Despite recent advances in inversion-based editing, text-guided image manipulation remains challenging for diffusion models. The primary bottlenecks include 1) the time-consuming nature of the inversion process; 2) the struggle to balance consistency with accuracy; 3) the lack of compatibility with efficient consistency sampling methods used in consistency models. To address the above issues, we start by asking ourselves if the inversion process can be eliminated for editing. We show that when the initial sample is known, a special variance schedule reduces the denoising step to the same form as the multi-step consistency sampling. We name this Denoising Diffusion Consistent Model (DDCM), and note that it implies a virtual inversion strategy without explicit inversion in sampling. We further unify the attention control mechanisms in a tuning-free framework for text-guided editing. Combining them, we present inversion-free editing (InfEdit), which allows for consistent and faithful editing for both rigid and non-rigid semantic changes, catering to intricate modifications without compromising on the image's integrity and explicit inversion. Through extensive experiments, InfEdit shows strong performance in various editing tasks and also maintains a seamless workflow (less than 3 seconds on one single A40), demonstrating the potential for real-time applications. Project Page: https://sled-group.github.io/InfEdit/

Currently in the petroleum industry, operators often flare the produced gas instead of commodifying it. The flaring magnitudes are large in some states, which constitute problems with energy waste and CO2 emissions. In North Dakota, operators are required to estimate and report the volume flared. The questions are, how good is the quality of this reporting, and what insights can be drawn from it? Apart from the company-reported statistics, which are available from the North Dakota Industrial Commission (NDIC), flared volumes can be estimated via satellite remote sensing, serving as an unbiased benchmark. Since interpretation of the Landsat 8 imagery is hindered by artifacts due to glow, the estimated volumes based on the Visible Infrared Imaging Radiometer Suite (VIIRS) are used. Reverse geocoding is performed for comparing and contrasting the NDIC and VIIRS data at different levels, such as county and oilfield. With all the data gathered and preprocessed, Bayesian learning implemented by MCMC methods is performed to address three problems: county level model development, flaring time series analytics, and distribution estimation. First, there is heterogeneity among the different counties, in the associations between the NDIC and VIIRS volumes. In light of such, models are developed for each county by exploiting hierarchical models. Second, the flaring time series, albeit noisy, contains information regarding trends and patterns, which provide some insights into operator approaches. Gaussian processes are found to be effective in many different pattern recognition scenarios. Third, distributional insights are obtained through unsupervised learning. The negative binomial and GMMs are found to effectively describe the oilfield flare count and flared volume distributions, respectively. Finally, a nearest-neighbor-based approach for operator level monitoring and analytics is introduced.

Marc Wojno has been a writer and editor in the financial field for more than two decades. A new report published this month by data analytics firm FICO shows that a growing percentage of younger U.S. consumers -- specifically Gen X, Millennial and Gen Z groups -- consider digital banks, such as Cash App, Chime and PayPal, as their primary checking account provider, not traditional megabanks such as Bank of America, JPMorgan Chase and Wells Fargo. The report identified five competitive threats to traditional banks and credit unions, and what those companies need to do to stay competitive: Overdraft; savings and investing; buy now, pay later (BNPL); niche neobanks; and open banking. The report, Counterattack: Banks Field Guide to Fintech Disruption, in conjunction with research from Cornerstone Advisors, notes that although many US consumers are pleased with the quality and services of traditional banks and credit unions, the percentage of those three younger generations who chose fintechs over brick-and-mortars as their primary banks have doubled, at 12% of customers since 2020. FICO's report stated that for Millennials and Gen X-ers, the percentages dropped by nearly half during that same period.

DeepBrain AI's AI human technology is a solution that creates a virtual human capable of real-time interactive communication. It implements AI that can communicate directly with users by fusion of speech synthesis, video synthesis, natural language processing, and speech recognition technologies. As a technology that can realize complete contactless service in various fields, banks have the effect of providing a secure counseling service to customers who prefer non-face-to-face in accordance with the COVID-19 situation, and shortening customer waiting time through faster response. First, the AI banker greets customers when they arrive at the kiosk and provides answers to their questions. All answers go through the process of deriving optimal information based on KB-STA, a financial language model developed by KB Kookmin Bank, and delivered to customers through the AI banker's video and voice implemented with DeepBrain AI's AI human technology.