3 research outputs found
Synchronization of stochastic genetic oscillator networks with time delays and Markovian jumping parameters
The official published version of the article can be found at the link below.Genetic oscillator networks (GONs) are inherently coupled complex systems where the nodes indicate the biochemicals and the couplings represent the biochemical interactions. This paper is concerned with the synchronization problem of a general class of stochastic GONs with time delays and Markovian jumping parameters, where the GONs are subject to both the stochastic disturbances and the Markovian parameter switching. The regulatory functions of the addressed GONs are described by the sector-like nonlinear functions. By applying up-to-date ‘delay-fractioning’ approach for achieving delay-dependent conditions, we construct novel matrix functional to derive the synchronization criteria for the GONs that are formulated in terms of linear matrix inequalities (LMIs). Note that LMIs are easily solvable by the Matlab toolbox. A simulation example is used to demonstrate the synchronization phenomena within biological organisms of a given GON and therefore shows the applicability of the obtained results.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Royal Society of the UK, the National Natural Science Foundation of China under Grant 60804028, the Teaching and Research Fund for Excellent Young Teachers at Southeast University of China, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in
generating reasonable responses with respect to multi-modal contents. However,
there is still a wide gap between the performance of recent MLLM-based
applications and the expectation of the broad public, even though the most
powerful OpenAI's GPT-4 and Google's Gemini have been deployed. This paper
strives to enhance understanding of the gap through the lens of a qualitative
study on the generalizability, trustworthiness, and causal reasoning
capabilities of recent proprietary and open-source MLLMs across four
modalities: ie, text, code, image, and video, ultimately aiming to improve the
transparency of MLLMs. We believe these properties are several representative
factors that define the reliability of MLLMs, in supporting various downstream
applications. To be specific, we evaluate the closed-source GPT-4 and Gemini
and 6 open-source LLMs and MLLMs. Overall we evaluate 230 manually designed
cases, where the qualitative results are then summarized into 12 scores (ie, 4
modalities times 3 properties). In total, we uncover 14 empirical findings that
are useful to understand the capabilities and limitations of both proprietary
and open-source MLLMs, towards more reliable downstream multi-modal
applications