46 research outputs found

    A Mechanism for Participatory Budgeting With Funding Constraints and Project Interactions

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    Participatory budgeting (PB) has been widely adopted and has attracted significant research efforts; however, there is a lack of mechanisms for PB which elicit project interactions, such as substitution and complementarity, from voters. Also, the outcomes of PB in practice are subject to various minimum/maximum funding constraints on 'types' of projects. There is an insufficient understanding of how these funding constraints affect PB's strategic and computational complexities. We propose a novel preference elicitation scheme for PB which allows voters to express how their utilities from projects within 'groups' interact. We consider preference aggregation done under minimum and maximum funding constraints on 'types' of projects, where a project can have multiple type labels as long as this classification can be defined by a 1-laminar structure (henceforth called 1-laminar funding constraints). Overall, we extend the Knapsack voting model of Goel et al. in two ways - enriching the preference elicitation scheme to include project interactions and generalizing the preference aggregation scheme to include 1-laminar funding constraints. We show that the strategyproofness results of Goel et al. for Knapsack voting continue to hold under 1-laminar funding constraints. Although project interactions often break the strategyproofness, we study a special case of vote profiles where truthful voting is a Nash equilibrium under substitution project interactions. We then turn to the study of the computational complexity of preference aggregation. Social welfare maximization under project interactions is NP-hard. As a workaround for practical instances, we give a fixed parameter tractable (FPT) algorithm for social welfare maximization with respect to the maximum number of projects in a group

    Maximum repetition rate in a large cross-sectional sample of typically developing Dutch-speaking children

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    Item does not contain fulltextPurpose: The current study aims to provide normative data for the maximum repetition rate (MRR) development of Dutch-speaking children based on a large cross-sectional study using a standardised protocol.Method: A group of 1014 typically developing children aged 3;0 to 6;11 years performed the MRR task of the Computer Articulation Instrument (CAI). The number of syllables per second was calculated for mono-, bi-, and trisyllabic sequences (MRR-pa, MRR-ta, MRR-ka, MRR-pata, MRR-taka, MRR-pataka). A two-way mixed ANOVA was conducted to compare the effects of age and gender on MRR scores in different MRR sequences.Result: The data analysis showed that overall MRR scores were affected by age group, gender and MRR sequence. For all MRR sequences the MRR increased significantly with age. MRR-pa was the fastest sequence, followed by respectively MRR-ta, MRR-pata, MRR-taka, MRR-ka and MRR-pataka. Overall MRR scores were higher for boys than for girls, for all MRR sequences.Conclusion: This study presents normative data of MRR of Dutch-speaking children aged 3;0 to 6;11 years. These norms might be useful in clinical practice to differentiate children with speech sound disorders from typically developing children. More research on this topic is necessary. It is also suggested to collect normative data for other individual languages, using the same protocol

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    An investigation into the influence of changes in static single leg standing posture on hip and thigh muscle activation in a pain free population

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    The influence that trunk and pelvis posture has on lower limb muscle activation in single leg stance is unknown. Normative surface EMG data on 8 hip and thigh muscles was collected in a single group containing 22 young asymptomatic males, monitored using the VICON motion analysis system. Paired clinically relevant test postures demonstrated changes in trunk position in the sagittal plane and pelvis position in the frontal plane had the greatest effect on muscle activation

    Resource portfolio management: bundling process

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    Managers within firms seek to align their portfolio of capabilities to best respond to their competitive environment. Processes used by firms to acquire resources, bundle those resources into capabilities, and then leverage those capabilities to obtain competitive advantage are of interest to scholars and practitioners alike. In this study I explore the bundling process and how firms create advantage from its use in different environmental conditions. Using policy capturing survey techniques analyzed with hierarchial linear modeling while manipulating environmental contexts of dynamism, munificence, and punctuated threats, I observe how firms vary their resource bundling processes to create advantage and improve performance. For each combination of environmental condition, hypotheses are presented and tested with respect to firm response. Due to a lack of differentiation between the three bundling sub-processes, several proposed hypotheses were not testable and thus, unsupported. Current theory details three bundling sub-processes; however, I demonstrate evidence that fewer or greater numbers of sub-processes may be required to capture the bundling process. Other evidence suggests that firms do alter bundling sub-processes in response to changing conditions of munificence, but fail to do so during punctuated events

