728 research outputs found
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
(X=Cl, I, Br) and CrXTe (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
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