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The boron emitter formation for tunnel oxide passivated contact (TOPCon) solar cells faces higher costs compared to the POCl 3 diffusion for passivated emitter and rear (PERC) solar cells due to the requirement for higher temperatures and longer process times. This work presents an alternative energy-efficient and low cost of ownership boron diffusion approach for TOPCon solar cells, enabling a highly increased throughput compared to the typically used gas phase diffusion. We use an atmospheric pressure chemical vapor deposition borosilicate glass layer as the boron dopant source and combine it with a subsequent thermal anneal in a quartz tube furnace for dopant drive-in. Here, we either use a conventional single-slot quartz boat configuration, or, for highly increased throughput, a vertical wafer stack configuration with the wafer surfaces in direct contact with each other. We show that this approach yields an emitter doping profile comparable to the state-of-the-art gas phase diffusion with sufficient uniformity across the wafer area. We further investigate the emitter dark saturation current densities j 0e as well as the energy conversion efficiency of TOPCon solar cells fabricated for each configuration and compare the results to those of a BBr 3 reference process. These solar cells achieve energy conversion efficiencies exceeding 23% for the stack diffusion approach. Additionally, we demonstrate a potential reduction in both the cost of ownership and the specific electricity consumption of the presented approach.

Cloud-Computing ist für Unternehmen eine kostengünstige und flexible Art der IT-Nutzung, ohne dass Investitionen in die eigene, teure und starre IT-Infrastruktur notwendig sind. Daher wird erwartet, dass die Nutzung von Cloud-Diensten sich in geringeren IT-Investitionen niederschlägt und zugleich die Produktivität erhöht. Ob dem tatsächlich so ist, wird mit Hilfe von aktuellen aggregierten Daten und speziellen Unternehmensdaten für die Jahre 2014 und 2016 nachgegangen. Es zeigt sich, dass die Verwendung von Cloud-Computing in keinem der untersuchten Wirtschaftszweige mit signifikant geringeren IT-Investitionen einhergeht. Allerdings findet sich für das Verarbeitende Gewerbe ein positiver Zusammenhang zwischen der Arbeitsproduktivität von Unternehmen und der Nutzung von Cloud-Computing. Um Chancen der Cloud-Technologie voll auszuschöpfen und die Wettbewerbsfähigkeit der Unternehmen zu erhalten oder gar zu steigern, sollte der Staat die Rahmenbedingungen verbessern, unter anderem indem er den Ausbau der Breitbandinfrastruktur – wo notwendig – unterstützt. Darüber hinaus ist eine aufgeschlossenere Haltung gegenüber innovativen Technologien in Unternehmen und Gesellschaft notwendig., DIW Wochenbericht

This dissertation seeks to enhance the understanding of University Industry Collaboration (UIC) context in Sub-Saharan Africa and to identify mechanisms to stimulate interactions and knowledge transfer between the two economic entities. Results from three studies on Rwanda and a systematic literature review are presented and discussed. They indicate that UIC activities in Rwanda are unidirectional and narrow in scope i.e., mainly taking place through educational-oriented channels. They are therefore less prone to the creation and diffusion of knowledge through relational learning. From the industry perspective, results reveal that companies are motivated by short-term benefits rather than long term innovation-oriented benefits, mainly due to a business context less auspicious to investing in long-term growth-related projects. Findings reveal also that the industry in Rwanda has a negative perception on the current UIC activities and on the capacity of Higher Education Institutions (HEIs) to meet firms’ growth and profitability needs. From this context, potential mechanisms to stimulate UIC are identified, and companies’ perception on the extent to which they stimulate UIC assessed. The latter is done using the Discrete Choice Experiment (DCE) methodology. Results from this DCE allow to conclude by proposing a mix of stimulating mechanisms for UIC, consisting of (1) instilling the commitment of both academia and industry (mainly through ensuring win-win between partners), (2) building trust (through enhancement of quality of graduates and research), (3) creating a conducive UIC governance and policy framework (through creation of an independent boundary spanner), and (4) establishing an effective information and communication system between actors. We suggest implementing the identified mechanisms concomitantly because they are interdependent and results from one influence the implementation of the others.

Sabemos que as urnas de Pólya aparecem inicialmente em trabalhos de Markov 1905-1907. O objetivo deste trabalho é estudar a lei forte de grandes números para o modelo do passeio aleatório do elefante e obter um teorema limite funcional para um processo gaussiano com a forma explícita da matriz de covariância limite. Para isso, relacionamos o modelo citado anteriormente com as urnas de Pólya e utizamos resultados já existente deste último. We know that Pólya urns first appear in work by Markov 1905-1907. O purpose of this work is to study the strong law of large numbers for the elephant random walk model elephant random walk model and to obtain a functional limit theorem for a Gaussian process with the explicit form of the limit covariance matrix. For this, we relate the model cited model with Pólya’s urns and use already-existing results from the latter. Dissertação (Mestrado)

PurposeThe purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.Design/methodology/approachThis article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.FindingsFindings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.Originality/valueThe originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.

Oscillatory systems of interacting Hawkes processes with Erlang memory kernels were introduced by Ditlevsen and Löcherbach (Stoch. Process. Appl., 2017). They are piecewise deterministic Markov processes (PDMP) and can be approximated by a stochastic diffusion. In this paper, first, a strong error bound between the PDMP and the diffusion is proved. Second, moment bounds for the resulting diffusion are derived. Third, approximation schemes for the diffusion, based on the numerical splitting approach, are proposed. These schemes are proved to converge with mean-square order 1 and to preserve the properties of the diffusion, in particular the hypoellipticity, the ergodicity, and the moment bounds. Finally, the PDMP and the diffusion are compared through numerical experiments, where the PDMP is simulated with an adapted thinning procedure.

As was found in our previous works, when Nafion swells in water, polymer fibers unwind into the bulk of the surrounding liquid. This effect is controlled by the content of deuterium in water. Here, we present the results of studying the dynamics of methylene blue (MB) adsorption on the Nafion surface for MB solutions based on natural water (deuterium content is 157 ppm, the unwinding effect occurs) and based on deuterium-depleted water (DDW; deuterium content is 3 ppm, there is no unwinding). In addition, we studied the dynamics of water desorption during drying of the Nafion polymer membrane after soaking in MB solution based on natural water and DDW. It turned out that in the case of natural water, the rate of MB adsorption and water desorption is higher than in the case of DDW. It also turned out that the amount of MB adsorbed on the membrane in the case of natural water is greater than in the case of DDW. Finally, it was found that the desorption of water during drying is accompanied by a rearrangement of the absorption spectrum of Nafion. This rearrangement occurs earlier in the case of DDW. Thus, by infinitesimal changes in the deuterium content (from 3 to 157 ppm) in an aqueous solution, in which a polymer membrane swells, we can control the dynamics of adsorption and desorption processes. A qualitative model, which connects the observed effects with the slowing down of diffusion processes inside the layer of unwound fibers, is proposed.

Сучасне суспільство викидає в навколишнє атмосферне повітря велику кількість різноманітних хімічних сполук, серед яких – СО2, якого викидаються мільйони тонн. Одним із напрямів його переробки є електроннокаталітична переробка СО2 в органічні сполуки. Важливою складовою цього методу є дифузійні процеси, які через нетривалий час існування частини сполук найкраще досліджувати за допомогою математичної моделі. Тому було складено і вирішено математичну модель дифузійних процесів на поверхні каталізатора при електроннокаталітичній переробці СО2. В результаті моделювання отримано залежності коефіцієнта дифузії, швидкості дрейфу частинок, загального об’ємного коефіцієнта масовіддачі, а також швидкості масовіддачі. Modern society emits a large number of various chemical substances into the ambient air. One of these compounds is carbon dioxide. Millions of tons are thrown away every year, and this pace is not decreasing. All these problems could be solved if an efficient way to use carbon dioxide as a source of carbon in the production of fuel and chemical raw materials is found. The conversion of CO2 into value-added chemicals and energy sources is considered to be one of the four priority areas of economic development in different countries. Electrocatalytic processing of CO2 into organic compounds can be used as one of such directions of processing. A peculiarity of this method consists in the use of low-temperature plasma barrier discharge in the presence of a heterogeneous catalyst. An important component of this method is diffusion processes on the catalyst surface, which after a short period of existence of some compounds are best investigated using a mathematical model. Therefore, a mathematical model of diffusion processes on the catalyst surface during electroncatalytic CO2 processing has been compiled and solved. Analyzing each individual diffusion parameter, we have found that: - when the field strength is applied, the diffusion coefficient increases several times; - - the action of the field strength causes all the studied compounds to move in a direction along the vectors of the electromagnetic field. The greatest is the velocity of atomic oxygen and hydrogen; - the value of the total volumetric coefficient of mass transfer is not significantly affected by the superposition of tension, and the available difference is a few percent. This does not apply to hydrogen, oxygen and CO atoms; - the rate of mass transfer to atomic oxygen is higher, other compounds do not significantly increase the rate of mass transfer

We outline a reduction scheme for a class of Brownian dynamics which leads to meaningful corrections to the Smoluchowski equation in the overdamped regime. The mobility coefficient of the reduced dynamics is obtained by exploiting the Dynamic Invariance principle, whereas the diffusion coefficient fulfils the Fluctuation-Dissipation theorem. Explicit calculations are carried out in the case of a harmonically bound particle. A quantitative pointwise representation of the reduction error is also provided and connections to both the Maximum Entropy method and the linear response theory are highlighted. Our study paves the way to the development of reduction procedures applicable to a wider class of diffusion processes.

