ethicalabs/Research-Intent-Collab huggingface dataset:
The Numerical Computation of Posterior Distributions in Bayesian Statistical Inference

SUMMARY A method is described in which distributions of parameters in problems of Bayesian statistical inference are handled as arrays in computer storage. This results in a very flexible approach to problems in nonlinear regression, fitting of frequency functions, model discrimination, etc. It is particularly valuable in finding marginal and conditional parameter distributions and distributions of functions of the parameters in a model. It does so with no requirements for linearization of models or for specific forms for distribution functions. If more than a few parameters are present the requirements for computer storage may be large.
Generalized diffusion equation for a growth and dispersion model of a population including individual behaviors on the boundary of the different habitats

The aim of this work is the study of a transmission problem in population dynamics between two juxtaposed habitats. In each habitat, we consider a partial differential equation, modeling the generalized dispersion, made up of a linear combination of Laplacian and Bilaplacian operators. We begin by studying and solving the same equation with various boundary conditions in a single habitat. This study is carried out using an operational formulation of the problem: we rewrite this PDE as a differential equation, set in a Banach space built on the spaces Lp with 1 < p < +∞, where the coefficients are unbounded linear operators. Thanks to functional calculus, analytic semigroup theory and interpolation theory, we obtain optimal results of existence, uniqueness and maximum regularity of the classical solution if and only if the data are in some interpolation spaces.
Relative-perfectness of discrete gradient vector fields and multi-parameter persistent homology

The combination of persistent homology and discrete Morse theory has proven very effective in visualizing and analyzing big and heterogeneous data. Indeed, topology provides computable and coarse summaries of data independently from specific coordinate systems and does so robustly to noise. Moreover, the geometric content of a discrete gradient vector field is very useful for visualization purposes. The specific case of multivariate data still demands for further investigations, on the one hand, for computational reasons, it is important to reduce the necessary amount of data to be processed. On the other hand, for analysis reasons, the multivariate case requires the detection and interpretation of the possible interdepedance among data components. To this end, in this paper we introduce and study a notion of perfectness for discrete gradient vector fields with respect to multi-parameter persistent homology, called relative-perfectness. As a natural generalization of usual perfectness in Morse theory for homology, relative-perfectness entails having the least number of critical cells relevant for multi-parameter persistence. As a first contribution, we support our definition of relative-perfectness by generalizing Morse inequalities to the filtration structure where homology groups involved are relative with respect to subsequent sublevel sets. In order to allow for an interpretation of critical cells in $2$-parameter persistence, our second contribution consists of two inequalities bounding Betti tables of persistence modules from above and below, via the number of critical cells. Our last result is the proof that existing algorithms based on local homotopy expansions allow for efficient computability over simplicial complexes up to dimension $2$.
Bugs below zero: Communicating science and engaging the public with winter active aquatic insects and stream food webs

Winter is typically viewed as a time when insects are thought to be inactive; however, some aquatic insects (e.g., chironomid flies, stoneflies, and mayflies) have species which complete multiple life stages and emerge as active, terrestrial adults during winter. These insects have adaptations that permit survival at low temperatures and are known to occur in seasonally cold environments worldwide. However, awareness and education around these specialized insects are lacking partially due to the limited research and education centered around winter aquatic ecology. The Bugs Below Zero project, started in 2019, aims to enhance awareness and increase appreciation for winter-adapted aquatic insects, providing opportunities for the public to engage in community science efforts collecting data on these insect groups. The program has received positive reception in classroom and outreach settings and has successfully provided multimedia educational materials to hundreds of educators and data collection opportunities to numerous volunteer groups and classrooms. With the help of volunteers, the Bugs Below Zero team aims to add to the body of scientific knowledge about winter active insects and to continue educating students and community members about these organisms, their role in food webs, and their conservation needs.
The Impact of Social Media Addiction on Adolescent Mental Health in Ambon City, Indonesia

The increasing prevalence of social media use among adolescents has raised concerns regarding its potential impact on mental health. This study investigated the relationship between social media addiction and adolescent mental health in Ambon, Indonesia. A quantitative cross-sectional research design was utilized, involving 336 adolescents aged 13-18 who actively used social media. Data were gathered through a structured questionnaire assessing demographic characteristics, social media usage patterns, and mental health status using the Depression Anxiety Stress Scale (DASS-21). Descriptive analysis, bivariate analysis using odds ratios (ORs), and multivariate analysis via multiple logistic regression were conducted. Results indicated that 64.88% of respondents had poor mental health scores. Bivariate analysis revealed significant associations between age, study frequency, duration of social media use, and social media usage scores with mental health conditions (p&lt;0.05). Multiple logistic regression identified that adolescents aged 18-20 years, those who studied infrequently, used social media for over three hours daily, and had high social media usage scores were at greater risk of poor mental health. These findings underscore the importance of collaborative efforts among stakeholders to design effective interventions and policies that foster responsible social media use and promote adolescent mental well-being in the digital era.
Right to Privacy of women and children in cyberspace: Legal Frameworks and challenges for protection against cybercrime and surveillance

