Self-Paced Multi-Task Learning

In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating all tasks and instances equally when training, SPMTL attempts to jointly learn the tasks by taking into consideration the complexities of both tasks and instances. This is inspired by the cognitive process of human brain that often learns from the easy to the hard. We construct a compact SPMTL formulation by proposing a new task-oriented regularizer that can jointly prioritize the tasks and the instances. Thus it can be interpreted as a self-paced learner for MTL. A simple yet effective algorithm is designed for optimizing the proposed objective function. An error bound for a simplified formulation is also analyzed theoretically. Experimental results on toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-art methods

Experimental Study on Ultrahigh Strength Concrete Filled Steel Tube Short Columns under Axial Load

Based on the project of Modaoxi Bridge, an experimental study on the compressive behavior of ultrahigh strength concrete filled steel tube (UHSCFST) short column was conducted. A total of 9 UHSCFST specimens were tested, and the cube strength (fcu) of the core concrete reached 115.4 MPa. Main parameters were the confining factor (ξ=0.608, 0.919, and 1.015), steel ratio (α=14.67%, 20.02%, and 21.98%), and steel strength (fy = 349 MPa, 352 MPa, and 427 MPa). The axially loading test results showed that the visible damage of steel tube occurred under the ultimate load. The higher the confining effect, the less the damage features. And all specimens basically presented a drum-type failure mode. The confining effect of steel tube effectively changed the brittle failure mode of ultrahigh strength concrete (UHSC) and tremendously improved the load bearing capacity and ductility of specimens. Moreover, the higher the steel ratio and steel strength of the specimens, the stronger the confining effect. Meanwhile the excellent mechanical properties will be obtained. Also it is recommended that the UHSCFST prefers Q345 or above strength steel tube to ensure sufficient ductility, and the steel ratio should be more than 20%. Furthermore, the confining effect of steel tubes can improve the ultimate bearing capacity of the ultrahigh strength CFST short columns

Social support and health behaviors of older adults during the COVID-19 pandemic in China: a moderated mediation model of loneliness and economic income

Background: The literature shows that social support is an important factor influencing health behaviors. This study aimed to explore the relationships and intrinsic pathways of social support, loneliness, economic income, and health behaviors among older adults during the Corona Virus Disease 2019 (COVID-19) pandemic, and to provide a theoretical basis for the implementation of health behaviors interventions for older adults. Methods: A cluster-random-sampling survey was adopted within two towns in Dongguan, China. Demographic characteristics, social support, loneliness, economic income and health behaviors were measured. The Social Support Appraisals scale (SS-A), the ULS-8 Loneliness Scale, and the Self-rated abilities for health practice scale (SRAHPS) were used to measure social support, loneliness, and health behaviors in older adults, respectively. A moderated mediation model was built to examine the relationships among social support, loneliness, economic income, and health behaviors using the SPSS PROCESS 4.0 macro. We conducted bootstrapping of regression estimates with 5000 samples and a 95% confidence interval. Results: 621 older adults completed the questionnaire. Most of the participants were female, accounting for 75.0%, and the average age was 81.11 years (SD = 8.11). The median (interquartile range) of the participants’ average monthly economic income was 800 (500–1000)RMB. The results of the mediation analysis showed that loneliness partly mediated the relationship between social support and health behaviors (B = 0.024, 95%CI: 0.007, 0.042), with the mediating effect accounting for 4.56% of the total effect. The moderation mediation analysis revealed a positive moderating role of economic income in the relationship between social support and loneliness (B = 0.114, 95%CI: 0.054, 0.174). Specifically, the relationship between social support and loneliness was found to be weaker for older adults with a high economic income compared to those with a lower economic income. Conclusion: The provision of enhanced social support and the alleviation of loneliness among older adults during an epidemic can facilitate the development of healthy behaviours, particularly among those who are economically disadvantaged

Combat molten aluminum corrosion of AISI H13 steel by lowtemperature liquid nitrocarburizing

Possibility of improving the resistance of AISI H13 steel to molten aluminum corrosion by liquid nitrocarburizing (LNC) was explored. The effects of the LNC parameters in terms of temperatures (703/723/743K) and soaking time (4/8/12h) on phase transformation, microstructure, and resistance to molten aluminum were fully studied. The surface phase compositions and the cross-sectional phase distribution of the LNC treated specimens were studied by implementable X-ray diffraction analysis. Microstructure, element distribution, microhardness, and the kinetics of the nitrocarburized case formation were fully researched. Immersion test of corrosion resistance to molten aluminum was carried out at 1023K for 30min. It is observed that an oxide layer can be produced on the top of the nitrocarburized case during LNC treatment, which cannot be regularly produced by other nitriding methods. The nitrocarburized case consists of a compound layer, a diffusion layer, and a transition layer. The growth of the nitrocarburized case is proportional to the squared treatment time and follows the Arrhenius law for the treatment temperature. The activation energy is estimated to be 195.4 kJ·mol−1. While the nitrocarburized case provided limited resistance to molten aluminum, the oxide layer formed on the top of the nitrocarburized case conferred significantly improved molten aluminum corrosion resistance, especially a duplex oxide layer produced at 743

