Real-time motion data annotation via action string

Even though there is an explosive growth of motion capture data, there is still a lack of efficient and reliable methods to automatically annotate all the motions in a database. Moreover, because of the popularity of mocap devices in home entertainment systems, real-time human motion annotation or recognition becomes more and more imperative. This paper presents a new motion annotation method that achieves both the aforementioned two targets at the same time. It uses a probabilistic pose feature based on the Gaussian Mixture Model to represent each pose. After training a clustered pose feature model, a motion clip could be represented as an action string. Then, a dynamic programming-based string matching method is introduced to compare the differences between action strings. Finally, in order to achieve the real-time target, we construct a hierarchical action string structure to quickly label each given action string. The experimental results demonstrate the efficacy and efficiency of our method

Epigenetic Down-Regulation of Sirt 1 via DNA Methylation and Oxidative Stress Signaling Contributes to the Gestational Diabetes Mellitus-Induced Fetal Programming of Heart Ischemia-Sensitive Phenotype in Late Life.

Rationale: The incidence of gestational diabetes mellitus (GDM) is increasing worldwide. However, whether and how GDM exposure induces fetal programming of adult cardiac dysfunctional phenotype, especially the underlying epigenetic molecular mechanisms and theranostics remain unclear. To address this problem, we developed a late GDM rat model. Methods: Pregnant rats were made diabetic on day 12 of gestation by streptozotocin (STZ). Experiments were conducted in 6 weeks old offspring. Results: There were significant increases in ischemia-induced cardiac infarction and gender-dependent left ventricular (LV) dysfunction in male offspring in GDM group as compared to controls. Exposure to GDM enhanced ROS level and caused a global DNA methylation in offspring cardiomyocytes. GDM attenuated cardiac Sirt 1 protein and p-Akt/Akt levels, but enhanced autophagy-related proteins expression (Atg 5 and LC3 II/LC3 I) as compared to controls. Ex-vivo treatment of DNA methylation inhibitor, 5-Aza directly inhibited Dnmt3A and enhanced Sirt 1 protein expression in fetal hearts. Furthermore, treatment with antioxidant, N-acetyl-cysteine (NAC) in offspring reversed GDM-mediated DNA hypermethylation, Sirt1 repression and autophagy-related gene protein overexpression in the hearts, and rescued GDM-induced deterioration in heart ischemic injury and LV dysfunction. Conclusion: Our data indicated that exposure to GDM induced offspring cardiac oxidative stress and DNA hypermethylation, resulting in an epigenetic down-regulation of Sirt1 gene and aberrant development of heart ischemia-sensitive phenotype, which suggests that Sirt 1-mediated signaling is the potential therapeutic target for the heart ischemic disease in offspring

The resilience of interdependent transportation networks under targeted attack

Modern world builds on the resilience of interdependent infrastructures characterized as complex networks. Recently, a framework for analysis of interdependent networks has been developed to explain the mechanism of resilience in interdependent networks. Here we extend this interdependent network model by considering flows in the networks and study the system's resilience under different attack strategies. In our model, nodes may fail due to either overload or loss of interdependency. Under the interaction between these two failure mechanisms, it is shown that interdependent scale-free networks show extreme vulnerability. The resilience of interdependent SF networks is found in our simulation much smaller than single SF network or interdependent SF networks without flows.Comment: 5 pages, 4 figure

Dynamical phase transition in quantum neural networks with large depth

Understanding the training dynamics of quantum neural networks is a fundamental task in quantum information science with wide impact in physics, chemistry and machine learning. In this work, we show that the late-time training dynamics of quantum neural networks can be described by the generalized Lotka-Volterra equations, which lead to a dynamical phase transition. When the targeted value of cost function crosses the minimum achievable value from above to below, the dynamics evolve from a frozen-kernel phase to a frozen-error phase, showing a duality between the quantum neural tangent kernel and the total error. In both phases, the convergence towards the fixed point is exponential, while at the critical point becomes polynomial. Via mapping the Hessian of the training dynamics to a Hamiltonian in the imaginary time, we reveal the nature of the phase transition to be second-order with the exponent $\nu=1$, where scale invariance and closing gap are observed at critical point. We also provide a non-perturbative analytical theory to explain the phase transition via a restricted Haar ensemble at late time, when the output state approaches the steady state. The theory findings are verified experimentally on IBM quantum devices.Comment: 11+35 pages, comments are welcome

A human motion feature based on semi-supervised learning of GMM

Using motion capture to create naturally looking motion sequences for virtual character animation has become a standard procedure in the games and visual effects industry. With the fast growth of motion data, the task of automatically annotating new motions is gaining an importance. In this paper, we present a novel statistic feature to represent each motion according to the pre-labeled categories of key-poses. A probabilistic model is trained with semi-supervised learning of the Gaussian mixture model (GMM). Each pose in a given motion could then be described by a feature vector of a series of probabilities by GMM. A motion feature descriptor is proposed based on the statistics of all pose features. The experimental results and comparison with existing work show that our method performs more accurately and efficiently in motion retrieval and annotation

Continuous Beam Steering Through Broadside Using Asymmetrically Modulated Goubau Line Leaky-Wave Antennas

Goubau line is a single-conductor transmission line, featuring easy integration and low-loss transmission properties. Here, we propose a periodic leaky-wave antenna (LWA) based on planar Goubau transmission line on a thin dielectric substrate. The leaky-wave radiations are generated by introducing periodic modulations along the Goubau line. In this way, the surface wave, which is slow-wave mode supported by the Goubau line, achieves an additional momentum and hence enters the fast-wave region for radiations. By employing the periodic modulations, the proposed Goubau line LWAs are able to continuously steer the main beam from backward to forward within the operational frequency range. However, the LWAs usually suffer from a low radiation efficiency at the broadside direction. To overcome this drawback, we explore both transversally and longitudinally asymmetrical modulations to the Goubau line. Theoretical analysis, numerical simulations and experimental results are given in comparison with the symmetrical LWAs. It is demonstrated that the asymmetrical modulations significantly improve the radiation efficiency of LWAs at the broadside. Furthermore, the measurement results agree well with the numerical ones, which experimentally validates the proposed LWA structures. These novel Goubau line LWAs, experimentally demonstrated and validated at microwave frequencies, show also great potential for millimeter-wave and terahertz systems

Consensus Graph Representation Learning for Better Grounded Image Captioning

The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words. The most common way is to encourage the captioning model to dynamically link generated object words or phrases to appropriate regions of the image, i.e., the grounded image captioning (GIC). However, GIC utilizes an auxiliary task (grounding objects) that has not solved the key issue of object hallucination, i.e., the semantic inconsistency. In this paper, we take a novel perspective on the issue above - exploiting the semantic coherency between the visual and language modalities. Specifically, we propose the Consensus Rraph Representation Learning framework (CGRL) for GIC that incorporates a consensus representation into the grounded captioning pipeline. The consensus is learned by aligning the visual graph (e.g., scene graph) to the language graph that consider both the nodes and edges in a graph. With the aligned consensus, the captioning model can capture both the correct linguistic characteristics and visual relevance, and then grounding appropriate image regions further. We validate the effectiveness of our model, with a significant decline in object hallucination (-9% CHAIRi) on the Flickr30k Entities dataset. Besides, our CGRL also evaluated by several automatic metrics and human evaluation, the results indicate that the proposed approach can simultaneously improve the performance of image captioning (+2.9 Cider) and grounding (+2.3 F1LOC).Comment: 9 pages, 5 figures, AAAI 202