Composite Objects Research Articles

Composite expectile estimation in partial functional linear regression model

Recent research and substantive studies have shown growing interest in expectile regression (ER) procedures. Similar to quantile regression, ER with respect to different expectile levels can provide a comprehensive picture of the conditional distribution of a response variable given predictors. This study proposes three composite-type ER estimators to improve estimation accuracy. The proposed ER estimators include the composite estimator, which minimizes the composite expectile objective function across expectiles; the weighted expectile average estimator, which takes the weighted average of expectile-specific estimators; and the weighted composite estimator, which minimizes the weighted composite expectile objective function across expectiles. Under certain regularity conditions, we derive the convergence rate of the slope function, obtain the mean squared prediction error, and establish the asymptotic normality of the slope vector. Simulations are conducted to assess the empirical performances of various estimators. An application to the analysis of capital bike share data is presented. The numerical evidence endorses our theoretical results and confirm the superiority of the composite-type ER estimators to the conventional least squares and single ER estimators.

Doubly heavy dibaryons as seen by string theory

We propose the stringy description of the system consisting of two heavy and four light quarks in the case of two light flavors of equal mass. As an application, we consider the three low-lying Born-Oppenheimer potentials as a function of the heavy quark separation. Our analysis shows that the ground state potential is described in terms of both hadroquarkonia and hadronic molecules. A connected string configuration makes the dominant contribution to the potential of an excited state at small separations, and for separations larger than 0.1 fm, it exhibits the diquark-diquark-diquark structure [Qq][Qq][qq]. For better understanding the quark organization inside the system, we introduce several critical separations related to the processes of string reconnection, breaking and junction annihilation. We also discuss the simplest string configurations including the five-string junctions and their implications for the system, in particular the emergence of composite quark objects different from diquarks and the process of junction fusion. Published by the American Physical Society 2024

Optimization-based planning of speech articulation using general Tau Theory

This paper presents a model of speech articulation planning and generation based on General Tau Theory and Optimal Control Theory. Because General Tau Theory assumes that articulatory targets are always reached, the model accounts for speech variation via context-dependent articulatory targets. Targets are chosen via the optimization of a composite objective function. This function models three different task requirements: maximal intelligibility, minimal articulatory effort and minimal utterance duration. The paper shows that systematic phonetic variability can be reproduced by adjusting the weights assigned to each task requirement. Weights can be adjusted globally to simulate different speech styles, and can be adjusted locally to simulate different levels of prosodic prominence. The solution of the optimization procedure contains Tau equation parameter values for each articulatory movement, namely position of the articulator at the movement offset, movement duration, and a parameter which relates to the shape of the movement’s velocity profile. The paper presents simulations which illustrate the ability of the model to predict or reproduce several well-known characteristics of speech. These phenomena include close-to-symmetric velocity profiles for articulatory movement, variation related to speech rate, centralization of unstressed vowels, lengthening of stressed vowels, lenition of unstressed lingual stop consonants, and coarticulation of stop consonants.

Novel process optimization based on machine learning: A study on biohydrogen production from waste resources

The biomass poultry litter and sewage sludge co-gasification is a good thermochemical method to produce hydrogen energy meanwhile to mitigate the consumption of fossil fuels. However, the operation optimization in the complex process system is important while computationally difficult because of high nonlinearity and many first-principle constraints. To address the optimization of this biohydrogen production process, a methodology framework for achieving the optimal operations is thus presented. The complete process system is first simulated and decomposed into upstream gasification process and downstream hydrogen purification for reducing computational complexity through the thermodynamic equilibrium-based simulation. Totally 1400 data points by pairing total 15 operating conditions with a composite sustainability index objective are generated through the random sampling and data classification strategies, which are then used for the construction of the artificial neural network (ANN)-based prediction models. ANN models of two subprocesses demonstrate the satisfactory prediction accuracy with R2 value of 0.98 and 0.99, respectively, which are then integrated with mixed-integer linear programming (MILP) for the optimization of upstream process and downstream process step-by-step. The MILP problems based on the ANN models are solved with lower optimization time of 45∼100 s compared to the heuristic algorithm optimization (3–6 h) based on the thermodynamic equilibrium-based simulation. The optimal sustainability index values of two processes are 0.80 and 0.91 which are both improved compared to the existing optimization results (0.79 and 0.84). This study emphasizes the optimization potential of the integration approach of machine learning-based modelling and mathematical programming for developing the optimal waste-to-energy processes.

