Reviewerly/exHarmony huggingface dataset:
Trap-integrated superconducting nanowire single-photon detectors with improved rf tolerance for trapped-ion qubit state readout

State readout of trapped-ion qubits with trap-integrated detectors can address important challenges for scalable quantum computing, but the strong rf electric fields used for trapping can impact detector performance. Here, we report on NbTiN superconducting nanowire single-photon detectors (SNSPDs) employing grounded aluminum mirrors as electrical shielding that are integrated into linear surface-electrode rf ion traps. The shielded SNSPDs can be operated at applied rf trapping potentials of up to 54 Vpeak at 70 MHz and temperatures of up to 6 K, with a maximum system detection efficiency of 68 %. This performance should be sufficient to enable parallel high-fidelity state readout of a wide range of trapped ion species in typical cryogenic apparatus.
Quantum Lazy Training

In the training of over-parameterized model functions via gradient descent, sometimes the parameters do not change significantly and remain close to their initial values. This phenomenon is called <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">lazy training</mml:mtext></mml:mrow></mml:math> and motivates consideration of the linear approximation of the model function around the initial parameters. In the lazy regime, this linear approximation imitates the behavior of the parameterized function whose associated kernel, called the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">tangent kernel</mml:mtext></mml:mrow></mml:math>, specifies the training performance of the model. Lazy training is known to occur in the case of (classical) neural networks with large widths. In this paper, we show that the training of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">geometrically local</mml:mtext></mml:mrow></mml:math> parameterized quantum circuits enters the lazy regime for large numbers of qubits. More precisely, we prove bounds on the rate of changes of the parameters of such a geometrically local parameterized quantum circuit in the training process, and on the precision of the linear approximation of the associated quantum model function; both of these bounds tend to zero as the number of qubits grows. We support our analytic results with numerical simulations.
Dynamical Gauge Fields with Bosonic Codes

The quantum simulation of dynamical gauge field theories offers the opportunity to study complex high-energy physics with controllable low-energy devices. For quantum computation, bosonic codes promise robust error correction that exploits multiparticle redundancy in bosons. Here, we demonstrate how bosonic codes can be used to simulate dynamical gauge fields. We encode both matter and dynamical gauge fields in a network of resonators that are coupled via three-wave mixing. The mapping to a Z_{2} dynamical lattice gauge theory is established when the gauge resonators operate as Schrödinger cat states. We explore the optimal conditions under which the system preserves the required gauge symmetries. Our findings promote realizing high-energy models using bosonic codes.
Determination of bromine, iodine, and other components in northern Hainan island soil

AbstractAbstractThe emergence of the integrated wavelength-dispersive and energy-dispersive Zetium X-ray fluorescence spectrometer technique offered new chances to analyze elemental distribution with minimal sample preparation in complex matrix geological samples-northern Hainan island soils. Wavelength-dispersive X-ray fluorescence was applied for the measurement of bromine in the northern Hainan island soil. To reduce the fluctuations of the low-bromine-content measurement results, a two-channel measurement of bromine was designed, and the average value of the two-channel measurements was used as the final result. The soil samples in northern Hainan Island were rich in iodine, with a content range of a few µg/g to more than 100 µg/g, and these were particularly suitable to be analyzed by energy-dispersive X-ray fluorescence spectrometer. The geological samples in northern Hainan Island were brown, purple, and brown-red soil containing kaolinite, ilmenite, rutile, anatase, and goethite. These soil samples were rich in titanium, aluminum, and iron. The titanium concentration in the northern Hainan Island soil was anomalously high (up to 15.05%), far exceeding the content range of the calibration curve. Owing to the empirical coefficient matrix correction method, unreliable or even unreasonable results were obtained for some elements. By systematically mitigating matrix effects through the development of custom reference materials and building matrix-matched measurement and calibration routines, the quantification of Vanadium, Barium, and major components in northern Hainan Island soil samples was optimized. The efficacy of the X-ray fluorescence spectrometer method was confirmed by comparison with inductively coupled plasma–optical emission spectroscopy analysis. This work demonstrates the capability of the integrated wavelength-dispersive and energy-dispersive Zetium X-ray fluorescence spectrometer technique for reliable quantitative elemental analysis of complex matrix geological samples.Keywords: BromineiodinesoilX-ray fluorescence spectrometer Additional informationFundingWe wish to thank the sponsor of the National Key Research and Development Project (Project number: 2021YFC2903001).
Scheduling of Jobs Allocation for Apache Spark Using Kubernetes for Efficient Execution of Big Data Application

