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1. HFGaussian: Learning Generalizable Gaussian Human with Integrated Human Features

Author

Dey, Arnab, Lu, Cheng-You, Comport, Andrew I., Sridhar, Srinath, Lin, Chin-Teng, and Martinet, Jean

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Recent advancements in radiance field rendering show promising results in 3D scene representation, where Gaussian splatting-based techniques emerge as state-of-the-art due to their quality and efficiency. Gaussian splatting is widely used for various applications, including 3D human representation. However, previous 3D Gaussian splatting methods either use parametric body models as additional information or fail to provide any underlying structure, like human biomechanical features, which are essential for different applications. In this paper, we present a novel approach called HFGaussian that can estimate novel views and human features, such as the 3D skeleton, 3D key points, and dense pose, from sparse input images in real time at 25 FPS. The proposed method leverages generalizable Gaussian splatting technique to represent the human subject and its associated features, enabling efficient and generalizable reconstruction. By incorporating a pose regression network and the feature splatting technique with Gaussian splatting, HFGaussian demonstrates improved capabilities over existing 3D human methods, showcasing the potential of 3D human representations with integrated biomechanics. We thoroughly evaluate our HFGaussian method against the latest state-of-the-art techniques in human Gaussian splatting and pose estimation, demonstrating its real-time, state-of-the-art performance.

Published
2024

4. GHNeRF: Learning Generalizable Human Features with Efficient Neural Radiance Fields

Author

Dey, Arnab, Yang, Di, Agaram, Rohith, Dantcheva, Antitza, Comport, Andrew I., Sridhar, Srinath, and Martinet, Jean

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Recent advances in Neural Radiance Fields (NeRF) have demonstrated promising results in 3D scene representations, including 3D human representations. However, these representations often lack crucial information on the underlying human pose and structure, which is crucial for AR/VR applications and games. In this paper, we introduce a novel approach, termed GHNeRF, designed to address these limitations by learning 2D/3D joint locations of human subjects with NeRF representation. GHNeRF uses a pre-trained 2D encoder streamlined to extract essential human features from 2D images, which are then incorporated into the NeRF framework in order to encode human biomechanic features. This allows our network to simultaneously learn biomechanic features, such as joint locations, along with human geometry and texture. To assess the effectiveness of our method, we conduct a comprehensive comparison with state-of-the-art human NeRF techniques and joint estimation algorithms. Our results show that GHNeRF can achieve state-of-the-art results in near real-time.

Published
2024

5. HFNeRF: Learning Human Biomechanic Features with Neural Radiance Fields

Author

Dey, Arnab, Yang, Di, Dantcheva, Antitza, and Martinet, Jean

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In recent advancements in novel view synthesis, generalizable Neural Radiance Fields (NeRF) based methods applied to human subjects have shown remarkable results in generating novel views from few images. However, this generalization ability cannot capture the underlying structural features of the skeleton shared across all instances. Building upon this, we introduce HFNeRF: a novel generalizable human feature NeRF aimed at generating human biomechanic features using a pre-trained image encoder. While previous human NeRF methods have shown promising results in the generation of photorealistic virtual avatars, such methods lack underlying human structure or biomechanic features such as skeleton or joint information that are crucial for downstream applications including Augmented Reality (AR)/Virtual Reality (VR). HFNeRF leverages 2D pre-trained foundation models toward learning human features in 3D using neural rendering, and then volume rendering towards generating 2D feature maps. We evaluate HFNeRF in the skeleton estimation task by predicting heatmaps as features. The proposed method is fully differentiable, allowing to successfully learn color, geometry, and human skeleton in a simultaneous manner. This paper presents preliminary results of HFNeRF, illustrating its potential in generating realistic virtual avatars with biomechanic features using NeRF.

Published
2024

6. Guaranteeing Service in Connected Microgrids: Storage Planning and Optimal Power Sharing Policy

Author

Dey, Arnab, Khatana, Vivek, Mani, Ankur, and Salapaka, Murti V.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

