Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrable hierarchy, $3\times 3$ constrained systems, and parametric and peaked stationary solutions

This paper gives a new integrable hierarchy of nonlinear evolution equations. The DP equation: $m_t+um_x+3mu_x=0, m=u-u_{xx}$, proposed recently by Desgaperis and Procesi \cite{DP[1999]}, is the first one in the negative hierarchy while the first one in the positive hierarchy is:\ $m_t=4(m^{-{2/3}})_x-5(m^{-{2/3}})_{xxx}+ (m^{-{2/3}})_{xxxxx}$. The whole hierarchy is shown Lax-integrable through solving a key matrix equation. To obtain the parametric solutions for the whole hierarchy, we separatedly discuss the negative and the positive hierarchies. For the negative hierarchy, its $3\times3$ Lax pairs and corresponding adjoint representations are nonlinearized to be Liouville-integrable Hamiltonian canonical systems under the so-called Dirac-Poisson bracket defined on a symplectic submanifold of $\R^{6N} $. Based on the integrability of those finite-dimensional canonical Hamiltonian systems we give the parametric solutions of the all equations in the negative hierarchy. In particular, we obtain the parametric solution of the DP equation. Moreover, for the positive hierarchy, we consider the different constraint and use a similar procudure to the negative case to obtain the parametric solutions of the positive hierarchy. In particular, we give the parametric solution of the 5th-order PDE $m_t=4(m^{-{2/3}})_x-5(m^{-{2/3}})_{xxx}+ (m^{-{2/3}})_{xxxxx}$. Finally, we discuss the stationary solutions of the 5th-order PDE, and particularly give its four peaked stationary solutions. The stationary solutions may be included in the parametric solution, but the peaked stationary solutions not. The 5th-order PDE does not have the cusp soliton although it looks like a higher order Harry-Dym equation.
Anomaly Detection Method of Unknown Protocol in Power Industrial Control System Based on RNN

The power industrial control system contains a large number of communication protocols. These protocols lack security consideration at the beginning of the design, which makes them face the risk of cyber attacks. Therefore, the anomaly detection of protocols plays an important role in improving the active defense capability of the power industrial control system. With the increasing number of private protocols, traditional methods based on protocol depth parsing cannot perform anomaly detection for unknown protocols. In this paper, we propose an anomaly detection method for unknown protocol in power industrial control system based on Recurrent Neural Network (RNN). Firstly, extract the payload of power industrial control traffic in application layer, and preprocess each field of the payload; Secondly, input the preprocessed field values as the feature quality to the built RNN model for training; Lastly, use the trained model for unknown protocol anomaly detection. The traffic data collected by the real substation is used for simulation. The experimental results show that the method in this paper can effectively detect the abnormality of unknown protocols, and has high accuracy and low false alarm rate. At the same time, it has significant advantages when compared with traditional machine algorithms.
A Scoping Review of the Use of Log Data for Evaluating Mobile Apps: Exploring Implications for mHealth Apps

There is a growing trend in the potential benefits and application of log data to evaluate mHealth Apps. Unfortunately, log analyses within this field are faced with challenges such as unregulated processes, questionable validity of the findings, and subjective assessment criteria resulting in the underutilization of mHealth data. To increase the use and benefit of mHealth data, there is a call for more complete data and process transparency to derive trustworthy evidence of the Apps’ efficacy. We aimed to explore extant literature and guidance through a scoping review of how log data analysis can be used to generate valuable insights supporting the evaluation of mobile Apps. The scoping review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines for a scoping review. The Scopus database and grey literature (through a Google search) delivered 105 articles, and we applied inclusion and exclusion criteria to retain 33 articles in the sample for analysis and synthesis. This scoping review sought to identify how log data are used for mobile App evaluations. By highlighting the existing trends found in the literature, identifying the similarities and differences between mHealth and General App analyses, and categorizing the indicators, insights, and improvements, this study contributes to the existing knowledge base of mHealth evaluations and future standardizations. The concepts and categories identified by this review are combined to form a conceptual framework that will be refined and incorporated into future research toward addressing the gap identified in the current literature.
High-temperature ultraviolet detection based on InGaN Schottky photodiodes

