3D simulation of navigation problem of people with cerebral visual impairment

Cerebral Visual Impairment (CVI) is a medical area that concerns the study of the effect of brain damages on the visual field (VF). People with CVI have difficulties in their mobility and they have behaviours that others find hard to understand due to their visual impairment. A branch of Artificial Intelligence (AI) is the simulation of behaviour by building computational models that help to explain how people solve problems or why they behave in a certain way. This paper describes a novel computational system that simulates the navigation problem that is faced by people with CVI. This will help relatives, friends, and ophthalmologists of CVI patients understand more about their difficulties in navigating their everyday environment. The navigation simulation system is implemented using the Unity3D game engine. Virtual scenes of different living environment are also created using the Unity modelling software. The vision of the avatar in the virtual environment is implemented using a camera provided by the 3D game engine. Filters that mimic visual defects are created automatically and placed in front of the visual field of the avatar. The filters are based on the visual field charts of individual patients. Algorithms for navigation based on the limited vision have also been developed to demonstrate navigation problems because of the visual defects. The results showed different actions for the navigation behaviours according to the patients’ vision, and the navigations differ from patient to another according to their different defects

CFD STUDY OF COMPLEX CIRCULATING FLUIDIZED BED SYSTEMS

Circulating fluidized bed (CFB) has been widely applied to many chemical engineering processes. Although significant developments have been made in understanding the performance using the complex CFB technology during the last decades, the detailed inner information cannot be obtained by experiments because of complicated flow pattern in the system and backward measuring equipment. Numerical simulation has become the primary method to accelerate the development of complex CFB technology, reduce the cost of design and operating time, as well as reduce the technical risks. This thesis aims to provide more detailed in-furnace phenomena of complex CFB systems, including the hydrodynamic behaviours and chemical reactions based on the numerical simulation method. The promising chemical looping combustion (CLC) technology, as an example of complex CFB systems, will be focused on in this thesis. Meanwhile, the non-uniformity phenomenon in complex CFB units is comprehensively investigated in two symmetrical CFB configurations connected in parallel and series. Sequentially, an integrated method to dynamically combine CFD modelling and the process simulation is developed as a solution to improve the CFB performance. Specifically, it covers the following five aspects: 1. The hydrodynamic characteristics in a full-loop dual CFB CLC unit are comprehensively investigated based on the Eulerian multi-fluid model to give more detailed information about the flow behaviours. 2. The hydrodynamic characteristics in a unique counter-current moving bed full-loop CLC unit are comprehensively investigated based on the Eulerian multi-fluid model to study the unique configuration and in-furnace fluidization. 3. The reaction characteristics in the unique counter-current moving bed full-loop CLC unit are firstly attempt based on the hybrid Eulerian- Eulerian-Lagrangian model to study the in-furnace reaction details. 4. The non-uniformity characteristics of the multiphase flow in two complex CFB units connected in parallel and series, respectively, are studied based on the Eulerian multi-fluid model. 5. A novel direct integrated method to dynamically combine CFD modelling and the process simulation is developed. A case study of real-time regulation of boundary and operating conditions of reactors in complex CFBs is realized. These studies contribute to the deep understanding and further optimization of complex CFB systems

Domain-Agnostic Neural Architecture for Class Incremental Continual Learning in Document Processing Platform

Production deployments in complex systems require ML architectures to be highly efficient and usable against multiple tasks. Particularly demanding are classification problems in which data arrives in a streaming fashion and each class is presented separately. Recent methods with stochastic gradient learning have been shown to struggle in such setups or have limitations like memory buffers, and being restricted to specific domains that disable its usage in real-world scenarios. For this reason, we present a fully differentiable architecture based on the Mixture of Experts model, that enables the training of high-performance classifiers when examples from each class are presented separately. We conducted exhaustive experiments that proved its applicability in various domains and ability to learn online in production environments. The proposed technique achieves SOTA results without a memory buffer and clearly outperforms the reference methods.Comment: arXiv admin note: text overlap with arXiv:2211.1496

