Hasil untuk "Computer Science"

Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu et al.

Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence under such constraints. We leverage a deep learning framework that combines neural operators with denoising diffusion probabilistic models to reconstruct high-resolution ocean states from extremely sparse Lagrangian observations. By conditioning the generative model on neural operator outputs, the framework accurately captures small-scale, high-wavenumber dynamics even at 99% sparsity (for synthetic data) and 99.9% sparsity (for real satellite observations). We validate our method on benchmark systems, synthetic float observations, and real satellite data, demonstrating robust performance under severe spatial sampling limitations as compared to other deep learning baselines.

The CMS collaboration, A. Tumasyan, W. Adam et al.

Nana Liu, Qisheng Wang, Mark M. Wilde et al.

Abstract Matrix geometric means between two positive definite matrices can be defined from distinct perspectives—as solutions to certain nonlinear systems of equations, as points along geodesics in Riemannian geometry, and as solutions to certain optimisation problems. We devise quantum subroutines for the matrix geometric means, and construct solutions to the algebraic Riccati equation—an important class of nonlinear systems of equations appearing in machine learning, optimal control, estimation, and filtering. Using these subroutines, we present a new class of quantum learning algorithms, for both classical and quantum data, called quantum geometric mean metric learning, for weakly supervised learning and anomaly detection. The subroutines are also useful for estimating geometric Rényi relative entropies and the Uhlmann fidelity, in particular achieving optimal dependence on precision for the Uhlmann and Matsumoto fidelities. Finally, we provide a BQP-complete problem based on matrix geometric means that can be solved by our subroutines.

Longjian Piao, Laurens de Vries, Mathijs de Weerdt et al.

Future energy markets for low voltage AC and DC distribution systems will facilitate prosumer participation in the market. To comply with market regulations and grid constraints, a tailored market design reflecting (DC) operational requirements is needed. Our previous work identified a locational energy market design. However, its real-life implementation faces challenges due to uncertainties in system operation, prosumer preferences, and bidding strategies. This article tests the market design under uncertain scenarios. To this end, we develop an agent-based model that simulates typical electric vehicle user preferences and bidding strategies, influenced by varying degrees of range anxiety. The market design is tested in challenging scenarios with a high share of solar panels and electric vehicles, modelled using the high-resolution Pecan Street database. Simulations indicate that the proposed market design maintains both economic efficiency and system reliability under real-life uncertainties. This in turn indicates the practical feasibility of locational energy markets in helping to integrate renewable generation sources and bidirectional power flows.

Rey Ortiz, Sharif Ahmed, Priscilla Salas et al.

Context: X, formerly known as Twitter, is one of the largest social media platforms and has been widely used for communication during research conferences. While previous studies have examined how users engage with X during these events, limited research has focused on analyzing the content posted by computer science conferences. Objective: This study investigates how conferences from different areas of computer science perform on social media by analyzing their activity, follower engagement, and the content posted on X. Method: We collect posts from 22 computer science conferences and conduct statistical experiments to identify variations in content. Additionally, we perform a manual analysis of the top five posts for each engagement metric. Results: Our findings indicate statistically significant differences in category, sentiment, and post length across computer science conference posts. Among all engagement metrics, likes were the most common way users interacted with conference content. Conclusion: This study provides insights into the social media presence of computer science conferences, highlighting key differences in content, sentiment, and engagement patterns across different venues.

Grace Wolf-Chase, Charles Kerton, Kathryn Devine et al.

We review participatory science programs that have contributed to the understanding of star formation. The Milky Way Project (MWP), one of the earliest participatory science projects launched on the Zooniverse platform, produced the largest catalog of ``bubbles'' associated with feedback from hot young stars to date, and enabled the identification of a new class of compact star-forming regions (SFRs) known as ``yellowballs'' (YBs). The analysis of YBs through their infrared colors and catalog cross-matching led to discovering that YBs are compact photodissociation regions generated by intermediate- and high-mass young stellar objects embedded in clumps that range in mass from 10 - 10,000 solar masses and luminosity from 10 - 1,000,000 solar luminosities. The MIRION catalog, assembled from 6176 YBs identified by citizen scientists, increases the number of candidate intermediate-mass SFRs by nearly two orders of magnitude. Ongoing work utilizing data from the Spitzer, Herschel and WISE missions involves analyzing infrared color trends to predict physical properties and ages of YB environments. Methods include applying summary statistics to histograms and color-color plots as well as SED fitting. Students in introductory astronomy classes contribute toward continued efforts refining photometric measurements of YBs while learning fundamental concepts in astronomy through a classroom-based participatory science experience, the PERYSCOPE project. We also describe an initiative that engaged seminaries, family groups, and interfaith communities in a wide variety of science projects on the Zooniverse platform. This initiative produced important guidance on attracting audiences that are underserved, underrepresented, or apprehensive about science.

