What does ASEAN economic community bring to older workers? Examining inequality in old age in Thailand's fast-ageing society

The ASEAN Economic Community is envisaged to promote economic integration initiatives to create a single market across Southeast Asian member countries. It is acknowledged that the intergovernmental initiatives need to be accommodative to national and regional contexts. Thailand, as a pivotal and active partnership, endeavours to facilitate economic transformation and regional integration within the ASEAN and cope with population ageing in Thai society. Since Thailand has been the third most rapidly ageing country in the world, demographic changes pose new challenges for how to achieve persistent economic growth, productive employment and decent work. This article is based on a qualitative approach to investigate the emergent inequality within and across age cohorts shaped by the AEC structural forces, as well as utilizes reliable secondary data to formulate argumentation, including academic publications, policy analysis, scientific reports. We are particularly concerned about the heterogeneity and poverty in old age from the perspective of cumulative advantages/disadvantages. In conclusion, this article suggests policy recommendations of mitigating inequality in old age and advocates a critical lens to examine how political economic structure shapes older individuals in the labour market

Mitigating Transformer Overconfidence via Lipschitz Regularization

Though Transformers have achieved promising results in many computer vision tasks, they tend to be over-confident in predictions, as the standard Dot Product Self-Attention (DPSA) can barely preserve distance for the unbounded input domain. In this work, we fill this gap by proposing a novel Lipschitz Regularized Transformer (LRFormer). Specifically, we present a new similarity function with the distance within Banach Space to ensure the Lipschitzness and also regularize the term by a contractive Lipschitz Bound. The proposed method is analyzed with a theoretical guarantee, providing a rigorous basis for its effectiveness and reliability. Extensive experiments conducted on standard vision benchmarks demonstrate that our method outperforms the state-of-the-art single forward pass approaches in prediction, calibration, and uncertainty estimation.Comment: Accepted by UAI 2023. (https://proceedings.mlr.press/v216/ye23a.html

What Motivates People to Share Online Rumors? Deconstructing the Ambiguity of Rumors from a Perspective of Digital Storytelling

With the proliferation of social networks and the development of digital technology, the content structure and propagation mode of rumors have become more complicated with ambiguity, which has greatly influenced people’s behaviors when facing digitalized rumors. Based on the digital storytelling theory, this study takes an early initiative by deconstructing and identifying the basic components of online rumors and revealing the conditions under which people’s sharing behaviors in a social environment. A data set of health-related rumors related to Covid-19 was used to test the research hypotheses. The results indicated that causality explicitness, element integrality and source explicitness have different influences on rumor sharing behavior. And rumor vividness plays a negative moderating effect during the sharing process. This research offers insight to viewers and website authorities on ways to monitor and debunk online rumors

Observation of Traveling Breathers and Their Scattering in a Two-Fluid System

The observation of traveling breathers (TBs) with large-amplitude oscillatory tails realizes an almost 50-year-old theoretical prediction (Kuznetsov 1975) and generalizes the notion of a breather. Two strongly nonlinear TB families are created in a core-annular flow by interacting a soliton and a nonlinear periodic (cnoidal) carrier. Bright and dark TBs are observed to move faster or slower, respectively, than the carrier while imparting a phase shift. Agreement with model equations is achieved. Scattering of the TBs are observed to be physically elastic. The observed TBs generalize to many continuum and discrete systems

InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions

We have recently seen tremendous progress in diffusion advances for generating realistic human motions. Yet, they largely disregard the multi-human interactions. In this paper, we present InterGen, an effective diffusion-based approach that incorporates human-to-human interactions into the motion diffusion process, which enables layman users to customize high-quality two-person interaction motions, with only text guidance. We first contribute a multimodal dataset, named InterHuman. It consists of about 107M frames for diverse two-person interactions, with accurate skeletal motions and 23,337 natural language descriptions. For the algorithm side, we carefully tailor the motion diffusion model to our two-person interaction setting. To handle the symmetry of human identities during interactions, we propose two cooperative transformer-based denoisers that explicitly share weights, with a mutual attention mechanism to further connect the two denoising processes. Then, we propose a novel representation for motion input in our interaction diffusion model, which explicitly formulates the global relations between the two performers in the world frame. We further introduce two novel regularization terms to encode spatial relations, equipped with a corresponding damping scheme during the training of our interaction diffusion model. Extensive experiments validate the effectiveness and generalizability of InterGen. Notably, it can generate more diverse and compelling two-person motions than previous methods and enables various downstream applications for human interactions.Comment: accepted by IJCV 202