The use of cloud services is in high demand due to their high storage and computing capacity.Apache spark provides an open deployment framework for data storage and computation using cluster computing.The specified spark core scheduler uses FIFO to manage job execution in batches.However, it may not be suitable for large-scale clusters due to the unevenness in managing resource allocation between different types of applications.Because of this, most of the work executors are still underutilized and resources are wasted in all the pods, leading to cost inefficiencies.Using apache spark on kubernetes to run cloud applications will ensure rapid resource management for workload execution.Incoming workloads vary widely across applications, so it is critical to manage workload allocation to ensure QoS and cost efficiency.This paper proposes a job scheduling mechanism (JSM) for apache spark on kubernetes to dynamically schedule job allocation for the efficient execution of various big data applications.The JSM process predicts the cluster load and suggests relocation of workloads to efficiently distribute the workload to the lower loaded pods in a standard cluster to optimize cost performance.It identifies the upcoming workload of a job and determines the best-fit pod and aims to reduce the usage of CPU and memory which result in enhancing cost efficiency.The JSM's effective management of job allocation and migration among the underload pods preserves the resource and enhances cost efficiency.Experimental settings are configured to evaluate cluster resources with benchmark statistics output for application job execution.The outcome results of cost, job performance, and scheduling overhead show improved cost efficiency for job execution.The comparison with the existing scheduler with varying request load shows an improvisation of 2% in cost efficiency and 3% lower scheduling overhead.
The Effect of STEAM on Science Learning on Student Learning Achievement: A Meta-Analysis

STEAM is a popular approach used in learning by integrating several fields of science in the form of science, technology, engineering, art, and mathematics. This study aims to determine the effect of size from studies that discuss the effect of STEAM on science learning on learning achievement. The moderator variable consists of the level of education and the distribution of regions. This research is a meta-analysis study by collecting quantitative data to calculate the effect size of each study. Studies were collected through national and international journal research using Google Scholar and ERIC in the range of 2012 to 2022. The effect of STEAM on science learning on student learning achievement had an average size effect score of 0.71 in the moderate category obtained from 20 studies. The distribution of studies discussing STEAM is most commonly found in the South Korean region. The influence of STEAM varies on the level of education dominated in elementary schools.
Data-ing and Un-Data-ing

research-article Share on Data-ing and Un-Data-ing Authors: Angelika Strohmayer Northumbria University Northumbria UniversityView Profile , Michael Muller IBM Research IBM ResearchView Profile Authors Info & Claims InteractionsVolume 30Issue 3May + June 2023pp 38–42https://doi.org/10.1145/3587240Published:01 May 2023Publication History 0citation248DownloadsMetricsTotal Citations0Total Downloads248Last 12 Months248Last 6 weeks248 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research Committee Update: Hot topics in geophysics: Progress, trends, and perspectives

Over the past decade, computing power has increased, new sensing technologies have been developed, and our understanding of how we interact with the earth has evolved, leading to new opportunities and priorities in geophysical research. These changes have been more rapid in some areas than others, and new topics have emerged as well. It is challenging for geophysicists, including junior staff and undergraduates starting their geophysics journeys, to stay abreast of scientific and industrial trends. Thus, the Early Career Subcommittee of the SEG Research Committee (RC) thought it imperative to survey members of the RC in 2022. To that end, a survey was conducted, and responses were collected from 43 RC members.
Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare.

Home healthcare (HHC) agencies provide care to more than 3.4 million adults per year. There is value in studying HHC narrative notes to identify patients at risk for deterioration. This study aimed to build machine learning algorithms to identify "concerning" narrative notes of HHC patients and identify emerging themes. Six algorithms were applied to narrative notes (n = 4,000) from a HHC agency to classify notes as either "concerning" or "not concerning." Topic modeling using Latent Dirichlet Allocation bag of words was conducted to identify emerging themes from the concerning notes. Gradient Boosted Trees demonstrated the best performance with a F-score = 0.74 and AUC = 0.96. Emerging themes were related to patient-clinician communication, HHC services provided, gait challenges, mobility concerns, wounds, and caregivers. Most themes have been cited by previous literature as increasing risk for adverse events. In the future, such algorithms can support early identification of patients at risk for deterioration.
Semantic Plane-Structure based motion detection with a nonstationary camera

This paper presents a novel method to accurately detect moving objects from a video sequence captured using a nonstationary camera. Although common methods provide effective motion detection for static backgrounds or through only planar-perspective transformation, many detection errors occur when the background contains complex dynamic interferences or the camera undergoes unknown motions. To solve this problem, this study proposed a motion detection method that incorporates temporal motion and spatial structure. In the proposed method, first, spatial semantic planes are segmented, and image registration based on stable background planes is applied to overcome the interferences of the foreground and dynamic background. Thus, the estimated dense temporal motion ensures that small moving objects are not missed. Second, motion pixels are mapped on semantic planes, and then, the spatial distribution constraints of motion pixels, regional shapes and plane semantics, which are integrated into a planar structure, are used to minimise false positives. Finally, based on the dense temporal motion and spatial structure, moving objects are accurately detected. The experimental results on CDnet dataset, Pbi dataset, Aeroscapes dataset, and other challenging self-captured videos under difficult conditions, such as fast camera movement, large zoom variation, video jitters, and dynamic background, revealed that the proposed method can remove background movements, dynamic interferences, and marginal noises and can effectively obtain complete moving objects. © 2017 ElsevierInc.Allrightsreserved.
An unsupervised feature selection approach for actionable warning identification

