FastVideoEdit: Leveraging Consistency Models for Efficient Text-to-Video Editing

Diffusion models have demonstrated remarkable capabilities in text-to-image and text-to-video generation, opening up possibilities for video editing based on textual input. However, the computational cost associated with sequential sampling in diffusion models poses challenges for efficient video editing. Existing approaches relying on image generation models for video editing suffer from time-consuming one-shot fine-tuning, additional condition extraction, or DDIM inversion, making real-time applications impractical. In this work, we propose FastVideoEdit, an efficient zero-shot video editing approach inspired by Consistency Models (CMs). By leveraging the self-consistency property of CMs, we eliminate the need for time-consuming inversion or additional condition extraction, reducing editing time. Our method enables direct mapping from source video to target video with strong preservation ability utilizing a special variance schedule. This results in improved speed advantages, as fewer sampling steps can be used while maintaining comparable generation quality. Experimental results validate the state-of-the-art performance and speed advantages of FastVideoEdit across evaluation metrics encompassing editing speed, temporal consistency, and text-video alignment

Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants

On-device Virtual Assistants (VAs) powered by Automatic Speech Recognition (ASR) require effective knowledge integration for the challenging entity-rich query recognition. In this paper, we conduct an empirical study of modeling strategies for server-side rescoring of spoken information domain queries using various categories of Language Models (LMs) (N-gram word LMs, sub-word neural LMs). We investigate the combination of on-device and server-side signals, and demonstrate significant WER improvements of 23%-35% on various entity-centric query subpopulations by integrating various server-side LMs compared to performing ASR on-device only. We also perform a comparison between LMs trained on domain data and a GPT-3 variant offered by OpenAI as a baseline. Furthermore, we also show that model fusion of multiple server-side LMs trained from scratch most effectively combines complementary strengths of each model and integrates knowledge learned from domain-specific data to a VA ASR system

Individual Parametric Insurance Product Design

This report presents a design of a parametric insurance product for individual consumers in two neighboring countries Ambernϊa and Palȍmϊnϊa. Unlike traditional insurance, this product issues a predetermined payout to a policyholder when a pre-agreed event has been triggered. By conducting analyses on given health data in the countries, we first projected individual losses and calculated premiums according to gender, age and risk factor information. Then we defined triggering events and modeled the payout scheme for our product. Comprehensive strategies are also provided for marketing and risk mitigation

Transnational Queerness and Educational Mobility among Chinese College Students in North America

This thesis focuses on the transnational education experiences of queer women who migrated to North America from mainland China, motivated by both individual and professional reasons. Influenced by the global queer movements and economic development in post-socialist China, young queer women are studying abroad in developed, anglophone countries. Drawing from the researcher’s private journey as a Chinese queer woman in the U.S. and the socio-political background of Chinese queer culture, this research delves into the intersections of national, social, gender, and sexual identities. Here this paper argues that Chinese queer women adopt a discursive practice of “disengagement from national identity” as a strategy for political activism, which allows them to negotiate an increasingly restrictive political environment in mainland China and to participate in various social justice and advocacy activities in a transnational context. The thesis also addresses the complexities and conflicts of gender and sexual dichotomies, emphasizing the political expressions and alliances behind these identities. In addition, the paper discusses the difficulties and injustices faced by Chinese diaspora women in the U.S. immigration justice system, as well as the struggles of Chinese queer activists in their migratory experiences

Population inversion in laser-driven N2+

The time-dependent population transfer process of N2+ generated in an intense laser pulse has been investigated using the quasi-stationary Floquet theory by assuming that N2+ experiences an intense laser pulse with the sudden turn-on. A light-dressed B state is formed with a significant amount of population when pulse is suddenly turned on and is adiabatically transformed to the vibrational ground state (v = 0) of the field-free B state when the pulse vanishes. In addition, a part of the population is transferred to the electronically excited A state through one-photon resonance, which also contributes to decreasing the final population in the X state, facilitating the population inversion between the B state and the X state

Investigation of Surface-inset Machines with Mixed Grade Magnets Considering Magnet Thickness

This paper presents a mixed grade magnet model for surface-inset machines considering the magnet thickness. In the polar coordinates, on the basis of the Laplace/quas i-Poisson equations and boundary conditions, the constructed matrix equations are solved and the air gap magnetic field in the machine is derived. Taking an 8-pole/12-slot surface-inset motor as an example, through the presented optimization process, the air gap field is optimized considering the magnet thickness, remanence and magnetization angle. In addition, the back-EMF and electromagnetic torque are analytically obtained. The optimized results show that the proposed mixed grade magnet model has larger electromagnetic torque and smaller torque ripple than the conventional one. Finally, the analytical predictions are evaluated by finite element analysis (FEA)