Sharif Digital Repository

In the mid twentieth century by discovering the existence of genetic strands and understanding their role in diseases and phenotypes of species, research initiated to decipher their content. Sequencing of the first human genome at early twenty-first century paved the way to study and even cure complex human deseases having genetic origin. Next Generation Sequencing (NGS) Technologies have significantly reduced the expenses and the timing complexity of DNA Sequencing and this has an improving trend. In this thesis, we evaluate Base Calling methods, a critical step in analyzing next generation sequencing information and deals with massive sequencing data. Base Calling tries to optimally detect... 

Causal discovery is of utmost importance for agents who must plan and decide based on observations. Since mistaking correlation with causation might lead to un- wanted consequences. The gold standard to discover causal relation is to perform experiments. However, experiments are in many cases expensive, unethical or impossible to perform. In these situations, there is a need for observational causal discovery. Causal discovery in the observational data setting involves making significant assumptions on the data and on the underlying causal model. This thesis aims to alleviate some of the assumptions and tries to identify the causal relationships and causal mechanisms using generative neural... 

e study of the brain’s neural activity is an active research area in computational neuroscience aiming to provide insights about the functionality of the brain as well as dysfunctions that underlie disorders. Functional Magnetic Resonance Imaging (fMRI) plays an important role in brain studies by providing non-invasive records of neural activities during a specific task with location sensitivity. Recent advances in statistics and machine learning offer powerful tools for paern recognition and processing of fMRI data. In this thesis, we decode information recorded via fMRI from the visual cortex to reconstruct images presented to subjects. Current reconstruction methods face numerous... 

Ever-increasing appetite for wireless data traffic is insatiable. Mobile data traffic was about 3.7 Exabytes per month in 2015. Owing to Cisco’s prognostications, this data traffic will surpass 30 Exabytes per month in 2020, which unveil exponentially growth in that. On the other hand, however, precedent techniques have undertaken this appetite till now, but most of them will not hold off this exponentially increasing demand in near future. Accordingly, one of the most precious resources for near future is millimeter Wave (mmWave). In this thesis, we present a cell planning problem for a cellular network containing two types of base stations (BS), to distinguish between them, BSs which have... 

Most of the existing visual SLAM methods heavily rely on a static world assumption and easily fail in dynamic environments. One solution is to eliminate the influence of dynamic objects by introducing deep learning-based semantic information to SLAM systems. In this project, we propose a real-time semantic RGB-D SLAM (built upon RTAB-Map) system for dynamic environments that is capable of detecting moving objects and maintaining a static map for robust camera tracking. Furthermore, we augment the semantic segmentation process using an Extended Kalman filter module to detect temporarily static moving objects by adding centroids to each found dynamic object and calculating their velocity. We... 

This thesis focuses on generating text from data. The Data-to-Text system must have three capabilities; First, it should be able to produce coherent, comprehensible, fluent text that is close to human natural language, in such a way that it is not possible to distinguish it from texts written by humans. Second, to be able to produce a variety of sentences to express the same concept. The third is to be able to express the information of the input data without repetition, redundancy, and omission in the output sentences. The latter is one of the main challenges of data-to-text systems because not being faithful to the input data can lead to se- rious problems in real-world applications. Until...