G\mathcal{G}-softmax: Improving Intra-class Compactness and Inter-class Separability of Features

Intra-class compactness and inter-class separability are crucial indicators to measure the effectiveness of a model to produce discriminative features, where intra-class compactness indicates how close the features with the same label are to each other and inter-class separability indicates how far away the features with different labels are. In this work, we investigate intra-class compactness and inter-class separability of features learned by convolutional networks and propose a Gaussian-based softmax ($\mathcal{G}$-softmax) function that can effectively improve intra-class compactness and inter-class separability. The proposed function is simple to implement and can easily replace the softmax function. We evaluate the proposed $\mathcal{G}$-softmax function on classification datasets (i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet) and on multi-label classification datasets (i.e., MS COCO and NUS-WIDE). The experimental results show that the proposed $\mathcal{G}$-softmax function improves the state-of-the-art models across all evaluated datasets. In addition, analysis of the intra-class compactness and inter-class separability demonstrates the advantages of the proposed function over the softmax function, which is consistent with the performance improvement. More importantly, we observe that high intra-class compactness and inter-class separability are linearly correlated to average precision on MS COCO and NUS-WIDE. This implies that improvement of intra-class compactness and inter-class separability would lead to improvement of average precision.Comment: 15 pages, published in TNNL

De novo human genome assemblies reveal spectrum of alternative haplotypes in diverse populations.

The human reference genome is used extensively in modern biological research. However, a single consensus representation is inadequate to provide a universal reference structure because it is a haplotype among many in the human population. Using 10× Genomics (10×G) "Linked-Read" technology, we perform whole genome sequencing (WGS) and de novo assembly on 17 individuals across five populations. We identify 1842 breakpoint-resolved non-reference unique insertions (NUIs) that, in aggregate, add up to 2.1 Mb of so far undescribed genomic content. Among these, 64% are considered ancestral to humans since they are found in non-human primate genomes. Furthermore, 37% of the NUIs can be found in the human transcriptome and 14% likely arose from Alu-recombination-mediated deletion. Our results underline the need of a set of human reference genomes that includes a comprehensive list of alternative haplotypes to depict the complete spectrum of genetic diversity across populations

Is Musical Emotion An Illusion?

The power of music to arouse garden-variety emotions has attracted attention from musicians, psychologists, and philosophers over decades. Despite its widespread acknowledgement, there is no agreement on how pure music with no propositional content can induce such a wide range of emotions. Jenefer Robinson coined this 1 problemthepuzzleofmusicalemotion. Inthisessay,Iwillfirstdiscusswhymusical emotion is a puzzle. Then, Jesse Prinz’s perceptual theory of emotion and his solution 2 to the puzzle will be discussed. Prinz regards an emotion as an embodied appraisal, and a musical emotion as an illusory embodied appraisal which is a by-product of the adaptive emotion system. I argue that it is problematic to regard musical emotions as illusions for two reasons: 1) the bodily responses aroused by music are not specific enough to produce an illusion of a real emotion; 2) musical emotion is adaptive by itself in the sense that it is a mirroring-based simulation of the emotion represented by music, and such mirroring system plays an important role in interpersonal emotion communications