Francesco Toscano, Costanza Fiorentino, Nicola Capece et al.

Digital Precision Agriculture (DPA) is a comprehensive approach to agronomic management that utilizes advanced technologies, such as sensor data analysis and automation, to optimize crop productivity, enhance farm income, and minimize environmental impacts. DPA encompasses various agricultural domains, including pest control, pest management, fertilization, irrigation management, sowing, transplanting, crop health monitoring, yield forecasting, harvesting, and post-harvest stages. Among the enabling technologies for DPA, Unmanned Aerial Vehicles (UAVs) have gained significant attention and market growth. The advancements in control systems, robotics, electronics, and artificial intelligence have led to the development of sophisticated agricultural drones. UAVs offer advantages such as versatility, quick and accurate remote sensing capabilities, and high-quality imaging at affordable prices. Furthermore, the miniaturization of sensors and advancements in nanotechnology enable UAVs to perform multiple operations simultaneously without compromising flight autonomy. However, various variables, including aircraft mass, payload capacity, size, battery characteristics, flight autonomy, cost, and environmental conditions, impact the performance and applicability of UAV systems in agriculture. The economic considerations involve the purchase of drones, equipment, and the expertise of trained pilots for flight management and data processing. Payload capacity, flight range, and financial factors influence agriculture’s choice and implementation of UAVs. The research and patent trends show the growing interest in UAVs for agricultural applications. This paper provides a general review of UAV types, construction architectures, and their diverse applications in agriculture until 2022.

Jian Zhao, Haixia Li, Jing Qu et al.

DNA methylation indicates the individual’s aging, so-called Epigenetic clocks, which will improve the research and diagnosis of aging diseases by investigating the correlation between methylation loci and human aging. Although this discovery has inspired many researchers to develop traditional computational methods to quantify the correlation and predict the chronological age, the performance bottleneck delayed access to the practical application. Since artificial intelligence technology brought great opportunities in research, we proposed a perceptron model integrating a channel attention mechanism named PerSEClock. The model was trained on 24,516 CpG loci that can utilize the samples from all types of methylation identification platforms and tested on 15 independent datasets against seven methylation-based age prediction methods. PerSEClock demonstrated the ability to assign varying weights to different CpG loci. This feature allows the model to enhance the weight of age-related loci while reducing the weight of irrelevant loci. The method is free to use for academics at www.dnamclock.com/#/original.

Abdalwali Lutfi, Ahmad Farhan Alshira’h, Malek Hamed Alshirah et al.

Despite tax being a fundamental method to redistribute wealth and achieve a sustainable economic and social system, tax agencies and institutions in most countries are struggling with low tax collections. This issue is often attributed to the level of compliance among taxpayers. To gain more insight into this problem, a study was conducted to examine how socio-economic determinants such as probability of detection, tax complexity, tax penalty, tax sanctions, tax ethics, tax justice, government spending, and tax services quality impact VAT compliance decisions. The study drew a random sample of 770 retail industry participants from Jordan, an Arabic country, for a self-administered survey. Smart-PLS structural equation modeling was used to analyze and estimate the compliance model. The results indicated that all proposed direct relationships were supported, and the interactions between tax knowledge and the socio-economic determinants on VAT compliance were found to be significant. The findings of this research can be useful for policymakers and institutions responsible for taxpayers' communities to understand the role of tax knowledge in VAT compliance in the retail industry. The study emphasizes the significance of instilling tax knowledge, social and moral values among VAT payers, establishing an equitable system, and launching awareness programs in Jordanian society. Additionally, it contributes to existing literature by confirming a practical compliance model rooted in the socio-economic theory of regulatory compliance. This model incorporates the moderating effect of tax knowledge within socio-economic aspects of VAT compliance. By understanding the importance of tax knowledge, policymakers and institutions can develop effective strategies to boost VAT funds and improve compliance in the retail industry. This can ultimately lead to increased government revenues without placing an undue economic burden on lower-income taxpayers.

Het Patel, Aditya Kansara, Boppuru Rudra Prathap et al.

The exponential rise in the use of social media has resulted in a massive increase in the volume of unstructured text created. This content is presented through messages, conversations, postings, and blogs. Microblogging has become a popular way for people to share what they are thinking. Many people express their thoughts on various issues relating to their hobbies. As a result, microblogging websites have become a valuable resource for opinion mining and sentiment research. Twitter is a well-known microblogging network, with over 500 million new tweets posted daily. The goal of this study was to mine tweets for political sentiments. The extraction of tweets relating to India's well-known political leaders of different states & parties in India and applying the polarity detection analysis of human behavior on the retweeted messages As a result, the sentiment classification algorithm is designed to determine whether tweets are more likely to predict the popularity of certain politicians among the general public. The subjectivity and polarity present in the tweets of political leaders are compared. The engagements of these leaders are then taken into account to determine their popularity. All these comparisons are then portrayed using data visualizations.

Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe et al.

Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and attract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science.

T. Williams

Hanumanth Srikanth Cheruvu, Xin Liu, Jeffrey E Grice et al.

The dataset represented in this article is referred to by the review article entitled “Topical drug delivery: history, percutaneous absorption, and product development” (MS Roberts et al., 2021) [1]. The dataset contains maximal flux (Jmax), and permeability coefficient (kp) values collated from In Vitro human skin Permeation Test (IVPT) reports published to date for various drugs, xenobiotics, and other solutes applied to human epidermis from aqueous solutions. Also included are each solute's physicochemical properties and the experimental conditions, such as temperature, skin thickness, and skin integrity, under which the data was generated. This database is limited to diluted or saturated aqueous solutions of solutes applied on human epidermal membranes or isolated stratum corneum in large volumes so that there was minimal change in the donor phase concentration. Included in this paper are univariate Quantitative Structure-epidermal Permeability Relationships (QSPR) in which the solute epidermal permeation parameters (kp, and Jmax) are related to potential individual solute physicochemical properties, such as molecular weight (MW), log octanol-water partition coefficient (log P), melting point (MP), hydrogen bonding (acceptor - Ha, donor – Hd), by scatter plots. This data was used in the associated review article to externally validate existing QSPR regression equations used to forecast the kp and Jmax for new therapeutic agents and chemicals. The data may also be useful in developing new QSPRs that may aid in: (1) drug choice and (2) product design for both topical and transdermal delivery, as well as (3) characterizing the potential skin exposure of hazardous substances.

Shobhit K. Patel, Juveriya Parmar, Vijay Katkar et al.

The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurface solar absorber based on Ge2Sb2Te5 (GST) substrate which increases the absorption in visible, infrared and ultraviolet regions. GST is a phase-changing material having two different phases amorphous (aGST) and crystalline (cGST). The absorber is also analyzed using machine learning algorithm to predict the absorption values for different wavelengths. The solar absorber is showing an ultra-broadband response covering a 0.2–1.5 µm wavelength. The absorption analysis for ultra-violet, visible, and near-infrared regions for aGST and cGST is presented. The absorption of aGST design is better compared to cGST design. Furthermore, the design is showing polarization insensitiveness. Experiments are performed to check the K-Nearest Neighbors (KNN)-Regressor model’s prediction efficiency for predicting missing/intermediate wavelengths values of absorption. Different values of K and test scenarios; C-30, C-50 are used to evaluate regressor models using adjusted R2 Score as an evaluation metric. It is detected from the experimental results that, high prediction proficiency (more than 0.9 adjusted R2 score) can be accomplished using a lower value of K in KNN-Regressor model. The design results are optimized for geometrical parameters like substrate thickness, metasurface thickness, and ground plane thickness. The proposed metasurface solar absorber is absorbing ultraviolet, visible, and near-infrared regions which will be used in solar thermal energy applications.

Andrea Mannocci, Ornella Irrera, Paolo Manghi

Authorship of scientific articles has profoundly changed from early science until now. While once upon a time a paper was authored by a handful of authors, scientific collaborations are much more prominent on average nowadays. As authorship (and citation) is essentially the primary reward mechanism according to the traditional research evaluation frameworks, it turned out to be a rather hot-button topic from which a significant portion of academic disputes stems. However, the novel Open Science practices could be an opportunity to disrupt such dynamics and diversify the credit of the different scientific contributors involved in the diverse phases of the lifecycle of the same research effort. In fact, a paper and research data (or software) contextually published could exhibit different authorship to give credit to the various contributors right where it feels most appropriate. As a preliminary study, in this paper, we leverage the wealth of information contained in Open Science Graphs, such as OpenAIRE, and conduct a focused analysis on a subset of publications with supplementary material drawn from the European Marine Science (MES) research community. The results are promising and suggest our hypothesis is worth exploring further as we registered in 22% of the cases substantial variations between the authors participating in the publication and the authors participating in the supplementary dataset (or software), thus posing the premises for a longitudinal, large-scale analysis of the phenomenon.

Martin Jedwabny, Pierre Bisquert, Madalina Croitoru

Normative ethics has been shown to help automated planners take ethically aware decisions. However, state- of-the-art planning technologies don’t provide a sim- ple and direct way to support ethical features. Here, we propose a new theoretical framework based on a con- struct, called ethical rule, that allows to model prefer- ences amongst ethically charged features and capture various ethical theories. We show how the framework can model and combine the strengths of these theories. Then, we demonstrate that classical planning domains extended with ethical rules can be compiled into soft goals in PDDL.