Multi-Label Knowledge Distillation

Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, which is very successful in multi-class single-label learning. However, these methods can hardly be extended to the multi-label learning scenario, where each instance is associated with multiple semantic labels, because the prediction probabilities do not sum to one and feature maps of the whole example may ignore minor classes in such a scenario. In this paper, we propose a novel multi-label knowledge distillation method. On one hand, it exploits the informative semantic knowledge from the logits by dividing the multi-label learning problem into a set of binary classification problems; on the other hand, it enhances the distinctiveness of the learned feature representations by leveraging the structural information of label-wise embeddings. Experimental results on multiple benchmark datasets validate that the proposed method can avoid knowledge counteraction among labels, thus achieving superior performance against diverse comparing methods. Our code is available at: https://github.com/penghui-yang/L2DComment: Accepted by ICCV 2023. The first two authors contributed equally to this wor

Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs

We developed the quantative structure-property relationships (QSPRs) models to correlate the molecular structures of surfactant, cosurfactant, oil, and drug with the solubility of poorly water-soluble 2-aryl propionic acid nonsteroidal anti-inflammatory drugs (2-APA-NSAIDs) in self-emulsifying drug delivery systems (SEDDSs). The compositions were encoded with electronic, geometrical, topological, and quantum chemical descriptors. To obtain reliable predictions, we used multiple linear regression (MLR) and artificial neural network (ANN) methods for model development. The obtained equations were validated using a test set of 42 formulations and showed a great predictive power, and linear models were found to be better than nonlinear ones. The obtained QSPR models would greatly facilitate fast screening for the optimal formulations of SEDDS at the early stage of drug development and minimize experimental effort

Transport of biomolecules in asymmetric nanofilter arrays

Abstract We propose a theoretical model for describing the electric-field-driven migration of rod-like biomolecules in nanofilters comprising a periodic array of shallow passages connecting deep wells. The electrophoretic migration of the biomolecules is modeled as transport of pointsized Brownian particles, with the orientational degree of freedom captured by an entropy term. Using appropriate projections, the formulation dimensionality is reduced to one physical dimension, requiring minimal computation and making it ideal for device design and optimization. Our formulation is used to assess the effect of slanted well walls on the energy landscape and resulting molecule mobility. Using this approach, we show that asymmetry in the well shape, such as a well with one slanted and one vertical wall, may be used for separation using low-frequency alternatingcurrent fields because the mobility of a biomolecule is different in the two directions of travel. Our results show that, compared to methods using direct-current fields, the proposed method remains effective at higher field strengths and can achieve comparable separation using a significantly shorter device

The effects of vegetation type on ecosystem carbon storage and distribution in subtropical plantations

Establishing plantation forests significantly increases the carbon (C) storage of terrestrial ecosystems. However, how vegetation types affect the ecosystem C sequestration capacity is not completely clear. Here, a slash pine plantation (SPP), a Schima superba plantation (SSP), and a Masson pine plantation (MPP), which have been planted for 30 years, were selected in subtropical China. The C storage and distribution patterns of plant, litter, and soil were investigated and calculated. The ecosystem C density was 17.7, 21.6, and 15.3 kg m–2 for SPP, SSP, and MPP, respectively. Ecosystem C stocks were mainly contributed by tree aboveground (39.9–46.0%) and soil C stocks (41.6–44.2%). The ecosystem C density of SSP was higher than that of SPP and MPP, and significant differences were found among three plantations for both aboveground and underground C densities. The aboveground and underground ecosystem C storage of SSP was 27.4 and 53.4% higher than that of MPP, respectively. Meanwhile, root C storage of MPP was lower than that of SPP and SSP, while soil C storage of MPP was lower than that of SSP. In the understory layer, SPP had the highest C density, followed by MPP, and there was a significant difference in C density among three plantations. However, no significant difference was found for the ecosystem C distribution among three plantations. Our results show that vegetation types significantly affect C storage but not C distribution in forest ecosystems and establishing the broad-leaved plantation has the highest ecosystem C storage in the subtropics. This study provides a theoretical basis for us to choose appropriate forest management measures