jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fernanda Paula Bicalho Pio | Plínio da Costa Temba | Marcelo Antônio Nero | Marcos Antônio Timbó Elmiro | Eliane Maria Vieira | Helder Lages Jardim
Source: Geociências (article, 2024-01-15)
Field: Chemistry — Analytical Chemistry

AVALIAÇÃO DA TÉCNICA DE MODELO LINEAR DE MISTURA ESPECTRAL COMO SUBSÍDIO À CLASSIFICAÇÃO DO USO E OCUPAÇÃO DO SOLO Resumo - O desenvolvimento das tecnologias de sensoriamento remoto tem incentivado a aplicação de imagens digitais em variadas circunstâncias, inclusive em estudos ambientais e do monitoramento da Terra. Para os estudos de classificação do uso e ocupação do solo diversas técnicas de classificação podem ser aplicadas. Estas, porém, variam em função das características dos sensores imageadores e da finalidade do experimento. Em estudos de classificação de imagens é comum se deparar com problemas de mistura espectral que podem ser limitadores, assim, métodos de trabalho são utilizados para extrair informações das imagens com maior riqueza de detalhes considerando as propriedades dos materiais presentes dentro de um pixel e para auxiliar nas técnicas de classificação. Este trabalho aborda o modelo linear de mistura espectral como subsídio à classificação do uso e ocupação do solo. Os resultados foram comparados com a classificação realizada a partir de imagens do satélite Sentinel 2-A. A precisão geral para as classificações originárias dos dados do Sentinel-2A e do MLME foi de 65% e 62%, respectivamente, e o coeficiente Kappa foi de 0,53 (dados espectrais do satélite) e 0,62 (resultado do MLME) indicando que a qualidade dos dois mapeamentos pode ser considerada como moderada, suficientemente boa ou boa. Palavras-chave: Mistura espectral. Modelo linear de mistura espectral. Classificação de imagens. Sentinel-2A. Abstract - The development of remote sensing technologies has encouraged the application of digital images in various circumstances, including environmental studies and Earth monitoring. For classification studies of land use and occupation, several classification techniques can be applied. These, however, vary depending on the characteristics of the imaging sensors and the purpose of the experiment. In image classification studies, it is common to encounter problems of spectral mixing that can be limiting, so working methods are used to extract information from images with greater detail, considering the properties of the materials present within a pixel and to help in classification techniques. This work approaches the linear model of spectral mixture as subsidy to the classification of land use and occupation. The results were compared with the classification performed from Sentinel 2-A satellite images. The overall accuracy for the ratings sourced from the Sentinel-2A and LMSM data was 65% and 62%, respectively, and the Kappa coefficient was 0.53 (satellite spectral data) and 0.62 (LMSM result) indicating that the quality of the two mappings can be considered as moderate, good enough or good. Keywords: Spectral mixing. Linear spectral mixing model. Image classification. Sentinel-2A. Physics ; Humanities ; Art Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Jamshid Namdari | Robert T. Krafty | Amita K. Manatunga
Source: arXiv (Cornell University) (preprint, 2024-12-15)
Field: Chemistry — Analytical Chemistry

P3LS: Point Process Partial Least Squares Many studies collect data that can be considered as a realization of a point process. Included are medical imaging data where photon counts are recorded by a gamma camera from patients being injected with a gamma emitting tracer. It is of interest to develop analytic methods that can help with diagnosis as well as in the training of inexpert radiologists. Partial least squares (PLS) is a popular analytic approach that combines features from linear modeling as well as dimension reduction to provide parsimonious prediction and classification. However, existing PLS methodologies do not include the analysis of point process predictors. In this article, we introduce point process PLS (P3LS) for analyzing latent time-varying intensity functions from collections of inhomogeneous point processes. A novel estimation procedure for $P^3LS$ is developed that utilizes the properties of log-Gaussian Cox processes, and its empirical properties are examined in simulation studies. The method is used to analyze kidney functionality in patients with renal disease in order to aid in the diagnosis of kidney obstruction. Partial least squares regression ; Process (computing) ; Point (geometry) ; Mathematics ; Applied mathematics ; Computer science ; Statistics ; Geometry Spectroscopy and Chemometric Analyses
Authors: Shuai Zhang | Nianhong Jiao | Tian Li | Chaojie Yang | Chenhui Xue | Boya Niu | Junwei Gao
Source: arXiv (Cornell University) (preprint, 2024-10-30)
Field: Materials Science — Polymers and Plastics

