Recently, the security of multimodal verification has become a grow-ing concern since many fusion systems have been known to be easily deceived by partial spoof attacks, i.e. only a subset of modalities is spoofed. In this paper, we verify such a vulnerability and propose to use two representation-based met-rics to close this gap. Firstly, we use the collaborative representation fidelity with non-target subjects to measure the affinity of a query sample to the claimed client. We further consider sparse coding as a competing comparison among the client and the non-target subjects, and hence explore two sparsity-based measures for recognition. Last, we select the representation-based measure, and assemble its score and the affinity score of each modality to train a support vector machine classifier. Our experimental results on a chimeric multimodal database with face and ear traits demonstrate that in both regular verification and partial spoof at-tacks, the proposed method significant
The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter assisted by particle flows (PF) has been shown to be promising for audio-visual multi-speaker tracking. A clustering step is often employed for calculating the particle flow, which leads to a substantial increase in the computational cost. To address this issue, we propose an alternative method based on the labelled non-zero particle flow (LNPF) to adjust the particle states. Results obtained from the AV16.3 dataset show improved performance by the proposed method in terms of computational efficiency and tracking accuracy as compared with baseline AV-NPF-SMC-PHD methods.
Hybrid lead halide perovskites have emerged as high-performance photovoltaic materials with their extraordinary optoelectronic properties. In particular, the remarkable device efficiency is strongly influenced by the perovskite crystallinity and the film morphology. Here, we investigate the perovskites crystallization kinetics and growth mechanism in real time from liquid precursor continually to the final uniform film. We utilize some advanced in-situ characterization techniques including synchrotron-based grazing incident X-ray diffraction to observe crystal structure and chemical transition of perovskites. The nano-assemble model from perovskite intermediated [PbI6]4- cage nanoparticles to bulk polycrystals is proposed to understand perovskites formation at a molecular- or nano-level. A crystallization-depletion mechanism is developed to elucidate the periodic crystallization and the kinetically trapped morphology at a mesoscopic level. Based on these in-situ dynamics studies, the whole process of the perovskites formation and transformation from the molecular to the microstructure over relevant temperature and time scales is successfully demonstrated.
Target tracking is a challenging task and generally no analytical solution is available, especially for the multi-target tracking systems. To address this problem, probability hypothesis density (PHD) filter is used by propagating the PHD instead of the full multi-target posterior. Recently, the particle flow filter based on the log homotopy provides a new way for state estimation. In this paper, we propose a novel sequential Monte Carlo (SMC) implementation for the PHD filter assisted by the particle flow (PF), which is called PF-SMCPHD filter. Experimental results show that our proposed filter has higher accuracy than the SMC-PHD filter and is computationally cheaper than the Gaussian mixture PHD (GM-PHD) filter.
Sequential Monte Carlo probability hypothesis density (SMC- PHD) ltering has been recently exploited for audio-visual (AV) based tracking of multiple speakers, where audio data are used to inform the particle distribution and propagation in the visual SMC-PHD lter. How- ever, the performance of the AV-SMC-PHD lter can be a ected by the mismatch between the proposal and the posterior distribution. In this pa- per, we present a new method to improve the particle distribution where audio information (i.e. DOA angles derived from microphone array mea- surements) is used to detect new born particles and visual information (i.e. histograms) is used to modify the particles with particle ow (PF). Using particle ow has the bene t of migrating particles smoothly from the prior to the posterior distribution. We compare the proposed algo- rithm with the baseline AV-SMC-PHD algorithm using experiments on the AV16.3 dataset with multi-speaker sequences.
The presence of low molar mass surfactants in latex films results in detrimental effects on their water permeability, gloss and adhesion. For applications as coatings, there is a need to develop formulations that do not contain surfactants and that have better water barrier properties. Having previously reported the synthesis of surfactant-free latex particles in water using low amounts (< 2 wt%) of controlled radical polymer chains (Lesage de la Haye et al. Macromolecules 2017, 50, 9315−9328), here we study the water barrier properties of films made from these particles and their application in anti-corrosion coatings. When films cast from aqueous dispersions of acrylate copolymer particles stabilized with poly(sodium 4-styrenesulfonate) (PSSNa) were immersed in water for three days, they sorbed only 4 wt.% water. This uptake is only slightly higher than the value predicted for the pure copolymer, indicating that the negative effects of any particle boundaries and hydrophilic stabilizing molecules are minimal. This sorption of liquid water is five times lower than what is found in films cast from particles stabilized with the same proportion of poly(methacrylic acid) (PMAA), which is more hydrophilic than PSSNa. In water vapor with 90% relative humidity, the PSSNa-based film had an equilibrium sorption of only 4 wt.%. A small increase in the PMAA content has a strong and negative impact on the barrier properties. Nuclear magnetic resonance relaxometry on polymer films after immersion in water shows that water clusters have the smallest size in the films containing PSSNa. Furthermore, these films retain their optical clarity during immersion in liquid water for up to 90 minutes, whereas all other compositions quickly develop opacity (“water whitening”) as a result of light scattering from sorbed water. This implies a remarkably complete coalescence and a very small density of defects, which yields properties matching those of some solvent borne films. The latex stabilized with PSSNa is implemented as the binder in a paint formulation for application as an anti-corrosive barrier coating on steel substrates and evaluated in accelerated weathering and corrosion tests. Our results demonstrate the potential of self-stabilized latex particles for the development of different applications, such as waterborne protective coatings and pressure-sensitive adhesives.
Prediction uncertainty has rarely been integrated into traditional soft sensors in industrial processes. In this work, a novel autoswitch probabilistic soft sensor modeling method is proposed for online quality prediction of a whole industrial multigrade process with several steady-state grades and transitional modes. It is different from traditional deterministic soft sensors. Several single Gaussian process regression (GPR) models are first constructed for each steady-state grade. A new index is proposed to evaluate each GPR-based steady-state grade model. For the online prediction of a new sample, a prediction variance-based Bayesian inference method is proposed to explore the reliability of existing GPR-based steady-state models. The prediction can be achieved using the related steady-state GPR model if its reliability using this model is large enough. Otherwise, the query sample can be treated as in transitional modes and a local GPR model in a just-in-time manner is online built. Moreover, to improve the efficiency, detailed implementation steps of the autoswitch GPR soft sensors for a whole multigrade process are developed. The superiority of the proposed method is demonstrated and compared with other soft sensors in an industrial process in Taiwan, in terms of online quality prediction.
Sequential Monte Carlo probability hypothesis density (SMC-PHD) filtering is a popular method used recently for audio-visual (AV) multi-speaker tracking. However, due to the weight degeneracy problem, the posterior distribution can be represented poorly by the estimated probability, when only a few particles are present around the peak of the likelihood density function. To address this issue, we propose a new framework where particle flow (PF) is used to migrate particles smoothly from the prior to the posterior probability density. We consider both zero and non-zero diffusion particle flows (ZPF/NPF), and developed two new algorithms, AV-ZPF-SMC-PHD and AV-NPFSMC- PHD, where the speaker states from the previous frames are also considered for particle relocation. The proposed algorithms are compared systematically with several baseline tracking methods using the AV16.3, AVDIAR and CLEAR datasets, and are shown to offer improved tracking accuracy and average effective sample size (ESS).
The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been shown to be promising for audio-visual multi-speaker tracking. Recently, the zero diffusion particle flow (ZPF) has been used to mitigate the weight degeneracy problem in the SMC-PHD filter. However, this leads to a substantial increase in the computational cost due to the migration of particles from prior to posterior distribution with a partial differential equation. This paper proposes an alternative method based on the non-zero diffusion particle flow (NPF) to adjust the particle states by fitting the particle distribution with the posterior probability density using the nonzero diffusion. This property allows efficient computation of the migration of particles. Results from the AV16.3 dataset demonstrate that we can significantly mitigate the weight degeneracy problem with a smaller computational cost as compared with the ZPF based SMC-PHD filter.
Sequential Monte Carlo probability hypothesis density (SMC- PHD) filtering has been recently exploited for audio-visual (AV) based tracking of multiple speakers, where audio data are used to inform the particle distribution and propagation in the visual SMC-PHD filter. However, the performance of the AV-SMC-PHD filter can be affected by the mismatch between the proposal and the posterior distribution. In this paper, we present a new method to improve the particle distribution where audio information (i.e. DOA angles derived from microphone array measurements) is used to detect new born particles and visual information (i.e. histograms) is used to modify the particles with particle flow (PF). Using particle flow has the benefit of migrating particles smoothly from the prior to the posterior distribution. We compare the proposed algorithm with the baseline AV-SMC-PHD algorithm using experiments on the AV16.3 dataset with multi-speaker sequences.