The integration of renewable energy sources (RES) into power distribution grids poses challenges to system reliability due to the inherent uncertainty in their power production. To address this issue, battery energy sources (BESs) are being increasingly used as a promising solution to counter the uncertainty associated with RES power production. During the overall system planning stage, the optimal capacity of the BES has to be decided. In the operational phase, policies on when to charge the BESs and when to use them to support loads must be determined so that the BES remains within its operating range, avoiding depletion of charge on one hand and remaining within acceptable margins of maximum charge on the other. In this paper, a stochastic control framework is used to determine battery capacity, for microgrids, which ensures that during the operational phase, BESs' operating range is respected with pre-specified high probability. We provide an explicit analytical expression of the required BESs energy capacity for a single microgrid with RES as the main power source. Leveraging insights from the single microgrid case, the article focuses on the design and planning of BESs for the two-microgrid scenario. In this setting, microgrids are allowed to share power while respecting the capacity constraints imposed by the power lines. We characterize the optimal power transfer policy between the microgrids and the optimal BES capacity for multiple microgrids. This provides the BES savings arising from connecting the microgrids.

Published
2024

7. DiVa-360: The Dynamic Visual Dataset for Immersive Neural Fields

Author

Lu, Cheng-You, Zhou, Peisen, Xing, Angela, Pokhariya, Chandradeep, Dey, Arnab, Shah, Ishaan, Mavidipalli, Rugved, Hu, Dylan, Comport, Andrew, Chen, Kefan, and Sridhar, Srinath

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Advances in neural fields are enabling high-fidelity capture of the shape and appearance of dynamic 3D scenes. However, their capabilities lag behind those offered by conventional representations such as 2D videos because of algorithmic challenges and the lack of large-scale multi-view real-world datasets. We address the dataset limitation with DiVa-360, a real-world 360 dynamic visual dataset that contains synchronized high-resolution and long-duration multi-view video sequences of table-scale scenes captured using a customized low-cost system with 53 cameras. It contains 21 object-centric sequences categorized by different motion types, 25 intricate hand-object interaction sequences, and 8 long-duration sequences for a total of 17.4 M image frames. In addition, we provide foreground-background segmentation masks, synchronized audio, and text descriptions. We benchmark the state-of-the-art dynamic neural field methods on DiVa-360 and provide insights about existing methods and future challenges on long-duration neural field capture.

Published
2023

8. Provider-patient experiences and HIV care utilization among people living with HIV who inject drugs in St. Petersburg, Russia.

Author

Raj, Anita, Gnatienko, Natalia, Cheng, Debbie, Blokhina, Elena, Dey, Arnab, Wagman, Jennifer, Toussova, Olga, Truong, Ve, Rateau, Lindsey, Lunze, Karsten, Krupitsky, Evgeny, and Samet, Jeffrey

Subjects
HIV, PWID, Russia, provider disrespect, Humans, Female, Cross-Sectional Studies, Delivery of Health Care, Health Facilities, HIV Infections, Patient Outcome Assessment, Russia
Abstract

Providers disrespect and abuse of patients is a recognized but understudied issue affecting quality of care and likely affecting healthcare utilization. Little research has examined this issue among people living with HIV (PWH) who inject drugs, despite high stigmatization of this population. No research has examined this issue in the context of Russia. This study assesses patients reports of disrespect and abuse from providers as a barrier to healthcare and examines the association between these reports and HIV care outcomes.We conducted a cross-sectional analysis of the associations between disrespect/abuse from health providers as a barrier to care and the following HIV care outcomes: (i) anti-retroviral treatment (ART) uptake ever, (ii) past 6-month visit to HIV provider, and (iii) CD4 count. Participants (N = 221) were people living with HIV who injected drugs and were not on ART at enrollment.Two in five participants (42%) reported a history disrespect/abuse from a healthcare provider that they cited as a barrier to care. Those reporting this concern had lower odds of ever use of ART (adjusted odds ratio 0.46 [95% CI 0.22, 0.95]); we found no significant associations for the other HIV outcomes. We additionally found higher representation of women among those reporting prevalence of disrespect/abuse from provider as a barrier to care compared to those not reporting this barrier (58.1% versus 27.3%).Almost half of this sample of PWH who inject drugs report disrespect/abuse from a provider as a barrier to healthcare, and this is associated with lower odds of receipt of ART but not with other HIV outcomes studied. There is need for improved focus on quality of respectful and dignified care from providers for PWH who inject drugs, and such focus may improve ART uptake in Russia.

