Micro venture capital

Recently, the venture capital (VC) industry has experienced the entry of several new capital providers. Using US data on investors and their portfolio startups from 2000 to 2022, we document the emergence of a new type of investors: the micro VC. Our analysis reveals that micro Vencture Capitalists (VCs) have an idiosyncratic investment strategy, which differs from traditional VCs. Compared with these investors, micro VCs invest in riskier startups, that is, early-stage ventures initiated by less experienced founders; yet, micro VCs are less likely to syndicate, stage their investments, and replace the startup founders. Additionally, startups funded by micro VCs are less likely to experience successful exits than those backed by traditional VCs. These results can be traced to a mix of smaller capital endowments, less sophisticated limited partners, and lesser human capital of which micro VCs dispose, and that may induce them to spread their thin capital across many investments to maximize returns. Our analysis also uncovers important differences in the strategies pursued by micro VCs and business angels.Managerial SummaryThe VC industry is increasingly populated by a variety of investors with disparate characteristics and objectives. One such type of investors is represented by the so-called micro VC firms. These are VC firms that manage funds typically below $50 million and focused primarily on investing in founder-led startups. We leverage comprehensive VC data in the United States to answer three questions: (1) Who leads micro VC firms? (2) How do micro VC firms invest? (3) How do startups backed by micro VC perform? We find that micro VC firms are often led by relatively inexperienced entrepreneurs with little VC experience, and these firms are supported by less sophisticated limited partners. Although micro VC firms invest in riskier startups, they are less engaged in syndication and investment staging than traditional VC firms. Finally, micro VC-backed startups have a lower probability of successful exit as compared with those backed by traditional VC firms. Collectively, our results suggest that micro VCs differ from traditional VCs beyond being "micro.

Exploring the Impact of Disrupted Peer-to-Peer Communications on Fully Decentralized Learning in Disaster Scenarios

Fully decentralized learning enables the distribution of learning resources and decision-making capabilities across multiple user devices or nodes, and is rapidly gaining popularity due to its privacy-preserving and decentralized nature. Importantly, this crowdsourcing of the learning process allows the system to continue functioning even if some nodes are affected or disconnected. In a disaster scenario, communication infrastructure and centralized systems may be disrupted or completely unavailable, hindering the possibility of carrying out standard centralized learning tasks in these settings. Thus, fully decentralized learning can help in this case. However, transitioning from centralized to peer-to-peer communications introduces a dependency between the learning process and the topology of the communication graph among nodes. In a disaster scenario, even peer-to-peer communications are susceptible to abrupt changes, such as devices running out of battery or getting disconnected from others due to their position. In this study, we investigate the effects of various disruptions to peer-to-peer communications on decentralized learning in a disaster setting. We examine the resilience of a decentralized learning process when a subset of devices drop from the process abruptly. To this end, we analyze the difference between losing devices holding data, i.e., potential knowledge, vs. devices contributing only to the graph connectivity, i.e., with no data. Our findings on a Barabasi-Albert graph topology, where training data is distributed across nodes in an IID fashion, indicate that the accuracy of the learning process is more affected by a loss of connectivity than by a loss of data. Nevertheless, the network remains relatively robust, and the learning process can achieve a good level of accuracy

Laser scanning and modelling of barely visible features: the survey of the Grotto of the Animals at the Villa of Castello (Florence)

