Diaphragmatic hernia following oesophagectomy for oesophageal cancer – Are we too radical?

Background: Diaphragmatic herniation (DH) of abdominal contents into the thorax after oesophageal resection is a recognised and serious complication of surgery. While differences in pressure between the abdominal and thoracic cavities are important, the size of the hiatal defect is something that can be influenced surgically. As with all oncological surgery, safe resection margins are essential without adversely affecting necessary anatomical structure and function. However very little has been published looking at the extent of the hiatal resection. We aim to present a case series of patients who developed DH herniation post operatively in order to raise discussion about the ideal extent of surgical resection required. Methods: We present a series of cases of two male and one female who had oesophagectomies for moderately and poorly differentiated adenocarcinomas of the lower oesophagus who developed post-operative DH. We then conducted a detailed literature review using Medline, Pubmed and Google Scholar to identify existing guidance to avoid this complication with particular emphasis on the extent of hiatal resection. Discussion: Extended incision and partial resection of the diaphragm are associated with an increased risk of postoperative DH formation. However, these more extensive excisions can ensure clear surgical margins. Post-operative herniation can be an early or late complication of surgery and despite the clear importance of hiatal resection only one paper has been published on this subject which recommends a more limited resection than was carried out in our cases. Conclusion: This case series investigated the recommended extent of hiatal dissection in oesophageal surgery. Currently there is no clear guidance available on this subject and further studies are needed to ascertain the optimum resection margin that results in the best balance of oncological parameters vs. post operative morbidity

Analysis of a consensus protocol for extending consistent subchains on the bitcoin blockchain

Currently, an increasing number of third-party applications exploit the Bitcoin blockchain to store tamper-proof records of their executions, immutably. For this purpose, they leverage the few extra bytes available for encoding custom metadata in Bitcoin transactions. A sequence of records of the same application can thus be abstracted as a stand-alone subchain inside the Bitcoin blockchain. However, several existing approaches do not make any assumptions about the consistency of their subchains, either (i) neglecting the possibility that this sequence of messages can be altered, mainly due to unhandled concurrency, network malfunctions, application bugs, or malicious users, or (ii) giving weak guarantees about their security. To tackle this issue, in this paper, we propose an improved version of a consensus protocol formalized in our previous work, built on top of the Bitcoin protocol, to incentivize third-party nodes to consistently extend their subchains. Besides, we perform an extensive analysis of this protocol, both defining its properties and presenting some real-world attack scenarios, to show how its specific design choices and parameter configurations can be crucial to prevent malicious practices

A local feature engineering strategy to improve network anomaly detection

The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions

Statistical arbitrage powered by Explainable Artificial Intelligence

Machine learning techniques have recently become the norm for detecting patterns in financial markets. However, relying solely on machine learning algorithms for decision-making can have negative consequences, especially in a critical domain such as the financial one. On the other hand, it is well-known that transforming data into actionable insights can pose a challenge even for seasoned practitioners, particularly in the financial world. Given these compelling reasons, this work proposes a machine learning approach powered by eXplainable Artificial Intelligence techniques integrated into a statistical arbitrage trading pipeline. Specifically, we propose three methods to discard irrelevant features for the prediction task. We evaluate the approaches on historical data of component stocks of the S&P500 index and aim at improving not only the prediction performance at the stock level but also overall at the stock set level. Our analysis shows that our trading strategies that include such feature selection methods improve the portfolio performances by providing predictive signals whose information content suffices and is less noisy than the one embedded in the whole feature set. By performing an in-depth risk-return analysis, we show that the proposed trading strategies powered by explainable AI outperform highly competitive trading strategies considered as baselines

Popularity prediction of instagram posts

Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction, with the aim of exploiting advanced techniques such as machine learning, deep learning, natural language processing, etc., to support such tools. In light of the above, in this work we aim to address the challenge of predicting the popularity of a future post on Instagram, by defining the problem as a classification task and by proposing an original approach based on Gradient Boosting and feature engineering, which led us to promising experimental results. The proposed approach exploits big data technologies for scalability and efficiency, and it is general enough to be applied to other social media as well

Exploring relationships between the distribution of giant red shrimp Aristaeomorpha foliacea (Risso, 1827) and environmental factors in the Central-Western Mediterranean Sea

Mediterranean giant red shrimp Aristaeomorpha foliacea (Risso, 1827) is one of the dominant species in deep-sea megafaunal assemblages, plays a key role in deep-sea communities and it is considered one of the most important targets of deep-water trawl fishing. Although a large number of studies have analysed the spatial distribution of epibenthic crustaceans in bathyal habitats with respect to environmental, geomorphological and hydrological factors, as well as fishing pressure, the manner in which these variables synergistically affect the spatio-temporal changes of giant red shrimp is unclear. To analyse the possible effects of abiotic predictors on the spatio-temporal distribution of giant red shrimp, Generalized Additived Models (GAMs) and Regression Trees were produced. Biological data were collected during the MEDITS trawl surveys carried out in the Sea of Sardinia (2009-2014), during which environmental data were obtained with a multiparametric probe. A longitudinal (west-east) trend was found, with higher abundances at depths of 400-600 m, corresponding to salinity values of 38.1-38.5 psu and temperatures of 13.6-13.8°C. Our results confirm the existence of a tight linkage between the distribution of the Levantine Intermediate Water (LIW) from the eastern Mediterranean Sea and the preferential habitat characteristics of the giant red shrimp. We suggest that a deeper knowledge of the relationships between abiotic (hydrological) factors in the water column and the distribution of Mediterranean resources, such as the giant red shrimp, can provide valuable support for their better management, at the local scale (Sardinia) and across the whole Mediterranean Sea. al use only

SEM Remote Control with a 3D Option

Abstract Remote control of a scientific instrument is a topic gaining more and more attention between instrument users and operators. The project presented in this article reports results obtained from two distinct research efforts. The main outcome from the first research was the realization of an application to remote-control a Scanning Electron Microscope (SEM), while the main outcome from the second research was the implementation of a procedure to reconstruct 3D surfaces

FootApp: An AI-powered system for football match annotation

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players’ and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario

CulturAI: Semantic Enrichment of Cultural Data Leveraging Artificial Intelligence

In this paper, we propose an innovative tool able to enrich cultural and creative spots (gems, hereinafter) extracted from the European Commission Cultural Gems portal, by suggesting relevant keywords (tags) and YouTube videos (represented with proper thumbnails). On the one hand, the system queries the YouTube search portal, selects the videos most related to the given gem, and extracts a set of meaningful thumbnails for each video. On the other hand, each tag is selected by identifying semantically related popular search queries (i.e., trends). In particular, trends are retrieved by querying the Google Trends platform. A further novelty is that our system suggests contents in a dynamic way. Indeed, as for both YouTube and Google Trends platforms the results of a given query include the most popular videos/trends, such that a gem may constantly be updated with trendy content by periodically running the tool. The system has been tested on a set of gems and evaluated with the support of human annotators. The results highlighted the effectiveness of our proposal