Viewing strategy of Cebus monkeys during free exploration of natural images

This study focuses on the rat activity in a hole–board setting that we considered a type of exploratory behavior. The general hypothesis is based on the claim that a motivational mechanism is central to both the response to novelty in a highly familiarized environment and the activity in the hole–board apparatus. Our sample consisted of 80 experimentally naive Lister Hooded rats. All rats were tested in the hole–board apparatus. Twenty individuals with the highest hole-board scores and twenty subjects with the lowest hole–board scores subsequently underwent an established free-exploration test. In our study, the scores obtained in the hole–board test had little predictive value for the rats’ activity in the free-exploration test. Based on our previous experience in studying exploratory behavior in the free-exploration test and the data presented in this paper, we suggest that the hole–board test is not an appropriate tool for measuring exploratory behavior in laboratory rodents.

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Scale free is not rare in international trade networks

Scientific Reports ◽

10.1038/s41598-021-92764-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Linqing Liu ◽

Mengyun Shen ◽

Chang Tan

Keyword(s):

International Trade ◽

Preferential Attachment ◽

Developed Countries ◽

Total Trade ◽

Trade Networks ◽

Scale Free ◽

Weighted Networks ◽

Free Exploration ◽

Technology Products ◽

Attachment Mechanism

AbstractFailing to consider the strong correlations between weights and topological properties in capacity-weighted networks renders test results on the scale-free property unreliable. According to the preferential attachment mechanism, existing high-degree nodes normally attract new nodes. However, in capacity-weighted networks, the weights of existing edges increase as the network grows. We propose an optimized simplification method and apply it to international trade networks. Our study covers more than 1200 product categories annually from 1995 to 2018. We find that, on average, 38%, 38% and 69% of product networks in export, import and total trade are scale-free. Furthermore, the scale-free characteristics differ depending on the technology. Counter to expectations, the exports of high-technology products are distributed worldwide rather than concentrated in a few developed countries. Our research extends the scale-free exploration of capacity-weighted networks and demonstrates that choosing appropriate filtering methods can clarify the properties of complex networks.

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Learning About the World by Learning About Images

Current Directions in Psychological Science ◽

10.1177/0963721421990334 ◽

2021 ◽

pp. 096372142199033

Author(s):

Katherine R. Storrs ◽

Roland W. Fleming

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Visual Processing ◽

Visual Experience ◽

Natural Images ◽

Sensory Coding ◽

The World ◽

Unsupervised Deep Learning ◽

Statistical Regularities ◽

Sensory Encoding

One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of visual processing. But recent advances in unsupervised deep learning have changed that. Neural networks can be taught to compress images or make predictions in space or time. In the process, they learn the statistical regularities that structure images, which in turn often reflect physical objects and processes in the outside world. The astonishing accomplishments of unsupervised deep learning reaffirm the importance of learning statistical regularities for sensory coding and provide a coherent framework for how knowledge of the outside world gets into visual cortex.

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A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

Journal of Data and Information Quality ◽

10.1145/3418035 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1-19

Author(s):

Sreelakshmy I. J. ◽

Binsu C. Kovoor

Keyword(s):

Patch Size ◽

Image Inpainting ◽

Qualitative Evaluation ◽

Natural Images ◽

External Information ◽

Image Editing ◽

Model Combining ◽

Proposed Model ◽

The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

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Second-order statistics of natural images

Neurocomputing ◽

10.1016/s0925-2312(02)00734-8 ◽

2003 ◽

Vol 52-54 ◽

pp. 467-472 ◽

Cited By ~ 6

Author(s):

Hauke Bartsch ◽

Klaus Obermayer

Keyword(s):

Order Statistics ◽

Second Order ◽

Natural Images ◽

Second Order Statistics

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