A thermally stable metal-insulator-semiconductor (MIS) Schottky-type photodiode with high performance based on the InGaN film is demonstrated at high temperatures up to 523 K. The reverse leakage current remains at a low level (10−7−10−8 A), while the UV responsivity is as high as 5.6 A/W at −3 V under 523 K, without observing the persistent photoconductivity. The discrimination ratio between ultraviolet (378 nm) and visible light (600 nm) is maintained to be more than 105. The temperature-dependent current-voltage characteristics of the MIS diode were analyzed. The photocurrent gain at reverse biases was interpreted in term of thermionic-field emission (TFE) and field-emission tunneling mechanism from room-temperature to 463 K, while TFE becomes the dominant mechanism at high temperatures.
Correlation of Signals Having a Linear Delay

The output signal of a cross‐correlation detector is calculated for a general signal spectrum and integration time under the assumption of a constant delay rate but otherwise ideal conditions. The delay‐rate degradation of the output signal is shown to be equivalent to passing the ideal cross‐correlation function through a low‐pass filter. The output signal‐to‐noise ratio is evaluated for the threshold case by introducing straight‐forward noise statistics. Subsequently, low‐pass rectangular signal spectra are assumed, and the output signal‐to‐noise ratios for various delay rates and time‐bandwidth products are numerically evaluated. For zero delay rates, the output signal‐to‐noise ratio is a linear function of the time‐bandwidth product, but for nonzero delay rates, optimum points exist.
Traffic Flow Video Image Recognition and Analysis Based on Multi-Target Tracking Algorithm and Deep Learning

Traffic flow parameters are an important data support for the research and development of several technologies in the intelligent transportation system. Therefore, accurate and real-time estimation of traffic flow is particularly important for urban traffic. In this study, a real-time traffic flow detection system framework was constructed based on video image collection and analysis. According to the vehicle detection and tracking results, a traffic flow parameter estimation model and an improved LSTM network are proposed for spatiotemporal counting feature recognition. The results conclude that the developed framework can estimate the traffic flow density and count vehicles, as well as estimate the traffic flow velocity and traffic volume to estimate and optimize traffic flow, respectively. Additionally, the simulation results show that the proposed method can not only counts the two-way traffic vehicles quickly and accurately, but also avoids the use of the complex multi-target tracking method to spatiotemporal correlation of a single target, increases the speed and accuracy of the spatiotemporal information processing procedure, and has stronger scene adaptability.
A Deeper Look Into the 2021 Tyrnavos Earthquake Sequence (TES) Reveals Coseismic Breaching of an Unrecognized Large‐Scale Fault Relay Zone in Continental Greece

Large magnitude (Mw ∼ ≥6) earthquakes in extensional settings are often associated with simultaneous rupture of multiple normal faults as a result of static and/or dynamic stress transfer. Here, we report details of the coseismic breaching of a previously unrecognized large‐scale fault relay zone in central Greece, through three successive normal fault earthquakes of moderate magnitude (Mw 5.7–6.3) that occurred over a period of ∼10 days in March 2021. Specifically, joint analysis of InSAR, GNSS and seismological data, coupled with detailed field and digital fault mapping, reveals that the Tyrnavos Earthquake Sequence (TES) was accommodated at the northern end of a ∼100 km wide transfer structure, by faults largely unbroken during the Holocene. By contrast, the southern section of this relay zone appears to have accrued significant slip during Holocene. InSAR‐derived displacements agree with the loci of eight subtle, previously undetected, faults that accommodated coseismic and/or syn‐seismic normal fault slip during the TES. Kinematic modeling coupled with fault mapping suggests that all involved faults are interconnected at depth, with their conjugate fault‐intersections acting largely as barriers to coseismic rupture propagation. We also find that the TES mainshocks were characterized by unusually high (>6 MPa) stress‐drop values that scale inversely with rupture length and earthquake magnitude. These findings, collectively suggest that the TES propagated north‐westward to rupture increasingly stronger asperities at fault intersections, transferring slip between the tips of a well‐established, but previously unrecognized, relay structure. Fault relay zones may be prone to high stress‐drop earthquakes and associated elevated seismic hazard.
The Influence of Health-Promoting Leadership on Employees’ Positive Workplace Outcomes: The Mediating Role of Employability and the Moderating Role of Workplace Civility

