Reviewerly/exHarmony huggingface dataset:
Tuning atom-field interaction via phase shaping

A coherent electromagnetic field can be described by its amplitude, frequency, and phase. All these properties can influence the interaction between the field and an atom. Here we demonstrate the phase shaping of microwaves that are scattered by a superconducting artificial atom coupled to the end of a semi-infinite one-dimensional transmission line. In particular, we input a weak exponentially rising pulse with phase modulation to a transmon qubit. We observe that atom-field interaction can be tuned from a nearly full interaction (interaction efficiency, i.e., amount of the field energy interacting with the atom, of 94.5 %) to effectively no interaction (interaction efficiency of 3.5 %).
Solar Radiation Forecasting Models and their Thermodynamic Analysis in Asaba: Least Square Regression and Machine Learning Approach

Least square regression and machine learning tools were used for the development of global solar radiation forecasting models for Asaba region. Data from the year 2013-2022 from Nigerian Meteorological Agency, Asaba was used for this study. The least square regression method was used to develop four global solar radiation -based models, tagged H1, H2, H3 and H4 with characteristic day length, solar declination angle, rainfall amount, etc. as its model terms while the machine learning models produced multilayer perceptron, coarse Gaussian model (SVM-based model) and XGBoost model. The prediction factors like mean bias error, mean percentage error, root mean square error, Nash-Sutcliffe equation, coefficient of correlation (R), t-test, and coefficient of determination (R2) were considered using the model terms. The results indicates that H4 model outperformed H1, H2, H3, machine learning models (SVM-based model, multilayer perceptron and XGBoost) and other existing models (MA-MME and MLR) with a mean percentage error value of 0.740, RMSE value of 46.588, Nash-Sutcliffe equation value of 0.739, higher R2 value of 0.7391, t-test value of 2.595E-24 and mean bias error value of -6.88E-12. Thus, H4 model results fell within accepted range. Additionally, the exergy of the global solar radiation of Asaba varied from 20-185 W/m2 which are good. This shows that a more efficient and ideal global solar radiation prediction model (H4) has been developed for Asaba and other regions that share similar climatic conditions.
Automated Detection and Machine Learning‐Based Classification of Seismic Tremors Associated With a Non‐Volcanic Gas Emission (Mefite d’Ansanto, Southern Italy)

Abstract A major aim in the study of crustal fluids is the development of automatic methodologies for monitoring deep‐source, non‐volcanic gas emissions’ spatio‐temporal evolution. Crustal fluids play a significant role in the generation of large earthquakes and the characterization of their emissions on the surface can be essential for better understanding crustal processes generating earthquakes. We investigate seismic tremors recorded over 4 days in 2019 at the Mefite d’Ansanto (southern Apennines, Italy) that is located at the northern end of the fault system that generated the Mw 6.9 1980 Irpinia Earthquake. The Mefite d’Ansanto is hypothesized to be the largest natural, non‐volcanic, CO 2 ‐rich gas emission on Earth. The seismic tremor is studied by employing a dense temporary seismic network and an automated detection algorithm based on non‐parametric statistics of the recorded signal amplitudes. We extracted signal characteristics (RMS amplitude and statistical moments of amplitudes both in time and frequency domains) for use in the subsequent supervised machine‐learning classification of the target tremor and accidently detected anthropogenic and background noise. The data set is used for the training and optimization of station‐based KNN (k‐Nearest‐Neighbors) binary classifiers obtaining good classification performances with a median overall accuracy across all stations of 92.8%. The classified tremor displayed common features at all stations: variable duration (16 s to 30–40 min), broad peak frequency (4–20 Hz) with varying amplitudes, and two types of signals: (a) long‐duration, high‐amplitude tremor and (b) pulsating tremor. Higher tremor amplitudes recorded at stations closer to local bubbling and pressurized vents suggest multiple local tremor sources.
Offline Handwritten Signature Identification based on Hybrid Features and Proposed Deep Model

Handwritten signature identification is the process of identifying the true identity of an individual by analyzing their signature. This is an important task in applications such as financial transactions, legal documents, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. This paper proposes an authentic signature identification method based on integrating static (off-line) signature data and proposed deep-based model, this is done by fused three types of signature features, Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency-features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, the fused features are inputted to the proposed deep-based model of 25 layers for identifying each person. For experiments, we have used three datasets: Our own private collected dataset, called SigArab, and two public datasets called SigComp11 and CEDAR respectively. The proposed deep model achieves 99.23%, 100%, 100% accuracy on SigArab, CEDAR and SigComp2011 datasets.
Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

Abstract The Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.
LoHDP: Adaptive local differential privacy for high‐dimensional data publishing

