Dualities and Symmetries of Quantum Field Theories from Brane Engineering

This dissertation presents a study of dualities and generalized global symmetries in quantum field theories (QFTs) from the string theory perspective. Chapter 2 is based on the work arXiv:2110.03696 with Sebasti\'{a}n Franco, Alessandro Minino and \'{A}ngel M. Uranga . It introduces a new class of string theory backgrounds, Spin(7) orientifolds, allowing for the engineering of 2$d$ $\mathcal{N}=(0,1)$ gauge theories on D1-branes and illustrating the perspective on 2$d$ $\mathcal{N}=(0,1)$ theories as real slices of 2$d$ $\mathcal{N}=(0,2)$ ones. Chapter 3 is based on the work arXiv:2112.03929 with Sebasti\'{a}n Franco, Alessandro Minino and \'{A}ngel M. Uranga. It presents a new, geometric perspective on the triality of 2$d$ $\mathcal{N}=(0,1)$ gauge theories, based on their brane engineering introduced in Chapter 2. It also shows that general Spin(7) orientifolds extend triality to theories consisting of coupled 2$d$ $\mathcal{N}=(0,2)$ and $\mathcal{N}=(0,1)$ sectors, leading to extensions of triality that interpolate between pure 2$d$ $\mathcal{N}=(0,2)$ and $\mathcal{N}=(0,1)$ cases. Chapter 4 is based on the work arXiv:2212.09743 with Jonathan J. Heckman, Max Hubner, Ethan Torres and Hao Y. Zhang. It presents a top-down construction of non-invertible duality symmetries in 4$d$ QFTs. The realization of QFTs is through D3-branes probing a Calabi-Yau threefold with an isolated singularity. The non-invertible duality defect then arises from configurations of 7-branes "at infinity". The study shows that different field-theoretic realizations of duality defects simply amount to distinct choices of where to place 7-brane branch cuts in the 5D bulk.Comment: Ph.D. Dissertatio

Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels

Computer vision algorithms with pixel-wise labeling tasks, such as semantic segmentation and salient object detection, have gone through a significant accuracy increase with the incorporation of deep learning. Deep segmentation methods slightly modify and fine-tune pre-trained networks that have hundreds of millions of parameters. In this work, we question the need to have such memory demanding networks for the specific task of salient object segmentation. To this end, we propose a way to learn a memory-efficient network from scratch by training it only on salient object detection datasets. Our method encodes images to gridized superpixels that preserve both the object boundaries and the connectivity rules of regular pixels. This representation allows us to use convolutional neural networks that operate on regular grids. By using these encoded images, we train a memory-efficient network using only 0.048\% of the number of parameters that other deep salient object detection networks have. Our method shows comparable accuracy with the state-of-the-art deep salient object detection methods and provides a faster and a much more memory-efficient alternative to them. Due to its easy deployment, such a network is preferable for applications in memory limited devices such as mobile phones and IoT devices.Comment: 6 pages, submitted to MMSP 201

BFT2_2: a General Class of 2d2d N=(0,2)\mathcal{N}=(0,2) Theories, 3-Manifolds and Toric Geometry

We introduce and initiate the study of a general class of $2d$ $\mathcal{N}=(0,2)$ quiver gauge theories, defined in terms of certain 2-dimensional CW complexes on oriented 3-manifolds. We refer to this class of theories as BFT$_2$\mbox{'}s. They are natural generalizations of Brane Brick Models, which capture the gauge theories on D1-branes probing toric Calabi-Yau 4-folds. The dynamics and triality of the gauge theories translates into simple transformation of the underlying CW complexes. We introduce various combinatorial tools for analyzing these theories and investigate their connections to toric Calabi-Yau manifolds, which arise as their master and moduli spaces. Invariance of the moduli space is indeed a powerful criterion for identifying theories in the same triality class. We also investigate the reducibility of these theories.Comment: 44 pages, 32 figure