In this article, we approximate the invariant distributionνof an ergodic Jump Diffusion driven by the sum of a Brownian motion and a Compound Poisson process with sub-Gaussian jumps. We first construct an Euler discretization scheme with decreasing time steps. This scheme is similar to those introduced in Lamberton and PagèsBernoulli8(2002) 367-405. for a Brownian diffusion and extended in F. Panloup,Ann. Appl. Probab.18(2008) 379-426. to a diffusion with Lévy jumps. We obtain a non-asymptoticquasiGaussian (asymptotically Gaussian) concentration bound for the difference between the invariant distribution and the empirical distribution computed with the scheme of decreasing time step along appropriate test functionsfsuch thatf−ν(f) is a coboundary of the infinitesimal generator.

In a consumer advice network, users' opinions of products influence the opinions of others, and users may establish new links with each other or cancel existing ones. This study aimed to explore the dynamic features of the consumer advice network using data crawled from online social networks, verifying that such a network's degree of distribution follows a scale-free law and that the network is disassortative. This article also determines that opinion distance has a significant negative correlation with network directed links, and in-degree centrality has a significant positive correlation with network directed links. To study how this correlation affects the dynamic mechanism of network evolution, a time variable is introduced, and a continuous opinion model based on a complex network and bounded confidence is used to establish a generalized adaptive model of a consumer advice network. Through computational experiments and visualization methods, the co-evolutionary process of directed links and users' opinions in a consumer advice network is analyzed. Users in a consumer advice network with similar opinions show a gradual accumulation trend, with opinions ultimately converging in the middle area of the initial opinion distribution.

This paper explores the role that IT capability (IT-c) plays in firm innovation performance through the channel of organisational learning (OL) in Kenya. It frames OL in two dimensions: explorative and exploitative OL. The former entails seeking new knowledge, which mainly exists outside the firm’s competence. The latter is an activity or process that builds on existing competence and knowledge in the firm. Using mediation analysis of 481 firms drawn from the World Bank Enterprise Survey (2013) and Innovation Follow-up Survey 2014, it demonstrates that IT-c has a significant effect on innovation performance of firms (capability to simultaneously improve products, processes, organising and market development). It finds that the mediative role of OL in the relationship between IT-c and innovation performance is realised mainly through explorative learning, whilst enabling the firm exploit existing (in-house) knowledge base. The paper puts forward some managerial, policy and further research suggestions.

In der Diskussion über die staatliche Förderung des Breitbandausbaus wird immer wieder die Frage aufgeworfen, ob und in welchem Umfang Unternehmen von neuen Netzen profitieren. Empirische Analysen auf Grundlage von Unternehmensdaten und Daten zur Breitbandverfügbarkeit in Deutschland zeigen, dass ein Teil der Unternehmen im Untersuchungszeitraum (2010 bis 2015) vom Breitbandausbau profitiert hat. Vor allem Dienstleistungsunternehmen konnten ihre Produktivität erhöhen. Die Stärke der Effekte unterscheidet sich aber deutlich zwischen den verschiedenen Wirtschaftszweigen. Wichtig sind außerdem möglichst geringe Entfernungen zwischen den Endpunkten des Glasfasernetzes und den Breitbandnutzern. Nur dann lassen sich positive Effekte sicherstellen. Die Ergebnisse der Untersuchung unterstützen den wirtschaftspolitischen Ansatz, den Ausbau der Breitbandversorgung zu fördern, da er die Wettbewerbsfähigkeit der Unternehmen und damit ihre Überlebensfähigkeit verbessert., DIW Wochenbericht

We deal with a continuous-time Ehrenfest model defined over an extended star graph, defined as a lattice formed by the integers of $d$ semiaxis joined at the origin. The dynamics on each ray are regulated by linear transition rates, whereas the switching among rays at the origin occurs according to a general stochastic matrix. We perform a detailed investigation of the transient and asymptotic behavior of this process. We also obtain a diffusive approximation of the considered model, which leads to an Ornstein-Uhlenbeck diffusion process over a domain formed by semiaxis joined at the origin, named spider. We show that the approximating process possesses a truncated Gaussian stationary density. Finally, the goodness of the approximation is discussed through comparison of stationary distributions, means and variances., Comment: 30 pages, 9 figures, 3 tables, to be published in "Mathematical Methods in the Applied Sciences"