Static Analysis Tools (SATs) are widely applied to detect defects in software projects. However, SATs are overshadowed by a large number of unactionable warnings, which severely hinder the usability of SATs. To address this problem, the existing approaches commonly use Machine Learning (ML) techniques for Actionable Warning Identification (AWI). For these ML-based AWI approaches, the warning feature determination is one of the most critical parts to effectively identify actionable warnings. To eliminate redundant and irrelevant warning features, ML-based AWI approaches usually incorporate feature selection to determine the feature subset by calculating the importance or correlation of features with warning labels. Nevertheless, warning labels are not always available directly in practice. Thus, it is vital and challenging to select warning features for ML-based AWI approaches when warning labels are absent. To address the above problem, we propose an UNsupervised fEAture SElection approach called UNEASE for ML-based AWI. (1) UNEASE first performs the feature clustering to gather warning features into clusters, where the number of clusters is automatically determined and features in the same cluster are considered redundant. (2) Subsequently, UNEASE performs the feature ranking to sort warning features in each cluster with three newly proposed ranking strategies and selects the top-ranked warning feature from each cluster. Based on the selected features, we train a ML classifier to identify actionable warnings. We conduct experiments in eight large-scale and real-world warning datasets. Comparing UNEASE with nine typical feature reduction techniques, the experimental results show that while taking the low cost to perform the feature selection and maintaining the low redundancy rate in the selected warning features, UNEASE obtains the top-ranked AUC.
ENHANCHING STUDENTSâ€™ WRITING SKILL THROUGH MALL: WHATSAPP MESSAGES IN TEACHING WRITING

Mobile technologies offer numerous practical uses in language learning. The objective of this study to investigate whether or not the application of whatsapp group could improve studentsâ€™ English writing achievement. The participant of this study were the second semester of English study program that consisted of 40 students. Quasi â€“ Experimental design was applied in this study. In collecting the data, written test is given to sample. The result of data analysis showed that the students got better improvement in English writing ability after being treated by using Whatsapp Group Discussion. It based on the result of independent t-test whereas the t obtain was 5.298 and the significant level 0.00. Futhermore, it can be concluded that there was a significant difference between the student who were taught by using whatsapp group discussion and those who were not. In other words, Whatsapp group is a good teaching media and strategy in increasing studentsâ€™ writing achievement. Therefore, it can be said that MALL is an appropriate teaching strategy in teaching writing for EFL students.
THE CORRELATION AMONG SELF-CONCEPT, READING ATTITUDE, AND THE NINTH GRADE STUDENTSâ€™ READING COMPREHENSION OF STATE JUNIOR HIGH SCHOOL 51 PALEMBANG

This research aims to determine the relationship between self-concept, reading attitudes, and reading comprehension of the ninth grade students of SMPN 51 Palembang. In this study 30 ninth grade students were selected as a stratified random sample. and tests are used to collect data. The analysis was performed using the Pearson product moment correlation coefficient, Normality Test, Homogeneity Test, Linearity Test and multiple regression. The results showed that the correlation coefficient between self concept showed the level of significant was 0.000 &lt;0.05. showed the level of significant was 0.000 &lt;0.05. The correlation coefficient between self concept showed the level of significant was 0.000 &lt;0.05. The correlation coefficient between self concept showed the level of significant was 0.000 &lt;0.05.
[A survey of loss function of medical image segmentation algorithms].

