Miracle-dz/UniFAR-FTUs huggingface dataset:
Melting of LiF and NaCl to 1 Mbar: Systematics of Ionic Solids at Extreme Conditions

We report the first diamond cell data for the melting curves of LiF and NaCl up to 1 Mbar (100 GPa). The melting curve of NaCl is in agreement with shock data and shows a break in slope at 290 kbar due to the $B1(\mathrm{NaCl})\ensuremath{-}B2(\mathrm{CsCl})$ transition. LiF remains $B1$ to the highest pressure, and molecular dynamic simulations predict that molten LiF retains a simple-cubic $B1$-like structure. A plot of the recent Mainz diamond anvil melting curves for MgO, LiF, KBr, KCl, CsI, and NaCl shows that at very high pressure all the melting curves have very similar slopes approaching a value of order 1--2 K/kbar.
Entrepreneurship through Employee Mobility, Innovation, and Growth

Firm-level productivity differences are big and largely ascribed to ex-ante heterogeneity in the entrepreneurs’ growth potential at birth. Where do these ex-ante differences come from, and what can the policy do to encourage the entry of high-growth entrepreneurs? I study empirically and by means of a quantitative growth model the spinout firms: the firms founded by former employees of the incumbent firms. By focusing on innovating spinouts identified through the inventor mobility in the patent data, I document that spinout entrants significantly outperform regular entrants throughout their life. Firms with a bigger technological lead spawn more successful spinouts. Building on these observations, I build a structural model of innovation and firm dynamics, where firm heterogeneity arises from endogenous decisions of innovation workers to become entrepreneurs and create spinouts. The spinout dynamics affect productivity growth through four main channels: direct entry, incumbents’ disincentive effect, knowledge diffusion, and the firm composition channel. Growth decompositions show that accounting for spinout dynamics is quantitatively important for our understanding of the growth process. I analyze the role of noncompete laws affecting employee entrepreneurship for aggregate innovation and growth. JEL classification: O30, O43
Ionic contrast terahertz near field imaging

We demonstrated the direct and noninvasive imaging of functional neurons by Ionic Contrast Terahertz (ICT) near-field microscopy. This technique provides quantitative measurements of ionic concentrations in both the intracellular and extracellular compartments and opens the way to direct noninvasive imaging of neurons during electrical, toxin, or thermal stresses. Furthermore, neuronal activity results from both a precise control of transient variations in ionic conductance and a much less studied water exchange between the extracellular matrix and the intraaxonal compartment. The developed ICT technique associated with a full three-dimensional simulation of the axon-aperture near-field system allows a precise measurement of the axon geometry and therefore the direct visualization of neuron swelling induced by temperature change or neurotoxin poisoning. We also developed Terahertz Attenuated Total Reflection (ATR) devices perfectly suited for studying cell layers. Inserted in a terahertz time-domain system, and using a high resistivity low loss silicon prism to couple the terahertz wave into the sample, the detection scheme is based on the relative differential spectral phase of two orthogonal polarizations. Biological sample imaging as well as subwavelength (λ/16) longitudinal resolution are demonstrated.
How Does the Codi�ed Knowledge Relatedness In�uence Foreign Intelligence Import in Emerging Countries: Evidence from Chinese PCT Activities

With the accelerated evolution of the global innovation pattern, the micro-foundation of the participation of emerging countries’ innovation subjects in the global ‘war for talent’ has gradually become the focus of relevant studies. Based on the perspective of citation coupling, this study uses the bibliographic-patent-coupling (BPC) indicators between 214 innovation subjects owned by 96 popular innovative organisations in China and 431 overseas regions with patent cooperation treaty (PCT) activity records to describe the bilateral codi�ed knowledge (CK) relatedness and examine its impact on the allocation of overseas PCT inventors by Chinese innovation subjects. The results show that the BPC relationship signi�cantly increases the scale of Chinese innovation subjects’ intelligence import from overseas regions, especially the regions with rapid innovation. The effect of the BPC relationship on increasing the foreign intelligence import scale of innovation subjects is positively affected by the quality of the patents jointly cited by both parties. The study further �nds that the heterogeneity of innovation subjects can affect the effect of foreign intelligence import on BPC strength. Finally, the tacit knowledge (TK) relatedness at the geographic and organisational levels can promote the import of foreign intelligence of innovation subjects and have a coordinated effect on the BPC relationship. This coordinated effect mainly exists in high-quality innovation activities driven by enterprise innovation subjects.
Hybrid Manycore Dataflow Processor

