Proteomics for Cerebrospinal Fluid Biomarker Identification in Parkinsons Disease: Methods and Critical Aspects

Parkinson's disease (PD), similar with other neurodegenerative disorders, would benefit from the identification of early biomarkers for differential diagnosis and prognosis to address prompt clinical treatments. Together with hypothesis driven approaches, PD has been investigated by high-throughput differential proteomic analysis of cerebrospinal fluid (CSF) protein content. The principal methodologies and techniques utilized in the proteomics field for PD biomarker discovery from CSF are presented in this mini review. The positive aspects and challenges in proteome-based biomarker research are also discussed

Il problema della collisione tra readers nei sistemi RFId

I sistemi RFId sono una tecnologia in espansione nel panorama moderno del riconoscimento automizzato. Tuttavia tali sistemi presantano problematiche intrinseche alla comunicazione su radiofrequenze quali interferenze e collisioni a seguito di trasmissioni multiple contemporanee. Nella tesi si propone, dopo una rassegna delle soluzione esistenti, un approccio alternativo e originale al problema delle collisioni tra readers

On the estimation of the Lorenz curve under complex sampling designs

This paper focuses on the estimation of the concentration curve of a finite population, when data are collected according to a complex sampling design with different inclusion probabilities. A (design-based) Hajek type estimator for the Lorenz curve is proposed, and its asymptotic properties are studied. Then, a resampling scheme able to approximate the asymptotic law of the Lorenz curve estimator is constructed. Applications are given to the construction of (i) a confidence band for the Lorenz curve, (ii) confidence intervals for the Gini concentration ratio, and (iii) a test for Lorenz dominance. The merits of the proposed resampling procedure are evaluated through a simulation study

Alcune riflessioni sulla pratica regolatoria, con riferimento ad alcuni settori dell’industria dei trasporti

La regolazione dei mercati rappresenta un elemento fondamentale per il raggiungimento di un’organizzazione più efficiente del sistema dei trasporti insieme ad una distribuzione equa dei vantaggi derivanti dallo stesso. In tale ottica la tutela del consumatore, le norme legate alla qualità e alla sicurezza dei beni e servizi scambiati su un mercato e una consona valutazione delle esternalità sono solo alcuni degli elementi di criticità di cui il regolatore dovrebbe tenere conto. In molti paesi, a partire dagli anni ’80, il settore dei trasporti è stato coinvolto dal processo di privatizzazioni e liberalizzazioni che ha interessato molte public utilities e che ha portato, in particolare, alla creazione di mercati regolati in cui le infrastrutture sono tipicamente gestite in concessione, mentre i servizi, laddove non sia possibile l’introduzione di forme di concorrenza, vengono invece regolamentati e le tariffe determinate secondo regole pre-definite. Nel corso del tempo alcune forme di regolazione tariffaria sembrano essersi imposte senza che la loro adozione nel caso di un particolare settore sia stata adeguatamente valutata alla luce delle caratteristiche economico-tecnologiche del settore stesso. Lo studio qui proposto si prefigge di effettuare un confronto tra i due principali metodi di regolamentazione tariffaria (price cap e regolazione del tasso di rendimento, RoR) tipicamente applicati nei settori altamente regolati, in cui la concorrenza è limitata per motivi tecnici o legati alla sicurezza. L’articolo è organizzato come segue. Il primo paragrafo è dedicato ad una breve discussione della letteratura, mentre nel secondo paragrafo vengono analizzate criticamente le principali caratteristiche dei metodi di regolazione tariffaria ispirati al price cap e al RoR. Quindi, nel terzo paragrafo viene analizzato, alla luce della discussione teorica sviluppata precedentemente, il caso di due diversi settori dei trasporti (autostrade e ormeggio) regolati in Italia con metodi di tipo price cap e RoR. Infine,il quarto paragrafo è dedicato alle conclusioni e alla discussione di possibili sviluppi futuri della ricerca

