An alternative approach to dimension reduction for pareto distributed data: a case study

Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the prediction accuracy can occur if one tries to train a deep learning model by adding specific device descriptors, based on categorical data. This can happen because of an excessive increase in the dimensions of the data, with a correspondent loss of statistical significance. After several unsuccessful experiments conducted with alternative methodologies that either permit to reduce the data space dimensionality or employ more traditional machine learning algorithms, we changed the training strategy, reconsidering that categorical data, in the light of a Pareto analysis. In essence, we used those categorical descriptors, not as an input on which to train our deep learning model, but as a tool to give a new shape to the dataset, based on the Pareto rule. With this data adjustment, we trained a more performative deep learning model able to detect defective water meter devices with a prediction accuracy in the range 87-90%, even in the presence of categorical descriptors

Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures

Abstract In this paper, we describe the design of a machine learning-based classifier, tailored to predict whether a water meter will fail or need a replacement. Our initial attempt to train a recurrent deep neural network (RNN), based on the use of 15 million of readings gathered from 1 million of mechanical water meters, spread throughout Northern Italy, led to non-positive results. We learned this was due to a lack of specific attention devoted to the quality of the analyzed data. We, hence, developed a novel methodology, based on a new semantics which we enforced on the training data. This allowed us to extract only those samples which are representative of the complex phenomenon of defective water meters. Adopting such a methodology, the accuracy of our RNN exceeded the 80% threshold. We simultaneously realized that the new training dataset differed significantly, in statistical terms, from the initial dataset, leading to an apparent paradox. Thus, with our contribution, we have demonstrated how to reconcile such a paradox, showing that our classifier can help detecting defective meters, while simplifying replacement procedures

Applied deep learning and data science with a human-centric and data-centric approach

The term Artificial intelligence acquired a lot of baggage since its introduction and in its current incarnation is synonymous with Deep Learning. The sudden availability of data and computing resources has opened the gates to myriads of applications. Not all are created equal though, and problems might arise especially for fields not closely related to the tasks that pertain tech companies that spearheaded DL. The perspective of practitioners seems to be changing, however. Human-Centric AI emerged in the last few years as a new way of thinking DL and AI applications from the ground up, with a special attention at their relationship with humans. The goal is designing a system that can gracefully integrate in already established workflows, as in many real-world scenarios AI may not be good enough to completely replace its humans. Often this replacement may even be unneeded or undesirable. Another important perspective comes from, Andrew Ng, a DL pioneer, who recently started shifting the focus of development from “better models” towards better, and smaller, data. He defined his approach Data-Centric AI. Without downplaying the importance of pushing the state of the art in DL, we must recognize that if the goal is creating a tool for humans to use, more raw performance may not align with more utility for the final user. A Human-Centric approach is compatible with a Data-Centric one, and we find that the two overlap nicely when human expertise is used as the driving force behind data quality. This thesis documents a series of case-studies where these approaches were employed, to different extents, to guide the design and implementation of intelligent systems. We found human expertise proved crucial in improving datasets and models. The last chapter includes a slight deviation, with studies on the pandemic, still preserving the human and data centric perspective

Constant bandwidth servers with constrained deadlines

The Hard Constant Bandwidth Server (H-CBS) is a reservation-based scheduling algorithm often used to mix hard and soft real-time tasks on the same system. A number of variants of the H-CBS algorithm have been proposed in the last years, but all of them have been conceived for implicit server deadlines (i.e., equal to the server period). However, recent promising results on semi-partitioned scheduling together with the demand for new functionality claimed by the Linux community, urge the need for a reservation algorithm that is able to work with constrained deadlines. This paper presents three novel H-CBS algorithms that support constrained deadlines. The three algorithms are formally analyzed, and their performance are compared through an extensive set of simulations

Recycling of yttria-stabilized zirconia waste powders in glazes suitable for ceramic tiles

The aim of this work is to demonstrate the feasibility of valorizing and recycling Yttria-stabilized Zirconia thermal spray waste into high value products for industrial and residential use. Based on the powders chemistry and morphology, this work aims to realize products, like frits suitable for white glazes and ceramic tiles. The focus is on one class of powder: high-temperature and abrasion-resistant ceramics, like Yttria-stabilized zirconia. This study has revealed that the substitution of pure zirconia with waste Yttria-stabilized zirconia is possible in high percentages, up to 100% to prepare frits suitable for white glaze

Surgical treatment of retrosternal extraosseous Ewing Sarcoma in a 6-years old female: a clamshell approach with hemysternectomy and application of a non-crosslinked extracellular matrix

Background Ewing Sarcoma (ES) and Neuroblastoma (NB) belong to a family of tumours of primitive neuroectodermal origin (PNET) that occurs in both bone and soft tissue. Notwithstanding ES and NB are two distinct malignant tumours, sometimes there could be a link between them. Case report We describe a case of an extraosseous ES localized in the retrosternal region and the upper lobe of the right lung, which had been previously treated for NB in a 6 years old female. We treated this case with a clamshell approach which allows, in a one-step surgery, a complete excision of the mass reconstructing the hemysternectomy with a non-crosslinked matrix. Conclusion the clamshell approach is therefore useful to achieve the retrosternal space and the lung with a single surgical access. According to our experience, we consider appropriate to use a non-crosslinked matrix for sternal reconstruction

Inhibition mechanism of urease by Au(III) compounds unveiled by x-ray diffraction analysis

The nickel-dependent enzyme urease is a virulence factor for a large number of critical human pathogens, making this enzyme a potential target of therapeutics for the treatment of resistant bacterial infections. In the search for novel urease inhibitors, five selected coordination and organometallic Au(III) compounds containing N∧N or C∧N and C∧N∧N ligands were tested for their inhibitory effects against Canavalia ensiformis (jack bean) urease. The results showed potent inhibition effects with IC50 values in the nanomolar range. The 2.14 Å resolution crystal structure of Sporosarcina pasteurii urease inhibited by the most effective Au(III) compound [Au(PbImMe)Cl2]PF6 (PbImMe = 1-methyl-2-(pyridin-2-yl)-benzimidazole) reveals the presence of two Au ions bound to the conserved triad αCys322/αHis323/αMet367. The binding of the Au ions to these residues blocks the movement of a flap, located at the edge of the active site channel and essential for enzyme catalysis, completely obliterating the catalytic activity of urease. Overall, the obtained results constitute the basis for the design of new gold complexes as selective urease inhibitors with future antibacterial applications

The Magnetic Sensitivity of the Ba II D1 and D2 Lines of the Fraunhofer Spectrum

The physical interpretation of the spectral line polarization produced by the joint action of the Hanle and Zeeman effects offers a unique opportunity to obtain empirical information about hidden aspects of solar and stellar magnetism. To this end, it is important to achieve a complete understanding of the sensitivity of the emergent spectral line polarization to the presence of a magnetic field. Here we present a detailed theoretical investigation on the role of resonance scattering and magnetic fields on the polarization signals of the Ba II D1 and D2 lines of the Fraunhofer spectrum, respectively at 4934 \AA\ and 4554 \AA. We adopt a three-level model of Ba II, and we take into account the hyperfine structure that is shown by the $^{135}$Ba and $^{137}$Ba isotopes. Despite of their relatively small abundance (18%), the contribution coming from these two isotopes is indeed fundamental for the interpretation of the polarization signals observed in these lines. We consider an optically thin slab model, through which we can investigate in a rigorous way the essential physical mechanisms involved (resonance polarization, Zeeman, Paschen-Back and Hanle effects), avoiding complications due to radiative transfer effects. We assume the slab to be illuminated from below by the photospheric solar continuum radiation field, and we investigate the radiation scattered at 90 degrees, both in the absence and in the presence of magnetic fields, deterministic and microturbulent. We show in particular the existence of a differential magnetic sensitivity of the three-peak Q/I profile that is observed in the D2 line in quiet regions close to the solar limb, which is of great interest for magnetic field diagnostics.Comment: 40 pages, 1 table and 19 figures. Accepted for publication in The Astrophysical Journal (ApJ

Influence of feedstock and operational conditions on bio-chars derived from the pyrolysis of selected biomasses

The proprieties of bio-char, the solid product from biomass pyrolysis, depends on both the feedstock and process conditions during thermochemical conversion[1]. As regards the interaction of the char with soil (i.e. as soil amendment), surface areas, size and shape of pores are among the most important factors to be considered. [1] P. R. Bonelli , G. Nunell , M. E. Fernández , E. L. Buonomo & A. L. Cukierman (2012) The Potential Applications of the Bio-char Derived from the Pyrolysis of an Agro-industrial Waste. Effects of Temperature and Acid-pretreatment, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 34:8, 746-755, DOI: 10.1080/15567031003681937 Please click Additional Files below to see the full abstract

Efficiency and costs of the health management in an organic dairy farm where we use unconventional medicines

The EU organic regulation explicitly promote the use of unconventional therapies, like homoeopathy and phytotherapy. The aim of the present study was to investigate the efficiency and the costs of these treatment methods. From December 2006 to September 2008, we analyzed the data recorded in an organic dairy farm where the animals are normally treated by classical unicistic homeopathy and phytotherapy, and only when indispensable, by allophaty, antiparasitic drugs, surgery and vaccines. The use of homeopathy resulted to be predominant in comparison with the others treatments. Besides, our trial showed that homeopathy and phytotherapy could be used to treat, with good outcomes, the majority of diseases that occur in a dairy cattle farm, even if, sometimes, conventional medicines have to be used. The costs for unconventional treatments are very low in comparison with conventional ones. This will allow the spreading of unconventional medicines in the Italian organic farms