— This work addresses high performance computing architectures, presenting a hybrid processor that includes multiple computing architectures on a single chip die. Beside commonly used multicore processor, a personal computer might include manycore graphical processor. This work advocates for a combination of these two architectures along with a dataflow processor that usually appears in the form of a FPGA chip able to perform parallel tasks at the same time. A quick overview of these computer architectures and appropriate programming paradigms is followed by the comparison based on flexibility and speed, price and development time, and speed and power consumption. Finally, the proposed hybrid processor is analyzed against computationally demanding algorithms that are often executed on high performance computing architectures. Future work will include the comparison of the proposed computer paradigms and the comparison of the proposed hybrid architecture with existing ones.
Identification of research trends in emerging technologies implementation on public services using text mining analysis

Purpose – This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery. Design/methodology/approach – VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services. Findings – The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge.Inaddition,onthewhole,findings identifyfourstagesintheevolutionofETsandtheirapplication to public services: the “ electronic administration ” stage, the “ technological baseline ” stage, the “ managerial ” stage and the “ disruptive technological ” stage. Practical implications – The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services. Socialimplications – TheresearchhelpsresearcherstotrackresearchtrendsanduncovernewpathsonETs and its implementation in public services. Originality/value – Recent research has focused on the need of implementing ETs for improving public services,whichcouldhelpcitiestoimprovethe citizens ’ qualityoflifeinurbanareas. Thispapercontributesto expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.
Mobile robot decision-making system based on deep machine learning

—One of the major aspects of Industry 4.0 is enabling the manufacturing entities to operate in the dynamical systems autonomously. Therefore, to be autonomous, manufacturing entities need to have sensors to perceive their environment and utilize that information to make decisions regarding their actions. Having that in mind, in this paper, the authors propose a mobile robot decision-making system based on the integration of visual data and mobile robot pose. Mobile robot pose (current position and orientation) is integrated with two images gathered by two cameras and utilized to predict the possibility of gripping the part to be manufactured. A decision-making system is created by utilizing the deep learning model Resnet18 with an additional input for the mobile robot pose. The model is trained end-to-end and experimental evaluation is performed by using the mobile robot RACIO (Robot with Artificial Intelligence based COgnition).
Diffusion by Continuous Movements

The influence of velocity distribution on one‐particle dispersion is studied. The ensemble of particle releases is subdivided into subensembles characterized by particle velocity at the instant of release. Under certain simplifying assumptions, the motion of the particles of each subensemble is a systematic drift, combined with dispersal about the subensemble drift position. The displacement variance due to subensemble drift is dominant for small time t since particle release, whereas that due to subensemble is the more important at large t. The assumption that subensemble dispersal is Gaussian yields an estimate of the probability distribution density function θ(x, t) governing particle position which is correct both in the limits of t small and t large. The estimate is thus better than that given by the usual assumption that θ is Gaussian, and appears useful for t values less than the Lagrangian integral scale. The subensemble approach offers some new insights into the problem of the instantaneous struc.
Low-cost real-time human motion capturing system

— In recent years motion capturing technology found numerous applications in industry and research areas like human-robot interaction, medical applications, etc. Those systems can be very expensive and might require a lot of setup time. In this paper, leveraging the advances in deep learning and computer science, the low-cost real-time motion capturing system is presented. The system was designed to use off-the-shelf inexpensive cameras, freely available software, and a gaming laptop. The system design, underlying math principles, reconstruction pipeline, and reconstruction results will be discussed in the paper. The presented motion capturing system can reconstruct a human pose with 33 keypoints in real-time at 17Hz. The whole setup costs less than $2500 including the price of the dedicated PC.
Portfolio Performance Evaluation Using the DEA Based on That Variance is Placed Under Shadow of Mean or Vice Versa

Application of Data Envelopment Analysis (DEA) in selection of portfolio is for occasions when we are to consume rate of a capital in a financial market can be useful. In enforcement of this application, the most significant factors influencing on the performance evaluation of the investment companies are mean of the profit achieved from investment and it's variance. One of the discussed assumption in performance evaluation of portfolio can be in such a manner that the more the mean of the resulted profit and the less the variance, the better will be the performance. For this reasons mean as output and variance as input have been taken into consideration in the technique of DEA. But, since efficiency in DEA results from division of output by input, thus, variance may overshadow the mean, and a DMU which has a very low mean and little variance is to be placed better than a DMU which has a high mean and relatively-high variance. For this reason, in this article, a criterion is presented when variance as an input is supposed, but it is placed under shadow of mean. In other words, the first priority is evaluation via mean, but this evaluation must not be absolute priority. Supposing that there are n portfolios with equal inputs (purchase price) and various outputs (mean and variance) and considering variance as an undesirable output, we create a change in the mean with aid of the suggested relationship, and, then, technique of DEA will be enforced. Finally, this technique is to be employed on a real data set.
Prevalence of Internet Addiction among College students of Ahmedabad city

