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arXiv 2023年

作者： Zhang, Ji-Guo Zhao, Ze-Wei Li, Yichao Zhang, Jing-Fei Li, Di Zhang, Xin Department of Physics College of Sciences Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

We present a forecast of the cosmological parameter estimation using fast radio bursts (FRBs) from the upcoming Square Kilometre Array (SKA), focusing on the issues of dark energy, the Hubble constant, and baryon density. We simulate 105 and 106 localized FRBs from a 10-year SKA observation, and find that: (i) using 106 FRB data alone can tightly constrain dark-energy equation of state parameters better than CMB+BAO+SNe, providing an independent cosmological probe to explore dark energy;(ii) combining the FRB data with gravitational-wave standard siren data from 10-year observation with the Einstein Telescope, the Hubble constant can be constrained to a sub-percent level, serving as a powerful low-redshift probe;(iii) using 106 FRB data can constrain the baryon density Ωbh to a precision of ∼ 0.1%. Our results indicate that SKA-era FRBs will provide precise cosmological measurements to shed light on both dark energy and the missing baryon problem, and help resolve the Hubble tension. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology

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Gourds: A sliding-block puzzle with turning

arXiv

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arXiv 2020年

作者： Hamersma, Joep van Kreveld, Marc Uno, Yushi van der Zanden, Tom C. Department of Information and Computing Sciences Utrecht University Netherlands Graduate School of Engineering Osaka Prefecture University Japan Department of Data Analytics and Digitalisation Maastricht University Netherlands

We propose a new kind of sliding-block puzzle, called Gourds, where the objective is to rearrange 1 × 2 pieces on a hexagonal grid board of 2n + 1 cells with n pieces, using sliding, turning and pivoting moves. This puzzle has a single empty cell on a board and forms a natural extension of the 15-puzzle to include rotational moves. We analyze the puzzle and completely characterize the cases when the puzzle can always be solved. We also study the complexity of determining whether a given set of colored pieces can be placed on a colored hexagonal grid board with matching colors. We show this problem is NP-complete for arbitrarily many colors, but solvable in randomized polynomial time if the number of colors is a fixed constant. © 2020, CC BY.

关键词： Hamiltonians

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229TiP A phase II trial of a neural network-based treatment decision support tool in patients (pts) with refractory solid organ malignancies

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Annals of Oncology 2024年 35卷 S307-S308页

作者： R.J. Walsh A. Jayagopal T.Z. Tan J. Pitt R. Sundar S.C. Lee B-C. Goh V. Rajan D.S. Tan A.D. Jeyasekharan Department of Haematology-Oncology National University Cancer Institute Singapore Singapore Department of Information Systems and Analytics School of Computing NUS - National University of Singapore Singapore Genomics and Data Analytics Cancer Science Institute (CSI) - National University of Singapore (NUS) Singapore National University Singapore Cancer Science Institute (CSI) - National University of Singapore (NUS) Singapore Medical Oncology Department NUHS - National University Health System Singapore

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Predicting elections: Social media data and techniques

Predicting elections: Social media data and techniques

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2019 International Conference on Engineering and Emerging Technologies, ICEET 2019

作者： Bilal, Muhammad Gani, Abdullah Marjani, Mohsen Malik, Nadia Centre for Data Science and Analytics School of Computing and IT Taylor's University Subang Jaya Malaysia Department of Management Sciences COMSATS University Islamabad Islamabad Pakistan

ISBN: (纸本)9781538621707

During the last decade, the use social media has provided a virtual community where the users express their intention, opinion and communicate with others. Organizations and researchers are very much interested to investigate the polarity of the user's opinions for making predictions and planning of future plans. The use and influence of social media in politics have been recognized by many researchers and political parties in making electoral predictions and devising future campaign strategies. As the literature on making electoral predictions is increasing day by day, this study aims to give an overview of the current state of approaches being utilized, social media used for data collection and the outcomes of the proposed approaches, either succeeded in making accurate electoral predictions using social media data and approaches used or not. This study gives a quick overview and guides researchers in understanding the approaches previously adopted and data sources utilized in predicting electoral results. © 2019 IEEE.

关键词： Forecasting

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Influence of relative Quality and experience in female single tennis match performance using cluster and discriminant analysis

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AIP Conference Proceedings 2024年第1期3123卷

作者： Norhalilah Che Husain Aida Wati Zainan Abidin Kadzim Mohd Rosdi Zan Azma Nasruddin Hafiszah Ismail Big Data Analytics Core Team Department of Statistics Malaysia Presint 1 62514 Putrajaya Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam 40450 Selangor Malaysia Intelligent Data Analytics Competence Centre College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam 40450 Selangor Malaysia Department of Geomatics Sarawak Shell Berhad Miri 98000 Sarawak Malaysia School of Computer Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam 40450 Selangor Malaysia School of Real Estate and Building Surveying College of Built Environment Universiti Teknologi MARA Shah Alam 40450 Selangor Malaysia

