724 research outputs found

    Development of variable and robust brain wiring patterns in the fly visual system

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    Precise generation of synapse-specific neuronal connections are crucial for establishing a robust and functional brain. Neuronal wiring patterns emerge from proper spatiotemporal regulation of axon branching and synapse formation during development. Several neuropsychiatric and neurodevelopmental disorders exhibit defects in neuronal wiring owing to synapse loss and/or dys-regulated axon branching. Despite decades of research, how the two inter-dependent cellular processes: axon branching and synaptogenesis are coupled locally in the presynaptic arborizations is still unclear. In my doctoral work, I investigated the possible role of EGF receptor (EGFR) activity in coregulating axon branching and synapse formation in a spatiotemporally restricted fashion, locally in the medulla innervating Dorsal Cluster Neuron (M- DCN)/LC14 axon terminals. In this work I have explored how genetically encoded EGFR randomly recycles in the axon branch terminals, thus creating an asymmetric, non-deterministic distribution pattern. Asymmetric EGFR activity in the branches acts as a permissive signal for axon branch pruning. I observed that the M-DCN branches which stochastically becomes EGFR ‘+’ during development are synaptogenic, which means they can recruit synaptic machineries like Syd1 and Bruchpilot (Brp). My work showed that EGFR activity has a dual role in establishing proper M-DCN wiring; first in regulating primary branch consolidation possibly via actin regulation prior to synaptogenesis. Later in maintaining/protecting the levels of late Active Zone (AZ) protein Brp in the presynaptic branches by suppressing basal autophagy level during synaptogenesis. When M-DCNs lack optimal EGFR activity, the basal autophagy level increases resulting in loss of Brp marked synapses which is causal to increased exploratory branches and post-synaptic target loss. Lack of EGFR activity affects the M-DCN wiring pattern that makes adult flies more active and behave like obsessive compulsive in object fixation assay. In the second part of my doctoral work, I have asked how non-genetic factors like developmental temperature affects adult brain wiring. To test that, I increased or decreased rearing temperature which is known to inversely affect pupal developmental rate. We asked if all the noisy cellular processes of neuronal assembly: filopodial dynamics, axon branching, synapse formation and postsynaptic connections scale up or down accordingly. I observed that indeed all the cellular processes slow down at lower developmental temperature and vice versa, which changes the DCN wiring pattern accordingly. Interestingly, behavior of flies adapts to their developmental temperature, performing best at the temperature they have been raised at. This shows that optimal brain function is an adaptation of robust brain wiring patterns which are specified by noisy developmental processes. In conclusion, my doctoral work helps us better understand the developmental regulation of axon branching and synapse formation for establishing precise brain wiring pattern. We need all the cell intrinsic developmental processes to be highly regulated in space and time. It is infact a combinatorial effect of such stochastic processes and external factors that contribute to the final outcome, a functional and robust adult brain

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Current and Future Challenges in Knowledge Representation and Reasoning

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    Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

    Decision Heuristics in a Constraint-based Product Configurator

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    This paper presents an evaluation of decision heuristics of solvers of the Boolean satisfiability problem (SAT) in the context of constraint-based product configuration. In product configuration, variable assignments are searched in real-time, based on interactively formulated user requirements. Operating on user’s successive input poses new requirements, such as low-latency interactivity as well as deterministic and minimal implicit product changes. This work presents a performance evaluation of several heuristics from the SAT literature along with new variants that address the special real-time requirements of incremental product configuration. Our results show that the execution time on an industrial benchmark can be significantly improved with our new heuristic

    Offset-free model predictive control using Koopman-Wiener models

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    Abstract. This master’s thesis was built on the previously developed Koopman-Wiener nonlinear model predictive controller, and the goal of this thesis was to find a suitable strategy for rejecting steady-state offset, caused by plant model mismatch. This thesis also aimed to enable the controller to perform in applications where the full state is not measured and the available measurements are corrupted with noise. The work in this thesis considered multiple strategies for handling plant model mismatch, but disturbance rejection was selected as the main approach. It is proposed in this thesis that the disturbance model for disturbance rejection can be chosen by calculating empirical observability Gramian at a single initial point for every considered augmented model option and then picking the model which is interpreted as the most observable. The proposed observability analysis provides information about weak observability of the disturbance augmented model only at the single initial point. Nevertheless, it was argued in this thesis that the results can be assumed to represent the relevant operation region, and thus the method is applicable for choosing a disturbance model. As an alternative to compare against disturbance rejection, this thesis also investigated recursive least squares method that adapts the Koopman-Wiener model within the controller online. For state estimation, this thesis utilized unscented Kalman filter. This thesis demonstrated performance of the chosen methods with two nonlinear system case studies commonly studied in the literature: a simulated continuous stirred tank reactor and a simulated distillation column. This paper provides three main results. Firstly, the controller with disturbance rejection is successful in eliminating steady-state offset in a closed-loop system. Secondly, the controller is unable to reach satisfactory performance while using the recursive least squares method. Thirdly, the results from case studies support the chosen disturbance modeling approach, since the disturbance models chosen with the approach lead to improved or equal controller performance compared to using other disturbance models. Furthermore, the results support presenting a useful heuristic about how to perform disturbance modeling with Koopman-Wiener models by having the disturbances affect the slow dynamics of the model.Säätöpoikkeamasta vapaa malliprediktiivinen säädin käyttäen Koopman-Wiener malleja. Tiivistelmä. Tämä diplomityö perustui aiemmin kehitettyyn epälineaariseen Koopman-Wiener malliprediktiiviseen säätimeen. Diplomityön tavoitteena oli löytää sopiva strategia eliminoimaan tasapainotilan säätöpoikkeama, joka on seurausta tilanteesta, jossa säätimen käyttämä malli ei vastaa ohjattavaa prosessia. Työssä tavoiteltiin myös säätimen toiminnan mahdollistamista sovelluksissa, joissa prosessin jokaista tilamuuttujaa ei mitata, ja saatavilla olevissa mittauksissa on kohinaa. Diplomityössä harkittiin useita eri strategioita vastaamaan säätimen ja prosessin mallien yhteensopimattomuuteen, mutta häiriön torjunta valikoitui pääasialliseksi lähestymistavaksi. Diplomityössä ehdotetaan, että häiriön torjuntaan käytettävä häiriömalli voidaan valita laskemalla empiirinen havaittavuus Gramin matriisi yhdessä alkupisteessä jokaiselle harkitulle häiriömallille ja sitten valitsemalla malli, joka tulkitaan eniten havaittavaksi. Ehdotettu havaittavuusanalyysi tuottaa tietoa heikosta havaittavuudesta häiriöaugmentoidulle mallille vain valitussa alkupisteessä. Siitä huolimatta, tässä työssä argumentoitiin, että tulosten voidaan olettaa kuvastavan olennaista prosessin toiminta-aluetta, ja menetelmä soveltuu täten häiriömallin valitsemiseen. Vaihtoehtona häiriön torjunnalle, tässä työssä tutkittiin myös rekursiivista pienimmän neliösumman menetelmää adaptoimaan säätimessä käytettävää Koopman-Wiener-mallia ajon aikana. Tilaestoimointiin tässä työssä käytettiin hajustamatonta Kalman suodinta. Diplomityö demonstroi valittujen menetelmien suorituskykyä kahdella epälineaarisella tapaustutkimuksella: simuloitu jatkuvatoiminen sekoitusreaktori ja simuloitu tislauskolonni. Tässä työssä esitetään kolme tärkeää tulosta. Ensimmäiseksi, säädin joka käyttää häiriön torjuntaa, onnistuu poistamaan tasapainotilan säätöpoikkeaman takaisinkytketyssä systeemissä. Toiseksi, säädin ei saavuta tyydyttävää suorituskykyä rekursiivista pienimmän neliösumman menetelmää käytettäessä. Kolmanneksi, tapaustutkimukset tukevat ehdotettua lähestymistapaa häiriömallinnukseen, koska valitut häiriömallit johtavat parempaan tai yhtä hyvään säätimen suorituskykyyn verrattuna muiden häiriömallien käyttämiseen. Lisäksi tulokset tukevat hyödyllisen heuristisen säännön esittämistä Koopman-Wiener-mallien häiriömallintamiselle siten, että häiriömuuttujat vaikuttavat mallin dynaamisesti hitaisiin tilamuuttujiin

    A Survey on Explainable Anomaly Detection

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    In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from Data (TKDD) for publication (preprint version

    Contributions to time series analysis, modelling and forecasting to increase reliability in industrial environments.

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    356 p.La integración del Internet of Things en el sector industrial es clave para alcanzar la inteligencia empresarial. Este estudio se enfoca en mejorar o proponer nuevos enfoques para aumentar la confiabilidad de las soluciones de IA basadas en datos de series temporales en la industria. Se abordan tres fases: mejora de la calidad de los datos, modelos y errores. Se propone una definición estándar de métricas de calidad y se incluyen en el paquete dqts de R. Se exploran los pasos del modelado de series temporales, desde la extracción de características hasta la elección y aplicación del modelo de predicción más eficiente. El método KNPTS, basado en la búsqueda de patrones en el histórico, se presenta como un paquete de R para estimar datos futuros. Además, se sugiere el uso de medidas elásticas de similitud para evaluar modelos de regresión y la importancia de métricas adecuadas en problemas de clases desbalanceadas. Las contribuciones se validaron en casos de uso industrial de diferentes campos: calidad de producto, previsión de consumo eléctrico, detección de porosidad y diagnóstico de máquinas

    End-of-Horizon Load Balancing Problems: Algorithms and Insights

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    Effective load balancing is at the heart of many applications in operations. Often tackled via the balls-into-bins paradigm, seminal results have shown that a limited amount of flexibility goes a long way in order to maintain (approximately) balanced loads throughout the decision-making horizon. This paper is motivated by the fact that balance across time is too stringent a requirement for some applications; rather, the only desideratum is approximate balance at the end of the horizon. In this work we design ``limited-flexibility'' algorithms for three instantiations of the end-of-horizon balance problem: the balls-into-bins problem, opaque selling strategies for inventory management, and parcel delivery for e-commerce fulfillment. For the balls-into-bins model, we show that a simple policy which begins exerting flexibility toward the end of the time horizon (i.e., when Θ(TlogT)\Theta\left(\sqrt{T\log T}\right) periods remain), suffices to achieve an approximately balanced load (i.e., a maximum load within O(1){O}(1) of the average load). Moreover, with just a small amount of adaptivity, a threshold policy achieves the same result, while only exerting flexibility in O(T){O}\left(\sqrt{T}\right) periods, matching a natural lower bound. We then adapt these algorithms to develop order-wise optimal policies for the opaque selling problem. Finally, we show via a data-driven case study that the adaptive policy designed for the balls-into-bins model can be modified to (i) achieve approximate balance at the end of the horizon and (ii) yield significant cost savings relative to policies which either never exert flexibility, or exert flexibility aggressively enough to achieve anytime balance. The unifying motivation behind our algorithms is the observation that exerting flexibility at the beginning of the horizon is likely wasted when system balance is only evaluated at the end
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