Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evolution Analysis of Collaborative Innovation Network considering Government Subsidies and Supervision

Collaborative innovation networks have the basic attributes of complex networks. The interaction of innovation network members has promoted the development of collaborative innovation networks. Using the game-based theory in the B-A scale-free network context, this paper builds an evolutionary game model of network members and explores the emergence mechanism from collaborative innovation behavior to the macroevolution of networks. The results show that revenue distribution, compensation of the betrayer, government subsidies, and supervision have positively contributed to the continued stability of collaborative innovation networks. However, the effect mechanisms are dissimilar for networks of different scales. In small networks, the rationality of the revenue distribution among members that have similar strengths should receive more attention, and the government should implement medium-intensity supervision measures. In large networks, however, compensation of the betrayer should be attached greater importance to, and financial support from the government can promote stable evolution more effectively.
High Quality Compatible Triangulations for Planar Shape Animation

We propose a new method to compute compatible triangulations of two polygons in order to create a smooth geometric transformation between them. Compared with existing methods, our approach creates triangulations of better quality, that is, triangulations with fewer long thin triangles and Steiner points. This results in visually appealing morphing when transforming the shape from one to another. Our method consists of three stages. First, we use the common valid vertex pair to uniquely decompose the source and target polygons into pairs of sub-polygons, in which each concave sub-polygon is triangulated. Second, within each sub-polygon pair, we map the triangulation of a concave sub-polygon onto the corresponding sub-polygon using linear transformation, thereby generating compatible meshes between the source and the target. Third, we refine the compatible meshes, which can create better quality planar shape morphing with detailed textures. Experimental results show that our method can create compatible meshes of higher quality compared to existing methods with fewer long thin triangles and smaller triangle deformation values during shape morphing. These advantages enable us to create more consistent rotations for rigid shape interpolation algorithm and facilitate a smoother morphing process. The proposed algorithm is robust and computationally efficient. It can be applied to produce convincing transformations such as interactive 2D animation creation and texture mapping.
DOA Estimation in Impulsive Noise via Low-Rank Matrix Approximation and Weakly Convex Optimization

Conventional direction-of-arrival (DOA) estimators are vulnerable to impulsive noise. In this paper, to tackle this issue, a class of weakly convex-inducing penalties is introduced for robust DOA estimation via low-rank matrix approximation, where $\ell _{2,1}$-norm is adopted as the metric for suppressing the outliers. Two iterative algorithms are developed to construct the noise-free data matrix. To avoid determining the number of sources, the DOAs are estimated by exploiting the special joint diagonalization structure of the constructed signal covariance matrix. Compared with several existing algorithms, the proposed methods enjoy faster computation, similar DOA estimation performance against impulsive noise and requiring no a priori information of the source number. Numerical experiments are included to demonstrate the outlier-resistance of our solutions.
STURM-LIOUVILLE OPERATOR WITH PARAMETER-DEPENDENT BOUNDARY CONDITIONS ON TIME SCALES

In this study, we consider a boundary-value problem generated by a Sturm-Liouville dynamic equation on a time scale with boundary conditions depending on a spectral parameter. We introduce the operator formulation of the problem and give some properties of eigenvalues and eigenfunctions. We also formulate the number of eigenvalues when the time scale is finite.
Numerical Double Sumudu Transform for Nonlinear Mixed Fractional Partial Differential Equations

In this paper, we are going to study a nonlinear fractional partial differential equations by double Sumudu transformation coupled with Adomian decomposition method or with variational iteration method. For this case, we choose an important fractional partial differential equations, such as parabolic-hyperbolic with mixed fractional derivatives (homogeneous and nonhomogeneous) types. Some examples are given here to illustrate efficiency of this method.
The Hosoya Entropy of Graphs Revisited

In this paper we extend earlier results on Hosoya entropy (H-entropy) of graphs, and establish connections between H-entropy and automorphisms of graphs. In particular, we determine the H-entropy of graphs whose automorphism group has exactly two orbits, and characterize some classes of graphs with zero H-entropy.
Learning to scale up search-driven data integration

A recent movement to tackle the long-standing data integration problem is a compositional and iterative approach, termed “pay-as-you-go” data integration. Under this model, the objective is to immediately support queries over “partly integrated” data, and to enable the user community to drive integration of the data that relate to their actual information needs. Over time, data will be gradually integrated. While the pay-as-you-go vision has been well-articulated for some time, only recently have we begun to understand how it can be manifested into a system implementation. One branch of this effort has focused on enabling queries through keyword search-driven data integration, in which users pose queries over partly integrated data encoded as a graph, receive ranked answers generated from data and metadata that is linked at query-time, and provide feedback on those answers. From this user feedback, the system learns to repair bad schema matches or record links. Many real world issues of uncertainty and diversity in search-driven integration remain open. Such tasks in search-driven integration require a combination of human guidance and machine learning. The challenge is how to make maximal use of limited human input. This thesis develops three methods to scale up searchdriven integration, through learning from expert feedback: (1) active learning techniques to repair links from small amounts of user feedback; (2) collaborative learning techniques to combine users’ conflicting feedback; and (3) debugging techniques to identify where data experts could best improve integration quality. We implement these methods within the Q System, a prototype of search-driven integration, and validate their effectiveness over real-world datasets. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Zachary G. Ives Subject Categories Computer Sciences This dissertation is available at ScholarlyCommons: https://repository.upenn. edu/edissertations/2653 LEARNING TO SCALE UP SEARCH-DRIVEN DATA INTEGRATION
Speedup Query Processing in Hadoop Using Mapreduce Framework

: The Internet used by 3.2 billion people in 2015. Nearly half of the global population will be using the internet by the end of this year, according to a new report. Enterprises today gain vast volumes of data from different sources and influence this information by means of data analysis to support effective decision-making and provide new functionality and services. The key requirement of data analytics is scalability, simply due to the immense volume of data that need to be extracted, processed, and analyzed in a timeline fashion. Possibly the most popular framework for current large-scale data analytics is Map-Reduce, mainly due to its salient features that include scalability, fault-tolerance, ease of programming, and edibility. However, despite its merits, MapReduce has evident performance limitations in miscellaneous analytical tasks, and this has given rise to a significant body of research that aim at improving its efficiency, while maintaining its desirable properties. The aims of this review the state-of-the-art in improving the performance of parallel query processing using MapReduce. A set of the most significant weaknesses and limitations of Map-Reduce is discussed at a high level, along with solving techniques. Taxonomy is presented for categorizing existing research on MapReduce improvements according to the specific problem they target. Based on the proposed taxonomy, a classification of existing research is provided focusing on the optimization objective. Concluding, this research article outlines interesting directions for future parallel data processing systems.
Sectional lumped parameter model using bond graph and simulation for hot asphalt pipelines

ABSTRACT In order to achieve the matching control of hot asphalt and foaming water and improve the quality of foamed asphalt, fully considering the characteristics of the asphalt pipeline is necessary. Our dynamic model of the hot asphalt pipeline system was constructed by a sectional lumped parameter method, and the dynamic characteristics of the hot asphalt pipeline system were analyzed using Simulink software. The results showed that with decreasing pipe diameter and increasing pipe length and asphalt viscosity, the fluid pressure, flow vibration frequency, and amplitude reduced at the pipeline nozzle, whereas the loss increased. Under these conditions, the dynamic response of the pressure and flow reached the steady state faster. In contrast, with increasing pipe diameter and decreasing pipe length and asphalt viscosity, the fluid pressure, flow vibration frequency, and amplitude increased at the pipeline nozzle, whereas the loss decreased. The dynamic response speed of the pressure and flow attained steady state at a slower pace.
Algorithmic Mechanism Design via Relaxation and Rounding

Maximizing social welfare for environments with selfish agents, is hard but of central importance. This problem arises in many scenarios from combinatorial auctions to matching markets. In this thesis, we propose solutions that are dominant strategy incentive compatible (DSIC), and run computationally in polynomial time for two variants of the problem. Furthermore, we propose general-purpose machineries that are useful in implementing DSIC solutions with efficient computational runtime.
Modeling heteroscedastic, skewed and leptokurtic returns in discrete time

Popular models of finance, fall short of accounting for most empiricallyÂ found stylized features of financial time series data, such as volatility clustering,Â skewness and leptokurtic nature of log returns. In this study we propose aÂ general framework for modeling asset returns which account for serial dependenciesÂ in higher moments and leptokurtic nature of scaled GARCH filteredÂ residuals. Such residuals are calibrated to normal inverse Gaussian and hyperbolicÂ distribution. Dynamics of risky assets assumed in Black Scholes model,Â Duans GARCH model and other benchmark models for contract valuation,Â are shown to be nested in the the proposed framework.Keywords: Stylized facts; Normal inverse Gaussian; GARCH model; HyperbolicÂ distribution; leptokurtic returns
Robust consensus of fractional‐order singular uncertain multi‐agent systems

