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ζ-QVAE: A Quantum Variational Autoencoder utilizing Regularized Mixed-state Latent Representations

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arXiv 2024年

作者： Wang, Gaoyuan Warrell, Jonathan Emani, Prashant S. Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Computer Science Yale University New HavenCT06520 United States Department of Statistics & Data Science Yale University New HavenCT06520 United States Department of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States NEC Laboratories America PrincetonNJ08540 United States

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves compressing the original data to manageable dimensions while still representing essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To bridge this gap, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied for both classical and quantum data compression. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full-data density matrix and allow for a"global"training objective. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results consistently indicate that ζ-QVAE exhibits similar or better performance compared to matched classical models. © 2024, CC BY-NC-ND.

关键词： Large datasets

Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia 2

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Lockdown strategy worth lives: The SEIRD modelling in COVID-...

2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Nurlaila, I. Hidayat, A.A. Pardamean, B. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Lockdowns, despite their conflicting restrictions and consequences they might offer when enforced as a national strategy, are deemed to be suggestive for a prompt conquer to the Coronavirus Disease-19 (COVID-19) outbreak. There have been some success stories such as in China, South Korea and our ASEAN member fellow, Vietnam, which exhibited extremely fallen numbers of COVID-19 cases post the enforcement of lockdown. Indonesia, however, remains in the crux of dispute whether or not the lockdown is opted to force COVID-19 transmission down under control. We, in this respect, employ the most popular model which has been broadly applied in the field of epidemiology, referred to as SEIRD (Susceptible, Exposed, Infectious, Recovered, and Death), the extension form of an age-structured SEIR, where the Death (D) is included to provide more factual situation. We modify the I (Infectious) fraction as symp-tomatic (Is) and asymptomatic (Ia) infectives. There are three lockdown scenarios simulated in our modified SEIRD with the starting date are 26 April 2020 when the immediate lockdown was enacted-and a-week and two-week-gaps, respectively. We figured out that by stipulating the lockdown without delay (26 April 2020), the new cases could be kept below 10,000. A week delay escalates case number to 5,000 and delay for one more week results in beyond 20,000 new cases. Furthermore, when the intervention is delayed following the delayed lockdown, normalization would demand a longer period within which dealing with more critical and dying patients is unavoidable and should be more anticipated. © Published under licence by IOP Publishing Ltd.

关键词： Coronavirus

KRONECKER PRODUCT OF TENSORS AND HYPERGRAPHS: STRUCTURE AND DYNAMICS∗

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arXiv 2023年

作者： Pickard, Joshua Chen, Can Stansbury, Cooper Surana, Amit Bloch, Anthony Rajapakse, Indika Department of Computational Medicine & Bioinformatics University of Michigan Ann ArborMI48109 United States School of Data Science and Society Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Raytheon Technologies Research Center East HartfordCT06108 United States Department of Mathematics University of Michigan Ann ArborMI48109 United States Department of Computational Medicine & Bioinformatics Medical School Department of Mathematics University of Michigan Ann ArborMI48109 United States

Hypergraphs and tensors extend classic graph and matrix theory to account for multiway relationships, which are ubiquitous in engineering, biological, and social systems. While the Kronecker product is a potent tool for analyzing the coupling of systems in graph or matrix contexts, its utility in studying multiway interactions, such as those represented by tensors and hypergraphs, remains elusive. In this article, we present a comprehensive exploration of algebraic, structural, and spectral properties of the tensor Kronecker product. We express Tucker and tensor train decompositions and various tensor eigenvalues in terms of the tensor Kronecker product. Additionally, we utilize the tensor Kronecker product to form Kronecker hypergraphs, a tensor-based hypergraph product, and investigate the structure and stability of polynomial dynamics on Kronecker hypergraphs. Finally, we provide numerical examples to demonstrate the utility of the tensor Kronecker product in computing Z-eigenvalues, various tensor decompositions, and determining the stability of polynomial systems. © 2023, CC BY.

关键词： Tensors

South jakarta's air quality using PM 2.5 data at the beginning of COVID-19 Restriction 6

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South jakarta's air quality using PM 2.5 data at the beginni...

6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Rahutomo, Reza Purwandari, Kartika Hidayat, Alam Ahmad Pardamean, Bens Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

As soon as the lockdown was established in Jakarta, Indonesia, people experienced a change in Jakarta's air quality. This research was conducted to answer several challenges such as: where to find Indonesia's air quality open data platforms, how to handle the acquired data with SQL technology, and how to validate whether the air quality of Jakarta was significantly changed from a period to another or not. With particulate matter 2.5 (PM 2.5) data acquired by The United States Diplomatic Post in South Jakarta, a data visualization was generated to describe the fluctuation of PM 2.5 from February 29th to June 29th. By conducting a two-sample t-test, a significant change in South Jakarta's air quality was revealed. The test concluded that South Jakarta's air quality got worse even after a lockdown was established. © 2021 IEEE.

关键词： COVID-19

Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series data

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Procedia Computer Science 2023年 227卷 1062-1069页

作者： Alam Ahmad Hidayat Arif Budiarto Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters.

关键词： deep learning forecasting sleep quality time series wearable devices

Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization

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arXiv 2021年

作者： Luo, Yuetian Li, Xudong Zhang, Anru R. Data Science Institute University of Chicago United States School of Data Science Fudan University China Departments of Biostatistics & Bioinformatics Computer Science Electrical & Computer Engineering Mathematics and Statistical Science Duke University United States

In this paper, we consider the geometric landscape connection of the widely studied manifold and factorization formulations in low-rank positive semidefinite (PSD) and general matrix optimization. We establish a sandwich relation on the spectrum of Riemannian and Euclidean Hessians at first-order stationary points (FOSPs). As a result of that, we obtain an equivalence on the set of FOSPs, second-order stationary points (SOSPs), and strict saddles between the manifold and the factorization formulations. In addition, we show the sandwich relation can be used to transfer more quantitative geometric properties from one formulation to another. Similarities and differences in the landscape connection under the PSD case and the general case are discussed. To the best of our knowledge, this is the first geometric landscape connection between the manifold and the factorization formulations for handling rank constraints, and it provides a geometric explanation for the similar empirical performance of factorization and manifold approaches in low-rank matrix optimization observed in the literature. In the general low-rank matrix optimization, the landscape connection of two factorization formulations (unregularized and regularized ones) is also provided. By applying these geometric landscape connections, in particular, the sandwich relation, we are able to solve unanswered questions in literature and establish stronger results in the applications on geometric analysis of phase retrieval, well-conditioned low-rank matrix optimization, and the role of regularization in factorization arising from machine learning and signal processing. © 2021, CC BY-SA.

关键词： Matrix factorization

The microcirculation: master in normal pregnancy, puppet in preeclampsia

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American Journal of Obstetrics and Gynecology 2025年

作者： Thevissen, Kristof Cornette, Jerome Bruckers, Liesbeth Gyselaers, Wilfried Department of Rheumatology Ziekenhuis Oost-Limburg Genk Belgium Faculty of Medicine and Life Sciences Hasselt University Diepenbeek Belgium Department of Obstetrics and Fetal Medicine Erasmus Medical Center Rotterdam Netherlands Data Science Institute Interuniversity Institute for Biostatistics and Statistical Bioinformatics Hasselt University Diepenbeek Belgium Department of Obstetrics and Gynaecology Ziekenhuis Oost-Limburg Genk Belgium

Background: The microcirculation is studied sparsely in the field of maternal hemodynamics. With nailfold video capillaroscopy, further insight is possible in this interesting field within maternal hemodynamics. Objective: This study aimed to investigate the association between functional parameters of the microcirculation and the systemic cardiovascular system in pregnant women at risk for gestational hypertension disorders. Study Design: For this observational study, women with high cardiovascular risk according to maternal anthropometrics and obstetrical and medical history were recruited at random gestational ages, depending on the time of referral to the outpatient clinic for high-risk prenatal care at Ziekenhuis Oost-Limburg, Genk, Belgium. After birth, data on maternal and neonatal outcomes were obtained from hospital records, and only women with normal pregnancy (n=142) and preeclampsia (n=34) were included in this analysis. Nailfold video capillaroscopy measurements were performed in the first, second, and/or third trimesters. Video magnification of 200× was used for all fingers except the thumbs, and the stored images were analyzed offline. Capillary density was quantified (n/mm²), mean capillary diameter measured (μm), and capillary bed surface calculated as density × diameter. Cardiac output and total peripheral resistance were measured using impedance cardiography, together with sphygmomanometric blood pressure measurement. A linear mixed model for repeated measures was used to investigate the association between the microvascular and macrovascular parameters. No corrections for multiple testing were applied. Results: In normal pregnancies, a positive association was observed between the capillary bed surface and total vascular resistance (1.807;P=.01) and a negative association between capillary density and cardiac output (−0.269;P=.037). In preeclampsia, a negative association was observed between capillary density and mean arterial pressur

关键词： capillary density capillary diameter capillary perfusion cardiac output gestational hypertension disorder hypertension maternal hemodynamics microcirculation nailfold video capillaroscopy preeclampsia systemic circulation vascular resistance

Mind the Context: Attention-Guided Weak-to-Strong Consistency for Enhanced Semi-Supervised Medical Image Segmentation

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arXiv 2024年

作者： Cheng, Yuxuan Shao, Chenxi Ma, Jie Li, Guoliang Huazhong Agricultural University Wuhan430070 China National Key Laboratory of Crop Genetic Improvement Hubei Hongshan Laboratory Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big Data Key Laboratory of Smart Farming Technology for Agricultural Animals 3D Genomics Research Center Huazhong Agricultural University Wuhan430070 China

Medical image segmentation is a pivotal step in diagnostic and therapeutic processes, relying on high-quality annotated data that is often challenging and costly to obtain. Semi-supervised learning offers a promising approach to enhance model performance by leveraging unlabeled data. Although weak-to-strong consistency is a prevalent method in semi-supervised image segmentation, there is a scarcity of research on perturbation strategies specifically tailored for semi-supervised medical image segmentation tasks. To address this challenge, this paper introduces a simple yet efficient semisupervised learning framework named Attention-Guided weak-to-strong Consistency Match (AIGCMatch). The AIGCMatch framework incorporates attention-guided perturbation strategies at both the image and feature levels to achieve weak-to-strong consistency regularization. This method not only preserves the structural information of medical images but also enhances the model’s ability to process complex semantic information. Extensive experiments conducted on the ACDC and ISIC-2017 datasets have validated the effectiveness of AIGCMatch. Our method achieved a 90.4% Dice score in the 7-case scenario on the ACDC dataset, surpassing the state-of-the-art methods and demonstrating its potential and efficacy in clinical settings. Additionally, on the ISIC-2017 dataset, we significantly outperformed our baseline, indicating the robustness and generalizability of AIGCMatch across different medical image segmentation tasks. © 2024, CC BY.

关键词： Self-supervised learning

Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images 5

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Pyramid Scene Parsing Net Model for Automated Paddy Field Ma...

5th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2022

作者： Heryadi, Yaya Irwansyah, Edy Miranda, Eka Soeparno, Haryono Herlawati Hashimoto, Kiyota Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Faculty of Technology and Environment Phuket83120 Thailand

ISBN: (纸本)9798350398212

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops. © 2022 IEEE.

关键词： Image enhancement