ICT from Below: ELISA Program and the Innovation of Local Government in Italy

In the last two decades, there has been a shift in the fundamental paradigm of Public Administration, from New Public Management, characterized by a managerial, microeconomic and sectorial approach, to Multilevel Public Governance, characterized by an integrated, strategic and holistic approach. The current Multilevel Public Governance paradigm is particularly useful to study and to approach to the Italian local government sector, due to its extreme fragmentation that needs cooperation to overcome its limits. It is in this theoretical framework that we present the ELISA program and its efforts in modernizing the local administration with ICT… from below.ELISA program (Enti Locali – Innovazione di SistemA, Local Government – System Innovation) is a national government project for the innovation in the Public Administration, guided by the Department for Regional and Local Affairs. The project promotes innovative instruments to develop software and technological public platform in three different main fields: taxation and cadastre; info-mobility; quality of services. The ultimate goal is to increase the efficiency of the administrative structure and provide better and technologically advanced solutions to respond to the needs of the citizens.Considering the success of the ELISA program, the Department for Regional and Local Affairs, in cooperation with Politecnico di Milano and Invitalia s.p.a., activates the eGovernment Laboratory with the aim in guaranteeing the replicability and the uniform dissemination of the best solutions all along the country. One of its action lines is to develop and implement the Innovation Communities raised in connection with the best practises of the ELISA program. For this purpose, it encourages sustainable and innovative management models, which are able to spread significant benefits by passing on administrative skills and knowledge to other Public Administrations and, furthermore, promoting the implementation mechanism and the deployment of the experiences.For that to happen, system-oriented measures are needed in order to redefine the relationship between the Local Authorities and the Central Administration: the first ones have the task to identifying both problems and needs of their territories and design proper solutions accordingly; the second one has to guarantee constant and consistent funds, coordinate the actions and oversee the whole process. One example of system-oriented measure is ItaliAE project, a complex and technological program, financed by European structural funds and born to follow the implementation of the local authority reform provided for by the law n. 56/2014 and to support the transformation of the Italian administrative geography and to improving its efficiency

Monitoring social distancing with single image depth estimation

The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing the virus spread, and constraining the minimum distance between people resulted in one of the most effective strategies. Thus, the implementation of autonomous systems capable of monitoring the so-called social distance gained much interest. In this paper, we aim to address this task leveraging a single RGB frame without additional depth sensors. In contrast to existing single-image alternatives failing when ground localization is not available, we rely on single image depth estimation to perceive the 3D structure of the observed scene and estimate the distance between people. During the setup phase, a straightforward calibration procedure, leveraging a scale-aware SLAM algorithm available even on consumer smartphones, allows us to address the scale ambiguity affecting single image depth estimation. We validate our approach through indoor and outdoor images employing a calibrated LiDAR + RGB camera asset. Experimental results highlight that our proposal enables sufficiently reliable estimation of the inter-personal distance to monitor social distancing effectively. This fact confirms that despite its intrinsic ambiguity, if appropriately driven single image depth estimation can be a viable alternative to other depth perception techniques, more expensive and not always feasible in practical applications. Our evaluation also highlights that our framework can run reasonably fast and comparably to competitors, even on pure CPU systems. Moreover, its practical deployment on low-power systems is around the corner.Comment: Accepted for pubblication on IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI

DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs

The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applications. Low-Cost MCU-based end-nodes have limited on-chip memory and often replace caches with scratchpads, to reduce area overheads and increase energy efficiency -- requiring explicit DMA-based memory transfers between different levels of the memory hierarchy. Mapping modern DNNs on these systems requires aggressive topology-dependent tiling and double-buffering. In this work, we propose DORY (Deployment Oriented to memoRY) - an automatic tool to deploy DNNs on low cost MCUs with typically less than 1MB of on-chip SRAM memory. DORY abstracts tiling as a Constraint Programming (CP) problem: it maximizes L1 memory utilization under the topological constraints imposed by each DNN layer. Then, it generates ANSI C code to orchestrate off- and on-chip transfers and computation phases. Furthermore, to maximize speed, DORY augments the CP formulation with heuristics promoting performance-effective tile sizes. As a case study for DORY, we target GreenWaves Technologies GAP8, one of the most advanced parallel ultra-low power MCU-class devices on the market. On this device, DORY achieves up to 2.5x better MAC/cycle than the GreenWaves proprietary software solution and 18.1x better than the state-of-the-art result on an STM32-F746 MCU on single layers. Using our tool, GAP-8 can perform end-to-end inference of a 1.0-MobileNet-128 network consuming just 63 pJ/MAC on average @ 4.3 fps - 15.4x better than an STM32-F746. We release all our developments - the DORY framework, the optimized backend kernels, and the related heuristics - as open-source software.Comment: 14 pages, 12 figures, 4 tables, 2 listings. Accepted for publication in IEEE Transactions on Computers (https://ieeexplore.ieee.org/document/9381618