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Hyperspectral imaging systems based on multiple-beam interference (MBI), such as Fabry-Perot interferometry, are attracting interest due to their compact design, high throughput, and fine resolution. Unlike dispersive devices, which measure spectra directly, the desired spectra in interferometric systems are reconstructed from measured interferograms. Although the response of MBI devices is modeled by the Airy function, existing reconstruction techniques are often limited to Fourier-transform spectroscopy, which is tailored for two-beam interference (TBI). These methods impose limitations for MBI and are susceptible to non-idealities like irregular sampling and noise, highlighting the need for an in-depth numerical framework. To fill this gap, we propose a rigorous taxonomy of the TBI and MBI instrument description and propose a unified Bayesian formulation which both embeds the description of existing literature works and adds some of the real-world non-idealities of the acquisition process. Under this framework, we provide a comprehensive review of spectroscopy forward and inverse models. In the forward model, we propose a thorough analysis of the discretization of the continuous model and the ill-posedness of the problem. In the inverse model, we extend the range of existing solutions for spectrum reconstruction, framing them as an optimization problem. Specifically, we provide a progressive comparative analysis of reconstruction methods from more specific to more general scenarios, up to employing the proposed Bayesian framework with prior knowledge, such as sparsity constraints. Experiments on simulated and real data demonstrate the framework's flexibility and noise robustness. The code is available at https://github.com/mhmdjouni/inverspyctrometry., Comment: Preprent, 15 pages, 13 figures, full article

In this study, we conducted a comprehensive data collection on the 2022 Qatar FIFA World Cup event and used a multilayer network approach to visualize the main topics, while considering their context and meaning relationships. We structured the data into layers that corresponded with the stages of the tournament and utilized Gephi software to generate the multilayer networks. Our visualizations displayed both the relationships between topics and words, showing the word-context relationship, as well as the dynamics and changes over time by layer of the most frequently discussed topics., Comment: 8 pages, 1 figure, 1 table, 3 link

In the last decade, novel hyperspectral cameras have been developed with particularly desirable characteristics of compactness and short acquisition time, retaining their potential to obtain spectral/spatial resolution competitive with respect to traditional cameras. However, a computational effort is required to recover an interpretable data cube. In this work we focus our attention on imaging spectrometers based on interferometry, for which the raw acquisition is an image whose spectral component is expressed as an interferogram. Previous works have focused on the inversion of such acquisition on a pixel-by-pixel basis within a Bayesian framework, leaving behind critical information on the spatial structure of the image data cube. In this work, we address this problem by integrating a spatial regularization for image reconstruction, showing that the combination of spectral and spatial regularizers leads to enhanced performances with respect to the pixelwise case. We compare our results with Plug-and-Play techniques, as its strategy to inject a set of denoisers from the literature can be implemented seamlessly with our physics-based formulation of the optimization problem., Comment: 5 pages, 2 figures

Hyperspectral imaging is gathering significant attention due to its potential in various domains such as geology, agriculture, ecology, and surveillance. However, the associated processing algorithms, which are essential for enhancing output quality and extracting relevant information, are often computationally intensive and have to deal with substantial data volumes. Our focus lies on reconfigurable hardware, particularly recent FPGAs. While FPGA design can be complex, High Level Synthesis (HLS) workflows have emerged as a solution, abstracting low-level design intricacies and enhancing productivity. Despite successful prior efforts using HLS for hyperspectral imaging acceleration, we lack a comprehensive research to benchmark various algorithms and architectures within a unified framework. This study aims to quantitatively evaluate performance across different inversion algorithms and design architectures, providing insights for optimal trade-offs for specific applications. We apply this analysis to the case study of spectrum reconstruction processed from interferometric acquisitions taken by Fourier transform spectrometers., Comment: 12 pages, 8 figures