Due to the fast growth of advanced technology, the volume of sports data has increased, which has led to researchers’ interest in studying the tennis data set. Tennis is an example of an individual sport where the opponent's skills and anticipation are just as significant as the player's personal qualities and abilities in determining the outcome of a match. Winning is greatly influenced by the experience as well as the comparative talent of the opponents. Hence, this study has two aims: first, to calculate the Relative Quality of each player and then classify the similar patterns of the female single players’ performance based on their experience and Relative Quality, and second, to determine which weightings of the variables best discriminate between winners and losers for each cluster. The match statistics for four 2018 Grand Slam main-draw women’s singles matches were collected from separate official tournament websites: the Australian Open, the French Open, Wimbledon, and the US Open, which consist of 1016 observations. The methods used in this study are hierarchical-based clustering analysis, which groups the players based on these two attributes, and discriminant analysis, which governs which attributes are best to discriminate among the winners and losers for each of the four clusters. Based on the cluster analysis results, four clusters were identified and designated as LEHRQ, LELRQ, HEHRQ, and HEHRQ. The outcome of the discriminant analysis shows that the performance of a tennis player is most likely reliant on holding their serve and remaining powerful to break their opponent's serve. Moreover, there are variations in the components among clusters. Identifying to which group a player belongs provides valuable assistance to both players and coaches in optimizing their training strategies and augmenting their performance by refining their serving and return abilities.

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Discriminative Multimodal Learning via Conditional Priors in Generative Models

arXiv

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arXiv 2021年

作者： Mancisidor, Rogelio A. Kampffmeyer, Michael Aas, Kjersti Jenssen, Robert Department of Data Science and Analytics BI Norwegian Business School Nydalsveien 37 Oslo0484 Norway Department of Physics and Technology Faculty of Science and Technology UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9037 Norway Norwegian Computing Center P.O. Box 114 Blindern Oslo Norway

Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all modalities and class labels are available for model training, but where some modalities and labels required for downstream tasks are missing. We show, in this scenario, that the variational lower bound limits mutual information between joint representations and missing modalities. We, to counteract these problems, introduce a novel conditional multi-modal discriminative model that uses an informative prior distribution and optimizes a likelihood-free objective function that maximizes mutual information between joint representations and missing modalities. Extensive experimentation demonstrates the benefits of our proposed model, empirical results show that our model achieves state-of-the-art results in representative problems such as downstream classification, acoustic inversion, and image and annotation generation. Copyright © 2021, The Authors. All rights reserved.

关键词： Modal analysis

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A two-stage structural equation modeling-neural network approach for understanding and predicting the determinants of m-government service adoption

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Journal of Systems and Information Technology 2019年第4期21卷 419-438页

作者： Talukder, Shamim Chiong, Raymond Dhakal, Sandeep Sorwar, Golam Bao, Yukun Department of Management North South University Dhaka Bangladesh School of Electrical Engineering and Computing The University of Newcastle Newcastle Australia School of Business and Tourism Southern Cross University Bilinga Australia Centre for Big Data Analytics Jiangxi University of Engineering Xinyu China

Purpose: Despite the widespread use of mobile government (m-government) services in developed countries, the adoption and acceptance of m-government services among citizens in developing countries is relatively low. The purpose of this study is to explore the most critical determinants of acceptance and use of m-government services in a developing country context. Design/methodology/approach: The unified theory of acceptance and use of technology (UTAUT) extended with perceived mobility and mobile communication services (MCS) was used as the theoretical framework. data was collected from 216 m-government users across Bangladesh and analyzed in two stages. First, structural equation modeling (SEM) was used to identify significant determinants affecting users' acceptance of m-government services. In the second stage, a neural network model was used to validate SEM results and determine the relative importance of the determinants of acceptance of m-government services. Findings: The results show that facilitating conditions and performance expectancy are the two important precedents of behavioral intention to use m-government services, and performance expectancy mediates the relationship between MCS, mobility and the intention to use m-government services. Research limitations/implications: Academically, this study extended and validated the underlying concept of UTAUT to capture the adoption behavior of individuals in a different cultural context. In particular, MCS might be the most critical antecedent towards mobile application studies. From a practical perspective, this study may provide valuable guidelines to government policymakers and system developers towards the development and effective implementation of m-government systems. Originality/value: This study has contributed to the existing, but limited, literature on m-government service adoption in the context of a developing country. The predictive modeling approach is an innovative approach in the field of te

关键词： Developing countries

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Quantum generative training using rényi divergences

arXiv

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arXiv 2021年

作者： Kieferová, Mária Marrero, Carlos Ortiz Wiebe, Nathan Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney Data Sciences & Analytics Group Department of Electrical & Computer Engineering Pacific Northwest National Laboratory North Carolina State University Department of Computer Science Department of Physics High Performance Computing Group University of Toronto University of Washington Pacific Northwest National Laboratory

Quantum neural networks (QNNs) are a framework for creating quantum algorithms that promises to combine the speedups of quantum computation with the widespread successes of machine learning. A major challenge in QNN development is a concentration of measure phenomenon known as a barren plateau that leads to exponentially small gradients for a range of QNNs models. In this work, we examine the assumptions that give rise to barren plateaus and show that an unbounded loss function can circumvent the existing no-go results. We propose a training algorithm that minimizes the maximal Rényi divergence of order two and present techniques for gradient computation. We compute the closed form of the gradients for Unitary QNNs and Quantum Boltzmann Machines and provide sufficient conditions for the absence of barren plateaus in these models. We demonstrate our approach in two use cases: thermal state learning and Hamiltonian learning. In our numerical experiments, we observed rapid convergence of our training loss function and frequently archived a 99% average fidelity in fewer than 100 epochs. © 2021, CC BY.