Selbstständige haben während der Covid-19-Pandemie im Vergleich zu abhängig Beschäftigten besonders starke Einkommensverluste erlitten. Zu ihrer Unterstützung hat die Bundesregierung daher verschiedene Programme mit Liquiditätshilfen aufgelegt. Im Frühjahr 2020 startete der Bund die Soforthilfe, die für von Umsatzrückgängen betroffene Selbstständige Einmalzahlungen von bis zu 15 000 Euro vorsah. Für die Hilfen gab es rund 2,2 Millionen Anträge – rund die Hälfte aller Selbstständigen wollte diese somit in Anspruch nehmen. Analysen zur Wirkung des Programms auf Grundlage von Online-Umfragedaten mit 27 000 Selbstständigen zeigen: Unter den Selbstständigen, die die Soforthilfe bezogen haben, hat sich die Einschätzung, dass ihre Unternehmung die Krise überstehen würde, im Durchschnitt nur moderat erhöht. Allerdings wirken die staatlichen Hilfen bei Selbstständigen mit stark digitalisierten Unternehmungen deutlich stärker als bei Selbstständigen mit weniger digitalisierten Unternehmungen. Dies gilt allerdings nur dann, wenn die Selbstständigen bereits vor der Krise mit den Digitalisierungsprozessen begonnen haben. Die Ergebnisse machen damit wichtige Zusammenhänge zwischen staatlichen Förderprogrammen und dem Digitalisierungsgrad von Unternehmungen deutlich. Die Studie hilft somit der Politik, die Wirkung krisenbezogener Politikinstrumente zu verbessern. Dazu gehören nicht nur kurzfristige Politikinstrumente wie die Soforthilfe, sondern auch Instrumente, die die Digitalisierung unternehmerischer Tätigkeit langfristig erleichtern., DIW Wochenbericht

Identifying the source of spread through a network, termed diffusion source identification, is a problem of fundamental importance in a broad class of applications such as rumor controlling and virus identification. Though this problem has received significant recent attention, most studies have focused only on very restrictive settings and lack theoretical guarantees for real-world network topologies. This work introduces a statistical framework for diffusion source identification for a broad class of diffusion processes on general network topologies. It also develops a confidence set inference approach inspired by statistical hypothesis testing. Our method efficiently produces a small subset of nodes, which provably covers the source node with any pre-specified confidence level. Multiple Monte Carlo strategies are presented for the inference procedure based on network topology and the probabilistic properties that significantly improve the scalability. To our knowledge, this is the first diffusion source identification method with a practically useful theoretical guarantee on general networks. The proposed method is evaluated by extensive synthetic experiments based on well-known random network models and real-world networks from several domains. The method is also demonstrated in a real-world diffusion example concerning the spread of COVID-19 between cities.

For a one-dimensional diffusion process X , we derive the Laplace transform and the moments of the first time at which the age of an excursion above (or below) the level x is longer than u . The result is then illustrated for diffusion processes that are found relevant in applications. In the context of pricing Parisian options, the Brownian motion and the Geometric Brownian motion are considered and the Laplace transform can be made explicit and explicit expression for the moments can be derived. In the context of neuronal modeling, the Ornstein–Uhlenbeck process and the Cox–Ingersoll–Ross process are considered and the Laplace transform and the moments must be approximated by numerical inversion.

Purpose: The outcomes from agricultural knowledge transfer (KT) are dependent on the access to and the quality of services available, coupled with the motivation of prospective clients to implement new skills. Within this context, the allocation of resources particularly in terms of the location of KT offices and number of agricultural advisers are important considerations for understanding KT impact. This quantitative study evaluates the impact of the rationalisation of KT resources on farm profitability for KT clients in Ireland during the recessionary period 2008-2014. Design/Methodology: Teagasc, the public KT service provider in Ireland, experienced significant office closures (43%) and staff reduction (38%) during the economic crisis, yet client numbers declined only slightly (4.5%). Administrative data is merged with a panel data set on farm level performance to test the impact of KT through Random Effects estimation. Findings: The results show that annual contract holders gained a 12.3% benefit to their market gross margin per hectare over the period. However, there was a negative effect of 0.2% for each additional client assigned to the adviser which averaged at 9.6% per adviser. Practical Implications: The quantitative findings provide a measure of impact that represents the value for money for the KT service. The key implication is that the client ratio for advisers should be considered when allocating resources and lower ratios would positively impact client margins. Theoretical Implications: This article outlines the value of quantitative studies to estimate impact in a clear translatable manner which can aid the policy discussion around resource deployment particularly in a recessionary period. The employment of a Random Effects estimator on a panel data set provides a solid base for the analysis. Originality/Value: This study evaluates the impact of KT on farm level profitability during a recessionary period when resources were constrained, and uses spatial variables and client densities to examine the regional effects.

Online consumer social networks have become an important marketing channel for product-related information diffusion in the technological landscape. This paper aims to identify the factors that drive opinion diffusion behavior in consumers on online social networks. To empirically test the theoretically driven hypotheses, the study uses firsthand survey responses from 614 respondents based in two major cities in China: Shenzhen, an eastern city, and Xi'an, a western city. A survey of online shopping consumers shows that both network heterogeneity and network influence have a positive impact on consumers' level of online opinion leadership and online opinion seeking behavior. However, network acquaintance has an insignificant impact on the level of opinion diffusion. Furthermore, online opinion diffusion has a positive impact on behavioral consequence, which has two dimensions: online forwarding and online communication. An online opinion leader is more likely to forward and communicate information than an online opinion seeker. Multiple-group analysis indicates that gender and city moderate the effect of opinion diffusion. Theoretically, this paper makes several contributions to marketing literature, particularly from a social networking perspective. The results from this study can help online retailers and social network service providers utilize consumer networks to improve online opinion diffusion. Within social networking literature, this is a unique attempt to explore the factors driving opinion diffusion behavior in consumers' online social networks.

