Sharif Digital Repository

Most elderly and physically disabled people suffer from lack of functionality and dexterity in their elbow or wrist. These disabilities are due to the damages mostly caused by sport surgery, spinal surgery, or stroke. Therefore, design of an assistive exoskeleton robot for upper limb movements seems necessary. The purpose of this study is to design, fabricate and deliver a control algorithm for an assistive wearable robot. The robot has five degrees of freedom in order to help the flexion/extension and abduction/adduction shoulder. Dynamic and kinematic model of elbow, forearm, and wrist is developed to determine the amounts of torques which are required in the joint actuators mounted on the... 

In this project, a ceramic biocompatible coating was applied on Titanium which is included Hydroxyapatite by AC Plasma Electrolyte Oxidation method. First, coating was performed in 5 different solutions and various duration in 500mA/Cm2 as a current density. Then, by microscopic and macroscopic images and size of porosity and also thickness of coat, 3 solutions were chosen for adding nanoparticles: Solution 1 which includes NaH2PO4 and Ca(CH3COO)2 , Solution 3 which includes Ca(CH3COO)2 and Na-Beta G and Solution 4 which includes Ca(H2PO4)2, HMP, NA2(EDTA) and Ca(CH3COO)2 . Also the time 10 minutes was chose as a appropriate time. The average of the porosities size in solution 1 was about... 

Solving the visual symbol grounding problem has long been a goal of modern aritificial intelligence. Due to recent breakthroughs in deep learning methods for natural language processing and visual interpretation tasks‚ the field now seems to be as near to achieving this goal as it ever was. Also recent progress in using recurrent neural netowrks (RNNs) for image description‚ has motivated the exploration of their application for video description tasks. However, while images remain static‚ interpreting videos require modeling complex dynamic temporal sturctures and then properly integrating that information into a natural language description. Recurrent neural networks can be both used to... 

Few-shot learning methods have achieved notable performance in recent years. However, fewshot learning in large-scale settings with hundreds of classes is still challenging. In this dissertation, we tackle the problems of large-scale few-shot learning by taking advantage of pre-trained foundation models. We recast the original problem in two levels with different granularity. At the coarse-grained level, we introduce a novel object recognition approach with robustness to sub-population shifts. At the fine-grained level, generative experts are designed for few-shot learning, specialized for different superclasses. A Bayesian schema is considered to combine coarse-grained information with... 

Despite the remarkable success of deep learning algorithms in recent years, it still has a long way to reach the status of human natural intelligence and to acquire the expected self-autonomy. As a result, many researchers in this field have focused on the development of these algorithms while taking inspiration from human cognitive behaviors. One of the disadvantages of current algorithms is the lack of their ability to learn in a continual manner while deployed in the environment. More precisely, deep learning models are not able to gradually gather knowledge from the environment and if they are in a situation of limited access to data, they will suffer from catastrophic forgetting; a... 

Recent advances in deep neural networks have shown significant potential; however, they still face challenges when it comes to non-stationary environments. Continual learning is related to deep neural networks with limited capacity that should perform well on a sequence of tasks. On the other hand, studies have shown that neural networks are sensitive to covariate shifts. But in many cases, the distribution of data varies with time. Domain Adaptation tries to improve the performance of a model on an unlabeled target domain by using the knowledge of other related labeled data coming from a different distribution. Many studies on domain adaptation have optimistic assumptions that are not... 

Representation learning refers to mapping the input data to another space, usually with lower dimensions than the input space. This task can be helpful in improving the performance of methods in downstream tasks, compression, and improving sample generation in generative models. Representation learning is a problem connected to information theory, and information theory's concepts and quantities are used widely in representation learning models. Besides, the representation learning problem is closely related to latent variable generative models. These models usually learn useful representations in their process of training, implicitly or explicitly. So, the usage of latent variable... 

Ensuring the reliability of Large Language Models (LLMs) in complex reasoning tasks remains a formidable challenge, particularly in scenarios that demand precise mathematical calculations and knowledge-intensive open-domain generation. In this work, we introduce an uncertainty-aware framework designed to enhance the accuracy of LLM responses by systematically incorporating model confidence at critical deci- sion points. We propose an approach that encourages multi-step reasoning in LLMs and quantify the confidence of intermediate answers such as numerical results in math- ematical reasoning and proper nouns in open-domain generation. Then, the overall confidence of each reasoning chain is... 

