A Framework for Unlocking and Linking WWII Japanese American Incarceration Biographical Data

Entity resolution is the process of identifying the records that refer to the same entity. Blocking is the most important step in the entity resolution process, which allows optimizing the matching step by grouping the records into blocks in a way that only the records in the same block will be compared to each other. However, the blocking process can’t be done with certainty and confidence, such that records may be assigned to the wrong blocks causing the ignorance of many necessary comparisons. In this paper, we propose a Fuzzy/Probabilistic approach to overcome the uncertainty problem. Our approach defines two steps: Fuzzy Block Generator and Probabilistic filtering which aim to minimize the effect of uncertainty by assigning records to multiple possible blocks, and to reduce the number of unnecessary comparisons based on a probabilistic approach, respectively. Conducted experiments showcased the effectiveness and efficiency of our solution.

Entity resolution (ER) is an important step of data preprocessing. Deep learning based entity resolution is a growing topic in research communities. Considering that record structure is hierarchical: token, attribute, record, we propose a hybrid attention-based network framework for entity resolution. It synthesizes information from different abstract levels of record hierarchy. Systematic attention mechanisms are exploited in several aspects of ER: self-attention for internal dependency capture, inter-attention for alignments, and multi-dimensional weight attention for importance discrimination. Also attribute order is taken into account in ER learning for better similarity representations. Moreover, we tackle ER over low-quality data by hybrid soft token alignments. Extensive experiments on 4 datasets are conducted, and the resultsshow that our approach surpasses existing ER approaches.

Entity Resolution (ER) is one of the techniques used to disambiguate the various manifestations of same object to improve search results in databases. Recently, Crowd sourcing has been utilized to improve entity resolution to gain positive impact when searching for particular information in a database. In this paper, we consider the domain of Customer Relationship Management (CRM) and utilize the approach of Crowd sourcing to enrich the process of achieving ER. Specifically our focus is to identify the right customer that has been manifested in various ways under a common name in a database using In-house Crowd sourcing-based Entity Resolution approach (ICER). The ICER takes the list of possible duplicates into consideration (which are pre-determined) and identifies the pair of record that has the maximum impact in achieving ER. Then, this pair is crowd sourced to Customer Service Representatives (CSRs) to have their input (labeling). ICER incorporates the principles of Human Intelligence Task (HIT) that aims to keep the questions asked to the CSR to a minimum. Two ICER approaches are proposed in this study based on probabilistic (modified approach of Whang et al) and active learning schemas. The applicability of the proposed ICER approaches and comparison of their results have been highlighted by using an example.

Reviewed by: Trans-Pacific Japanese American Studies: Conversations on Race and Racializations eds. by Yasuko Takezawa and Gary Y. Okihiro Lily Anne Welty Tamai Trans-Pacific Japanese American Studies: Conversations on Race and Racializations, edited by Yasuko Takezawa and Gary Y. Okihiro. Honolulu: University of Hawai'i Press, 2016. Xii+ 400 pp. $68.00 cloth. ISBN: 9780-8248-4758-6. In the bibliographic essay of Paul Spickard's book, Japanese Americans: The Formation and Transformation of an Ethnic Group, he reflected on the historiography of Japanese American Studies in 1994: [I]t is curious that one topic, Japanese American imprisonment in the World War II concentration camps, has commanded so much attention, that there are still so many topics left almost untouched, and that the range of theoretical perspectives among students of Japanese American history has been so limited. Clearly, there is still a great deal of work to be done. (191) Yasuko Takezawa and Gary Okihiro have answered that call in this volume on transpacific Japanese American studies. Japanese American studies has long been dominated by scholarship focused on the racialization of the Japanese American community through the lens of the World War II incarceration camps in the United States and, more recently, in Canada. The transpacific characteristic of the Japanese American community, and Japanese American studies has emphasized the Issei generation and the Kibei-Nisei by U.S.-based scholars and increasingly Japan-based scholars. Takezawa and Okihiro edited a volume that expands the field of Japanese American studies by engaging in and centering on transpacific people and their communities. The authors in the book are well-known scholars from both the United States and Japan, providing a transpacific framework that includes not only the content but also the conversations between the transnational contributors. The book discusses the questions, "Who is Japanese American?," "What is Japanese American studies?," and "Can dialogue on the topic between scholars in Japan and in the U.S. be on equal footing when English language texts dominate the field?" The volume covers topics in history but also art, music, [End Page 472] education, diaspora, and the post-1965 Japanese American community of Shin-Issei. The last section, titled "Dialoging Subject Positions," is especially informative because scholars address their positionalities and current perspectives on the state of the field in shorter-length chapters. Trans-Pacific Japanese American Studies expands the intellectual breadth and depth of Japanese American studies because it deliberately brings together the conversations of scholars in North America and Japan to dismantle the hierarchy within the scholarship in the field. Japanese American studies grew out of the need for political action, coming off the heels of the World War II Japanese American incarceration, and the need for ethnic studies in the United States. More broadly speaking, the scholarly focus has been on the legacies of World War II, and on race, inclusion, and identity primarily within a U.S. framework in English. As a result of this need from the 1970s, the World War II Japanese American incarceration experience has eclipsed most other topics of scholarship, leaving the experiences of women, Okinawans, Ainu, burakumin, Nikkei in Latin America, and the mixed-race experience largely understudied. The editors and the authors in the volume have centered the transpacific narrative of the Japanese diaspora and made it the point of departure. Eiichiro Azuma's deftly written chapter highlights why the generational labels of Issei and Nisei by definition have prevented the inclusion of Shin-Issei and other post-World War II immigrations into the fold of the Japanese American studies. This book has made a space to include discussions on the aforementioned groups and also Zainichi Koreans, Shin Issei, war brides, and Japanese Latinos more critically. Valerie Matsumoto's chapter on Sansei, specifically Sansei women, breathes new life into contemporary Japanese American studies, which has had a spotlight on the Issei and Nisei and their immigration and incarceration experiences. Matsumoto's discussion centers on the Sansei and how they were influenced by the activism of the 1960s and 1970s and highlights the transnational solidarity of Sansei Japanese Americans during the Vietnam War era and the Asian American movement. Many Sansei had connections to Japan...