This research paper analysis the legal frameworks and determined challenges in safeguarding the right to privacy of women and children from cybercrimes and digital monitoring in cyberspace in India. The paper examines prevailing legal provisions, which includes the Information Technology Act, 2000, the Protection of Children from Sexual Offences Act, 2012, Sexual Harassment of Women at Workplace (Prevention, Prohibition and Redressal) Act, 2013 and the recently enacted Digital Personal Data Protection Act, 2023. The study deals with the particular susceptibilities of women and children in context to online threats such as sexual harassment, cyber stalking, and without consent transmitting of private intimate images. The paper recognizes important key challenges, involving the cross-jurisdictional nature of cybercrime, absence of digital literacy, and the fast growth of technological developments that frequently overtake legal provisions.           To conclude, it recommends policy solutions and legal improvements to reinforce protecting measures and guarantees a harmless digital environment for the women’s and children’s. The study suggests strong recommendations to fortify Indian cyber legal framework, highlighting the requirement of vigorous legislation to protect women and children in this digital world.
Chromatin Remodeling and Epigenetic Regulation in Plant DNA Damage Repair

DNA damage response (DDR) in eukaryotic cells is initiated in the chromatin context. DNA damage and repair depend on or have influence on the chromatin dynamics associated with genome stability. Epigenetic modifiers, such as chromatin remodelers, histone modifiers, DNA (de-)methylation enzymes, and noncoding RNAs regulate DDR signaling and DNA repair by affecting chromatin dynamics. In recent years, significant progress has been made in the understanding of plant DDR and DNA repair. SUPPRESSOR OF GAMMA RESPONSE1, RETINOBLASTOMA RELATED1 (RBR1)/E2FA, and NAC103 have been proven to be key players in the mediation of DDR signaling in plants, while plant-specific chromatin remodelers, such as DECREASED DNA METHYLATION1, contribute to chromatin dynamics for DNA repair. There is accumulating evidence that plant epigenetic modifiers are involved in DDR and DNA repair. In this review, I examine how DDR and DNA repair machineries are concertedly regulated in Arabidopsis thaliana by a variety of epigenetic modifiers directing chromatin remodeling and epigenetic modification. This review will aid in updating our knowledge on DDR and DNA repair in plants.
Foundations of Statistical Mechanics and Theory of Phase Transition

19 pages, Latex
SECURING THE RIGHT TO PRIVACY AGAINST THE STATE SURVEILLANCE

This research paper gives an overview on right to privacy as a fundamental and human right of every citizen and the important aspects are being covered subject to restrictions imposed by state. Privacy as a fundamental right has been upheld by the apex court of the nation and the activities of state which violates this essential and basic right needs to be considered by the judiciary under their power of judicial review and judicial activism Data in today’s society is used by the Big Tech companies to make millions of dollar and this trade of data undermines the basic right of citizens who do not want their nasty algorithm to affect the privacy of citizens. A number of cases has been heard by judiciary and the landmark judgments have provided for protection of right to privacy. Article 21 of Indian constitution has a very wide scope and the intent of the framers of the constitution when they framed this article was not to limit its applicability to life and liberty but also cover various other facets of it. This paper will further provide a comprehensive analysis on right to privacy in India.
Lead-lag detection and network clustering for multivariate time series with an application to the US equity market

In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead-lag structure amongst the time series variables. In this paper, we propose a method for the detection of lead-lag clusters of time series in multivariate systems. We demonstrate that the web of pairwise lead-lag relationships between time series can be helpfully construed as a directed network, for which there exist suitable algorithms for the detection of pairs of lead-lag clusters with high pairwise imbalance. Within our framework, we consider a number of choices for the pairwise lead-lag metric and directed network clustering components. Our framework is validated on both a synthetic generative model for multivariate lead-lag time series systems and daily real-world US equity prices data. We showcase that our method is able to detect statistically significant lead-lag clusters in the US equity market. We study the nature of these clusters in the context of the empirical finance literature on lead-lag relations and demonstrate how these can be used for the construction of predictive financial signals.
Incorporating Affordability Concerns Within Cost-Effectiveness Analysis for Health Technology Assessment

Recent policy developments and journal articles have emphasized a divergence: when interventions are found to be cost-effective but unaffordable. This apparent paradox reflects a conventional practice of cost-effectiveness analysis that does not properly evaluate the opportunity costs of an intervention that imposes non-marginal costs on the healthcare system.Taking the perspective of an exogenously resource constrained decision maker, this paper presents a framework by which concerns for affordability can be appropriately incorporated within cost-effectiveness analysis.A net benefit framework is proposed where health opportunity costs are estimated for each simulation iteration within each time period. The framework is applied to a hypothetical case study based on the recent experience of the English NHS with new hepatitis C drugs.Under the proposed framework, but not under conventional cost-effectiveness analysis, estimates of health opportunity costs differ between scenarios involving different profiles of budget impact even when their net present value, or expected value, are the same.The framework presented here reflects the importance of the scale of budget impacts along with their uncertainty distribution and time profile. In doing so it resolves issues with the conduct of conventional cost-effectiveness analysis where affordability concerns are not explicitly incorporated.
A context-aware e-bike system to reduce pollution inhalation while cycling

The effect of transport-related pollution on human health is fast becoming recognised as a major issue in cities worldwide. Cyclists, in particular, face great risks, as they typically are most exposed to tail-pipe emissions. Three avenues are being explored worldwide in the fight against urban pollution: (i) outright bans on polluting vehicles and embracing zero tailpipe emission vehicles; (ii) measuring air-quality as a means to better informing citizens of zones of higher pollution; and (iii) developing smart mobility devices that seek to minimize the effect of polluting devices on citizens as they transport goods and individuals in our cities. Following this latter direction, in this paper we present a new way to protect cyclists from the effect of urban pollution. Namely, by exploiting the actuation possibilities afforded by pedelecs or e-bikes (electric bikes), we design a cyber-physical system that mitigates the effect of urban pollution by indirectly controlling the breathing rate of cyclists in polluted areas. Results from a real device are presented to illustrate the efficacy of our system.
BioAutoML-NAS: An End-to-End AutoML Framework for Multimodal Insect Classification via Neural Architecture Search on Large-Scale Biodiversity Data

Insect classification is important for agricultural management and ecological research, as it directly affects crop health and production. However, this task remains challenging due to the complex characteristics of insects, class imbalance, and large-scale datasets. To address these issues, we propose BioAutoML-NAS, the first BioAutoML model using multimodal data, including images, and metadata, which applies neural architecture search (NAS) for images to automatically learn the best operations for each connection within each cell. Multiple cells are stacked to form the full network, each extracting detailed image feature representations. A multimodal fusion module combines image embeddings with metadata, allowing the model to use both visual and categorical biological information to classify insects. An alternating bi-level optimization training strategy jointly updates network weights and architecture parameters, while zero operations remove less important connections, producing sparse, efficient, and high-performing architectures. Extensive evaluation on the BIOSCAN-5M dataset demonstrates that BioAutoML-NAS achieves 96.81% accuracy, 97.46% precision, 96.81% recall, and a 97.05% F1 score, outperforming state-of-the-art transfer learning, transformer, AutoML, and NAS methods by approximately 16%, 10%, and 8% respectively. Further validation on the Insects-1M dataset obtains 93.25% accuracy, 93.71% precision, 92.74% recall, and a 93.22% F1 score. These results demonstrate that BioAutoML-NAS provides accurate, confident insect classification that supports modern sustainable farming.
FDA rules for R&amp;D also: root cause analysis and failure investigations

This session discusses successful methodologies and regulatory documentation required for Research and Development scientists and engineers during development, animal and clinical testing and marketing of medical devices and drug device combinations. The session will be a guided roundtable in which the development lifecycle will be discussed in terms of common regulatory pitfalls. Group experiences will be shared during the session. Topics discussed will include: documentation required for various regulatory pathways, lab organization under GMPs for R&D vs. GMPs for QC, and FDA audit experiences in the R&D lab.
The role of the land-surface model for climate change projections over the Iberian Peninsula

The importance of land-surface processes within Regional Climate Models for accurately reproducing the present-day climate is well known. However, their role when projecting future climate is still poorly reported. Hence, this work assesses the influence of the land-surface processes, particularly the contribution of soil moisture, when projecting future changes for temperature, precipitation and wind over a complex area as the Iberian Peninsula, which, in addition, shows great sensitivity to climate change. The main signals are found for the summer season, when the results indicate a strengthening in the increases projected for both mean temperature and temperature variability as a consequence of the future intensification of the positive soil moisture-temperature feedback. The more severe warming over the inner dry Iberian Peninsula further implies an intensification of the Iberian thermal low and, thus, of the cyclonic circulation. Furthermore, the land-atmosphere coupling leads to the projection of a wider future daily temperature range, since maximum temperatures are more affected than minima, a feature absent in non-coupled simulations. Regarding variability, the areas where the land-atmosphere coupling introduces larger changes are those where the reduction in the soil moisture content is more dramatic in future simulations, i.e., the so-called transitional zones. As regards precipitation, weaker positive signals for convective precipitation and more intense negative signals for non-convective precipitation are obtained as a result of the soil moisture-atmosphere interactions. These results highlight the crucial contribution of soil moisture to climate change projections and suggest its plausible key role for future projections of extreme events.
Candidate genes for predicting the survival of patients with gastric cancer: a study based on The Cancer Genome Atlas (TCGA) database

Gastric cancer (GC) is the second most frequent cause of cancer-related mortality in the world, and the five-year survival rate for GC remains very low universally. In recent years, it has become a consensus that genetic changes are associated with carcinogenesis of GC, and precision medicine based on genetic changes is one of the most popular treatments for GC patients. However, the association between some genes and GC-related protein signaling pathways is still not well understood. This study revealed that seven genes were closely related to the survival probability in GC patients.We downloaded the gene expression data of GC patients from The Cancer Genome Atlas (TCGA) databases, and integrated bioinformatic analysis was performed, such as differential gene expression analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathways analyses, as well as survival analysis. The r package survival was used to analyze the Kaplan-Meier survival analysis, which showed the associations between specific gene expressions and the outcomes of patients with GC to identify which genes could be potential prognostic biomarkers.This study revealed that seven genes: alcohol dehydrogenase 4 (ADH4), histamine receptor H3 (HRH3), neuropeptide Y2 receptor (NPY2R), apolipoprotein AI (APOA1), N-acetylgalactosaminyltransferase 14 (GALNT14), leucine-rich repeats and IQ motif containing 1 (LRRIQ1), and coiled-coil-domain-containing 57 (CCDC57). These seven genes were closely related to the survival probability of GC patients (P<0.05).Our study found seven genes which could be considered as candidate prognostic biomarkers and therapeutic targets.
Serverless Computing &amp; Function-as-a-Service (FaaS) Optimization

Function-as-a-Service (FaaS) in cloud computing is a critical optimization problem that needs to be tackled, including cold start latency, resource inefficiency, state management, and more. While FaaS provides obvious scalability and lower cost benefits, the lack of availability of resources and the problem of cold starts to prevent it from being used for high-performance applications. Pre-warming, snapshotting, and on-demand instantiation with lightweight runtimes, such as WebAssembly, are other ways to minimize cold start delays. It also benchmarks major FaaS platforms (AWS Lambda, Google Cloud Functions, Azure Functions, and OpenFaaS) and measures latency, throughput, and scalability metrics. The study also considers how to manage the resource, for instance, using auto-scaling, memory allocation, and request batching to enhance cost efficiency and performance. The study covers security challenges in multi-tenant environments and solutions for stateful applications in usually stateless serverless architectures with Faast.js, Knative, and OpenWhisk. Another area of research is edge computing and architectures for multiple clouds to improve the deployment of FaaS. Incorporating the lessons from this study gives it enough flexibility to adjust functions as applications in a real-world enterprise environment, especially in high-performance and data-sensitive applications. The study also provides security practices like function isolation and encryption to secure data in multi-tenancy environments for reliable, secure, and efficient serverless computing. The contribution to FaaS optimization and security for various use cases is achieved.
Experimental Comparison of Six Population-Based Algorithms for Continuous Black Box Optimization

Six population-based methods for real-valued black box optimization are thoroughly compared in this article. One of them, Nelder-Mead simplex search, is rather old, but still a popular technique of direct search. The remaining five (POEMS, G3PCX, Cauchy EDA, BIPOP-CMA-ES, and CMA-ES) are more recent and came from the evolutionary computation community. The recently proposed comparing continuous optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that BIPOP-CMA-ES reaches the highest success rates and is often also quite fast. The results of the remaining algorithms are mixed, but Cauchy EDA and POEMS are usually slow.
About the non vanishing of boundary terms in correlation functions as origin of phase transitions. A microscopic proof of a Goldstone theorem in classical statistical mechanics

We give a general microscopic proof that the well known Goldstone theorem connected with spontaneous symmetry breaking in quantum statistical mechanics and quantum field theory has a counterpart in classical statistical mechanics. Our approach is mainly based on the replacement of commutators by Poisson brackets as infinitesimal generators of symmetries and on the Fourier transformed version of the so called Kubo-Martin-Schwinger property of equilibrium states. Especially we show that the phase transition is related to the non vanishing of certain boundary contributions in Poisson brackets which from the naive point of view should vanish.