Frequency and precision of feedback and the adaptive process of learning a dual motor task

This work investigated the effects of frequency and precision of feedback on the learning of a dual-motor task. One hundred and twenty adults were randomly assigned to six groups of different knowledge of results (KR), frequency (100%, 66% or 33%) and precision (specific or general) levels. In the stabilization phase, participants performed the dual task (combination of linear positioning and manual force control) with the provision of KR. Ten non-KR adaptation trials were performed for the same task, but with the introduction of an electromagnetic opposite traction force. The analysis showed a significant main effect for frequency of KR. The participants who received KR in 66% of the stabilization trials showed superior adaptation performance than those who received 100% or 33%. This finding reinforces that there is an optimal level of information, neither too high nor too low, for motor learning to be effective.

Effects of visual and auditory stimuli in a choice reaction time task

The effect produced by a warning stimulus(i) (WS) in reaction time (RT) tasks is commonly attributed to a facilitation of sensorimotor mechanisms by alertness. Recently, evidence was presented that this effect is also related to a proactive inhibition of motor control mechanisms. This inhibition would hinder responding to the WS instead of the target stimulus (TS). Some studies have shown that auditory WS produce a stronger facilitatory effect than visual WS. The present study investigated whether the former WS also produces a stronger inhibitory effect than the latter WS. In one session, the RTs to a visual target in two groups of volunteers were evaluated. In a second session, subjects reacted to the visual target both with (50% of the trials) and without (50% of the trials) a WS. During trials, when subjects received a WS, one group received a visual WS and the other group was presented with an auditory WS. In the first session, the mean RTs of the two groups did not differ significantly. In the second session, the mean RT of the two groups in the presence of the WS was shorter than in their absence. The mean RT in the absence of the auditory WS was significantly longer than the mean RT in the absence of the visual WS. Mean RTs did not differ significantly between the present conditions of the visual and auditory WS. The longer RTs of the auditory WS group as opposed to the visual WS group in the WS-absent trials suggest that auditory WS exert a stronger inhibitory influence on responsivity than visual WS.

Differential effects of task-specific practice on performance in a simulated penalty kick under high-pressure

Objectives The current study investigated to what extent task-specific practice can help reduce the adverse effects of high-pressure on performance in a simulated penalty kick task. Based on the assumption that practice attenuates the required attentional resources, it was hypothesized that task-specific practice would enhance resilience against high-pressure. Method Participants practiced a simulated penalty kick in which they had to move a lever to the side opposite to the goalkeeper's dive. The goalkeeper moved at different times before ball-contact. Design Before and after task-specific practice, participants were tested on the same task both under low- and high-pressure conditions. Results Before practice, performance of all participants worsened under high-pressure; however, whereas one group of participants merely required more time to correctly respond to the goalkeeper movement and showed a typical logistic relation between the percentage of correct responses and the time available to respond, a second group of participants showed a linear relationship between the percentage of correct responses and the time available to respond. This implies that they tended to make systematic errors for the shortest times available. Practice eliminated the debilitating effects of high-pressure in the former group, whereas in the latter group high-pressure continued to negatively affect performance. Conclusions Task-specific practice increased resilience to high-pressure. However, the effect was a function of how participants responded initially to high-pressure, that is, prior to practice. The results are discussed within the framework of attentional control theory (ACT).

Self-Organizing Mechanisms for Task Allocation in a Knowledge-Based Economy

A prevalent claim is that we are in knowledge economy. When we talk about knowledge economy, we generally mean the concept of “Knowledge-based economy” indicating the use of knowledge and technologies to produce economic benefits. Hence knowledge is both tool and raw material (people’s skill) for producing some kind of product or service. In this kind of environment economic organization is undergoing several changes. For example authority relations are less important, legal and ownership-based definitions of the boundaries of the firm are becoming irrelevant and there are only few constraints on the set of coordination mechanisms. Hence what characterises a knowledge economy is the growing importance of human capital in productive processes (Foss, 2005) and the increasing knowledge intensity of jobs (Hodgson, 1999). Economic processes are also highly intertwined with social processes: they are likely to be informal and reciprocal rather than formal and negotiated. Another important point is also the problem of the division of labor: as economic activity becomes mainly intellectual and requires the integration of specific and idiosyncratic skills, the task of dividing the job and assigning it to the most appropriate individuals becomes arduous, a “supervisory problem” (Hogdson, 1999) emerges and traditional hierarchical control may result increasingly ineffective. Not only specificity of know how makes it awkward to monitor the execution of tasks, more importantly, top-down integration of skills may be difficult because ‘the nominal supervisors will not know the best way of doing the job – or even the precise purpose of the specialist job itself – and the worker will know better’ (Hogdson,1999). We, therefore, expect that the organization of the economic activity of specialists should be, at least partially, self-organized. The aim of this thesis is to bridge studies from computer science and in particular from Peer-to-Peer Networks (P2P) to organization theories. We think that the P2P paradigm well fits with organization problems related to all those situation in which a central authority is not possible. We believe that P2P Networks show a number of characteristics similar to firms working in a knowledge-based economy and hence that the methodology used for studying P2P Networks can be applied to organization studies. Three are the main characteristics we think P2P have in common with firms involved in knowledge economy: - Decentralization: in a pure P2P system every peer is an equal participant, there is no central authority governing the actions of the single peers; - Cost of ownership: P2P computing implies shared ownership reducing the cost of owing the systems and the content, and the cost of maintaining them; - Self-Organization: it refers to the process in a system leading to the emergence of global order within the system without the presence of another system dictating this order. These characteristics are present also in the kind of firm that we try to address and that’ why we have shifted the techniques we adopted for studies in computer science (Marcozzi et al., 2005; Hales et al., 2007 [39]) to management science.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Un acercamiento del "Titulado Arquitecto" en España, al ejercicio profesional del project manager en Europa

