1,366 research outputs found

    Language Design for Reactive Systems: On Modal Models, Time, and Object Orientation in Lingua Franca and SCCharts

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    Reactive systems play a crucial role in the embedded domain. They continuously interact with their environment, handle concurrent operations, and are commonly expected to provide deterministic behavior to enable application in safety-critical systems. In this context, language design is a key aspect, since carefully tailored language constructs can aid in addressing the challenges faced in this domain, as illustrated by the various concurrency models that prevent the known pitfalls of regular threads. Today, many languages exist in this domain and often provide unique characteristics that make them specifically fit for certain use cases. This thesis evolves around two distinctive languages: the actor-oriented polyglot coordination language Lingua Franca and the synchronous statecharts dialect SCCharts. While they take different approaches in providing reactive modeling capabilities, they share clear similarities in their semantics and complement each other in design principles. This thesis analyzes and compares key design aspects in the context of these two languages. For three particularly relevant concepts, it provides and evaluates lean and seamless language extensions that are carefully aligned with the fundamental principles of the underlying language. Specifically, Lingua Franca is extended toward coordinating modal behavior, while SCCharts receives a timed automaton notation with an efficient execution model using dynamic ticks and an extension toward the object-oriented modeling paradigm

    Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data

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    Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp

    Exploring the impact of food composition and eating context on food choice, energy intake and body mass index

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    Research suggests that traditional behaviour-based weight loss approaches requiring individuals to change their dietary habits or physical activity results in only modest weight loss which often isn’t maintained. Given the need to improve population health, the broader food environment (e.g., food price or composition) and the more immediate eating context have been identified as possible intervention targets.For product reformulation to be a successful public health strategy, consumers are required to be ‘insensitive’ to changes to the reformulated product. This raises a more general question regarding whether humans are sensitive to food composition and whether this influences food choice and energy intake. The studies presented in Part A suggest that people are sensitive to both the energy content and macronutrient composition of food. Specifically, the results presented in chapters two to four indicate a non-linear pattern in meal caloric intake in response to meal energy density (kcal/g), and this pattern was captured in a theoretical two-component model of meal size (g, chapter five). The remaining two chapters in Part A (chapters six and seven) explore human sensitivity to food macronutrient composition. Chapter six describes the development of a new paradigm and task to assess protein discrimination by humans. Chapter seven focuses on the remaining two macronutrients, fat and carbohydrate, and demonstrates that, alongside being more liked, foods containing a combination of fat and carbohydrate are selected in larger portions than foods high in either fat or carbohydrate.The effect of eating contexts (e.g., social or distracted eating) on acute energy intake is well-researched, but their chronic impact on energy balance is unclear. The results of chapter nine (Part B) indicated that more frequently watching TV was associated with a higher body mass index (BMI) in young adults. More generally, the work identified eating contexts as potential targets for public health messaging which could effect changes in BMI on a population level. Together, the work presented in this thesis highlights new complexity in human dietary behaviour which presents both challenges and opportunities for successful food reformulation as a public health strategy, and it also demonstrates that the context in which we eat our meals could be leveraged to improve population-level health