: Introduction: With increased access to the cheaper and faster internet, the people are more prone to develop addiction to the internet. This internet addiction may interfere with essential activities like acquiring formal education. Therefore it is important to assess the level of internet addiction and associated factors with the same amongst students. Objectives : To know the prevalence of internet usage among college students of various fields, to assess the patterns and risk factors of internet usage among the students and to evaluate internet addiction and behavior issues related to internet use. Method: The study was a cross-sectional study, which was carried out in five different colleges across different streams (Medicine, Arts, Commerce, Science and Law) in the city of Ahmedabad during the period of October 2017 to November 2018. The sample size was 465 students. Young's Internet Addiction Test (IAT) was utilized to assess the internet addiction amongst the study participants. Results : The study revealed that the overall prevalence of internet addiction was 56.8% which included 17.4% moderate and 1.3% of severe addiction. Highest prevalence was found in the students from commerce stream. The factors which were found significantly associated were;average length of internet use (highest with use of 7 to 10 years), average daily use of internet (highest with daily use of 11 hours or more), and continuous access to the internet. Conclusion : Majority of the students had some form of internet addiction in the present study. Duration of internet use, daily time spent on internet and continuous access to the internet were among the major attributes associated with internet addiction among college students.
COMPRESSION OF COLOUR IMAGES USING MACHINE LEARNING ALGORITHM

The increase use of transfer of images from one device to another created a need to compress them without compromising their quality. Machine learning algorithm forms a potential method to compress an image by clustering thus leading to elimination of redundant colours . k-means algorithm is an unsupervised machine learning algorithm which clusters data and hence elected as best method for compression of colours . In this paper we propose compression of an image using k-means algorithm and variation of size of image for different values of k. Moreover comparison of k-means with JPEG and wavelet compression algorithms is performed deriving their advantages, disadvantages and compression ratio.
A novel method for fabric color transfer

Fabric color design is a complex process in textiles and clothing industry. A new method for fabric color selection and transferring is proposed in this study. An automatic way to select the colors from the natural images is developed for fabric color design. Based on these colors, a fabric image is then used for color transferring. The fabric image is processed by a bias field estimation operation, and the membership function of the color deviations of the image has been obtained. According to the selected colors and the color membership function, the fabric image colors can be changed and transferred to a new image that preserves the similar texture appearance but with significantly different color effects. The experimental results confirm the effectiveness of the proposed method. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 304–310, 2015
Efficient parametric terahertz generation in quasi-phase-matched GaP through cavity enhanced difference-frequency generation

We report an efficient parametric terahertz (THz) source by using bonded quasi-phase-matched (QPM) GaP crystals pumped by the C-band pulsed fiber lasers in a master oscillator power amplifier configuration, based on difference frequency generation (DFG). We observed that the QPM-GaP crystals can effectively increase the THz generation power and efficiency by increasing the number of periods. Moreover, we observed external cavity enhanced THz DFG by placing the QPM-GaP crystal in an external ring cavity. The THz cavity enhancement factor of approximately 250 has been achieved in comparison with a single-pass THz DFG. The maximum THz average power can reach 339 μW, corresponding to a power conversion efficiency of 2.43×10−4 and a quantum efficiency of 3.16%.
GAN-based Data Augmentation in the Design of Cyber-attack Detection Methods

 Abstract —The advent of the Industry 4.0 paradigm that relies on the concepts of Cyber-Physical Systems (CPS) and the Industrial Internet of Things (IIoT) leads to the transition from centralized to distributed control. In this approach, interconnected smart devices (sensors, actuators, etc.) as the key enablers achieve system control through coordinated work. Introduction of IIoT leads to ubiquitous communication between smart devices, thus opening up a vast area for potential malicious threats and attacks which can cause serious consequences, take to system dysfunction or even endanger human lives. Therefore, security mechanisms have to be developed to provide timely detection of different cyber-attacks and to keep the system safe and protected. Since industrial processes are often very complex and their analytical model is very difficult to determine, deep learning based methods for cyber-security mechanisms development are imposed as a technique of choice. Successful employment of data-driven solutions, particularly based on deep learning approaches usually requires a big amount of data. However, due to various limitations in the acquisition of data from the real process, its availability is still a major challenge. For instance, the Industry 4.0 factory implies frequent reconfiguration which reduces the time intervals available for experimental procedures such as data acquisition. One of the ways to deal with this issue is called data augmentation. In this paper, we apply data augmentation in the design of cyber-attack detection methods in Industrial Control Systems (ICS). In particular, we explore the possibilities for utilization of Generative Adversarial Networks (GAN) to generate the necessary amount of data for deep learning based modeling using a relatively small number of available samples on input.
Intra-Industry Trade Between Japan and Korea: Vertical Intra-Industry Trade, Fragmentation and Export Margins (Very preliminary, work in progress)

