The Effect of Trust and its Antecedents on Robot Acceptance

As social and socially assistive robots are becoming more prevalent in our society, it is beneficial to understand how people form first impressions of them and eventually come to trust and accept them. This paper describes an Amazon Mechanical Turk study (n = 239) that investigated trust and its antecedents trustworthiness and first impressions. Participants evaluated the social robot Pepper's warmth and competence as well as trustworthiness characteristics ability, benevolence and integrity followed by their trust in and intention to use the robot. Mediation analyses assessed to what degree participants' first impressions affected their willingness to trust and use it. Known constructs from user acceptance and trust research were introduced to explain the pathways in which one perception predicted the next. Results showed that trustworthiness and trust, in serial, mediated the relationship between first impressions and behavioral intention.Comment: In SCRITA 2023 Workshop Proceedings (arXiv:2311.05401) held in conjunction with 32nd IEEE International Conference on Robot & Human Interactive Communication, 28/08 - 31/08 2023, Busan (Korea

Continual Learning with Extended Kronecker-factored Approximate Curvature

We propose a quadratic penalty method for continual learning of neural networks that contain batch normalization (BN) layers. The Hessian of a loss function represents the curvature of the quadratic penalty function, and a Kronecker-factored approximate curvature (K-FAC) is used widely to practically compute the Hessian of a neural network. However, the approximation is not valid if there is dependence between examples, typically caused by BN layers in deep network architectures. We extend the K-FAC method so that the inter-example relations are taken into account and the Hessian of deep neural networks can be properly approximated under practical assumptions. We also propose a method of weight merging and reparameterization to properly handle statistical parameters of BN, which plays a critical role for continual learning with BN, and a method that selects hyperparameters without source task data. Our method shows better performance than baselines in the permuted MNIST task with BN layers and in sequential learning from the ImageNet classification task to fine-grained classification tasks with ResNet-50, without any explicit or implicit use of source task data for hyperparameter selection.Comment: CVPR 202

Residual Continual Learning

We propose a novel continual learning method called Residual Continual Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon in sequential learning of multiple tasks, without any source task information except the original network. ResCL reparameterizes network parameters by linearly combining each layer of the original network and a fine-tuned network; therefore, the size of the network does not increase at all. To apply the proposed method to general convolutional neural networks, the effects of batch normalization layers are also considered. By utilizing residual-learning-like reparameterization and a special weight decay loss, the trade-off between source and target performance is effectively controlled. The proposed method exhibits state-of-the-art performance in various continual learning scenarios.Comment: AAAI 202

SwiFT: Swin 4D fMRI Transformer

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI.Comment: NeurIPS 202

SPON1 Can Reduce Amyloid Beta and Reverse Cognitive Impairment and Memory Dysfunction in Alzheimer's Disease Mouse Model

Alzheimer's disease (AD) is a complex, age-related neurodegenerative disease that is the most common form of dementia. However, the cure for AD has not yet been founded. The accumulation of amyloid beta (A beta) is considered to be a hallmark of AD. Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), also known as beta secretase is the initiating enzyme in the amyloidogenic pathway. Blocking BACE1 could reduce the amount of A beta, but this would also prohibit the other functions of BACE1 in brain physiological activity. SPONDIN1 (SPON1) is known to bind to the BACE1 binding site of the amyloid precursor protein (APP) and blocks the initiating amyloidogenesis. Here, we show the effect of SPON1 in A beta reduction in vitro in neural cells and in an in vivo AD mouse model. We engineered mouse induced neural stem cells (iNSCs) to express Spon1. iNSCs harboring mouse Spon1 secreted SPON1 protein and reduced the quantity of A beta when co-cultured with A beta -secreting Neuro 2a cells. The human SPON1 gene itself also reduced A beta in HEK 293T cells expressing the human APP transgene with AD-linked mutations through lentiviral-mediated delivery. We also demonstrated that injecting SPON1 reduced the amount of A beta and ameliorated cognitive dysfunction and memory impairment in 5xFAD mice expressing human APP and PSEN1 transgenes with five AD-linked mutations

Linearly Replaceable Filters for Deep Network Channel Pruning

Convolutional neural networks (CNNs) have achieved remarkable results; however, despite the development of deep learning, practical user applications are fairly limited because heavy networks can be used solely with the latest hardware and software supports. Therefore, network pruning is gaining attention for general applications in various fields. This paper proposes a novel channel pruning method, Linearly Replaceable Filter (LRF), which suggests that a filter that can be approximated by the linear combination of other filters is replaceable. Moreover, an additional method called Weights Compensation is proposed to support the LRF method. This is a technique that effectively reduces the output difference caused by removing filters via direct weight modification. Through various experiments, we have confirmed that our method achieves state-of-the-art performance in several benchmarks. In particular, on ImageNet, LRF-60 reduces approximately 56% of FLOPs on ResNet-50 without top-5 accuracy drop. Further, through extensive analyses, we proved the effectiveness of our approaches

The role of YKL-40 in the pathogenesis of autoimmune diseases: a comprehensive review

YKL-40, a chitinase-3-like protein 1 (CHI3L1) or human cartilage glycoprotein 39 (HC gp-39), is expressed and secreted by various cell-types including macrophages, chondrocytes, fibroblast-like synovial cells and vascular smooth muscle cells. Its biological function is not well elucidated, but it is speculated to have some connection with inflammatory reactions and autoimmune diseases. Although having important biological roles in autoimmunity, there were only attempts to elucidate relationships of YKL-40 with a single or couple of diseases in the literature. Therefore, in order to analyze the relationship between YKL-40 and the overall diseases, we reviewed 51 articles that discussed the association of YKL-40 with rheumatoid arthritis, psoriasis, systemic lupus erythematosus, Behçet disease and inflammatory bowel disease. Several studies showed that YKL-40 could be assumed as a marker for disease diagnosis, prognosis, disease activity and severity. It is also shown to be involved in response to disease treatment. However, other studies showed controversial results particularly in the case of Behçet disease activity. Therefore, further studies are needed to elucidate the exact role of YKL-40 in autoimmunity and to investigate its potential in therapeutics

Diclofenac, a Non-steroidal Anti-inflammatory Drug, Inhibits L-type Ca2+ Channels in Neonatal Rat Ventricular Cardiomyocytes

A non-steroidal anti-inflammatory drug (NSAID) has many adverse effects including cardiovascular (CV) risk. Diclofenac among the nonselective NSAIDs has the highest CV risk such as congestive heart failure, which resulted commonly from the impaired cardiac pumping due to a disrupted excitation-contraction (E-C) coupling. We investigated the effects of diclofenac on the L-type calcium channels which are essential to the E-C coupling at the level of single ventricular myocytes isolated from neonatal rat heart, using the whole-cell voltage-clamp technique. Only diclofenac of three NSAIDs, including naproxen and ibuprofen, significantly reduced inward whole cell currents. At concentrations higher than 3 µM, diclofenac inhibited reversibly the Na+ current and did irreversibly the L-type Ca2+ channels-mediated inward current (IC50=12.89±0.43 µM) in a dose-dependent manner. However, nifedipine, a well-known L-type channel blocker, effectively inhibited the L-type Ca2+ currents but not the Na+ current. Our finding may explain that diclofenac causes the CV risk by the inhibition of L-type Ca2+ channel, leading to the impairment of E-C coupling in cardiac myocytes