HelloMeme: Integrating Spatial Knitting Attentions to Embed High-Level and Fidelity-Rich Conditions in Diffusion Models We propose an effective method for inserting adapters into text-to-image foundation models, which enables the execution of complex downstream tasks while preserving the generalization ability of the base model. The core idea of this method is to optimize the attention mechanism related to 2D feature maps, which enhances the performance of the adapter. This approach was validated on the task of meme video generation and achieved significant results. We hope this work can provide insights for post-training tasks of large text-to-image models. Additionally, as this method demonstrates good compatibility with SD1.5 derivative models, it holds certain value for the open-source community. Therefore, we will release the related code (\url{https://songkey.github.io/hellomeme}). High fidelity ; Diffusion ; Fidelity ; Computer science ; Engineering ; Physics ; Electrical engineering ; Telecommunications Textile materials and evaluations
Authors: L. J. Garcia | Daniel Foreman-Mackey | C. A. Murray | S. Aigrain | Dax L. Feliz | F. J. Pozuelos
Source: arXiv (Cornell University) (preprint, 2024-02-09)
Field: Chemistry — Organic Chemistry

nuance: Efficient detection of planets transiting active stars The detection of planetary transits in the light curves of active stars, featuring correlated noise in the form of stellar variability, remains a challenge. Depending on the noise characteristics, we show that the traditional technique that consists of detrending a light curve before searching for transits alters their signal-to-noise ratio, and hinders our capability to discover exoplanets transiting rapidly-rotating active stars. We present nuance, an algorithm to search for transits in light curves while simultaneously accounting for the presence of correlated noise, such as stellar variability and instrumental signals. We assess the performance of nuance on simulated light curves as well as on the TESS light curves of 438 rapidly-rotating M dwarfs. For each dataset, we compare our method to 5 commonly-used detrending techniques followed by a search with the Box-Least-Square algorithm. Overall, we demonstrate that nuance is the most performant method in 93% of cases, leading to both the highest number of true positives and the lowest number of false positive detections. Although simultaneously searching for transits while modeling correlated noise is expected to be computationally expensive, we make our algorithm tractable and available as the JAX-powered Python package nuance, allowing its use on distributed environments and GPU devices. Finally, we explore the prospects offered by the nuance formalism, and its use to advance our knowledge of planetary systems around active stars, both using space-based surveys and sparse ground-based observations. Planet ; Stars ; Exoplanet ; Astronomy ; Astrobiology ; Physics ; Astrophysics Chemistry and Stereochemistry Studies
Authors: Alain Théophile Sané | Ajita Suhrid Annachhatre | Klaus Kuehn
Source: Journal of Student Research (article, 2024-11-30)
Field: Chemistry — Inorganic Chemistry