Two Physicalist Arguments for Microphysical Manyism

AbstractI here defend microphysical manyism. According to microphysical manyism, each composite or higher-level object is a mere plurality of microphysical particles. After clarifying the commitments of the view, I offer two physicalist-friendly arguments in its favour. The first argument appeals to the Canberra Plan. Here I argue that microphysical particles acting in unison play the theoretical roles associated with composite objects - that they do everything that we think of composite objects as doing - and thus that composite objects are to be identified with pluralities ofmicrophysical particles. Along the way I rebut the objections that pluralities of particles don’t display the right emergent, ‘lingering’, or modal properties to be good candidates for identification with higher-level objects. In the second argument I claim that microphysical manyism is uniquely able to capture a compelling and widespread physicalist intuition concerning the intimate nature of the relationshipbetween higher-level, composite objects and the microphysical world.

Charged anisotropic composite stellar object with strange, polytropic and gaseous matter

In this study, we build a charged star model with three layers with distinct equations of state. The core, intermediate layer, and envelope layer obey linear, polytropic, and Chaplygin equations of state, respectively. The model satisfies the physical requirements for the matter variables, gravitational potentials, and stability conditions. We have generated within-acceptable range masses and radii for stars. We also recover the masses and radii for the stars PSRJ1903+0327, PSRJ1614-2230, SAXJ1808.4-3658, Vela X-1, and EXO1785-248 from earlier investigations which shows astrophysical significance of our model.

Painterly Image Harmonization by Learning from Painterly Objects

Given a composite image with photographic object and painterly background, painterly image harmonization targets at stylizing the composite object to be compatible with the background. Despite the competitive performance of existing painterly harmonization works, they did not fully leverage the painterly objects in artistic paintings. In this work, we explore learning from painterly objects for painterly image harmonization. In particular, we learn a mapping from background style and object information to object style based on painterly objects in artistic paintings. With the learnt mapping, we can hallucinate the target style of composite object, which is used to harmonize encoder feature maps to produce the harmonized image. Extensive experiments on the benchmark dataset demonstrate the effectiveness of our proposed method.

Electron-Assisted Generation and Straight Movement of Skyrmion Bubble in Kagome TbMn6Sn6.

Topological magnetic textures are promising candidates as binary data units for the next-generation memory device. The precise generation and convenient control of nontrivial spin topology at zero field near room temperature endows the critical advantages in skyrmionic devices but is not simultaneously integrated into one material. Here, in the Kagome plane of quantum TbMn6Sn6, the expedient generation of the skyrmion bubbles in versatile forms of lattice, chain, and isolated one by converging the electron beam, where the electron intensity gradient contributes to the dynamic generation from local anisotropy variation near spin reorientation transition (SRT) is reported. Encouragingly, by utilizing the dynamic shift of the SRT domain interface, the straight movement is actualized with the skyrmion bubble slave to the SRT domain interface forming an elastic composite object, avoiding the usual deflection from the skyrmion Hall effect. The critical contribution of the SRT domain interface via conveniently electron-assisted heating is further theoretically validated in micromagnetic simulation, highlighting the compatible application possibility in advanced devices.

Harnessing AI and Gut Microbiome Research for Precision Health

The gut microbiome's impact on physiological processes, influenced by diet and lifestyle, is profound. Dysbiosis, an imbalance in microbiota composition, is associated with diseases like obesity. This review explores the gut microbiome's role in metabolism and calorie intake, alongside recent AI advancements impacting personalized nutrition. AI has revolutionized microbiome research, especially in multi-omics data analysis. AI-driven approaches enable the integration and interpretation of diverse omics datasets, including genomics, metabolomics, and proteomics, providing comprehensive insights into the gut microbiome's functional dynamics and its impact on host metabolism. These facilitate the identification of microbial biomarkers associated with disease states and dietary interventions, paving the way for personalized nutrition strategies tailored to individual gut microbiome profiles. AI platforms can also offer tailored dietary recommendations based on microbiome composition and health objectives. Healthcare professionals leverage AI to optimize dietary interventions, promoting gut microbiome modulation and preventing chronic diseases. Challenges like data standardization and privacy persist, yet addressing them is vital for maximizing AI's benefits in health outcomes and precision medicine. Ongoing AI and microbiome research promise to revolutionize personalized nutrition and metabolic health worldwide.

Composite Backbone Small Object Detection Based on Context and Multi-Scale Information with Attention Mechanism

Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility to interference from neighboring elements. To improve detection accuracy of small objects, this study presents a novel method that integrates context information, attention mechanism, and multi-scale information. First, to realize feature augmentation, a composite backbone network is employed which can jointly extract object features. On this basis, to efficiently incorporate context information and focus on key features, the composite dilated convolution and attention module (CDAM) is designed, consisting of a composite dilated convolution module (CDM) and convolutional block attention module (CBAM). Then, a feature elimination module (FEM) is introduced to reduce the feature proportion of medium and large objects on feature layers; the impact of neighboring objects on small object detection can thereby be mitigated. Experiments conducted on MS COCO validate the superior performance of the method compared with baseline detectors, while it yields an average enhancement of 0.8% in overall detection accuracy, with a notable enhancement of 2.7% in small object detection.

Homogeneous fine classification of Markarian galaxies based on SDSS medium-resolution spectroscopy

Context. Markarian (Mrk) galaxies, revealed due to their ultraviolet excess are very famous; they play a significant role in many astro-physical problems and come in a variety of types: Seyferts (Sys), low-ionization narrow emission-line regions (LINERs), starbursts (SBs), blue compact dwarf galaxies (BCDGs), as well as some quasars (QSOs) and blazars. They also appear as sources of non-optical radiation, such as gamma-ray, X-ray, ultraviolet (UV), infrared (IR), and radio, including some extremely high-energy gamma-ray sources (Mrk 421, Mrk 501) and very high-luminosity IR galaxies (Mrk 231). Aims. The classifications of Mrk galaxies for activity types have been carried out based on old mostly poor-quality and inhomoge-neous spectra (relatively low-resolution photographic spectra and low S/N spectra). The Sloan Digital Sky Surveys (SDSS) provides ~90% of the existing medium-resolution spectra, and is a relevant homogeneous database for classification of galaxies. Our aim was to obtain homogeneous classifications of Mrk galaxies by means of the SDSS spectra. Methods. Based on our work on the classification of newly revealed objects from various recent catalogs, we developed a fine classification scheme for activity types based on the homogeneous database of the SDSS spectroscopy. This scheme was applied to Mrk galaxies having spectra in the SDSS (779 out of 1544, 50.45%). Results. As a result, 779 Mrk galaxies now have homogeneous optical spectral classification that can be used for further studies and statistics of their physical and spatial properties. We revealed 2 QSOs, 49 classical Seyferts (broad-line Seyferts 1, BLS1s), 17 narrow-line Seyferts 1 (NLS1s), 4 Sy2s, 12 LINERs, 11 active galactic nuclei (AGN) without a definite type, 31 Composite spectrum objects, and 533 HII/Starbursts. Due to low-quality spectra, some objects were left without definite activity types and were classified as emission or absorption galaxies.

Translucency measurement system based on a polarized camera.