Advances in Automatic Keyphrase Extraction

The main purpose of this thesis is to analyze and propose new improvements in the field of Automatic Keyphrase Extraction, i.e., the field of automatically detecting the key concepts in a document. We will discuss, in particular, supervised machine learning algorithms for keyphrase extraction, by first identifying their shortcomings and then proposing new techniques which exploit contextual information to overcome them. Keyphrase extraction requires that the key concepts, or \emph{keyphrases}, appear verbatim in the body of the document. We will identify the fact that current algorithms do not use contextual information when detecting keyphrases as one of the main shortcomings of supervised keyphrase extraction. Instead, statistical and positional cues, like the frequency of the candidate keyphrase or its first appearance in the document, are mainly used to determine if a phrase appearing in a document is a keyphrase or not. For this reason, we will prove that a supervised keyphrase extraction algorithm, by using only statistical and positional features, is actually able to extract good keyphrases from documents written in languages that it has never seen. The algorithm will be trained over a common dataset for the English language, a purpose-collected dataset for the Arabic language, and evaluated on the Italian, Romanian and Portuguese languages as well. This result is then used as a starting point to develop new algorithms that use contextual information to increase the performance in automatic keyphrase extraction. The first algorithm that we present uses new linguistics features based on anaphora resolution, which is a field of natural language processing that exploits the relations between elements of the discourse as, e.g., pronouns. We evaluate several supervised AKE pipelines based on these features on the well-known SEMEVAL 2010 dataset, and we show that the performance increases when we add such features to a model that employs statistical and positional knowledge only. Finally, we investigate the possibilities offered by the field of Deep Learning, by proposing six different deep neural networks that perform automatic keyphrase extraction. Such networks are based on bidirectional long-short term memory networks, or on convolutional neural networks, or on a combination of both of them, and on a neural language model which creates a vector representation of each word of the document. These networks are able to learn new features using the the whole document when extracting keyphrases, and they have the advantage of not needing a corpus after being trained to extract keyphrases from new documents. We show that with deep learning based architectures we are able to outperform several other keyphrase extraction algorithms, both supervised and not supervised, used in literature and that the best performances are obtained when we build an additional neural representation of the input document and we append it to the neural language model. Both the anaphora-based and the deep-learning based approaches show that using contextual information, the performance in supervised algorithms for automatic keyphrase extraction improves. In fact, in the methods presented in this thesis, the algorithms which obtained the best performance are the ones receiving more contextual information, both about the relations of the potential keyphrase with other parts of the document, as in the anaphora based approach, and in the shape of a neural representation of the input document, as in the deep learning approach. In contrast, the approach of using statistical and positional knowledge only allows the building of language agnostic keyphrase extraction algorithms, at the cost of decreased precision and recall

Computational methods to explore hierarchical and modular structure of biological networks

Networks have been widely used to understand structure of complex systems. From studying biological networks of protein-protein, genetic and other types of interactions, we gain insights into functional organization of static biological systems that could hardly be measured experimentally in current state-of-the-art technology. Biological networks also serve as a principled framework that integrates multiple sources of genome-wide data sets such as gene expression arrays and sequencing. Yet, a large-scale network is often intractable for intuitive visualization and computation. We developed novel network clustering algorithms to harness the power of genome-scale biological networks of all genes/proteins. Especially our algorithms were capable of finding hidden modular structures in hierarchical stochastic block model. Since the modules are organized hierarchically, our algorithms facilitate downstream analysis and design of in-depth validation experiments in ``divide-and-conquer'' strategy. Moreover, we present empirical evidence that the hierarchical and modular structure best explains observed biological networks. We used the static clustering methods in two ways. First we sought to extend the static methods to dynamic clustering problems, and observed general patterns of dynamics of network modules. For examples we demonstrate dynamics of yeast metabolic cycle and Arabidopsis root developmental process. Moreover, we propose a prioritization scheme that sorts identified network modules in the order of discriminative power. In the course of research we conclude that biological networks are best understood as hierarchically organized modules, and the modules remain stable in unperturbed biological process, but they can respond differently to abnormal / external perturbations such as knock-down of key enzymes

Scaling laws for density correlations and fluctuations in multiparticle dynamics

Experimental data are presented on particle correlations and fluctuations in various high-energy multiparticle collisions, with special emphasis on evidence for scaling-law evolution in small phase-space domains. The notions of intermittency and fractality as related to the above findings are described. Phenomenological and theoretical work on the subject is reviewed.Comment: 139 pages, LATEX, 67 figures (hard copies on request from [email protected]

Characterization of chloroplast transit peptides and the major stromal Hsp70, CSS1 : implications for an ATP-dependent chloroplast protein import molecular motor

Chloroplast protein import is a relatively poorly understood protein trafficking system. In other protein import systems, the translocation machinery has been identified and well studied, including the mechanism by which proteins are unidirectionally transported across the importing membrane. In the chloroplast, no such molecular motor is acknowledged. The work described in this dissertation is a preliminary attempt to assign that role to the major stromal Hsp70, CSS1. We have shown, through a variety of in vivo and in vitro techniques, interaction between a chloroplast transit peptide and two members of the Hsp70 class of molecular chaperones, DnaK and CSS1. We have also mapped this specific interaction to the N-terminus of one transit peptide and generallized this N-terminal bias to all transit peptides through statistical analyses. Futhermore, we have generated a recombinant form of CSS1 and have developed a novel chromatographic technique to purify it in an active form. Finally, we have biochemically characterized CSS1, relating its place within the Hsp70 protein family and describing its catalytic and chaperone activities in detail. This work provides the basis for further in vivo and in vitro studies which our data predict will prove that CSS1 is the chloroplast protein import molecular motor

Statistical modelling of masked gene regulatory pathway changes across microarray studies of interferon gamma activated macrophages

