General Greedy De-bias Learning

Neural networks often make predictions relying on the spurious correlations from the datasets rather than the intrinsic properties of the task of interest, facing sharp degradation on out-of-distribution (OOD) test data. Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios. Others implicitly identify the dataset bias by special design low capability biased models or losses, but they degrade when the training and testing data are from the same distribution. In this paper, we propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model. The base model is encouraged to focus on examples that are hard to solve with biased models, thus remaining robust against spurious correlations in the test stage. GGD largely improves models' OOD generalization ability on various tasks, but sometimes over-estimates the bias level and degrades on the in-distribution test. We further re-analyze the ensemble process of GGD and introduce the Curriculum Regularization inspired by curriculum learning, which achieves a good trade-off between in-distribution and out-of-distribution performance. Extensive experiments on image classification, adversarial question answering, and visual question answering demonstrate the effectiveness of our method. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Controllable nonlinear optical properties of different-sized iron phosphorus trichalcogenide (FePS3) nanosheets

Two-dimensional iron phosphorus trichalcogenide (FePS3) has attracted significant attention for its use in electricity, magnetism and optical fields due to its outstanding physical and chemical properties. Herein, FePS3 was prepared using the chemical vapor transportation (CVT) method and then exfoliated by using fast electrochemical exfoliation. After gradient centrifugation, FePS3 nanosheets with thicknesses ranging from 1.5 to 20 nm and lateral dimensions of 0.5–3 μm were obtained. By utilizing the spatial self-phase modulation (SSPM) effect, the relationships between the nonlinear refractive index and the size of the FePS3 nanosheets were investigated in detail which revealed that the nonlinear refractive index can be effectively controlled by the size of the FePS3 nanosheets. It is worth noting that the optimal FePS3 nanosheets (3–5 layers thick and ∼1 μm in lateral dimensions) displayed the highest nonlinear refractive index of ∼10−5 cm2 W−1. This work demonstrates the potential that FePS3 nanosheets have for use in nonlinear optics or nonlinear optical devices

Density Functional Study of Trimetallic Au_<i>x</i>Pd_<i>y</i>Pt_<i>z</i> (<i>x</i> + <i>y</i> + <i>z</i> = 7) Clusters and Their Interactions with the O₂ Molecule

Density functional theory calculations were performed to investigate the structural and energetic properties of trimetallic Au_<i>x</i>Pd_<i>y</i>Pt_<i>z</i> clusters with <i>x</i> + <i>y</i> + <i>z</i> = 7. The possible stable geometrical configurations with their electronic states are determined. We analyze the chemical order, binding energies, vertical ionization potential, electron affinity, and HOMO–LUMO gaps as a function of the whole concentration range. The affinity of Au_<i>x</i>Pd_<i>y</i>Pt_<i>z</i> clusters toward one O₂ molecule is also evaluated in terms of the changes in geometry, adsorption energy, and charge transfer

Atomically precise single metal oxide cluster catalyst with oxygen-controlled activity

Single cluster catalysts (SCCs) consisting of atomically precise metal nanoclusters dispersed on supports represent a new frontier of heterogeneous catalysis. However, the ability to synthesize SCCs with high loading and to precisely introduce non-metal atoms to further tune their catalytic activity and reaction scope of SCCs have been longstanding challenges. Here, a new interface confinement strategy is developed for the synthesis of a high density of atomically precise Ru oxide nanoclusters (Ru3O2) on reduced graphene oxide (rGO), attributed to the suppression of diffusion-induced metal cluster aggregation. Ru3O2/rGO exhibits a significantly enhanced activity for oxidative dehydrogenation of 1,2,3,4-tetrahydroquinoline (THQ) to quinoline with a high yield (≈86%) and selectivity (≈99%), superior to Ru and RuO2 nanoparticles, and homogeneous single/multiple-site Ru catalysts. In addition, Ru3O2/rGO is also capable of efficiently catalyzing more complex oxidative reactions involving three reactants. The theoretical calculations reveal that the presence of two oxygen atoms in the Ru3O2 motif not only leads to a weak hydrogen bonding interaction between the THQ reactant and the active site, but also dramatically depletes the density of states near the Fermi level, which is attributed to the increased positive valence state of Ru and the enhanced oxidative activity of the Ru3O2 cluster for hydrogen abstraction.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)J.L. acknowledges the support from MOE grants (MOE2019-T2-2-044 and R-143-000-B47-114) and the support from Agency for Science, Technology and Research (A*STAR) under its AME IRG Grant (Project No. A20E5c0096) and NUS Green Energy Program. Y.Y.F thanks the support from National Natural Science Foundation of China (22005244) and Natural Science Foundation of Ningbo City (202003N4052)