Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Thermal QCD Matter from Canonical Sectors

We discuss the thermal phase structure of quantum chromodynamics (QCD) at zero real chemical potential (μR=0) from the viewpoint of canonical sectors. The canonical sectors take the system to pieces of each elementary excitation mode and thus seem to be useful in the investigation of the confinement–deconfinement nature of QCD. Since the canonical sectors themselves are difficult to compute, we propose a convenient quantity which may determine the structural changes of the canonical sectors. We discuss the quantity qualitatively by adopting lattice QCD prediction for the phase structure with finite imaginary chemical potential. In addition, we numerically estimate this quantity by using the simple QCD effective model. It is shown that there should be a sharp change of the canonical sectors near the Roberge–Weiss endpoint temperature at μR=0. Then, the behavior of the quark number density at finite imaginary chemical potential plays a crucial role in clarifying the thermal QCD properties.
Feature Ranking and Differential Evolution for Feature Selection in Brushless DC Motor Fault Diagnosis

A fault diagnosis system with the ability to recognize many different faults obviously has a certain complexity. Therefore, improving the performance of similar systems has attracted much research interest. This article proposes a system of feature ranking and differential evolution for feature selection in BLDC fault diagnosis. First, this study used the Hilbert–Huang transform (HHT) to extract the features of four different types of brushless DC motor Hall signal. When there is a fault, the symmetry of the Hall signal will be influenced. Second, we used feature selection based on a distance discriminant (FSDD) to calculate the feature factors which base on the category separability of features to select the features which have a positive correlation with the types. The features were entered sequentially into the two supervised classifiers: backpropagation neural network (BPNN) and linear discriminant analysis (LDA), and the identification results were then evaluated. The feature input for the classifier was derived from the FSDD, and then we optimized the feature rank using differential evolution (DE). Finally, the results were verified from the BLDC motor’s operating environment simulation with the same features by adding appropriate signal-to-noise ratio magnitudes. The identification system obtained an accuracy rate of 96% when there were 14 features. Additionally, the experimental results show that the proposed system has a robust anti-noise ability, and the accuracy rate is 92.04%, even when 20 dB of white Gaussian noise is added to the signal. Moreover, compared with the systems established from the discrete wavelet transform (DWT) and a variety of classifiers, our proposed system has a higher accuracy with fewer features.
Bisimulation by Partitioning Is Ω((m+n)log n)

An asymptotic lowerbound of Ω(( m + n )log n ) is established for partition refinement algorithms that decide bisimilarity on labeled transition systems. The lowerbound is obtained by subsequently analysing two families of deterministic transition systems – one with a growing action set and another with a fixed action set. For deterministic transition systems with a one-letter action set, bisimilarity can be decided with fundamentally different techniques than partition refinement. In particular, Paige, Tarjan, and Bonic give a linear algorithm for this specific situation. We show, exploiting the concept of an oracle, that the approach of Paige, Tarjan, and Bonic is not of help to develop a generic algorithm for deciding bisimilarity on labeled transition systems that is faster than the established lowerbound of Ω(( m + n )log n ).
Pseudo-Laminarization of Mixed Microbubble Water and Complex Fluids in Capillary Flows

Symmetry in fluid flow is determined by external factors such as magnetic or electric fields. Owing to the electrical properties of fluids, liquids containing fine bubble or ionic surfactant molecules differ from the normal fluid flow. Therefore, the pressure drop and frictional coefficient in a pipe are measured and estimated for water alone, for mixed microbubble water (MB), and for complex fluids (anionic surfactant solution and polymer solution) in capillary flow with constant flow rate. For water alone, good agreement is obtained between the experimental and theoretical values for laminar Hagen–Poiseuille flow and the Blasius expression in turbulent flow. For the MB, pseudo-laminarization is suggested up to a Reynolds number of 4.2×103. Both the anionic surfactant solution and the polymer solution exhibit the same tendency as that of the MB. In explaining these behaviors, elasticity and electrical interaction are discussed, and electrical interaction on the capillary wall is strongly suggested to be a contributing factor. Additionally, surface tension is investigated and supports the discussion.
The Use of a Game Theory Model to Explore the Emergence of Core/Periphery Structure in Networks and Its Symmetry

