Polyphonic Sound Event Detection by using Capsule Neural Networks

Artificial sound event detection (SED) has the aim to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, Deep Learning offers valuable techniques for this goal such as Convolutional Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has been recently introduced in the image processing field with the intent to overcome some of the known limitations of CNNs, specifically regarding the scarce robustness to affine transformations (i.e., perspective, size, orientation) and the detection of overlapped images. This motivated the authors to employ CapsNets to deal with the polyphonic-SED task, in which multiple sound events occur simultaneously. Specifically, we propose to exploit the capsule units to represent a set of distinctive properties for each individual sound event. Capsule units are connected through a so-called "dynamic routing" that encourages learning part-whole relationships and improves the detection performance in a polyphonic context. This paper reports extensive evaluations carried out on three publicly available datasets, showing how the CapsNet-based algorithm not only outperforms standard CNNs but also allows to achieve the best results with respect to the state of the art algorithms

The scientific influence of nations on global scientific and technological development

Determining how scientific achievements influence the subsequent process of knowledge creation is a fundamental step in order to build a unified ecosystem for studying the dynamics of innovation and competitiveness. Relying separately on data about scientific production on one side, through bibliometric indicators, and about technological advancements on the other side, through patents statistics, gives only a limited insight on the key interplay between science and technology which, as a matter of fact, move forward together within the innovation space. In this paper, using citation data of both research papers and patents, we quantify the direct influence of the scientific outputs of nations on further advancements in science and on the introduction of new technologies. Our analysis highlights the presence of geo-cultural clusters of nations with similar innovation system features, and unveils the heterogeneous coupled dynamics of scientific and technological advancements. This study represents a step forward in the buildup of an inclusive framework for knowledge creation and innovation

No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone

It is generally recognized that the traffic generated by an individual connected to a network acts as his biometric signature. Several tools exploit this fact to fingerprint and monitor users. Often, though, these tools assume to access the entire traffic, including IP addresses and payloads. This is not feasible on the grounds that both performance and privacy would be negatively affected. In reality, most ISPs convert user traffic into NetFlow records for a concise representation that does not include, for instance, any payloads. More importantly, large and distributed networks are usually NAT'd, thus a few IP addresses may be associated to thousands of users. We devised a new fingerprinting framework that overcomes these hurdles. Our system is able to analyze a huge amount of network traffic represented as NetFlows, with the intent to track people. It does so by accurately inferring when users are connected to the network and which IP addresses they are using, even though thousands of users are hidden behind NAT. Our prototype implementation was deployed and tested within an existing large metropolitan WiFi network serving about 200,000 users, with an average load of more than 1,000 users simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned out to be very effective, with an accuracy greater than 90%. We also devised new tools and refined existing ones that may be applied to other contexts related to NetFlow analysis

Dynamics in the Fitness-Income plane: Brazilian states vs World countries

In this paper we introduce a novel algorithm, called Exogenous Fitness, to calculate the Fitness of subnational entities and we apply it to the states of Brazil. In the last decade, several indices were introduced to measure the competitiveness of countries by looking at the complexity of their export basket. Tacchella et al (2012) developed a non-monetary metric called Fitness. In this paper, after an overview about Brazil as a whole and the comparison with the other BRIC countries, we introduce a new methodology based on the Fitness algorithm, called Exogenous Fitness. Combining the results with the Gross Domestic Product per capita (GDPp), we look at the dynamics of the Brazilian states in the Fitness-Income plane. Two regimes are distinguishable: one with high predictability and the other with low predictability, showing a deep analogy with the heterogeneous dynamics of the World countries. Furthermore, we compare the ranking of the Brazilian states according to the Exogenous Fitness with the ranking obtained through two other techniques, namely Endogenous Fitness and Economic Complexity Index

