In this paper, we propose a novel tensor completion framework, Overlapping Tensor Train Completion with TV Regularization (OTTC-TV), which integrates the strengths of both Overlapping Ket Augmentation (OKA) and Total ...
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We consider an interacting particle system on trees known as the frog model: initially, a single active particle begins at the root and i.i.d. Poiss(λ) many inactive particles are placed at each nonroot vertex. Activ...
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Modern large-scale computing systems always demand better connectivity indicators for reliability evaluation. However, as more processing units have been rapidly incorporated into emerging computing systems, existing ...
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Large language models (LLMs) have demonstrated promising in-context learning capabilities, especially with instructive prompts. However, recent studies have shown that existing large models still face challenges in sp...
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The Inverse Rayleigh distribution has many applications in the area of reliability studies. It is regarded as a model for a lifetime random variable. It is essential to develop an efficient goodness-of-fit test for th...
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Oja's algorithm for Streaming Principal Component Analysis (PCA) for n datapoints in a d dimensional space achieves the same sin-squared error O(reff/n) as the offline algorithm in O(d) space and O(nd) time and a ...
Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics *** method outputs the unbiased e...
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Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics *** method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend *** this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID ***/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID ***,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the ***,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work ***:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the *** a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning *** limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical *** implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics ***/value:This study gains insights into the usage of econometric tools in science of science.
Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender *** existing floor localization systems have many drawbacks,like low accuracy,poor scalab...
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Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender *** existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational *** this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a ***,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural *** approach offers high accuracy,easy scalability to new buildings,and computational *** results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art ***,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.
Supervised contrastive representation learning has been shown to be effective in various transfer learning scenarios. However, while asymmetric non-contrastive learning (ANCL) often outperforms its contrastive learnin...
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Supervised contrastive representation learning has been shown to be effective in various transfer learning scenarios. However, while asymmetric non-contrastive learning (ANCL) often outperforms its contrastive learning counterpart in self-supervised representation learning, the extension of ANCL to supervised scenarios is less explored. To bridge the gap, we study ANCL for supervised representation learning, coined SUPSIAM and SUPBYOL, leveraging labels in ANCL to achieve better representations. The proposed supervised ANCL framework improves representation learning while avoiding collapse. Our analysis reveals that providing supervision to ANCL reduces intra-class variance, and the contribution of supervision should be adjusted to achieve the best performance. Experiments demonstrate the superiority of supervised ANCL across various datasets and tasks. The code is available at: https://***/JH-Oh-23/Sup-ANCL. Copyright 2024 by the author(s)
Hate speech is a prominent, growing problem within our society, and this problem and its effects have only increased with time. According to the Center for Technology and Society, 52% of people reported being harassed...
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