This study aims to investigate the possible relationship between the mechanisms of social influence and the choice of airline, through the use of new tools, with the aim of understanding whether they can contribute to a better understanding of the factors influencing the decisions of consumers in the aviation sector. We have chosen to extract user reviews from well-known platforms: Trustpilot, Google, and Twitter. By combining web scraping techniques, we have been able to collect a comprehensive dataset comprising a wide range of user opinions, feedback, and ratings. We then refined the BERT model to focus on insightful sentiment in the context of airline reviews. Through our analysis, we observed an intriguing trend of average negative sentiment scores across various airlines, giving us deeper insight into the dynamics between airlines and helping us identify key partnerships, popular routes, and airlines that play a central role in the aeronautical ecosystem of Catania airport during the specified period. Our investigation led us to find that, despite an airline having received prestigious awards as a low-cost leader in Europe for two consecutive years 2021 and 2022, the "Catanese" user tends to suffer the dominant position of other companies. Understanding the impact of positive reviews and leveraging sentiment analysis can help airlines improve their reputation, attract more customers, and ultimately gain a competitive edge in the marketplace., Comment: arXiv admin note: text overlap with arXiv:1311.3475 by other authors

Microsatellites and drones are often equipped with digital cameras whose sensing system is based on color filter arrays (CFAs), which define a pattern of color filter overlaid over the focal plane. Recent commercial cameras have started implementing RGBW patterns, which include some filters with a wideband spectral response together with the more classical RGB ones. This allows for additional light energy to be captured by the relevant pixels and increases the overall SNR of the acquisition. Demosaicking defines reconstructing a multi-spectral image from the raw image and recovering the full color components for all pixels. However, this operation is often tailored for the most widespread patterns, such as the Bayer pattern. Consequently, less common patterns that are still employed in commercial cameras are often neglected. In this work, we present a generalized framework to represent the image formation model of such cameras. This model is then exploited by our proposed demosaicking algorithm to reconstruct the datacube of interest with a Bayesian approach, using a total variation regularizer as prior. Some preliminary experimental results are also presented, which apply to the reconstruction of acquisitions of various RGBW cameras.

Recently, an increasing interest in the management of water and health resources has been recorded. This interest is fed by the global sustainability challenges posed to the humanity that have water scarcity and quality at their core. Thus, the availability of effective, meaningful and open data is crucial to address those issues in the broader context of the Sustainable Development Goals of clean water and sanitation as targeted by the United Nations. In this paper, we present the Water Health Open Knowledge Graph (WHOW-KG) along with its design methodology and analysis on impact. WHOW-KG is a semantic knowledge graph that models data on water consumption, pollution, infectious disease rates and drug distribution. The WHOW-KG is developed in the context of the EU-funded WHOW (Water Health Open Knowledge) project and aims at supporting a wide range of applications: from knowledge discovery to decision-making, making it a valuable resource for researchers, policymakers, and practitioners in the water and health domains. The WHOW-KG consists of a network of five ontologies and related linked open data, modelled according to those ontologies., Comment: 17 pages

In recent years, the demand for hyperspectral imaging devices has grown significantly, driven by their ability of capturing high-resolution spectral information. Among the several possible optical designs for acquiring hyperspectral images, there is a growing interest in interferometric spectral imaging systems based on division of aperture. These systems have the advantage of capturing snapshot acquisitions while maintaining a compact design. However, they require a careful calibration to operate properly. In this work, we present the interferometer response characterization algorithm (IRCA), a robust three-step procedure designed to characterize the transmittance response of multi-aperture imaging spectrometers based on the interferometry of Fabry-Perot. Additionally, we propose a formulation of the image formation model for such devices suitable to estimate the parameters of interest by considering the model under various regimes of finesse. The proposed algorithm processes the image output obtained from a set of monochromatic light sources and refines the results using nonlinear regression after an ad-hoc initialization. Through experimental analysis conducted on four different prototypes from the Image SPectrometer On Chip (ImSPOC) family, we validate the performance of our approach for characterization. The associated source code for this paper is available at https://github.com/danaroth83/irca., Comment: 20 pages, 11 figures

