1,720,955 research outputs found
Templated from LOIS: Learning-Optimized Inference System for Cost-Efficient Large Language Model Deployment
No full textThis paper presents LOIS (Learning-Optimized Inference System), a novel self-optimizing infrastructure framework that dynamically adapts Large Language Model deployment strategies through reinforcement learning and predictive analytics. LOIS addresses the critical challenge that inference costs represent 60-80% of total AI operational expenditure in production systems. The framework incorporates four key innovations: adaptive model routing using learned policies, dynamic quantization that adjusts precision in real-time, predictive resource allocation through LSTM-based workload forecasting, and multi-tenant optimization for compute sharing. Our evaluation combines analytical modeling and discrete-event simulation on production-representative workloads (347K queries over 7 days), indicating projected improvements of 47% reduction in inference costs, 34% improvement in P95 latency, and 3.2x better GPU utilization compared to baseline Kubernetes orchestration. While results are simulation-based rather than live deployment, the methodology employs empirically-validated performance models from established systems (vLLM, TensorRT-LLM) and conservative assumptions grounded in published benchmarks. LOIS represents a paradigm shift from reactive infrastructure management to proactive, learning-based optimization for economically sustainable LLM deployment at scale
Reinforcement Learning for Self-Optimizing Infrastructure as Code (IaC)
No full textReinforcement Learning for Self-Optimizing Infrastructure as Code introduces a paradigm shift that fundamentally transforms cloud operations, moving beyond mere infrastructure improvement to reimagine the entire operational model. This article examines how reinforcement learning techniques create autonomous infrastructure systems that continuously evolve through operational feedback loops, eliminating traditional boundaries between deployment, monitoring, and optimization phases. By replacing manual intervention with intelligent, self-directing systems, RL-based approaches revolutionize how organizations interact with cloud environments—transitioning from hands-on management to strategic governance of self-optimizing infrastructure ecosystems. The architecture, implementation challenges, and practical applications showcase how this approach represents not just an advancement in infrastructure tooling but a complete reconceptualization of cloud operations that promises to reshape enterprise IT management fundamentally
Templated from LOIS: Learning-Optimized Inference System for Cost-Efficient Large Language Model Deployment
No full textThis paper presents LOIS (Learning-Optimized Inference System), a novel self-optimizing infrastructure framework that dynamically adapts Large Language Model deployment strategies through reinforcement learning and predictive analytics. LOIS addresses the critical challenge that inference costs represent 60-80% of total AI operational expenditure in production systems. The framework incorporates four key innovations: adaptive model routing using learned policies, dynamic quantization that adjusts precision in real-time, predictive resource allocation through LSTM-based workload forecasting, and multi-tenant optimization for compute sharing. Our evaluation combines analytical modeling and discrete-event simulation on production-representative workloads (347K queries over 7 days), indicating projected improvements of 47% reduction in inference costs, 34% improvement in P95 latency, and 3.2x better GPU utilization compared to baseline Kubernetes orchestration. While results are simulation-based rather than live deployment, the methodology employs empirically-validated performance models from established systems (vLLM, TensorRT-LLM) and conservative assumptions grounded in published benchmarks. LOIS represents a paradigm shift from reactive infrastructure management to proactive, learning-based optimization for economically sustainable LLM deployment at scale
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe 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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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