Robust Kernel-based Feature Representation for 3D Point Cloud Analysis via Circular Graph Convolutional Network

Feature descriptors of point clouds are used in several applications, such as registration and part segmentation of 3D point clouds. Learning discriminative representations of local geometric features is unquestionably the most important task for accurate point cloud analyses. However, it is challenging to develop rotation or scale-invariant descriptors. Most previous studies have either ignored rotations or empirically studied optimal scale parameters, which hinders the applicability of the methods for real-world datasets. In this paper, we present a new local feature description method that is robust to rotation, density, and scale variations. Moreover, to improve representations of the local descriptors, we propose a global aggregation method. First, we place kernels aligned around each point in the normal direction. To avoid the sign problem of the normal vector, we use a symmetric kernel point distribution in the tangential plane. From each kernel point, we first projected the points from the spatial space to the feature space, which is robust to multiple scales and rotation, based on angles and distances. Subsequently, we perform graph convolutions by considering local kernel point structures and long-range global context, obtained by a global aggregation method. We experimented with our proposed descriptors on benchmark datasets (i.e., ModelNet40 and ShapeNetPart) to evaluate the performance of registration, classification, and part segmentation on 3D point clouds. Our method showed superior performances when compared to the state-of-the-art methods by reducing 70$\%$ of the rotation and translation errors in the registration task. Our method also showed comparable performance in the classification and part-segmentation tasks with simple and low-dimensional architectures.Comment: 10 pages, 9 figure

IL-1α Stimulation Restores Epidermal Permeability and Antimicrobial Barriers Compromised by Topical Tacrolimus

In a previous study, we showed that barrier recovery was delayed after acute barrier disruption in the skin treated with topical calcineurin inhibitors. Tacrolimus decreases lipid synthesis and the expressions of antimicrobial peptide (AMP) and IL-1α in the epidermis. IL-1α is an important cytokine for improving barrier function, lamellar body (LB) production, and lipid synthesis in keratinocytes (KCs). We aimed to evaluate whether IL-1α stimulation could restore the barrier dysfunction observed in tacrolimus-treated skin. Topical imiquimod, an IL-1α inducer, restored the epidermal permeability barrier recovery that had been inhibited by tacrolimus treatment in human (n=15) and murine (n=10) skins, and improved stratum corneum integrity by restoring corneodosmosomes in murine skin (n=6). Imiquimod co-applied on the epidermis resulted in an increase in the production of LB and three major lipid synthesis-related enzymes, and in the expressions of mBD3, CRAMP, and IL-1α (n=5). Furthermore, intracutaneous injection of IL-1α restored permeability barrier recovery (n=6). In IL-1 type 1 receptor knockout mice, topical imiquimod was unable to restore permeability barrier recovery after tacrolimus treatment (n=21). In conclusion, IL-1α stimulation induced positive effects on epidermal permeability and antimicrobial barrier functions in tacrolimus-treated skin. These positive effects were mediated by an increase in epidermal lipid synthesis, LB production, and AMP expression.JID JOURNAL CLUB ARTICLE: For questions, answers, and open discussion about this article, please go to http://www.nature.com/jid/journalclu

ProtoFL: Unsupervised Federated Learning via Prototypical Distillation

Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems. However, limited round communications, scarce representation, and scalability pose significant challenges to its deployment, hindering its full potential. In this paper, we propose 'ProtoFL', Prototypical Representation Distillation based unsupervised Federated Learning to enhance the representation power of a global model and reduce round communication costs. Additionally, we introduce a local one-class classifier based on normalizing flows to improve performance with limited data. Our study represents the first investigation of using FL to improve one-class classification performance. We conduct extensive experiments on five widely used benchmarks, namely MNIST, CIFAR-10, CIFAR-100, ImageNet-30, and Keystroke-Dynamics, to demonstrate the superior performance of our proposed framework over previous methods in the literature.Comment: Accepted by ICCV 2023. Hansol Kim and Youngjun Kwak contributed equally to this wor

Neural Basis of Psychological Growth following Adverse Experiences: A Resting-State Functional MRI Study

