Cartan Connections and Atiyah Lie Algebroids

This work extends previous developments carried out by some of the authors on Ehresmann connections on Atiyah Lie algebroids. In this paper, we study Cartan connections in a framework relying on two Atiyah Lie algebroids based on a $H$-principal fiber bundle $\mathcal{P}$ and its associated $G$-principal fiber bundle $\mathcal{Q} := \mathcal{P} \times_H G$, where $H \subset G$ defines the model for a Cartan geometry. The first main result of this study is a commutative and exact diagram relating these two Atiyah Lie algebroids, which allows to completely characterize Cartan connections on $\mathcal{P}$. Furthermore, in the context of gravity and mixed anomalies, our construction answers a long standing mathematical question about the correct geometrico-algebraic setting in which to combine inner gauge transformations and infinitesimal diffeomorphisms.Comment: 27 pages. Published versio

Semantic data ingestion for intelligent, value-driven big data analytics

In this position paper we describe a conceptual model for intelligent Big Data analytics based on both semantic and machine learning AI techniques (called AI ensembles). These processes are linked to business outcomes by explicitly modelling data value and using semantic technologies as the underlying mode for communication between the diverse processes and organisations creating AI ensembles. Furthermore, we show how data governance can direct and enhance these ensembles by providing recommendations and insights that to ensure the output generated produces the highest possible value for the organisation

Vers un modèle unitaire de la scientificité

Le présent travail s'inscrit à l'intersection de deux problèmes épistémologiques majeurs. D'une part, le problème de la démarcation scientifique, qui consiste à identifier ce qui distingue intrinsèquement un système (un énoncé, une théorie, ...) scientifique d'un système non scientifique ou pseudo-scientifique. D'autre part, le problème de l'unité épistémologique des sciences, qui consiste à se demander si toutes les disciplines à vocation scientifique peuvent être vues comme des instanciations d'une notion unique de la scientificité. Ces deux problèmes ont soulevé de nombreux débats ayant mis en évidence un ensemble important de difficultés. Le terme « scientifique » désigne en effet des méthodes empiriques, des constructions théoriques et des pratiques de recherche si hétérogènes qu’il semble voué à l’échec d’en chercher une définition aisée à circonscrire. De plus, les objets des disciplines scientifiques sont eux-mêmes de natures très diverses, ce qui semble rendre pareillement caduque toute recherche d’un concept unique de science qui pourrait s’appliquer indépendamment de la discipline en question. Dans cette thèse, je me propose de prendre à contre-pied cet état de fait en soutenant la possibilité et la pertinence d'un modèle unitaire de la scientificité, tout en me restreignant à une approche épistémologique comparée entre les sciences physiques et les sciences sociales. Pour défendre mon propos, j’ai mobilisé deux types de réponses pouvant être opposées au constat présenté plus haut. D’une part, des réponses de principe, où j’examine et m’oppose à des arguments théoriques soutenant l’impossibilité ou en tout cas la difficulté de définir la scientificité en général, et la nécessité pour les sciences sociales de jouir d’une définition à part. D’autre part, je mobilise également des réponses par l’exemple. J’étudie alors plus en détail l’approche dite « analytique » en sociologie. Ce courant a cela d’intéressant pour mon propos qu’il ne semble pas nécessiter d’épistémologie alternative à celle ayant cours, par exemple, en physique ou en biologie, tout en prétendant bien produire des connaissances sur le monde social. Il s’agit donc d’un contre-exemple concret et manifeste de la thèse soutenant que la sociologie ne peut pas jouir du même type d’épistémologie que les autres disciplines. Plus concrètement, j’élabore un (méta-)modèle unitaire de la scientificité en me concentrant sur une unité d’analyse bien circonscrite : les modèles. Je distingue chez ses derniers, classiquement, deux composantes principales : une composante empirique, qui a pour vocation à identifier des régularités dans le réel auquel on a accès à travers des données, et une composante théorique, à visée explicative et classificatoire. Je propose alors de construire formellement un degré de scientificité global dans lequel se combinent la maximisation d’une certaine quantité d’information définie sur la composante empirique et un critère d’invariance structurelle défini sur la composante théorique. Ces diverses constructions, bien que formelles, permettent d’éclairer efficacement les questions épistémologiques que je me suis données au départ de mon travail, ce dernier ayant vocation à constituer une étape supplémentaire vers un modèle unitaire de la scientificité

Saffron: a data value assessment tool for quantifying the value of data assets

Data has become an indispensable commodity and it is the basis for many products and services. It has become increasingly important to understand the value of this data in order to be able to exploit it and reap the full benefits. Yet, many businesses and entities are simply hoarding data without understanding its true potential. We here present Saffron; a Data Value Assessment Tool that enables the quantification of the value of data assets based on a number of different data value dimensions. Based on the Data Value Vocabulary (DaVe), Saffron enables the extensible representation of the calculated value of data assets, whilst also catering for the subjective and contextual nature of data value. The tool exploits semantic technologies in order to provide traceable explanations of the calculated data value. Saffron therefore provides the first step towards the efficient and effective exploitation of data assets

What is to be explained?

The deductive nomological (DN) model has been the basis for discussions about scientific explanations for decades. The overcoming of the logical empiricist program together with the raise of several counter-examples to the DN model have progressively led to a renewal of the reflections on this topic. The first step of this paper is to clarify the framework in which the epistemological question of scientific explanation is adressed. We make a proposal for a universal structure of scientific models, which constitute the basic epistemic unit of our analysis. It is then possible, within this framework, to clarify some discussions about scientific explanation, and to offer a new account of it. The latter tries to benefit from the advantages of the DN model, as resting on a law, together with neutralizing some of its typical criticisms. The work presented here is both abstract, exhibiting formal structures as general patterns of explanation, and concrete with real examples taken from actual scientific disciplines

Epistemological status of rationality principles in the social sciences: a structural invariance criterion

In the social sciences, within the explanatory paradigm of structural individualism, a theory of action – like rational choice theory – models how individuals behave and interact at the micro level in order to explain macro observations as the aggregation of these individuals actions. A central epistemological issue is that such theoretical models are stuck in a dilemma between falsity of their basic assumptions and triviality of their explanation. On the one hand, models which have a great empirical success often rest on unrealistic or even knowingly false assumptions; on the other hand, more complex models, with additional more realistic hypotheses, can (trivially) adapt to a wide range of situations and thus loose their explanatory power. Our purpose here is epistemological and consists in wondering to which extent demanding realistic assumptions in such cases is a relevant criterion with respect to the acceptance of a given explanatory model. Via an analogical reasoning with physics, we argue that this criterion seems too strong and actually irrelevant. General physical principles are not just idealized or unrealistic, they can also be formulated in many different yet equivalent ways which do not imply the same fundamental unobservable entities or phenomena. However, the classification of phenomena that such principles allow to highlight does not depend, at the end, on any particular formulation of these basic assumptions. This suggests that some hypotheses in theoretical models are actually not genuine empirical statements that could be independently tested but only substrates of modeling embodying a classification principle. Thus, we develop a structural invariance criterion that we then apply to rational choice models in the social sciences. We argue that this criterion allows to escape from the epistemological dilemma without condemning formal approaches like rational choice theory for their lack of realisticness nor being stuck to any antirealist viewpoint

Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue.

Genome-wide DNA sequencing was used to decrypt the phylogeny of multiple samples from distinct areas of cancer and morphologically normal tissue taken from the prostates of three men. Mutations were present at high levels in morphologically normal tissue distant from the cancer, reflecting clonal expansions, and the underlying mutational processes at work in morphologically normal tissue were also at work in cancer. Our observations demonstrate the existence of ongoing abnormal mutational processes, consistent with field effects, underlying carcinogenesis. This mechanism gives rise to extensive branching evolution and cancer clone mixing, as exemplified by the coexistence of multiple cancer lineages harboring distinct ERG fusions within a single cancer nodule. Subsets of mutations were shared either by morphologically normal and malignant tissues or between different ERG lineages, indicating earlier or separate clonal cell expansions. Our observations inform on the origin of multifocal disease and have implications for prostate cancer therapy in individual cases

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies

The scientific demarcation problem: a formal and model-based approach to falsificationism

The problem of finding a clear demarcation between what is scientific and what is pseudoscientific or merely unscientific remains open. Over the past century, the debate has been structured around Karl Popper's falsificationist epistemology. Its central idea is that what makes something scientific is not so much how adequate it is with data, but rather to what extent it might not have been so. Among all criticisms that have been rising in the wake of this idea, a central one is the Duhem-Quine thesis which questions the very fact, for a single statement as well as for whole theories, to be really falsifiable at all. Currently, most of the current approaches to the scientific demarcation problem are multi-criteria and holistic. The approach presented in this paper takes the other way around. We use the language of probability theory to precisely and formally describe what we set as the elementary unit of analysis: empirical models. This framework allows us to clearly define the popperian corroboration degree on empirical models rather than on mere statements or systems of statements. Scientificity is seen as the maximization of this degree of corroboration over all available models and data. We eventually recover, in a natural way, well-accepted scientificity criteria: empirical adequacy, Lakatos' progressive problemshifts, balance between strength and simplicity, parsimony, and coherence as special cases of this general scientificity principle. From this viewpoint, the language dependency of our empirical knowledge no longer appears as a limitation of falsificationism but as one more reason to take it as a good epistemological framework