This paper contributes to the existing empirical investigation of Japan-Korea international trade by providing new evidence of intra-industry trade between Korea and Japanese sub-regions. Taking advantage of a Japanese international trade dataset disaggregated by sub-regions, we calculate the Grubel-Lloyd intra-industry trade index for 41 regions of Japan with respect to Korea for the period between 1988 and 2006. By restricting the flows of intra-industry trade to sub-regions, the Grubel-Lloyd index is more likely to capture the effect of the fragmentation of production than the traditional index, which is based on the national level. By using Japanese prefecture international trade data, it is revealed that intra-industry trade is still pervasive even when it is restricted to trade flows between prefectures and Korea. In intra-industry trade regression models, we introduce extensive and intensive margins of prefecture exports as new explanatory variables. We find that a rise in intra-industry trade is driven by the introduction of a new variety of exports, while intra-industry trade is discouraged by an increase in the trade value of products already exported.
Optimal Operation of Multi-Energy System Integrated with an Alkaline Electrolyzer Dynamic Power-to-Hydrogen&Heat (P2H2) Model

Apart from producing green hydrogen, the commercial alkaline electrolyzer (AE) could also be a potential heat supply source by recycling the waste heat during the electrolysis process. In this paper, a dynamic power-to-hydrogen&heat (P2H) model is proposed to characterize the interaction of hydrogen and heat supplying in an AE stack. Based on the presented model, furthermore, a rule-based strategy (RBS) and an operation cost-minimization oriented Mixed Integer Linear Programming (MILP)-based strategy (MILPBS) are developed for enabling the operational flexibility of a multi-energy system (MES) integrated with the P2H model. Several cases are studied to show the effectiveness and limitations of the two strategies with comparative performance analysis for different application scenarios.
Condensed Matter and Interphases

The purpose of our study was to analyse the effect of the moisture content in benzoic acid on the electrical conductivity of its melts. The measurements were performed using impedance spectroscopy in a hermetically sealed metal cell with the temperature of the melts being 160–200 °С. Samples of benzoic acid with different moisture content were used: (i) as-received benzoic acid; (ii) acid dried over anhydrous calcium chloride; (iii) acid exposed to air at 100 % relative humidity. The study demonstrated that electrical conductivity increased with an increase in the amount of moisture in the acid (the conductivity of the sample with the highest moisture content was about 2.5 times higher than that of the driest sample). The results obtained are of importance for understanding the mechanisms of proton exchange processes on lithium niobate crystals and can be used for the production of proton-exchange waveguides with stable characteristics.
Colla-Con g: a stakeholders preferences-based approach for product lines collaborative con guration

During collaborative configuration of software product lines (SPL), multiple stakeholders contribute together in building a single product specification. Conflicting situations can arise during the configuration process due to contradictions between some/all stakeholders’ configuration choices. Detecting and resolving such situation rise two major challenges: choosing which choices to omit and taking stakeholders’ preferences into account. While several approaches are available for SPL collaborative configuration, most of the existing ones either do not present detailed information on the strategies for conflict resolution or they rely on a systematic process which resolves conflicts by prioritizing configuration decisions made at earlier stage, constraining therefore some stakeholders’ choices. The lack of flexibility may hinder conflict resolution as choices taken in earlier stages overlay those in later phases. To mitigate these limitations, we propose a new collaborative configuration approach (Colla-Config) which provides a preference-based conflict resolution method within a free-order configuration process; each stakeholder expresses his/her preferences through a set of substituCorresponding author Email addresses: Sihem.BenSassi@gmail.com (Sihem Ben Sassi), Sabrineedded@gmail.com (Sabrine Edded), Raul.mazo@ensta-bretagne.fr (Raúl Mazo), Henda.benghezala@ensi.rnu. tn (Henda Ben Ghezala), Camille.salinesi@univ-paris1.fr (Camille Salinesi) Preprint submitted to Journal of Systems and Software August 13, 2022 tion rules, and freely makes his/her configuration decisions towards the desired product without being constrained by the configuration decisions made by the other ones. To assess the feasibility and the usability of the proposed approach, we conducted a usability test designed by following the ISO/IEC 25062:2006 Common Industry Format for usability tests. Results of the experiments provided preliminary evidence of the approach feasibility and the tool ability to properly support the SPL collaborative configuration.