Yao-Shan of traditional Chinese medicine: an old story for metabolic health

Type 2 diabetes mellitus, nonalcoholic fatty liver disease (NAFLD), cardio-cerebrovascular diseases (CCVDs), hyperuricemia and gout, and metabolic-related sexual dysfunction are metabolic diseases that affect human health in modern society. Scientists have made great efforts to investigate metabolic diseases using cell models in vitro or animal models in the past. However, the findings from cells or animals are difficult to translate into clinical applications due to factors such as the in vitro and in vivo differences; the differences in anatomy, physiology, and genetics between humans and animals; and the differences in microbiome–host interaction. The Chinese have extensively used the medicated diet of traditional Chinese medicine (TCM) (also named as Yao-Shan of TCM, Chinese Yao-Shan et al.) to maintain or improve cardiometabolic health for more than 2,200 years. These ancient classic diets of TCM are essential summaries of long-term life and clinical practices. Over the past 5 years, our group has made every effort to collect and sort out the classic Yao-Shan of TCM from the ancient TCM literature since Spring and Autumn and Warring States Period, especially these are involved in the prevention and treatment of metabolic diseases, such as diabetes, NAFLD, CCVDs, hyperuricemia and gout, and sexual dysfunction. Here, we summarized and discussed the classic Yao-Shan of TCM for metabolic diseases according to the time recorded in the ancient literature, and revised the Latin names of the raw materials in these Yao-Shan of TCM. Moreover, the modern medicine evidences of some Yao-Shan of TCM on metabolic diseases have also been summarized and emphasized in here. However, the exact composition (in terms of ratios), preparation process, and dosage of many Yao-Shan are not standardized, and their main active ingredients are vague. Uncovering the mystery of Yao-Shan of TCM through modern biological and chemical strategies will help us open a door, which is ancient but now looks new, to modulate metabolic homeostasis and diseases

Unpacking People\u27s Understandings of Bluetooth Beacon Systems - A Location-Based IoT Technology

Bluetooth beacon technology is an emerging location-based Internet of Things (IoT) technology, designed to transform proximity-based services in various domains such as retail. Beacons are part of the IoT infrastructure, but people rarely interact with them directly and yet they could still pose privacy risks to users. However, little is known about people\u27s understandings of how beacon-based systems work. This is an important question since it can influence people\u27s perceptions, adoption, and usage of this emerging technology. Drawing from 22 semi-structured interviews, we studied people\u27s understandings of how beacon-based systems work and identified several factors that shaped their understandings or misunderstandings, such as how information flows among the components and who owns the beacons. These understandings and misunderstandings can potentially pose significant privacy risks to beacon users

Unpacking People's Understandings of Bluetooth Beacon Systems - A Location-Based IoT Technology

Bluetooth beacon technology is an emerging location-based Internet of Things (IoT) technology, designed to transform proximity-based services in various domains such as retail. Beacons are part of the IoT infrastructure, but people rarely interact with them directly and yet they could still pose privacy risks to users. However, little is known about people's understandings of how beacon-based systems work. This is an important question since it can influence people's perceptions, adoption, and usage of this emerging technology. Drawing from 22 semi-structured interviews, we studied people's understandings of how beacon-based systems work and identified several factors that shaped their understandings or misunderstandings, such as how information flows among the components and who owns the beacons. These understandings and misunderstandings can potentially pose significant privacy risks to beacon users

How Generative Music Affects the ISO Principle-Based Emotion-Focused Therapy: An EEG Study

Recently, AI-generated content (AIGC) technologies have made remarkable advancements, even achieving superhuman performance across various domains. However, few previous studies have investigated its impact on emotion-focused therapy with artistic content, e.g., music. In this paper, we conducted an EEG experiment to explore the effects of generative music on emotion-focused music therapy based on the ISO principle. This experiment compared AI-generated and human-created music regarding the changes in participants' valence and arousal following negative emotion induction with the ISO principle adherence and non-adherence. The results show that generative music, with its harmonic consistency and simple rhythm, is more effective in supporting positive emotions and improving temporal lobe activity. Besides, the therapeutic effectiveness of generative music adhering to the ISO principle has also been validated. This study highlights the distinct emotional and neural mechanisms of AI-generated music, offering valuable insights into future AI-powered emotion-focused therapy strategies

Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering

Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets.Comment: Accepted by IEEE TPAMI, extended version of conference paper arXiv:2110.0420

Casting a BAIT for Offline and Online Source-free Domain Adaptation

We address the source-free domain adaptation (SFDA) problem, where only the source model is available during adaptation to the target domain. We consider two settings: the offline setting where all target data can be visited multiple times (epochs) to arrive at a prediction for each target sample, and the online setting where the target data needs to be directly classified upon arrival. Inspired by diverse classifier based domain adaptation methods, in this paper we introduce a second classifier, but with another classifier head fixed. When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features. Next, when updating the feature extractor, those features will be pushed towards the right side of the source decision boundary, thus achieving source-free domain adaptation. Experimental results show that the proposed method achieves competitive results for offline SFDA on several benchmark datasets compared with existing DA and SFDA methods, and our method surpasses by a large margin other SFDA methods under online source-free domain adaptation setting

Generalized Source-free Domain Adaptation

Altres ajuts: CERCA Programme/Generalitat de CatalunyaDomain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only a source pre-trained model is available for adaptation to the target domain. However, those methods do not consider keeping source performance which is of high practical value in real world applications. In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation. First, we propose local structure clustering (LSC), aiming to cluster the target features with its semantically similar neighbors, which successfully adapts the model to the target domain in the absence of source data. Second, we propose sparse domain attention (SDA), it produces a binary domain specific attention to activate different feature channels for different domains, meanwhile the domain attention will be utilized to regularize the gradient during adaptation to keep source information. In the experiments, for target performance our method is on par with or better than existing DA and SFDA methods, specifically it achieves state-of-the-art performance (85.4%) on VisDA, and our method works well for all domains after adapting to single or multiple target domains

From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX

The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer's perspective on education and advocacy for ethical designs; and (2) the policymaker's perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users' needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users' awareness of and action on dark patterns being empowered in a realistic in-situ setting.Comment: Conditionally Accepted at CSCW 202

Provable Multi-instance Deep AUC Maximization with Stochastic Pooling

This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e.g., multiple 2D slices of a CT scan for a patient). We address a neglected yet non-negligible computational challenge of MIL in the context of DAM, i.e., bag size is too large to be loaded into {GPU} memory for backpropagation, which is required by the standard pooling methods of MIL. To tackle this challenge, we propose variance-reduced stochastic pooling methods in the spirit of stochastic optimization by formulating the loss function over the pooled prediction as a multi-level compositional function. By synthesizing techniques from stochastic compositional optimization and non-convex min-max optimization, we propose a unified and provable muli-instance DAM (MIDAM) algorithm with stochastic smoothed-max pooling or stochastic attention-based pooling, which only samples a few instances for each bag to compute a stochastic gradient estimator and to update the model parameter. We establish a similar convergence rate of the proposed MIDAM algorithm as the state-of-the-art DAM algorithms. Our extensive experiments on conventional MIL datasets and medical datasets demonstrate the superiority of our MIDAM algorithm.Comment: 22 page

The stability of unevenly spaced planetary systems

Studying the orbital stability of multi-planet systems is essential to understand planet formation, estimate the stable time of an observed planetary system, and advance population synthesis models. Although previous studies have primarily focused on ideal systems characterized by uniform orbital separations, in reality a diverse range of orbital separations exists among planets within the same system. This study focuses on investigating the dynamical stability of systems with non-uniform separation. We considered a system with 10 planets with masses of $10^{-7}$ solar masses around a central star with a mass of $1$ solar mass. We performed more than 100,000 runs of N-body simulations with different parameters. Results demonstrate that reducing merely one pair of planetary spacing leads to an order of magnitude shorter orbital crossing times that could be formulated based on the Keplerian periods of the closest separation pair. Furthermore, the first collisions are found to be closely associated with the first encounter pair that is likely to be the closest separation pair initially. We conclude that when estimating the orbital crossing time and colliding pairs in a realistic situation, updating the formula derived for evenly spaced systems would be necessary.Comment: 6 pages, 3 figures, accepted for publication in Icaru