The Effect of Coconut Water on Blood Glucose and Insulin in Humans Depends on their Metabolic Status Coconut water, a popular health food in Asia, is gaining popularity in the US due to its low glycemic index and is being promoted as a good beverage alternative to sugary drinks for people preferring a low carbohydrate diet or who are on intermittent fasting regimens. Given its increased popularity and confusing information in the public domain, some suggesting no impact on blood glucose, we aimed to establish the effect systematically. We hypothesized that coconut water may mildly increase blood glucose and insulin in all subjects as it contains at least ~9 g of total carbohydrates. We conducted a clinical study in humans (n=11, ages 16 and above) wherein fasting blood glucose and insulin were measured at baseline and again at 40 min and 120 min after drinking 250 mL of either fresh or bottled coconut water. As hypothesized, coconut water increased blood glucose and insulin in the majority of subjects in 45 min, and returned to baseline by 2 h post consumption. The mean increase from baseline was 8.90 mg/dL for glucose and 9.53 uIU/mL for insulin. Further, there was a clear trend of higher increase in prediabetic subjects (n=5) compared to the normal subjects (n=5), with insulin increase being statistically significant. There was no difference in the response to fresh versus bottled coconut water. In conclusion, the effect of coconut water on blood glucose and insulin was mild, however, the magnitude may depend on a person's baseline metabolic status. Insulin ; Metabolic activity ; Internal medicine ; Chemistry ; Medicine ; Physiology Coconut Research and Applications ; Fatty Acid Research and Health
Authors: Din Ping Tsai | Shumin Xiao
Source: Opto-Electronic Science (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Special issue on meta-lenses Lens (geology) ; Computer science ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Photochromic and Fluorescence Chemistry ; Advanced Optical Imaging Technologies
Authors: В А Золотарев | Ya.V. Stroinov | Natalia Kosolapova
Source: Limnology and Freshwater Biology (article, 2024-01-01)
Field: Materials Science — Biomaterials

Microperiphyton development in lakes and reservoirs Biofilms play an important role in the material flux of lakes and other aquatic ecosystems, but little is known about the mechanisms controlling their community structure under natural conditions. In the present study, we focused on the use of artificial substrates for the life strategies of microbial communities investigation. Heterotrophic nanoflagellates (HNF) are known as most important grazers of bacteria in the biofilm communities. The Choanoflagellida have an essential role in microbial food webs contributing to a process of self-purification of water bodies as well as sponge choanocytes, filtering large water volumes. Effects of Cyanobacteria and other pathogenic microorganisms on sponges are very similar to the same effects on Choanoflagellida and HNF in the early phase of biofilm colonization. Standard microbial biofilms (model communities) can serve for ecorobots development. Development (topology) ; Geology ; Petroleum engineering ; Environmental science ; Mathematics Diatoms and Algae Research
Authors: Subhasis Das | Anindya Ghosh
Source: Indian Journal of Fibre & Textile Research (article, 2024-09-01)
Field: Materials Science — Polymers and Plastics

Selection of raw material parameters for multi-response optimization of cotton yarn qualities In this work, a multi-response optimization of cotton yarn quality using desirability function approach has been attempted. Being a natural product, cotton yarn qualities are primarily governed by raw material characteristics. This work aims to resolve the complexity of simultaneous optimization of raw material properties using a hybrid multi-response optimization model, where predictive power of support vector regression and optimization capability of genetic algorithm are employed with desirability function. The individual desirability of cotton fibre qualities is assessed from the six properties, such as fibre strength, elongation, fineness, upper half mean length, uniformity index and short fibre content. The yarn quality parameters, such as yarn strength, yarn elongation, hairiness and unevenness, are combined together to express overall desirability. The optimum cotton quality parameters essential to produce good quality yarn can be determined from the proposed multi-response optimization model. Yarn ; Selection (genetic algorithm) ; Raw material ; Materials science ; Composite material ; Pulp and paper industry ; Computer science ; Engineering ; Artificial intelligence ; Chemistry Textile materials and evaluations
Source: Pharmacy Education (article, 2024-09-12)
Field: Chemistry — Analytical Chemistry