In network systems characterized by complex interactions of various types, core-periphery structures can be found. In this paper, we deal with such questions as what processes can lead to the emergence of core-periphery formation, whether this structure is symmetric, and to what extent. Namely, the question of symmetry in a complex network is still the subject of intense research interest. Symmetry can relate to network topology, network relationships, and other processes on networks. To answer these questions, we modified the model of the classic social dilemma called the repeated prisoner’s dilemma (or repeated PD game) by adding the cost of maintaining relationships between the pairs of players (partners) and especially by adding the possibility of ending some relationships. We present the results of simulations that suggest that the players’ network strategy (i.e., partner selection or termination of relationships with some partners) is the driving force behind the emergence of a core-periphery structure in networks rather than the player’s strategy in PD. Our results also suggest that the formed core is symmetric, and this symmetry is a result of the symmetric interactions of core players. Our outcomes can help understand various economic or social questions related to creating centers or peripheries, including their symmetry in different network systems.
Application of Anti-Diagonal Averaging in Response Reconstruction

Response reconstruction is used to obtain accurate replication of vehicle structural responses of field recorded measurements in a laboratory environment, a crucial step in the process of Accelerated Destructive Testing (ADA). Response Reconstruction is cast as an inverse problem whereby an input signal is inferred to generate the desired outputs of a system. By casting the problem as an inverse problem we veer away from the familiarity of symmetry in physical systems since multiple inputs may generate the same output. We differ in our approach from standard force reconstruction problems in that the optimisation goal is the recreated output of the system. This alleviates the need for highly accurate inputs. We focus on offline non-causal linear regression methods to obtain input signals. A new windowing method called AntiDiagonal Averaging (ADA) is proposed to improve the regression techniques’ performance. ADA introduces overlaps within the predicted time signal windows and averages them. The newly proposed method is tested on a numerical quarter car model and shown to accurately reproduce the system’s outputs, which outperform related Finite Impulse Response (FIR) methods. In the nonlinear configuration of the numerical quarter car, ADA achieved a recreated output Mean Fit Function Error (MFFE) score of 0.40% compared to the next best performing FIR method, which generated a score of 4.89%. Similar performance was shown for the linear case.
Comprehensive View of Topological Optimization Scooter Frame Design and Manufacturing

The combination of topology optimization (TO) and 3D printing has revolutionized the way components are designed and fabricated. In view of this, this manuscript presents a TO workflow considering the frame of a scooter. In particular, TO is employed to redesign the scooter frame based on a commercial one. The topologically optimized frame is then fabricated with stainless steel 316L utilizing the selective laser melting (SLM) method. In particular, technical obstacles encountered during the process and according solutions are recorded. Given the herein notes, readers who are working with the two technologies can anticipate the technical problems and deliver more effective solutions should any of them arise.
Two-Roller Continuous Calibration Process by Compression for Submarine Pipelines

Submarine pipeline is a key part in the development of deep sea and ultra-deep sea oil and gas. In order to reduce the ovality of pipes and improve their compressive strength, a two-roller continuous calibration (TRCC) process by compression is proposed. A springback analysis of compress bending is carried out, and an analytical model is established, which predicts ovality after calibration and provides a theoretical basis for roller shape design and process parameter formulation. Numerical simulation and physical experiments are carried out. The distribution of stress and strain is analyzed. The effects of initial ovality, reduction ratio and initial placement angle on the ovality after calibration are studied. When the reduction ratio is about 1%, the ovality is optimal. The theoretical analysis shows that the ovality after calibration is about 0.03%, and the ovality after calibration by numerical simulation and experiment is less than 0.45%, proving the feasibility of the process.
Quantum Behavior of a Nonextensive Oscillatory Dissipative System in the Coherent State

We investigate the nonextensivity of a generalized dissipative oscillatory system in the Glauber coherent state. We introduce a parameter q as a measure of the nonextensivity of the system. Considering the characteristic of nonextensivity, the system is described by a deformed Caldirola–Kanai oscillator, which is represented in terms of q. We manage the system by describing the associated Hamiltonian in terms of the harmonic oscillator ladder operators. The time evolutions of the canonical variables, the Hamiltonian expectation value, the quantum energy, and the symmetry-breaking in the evolution of the system, are analyzed in detail regarding their dependence on q, damping factor, and the external driving force. The amplitude of the oscillator is slightly quenched as q becomes large, whereas the amplitude of the canonical momentum is enhanced in response to the growth in q. As q increases, the dissipation of the quantum energy becomes a little faster as a manifestation of the nonextensivity of the system. Our results are compared to the classical results, as well as to those in the previous research performed on the basis of the SU(1,1) coherent states. The coherent states, including the Glauber coherent states, can be convenient resources for carrying information, which is crucial in quantum information processing.
Implementing an Efficient Secure Attribute-Based Encryption System for IoV Using Association Rules

