147 research outputs found
Global stability of spatially nonhomogeneous steady state solution in a diffusive Holling-Tanner predator-prey model
The global stability of the nonhomogeneous positive steady state solution to
a diffusive Holling-Tanner predator-prey model in a heterogeneous environment
is proved by using a newly constructed Lyapunov function and estimates of
nonconstant steady state solutions. The techniques developed here can be
adapted for other spatially heterogeneous consumer-resource models.Comment: 14 page
Magnetoelastic coupling and charge correlation lengths in a twin domain of Ba(FeCo)As (): A high-resolution X-ray diffraction study
The interplay between structure, magnetism and superconductivity in single
crystal Ba(FeCo)As (x=0.047) has been studied using
high-resolution X-ray diffraction by monitoring charge Bragg reflections in
each twin domain separately. The emergence of the superconducting state is
correlated with the suppression of the orthorhombic distortion around
\emph{T}, exhibiting competition between orthorhombicity and
superconductivity. Above \emph{T}, the in-plane charge correlation
length increases with the decrease of temperature, possibly induced by nematic
fluctuations in the paramagnetic tetragonal phase. Upon cooling, anomalies in
the in-plane charge correlation lengths along () and axes
() are observed at \emph{T} and also at
\emph{T} indicative of strong magnetoelastic coupling. The
in-plane charge correlation lengths are found to exhibit anisotropic behavior
along and perpendicular to the in-plane component of stripe-type AFM wave
vector (101) below around \emph{T}. The temperature
dependence of the out-of-plane charge correlation length shows a single anomaly
at \emph{T}, reflecting the connection between Fe-As distance and
Fe local moment. The origin of the anisotropic in-plane charge correlation
lengths and is discussed on the basis of the antiphase
magnetic domains and their dynamic fluctuations.Comment: 7 pages, 6 figures, 34 references, submitted for publication in
Physical Review
3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models
3D point cloud models are widely applied in safety-critical scenes, which
delivers an urgent need to obtain more solid proofs to verify the robustness of
models. Existing verification method for point cloud model is time-expensive
and computationally unattainable on large networks. Additionally, they cannot
handle the complete PointNet model with joint alignment network (JANet) that
contains multiplication layers, which effectively boosts the performance of 3D
models. This motivates us to design a more efficient and general framework to
verify various architectures of point cloud models. The key challenges in
verifying the large-scale complete PointNet models are addressed as dealing
with the cross-non-linearity operations in the multiplication layers and the
high computational complexity of high-dimensional point cloud inputs and added
layers. Thus, we propose an efficient verification framework, 3DVerifier, to
tackle both challenges by adopting a linear relaxation function to bound the
multiplication layer and combining forward and backward propagation to compute
the certified bounds of the outputs of the point cloud models. Our
comprehensive experiments demonstrate that 3DVerifier outperforms existing
verification algorithms for 3D models in terms of both efficiency and accuracy.
Notably, our approach achieves an orders-of-magnitude improvement in
verification efficiency for the large network, and the obtained certified
bounds are also significantly tighter than the state-of-the-art verifiers. We
release our tool 3DVerifier via https://github.com/TrustAI/3DVerifier for use
by the community
Correlation between sequence conservation and structural thermodynamics of microRNA precursors from human, mouse, and chicken genomes
<p>Abstract</p> <p>Background</p> <p>Previous studies have shown that microRNA precursors (pre-miRNAs) have considerably more stable secondary structures than other native RNAs (tRNA, rRNA, and mRNA) and artificial RNA sequences. However, pre-miRNAs with ultra stable secondary structures have not been investigated. It is not known if there is a tendency in pre-miRNA sequences towards or against ultra stable structures? Furthermore, the relationship between the structural thermodynamic stability of pre-miRNA and their evolution remains unclear.</p> <p>Results</p> <p>We investigated the correlation between pre-miRNA sequence conservation and structural stability as measured by adjusted minimum folding free energies in pre-miRNAs isolated from human, mouse, and chicken. The analysis revealed that conserved and non-conserved pre-miRNA sequences had structures with similar average stabilities. However, the relatively ultra stable and unstable pre-miRNAs were more likely to be non-conserved than pre-miRNAs with moderate stability. Non-conserved pre-miRNAs had more G+C than A+U nucleotides, while conserved pre-miRNAs contained more A+U nucleotides. Notably, the U content of conserved pre-miRNAs was especially higher than that of non-conserved pre-miRNAs. Further investigations showed that conserved and non-conserved pre-miRNAs exhibited different structural element features, even though they had comparable levels of stability.</p> <p>Conclusions</p> <p>We proposed that there is a correlation between structural thermodynamic stability and sequence conservation for pre-miRNAs from human, mouse, and chicken genomes. Our analyses suggested that pre-miRNAs with relatively ultra stable or unstable structures were less favoured by natural selection than those with moderately stable structures. Comparison of nucleotide compositions between non-conserved and conserved pre-miRNAs indicated the importance of U nucleotides in the pre-miRNA evolutionary process. Several characteristic structural elements were also detected in conserved pre-miRNAs.</p