Summary The increasing availability of high‐dimensional data collected from numerous users has led to the need for multi‐dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters. These models are then used to derive low‐dimensional marginals of these clusters, allowing for an approximation of the distribution of the original dataset and the generation of synthetic datasets. Existing solutions have limitations in computing the marginals of pairwise attributes and multi‐dimensional distribution on attribute clusters, as well as constructing relational dependency graphs that contain large clusters. To address these problems, we propose LoHDP, a high‐dimensional data publishing method composed of adaptive marginal computing and an effective attribute clustering method. The adaptive local marginal calculates any k ‐dimensional marginals required in the algorithm. In particular, methods such as sampling‐based randomized response are used instead of privacy budget splits to perturb user data. The attribute clustering method measures the correlation between pairwise attributes using an effective method, reduces the search space during the construction of the dependency graph using high‐pass filtering technology, and realizes dimensionality reduction by combining sufficient triangulation operation. We demonstrate through extensive experiments on real datasets that our LoHDP method outperforms existing methods in terms of synthetic dataset quality.
Research on Governance of Higher Engineering Education Quality in China after Accessing the Washington Accord

Abstract As an important quality assurance mechanism, the program accreditation of engineering education has become the basic mechanism for improving the quality of engineering education in various countries. Under the guidance of the CPC Central Committee and the State Council, China has established quality assurance mechanism of engineering education. In June 2016, China officially accessed the Washington Accord, which symbolized that its engineering education accreditation system has been recognized by the international engineering community. However, China's accessing the Washington Accord as a full signatory is only the beginning of integrating into the international engineering community. China must actively respond to the contrasts with international engineering education, and optimize the accreditation system of engineering education. Thus, this research attempts to propose an emerging concept, i.e., governance of higher engineering education quality; draw on the Governance Theory to establish a logical framework for the governance of higher engineering education quality in China around the dimensions of governance motivation, subjects, objects, purposes and approaches, so as to provide "China Solution" in the context of global quality assurance in engineering education.
Implementation of Actionable Gamification Design Framework in Machining Training

Abstract With the development of Industry 4.0 technologies adopted in machining industry, more and more machine tools and systems become smarter and automatic. However, contrary to our expectations, the machining automation has not much reduced the labor shortage issue in the machining industry, and this demand gap is gradually expanding in recent years. Although this issue is multiaspect and complicated, it is evident that one of the main reasons comes from the conventional training system. By comparing with the outdated training content, which has been realized and emphasized by machining training providers, the problems regarding the training method have not yet attracted sufficient attention. In order to realize the training outcomes efficiently, instructor-led and on-the-job training methods are widely adopted by most of the machining training programs. However, trainee's engagement, i.e., interest and involvement, is often ignored, which is one of the main reasons for downward enrollment and retention rate of many machining related programs from trade schools or community colleges. To engage the younger generation into this career field, more attention should be paid to incorporate more training methods favorite with young students, and the interactive training method with game-based elements has been proved to be a good solution in many similar applications. Gamification, a technique to facilitate training and learning within the playable context, has the potential to enhance training engagement. In a gamified application, game design elements and gameplay mechanics are added to motivate trainees to stay in the loop of repeating necessary equipment operations while mitigating the mental fatigue they might have due to lack of interest and the boreout syndrome at machining workspace. Gamification is promising to improve self-motivation by stimulating the desire to pursue a gaming-inspired goal (i.e., reaching a winning condition in the game or earning a level-up badge to show off on the profile). By connecting the training tasks to a gaming goal, trainees would be more likely to enjoy doing the practice and feel less forced. The objective of this study is to systematically evaluate the conventional machining training under the guidance of the Octalysis Framework with eight core drives of gamification and come up with the Octalysis score to each core drive. In addition, the Octalysis for conventional machining training will be compared with the designs and mechanics of successful skill-learning games to identify the strong and weak aspects of learning engagement. From this study, it is expected to propose a new perspective to evaluate the current machining training method on student attraction and engagement and improve it with appropriate game-based elements.
Reweighting trajectory segments into steady state without knowledge of the sampling distribution

Molecular dynamics (MD) and enhanced sampling simulations have become fundamental tools for studying biomolecular events. A significant challenge in these simulations is ensuring that sampled configurations and transitions converge to the stationary distribution of interest, whether equilibrium or nonequilibrium. Lack of convergence constrains the estimation of mechanisms, free energy, and rates of complex molecular events. Here, we introduce the “randomized iterative trajectory reweighting” algorithm to estimate a stationary distribution from unconverged simulation data, applicable to trajectories generated without biasing forces. This method iteratively reweights trajectories based on the stationary solution of a coarse-grained transition matrix, employing a new random clustering in each iteration. The iterative random clustering mitigates the phase-space discretization error inherent in existing trajectory reweighting techniques based on one-shot clustering and ultimately yields numerically unbiased quasi-continuous configuration-space distributions and estimates of observables. We demonstrate this procedure for both equilibrium and nonequilibrium steady states, using synthetic molecular dynamics (synMD) trajectories to enable comparison with exact reference values.
Classifying Negative Objects With Neural Networks