This paper examines the adoption and diffusion of medical technology as associated with the dramatic recent increase in the surgical use of robots. We consider specifically the sequential adoption and diffusion patterns of three interrelated surgical technologies within a single healthcare system (the English NHS): robotic, laparoscopic and open radical prostatectomy. Robotic and laparoscopic techniques are minimally invasive procedures with similar patient benefits, but the newer robotic technique requires a high initial investment cost to purchase the robot and carries high maintenance costs over time. Using data from a large UK administrative database, Hospital Episodes Statistics, for the period 2000-2018, we analyse 173 hospitals performing radical prostatectomy, the most prevalent and earliest surgical area of adoption of robotic surgery. Our empirical analysis first identifies substitution effects, with robotic surgery replacing the incumbent technology, including the recently diffused laparoscopic technology. We then quantify the spillover of robotic surgery as it diffuses to other surgical specialties. Finally, we perform time-to-event analysis at the hospital level to quantitatively examine the adoption. Results show that a higher number of urologists and a wealthier referral area favor robot adoption.

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Josep Vives i Santa Eulàlia, [en] Since the creation in 1973 of the first stock Exchange exclusively dedicated to financial options, and the irruption of Mathematics in this discipline, leaded by Fisher Black and Myron Scholes, there have been numerous investors who have predicted the option’s price behavior backed by the principles of Black-Scholes model in order to perform safer investments. However, focusing on the current situation of Stock Market, where uncertainty prevails due to the sanitary crisis provoking huge variations in stock prices in short periods of time and the growing preference by investors in assets such as cryptocurrencies, highly volatile assets, the Black-Scholes model is found to be useless, forcing investors to use other models. The aim of this project is to perform an introduction to 1976 Robert C. Merton Jump-Diffusion model, introducing this way an alternative model to Black-Scholes one, which will take in account situations in which stock prices experiment significant variations in short periods of time, just as it occurs in nowadays market. It will be also introduced the 2002 Kou model, as an alternative Jump-Diffusion model to Merton’s in the particular case of dependent option pricing. It will finish with the statement and demonstrations of 2004 Kou-Wang theorem, which gives the investors a barrier options pricing formula.

We propose a new statistical observation scheme of diffusion processes named convolutional observation, where it is possible to deal with smoother observation than ordinary diffusion processes by considering convolution of diffusion processes and some kernel functions with respect to time parameter. We discuss the estimation and test theories for the parameter determining the smoothness of the observation, as well as the least-square-type estimation for the parameters in the diffusion coefficient and the drift one of the latent diffusion process. In addition to the theoretical discussion, we also examine the performance of the estimation and the test with computational simulation, and show an example of real data analysis for one EEG data whose observation can be regarded as smoother one than ordinary diffusion processes with statistical significance.

The assessment of semantic relatedness for a given pair of entities in a knowledge graph has become a critical step in a wide variety of artificial intelligence tasks, including but not restricted to fields such as machine learning, natural language processing, and information retrieval. Semantic relatedness is a generalisation of semantic similarity; entities are semantically assessed by virtue of their relationships in the knowledge graph rather than by their inherent similarity. Semantic relatedness measures have been widely addressed in the research literature, producing a wide range of relatedness functions. These functions require to enumerate paths between entities, using a simple and yet powerful intuition: the more the number of paths connecting the entities, the more related. However, extracting paths from knowledge graphs is computationally expensive. Since the number of paths increases exponentially with the number of edges, a denser graph affects the tractability of the assessment. This issue becomes critical in online services where potential computational bottlenecks can be a point of failure and delays. In this thesis, we introduce an approach to semantic relatedness based on diffusion processes over knowledge graphs. We argue that diffusion processes can replace paths as the source of semantics without affecting the performance of these measures in real-world applications. We formalise this form of relatedness, and we compare them against their path-based cousins. We also study the methods to compute diffusion in large knowledge graphs. Our findings show that diffusion-based models behave similarly to path-based ones in terms of ranking of entity pairs and have a better computational performance. We evaluate the computational cost of our models and give recommendations to build real-world applications from them. To this end, we tested and evaluated our model in two relevant applications: entity retrieval in knowledge graphs and entity linking over streams of text. In the former, we introduced a two-stage retrieval model that combined a standard information retrieval model with a re-rank function based on our form of relatedness. In the latter, our model was able to overcome the computational bottleneck of linking entities in microblog posts using diffusion-based relatedness models and producing annotations in the text.

