ConvNets achieve good results when training from clean data, but learning from noisy labels significantly degrades performances and remains challenging. Unlike previous works constrained by many conditions, making them infeasible to real noisy cases, this work presents a novel deep self-learning framework to train a robust network on the real noisy datasets without extra supervision. The proposed approach has several appealing benefits. (1) Different from most existing work, it does not rely on any assumption on the distribution of the noisy labels, making it robust to real noises. (2) It does not need extra clean supervision or accessorial network to help training. (3) A self-learning framework is proposed to train the network in an iterative end-to-end manner, which is effective and efficient. Extensive experiments in challenging benchmarks such as Cloth-ing1M and Food101-N show that our approach outperforms its counterparts in all empirical settings.

Knowledge on the transformation of nanoscale zero-valent iron (nZVI) in water is essential to predict its surface chemistry including surface charge, colloidal stability and aggregation, reduction and sorption of organic contaminants, heavy metal ions and other pollutants in the environment. In this work, transmission electronic microscopy (TEM), X-ray diffraction (XRD) and Raman spectroscopy are applied to study the compositional and structural evolution of nZVI under oxic and anoxic conditions. Under anoxic conditions, the core-shell structure of nZVI is well maintained even after 72h, and the corrosion products usually contain a mixture of wustite (FeO), goethite (α-FeOOH) and akaganeite (β-FeOOH). Under oxic conditions, the core-shell structure quickly collapses to flakes or acicular-shaped structures with crystalline lepidocrocite (γ-FeOOH) as the primary end product. This work provides detailed information and fills an important knowledge gap on the physicochemical characteristics and structural evolution of engineered nanomaterials in the environment.

The ordered aggregation of non-fullerene small molecular acceptors (SMAs) plays a key role in determining the charge transport and bimolecular recombination in polymer/SMA solar cells.

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