La disabilità in azienda: verso una gestione strategica della diversità

In Italia, nonostante legislazione nazionale in materia di diritto al lavoro dei disabili offra numerosi strumenti per favorire l'inserimento e l'integrazione lavorativa, continua a registrarsi un inserimento ridotto delle persone con disabilità rispetto ai soggetti normodotati in tutte le fasce di età. Affiancare alla normativa una tecnica di gestione strategica della risorsa umana disabile, che coniughi le esigenze di realizzazione del lavoratore con le esigenze economico-produttive dell'azienda, rappresenta una delle possibili soluzioni

Design of integrated mixed-signal IPs for automotive applications

An ever-increasing range of sophisticated and leading-edge electronic technologies emerging into the automotive field makes this one of the most dynamic but even complicated manufacturing sector in the world. Car companies are convinced that electronic is the key to meet different and often divergent requirements such as high-safety vehicles, comfort, infotainment, gas emission reduction, power saving, low cost technologies and short time to market. For these reasons the expectations on the electronic automotive systems are very high since it seems to be the major factor of innovation technology and differentiation in a more and more competitive market field. The hardness of this market scenario has a direct impact on the complexity of electronic systems. Many features on modern medium-segment cars are based on high performance Electronic Control Units dealing with up to 2500 signals. As a consequence the number of sensing and actuating elements hidden into the body or the chassis of a car is growing continuously. Moreover as many hydraulic and mechanical actuators are replaced by power consuming electronic components and new entertainment features are provided to meet customer’s requests, power saving becomes an issue even in the automotive field. The PhD research activity has been focused on the design of integrated electronic systems for the automotive fields. The new requirements of the automotive market together with the necessity to reduce time to market imply a complete review of the electronic systems design flows. For these reasons the PhD activities have been always leaded following a platform based design approach in order to give a proper answer to the aforementioned requirements and to give a more efficient alternative to the actual design approaches. Chapter 1 of this thesis explains in much more details the automotive market requirements focusing on the characteristics of each particular segment and presenting the main actual and future automotive applications. A particular attention is given to the electronic automotive challenges and design issues implied by this scenario that has motivated the overall PhD activities. Chapter 2 starts with the presentation of the actual state of the art of the methodologies used in the electronic automotive field and continues with the description of the proposed platform called Intelligent Sensor InterFace (ISIF). ISIF is a platform targeted to interface automotive sensors and is composed by a high number of highly programmable software and hardware IPs. The platform has been integrated in a 0.35 um Bipolar CMOS DMOS (BCD) technology supplied by STMicroelectronics. Some case studies regarding fast prototyping possibilities with ISIF are presented: a magneto-resistive position sensor and two capacitive inertial sensors (in collaboration with SensorDynamics AG), a gyro and a low-g YZ accelerometer. Chapter 3 describes the extension of the ISIF application space to high power automotive systems and to laser based video projection systems: High power automotive systems are gaining importance during the last years since many mechanical and hydraulic features are completely transferred to electronic systems. In this thesis the design and test of a programmable MOS half bridge driver featuring low ElectroMagnetic Interferences (EMIs) and targeted to electric motor and antenna driving is presented. Laser based video projection systems are expected to find a wide utilization for the realization of new generation automotive head up displays thanks to the recent advance of Micro-Opto-Electromechanical Systems (MOEMS) and visible laser sources. The thesis shows in details the technical characteristics of this topic and describes the design and simulation results of a scanning micromirrors high voltage driver in a 0.18 um BCD technology supplied by STMicroelectronics

A jamming transition from under- to over-parametrization affects loss landscape and generalization

We argue that in fully-connected networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. Under some general conditions, we show that this transition is sharp for the hinge loss. In the whole over-parametrized regime, poor minima of the loss are not encountered during training since the number of constraints to satisfy is too small to hamper minimization. Our findings support a link between this transition and the generalization properties of the network: as we increase the number of parameters of a given model, starting from an under-parametrized network, we observe that the generalization error displays three phases: (i) initial decay, (ii) increase until the transition point --- where it displays a cusp --- and (iii) slow decay toward a constant for the rest of the over-parametrized regime. Thereby we identify the region where the classical phenomenon of over-fitting takes place, and the region where the model keeps improving, in line with previous empirical observations for modern neural networks.Comment: arXiv admin note: text overlap with arXiv:1809.0934

The Lure of Gambling: What State Governments Can Gain from the Legalization and Expansion of Gambling

Thesis advisor: Richard McGowanGambling, both in the casino-style and lottery forms, has risen to become a major component of the entertainment industry in the United States. State governments are the gatekeepers of this growing industry, holding the power to legalize and regulate all aspects of gambling. This thesis explores the rationale state governments have for legalizing gambling as well as the impact gambling tax revenues have for state budgets. The main focus is casino-style gambling, as casino-style gambling in particular is being pursued for expansion by numerous states in a variety of forms. As various forms of gambling are legalized throughout the country, a state's gambling interests begin to face competition from both neighboring states and other forms of gambling within the state. Econometric models attempted to predict the tax revenues a state can obtain from legalized gambling based on such competition and a states own demographics. The results support a first-mover advantage for states expanding casino-style gambling and finds that new forms of gambling significantly erode established gambling industries.Thesis (BS) — Boston College, 2006.Submitted to: Boston College. College of Arts and Sciences.Discipline: Economics Honors Program

Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime

Deep neural networks can achieve remarkable generalization performances while interpolating the training data perfectly. Rather than the U-curve emblematic of the bias-variance trade-off, their test error often follows a "double descent" - a mark of the beneficial role of overparametrization. In this work, we develop a quantitative theory for this phenomenon in the so-called lazy learning regime of neural networks, by considering the problem of learning a high-dimensional function with random features regression. We obtain a precise asymptotic expression for the bias-variance decomposition of the test error, and show that the bias displays a phase transition at the interpolation threshold, beyond which it remains constant. We disentangle the variances stemming from the sampling of the dataset, from the additive noise corrupting the labels, and from the initialization of the weights. Following up on Geiger et al. 2019, we first show that the latter two contributions are the crux of the double descent: they lead to the overfitting peak at the interpolation threshold and to the decay of the test error upon overparametrization. We then quantify how they are suppressed by ensemble averaging the outputs of K independently initialized estimators. When K is sent to infinity, the test error remains constant beyond the interpolation threshold. We further compare the effects of overparametrizing, ensembling and regularizing. Finally, we present numerical experiments on classic deep learning setups to show that our results hold qualitatively in realistic lazy learning scenarios.Comment: 29 pages, 12 figure