160 research outputs found
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
Traditional recommendation systems are faced with two long-standing
obstacles, namely, data sparsity and cold-start problems, which promote the
emergence and development of Cross-Domain Recommendation (CDR). The core idea
of CDR is to leverage information collected from other domains to alleviate the
two problems in one domain. Over the last decade, many efforts have been
engaged for cross-domain recommendation. Recently, with the development of deep
learning and neural networks, a large number of methods have emerged. However,
there is a limited number of systematic surveys on CDR, especially regarding
the latest proposed methods as well as the recommendation scenarios and
recommendation tasks they address. In this survey paper, we first proposed a
two-level taxonomy of cross-domain recommendation which classifies different
recommendation scenarios and recommendation tasks. We then introduce and
summarize existing cross-domain recommendation approaches under different
recommendation scenarios in a structured manner. We also organize datasets
commonly used. We conclude this survey by providing several potential research
directions about this field
A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture
The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach
Learning Object-Centric Neural Scattering Functions for Free-viewpoint Relighting and Scene Composition
Photorealistic object appearance modeling from 2D images is a constant topic
in vision and graphics. While neural implicit methods (such as Neural Radiance
Fields) have shown high-fidelity view synthesis results, they cannot relight
the captured objects. More recent neural inverse rendering approaches have
enabled object relighting, but they represent surface properties as simple
BRDFs, and therefore cannot handle translucent objects. We propose
Object-Centric Neural Scattering Functions (OSFs) for learning to reconstruct
object appearance from only images. OSFs not only support free-viewpoint object
relighting, but also can model both opaque and translucent objects. While
accurately modeling subsurface light transport for translucent objects can be
highly complex and even intractable for neural methods, OSFs learn to
approximate the radiance transfer from a distant light to an outgoing direction
at any spatial location. This approximation avoids explicitly modeling complex
subsurface scattering, making learning a neural implicit model tractable.
Experiments on real and synthetic data show that OSFs accurately reconstruct
appearances for both opaque and translucent objects, allowing faithful
free-viewpoint relighting as well as scene composition. Project website:
https://kovenyu.com/osf/Comment: Project website: https://kovenyu.com/osf/ Journal extension of
arXiv:2012.08503. The first two authors contributed equally to this wor
Differentiable Physics Simulation of Dynamics-Augmented Neural Objects
We present a differentiable pipeline for simulating the motion of objects
that represent their geometry as a continuous density field parameterized as a
deep network. This includes Neural Radiance Fields (NeRFs), and other related
models. From the density field, we estimate the dynamical properties of the
object, including its mass, center of mass, and inertia matrix. We then
introduce a differentiable contact model based on the density field for
computing normal and friction forces resulting from collisions. This allows a
robot to autonomously build object models that are visually and
\emph{dynamically} accurate from still images and videos of objects in motion.
The resulting Dynamics-Augmented Neural Objects (DANOs) are simulated with an
existing differentiable simulation engine, Dojo, interacting with other
standard simulation objects, such as spheres, planes, and robots specified as
URDFs. A robot can use this simulation to optimize grasps and manipulation
trajectories of neural objects, or to improve the neural object models through
gradient-based real-to-simulation transfer. We demonstrate the pipeline to
learn the coefficient of friction of a bar of soap from a real video of the
soap sliding on a table. We also learn the coefficient of friction and mass of
a Stanford bunny through interactions with a Panda robot arm from synthetic
data, and we optimize trajectories in simulation for the Panda arm to push the
bunny to a goal location
Impact of Minor Alloy Components on the Electrocapillarity and Electrochemistry of Liquid Metal Fractals
Exploring and controlling surface tensionâdriven phenomena in liquid metals may lead to unprecedented possibilities for nextâgeneration microfluidics, electronics, catalysis, and materials synthesis. In pursuit of these goals, the impact of minor constituents within liquid alloys is largely overlooked. Herein, it is showed that the presence of a fraction of solute metals such as tin, bismuth, and zinc in liquid gallium can significantly influence their electrocapillarity and electrochemistry. The instabilityâdriven fractal formation of liquid alloy droplets is investigated with different solutes and reveals the formation of distinctive nonâbranched droplets, unstable fractals, and stable fractal modes under controlled voltage and alkaline solution conditions. In their individually unique fractal morphology diagrams, different liquid alloys demonstrate significantly shifted voltage thresholds in transition between the three fractal modes, depending on the choice of the solute metal. Surface tension measurements, cycle voltammetry and surface compositional characterizations provide strong evidence that the minor alloy components drastically alter the surface tension, surface electrochemical oxidation, and oxide dissolution processes that govern the droplet deformation and instability dynamics. The findings that minor components are able to regulate liquid alloysâ surface tensions, surface element distributions and electrochemical activities offer great promises for harnessing the tunability and functionality of liquid metals
Expected geoneutrino signal at JUNO using local integrated 3-D refined crustal model
Geoneutrinos serve as a potent tool for comprehending the radiogenic power
and composition of Earth. Although geoneutrinos have been observed in prior
experiments, the forthcoming generation of experiments,such as JUNO, will be
necessary for fully harnessing their potential. Precise prediction of the
crustal contribution is vital for interpreting particlephysics measurements in
the context of geo-scientific inquiries. Nonetheless, existing models such as
JULOC and GIGJ have limitations in accurately forecasting the crustal
contribution. This paper introduces JULOCI, the novel 3-D integrated crustal
model of JUNO, which employs seismic, gravity, rock sample, and heat flow data
to precisely estimate the geoneutrino signal of the lithosphere. The model
indicates elevated concentrations of uranium and thorium in southern China,
resulting in unexpectedly strong geoneutrino signals.The accuracy of JULOC-I,
coupled with a decade of experimental data, affords JUNO the opportunity to
test multiple mantle models. Once operational, JUNO can validate the model
predictions and enhance the precision of mantle measurements. All in all, the
improved accuracy ofJULOC-I represents a substantial stride towards
comprehending the geochemical distribution of the South China crust, offering a
valuable tool for investigating the composition and evolution of the Earth
through geoneutrinos.Comment: Substantial updates on the model and predictions, submitted versio
Bearing behavior of high-performance concrete-filled high-strength steel tube composite columns subjected to eccentrical load
In order to study the bearing capacity of high-performance concrete-filled high-strength steel tube (HCHST) composite stub columns subjected to eccentrical load, 22 HCHST composite stub columns, 4 concrete-filled steel tube (CFST) composite stub columns and 8 high-performance concrete-filled steel tube (HCST) composite stub columns were designed with the cubic compressive strength of concrete (fcu), the yield strength of steel tube (fy), the thickness of tube wall (t), the eccentricity (e) and slenderness ratio (λ) as the main parameters. Considering the nonlinear constitutive model of concrete and simplified constitutive model of steel, the finite element (FE) model of HCHST composite stub columns was established by ABAQUS software. By comparison with the existing test results, the rationality of the constitutive model and boundary conditions was verified. The variation of ultimate bearing capacity and the typical failure modes of HCHST composite stub columns under different parameters was analyzed. The results show that the specimens exhibit obvious bulge outward at the end of the steel tube and shear failure at the end of concrete. High-performance concrete (HPC) can significantly improve the ultimate eccentrical compression bearing capacity of composite columns, and high-strength steel tubes have better restraint effect on HPC. With the increasing of t, the ultimate eccentrical compression bearing capacity and the load-holding capacity of HCHST columns increases gradually, while the ultimate eccentrical compression bearing capacity decreases gradually with the increasing of λ and e. By introducing the reduction coefficient of eccentricity (Ï1) and the reduction coefficient of slenderness ratio (Ï2), the calculation formula of the eccentric bearing capacity of HCHST columns is proposed by statistical regression, which can lay a foundation for the application of HCHST columns in practical engineering
Surface passivation for highly active, selective, stable, and scalable CO2 electroreduction
Electrochemical conversion of CO2 to formic acid using Bismuth catalysts is one the most promising pathways for industrialization. However, it is still difficult to achieve high formic acid production at wide voltage intervals and industrial current densities because the Bi catalysts are often poisoned by oxygenated species. Herein, we report a Bi3S2 nanowire-ascorbic acid hybrid catalyst that simultaneously improves formic acid selectivity, activity, and stability at high applied voltages. Specifically, a more than 95% faraday efficiency was achieved for the formate formation over a wide potential range above 1.0âV and at ampere-level current densities. The observed excellent catalytic performance was attributable to a unique reconstruction mechanism to form more defective sites while the ascorbic acid layer further stabilized the defective sites by trapping the poisoning hydroxyl groups. When used in an all-solid-state reactor system, the newly developed catalyst achieved efficient production of pure formic acid over 120âhours at 50âmAâcmâ2 (200âmA cell current)
- âŠ