Critical Repetition Rates for Perceptual Segregation of Time-Varying Auditory, Visual and Vibrotactile Stimulation

What sound quality has led to exclude infrasound from sound in the conventional hearing range? We examined whether temporal segregation of pressure pulses is a distinctive property and evaluated this perceptual limit via an adaptive psychophysical procedure for pure tones and carriers of different envelopes. Further, to examine across-domain similarity and individual covariation of this limit, here called the critical segregation rate (CSR), it was also measured for various periodic visual and vibrotactile stimuli. Results showed that sequential auditory or vibrotactile stimuli separated by at least ~80‒90 ms (~11‒12-Hz repetition rates), will be perceived as perceptually segregated from one another. While this limit did not statistically differ between these two modalities, it was significantly lower than the ~150 ms necessary to perceptually segregate successive visual stimuli. For the three sensory modalities, stimulus periodicity was the main factor determining the CSR, which apparently reflects neural recovery times of the different sensory systems. Among all experimental conditions, significant within- and across-modality individual CSR correlations were observed, despite the visual CSR (mean: 6.8 Hz) being significantly lower than that of both other modalities. The auditory CSR was found to be significantly lower than the frequency above which sinusoids start to elicit a tonal quality (19 Hz; recently published for the same subjects). Returning to our initial question, the latter suggests that the cessation of tonal quality — not the segregation of pressure fluctuations — is the perceptual quality that has led to exclude infrasound (sound with frequencies < 20 Hz) from the conventional hearing range.

A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

Differential abilities to engage inaccessible chromatin diversify vertebrate Hox binding patterns

ABSTRACTAlthough Hox genes encode for conserved transcription factors (TFs), they are further divided into anterior, central and posterior groups based on their DNA-binding domain similarity. The posterior Hox group expanded in the deuterostome clade and patterns caudal and distal structures. We aimed to address how similar Hox TFs diverge to induce different positional identities. We studied Hox TF DNA-binding and regulatory activity during an in vitro motor neuron differentiation system that recapitulates embryonic development. We found diversity in the genomic binding profiles of different Hox TFs, even among the posterior group paralogs that share similar DNA-binding domains. These differences in genomic binding were explained by differing abilities to bind to previously inaccessible sites. For example, the posterior group HOXC9 had a greater ability to bind occluded sites than the posterior HOXC10, producing different binding patterns and driving differential gene expression programs. From these results, we propose that the differential abilities of posterior Hox TFs to bind to previously inaccessible chromatin drive patterning diversification.This article has an associated ‘The people behind the papers’ interview.

Domain Adaptive Classification on Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are ubiquitous structures in that they can depict complex relational data. Due to their complexity, it is hard to obtain sufficient labeled data on HINs, hampering classification on HINs. While domain adaptation (DA) techniques have been widely utilized in images and texts, the heterogeneity and complex semantics pose specific challenges towards domain adaptive classification on HINs. On one hand, HINs involve multiple levels of semantics, making it demanding to do domain alignment among them. On the other hand, the trade-off between domain similarity and distinguishability must be elaborately chosen, in that domain invariant features have been shown to be homogeneous and uninformative for classification. In this paper, we propose Multi-space Domain Adaptive Classification (MuSDAC) to handle the problem of DA on HINs. Specifically, we utilize multi-channel shared weight GCNs, projecting nodes in HINs to multiple spaces where pairwise alignment is carried out. In addition, we propose a heuristic sampling algorithm that efficiently chooses the combination of channels featuring distinguishability, and moving-averaged weighted voting scheme to fuse the selected channels, minimizing both transfer and classification loss. Extensive experiments on pairwise datasets endorse not only our model's performance on domain adaptive classification on HINs and contributions by individual components.