On February 19, 1942, President Franklin Delano Roosevelt signed Executive Order 9066 authorizing the incarceration of 120,000 Japanese Americans, living primarily on the West Coast of the continental United States. On August 10, 1988, President Ronald Reagan signed legislation authorizing formal apologies and checks for $20,000 to those still alive who had been unjustly imprisoned during WWII. In the interim period, nearly a half century, there were enormous shifts in memories of the events, mainstream accounts, and internal ethnic accountabilities. To be sure, there were significant acts of resistance, from the beginning of mass forced removal to the Supreme Court decisions toward the end of the war. But for a quarter of a century, between 1945 and approximately 1970, there was little to threaten a master narrative that posited Japanese Americans, led by the Japanese American Citizens League (JACL), as a once-embattled ethnic/racial minority that had transcended its victimized past to become America’s treasured model minority. The fact that the Japanese American community began effective mobilization for government apology and reparations in the 1970s only confirmed its emergence as a bona fide part of the American body politic. But where the earlier narrative extolled the memories of Japanese American war heroes and leaders of the JACL, memory making changed dramatically in the 1990s and 2000s. In the years since Reagan’s affirmation that “here we admit a wrong,” Japanese Americans have unleashed a torrent of memorials, museums, and monuments honoring those who fought the injustices and who swore they would resist current or future attempts to scapegoat other groups in the name of national security.

The History and Culture of a People highlights the enormous contributions of Japanese Americans in history, civil rights, politics, economic development, arts, literature, film, popular culture, sports, and religious landscapes. It not only provides context to important events in Japanese American history and in-depth information about the lives and backgrounds of well-known Japanese Americans, but also captures the essence of everyday life for Japanese Americans as they have adjusted their identities, established communities, and interacted with other ethnic groups. This innovative volume will become the standard resource for exploring why the Japanese came to America more than 130 years ago, where they settled, and what experiences played a role in forming the distinctive Japanese American identity.