Traditionally, machine learning models for classification tasks rely on statistical methods to find correlations between patterns in the input data and their correspond- ing labels. However, these correlations are not necessarily consistent across different data partitions and may change at test time. Such unstable correlations are referred to as spurious correlations. When the spurious correlation relied upon during training changes at test time, the model’s accuracy can degrade. To improve robustness to shifts in spurious correlations, most research in this area assumes that group annota- tions based on different values of the spurious attribute are available during training or validation.... 

The production of agricultural products is one of the most important human economic activities. The issue of mechanized and optimal use of pesticides is vital for human health and the environment, and the use of helicopters makes this possible. Although spray-based systems in helicopters are one of the most effective ways to produce agricultural products, it is still unclear how droplet movement in aerial spraying is affected by the complex downwash flow created by rotors. Modeling agricultural air spray to identify the spray trend of droplets in the air stream, downwash flows, and consequent vortices has attracted more attention as a result of the development of computational fluid... 

The rapid emergence of powerful text-to-image generative models in recent years has attracted significant attention. Trained conditionally on large-scale text–image pairs, these models have achieved remarkable results in terms of quality, realism, and diversity of generated images. Nevertheless, they exhibit substantial weaknesses in visual compositional generation. In this context, compositional generation refers to the model’s ability to correctly interpret and render entities, attributes (such as colors and sizes), inter-object relationships, and object counts specified in the input text, while maintaining overall image quality. In this thesis, we first analyze and categorize the... 

In recent years, Large Language Models have demonstrated remarkable capabilities in solving a wide range of natural language processing tasks. However, their high computational cost has made their use in resource-constrained environments a significant challenge. Given these challenges, Small Language Models (SLMs) have emerged as an efficient alternative, especially for specialized applications under severe hardware constraints. This thesis proposes a novel framework for structured, dynamic, and task-specific pruning of SLM layers, aimed at reducing computational cost and inference latency, specifically optimized for the tool-calling task. The proposed method introduces a multi-stage... 

Dithiocarbamate is one of the most useful components in the science of chemistry. They are used not only in agriculture as fungicides but also in photochemistry in order to determine and analyse NO in biological processes as well as in polymerization routes. Dithiocarbamates which are known as bidentate ligands with high yield are applied in many fields such as producing chemical drugs, functioning as intermediates in organic synthesis, protecting groups in peptide synthesis as well as highly chelating ligands. Regarding the high practically of dithiocarbamates and by focusing on their synthesis process, we have applied them as bidentate ligands. Cinnamaldehyde was condensed in ethanol with... 

The goal of this project is the development of a control algorithm for a flock of non-holonomic aerial agents. For this purpose,the swarm architecture having some unique features such as robustness, flexibility, and scalability is utilized. Swarm is defined as a group of simple agents having local interactions between themselves and the environmentwhich shows an unpredictable emergent behavior.Behavior based control which is inspired from the animal behaviors is employed to control the swarm of mobile agents. Accordingly, the necessary behaviors which are distance adjustment, velocity agreement, and virtual leader tracking together with a fuzzy coordinator are designed. In this study, in... 

In many real-world applications, data contain multiple input channels (e.g., web pages include text, images and etc). In these cases, supervisory information may also be available in the form of distance constraints such as similar and dissimilar pairs from user feedbacks. Distance metric learning in these environments can be used for different goals such as retrieval and recommendation. In this research, we used from dual-wing harmoniums to combining text and image modals to a unified latent space when similar-dissimilar pairs are available. Euclidean distance of data represented in this latent space used as a distance metric. In this thesis, we extend the dual-wing harmoniums for... 

The existing learning methods usually assume that training and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these learning methods on the test data will be severely degraded. We often have sufficient labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and no labeled training data. In this thesis, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. We propose a framework which finds a new representation for both the source and the target domain in which the distance between these...