Behaviour of High- and Ultra-High Performance Fibre Reinforced Concrete Flexural Members

Concrete is a quasi-brittle material that increases in brittleness as the compressive strength increases and as such plain high-strength concrete always fails in an explosive manner. Incorporation of randomly distributed discrete non-metallic or metallic fibres into conventional concrete mixes has now been well-recognized as a feasible solution to address the issue of the low material ductility of concrete. The presence of fibres in concrete can prevent wider cracks on the concrete structural elements under instantaneous and sustained loads and an associated refinement of the pore structure and mitigations of micro-cracks also effectively contributes to enhancing the durability-related material properties. This thesis presents a series of research work investigating the behaviour of various types of high- and ultra-high performance concrete flexural members. The work starts with an investigation to develop mix proportions for ultra-performance concrete with and without fibres to achieve the desired dimensional stability as reported in Chapters 2 and 3 in this thesis. The optimal concrete recipe is then used continuously through the entire experimental program to manufacture the flexural members for investigations, as presented in Chapters 5 to 8, where the behaviour of a series of high- and ultra-high performance concrete flexural members, including fibre-reinforced concrete (FRC) simply-supported beams, ultra-high performance fibre reinforced concrete (UHPFRC) continuous beams, curved beams, skew slabs and also sandwich panels using ultra-high performance concrete (UHPC) face sheets, are experimentally studied. Having experimentally investigated the performances of these high- and ultra-high performance concrete flexural members, in Chapter 4, a generic analysis technique, (the segmental based moment-rotation approach), is extended to simulate the behaviour of highand ultra-high fibre reinforced concrete flexural members. This approach is based on the fundamental Euler-Bernoulli postulation that plane remains plane and applies the wellestablished mechanics of partial interaction (PI) theory to simulate crack formation and crack widening including the influence of the discrete fibre reinforcement. Moreover, in Chapters 6 to 8, analytical approaches, in terms of the fundamental mechanics based closed-form models are also derived using energy theorem to predict the performance of non-orthogonal ultra-high performance fibre-reinforced concrete flexural members.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

MoEC: Mixture of Expert Clusters

Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation. Such problems are especially severe on tasks with limited data, thus hindering the progress for MoE models to improve performance by scaling up. In this work, we propose Mixture of Expert Clusters - a general approach to enable expert layers to learn more diverse and appropriate knowledge by imposing variance-based constraints on the routing stage. We further propose a cluster-level expert dropout strategy specifically designed for the expert cluster structure. Our experiments reveal that MoEC could improve performance on machine translation and natural language understanding tasks, and raise the performance upper bound for scaling up experts under limited data. We also verify that MoEC plays a positive role in mitigating overfitting and sparse data allocation

Chiral Anomaly Beyond Fermionic Paradigm

Two-dimensional magnets have manifested themselves as promising candidates for quantum devices. We here report that the edge and strain effects during the device fabrication with two-dimensional honeycomb ferromagnets such as CrX$_3$ (X=Cl, I, Br) and CrXTe$_3$ (X=Si, Ge) can be characterized by a (1+1)-dimensional magnon chiral anomaly beyond the fermionic paradigm. In the presence of zigzag edges, a pair of chiral bulk-edge magnon bands appear and cause an imbalance of left- and right-chirality magnons when subjected to nonuniform temperature or magnetic fields. In the presence of a uniaxial strain, the bulk Dirac magnons are broken into chiral magnon pseudo-Landau levels, resulting in a magnon chiral anomaly observable through a negative strain-resistivity of the magnetic dipole and heat. Our work demonstrates a chiral anomaly with (quasi)particles obeying non-fermionic statistics and will be instructive in understanding anomalous magnon transport.Comment: 4.5 pages, 4 figure

Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration

Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical manner. However, the single-layer architecture commonly used in current SIVI methods can be insufficient when the target posterior has complicated structures. In this paper, we propose hierarchical semi-implicit variational inference, called HSIVI, which generalizes SIVI to allow more expressive multi-layer construction of semi-implicit distributions. By introducing auxiliary distributions that interpolate between a simple base distribution and the target distribution, the conditional layers can be trained by progressively matching these auxiliary distributions one layer after another. Moreover, given pre-trained score networks, HSIVI can be used to accelerate the sampling process of diffusion models with the score matching objective. We show that HSIVI significantly enhances the expressiveness of SIVI on several Bayesian inference problems with complicated target distributions. When used for diffusion model acceleration, we show that HSIVI can produce high quality samples comparable to or better than the existing fast diffusion model based samplers with a small number of function evaluations on various datasets.Comment: 25 pages, 13 figures, NeurIPS 202

Neural Snowball for Few-Shot Relation Learning

Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient training data. To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. More specifically, we use Relational Siamese Networks (RSN) to learn the metric of relational similarities between instances based on existing relations and their labeled data. Afterwards, given a new relation and its few-shot instances, we use RSN to accumulate reliable instances from unlabeled corpora; these instances are used to train a relation classifier, which can further identify new facts of the new relation. The process is conducted iteratively like a snowball. Experiments show that our model can gather high-quality instances for better few-shot relation learning and achieves significant improvement compared to baselines. Codes and datasets are released on https://github.com/thunlp/Neural-Snowball.Comment: Accepted by AAAI202

Identifying Vulnerable Third-Party Java Libraries from Textual Descriptions of Vulnerabilities and Libraries

To address security vulnerabilities arising from third-party libraries, security researchers maintain databases monitoring and curating vulnerability reports. Application developers can identify vulnerable libraries by directly querying the databases with their used libraries. However, the querying results of vulnerable libraries are not reliable due to the incompleteness of vulnerability reports. Thus, current approaches model the task of identifying vulnerable libraries as a named-entity-recognition (NER) task or an extreme multi-label learning (XML) task. These approaches suffer from highly inaccurate results in identifying vulnerable libraries with complex and similar names, e.g., Java libraries. To address these limitations, in this paper, we propose VulLibMiner, the first to identify vulnerable libraries from textual descriptions of both vulnerabilities and libraries, together with VulLib, a Java vulnerability dataset with their affected libraries. VulLibMiner consists of a TF-IDF matcher to efficiently screen out a small set of candidate libraries and a BERT-FNN model to identify vulnerable libraries from these candidates effectively. We evaluate VulLibMiner using four state-of-the-art/practice approaches of identifying vulnerable libraries on both their dataset named VeraJava and our VulLib dataset. Our evaluation results show that VulLibMiner can effectively identify vulnerable libraries with an average F1 score of 0.657 while the state-of-the-art/practice approaches achieve only 0.521