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

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

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Research on 3D Defect Information Management of Drainage Pipeline Based on BIM

With the age of pipeline and increase in the volume of urban sewage, the pipeline has different degrees of defects, which can cause safety problems such as road collapse and urban flooding. The service life of drainage pipes is closely related to daily maintenance and inspection, so it is very important to inspect the defects and monitor the operation of drainage pipes regularly. However, the existing research lacks quantitative detection and intelligent management of pipeline defect information. Therefore, the depth camera is used as the sensor to quantitatively detect the volume and area of the pit on the concrete pipe, and a defect information management platform is constructed in this paper. Firstly, combined BIM model with 3D point cloud, this paper proposes a 3D defect information management platform of drainage pipeline. Then, the depth camera is used to collect the damage data and preprocess the data, and a method for calculating the damage volume and surface area of drainage pipeline based on 3D mesh reconstruction of the defect point cloud is proposed. The verification experiment results show that the error between the quantized volume and the real volume is mostly within 10%, and the maximum error is 17.54%, indicating high accuracy. The drainage pipeline information model is created. Finally, the data is uploaded to the information management platform to realize the visualization and informatization of pipeline defects and the later operation and maintenance requirements of the pipeline