This study investigates the effectiveness of unsupervised outlier detection methods in audit analytics, utilizing USA spending data from the U.S. Department of Health and Human Services (DHHS) as a case example. We employ and compare multiple outlier detection algorithms, including Histogram-based Outlier Score (HBOS), Robust Principal Component Analysis (PCA), Minimum Covariance Determinant (MCD), and K-Nearest Neighbors (KNN) to identify anomalies in federal spending patterns. The research addresses the growing need for efficient and accurate anomaly detection in large-scale governmental datasets, where traditional auditing methods may fall short. Our methodology involves data preparation, algorithm implementation, and performance evaluation using precision, recall, and F1 scores. Results indicate that a hybrid approach, combining multiple detection strategies, enhances the robustness and accuracy of outlier identification in complex financial data. This study contributes to the field of audit analytics by providing insights into the comparative effectiveness of various outlier detection models and demonstrating the potential of unsupervised learning techniques in improving audit quality and efficiency. The findings have implications for auditors, policymakers, and researchers seeking to leverage advanced analytics in governmental financial oversight and risk management.

The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA). However, few works explore this paradigm in the scenario of multi-document question answering (MD-QA), a task demanding a thorough understanding of the logical associations among the contents and structures of different documents. To fill this crucial gap, we propose a Knowledge Graph Prompting (KGP) method to formulate the right context in prompting LLMs for MD-QA, which consists of a graph construction module and a graph traversal module. For graph construction, we create a knowledge graph (KG) over multiple documents with nodes symbolizing passages or document structures (e.g., pages/tables), and edges denoting the semantic/lexical similarity between passages or intra-document structural relations. For graph traversal, we design an LLM-based graph traversal agent that navigates across nodes and gathers supporting passages assisting LLMs in MD-QA. The constructed graph serves as the global ruler that regulates the transitional space among passages and reduces retrieval latency. Concurrently, the graph traversal agent acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality. Extensive experiments underscore the efficacy of KGP for MD-QA, signifying the potential of leveraging graphs in enhancing the prompt design for LLMs. Our code: https://github.com/YuWVandy/KG-LLM-MDQA.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A New Jersey man who used dating apps to lure and kill three women five years ago was sentenced Wednesday to 160 years in prison after a trial in which it was revealed that friends of one victim did their own detective work on social media to ferret out the suspect. Khalil Wheeler-Weaver, 25, sat motionless as the judge gave the sentence in state court in Newark. The sentencing was preceded by emotional statements by family members of victims Robin West and Sarah Butler.

Newark Venture Partners hosted a full house at its biannual Demo Day, for its 7th NVP Labs class, at the Audible Innovation Cathedral. The event featured presentations from founders of the graduating companies, Botmock, Brahmin Solutions, Galaxy.AI, MindRight Health, omniX, Speak2 Software and SpeechKit. Other featured speakers included Don Katz, Founder and Executive Chairman of Audible, Newark Assemblywoman Eliana Pintor Marin, and Wole Coaxum, Founder and CEO of MoCaFi (an NVP portfolio company) who paid tribute to Dr. Martin Luther King, Jr.'s birthday. Don Katz, Founder and Executive Chairman of Audible said, "Everyone loves a comeback story and Newark has a great one, including Newark Venture Partners, which is an internationally acknowledged phenomenon that has exceeded all of my founder expectations. When I recently visited NVP labs I was dazzled by one impassioned founder, team, and company after another. Now it is time to double down on NVP's measurable success."

Artificial intelligence and its offshoot, machine learning, have been attracting a lot of interest across the manufacturing verticals with the promise of converting in-depth data analysis into improved production operations. The technology is also being applied directly to production in tangible ways, as can be seen in Frito-Lay's use of machine learning in its quality and weighing process. But just how do you get your hands on this technology to begin using it? One answer to that question is through Newark's artificial intelligence (AI) online resource site. To help visitors to the site avoid getting bogged down in the AI-related possibilities, the Newark AI site features the company's AI Configurator--an interactive tool that allows engineers to determine the most appropriate products for their AI projects. According to Newark, the AI Configurator "identifies the various development boards, sensors, and software that best meet the needs of the inputted application from a wide range of vendors.

Not a week goes by that I don't see dozens of headlines blaming "AI" or "deep learning" for yet another ethical conundrum that will doom human society. Whether it is predictive policing or facial recognition or autonomous weapons, it seems nearly every facet of society is facing an "AI revolution" that will destroy humankind. If one peels back the breathless hype and viral buzzwords, however, is it really AI that we are worried about or is it the shift towards a data-centric society with or without deep learning advances? To the general public, "big data" and "deep learning" are increasingly becoming synonymous, fueled by the never-ending hype machine of Silicon Valley and the very legitimate advances occurring in deep learning powered largely by the massive availability of large datasets and the unique abilities of those models to make sense of all that data. From a technical standpoint, however, these are two entirely distinct concepts.

Not a week goes by that I don't see dozens of headlines blaming "AI" or "deep learning" for yet another ethical conundrum that will doom human society. Whether it is predictive policing or facial recognition or autonomous weapons, it seems nearly every facet of society is facing an "AI revolution" that will destroy humankind. If one peels back the breathless hype and viral buzzwords, however, is it really AI that we are worried about or is it the shift towards a data-centric society with or without deep learning advances? To the general public, "big data" and "deep learning" are increasingly becoming synonymous, fueled by the never-ending hype machine of Silicon Valley and the very legitimate advances occurring in deep learning powered largely by the massive availability of large datasets and the unique abilities of those models to make sense of all that data. From a technical standpoint, however, these are two entirely distinct concepts.

We don't understand AI because we don't understand intelligence