Towards Better Image Embeddings Using Neural Networks

The primary focus of this dissertation is to study image embeddings extracted by neural networks. Deep Learning (DL) is preferred over traditional Machine Learning (ML) for the reason that feature representations can be automatically constructed from data without human involvement. On account of the effectiveness of deep features, the last decade has witnessed unprecedented advances in Computer Vision (CV), and more real-world applications are expected to be introduced in the coming years. A diverse collection of studies has been included, covering areas such as person re-identification, vehicle attribute recognition, neural image compression, clustering and unsupervised anomaly detection. More specifically, three aspects of feature representations have been thoroughly analyzed. Firstly, features should be distinctive, i.e., features of samples from distinct categories ought to differ significantly. Extracting distinctive features is essential for image retrieval systems, in which an algorithm finds the gallery sample that is closest to a query sample. Secondly, features should be privacy-preserving, i.e., inferring sensitive information from features must be infeasible. With the widespread adoption of Machine Learning as a Service (MLaaS), utilizing privacy-preserving features prevents privacy violations even if the server has been compromised. Thirdly, features should be compressible, i.e., compact features are preferable as they require less storage space. Obtaining compressible features plays a vital role in data compression. Towards the goal of deriving distinctive, privacy-preserving and compressible feature representations, research articles included in this dissertation reveal different approaches to improving image embeddings learned by neural networks. This topic remains a fundamental challenge in Machine Learning, and further research is needed to gain a deeper understanding

Study of Catalytic Dephosphorylation using Cerium Dioxide Nano-Crystal

Cerium oxides emerge as a competitive candidate for wastewater treatment due to its exceptional ability to cleave P-O ester bond. In this paper, we want to validate its universal catalysis ability on wider range of organic phosphates; furthermore, we want to probe what factor(s) inhibits and/or promotes the reaction, and what pathway this dephosphorylation reactions take. Understanding the reaction mechanism helps commercialize cerium oxide nano-particles

Competitions in Education: Case Study on Face Verification

All genuine knowledge originates in direct experience, especially for engineering courses. To help the students grasp hands-on experience of solving practical problems, a Machine Learning competition named TUGraz-TUT Face Verification Challenge was jointly organized by Graz University of Technology and Tampere University of Technology. The objective of the competition was to identify whether two facial images represent the same person. During the two-month period, the competition received 137 entries submitted by 28 players in 20 teams. This thesis summarizes the outcome of the competition. To scrutinize the face verification system systematically, the processing workflow was divided into several parts. In the procedure of face alignment, Unsupervised Joint Alignment and Ensemble of Regression Trees were compared. Subsequently, the OpenFace and VGG Face features were retrieved from the aligned images. In the classification system, the performance of neural network and support vector classification were evaluated. Moreover, the influence of the ensemble strategies and the result of different error metrics were investigated. Based on the cutting-edge deep neural networks proposed by the research community, the winning solutions attained excellent results as the Weighted AUC scores exceeded 0.9990. In addition to the preceding accomplishments, the findings suggested that there were still opportunities for further enhancements of the face verification systems. The limitations of current work and a handful of conceivable directions for future research had been deduced

Disorder Averaging and its UV (Dis)Contents

We present a stringy realization of quantum field theory ensembles in $D \le 4$ spacetime dimensions, thus realizing a disorder averaging over coupling constants. When each member of the ensemble is a conformal field theory with a standard semi-classical holographic dual of the same radius, the resulting bulk can be interpreted as a single asymptotically Anti-de Sitter space geometry with a distribution of boundary components joined by wormhole configurations, as dictated by the Hartle-Hawking wave function. This provides a UV completion of a recent proposal by Marolf and Maxfield that there is a high-dimensional Hilbert space for baby universes, but one that is compatible with the proposed Swampland constraints of McNamara and Vafa. This is possible because our construction is really an approximation that breaks down both at short distances, but also at low energies for objects with a large number of microstates. The construction thus provides an explicit set of counterexamples to various claims in the literature that holographic and effective field theory considerations can be reliably developed without reference to any UV completion.Comment: v2: 27 pages, 6 figures, typos corrected, references and clarifications adde

Improved Extreme Learning Machine and Its Application in Image Quality Assessment

Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted) ELM, and propose a modified (weighted) extreme learning machine (M-ELM and M-WELM). Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment