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We examine a quantity competition among branded and nonbranded firms. The market comprises two consumer segments: one purchases only branded products (the high-end market), while the other segment's consumers purchase less expensive products (the low-end market). When branded firms take actions sequentially, we show that the branded leader has an incentive to restrict its quantity to avoid entering the low-end market. As the follower recognizes this incentive, it can restrict the leader by implementing a quantity constraint, which is affected by the number of nonbranded firms. We find that both the branded leader and follower could benefit from the nonbranded firms and that the leader prefers to have more nonbranded firms in the market than the follower does. Furthermore, we show that the free entry of nonbranded firms could negatively affect total surplus as well as consumer surplus even without any costs, because of the premium pricing of branded products.

Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.

To improve software reliability, software defect prediction is used to find software bugs and prioritize testing efforts. Recently, some researchers introduced deep learning models, such as the deep belief network (DBN) and the state-of-the-art convolutional neural network (CNN), and used automatically generated features extracted from abstract syntax trees (ASTs) and deep learning models to improve defect prediction performance. However, the research on the CNN model failed to reveal clear conclusions due to its limited dataset size, insufficiently repeated experiments, and outdated baseline selection. To solve these problems, we built the PROMISE Source Code (PSC) dataset to enlarge the original dataset in the CNN research, which we named the Simplified PROMISE Source Code (SPSC) dataset. Then, we proposed an improved CNN model for within-project defect prediction (WPDP) and compared our results to existing CNN results and an empirical study. Our experiment was based on a 30-repetition holdout validation and a 10 * 10 cross-validation. Experimental results showed that our improved CNN model was comparable to the existing CNN model, and it outperformed the state-of-the-art machine learning models significantly for WPDP. Furthermore, we defined hyperparameter instability and examined the threat and opportunity it presents for deep learning models on defect prediction.

The long-standing problem of geometric problem solving in artificial intelligence education has attracted widespread attention. It is necessary to combine geometry diagrams and text descriptions to form a logical representation. This involves combining the knowledge of mathematical theorems, generating a solution sequence, and executing to obtain the answer. However, deficiencies in the feature extraction of geometry diagrams and the fusion of diagram text information can lead to poor performance in solving geometry problems. To effectively extract geometry diagram features, this study proposes an improved diagram parser DenseNet, and enhances the semantic representation of cross-modal information by adding auxiliary tasks. A structural and semantic pre-training strategy was used to parse the text description to avoid different problem solving schemes due to subtle differences in the interpretation of text content. Information fusion was realized by connecting the two modal labels, and then the information was sent to the encoder for fusion. The geometric knowledge was generated under the guidance of multi-modal information, and these programs were executed to obtain the results. Additionally, the performance of the proposed geometric neural solution method on the PGPS9K dataset is improved by 1.3% on average. Compared with the Geometry3K dataset, the effectiveness was proven.

Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust. Besides, they are less efficient during training or slow during inference, which is not friendly to real-time applications. In this paper, we propose a scale-transferrable architecture for practical object detection based on the analysis of the connection between dilation rate and effective receptive field. Our method firstly predicts a global continuous scale, which is shared by all positions, for each convolution filter of each network stage. Secondly, we average the spatial features and distill the scale from channels to effectively learn the scale. Thirdly, for fast-deployment, we propose a scale decomposition method that transfers the robust fractional scale into the combination of fixed integral scales for each convolution filter, which exploits the dilated convolution. Moreover, to overcome the shortcomings of our method for large-scale object detection, we modify the Feature Pyramid Network structure. Finally, we illustrate the orthogonality role of our method for sampling strategy. We demonstrate the effectiveness of our method on one-stage and two-stage algorithms under different configurations and compare them with different dilated convolution blocks. For practical applications, the training strategy of our method is simple and efficient, avoiding complex data sampling or optimization strategy. During inference, we reduce the latency of the proposed method by using the hardware accelerator TensorRT without extra operation. On the COCO test-dev, our model achieves 41.7% mAP on one-stage detector and 42.5% mAP on two-stage detector based on ResNet-101, and outperforms baselines by 3.2% and 3.1% mAP, respectively.

A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.