In silico genetic robustness analysis of microRNA secondary structures: potential evidence of congruent evolution in microRNA
<p>Abstract</p> <p>Background</p> <p>Robustness is a fundamental property of biological systems and is defined as the ability to maintain stable functioning in the face of various perturbations. Understanding how robustness has evolved has become one of the most attractive areas of research for evolutionary biologists, as it is still unclear whether genetic robustness evolved as a direct consequence of natural selection, as an intrinsic property of adaptations, or as congruent correlate of environment robustness. Recent studies have demonstrated that the stem-loop structures of microRNA (miRNA) are tolerant to some structural changes and show thermodynamic stability. We therefore hypothesize that genetic robustness may evolve as a correlated side effect of the evolution for environmental robustness.</p> <p>Results</p> <p>We examine the robustness of 1,082 miRNA genes covering six species. Our data suggest the stem-loop structures of miRNA precursors exhibit a significantly higher level of genetic robustness, which goes beyond the intrinsic robustness of the stem-loop structure and is not a byproduct of the base composition bias. Furthermore, we demonstrate that the phenotype of miRNA buffers against genetic perturbations, and at the same time is also insensitive to environmental perturbations.</p> <p>Conclusion</p> <p>The results suggest that the increased robustness of miRNA stem-loops may result from congruent evolution for environment robustness. Potential applications of our findings are also discussed.</p
General Debiasing for Multimodal Sentiment Analysis
Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal
information for prediction yet unavoidably suffers from fitting the spurious
correlations between multimodal features and sentiment labels. For example, if
most videos with a blue background have positive labels in a dataset, the model
will rely on such correlations for prediction, while ``blue background'' is not
a sentiment-related feature. To address this problem, we define a general
debiasing MSA task, which aims to enhance the Out-Of-Distribution (OOD)
generalization ability of MSA models by reducing their reliance on spurious
correlations. To this end, we propose a general debiasing framework based on
Inverse Probability Weighting (IPW), which adaptively assigns small weights to
the samples with larger bias i.e., the severer spurious correlations). The key
to this debiasing framework is to estimate the bias of each sample, which is
achieved by two steps: 1) disentangling the robust features and biased features
in each modality, and 2) utilizing the biased features to estimate the bias.
Finally, we employ IPW to reduce the effects of large-biased samples,
facilitating robust feature learning for sentiment prediction. To examine the
model's generalization ability, we keep the original testing sets on two
benchmarks and additionally construct multiple unimodal and multimodal OOD
testing sets. The empirical results demonstrate the superior generalization
ability of our proposed framework. We have released the code and data to
facilitate the reproduction
Enhancing robustness in video recognition models : Sparse adversarial attacks and beyond
Recent years have witnessed increasing interest in adversarial attacks on images, while adversarial video attacks have seldom been explored. In this paper, we propose a sparse adversarial attack strategy on videos (DeepSAVA). Our model aims to add a small human-imperceptible perturbation to the key frame of the input video to fool the classifiers. To carry out an effective attack that mirrors real-world scenarios, our algorithm integrates spatial transformation perturbations into the frame. Instead of using the norm to gauge the disparity between the perturbed frame and the original frame, we employ the structural similarity index (SSIM), which has been established as a more suitable metric for quantifying image alterations resulting from spatial perturbations. We employ a unified optimisation framework to combine spatial transformation with additive perturbation, thereby attaining a more potent attack. We design an effective and novel optimisation scheme that alternatively utilises Bayesian Optimisation (BO) to identify the most critical frame in a video and stochastic gradient descent (SGD) based optimisation to produce both additive and spatial-transformed perturbations. Doing so enables DeepSAVA to perform a very sparse attack on videos for maintaining human imperceptibility while still achieving state-of-the-art performance in terms of both attack success rate and adversarial transferability. Furthermore, built upon the strong perturbations produced by DeepSAVA, we design a novel adversarial training framework to improve the robustness of video classification models. Our intensive experiments on various types of deep neural networks and video datasets confirm the superiority of DeepSAVA in terms of attacking performance and efficiency. When compared to the baseline techniques, DeepSAVA exhibits the highest level of performance in generating adversarial videos for three distinct video classifiers. Remarkably, it achieves an impressive fooling rate ranging from 99.5% to 100% for the I3D model, with the perturbation of just a single frame. Additionally, DeepSAVA demonstrates favorable transferability across various time series models. The proposed adversarial training strategy is also empirically demonstrated with better performance on training robust video classifiers compared with the state-of-the-art adversarial training with projected gradient descent (PGD) adversary
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