In order to describe or estimate different quantities related to a specific random variable, it is of prime interest to numerically generate such a variate. In specific situations, the exact generation of random variables might be either momentarily unavailable or too expensive in terms of computation time. It therefore needs to be replaced by an approximation procedure. As was previously the case, the ambitious exact simulation of exit times for diffusion processes was unreachable though it concerns many applications in different fields like mathematical finance, neuroscience or reliability. The usual way to describe exit times was to use discretization schemes, that are of course approximation procedures. Recently, Herrmann and Zucca \cite{Herrmann-Zucca-2} proposed a new algorithm, the so-called GDET-algorithm (General Diffusion Exit Time), which permits to simulate exactly the exit time for one-dimensional diffusions. The only drawback of exact simulation methods using an acceptance-rejection sampling is their time consumption. In this paper the authors highlight an acceleration procedure for the GDET-algorithm based on a multi-armed bandit model. The efficiency of this acceleration is pointed out through numerical examples.

Received theory of production is not very useful if we try to understand what the 'sources' of growth are when we deal with natural resource-based sectors of economic activity. In these industries, a complex set of interactions and co-evolution prevails between firms producing the commodity and leading the value chain, subcontractors supplying them with machinery, equipment, services and process engineering knowhow, public sector agencies monitoring their environmental impact and local communities engaged in the exploitation of the resource. These agents interact on a daily basis giving rise to a complex set of 'sector specific' rules of governance which vary from country to country and from sector to sector.In this paper we look at the mining industry, that has experienced a very rapid process of change due to the dramatic expansion of demand from China, India and other economies, and to major changes in the international knowledge frontier in many different scientific and technological disciplines (e.g. geology, biotechnologies, digital and computer sciences, health sciences and engineering). These developments have induced dramatic changes in the industry and most notably in the patterns of interaction among the various agents mentioned above. A similar process of sector-specific dynamic interdependencies seems to prevail in other natural resource based sectors, such as aquaculture, forestry products and others. In this paper we present a model of these interactions and sketch out an analytical view as to how production organization takes place in the mining sector, and how these location-specific forces induce change in the industry over time. Our way of looking at these issues has strong policy implications which we briefly examine in the final pages of the paper.

Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph signal processing tools cannot be used anymore. Researchers have proposed approaches to infer a graph topology from observations of signals on its nodes. Since the problem is ill-posed, these approaches make assumptions, such as smoothness of the signals on the graph, or sparsity priors. In this paper, we propose a characterization of the space of valid graphs, in the sense that they can explain stationary signals. To simplify the exposition in this paper, we focus here on the case where signals were i.i.d. at some point back in time and were observed after diffusion on a graph. We show that the set of graphs verifying this assumption has a strong connection with the eigenvectors of the covariance matrix, and forms a convex set. Along with a theoretical study in which these eigenvectors are assumed to be known, we consider the practical case when the observations are noisy, and experimentally observe how fast the set of valid graphs converges to the set obtained when the exact eigenvectors are known, as the number of observations grows. To illustrate how this characterization can be used for graph recovery, we present two methods for selecting a particular point in this set under chosen criteria, namely graph simplicity and sparsity. Additionally, we introduce a measure to evaluate how much a graph is adapted to signals under a stationarity assumption. Finally, we evaluate how state-of-the-art methods relate to this framework through experiments on a dataset of temperatures., Submitted to IEEE Transactions on Signal and Information Processing over Networks

This paper proposes a new chaotic image encryption scheme, which employs Josephus traversing and mixed chaotic map. The scheme consists of three processes: key stream generation process; three-round scrambling process; and one-round diffusion process. The proposed mathematical model is applied for the key stream generator in the first process. The initial values and parameters are sensitive to both the secret keys in the new scheme and plain images. The second process employs the Josephus traversing in scrambling; then the rows and columns of pixels are exchanged. The third process can modify the pixel gray-level values and crack the strong correlations between adjacent pixels simultaneously. The initial conditions for chaotic systems are derived using external secret keys by applying some algebraic transformations to the key. Security analysis indicates that the new scheme is effective, which can resist common attacks.

We relate some basic constructions of stochastic analysis to differential geometry, via random walk approximations. We consider walks on both Riemannian and sub-Riemannian manifolds in which the steps consist of travel along either geodesics or integral curves associated to orthonormal frames, and we give particular attention to walks where the choice of step is influenced by a volume on the manifold. A primary motivation is to explore how one can pass, in the parabolic scaling limit, from geodesics, orthonormal frames, and/or volumes to diffusions, and hence their infinitesimal generators, on sub-Riemannian manifolds, which is interesting in light of the fact that there is no completely canonical notion of sub-Laplacian on a general sub-Riemannian manifold. However, even in the Riemannian case, this random walk approach illuminates the geometric significance of Ito and Stratonovich stochastic differential equations as well as the role played by the volume., 32 pages, 3 figures. v2: typos corrected and minor revisions. v3: final version, to appear on ESAIM: COCV

Recent studies report that technological developments in machine learning and artificial intelligence present a significant risk to jobs in advanced countries. We re-estimate automation risk at the job level, finding sectoral employment structure to be key in determining automation risk at the country level. At the country level, we find a negative relationship between automation risk and labour productivity. We then analyse the role of trade as a factor leading to structural changes and consider the relation between trade and aggregate automation risk by comparing automation risk between a hypothetical autarky and the actual situation. Results indicate that with trade, automation risk is higher in Europe, although moderately so. Automation risk in the high-productivity European countries is higher with trade, with trade between European and non-European nations driving these results. This implies that these countries do not, on balance, offshore automation risk, but rather import it. The sectors that show the largest automation risk relation to trade are manufacturing, trade, transport and finance.

