153 research outputs found
Fully-coupled hydro-mechanical analysis of water saturated porous geomaterials under complex loading conditions
In this study, we integrate a novel stabilized enhanced strain mixed finite element procedure for poromechanics with an elasto-plastic geomodel to simulate the hydro-mechanical responses of water saturated porous geomaterials such as porous rocks and sands. We present a quantitative analysis on how macroscopic plastic response affects the seepage of pore fluid, and vice versa. We are particular interested in hydromechanical coupling effects on the shear failure behavior of the porous geomaterials as well as its potential regularization effects on pathological mesh dependence. Finite element simulations of shear failure problems of water-saturated porous geomaterials will be presented to study the effect of pore pressure diffusion on the stress path and plastic response of the porous geomaterials
Modeling the hydro-mechanical responses of strip and circular punch loadings on water-saturated collapsible geomaterials
A stabilized enhanced strain finite element procedure for poromechanics is fully integrated with an elasto-plastic cap model to simulate the hydro-mechanical interactions of fluid-infiltrating porous rocks with associative and non-associative plastic flow. We present a quantitative analysis on how macroscopic plastic volumetric response caused by pore collapse and grain rearrangement affects the seepage of pore fluid, and vice versa. Results of finite element simulations imply that the dissipation of excess pore pressure may significantly affect the stress path and thus alter the volumetric plastic responses
Glycyrrhizin could reduce ocular hypertension induced by triamcinolone acetonide in rabbits
Purpose: To evaluate the hypotensive effects of glycyrrhizin (GL) on a rabbit model of ocular hypertension (OH) induced by triamcinolone acetonide (TA). Methods: Forty New Zealand White Rabbits were divided as follows: control (intravitreal injection of sterile saline solution); GL (intravitreal injection of sterile saline solution, then fed with 25mg GL/day); TA (intravitreal TA injection); TA+GL (intravitreal TA injection, then fed with GL) and GL+TA (pre-treated with GL for 3 days, then got TA injection and the following GL treatment). Intraocular pressure (IOP), flash electroretinogram (flash ERG) and flash visual evoked potential (flash VEP) were measured during the follow-up (28 days). The aqueous humor was analyzed, using (1)H-nuclear magnetic resonance spectroscopy and principal components analysis (PCA). Results: IOP elevation was observed in the TA group during the follow-up, compared to the controls (p<0.01). The IOP was decreased in the TA+GL group and the GL+TA group, compared to the TA group (p<0.05). Both in flash ERG and VEP, the amplitudes were decreased, and the implicit time was prolonged in the TA group, compared to the controls (p<0.05); and the parameters were improved after intervention of GL, compared to the TA group (p<0.05). PCA results indicated that TA could affect ocular metabolism (especially the sugar metabolism), and GL could inhibit it. Conclusions: The administration of GL could suppress OH induced by TA in rabbits, and improve their electrophysiological parameters. Metabolomics is a useful tool in ophthalmology research. Our results indicate that TA-induced ocular metabolism changes could be compensated by GL.Biochemistry & Molecular BiologyOphthalmologySCI(E)6ARTICLE2242056-20641
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
Recently, Chain-of-Thought (CoT) prompting has delivered success on complex
reasoning tasks, which aims at designing a simple prompt like ``Let's think
step by step'' or multiple in-context exemplars with well-designed rationales
to elicit Large Language Models (LLMs) to generate intermediate reasoning
steps. However, the generated rationales often come with mistakes, making
unfactual and unfaithful reasoning chains. To mitigate this brittleness, we
propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting
LLMs to generate explicit pieces of knowledge evidence in the form of structure
triple. This is inspired by our human behaviors, i.e., we can draw a mind map
or knowledge map as the reasoning evidence in the brain before answering a
complex question. Benefiting from CoK, we additionally introduce a
F^2-Verification method to estimate the reliability of the reasoning chains in
terms of factuality and faithfulness. For the unreliable response, the wrong
evidence can be indicated to prompt the LLM to rethink. Extensive experiments
demonstrate that our method can further improve the performance of commonsense,
factual, symbolic, and arithmetic reasoning tasks.Comment: Work in progres
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
Code pre-trained models (CodePTMs) have recently demonstrated a solid
capacity to process various software intelligence tasks, e.g., code clone
detection, code translation, and code summarization. The current mainstream
method that deploys these models to downstream tasks is to fine-tune them on
individual tasks, which is generally costly and needs sufficient data for large
models. To tackle the issue, in this paper, we present TransCoder, a unified
Transferable fine-tuning strategy for Code representation learning. Inspired by
human inherent skills of knowledge generalization, TransCoder drives the model
to learn better code-related meta-knowledge like human programmers.
Specifically, we employ a tunable prefix encoder as the meta-learner to capture
cross-task and cross-language transferable knowledge, respectively. Besides,
tasks with minor training sample sizes and languages with small corpus can be
remarkably benefited from our approach. Extensive experiments conducted on
benchmark datasets clearly demonstrate that our method can lead to superior
performance on various code-related tasks and encourage mutual reinforcement.
