Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks

Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little research on how the concept of distillation can be leveraged to distill the knowledge encapsulated in the training data itself into a reduced form. In this study, we explore the concept of progressive label distillation, where we leverage a series of teacher-student network pairs to progressively generate distilled training data for learning deep neural networks with greatly reduced input dimensions. To investigate the efficacy of the proposed progressive label distillation approach, we experimented with learning a deep limited vocabulary speech recognition network based on generated 500ms input utterances distilled progressively from 1000ms source training data, and demonstrated a significant increase in test accuracy of almost 78% compared to direct learning.Comment: 9 page

Quantifying the Performance of Explainability Algorithms

Given the complexity of the deep neural network (DNN), DNN has long been criticized for its lack of interpretability in its decision-making process. This 'black box' nature has been preventing the adaption of DNN in life-critical tasks. In recent years, there has been a surge of interest around the concept of artificial intelligence explainability/interpretability (XAI), where the goal is to produce an interpretation for a decision made by a DNN algorithm. While many explainability algorithms have been proposed for peaking into the decision-making process of DNN, there has been a limited exploration into the assessment of the performance of explainability methods, with most evaluations centred around subjective human visual perception of the produced interpretations. In this study, we explore a more objective strategy for quantifying the performance of explainability algorithms on DNNs. More specifically, we propose two quantitative performance metrics: i) \textbf{Impact Score} and ii) \textbf{Impact Coverage}. Impact Score assesses the percentage of critical factors with either strong confidence reduction impact or decision shifting impact. Impact Coverage accesses the percentage overlapping of adversarially impacted factors in the input. Furthermore, a comprehensive analysis using this approach was conducted on several explainability methods (LIME, SHAP, and Expected Gradients) on different task domains, such as visual perception, speech recognition and natural language processing (NLP). The empirical evidence suggests that there is significant room for improvement for all evaluated explainability methods. At the same time, the evidence also suggests that even the latest explainability methods can not produce steady better results across different task domains and different test scenarios

8-Phenyl-10-oxa-8-aza­tricyclo­[4.3.0.12,5]decane-7,9-dione

The reaction of aniline with norcantharidin produced the imide title compound, C14H13NO3, which shows no significant hydrogen bonds in the crystal structure. The dihedral angle between the phenyl and pyrrolidine rings is 48.48 (6)°

A potential three-gene-based diagnostic signature for idiopathic pulmonary fibrosis

Background: Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease whose etiology remains unknown. This study aims to explore diagnostic biomarkers and pathways involved in IPF using bioinformatics analysis.Methods: IPF-related gene expression datasets were retrieved and downloaded from the NCBI Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened, and weighted correlation network analysis (WGCNA) was performed to identify key module and genes. Functional enrichment analysis was performed on genes in the clinically significant module. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were run to screen candidate biomarkers. The expression and diagnostic value of the biomarkers in IPF were further validated in external test datasets (GSE110147).Results: 292 samples and 1,163 DEGs were screened to construct WGCNA. In WGCNA, the blue module was identified as the key module, and 59 genes in this module correlated highly with IPF. Functional enrichment analysis of blue module genes revealed the importance of extracellular matrix-associated pathways in IPF. IL13RA2, CDH3, and COMP were identified as diagnostic markers of IPF via LASSO and SVM-RFE. These genes showed good diagnostic value for IPF and were significantly upregulated in IPF.Conclusion: This study indicates that IL13RA2, CDH3, and COMP could serve as diagnostic signature for IPF and might offer new insights in the underlying diagnosis of IPF

catena-Poly[[diaqua­nickel(II)]-μ-7-oxabicyclo­[2.2.1]heptane-2,3-di­carboxyl­ato]

In the crystal structure of the title compound, [Ni(C8H8O5)(H2O)2]n, the NiII cation is in a Jahn–Teller-distorted octahedral coordination environment binding to two O atoms from water molecules, the bridging O atom of the bicycloheptane unit, two carboxylate O atoms from different carboxylate groups and one carboxylate O atom from a symmetry-related bridging ligand. The crystal structure is made up from layers propagating parallel to the bc plane