Many inter-organizational networks exhibit small-world properties in that they consist of close-knit sub-networks or blocks, along with sparse ties spanning those blocks, so that an average firm in the network has short connections to a wide range of partners. While extant literature has shown that networks exhibiting these properties outperform those that do not, little empirical research exists on the process of knowledge exchanges among firms within and across blocks in a network. This paper aims to fill this gap in the context of inter-firm technology alliance networks in the biopharmaceutical industry. Adopting an intertemporal perspective, we examine how particular alliance relationships within a network affect learning and information flows and therefore the co-evolution and similarity of the firms' technology profiles. Results of a Quasi-Maximum Likelihood analysis on a 10-year panel data set consisting of 217 firms reveal that firms in blocks tend to develop similar technology profiles over time. The results further demonstrate that firms located closer to each other in a network (shorter path length) display higher levels of resemblance compared with firms that are farther apart. However, perhaps more importantly and contrary to expectations, our results also show that the length of ties between two firms has a smaller effect on the similarity between their profiles when firms are not members of the same block.

Online food retail has the potential to broaden access to systems of food provision which promote social and environmental quality attributes. This possibility is explored using data from a survey of 365 consumers who purchased food either via internet retailers of local and organic food, or via farmers' markets, in Vancouver, Canada and Melbourne, Australia. Survey results are analyzed using principal component and regression techniques and interpreted via the theoretical framework of conventions theory. Key findings show that while online retailers of local organic food are not currently attracting more resource constrained consumers, they do appeal to a similar, although broader, array of quality conventions. This research provides new insights into the challenges and opportunities associated with increasing consumer access to alternative food networks, as well as adding to the small number of quantitative studies in the conventions theory literature.

Our research studies the construction and estimation of copula-based semi parametric Markov model for the processes, which involved in water flows in the hydro plants. As a rule analyzing the dependence structure of stationary time series regressive models defined by invariant marginal distributions and copula functions that capture the temporal dependence of the processes is considered. This permits to separate out the temporal dependence (such as tail dependence) from the marginal behavior (such as fat tails) of a time series. Dealing with utility company data we have found the best copula describing data - Gumbel copula. As a result constructed algorithm was used for an imitation of low probability events (in a hydro power industry) and predictions.

Graphical abstract, We present a generic method for considering incomplete but gradually expandable sociological data in agent-based modeling based on the classic model of cultural dissemination by Axelrod. Our method extension was inspired by research on the diffusion of citizen photovoltaic initiatives, i.e. by initiatives in which citizens collectively invest in photovoltaic plants and share the profits. Owing to the absence of empirical interaction parameters, the Axelrod model was used as basis for considering peer effects with contrived interaction data that can be updated from empirical surveys later on. The Axelrod model was extended to cover the following additional features: • Consideration of empirical social science data for concrete social interaction. • Development of a variable and fine-tunable interaction function for agents. • Deployment of a generic procedure for modeling peer effects in agent-based models.

The regular Dirichlet extension is the dual concept of regular Dirichlet subspace. The main purpose of this paper is to characterize all the regular Dirichlet extensions of one-dimensional Brownian motion and to explore their structures. It is shown that every regular Dirichlet extension of one-dimensional Brownian motion may essentially decomposed into at most countable disjoint invariant intervals and an $\mathcal{E}$-polar set relative to this regular Dirichlet extension. On each invariant interval the regular Dirichlet extension is characterized uniquely by a scale function in a given class. To explore the structure of regular Dirichlet extension we apply the idea introduced in [17], we formulate the trace Dirichlet forms and attain the darning process associated with the restriction to each invariant interval of the orthogonal complement of $H^1_\mathrm{e}(\mathbb{R})$ in the extended Dirichlet space of the regular Dirichlet extension. As a result, we find an answer to a long-standing problem whether a pure jump Dirichlet form has proper regular Dirichlet subspaces., Comment: 29 pages with 2 figures

International audience; This milestone 50th volume of the "Séminaire de Probabilités" pays tribute with a series of memorial texts to one of its former editors, Jacques Azéma, who passed away in January. The founders of the "Séminaire de Strasbourg", which included Jacques Azéma, probably had no idea of the possible longevity and success of the process they initiated in 1967. Continuing in this long tradition, this volume contains contributions on state-of-art research on Brownian filtrations, stochastic differential equations and their applications, regularity structures, quantum diffusion, interlacing diffusions, mod-Ø convergence, Markov soup, stochastic billiards and other current streams of research.

