10,876 research outputs found
BINN: A deep learning approach for computational mechanics problems based on boundary integral equations
We proposed the boundary-integral type neural networks (BINN) for the
boundary value problems in computational mechanics. The boundary integral
equations are employed to transfer all the unknowns to the boundary, then the
unknowns are approximated using neural networks and solved through a training
process. The loss function is chosen as the residuals of the boundary integral
equations. Regularization techniques are adopted to efficiently evaluate the
weakly singular and Cauchy principle integrals in boundary integral equations.
Potential problems and elastostatic problems are mainly concerned in this
article as a demonstration. The proposed method has several outstanding
advantages: First, the dimensions of the original problem are reduced by one,
thus the freedoms are greatly reduced. Second, the proposed method does not
require any extra treatment to introduce the boundary conditions, since they
are naturally considered through the boundary integral equations. Therefore,
the method is suitable for complex geometries. Third, BINN is suitable for
problems on the infinite or semi-infinite domains. Moreover, BINN can easily
handle heterogeneous problems with a single neural network without domain
decomposition
Model Sparsification Can Simplify Machine Unlearning
Recent data regulations necessitate machine unlearning (MU): The removal of
the effect of specific examples from the model. While exact unlearning is
possible by conducting a model retraining with the remaining data from scratch,
its computational cost has led to the development of approximate but efficient
unlearning schemes. Beyond data-centric MU solutions, we advance MU through a
novel model-based viewpoint: sparsification via weight pruning. Our results in
both theory and practice indicate that model sparsity can boost the
multi-criteria unlearning performance of an approximate unlearner, closing the
approximation gap, while continuing to be efficient. With this insight, we
develop two new sparsity-aware unlearning meta-schemes, termed `prune first,
then unlearn' and `sparsity-aware unlearning'. Extensive experiments show that
our findings and proposals consistently benefit MU in various scenarios,
including class-wise data scrubbing, random data scrubbing, and backdoor data
forgetting. One highlight is the 77% unlearning efficacy gain of fine-tuning
(one of the simplest approximate unlearning methods) in the proposed
sparsity-aware unlearning paradigm. Codes are available at
https://github.com/OPTML-Group/Unlearn-Sparse
Orbit- and Atom-Resolved Spin Textures of Intrinsic, Extrinsic and Hybridized Dirac Cone States
Combining first-principles calculations and spin- and angle-resolved
photoemission spectroscopy measurements, we identify the helical spin textures
for three different Dirac cone states in the interfaced systems of a 2D
topological insulator (TI) of Bi(111) bilayer and a 3D TI Bi2Se3 or Bi2Te3. The
spin texture is found to be the same for the intrinsic Dirac cone of Bi2Se3 or
Bi2Te3 surface state, the extrinsic Dirac cone of Bi bilayer state induced by
Rashba effect, and the hybridized Dirac cone between the former two states.
Further orbit- and atom-resolved analysis shows that s and pz orbits have a
clockwise (counterclockwise) spin rotation tangent to the iso-energy contour of
upper (lower) Dirac cone, while px and py orbits have an additional radial spin
component. The Dirac cone states may reside on different atomic layers, but
have the same spin texture. Our results suggest that the unique spin texture of
Dirac cone states is a signature property of spin-orbit coupling, independent
of topology
Characteristics of multipleâyear nitrous oxide emissions from conventional vegetable fields in southeastern China
The annual and interannual characteristics of nitrous oxide (N2O) emissions from conventional vegetable fields are poorly understood. We carried out 4 year measurements of N2O fluxes from a conventional vegetable cultivation area in the Yangtze River delta. Under fertilized conditions subject to farming practices, approximately 86% of the annual total N2O release occurred following fertilization events. The direct emission factors (EFd) of the 12 individual vegetable seasons investigated ranged from 0.06 to 14.20%, with a mean of 3.09% and a coefficient of variation (CV) of 142%. The annual EFd varied from 0.59 to 4.98%, with a mean of 2.88% and an interannual CV of 74%. The mean value is much larger than the latest default value (1.00%) of the Intergovernmental Panel on Climate Change. Occasional application of lagoonâstored manure slurry coupled with other nitrogen fertilizers, or basal nitrogen addition immediately followed by heavy rainfall, accounted for a substantial portion of the large EFds observed in warm seasons. The large CVs suggest that the emission factors obtained from shortâterm observations that poorly represent seasonality and/or interannual variability will inevitably yield large uncertainties in inventory estimation. The results of this study indicate that conventional vegetable fields associated with intensive nitrogen addition, as well as occasional applications of manure slurry, may substantially account for regional N2O emissions. However, this conclusion needs to be further confirmed through studies at multiple field sites. Moreover, further experimental studies are needed to test the mitigation options suggested by this study for N2O emissions from open vegetable fields
Electronic Structures of Graphene Layers on Metal Foil: Effect of Point Defects
Here we report a facile method to generate a high density of point defects in
graphene on metal foil and show how the point defects affect the electronic
structures of graphene layers. Our scanning tunneling microscopy (STM)
measurements, complemented by first principle calculations, reveal that the
point defects result in both the intervalley and intravalley scattering of
graphene. The Fermi velocity is reduced in the vicinity area of the defect due
to the enhanced scattering. Additionally, our analysis further points out that
periodic point defects can tailor the electronic properties of graphene by
introducing a significant bandgap, which opens an avenue towards all-graphene
electronics.Comment: 4 figure
Variation of adsorption effects in coals with different particle sizes induced by differences in microscopic adhesion
Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067) and the 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035). We are very grateful to the reviewers and editors for their valuable comments and suggestionsPeer reviewedPostprin
- âŠ