Published
2023

10. PNeRF: Probabilistic Neural Scene Representations for Uncertain 3D Visual Mapping

Author

Ahmine, Yassine, Dey, Arnab, and Comport, Andrew I.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently neural scene representations have provided very impressive results for representing 3D scenes visually, however, their study and progress have mainly been limited to visualization of virtual models in computer graphics or scene reconstruction in computer vision without explicitly accounting for sensor and pose uncertainty. Using this novel scene representation in robotics applications, however, would require accounting for this uncertainty in the neural map. The aim of this paper is therefore to propose a novel method for training {\em probabilistic neural scene representations} with uncertain training data that could enable the inclusion of these representations in robotics applications. Acquiring images using cameras or depth sensors contains inherent uncertainty, and furthermore, the camera poses used for learning a 3D model are also imperfect. If these measurements are used for training without accounting for their uncertainty, then the resulting models are non-optimal, and the resulting scene representations are likely to contain artifacts such as blur and un-even geometry. In this work, the problem of uncertainty integration to the learning process is investigated by focusing on training with uncertain information in a probabilistic manner. The proposed method involves explicitly augmenting the training likelihood with an uncertainty term such that the learnt probability distribution of the network is minimized with respect to the training uncertainty. It will be shown that this leads to more accurate image rendering quality, in addition to more precise and consistent geometry. Validation has been carried out on both synthetic and real datasets showing that the proposed approach outperforms state-of-the-art methods. The results show notably that the proposed method is capable of rendering novel high-quality views even when the training data is limited., Comment: 7 Pages, 6 Figures, 5 Tables. Submitted to IEEE International Conference on Robotics and Automation 2023 (ICRA 2023)

Published
2022

11. Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact

Author

Dey, Arnab, Goswami, Mononito, Yoon, Joo Heung, Clermont, Gilles, Pinsky, Michael, Hravnak, Marilyn, and Dubrawski, Artur

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning (ML) models capable of accurately adjudicating Vital Sign (VS) alerts raised at the bedside of hemodynamically monitored patients as real or artifact. Previous studies have utilized supervised ML techniques that require substantial amounts of hand-labeled data. However, manually harvesting such data can be costly, time-consuming, and mundane, and is a key factor limiting the widespread adoption of ML in healthcare (HC). Instead, we explore the use of multiple, individually imperfect heuristics to automatically assign probabilistic labels to unlabeled training data using weak supervision. Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML., Comment: Accepted at American Medical Informatics Association (AMIA) Annual Symposium 2022. 10 pages, 4 figures and 2 tables

Published
2022

12. The associations of everyday and major discrimination exposure with violence and poor mental health outcomes during the COVID-19 pandemic

Author

Raj, Anita, Chatterji, Sangeeta, Johns, Nicole E, Yore, Jennifer, Dey, Arnab K, and Williams, David R

Subjects
Human Society, Brain Disorders, Violence Research, Behavioral and Social Science, Mental Health, Aetiology, 2.3 Psychological, social and economic factors, Mental health, Good Health and Well Being, Peace, Justice and Strong Institutions, Gender Equality, Adult, Humans, Pandemics, Cross-Sectional Studies, COVID-19, Violence, Outcome Assessment, Health Care, Sexual violence, Sexual harassment, Gender -based violence, Economic deprivation, Poverty, Gender-based violence, Medical and Health Sciences, Economics, Studies in Human Society, Public Health, Health sciences, Human society
Abstract

Research on discrimination and risks for violence and mental health issues under the pandemic is notably absent. We examined the relative effects of perceived everyday discrimination (e.g., poorer service, disrespectful treatment in a typical week) and major experiences of race-based discrimination (e.g., racial/ethnic discrimination in housing or employment at any point in the lifetime) on experiences of violence and the PHQ-4 assessment of symptoms of depression and anxiety under the pandemic. We analyzed state-representative cross-sectional survey data from California adults (N = 2114) collected in March 2021. We conducted multivariate regression models adjusting for age, race/ethnicity, gender, sexual identity, income, and disability. One in four Californians (26.1%) experienced everyday discrimination in public spaces, due most often to race/ethnicity and gender. We found that everyday discrimination was significantly associated with past year physical violence (single form Adjusted Odds Ratio [AOR] 5.0, 95% CI 2.5-10.3; multiple forms AOR 2.6, 95% CI 1.1-5.8), past year sexual violence (multiple forms AOR 2.5, 95% CI 1.4-4.4), and mental health symptoms (e.g., severe symptoms, multiple forms AOR 3.3, 95% CI 1.6-6.7). Major experiences of race-based discrimination (reported by 10.0% of Californians) were associated with past year sexual violence (AOR 2.0, 95% CI 1.1-3.8) and severe mental health symptoms (AOR 2.7, 95% CI 1.2-6.2). Non-race-based major discrimination (reported by 23.9% of Californians) was also associated with violence and mental health outcomes Everyday discrimination, more than major experiences of discrimination, was associated with higher risk for violence and poor mental health outcomes during the pandemic. Non-race-based forms of major discrimination independently were also associated with these negative outcomes. Findings indicate that efforts to reduce and ultimately eliminate discrimination should be a focus of public health and COVID-19 rebuilding efforts.

Published
2023

14. Impulsivity and linkage to HIV Care among People living with HIV in St. Petersburg, Russia

Author

Dey, Arnab K, Ennis, Nicole, Cheng, Debbie M, Blokhina, Elena, Raj, Anita, Quinn, Emily, Bendiks, Sally, Palfai, Tibor, Dunne, Eugene M, Cook, Robert L, Krupitsky, Evgeny, and Samet, Jeffrey H

Subjects
Public Health, Health Sciences, Clinical Research, HIV/AIDS, Behavioral and Social Science, Infectious Diseases, Health and social care services research, 8.1 Organisation and delivery of services, Infection, Good Health and Well Being, Adult, Male, Humans, Female, Substance Abuse, Intravenous, HIV Infections, CD4 Lymphocyte Count, Impulsive Behavior, Substance-Related Disorders, Russia, Impulsivity, HIV, PWID, Public Health and Health Services, Social Work, Public health
Abstract

This study evaluated the association between impulsivity and linkage to HIV care among Russians living with HIV recruited from an inpatient narcology hospital. Linking Infectious and Narcology Care (LINC) study participants who completed the Barratt Impulsiveness Scale (BIS) were included in these analyses. The primary independent variable was impulsivity score which was categorized as high impulsivity (BIS score > 71) vs. low impulsivity (BIS score

Published
2022

16. Mip-NeRF RGB-D: Depth Assisted Fast Neural Radiance Fields

Author

Dey, Arnab, Ahmine, Yassine, and Comport, Andrew I.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Neural scene representations, such as Neural Radiance Fields (NeRF), are based on training a multilayer perceptron (MLP) using a set of color images with known poses. An increasing number of devices now produce RGB-D(color + depth) information, which has been shown to be very important for a wide range of tasks. Therefore, the aim of this paper is to investigate what improvements can be made to these promising implicit representations by incorporating depth information with the color images. In particular, the recently proposed Mip-NeRF approach, which uses conical frustums instead of rays for volume rendering, allows one to account for the varying area of a pixel with distance from the camera center. The proposed method additionally models depth uncertainty. This allows to address major limitations of NeRF-based approaches including improving the accuracy of geometry, reduced artifacts, faster training time, and shortened prediction time. Experiments are performed on well-known benchmark scenes, and comparisons show improved accuracy in scene geometry and photometric reconstruction, while reducing the training time by 3 - 5 times.

Published
2022
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17. RGB-D Neural Radiance Fields: Local Sampling for Faster Training

Author

Dey, Arnab and Comport, Andrew I.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Learning a 3D representation of a scene has been a challenging problem for decades in computer vision. Recent advances in implicit neural representation from images using neural radiance fields(NeRF) have shown promising results. Some of the limitations of previous NeRF based methods include longer training time, and inaccurate underlying geometry. The proposed method takes advantage of RGB-D data to reduce training time by leveraging depth sensing to improve local sampling. This paper proposes a depth-guided local sampling strategy and a smaller neural network architecture to achieve faster training time without compromising quality.

Published
2022

18. Factors affecting delayed and non-receipt of healthcare during the COVID-19 pandemic for women in rural Maharashtra, India: Evidence from a cross-sectional study

Author

Dey, Arnab K, Bhan, Nandita, Rao, Namratha, Ghule, Mohan, Chatterji, Sangeeta, and Raj, Anita

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Prevention, Clinical Research, Health Services, Reproductive health and childbirth, Good Health and Well Being, COVID-19, Maternal and Child Health, Healthcare utilisation, Delay in seeking healthcare, Health system perception, Mediation, India, Clinical sciences, Health services and systems, Public health
Abstract

BackgroundPathways to low healthcare utilisation under the COVID-19 pandemic are not well understood. This study aims to understand women's concerns about the health system's priorities and their increased burden of domestic responsibilities during COVID-19 as predictors of delayed or non-receipt of needed care for themselves or their children.MethodsWe surveyed married women in rural Maharashtra, India (N = 1021) on their health and economic concerns between Feb 1 and March 26, 2021. This study period was when India emerged from the first wave of the pandemic, which had severely impacted the health systems, and before the second-even more devastating wave had started. We captured if women were concerned about access to non-COVID health services due to healthcare being directed solely to COVID-19) (exposure 1) and whether their domestic responsibilities increased during the pandemic (exposure 2). Our outcomes included women's reports on whether they delayed healthcare seeking (secondary outcome and mediator) and whether they received healthcare for themselves or their children when needed (primary outcome). We conducted adjusted regression models on our predictor variables with each outcome and assessed the mediation effects of delayed healthcare seeking for each of the exposure variables.FindingsWe found that women who were concerned that healthcare was directed solely towards COVID-19 were more likely not to receive healthcare when needed (Adjusted Risk Ratio [ARR] = 1.49, 95% CI = 1.14, 1.95). We also found that women whose domestic care burden increased under the pandemic were more likely to delay healthcare seeking (ARR = 1.84, 95% CI = 1.05, 3.21). Delayed healthcare seeking mediated the associations between each of our exposure variables with our outcome variable, non-receipt of needed healthcare.InterpretationOur findings suggested that women's perceptions of healthcare systems and their domestic labour burdens affected healthcare seeking during the pandemic in India, even before the second wave of COVID-19 incapacitated the health system. Support for women and health systems is needed to ensure healthcare uptake during crises.FundingEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, USA (grant numbers: R01HD084453- 01A1 and RO1HD61115); Department of Biotechnology, Government of India (grant #BT/IN/US/01/BD/2010); the EMERGE project (Bill and Melinda Gates Foundation Grants: OPP1163682 and INV018007; PI Anita Raj), and Bill and Melinda Gates Foundation Grant number INV-002967.

Published
2022

19. Gender differences in work attendance among health care workers in Northern Nigeria during the COVID-19 pandemic

Author

Taiwo, Modupe, Oyekenu, Oluwatoyin, Ekeh, Ferdinard, Dey, Arnab K, and Raj, Anita

Subjects
Biomedical and Clinical Sciences, Health Services and Systems, Public Health, Clinical Sciences, Health Sciences, Clinical Research, Good Health and Well Being, Health workers, COVID-19, Northern nigeria, Gender, Household burden, COVID -19, Clinical sciences, Health services and systems, Public health
Abstract

BackgroundThe COVID-19 pandemic has resulted in the overwork of health care workers (HCWs) and greater household burdens for women. This study examines gender differences in HCWs' work attendance under COVID-19 and household burdens as a potential mediator of the gender difference in Northern Nigeria.MethodsFrom April to May 2021, we conducted a cross-sectional survey on work and household burdens with a convenience sample of male and female HCWs (N=334) across 16 facilities in the Gombe, Katsina, and Zamfara states in Northern Nigeria. We used a series of multilevel modified Poisson regression models to examine the associations between gender and HCW work attendance. We also tested the mediation effect of household burdens on this observed association.FindingsOnly 2·10% of HCWs reported

Published
2022

20. Using machine learning to understand determinants of IUD use in India: Analyses of the National Family Health Surveys (NFHS-4)

Author

Dey, Arnab K, Dehingia, Nabamallika, Bhan, Nandita, Thomas, Edwin Elizabeth, McDougal, Lotus, Averbach, Sarah, McAuley, Julian, Singh, Abhishek, and Raj, Anita

Subjects
Human Society, Demography, Contraception/Reproduction, Prevention, Clinical Research, Pediatric, Behavioral and Social Science, Good Health and Well Being, Intra-uterine devices, IUD, Male engagement, Couple dynamics, Family planning, Reproductive health, Machine learning, NFHS, India, Public Health and Health Services, Epidemiology, Public health, Sociology
Abstract

Intra-uterine devices (IUDs) are a safe and effective method to delay or space pregnancies and are available for free or at low cost in the Indian public health system; yet, IUD uptake in India remains low. Limited quantitative research using national data has explored factors that may affect IUD use. Machine Learning (ML) techniques allow us to explore determinants of low prevalence behaviors in survey research, such as IUD use. We applied ML to explore the determinants of IUD use in India among married women in the 4th National Family Health Survey (NFHS-4; N = 499,627), which collects data on demographic and health indicators among women of childbearing age. We conducted ML logistic regression (lasso and ridge) and neural network approaches to assess significant determinants and used iterative thematic analysis (ITA) to offer insight into related variable constructs generated from a series of regularized models. We found that couples' shared family planning (FP) goals were the strongest determinants of IUD use, followed by receipt of FP services and desire for no more children, higher wealth and education, and receipt of maternal and child health services. Findings highlight the importance of male engagement and family planning services for IUD uptake and the need for more targeted efforts to support awareness of IUD as an option for spacing, especially for those of lower SES and with lower access to care.

Published
2022

21. Urban Fire Station Location Planning using Predicted Demand and Service Quality Index

Author

Dey, Arnab, Heger, Andrew, and England, Darin

Subjects
Computer Science - Machine Learning, Computer Science - Information Retrieval, Mathematics - Optimization and Control
Abstract

In this article, we propose a systematic approach for fire station location planning. We develop machine learning models, based on Random Forest and Extreme Gradient Boosting, for demand prediction and utilize the models further to define a generalized index to measure quality of fire service in urban settings. Our model is built upon spatial data collected from multiple different sources. Efficacy of proper facility planning depends on choice of candidates where fire stations can be located along with existing stations, if any. Also, the travel time from these candidates to demand locations need to be taken care of to maintain fire safety standard. Here, we propose a travel time based clustering technique to identify suitable candidates. Finally, we develop an optimization problem to select best locations to install new fire stations. Our optimization problem is built upon maximum coverage problem, based on integer programming. We further develop a two-stage stochastic optimization model to characterize the confidence in our decision outcome. We present a detailed experimental study of our proposed approach in collaboration with city of Victoria Fire Department, MN, USA. Our demand prediction model achieves true positive rate of 80% and false positive rate of 20% approximately. We aid Victoria Fire Department to select a location for a new fire station using our approach. We present detailed results on improvement statistics by locating a new facility, as suggested by our methodology, in the city of Victoria.

Published
2021

24. Help seeking behavior by women experiencing intimate partner violence in india: A machine learning approach to identifying risk factors

Author

Dehingia, Nabamallika, Dey, Arnab K, McDougal, Lotus, McAuley, Julian, Singh, Abhishek, and Raj, Anita

Subjects
Prevention, Violence Against Women, Basic Behavioral and Social Science, Mental Health, Violence Research, Behavioral and Social Science, Mental health, Gender Equality, Good Health and Well Being, Help-Seeking Behavior, General Science & Technology
Abstract

BackgroundDespite the low prevalence of help-seeking behavior among victims of intimate partner violence (IPV) in India, quantitative evidence on risk factors, is limited. We use a previously validated exploratory approach, to examine correlates of help-seeking from anyone (e.g. family, friends, police, doctor etc.), as well as help-seeking from any formal sources.MethodsWe used data from a nationally-representative health survey conducted in 2015-16 in India, and included all variables in the dataset (~6000 variables) as independent variables. Two machine learning (ML) models were used- L-1, and L-2 regularized logistic regression models. The results from these models were qualitatively coded by researchers to identify broad themes associated with help-seeking behavior. This process of implementing ML models followed by qualitative coding was repeated until pre-specified criteria were met.ResultsIdentified themes associated with help-seeking behavior included experience of injury from violence, husband's controlling behavior, husband's consumption of alcohol, and being currently separated from husband. Themes related to women's access to social and economic resources, such as women's employment, and receipt of maternal and reproductive health services were also noted to be related factors. We observed similarity in correlates for seeking help from anyone, vs from formal sources, with a greater focus on women being separated for help-seeking from formal sources.ConclusionFindings highlight the need for community programs to reach out to women trapped in abusive relationships, as well as the importance of women's social and economic connectedness; future work should consider holistic interventions that integrate IPV screening and support services with women's health related services.

Published
2022

25. Transactive Framework for Dynamic Energy Storage Allocation for Critical Load Management

Author

Dey, Arnab, Khatana, Vivek, Mani, Ankur, and Salapaka, Murti V.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Increased penetration of Distributed Energy Resources (DER) and Renewable Energy Systems (RES) transforming the conventional distribution grid into a transactive framework supervised by a distribution system operator (DSO). Although the emerging transactive energy management techniques improve the grid reliability, the inherent uncertainty of RES poses a challenge in meeting the power demand of the critical infrastructure in the microgrid unless sufficient battery energy storage is maintained. However, maintaining expensive battery storage increases the operating cost of the DSO. In this article, we propose a cost-effective dynamic resource allocation strategy to optimize the battery reserve requirement while ensuring the critical demand is met with a provable guarantee. Our proposed scheme enables the DSO to optimize the RES and battery reserve allocation to eliminate the risk of over or underproduction. We present numerical simulations under three different scenarios of multiple microgrids with uncertain renewable generation. The simulation results demonstrate the efficacy of the proposed transactive stochastic control algorithm.

Published
2021

26. How does the sex composition of children affect men's higher ideal family size preference relative to women and contraceptive use patterns among couples? A cross-sectional analysis of dyadic couple's data in India

Author

Dey, Arnab K, Acharya, Rajib, Tomar, Shweta, Silverman, Jay G, and Raj, Anita

Subjects
Human Society, Demography, Behavioral and Social Science, Prevention, Clinical Research, Contraception/Reproduction, Basic Behavioral and Social Science, Pediatric, Good Health and Well Being, Ideal family size, Sex composition, Parity, Modern contraceptive use, Couples, India, Public Health and Health Services, Epidemiology, Public health, Sociology
Abstract

IntroductionSon preference, an ongoing concern in India, is a known driver of ideal family size preferences and contraceptive use among couples. These associations can vary substantially with parity and can influence men and women differently. This study assesses the association of sex composition of children by parity and a) men's higher ideal family size preference relative to women and b) use of modern contraceptives by couples.MethodsWe used the Couples Recode dataset from National Family Health Survey (NFHS) 2015-16 and identified couples who had at least one child and had complete responses for variables used in the study (N = 56,731 couples). We developed multivariable linear and logistic regression models to study the association between sex composition of children by parity and our dependent variables.ResultsOur findings indicate that the sex composition of children is associated with men's higher ideal family size preference, relative to women, among couples with four or more children. We also find that couples with less than four children are less likely to use modern contraceptives when they have an equal or a greater number of daughters than sons compared to those who have no daughters. Findings suggest that couples with four or more children are more likely to use modern contraceptives when they have at least one son and one daughter and are less likely to use contraceptives when they have all daughters and no sons, than couples who have no daughters.ConclusionThis study contributes to existing research on the relationship between sex composition of children with ideal family size preferences and contraceptive use by highlighting meaningful differences between higher and lower parity couples. Findings from the study can be used by family planning programs in India to customize family planning counselling messages by both sex composition and parity.

Published
2021

29. On Mitigating the Uncertainty in Renewable Generation in Distribution Microgrids

Author

Dey, Arnab, Khatana, Vivek, Mani, Ankur, and Salapaka, Murti V.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In this article, we focus on the problem of mitigating the risk of not being able to meet the power demand, due to the inherent uncertainty of renewable energy generation sources in microgrids. We consider three different demand scenarios, namely meeting short-time horizon power demand, a sustained energy demand and a scenario where the power demand at a prescribed future time has to be met with almost sure guarantee with power generation being stochastic and following dynamics governed by geometric Brownian motion. For each of these scenarios we provide solutions to meet the electrical demand. We present results of numerical experiments to demonstrate the applicability of our schemes.

Published
2020

31. Fast 2D NMR for Metabolomics

Author

Praud, Clément, primary, Letertre, Marine P. M., additional, Dey, Arnab, additional, Dumez, Jean-Nicolas, additional, and Giraudeau, Patrick, additional

Published
2023
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33. Associations of intimate partner violence and reproductive coercion with contraceptive use in Uttar Pradesh, India: How associations differ across contraceptive methods

Author

Tomar, Shweta, Dehingia, Nabamallika, Dey, Arnab K, Chandurkar, Dharmendra, Raj, Anita, and Silverman, Jay G

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Violence Against Women, Behavioral and Social Science, HIV/AIDS, Contraception/Reproduction, Prevention, Adolescent Sexual Activity, Pediatric, Mental Health, Violence Research, Teenage Pregnancy, Clinical Research, Reproductive health and childbirth, Gender Equality, Good Health and Well Being, Peace, Justice and Strong Institutions, Adolescent, Adult, Coercion, Contraception, Contraception Behavior, Cross-Sectional Studies, Female, Humans, India, Intimate Partner Violence, Male, Middle Aged, Spouses, Young Adult, General Science & Technology
Abstract

Intimate partner violence (IPV) and reproductive coercion (RC)-largely in the form of pressuring pregnancy-appear to contribute to low use of contraceptives in India; however, little is known about the extent to which these experiences differentially affect use of specific contraceptive methods. The current study assessed the association of IPV and RC with specific contraceptive methods (Intrauterine Devices [IUDs], pills, condoms) among a large population-based sample of currently married women (15-49 years, n = 1424) living in Uttar Pradesh. Outcomes variables included past year modern contraceptive use and type of contraceptive used. Primary independent variables included lifetime experience of RC by current husband or in-laws, and lifetime experiences of physical IPV and sexual IPV by current husband. Multivariate logistic regression models were developed to determine the effect of each form of abuse on women's contraceptive use. Approximately 1 in 7 women (15.1%) reported experiencing RC from their current husband or in-laws ever in their lifetime, 37.4% reported experience of physical IPV and 8.3% reported experience of sexual IPV by their current husband ever in their lifetime. Women experiencing RC were less likely to use any modern contraceptive (AOR: 0.18; 95% CI: 0.9-0.36). Such women also less likely to report pill and condom use but were more likely to report IUD use. Neither form of IPV were associated with either overall or method specific contraceptive use. Study findings highlight that RC may influence contraceptive use differently based on type of contraceptive, with less detectable, female-controlled contraceptives such as IUD preferred in the context of women facing RC. Unfortunately, IUD uptake remains low in India. Increased access and support for use, particularly for women contending with RC, may be important for improving women's control over contraceptive use and reducing unintended pregnancy.

Published
2020

34. COV-XDCNN: Deep Learning Model with External Filter for Detecting COVID-19 on Chest X-Rays

Author

Dey, Arnab, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Fernando, Xavier, editor, Lu, Joan, editor, Piramuthu, Selwyn, editor, and Chandrabose, Aravindan, editor

Published
2022
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35. Emotion Recognition Using Deep Learning in Pandemic with Real-time Email Alert

Author

Dey, Arnab, Dasgupta, Kousik, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Bindhu, V., editor, Tavares, João Manuel R. S., editor, and Du, Ke-Lin, editor

Published
2022
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37. Disrupting gender norms in health systems: making the case for change

Author

Hay, Katherine, McDougal, Lotus, Percival, Valerie, Henry, Sarah, Klugman, Jeni, Wurie, Haja, Raven, Joanna, Shabalala, Fortunate, Fielding-Miller, Rebecca, Dey, Arnab, Dehingia, Nabamallika, Morgan, Rosemary, Atmavilas, Yamini, Saggurti, Niranjan, Yore, Jennifer, Blokhina, Elena, Huque, Rumana, Barasa, Edwine, Bhan, Nandita, Kharel, Chandani, Silverman, Jay G, Raj, Anita, Gender Equality, Norms, Darmstadt, Gary L, Greene, Margaret Eleanor, Hawkes, Sarah, Heise, Lori, Heymann, Jody, Levine, Ruth, and Gupta, Geeta Rao

Subjects
Behavioral and Social Science, Generic health relevance, Gender Equality, Reduced Inequalities, Female, Global Health, Healthcare Disparities, Humans, Male, Nurse's Role, Occupational Health, Sexism, Gender Equality, Norms, and Health Steering Committee, Medical and Health Sciences, General & Internal Medicine
Abstract

Restrictive gender norms and gender inequalities are replicated and reinforced in health systems, contributing to gender inequalities in health. In this Series paper, we explore how to address all three through recognition and then with disruptive solutions. We used intersectional feminist theory to guide our systematic reviews, qualitative case studies based on lived experiences, and quantitative analyses based on cross-sectional and evaluation research. We found that health systems reinforce patients' traditional gender roles and neglect gender inequalities in health, health system models and clinic-based programmes are rarely gender responsive, and women have less authority as health workers than men and are often devalued and abused. With regard to potential for disruption, we found that gender equality policies are associated with greater representation of female physicians, which in turn is associated with better health outcomes, but that gender parity is insufficient to achieve gender equality. We found that institutional support and respect of nurses improves quality of care, and that women's empowerment collectives can increase health-care access and provider responsiveness. We see promise from social movements in supporting women's reproductive rights and policies. Our findings suggest we must view gender as a fundamental factor that predetermines and shapes health systems and outcomes. Without addressing the role of restrictive gender norms and gender inequalities within and outside health systems, we will not reach our collective ambitions of universal health coverage and the Sustainable Development Goals. We propose action to systematically identify and address restrictive gender norms and gender inequalities in health systems.

Published
2019

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