Continual learning for computer vision applications

One of the most visionary goals of Artificial Intelligence is to create a system able to mimic and eventually surpass the intelligence observed in biological systems including, ambitiously, the one observed in humans. The main distinctive strength of humans is their ability to build a deep understanding of the world by learning continuously and drawing from their experiences. This ability, which is found in various degrees in all intelligent biological beings, allows them to adapt and properly react to changes by incrementally expanding and refining their knowledge. Arguably, achieving this ability is one of the main goals of Artificial Intelligence and a cornerstone towards the creation of intelligent artificial agents. Modern Deep Learning approaches allowed researchers and industries to achieve great advancements towards the resolution of many long-standing problems in areas like Computer Vision and Natural Language Processing. However, while this current age of renewed interest in AI allowed for the creation of extremely useful applications, a concerningly limited effort is being directed towards the design of systems able to learn continuously. The biggest problem that hinders an AI system from learning incrementally is the catastrophic forgetting phenomenon. This phenomenon, which was discovered in the 90s, naturally occurs in Deep Learning architectures where classic learning paradigms are applied when learning incrementally from a stream of experiences. This dissertation revolves around the Continual Learning field, a sub-field of Machine Learning research that has recently made a comeback following the renewed interest in Deep Learning approaches. This work will focus on a comprehensive view of continual learning by considering algorithmic, benchmarking, and applicative aspects of this field. This dissertation will also touch on community aspects such as the design and creation of research tools aimed at supporting Continual Learning research, and the theoretical and practical aspects concerning public competitions in this field.

PCM-based in-memory computing: architectures, circuits and applications

Analog In-memory Computing (AIMC) has been proposed in the context of Beyond Von Neumann architectures as a valid strategy to reduce internal data transfers energy consumption and latency, and to improve compute efficiency. The aim of AIMC is to perform computations within the memory unit, typically leveraging the physical features of memory devices. Among resistive Non-volatile Memories (NVMs), Phase-change Memory (PCM) has become a promising technology due to its intrinsic capability to store multilevel data. Hence, PCM technology is currently investigated to enhance the possibilities and the applications of AIMC. This thesis aims at exploring the potential of new PCM-based architectures as in-memory computational accelerators. In a first step, a preliminar experimental characterization of PCM devices has been carried out in an AIMC perspective. PCM cells non-idealities, such as time-drift, noise, and non-linearity have been studied to develop a dedicated multilevel programming algorithm. Measurement-based simulations have been then employed to evaluate the feasibility of PCM-based operations in the fields of Deep Neural Networks (DNNs) and Structural Health Monitoring (SHM). Moreover, a first testchip has been designed and tested to evaluate the hardware implementation of Multiply-and-Accumulate (MAC) operations employing PCM cells. This prototype experimentally demonstrates the possibility to reach a 95% MAC accuracy with a circuit-level compensation of cells time drift and non-linearity. Finally, empirical circuit behavior models have been included in simulations to assess the use of this technology in specific DNN applications, and to enhance the potentiality of this innovative computation approach.

Processi di biorefining per l'estrazione di secondary chemical building blocks da sottoprotti dell'agro-industria