Ten weeks after the 1941 Japanese military attack on Pearl Harbor, Hawaii, the U.S. government authorized the removal of more than 110,000 Japanese American men, women, and children from their homes in Western portions of the country to incarceration camps in desolate areas of the United States. The mass incarceration was portrayed as necessary to protect the country from potential acts of espionage or sabotage that might be committed by someone of Japanese ancestry. However, an extensive government review initiated in 1980 found no evidence of military necessity to support the removal decision and concluded that the incarceration was a grave injustice fueled by racism and war hysteria. The Japanese American wartime experience represents a powerful case example of race-based historical trauma. This article describes the consequences of the incarceration for Japanese Americans during and after their unjust imprisonment, their coping responses and healing strategies, as well as the impacts of receiving governmental redress more than four decades after the war's end. Examination of this specific event provides a perspective for understanding the long-term, radiating effects of racial trauma and the process of healing, over a broad arc of time and across social contexts. Current relevance of the Japanese American incarceration and implications for the field of psychology are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Geospatial data is ubiquitous in today’s world, playing a crucial role in various aspects of modern life. As businesses increasingly establish and expand their online presence, they leverage location-based insights to better understand customer behavior and optimize services. Simultaneously, the proliferation of GPS-enabled devices enables users to continuously share their locations and activities online, contributing to an ever-growing pool of geospatial information. Social networks and web services, in particular, heavily rely on this data, integrating it into their platforms to provide more personalized and relevant experiences. This surge in geospatial data generation necessitates the development of novel algorithms to mine meaningful patterns and enhancing user experiences by delivering accurate information at the right place and time. This dissertation presents a pipeline of novel algorithms, spanning from the integration of geospatial data, to its effective application in real-world use cases. Specifically it introduces (1) an algorithm for deduplicating and consolidating geospatial data from multiple sources, a key step in creating a comprehensive geospatial database; it studies (2) the problem of Geospatial Knowledge Graph (GeoKG) construction, and presents an algorithm to automatically mine fine-grained spatial relationships between entities from a database, within an open-world setting; it presents (3) a framework for self-supervised pre-training of geospatial foundation models, capable of generating task-agnostic representations that are readily applicable to a wide range of downstream tasks. Finally, (4) this thesis explores the fine-tuning of pre-trained large language models (LLMs) on city-specific data, to build urban virtual assistants, capable of delivering accurate geospatial recommendations in a conversational manner while minimizing hallucinations. The study on geospatial Entity Resolution (ER) proposes a state-of-the-art approach to link records from different sources, that refer to the same real-world geospatial entity. This is a crucial step for deduplicating merged data. The algorithm, called Geo-ER, leverages textual, spatial, and contextual information, making a step forward towards automatic geospatial data integration. A novel neighborhood embedding component is designed, using Graph Attention Networks, to produce a more contextualized representation of an entity. Extensive experiments, on eight real-world datasets, are performed to demonstrate the advantages of Geo-ER compared to existing state-of-the-art solution for geospatial and traditional ER. Cross-city validation experiments showcase the algorithm's robustness in scenarios where training data for a specific city is unavailable. Subsequently, this thesis presents GTMiner, a novel framework for automatically constructing a Geospatial Knowledge Graph (GeoKG) from tabular data, derived from the merging and consolidation of multiple data sources. The architecture for geospatial relation prediction is an open-world Knowledge Graph Completion solution, consisting of a pre-trained language model, a geospatial encoder, and a Geo-Textual interaction component, which jointly model the textual and geospatial characteristics of an entity. GTMiner further includes a refinement module, specifically designed to improve both coverage and correctness of a GeoKG. The study further introduces four real-world datasets, collected from publicly available sources, and carries out extensive experiments to evaluate the performance of each of the system’s components and perform ablation studies. A study on general-purpose representation learning in the geospatial domain introduces CityFM, a new framework that leverages mutual information maximization and incorporates Nodes, Ways, and Relations into its learning frame-work to train geospatial Pre-trained Foundation Models (PFMs) for a selected area of interest. CityFM-trained models are task-agnostic and multimodal, capable of integrating an entity’s textual, visual, and spatial attributes. The study conducts experiments on road, building, and region-level downstream tasks to demonstrate the effectiveness of the embeddings produced by the foundation models in real-world applications, compared to task-specific algorithms. The last study in this dissertation presents LAMP, a virtual assistant for urban applications. It introduces a novel strategy to generate synthetic conversational data about geospatial entities, to fine-tune a pre-trained LLM. The generated data enables the language model to learn and memorize thousands of places within a city, while also equipping it with spatial (proximity) awareness specific to the urban area of interest.

Human trafficking is a challenging law enforcement problem, and traces of victims of such activity manifest as ‘escort advertisements’ on various online forums. Given the large, heterogeneous and noisy structure of this data, building models to predict instances of trafficking is a convoluted task. In this paper we propose an entity resolution pipeline using a notion of proxy labels, in order to extract clusters from this data with prior history of human trafficking activity. We apply this pipeline to 5M records from backpage.com and report on the performance of this approach, challenges in terms of scalability, and some significant domain specific characteristics of our resolved entities.