    Binding processes in episodic memory: Measurement, structure, and moderators

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    Episodic memory enables people to remember personally experienced events. While these events consist of different elements, people are able to form coherent memory representations. This requires that an event’s constituent elements are bound together in memory. Despite the importance of these binding processes for episodic memory, they are still only poorly understood and our abilities to measure them are limited. In this thesis, comprising three articles, I provide a new approach for measuring binding effects and use this measure to probe properties of binding processes in episodic memory. In the first article, I introduce the new measurement approach and evaluate its suitability for measuring binding effects in comparison to previous approaches. I show that the approach has good measurement properties and is better suited for measuring binding effects than previous approaches. In the second article, I examine the structure in which event elements are bound together and whether animacy influences binding processes. I show that different binding structures are possible, such as an integrated binding structure, in which event elements are bound into a unitary representation, and a hierarchical binding structure, in which event elements are preferentially bound to particular types of elements. These may lie on a continuum of memory representations with varying degrees of integration. I further show that the presence of an animate element in an event facilitates binding, enabling more coherent memory representations with a higher degree of integration. In addition, awareness regarding commonalities of types of event elements across events may facilitate binding. In the third article, I examine whether agency influences binding processes. I show that the presence of an agentic element in an event may facilitate binding, but evidence was not conclusive and effects may have been concealed due to low memory performance. Agency may thus underlie the previously found facilitating effect of animacy on binding, since animate elements may exert their influence by providing a potential agent in an event. One aim of my thesis is to provide a new tool for investigating binding processes in episodic memory. An additional aim is to extend our current understanding of binding structures that link together the elements of an event, as well as the factors that moderate binding processes. In doing so, I hope to advance our understanding of binding processes and enable and inform future exploration, as well as theory development and refinement, of this fundamental property underlying episodic memory

    Fictocritical Cyberfeminism: A Paralogical Model for Post-Internet Communication

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    This dissertation positions the understudied and experimental writing practice of fictocriticism as an analog for the convergent and indeterminate nature of “post-Internet” communication as well a cyberfeminist technology for interfering and in-tervening in metanarratives of technoscience and technocapitalism that structure contemporary media. Significant theoretical valences are established between twen-tieth century literary works of fictocriticism and the hybrid and ephemeral modes of writing endemic to emergent, twenty-first century forms of networked communica-tion such as social media. Through a critical theoretical understanding of paralogy, or that countercultural logic of deploying language outside legitimate discourses, in-volving various tactics of multivocity, mimesis and metagraphy, fictocriticism is ex-plored as a self-referencing linguistic machine which exists intentionally to occupy those liminal territories “somewhere in among/between criticism, autobiography and fiction” (Hunter qtd. in Kerr 1996). Additionally, as a writing practice that orig-inated in Canada and yet remains marginal to national and international literary scholarship, this dissertation elevates the origins and ongoing relevance of fictocriti-cism by mapping its shared aims and concerns onto proximal discourses of post-structuralism, cyberfeminism, network ecology, media art, the avant-garde, glitch feminism, and radical self-authorship in online environments. Theorized in such a matrix, I argue that fictocriticism represents a capacious framework for writing and reading media that embodies the self-reflexive politics of second-order cybernetic theory while disrupting the rhetoric of technoscientific and neoliberal economic forc-es with speech acts of calculated incoherence. Additionally, through the inclusion of my own fictocritical writing as works of research-creation that interpolate the more traditional chapters and subchapters, I theorize and demonstrate praxis of this dis-tinctively indeterminate form of criticism to empirically and meaningfully juxtapose different modes of knowing and speaking about entangled matters of language, bod-ies, and technologies. In its conclusion, this dissertation contends that the “creative paranoia” engendered by fictocritical cyberfeminism in both print and digital media environments offers a pathway towards a more paralogical media literacy that can transform the terms and expectations of our future media ecology

    Recombinant spidroins from infinite circRNA translation

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    Spidroins are a diverse family of peptides and the main components of spider silk. They can be used to produce sustainable, lightweight and durable materials for a large variety of medical and engineering applications. Spiders’ territorial behaviour and cannibalism precludes farming them for silk. Recombinant protein synthesis is the most promising way of producing these peptides. However, many approaches have been unsuccessful in obtaining large titres of recombinant spidroins or ones of sufficient molecular weight. The work described here is focused on expressing high molecular weight spidroins from short circular RNA molecules. Mammalian host cells were transfected with designed circular-RNA-producing plasmid vectors. A backsplicing approach was implemented to successfully circularise RNA in a variety of mammalian cell types. This approach could not express any recombinant spidroins based on a variety of qualitative protein assays. Further experiments investigated the reasons behind this. Additionally, due to the diversity of spidroins in a large number of spider lineages, there are potentially many spidroin sequences left to be discovered. A bioinformatic pipeline was developed that accepts transcriptome datasets from RNA sequencing and uses tandem repeat detection and profile HMM annotation to identify novel sequences. This pipeline was specifically designed for the identification of repeat domains in expressed sequences. 21 transcriptomes from 17 different species, encompassing a wide selection of basal and derived spider lineages, were investigated using this pipeline. Six previously undescribed spidroin sequences were discovered. This pipeline was additionally tested in the context of the suckerin protein family. These proteins have recently been investigated for their potential properties in medicine and engineering including adhesion in wet environments. The computational pipeline was able to double the number of suckerins known to date. Further phylogenetic analysis was implemented to expand on the knowledge of suckerins. This pipeline enables the identification of transcripts that may have been overlooked by more mainstream analysis methods such as pairwise homology searches. The spidroins and suckerins discovered by this pipeline may contribute to the large repertoire of potentially useful properties characteristic of this diverse peptide family