关键词： Quantum computers

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Deep-learning enabled combined measurement of tumour cell density and tumour infiltrating lymphocyte density as a prognostic biomarker in colorectal cancer

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BJC reports 2025年第1期3卷 12页

作者： Alice C Westwood Benjamin I Wilson Jon Laye Heike I Grabsch Wolfram Mueller Derek R Magee Phillip Quirke Nicholas P West Division of Pathology and Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds UK. School of Computing University of Leeds Leeds UK. Department of Pathology GROW - Research Institute for Oncology and Reproduction Maastricht University Medical Center+ Maastricht Netherlands. Gemeinschaftspraxis Pathologie Starnberg Germany. Division of Pathology and Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds UK. N.P.West@leeds.ac.uk.

BACKGROUND:Within the colorectal cancer (CRC) tumour microenvironment, tumour infiltrating lymphocytes (TILs) and tumour cell density (TCD) are recognised prognostic markers. Measurement of TILs and TCD using deep-learning (DL) on haematoxylin and eosin (HE) whole slide images (WSIs) could aid management. METHODS:HE WSIs from the primary tumours of 127 CRC patients were included. DL was used to quantify TILs across different regions of the tumour and TCD at the luminal surface. The relationship between TILs, TCD, and cancer-specific survival was analysed. RESULTS:Median TIL density was higher at the invasive margin than the luminal surface (963 vs 795 TILs/mm, P = 0.010). TILs and TCD were independently prognostic in multivariate analyses (HR 4.28, 95% CI 1.87-11.71, P = 0.004; HR 2.72, 95% CI 1.19-6.17, P = 0.017, respectively). Patients with both low TCD and low TILs had the poorest survival (HR 10.0, 95% CI 2.51-39.78, P = 0.001), when compared to those with a high TCD and TILs score. CONCLUSIONS:DL derived TIL and TCD score were independently prognostic in CRC. Patients with low TILs and TCD are at the highest risk of cancer-specific death. DL quantification of TILs and TCD could be used in combination alongside other validated prognostic biomarkers in routine clinical practice.

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ConBERT-RL: A policy-driven deep reinforcement learning based approach for detecting homophobia and transphobia in low-resource languages

Natural Language Processing Journal

引用

Natural Language Processing Journal 2024年 6卷

作者： Vivek Suresh Raj Chinnaudayar Navaneethakrishnan Subalalitha Lavanya Sambath Frank Glavin Bharathi Raja Chakravarthi University of Calgary Calgary Alberta Canada Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Information Technology Madras Institute of Technology Chennai India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Galway Ireland School of Computer Science University of Galway Galway Ireland

In this work, we present a novel framework for discriminatory comment classification in targeted low-resource languages thereby enabling identification of discriminatory comments for promoting safer online environment in linguistically diverse contexts. Recently proposed literatures with Bidirectional Encoder Representations from Transformers (BERT) and its variants, have produced promising results, particularly in the case of transliterated Tamil words in English. Such approaches are seen as transfer learning and fine-tuning between a general environment and targeted downstream task. However, for an effective transfer of knowledge, from a source task to a targeted task, their feature space has to be correlated (similarity measure as metric, to measure knowledge transfer), which is unexplored in the previous literature works. In practice, such similarity conditions are often violated. We propose a Con catenated representation, powered with BERT in R einforcement L earning (RL) - ConBERT-RL framework, to capture problem-specific features, as well as to understand nuances in case of transliterated Tamil words into English, to make decision in classifying discriminatory comments. ConBERT-RL incorporates a fusion of learned hidden-state representation from our pre-trained Classifier-Model (CM), along with the broader contextualized representation (pooled output) from BERT. The key idea is to utilize this concatenated representation to drive our policy network for hate comment classification. To effectively learn such a policy, we use the REINFORCE algorithm in a reinforcement learning setting, to guide our ConBERT-RL agent, in making informative decisions. To demonstrate the general aspects of ConBERT-RL, we conduct experiments for offensive comment classification on transliterated Tamil words in English dataset. ConBERT-RL obtains results, where it significantly improved the score of micro average accuracy with 90% ( ≈ 1 . 0 % absolute improvement over BERT+FC), 93% (

关键词： Deep reinforcement learning Low resourced language Homophobia detection Transphobia detection

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