FIP Pharmacy practice research summer meeting for PhD students, postdoctoral fellows and supervisors conference abstracts 2024 We are pleased to confirm the publication of the pharmacy practice research summer virtual meeting for PhD students, postdoctoral fellows and supervisors conference abstracts 2024. The research summer virtual meeting was hosted by the FIP Pharmacy Practice Research Special Interest Group (PPR SIG). Pharmacy ; Medical education ; Pharmacy practice ; Medicine ; Family medicine Analytical Methods in Pharmaceuticals
Authors: А.М. Петров | А.Н. Попов | A.M. Petrov | A.N. Popov
Source: Электронный архив ЮУрГУ (South Ural State University) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Thermodynamic physical-mathematical models of multi-phase flows of external heat supply networks Петров Алексей Михайлович, канд. техн. наук, доц., заведующий кафедрой электроэнергетики и автоматики, Заполярный государственный университет им. Н.М. Федоровского, Норильск, Россия; darker2012@yandex.ru. Попов Антон Николаевич, старший преподаватель Академического департамента, Тюменский государственный университет, Тюмень, Россия; старший преподаватель, Государственный аграрный университет Северного Зауралья, Тюмень, Россия; 264241@mail.ru. Aleksey M. Petrov, Cand. Sci. (Eng.), Ass. Prof., Head of the Department of Power Engineering and Automation, Fedorovsky Polar State University, Norilsk, Russia; darker2012@yandex.ru. Anton N. Popov, Senior Lecturer of the Academic Department, University of Tyumen, Tyumen, Russia; Senior Lecturer, Northern Trans-Ural State Agricultural University, Tyumen, Russia; 264241@mail.ru. State (computer science) ; Head (geology) ; Power (physics) ; Heat flow ; Work (physics) Advanced Materials and Semiconductor Technologies ; Integrated Energy Systems Optimization ; Advanced Power Generation Technologies
Authors: Novia Hasdyna
Source: Jurnal Techno Nusa Mandiri (article, 2024-03-28)
Field: Chemistry — Analytical Chemistry

PREDICTIVE MODELING OF BROILER CHICKEN PRODUCTION USING THE NAIVE BAYES CLASSIFICATION ALGORITHM Serious challenges are faced by broiler chicken farmers in Seumirah Village, Nisam Antara Subdistrict, North Aceh Regency, in their efforts to create high-quality and productive chickens. These difficulties not only impact the farmers' income but also result in recurring losses every year. This research aims to design a system using the Naive Bayes Classifier algorithm to assess the capacity and classify production types based on specific criteria such as population, age, depletion, FCR (Feed Conversion Ratio), IP (Index Performance), and BW (Body Weight). The system aims to classify broiler chicken production as either increasing (profitable) or decreasing (unprofitable). In the development of this predictive system, the PHP programming language is employed, with a MySQL database as the data storage medium. The results of this broiler chicken production prediction system have proven effective in providing information in the form of profit or loss reports based on the harvest results for each monthly period. The implementation of this system is expected to assist in optimizing farmers' production management, increasing business profitability, and providing better guidance for future business decisions. The classification results using the Naive Bayes method indicate an accuracy rate of 86,67 and error rate of 13,3%. Broiler ; Naive Bayes classifier ; Production (economics) ; Bayes' theorem ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Bayesian probability ; Support vector machine ; Biology ; Animal science ; Economics Spectroscopy and Chemometric Analyses ; Fuzzy Logic and Control Systems
Authors: Kendrick Li | George C. Linderman | Xu Shi | Eric J. Tchetgen Tchetgen
Source: arXiv (Cornell University) (preprint, 2024-09-13)
Field: Chemistry — Electrochemistry

Regression-based proximal causal inference for right-censored time-to-event data Unmeasured confounding is one of the major concerns in causal inference from observational data. Proximal causal inference (PCI) is an emerging methodological framework to detect and potentially account for confounding bias by carefully leveraging a pair of negative control exposure (NCE) and outcome (NCO) variables, also known as treatment and outcome confounding proxies. Although regression-based PCI is well developed for binary and continuous outcomes, analogous PCI regression methods for right-censored time-to-event outcomes are currently lacking. In this paper, we propose a novel two-stage regression PCI approach for right-censored survival data under an additive hazard structural model. We provide theoretical justification for the proposed approach tailored to different types of NCOs, including continuous, count, and right-censored time-to-event variables. We illustrate the approach with an evaluation of the effectiveness of right heart catheterization among critically ill patients using data from the SUPPORT study. Our method is implemented in the open-access R package 'pci2s'. Inference ; Causal inference ; Regression ; Event (particle physics) ; Computer science ; Econometrics ; Event data ; Statistics ; Artificial intelligence ; Mathematics Electrochemical Analysis and Applications ; Advanced Causal Inference Techniques ; Statistical Methods and Inference
Authors: Titin Agustina | Anwar Fitrianto | INDAHWATI INDAHWATI
Source: International Journal of Scientific Research in Science Engineering and Technology (article, 2024-03-25)
Field: Chemistry — Analytical Chemistry