As the Internet of vehicles (IoV) is the perceptual information subject, the intelligent connected vehicle (ICV) is establishing an interconnected information transmission system through opening more external interfaces. However, security communication problems thereby are generated, attracting massive attention for researchers. Hence, the in-vehicle network system is responsible for controlling the state of the ICV and has a major impact on driving safety. In this paper, we designed an efficient secure ciphertext-policy attribute-based encryption (CP-ABE) system for protecting communication. The research focuses on mining the frequency features between vehicle nodes through the max-miner association rules algorithm, aiming to build frequent item sets. Furthermore, an improved asymmetric ABE scheme can implement secure communication in-vehicle nodes that belong to the same classification set. Through the hardware platform and in-vehicle network simulator (INVS) to evaluate our scheme, the results demonstrate that the work possesses enough security without reducing communication efficiency, meanwhile improve bus load performance.
New Method for Calculation of Radiation Defect Dipole Tensor and Its Application to Di-Interstitials in Copper

The effect of external and internal elastic strain fields on the anisotropic diffusion of radiation defects (RDs) can be taken into account if one knows the dipole tensor of saddle-point configurations of the diffusing RDs. It is usually calculated by molecular statics, since the insufficient accuracy of the available experimental techniques makes determining it experimentally difficult. However, for an RD with multiple crystallographically non-equivalent metastable and saddle-point configurations (as in the case of di-interstitials), the problem becomes practically unsolvable due to its complexity. In this paper, we used a different approach to solving this problem. The molecular dynamics (MD) method was used to calculate the strain dependences of the RD diffusion tensor for various types of strain states. These dependences were used to determine the dipole tensor of the effective RD saddle-point configuration, which takes into account the contributions of all real saddle-point configurations. The proposed approach was used for studying the diffusion characteristics of RDs, such as di-interstitials in FCC copper (used in plasma-facing components of fusion reactors under development). The effect of the external elastic field on the MD-calculated normalized diffusion tensor (ratio of the diffusion tensor to a third of its trace) of di-interstitials was fully consistent with analytical predictions based on the kinetic theory, the parameters of which were the components of the dipole tensors, including the range of non-linear dependence of the diffusion tensor on strains. The results obtained allowed for one to simulate the anisotropic diffusion of di-interstitials in external and internal elastic fields, and to take into account the contribution of di-interstitials to the radiation deformation of crystals. This contribution can be significant, as MD data on the primary radiation damage in copper shows that ~20% of self-interstitial atoms produced by cascades of atomic collisions are combined into di-interstitials.
Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks

To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.
Compound-Specific Chlorine Isotope Analysis of Organochlorines Using Gas Chromatography-Double Focus Magnetic-Sector High Resolution Mass Spectrometry

Compound-specific chlorine isotope analysis (CSIA-Cl) is a practicable and high-performance approach for quantification of transformation processes and pollution source apportionment of chlorinated organic compounds. This study developed a CSIA-Cl method for perchlorethylene (PCE) and trichloroethylene (TCE) using gas chromatography-double focus magnetic-sector high resolution mass spectrometry (GC-DFS-HRMS) with a bracketing injection mode. The achieved highest precision for PCE was 0.021% (standard deviation of isotope ratios), and that for TCE was 0.025%. When one standard was used as the external isotopic standard for another of the same analyte, the lowest standard deviations of relative isotope-ratio variations ({\delta}37Cl') between the two corresponding standards were 0.064% and 0.080% for PCE and TCE, respectively. As a result, the critical {\delta}37Cl' for differentiating two isotope ratios are 0.26% and 0.32% for PCE and TCE, respectively, which are comparable with those in some reported studies using GC-quadrupole MS (GC-qMS). The lower limit of detection for CSIA-Cl of PCE was 0.1 ug/mL (0.1 ng on column), and that for TCE was determined to be 1.0 ug/mL (1.0 ng on column). Two isotope ratio calculation schemes, i.e., a scheme using complete molecular-ion isotopologues and another one using a pair of neighboring isotopologues, were evaluated in terms of precision and accuracy. The complete-isotopologue scheme showed evidently higher precision and was deduced to be more competent to reflect trueness in comparison with the isotopologue-pair scheme. The CSIA-Cl method developed in this study will be conducive to future studies concerning transformation processes and source apportionment of PCE and TCE, and light the ways to method development of CSIA-Cl for more organochlorines.
Analysis and digital processing of sem images of anodic alumina films with nanoporous structure

