Thesis advisor: Brian B. Zhou / This thesis presents work in the context of multimodal magnetometry for two-dimensional (2d) materials. Research on van der Waals materials has been rapidly emerging and several imaging techniques have been developed in the past decades. Among the modern techniques, solid-state spins feature outstanding sensitivity and nano-scale spatial resolution. Yet their full capacity in sensing still has room for improvement, as the quantum nature of their properties haven't been fully utilized. My research involves developing state-of-the-art sensing techniques to add new `function modules' to the nitrogen-vacancy (NV) centers, with the goal of uncovering dynamical magnetic and electrical phenomena of 2d materials. In the first chapter I will briefly discuss the basic opto-spin properties of the NV center. One shall see why NV is preferred as a quantum sensing probe: the opto-spin property comes handy as one simply counts photons to manipulate and read out quantum states, and the stability and long quantum coherence time makes NV adaptive with various environments and engineering. In the second chapter I will discuss the experimental setup with the focus on the home-built confocal microscope, which equips our sensing technique with the pump-probe scanning ability of sub-um 2d resolution. In the third chapter I will discuss the developments of the sensing protocols, including the ac susceptometry and the opto-magnetization mapping, based on the lock-in method using the quantum dynamical decoupling sequences. In the fourth chapter I will describe the ac susceptibility measurements on thin CrBr3 flakes. The magnetization behaviors under kHz to MHz excitations reveal the domain morphology and domain wall mobility, providing insights to the exchange interaction of the chromium trihalides in the 2d limit. In the fifth chapter I will describe the pump-probe measurements on few-layer CrCl3 flakes. The mapping result demonstrates a photo-generated enhancement of the in-plane magnetization. Along with the time-resolved photoluminescence measurement, the results are indicative of a defect-assisted Auger recombination process of excitons. To conclude, the multimodal sensing techniques with NV developed in this thesis allow for more versatile experiments with sensitivity for low-dimensional systems. The developments bring up new perspectives on fundamental physics in atomically thin materials, providing new ideas for future technological applications such as spintronics and quantum memory. / Thesis (PhD) — Boston College, 2024. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Physics.

Automatic movie analysis is the task of employing Machine Learning methods to the field of screenplays, movie scripts, and motion pictures to facilitate or enable various tasks throughout the entirety of a movie’s life-cycle. From helping with making informed decisions about a new movie script with respect to aspects such as its originality, similarity to other movies, or even commercial viability, all the way to offering consumers new and interesting ways of viewing the final movie, many stages in the life-cycle of a movie stand to benefit from Machine Learning techniques that promise to reduce human effort, time, or both. Within this field of automatic movie analysis, this thesis addresses the task of summarising the content of screenplays, enabling users at any stage to gain a broad understanding of a movie from greatly reduced data. The contributions of this thesis are four-fold: (i)We introduce ScriptBase, a new large-scale data set of original movie scripts, annotated with additional meta-information such as genre and plot tags, cast information, and log- and tag-lines. To our knowledge, Script- Base is the largest data set of its kind, containing scripts and information for almost 1,000 Hollywood movies. (ii) We present a dynamic summarisation model for the screenplay domain, which allows for extraction of highly informative and important scenes from movie scripts. The extracted summaries allow for the content of the original script to stay largely intact and provide the user with its important parts, while greatly reducing the script-reading time. (iii) We extend our summarisation model to capture additional modalities beyond the screenplay text. The model is rendered multi-modal by introducing visual information obtained from the actual movie and by extracting scenes from the movie, allowing users to generate visual summaries of motion pictures. (iv) We devise a novel end-to-end neural network model for generating natural language screenplay overviews. This model enables the user to generate short descriptive and informative texts that capture certain aspects of a movie script, such as its genres, approximate content, or style, allowing them to gain a fast, high-level understanding of the screenplay. Multiple automatic and human evaluations were carried out to assess the performance of our models, demonstrating that they are well-suited for the tasks set out in this thesis, outperforming strong baselines. Furthermore, the ScriptBase data set has started to gain traction, and is currently used by a number of other researchers in the field to tackle various tasks relating to screenplays and their analysis.

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