Recent studies revealed that sleep and wakefulness are locally regulated and that ‘islands’ of these two states may often coexist in the same individual. Changes in brain activity during wakefulness are known to affect sleep ‘intensity’ within circumscribed cortical areas , and such local variations may in turn modulate sleep-dependent processes including memory consolidation or emotional reactivity. Episodes of ‘local sleep’ may occur during wakefulness as a consequence of ‘functional fatigue’ induced by the reiterated ‘use’ of task- related brain areas, and have a proved impact on behavioral performance . The observation that conscious experiences that occur during sleep (dreams) may depend on a ‘partial awakening’ of specific brain regions opened new opportunities for investigating the fundamental function(s) of dreams, but also for exploring the anatomical and functional bases of consciousness . Finally, alterations in the local regulation of sleep and wakefulness have been suggested to represent the bases for symptoms observed in pathological conditions, such as major depression or insomnia.
Topics of interest related to this research field include (but are not limited to):
Regulation of local sleep during wakefulness and implications for behavior and cognition;
The effects of sleep deprivation/restriction on brain structure and function;
The role of sleep and dreams in experience-dependent brain plasticity (memory/learning) and in emotional regulation;
The relationship between local sleep regulation and pathological conditions.
Projects may involve one or more of the following investigation techniques (in wakefulness and/or in sleep): psychometric questionnaires, behavioral testing, high-density EEG recordings, functional/structural MRI, combined fMRI-EEG.
Sleep, consciousness, dreams, learning, memory, emotion.
- Bernardi G, Betta M, Cataldi J, Leo A, Haba-Rubio J, Heinzer R, et al. Visual imagery and visual perception induce similar changes in occipital slow waves of sleep. Under Review (also see the abstract submitted to the 2017 WorldSleep Conference: The effect of acute, short-term visual deprivation on low-frequency EEG activity during wakefulness and sleep, Sleep Medicine 2017; 40: e32)
- Bernardi G, Siclari F, Yu X, Zennig C, Bellesi M, Ricciardi E, et al. Neural and behavioral correlates of extended training during sleep deprivation in humans: evidence for local, task-specific effects. J Neurosci 2015;35: 4487–4500.
- Siclari F, Baird B, Perogamvros L, Bernardi G, LaRocque JJ, Riedner B, et al. The neural correlates of dreaming. Nat Neurosci 2017;20: 872–878.
Much of what we observe in the adult brain reflects how neural circuitries have been sculpted by experience along the life cycle. A powerful way to investigate the impact of experience on the functional and structural organization of the brain is provided by the sensory deprivation model. By perturbing the availability of a sensory input, as for instance adopting (1) permanent sensory deprivation (e.g. deafness and blindness), (2) sensory re-afferentation (e.g. individuals whom recover vision or audition after a period of deprivation) or (3) short-lasting deprivation in the adult, as models of investigation, we can understand the degree of plasticity of sensory systems and of their interactions.
This research field has always been of major interest at the MoMiLab and thus, multiple topics have been and are currently explored, such as:
Modality independent brain development, what the brain develops despite the lack of a sensory modality. Seminal studies by RU MoMiLab (e.g. Ricciardi et al., 2011) and other research groups have provided evidence that most of the brain morpho-functional architecture forms and develops independently by the sensory experience which carries the information.
Conceptual representation and its sensory dependence (Handjaras et al., 2016)
Degree of plasticity and functional recovery in case of temporary sensory deprivation and restoration (Bottari et al., 2018)
Impact of visual impairment on auditory development
Structural changes in the sensory deprived blind brain
Methods of investigation include: fMRI, EEG and psychophysics
Experience dependence, sensory deprivation and restoration, neural plasticity, supramodality
- Ricciardi E. Pietrini P. New light from the dark: what blindness can teach us about brain function. Current Opinion in Neurology, 24 (4), 357–363, 2011
- Handjaras G. Ricciardi E. Leo A. Lenci A. Cecchetti L. Cosottini M. Marotta G. Pietrini P. How concepts are encoded in the human brain: A modality independent, category-based cortical organization of semantic knowledge. Neuroimage, 135, 232-242, 2016
- Bottari D. Kekunnaya R. Hense M. Troje N. Sourav S. Röder B. Motion processing after sight restoration: No competition between visual recovery and auditory compensation. NeuroImage 167, 284-296, 2018
‘Neuroeconomics’ and ‘neuromarketing’ are emerging interdisciplinary fields promoting a dialogue between neuroscience, psychology, behavioral science, economics and marketing.
A new laboratory called Innovation Center Lab-Neuroscience has been created to foster this interdisciplinary research fields within a multidisciplinary team to:
- apply behavioral science and design thinking to optimize consumer behavior, businesses, and policy
- investigate behavioral and neural correlates of consumers’ response to marketing stimuli
- design solutions that make products and services more responsive to human behavior and drive behavior change
The research projects will engage the use of brain imaging methodologies, biometrics and other technologies in assessing how specific samples (e.g., potential consumers, branch managers, trafers, etc.) respond when presented with specific products and/or related stimuli. In particular, the comprehension of how information on a specific product/item is conveyed through different sensory modalities and media channels and influence decision-making processes represent a current challenge for neuroscientific research in the field of marketing. Original approaches in neuroimaging studies applied to social sciences, behavior, decision-making processes should, not simply, characterize the individual (social and personological) profile, behavioral and brain functional processes, but mainly look for potential predictive biomarkers of individual choices or social outcome and contribute to the design of behavioral-change interventions.