Comparison of SARIMA, Bagging Exponential Smoothing with STL Decomposition and Robust STL Decomposition for Forecasting Red Chili Production Time series analysis enables the identification of trends and patterns in data, allowing for the development of forecasting models that predict future values. One effective approach for forecasting seasonal time series data is the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Bagging Exponential Smoothing with STL Decomposition (BES-STL) is an ensemble machine learning method aimed at enhancing forecasting accuracy. STL Method, which stands for Seasonal-Trend decomposition using Loess, is utilized to decompose time series data into three components, namely trend, seasonal, and remainder components. In the remainder component, the process of bootstrap aggregation (bagging) with Moving Block Bootstrapping (MBB) is used to obtain synthetic data, followed by averaging the value by month from the entire series as the forecast results. A comparative analysis was conducted using the SARIMA, BES-STL, and BES-RSTL models. The optimal model, with the lowest MAPE and RMSE, is then implemented to predict national red chili production. The results indicate that the SARIMA(1,1,1)(0,1,1)12 model has the best performance with a MAPE of 7.06 and a RMSE of 95,473. The top-performing model is utilized to forecast data from January to December 2022. Additionally, the forecasted results are compared to the actual data, resulting in a highly precise MAPE of 5.39. Exponential smoothing ; Decomposition ; Production (economics) ; Exponential function ; Mathematics ; Computer science ; Statistics ; Economics ; Chemistry Spectroscopy and Chemometric Analyses
Authors: José E. Chacón | Carlos Tenreiro
Source: arXiv (Cornell University) (preprint, 2024-04-21)
Field: Materials Science — Materials Chemistry

Estimation of density functionals via cross-validation In density estimation, the mean integrated squared error (MISE) is commonly used as a measure of performance. In that setting, the cross-validation criterion provides an unbiased estimator of the MISE minus the integral of the squared density. Since the minimum MISE is known to converge to zero, this suggests that the minimum value of the cross-validation criterion could be regarded as an estimator of minus the integrated squared density. This novel proposal presents the outstanding feature that, unlike all other existing estimators, it does not need the choice of any tuning parameter. Indeed, it is proved here that this approach results in a consistent and efficient estimator, with remarkable performance in practice. Moreover, apart from this base case, it is shown how several other problems on density functional estimation can be similarly handled using this new principle, thus demonstrating full potential for further applications. Cross-validation ; Econometrics ; Mathematics ; Computer science ; Statistics Crystallization and Solubility Studies
Authors: 高克林
Source: Acta Physica Sinica (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Preface to the special topic: Precision spectroscopy of few-electron atoms and molecules 量子电动力学(QED)作为原子分子精密谱的理论基础, 为理解微观物理世界提供了坚实的框架. 少电子原子分子体系由于其相对简单的电子结构, 成为高精度计算与测量的理想对象, 是检验束缚态QED理论的独特平台. 随着冷原子物理和激光技术的迅猛发展, 原子分子光谱的精密测量也不断取得突破. 精度的提升使得能级的位移揭示出越来越多的物理效应, 为检验QED理论、测量基本物理常数、揭示原子核结构以及探索新物理开辟了重要的科学路径. 可以说, 历经一个多世纪的发展, 少电子原子分子精密谱研究仍然在推动着物理学的前沿进展. Electron ; Spectroscopy ; Atomic physics ; Molecule ; Electron spectroscopy ; Physics ; Materials science ; Nuclear physics ; Quantum mechanics Various Chemistry Research Topics ; Atomic and Molecular Physics