3,962 research outputs found

    Computational techniques to interpret the neural code underlying complex cognitive processes

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    Advances in large-scale neural recording technology have significantly improved the capacity to further elucidate the neural code underlying complex cognitive processes. This thesis aimed to investigate two research questions in rodent models. First, what is the role of the hippocampus in memory and specifically what is the underlying neural code that contributes to spatial memory and navigational decision-making. Second, how is social cognition represented in the medial prefrontal cortex at the level of individual neurons. To start, the thesis begins by investigating memory and social cognition in the context of healthy and diseased states that use non-invasive methods (i.e. fMRI and animal behavioural studies). The main body of the thesis then shifts to developing our fundamental understanding of the neural mechanisms underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging and behaviour preprocessing computational pipelines were developed and optimised for use in social interaction and spatial navigation experimental analysis. In parallel, a review was conducted on methods for multivariate/neural population analysis. A comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise and behavioural complexity. Furthermore, the review visualises how NML can be applied to disease states in the brain and introduces the secondary analyses that can be used to enhance or characterise a neural manifold. Lastly, the preprocessing and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored how neural firing in the medial Prefrontal cortex changed as a function of the social dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble of behavioural-tuned neurons that fire preferentially during specific behaviours such as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s behaviour. Furthermore, in dominant animals, the neural population exhibited greater average firing than that of subordinate animals. Next, to investigate spatial memory, a spatial recency task was used, where rats learnt to navigate towards one of three reward locations and then recall the rewarded location of the session. During the task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active in the decision period before the animal navigates to the rewarded location. The result posits that prospective replay of neural sequences in the hippocampal CA1 region could provide a mechanism by which decision-making is supported

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation

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    Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developer’s profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts. However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of components’ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifier’s F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation

    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

    Optimal speed trajectory and energy management control for connected and automated vehicles

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    Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle). The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles. In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation. The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces

    Production of novel taxane intermediates of anticancer drug Taxol using microbial consortia

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    Paclitaxel —commercially known as taxol— is one of the most widely used drugs to treat different types of cancer. Its production using microbial platforms has been challenging due to the complexity of the molecule and the difficulty of expressing functional enzymes. Expression of many heterologous enzymes in microorganisms often leads to an increased metabolic burden that usually causes growth and production decrease. Division of Labour can offer great advantages as the metabolic burden can be divided into two or more specialised organisms. In this work, the use of microbial consortia to produce oxygenated and acetylated paclitaxel precursors was examined. The aim of Chapter 3 was specialisation by engineering various GAL80 mutant strains which could produce taxanes without the need for galactose for induction, and to further expand the Taxol metabolic pathway by incorporating the enzyme Taxane-10-Hydroxylase (T10ßOH). Using glucose showed a decreased Taxadiene production of up to 50% in comparison to galactose, probably because MIG1 is believed to cause repression in the presence of this sugar. Sucrose and fructose yielded very poor taxadiene results around 90% lower than galactose to produce taxanes. Conversely, using raffinose yielded the best production for Taxa-4, 11-dien-5a-yl acetate (T5αAc) at 72 ± 15 mg/L which represented a 7-fold increase compared to previous literature data. Despite the good production results with raffinose and galactose, the T10ßOH product was not detected. The next section aimed to develop microbial consortia and an inoculum engineering strategy to enable the production of T10ß-ol. Different plasmids carrying the T5αOH, TAT and T10OH enzymes were assembled for expression in an engineered ethanologenic E. coli capable of growing on xylose. As result, the product of the T10ßOH enzyme was detected and quantified for the first time using the consortia. A production of 8 ± 0.30 mg/L of T10ßol was found when fermenting glucose with a yeast strain with GAL80 deletion which was only capable of producing taxadiene proving that the pathway was efficiently divided. Finally, as a second approach, two plasmids carrying TAT and T10ßOH enzymes were assembled to form a consortium integrated by two S. cerevisiae strains. The inoculation ratio of 3:1 (EJ2 10ß: EJ2 TAT) resulted in a production of 26.5 ±5 mg/L of Taxa-4, 11-dien-5α-acetoxy-10-ol (T10ßol), the highest ever reported for this compound. It is important to mention that this consortium was the most ambitious of all as it involved 2 different yeast strains plus 3 different bacteria strains, which to the best of our knowledge is one of the most successful and complex consortia including five microorganisms diving a synthetic pathway and achieving true synergy to divide the metabolic burden among the consortia. Following the detection of T10ßol, we aimed to test a novel enzyme candidate (T1ßOH) to produce the next paclitaxel intermediate taxadiene-5a-acetoxy-1a,10b-diol (T1ß-ol) using optimized co-cultures described in chapter 4. After adjusting inoculation ratios and other parameters based on the evidence obtained from previous experiments we managed to detect and quantify the new T1ß-ol compound with an estimated production of 45 ±3 mg/L. The strategies of inoculum engineering, microbial consortia and conversion towards constitutive expression have shown to be very suitable to expand and optimize the paclitaxel metabolic pathway. These strategies also proved effective for the detection and production of a new compound highlighting the potential for its application in other metabolic routes practically and rapidly