The deep fusion of natural and artificial elements typical of Italian Renaissance gardens is particularly evident in the park of Villa di Castello and in the Grotto of the Animals, also called Grotto of the Flood. The soil slope is the essential element of a huge underlying hydraulic machine and it is the result of extensive earthworks which led to the construction of the big retaining wall limiting the grotto and the adjacent fountains. Hence, this grotto represents only the visible part of a mechanism running all around it. It is formed by a single chamber vaulted and covered with sponge-like stones, as well as decorations made of pebbles and shells. The space is divided into three wings, with big marble basins at their end. Over them there are reliefs of animals made of different stones and marbles. Animals recur also in the compositions of fish and shellfish decorating the side basins and in the bronze birds currently kept in the Museo del Bargello. The name “Grotto of the Flood” comes from the water feature that characterised this place: visitors were surprised by tens of jets hidden among the stones in the vault and in the floor. To obtain this effect, the whole grotto is surrounded by multi-storey tunnels, hiding the hydraulic system and people activating the mechanisms. Research agreements were drawn up between the Special Superintendence for the Historical, Artistic and Ethnoanthropological Heritage, the Florence museums group and the GeCO Lab, for the realization of the survey presented in this paper. The task of the GeCO Lab was thus identifying the best solutions to check the spatial relations between the grotto and the area above, as well as the geometric and functional connections between the building and the ancient hydraulic system, composed by pipes and nozzles concealed between the stones. Besides, the overall survey was intended as a documentation of the on-going restoration work

Impact of network topology on the performance of Decentralized Federated Learning

Fully decentralized learning is gaining momentum for training AI models at the Internet’s edge, addressing infrastructure challenges and privacy concerns. In a decentralized machine learning system, data is distributed across multiple nodes, with each node training a local model based on its respective dataset. The local models are then shared and combined to form a global model capable of making accurate predictions on new data. Our exploration focuses on how different types of network structures influence the spreading of knowledge – the process by which nodes incorporate insights gained from learning patterns in data available on other nodes across the network. Specifically, this study investigates the intricate interplay between network structure and learning performance using three network topologies and six data distribution methods. These methods consider different vertex properties, including degree centrality, betweenness centrality, and clustering coefficient, along with whether nodes exhibit high or low values of these metrics. Our findings underscore the significance of global centrality metrics (degree, betweenness) in correlating with learning performance, while local clustering proves less predictive. We highlight the challenges in transferring knowledge from peripheral to central nodes, attributed to a dilution effect during model aggregation. Additionally, we observe that central nodes exert a pull effect, facilitating the spread of knowledge. In examining degree distribution, hubs in Barabási–Albert networks positively impact learning for central nodes but exacerbate dilution when knowledge originates from peripheral nodes. Finally, we demonstrate the formidable challenge of knowledge circulation outside of segregated communities, and discuss the impact of class cross-correlations

Emergency department visits by nursing home residents. A retrospective Italian study of administrative databases from 2015 to 2019

Background Visits to Emergency Departments (ED) can be traumatic for Nursing Home (NH) residents. In Italy, the rate of ED visits by NH residents was recently calculated as 3.3%. The reduction of inappropriate ED visits represents a priority for National Healthcare Systems worldwide. Nevertheless, research on factors associated with ED visits is still under-studied in the Italian setting. This study has two main aims: (i) to describe the baseline characteristics of NH residents visiting ED at regional level; (ii) to assess the characteristics, trends, and factors associated with these visits.Methods A retrospective study of administrative data for five years was performed in the Piedmont Region. Data from 24,208 NH residents were analysed. Data were obtained by merging two ministerial databases of residential care and ED use. Sociodemographic and clinical characteristics of the residents, trends, and rates of ED visits were collected. A Generalized Linear Model (GLM) regression was used to evaluate the factors associated with ED visits.Results In 5 years, 12,672 residents made 24,609 ED visits. Aspecific symptoms (45%), dyspnea (17%) and trauma (16%) were the most frequent problems reported at ED. 51% of these visits were coded as non-critical, and 58% were discharged to the NH. The regression analysis showed an increased risk of ED visits for men (OR = 1.61, 95% CI 1.51-1.70) and for residents with a stay in NH longer than 400 days (OR = 2.19, 95% CI 2.08-2.31).Conclusions Our study indicates that more than half of NH residents' ED visits could potentially be prevented by treating residents in NH. Investments in the creation of a structured and effective network within primary care services, promoting the use of health technology and palliative care approaches, could reduce ED visits and help clinicians manage residents on-site and remotely