14,192 research outputs found

    Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules

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    We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for a given grammar which can represent straight-line programs with function calls and multiple types. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, S4Eq, achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programsComment: 30 pages including appendi

    Complement mediated synapse elimination in schizophrenia

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    Schizophrenia (SCZ) is a devastating psychiatric disorder with a typically age of onset in late adolescence. The heritability is estimated to be in between 60-80% and large-scale genome-wide studies have revealed a prominent polygenic component to SCZ risk and identified more than three-hundred common risk variants. Despite a better understanding of which genetic risk variants that increases SCZ risk, it has been challenging to map out the pathophysiology of the disorder. This has stalled the development of target drugs and current treatment options display moderate efficacy and are prone to produce side-effects. SCZ is generally considered a neurodevelopmental disorder and it was proposed more than forty years ago that physiological removal of less active synapses in adolescence, i.e., synaptic pruning, is increased in SCZ and hereby causes the core symptoms of the disorder. This theory has then been supported by post-mortem brain tissue and imaging studies displaying decreased synapse density in SCZ. More recently, it was then shown that the most strongly associated risk loci can largely be explained by copy numbers of a gene coding for the complement factor 4A (C4A). As microglia prune synapses with the help of complement signalling, we therefore decided to use a recently developed human 2D in vitro assay to assess microglial uptake of synaptic structures in models based on cells from individuals with SCZ and healthy controls (study I). We observed excessive uptake of synaptic structures in SCZ models and by mixing synapses from healthy controls with microglia from SCZ patients, and vice versa, we showed the contribution of microglial and neuronal factors contributing to this excessive uptake of synaptic structures. We then developed an in vitro assay to study neuronal complement deposition dependent on copy numbers of C4A in the neuronal lines. Complement 3 (C3) deposition increased by C4A copy numbers but was independent of C4B copy numbers (also unrelated to SCZ risk). Similar C4A copy numbers correlated with the extent of microglial uptake of synapses. Microglial uptake of synaptic structures could also be inhibited by the tetracycline minocycline that also decreased risk of developing SCZ in an electronic health record cohort. In study II, we cerebrospinal fluid (CSF) from first-episode psychosis patients to measure protein levels of C4A. In two independent cohorts, we observed elevated C4A levels (although not C4B levels) in first-episode patients that later were to develop SCZ and could show correlations with markers of synapse density. However, elevated C4A levels could not fully be explained by more copy numbers of C4A in individuals with SCZ and in vitro experiments revealed that SCZ-associated cytokines can induce C4A mRNA expression while also correlating with C4A in patient-derived CSF. In study III, we set-up a 3D brain organoid models to more fully comprehensively capture processes in the developing human brain and then also included innately developing microglia. We display synaptic pruning within these models and use single cell RNA sequencing to validate them. In conclusion, this thesis uses patient-derived cellular modelling to uncover a disease mechanism in SCZ that link genetic risk variants with bona fide protein changes in living patients

    Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends

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    Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state-of-the-art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher-level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation

    Preferentialism and the conditionality of trade agreements. An application of the gravity model