Genes in Intelligent Agents

Training intelligent agents in Reinforcement Learning (RL) is much more time-consuming than animal learning. This is because agents learn from scratch, but animals learn with genes inherited from ancestors and are born with some innate abilities. Inspired by genes in animals, here we conceptualize the gene in intelligent agents and introduce Genetic Reinforcement Learning (GRL), a computational framework to represent, evaluate, and evolve genes (in agents). Leveraging GRL we identify genes and demonstrate several advantages of genes. First, we find that genes take the form of the fragment of agents' neural networks and can be inherited across generations. Second, we validate that genes bring better and stabler learning ability to agents, since genes condense knowledge from ancestors and bring agent with innate abilities. Third, we present evidence of Lamarckian evolution in intelligent agents. The continuous encoding of knowledge into genes across generations facilitates the evolution of genes. Overall, our work promotes a novel paradigm to train agents by incorporating genes

PAC Learnability under Explanation-Preserving Graph Perturbations

Graphical models capture relations between entities in a wide range of applications including social networks, biology, and natural language processing, among others. Graph neural networks (GNN) are neural models that operate over graphs, enabling the model to leverage the complex relationships and dependencies in graph-structured data. A graph explanation is a subgraph which is an `almost sufficient' statistic of the input graph with respect to its classification label. Consequently, the classification label is invariant, with high probability, to perturbations of graph edges not belonging to its explanation subgraph. This work considers two methods for leveraging such perturbation invariances in the design and training of GNNs. First, explanation-assisted learning rules are considered. It is shown that the sample complexity of explanation-assisted learning can be arbitrarily smaller than explanation-agnostic learning. Next, explanation-assisted data augmentation is considered, where the training set is enlarged by artificially producing new training samples via perturbation of the non-explanation edges in the original training set. It is shown that such data augmentation methods may improve performance if the augmented data is in-distribution, however, it may also lead to worse sample complexity compared to explanation-agnostic learning rules if the augmented data is out-of-distribution. Extensive empirical evaluations are provided to verify the theoretical analysis.Comment: 21 pages, 6 figures, 4 table

ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder with very high prevalence around the world. Research progress in the field of ASD facial analysis in pediatric patients has been hindered due to a lack of well-established baselines. In this paper, we propose the use of the Vision Transformer (ViT) for the computational analysis of pediatric ASD. The presented model, known as ViTASD, distills knowledge from large facial expression datasets and offers model structure transferability. Specifically, ViTASD employs a vanilla ViT to extract features from patients' face images and adopts a lightweight decoder with a Gaussian Process layer to enhance the robustness for ASD analysis. Extensive experiments conducted on standard ASD facial analysis benchmarks show that our method outperforms all of the representative approaches in ASD facial analysis, while the ViTASD-L achieves a new state-of-the-art. Our code and pretrained models are available at https://github.com/IrohXu/ViTASD.Comment: 5 pages, 3 figures, Accepted by the ICASSP 202

ODTC: An online darknet traffic classification model based on multimodal self-attention chaotic mapping features

Darknet traffic classification is significantly important to network management and security. To achieve fast and accurate classification performance, this paper proposes an online classification model based on multimodal self-attention chaotic mapping features. On the one hand, the payload content of the packet is input into the network integrating CNN and BiGRU to extract local space-time features. On the other hand, the flow level abstract features processed by the MLP are introduced. To make up for the lack of the indistinct feature learning, a feature amplification module that uses logistic chaotic mapping to amplify fuzzy features is introduced. In addition, a multi-head attention mechanism is used to excavate the hidden relationships between different features. Besides, to better support new traffic classes, a class incremental learning model is developed with the weighted loss function to achieve continuous learning with reduced network parameters. The experimental results on the public CICDarketSec2020 dataset show that the accuracy of the proposed model is improved in multiple categories; however, the time and memory consumption is reduced by about 50. Compared with the existing state-of-the-art traffic classification models, the proposed model has better classification performance

Implications for policy and planning to foster solidarity between the generations and enhance healthy life among older adults.

This policy document is a compilation of the studies of the five Early Stage Researchers (ESRs) in Working Package 2 of the EuroAgeism project (WP2). WP2 explores ageism in access to goods and services: social and health services (formal, informal), and appropriate drug treatment. It examines the origins, manifestations, and consequences of ageism in social life, social and health care services, and medication treatment. The integration of the five programmes of individual study provides an opportunity to identify common threads of contextual influences on the manifestation of ageism and its negative outcomes, such as poor access to goods and services, including access for people living with dementia; social support; and health and social care, including timely and appropriate medications. Given its multidimensional nature, ageism has to be studied from a multi-disciplinary perspective to consider the individual (micro), social (meso) and structural (macro) levels together. As such, through the five ESRs programmes of study, WP2 will compare and contrast the intersections between individuals and society. This analysis involves ageist attitudes and behaviours perpetuated by the media and held by older adults, by others in their social environment and those of service providers. WP2 addresses ageism as a factor that impacts all aspects of life, including social relations, media, social care and health care. This policy document provides a synthesis of the five programmes of work to highlight the implications for, and to inform policy on, fostering solidarity between generations and enhancing healthy life among older adults. The EuroAgeism project has received funding from the European Union's Horizon 2020 research and innovation programme, under the Marie Skłodowska-Curie grant agreement number 764632