Programa de doctorado: Restauración y rehabilitación arquitetónica. Investigación, tendencias e innovaciones. La fecha de publicación es la fecha de lectura

Workout Manager Android App

L’obiettivo di questa tesi è quello di progettare e sviluppare un'applicazione Android inerente alle palestre. In particolare l'utente finale dovrà essere in grado di poter scaricare i propri allenamenti da un server SQL remoto e visualizzarli con un layout grafico che ne ricordi la struttura di un allenamento cartaceo (con l’utilizzo quindi di schede, esercizi, serie, ripetizioni,...). Attraverso una procedura guidata, l’applicazione dovrà seguire l’utente durante il suo allenamento e registrarne i progressi. Questi ultimi dovranno essere memorizzati permanentemente e visualizzati attraverso dei grafici.

OpenMP task scheduling strategies to mitigate hardware variability in tightly-coupled shared memory clusters

In questa tesi sono stati apportati due importanti contributi nel campo degli acceleratori embedded many-core. Abbiamo implementato un runtime OpenMP ottimizzato per la gestione del tasking model per sistemi a processori strettamente accoppiati in cluster e poi interconnessi attraverso una network on chip. Ci siamo focalizzati sulla loro scalabilità e sul supporto di task di granularità fine, come è tipico nelle applicazioni embedded. Il secondo contributo di questa tesi è stata proporre una estensione del runtime di OpenMP che cerca di prevedere la manifestazione di errori dati da fenomeni di variability tramite una schedulazione efficiente del carico di lavoro.

Ricostruzione delle sorgenti corticali da segnali EEG in task di discriminazione visiva: Effetto di training multisensoriale

I neuroni in alcune regioni del nostro cervello mostrano una risposta a stimoli multisensoriali (ad es. audio-visivi) temporalmente e spazialmente coincidenti maggiore della risposta agli stessi stimoli presi singolarmente (integrazione multisensoriale). Questa abilità può essere sfruttata per compensare deficit unisensoriali, attraverso training multisensoriali che promuovano il rafforzamento sinaptico all’interno di circuiti comprendenti le regioni multisensoriali stimolate. Obiettivo della presente tesi è stato quello di studiare quali strutture e circuiti possono essere stimolate e rinforzate da un training multisensoriale audio-visivo. A tale scopo, sono stati analizzati segnali elettroencefalografici (EEG) registrati durante due diversi task di discriminazione visiva (discriminazione della direzione di movimento e discriminazione di orientazione di una griglia) eseguiti prima e dopo un training audio-visivo con stimoli temporalmente e spazialmente coincidenti, per i soggetti sperimentali, o spazialmente disparati, per i soggetti di controllo. Dai segnali EEG di ogni soggetto è stato ricavato il potenziale evento correlato (ERP) sullo scalpo, di cui si è analizzata la componente N100 (picco in 140÷180 ms post stimolo) verificandone variazioni pre/post training mediante test statistici. Inoltre, è stata ricostruita l’attivazione delle sorgenti corticali in 6239 voxel (suddivisi tra le 84 ROI coincidenti con le Aree di Brodmann) con l’ausilio del software sLORETA. Differenti attivazioni delle ROI pre/post training in 140÷180 ms sono state evidenziate mediante test statistici. I risultati suggeriscono che il training multisensoriale abbia rinforzato i collegamenti sinaptici tra il Collicolo Superiore e il Lobulo Parietale Inferiore (nell’area Area di Brodmann 7), una regione con funzioni visuo-motorie e di attenzione spaziale.

Human Presence Detection: analisi comparativa e caso di studio in Home Manager

La possibilità di monitorare l’attività degli utenti in un sistema domotico, sia considerando le azioni effettuate direttamente sul sistema che le informazioni ricavabili da strumenti esterni come la loro posizione GPS, è un fattore importante per anticipare i bisogni e comprendere le preferenze degli utenti stessi, rendendo sempre più intelligenti ed autonomi i sistemi domotici. Mentre i sistemi attualmente disponibili non includono o non sfruttano appieno queste potenzialità, l'obiettivo di sistemi prototipali sviluppati per fini di ricerca, quali ad esempio Home Manager, è invece quello di utilizzare le informazioni ricavabili dai dispositivi e dal loro utilizzo per abilitare ragionamenti e politiche di ordine superiore. Gli obiettivi di questo lavoro sono: - Classificare ed elencare i diversi sensori disponibili al fine di presentare lo stato attuale della ricerca nel campo dello Human Sensing, ovvero del rilevamento di persone in un ambiente. - Giustificare la scelta della telecamera come sensore per il rilevamento di persone in un ambiente domestico, riportando metodi per l’analisi video in grado di interpretare i fotogrammi e rilevare eventuali figure in movimento al loro interno. - Presentare un’architettura generica per integrare dei sensori in un sistema di sorveglianza, implementando tale architettura ed alcuni algoritmi per l’analisi video all’interno di Home Manager con l’aiuto della libreria OpenCV .