Visual-Language Models (VLMs) have shown remarkable performance across various tasks, particularly in recognizing geographic information from images. However, significant challenges remain, including biases and privacy concerns. To systematically address these issues in the context of geographic information recognition, we introduce a benchmark dataset consisting of 1,200 images paired with detailed geographic metadata. Evaluating four VLMs, we find that while these models demonstrate the ability to recognize geographic information from images, achieving up to $53.8\%$ accuracy in city prediction, they exhibit significant regional biases. Specifically, performance is substantially higher for economically developed and densely populated regions compared to less developed ($-12.5\%$) and sparsely populated ($-17.0\%$) areas. Moreover, the models exhibit regional biases, frequently overpredicting certain locations; for instance, they consistently predict Sydney for images taken in Australia. The strong performance of VLMs also raises privacy concerns, particularly for users who share images online without the intent of being identified. Our code and dataset are publicly available at https://github.com/uscnlp-lime/FairLocator.

Vision is a primary means of how humans perceive the environment, but Blind and Low-Vision (BLV) people need assistance understanding their surroundings, especially in unfamiliar environments. The emergence of semantic-based systems as assistance tools for BLV users has motivated many researchers to explore responses from Large Vision-Language Models (LVLMs). However, it has yet been studied preferences of BLV users on diverse types/styles of responses from LVLMs, specifically for navigational aid. To fill this gap, we first construct Eye4B dataset, consisting of human-validated 1.1k curated outdoor/indoor scenes with 5-10 relevant requests per scene. Then, we conduct an in-depth user study with eight BLV users to evaluate their preferences on six LVLMs from five perspectives: Afraidness, Nonactionability, Sufficiency, and Conciseness. Finally, we introduce Eye4B benchmark for evaluating alignment between widely used model-based image-text metrics and our collected BLV preferences. Our work can be set as a guideline for developing BLV-aware LVLMs towards a Barrier-Free AI system.

After months at sea, the U.S. Coast Guard Cutter Waesche returned to San Diego on Thursday, with over 37,000 pounds of confiscated cocaine and one less crew member, lost at sea, officials said. The offloading of their massive narcotics haul -- which weighs about as much as a full grown humpback whale and is estimated to be worth 275 million -- comes days after search efforts were ended for 23-year-old Seaman Bryan Lee, according to the Coast Guard. Lee, who hails from Rancho Cordova, was discovered missing at 6:45 a.m. last Tuesday while the Waesche was conducting a routine counter-drug patrol around 300 nautical miles south of Mexico. Search crews dedicated over 190 hours to scouring 19,000 nautical miles for Lee using drones, aircraft and vessels, before suspending the search on Monday. The confiscated cocaine was netted through 11 drug interdiction missions off the coasts of Mexico and Central and South America from December through mid February.

More than 100 people have been killed in air crashes this year already, including in a midair collision between a commercial airliner and a helicopter near Washington, DC, and a plane crashing into a bus on a Sao Paulo street. The fatal incidents in the first two months of the new year came after last year was declared one of the deadliest in aviation history with at least 318 deaths in 11 civilian airplane crashes, including two incidents in the last week of December. While fatal air crashes are rare, they attract extraordinary attention, often reinstilling the fear of flying. At least 25 million adults in the United States alone have a fear of flying, according to the Cleveland Clinic. The fear is often exacerbated not just by the crashes but also incidents like emergency landings, a door blowing off a plane and aircraft skidding off runways.

In this book, written in Portuguese, we discuss what ill-posed problems are and how the regularization method is used to solve them. In the form of questions and answers, we reflect on the origins and future of regularization, relating the similarities and differences of its meaning in different areas, including inverse problems, statistics, machine learning, and deep learning.

Large language models (LLMs) have been shown to propagate and even amplify gender bias, in English and other languages, in specific or constrained contexts. However, no studies so far have focused on gender biases conveyed by LLMs' responses to generic instructions, especially with regard to masculine generics (MG). MG are a linguistic feature found in many gender-marked languages, denoting the use of the masculine gender as a "default" or supposedly neutral gender to refer to mixed group of men and women, or of a person whose gender is irrelevant or unknown. Numerous psycholinguistics studies have shown that MG are not neutral and induce gender bias. This work aims to analyze the use of MG by both proprietary and local LLMs in responses to generic instructions and evaluate their MG bias rate. We focus on French and create a human noun database from existing lexical resources. We filter existing French instruction datasets to retrieve generic instructions and analyze the responses of 6 different LLMs. Overall, we find that $\approx$39.5\% of LLMs' responses to generic instructions are MG-biased ($\approx$73.1\% across responses with human nouns). Our findings also reveal that LLMs are reluctant to using gender-fair language spontaneously.