Interferon gamma (IFN-γ) regulation of macrophages plays an essential role in innate immunity and pathogenicity of viral infections by directing large and small genome-wide changes in the transcriptional program of macrophages. Smaller changes at the transcriptional level are difficult to detect but can have profound biological effects, motivating the hypothesis of this thesis that responses of macrophages to immune activation by IFN-γ include small quantitative changes that are masked by noise but represent meaningful transcriptional systems in pathways against infection. To test this hypothesis, statistical meta-analysis of microarray studies is investigated as a tool to obtain the necessary increase in analysis sensitivity. Three meta-analysis models (Effect size model, Rank Product model, Fisher’s sum of logs) and three further modified versions were applied to a heterogeneous set of four microarray studies on the effect of IFN-γ on murine macrophages. Performance assessments include recovery of known biology and are followed by development of novel biological hypotheses through secondary analysis of meta-analysis outcomes in context of independent biological data sources. A separate network analysis of a microarray time course study investigate s if gene sets with coordinated time-dependent relationships overlap can also identify subtle IFN-γ related transcriptional changes in macrophages that match those identified through meta-analysis. It was found that all meta-analysis models can identify biologically meaningful transcription at enhanced sensitivity levels, with slightly improved performance advantages for a non-parametric model (Rank Product meta-analysis). Meta-analysis yielded consistently regulated genes, hidden in individual microarray studies, related to sterol biosynthesis (Stard3, Pgrmc1, Galnt6, Rab11a, Golga4, Lrp10), implicated in cross-talk between type II and type I interferon or IL-10 signalling (Tbk1, Ikbke, Clic4, Ptpre, Batf), and circadian rhythm (Csnk1e). Further network analysis confirms that meta-analysis findings are highly concentrated in a distinct immune response cluster of co-expressed genes, and also identifies global expression modularisation in IFN-γ treated macrophages, pointing to Trafd1 as a central anti-correlated node topologically linked to interactions with down-regulated sterol biosynthesis pathway members. Outcomes from this thesis suggest that small transcriptional changes in IFN-γ activated macrophages can be detected by enhancing sensitivity through combination of multiple microarray studies. Together with use of bioinformatical resources, independent data sets and network analysis, further validation assigns a potential role for low or variable transcription genes in linking type II interferon signalling to type I and TLR signalling, as well as the sterol metabolic network

Intelligent computational techniques and virtual environment for understanding cerebral visual impairment patients

Cerebral Visual Impairment (CVI) is a medical area that concerns the study of the effect of brain damages on the visual field (VF). People with CVI are not able to construct a perfect 3-Dimensional view of what they see through their eyes in their brain. Therefore, they have difficulties in their mobility and behaviours that others find hard to understand due to their visual impairment. A branch of Artificial Intelligence (AI) is the simulation of behaviour by building computational models that help to explain how people solve problems or why they behave in a certain way. This project describes a novel intelligent system that simulates the navigation problems faced by people with CVI. This will help relatives, friends, and ophthalmologists of CVI patients understand more about their difficulties in navigating their everyday environment. The navigation simulation system is implemented using the Unity3D game engine. Virtual scenes of different living environments are also created using the Unity modelling software. The vision of the avatar in the virtual environment is implemented using a camera provided by the 3D game engine. Given a visual field chart of a CVI patient with visual impairment, the system automatically creates a filter (mask) that mimics a visual defect and places it in front of the visual field of the avatar. The filters are created by extracting, classifying and converting the symbols of the defected areas in the visual field chart to numerical values and then converted to textures to mask the vision. Each numeric value represents a level of transparency and opacity according to the severity of the visual defect in that region. The filters represent the vision masks. Unity3D supports physical properties to facilitate the representation of the VF defects into a form of structures of rays. The length of each ray depends on the VF defect s numeric value. Such that, the greater values (means a greater percentage of opacity) represented by short rays in length. While the smaller values (means a greater percentage of transparency) represented by longer rays. The lengths of all rays are representing the vision map (how far the patient can see). Algorithms for navigation based on the generated rays have been developed to enable the avatar to move around in given virtual environments. The avatar depends on the generated vision map and will exhibit different behaviours to simulate the navigation problem of real patients. The avatar s behaviour of navigation differs from patient to another according to their different defects. An experiment of navigating virtual environments (scenes) using the HTC Oculus Vive Headset was conducted using different scenarios. The scenarios are designed to use different VF defects within different scenes. The experiment simulates the patient s navigation in virtual environments with static objects (rooms) and in virtual environments with moving objects. The behaviours of the experiment participants actions (avoid/bump) match the avatar s using the same scenario. This project has created a system that enables the CVI patient s parents and relatives to aid the understanding what the CVI patient encounter. Besides, it aids the specialists and educators to take into account all the difficulties that the patients experience. Then, is to design and develop appropriate educational programs that can help each individual patient