This paper focuses on consensus analysis of fractional‐order singular multi‐agent systems (FOSMASs) with uncertainties under fixed topology. The definition of admissible consensus or robust admissible consensus of FOSMASs is proposed and the communication network topology is assumed to be an undirected or a directed graph. Through a coordinate transformation, the admissible consensus of FOSMASs is converted into two parts, one is regularity and impulse‐free of single fractional‐order singular (FOS) linear system, another one is admissibility of multiple FOS linear systems. The consensus conditions are expressed by linear matrix inequalities (LMIs) via SVD technique. Some simulation examples are given to show how to solve LMIs to get the control gain. The simulation results display that the states of the FOSMAS can achieve consensus by using the solved control gain.
RNA structure prediction including pseudoknots through direct enumeration of states

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past forty years. While many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges. Here, we describe a new method to compute the entire free energy landscape of secondary structures of RNA resulting from a primary RNA sequence, by combining a polymer physics model for the entropy of pseudoknots with exhaustive enumeration of the set of possible structures. Our polymer physics model can address arbitrarily complex pseudoknots and has only two free loop entropy parameters that correspond to concrete physical quantities, over an order of magnitude fewer than even the sparsest state-of-the-art algorithms. Our model outperforms previously published methods in predicting pseudoknots, while performing on par with current methods in the prediction of non-pseudoknotted structures. For RNA sequences of ~ 45 nucleotides, or ~ 90 with minimal heuristics, the complet–e enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem.
Assessing the suitability of H4RG near-infrared detectors for precise Doppler radial velocity measurements

Abstract. At wavelengths longwards of the sensitivity of silicon, hybrid structured mercury–cadmium–telluride (HgCdTe) detectors show promise to enable extremely precise radial velocity (RV) measurements of late-type stars. The most advanced near-infrared (NIR) detector commercially available is the HAWAII series (HxRG) of NIR detectors. While the quantum efficiency of such devices has been shown to be ≈90 % , the noise characteristics of these devices, and how they relate to RV measurements, have yet to be quantified. We characterize the various noise sources generated by H4RG arrays using numerical simulations. We present recent results using our end-to-end spectrograph simulator in combination with the HxRG Noise Generator, which emulates the effects of read noise, parameterized by white noise, correlated and uncorrelated pink (1 / f) noise, alternating column noise, and picture frame noise. The effects of nonlinear pixel response, dark current, persistence, and interpixel capacitance on RV precision are also considered. Our results have implications for RV error budgets and instrument noise floors that can be achieved with NIR Doppler spectrographs that utilize this kind of detector.
Overview of Spintronic Sensors With Internet of Things for Smart Living

Smart living is a trending and connected lifestyle that envisions efficient and sustainable energy utilization, stable and reliable power supply, intelligent and coordinated transportation and mobility, and personalized and cost-effective healthcare. Its realization needs the Internet of Things (IoT). IoT is a compelling platform connecting trillions of sensors and collecting data for connectivity and analytics. It is more advanced than traditional monitoring systems where limited sensors and wired communication can merely collect fragmented data in the application domains. Spintronic sensors with the superb measuring ability and multiple unique advantages can be one of the critical sensing devices supporting the IoT and enabling smart living. In this paper, we review successful applications of individual spintronic sensors in electrical current sensing, transmission and distribution lines monitoring, vehicle detection, and biodetection that can help to fulfill the promises of smart living in energy management, power delivery, transport, and healthcare. The wireless spintronic sensor networks (WSSNs) working at the massive interconnected network level are proposed and illustrated to provide pervasive monitoring systems, which facilitate the intelligent surveillance and management over building, power grid, transport, and healthcare. The database of collected information will be of great use to policy making in public services and city planning. This paper provides insights for realizing smart living through the integration of IoT with spintronic sensor technology.
The apparent mass and transmissibility of a bicycle-rider system

