176,991 research outputs found
RAG-Driven Memory Architectures in Conversational LLMs—A Literature Review With Insights Into Emerging Agriculture Data Sharing
No full textDespite significant advances in natural language processing, conversational AI systems face persistent challenges in maintaining extensive and contextually coherent dialogues, particularly regarding long-term memory management. This literature review synthesizes current approaches to memory architectures in conversational AI, examining the transition from basic dialogue agents to more sophisticated, agentic frameworks.We analyze how vector databases and Retrieval-Augmented Generation (RAG) address fundamental challenges in storing and retrieving conversational context, maintaining system responsiveness, managing user-specific data ethically, and integrating domain-specific information. Through systematic review of papers, we identify critical limitations of vector embeddings in capturing extended conversational context, particularly in agentic domains requiring semantic, episodic, procedural, and emotional memory. We evaluate how RAG frameworks can augment vector databases to handle memory-intensive tasks requiring real-time updates and domain-specific knowledge integration. Furthermore, we examine alternative architectures including knowledge graphs, finite state machines, and hybrid solutions, highlighting the data quality and ethical challenges that must be addressed for scalable, reliable AI memory management. Our analysis provides a structured framework for understanding memory evolution in conversational AI, identifies gaps in current RAG solutions, proposes hybrid memory designs, and outlines future research directions emphasizing cross-domain applications in agriculture
Modified xLSTM for Compression and Decompression of Multimodal Agricultural Data in Low-Resource Settings
No full textEfficient compression techniques are essential for handling large datasets, especially in low-resource agricultural settings where bandwidth and storage are limited. This paper introduces a novel approach that combines a modified extended Long Short-Term Memory (xLSTM) network with multiplicative LSTM (mLSTM) cells for compressing and decompressing multimodal agricultural data. The key contribution lies in tailoring the xLSTM-mLSTM architecture specifically for agricultural data, capturing unique patterns to enhance compression efficiency. Specifically, we focus on agricultural datasets comprising textual labels and images. The proposed method combines xLSTM with mLSTM cells for text data compression and employs a convolutional autoencoder for image data. We compare our approach with existing compression methods applied to agricultural data, demonstrating superior performance in terms of compression ratio and reconstruction quality. Experimental results demonstrate the effectiveness of the approach, achieving significant data size reduction while maintaining acceptable reconstruction quality, as evidenced by metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM)
Agricultural data privacy and federated learning: A review of challenges and opportunities
No full textThe rapid digitalization of agriculture has resulted in an unprecedented surge in data collection, necessitating this way the privacy protection in innovative data analytics solutions. Federated Learning emerges as a promising solution since it allows for collaborative model training across decentralized data sources without sharing raw data. This review explores the use of Federated Learning in agriculture, focusing on privacy-preserving methods. We thoroughly reviewed a large corpus of relevant research, examining several Federated Learning types and their application to agricultural scenarios, such as pest and disease detection, crop yield prediction, and resource management. Our findings underscore Federated Learning's potential to revolutionize privacy-preserving data analysis in agriculture by enabling better decision-making through aggregated insights from various farms, while retaining data confidentiality. At the same time, a number of technical complications arise, including data heterogeneity, communication impediments, and limited computational capabilities in rural areas. Data ownership, fairness, and stakeholder trust are significant barriers to widespread use in practice. The present study provides research gaps that need to be addressed to fully use the potential of Federated Learning in agriculture. Tailoring the design of Federated Learning algorithms and adhering to the nature of agricultural data and its peculiarities can promote the enhancement of agriculture-friendly frameworks to ensure privacy-preserving mechanisms for agriculture-oriented applications, and the development of frameworks that bear ethical issues in mind and facilitate farmers-based equitable benefit distribution. Since Federated Learning can potentially change the landscape of data-driven agriculture by allowing collaborative data analytics without compromising privacy, it is highly important to overcome the technological and ethical barriers demonstrated in this study, maximizing its impact on sustainable farming practices and innovations
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Letter from R. R. Zellick, Assistant Trust Officer, Anglo California National Bank of San Francisco, to Joseph R. Goodman, October 2, 1942
No full textLetter from R. R. Zellick, Assistant Trust Officer at The Anglo California National Bank of San Francisco, to Joseph R. Goodman, regarding property owned by Dave Tatsuno. Zellick mentions a dispute between current tenants and Tatsuno, and that Tatsuno has asked Goodman to help locate trustworthy tenants.Personal correspondence, organizational records, government documents, publications, and other papers created or collected by Joseph R. Goodman documenting the forced removal and incarceration of Japanese Americans during World War II, as well as organized resistance to incarceration. Included in the collection are records of the Japanese Young Men's Christian Association and the Japanese American Citizens' League in San Francisco, including papers of the Japanese YMCA's executive secretary Lincoln Kanai; Sakai family papers; Goodman's correspondence to and from Japanese American incarcerees, organizations opposing forced removal and incarceration of Japanese Americans, the War Relocation Authority, and others; publications, photographs, and ephemera from the Topaz Relocation Center, where Goodman taught high school; War Relocation Authority records and publications; and newspaper clippings, pamphlets, and reports about forced removal and incarceration created by various government, religious, and civic organizations, in California and nationwide
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Liftings for noncomplete probability spaces
No full textThe current state of knowledge concerning liftings for noncomplete probability spaces is discussed. This is a somewhat expanded version of the author's talk given at the 1991 Summer Conference on General Topology and Applications in Honor of Mary Ellen Rudin and Her Work.PT: S; CR: BURKE MR, IN PRESS P AM MATH S BURKE MR, 1991, ISRAEL J MATH, V73, P33 BURKE MR, 1992, ISRAEL J MATH, V79, P289 CARLSON T, THEOREM LIFTING CHRISTENSEN JPR, 1974, TOPOLOGY BOREL STRUC FREMLIN DH, 1989, HDB BOOLEAN ALGEBRAS, P877 INOESCUTULCEA A, 1966, 5TH P BERK S MATH ST, V2 IONESCUTULCEA A, 1967, CONTRIBUTIONS PROB 1, P63 IONESCUTULCEA A, 1969, TOPICS THEORY LIFTIN JECH TJ, 1978, SET THEORY JOHNSON RA, 1980, P AM MATH SOC, V80, P234 JUST W, IN PRESS T AM MATH S KUPKA J, 1983, INDIANA U MATH J, V32, P717 LOSERT V, 1983, LNM, V1080, P95 MAHARAM D, 1958, P AM MATH SOC, V9, P987 SHELAH S, 1983, ISRAEL J MATH, V45, P90 TALAGRAND M, 1982, P AM MATH SOC, V84, P379 VONNEUMANN J, 1931, CRELLES J MATH, V165, P109; NR: 18; TC: 0; J9: ANN N Y ACAD SCI; PG: 4; GA: BZ86BSource type: Electronic(1
Hansen, Lee (Lee R.). Union, non-union, and managerial pay plan state employees, 2008-2019
No full text1 online resource (2 pages)"July 1, 2021."Provides the number of union and non-union state employees in each of the last 14 years. Also provides the number of state employees paid under the state's managerial pay plan during each of those years. Updates OLR research report 2019-R-011
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