The COVID-19 pandemic has severely accelerated the transformation and rapid organisational change in the workplace. The impact of the COVID-19 pandemic on the hotel industry will not fade in a short time, and the long-term coexistence with the COVID-19 pandemic pressure is a real dilemma for the hotel industry. The topic of How to create employee positive workplace outcomes (task performance and innovative work behaviour) during the COVID-19 pandemic has garnered increasing interest in both practical and academic fields. Leaders play a critical role in influencing employee workplace outcomes, yet few studies have explored the predicting role of health-promoting leadership. Drawing upon the conservation of resources (COR) theory, this study aims to examine the employability mediator effect and workplace civility as the moderator effect in the relationship between health-promoting leadership and employee-positive workplace outcomes (task performance and innovative work behaviour). We conducted a two-wave survey of 421 participants from the hotel industry in China and formulated a series of hypotheses that were tested with structural equation modelling. The results showed that health-promoting leadership has a significant positive effect on employees’ employability (β = 0.479, p < 0.001), task performance (β = 0.250, p < 0.001), and innovative work behaviour (β = 0.446, p < 0.001). Employability has a significant positive effect on task performance (β = 0.438, p < 0.001) and innovative work behaviour (β = 0.296, p < 0.001). This study makes certain contributions to the extant hotel industry employees’ positive workplace outcomes literature by attending to the healthy leadership styles that promote employability during the COVID-19 pandemic, and its novel point is to evaluate the workplace civility moderating effect between the above model. It also provides practical insight that mutual transformation in workplace relationships inspire those positive outcomes.
Radar Pulse Signal Design and Imaging Method Based on Coded Modulation

Radar faces various interferences from the reconnaissance and other devices when conducting target imaging and recognition. The properties of traditional modulation methods, such as linear frequency modulation (LFM) and phase code modulation (PCM), have been investigated in detail. Therefore, it can be intercepted and recognized easily so that the target image is difficult to be obtained. To improve the complexity of radar signals, the pulse signal design and imaging method is proposed in this article based on coded modulation. Taking LFM signal as an example, it is modulated by the code sequences first. As the spectrum of LFM signal is aliasing after coded modulation, the false peaks appear in target high-resolution range profile (HRRP). To eliminate the false peaks in HRRP, the plus-minus coded modulation is designed according to the duty ratio of the first coded modulation signal. Then, the HRRP can be recovered for target imaging. The whole procedure of the coded modulation and imaging is provided and analyzed. In addition, based on different coded modulation signals, the entropy of signal time–frequency image is discussed to illustrate the antijamming performance. Simulations and experiments are conducted to verify the effectiveness of the proposed signal design and imaging method.
Diophantine equations in moderately many variables

We give upper bounds for the number of integral solutions of bounded height to a system of equations $f_i(x_1,\ldots,x_n) = 0$, $1 \leq i \leq r$, where the $f_i$ are polynomials with integer coefficients. The estimates are obtained by generalising an approach due to Heath-Brown, using a certain $q$-analogue of van der Corput's method, to the case of systems of polynomials of differing degree. Our results apply for a wider range of $n$, in terms of the degrees of the polynomials $f_i$, than bounds obtained with the circle method.
Pre-processing Algorithm for Rectification of Geometric Distortions in Satellite Images

A number of algorithms have been reported to process and remove geometric distortions in satellite images. Ortho-correction, geometric error correction, radiometric error removal, etc are a few important examples. These algorithm require supplementary meta-information of the satellite images such as ground control points and correspondence, sensor orientation details, elevation profile of the terrain, etc to establish corresponding transformations. In this paper, a pre-processing algorithm has been proposed which removes systematic distortions of a satellite image and thereby removes the blank portion of the image. It is an input-to-output mapping of image pixels, where the transformation computes the coordinate of each output pixel corresponding to the input pixel of an image. The transformation is established by the exact amount of scaling, rotation and translation needed for each pixel in the input image so that the distortion induced during the recording stage is corrected.Defence Science Journal, 2011, 61(2), pp.174-179, DOI:http://dx.doi. org/10.14429/dsj.61. 421
Computing derangement probabilities of the symmetric group acting on k-sets

Let $i(\infty,k)$ be the limiting proportion, as $n \rightarrow \infty$, of permutations in the symmetric group of degree $n$ that fix a $k$-set. We give an algorithm for computing $i(\infty,k)$ and state the values of $i(\infty,k)$ for $k \le 30$. These values are consistent with a conjecture of Peter Cameron that $i(\infty,k)$ is a decreasing function of $k$.
Local spectral equidistribution for degree 2 Siegel modular forms in level and weight aspects

We prove an equidistribution statement for the Satake parameters of the local representations attached to Siegel cusp forms of degree $2$ of increasing level and weight, counted with a certain arithmetic weight. We then apply this to compute the symmetry type of a similarly weighted distribution of the low-lying zeros of $L$-functions attached to these cusp forms.
Artificial Intelligence Model for the Identification of the Personality of Twitter Users through the Analysis of Their Behavior in the Social Network

Currently, social networks have become one of the most used channels by society to share their ideas, their status, generate trends, etc. By applying artificial intelligence techniques and sentiment analysis to the large volume of data found in social networks, it is possible to predict the personality of people. In this work, the development of a data analysis model with machine learning algorithms with the ability to predict the personality of a user based on their activity on Twitter is proposed. To do this, a data collection and transformation process is carried out to be analyzed with sentiment analysis techniques and the linguistic analysis of tweets. Very successful results were obtained by developing a training process for the machine learning algorithm. By generating comparisons of this model, with the related literature, it is shown that social networks today house a large volume of data that contains significant value if your approach is appropriate. Through the analysis of tweets, retweets, and other factors, there is the possibility of creating a virtual profile on the Internet for each person; the uses can vary, from creating marketing campaigns to optimizing recruitment processes.
Benefits and Difficulties of Postgraduate Student-Researchers: A Qualitative Inquiry

It is a general objective of an educational institution to facilitate the needs of the students. Considering research to be a critical component of postgraduate education, this qualitative inquiry sought to ascertain the benefits and difficulties associated with conducting research by postgraduate students. These elements have an effect on their contentment and motivation, which in turn has an effect on the success of the research program's efforts. Ten postgraduate students who had taken Thesis 1 and 2 courses at a private university in Northern Mindanao were chosen for the study via a purposive sample technique. Findings revealed that undertaking research enables researchers to grow personally and professionally. The majority of postgraduate students asserted that conducting research enabled them to discover new information and allowed them to solve problems. On the other hand, the students' challenges highlighted areas of the research process where student-researchers require further direction and assistance. Concerning the impacts of conducting research on an individual's physical, intellectual, social, and emotional well-being, the study discovered that research has a positive or negative effect on an individual's well-being, depending on how they deal with the experience. This study disclosed that research students must understand the value of effective time management and must learn to create and apply it when conducting research in order to work around time restrictions. Research programs may place a greater emphasis on guiding and aiding students with the formulation of research questions, the examination of literature, the collection and analysis of data, as these were perceived as difficult steps in the research process by student-researchers.
Compact Functional Testing for Neuromorphic Computing Circuits

