The role of venture capital in the emerging entrepreneurial finance ecosystem: future threats and opportunities

The last decade has seen the emergence of alternative sources of early-stage finance, which are radically changing and reshaping the start-up eco-system. These include incubators, accelerators, science and technology parks, university-affiliated seed funds, corporate seed funds, business angels \u2013 including \u201csuper-angels\u201d, angel groups, business angel networks and angel investment funds \u2013 and both equity- and debt-based crowdfunding platforms. In parallel with this development, large financial institutions that have traditionally invested in late-stage and mature companies, have increasingly diversified their investment portfolios to \u201cget into the venture game\u201d, in some cases, through the traditional closed-end funds model and, in other cases through direct investments and coinvestments alongside the closed-end funds. This paper reviews the main features, investment policies and risk-return profiles of the institutional and informal investors operating in the very early stage of the life cycle of entrepreneurial firms. It concludes that traditional closed-end venture capital funds continue to play an important role in early stage finance because of their unique competences (e.g. screening, negotiating and monitoring) in what has become a wider and more complex financing ecosystem

A hybrid neuro--wavelet predictor for QoS control and stability

For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use \emph{wavelet analysis}, providing compression and denoising on the observed time series of the amount of past user requests; and a \emph{recurrent neural network} trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06\%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations

Emerging trends in entrepreneurial finance

The emergence of new sources of financing in the aftermath of the financial crisis has substantially increased the funding options available to new entrepreneurial ventures. Technology parks, startup incubators and accelerators, business angels and angel investment organizations, equity crowdfunding platforms, venture capital funds, corporate seed funds and institutional investors directly investing in new ventures, have significantly increased the menu of funding channels, in many cases by leveraging the disrupting effects of Fintech companies and the emergence of internet-based segments of the capital market. As a consequence, a new financing eco-system for new ventures has emerged in recent years that has significant implications for both investors and entrepreneurs, impacting on entrepreneurial growth paths and creating new policy challenges at both the national and global scales. The substantially larger set of funding channels has not only been instrumental in the unprecedented growth in the number of early stage companies but has also raised new questions that have challenged scholars and practitioners and policymakers alike. Idiosyncratic risk-return profiles and investment philosophies, unorthodox investment practices, innovative value-adding contributions to portfolio companies ventures and structurally different exit options are some of the areas that require urgent investigation. The first \u201cEmerging Trends in Entrepreneurial Finance\u201d Conference, 1\u20132 June 2017 organized by the Stevens School of Business, the University of Piemonte Orientale and the Editors of Venture Capital: an International Journal of Entrepreneurial Finance at the Stevens Institute of Technology (Hoboken, NJ, USA) with the sponsorship of Hanlon Financial Systems Center and the Stevens Venture Center, aimed at gathering world-class scholars in the field of entrepreneurial finance to stimulate a debate on the evolution of the financing ecosystem for new ventures. From the close to 75 submissions, of which 16 were accepted for presentation. the Guest Editors of this special Issue have selected six outstanding papers that address crucial topics and recent developments

A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP

Surface plasmon polaritons (SPPs) confined along metal-dielectric interface have attracted a relevant interest in the area of ultracompact photonic circuits, photovoltaic devices and other applications due to their strong field confinement and enhancement. This paper investigates a novel cascade neural network (NN) architecture to find the dependance of metal thickness on the SPP propagation. Additionally, a novel training procedure for the proposed cascade NN has been developed using an OpenMP-based framework, thus greatly reducing training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand

Correlazioni tra le velocit\ue0 ultrasoniche e le caratteristiche petrografiche in ceramiche archeologiche: un primo approccio metodologico

L\u2019analisi delle velocit\ue0 degli ultrasuoni permette di investigare i caratteri tessiturali e strutturali e di individuare la presenza di eventuali difetti localizzati all\u2019interno del campione. La loro diffusione negli studi archeometrici \ue8 legata alla non distruttivit\ue0 e alla possibilit\ue0 di fornire risultati accurati in tempo reale. In questo lavoro sono state effettuate numerose analisi petrografiche in sezione sottile e misure di velocit\ue0 di propagazione degli ultrasuoni su reperti ceramici di interesse archeologico. In particolare sono stati selezionati ed analizzati ceramiche preistoriche ed anfore da trasporto del V-IV sec. a.C., caratterizzati da differente granulometria, composizione e struttura. Da questo primo approccio \ue8 stato possibile evidenziare che il parametro petrografico che influenza maggiormente la velocit\ue0 degli ultrasuoni \ue8 la forma e la disposizione spaziale dei pori, e la dimensione media degli inclusi, mentre correlazioni poco significative si ottengono prendendo in considerazione la percentuale e il tipo di inerte presente nell\u2019impasto ceramico

An entropy evaluation algorithm to improve transmission efficiency of compressed data in pervasive healthcare mobile sensor networks

Data transmission is the most critical operation for mobile sensors networks in term of energy waste. Particularly in pervasive healthcare sensors network it is paramount to preserve the quality of service also by means of energy saving policies. Communication and data transmission are among the most critical operation for such devises in term of energy waste. In this paper we present a novel approach to increase battery life-span by means of shorter transmission due to data compression. On the other hand, since this latter operation has a non-neglectable energy cost, we developed a compression efficiency estimator based on the evaluation of the absolute and relative entropy. Such algorithm provides us with a fast mean for the evaluation of data compressibility. Since mobile wireless sensor networks are prone to battery discharge-related problems, such an evaluation can be used to improve the electrical efficiency of data communication. In facts the developed technique, due to its independence from the string or file length, is extremely robust both for small and big data files, as well as to evaluate whether or not to compress data before transmission. Since the proposed solution provides a quantitative analysis of the source's entropy and the related statistics, it has been implemented as a preprocessing step before transmission. A dynamic threshold defines whether or not to invoke a compression subroutine. Such a subroutine should be expected to greatly reduce the transmission length. On the other hand a data compression algorithm should be used only when the energy gain of the reduced transmission time is presumably greater than the energy used to run the compression software. In this paper we developed an automatic evaluation system in order to optimize the data transmission in mobile sensor networks, by compressing data only when this action is presumed to be energetically efficient. We tested the proposed algorithm by using the Canterbury Corpus as well as standard pictorial data as benchmark test. The implemented system has been proven to be time-inexpensive with respect to a compression algorithm. Finally the computational complexity of the proposed approach is virtually neglectable with respect to the compression and transmission routines themselves