Stochastic representation for solutions of a system of coupled HJB-Isaacs equations with integral-partial operators

In this paper, we focus on the stochastic representation of a system of coupled Hamilton-Jacobi-Bellman-Isaacs (HJB-Isaacs (HJBI), for short) equations which is in fact a system of coupled Isaacs' type integral-partial differential equation. For this, we introduce an associated zero-sum stochastic differential game, where the state process is described by a classical stochastic differential equation (SDE, for short) with jumps, and the cost functional of recursive type is defined by a new type of backward stochastic differential equation (BSDE, for short) with two Poisson random measures, whose wellposedness and a prior estimate as well as the comparison theorem are investigated for the first time. One of the Poisson random measures appearing in the SDE and the BSDE stems from the integral term of the HJBI equations; the other random measure in BSDE is introduced to link the coupling factor of the HJBI equations. We show through an extension of the dynamic programming principle that the lower value function of this game problem is the viscosity solution of the system of our coupled HJBI equations. The uniqueness of the viscosity solution is also obtained in a space of continuous functions satisfying certain growth condition. In addition, also the upper value function of the game is shown to be the solution of the associated system of coupled Issacs' type of integral-partial differential equations. As a byproduct, we obtain the existence of the value for the game problem under the well-known Isaacs' condition.Comment: 37 page

Dynamic changes and multi-dimensional evolution of portfolio optimization

Although there has been an increasing number of studies investigate portfolio optimization from different perspectives, few attempts could be found that focus on the development trend and hotspots of this research area. Therefore, it motivates us to comprehensively investigate the development of portfolio optimization research and give some deep insights into this knowledge domain. In this paper, some bibliometric methods are utilized to analyse the status quo and emerging trends of portfolio optimization research on various aspects such as authors, countries and journals. Besides, ‘theories’, ‘models’ and ‘algorithms’, especially heuristic algorithms are identified as the hotspots in the given periods. Furthermore, the evolutionary analysis tends to presents the dynamic changes of the cutting-edge concepts of this research area in the time dimension. It is found that more portfolio optimization studies were at an exploration stage from mean-variance analysis to consideration of multiple constraints. However, heuristic algorithms have become the driving force of portfolio optimization research in recent years. Multidisciplinary analyses and applications are also the main trends of portfolio optimization research. By analysing the dynamic changes and multi-dimensional evolution in recent decades, we contribute to presenting some deep insights of the portfolio optimization research directly, which assists researchers especially beginners to comprehensively learn this research field

Atomistic tensile deformation mechanisms in a CrCoNi medium-entropy alloy with gradient nano-grained structure at cryogenic temperature

Large-scale molecular dynamics (MD) simulations have been utilized to reveal the atomistic deformation mechanisms of a CrCoNi medium-entropy alloy (MEA) with gradient nano-grained (GNG) structure in the present study. Strong strain hardening was observed in the gradient nano-grained structure at the elasto-plastic transition stage, which could be attributed to the Masing hardening. After yielding, obvious partitioning of tensile strain was detected in the gradient nano-grained structure, which indicates the existence of hetero-deformation induced (HDI) hardening effect and could account for the higher flow stress of the gradient nano-grained structure than that calculated by the rule of mixture from its homogenous nano-grained (NG) structured counterparts. Moreover, partitioning of stacking fault factor (corresponding to the partitioning of tensile strain), which demonstrates the intensity of dislocation behaviors, was also observed in the gradient nano-grained structure. The differences of factors for each grain size area were found to be smaller in the gradient nano-grained structure than those of its homogeneous nano-grained structured counterparts, which indicates the influence of forward stress and back stress on dislocation motion near the zone boundary between the hard zone with smaller grains and the soft zone with larger grains, further verifying the presence of hetero-deformation induced hardening in the gradient nano-grained structure

SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation

Unsupervised video object segmentation (UVOS) aims at detecting the primary objects in a given video sequence without any human interposing. Most existing methods rely on two-stream architectures that separately encode the appearance and motion information before fusing them to identify the target and generate object masks. However, this pipeline is computationally expensive and can lead to suboptimal performance due to the difficulty of fusing the two modalities properly. In this paper, we propose a novel UVOS model called SimulFlow that simultaneously performs feature extraction and target identification, enabling efficient and effective unsupervised video object segmentation. Concretely, we design a novel SimulFlow Attention mechanism to bridege the image and motion by utilizing the flexibility of attention operation, where coarse masks predicted from fused feature at each stage are used to constrain the attention operation within the mask area and exclude the impact of noise. Because of the bidirectional information flow between visual and optical flow features in SimulFlow Attention, no extra hand-designed fusing module is required and we only adopt a light decoder to obtain the final prediction. We evaluate our method on several benchmark datasets and achieve state-of-the-art results. Our proposed approach not only outperforms existing methods but also addresses the computational complexity and fusion difficulties caused by two-stream architectures. Our models achieve 87.4% J & F on DAVIS-16 with the highest speed (63.7 FPS on a 3090) and the lowest parameters (13.7 M). Our SimulFlow also obtains competitive results on video salient object detection datasets.Comment: Accepted to ACM MM 202

Weakly Supervised Video Salient Object Detection via Point Supervision

Video salient object detection models trained on pixel-wise dense annotation have achieved excellent performance, yet obtaining pixel-by-pixel annotated datasets is laborious. Several works attempt to use scribble annotations to mitigate this problem, but point supervision as a more labor-saving annotation method (even the most labor-saving method among manual annotation methods for dense prediction), has not been explored. In this paper, we propose a strong baseline model based on point supervision. To infer saliency maps with temporal information, we mine inter-frame complementary information from short-term and long-term perspectives, respectively. Specifically, we propose a hybrid token attention module, which mixes optical flow and image information from orthogonal directions, adaptively highlighting critical optical flow information (channel dimension) and critical token information (spatial dimension). To exploit long-term cues, we develop the Long-term Cross-Frame Attention module (LCFA), which assists the current frame in inferring salient objects based on multi-frame tokens. Furthermore, we label two point-supervised datasets, P-DAVIS and P-DAVSOD, by relabeling the DAVIS and the DAVSOD dataset. Experiments on the six benchmark datasets illustrate our method outperforms the previous state-of-the-art weakly supervised methods and even is comparable with some fully supervised approaches. Source code and datasets are available.Comment: accepted by ACM MM 202

Coronavirus accessory protein ORF3 biology and its contribution to viral behavior and pathogenesis

Coronavirus porcine epidemic diarrhea virus (PEDV) is classified in the genu

PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation

Panoramic videos contain richer spatial information and have attracted tremendous amounts of attention due to their exceptional experience in some fields such as autonomous driving and virtual reality. However, existing datasets for video segmentation only focus on conventional planar images. To address the challenge, in this paper, we present a panoramic video dataset, PanoVOS. The dataset provides 150 videos with high video resolutions and diverse motions. To quantify the domain gap between 2D planar videos and panoramic videos, we evaluate 15 off-the-shelf video object segmentation (VOS) models on PanoVOS. Through error analysis, we found that all of them fail to tackle pixel-level content discontinues of panoramic videos. Thus, we present a Panoramic Space Consistency Transformer (PSCFormer), which can effectively utilize the semantic boundary information of the previous frame for pixel-level matching with the current frame. Extensive experiments demonstrate that compared with the previous SOTA models, our PSCFormer network exhibits a great advantage in terms of segmentation results under the panoramic setting. Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking

Synthesis, characterization, and antifungal evaluation of novel 1,2,3-triazolium-functionalized starch derivative

1,2,3-Triazolium-functionalized starch derivative was obtained by straightforward quaternization of the synthesized starch derivative bearing 1,2,3-triazole with benzyl bromide by combining the robust attributes of cuprous-catalyzed azide-alkyne cycloaddition. These novel starch derivatives were characterized by FTIR, UV-vis, H-1 NMR, C-13 NMR, and elemental analysis. Their antifungal activities against Colletotrichum lagenarium, Watermelon fusarium, and Phomopsis asparagi were investigated by hypha measurement in vitro. The fungicidal assessment revealed that compared with starch and starch derivative bearing 1,2,3-triazole with inhibitory indices of below 15% at 1.0 mg/mL, 1,2,3-triazolium-functionalized starch derivative had superior antifungal activity with inhibitory rates of over 60%. Especially, the best inhibitory index of 1,2,3-triazolium-functionalized starch derivative against Colletotrichum lagenarium attained 90% above at 1.0mg/mL. The results obviously showed that quaternization of 1,2,3-triazole with benzyl bromide could effectively enhance antifungal activity of the synthesized starch derivatives. The synthetic strategy described here could be utilized for the development of starch as novel antifungal biomaterial. (C) 2017 Elsevier B.V. All rights reserved