Logical gaps in the approximate solutions of the social learning game and an exact solution

After the social learning models were proposed, finding the solutions of the games becomes a well-defined mathematical question. However, almost all papers on the games and their applications are based on solutions built upon either an add-hoc argument or a twisted Bayesian analysis of the games. Here, we present logical gaps in those solutions and an exact solution of our own. We also introduced a minor extension to the original game such that not only logical difference but also difference in action outcomes among those solutions become visible.Comment: A major revisio

DFF-ResNet : An Insect Pest Recognition Model Based on Residual Networks

Insect pest control is considered as a significant factor in the yield of commercial crops. Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. Based on the original residual block, we fused the feature from a previous layer between two 1×1 convolution layers in a residual signal branch to improve the capacity of the block. Furthermore, we explored the contribution of each residual group to the model performance. We found that adding the residual blocks of earlier residual groups promotes the model performance significantly, which improves the capacity of generalization of the model. By stacking the feature fusion residual block, we constructed the Deep Feature Fusion Residual Network (DFF-ResNet). To prove the validity and adaptivity of our approach, we constructed it with two common residual networks (Pre-ResNet and Wide Residual Network (WRN)) and validated these models on the Canadian Institute For Advanced Research (CIFAR) and Street View House Number (SVHN) benchmark datasets. The experimental results indicate that our models have a lower test error than those of baseline models. Then, we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset. The experimental results show that our models outperform the original ResNet and other state-of-the-art methods

EQG-RACE: Examination-Type Question Generation

Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments. However, existing QG technologies encounter several key issues concerning the biased and unnatural language sources of datasets which are mainly obtained from the Web (e.g. SQuAD). In this paper, we propose an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE. Two main strategies are employed in EQG-RACE for dealing with discrete answer information and reasoning among long contexts. A Rough Answer and Key Sentence Tagging scheme is utilized to enhance the representations of input. An Answer-guided Graph Convolutional Network (AG-GCN) is designed to capture structure information in revealing the inter-sentences and intra-sentence relations. Experimental results show a state-of-the-art performance of EQG-RACE, which is apparently superior to the baselines. In addition, our work has established a new QG prototype with a reshaped dataset and QG method, which provides an important benchmark for related research in future work. We will make our data and code publicly available for further research.Comment: Accepted by AAAI-202

Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration

While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data. In this paper we present a novel model for ultra high-resolution image restoration, referred to as the Global-Local Stepwise Generative Network (GLSGN), which employs a stepwise restoring strategy involving four restoring pathways: three local pathways and one global pathway. The local pathways focus on conducting image restoration in a fine-grained manner over local but high-resolution image patches, while the global pathway performs image restoration coarsely on the scale-down but intact image to provide cues for the local pathways in a global view including semantics and noise patterns. To smooth the mutual collaboration between these four pathways, our GLSGN is designed to ensure the inter-pathway consistency in four aspects in terms of low-level content, perceptual attention, restoring intensity and high-level semantics, respectively. As another major contribution of this work, we also introduce the first ultra high-resolution dataset to date for both reflection removal and rain streak removal, comprising 4,670 real-world and synthetic images. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain streak removal and image dehazing, show that our GLSGN consistently outperforms state-of-the-art methods.Comment: submmitted to Transactions on Image Processin

Mineralogical and geochemical study of carp otoliths from Baiyangdian Lake and Miyun Water Reservoir in China

Carp otoliths from two different freshwaters (Baiyangdian Lake and Miyun Water Reservoir) were mineralogically and chemically analyzed. The water quality standard of Miyun Water Reservoir is Grade 2 which is much better than the Grade 5 of Baiyangdian Lake. The aim of this study was to examine the differences in otoliths in mineralogy and chemistry from the two sites with quite different qualities. All the analyzed carps showed lapillus and sagitta otoliths made of aragonite, except for B-22 (from Baiyangdian Lake) whose lapillus consisted of vaterite and sagitta consisted of aragonite and vaterite; all asteriscus are composed of vaterite. It is inferred that the occurrence of vaterictic otoliths is linked to poor water quality. Chemical analysis showed that significant difference of Pb concentration between sites was tested by t-test of the compare means (t-test comparison: t = 2.043, P<0.05). While the sitespecific differences of the other metals were not significant. In addition, a significant difference of Sn concentration was tested as well (t-test comparison: t = 2.652, P<0.05). Average content of lapilli Pb is consistent with the water dissolved Pb measurement, with higher dissolved Pb concentration in Baiyangdian Lake relative to the Miyun Water Reservoir.Key words: Carp otoliths, water quality, mineralogy, chemistry, Pb

Feature Decoupling-Recycling Network for Fast Interactive Segmentation

Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input without considering the invariant nature of the source image. As a result, extracting features from the source image is repeated in each interaction, resulting in substantial computational redundancy. In this work, we propose the Feature Decoupling-Recycling Network (FDRN), which decouples the modeling components based on their intrinsic discrepancies and then recycles components for each user interaction. Thus, the efficiency of the whole interactive process can be significantly improved. To be specific, we apply the Decoupling-Recycling strategy from three perspectives to address three types of discrepancies, respectively. First, our model decouples the learning of source image semantics from the encoding of user guidance to process two types of input domains separately. Second, FDRN decouples high-level and low-level features from stratified semantic representations to enhance feature learning. Third, during the encoding of user guidance, current user guidance is decoupled from historical guidance to highlight the effect of current user guidance. We conduct extensive experiments on 6 datasets from different domains and modalities, which demonstrate the following merits of our model: 1) superior efficiency than other methods, particularly advantageous in challenging scenarios requiring long-term interactions (up to 4.25x faster), while achieving favorable segmentation performance; 2) strong applicability to various methods serving as a universal enhancement technique; 3) well cross-task generalizability, e.g., to medical image segmentation, and robustness against misleading user guidance.Comment: Accepted to ACM MM 202

Hydrogen sulfide is involved in the chilling stress response in Vitis vinifera L.

Hydrogen sulfide (H2S) is an important signaling molecule involved in several stress-resistance processes in plants, such as drought and heavy metal stresses. However, little is known about the roles of H2S in responses to chilling stress. In this paper, we demonstrated that chilling stress enhance the H2S levels, the H2S synthetase (L-/D-cysteine desulfhydrase, L/DCD) activities, and the expression of L/DCD gene in Vitis vinifera L. ‘F-242’. Furthermore, the seedlings were treated with sodium hydrosulfide (NaHS, a H2S donor) and hypotaurine (HT, a H2S scavenger) at 4°C to examine the effects of exogenous H2S on grape. The results revealed that the high activity of superoxide dismutase and enhanced expression of VvICE1 and VvCBF3 genes, but low level of super oxide anion radical, malondialdehyde content and cell membrane permeability were detected after addition of NaHS. In contrast, HT treatment displayed contrary effect under the chilling temperature. Taken together, these data suggested that H2S might be directly involved in the cold signal transduction pathway of grape