Social networks play an important role in people's daily socialization, particularly through social media platforms, which have become key channels for communication and information dissemination. The digital ecosystem does not only evolve communication on multi-network (like TV, social media, and online newspapers) but also provides the social researcher with useful data to explain social-complex dynamics. Our work focus on cultural dynamics-reactions that occurred during the 2020 Emilia-Romagna elections'' in Italy, where a stronghold culture felt in danger of losing against the strong populism and Euro-scepticism present in digital ecosystems. We would like to show how the interaction between parts of the society, during cultural and/or political shifting, can lead to or induce emerging behaviour from society, creating groups that react against or improve the status quo. We developed a word-entry network based on three different levels of participation: pro, con, and neutral. We have analyzed the tweets collected (as text) with the word embedding tools, to see, the most used words (which may suggest the main topics) and the most related words among the various groups. We show how a careful analysis of groups through networks, can give important information about the current event., Comment: 13 pages, 5 figures, 3 tables

App IO is an app developed for the Italian PA. It is definitely useful for citizens to interact with the PA and to get services that were not digitized yet. Nevertheless, it was not perceived in a good way by the citizens and it has been criticized. As we wanted to find the root that caused all these bad reviews we scraped feedback from mobile app stores using custom-coded automated tools and - after that - we trained two machine learning models to perform both sentiment analysis and emotion detection to understand what caused the bad reviews.

This paper aims to show how some popular topics on social networks can be used to predict online newspaper views, related to the topics. Newspapers site and many social networks, become a good source of data to analyse and explain complex phenomena. Understanding the entropy of a topic, could help all organizations that need to share information like government, institution, newspaper or company, to expect an higher activity over their channels, and in some cases predict what the receiver expect from the senders or what is wrong about the communication. For some organization such political party, leaders, company and many others, the reputation and the communication are (for most of them) the key part of a more and complex huge system. To reach our goal, we use gathering tools and information theory to detect and analyse trends topic on social networks, with the purpose of proved a method that helps organization, newspapers to predict how many articles or communication they will have to do on a topic, and how much flow of views they will have in a given period, starting with the entropy-article ratio. Our work address the issue to explore in which entropy-rate, and through which dynamics, a suitable information diffusion performance is expected on social network and then on newspaper. We have identified some cross-cutting dynamics that, associated with the contexts, might explain how people discuss about a topic, can move on to argue and informs on newspapers sites., Comment: 13 pages, 8 figures, journal

Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by a single focal plane array. In this work, we propose to model the capturing system of a multiresolution coded acquisition (MRCA) in a unified framework, which natively includes conventional systems such as those based on spectral/color filter arrays, compressed coded apertures, and multiresolution sensing. We also propose a model-based image reconstruction algorithm performing a joint demosaicing and fusion (JoDeFu) of any acquisition modeled in the MRCA framework. The JoDeFu reconstruction algorithm solves an inverse problem with a proximal splitting technique and is able to reconstruct an uncompressed image datacube at the highest available spatial and spectral resolution. An implementation of the code is available at https://github.com/danaroth83/jodefu., Comment: 15 pages, 7 figures; regular paper

Digital Twins (DTs) are rapidly emerging as a fundamental brick of engineering cyber-physical systems, but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or application domains (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents and multiagent systems, as the latter are arguably the most rich abstractions available for the engineering of complex socio-technical and cyber-physical systems, and the former could both fill in some gaps in agent-oriented engineering and benefit from an agent-oriented interpretation -- in a cross-fertilisation journey., Comment: Digital Twin, Agent, Multiagent system, Cyber-physical system;16 pages; accepted and presented at EMAS2022 (workshop of AAMAS: https://emas.in.tu-clausthal.de/2022/#accepted)

Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal, which makes the systems inherently non-causal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance. The proposed system is trained end-to-end with only very simple pre- and postprocessing operations which are computationally lightweight and have low latency, making them conducive to clinical applications that require real-time processing. We have achieved a performance of 42.05% sensitivity with 5.78 false alarms per 24 hours on the development dataset of v1.5.2 of the Temple University Hospital Seizure Detection Corpus. On a single-core CPU operating at 1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of memory, and has a latency of 300 msec.

