1,720,960 research outputs found
Adaptive Estimation of the Pennes' Bio-Heat Equation - I: Observer Design
Full text linkIn this paper, we propose a multiple-model adaptive estimation setup for a class of uncertain parabolic reaction-diffusion PDEs encompassing the Pennes' bio-heat equation, which is a motivating case study from the perspective of biomedical applications such as hyperthermia. The efficacy of the approach in estimating the system solution and recovering the value of the reaction coefficient is validated through numerical simulations in MATLAB. The validation step has highlited some limitations of classical numerical simulation tools that we propose to handle through an implementation of the estimator relying on Deep Learning libraries. This alternative approach is reported in a companion paper (Part II of this work)
Adaptive Estimation of the Pennes' Bio-Heat Equation - II: A NN-Based Implementation for Real-Time Applications
No full textThis is the companion paper of a two-part work on the observation of the heat transfer phenomenon in biological tissues. In particular, we are interested in real-time estimation of the temperature in the interior of a spatial domain of interest using measurements at its boundary. The prevailing model for heat transfer in biological tissues, pioneered by Pennes [1], relies on a parabolic reaction-diffusion partial differential equation. However, neither the observation problem has been fully explored nor have the available solutions proved suitable for real-time applications. In the companion paper [2], we propose the design of an observer whose formal properties, however, cannot be easily reflected in its practical performance, due to computational issues arising with the use of common numerical solvers. The difficulties are mostly related to the integration of a system of coupled PDEs/ODE, required by the algorithm. In this paper, we propose an alternative implementation of the observer that makes use of deep neural networks for predicting the PDEs state, thus avoiding the online integration. Preliminary results show that this approach is very effective in solving the considered problem and is amenable to extension to other classes of PDEs and to higher dimensions
Multimodal Neural Databases
Full text linkThe rise in loosely-structured data available through text, images,
and other modalities has called for new ways of querying them.
Multimedia Information Retrieval has filled this gap and has witnessed exciting progress in recent years. Tasks such as search and
retrieval of extensive multimedia archives have undergone massive
performance improvements, driven to a large extent by recent developments in multimodal deep learning. However, methods in this
field remain limited in the kinds of queries they support and, in
particular, their inability to answer database-like queries. For this
reason, inspired by recent work on neural databases, we propose
a new framework, which we name Multimodal Neural Databases
(MMNDBs). MMNDBs can answer complex database-like queries
that involve reasoning over different input modalities, such as text
and images, at scale. In this paper, we present the first architecture able to fulfill this set of requirements and test it with several
baselines, showing the limitations of currently available models.
The results show the potential of these new techniques to process
unstructured data coming from different modalities, paving the way
for future research in the area
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
RRAML: Reinforced Retrieval Augmented Machine Learning
Full text linkThe emergence of large language models (LLMs) has revolutionized machine learning and related fields,
showcasing remarkable abilities in comprehending, generating, and manipulating human language.
However, their conventional usage through API-based text prompt submissions imposes certain limitations in terms of context constraints and external source availability. LLMs suffer from the problem of
hallucinating text, and in the last year, several approaches have been devised to overcome this issue:
adding an external Knowledge Base or an external memory consisting of embeddings stored and retrieved
by vector databases. In all the current approaches, though, the main issues are: (i) they need to access
an embedding model and then adapt it to the task they have to solve; (ii) in case they have to optimize
the embedding model, they need to have access to the parameters of the LLM, which in many cases are
"black boxes". To address these challenges, we propose a novel framework called Reinforced Retrieval
Augmented Machine Learning (RRAML). RRAML integrates the reasoning capabilities of LLMs with
supporting information retrieved by a purpose-built retriever from a vast user-provided database. By
leveraging recent advancements in reinforcement learning, our method effectively addresses several
critical challenges. Firstly, it circumvents the need for accessing LLM gradients. Secondly, our method
alleviates the burden of retraining LLMs for specific tasks, as it is often impractical or impossible due to
restricted access to the model and the computational intensity involved. Additionally, we seamlessly link
the retriever’s task with the reasoner, mitigating hallucinations and reducing irrelevant and potentially
damaging retrieved documents. We believe that the research agenda outlined in this paper has the
potential to profoundly impact the field of AI, democratizing access to and utilization of LLMs for a wide
range of entities
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
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