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    Modern economic growth is driven by international trade, and the preferential trade agreement constitutes the primary fit-for-purpose mechanism of choice for establishing, facilitating, and governing its flows. However, too little attention has been afforded to the differences in content and conditionality associated with different trade agreements. This has led to an under-considered mischaracterisation of the design-flow relationship. Similarly, while the relationship between trade facilitation and trade is clear, the way trade facilitation affects other areas of economic activity, with respect to preferential trade agreements, has received considerably less attention. Particularly, in light of an increasingly globalised and interdependent trading system, the interplay between trade facilitation and foreign direct investment is of particular importance. Accordingly, this thesis explores the bilateral trade and investment effects of specific conditionality sets, as established within Preferential Trade Agreements (PTAs). Chapter one utilises recent content condition-indexes for depth, flexibility, and constraints on flexibility, established by Dür et al. (2014) and Baccini et al. (2015), within a gravity framework to estimate the average treatment effect of trade agreement characteristics across bilateral trade relationships in the Association of Southeast Asian Nations (ASEAN) from 1948-2015. This chapter finds that the composition of a given ASEAN trade agreement’s characteristic set has significantly determined the concomitant bilateral trade flows. Conditions determining the classification of a trade agreements depth are positively associated with an increase to bilateral trade; hereby representing the furthered removal of trade barriers and frictions as facilitated by deeper trade agreements. Flexibility conditions, and constraint on flexibility conditions, are also identified as significant determiners for a given trade agreement’s treatment effect of subsequent bilateral trade flows. Given the political nature of their inclusion (i.e., the appropriate address to short term domestic discontent) this influence is negative as regards trade flows. These results highlight the longer implementation and time frame requirements for trade impediments to be removed in a market with higher domestic uncertainty. Chapter two explores the incorporation of non-trade issue (NTI) conditions in PTAs. Such conditions are increasing both at the intensive and extensive margins. There is a concern from developing nations that this growth of NTI inclusions serves as a way for high-income (HI) nations to dictate the trade agenda, such that developing nations are subject to ‘principled protectionism’. There is evidence that NTI provisions are partly driven by protectionist motives but the effect on trade flows remains largely undiscussed. Utilising the Gravity Model for trade, I test Lechner’s (2016) comprehensive NTI dataset for 202 bilateral country pairs across a 32-year timeframe and find that, on average, NTIs are associated with an increase to bilateral trade. Primarily this boost can be associated with the market access that a PTA utilising NTIs facilitates. In addition, these results are aligned theoretically with the discussions on market harmonisation, shared values, and the erosion of artificial production advantages. Instead of inhibiting trade through burdensome cost, NTIs are acting to support a more stable production and trading environment, motivated by enhanced market access. Employing a novel classification to capture the power supremacy associated with shaping NTIs, this chapter highlights that the positive impact of NTIs is largely driven by the relationship between HI nations and middle-to-low-income (MTLI) counterparts. Chapter Three employs the gravity model, theoretically augmented for foreign direct investment (FDI), to estimate the effects of trade facilitation conditions utilising indexes established by Neufeld (2014) and the bilateral FDI data curated by UNCTAD (2014). The resultant dataset covers 104 countries, covering a period of 12 years (2001–2012), containing 23,640 observations. The results highlight the bilateral-FDI enhancing effects of trade facilitation conditions in the ASEAN context, aligning itself with the theoretical branch of FDI-PTA literature that has outlined how the ratification of a trade agreement results in increased and positive economic prospect between partners (Medvedev, 2012) resulting from the interrelation between trade and investment as set within an improving regulatory environment. The results align with the expectation that an enhanced trade facilitation landscape (one in which such formalities, procedures, information, and expectations around trade facilitation are conditioned for) is expected to incentivise and attract FDI

    Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond

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    [ES] Esta tesis se enmarca en la intersección entre las técnicas modernas de Machine Learning, como las Redes Neuronales Profundas, y el modelado probabilístico confiable. En muchas aplicaciones, no solo nos importa la predicción hecha por un modelo (por ejemplo esta imagen de pulmón presenta cáncer) sino también la confianza que tiene el modelo para hacer esta predicción (por ejemplo esta imagen de pulmón presenta cáncer con 67% probabilidad). En tales aplicaciones, el modelo ayuda al tomador de decisiones (en este caso un médico) a tomar la decisión final. Como consecuencia, es necesario que las probabilidades proporcionadas por un modelo reflejen las proporciones reales presentes en el conjunto al que se ha asignado dichas probabilidades; de lo contrario, el modelo es inútil en la práctica. Cuando esto sucede, decimos que un modelo está perfectamente calibrado. En esta tesis se exploran tres vias para proveer modelos más calibrados. Primero se muestra como calibrar modelos de manera implicita, que son descalibrados por técnicas de aumentación de datos. Se introduce una función de coste que resuelve esta descalibración tomando como partida las ideas derivadas de la toma de decisiones con la regla de Bayes. Segundo, se muestra como calibrar modelos utilizando una etapa de post calibración implementada con una red neuronal Bayesiana. Finalmente, y en base a las limitaciones estudiadas en la red neuronal Bayesiana, que hipotetizamos que se basan en un prior mispecificado, se introduce un nuevo proceso estocástico que sirve como distribución a priori en un problema de inferencia Bayesiana.[CA] Aquesta tesi s'emmarca en la intersecció entre les tècniques modernes de Machine Learning, com ara les Xarxes Neuronals Profundes, i el modelatge probabilístic fiable. En moltes aplicacions, no només ens importa la predicció feta per un model (per ejemplem aquesta imatge de pulmó presenta càncer) sinó també la confiança que té el model per fer aquesta predicció (per exemple aquesta imatge de pulmó presenta càncer amb 67% probabilitat). En aquestes aplicacions, el model ajuda el prenedor de decisions (en aquest cas un metge) a prendre la decisió final. Com a conseqüència, cal que les probabilitats proporcionades per un model reflecteixin les proporcions reals presents en el conjunt a què s'han assignat aquestes probabilitats; altrament, el model és inútil a la pràctica. Quan això passa, diem que un model està perfectament calibrat. En aquesta tesi s'exploren tres vies per proveir models més calibrats. Primer es mostra com calibrar models de manera implícita, que són descalibrats per tècniques d'augmentació de dades. S'introdueix una funció de cost que resol aquesta descalibració prenent com a partida les idees derivades de la presa de decisions amb la regla de Bayes. Segon, es mostra com calibrar models utilitzant una etapa de post calibratge implementada amb una xarxa neuronal Bayesiana. Finalment, i segons les limitacions estudiades a la xarxa neuronal Bayesiana, que es basen en un prior mispecificat, s'introdueix un nou procés estocàstic que serveix com a distribució a priori en un problema d'inferència Bayesiana.[EN] This thesis is framed at the intersection between modern Machine Learning techniques, such as Deep Neural Networks, and reliable probabilistic modeling. In many machine learning applications, we do not only care about the prediction made by a model (e.g. this lung image presents cancer) but also in how confident is the model in making this prediction (e.g. this lung image presents cancer with 67% probability). In such applications, the model assists the decision-maker (in this case a doctor) towards making the final decision. As a consequence, one needs that the probabilities provided by a model reflects the true underlying set of outcomes, otherwise the model is useless in practice. When this happens, we say that a model is perfectly calibrated. In this thesis three ways are explored to provide more calibrated models. First, it is shown how to calibrate models implicitly, which are decalibrated by data augmentation techniques. A cost function is introduced that solves this decalibration taking as a starting point the ideas derived from decision making with Bayes' rule. Second, it shows how to calibrate models using a post-calibration stage implemented with a Bayesian neural network. Finally, and based on the limitations studied in the Bayesian neural network, which we hypothesize that came from a mispecified prior, a new stochastic process is introduced that serves as a priori distribution in a Bayesian inference problem.Maroñas Molano, J. (2022). Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181582TESI

