Short videos on social media are the dominant way young people consume content. News outlets aim to reach audiences through news reels—short videos conveying news—but struggle to translate traditional journalistic formats into short, entertaining videos. To translate news into social media reels, we support journalists in reframing the narrative. In literature, narrative framing is a high-level structure that shapes the overall presentation of a story. We identified three narrative framings for reels that adapt social media norms but preserve news value, each with a different balance of information and entertainment. We introduce ReelFramer, a human-AI co-creative system that helps journalists translate print articles into scripts and storyboards. ReelFramer supports exploring multiple narrative framings to find one appropriate to the story. AI suggests foundational narrative details, including characters, plot, setting, and key information. ReelFramer also supports visual framing; AI suggests character and visual detail designs before generating a full storyboard. Our studies show that narrative framing introduces the necessary diversity to translate various articles into reels, and establishing foundational details helps generate scripts that are more relevant and coherent. We also discuss the benefits of using narrative framing and foundational details in content retargeting.

Alexander Spangher, Zihan Xue, Te-Lin Wu, Mark Hansen, Jonathan May. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

News media often leverage documents to find ideas for stories, while being critical of the frames and narratives present. Developing angles from a document such as a press release is a cognitively taxing process, in which journalists critically examine the implicit meaning of its claims. Informed by interviews with journalists, we developed AngleKindling, an interactive tool which employs the common sense reasoning of large language models to help journalists explore angles for reporting on a press release. In a study with 12 professional journalists, we show that participants found AngleKindling significantly more helpful and less mentally demanding to use for brainstorming ideas, compared to a prior journalistic angle ideation tool. AngleKindling helped journalists deeply engage with the press release and recognize angles that were useful for multiple types of stories. From our findings, we discuss how to help journalists customize and identify promising angles, and extending AngleKindling to other knowledge-work domains.

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes are coupled and interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. Collaborative decision-making has shown significant benefits at the airport level, both in the U.S. and in Europe. This paper examines how it could be extended to the multimodal network level, discusses the supporting evidence, and provides recommendations for implementation. A case study on the disruption management following the Asiana Crash at San Francisco International Airport is presented. The crash led to a large number of flight diversions to many airports, such as Oakland, Los Angeles, but also Seattle for instance, disrupting the journeys of thousands of passengers. Passenger reaccommodation varied greatly from airline to airline and airport to airport. First, a passenger-centric reaccommodation scheme is developed to balance costs and delays, for each diversion airport. Second, assuming better information sharing and collaborative decision-making, we show that there was enough capacity at the neighboring airports, Oakland and San Jose, to accommodate most of the diverted flights and reoptimize the allocation of flight diversions to the Bay Area airports. Based on this case study, recommendations for the adoption of multimodal CDM are elaborated. This paper paves the way for further data-driven research for increased resilience of passenger door-to-door journeys.

Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes, whether air, road, or rail, are intrinsically coupled through passenger transfers and are interdependent. The frequent occurrence of perturbations on one or several modes disrupts passengers' entire journeys, directly and through ripple effects. This paper provides a case report of the Asiana crash in San Francisco International Airport (SFO) on July 6, 2013, and its repercussions on the multimodal transportation network. It studies the resulting propagation of disturbances on the transportation infrastructure in the USA, particularly on the U.S. air transport network and the ground transportation in the Bay Area. The perturbation takes different forms and varies in scale and time frame: cancelations and delays snowball in the airspace, with up to 86% of cancelations in the U.S. due to the SFO crash; highway traffic near the airport is impacted by congestion in previously not congested locations, with low speed and high delays on US 101; and transit passenger demand exhibits unusual traffic peaks in between airports in the Bay Area, with up to 180 passengers more per hour between SFO and Oakland International Airport Bay Area Rapid Transit stations. This paper also investigated the effect of the crash on the social media Twitter. This paper, through a case study, aims at stressing the importance of further data-driven research on interdependent infrastructure networks. The end goal is to form the basis for optimization models behind providing more reliable passenger door-to-door journeys and improved transport network resilience.

Existing 3D face recognition algorithms have achieved high enough performances against public datasets like FRGC v2, that it is difficult to achieve further significant increases in recognition performance. However, the 3D TEC dataset is a more challenging dataset which consists of 3D scans of 107 pairs of twins that were acquired in a single session, with each subject having a scan of a neutral expression and a smiling expression. The combination of factors related to the facial similarity of identical twins and the variation in facial expression makes this a challenging dataset. We conduct experiments using state of the art face recognition algorithms and present the results. Our results indicate that 3D face recognition of identical twins in the presence of varying facial expressions is far from a solved problem, but that good performance is possible.

As part of the process of delivering content, devices like proxies and gateways log valuable information about the activities and navigation patterns of users on the Web. In this study, we consider how this navigation data can be used to improve Web search. A query posted to a search engine together with the set of pages accessed during a search task is known as a search session. We develop a mixture model for the observed set of search sessions, and propose variants of the classical EM algorithm for training. The model itself yields a type of navigation-based query clustering. By implicitly borrowing strength between related queries, the mixture formulation allows us to identify the highly relevant URLs for each query cluster. Next, we explore methods for incorporating existing labeled data (the Yahoo! directory, for example) to speed convergence and help resolve low-traffic clusters. Finally, the mixture formulation also provides for a simple, hierarchical display of search results based on the query clusters. The effectiveness of our approach is evaluated using proxy access logs for the outgoing Lucent proxy.

We study the application of Rissanen's (1989) principle of minimum description length (MDL) to the problem of wavelet denoising and compression for natural images. After making a connection between thresholding and model selection, we derive an MDL criterion based on a Laplacian model for noiseless wavelet coefficients. We find that this approach leads to an adaptive thresholding rule. While achieving mean-squared-error performance comparable with other popular thresholding schemes, the MDL procedure tends to keep far fewer coefficients. From this property, we demonstrate that our method is an excellent tool for simultaneous denoising and compression. We make this claim precise by analyzing MDL thresholding in two optimality frameworks; one in which we measure rate and distortion based on quantized coefficients and one in which we do not quantize, but instead record rate simply as the number of nonzero coefficients.

In this paper, we describe a new technique for solving a variety of query-retrieval problems in optimal time with optimal or near-optimal space.In particular, we use the technique to construct algorithms and data structures for circular range searching, half-space range searching, and computing k-nearest neighbors in a variety of metrics.For each problem and each query, the response to the query is provided in O(k) or O(k + log n) time where k is the size of the response and n is the size of the problem.(E.g., for the n-point k-nearest neighbors problem, the k-nearest neighbors of any query point are provided in O(k -4log n) steps.)Depending on the problem being solved, the space required for the data structure is either linear or O(n log n).Hence, the time bounds are optimal and the space bounds are optimal or near-optimal.Previously known data structures for these problems required a factor of ~(log n(log log n) 2) or ~(log n log log n) more space and/or more time to answer each query.Our compaction technique incorporates planar separators, filtering search, and the probabilistic method for discrepancy problems.The fundamental idea is that k'h-order Voronoi diagrams (and other suitable proximity diagrams) can be compacted from k°(1)n space to O(n) space and still retain all the information that is essential for solving query problems.This result is of independent interest and may be useful in improving the memory space requirement or the query-time bound for other query-retrieval problems.