Topology of Social and Managerial Networks

With the explosion of innovative technologies in recent years, organizational and man- agerial networks have reached high levels of intricacy. These are one of the many complex systems consisting of a large number of highly interconnected heterogeneous agents. The dominant paradigm in the representation of intricate relations between agents and their evolution is a network (graph). The study of network properties, and their implications on dynamical processes, up to now mostly focused on locally defined quantities of nodes and edges. These methods grounded in statistical mechanics gave deep insight and explanations on real world phenomena; however there is a strong need for a more versatile approach which would rely on new topological methods either separately or in combination with the classical techniques. In this thesis we approach this problem introducing new topological methods for network analysis relying on persistent homology. The results gained by the new methods apply both to weighted and unweighted networks; showing that classi- cal connectivity measures on managerial and societal networks can be very imprecise and extending them to weighted networks with the aim of uncovering regions of weak connectivity. In the first two chapters of the thesis we introduce the main instruments that will be used in the subsequent chapters, namely basic techniques from network theory and persistent homology from the field of computational algebraic topology. The third chapter of the thesis approaches social and organizational networks studying their con- nectivity in relation to the concept of social capital. Many sociological theories such as the theory of structural holes and of weak ties relate social capital, in terms of profitable managerial strategies and the chance of rewarding opportunities, to the topology of the underlying social structure. We review the known connectivity measures for social networks, stressing the fact that they are all local measures, calculated on a node’s Ego network, i.e considering a nodes direct contacts. By analyzing real cases it, nevertheless, turns out that the above measures can be very imprecise for strategical individuals in social networks, revealing fake brokerage opportunities. We, therefore, propose a new set of measures, complementary to the existing ones and focused on detecting the position of links, rather than their density, therefore extending the standard approach to a mesoscopic one. Widening the view from considering direct neighbors to considering also non-direct ones, using the “neighbor filtration”, we give a measure of height and weight for structural holes, obtaining a more accurate description of a node’s strategical position within its contacts. We also provide a refined version of the network efficiency measure, which collects in a compact form the height of all structural holes. The methods are implemented and have been tested on real world organizational and managerial networks. In pursuing the objective of improving the existing methods we faced some technical difficulties which obliged us to develop new mathematical tools. The fourth chapter of the thesis deals with the general problem of detecting structural holes in weighted networks. We introduce thereby the weight clique rank filtration, to detect particular non-local structures, akin to weighted structural holes within the link-weight network fabric, which are invisible to existing methods. Their properties divide weighted networks in two broad classes: one is characterized by small hierarchi- cally nested holes, while the second displays larger and longer living inhomogeneities. These classes cannot be reduced to known local or quasi local network properties, because of the intrinsic non-locality of homology, and thus yield a new classification built on high order coordination patterns. Our results show that topology can provide novel insights relevant for many-body interactions in social and spatial networks. In the fifth chapter of the thesis, we develop new insights in the mathematical setting underlying multipersistent homology. More specifically we calculate combinatorial resolutions and efficient Gro ̈bner bases for multipersistence homology modules. In this new frontier of persistent homology, filtrations are parametrized by multiple elements. Using multipersistent homology temporal networks can be studied and the weight filtration and neighbor filtration can be combined

Shape-based invariant features extraction for object recognition

International audienceThe emergence of new technologies enables generating large quantity of digital information including images; this leads to an increasing number of generated digital images. Therefore it appears a necessity for automatic systems for image retrieval. These systems consist of techniques used for query specification and re-trieval of images from an image collection. The most frequent and the most com-mon means for image retrieval is the indexing using textual keywords. But for some special application domains and face to the huge quantity of images, key-words are no more sufficient or unpractical. Moreover, images are rich in content; so in order to overcome these mentioned difficulties, some approaches are pro-posed based on visual features derived directly from the content of the image: these are the content-based image retrieval (CBIR) approaches. They allow users to search the desired image by specifying image queries: a query can be an exam-ple, a sketch or visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring simi-larity between image features. An important property of these features is to be in-variant under various deformations that the observed image could undergo. In this chapter, we will present a number of existing methods for CBIR applica-tions. We will also describe some measures that are usually used for similarity measurement. At the end, and as an application example, we present a specific ap-proach, that we are developing, to illustrate the topic by providing experimental results

The role of ventrolateral prefrontal cortex in performance of spatial self-ordered response sequences in the marmoset