Phenol and cresols represent a good example of primary chemical building blocks of which 2.8 million tons are currently produced in Europe each year. Currently, these primary phenolic building blocks are produced by refining processes from fossil hydrocarbons: 5% of the world-wide production comes from coal (which contains 0.2% of phenols) through the distillation of the tar residue after the production of coke, while 95% of current world production of phenol is produced by the distillation and cracking of crude oil. In nature phenolic compounds are present in terrestrial higher plants and ferns in several different chemical structures while they are essentially absent in lower organisms and in animals. Biomass (which contain 3-8% of phenols) represents a substantial source of secondary chemical building blocks presently underexploited. These phenolic derivatives are currently used in tens thousand of tons to produce high cost products such as food additives and flavours (i.e. vanillin), fine chemicals (i.e. non-steroidal anti-inflammatory drugs such as ibuprofen or flurbiprofen) and polymers (i.e. poly p-vinylphenol, a photosensitive polymer for electronic and optoelectronic applications). European agrifood waste represents a low cost abundant raw material (250 millions tons per year) which does not subtract land use and processing resources from necessary sustainable food production. The class of phenolic compounds is essentially constituted by simple phenols, phenolic acids, hydroxycinnamic acid derivatives, flavonoids and lignans. As in the case of coke production, the removal of the phenolic contents from biomass upgrades also the residual biomass. Focusing on the phenolic component of agrifood wastes, huge processing and marketing opportunities open since phenols are used as chemical intermediates for a large number of applications, ranging from pharmaceuticals, agricultural chemicals, food ingredients etc. Following this approach we developed a biorefining process to recover the phenolic fraction of wheat bran based on enzymatic commercial biocatalysts in completely water based process, and polymeric resins with the aim of substituting secondary chemical building blocks with the same compounds naturally present in biomass. We characterized several industrial enzymatic product for their ability to hydrolize the different molecular features that are present in wheat bran cell walls structures, focusing on the hydrolysis of polysaccharidic chains and phenolics cross links. This industrial biocatalysts were tested on wheat bran and the optimized process allowed to liquefy up to the 60 % of the treated matter. The enzymatic treatment was also able to solubilise up to the 30 % of the alkali extractable ferulic acid. An extraction process of the phenolic fraction of the hydrolyzed wheat bran based on an adsorbtion/desorption process on styrene-polyvinyl benzene weak cation-exchange resin Amberlite IRA 95 was developed. The efficiency of the resin was tested on different model system containing ferulic acid and the adsorption and desorption working parameters optimized for the crude enzymatic hydrolyzed wheat bran. The extraction process developed had an overall yield of the 82% and allowed to obtain concentrated extracts containing up to 3000 ppm of ferulic acid. The crude enzymatic hydrolyzed wheat bran and the concentrated extract were finally used as substrate in a bioconversion process of ferulic acid into vanillin through resting cells fermentation. The bioconversion process had a yields in vanillin of 60-70% within 5-6 hours of fermentation. Our findings are the first step on the way to demonstrating the economical feasibility for the recovery of biophenols from agrifood wastes through a whole crop approach in a sustainable biorefining process.

Electrode Materials for Ionic Liquid Based-Supercapacitors

The development of safe, high energy and power electrochemical energy-conversion systems can be a response to the worldwide demand for a clean and low-fuel-consuming transport. This thesis work, starting from a basic studies on the ionic liquid (IL) electrolytes and carbon electrodes and concluding with tests on large-size IL-based supercapacitor prototypes demonstrated that the IL-based asymmetric configuration (AEDLCs) is a powerful strategy to develop safe, high-energy supercapacitors that might compete with lithium-ion batteries in power assist-hybrid electric vehicles (HEVs). The increase of specific energy in EDLCs was achieved following three routes: i) the use of hydrophobic ionic liquids (ILs) as electrolytes; ii) the design and preparation of carbon electrode materials of tailored morphology and surface chemistry to feature high capacitance response in IL and iii) the asymmetric double-layer carbon supercapacitor configuration (AEDLC) which consists of assembling the supercapacitor with different carbon loadings at the two electrodes in order to exploit the wide electrochemical stability window (ESW) of IL and to reach high maximum cell voltage (Vmax). Among the various ILs investigated the N-methoxyethyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide (PYR1(2O1)TFSI) was selected because of its hydrophobicity and high thermal stability up to 350 °C together with good conductivity and wide ESW, exploitable in a wide temperature range, below 0°C. For such exceptional properties PYR1(2O1)TFSI was used for the whole study to develop large size IL-based carbon supercapacitor prototype. This work also highlights that the use of ILs determines different chemical-physical properties at the interface electrode/electrolyte with respect to that formed by conventional electrolytes. Indeed, the absence of solvent in ILs makes the properties of the interface not mediated by the solvent and, thus, the dielectric constant and double-layer thickness strictly depend on the chemistry of the IL ions. The study of carbon electrode materials evidences several factors that have to be taken into account for designing performing carbon electrodes in IL. The heat-treatment in inert atmosphere of the activated carbon AC which gave ACT carbon featuring ca. 100 F/g in IL demonstrated the importance of surface chemistry in the capacitive response of the carbons in hydrophobic ILs. The tailored mesoporosity of the xerogel carbons is a key parameter to achieve high capacitance response. The CO2-treated xerogel carbon X3a featured a high specific capacitance of 120 F/g in PYR14TFSI, however, exhibiting high pore volume, an excess of IL is required to fill the pores with respect to that necessary for the charge-discharge process. Further advances were achieved with electrodes based on the disordered template carbon DTC7 with pore size distribution centred at 2.7 nm which featured a notably high specific capacitance of 140 F/g in PYR14TFSI and a moderate pore volume, V>1.5 nm of 0.70 cm3/g. This thesis work demonstrated that by means of the asymmetric configuration (AEDLC) it was possible to reach high cell voltage up to 3.9 V. Indeed, IL-based AEDLCs with the X3a or ACT carbon electrodes exhibited specific energy and power of ca. 30 Wh/kg and 10 kW/kg, respectively. The DTC7 carbon electrodes, featuring a capacitance response higher of 20%-40% than those of X3a and ACT, respectively, enabled the development of a PYR14TFSI-based AEDLC with specific energy and power of 47 Wh/kg and 13 kW/kg at 60°C with Vmax of 3.9 V. Given the availability of the ACT carbon (obtained from a commercial material), the PYR1(2O1)TFSI-based AEDLCs assembled with ACT carbon electrodes were selected within the EU ILHYPOS project for the development of large-size prototypes. This study demonstrated that PYR1(2O1)TFSI-based AEDLC can operate between -30°C and +60°C and its cycling stability was proved at 60°C up to 27,000 cycles with high Vmax up to 3.8 V. Such AEDLC was further investigated following USABC and DOE FreedomCAR reference protocols for HEV to evaluate its dynamic pulse-power and energy features. It was demonstrated that with Vmax of 3.7 V at T> 30 °C the challenging energy and power targets stated by DOE for power-assist HEVs, and at T> 0 °C the standards for the 12V-TSS and 42V-FSS and TPA 2s-pulse applications are satisfied, if the ratio wmodule/wSC = 2 is accomplished, which, however, is a very demanding condition. Finally, suggestions for further advances in IL-based AEDLC performance were found. Particularly, given that the main contribution to the ESR is the electrode charging resistance, which in turn is affected by the ionic resistance in the pores that is also modulated by pore length, the pore geometry is a key parameter in carbon design not only because it defines the carbon surface but also because it can differentially “amplify” the effect of IL conductivity on the electrode charging-discharging process and, thus, supercapacitor time constant.

Exploring domain-informed and physics-guided learning in image-to-image translation

Image-to-image (i2i) translation networks can generate fake images beneficial for many applications in augmented reality, computer graphics, and robotics. However, they require large scale datasets and high contextual understanding to be trained correctly. In this thesis, we propose strategies for solving these problems, improving performances of i2i translation networks by using domain- or physics-related priors. The thesis is divided into two parts. In Part I, we exploit human abstraction capabilities to identify existing relationships in images, thus defining domains that can be leveraged to improve data usage efficiency. We use additional domain-related information to train networks on web-crawled data, hallucinate scenarios unseen during training, and perform few-shot learning. In Part II, we instead rely on physics priors. First, we combine realistic physics-based rendering with generative networks to boost outputs realism and controllability. Then, we exploit naive physical guidance to drive a manifold reorganization, which allowed generating continuous conditions such as timelapses.

Analisi e ottimizzazione del servizio di raccolta e trasporto dei rifiuti anche in relazione alle tematiche ambientali: un'applicazione alla rete urbana di Bologna

Questo studio, che è stato realizzato in collaborazione con Hera, è un'analisi della gestione dei rifiuti a Bologna. La ricerca è stata effettuata su diversi livelli: un livello strategico il cui scopo è quello di identificare nuovi metodi per la raccolta dei rifiuti in funzione delle caratteristiche del territorio della città, un livello analitico che riguarda il miglioramento delle applicazioni informatiche di supporto, e livello ambientale che riguarda il calcolo delle emissioni in atmosfera di veicoli adibiti alla raccolta e al trasporto dei rifiuti. innanzitutto è stato necessario studiare Bologna e lo stato attuale dei servizi di raccolta dei rifiuti. È incrociando questi componenti che in questi ultimi tre anni sono state effettuate modifiche nel settore della gestione dei rifiuti. I capitoli seguenti sono inerenti le applicazioni informatiche a sostegno di tali attività: Siget e Optit. Siget è il programma di gestione del servizio, che attualmente viene utilizzato per tutte le attività connesse alla raccolta di rifiuti. È un programma costituito da moduli diversi, ma di sola la gestione dati. la sperimentazione con Optit ha aggiunto alla gestione dei dati la possibilità di avere tali dati in cartografia e di associare un algoritmo di routing. I dati archiviati in Siget hanno rappresentato il punto di partenza, l'input, e il raggiungimento di tutti punti raccolta l'obiettivo finale. L'ultimo capitolo è relativo allo studio dell'impatto ambientale di questi percorsi di raccolta dei rifiuti. Tale analisi, basata sulla valutazione empirica e sull'implementazione in Excel delle formule del Corinair mostra la fotografia del servizio nel 2010. Su questo aspetto Optit ha fornito il suo valore aggiunto, implementando nell'algoritmo anche le formule per il calcolo delle emissioni.