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How do I find U.S. Census data?
Visit census.gov (http://census.gov/) to browse quality information current and historical facts and figures about Americas people, places, and economy. An additional tool offered by the U.S. Census Bureau, the data.census.gov (https://data.census.gov/) is a platform designed to help users access demographic and economic data digitally. The Census Academy (https://www.census.gov/data/academy.html) has many short tutorials for searching this website. For more information, consult the Census Bureau's FAQ (https://ask.census.gov/) , or schedule an appointment with an LSU Libraries Librarian here (https://lsu.libcal.com/appointments/caple) . The census on microfilm LSU owns is limited. The only states in this collection include: Alabama, Arkansas, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, Missouri, Pennsylvania, South Carolina, Tennessee, Texas, Virginia (and scattered census material for West Virginia). Information on other states may be located at the National Archives (http://www.archives.gov/research/start/index.html) in Washington D.C., the regional branches (http://www.archives.gov/locations/index.html) of the National Archives, as well as the Bluebonnet Regional Branch of the East Baton Rouge Parish Library (https://www.ebrpl.com/) . The collection of census material at LSU Libraries includes population schedules, agricultural census data, lists of manufactures, slave schedules, passenger lists for the port of New Orleans covering 1853-1899, social statistics, and scattered information concerning Defective, Dependent and Delinquent Classes. Other material that may be helpful for researching archives for genealogy information include Records of the Diocese of Louisiana and the "Floridas", New Orleans City Directories for years 1805-1945, New Orleans Christian Advocate concerning Marriage and Death Notices, Military Academy Letters, and Indian Affairs, just to name a few. If you would like to access any of these materials, contact libgovdocs@lsu.edu . Answered by: Kendall Caple

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2065111
Catch Me If You Can: A Multi-Agent Synthetic Fraud Detection Framework for Complex Networks
Detecting fraudulent behavior across diverse domains presents a significant challenge due to the adaptive and elusive activities of fraud agents. Furthermore, imbalanced data distributions and limited labeled examples increase the difficulty of detecting fraud agents. To address these challenges, we propose Catch Me If You Can—a Multi-Agent Framework to generate synthetic datasets and simulate various types of fraudulent behavior, including but not limited to anti-money laundering (AML), credit card fraud, bot attacks, and malicious traffic. Our framework comprises two core agent types: (1) Detectors, trained to identify suspicious patterns in scenarios, and (2) Transaction Agents, including both legitimate participants and adversarial fraud agents employing strategies to evade detection. In this framework, detectors iteratively refine their detection strategies while fraud agents evolve adaptive tactics to disguise illicit activities, creating an adversarial coevolutionary environment. This dynamic fosters the generation of high-dimensional and realistic datasets for training and testing. By integrating synthetic pre-training with transfer learning, the framework leverages a variety of real-world datasets—including IEEE-CIS Fraud Detection, Credit Card Fraud Detection, and Elliptic++—demonstrating its broad applicability across multiple fraud domains. Our approach significantly improves detection performance, bridging the gap between simulation and real-world applications. It enables robust training across heterogeneous fraud behaviors, contributing to the development of resilient, generalizable solutions for financial security and fraud prevention.