    Proof of Concept of Therapeutic Gene Modulation of MBNL1/2 in Myotonic Dystrophy

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    La distrofia miotónica tipo 1 es una enfermedad genética rara multisistémica que afecta a 1 de cada 3000-8000 personas. La causa molecular de la enfermedad proviene de repeticiones tóxicas “CTG” en el gen DMPK (DM Protein Kinase). Tras la transcripción, estas repeticiones forman una estructura de horquilla que se une con alta afinidad a la familia de proteínas MBNL (Muscleblind-like) que agota su función de regulación de la poliadenilación y el splicing alternativo postranscripcional en numerosos transcritos. La pérdida de función de MBNL provoca una cascada de efectos posteriores, que eventualmente conducen a síntomas clínicos que incluyen miotonía, debilidad y atrofia muscular, cataratas, disfunción cardíaca y trastorno cognitivo. La restauración de la función de la proteína MBNL es clave para aliviar los síntomas debilitantes de esta enfermedad. Se han utilizado oligonucleótidos antisentido (AON) para apuntar a las repeticiones de DMPK y liberar MBNL del secuestro, lo que da como resultado resultados terapéuticos prometedores en modelos celulares y animales de la enfermedad. Otro factor que interviene en la pérdida de función de las proteínas MBNL son los miRNAs que regulan su traducción. Aquí se muestra el uso de AON dirigidos a la actividad de miR-23b y miR-218, que se ha demostrado previamente que regulan directamente MBNL1 y MBNL2. Estos antimiRs recibieron modificaciones FANA para aumentar su entrega en las células y reducir la toxicidad. También se probaron los AON, denominados blockmiRs, que se unen de manera complementaria a los sitios de unión confirmados de miR-23b y miR-218 en los 3'-UTR de las transcripciones de MBNL1 y MBNL2. De esta manera, los miRNAs no pueden unirse y regular la traducción de MBNL, lo que aumenta la cantidad de proteína MBNL producida en una célula deficiente. Aquí se propone el uso de AON de nuevo diseño dirigidos a la actividad de miR-23b y miR-218 para regular MBNL1 y MBNL2 a través de (1) exploración del bloqueo de miRNA a través de FANA-antimiR AON in vitro, (2) exploración del bloqueo del sitio de unión de miRNA a través de la estrategia blockmiR in vitro e in vivo con el uso de modificaciones químicas de LNA, y (3) mejora de la química de la estrategia blockmiR mediante el uso de tecnología de péptidos de penetración celular in vitro e in vivo.Myotonic Dystrophy Type 1 is a multi-systemic rare genetic disease affecting 1 in 3000-8000 people. The molecular cause of the disease stems from toxic “CTG” repetitions in the DMPK (DM Protein Kinase) gene. Upon transcription, these repetitions form a hairpin structure that binds with high affinity to the MBNL (Muscleblind-like) family of proteins depleting their function of post-transcriptional alternative splicing and polyadenylation regulation on numerous transcripts. MBNL loss-of-function causes a cascade of downstream effects, which eventually lead to clinical symptoms including myotonia, muscle weakness and atrophy, cataracts, cardiac dysfunction, and cognitive disorder. The restoration of MBNL protein function is key to relieving the debilitating symptoms of this disease. Antisense oligonucleotides (AONs) have been used to target the DMPK repeats and release MBNL from sequestration resulting in promising therapeutic results in cellular and animal models of the disease. Another factor playing a role in the loss-of-function of MBNL proteins are the miRNAs that regulate their translation. Here is shown the use of AONs targeting miR-23b and miR-218 activity, which have been previously shown to directly regulate MBNL1 and MBNL2. These antimiRs were given FANA modifications to increase their delivery in cells and lower toxicity. Also tested are AONs, termed blockmiRs, that complementary bind to the confirmed binding sites of miR-23b and miR-218 in the 3’-UTRs of MBNL1 and MBNL2 transcripts. In this way, the miRNAs are unable to bind and regulate the translation of MBNL thereby augmenting the amount of MBNL protein made in an otherwise deficient cell. Proposed here is the use of newly designed AONs targeting miR-23b and miR-218 activity in order to regulate MBNL1 and MBNL2 through (1) exploration of miRNA blocking through FANA-antimiR AONs in vitro, (2) exploration of miRNA binding site blocking through blockmiR strategy in vitro and in vivo with the use of LNA chemical modifications, and (3) improvement of the chemistry of the blockmiR strategy through the use of cell penetrating peptide technology in vitro and in vivo