Methods of investigation include: behavioral and psychophysical, brain imaging (e.g., fMRI and EEG)
neuroeconomics, decision making, neuromarketing, social sciences
- Genevsky A, Yoon C, Knutson B.When Brain Beats Behavior: Neuroforecasting Crowdfunding Outcomes. J Neurosci. 2017 Sep 6;37(36):8625-8634
- Casarotto S, Ricciardi E, Romani S, Dalli D, Pietrini P. Covert brand recognition engages emotion-specific brain networks. Arch Ital Biol. 2012 Dec;150(4):259-73
- Pietrini P. Toward a biochemistry of mind? Am J Psychiatry. 2003 Nov;160(11):1907-8
Social cognition represents the cornerstone of successful human interactions. Any social contact requires the interaction of several abilities: to observe other people’s behavior, to predict their reaction and to respond adequately. Altogether, these processes give rise to the complexity of the social world. In our everyday life, it is fundamental to recognize someone as a different person from ourselves, understand their feelings, emotions, beliefs and desires, infer the reasons behind their behavior and give a socially appropriate response to it. Therefore, we can easily imagine the consequences of not being able to understand when someone is angry, or not feeling anything when others are suffering. The total lack or severe impairment of social cognition abilities characterize several disorders, ranging from psychiatric (e.g., psychopathy) to neurodegenerative conditions (e.g., behavioral variant of frontotemporal dementia). The brain and psychological mechanisms at the basis of normal and pathological social cognition processes are still unclear and, among several others, different topics are relevant in the social and affective neuroscience field:
The investigation of how emotions are processed and represented in the brain
How the brain is able to analyze complex social scenarios and decipher social relations from minimal visual information.
Brain characteristics of psychopath subjects and their association with empathic processing
Methods of investigation include
structural and functional MRI, behavioral measurement and questionnaires, recording of autonomic activity (e.g., skin conductance, respiration and heart rate)
social cognition, emotion, mentalizing, perspective taking, empathy
- Lettieri, G., Handjaras, G., Ricciardi, E., Leo, A., Papale, P., Betta, M., ... & Cecchetti, L. (2018). Emotionotopy: Gradients encode emotion dimensions in right temporo-parietal territories. bioRxiv, 463166.
- Pietrini P. Toward a biochemistry of mind? Am J Psychiatry. 2003 Nov;160(11):1907-8
- Gobbini MI, Gentili C, Ricciardi E, Bellucci C, Salvini P, Laschi C, Guazzelli M, Pietrini P.Distinct neural systems involved in agency and animacy detection. J Cogn Neurosci. 2011 Aug;23(8):1911-20
A key function in our brain is the coding of information related to movements. Many questions regarding how motor schemes are represented in the brain, how we process sensory information relevant for planning movements or to understand others’ actions, are still open and debated. In addition, the importance of exploiting our knowledge about the motor system to develop more efficient strategies for rehabilitation and restoration of movement in patients who suffered brain damage is consistently growing. The study of this topic can be pursued through different methods, ranging from the recording of peripheral signals or specific limb positions to the usage of functional imaging to investigate the brain correlates of motor planning or perception.
In the last years, the MoMiLab has conducted much work on this field, leveraging also on collaborations with researchers from the engineering and bionics field, for the investigation of multiple topics related to motor control, such as:
Models for coding hand and upper limb movements. In the last years, the MoMiLab has conducted studies to demonstrate that the human motor system encodes low-dimensional models based on synergies. These models are particularly important for the design of prostheses, and the study of their direct coding in the brain is important for both research and application purposes.
Representation of different classes of movements in the human action observation systems.
Development of novel upper-limb prosthetic devices based on a better knowledge of movement representation. This work exploits the collaboration with Research Center “E. Piaggio” at the University of Pisa and the research partners of the EU-funded project SoftPro (https://softpro.eu)
Visual information on objects and its role on motor planning.
Methods of investigation include: fMRI, EEG, EMG, optical joint tracking and psychophysics
motor representation, action observation system, synergies, prostheses , object processing
- Leo, A., Handjaras, G., Bianchi, M., Marino, H., Gabiccini, M., Guidi, A., Scilingo, E.P., Pietrini, P., Bicchi, A., Santello, M., & Ricciardi, E. (2016). A synergy-based hand control is encoded in human motor cortical areas. Elife, 5, e13420
- Handjaras, G., Bernardi, G., Benuzzi, F., Nichelli, P. F., Pietrini, P., & Ricciardi, E. (2015). A topographical organization for action representation in the human brain. Human brain mapping, 36(10), 3832-3844.
- Piazza, C., Catalano, M. G., Bianchi, M., Ricciardi, E., Prattichizzo, D., Haddadin, S., ... & Van Der Kooij, H. (2018, October). The SoftPro project: Synergy-based open-source technologies for prosthetics and rehabilitation. In International Symposium on Wearable Robotics (pp. 370-374). Springer, Cham.
In the past decades, visual neuroscience has greatly advanced our knowledge of the human visual system. From the retina, visual signaling steps through a relay station in the lateral geniculate nuclei, and then reach the cerebral cortex targeting the primary visual area (area V1). V1 provides an optimal encoding of natural image statistics based on local contrast, orientation and spatial frequencies, while later cortical modules process higher-level features, such as texture and shape and determine figure-ground organization. The basic features of visual processing are well preserved and commonly represented in our species and in other mammals as well. Nonetheless, given the high inter-individual and cross-cultural variability of aesthetic responses to the same visual contents, an unanswered question in cognitive neuroscience remains whether our perception of the world is stable across time and cultural changes. In this light, the popularity of neuroimaging has grown in the last years and has spread well beyond the neuroscientific field, getting in touch with apparently distant fields. From those high-value inter-disciplinary collaborations with experts in aesthetics, architecture and visual arts, the field has started gaining a greater comprehension of what happens in our brain aside from the mere recognition of the content of a visual scene. By combining an art historian and a neuroscientific perspective, the following project will investigate the visual aesthetic response. In particular, it will focus on the relevance of cultural and social context in shaping the relationship between different visual representations and their evoked aesthetic response. This issue will represent a key advancement in the study of the visual system, which is often assumed to be unaffected by those factors. Methods of investigation include: recording of autonomic responses, EEG, fMRI and visual psychophysics.
Aesthetic response, beauty, vision, reward, emotion
- Papale, P., Chiesi, L., Rampinini, A. C., Pietrini, P., & Ricciardi, E. (2016). When neuroscience ‘touches’ architecture: from hapticity to a supramodal functioning of the human brain. Frontiers in psychology, 7, 866
- Chatterjee A, Vartanian O. Neuroaesthetics. Trends Cogn Sci. 2014 Jul;18(7):370-5
- Freedberg D, Gallese V. Motion, emotion and empathy in esthetic experience. Trends Cogn Sci. 2007
Nonlinear coupled problems governed by partial differential equations in solid and fluid mechanics arise in many engineering and biological applications where multiple fields (displacement, damage, thermal, humidity, electric, etc.) are strongly interacting with each other. The present research topic envisages a critical analysis and development of novel numerical strategies for the solution of nonlinearly coupled boundary value problems within the finite element method. Specifically, implicit and explicit numerical schemes, as well as monolithic and staggered solvers, along with suitable high performance computing strategies, will be developed for a wide range of problems selected for their relevance in industrial applications and failure analysis. Prospective applicants are expected to hold a degree in engineering, mathematics, physics, or computer science.
- Reinoso J, Paggi M, Linder C (2017). Phase field modeling of brittle fracture for enhanced assumed strain shells at large deformations: formulation and finite element implementation. COMPUTATIONAL MECHANICS, vol. 59, p. 981-1001, doi: 10.1007/s00466-017-1386-3
- Lenarda P, Paggi M, Ruiz Baier R (2017). Partitioned coupling of advection–diffusion–reaction systems and Brinkman flows. JOURNAL OF COMPUTATIONAL PHYSICS, vol. 344, p. 281-302, doi: 10.1016/j.jcp.2017.05.011
- Lenarda P, Gizzi A, Paggi M (2018). A modeling framework for electro-mechanical interaction between excitable deformable cells. EUROPEAN JOURNAL OF MECHANICS. A, SOLIDS, vol. 72, p. 374-392, doi: 10.1016/j.euromechsol.2018.06.001
- Reinoso J, Paggi M (2014). A consistent interface element formulation for geometrical and material nonlinearities. COMPUTATIONAL MECHANICS, vol. 54, p. 1569-1581, doi: 10.1007/s00466-014-1077-2
- Paggi M, Reinoso J (2017). Revisiting the problem of a crack impinging on an interface: A modeling framework for the interaction between the phase field approach for brittle fracture and the interface cohesive zone model. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, vol. 321, p. 145-172, doi: 10.1016/j.cma.2017.04.004
- Mariggiò G, Reinoso J, Paggi M, Corrado M (2018). Peeling of thick adhesive interfaces: The role of dynamics and geometrical nonlinearity. MECHANICS RESEARCH COMMUNICATIONS, vol. 94, p. 21-27, doi: 10.1016/j.mechrescom.2018.08.018
- Paggi M, Barber JR (2011). Contact conductance of rough surfaces composed of modified RMD patches. INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, vol. 54, p. 4664-4672, doi:10.1016/j.ijheatmasstransfer.2011.06.011
- Vakis AI, Yastrebov VA, Scheibert J, Nicola L, Dini D, Minfray C, Almqvist A, Paggi M, Lee S, Limbert G, Molinari JF, Anciaux G, Aghababaei R, Echeverri Restrepo S, Papangelo A, Cammarata A, Nicolini P, Putignano C, Carbone G, Stupkiewicz S, Lengiewicz J, Costagliola G, Bosia F, Guarino R, Pugno NM, Müser MH, Ciavarella M (2018). Modeling and simulation in tribology across scales: An overview. TRIBOLOGY INTERNATIONAL, vol. 125, p. 169-199, doi: 10.1016/j.triboint.2018.02.005
- Paggi M, Reinoso J (2018). A variational approach with embedded roughness for adhesive contact problems, MECHANICS OF ADVANCED MATERIALS AND STRUCTURES, in press, doi:10.1080/15376494.2018.1525454
Response time, throughput and utilization are extra-functional properties of software that are especially relevant in resource-constrained environments such as mobile phones and in the Internet-of-Things. Ideally, one would like to use an application that automatically meets given user-defined performance requirements. This requires the availability of a mechanism that can identify the current state of the software system and predict its future behaviour under a range of assumptions of the environment, with an algorithm that returns the optimal configuration meeting the desired performance target. The candidate will have the opportunity to work on the development of self-adaptive methods for software performance using a range of techniques including black-box representations based on machine learning and white-box analytical models built from first principles.
software performance engineering; self-adaptive systems; predictive modelling
E. Incerto, M. Tribastone, and C. Trubiani, “Software performance self-adaptation through efficient model predictive control,” in 32nd ACM/IEEE International Conference on Automated Software Engineering (ASE), 2017. http://cse.lab.imtlucca.it/∼mirco.tribastone/papers/ase2017.pdf
E. Incerto, M. Tribastone, and C. Trubiani, “Combined vertical and horizontal autoscaling through model predictive control,” in 24th International European Conference on Parallel and Distributed Computing (EURO-PAR), 2018. http://cse.lab.imtlucca.it/∼mirco.tribastone/papers/europar18.pdf
E. Incerto, A. Napolitano, and M. Tribastone, “Moving horizon estimation of service demands in queuing networks,” in 26th IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2018. http://cse.lab.imtlucca.it/∼mirco.tribastone/papers/mascots18.pdf
Dynamical systems are a fundamental mathematical model to describe predict the behavior of natural as well as engineered processes. Our capability to gain relevant insights details is however hindered by the large dimensionality of such models when describing systems characterized by a large degree of complexity. Coarse graining, model reduction, and model abstraction are among the several different keywords with which a wide range of disciplines refer to the topic of simplifying a given dynamical system into a smaller one that preserves key observables of interest to the modeler. The candidate will have the opportunity to work on the development of new coarse-graining methods and algorithms, with applications related to models in various disciplines including automation, computer science, statistical physics, and systems biology.
coarse graining; model reduction; dynamical systems
Mirco Tribastone (SYSMA), Guido Caldarelli (NETWORKS), and Diego Garlaschelli (NETWORKS)
- S. Tognazzi, M. Tribastone, M. Tschaikowski, and A. Vandin, “Backward invariance for linear differential algebraic equations,” in 57th IEEE Conference on Decision and Control (CDC), 2018
- L. Cardelli, M. Tribastone, M. Tschaikowski, and A. Vandin, “Maximal aggregation of polynomial dynamical systems,” Proceedings of the National Academy of Sciences, vol. 114, no. 38, pp. 10 029–10 034, 2017.
- L. Cardelli, M. Tribastone, A. Vandin, and M. Tschaikowski, “ERODE: A tool for the evaluation and reduction of ordinary differential equations,” in Tools and Algorithms for the Construction and Analysis of Systems - 23rd International Conference, TACAS, 2017.
The goal is to develop autonomous, self-reconfigurable, control systems that are able to learn how to achieve their objectives from data, adapting themselves to external stimuli such as changing environmental conditions and variations of the dynamic properties of the process. In particular, new approaches will be developed for synthesizing control systems from data that are optimal, robust, and can cope with operating constraints on input and output variables, addressing both model-based methods, where an open-loop model of the process is identified from data, and model-free methods, that directly synthesize the control law from data.
Systems identification, machine learning, reinforcement learning, model predictive control
- R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. Second Edition. MIT press Cambridge, 2018
- F. L. Lewis, D. Vrabie, and K. G. Vamvoudakis, “Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers,” IEEE Control Systems, vol. 32, no. 6, pp. 76–105, 2012.
- B. Recht, "A Tour of Reinforcement Learning: The View from Continuous Control", 2018.
- D. Selvi, D. Piga, and A. Bemporad, “Towards direct data-driven control design of optimal controllers,” in Proc. European Control Conf., Limassol, Cyprus, 2018, pp. 2836–2841.
- V. Breschi, D. Piga, and A. Bemporad, “Piecewise aﬃne regression via recursive multiple least squares and multicategory discrimination,” Automatica, vol. 73, pp. 155–162, Nov. 2016.
- D. Piga, S. Formentin, and A. Bemporad, “Direct data-driven control of constrained systems,” IEEE Transactions on Control Systems Technology, vol. 26, no. 4, pp. 1422–1429, Jul 2018.
- J. R. Salvador, D. Munoz de la Pena, T. Alamo, and A. Bemporad, “Data-based predictive control via direct weight optimization,” in 6th IFAC Conference on Nonlinear Model Predictive Control, pp. 437-442, Madison, WI, USA, 2018.
