Zobrazeno 1 - 10
of 193
pro vyhledávání: '"ARORA, ABHISHEK"'
This manuscript presents the Quantum Finite Element Method (Q-FEM) developed for use in noisy intermediate-scale quantum (NISQ) computers, and employs the variational quantum linear solver (VQLS) algorithm. The proposed method leverages the classical
Externí odkaz:
http://arxiv.org/abs/2411.09038
Social scientists and the general public often analyze contemporary events by drawing parallels with the past, a process complicated by the vast, noisy, and unstructured nature of historical texts. For example, hundreds of millions of page scans from
Externí odkaz:
http://arxiv.org/abs/2406.15593
Massive-scale historical document collections are crucial for social science research. Despite increasing digitization, these documents typically lack unique cross-document identifiers for individuals mentioned within the texts, as well as individual
Externí odkaz:
http://arxiv.org/abs/2406.15576
In the U.S. historically, local newspapers drew their content largely from newswires like the Associated Press. Historians argue that newswires played a pivotal role in creating a national identity and shared understanding of the world, but there is
Externí odkaz:
http://arxiv.org/abs/2406.09490
Autor:
Arora, Abhishek, Acharya, Amit
Important physical observations in rupture dynamics such as static fault friction, short-slip, self-healing, and supershear phenomenon in cracks are studied. A continuum model of rupture dynamics is developed using the field dislocation mechanics (FD
Externí odkaz:
http://arxiv.org/abs/2312.09378
Billions of public domain documents remain trapped in hard copy or lack an accurate digitization. Modern natural language processing methods cannot be used to index, retrieve, and summarize their texts; conduct computational textual analyses; or extr
Externí odkaz:
http://arxiv.org/abs/2310.10050
Autor:
Arora, Abhishek, Dell, Melissa
Linking information across sources is fundamental to a variety of analyses in social science, business, and government. While large language models (LLMs) offer enormous promise for improving record linkage in noisy datasets, in many domains approxim
Externí odkaz:
http://arxiv.org/abs/2309.00789
Autor:
Dell, Melissa, Carlson, Jacob, Bryan, Tom, Silcock, Emily, Arora, Abhishek, Shen, Zejiang, D'Amico-Wong, Luca, Le, Quan, Querubin, Pablo, Heldring, Leander
Existing full text datasets of U.S. public domain newspapers do not recognize the often complex layouts of newspaper scans, and as a result the digitized content scrambles texts from articles, headlines, captions, advertisements, and other layout reg
Externí odkaz:
http://arxiv.org/abs/2308.12477
Record linkage is a bedrock of quantitative social science, as analyses often require linking data from multiple, noisy sources. Off-the-shelf string matching methods are widely used, as they are straightforward and cheap to implement and scale. Not
Externí odkaz:
http://arxiv.org/abs/2305.14672
Many applications require linking individuals, firms, or locations across datasets. Most widely used methods, especially in social science, do not employ deep learning, with record linkage commonly approached using string matching techniques. Moreove
Externí odkaz:
http://arxiv.org/abs/2304.03464