We also show that TransCoder is applicable in low-resource scenarios.Comment: work in progres
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure
Code pre-trained models (CodePTMs) have recently demonstrated significant
success in code intelligence. To interpret these models, some probing methods
have been applied. However, these methods fail to consider the inherent
characteristics of codes. In this paper, to address the problem, we propose a
novel probing method CAT-probing to quantitatively interpret how CodePTMs
attend code structure. We first denoise the input code sequences based on the
token types pre-defined by the compilers to filter those tokens whose attention
scores are too small. After that, we define a new metric CAT-score to measure
the commonality between the token-level attention scores generated in CodePTMs
and the pair-wise distances between corresponding AST nodes. The higher the
CAT-score, the stronger the ability of CodePTMs to capture code structure. We
conduct extensive experiments to integrate CAT-probing with representative
CodePTMs for different programming languages. Experimental results show the
effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are
publicly available at https://github.com/nchen909/CodeAttention.Comment: Accepted by EMNLP 202
Do Large Language Models Know What They Don't Know?
Large language models (LLMs) have a wealth of knowledge that allows them to
excel in various Natural Language Processing (NLP) tasks. Current research
focuses on enhancing their performance within their existing knowledge. Despite
their vast knowledge, LLMs are still limited by the amount of information they
can accommodate and comprehend. Therefore, the ability to understand their own
limitations on the unknows, referred to as self-knowledge, is of paramount
importance. This study aims to evaluate LLMs' self-knowledge by assessing their
ability to identify unanswerable or unknowable questions. We introduce an
automated methodology to detect uncertainty in the responses of these models,
providing a novel measure of their self-knowledge. We further introduce a
unique dataset, SelfAware, consisting of unanswerable questions from five
diverse categories and their answerable counterparts. Our extensive analysis,
involving 20 LLMs including GPT-3, InstructGPT, and LLaMA, discovering an
intrinsic capacity for self-knowledge within these models. Moreover, we
demonstrate that in-context learning and instruction tuning can further enhance
this self-knowledge. Despite this promising insight, our findings also
highlight a considerable gap between the capabilities of these models and human
proficiency in recognizing the limits of their knowledge.Comment: 10 pages, 9 figures, accepted by Findings of ACL202
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents
Graphical User Interface (GUI) agents are designed to automate complex tasks
on digital devices, such as smartphones and desktops. Most existing GUI agents
interact with the environment through extracted structured data, which can be
notably lengthy (e.g., HTML) and occasionally inaccessible (e.g., on desktops).
To alleviate this issue, we propose a novel visual GUI agent -- SeeClick, which
only relies on screenshots for task automation. In our preliminary study, we
have discovered a key challenge in developing visual GUI agents: GUI grounding
-- the capacity to accurately locate screen elements based on instructions. To
tackle this challenge, we propose to enhance SeeClick with GUI grounding
pre-training and devise a method to automate the curation of GUI grounding
data. Along with the efforts above, we have also created ScreenSpot, the first
realistic GUI grounding benchmark that encompasses mobile, desktop, and web
environments. After pre-training, SeeClick demonstrates significant improvement
in ScreenSpot over various baselines. Moreover, comprehensive evaluations on
three widely used benchmarks consistently support our finding that advancements
in GUI grounding directly correlate with enhanced performance in downstream GUI
agent tasks. The model, data and code are available at
https://github.com/njucckevin/SeeClick
Exchanging-based Multimodal Fusion with Transformer
We study the problem of multimodal fusion in this paper. Recent
exchanging-based methods have been proposed for vision-vision fusion, which aim
to exchange embeddings learned from one modality to the other. However, most of
them project inputs of multimodalities into different low-dimensional spaces
and cannot be applied to the sequential input data. To solve these issues, in
this paper, we propose a novel exchanging-based multimodal fusion model MuSE
for text-vision fusion based on Transformer. We first use two encoders to
separately map multimodal inputs into different low-dimensional spaces. Then we
employ two decoders to regularize the embeddings and pull them into the same
space. The two decoders capture the correlations between texts and images with
the image captioning task and the text-to-image generation task, respectively.
Further, based on the regularized embeddings, we present CrossTransformer,
which uses two Transformer encoders with shared parameters as the backbone
model to exchange knowledge between multimodalities. Specifically,
CrossTransformer first learns the global contextual information of the inputs
in the shallow layers. After that, it performs inter-modal exchange by
selecting a proportion of tokens in one modality and replacing their embeddings
with the average of embeddings in the other modality. We conduct extensive
experiments to evaluate the performance of MuSE on the Multimodal Named Entity
Recognition task and the Multimodal Sentiment Analysis task. Our results show
the superiority of MuSE against other competitors. Our code and data are
provided at https://github.com/RecklessRonan/MuSE
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