Scaffold Structural Microenvironmental Cues to Guide Tissue Regeneration in Bone Tissue Applications

In the process of bone regeneration, new bone formation is largely affected by physico-chemical cues in the surrounding microenvironment. Tissue cells reside in a complex scaffold physiological microenvironment. The scaffold should provide certain circumstance full of structural cues to enhance multipotent mesenchymal stem cell (MSC) differentiation, osteoblast growth, extracellular matrix (ECM) deposition, and subsequent new bone formation. This article reviewed advances in fabrication technology that enable the creation of biomaterials with well-defined pore structure and surface topography, which can be sensed by host tissue cells (esp., stem cells) and subsequently determine cell fates during differentiation. Three important cues, including scaffold pore structure (i.e., porosity and pore size), grain size, and surface topography were studied. These findings improve our understanding of how the mechanism scaffold microenvironmental cues guide bone tissue regeneration

Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning

Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and help improve the prediction performance of event sequence models. We design LAMP, a framework that integrates a large language model in event prediction. Particularly, the language model performs abductive reasoning to assist an event sequence model: the event model proposes predictions on future events given the past; instructed by a few expert-annotated demonstrations, the language model learns to suggest possible causes for each proposal; a search module finds out the previous events that match the causes; a scoring function learns to examine whether the retrieved events could actually cause the proposal. Through extensive experiments on several challenging real-world datasets, we demonstrate that our framework -- thanks to the reasoning capabilities of large language models -- could significantly outperform the state-of-the-art event sequence models.Comment: NeurIPS 2023 camera-read

LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning

Recent advances in Large Multimodal Models (LMM) have made it possible for various applications in human-machine interactions. However, developing LMMs that can comprehend, reason, and plan in complex and diverse 3D environments remains a challenging topic, especially considering the demand for understanding permutation-invariant point cloud 3D representations of the 3D scene. Existing works seek help from multi-view images, and project 2D features to 3D space as 3D scene representations. This, however, leads to huge computational overhead and performance degradation. In this paper, we present LL3DA, a Large Language 3D Assistant that takes point cloud as direct input and respond to both textual-instructions and visual-prompts. This help LMMs better comprehend human interactions and further help to remove the ambiguities in cluttered 3D scenes. Experiments show that LL3DA achieves remarkable results, and surpasses various 3D vision-language models on both 3D Dense Captioning and 3D Question Answering.Comment: Project Page: https://ll3da.github.io

WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine

We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain. Our system harnesses a large language model of GPT architecture that has been tuned using extensive corpora of finance-related text. As a result, our system possesses the capability to understand complex financial queries, such as "How should I manage my investments during inflation?", and provide informed responses. Furthermore, our system incorporates a local knowledge base and a search engine to retrieve relevant information. The final responses are conditioned on the search results and include proper citations to the sources, thus enjoying an enhanced credibility. Through a range of finance-related questions, we have demonstrated the superior performance of our system compared to other models. To experience our system firsthand, users can interact with our live demo at https://weaverbird.ttic.edu, as well as watch our 2-min video illustration at https://www.youtube.com/watch?v=fyV2qQkX6Tc

EasyTPP: Towards Open Benchmarking Temporal Point Processes

Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point processes (TPPs) have emerged as the most natural and competitive models, making a significant impact in both academic and application communities. Despite the emergence of many powerful models in recent years, there hasn't been a central benchmark for these models and future research endeavors. This lack of standardization impedes researchers and practitioners from comparing methods and reproducing results, potentially slowing down progress in this field. In this paper, we present EasyTPP, the first central repository of research assets (e.g., data, models, evaluation programs, documentations) in the area of event sequence modeling. Our EasyTPP makes several unique contributions to this area: a unified interface of using existing datasets and adding new datasets; a wide range of evaluation programs that are easy to use and extend as well as facilitate reproducible research; implementations of popular neural TPPs, together with a rich library of modules by composing which one could quickly build complex models. All the data and implementation can be found at https://github.com/ant-research/EasyTemporalPointProcess. We will actively maintain this benchmark and welcome contributions from other researchers and practitioners. Our benchmark will help promote reproducible research in this field, thus accelerating research progress as well as making more significant real-world impacts.Comment: ICLR 2024 camera read