    Temperature selectivity in Icelandic and Northeast-Arctic cod

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    Increasing water temperatures are predicted worldwide, with high amplitudes in the Arctic and sub-Arctic regions exceeding predictions for other regions. An understanding how Atlantic cod (Gadus morhua) reacted to changing environmental conditions in the past is essential for predicting re-distribution under climate change. In this thesis, I examined the temperature selectivity of Icelandic and Northeast-Arctic (NEA) cod in response to fluctuating temperature conditions and changes in the stock dynamics. Multiple century-long data time series and linear mixed-effect models were used to investigate the effect of fluctuating water temperatures and changes in stock dynamics on the temperature selectivity of cod, using stable oxygen isotope composition in otoliths as a proxy of ambient temperature. Icelandic cod δ18Ootolith values were significantly correlated with water temperature time series, indicating that they were exposed to fluctuating water temperatures during the past 100 years and did not move appreciably in response to increasing ocean temperatures. Furthermore, abundance changes have affected the temperature selectivity of Icelandic and NEA cod as a density-driven response; however, the response of the two populations was different. Increasing abundance resulted in increasing intraspecific competition and decreasing individual fitness levels, which expanded the distribution area of both cod stocks into previously unfavorable thermal habitats. To validate the accuracy of high-resolution otolith isotopic records as a temperature proxy, stable oxygen isotope records of wild, free-swimming Icelandic cod tracked with data-storage tags (DST) were analyzed with high-resolution secondary-ion mass spectrometry (SIMS). Results demonstrated that the method is well-suited as a relative index of temperature exposure, although maximum ambient temperatures were often overestimated. Using century-long data time series and oxygen isotope chronologies of cod otoliths have markedly improved our understanding of the Icelandic and NEA cod temperature selectivity. Thus, this thesis provides important information that helps to predict how two commercially important cod stocks might respond to global warming and fluctuating stock dynamics.Hækkandi sjávarhita er nú spáð um allan heim og á norðurslóðum er auk þess búist við meiri hitasveiflum en annars staðar. Betri skilningur á viðbrögðum Atlantshafsþorsks (Gadus morhua) við breyttum umhverfisaðstæðum á liðnum áratugum opnar möguleika á að spá fyrir um mögulega breytta dreifingu þorskins á tímum loftslagsbreytinga. Hér er greint frá rannsóknum þar sem leitast var við að skýra hvort og hvernig þorskar af íslenska stofninum og norðaustur heimsskautsstofninum bregðast við breytingum á hitastigi sjávar og stofnstærð. Gagnasett með mælingum 100 ár aftur í tíman ásamt línulegum líkönum með blönduðum áhrifaþáttum voru notuð til þess að meta hvort stofnstærð og sjávarhiti hafi áhrif á hitastigsval einstaklings, sem hægt er að meta með stöðugum ísótópum súrefnis. Marktæk fylgni fannst milli δ18O gilda íslenska þorskins og sjávarhita yfir tíma, sem bendir til þess að þorskurinn hafi verið útsettur fyrir sveiflukenndum sjávarhita síðastliðin 100 ár, en virðist ekki hafa hörfað að marki frá hækkandi hitastigi. Aukinn stofnþéttleiki á svæðum með kjörhita getur leitt til aukinnar samkeppni og minnkandi hæfni einstaklinga en slíkt getur valdið því að sumir einstaklingar leiti á önnur óhagstæðari hitastigs mið. Gögn okkar benda til þess að slíkt hafi átt sér stað í báðum stofnunum, þrátt fyrir ólík viðbrögð. Til frekari staðfestingar á notagildi kvarna til að varpa ljósi á hitastig sjávar á mismunandi stigum lífsferils voru ísótópar súrefnis mældir í kvörnum þorska sem á voru fest gagnasöfnunarmerki (DST-tag) sem mæla hitastig og dýpi. Í þessu tilfelli voru ísótópar í vaxtarhringjum kvarna mældir með meiri upplausn í nákvæmum massalitrófsgreini (SIMS) sem staðfesti að þessar aðferðir eru vel til þess fallnar að meta hitastig sjávar sem fiskar hafa dvalið í aftur í tíman, þó svo að hámarks hiti sé gjarnan ofmetin. Rannsóknirnar sýna hvernig nota má mælingar á ísótópum súrefnis í árhringjum kvarna ásamt sambærilegum langtímagögnum um hitastig sjávar og stofnsveiflur til þess að greina hvernig þessir þættir hafa áhrif á hitastigsval þorsks. Slíkar upplýsingar munu reynast gagnlegar við að spá fyrir um hvernig þorskstofnar bregðast við hnattrænni hlýnun og stofnsveiflum
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