The ventrolateral prefrontal cortex (vlPFC) in primates plays an important role in cognitive control and working memory, but as argued in the Introduction its contribution to those aspects of goal-directed behaviour such as planning and executing spatial response sequences requires further analysis, using more refined methods than have been employed hitherto. These studies investigated the role of vlPFC in performance of self-ordered response sequences using intra-cerebral microinfusions of specific pharmacologic agents in the common marmoset. Following a description of the necessary methodology, including behavioural training and surgical details (Chapter 2), a causal role for vlPFC in performance of spatial-self ordered sequences was confirmed in Chapter 3 by demonstrating that local inactivation of vlPFC using muscimol/baclofen infusions impairs sequencing. This effect was shown to be selective to performance of sequences that varied spatially from trial to trial; thus, no effects of vlPFC inactivation were observed for performance of a fixed response sequence. Once animals could learn a heuristical strategy for a self-ordered fixed sequence, vlPFC inactivation no longer impaired performance. Chapter 4 investigated the effects of the chemical neuromodulation of vlPFC on self-ordered sequencing using microinfusions of dopamine receptor D2 antagonist, sulpiride, and 5HT2A receptor antagonist, M100907 on performance of variable sequences. These drugs produced contrasting, dose-dependent impairments. M100907 impaired accuracy, while sulpiride impaired error correction. Chapter 5 studied effects of blocking glutamatergic receptors in a region of the caudate nucleus to which the vlPFC projects, but no significant effects on sequencing accuracy were observed, although there were large effects on perseverative errors in 2 out of 3 animals. The findings are discussed in Chapter 6 in terms of the functioning of the vlPFC and its possible role in controlling flexible response sequencing and working memory. The findings are shown to be of relevance for psychiatric disorders such as obsessive compulsive disorder (OCD) and schizophrenia, which show functional dysconnectivity of the vlPFC in association with response sequencing impairments

Exploiting Latent Features of Text and Graphs

As the size and scope of online data continues to grow, new machine learning techniques become necessary to best capitalize on the wealth of available information. However, the models that help convert data into knowledge require nontrivial processes to make sense of large collections of text and massive online graphs. In both scenarios, modern machine learning pipelines produce embeddings --- semantically rich vectors of latent features --- to convert human constructs for machine understanding. In this dissertation we focus on information available within biomedical science, including human-written abstracts of scientific papers, as well as machine-generated graphs of biomedical entity relationships. We present the Moliere system, and our method for identifying new discoveries through the use of natural language processing and graph mining algorithms. We propose heuristically-based ranking criteria to augment Moliere, and leverage this ranking to identify a new gene-treatment target for HIV-associated Neurodegenerative Disorders. We additionally focus on the latent features of graphs, and propose a new bipartite graph embedding technique. Using our graph embedding, we advance the state-of-the-art in hypergraph partitioning quality. Having newfound intuition of graph embeddings, we present Agatha, a deep-learning approach to hypothesis generation. This system learns a data-driven ranking criteria derived from the embeddings of our large proposed biomedical semantic graph. To produce human-readable results, we additionally propose CBAG, a technique for conditional biomedical abstract generation

Preliminary finite element modeling of a piezoelectric actuated marine propulsion fin

New technologies surrounding composite materials and autonomous underwater vehicle (AUV) design have led to numerous studies involving the marine propulsion for these AUVs. AUVs traditionally are classified as highly efficient, payload capable, and can be utilized as reconnaissance or surveillance vehicles. Undullatory and oscillatory propulsion devices have been conceived to replace the present propulsion technologies, of propellers, with highly maneuverable, efficient, and quiet propulsion systems. Undullatory and oscillatory propulsion has been around for centuries employed by aquatic life, but only recently have the mini-technologies been available to present such propulsion devices economically and with enough materials research as to mimic biologic life on the same scale. Piezoelectric properties coupled with a thin plate allow for actuation properties, similar to bimetallic metals. Applying two piezoelectrics to the fixed end of a cantilevered beam or plate, on opposite sides, and actuating them with an opposite phase shift in electrical voltage potential results in transverse motion of the beam from the orthogonal plane to the vertical axis of the piezoelectric device. Coupling this property to a particular fiber orientation, composite thin plate, significantly increases the actuation properties. In addition, placing more than two piezoelectrics along the length of the thin composite plate gives the potential to increase actuation properties and change the motion from oscillatory to undullatory. These motions can again be increased by utilizing the natural vibration modes of the thin composite plate with piezoelectrics near resonance actuation. The current research is involved with modeling a piezoelectric actuated marine propulsion fin using the Galerkin finite element technique. An experimental proof of concept was developed to compare results. Using fluid-structure interaction (FSI) methods, it is proposed that the fluid and structure programs are resolved within one program. This is in contrast to traditional attempts at FSI problems that utilize a computational fluid dynamics (CFD) solver transferring load data between a structural dynamics/finite element (FE) program