This paper proposes a measurement system capable of estimating the transmittance and haze values of a composite object. The system, comprising a polarized camera, linear polarizer, and backlight, was calibrated to obtain four phase polarization images. Forty-one samples, which covered a wide range of transmittance and haze values, were manufactured to assist in correlating the polarization images and the referenced ground truth from the BKY-Gardner instrument. After the data regression, two linear equations were selected to estimate the transmittance and haze values of transparent objects. The verification experiment for 52 samples demonstrated that the proposed method accurately estimated the transmittance of the samples with a coefficient of determination (R 2) as high as 0.96 and an average error of less than 4.1%. The haze estimation had an R 2 of 0.94 and an average error of 5.08%. Pseudo color maps were used to present the different transmittance and haze values of a single object. The proposed system can perform image-based translucency measurements and obtain individual values of a composite object.

Relational Part-Aware Learning for Complex Composite Object Detection in High-Resolution Remote Sensing Images.

In high-resolution remote sensing images (RSIs), complex composite object detection (e.g., coal-fired power plant detection and harbor detection) is challenging due to multiple discrete parts with variable layouts leading to complex weak inter-relationship and blurred boundaries, instead of a clearly defined single object. To address this issue, this article proposes an end-to-end framework, i.e., relational part-aware network (REPAN), to explore the semantic correlation and extract discriminative features among multiple parts. Specifically, we first design a part region proposal network (P-RPN) to locate discriminative yet subtle regions. With butterfly units (BFUs) embedded, feature-scale confusion problems stemming from aliasing effects can be largely alleviated. Second, a feature relation Transformer (FRT) plumbs the depths of the spatial relationships by part-and-global joint learning, exploring correlations between various parts to enhance significant part representation. Finally, a contextual detector (CD) classifies and detects parts and the whole composite object through multirelation-aware features, where part information guides to locate the whole object. We collect three remote sensing object detection datasets with four categories to evaluate our method. Consistently surpassing the performance of state-of-the-art methods, the results of extensive experiments underscore the effectiveness and superiority of our proposed method.

Fast Direct Solution of Composite Dielectric Objects by M-HODLR

Fast Direct Solution of Composite Dielectric Objects by M-HODLR

Additively Composite Model Objective Function for Routing Protocol for Low-Power and Lossy Network Protocol

Additively Composite Model Objective Function for Routing Protocol for Low-Power and Lossy Network Protocol

Role of composite objective nutritional indexes in patients with chronic kidney disease.

Malnutrition persists as one of the most severe symptoms in patients with chronic kidney disease (CKD) globally. It is a critical risk factor for cardiovascular and all-cause mortality in patients with CKD. Readily available objective indicators are used to calculate composite objective nutritional assessment indexes, including the geriatric nutritional risk index, prognostic nutritional index, and controlling nutritional status score. These indexes offer a straightforward and effective method for evaluating nutritional status and predicting clinical outcomes in patients with CKD. This review presents supporting evidence on the significance of composite nutritional indexes.

Neolithic Liner Dagger from Ust-Narym in the Irtysh River Valley

The present research featured a slotted, or liner, bone dagger from the Neolithic layer of the Ust-Narym settlement on the Irtysh River. Its design, material processing technology, and functional purpose make it a rare, as well as the most ancient example of hand-to-hand combat weapons found in Kazakhstan. This composite object was carved from the posterior left metatarsal bone of a wild auroch with the help of several tools. It includes thin and sharp flint inserts, which were attached into the grooves on the side faces of the dagger frame with a special adhesive substance. An additional nozzle for a short handle made it possible to use this piercing-cutting weapon in battle. It is decorated with dots and circles connected by straight lines, which probably means it was a socialized object of ritual use associated with sacrifice. Typologically, the dagger from Ust-Narym is similar to artifacts found in the Late Paleolithic and Mesolithic sites in the south of Western Siberia, in the Urals, in the Baikal region, and in Eastern Europe. The dagger marks a milestone in the technical and technological development of ancient Eurasian peoples. It also illustrates the ethno-social and cultural processes across the vast territory of Eurasia.

