Hasil untuk "American literature"

T. Horváth

Abstract:The Summer 2024 Issue. Ploughshares is an award-winning journal of new writing. Since 1971, Ploughshares has discovered and cultivated the freshest voices in contemporary American literature, and now provides readers with thoughtful and entertaining literature in a variety of formats. Find out why the New York Times named Ploughshares “the Triton among minnows.”The Summer 2024 Issue, guest-edited by Rebecca Makkai, features prose by Dur e Aziz Amna, Ramona Ausubel, Peter Mountford, Khaddafina Mbabazi, DK Nnuro, and more.

E. Basch, A. Prestrud, P. Hesketh et al.

K. Meyer, G. Raghu, R. Baughman et al.

A. Bharucha, J. Pembérton, G. Locke

L. Harris, H. Fritsche, R. Mennel et al.

A. Benson, D. Schrag, M. Somerfield et al.

H. Gates

D. Pfister, David H. Johnson, C. Azzoli et al.

L. Margolis, G. Esch, J. Holmes et al.

D. Felson, Jennifer J. Anderson, M. Boers et al.

Anthony F Jorm, A. Korten, A. Henderson

F. Jameson

T. Lowi

Chris J. Park

J. Uscinski, Joseph M. Parent

J. Apfelbaum

C. Fox, S. Golden, C. Anderson et al.

Phoebe Chua, Cathy Mengying Fang, Takehiko Ohkawa et al.

Unlike spoken languages where the use of prosodic features to convey emotion is well studied, indicators of emotion in sign language remain poorly understood, creating communication barriers in critical settings. Sign languages present unique challenges as facial expressions and hand movements simultaneously serve both grammatical and emotional functions. To address this gap, we introduce EmoSign, the first sign video dataset containing sentiment and emotion labels for 200 American Sign Language (ASL) videos. We also collect open-ended descriptions of emotion cues. Annotations were done by 3 Deaf ASL signers with professional interpretation experience. Alongside the annotations, we include baseline models for sentiment and emotion classification. This dataset not only addresses a critical gap in existing sign language research but also establishes a new benchmark for understanding model capabilities in multimodal emotion recognition for sign languages. The dataset is made available at https://huggingface.co/datasets/catfang/emosign.

Hugo Massaroli, Hernan Chaves, Pilar Anania et al.

Deep learning models have shown strong performance in diagnosing Alzheimer's disease (AD) using neuroimaging data, particularly 18F-FDG PET scans, with training datasets largely composed of North American cohorts such as those in the Alzheimer's Disease Neuroimaging Initiative (ADNI). However, their generalization to underrepresented populations remains underexplored. In this study, we benchmark convolutional and Transformer-based models on the ADNI dataset and assess their generalization performance on a novel Latin American clinical cohort from the FLENI Institute in Buenos Aires, Argentina. We show that while all models achieve high AUCs on ADNI (up to .96, .97), their performance drops substantially on FLENI (down to .82, .80, respectively), revealing a significant domain shift. The tested architectures demonstrated similar performance, calling into question the supposed advantages of transformers for this specific task. Through ablation studies, we identify per-image normalization and a correct sampling selection as key factors for generalization. Occlusion sensitivity analysis further reveals that models trained on ADNI, generally attend to canonical hypometabolic regions for the AD class, but focus becomes unclear for the other classes and for FLENI scans. These findings highlight the need for population-aware validation of diagnostic AI models and motivate future work on domain adaptation and cohort diversification.

Cindy Kim, Daniela Puchall, Jiangyi Liang et al.

The advent of the COVID-19 pandemic has undoubtedly affected the political scene worldwide and the introduction of new terminology and public opinions regarding the virus has further polarized partisan stances. Using a collection of tweets gathered from leading American political figures online (Republican and Democratic), we explored the partisan differences in approach, response, and attitude towards handling the international crisis. Implementation of the bag-of-words, bigram, and TF-IDF models was used to identify and analyze keywords, topics, and overall sentiments from each party. Results suggest that Democrats are more concerned with the casualties of the pandemic, and give more medical precautions and recommendations to the public whereas Republicans are more invested in political responsibilities such as keeping the public updated through media and carefully watching the progress of the virus. We propose a systematic approach to predict and distinguish a tweet's political stance (left or right leaning) based on its COVID-19 related terms using different classification algorithms on different language models.