We address the problem of testing artificial intelligence (AI) hardware accelerators implementing spiking neural networks (SNNs). We define a metric to quickly rank available samples for training and testing based on their fault detection capability. The metric measures the interclass spike count difference of a sample for the fault-free design. In particular, each sample is assigned a score equal to the spike count difference between the first two top classes. The hypothesis is that samples with small scores achieve high fault coverage because they are prone to misclassification, i.e., a small perturbation in the network due to a fault will result in these samples being misclassified with high probability. We show that the proposed metric correlates with the per-sample fault coverage and that retaining a set of high-ranked samples in the order of ten achieves near-perfect fault coverage for critical faults that affect the SNN accuracy. The proposed test generation approach is demonstrated on two SNNs modeled in Python and on actual neuromorphic hardware. We discuss fault modeling and perform an analysis to reduce the fault space so as to speed up test generation time.
Water masers in Southern FIR-bright galaxies: detection and interferometric follow-ups

G. Surcis∗,a,b,c†A. Tarchi,a,d C. Henkel,c J. Ott,e, f ‡J. Lovell,g and P. Castangia,a a INAF-Osservatorio astronomico di Cagliari Loc. Poggio dei Pini, Strada 54, 09012 Capoterra (CA), Italy b Argelander-Institut für Astronomie (Universität Bonn) Auf dem Hügel 71, 53121 Bonn, Germany c Max-Planck Institut für Radioastronomie Auf dem Hügel 69, 53121 Bonn, Germany d INAF-Istituto di Radioastronomia Via Gobetti 101, 40129 Bologna, Italy e National Radio Astronomy Observatory 520 Edgemont Road, Charlottesville, VA 22903, USA f California Institute of Technology 1200 E California Blvd, Caltech Astronomy 105-24, Pasadena , CA 91125, USA g Department of Maths and Physics, Univerisity of Tasmania Private Bag 21, Hobart, Tasmania 7001, Australia E-mail: gsurcis@astro.uni-bonn.de,atarchi@ca.astro. it, p220hen@mpifr-bonn.mpg. de,jott@nrao.edu, Jim.Lovell@utas.edu. au, pcastang@ca.astro. it
A handbook of parametric survival models for actuarial use

Traditional actuarial techniques for mortality analysis are being supplanted by statistical models. Chief amongst these are survival models, which model mortality continuously at the level of the individual. An assumption of a mathematical form for the hazard function or, equivalently, the assumption of a continuous distribution for an individual's lifetime, leads automatically to smooth fitted mortality rates. This note gives an overview of the survival models commonly found in statistical packages and compares their suitability for actuarial work with the mortality ‘laws’ proposed by actuaries over the past two centuries. We find that the actuarial laws provide substantially better fits at post-retirement ages. We also give a common structure of parameterisation which gives consistent behaviour and interpretation of risk factors across all 16 survival models listed here. Finally, we consider the benefits of working directly with the log-likelihood function, including making allowance for the left truncation which is common for the data with which actuaries work.
Correlations in the scattering cross section of regular systems.

Recently, several authors recognized and discussed the fact that regular closed quantum systems, i.e., systems which do not show correlations in their level spectrum, are nevertheless likely to exhibit correlations in the scattering cross section if they are coupled with sufficient strength to decay channels. In this paper a new averaging method is developed, especially designed to deal with S-matrices constructed on the basis of Hamiltonians with uncorrelated eigenvalues. The method is applied to the correlation function of the S-matrix in the one-channel case. It leads to a two-dimensional integral representation of the correlation function which is accessible to an asymptotic series expansion. Depending on the statistical properties of the coupling matrix elements a `correlation hole' appears in the real part of the correlation function. It becomes fully developed in the strong coupling limit, but in some cases already appears before the coupling strength has reached the point of maximal transmission (critical point).