    Early Neanderthal social and behavioural complexity during the Purfleet Interglacial: handaxes in the latest Lower Palaeolithic.

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    Only a handful of ‘flagship’ sites from the Purfleet Interglacial (Marine Isotope Stage 9, c. 350-290,000 years ago) have been properly examined, but the archaeological succession at the proposed type-site at Purfleet suggests a period of complexity and transition, with three techno-cultural groups represented in Britain. The first was a simple toolkit lacking handaxes (the Clactonian), and the last a more sophisticated technology presaging the coming Middle Palaeolithic (simple prepared core or proto-Levallois technology). Sandwiched between were Acheulean groups, whose handaxes comprise the great majority of the extant archaeological record of the period – these are the focus of this study. It has previously been suggested that some features of the Acheulean in the Purfleet Interglacial were chronologically restricted, particularly the co-occurrence of ficrons and cleavers. These distinctive forms may have exceeded pure functionality and were perhaps imbued with a deeper social and cultural meaning. This study supports both the previously suggested preference for narrow, pointed morphologies, and the chronologically restricted pairing of ficrons and cleavers. By drawing on a wide spatial and temporal range of sites these patterns could be identified beyond the handful of ‘flagship’ sites previously studied. Hypertrophic ‘giants’ have now also been identified as a chronologically restricted form. Greater metrical variability was found than had been anticipated, leading to the creation of two new sub-groups (IA and IB) which are tentatively suggested to represent spatial and perhaps temporal patterning. The picture in the far west of Britain remains unclear, but the possibility of different Acheulean groups operating in the Solent area, and a late survival of the Acheulean, are both suggested. Handaxes with backing and macroscopic asymmetry may represent prehensile or ergonomic considerations not commonly found on handaxes from earlier interglacial periods. It is argued that these forms anticipate similar developments in the Late Middle Palaeolithic in an example of convergent evolution

    Evolution of ligand specificity of protein kinase A isoforms in the phylum Euglenozoa

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    Development of in-vitro in-silico technologies for modelling and analysis of haematological malignancies

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    Worldwide, haematological malignancies are responsible for roughly 6% of all the cancer-related deaths. Leukaemias are one of the most severe types of cancer, as only about 40% of the patients have an overall survival of 10 years or more. Myelodysplastic Syndrome (MDS), a pre-leukaemic condition, is a blood disorder characterized by the presence of dysplastic, irregular, immature cells, or blasts, in the peripheral blood (PB) and in the bone marrow (BM), as well as multi-lineage cytopenias. We have created a detailed, lineage-specific, high-fidelity in-silico erythroid model that incorporates known biological stimuli (cytokines and hormones) and a competing diseased haematopoietic population, correctly capturing crucial biological checkpoints (EPO-dependent CFU-E differentiation) and replicating the in-vivo erythroid differentiation dynamics. In parallel, we have also proposed a long-term, cytokine-free 3D cell culture system for primary MDS cells, which was firstly optimized using easily-accessible healthy controls. This system enabled long-term (24-day) maintenance in culture with high (>75%) cell viability, promoting spontaneous expansion of erythroid phenotypes (CD71+/CD235a+) without the addition of any exogenous cytokines. Lastly, we have proposed a novel in-vitro in-silico framework using GC-MS metabolomics for the metabolic profiling of BM and PB plasma, aiming not only to discretize between haematological conditions but also to sub-classify MDS patients, potentially based on candidate biomarkers. Unsupervised multivariate statistical analysis showed clear intra- and inter-disease separation of samples of 5 distinct haematological malignancies, demonstrating the potential of this approach for disease characterization. The work herein presented paves the way for the development of in-vitro in-silico technologies to better, characterize, diagnose, model and target haematological malignancies such as MDS and AML.Open Acces
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