- D. Masti and A. Bemporad, “Learning nonlinear state-space models using deep autoencoders,” in Proc. 57th IEEE Conf. on Decision and Control. 2018./
In recent years, robots have spread everywhere and are able to accomplish numerous tasks in various applications. While most success has been obtained in restricted environments, the current challenges include being able to operate safely (e.g. not harming people around the robots), coordinating multiple robots and adapting to changing environments.In this context, Model Predictive Control is a valuable tool for tackling constrained multiple input multiple output (possibly nonlinear) systems. While both theory and algorithms have been largely investigated in the literature, many questions still need to be answered before it will be possible to safely and effectively deploy robots to cooperate with human beings. Autonomous driving is currently perhaps the most thrilling field of research in this context.
Robotics, autonomous driving, model predictive control
- Craig, J. J. (2005). Introduction to robotics: mechanics and control (Vol. 3, pp. 48-70). Upper Saddle River, NJ, USA:: Pearson/Prentice Hall.
- Borrelli, F., Bemporad, A., & Morari, M. (2017). Predictive control for linear and hybrid systems. Cambridge University Press.- Rawlings, J. B., & Mayne, D. Q. (2009). Model predictive control: Theory and design.
- M. Graf Plessen, D. Bernardini, H. Esen, and A. Bemporad. Spatial-based predictive control and geometric corridor planning for adaptive cruise control coupled with obstacle avoidance. IEEE Trans. Contr. Systems Technology, vol. 26, no. 1, pp. 38–50, 2018.
- R. Hult, M. Zanon, S. Gros, and P. Falcone. Optimal Coordination of Automated Vehicles at Intersections: Theory and Experiments. IEEE Transactions on Control Systems Technology, (in press, available online)
- I. Batkovic, M. Zanon, N. Lubbe and P. Falcone. A Computationally Efficient Model for Pedestrian Motion Prediction. Proceedings of the European Control Conference (ECC), 2018
Distributed Optimization has been mostly investigated in order to address problems which have an intrinsically distributed nature, including smart cities, autonomous driving, power grids, etc. By restricting the focus on convex problems, it is possible to apply a wide range of algorithms. Moreover, the assumption of lossless and instantaneous communication links makes it possible to deploy algorithms which require simple computations but rely on large amounts of communication. In order to extend the applicability of distributed optimization, investigating nonconvex optimization strategies over lossy communication channels becomes of paramount importance. While only few results are currently available for this setting, some encouraging results have recently been obtained, which pave the road for further investigations on the topic. Notable examples include networked and cooperative control scenarios applied to, e.g., autonomous driving, formation flight, etc.
Nonconvex optimization, distributed optimization, networked optimization
- S. Boyd, L. Vandenberghe, "Convex optimization", Cambridge Univ. Press, 2004
- J. Nocedal, S.J. Wright, Numerical optimization 2nd ed, 2006
- R. Hult, M. Zanon, S. Gros and P. Falcone. Primal Decomposition of the Optimal Coordination of Vehicles at Traffic Intersections.Proceedings of the Conference on Decision and Control (CDC), 2016
- M. Zanon, S. Gros, P. Falcone and H. Wymeersch. An Asynchronous Algorithm for Optimal Vehicle Coordination at Traffic Intersections. Proceedings of the World Congress of the International Federation of Automatic Control, 2017
Model Predictive Control (MPC) is one of the most successful techniques adopted in industry to control multivariable systems in an optimized way under constraints on input and output variables. In MPC, the manipulated inputs are computed in real time by solving a mathematical programming problem, most frequently a Quadratic Program (QP). Topics of research are available on how to formulate and solve MPC problems with higher throughput than is currently available, while maintaining the numerical optimization code simple, certifiable for the worst-case execution time, and robust with respect to limited machine precision.
Convex optimization, numerical methods, model predictive control
- G. Cimini and A. Bemporad, "Exact complexity certiﬁcation of active-set methods for quadratic programming," IEEE Trans. Automatic Control, vol. 62, no. 12, pp. 6094–6109, 2017.
- A. Bemporad, "A numerically stable solver for positive semi-deﬁnite quadratic programs based on nonnegative least squares," IEEE Trans. Automatic Control, vol. 63, no. 2, pp. 525–531, 2018.
- A. Bemporad and V.V. Naik, "A numerically robust mixed-integer quadratic programming solver for embedded hybrid model predictive control," in 6th IFAC Conf. on Nonlinear Model Predictive Control, Madison, WI, 2018, pp. 502–507.
- N. Saraf and A. Bemporad, "Fast model predictive control based on linear input/output models and bounded-variable least squares," in Proc. 56th IEEE Conf. on Decision and Control, Melbourne, Australia, 2017.
- A. Bemporad, M. Morari, V. Dua, and E.N. Pistikopoulos, "The explicit linear quadratic regulator for constrained systems," Automatica, vol. 38, no. 1, pp. 3–20, 2002.