This paper provides an in-depth review about parametric estimation methods for stationary stochastic differential equations (SDEs) driven by Wiener noise with discrete time observations. The short-term interest rate dynamics are commonly described by continuous-time diffusion processes, whose parameters are subject to estimation bias, as data are highly persistent, and discretization bias, as data are discretely sampled despite the continuous-time nature of the model. To assess the role of persistence and the impact of sampling frequency on the estimation, we conducted a simulation study under different settings to compare the performance of the procedures and illustrate the finite sample behavior. To complete the survey, an application of the procedures to real data is provided The authors acknowledge support from grant MTM2016-76969-P from the Spanish Ministry of Economy and Competitiveness (cofunded with FEDER funds) and gratefully thank Spanish National Research Council for providing the computing resources of the Supercomputing Center of Galicia (CESGA) SI

A two-types, discrete-time population model with finite, constant size is constructed, allowing for a general form of frequency-dependent selection and skewed offspring distribution. Selection is defined based on the idea that individuals first choose a (random) number of \emph{potential} parents from the previous generation and then, from the selected pool, they inherit the type of the fittest parent. The probability distribution function of the number of potential parents per individual thus parametrises entirely the selection mechanism. Using sampling- and moment-duality, weak convergence is then proved both for the allele frequency process of the selectively weak type and for the population's ancestral process. The scaling limits are, respectively, a two-types $\Xi$-Fleming-Viot jump-diffusion process with frequency-dependent selection, and a branching-coalescing process with general branching and simultaneous multiple collisions. Duality also leads to a characterisation of the probability of extinction of the selectively weak allele, in terms of the ancestral process' ergodic properties.

We consider inference in the scalar diffusion model $\,\mathrm{d}X_{t}=b(X_{t})\,\mathrm{d}t+\sigma(X_{t})\,\mathrm{d}W_{t}$ with discrete data $(X_{j\Delta_{n}})_{0\leq j\leq n}$, $n\to\infty$, $\Delta_{n}\to0$ and periodic coefficients. For $\sigma$ given, we prove a general theorem detailing conditions under which Bayesian posteriors will contract in $L^{2}$-distance around the true drift function $b_{0}$ at the frequentist minimax rate (up to logarithmic factors) over Besov smoothness classes. We exhibit natural nonparametric priors which satisfy our conditions. Our results show that the Bayesian method adapts both to an unknown sampling regime and to unknown smoothness.

In this paper we present the Edgeworth expansion for the Euler approximation scheme of a continuous diffusion process driven by a Brownian motion. Our methodology is based upon a recent work (Stochastic Process. Appl. 123 (2013) 887–933), which establishes Edgeworth expansions associated with asymptotic mixed normality using elements of Malliavin calculus. Potential applications of our theoretical results include higher order expansions for weak and strong approximation errors associated to the Euler scheme, and for studentized version of the error process.

Is the German manufacturing industry, which has been leaving cities for less densely populated areas since World War II, being lured back into urban centers? This report analyses industrial start-ups from 2012 to 2016 and derives their preferred locations. The analysis shows that the start-up intensity in large agglomerations is on average almost 40 percent higher than in the other regions of Germany. Agglomerations attract start-ups with the advantages they offer, namely proximity to both research facilities and customers for the new (digital) industry. Accordingly, many new companies are being founded in city centers. To tap cities’ growth potential, not only must the influx of risk capital, knowledge, and skilled workers be secured, but space bottlenecks eliminated as well., DIW Weekly Report

We introduce a class of kinetic and anisotropic random motions $(x_t^{\sigma},v_t^{\sigma})_{t \geq 0}$ on the unit tangent bundle $T^1 \mathcal M$ of a general Riemannian manifold $(\mathcal M,g)$, where $\sigma$ is a positive parameter quantifying the amount of noise affecting the dynamics. As the latter goes to infinity, we then show that the time rescaled process $(x_{\sigma^2 t}^{\sigma})_{t \geq 0}$ converges in law to an explicit anisotropic Brownian motion on $\mathcal M$. Our approach is essentially based on the strong mixing properties of the underlying velocity process and on rough paths techniques, allowing us to reduce the general case to its Euclidean analogue. Using these methods, we are able to recover a range of classical results., Comment: 26 pages, 2 figures

This paper investigates the factors driving nanotechnology development in Chinese regions. Advanced regions of China have spearheaded the country’s rapid growth in nanotechnology, aided by substantial support from the government. While this head start could potentially perpetuate regional inequalities through agglomeration economies, the results suggest that knowledge spillovers exert a substantially greater impact in peripheral regions compared with the advanced ones, and may thus be compensating for the limited institutional support they receive and their weak technological capabilities. This research contributes to the regional innovation literature by highlighting that a formal scientific network can counteract the forces of agglomeration economies and spur innovation in peripheral regions.

International audience; We consider a stochastic growth model for which extinction eventually occurs almost surely. The associated complete Fokker–Planck equation describing the law of the process is established and studied. This equation combines a PDE and an ODE, connected one to each other. We then design a finite differences numerical scheme under a probabilistic viewpoint. The model and its approximation are evaluated through numerical simulations.