Topics

<> dependability (2) <> differentiable optics (2) <> differential geometry (2)

Discrete conformal mappings and Riemann Surfaces: Theory and Applications

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B9 L2 H2

The general idea of discrete differential geometry is to find and investigate discrete models that exhibit properties and structures characterisitic of the corresponding smooth geometric objects. This is a challenging problem, since equivalent points of view in the smooth setting may lead to a number of inequivalent treatments in the discrete setting. We will illustrate the paradigm of structure-preserving discretizations on the example of conformal maps by showing how simple definitions lead to surprisingly rich theories.

Anthony Ramos

Anthony Ramos is an MS/PhD Student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Helmut Pottman in his research group. Education and Early Career Anthony have obtained Bachelor's degree in Physicist from Yachay Tech University, Urcuqui, Ecuador - government project with the idea of making an international university for the country, from which he was part of the second generation of students. Research Interest Anthony Ramos is passionate about geometry and programming. He loves to bring problems to
<> digital health (2) <> distributed computing (2) <> Drug Combinations (2) <> drug discovery (2) <> edge computing (2) <> Electroencephalograms (2) <> energy efficiency (2) <> environment (2) <> Environmental Statistics (2) <> explainable AI framework (2) <> fault tolerance (2) <> Fault-Tolerant Computing (2) <> free boundary problems (2) <> functional prediction (2)

Semantic Prioritization of Novel Causative Genomic Variants in Mendelian and Oligogenic Diseases

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B9 R4223

Abstract Recent advances in Next Generation Sequencing (NGS) technologies have facilitated the generation of massive amounts of genomic data which in turn is bringing the promise that personalized medicine will soon become widely available. As a result, there is an increasing pressure to develop computational tools to analyze and interpret genomic data. In this dissertation, we present a systematic approach for interrogating patients' genomes to identify candidate causal genomic variants of Mendelian and oligogenic diseases. To achieve that, we leverage the use of biomedical data available

Imane Boudellioua

Imane Boudellioua obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Imene has a Master's degree in Computer Science from KAUST. During Master's her thesis supervisor was Professor Basem Shihada. Thesis title: " A Vehicular Guidance Wireless Sensor Actuator Network." Research Interests Imane Boudellioua's research interests include the application of machine learning and data mining algorithms for functional annotation of various biological
<> GaN (2) <> generative adversarial network (2) <> generative ai (2) <> genetic variation (2)

Semantic Prioritization of Novel Causative Genomic Variants in Mendelian and Oligogenic Diseases

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B9 R4223

Abstract Recent advances in Next Generation Sequencing (NGS) technologies have facilitated the generation of massive amounts of genomic data which in turn is bringing the promise that personalized medicine will soon become widely available. As a result, there is an increasing pressure to develop computational tools to analyze and interpret genomic data. In this dissertation, we present a systematic approach for interrogating patients' genomes to identify candidate causal genomic variants of Mendelian and oligogenic diseases. To achieve that, we leverage the use of biomedical data available

Imane Boudellioua

Imane Boudellioua obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Imene has a Master's degree in Computer Science from KAUST. During Master's her thesis supervisor was Professor Basem Shihada. Thesis title: " A Vehicular Guidance Wireless Sensor Actuator Network." Research Interests Imane Boudellioua's research interests include the application of machine learning and data mining algorithms for functional annotation of various biological
<> genome sequencing (2) <> Geometry (2)

Cohomology in fluids

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B4 L5 R5209

We present a topological analysis of the vorticity formulation in describing fluid dynamics. Despite its widespread use in fluid mechanics, this formulation is insufficient at describing fluid dynamics on a non-simply-connected domain. What is missing is an equation of motion for fluid's cohomology component, which exhibits fascinating dynamics previously under explored. Using geometric language, we derive the new equation of motion and establish new conservation laws, as Casimir invariants in Hamiltonian mechanics, for fluids on domains with general topology. Significantly, we present the first physically correct vortex method on curved surfaces with genus and boundaries.

