21 papers · ranked by Valyu relevance
Hongsheng Dai, Murray Pollock, Gareth O. Roberts
Summary. There has recently been considerable interest in addressing the problem of unifying distributed statistical analyses into a single coherent inference. This problem naturally arises in a number of situations, including in big-data settings, when working under privacy constraints, and in Bayesian model choice.…
Peng Wu, Tales Imbiriba, Víctor Daniel Elvira, Pau Closas
—The integration of data and knowledge from several sources is known as data fusion. When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential. In Bayesian settings, a priori information of the unknown quantities is available…
Candido, Antonio Leandro Martins, Maia, Jose Everardo Bessa
The automatic curation of discussion forums in online courses requires constant updates, making frequent retraining of Large Language Models (LLMs) a resource-intensive process. To circumvent the need for costly fine-tuning, this paper proposes and evaluates the use of Bayesian fusion. The approach combines the…
Eugene Santos Jr., Jacob Jurmain, Anthony Ragazzi, Alexander Zimper
The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real…
Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson
—This paper considers the problem of sequential fusion of predictions from neural networks (NN) and fusion of predictions from multiple NN. This fusion strategy increases the robustness, i.e., reduces the impact of one incorrect classification and detection of outliers the NN has not seen during training. This paper…
Da Zhi, Ladan Shahshahani, Caroline Nettekoven, Ana Lúısa Pinho + 2 more
One important barrier in the development of complex models of human brain organization is the lack of a large and comprehensive task-based neuro-imaging dataset. Therefore, current atlases of functional brain organization are mainly based on single and homogeneous resting-state datasets. Here, we propose a hierarchical…
Günther Koliander, Yousef El-Laham, Petar M. Djurić, Franz Hlawatsch
—Fusing probabilistic information is a fundamental task in signal and data processing with relevance to many fields of technology and science. In this work, we investigate the fusion of multiple probability density functions (pdfs) of a continuous random variable or vector. Although the case of continuous random…
Ofer Dagan, Nisar Ahmed
—In Bayesian peer-to-peer decentralized data fusion, the underlying distributions held locally by autonomous agents are frequently assumed to be over the same set of variables (homogeneous). This requires each agent to process and communicate the full global joint distribution, and thus leads to high computation and…
Pablo Torrijos, José M. Puerta, José A. Gámez, Juan A. Aledo
This paper presents the Greedy Min-Cut Bayesian Consensus (GMCBC) algorithm for the structural fusion of Bayesian Networks (BNs). The method is designed to preserve essential dependencies while controlling network complexity. It addresses the limitations of traditional fusion approaches, which often lead to excessively…
Robert J. Piechocki, Xiaoyang Wang, Mohammud J. Bocus
A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion…
Marcus Ghosh, Gabriel Béna, Volker Bormuth, Dan F. M. Goodman
Animals continuously detect information via multiple sensory channels, like vision and hearing, and integrate these signals to realise faster and more accurate decisions; a fundamental neural computation known as multisensory integration. A widespread view of this process is that multimodal neurons linearly fuse…
Tatiana Berlenko, Kirill Krinkin, Zheng Chen
Comparisons of Bayesian log-odds and Dempster’s combination rule for occupancy grid mapping typically parameterize the two sensor models independently, so that observed performance differences confound the fusion rule with the sensor parameterization. We develop a pignistic-transform-based matching methodology that…
Yujie Chen, Zexi Hua, Yongchuan Tang, Baoxin Li + 1 more
Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source…
David Meijer, Roberto Barumerli, Robert Baumgartner
Interpreting sensory prediction errors can be challenging in volatile environments because they can be caused by stochastic noise or by outdated predictions. Noisy signals should be integrated with prior beliefs to improve precision, but the two should be segregated when environmental changes render prior beliefs…
Zhuo Zhang, Hongfei Wang, Jianting Zhang, Wen Jiang + 2 more
'Philip Broadbridge' 'Éloi Bossé'] Measuring the correlation between belief functions is an important issue in Dempster-Shafer theory. From the perspective of uncertainty, analyzing the correlation may provide a more comprehensive reference for uncertain information processing. However, existing studies about…
Nour El Imane Hamda, Allel Hadjali, Mohand Lagha, Jose Manuel Molina López
'Jose Manuel Molina López'] In IoT environments, voluminous amounts of data are produced every single second. Due to multiple factors, these data are prone to various imperfections, they could be uncertain, conflicting, or even incorrect leading to wrong decisions. Multisensor data fusion has proved to be powerful for…
Yongchuan Tang, Yonghao Zhou, Xiangxuan Ren, Yufei Sun + 2 more
'Deyun Zhou'] Dempster-Shafer evidence theory is an effective method to deal with information fusion. However, how to deal with the fusion paradoxes while using the Dempster’s combination rule is still an open issue. To address this issue, a new basic probability assignment (BPA) generation method based on the cosine…
Authors not listed
Occupational chemical hazards pose profound risks to chemists in laboratory and industrial settings, encompassing acute and chronic exposures that imperil sensory organs (e.g., ocular, auditory, olfactory, dermal) and vital physiological systems. This manuscript delineates a multifaceted, innovative protocol suite…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
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Identifying molecular structure based on spectroscopic readings is a key task in a va- riety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless…