252 research outputs found
Deep-neural-network solution of the ab initio nuclear structure
Predicting the structure of quantum many-body systems from the first
principles of quantum mechanics is a common challenge in physics, chemistry,
and material science. Deep machine learning has proven to be a powerful tool
for solving condensed matter and chemistry problems, while for atomic nuclei,
it is still quite challenging because of the complicated nucleon-nucleon
interactions, which strongly couples the spatial, spin, and isospin degrees of
freedom. By combining essential physics of the nuclear wave functions and the
strong expressive power of artificial neural networks, we develop FeynmanNet, a
novel deep-learning variational quantum Monte Carlo approach for \emph{ab
initio} nuclear structure. We show that FeynmanNet can provide very accurate
ground-state energies and wave functions for He, Li, and even up to
O as emerging from the leading-order and next-to-leading-order
Hamiltonians of pionless effective field theory. Compared to the conventional
diffusion Monte Carlo approaches, which suffer from the severe inherent
fermion-sign problem, FeynmanNet reaches such a high accuracy in a variational
way and scales polynomially with the number of nucleons. Therefore, it paves
the way to a highly accurate and efficient \emph{ab initio} method for
predicting nuclear properties based on the realistic interactions between
nucleons.Comment: 13 pages, 3 figure
Tripartite evolutionary game analysis of power battery carbon footprint disclosure under the EU battery regulation
The EU's battery regulation aims to promote low-carbon and sustainable batteries and achieve carbon neutrality goals. However, in the actual implementation, limited government supervision, asymmetric information, and economic interests may induce battery manufacturers and third-party verification agencies to manipulate carbon footprint data. To prevent the occurrence of the above phenomena, this study constructs a tripartite evolutionary game model involving battery manufacturers, third-party verification agencies, and national market authorities. The model examines the strategic decision-making process, influential factors, and evolutionary stability of the three players, followed by simulation analysis. The results showed that the evolutionary system may exhibit two stable states: (0,0,1) and (1,1,0), corresponding to two strategy combinations {disclose false carbon footprints, intend rent-seeking, supervise} and {disclose true carbon footprint, reject rent-seeking, not supervise}, respectively. However, if the benefits of third-party agencies objectively assessing carbon footprints are not substantial enough, there will be only one stable state (0,0,1) in the system. To guide the evolutionary system towards the desired stable state (1,1,0), supportive policies should be implemented along with the EU battery regulation. Therefore, this study puts forward some policy recommendations in terms of institutional improvement, database construction, and the application of emerging technologies
Establishment of Neural Networks Robust to Label Noise
Label noise is a significant obstacle in deep learning model training. It can
have a considerable impact on the performance of image classification models,
particularly deep neural networks, which are especially susceptible because
they have a strong propensity to memorise noisy labels. In this paper, we have
examined the fundamental concept underlying related label noise approaches. A
transition matrix estimator has been created, and its effectiveness against the
actual transition matrix has been demonstrated. In addition, we examined the
label noise robustness of two convolutional neural network classifiers with
LeNet and AlexNet designs. The two FashionMINIST datasets have revealed the
robustness of both models. We are not efficiently able to demonstrate the
influence of the transition matrix noise correction on robustness enhancements
due to our inability to correctly tune the complex convolutional neural network
model due to time and computing resource constraints. There is a need for
additional effort to fine-tune the neural network model and explore the
precision of the estimated transition model in future research.Comment: 11 pages, 7 figure
Monitoring Efficiency of IoT Wireless Charging
Crowdsourcing wireless energy is a novel and convenient solution to charge
nearby IoT devices. Several applications have been proposed to enable
peer-to-peer wireless energy charging. However, none of them considered the
energy efficiency of the wireless transfer of energy. In this paper, we propose
an energy estimation framework that predicts the actual received energy. Our
framework uses two machine learning algorithms, namely XGBoost and Neural
Network, to estimate the received energy. The result shows that the Neural
Network model is better than XGBoost at predicting the received energy. We
train and evaluate our models by collecting a real wireless energy dataset.Comment: 3 pages, 4 figures. This is an accepted demo paper and it will appear
in The 21st International Conference on Pervasive Computing and
Communications (PerCom 2023
Towards peer-to-peer sharing of wireless energy services
Crowdsourcing wireless energy services is a novel convenient alternative to
charge IoT devices. We demonstrate peer-to-peer wireless energy services
sharing between smartphones over a distance. Our demo leverages (1) a
service-based technique to share energy services, (2) state-of-the-art power
transfer technology over a distance, and (3) a mobile application to enable
communication between energy providers and consumers. In addition, our
application monitors the charging process between IoT devices to collect a
dataset for further analysis. Moreover, in this demo, we compare the
peer-to-peer energy transfer between two smartphones using different charging
technologies, i.e., cable charging, reveres charging, and wireless charging
over a distance. A set of preliminary experiments has been conducted on a real
collected dataset to analyze and demonstrate the behavior of the current
wireless and traditional charging technologies.Comment: 4 pages, 4 figures. This is an accepted demo paper and it will appear
in the 20th International Conference on Service Oriented Computing (ICSOC
2022
A Personalized Facet-Weight Based Ranking Method for Service Component Retrieval
With the recent advanced computing, networking technologies and embedded systems, the computing paradigm has switched from mainframe and desktop computing to ubiquitous computing, one of whose visions is to provide intelligent, personalized and comprehensive services to users. As a new paradigm, Active Services is proposed to generate such services by retrieving, adapting, and composing of existing service components to satisfy user requirements. As the popularity of this paradigm and hence the number of service components increases, how to efficiently retrieve components to maximally meet user requirements has become a fundamental and significant problem. However, traditional facet-based retrieval methods only simply list out all the results without any kind of ranking and do not lay any emphasis on the differences of importance on each facet value in user requirements, which makes it hard for user to quickly select suitable components from the resulting list. To solve the problems, this paper proposes a novel personalized facet-weight based ranking method for service component retrieval, which assigns a weight for each facet to distinguish the importance of the facets, and constructs a personalized model to automatically calculate facet-weights for users according to their histo -rical retrieval records of the facet values and the weight setting. We optimize the parameters of the personalized model, evaluate the performance of the proposed retrieval method, and compare with the traditional facet-based matching methods. The experimental results show promising results in terms of retrieval accuracy and execution time
More complex encoder is not all you need
U-Net and its variants have been widely used in medical image segmentation.
However, most current U-Net variants confine their improvement strategies to
building more complex encoder, while leaving the decoder unchanged or adopting
a simple symmetric structure. These approaches overlook the true functionality
of the decoder: receiving low-resolution feature maps from the encoder and
restoring feature map resolution and lost information through upsampling. As a
result, the decoder, especially its upsampling component, plays a crucial role
in enhancing segmentation outcomes. However, in 3D medical image segmentation,
the commonly used transposed convolution can result in visual artifacts. This
issue stems from the absence of direct relationship between adjacent pixels in
the output feature map. Furthermore, plain encoder has already possessed
sufficient feature extraction capability because downsampling operation leads
to the gradual expansion of the receptive field, but the loss of information
during downsampling process is unignorable. To address the gap in relevant
research, we extend our focus beyond the encoder and introduce neU-Net (i.e.,
not complex encoder U-Net), which incorporates a novel Sub-pixel Convolution
for upsampling to construct a powerful decoder. Additionally, we introduce
multi-scale wavelet inputs module on the encoder side to provide additional
information. Our model design achieves excellent results, surpassing other
state-of-the-art methods on both the Synapse and ACDC datasets
Direct Laser Writing of Surface Micro-Domes by Plasmonic Bubbles
Plasmonic microbubbles produced by laser irradiated gold nanoparticles (GNPs)
in various liquids have emerged in numerous innovative applications. The
nucleation of these bubbles inherently involves rich phenomena. In this paper,
we systematically investigate the physicochemical hydrodynamics of plasmonic
bubbles upon irradiation of a continuous wave (CW) laser on a GNP decorated
sample surface in ferric nitrate solution. Surprisingly, we observe the direct
formation of well-defined micro-domes on the sample surface. It reveals that
the nucleation of a plasmonic bubble is associated with the solvothermal
decomposition of ferric nitrate in the solution. The plasmonic bubble acts as a
template for the deposition of iron oxide nanoparticles. It first forms a rim,
then a micro-shell, which eventually becomes a solid micro-dome. Experimental
results show that the micro-dome radius Rd exhibits an obvious dependence on
time t, which can be well interpreted theoretically. Our findings reveal the
rich phenomena associated with plasmonic bubble nucleation in a thermally
decomposable solution, paving a plasmonic bubble-based approach to fabricate
three dimensional microstructures by using an ordinary CW laser
NH3 sensor based on 3D hierarchical flower-shaped n-ZnO/p-NiO heterostructures yields outstanding sensing capabilities at ppb level
Hierarchical three-dimensional (3D) flower-like n-ZnO/p-NiO heterostructures with various ZnxNiy molar ratios (Zn5Ni1, Zn2Ni1, Zn1Ni1, Zn1Ni2 and Zn1Ni5) were synthesized by a facile hydrothermal method. Their crystal phase, surface morphology, elemental composition and chemical state were comprehensively investigated by XRD, SEM, EDS, TEM and XPS techniques. Gas sensing measurements were conducted on all the as-developed ZnxNiy-based sensors toward ammonia (NH3) detection under various working temperatures from 160 to 340 °C. In particular, the as-prepared Zn1Ni2 sensor exhibited superior NH3 sensing performance under optimum working temperature (280 °C) including high response (25 toward 100 ppm), fast response/recovery time (16 s/7 s), low detection limit (50 ppb), good selectivity and long-term stability. The enhanced NH3 sensing capabilities of Zn1Ni2 sensor could be attributed to both the specific hierarchical structure which facilitates the adsorption of NH3 molecules and produces much more contact sites, and the improved gas response characteristics of p-n heterojunctions. The obtained results clear demonstrated that the optimum n-ZnO/p-NiO heterostructure is indeed very promising sensing material toward NH3 detection for different applications
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