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Understanding Agreement and Disagreement in Listeners’ Perceived Emotion in Live Music Performance

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    Emotion perception of music is subjective and time dependent. Most computational music emotion recognition (MER) systems overlook time- and listener-dependent factors by averaging emotion judgments across listeners. In this work, we investigate the influence of music, setting (live vs lab vs online), and individual factors on music emotion perception over time. In an initial study, we explore changes in perceived music emotions among audience members during live classical music performances. Fifteen audience members used a mobile application to annotate time-varying emotion judgments based on the valence-arousal model. Inter-rater reliability analyses indicate that consistency in emotion judgments varies significantly across rehearsal segments, with systematic disagreements in certain segments. In a follow-up study, we examine listeners' reasons for their ratings in segments with high and low agreement. We relate these reasons to acoustic features and individual differences. Twenty-one listeners annotated perceived emotions while watching a recorded video of the live performance. They then reflected on their judgments and provided explanations retrospectively. Disagreements were attributed to listeners attending to different musical features or being uncertain about the expressed emotions. Emotion judgments were significantly associated with personality traits, gender, cultural background, and music preference. Thematic analysis of explanations revealed cognitive processes underlying music emotion perception, highlighting attributes less frequently discussed in MER studies, such as instrumentation, arrangement, musical structure, and multimodal factors related to performer expression. Exploratory models incorporating these semantic features and individual factors were developed to predict perceived music emotion over time. Regression analyses confirmed the significance of listener-informed semantic features as independent variables, with individual factors acting as moderators between loudness, pitch range, and arousal. In our final study, we analyzed the effects of individual differences on music emotion perception among 128 participants with diverse backgrounds. Participants annotated perceived emotions for 51 piano performances of different compositions from the Western canon, spanning various era. Linear mixed effects models revealed significant variations in valence and arousal ratings, as well as the frequency of emotion ratings, with regard to several individual factors: music sophistication, music preferences, personality traits, and mood states. Additionally, participants' ratings of arousal, valence, and emotional agreement were significantly associated to the historical time periods of the examined clips. This research highlights the complexity of music emotion perception, revealing it to be a dynamic, individual and context-dependent process. It paves the way for the development of more individually nuanced, time-based models in music psychology, opening up new avenues for personalised music emotion recognition and recommendation, music emotion-driven generation and therapeutic applications

    Modulation of motor vigour by expectation of reward probability trial-by-trial is preserved in healthy ageing and Parkinson's disease patients

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    Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigour dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a one-armed bandit decision-making paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger (HYA, 37 [13 males]) and older adults (HOA, 37 [20 males]), and medicated Parkinson's Disease patients (PD, 20 [13 males]). We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo-commensurate with movement time-on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA and PD, despite HOA and PD being slower than HYA. In Study 2 (HYA, 39 [10 males]), we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigour effects. Last, Study 3 (HYA, 33 [6 males]) revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial.Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigour to inferences about the action-reward mapping in ageing and medicated PD.SIGNIFICANCE STATEMENT:Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigour trial-by-trial in healthy younger and older adults, and in Parkinson's Disease patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigour. This positive bias was not compromised by age or Parkinson's disease
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