Discrete Surfaces from Developable Strips

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B4 L5 R5209

We will discuss several settings in which smooth developable strips are attached to each other or assembled in some other way to obtain surfaces with interesting geometric properties. Driven by the view towards applications we will investigate collections of strips which lie orthogonal or tangential to a reference surface and assume particular shapes. Such strips can serve as support structures or cladding panels of free-form shapes in architectural contexts. Our focus will lie on surfaces with a constant ratio of principal curvatures, cone nets, geodesic grid shells, and others.
<> health (2) <> Human Activity Recognition (2) <> human bioinformatics (2) <> ICT (2) <> ICVSS (2) <> image synthesis (2) <> Imaging (2) <> industrial control systems (2) <> infoclouds (2) <> Information retrieval (2) <> International Supercomputing Conference (2) <> internet of things security (2) <> KFUPM (2) <> knowledge graphs (2)

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Towards a Universal Knowledge Graph Chatbot: ChatGPT versus Traditional Question Answering for Knowledge Graphs

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B9 L2 H2 H2

Conversational AI and Question-Answering systems (QASs) for knowledge graphs (KGs) are both emerging research areas: they empower users with natural language interfaces for extracting information efficiently and effectively. While Conversational AI simulates human-like conversations, its effectiveness is limited by the available training data. However, QASs retrieve the most up-to-date information from KGs by translating natural language queries into formal queries that the database engine can process. In this talk, we examine the characteristics of existing approaches for combining Conversational AI and QASs to create novel KG chatbots. We also introduce KGQAn, a universal QA system that can be applied to any KG without the need for customization.
<> knowledge representation (2) <> large-scale computing (2) <> living systems (2) <> Localization (2) <> machine intelligence (2) <> machine learning for wireless communication (2) <> Machine Learning Systems (2) <> MEMS (2) <> Meta Learning (2) <> ML (2) <> mobile computing (2) <> multimodal sensing (2) <> nanomaterials (2) <> Nanopore Sequencing (2) <> Network Design (2) <> networking (2) <> Neuro-Symbolic AI (2) <> neuro-symbolic methods (2) <> Non-Terrestrial Networks (2) <> Numerical Optimization (2) <> ontology (2) <> optical computing (2) <> organ transplantation (2) <> Partial Differential Equations (2) <> pathway design (2) <> PDE (2) <> Performance optimization (2) <> PhenomeNET (2) <> phenotype ontologies (2) <> photonic integrated circuits (2) <> power systems (2) <> prediction tools (2)

Promoters identification and analysis

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B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

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B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> Programming language (2) <> promoters (2)

Promoters identification and analysis

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B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> protein coding (2)

Promoters identification and analysis

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B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> protein function prediction (2) <> protein structure prediction (2) <> proteins (2) <> quantum communication (2) <> quantum computing (2) <> resiliency (2) <> Robustness (2)

Reynolds-robust preconditioners for the stationary incompressible Navier--Stokes equations

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B9 L2 R2322 H1

Building on the work of Schöberl, Olshanskii, and Benzi, in this talk we present the first preconditioner for the Newton linearization of the stationary Navier--Stokes equations in three dimensions that achieve both optimal complexity in of count and Reynolds-robustness. The exact details of the preconditioner varies with discretization, but the general theme is to combine augmented Lagrangian stabilisation, a custom multigrid prolongation operator involving local solves on coarse cells, and an additive patchwise relaxation on each level that captures the kernel of the divergence operator.

Assessing DNS Resilience and Robustness

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B4 L5 R5220.

The Domain Name System (DNS) is a core protocol for the Internet. It resolves mappings between Internet Protocol (IP) addresses and their corresponding Fully Qualified Domain Names (FQDNs). Since all Internet communications rely on it, DNS structuring should therefore be resilient and robust against failure to avoid any service interruption. While the research community and experienced practitioners have established best practices to this end, many worldwide DNS implementations are still prone to many types of configuration errors. In this talk, I discuss the adoption of these approaches in some countries. Also, a case study is presented considering domains in Saudi Arabia (.sa) that illustrates the value of measuring the DNS at this scale. The results are valuable to improve the DNS infrastructure in the kingdom. Lastly, I provide recommendations to improve DNS service resilience and robustness.
<> scientific research (2) <> SDN (2) <> security (2) <> Security and Privacy (2) <> seismic imaging (2) <> semantic technologies (2)

Large-scale Text and Data Integration for Cancer Genomics Research

Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

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B19 L3 H2

Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Accelerating biomedical discovery with an Internet of FAIR data and services

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B9 L2 H1 R2322

In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
<> sensors (2) <> Signal and Image Processing (2) <> single cell biology (2) <> SOC (2) <> Social media (2) <> software/hardware co-design (2) <> spatial transcriptomics (2) <> Stochastic processes (2) <> sustainability (2) <> threat intelligence (2) <> Training (2) <> Trustworthy AI (2) <> TSS (2)

Promoters identification and analysis

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B1 L2 R2202

Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.

Novel computational methods for promoter identification and analysis

-

B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
<> ultrafast fiber lasers (2) <> uncertainty quantification (2)