Early breast cancer detection (BCD) through mammography screening continues to be a major focus in radiology as it plays a critical role in reducing mortality rates (Coleman (2017); Ginsburg et al. (2020)). Although artificial intelligence (AI) models can help radiologists to evaluate mammograms (Sahu et al. (2023); Evans et al. (2013); Maxwell (1999)), training such models face the challenge of limited datasets that may not fully represent all subgroups or cover variations in data distributions. Historically, certain racial groups face barriers to healthcare access because of many socio-economic factors (Azin et al. (2023); Hershman et al. (2005); Hussain-Gambles et al. (2004)). This lack of access can result in datasets that do not adequately represent these groups, potentially cause AI models to show biases for these groups. Even with seemingly balanced datasets, subtle biases may persist in the collected data due to systemic inequalities in the quality of healthcare (Obermeyer et al. (2019)). Among these groups, African American patients are often underrepresented in both breast imaging and broader healthcare datasets (Yedjou et al. (2019); Newman and Kaljee (2017)).

Recent progress in large language models (LLMs) has enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset. Yet, their zero-shot performance in complex tasks as opposed to straightforward information extraction tasks remains suboptimal. One feasible approach for tasks with lengthy, complex input is to first summarize the document and then apply supervised fine-tuning to the summary. However, the summarization process inevitably results in some loss of information. In this study we present a method for processing the summaries of long documents aimed to capture different important aspects of the original document. We hypothesize that LLM summaries generated with different aspect-oriented prompts contain different \textit{information signals}, and we propose methods to measure these differences. We introduce approaches to effectively integrate signals from these different summaries for supervised training of transformer models. We validate our hypotheses on a high-impact task -- 30-day readmission prediction from a psychiatric discharge -- using real-world data from four hospitals, and show that our proposed method increases the prediction performance for the complex task of predicting patient outcome.

Explainability remains a significant problem for AI models in medical imaging, making it challenging for clinicians to trust AI-driven predictions. We introduce 3D ReX, the first causality-based post-hoc explainability tool for 3D models. 3D ReX uses the theory of actual causality to generate responsibility maps which highlight the regions most crucial to the model's decision. We test 3D ReX on a stroke detection model, providing insight into the spatial distribution of features relevant to stroke.

Diabetes is a chronic metabolic disorder characterized by persistently high blood glucose levels (BGLs), leading to severe complications such as cardiovascular disease, neuropathy, and retinopathy. Predicting BGLs enables patients to maintain glucose levels within a safe range and allows caregivers to take proactive measures through lifestyle modifications. Continuous Glucose Monitoring (CGM) systems provide real-time tracking, offering a valuable tool for monitoring BGLs. However, accurately forecasting BGLs remains challenging due to fluctuations due to physical activity, diet, and other factors. Recent deep learning models show promise in improving BGL prediction. Nonetheless, forecasting BGLs accurately from multimodal, irregularly sampled data over long prediction horizons remains a challenging research problem. In this paper, we propose AttenGluco, a multimodal Transformer-based framework for long-term blood glucose prediction. AttenGluco employs cross-attention to effectively integrate CGM and activity data, addressing challenges in fusing data with different sampling rates. Moreover, it employs multi-scale attention to capture long-term dependencies in temporal data, enhancing forecasting accuracy. To evaluate the performance of AttenGluco, we conduct forecasting experiments on the recently released AIREADI dataset, analyzing its predictive accuracy across different subject cohorts including healthy individuals, people with prediabetes, and those with type 2 diabetes. Furthermore, we investigate its performance improvements and forgetting behavior as new cohorts are introduced. Our evaluations show that AttenGluco improves all error metrics, such as root mean square error (RMSE), mean absolute error (MAE), and correlation, compared to the multimodal LSTM model. AttenGluco outperforms this baseline model by about 10% and 15% in terms of RMSE and MAE, respectively.