    Exploring topological phonons in different length scales: microtubules and acoustic metamaterials

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    The topological concepts of electronic states have been extended to phononic systems, leading to the prediction of topological phonons in a variety of materials. These phonons play a crucial role in determining material properties such as thermal conductivity, thermoelectricity, superconductivity, and specific heat. The objective of this dissertation is to investigate the role of topological phonons at different length scales. Firstly, the acoustic resonator properties of tubulin proteins, which form microtubules, will be explored The microtubule has been proposed as an analog of a topological phononic insulator due to its unique properties. One key characteristic of topological materials is the existence of edge modes within the energy gap. These edge modes allow energy to be transferred at specific frequencies along the edges of the material, while the bulk remains unaffected. In the case of microtubules, its ability to store vibrational energy at its edges and the sensitivity to changes in local bulk structure align with the properties of topological insulators. Furthermore, the appearance of edge modes in topological phononic insulators is determined by the local interactions of the bulk material. Even small changes in the local structure can shift the resonant frequency of the edge mode or completely extinguish it. Similarly, the ability of microtubules to shorten or overcome energy barriers is greatly affected by changes in their local bulk structure. This suggests a parallel between the impact of local bulk structure on both topological insulators and microtubules. This similarity has led to the proposal that microtubules could serve as an analog of topological phononic insulators, providing insights into their dynamics and potential applications in fields such as chemotherapy drug development and nanoscale materials. Secondly, the application of topological phonons in the realm of acoustic metamaterials will be examined. Acoustic waves have recently become a versatile platform for exploring and studying various topological phases, showcasing their universality and diverse manifestations. The unique properties of topological insulators and their surface states heavily rely on the dimension and symmetries of the material, making it possible to classify them using a periodic table of topological insulators. However, certain combinations of dimensions and symmetries can impede the achievement of topological insulation. It is of utmost importance to preserve symmetries in order to maintain the desired topological properties, which necessitates careful consideration of coupling methods. In the context of discrete acoustic resonant models, efficiently coupling resonators while simultaneously preserving symmetry poses a challenging question. In this part, a clever experimental approach is proposed and discussed to couple acoustic crystals. This modular platform not only supports the existence of topologically protected edge and interface states but also offers a convenient setup that can be easily assembled and disassembled. Furthermore, inspired by recent theoretical advancements that draw on techniques from the field of C*-algebras for identifying topological metals, the present study provides direct observations of topological phenomena in gapless acoustic crystals. Through these observations, a general experimental technique is realized and developed to demonstrate the topology of such systems. By employing the method of coupling acoustic crystals, the investigation unveils robust boundary-localized states in a topological acoustic metal and presents a reinterpretation of a composite operator as a new Hamiltonian. This reinterpretation enables the direct observation of a topological spectral flow and facilitates the measurement of topological invariants. Through these investigations, the aim of this dissertation is to deepen our understanding of the significance and potential applications of topological phonons in diverse systems

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
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