161 research outputs found
Language Inclusion Checking of Timed Automata with Non-Zenoness
Ministry of Education, Singapore under its Academic Research Funding Tier
Automatically ‘Verifying’ Discrete-Time Complex Systems through Learning, Abstraction and Refinement
Precisely modeling complex systems like cyber-physical systems is
challenging, which often render model-based system verification techniques like
model checking infeasible. To overcome this challenge, we propose a method
called LAR to automatically `verify' such complex systems through a combination
of learning, abstraction and refinement from a set of system log traces. We
assume that log traces and sampling frequency are adequate to capture `enough'
behaviour of the system. Given a safety property and the concrete system log
traces as input, LAR automatically learns and refines system models, and
produces two kinds of outputs. One is a counterexample with a bounded
probability of being spurious. The other is a probabilistic model based on
which the given property is `verified'. The model can be viewed as a proof
obligation, i.e., the property is verified if the model is correct. It can also
be used for subsequent system analysis activities like runtime monitoring or
model-based testing. Our method has been implemented as a self-contained
software toolkit. The evaluation on multiple benchmark systems as well as a
real-world water treatment system shows promising results.Comment: Accepted by IEEE Transactions on Software Engineerin
Stack bound inference for abstract java bytecode
10.1109/TASE.2010.24Proceedings - 2010 4th International Symposium on Theoretical Aspects of Software Engineering, TASE 201057-6
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Recent advancements in conversational large language models (LLMs), such as
ChatGPT, have demonstrated remarkable promise in various domains, including
drug discovery. However, existing works mainly focus on investigating the
capabilities of conversational LLMs on chemical reaction and retrosynthesis.
While drug editing, a critical task in the drug discovery pipeline, remains
largely unexplored. To bridge this gap, we propose ChatDrug, a framework to
facilitate the systematic investigation of drug editing using LLMs. ChatDrug
jointly leverages a prompt module, a retrieval and domain feedback (ReDF)
module, and a conversation module to streamline effective drug editing. We
empirically show that ChatDrug reaches the best performance on 33 out of 39
drug editing tasks, encompassing small molecules, peptides, and proteins. We
further demonstrate, through 10 case studies, that ChatDrug can successfully
identify the key substructures (e.g., the molecule functional groups, peptide
motifs, and protein structures) for manipulation, generating diverse and valid
suggestions for drug editing. Promisingly, we also show that ChatDrug can offer
insightful explanations from a domain-specific perspective, enhancing
interpretability and enabling informed decision-making. This research sheds
light on the potential of ChatGPT and conversational LLMs for drug editing. It
paves the way for a more efficient and collaborative drug discovery pipeline,
contributing to the advancement of pharmaceutical research and development
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring
embeddings without expert labelling, a capability that carries profound
implications for molecular graphs due to the staggering number of potential
molecules and the high cost of obtaining labels. However, GSSL methods are
designed not for optimisation within a specific domain but rather for
transferability across a variety of downstream tasks. This broad applicability
complicates their evaluation. Addressing this challenge, we present "Molecular
Graph Representation Evaluation" (MOLGRAPHEVAL), generating detailed profiles
of molecular graph embeddings with interpretable and diversified attributes.
MOLGRAPHEVAL offers a suite of probing tasks grouped into three categories: (i)
generic graph, (ii) molecular substructure, and (iii) embedding space
properties. By leveraging MOLGRAPHEVAL to benchmark existing GSSL methods
against both current downstream datasets and our suite of tasks, we uncover
significant inconsistencies between inferences drawn solely from existing
datasets and those derived from more nuanced probing. These findings suggest
that current evaluation methodologies fail to capture the entirety of the
landscape.Comment: update result
Competition and Sustainability Development of a Multi-Airport Region: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area
new era in the development of multi-airport regions (MARs) has been in China, especially in the construction of global urban areas such as the Guangdong&ndash
Hong Kong&ndash
Macao Greater Bay Area (GBA). In order to better understand competition and provide significant advice to support sustainable development of Chinese MARs, this paper takes the GBA-MAR as an example, and then explores the competition between airports in this multi-airport region based on route level and its impact on passenger airport choice using three liner models. According to our findings, competition is concentrated on a few air routes in the Northeast&ndash
East Coastal region and the Asia-Pacific region, in which all three airports operate. Although the domestic market of the GBA-MAR has been relatively mature, the international air transport market is still left with considerable room, which is also reflected in connections to the world&rsquo
s cities and tourist destinations. In addition, the research also found that different airport brands created by route type significantly affected passenger airport choice. Overall, the study revealed that whether from a supply or demand aspect, competition between airports in a multi-airport region&mdash
not just airline competition in a single airport&mdash
is an important topic.
Document type: Articl
An Empirical Study of Retrieval-enhanced Graph Neural Networks
Graph Neural Networks (GNNs) are effective tools for graph representation
learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named
message passing, thereby their theoretical expressive power is limited to the
first-order Weisfeiler-Lehman test (1-WL). An effective approach to this
challenge is to explicitly retrieve some annotated examples used to enhance GNN
models. While retrieval-enhanced models have been proved to be effective in
many language and vision domains, it remains an open question how effective
retrieval-enhanced GNNs are when applied to graph datasets. Motivated by this,
we want to explore how the retrieval idea can help augment the useful
information learned in the graph neural networks, and we design a
retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the
choice of graph neural network models. In GRAPHRETRIEVAL, for each input graph,
similar graphs together with their ground-true labels are retrieved from an
existing database. Thus they can act as a potential enhancement to complete
various graph property predictive tasks. We conduct comprehensive experiments
over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach
substantial improvements over existing GNNs. Moreover, our empirical study also
illustrates that retrieval enhancement is a promising remedy for alleviating
the long-tailed label distribution problem.Comment: Accepted by ECAI 202
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Extending a first-principles primary production model to predict wheat yields
Climate exerts a major influence on crop development and yield. Despite extensive modelling efforts, there is still considerable uncertainty about the consequences of a changing climate for the yields of major crops. Existing crop models are complex and rely on many assumptions and parameters, motivating a quest for more parsimonious models with stronger theoretical and empirical foundations. This paper presents a prototype of such a model for wheat, informed by measurements of gross primary production (GPP), biomass and yield at research sites across the wheat-growing regions of China. First, GPP was predicted using a recently developed first-principles model driven only by climate, carbon dioxide (CO2) concentration, and light absorbed by leaves. Modelled GPP was shown to agree well with eddy-covariance measurements. Second, the data were used to show that above-ground biomass (AB) is proportional to time-integrated GPP, and that grain yield shows a saturating relationship with AB. Simple empirical equations based on these findings were combined with modelled GPP to predict yield, including propagated errors due to parameter uncertainty in both the GPP model and the empirical equations. The resulting 'hybrid' model, applied in a variety of climates, successfully predicted measured interannual variations in AB and yield. Third, the model was extended to include a phenology scheme, a mass-balance equation relating mean leaf area index to accumulated GPP over the growth phase, and an independently observed response of leaf mass-per-area to CO2. Sensitivity analyses and scenario runs with this extended model showed a positive but saturating (at ∼600 ppm) response of yield to rising CO2, consistent with experimental evidence. This positive effect was partially counteracted by a net negative response of yield to increasing temperature, caused by increasing photorespiration and an accelerated growth cycle
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