&lt;p&gt;The term &lt;em&gt;object&lt;/em&gt; is generally only associated with something that can be sensed. However, the empty space between and around objects can also be considered as objects, which we will define as negative objects. While incredibly important in some instances, they have been neglected by the research community in comparison to their positive counterparts. Without a properly developed lexicon for them, negative objects are hard to discuss and describe. This thesis develops the starting point for a lexicon for negative objects, builds a publicly accessible dataset, and demonstrates that they can be identified within an image with the application of a machine learning algorithm. The neural network performs at an average precision of 74% when identifying a certain type of negative object (holes). This algorithm also shows promise in being able to differentiate between holes and tunnels.&lt;/p&gt;
Distance Measure Concepts for Bayesian Inference of Chaotic Dynamical System Parameters

Abstract The problem of determining the parameters of dynamical systems often reduces to developing some notion of a "distance" between observed and simulated system trajectory data. The best parameter fit can then be found by adjusting the parameters that generate the simulated trajectory until the distance is minimized. However, in the case of chaotic dynamical systems, traditional distance measures such as the Mean Square Error (MSE) often fail to produce good results, a consequence of these systems' inherent sensitivity to changes in parameters and initial conditions. In this paper, we adopt the perspective that more robust distance measures can be formulated when the trajectories of these chaotic systems are treated as samples from probability distributions, rather than as time series data. Within this perspective, we evaluate the efficacy of three candidate distance measure concepts: the correlation integral likelihood (proposed by Haario et al.), the Wasserstein metric, and a family of information-theoretic distances based on the Kullback-Leibler divergence. We give particular emphasis to the performance of these methods on the Lorenz63 system, a canonical chaotic system with applications in modeling atmospheric convection.
Closed-loop circuit model of coagulation physiology

Clinical anticoagulation therapy is dosed at a standard dose or according to weight, with adjustments made based on laboratory test targets. Patients are at risk of over or under-anticoagulation given lengthy return times for many of these tests. To this end, the authors aim to develop a model of the physiologic coagulative response to anticoagulation administration that reflects real-time dose adjustments. Here, we present our closed-loop multi-element model of physiologic coagulation which is the foundation for this project. The authors have accomplished the basic circuit element model of 64 coupled differential equations describing the human coagulation cascade and feedback paths that incorporate rate kinetics of factor activation. The authors developed a Python script able to convert a large number of equations to a form compatible with SPICE circuit modeling software, which was then used to generate a multi-input circuit model of the differential equations. The equations were converted to a state space representation and used to derive a linearized approximation about the steady state. SPICE simulates internal feedback loops of the coagulation cascade over time with initial conditions representing normal coagulability. The authors are employing the law of conservation of mass to piecewise validate this model by observing species for concentration changes. Our model demonstrates changes in each coagulation factor concentration as an output over a specified time, given initial species concentrations as inputs. These initial parameters may be adjusted to reflect a starting physiologic state of coagulation. The authors observed little change in concentration for components whose eigenvalues equal 0, suggesting initial conditions are very close to steady state values. Coagulation physiology may be simplified into a circuit model that allows for real-time physiologic visualization. This will be useful in timely adjustments of anticoagulation therapy.
Continuous-Variable Quantum State Designs: Theory and Applications

We generalize the notion of quantum state designs to infinite-dimensional spaces. We first prove that, under the definition of continuous-variable (CV) state t-designs from [Blume-Kohout et al., Commun. Math. Phys. 326, 755 (2014)], no state designs exist for t≥2. Similarly, we prove that no CV unitary t-designs exist for t≥2. We propose an alternative definition for CV state designs, which we call rigged t-designs, and provide explicit constructions for t=2. As an application of rigged designs, we develop a design-based shadow-tomography protocol for CV states. Using energy-constrained versions of rigged designs, we define an average fidelity for CV quantum channels and relate this fidelity to the CV entanglement fidelity. As an additional result of independent interest, we establish a connection between torus 2-designs and complete sets of mutually unbiased bases.Received 13 December 2022Revised 25 October 2023Accepted 12 December 2023DOI:https://doi.org/10.1103/PhysRevX.14.011013Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum harmonic oscillatorQuantum information processingQuantum information processing with continuous variablesQuantum information theoryQuantum measurementsQuantum Information, Science & Technology
Visualisations for Bayesian Additive Regression Trees

Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).
An Algorithm to Analyze Arabic Verbs Morphologically

Morphological analysis describes a word in isolation from the context, which is an important stage when trying to automatically understand and interpret the text. Many studies have provided morphological analyzers of Arabic verbs. However, although accurate, they do not deal with all cases of Arabic verbs. This research introduces an algorithm to analyze Arabic verbs morphologically based on regular expressions then on Morphological Database. The advantages of this algorithm are that it deals with verbs in their morphological cases, extracting their possible structures and some of their morphological data. This algorithm yielded highly accurate results after being tested on a sample of vocabulary and text. Furthermore, the extracted data is completely error-free. KEYWORDS language processing, match, pattern, regular expression, root, word structure