Given two large lists of records, the task in entity resolution (ER) is to find the pairs from the Cartesian product of the lists that correspond to the same real world entity. Typically, passive learning methods on such tasks require large amounts of labeled data to yield useful models. Active Learning is a promising approach for ER in low resource settings. However, the search space, to find informative samples for the user to label, grows quadratically for instance-pair tasks making active learning hard to scale. Previous works, in this setting, rely on hand-crafted predicates, pre-trained language model embeddings, or rule learning to prune away unlikely pairs from the Cartesian product. This blocking step can miss out on important regions in the product space leading to low recall. We propose DIAL, a scalable active learning approach that jointly learns embeddings to maximize recall for blocking and accuracy for matching blocked pairs. DIAL uses an Index-By-Committee framework, where each committee member learns representations based on powerful pre-trained transformer language models. We highlight surprising differences between the matcher and the blocker in the creation of the training data and the objective used to train their parameters. Experiments on five benchmark datasets and a multilingual record matching dataset show the effectiveness of our approach in terms of precision, recall and running time.

Since the 442nd Regimental Combat Team trained in neighboring Mississippi, Japanese American women were regularly bused across the state line, for wholesome, racially partitioned dating and matrimony, signaling a new era of state-sanctioned courtship and institutionalized heterosexuality. These USO-sponsored events crystallized the paradox of incarceration and military service. And most women resisted. This chapter elaborates two vital but often neglected aspects of Japanese American experience, which help to clarify the paradox of incarceration and war service, as well as the complex identifications and affiliations crafted by all persons of Japanese descent in wartime America. To probe the slippery gray area between resistance and accommodation, it first considers the highly contingent, localized nature of racial categorization, especially given many Japanese Americans' ability to repeatedly pass in and out of the camps, into southern communities often ill-equipped to enforce anything other than a single, biracial, black/white color line. Second, it acknowledges and articulates the distinctive circumstances of incarceration for Japanese American women.

During World War II, the United States confined 120,000 Japanese Americans in incarceration camps based solely on their Japanese ancestry. Two thirds of those forced to live in the desolate camps were U.S. citizens. Decades later, the U.S. government concluded that Japanese Americans had suffered a grave injustice and issued a written apology and monetary award to each surviving incarceree. This article frames the incarceration as a race‐based personal and cultural trauma that had enduring consequences for Japanese Americans. Critical impacts of this historic event on identity and postwar trauma coping among U.S.‐born second generation Japanese American incarcerees and their offspring are described. We highlight how individual, intergenerational, and sociocultural processes interacted across decades to shape cultural trauma response and the movement to seek redress.

The internment of Japanese Americans during the Second World War neutralized the “enemy” abroad by controlling them at home. The rhetoric of Second World War exclusion characterized “Japanese” in opposition to “American” along “racial strains,” forcing polarized notions of identity onto the minority Japanese Americans in dividing loyal from disloyal, alien from citizen. In this chapter, I analyze John Okada’s No No Boy (1957), a novel about the residual effects of Japanese internment in the United States after the Second World War, and Joy Kogawa’s Obasan (1981), a novel about retrospective recollections of the Japanese evacuation in Canada. These works complicate any unified representation of the Japanese American internment experience. The contradictions within and between each artistic narrative encourage readers to define their own meanings or closures, positioning Japanese Americans not as a unitary alien body inassimilable to the nation, but as citizens with whom to engage and understand. The lack of resolution in the internment texts I analyze suggests that the project of narrating Japanese Americans from national exclusion to inclusion recurs in a discursive process. The experience of internment symbolizes the continued shuttling between national exclusion and inclusion that influences the formation of Japanese American identity.

Quantitative imaging of organelle dynamics provides crucial insights into cellular function, state, and organization; however, existing analysis workflows often require advanced coding expertise and multiple software tools. AutoMorphoTrack is an open-source Python toolkit that automates organelle detection, morphology classification, motility tracking, and colocalization from multichannel fluorescence microscopy image stacks. The platform includes adaptive segmentation, organelle trajectory reconstruction, and pixel-level overlap quantification within a unified, reproducible framework that can be executed as an interactive Jupyter notebook, a modular Python package, or through AI-assisted natural-language commands. Each analysis step outputs publication-ready images, time-lapse videos, and standardized quantitative data tables. To complement the main pipeline, an accompanying script— AMTComparison.py—is provided to demonstrate how AutoMorphoTrack’s outputs can be extended for comparative analysis across individual neurons or experimental conditions. Together, these tools provide an accessible framework for high-content, reproducible quantification of subcellular morphology, motility, and interactions at single-cell resolution.

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx