ethicalabs/Research-Intent-Collab huggingface dataset:
Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.
A Brief on Optimal Transport

The presentation covers prerequisite results from Topology and Measure Theory. This is then followed by an introduction into couplings and basic definitions for optimal transport. The Kantrorovich problem is then introduced and an existence theorem is presented. Following the setup of optimal transport is a brief overview of the Wasserstein distance and a short proof of how it metrizes the space of probability measures on a COMPACT domain. This presentation is a detailed examination of Villani's "Optimal Transport: Old and New" chapters 1-4 and part of 6.
Misappropriation of Trade Secrets Under the Indian Legal Framework: An Analytical  Study

The main purpose of this paper is to examine  the aspects of the misappropriation of trade secrets under the Indian legal framework, and how this can be prevented. The paper also tries  to understand the nature of trade secrets, the reasons for protecting them, and the practical challenges in identifying and protecting them as there is no legal framework in India to protect trade secrets from misappropriation. The  paper concludes by highlighting  the challenges to  the Indian legal framework, in handling and protecting the misappropriation of trade secrets.
Tackling climate change in Uganda through vulnerability assessment and health adaptation planning to build health system resilience

Introduction Climate change is a major threat to public health. Uganda is already experiencing climate change characterised by extreme weather events, including heavy rains, windstorms, hailstorms, drought, heat and cold waves. To address this challenge, in 2023, a vulnerability and adaptation assessment (VAA) was conducted. Findings were used to develop a Health National Adaptation Plan (H-NAP 2025 -2030) to enhance Uganda’s health system resilience by ensuring sustainable and continuous healthcare services during climate emergencies. We share key results from the VAA, and the H-NAP priorities.   Methods 43 districts were selected purposively in 15 health regions, based on past occurrences of health hazards such as droughts, storms, floods, and extreme temperatures. healthcare facility-based cross-sectional study applying qualitative and quantitative data collection methods. H-NAP development coordinated by MOH and involved a systematic approach: desk review, the VAA, co-development through stakeholder engagements and consultations.  Results VAA was conducted in 716 selected Health Care Facilities (HCFs), i.e., Health Centre IIs (40.9%), Health Centre IIIs (45.8%), Health Centre IVs (8.7%), and General Hospitals (4.3%).  Almost 90.4% of HFs assessed were government-owned, 9.2% were PNFPs, and 0.4% were Private for-profit. Exposure of the HCFs to climate risks: drought 47.6%, floods 39.7%, storms 31.1%, water level rise 12.0%, landslides 11.7%, lightning 8.9%, heat wave 2.0%, and cold waves 0.1%. RRHs have the least exposure to hazards in comparison to lower-level facilities.  Lower-level facilities have higher exposure to storms, high water levels, floods, drought and landslides. HCFs lack water management and safety plans to address contamination risks.  HCFs do not perform regular assessments of their energy systems to ensure they can cope with climate change events. H-NAP has 10 components originating from the health system building blocks aimed at building resilience.  Conclusion The H-NAP with 10 priority components is a clear statement and commitment by the Government of Uganda to address climate change and to build health system resilience.
THE ATACAMA COSMOLOGY TELESCOPE: A MEASUREMENT OF THE COSMIC MICROWAVE BACKGROUND POWER SPECTRUM AT 148 AND 218 GHz FROM THE 2008 SOUTHERN SURVEY

19 pages, 13 figures. Submitted to ApJ. This paper is a companion to Hajian et al. (2010) and Dunkley et al. (2010)
Improvement of Accessibility to Dental Care due to Expansion of National Health Insurance Coverage for Scaling in South Korea

Since 2013, adults aged over 20 can receive national health insurance scaling once a year in South Korea. In this study, we analyzed the usage status of national health insurance care service for periodontal disease in 2010-2018 by using Healthcare big data of the Health Insurance Review and Assessment Service. The increase rate of the dental care users was very high at 7.8 and 11.2% in 2013 and 2014, respectively. These are higher than the increase rate of all medical institution users, which is between -1.7 and 3.7%. In 2017, the rate of dental use was 44.4%, which has increased more than 10% compared to 2012. Percent receiver of national health insurance scaling was 19.5% in 2017. The 20s had the highest rate of 23.2%. The rate decreased with age. Based on these results, it can be evaluated that the expansion of national health insurance coverage for scaling improves accessibility to dental care. A more long-term assessment of the effect of periodic dental examination and scaling on reducing the prevalence of periodontal disease is needed. National health insurance coverage should be extended to oral hygiene education and supportive periodontal therapy in order to prevent periodontal disease.
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision

Accepted and will be published at TheWebConf2023 (WWW2023)
Determination of United Nations Security Council Resolutions by States

In United Nations history, the legality of Security Council Resolutions, in many cases, is challenged. Generally these challenges take by states that affected Security Councils decisions. With notice that states are the representative for implementation of Security Council Resolutions, they intervene their determination and interpretation in the way that implement Security Council Decisions. In some cases, domestic and regional courts determine the state action in implementation Security Council Resolutions. Although this cases couldn’t provide direct review on Resolutions but affected by way of implementation. Determination by states is probable and arises some concerns about decrease effectiveness of Security Council in maintenance of international peace and security.
The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset

The Radiological Society of North America (RSNA) Lumbar Degenerative Imaging Spine Classification (LumbarDISC) dataset is the largest publicly available dataset of adult MRI lumbar spine examinations annotated for degenerative changes. The dataset includes 2,697 patients with a total of 8,593 image series from 8 institutions across 6 countries and 5 continents. The dataset is available for free for non-commercial use via Kaggle and RSNA Medical Imaging Resource of AI (MIRA). The dataset was created for the RSNA 2024 Lumbar Spine Degenerative Classification competition where competitors developed deep learning models to grade degenerative changes in the lumbar spine. The degree of spinal canal, subarticular recess, and neural foraminal stenosis was graded at each intervertebral disc level in the lumbar spine. The images were annotated by expert volunteer neuroradiologists and musculoskeletal radiologists from the RSNA, American Society of Neuroradiology, and the American Society of Spine Radiology. This dataset aims to facilitate research and development in machine learning and lumbar spine imaging to lead to improved patient care and clinical efficiency.
Case 11 : Improving Sexual and Reproductive Health Rights: A Key Step in Achieving Gender Equality in Pakistan

The Pakistani Maternal Newborn Health Initiative (PAMNHI) project needs to shift its focus from providing solely maternal, neonatal, and child (MNCH) services in Pakistan to incorporating sexual and reproductive health (SRH) services in order to be approved by Global Affairs Canada (GAC). Aamir Khan, senior program manager at Daud Foundation Canada (DFA), and his team must weave SRH services into this project, which is being implemented in Pakistan, where sexual and reproductive health rights are a very sensitive issue. Aamir knows that achieving gender equality in Pakistan is a very complex issue due to many competing factors, such as the predominantly patriarchal structure of society and the lack of support from governing bodies. Aamir knew that, moving forward, the involvement of key stakeholders would be crucial in addressing gaps in gender disparities and responding to the local needs of the population. Key stakeholders have the ability to support and promote the integration of SRH services throughout PAMNHI in order to address gendered barriers and advance equitable outcomes and benefits from project interventions for women in Northern Pakistan. The goal is to identify key stakeholders who have the potential to influence the perceptions of communities in order to successfully promote SRH in Pakistan and align the project’s focus with GAC’s mandate of addressing sexual and reproductive health rights (SRHR). Also, Aamir and his team must discuss the roles, interest, and influence of each potential stakeholder (i.e., religious leaders and community partners) in helping to strengthen the project’s focus and achieve the underlying goal of decreasing gender disparities in Pakistan.
Sampling-based model order reduction for stochastic differential equations driven by fractional Brownian motion

In this paper, we study large-scale linear fractional stochastic systems representing, e.g., spatially discretized stochastic partial differential equations (SPDEs) driven by fractional Brownian motion (fBm) with Hurst parameter H &gt; ½. Such equations are more realistic in modeling real-world phenomena in comparison to frameworks not capturing memory effects. To the best of our knowledge, dimension reduction schemes for such settings have not been studied so far. In this work, we investigate empirical reduced order methods that are either based on snapshots (e.g. proper orthogonal decomposition) or on approximated Gramians. In each case, dominant subspaces are learned from data. Such model reduction techniques are introduced and analyzed for stochastic systems with fractional noise and later applied to spatially discretized SPDEs driven by fBm in order to reduce the computational cost arising from both the high dimension of the considered stochastic system and a large number of required Monte Carlo runs. We validate our proposed techniques with numerical experiments for some large-scale stochastic differential equations driven by fBm.
Integrating Refinery Placement, Storage Optimization, and Transportation Logistics for Resilient, Low-Carbon Biofuel Supply Chains in the U.S. Corn Belt