- J. Nocedal, S.J. Wright, Numerical optimization 2nd ed, 2006
- R. Verschueren, M. Zanon, R. Quirynen and M. Diehl. Time-optimal Race Car Driving using an Online Exact Hessian based Nonlinear MPC Algorithm. Proceedings of the European Control Conference (ECC), 2016
The Software Supply Chain (SSC) is a cornerstone of the industrial society on which many other Supply Chains (SCs) depend. The continuous demand/integration of the computing systems into SCs is pushing the development and distribution of software. To cope with this growing request many companies are including open source software (OSS) in their software products. OSS has many advantages, for example, it prevents that the software producer does not acquires a strong bargaining position on the consumer. However, the flip side is that the producer of a OSS has no obligation to maintain, improve or fix her software. All in all, the OSS ranges from small scale projects, with limited or even no security plan, to community projects that release periodic security updates. Such heterogeneity makes it difficult to understand the actual risks when one wants to integrate a OSS in his project. From a methodological point of view, the project aims at answering the following questions: (i) what are the conditions to make the formal verification a valuable asset in the SSC? (ii) can we design a mechanism based on economic rewards that push participant to find and fix bugs in OSS software? (iii) can the blockchain technology be used to implement a decentralized framework for the formal verification of security properties of OSS? From a practical point of view, the project aims at designing and implementing a blockchain service for the security analysis and patching of the OSS, where developers and security analysts cooperate efficiently.
Formal methods, software verification, blockchain, DLT, contract-driven development, mobile code security
Research units involved
Symbolic execution is a powerful technique to spot out corner cases, e.g., vulnerabilities, in the semantics of a program. As a matter of fact, it replaces the standard semantics (referring to specific values) with a symbolic one (manipulating abstract expressions). Unfortunately, symbolic execution does not scale on large programs. For this reason, hybrid techniques have been proposed (e.g. concolic testing and symbolic backward execution). The goal of this project is to combine the symbolic analysis of a program with a test execution environment driven by an evolutionary optimization strategy. The symbolic analysis is applied to obtain a compact (thus computable) specification of the conditions that a test must satisfy to trigger a certain vulnerability. Instead the evolutionary algorithm drives the refinement process that, starting from some random tests, leads to the convergence toward the desired ones. The convergence criteria is based on the optimization of a fitness function derived from the symbolic specification.
Formal methods, vulnerability analysis, security testing, evolutionary algorithms, white-box testing, binary analysis
Research units involved
- Eigenvector centrality for characterization of protein allosteric pathways
- Christian F. A. Negre, PNAS 115 (52) E12201-E12208 (2018)
- Cardelli, L., Tribastone, M., Tschaikowski, M., and Vandin, A. (2015). Forward and backward bisimulations for chemical reaction networks. arXiv preprint arXiv:1507.00163.
- Cardelli, L., Tribastone, M., Tschaikowski, M., and Vandin, A. (2017). Maximal aggregation of polynomial dynamical systems. Proceedings of the National Academy of Sciences, 114(38):10029–10034.
- J. Cardy Scaling and Renormalization in Statistical Physics OUP (1996)
- Hall, G. & Bialek, W. The statistical mechanics of Twitter. arXiv (2018).
- Weng, L., Flammini, A., Vespignani, A. and Menczer, F. , ‘Competition among memes in a world with limited attention’,Scientific Reports2, 1–9.11
- Caldarelli, G., Capocci, A., De Los Rios, P. & Muñoz, M. A. Scale-Free Networks from Varying Vertex Intrinsic Fitness. Phys. Rev. Lett. 89, (2002).
- Pastor-Satorras, R. & Vespignani, A. Epidemic Spreading in Scale-Free Networks. Phys. Rev. Lett. 86, 3200–3203 (2001).
- Tizzoni, M., Sun, K., Benusiglio, D., Karsai, M. & Perra, N. The Scaling of Human Contacts and Epidemic Processes in Metapopulation Networks. Sci. Rep. 5, 15111 (2015).
- Leontief, Wassily W. Input-Output Economics. 2nd ed., New York: Oxford University Press, 1986.
- The Product Space Conditions the Development of Nations. C. A. Hidalgo, B. Klinger, A.-L. Barabási, R. Hausmann 317 482-487 (2007)
- Complex Agent Based Models M. Gallegati Springer 2018
- The financial accelerator in an evolving credit network. Domenico Delli Gatti, Mauro Gallegati, Bruce Greenwald, Alberto Russo, Joseph E. Stiglitz. Journal of Economic Dynamics & Control 34 (2010) 1627–1650
- Thöni, Christian, and Simon Gächter. "Peer effects and social preferences in voluntary cooperation: A theoretical and experimental analysis." Journal of Economic Psychology 48 (2015): 72-88.
- Belloc, Marianna, Ennio Bilancini, Leonardo Boncinelli, and Simone D’Alessandro. "Intuition and Deliberation in the Stag Hunt Game" (2019), mimeo
- Bilancini, Ennio, and Leonardo Boncinelli. "The co-evolution of cooperation and defection under local interaction and endogenous network formation." Journal of Economic Behavior & Organization 70.1-2 (2009): 186-195.
Ennio Bilancini (AXES)
- Bilancini, Ennio, Leonardo Boncinelli, and Jiabin Wu. "The interplay of cultural intolerance and action-assortativity for the emergence of cooperation and homophily." European Economic Review 102 (2018): 1-18.
- Bilancini, Ennio, and Leonardo Boncinelli. "Social coordination with locally observable types." Economic Theory 65.4 (2018): 975-1009.
- Bilancini, Ennio, and Leonardo Boncinelli. "Instrumental cardinal concerns for social status in two-sided matching with non-transferable utility." European Economic Review 67 (2014): 174-189.
