digital skills that allow us to face problems and situations in this environment. In this sense, Higher Education Institutions (HEIs) have attached great importance to the training in digital skills of university students in their academic policies. Digital competence implies the knowledge, abilities, skills, and attitudes necessary to access, analyze, evaluate, reflect critically, create, and communicate in four key areas: technological, informational, multimedia, and communicative. The objective of this article is to analyze documents related to the digital competencies of university students. To achieve this, a descriptive bibliometric study was carried out. Among the conclusions, the need to strengthen the training in digital skills of university students is highlighted so that they can take advantage of the communication tools available in the knowledge society.
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection

Cereal grain plays a crucial role in the human diet as a major source of essential nutrients. Grain Appearance Inspection (GAI) serves as an essential process to determine grain quality and facilitate grain circulation and processing. However, GAI is routinely performed manually by inspectors with cumbersome procedures, which poses a significant bottleneck in smart agriculture. In this paper, we endeavor to develop an automated GAI system:AI4GrainInsp. By analyzing the distinctive characteristics of grain kernels, we formulate GAI as a ubiquitous problem: Anomaly Detection (AD), in which healthy and edible kernels are considered normal samples while damaged grains or unknown objects are regarded as anomalies. We further propose an AD model, called AD-GAI, which is trained using only normal samples yet can identify anomalies during inference. Moreover, we customize a prototype device for data acquisition and create a large-scale dataset including 220K high-quality images of wheat and maize kernels. Through extensive experiments, AD-GAI achieves considerable performance in comparison with advanced AD methods, and AI4GrainInsp has highly consistent performance compared to human experts and excels at inspection efficiency over 20x speedup. The dataset, code and models will be released at https://github.com/hellodfan/AI4GrainInsp.
Nutritional Knowledge and Dietary Habits of Student-Athletes in a State University: Towards a Contextualized Nutrition Education Plan

Sustainable Development Goal 2 aims to end hunger, achieve food security, improve nutrition, and promote healthy and sustainable diets. Student-athletes deal with unique challenges due to the demands of physical and mental requirements of their sports, making proper nutrition and dietary habits crucial for their overall health and performance. Therefore, this study aimed to analyze the relationship between nutritional knowledge and dietary habits among State Colleges and Universities Athletic Association (SCUAA) Athletes at a state university in Pampanga, Philippines, as a basis for the development of a contextualized Nutrition Education Plan (NEP). The study was grounded on a postpositivist philosophical worldview that employed a quantitative non-experimental approach, specifically a correlational design. A total of 177 student-athletes were assessed for their nutritional knowledge and dietary habits using survey questionnaires. It was determined that student-athletes’ level of nutritional knowledge was described as “knowledgeable” while their perceived dietary habits were described as “good”, but still, there is an area for continued improvement. Meanwhile, it was found that there was no significant difference between nutritional knowledge and dietary habits of student-athletes when grouped according to their demographic profile. On the other hand, there was a significant but weak positive relationship between nutritional knowledge and dietary habits of student-athletes. The results of student-athletes’ nutritional knowledge and dietary habits paved the way for a contextualized NEP anchored on the student-athlete. The study recommends generalizing the NEP and considering the other athletes who also need nutrition and dietary habits improvement.