The objective of this research is to identify the passive response of the rider’s body to translational and rotational random perturbations. A custom made bicycle mock-up equipped with a system of sensors has been developed, capable of measuring the linear accelerations, angular velocity and the rider’s force responses in all translational axes of all bicycle interfaces. The bicycle mock-up is driven by a hexapod that generates coloured noise perturbations in the range 0–10 Hz. Twenty four healthy male adults participated in this study and gave informed consent according to the guidelines of the ethical committee of Delft University of Technology. The responses of all subjects are represented in the frequency domain by means of frequency response functions. More specific, the interaction of the rider’s body at the seat, foot pegs and handlebars are expressed in terms of apparent mass and as seat-to-sternum transmissibility functions (STS). The apparent mass and STS transfer functions for the surge and heave motion suggest a simple underlying passive response system. For surge, a clear resonance peak was found at 2 Hz for nearly all interfaces and directions, whereas, for the heave motion a clear resonance peak at 5 Hz was found for the seat and handlebars and a resonance peak at 6 Hz for the foot pegs. The apparent mass of the pitch and yaw motion also suggest, to a certain extend, simple passive dynamics after 1 Hz characterised by resonance peaks at 1.8 and 2.3 Hz, respectively. Only the corresponding yaw STS transfer function showed a resonance peak around 2.3 Hz, as well. The sway and roll motion do not suggest simple passive dynamics showing similar trends in apparent mass characterised by an ever-decreasing gain and no resonance peaks. Finally, the surge, heave, pitch and yaw apparent mass transfer functions suggest that higher body mass in general yields higher peak magnitude and lower resonance frequency. This effect was most apparent for the surge motion.
Measurement of production cross sections and coupling properties of the Higgs boson in decays to two photons using the ATLAS detector

This report summarizes the results of the measurement of production cross sections and coupling properties of the Higgs boson in decays to two photons. The measurement is performed using 79.8 fb−1 of data from LHC proton-proton collisions at the center-of-mass energy of √ s = 13 TeV recorded by the ATLAS experiment. These results have improved precision compared to the results previously reported by ATLAS which used 36 fb−1 of data. Differential cross section measurements targeting gluon-gluon fusion production in association with b-jets have also been added.
Matrix Concentration & Computational Linear Algebra

These lecture notes were written to support the short course Matrix Concentration & Computational Linear Algebra delivered by the author at Ecole Normale Superieure in Paris from 1–5 July 2019 as part of the summer school “High-dimensional probability and algorithms. ” The aim of this course is to present some practical computational applications of matrix concentration.
Machine learning tools for detecting and visualizing attacks on computer systems

This dissertation focuses on three aspects of computer network security. First, the dissertation introduces several novel visualization approaches that can effectively be used in network anomaly detection research and development. It introduces advanced visualization methods using machine learning, statistical and artificial intelligence sciences. These methods include Principal Component Analysis, Self-Organizing Maps, k-means clustering, hierarchical clustering, Independent Component Analysis, Bi-plots, Mosaic plots and stars plots. The methods are compared in terms of their computational complexity, visual effectiveness and inherent ability to detect selected network attacks. One method that uses Principal Component Analysis for dimensionality reduction of input data and Bi-plots for visualization achieved 100% detection rate given a proposed threshold criterion. Other methods like Self-Organizing Maps and k-means demonstrated the ability to effectively distinguish normal traffic from anomalous traffic using effective graphical means. Second, it introduces a proposed unified software environment for developing, testing and evaluating network anomaly detection systems. This is aimed at addressing the lack of a homogeneous platform for developing and testing these systems. The proposed environment uses the S Language as a unified platform for developing an anomaly detection system with extensive visualization capabilities. The strength of the proposed system is demonstrated by using several machine learning and statistical methods to detect and visualize selected network attacks. The results of evaluating seven exploratory multivariate analysis methods for anomaly detection and visualization show the effectiveness of the proposed platform in streamlining the processes involved in developing and testing anomaly detection systems. Finally, low performances issues associated with software-based clustering methods are addressed by developing a hardware-based approach to anomaly detection. A circuit is developed using synthesizable Verilog Hardware Description Language that implements the k-means clustering algorithm in hardware. The circuit clusters network packet information into normal and anomalous traffic and generates an interrupt to indicate that the clustering process has finished. The circuit performance is 300 times faster when measured against a typical software-based version of the algorithm. The circuit was synthesized to produce a total gate count of 50K gates and can run with a clock frequency of 40MHZ.
Teacher Training in making through the co-design of learning environments.

The paper presents the preliminary results of a design-based research (DBR) about learning environments for teacher training in maker-centred education. The research has two main goals. The first one is to define principles for the design of learning environments for in-service teachers in maker-centred education. The second goal is to explore and understand the educational needs detected among the stakeholders during the training. After defining a theoretical framework based on the concepts of Learning Design, maker-centred education and Teacher Design Research, we present the development of a generalised teaching model by reconstructing the process of decision making. Finally we present a set of educational needs detected during the four implementations.