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1. Brain information processing capacity modeling

   https://doi.org/10.1038/s41598-022-05870-z  

   Li, Tongtong; Zheng, Yu; Wang, Zhe; Zhu, David C.; Ren, Jian; Liu, Taosheng; Friston, Karl (February 2022, Scientific Reports)

   Abstract Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that—for a given cognitive task and subject—higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity—as estimated from fMRI data—predicted task and age-related differences in reaction times, speaking to the model’s predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making. 

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2. DeepGD: A Deep Learning Framework for Graph Drawing Using GNN

   Xiaoqi Wang, Kevin Yen (September 2021, IEEE computer graphics and applications)

   Abstract—In the past decades, many graph drawing techniques have been proposed for generating aesthetically pleasing graph layouts. However, it remains a challenging task since different layout methods tend to highlight different characteristics of the graphs. Recently, studies on deep learning based graph drawing algorithm have emerged but they are often not generalizable to arbitrary graphs without re-training. In this paper, we propose a Convolutional Graph Neural Network based deep learning framework, DeepGD, which can draw arbitrary graphs once trained. It attempts to generate layouts by compromising among multiple pre-specified aesthetics considering a good graph layout usually complies with multiple aesthetics simultaneously. In order to balance the trade-off, we propose two adaptive training strategies which adjust the weight factor of each aesthetic dynamically during training. The quantitative and qualitative assessment of DeepGD demonstrates that it is capable of drawing arbitrary graphs effectively, while being flexible at accommodating different aesthetic criteria. 

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3. AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning

   https://doi.org/10.1109/IROS51168.2021.9636788  

   AlZayer, Hadi; Lin, Hubert; Bala, Kavita (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   The process of capturing a well-composed photo is difficult and it takes years of experience to master. We propose a novel pipeline for an autonomous agent to automatically capture an aesthetic photograph by navigating within a local region in a scene. Instead of classical optimization over heuristics such as the rule-of-thirds, we adopt a data-driven aesthetics estimator to assess photo quality. A reinforcement learning framework is used to optimize the model with respect to the learned aesthetics metric. We train our model in simulation with indoor scenes, and we demonstrate that our system can capture aesthetic photos in both simulation and real world environments on a ground robot. To our knowledge, this is the first system that can automatically explore an environment to capture an aesthetic photo with respect to a learned aesthetic estimator. Source code is at https://github.com/HadiZayer/AutoPhoto 

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4. Modeling naturalistic face processing in humans with deep convolutional neural networks

   https://doi.org/10.1073/pnas.2304085120  

   Jiahui, Guo; Feilong, Ma; Visconti di Oleggio Castello, Matteo; Nastase, Samuel A.; Haxby, James V.; Gobbini, M. Ida (October 2023, Proceedings of the National Academy of Sciences)

   Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features. 

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5. A deep convolutional neural network trained on representative samples for circulating tumor cell detection

   https://doi.org/10.1109/WACV.2016.7477603  

   Mao, Yunxiang; Yin, Zhaozheng; Schober, Joseph (March 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV))

   The number of Circulating Tumor Cells (CTCs) in blood indicates the tumor response to chemotherapeutic agents and disease progression. In early cancer diagnosis and treatment monitoring routine, detection and enumeration of CTCs in clinical blood samples have significant applications. In this paper, we design a Deep Convolutional Neural Network (DCNN) with automatically learned features for image-based CTC detection. We also present an effective training methodology which finds the most representative training samples to define the classification boundary between positive and negative samples. In the experiment, we compare the performance of auto-learned feature from DCNN and hand-crafted features, in which the DCNN outperforms hand-crafted feature. We also prove that the proposed training methodology is effective in improving the performance of DCNN classifiers. 

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