Over the past decade, research on the aftereffects of stressful or traumatic events has emphasized the negative outcomes from these experiences. However, the positive outcomes deriving from adversity are increasingly being examined, and such positive changes are described as posttraumatic growth (PTG). To investigate the relationship between basal whole-brain functional connectivity and PTG, we employed resting-state functional magnetic resonance imaging and analyzed the neural networks using independent component analysis in a sample of 33 healthy controls. Correlations were calculated between the network connectivity strength and the Posttraumatic Growth Inventory (PTGI) score. There were positive associations between the PTGI scores and brain activation in the rostral prefrontal cortex and superior parietal lobule (SPL) within the left central executive network (CEN) (respectively, r = 0.41, p < 0.001; r = 0.49, p < 0.001). Individuals with higher psychological growth following adverse experiences had stronger activation in prospective or working memory areas within the executive function network than did individuals with lower psychological growth (r = 0.40, p < 0.001). Moreover, we found that individuals with higher PTG demonstrated stronger connectivity between the SPL and supramarginal gyrus (SMG). The SMG is one of the brain regions associated with the ability to reason about the mental states of others, otherwise known as mentalizing. These findings suggest that individuals with higher psychological growth may have stronger functional connectivity between memory functions within the CEN and social functioning in the SMG, and that their better sociality may result from using more memory for mentalizing during their daily social interactions

Challenges of diet planning for children using artificial intelligence

BACKGROUND/OBJECTIVES: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications.MATERIALS/METHODS: We developed 2 AI solutions for children aged 3-5 yrs using a generative adversarial network (GAN) model and a reinforcement learning (RL) framework. After training these solutions to produce daily diet plans, experts evaluated the human-and AI-generated diets in 2 steps.RESULTS: In the evaluation of adequacy of nutrition, where experts were provided only with nutrient information and no food names, the proportion of strong positive responses to RL-generated diets was higher than that of the human-and GAN-generated diets (P &lt; 0.001). In contrast, in terms of diet composition, the experts&apos; responses to human-designed diets were more positive when experts were provided with food name information (i.e., composition information).CONCLUSIONS: To the best of our knowledge, this is the first study to demonstrate the development and evaluation of AI to support dietary planning for children. This study demonstrates the possibility of developing AI-assisted diet planning methods for children and highlights the importance of composition compliance in diet planning. Further integrative cooperation in the fields of nutrition, engineering, and medicine is needed to improve the suitability of our proposed AI solutions and benefit children&apos;s well-being by providing high-quality diet planning in terms of both compositional and nutritional criteria

Autistic empathy toward autistic others.

自閉スペクトラム症がある方々による、自閉スペクトラム症がある方々に対する共感. 京都大学プレスリリーズ. 2014-11-06.Individuals with Autism Spectrum Disorder (ASD) are thought to lack self-awareness and to experience difficulty empathising with others. Although these deficits have been demonstrated in previous studies, most of the target stimuli were constructed for typically developing (TD) individuals. We employed judgment tasks capable of indexing self-relevant processing in individuals with and without ASD. Fourteen Japanese males and one Japanese female with high-functioning ASD (17-41 years of age) and 13 Japanese males and two TD Japanese females ( 22-40 years of age), all of whom were matched for age and full and verbal intelligence quotient scores with the ASD participants, were enrolled in this study. The results demonstrated that the ventromedial prefrontal cortex was significantly activated in individuals with ASD in response to autistic characters and in TD individuals in response to non-autistic characters. Whereas the frontal-posterior network between the ventromedial prefrontal cortex and superior temporal gyrus participated in the processing of non-autistic characters in TD individuals, an alternative network was involved when individuals with ASD processed autistic characters. This suggests an atypical form of empathy in individuals with ASD toward others with ASD

Assessment of foodservice quality and identification of improvement strategies using hospital foodservice quality model

The purposes of this study were to assess hospital foodservice quality and to identify causes of quality problems and improvement strategies. Based on the review of literature, hospital foodservice quality was defined and the Hospital Foodservice Quality model was presented. The study was conducted in two steps. In Step 1, nutritional standards specified on diet manuals and nutrients of planned menus, served meals, and consumed meals for regular, diabetic, and low-sodium diets were assessed in three general hospitals. Quality problems were found in all three hospitals since patients consumed less than their nutritional requirements. Considering the effects of four gaps in the Hospital Foodservice Quality model, Gaps 3 and 4 were selected as critical control points (CCPs) for hospital foodservice quality management. In Step 2, the causes of the gaps and improvement strategies at CCPs were labeled as "quality hazards" and "corrective actions", respectively and were identified using a case study. At Gap 3, inaccurate forecasting and a lack of control during production were identified as quality hazards and corrective actions proposed were establishing an accurate forecasting system, improving standardized recipes, emphasizing the use of standardized recipes, and conducting employee training. At Gap 4, quality hazards were menus of low preferences, inconsistency of menu quality, a lack of menu variety, improper food temperatures, and patients' lack of understanding of their nutritional requirements. To reduce Gap 4, the dietary departments should conduct patient surveys on menu preferences on a regular basis, develop new menus, especially for therapeutic diets, maintain food temperatures during distribution, provide more choices, conduct meal rounds, and provide nutrition education and counseling. The Hospital Foodservice Quality Model was a useful tool for identifying causes of the foodservice quality problems and improvement strategies from a holistic point of view