There is growing interest in ASR systems that can recognize phones in a language-independent fashion. There is additionally interest in building language technologies for low-resource and endangered languages. However, there is a paucity of realistic data that can be used to test such systems and technologies. This paper presents a publicly available, phonetically transcribed corpus of 2255 utterances (words and short phrases) in the endangered Tangkhulic language East Tusom (no ISO 639-3 code), a Tibeto-Burman language variety spoken mostly in India. Because the dataset is transcribed in terms of phones, rather than phonemes, it is a better match for universal phone recognition systems than many larger (phonemically transcribed) datasets. This paper describes the dataset and the methodology used to produce it. It further presents basic benchmarks of state-of-the-art universal phone recognition systems on the dataset as baselines for future experiments., Comment: 4 pages, 3 figures

Six significant new methodological developments of the previously-presented "metastimuli architecture" for human learning through machine learning of spatially correlated structural position within a user's personal information management system (PIMS), providing the basis for haptic metastimuli, are presented. These include architectural innovation, recurrent (RNN) artificial neural network (ANN) application, a variety of atom embedding techniques (including a novel technique we call "nabla" embedding inspired by linguistics), ANN hyper-parameter (one that affects the network but is not trained, e.g. the learning rate) optimization, and meta-parameter (one that determines the system performance but is not trained and not a hyper-parameter, e.g. the atom embedding technique) optimization for exploring the large design space. A technique for using the system for automatic atom categorization in a user's PIMS is outlined. ANN training and hyper- and meta-parameter optimization results are presented and discussed in service of methodological recommendations., Comment: To appear in the Springer Lecture Notes in Artificial Intelligence for the Human-Computer Interaction Conference 2021, Augmented Cognition thematic area

For the paradigm of the quantum double-slit experiment (DSE), we apply Ockham's Razor to interpret quantum observations and to evaluate terminology associated with wave-particle duality. One finds that the Correspondence Principle (CP), combined with classical wave DSEs, e.g., Young [1804], is sufficient to separate and anticipate the observed quantum particle and wave phenomena. The empirical approach of Ockham infers that individual quanta are only whole particles during transit from source to detector; an individual quantum never acts as a wave. The wave nature of quanta emerges only in the distribution of large numbers of single-quantum observation events. That is, the "measurement problem" is no problem at all; "particle" and "wave" derive from separate and different aspects of a set of observations. Such artificial constructs as wave function collapse are irrelevant to the observation of individual quanta, each of which acts as a whole "particle" from emission to measurement. The histogram of detected events is a collective property identical to a classical wave interference pattern in the limit of large numbers, as the CP decrees. For a specific quantum, Ockham's Razor renders irrelevant any hypothesis regarding wave- or particle-like behavior of the quantum in the region between emission and detection. To analyze actual data sets consisting of a large number of identical observation events and to predict future DSEs, the CP of standard quantum (wave) mechanics is sufficient: in the limit, the distribution of observations approaches the continuous density defined by the standard quantum mechanical wave function. Scientific progress beyond this picture requires new, relevant experiments. From the DSE, Ockham's Razor infers that a theoretical quantum system consists of at least one quantum particle plus a wave function specifying the distribution of a large number of such particles., Comment: 25 Pages, 1 Figure. Revisions from original: Figure significantly redrawn to eliminate irrelevant elements and notation and to improve alignment of horizontal symmetry axis. Appendix added to define key terms. Wording of manuscript modified to render more specific and to reduce potential ambiguity. Inferred definition of quantum system added explicitly. Experiments referenced

By-products represent a major disposal problem for the food industry, but they are also promising sources of bioactive compounds. Olive pomace, one of the main by-products of olive oil production, is a potential low-cost, phenol-rich ingredient for the formulation of functional food. In this study, bakery products enriched with defatted olive pomace powder and their conventional counterparts were chemical characterized and in vitro digested. The bioaccessible fractions were supplemented to cultured human intestinal cells exposed to an inflammatory stimulus, and the anti-inflammatory effect and metabolome modification were evaluated. Although in all bakery products the enrichment with olive pomace significantly increased the total phenolic content, this increase was paralleled by an enhanced anti-inflammatory activity only in conventionally fermented bread. Therefore, while confirming olive oil by-products as functional ingredients for bakery food enrichment, our data highlight that changes in chemical composition cannot predict changes in functionality. Functionality should be first evaluated in biological in vitro systems, and then confirmed in human intervention studies.