Medical image segmentation based on deep learning has become a powerful tool in the field of medical image processing. Due to the special nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positive, false negative, etc. In view of these problems, researchers mostly improve the network structure, but rarely improve from the unstructured aspect. The loss function is an important part of the segmentation method based on deep learning. The improvement of the loss function can improve the segmentation effect of the network from the root, and the loss function is independent of the network structure, which can be used in various network models and segmentation tasks in plug and play. Starting from the difficulties in medical image segmentation, this paper first introduces the loss function and improvement strategies to solve the problems of sample imbalance, edge blur, false positive and false negative. Then the difficulties encountered in the improvement of the current loss function are analyzed. Finally, the future research directions are prospected. This paper provides a reference for the reasonable selection, improvement or innovation of loss function, and guides the direction for the follow-up research of loss function.基于深度学习的医学图像分割方法已经成为了医学图像处理领域的强大工具。由于医学图像的特殊性质，基于深度学习的图像分割算法面临样本不平衡、边缘模糊、假阳性、假阴性等问题，针对这些问题，研究人员大多对网络结构进行改进，而很少从非结构化方面做出改进。损失函数是基于深度学习的分割方法中重要的组成部分，对损失函数的改进可以从根源上提高网络的分割效果，并且损失函数与网络结构无关，可以即插即用地运用在各种网络模型和分割任务中。本文从医学图像分割任务中的困难出发，首先介绍了解决样本不平衡、边缘模糊、假阳性、假阴性问题的损失函数及改进策略；然后对目前损失函数改进过程中所遇到的困难进行分析；最后对未来的研究方向进行了展望。本文将为损失函数的合理选择、改进或创新提供参考，并为损失函数的后续研究指引方向。.
DEVELOPMENT OF INTERACTIVE MULTIMEDIA MATERIALS ON ANIMAL CLASSIFICATION BASED ON FOOD CATEGORY AT GRADE V ELEMENTARY SCHOOL

This paper reports a developmental product in the form of interactive multimedia on animal classification material based on food category in class V SD with valid and practical criteria.The method used is the Research and Development (RnD) method with the Plomp model.The Plomp model is carried out in three phases, namely Preliminary Research, Development or Prototyping, and Assessment.The researcher tested the validity of three validators, which consist of media, material experts, and language experts or linguists.To see the practicality of the product that is developed, the researcher conducted a limited trial in the form of a small group trial with teachers and students.The results of the validity test indicate that the developed interactive multimedia was in a very valid category with a percentage of 93.205%.Meanwhile, the practicality of interactive multimedia based on the teacher's response questionnaire was 90.55% in the very practical category and based on the student questionnaire the percentage was 92.25% in the very practical category.Based on the results of the research, it can be inferred that interactive multimedia meets valid and practical criteria, so it is recommended to apply it on natural science learning subjects at school.
Digital Literacy: Classroom Action Research for Vocational High School Students'

Purpose of the study: The purpose of this research is to find out students' digital literacy abilities and find out what actions are appropriate to take in order to increase students' digital literacy.&#x0D; Methodology: This study used a class action research method with the Jhon Elliot research model design. This research model consists of a cycle in which there are several stages, namely design, implementation (action), observation, and reflection&#x0D; Main Findings: The findings of this research is that students' digital literacy skills in the first cycle with conventional learning models are dominant in the bad category. Then in the second cycle by using a project-based learning model and utilizing technology directly it is known that students' digital literacy has increased where it is more dominant in the good category. So that the actions that can be taken by educators to improve students' digital literacy are by using a project-based learning model that is supported by sufficient learning media.&#x0D; Novelty/Originality of this study: The novelty of this research is that it was conducted at the vocational high school level, then focused on appropriate classroom actions to increase students' digital literacy in Indonesian language subjects.
Maximum Likelihood Distillation for Robust Modulation Classification

Deep Neural Networks are being extensively used in communication systems and Automatic Modulation Classification (AMC) in particular. However, they are very susceptible to small adversarial perturbations that are carefully crafted to change the network decision. In this work, we build on knowledge distillation ideas and adversarial training in order to build more robust AMC systems. We first outline the importance of the quality of the training data in terms of accuracy and robustness of the model. We then propose to use the Maximum Likelihood function, which could solve the AMC problem in offline settings, to generate better training labels. Those labels teach the model to be uncertain in challenging conditions, which permits to increase the accuracy, as well as the robustness of the model when combined with adversarial training. Interestingly, we observe that this increase in performance transfers to online settings, where the Maximum Likelihood function cannot be used in practice. Overall, this work highlights the potential of learning to be uncertain in difficult scenarios, compared to directly removing label noise.
Counterfactual Explanation for Multivariate Times Series Using A Contrastive Variational Autoencoder

We tackle the issue of anomaly detection for multivariate functional data in a supervised setting. Deep learning applied to multivariate time series has become common nowadays, especially for medical data such as electrocardiogram (ECG). There are not many explanability techniques that can handle multivariate time series. In this paper, we propose a model to understand abnormal class features on multivariate time series. We present a new variational autoencoder (VAE) training method that focuses on dividing the latent space into general and class-based features using supervised contrastive learning. The Contrastive VAE produces a well-organized latent space that enables us to modify only the class-based features and to use the generative part of the VAE to produce counterfactual examples. This method is able to easily provide plausible counterfactual observations, which highlights the differences between pathological and non-pathological data. We demonstrate the superiority of our approach over other counterfactual methods in terms of validity and performance.