The scientific impact of nations on scientific and technological development

Determining how scientific achievements influence the subsequent process of knowledge creation is a fundamental step in order to build a unified ecosystem for studying the dynamics of innovation and competitiveness. Yet, relying separately on data about scientific production on one side, through bibliometric indicators, and about technological advancements on the other side, through patents statistics, gives only a limited insight on the key interplay between science and technology which, as a matter of fact, move forward together within the innovation space. In this paper, using citation data of both scientific papers and patents, we quantify the direct impact of the scientific outputs of nations on further advancements in science and on the introduction of new technologies. Our analysis highlights the presence of geo-cultural clusters of nations with similar innovation system features, and unveils the heterogeneous coupled dynamics of scientific and technological success. This study represents a first step in the buildup of a comprehensive framework for knowledge creation and innovation

Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production

We show that the space in which scientific, technological and economic developments interplay with each other can be mathematically shaped using pioneering multilayer network and complexity techniques. We build the tri-layered network of human activities (scientific production, patenting, and industrial production) and study the interactions among them, also taking into account the possible time delays. Within this construction we can identify which capabilities and prerequisites are needed to be competitive in a given activity, and even measure how much time is needed to transform, for instance, the technological know-how into economic wealth and scientific innovation, being able to make predictions with a very long time horizon. Quite unexpectedly, we find empirical evidence that the naive knowledge flow from science, to patents, to products is not supported by data, being instead technology the best predictor for industrial and scientific production for the next decades

Optimal design of multi-energy systems with seasonal storage

Optimal design and operation of multi-energy systems involving seasonal energy storage are often hindered by the complexity of the optimization problem. Indeed, the description of seasonal cycles requires a year-long time horizon, while the system operation calls for hourly resolution; this turns into a large number of decision variables, including binary variables, when large systems are analyzed. This work presents novel mixed integer linear program methodologies that allow considering a year time horizon with hour resolution while significantly reducing the complexity of the optimization problem. First, the validity of the proposed techniques is tested by considering a simple system that can be solved in a reasonable computational time without resorting to design days. Findings show that the results of the proposed approaches are in good agreement with the full-scale optimization, thus allowing to correctly size the energy storage and to operate the system with a long-term policy, while significantly simplifying the optimization problem. Furthermore, the developed methodology is adopted to design a multi-energy system based on a neighborhood in Zurich, Switzerland, which is optimized in terms of total annual costs and carbon dioxide emissions. Finally the system behavior is revealed by performing a sensitivity analysis on different features of the energy system and by looking at the topology of the energy hub along the Pareto sets

Scattering Attenuation Images of the Control of Thrusts and Fluid Overpressure on the 2016–2017 Central Italy Seismic Sequence

Deep fluid circulation likely triggered the large extensional events of the 2016–2017 Central Italy seismic sequence. Nevertheless, the connection between fault mechanisms, main crustal-scale thrusts, and the circulation and interaction of fluids with tectonic structures controlling the sequence is still debated. Here, we show that the 3D temporal and spatial mapping of peak delays, proxy of scattering attenuation, detects thrusts and sedimentary structures and their control on fluid overpressure and release. After the mainshocks, scattering attenuation drastically increases across the hanging wall of the Monti Sibillini and Acquasanta thrusts, revealing fracturing and fluid migration. Before the sequence, low-scattering volumes within Triassic formations highlight regions of fluid overpressure, which enhances rock compaction. Our results highlight the control of thrusts and paleogeography on the sequence and hint at the monitoring potential of the technique for the seismic hazard assessment of the Central Apennines and other tectonic regions

Non Sequential Recursive Pair Substitution: Some Rigorous Results

We present rigorous results on some open questions on NSRPS, non sequential recursive pairs substitution method (see Grassberger in \cite{G}). In particular, starting from the action of NSRPS on finite strings we define a corresponding natural action on measures and we prove that the iterated measure becomes asymptotically Markov. This certify the effectiveness of NSRPS as a tool for data compression and entropy estimation.Comment: 20 page