Adaptation and assessement of projected Nesterov accelerated gradient flow to compute stationary states of nonlinear Schrödinger equations

The aim of the paper is to derive minimization algorithms based on the Nesterov accelerated gradient flow [Y. Nesterov, Gradient methods for minimizing composite objective function. Core discussion paper, (2007). Available at http://www.ecore.be/DPs/dp_1191313936.pdf; Y. Nesterov, A method of solving a convex programming problem with convergence rate O ( 1 / k 2 ) . In Doklady Akademii Nauk, Vol. 269, Russian Academy of Sciences, 1983, pp. 543–547; Y. Nesterov, Introductory Lectures on Convex Optimization: A basic course, Kluwer Academic Publishers, Massachusetts, 2004.] to compute the ground state of nonlinear Schrödinger equations, which can potentially include a fractional laplacian term. A comparison is developed with standard gradient flow formulations showing that the Nesterov accelerated gradient flow has some interesting properties but at the same time finds also some limitations due to the nature of the problem. A few simulations are finally reported to understand the behaviour of the algorithms and open the path to further complicate questions that require more advanced studies concerning the application of the Nesterov accelerated gradient flow to nonlinear Schrödinger equations.

A double-layer ring-structured equalizer for series-connected lithium-ion battery pack based on model predictive control

The inconsistency of lithium-ion batteries will seriously affect the performance and safety of the battery pack in series, resulting in a decrease in the available capacity and shortening of the life span of the series battery pack. To alleviate this inconsistency, a double-layer ring-structured equalization topology is proposed, which has the advantages of flexible equalization path and fast equalization speed. In order to further improve the equalization speed and reduce the equalization loss, this paper proposes an equalization strategy based on model predictive control (MPC). By establishing a prediction model and solving the composite optimization objective function, the optimal control strategy is obtained. To verify the feasibility of the proposed equalizer, the simulation study has been carried out in Matlab-Simulink. The simulation results in static operating condition show that, compared with the single-layer Buck-Boost equalizer based on the mean-difference algorithm, the equalization time of the proposed equalizer based on MPC is reduced by 38.15 % and the energy transfer efficiency is increased by 27.25 %. The rationality of the proposed equalizer and equalization strategy is further verified in constant current (CC) charging and New York Bus Cycle (NYBC) discharging operating conditions.

Vapor Nanobubbles around Heated Nanoparticles: Wetting Dependence of the Local Fluid Thermodynamics and Kinetics of Nucleation.

Plasmonic nanobubbles are composite objects resulting from the interaction between light and metallic nanoparticles immersed in a fluid. Plasmonic nanobubbles have applications in photothermal therapies, drug delivery, microfluidic manipulations, and solar energy conversion. Their early formation is, however, barely characterized due to the short time and length scales relevant to the process. Here, we investigate, using molecular dynamics (MD) simulations, the effect of nanoparticle wettability on both the local fluid thermodynamics and the kinetics of nanobubble generation in water. We first show that the local onset temperature of vapor nucleation decreases with the nanoparticle/water interfacial energy and may be 100 K below the water spinodal temperature in the case of weak nanoparticle/water interactions. Second, we demonstrate that vapor nucleation may be slower in the case of weak water/nanoparticle interactions. This result, which is qualitatively at odds with the predictions of isothermal classical nucleation theory, may be explained by the competition between two antagonist effects: while, classically, hydrophobicity increases the vapor nucleation rate, it also penalizes interfacial thermal transfer, slowing down kinetics. The kinetics of heat transfer from the nanoparticle to water is controlled by the interfacial thermal conductance. This quantity turns out not only to decrease with the nanoparticle hydrophobicity but also drops down prior to phase change, yielding even longer nucleation times. Such conclusions were reached by considering the comparison between MD and continuous heat transfer models. These results put forward the role of nanoparticle wettability in the generation of plasmonic nanobubbles observed experimentally and open the path to the control of boiling using nanopatterned surfaces.