The simulation of the magnetoresistance of a spin valve containing an antiferromagnetic fixing layer is performed. It is shown that the effect of exchange bias that occurs at the contact of the ferromagnetic and antiferromagnetic layers with certain combinations of parameters of the spin-valve can lead to an increase in the magnetoresistance in several times (from 1.5 up to 4) due to the presence of the field of exchange bias.
Deep Learning for Blocking in Entity Matching: A Design Space Exploration

Entity matching (EM) finds data instances that refer to the same real-world entity. Most EM solutions perform blocking then matching. Many works have applied deep learning (DL) to matching, but far fewer works have applied DL to blocking. These blocking works are also limited in that they consider only a simple form of DL and some of them require labeled training data. In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking for EM. We first define a large space of DL solutions for blocking, which contains solutions of varying complexity and subsumes most previous works. Next, we develop eight representative solutions in this space. These solutions do not require labeled training data and exploit recent advances in DL (e.g., sequence modeling, transformer, self supervision). We empirically determine which solutions perform best on what kind of datasets (structured, textual, or dirty). We show that the best solutions (among the above eight) outperform the best existing DL solution and the best existing non-DL solutions (including a state-of-the-art industrial non-DL solution), on dirty and textual data, and are comparable on structured data. Finally, we show that the combination of the best DL and non-DL solutions can perform even better, suggesting a new venue for research.
Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google

Google services continuously generate vast amounts of application data. This data provides valuable insights to business users. We need to store and serve these planet-scale data sets under the extremely demanding requirements of scalability, sub-second query response times, availability, and strong consistency; all this while ingesting a massive stream of updates from applications used around the globe. We have developed and deployed in production an analytical data management system, Napa, to meet these requirements. Napa is the backend for numerous clients in Google. These clients have a strong expectation of variance-free, robust query performance. At its core, Napa’s principal technologies for robust query performance include the aggressive use of materialized views, which are maintained consistently as new data is ingested across multiple data centers. Our clients also demand � exibility in being able to adjust their query performance, data freshness, and costs to suit their unique needs. Robust query processing and � exible con � guration of client databases are the hallmark of Napa design.
The determination of mono- and disaccharides in green Arabica and Robusta coffees using high performance liquid chromatography

Resultats d'analyses de douze cafes Arabica et huit cafes Robusta originaires de treize pays differents. La teneur totale en glucides libres est considerablement plus elevee chez les Arabica que chez les Robusta. Comparaison de leurs teneurs en saccharose, en sucres reducteurs, en rhamnose, en arabinose et en maltose
Unifying Instance-level and Type-level QP Frames for Natural Language Understanding

1 One important role for qualitative representations is as a constituent of natural language semantics. The incremental nature of language means that information about situations and models typically arrives piece by piece, and must be assembled in order to create a qualitative model that can be reasoned with. Our prior research created distinct QP frame systems for instance-level and type-level qualitative models. This is inelegant as well as problematic, since the choice of constructing instance versus type level models ought to be made based on the text and task context, not a priori. This paper describes the design of a unified QP frame system, where most of the frame contents are agnostic with respect to whether the final description will be instance-level or type level. Clues from the NLU system’s analysis plus context will determine whether the final model constructed is instance level, type level, or a mixture of the two.
Entrepreneurship education around the world: A possible comparison

Technological changes and innovation trends are quickly transforming jobs and professions. What is the added value of humans in a technological world? Many studies highlight the relevance of creativity and an entrepreneurial mindset for an individual to be able to seize opportunities and generate new ideas and values for the society, community , and economy. The main aim of this article is to illustrate the policies and programs of entrepreneurship education in Italy, South Korea and India. Starting from a review of definitions and based on the comparative method, our findings indicate that entrepreneurship education is emerging as an educational concept that could support students' growth to face new challenges today and in the future.