Ennio Bilancini (AXES)
- Alós-Ferrer, Carlos. "A Review Essay on Social Neuroscience: Can Research on the Social Brain and Economics Inform Each Other?." Journal of Economic Literature 56.1 (2018): 234-64.
- Griessinger, Thibaud, and Giorgio Coricelli. "The neuroeconomics of strategic interaction." Current Opinion in Behavioral Sciences 3 (2015): 73-79.
- Bilancini, Ennio, and Leonardo Boncinelli. "Rational attitude change by reference cues when information elaboration requires effort." Journal of Economic Psychology 65 (2018): 90-107.
Ennio Bilancini (AXES)
- Bilancini, Ennio, Leonardo Boncinelli, and Alan Mattiassi. "Assessing Actual Strategic Behavior to Construct a Measure of Strategic Ability." Frontiers in Psychology (2019) forthcoming
- Gill, David, and Victoria Prowse. "Cognitive ability, character skills, and learning to play equilibrium: A level-k analysis." Journal of Political Economy 124.6 (2016): 1619-1676.
- Alaoui, Larbi, and Antonio Penta. "Endogenous depth of reasoning." Review of Economic Studies 83.4 (2015): 1297-1333.
- Rice, Thomas. "The behavioral economics of health and health care." Annual review of public health 34 (2013): 431-447.
- Bellandi, Tommaso, Sara Albolino, Riccardo Tartaglia, and Sebastiano Bagnara. "Human factors and ergonomics in patient safety management." In Handbook of Human Factors and Ergonomics in Health Care and Patient Safety, pp. 698-717. CRC Press, 2016.
- Bartolini, Stefano, Ennio Bilancini, Luigino Bruni, and Pierluigi Porta, eds. Policies for happiness. Oxford University Press, 2016.
- Bakhshi, H., Downing, J., Osborne, M., & Schneider, P. (2017). The future of skills: employment in 2030. London: Nesta, Oxford-Martin, Pearson.
- Beaudry, Paul, David A. Green, and Benjamin M. Sand (2016). “The Great Reversal in the Demand for Skill and Cognitive Tasks”, Journal of Labor Economics, 34, 199–247.
- Deming, D. (2017). “The growing importance of social skills in the labor market”, The Quarterly Journal of Economics, 132, 1593-1640.
- Andrews, D. Criscuolo C. and Gal P. (2016), “The Best versus the Rest: The Global Productivity Slowdown, Divergence across Firms and the Role of Public Policy”, OECD Productivity Working Papers, No. 5
- Comin D. and Mestieri M. (2018). “If Technology Has Arrived Everywhere, Why Has Income Diverged?” American Economic Journal: Macroeconomics, 10 (3): 137-78.
- Grossman G. M. and Helpman E. (1994). “Endogenous Innovation in the Theory of Growth,” Journal of Economic Perspectives, American Economic Association, vol. 8(1), pages 23-44.
- Haldane A. G. (2017). Productivity Puzzles. Speech held by the Chief Economist of the Bank of England at the London School of Economics on 20 March 2017, available online at https://www.bankofengland.co.uk/speech/2017/productivity-puzzles.
- Romer, P. M. (1990). "Endogenous Technological Change," Journal of Political Economy, 98:5, S71–102.
- Bernard, A. and Moxnes, A. (2018). Networks and trade. Annual Review of Economics, vol. 10:65-85
- Bernard, A., A. Moxnes and Yukiko U. Saito, (2016). Production Networks, Geography and Firm Performance. CEP Discussion Papers dp1435, Centre for Economic Performance, LSE.
- Chaney, T. (2016). Networks in international trade. In Y. Bramoulle, A. Galeotti and B. Rogers (eds.), Oxford Handbook of the Economics of Networks, Oxford: Oxford University Press.
- Oberfield, E. (2018). A theory of input–output architecture. Econometrica, 86 (2), 559–589.
- Athey, S., Imbens, G. W. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences 113:7353-7360.
- Athey, S., Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives 31, 3-32.
- Imbens, G.W., and Rubin, D.B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. An Introduction. Cambridge University Press.
- Vapnik, V. N. (1998). Statistical Learning Theory. Wiley.
- Varian, H.R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives 28, 3-28.
Massimo Riccaboni, Armando Rungi
- Acemoglu D., Carvalho V. M., Ozdaglar A., Tahbaz‐Salehi A. (2016). “The Network Origins of Aggregate Fluctuations”, Econometrica, vol. 80(5): 1977-2016.
- Altomonte C., Di Mauro F., Ottaviano G., Rungi A and Vicard V. (2013) “Global value chains during the Great Trade collapse: a bullwhip effect” in: Firms in the international economy: firm heterogeneity meets international business. CESifo Seminar Series. MIT Press, pp. 277-308
- Carvalho, V. M. (2014). “From Micro to Macro via Production Networks.” Journal of Economic Perspectives, 28 (4): 23-48.
- di Giovanni J, Levchenko A. J., and Mejean I. (2018). "The Micro Origins of International Business-Cycle Comovement," American Economic Review, vol 108(1), pages 82-108.
- Escaith H., Lindenberg N. and Miroudot S. (2011). “Global Supply Chains, the Great Trade Collapse and Beyond: More Elasticity or More Volatility?” in “Recovery and Beyond: Lessons for Trade Adjustment and Competitiveness" Editors: F. di Mauro et B. Mandel. European Central Bank.
Irene Crimaldi (AXES)
- G. Aletti - I. Crimaldi - A. Ghiglietti, Networks of reinforced stochastic processes: asymptotics for the empirical means, forthcoming in Bernoulli.
- I. Crimaldi - P. Dai Pra - P-Y. Louis - I. G. Minelli (2019), Synchronization and functional central limit theorems for interacting reinforced random walks, Stochastic Processes and their Applications, 129(1), 70-101.
- G. Aletti - I. Crimaldi - A. Ghiglietti (2017), Synchronization of reinforced stochastic processes with a network-based interaction, The Annals of Applied Probability, 27(6), 3787-3844.
Decision making process is a main part of managing activities across all kinds of firms. Every decision is apt to product consequences for the firm, also impacting its performances. In the last decades, management research is increasingly focusing on the behavioral approach: integrating psychological theories and methods to management science can increase the understanding of how cognitive and emotional processes work and shape individuals’ decisions and actions. This line of research shall study how cognitive and social psychology they apply to strategic management theory and practice (namely, behavioral strategy). The behavioral approach uses the cognitive psychology, which is a branch of psychology that seeks to understand the internal mental processes of thought. The main research theme is business organization, not only the man. The candidate will embrace topics in the existing core of behavioral strategy (such as decision biases and cognitive schema), while encouraging innovations and de-biasing actions in a managerial context.
Nicola Lattanzi, Emiliano Ricciardi
- Powell, T. C., Lovallo, D., & Fox, C. R. (2011). Behavioral strategy. Strategic Management Journal, 32(13), 1369-1386.
- Lattanzi, N. (2013). Management Science and Neuroscience Impact. Decision Making Process, Entrepreneurship and Business Strategy. McGraw-Hill.
- Ricciardi, E., Bonino, D., Gentili, C., Sani, L., Pietrini, P., & Vecchi, T. (2006). Neural correlates of spatial working memory in humans: a functional magnetic resonance imaging study comparing visual and tactile processes. Neuroscience, 139(1), 339-349.
- Intesa San Paolo Innovation Center & IMT School for Advanced Studies Lucca (2018). Innovation Trend Report: Neuroscience Impact. Brain and Business.
Human beings manage all organizations; they are made of men and are imperfect systems. Firms’ long-term success greatly depends on how managers can select and govern human resources, also being able to understand the nature and complexities of human beings as multi-faceted individuals. Individuals also influence firm performances: employing the right type of person (with certain skills, behaviors and abilities) can help an organization increase productivity and maintain a competitive advantage. This line of research studies how personality influences job and firm performances.
Nicola Lattanzi, Massimo Riccaboni. Andrea Morescalchi
- Barrick, M. R., Stewart, G. L., & Piotrowski, M. (2002). Personality and job performance: test of the mediating effects of motivation among sales representatives. Journal of Applied Psychology, 87(1), 43.
- Seriki, O. K., Nath, P., Ingene, C. A., & Evans, K. R. (2018). How complexity impacts salesperson counterproductive behavior: The mediating role of moral disengagement. Journal of Business Research.
- Lattanzi, N., Menicagli, D., & Dal Maso, L. (2016). Neuroscience Evidence for Economic Humanism in Management Science: Organizational Implications and Strategy. Archives italiennes de biologie, 154(1), 25-36.
Family firms are a key component of the European economy, both for their high number and contributes to GDP and occupation. Although family businesses are not an Italian peculiarity, they strongly characterize the Italian economy. Research has not currently reached a unique paradigm on the relationships between the involvement of the family in the ownership and management of a firm. This line of research investigates the relationship between family firms and industrial districts, which is currently underdeveloped in the academic literature. The candidate will work both on developing the theoretical models and will analyse data using quantitative (econometrics and social network analysis) and qualitative methods (case studies).
Nicola Lattanzi, Marco Paggi e Alberto Bemporad, Armando Rungi
- Cucculelli, M., Storai, D. (2015). Family Firms and Industrial Districts: Evidence from the Italian Manufacturing Industry. Journal of Family Business Strategy, 6(4), p. 234-246
- Lattanzi, N. (2017). Le aziende familiari: Generazioni Società Mercato. G Giappichelli Editore.
Fintech (Financial Technology) includes a wide set of technologies and innovations that are revolutionizing traditional financial services. Blockchain's uses have recently evolved into many applications, such as banking, financial markets, insurance and leasing contracts. Today, blockchain has potential for application in various business fields, including accounting and in certifying financial statements. This line of research will follow blockchain’s fundamental concepts, providing perspectives on its challenges and opportunities in business, management and accounting practices, also using complex systems for management science.
Nicola Lattanzi, Diego Garlaschelli
- De Bruijn, H., & Ten Heuvelhof, E. (2018). Management in networks. Routledge.
- Dai, J., & Vasarhelyi, M. A. (2017). Toward blockchain-based accounting and assurance. Journal of Information Systems, 31(3), 5-21.
- Fanning, K., & Centers, D. P. (2016). Blockchain and its coming impact on financial services. Journal of Corporate Accounting & Finance, 27(5), 53-57.