Biofuels are a potential source of transportation energy and an alternative to transport electrification, but face challenges such as high costs, unclear emissions benefits, and vulnerability to extreme weather conditions. Understanding these dynamics is crucial for addressing gaps in previous studies, which often focus on either cost or emission minimization but fail to integrate multiple objectives while accounting for climate variability. By simultaneously optimizing refinery and storage facility placement, feedstock cultivation decisions, biomass flows (via truckloads) among farms, storage depots, and refineries, we explore trade-offs among cost, greenhouse gas emissions, and reliability under historical levels of yield variability. The central research question is: Do the benefits of adding biomass storage infrastructure outweigh the associated costs and limitations, such as temporal inflexibility? We hypothesize that, despite biomass being a perishable commodity with limited storage durations, strategic investment in storage, co-optimized with other supply chain components, can improve refinery utilization rates and reduce system-wide costs and emissions by mitigating the effects of inter-annual yield fluctuations. Our analysis demonstrates that the benefits of adding storage to biofuel supply chains substantially outweigh associated costs, with economic savings of 6-17% and GHG intensity reductions up to 7.5 g CO₂/MJ.
The Emerging Challenges of Customer Relationship Management with Customer Data Platforms (CDP): Constraints and Perspectives

Customers today are increasingly demanding a personalized shopping experience, coupled with stringent data security measures and prompt responses. As the landscape of online privacy evolves, particularly with the anticipated sunset of third-party cookies by 2024, businesses face a critical imperative to revamp their customer data management strategies. The emergence of Customer Data Platforms (CDPs) stands out as a pivotal solution in this evolving ecosystem. This article undertakes a detailed critique of CDPs, starting with insights gathered from both users and industry experts, and subsequently examining consumer perceptions and behaviors in the digital realm. At the heart of contemporary consumer expectations lies the desire for tailor-made interactions that reflect their preferences and behaviors. This demand necessitates robust data management tools capable of aggregating, analyzing, and activating customer data across multiple touchpoints. CDPs have emerged as a central tool in this endeavor, offering businesses a unified platform to integrate disparate data sources and derive actionable insights in real-time. By leveraging comprehensive customer profiles built from first-party data, CDPs enable businesses to deliver personalized experiences that foster customer loyalty and drive revenue growth. Moreover, the impending demise of third-party cookies, compounded by stringent data protection regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act), underscores the urgency for businesses to adopt privacy-centric data practices. CDPs play a crucial role in this paradigm shift by empowering businesses to ethically collect and utilize customer data, thereby enhancing transparency and trust. However, challenges persist, including the complexity of integrating CDPs into existing infrastructures and ensuring compliance with evolving regulatory frameworks. From a consumer standpoint, attitudes towards data privacy are evolving, with an increasing emphasis on transparency and control over personal information. Consumers are more inclined to engage with brands that demonstrate a commitment to data security and ethical data usage practices. This shift necessitates a nuanced approach from businesses using CDPs, emphasizing not only personalization but also data protection and consent management. While CDPs offer substantial benefits in navigating the complexities of modern data management and meeting consumer expectations for personalized experiences, their successful implementation hinges on addressing technical, regulatory, and ethical considerations. By doing so, businesses can not only adapt to the evolving landscape of digital privacy but also foster enduring customer relationships grounded in trust and mutual value.
On the Challenges of Collaborative Data Processing

The last 30 years have seen the creation of a variety of electronic collaboration tools for science and business. Some of the best-known collaboration tools support text editing (e.g., wikis). Wikipedia's success shows that large-scale collaboration can produce highly valuable content. Meanwhile much structured data is being collected and made publicly available. We have never had access to more powerful databases and statistical packages. Is large-scale collaborative data analysis now possible? Using a quantitative analysis of Web 2.0 data visualization sites, we find evidence that at least moderate open collaboration occurs. We then explore some of the limiting factors of collaboration over data.
An ab initio approach to the heliospheric modulation of galactic cosmic rays

In the present study we aim to further our understanding of charged particle transport in a magnetized medium. To this end, we perform direct numerical simulations of particle transport in a turbulent magnetic field. From the particle trajectories we calculate diffusion and drift coefficients. In contrast to previous numerical simulations of this nature, we also consider a background magnetic field that contains a gradient perpendicular to the magnetic field direction. By using a non-uniform background magnetic field, we can investigate the simultaneous large scale drift due to the gradient in the background magnetic field and the diffusion due to the turbulence which is superimposed on this background magnetic field. Upon comparison with the simulated diffusion coefficients, the newly proposed weakly non-linear theory (WNLT) of Shalchi et al. (2004b) seems to be the most appropriate theory for the simultaneous description of parallel and perpendicular diffusion over a wide range of fluctuation amplitude and particle rigidity. As for the effect of large scale drift on perpendicular diffusion, we find that under conditions of small amplitude turbulence and/ or high particle rigidity the transport perpendicular to the background field can exhibit super-diffusive behaviour. Diffusive behaviour seems to be recovered for the cases when the turbulence amplitude is sufficiently large and/ or the particle rigidity is sufficiently small. We furthermore find that both the drift coefficient and the drift speed are reduced from their weak scattering counterparts in the presence of scattering, with the reduction becoming more pronounced with increasing turbulence amplitude. For the drift coefficient in particular, the reduction from its weak scattering counterpart behaves differently for the cases in which the background magnetic field is either uniform or non-uniform. For the former case the reduction is predominantly at small rigidities, while for the latter case the reduction is predominantly at large rigidities. The latter result might be of significance for heliospheric modulation models in which the background magnetic field is highly non-uniform. Finally, we use a two-dimensional steadystate cosmic ray modulation model to see how our improved understanding of the underlying transport processes influences the overall cosmic ray modulation in the heliosphere. We conclude that in the absence of a theory which connects large scale drift with small scale diffusion, any statements about the inadequacy of a two-dimensional steady-state modulation model might be premature.
A community-based public engagement with health experiment: Using English for speakers of other languages (ESOL) classes to empower immigrant communities with science

Our previous work with immigrant communities identified a lack of awareness of infections such as gut worm (helminth) infections in their countries of origin, and a lack of English language skills around medical and scientific terms. Furthermore, people from minority ethnic and linguistic  backgrounds participate significantly less in public engagement with science practices or informal science learning activities. We suggest this may be due in part to their lack of language skills around scientific English. To start to address these issues of scientific language accessibility,  we developed a bespoke set of English lessons that dealt with the theme of infection. A set of six lessons were delivered to adult learners from a variety of ethnic backgrounds in English for speakers of other languages (ESOL) classes. Feedback from students was positive, with students taking  the opportunity to share their new knowledge with teachers, other students and their families. All students reported that they felt this would be invaluable to them in their everyday lives. We propose that developing programmes of this nature represents a potentially fruitful avenue for more-accessible  public engagement with research and health education practices.
The role of networks in science communication and engagement with non‐expert publics

AbstractIn this ongoing study, I examine associations among scientists by analyzing scholarly publication data and Internet hyperlink relationships using a social network analysis framework. Unlike other studies of collaboration, this study does not start with an extant network of scientists who share a research focus, but rather groups who are networked for purposes of public and political engagement. I have chosen as my nodes two presumed “networks” of scientists: the group of 16 scientists who authored a January 27th', 2012 op‐ed in The Wall Street Journal which suggested concerns about climate change were being exaggerated, and the group of 38 scientists who authored a response piece (supporting climate change science) on February 1st.I have chosen climate science because of its scientific complexity that is of necessity interdisciplinary, and its scrutiny by non‐expert publics. Building on the theoretical foundations of social epistemology, I examine how notions of the role of shared practices in engendering trust and knowledge‐sharing among expert groups can be applied when the lines between expert and non‐expert are contested in high‐visibility public arenas. In exploring some of those questions, this study aims to discover if a social network methodology combining co‐authorship data and hyperlink data can reveal patterns of association and engagement with traditional (e.g., scholarly publications) vs. non‐traditional (e.g., web sites or blog articles) methods of communication and legitimation.This study is intended as part of a larger research trajectory examining the role of social associations in influencing credibility and information trust decisions in areas where expert and non‐experts must meet and negotiate shared meanings. These findings could suggest future directions for collaborative data sharing practices as well as support theoretical work in understanding how trust and credibility are granted in networked information environments.