Hasil untuk "American literature"

W. Murphy

David Hsieh

John H. Pencavel

Andrew Bennett, Colin Elman

T. Abdullah, Tamara L. Brown

Y. Lelkes

C. Finney

A. Dajani, K. Taubert, W. Wilson et al.

L. Goldstein, R. Adams, K. Becker et al.

S. Grabe, J. Hyde

C. Jablecki, M. Andary, M. Floeter et al.

Andrey Itkin

This paper introduces a semi-analytical method for pricing American options on assets (stocks, ETFs) that pay discrete and/or continuous dividends. The problem is notoriously complex because discrete dividends create abrupt price drops and affect the optimal exercise timing, making traditional continuous-dividend models unsuitable. Our approach utilizes the Generalized Integral Transform (GIT) method introduced by the author and his co-authors in a number of papers, which transforms the pricing problem from a complex partial differential equation with a free boundary into an integral Volterra equation of the second or first kind. In this paper we illustrate this approach by considering a popular GBM model that accounts for discrete cash and proportional dividends using Dirac delta functions. By reframing the problem as an integral equation, we can sequentially solve for the option price and the early exercise boundary, effectively handling the discontinuities caused by the dividends. Our methodology provides a powerful alternative to standard numerical techniques like binomial trees or finite difference methods, which can struggle with the jump conditions of discrete dividends by losing accuracy or performance. Several examples demonstrate that the GIT method is highly accurate and computationally efficient, bypassing the need for extensive computational grids or complex backward induction steps.

Dawnena Key

This paper presents a real-time American Sign Language (ASL) recognition system utilizing a hybrid deep learning architecture combining 3D Convolutional Neural Networks (3D CNN) with Long Short-Term Memory (LSTM) networks. The system processes webcam video streams to recognize word-level ASL signs, addressing communication barriers for over 70 million deaf and hard-of-hearing individuals worldwide. Our architecture leverages 3D convolutions to capture spatial-temporal features from video frames, followed by LSTM layers that model sequential dependencies inherent in sign language gestures. Trained on the WLASL dataset (2,000 common words), ASL-LEX lexical database (~2,700 signs), and a curated set of 100 expert-annotated ASL signs, the system achieves F1-scores ranging from 0.71 to 0.99 across sign classes. The model is deployed on AWS infrastructure with edge deployment capability on OAK-D cameras for real-time inference. We discuss the architecture design, training methodology, evaluation metrics, and deployment considerations for practical accessibility applications.

Xavier Vasques, Renaud Richardet, Sean L Hill et al.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com

Xing-Yue Chen, Li-Jia Zhi, Jun Chen et al.

ObjectivesSepsis-associated acute kidney injury (SA-AKI) commonly occurs in critically ill patients and is closely associated with adverse outcomes. A comprehensive analysis of the current research landscape in SA-AKI can help uncover trends and key issues in this field. This study aims to provide a scientific basis for research directions and critical issues through bibliometric analysis.MethodsWe searched all articles on SA-AKI indexed in the SCI-Expanded of WoSCC up to May 7, 2024, and conducted bibliometric and visual analyses using bibliometric software CiteSpace and VOSviewer.ResultsOver the past 20 years, there has been a steady increase in literature related to renal repair following AKI. China and the United States contribute over 60% of the publications, driving research in this field. The University of Pittsburgh is the most active academic institution, producing the highest number of publications. J. A. Kellum is both the most prolific and the most cited author in this area. “Shock” and “American Journal of Physiology-Renal Physiology” are the most popular journals, publishing the highest number of articles. Recent high-frequency keywords in this field include “septic AKI,” “mitochondrial dysfunction,” “inflammasome,” “ferroptosis,” and “macrophage.” The terms “mitochondrial dysfunction,” “inflammasome,” “ferroptosis,” and “macrophage” represent current research hotspots and potential targets in this area.ConclusionThis is the first comprehensive bibliometric study to summarize the trends and advancements in SA-AKI research in recent years. These findings identify current research frontiers and hot topics, providing valuable insights for scholars studying SA-AKI.

N. O’grady, P. Barie, J. Bartlett et al.

Shubham Agarwal, Gaurav Sahu, Abhay Puri et al.

Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work. It is a tedious task which makes an automatic literature review generator appealing. Unfortunately, many existing works that generate such reviews using Large Language Models (LLMs) have significant limitations. They tend to hallucinate-generate non-factual information-and ignore the latest research they have not been trained on. To address these limitations, we propose a toolkit that operates on Retrieval Augmented Generation (RAG) principles, specialized prompting and instructing techniques with the help of LLMs. Our system first initiates a web search to retrieve relevant papers by summarizing user-provided abstracts into keywords using an off-the-shelf LLM. Authors can enhance the search by supplementing it with relevant papers or keywords, contributing to a tailored retrieval process. Second, the system re-ranks the retrieved papers based on the user-provided abstract. Finally, the related work section is generated based on the re-ranked results and the abstract. There is a substantial reduction in time and effort for literature review compared to traditional methods, establishing our toolkit as an efficient alternative. Our project page including the demo and toolkit can be accessed here: https://litllm.github.io

Joshua Montaño Paredes

Este artículo es resultado de la investigación realizada para el trabajo de fin de máster“El debate cultural en el ensayo afroecuatoriano del siglo XX: los casos de Adalberto Ortiz y Nelson Estupiñán Bass” (2023). Este trabajo es un esfuerzo para presentar de manera contextualizada las reflexiones de Adalberto Ortiz sobre la negritud. Para lograr esto, el escrito comienza presentando antecedentes tanto de los estudios literarios dedicados al autor, así como de la vida y obra ensayística de Ortiz, que explican el surgimiento de sus ensayos dedicados a la reflexión de la negritud. Ya en materia, se hace una descripción de los ensayos de Ortiz sobre la negritud y se los pone en debate con ensayos de teóricos culturales relevantes. Finalmente, se hace un recuento de los principales hallazgos del análisis propuesto.

Michael J. Horswell

Archivo histórico de Kipus: Revista Andina de Letras y Estudios Culturales, 2000.

D. Montague, J. Jarow, G. Broderick et al.