678 research outputs found
Exploring the effects of robotic design on learning and neural control
The ongoing deep learning revolution has allowed computers to outclass humans
in various games and perceive features imperceptible to humans during
classification tasks. Current machine learning techniques have clearly
distinguished themselves in specialized tasks. However, we have yet to see
robots capable of performing multiple tasks at an expert level. Most work in
this field is focused on the development of more sophisticated learning
algorithms for a robot's controller given a largely static and presupposed
robotic design. By focusing on the development of robotic bodies, rather than
neural controllers, I have discovered that robots can be designed such that
they overcome many of the current pitfalls encountered by neural controllers in
multitask settings. Through this discovery, I also present novel metrics to
explicitly measure the learning ability of a robotic design and its resistance
to common problems such as catastrophic interference.
Traditionally, the physical robot design requires human engineers to plan
every aspect of the system, which is expensive and often relies on human
intuition. In contrast, within the field of evolutionary robotics, evolutionary
algorithms are used to automatically create optimized designs, however, such
designs are often still limited in their ability to perform in a multitask
setting. The metrics created and presented here give a novel path to automated
design that allow evolved robots to synergize with their controller to improve
the computational efficiency of their learning while overcoming catastrophic
interference.
Overall, this dissertation intimates the ability to automatically design
robots that are more general purpose than current robots and that can perform
various tasks while requiring less computation.Comment: arXiv admin note: text overlap with arXiv:2008.0639
Evaluating Architectural Safeguards for Uncertain AI Black-Box Components
Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability
Reshaping the Museum of Zoology in Rome by Visual Storytelling and Interactive Iconography
This article summarizes the concept of a new immersive and interactive setting for the Zoology Museum in Rome, Italy. The concept, co-designed with all the museum’s curators, is aimed at enhancing the experiential involvement of the visitors by visual storytelling and interactive iconography. Thanks to immersive and interactive technologies designed by Centro Studi Logos, developed by Logosnet and known as e-REALâ and MirrorMeä, zoological findings and memoirs come to life and interact directly with the visitors in order to deepen their understanding, visualize stories and live experiences, and interact with the founder of the Museum (Mr. Arrigoni degli Oddi) who is now a virtualized avatar, or digital human, able to talk with the visitors. All the interactions are powered through simple hand gestures and, in a few cases, vocal inputs that transform into recognized commands from multimedia systems
Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals
General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/
Surface Reactions of Biomass Derived Oxygenates on Lewis Acidic Metal Oxides
Lignocellulosic biomass is currently the only source of organic carbon making it a sustainable source for production of liquid hydrocarbon fuels. One main challenge for valorization of biomass is reducing the oxygen content of the starting feedstock and producing high value chemicals. Using heterogeneous catalysts for conversion of biomass feedstock to commodity chemicals is one strategy for the valorization process. Specifically, using Lewis acidic metal oxides for this upgrading process has shown promise due to its ability to catalyze relevant reactions such as isomerization and (retro-) aldol condensation. This work seeks to elucidate the surface interactions of biomass derived oxygenates with solid Lewis acid sites. This is done using in-situ spectroscopic techniques such as Fourier transformed infrared, nuclear magnetic resonance and ultra-violet spectroscopies. These techniques were applied for studying the following reactions: (i) aldol condensation of ethanol and acetaldehyde over reduced molybdenum oxide; (ii) aldol condensation of acetaldehyde over supported molybdenum oxides; (iii) dehydration and retro-aldol condensation of C4 polyoxygenates using various Lewis acidic metal oxides and (iv) ring opening and esterification of erythrose using various Lewis acidic metal oxides. Surface properties such as Lewis and Brønsted acid site and reducibility of metal center are essential to rationalizing the reaction pathway of the above reactions. The aforementioned studies provide fundamental knowledge regarding how different oxygenates can interact with solid Lewis acid sites.Ph.D
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Exploring the socioeconomic and environmental factors influencing smallholder macadamia production and productivity in Malawi.
Macadamia (Macadamia integrifolia Maiden & Betche) is a highly valued crop in Malawi. The crop is a vital source of food security and ecosystem services, and its high-export cash value makes it a key contributor to the country's economy. Malawi ranks seventh in global macadamia production, comprising two subsectors: smallholders and commercial estates. However, significant yield gaps have been reported between smallholder and commercial estate producers. While commercial estates achieve higher average annual tree yields (30 kg), smallholder yields remain consistently low, averaging at or below 10 kg tree-1 year-1. Improving macadamia productivity among smallholders can help reduce poverty, improve household food security, and promote economic growth in Malawi.
Despite the significant contributions of smallholders in the Malawian macadamia subsector, research on the factors influencing the crop's productivity has primarily focused on commercial estate production. To address this knowledge gap, this Ph.D thesis focuses on smallholder macadamia production in Malawi. The thesis examines the socioeconomic characteristics of smallholder macadamia farmers, including demographics, cultivar preferences, and production constraints. Secondly, it evaluates the climatic factors influencing smallholder macadamia production and predicts the current and future suitable geographical areas for the crop. Lastly, it assesses the soil fertility status of smallholder macadamia farms in relation to macadamia production requirements.
Results of this study reveal that the majority (62%) of macadamia smallholders are over 50 years of age and consider farming their main occupation. However, this poses significant risks to the macadamia subsector, as older farmers are risk-averse and less innovative, hindering their willingness to adopt new agricultural technologies and ability to learn. Regarding cultivar preferences, the study finds that smallholder macadamia farmers prefer high-yielding cultivars with superior nut qualities, such as large and heavy nuts, and extended flowering periods. The most preferred macadamia cultivars in descending order are Hawaiian Agricultural Experimental Station (HAES) 660, 800, 816, and 246, which are the "core" of established cultivars in Malawi. The study identifies insect pests, diseases, market availability, strong winds, and a lack of agricultural extension services as the most significant challenges affecting smallholder macadamia farmers.
The study's suitability analysis reveals that the ensemble model has an excellent fit and high performance in predicting the current agro-climatically suitable areas for macadamia production (AUC = 0.90). The findings show that precipitation related variables (60.2%) are more important in determining the suitable areas for growing macadamia than temperature related variables (39.8%). The model results show that 57% (53,925 km2) of Malawi is currently suitable for macadamia cultivation, with the central region having the highest suitability (25.8%, 24,327 km2) and the southern region the lowest (10.7%, 10,257 km2). Optimal suitability (26%, 24,565 km2) is observed in the highland areas with elevations ranging from 1000–1400 metres above sea level (m.a.s.l.). Under the intermediate emission scenario (RCP 4.5) and the pessimistic scenario (RCP 8.5), the impact models predict net losses of 18% (17,015 km2) and 21.6% (20,414 km2), respectively, in the extent of suitable areas for macadamia in the 2050s.
The results of the soil fertility analysis indicate suboptimal fertility among the sampled macadamia farms. The majority of the soils are strongly acidic and deficient in essential nutrients required for the healthy growth of macadamia trees. Moreover, the average cation exchange capacity (1.67 cmol (+) kg-1) and the soil organic matter content (≤ 1%) are below the minimum optimal levels required for macadamia trees. These findings indicate that soil fertility is one of the primary limiting factors to the crop's productivity, even in areas with suitable climatic conditions. Therefore, addressing the soil fertility issues is crucial to improving the land suitability of the smallholder farms for macadamia, which can lead to optimal yields.
This study extends the frontiers of knowledge concerning the macadamia subsector in Malawi by providing insights into the smallholder macadamia farming systems, including demographics, cultivar preferences, and production constraints. It also provides novel empirical evidence on the climate factors that influence the suitability of rainfed macadamia cultivation and identifies current and future suitable growing areas in the country. Additionally, the study addresses the research gap on the soil fertility status of Malawian smallholder macadamia farms. Therefore, the findings of this research have practical implications for various areas such as macadamia cultivar introductions and breeding, land use planning, soil fertility management, and policy formulation for agricultural extension services, inputs, and marketing of the crop
How Reinforcement Learning can improve Video Games Development: Dreamer and P2E Algorithms in the SheepRL Framework
Artificial Intelligence (AI) in video games is along-standing research area. It studies how to use AI technologies to achieve human-level performance when playing games. For years now, ReinforcementLearning (RL) algorithms have outperformed the best human players in most video games. For this reason, it is interesting to investigate whether RL can still be used in the video game industry or whether the relationship between RL and the video game industry should remain purely academic.
This work focuses on two primary objectives within the video game industry: (i) Testing and Debugging: how RL can be exploited in order to uncover latent bugs, assess game difficulty, and refine the design of the video game. (ii) Non-Playable Characters (NPC) Creation and Generalization: is RL the best strategy to efficiently create NPCs or the RL algorithms have become too advanced?
This thesis explores the feasibility of using the state-of-the-art Dreamer algorithm in automated testing and NPCs creation for video games; in addition, it proposes SheepRL a scalable open source framework for running experiments in a distributed manner
Hand interaction designs in mixed and augmented reality head mounted display: a scoping review and classification
Mixed reality has made its first step towards democratization in 2017 with the launch of a first generation of commercial devices. As a new medium, one of the challenges is to develop interactions using its endowed spatial awareness and body tracking. More specifically, at the crossroad between artificial intelligence and human-computer interaction, the goal is to go beyond the Window, Icon, Menu, Pointer (WIMP) paradigm humans are mainly using on desktop computer. Hand interactions either as a standalone modality or as a component of a multimodal modality are one of the most popular and supported techniques across mixed reality prototypes and commercial devices. In this context, this paper presents scoping literature review of hand interactions in mixed reality. The goal of this review is to identify the recent findings on hand interactions about their design and the place of artificial intelligence in their development and behavior. This review resulted in the highlight of the main interaction techniques and their technical requirements between 2017 and 2022 as well as the design of the Metaphor-behavior taxonomy to classify those interactions
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