Abnormal angiogenesis plays an important role in impaired wound healing and development of chronic wounds in diabetes mellitus. Angelica dahurica radix is a common traditional Chinese medicine with wide spectrum medicinal effects. In this study, we analyzed the potential roles of Angelica dahurica ethanolic extract (ADEE) in correcting impaired angiogenesis and delayed wound healing in diabetes by using streptozotocin-induced diabetic rats. ADEE treatment accelerated diabetic wound healing through inducing angiogenesis and granulation tissue formation. The angiogenic property of ADEE was subsequently verified ex vivo using aortic ring assays. Furthermore, we investigated the in vitro angiogenic activity of ADEE and its underlying mechanisms using human umbilical vein endothelial cells. ADEE treatment induced HUVECs proliferation, migration, and tube formation, which are typical phenomena of angiogenesis, in dose-dependent manners. These effects were associated with activation of angiogenic signal modulators, including extracellular signal-regulated kinase 1/2 (ERK1/2), Akt, endothelial nitric oxide synthase (eNOS) as well as increased NO production, and independent of affecting VEGF expression. ADEE-induced angiogenic events were inhibited by the MEK inhibitor PD98059, the PI3K inhibitor Wortmannin, and the eNOS inhibitor L-NAME. Our findings highlight an angiogenic role of ADEE and its ability to protect against impaired wound healing, which may be developed as a promising therapy for impaired angiogenesis and delayed wound healing in diabetes.

With the development of resting-state functional magnetic resonance imaging (rs-fMRI) technology, the functional connectivity network (FCN) which reflects the statistical similarity of temporal activity between brain regions has shown promising results for the identification of neuropsychiatric disorders. Alteration in FCN is believed to have the potential to locate biomarkers for classifying or predicting schizophrenia (SZ) from healthy control. However, the traditional FCN analysis with stationary assumption, i.e., static functional connectivity network (SFCN) at the time only measures the simple functional connectivity among brain regions, ignoring the dynamic changes of functional connectivity and the high-order dynamic interactions. In this paper, the dynamic functional connectivity network (DFCN) is constructed to delineate the characteristic of connectivity variation across time. A high-order functional connectivity network (HFCN) designed based on DFCN, could characterize more complex spatial interactions across multiple brain regions with the potential to reflect complex functional segregation and integration. Specifically, the temporal variability and the high-order network topology features, which characterize the brain FCNs from region and connectivity aspects, are extracted from DFCN and HFCN respectively. Experiment results on SZ identification prove that our method is more effective (i.e., obtaining a significantly higher classification accuracy, 81.82%) than other competing methods. Post hoc inspection of the informative features in the individualized classification task further could serve as the potential biomarkers for identifying associated aberrant connectivity in SZ.

This study investigated neurologic changes in patients with dry eye (DE) by functional magnetic resonance imaging (fMRI) and to used regional homogeneity (ReHo) analysis to clarify the relationship between these changes and clinical features of DE. A total of 28 patients with DE and 28 matched healthy control (HC) subjects (10 males and 18 females in each group) were enrolled. fMRI scans were performed in both groups. We carried out ReHo analysis to assess differences in neural activity between the 2 groups, and receiver operating characteristic curve (ROC) analysis was performed to evaluate the performance of ReHo values of specific brain areas in distinguishing DE patients from HCs. The relationship between average ReHo values and clinical characteristics was assessed by correlation analysis. ReHo values of the middle frontal gyrus, inferior frontal gyrus, and superior frontal gyrus were significantly lower in DE patients compared to HCs. The ROC analysis showed that ReHo value had high accuracy in distinguishing between DE patients and HCs (P < 0.0001). The ReHo values of the middle frontal gyrus and dorsolateral superior frontal gyrus were correlated to disease duration (P < 0.05). Symptoms of ocular surface injury in DE patients are associated with dysfunction in specific brain regions, which may underlie the cognitive impairment, psychiatric symptoms, and depressive mood observed in DE patients. The decreased ReHo values of some brain gyri in this study may provide a reference for clinical diagnosis and determination of treatment efficacy.

Proteins exhibit remarkable molecular recognition by dynamically adjusting their conformations to selectively interact with ligands at specialized binding sites. To bind hydrated ligands, proteins leverage amino acid residues with similar water affinities as the substrate, minimizing the energy required to strip water molecules from the hydrophilic substrates. In synthetic receptor design, replicating this sophisticated adaptability remains a challenge, as most artificial receptors are optimized to bind desolvated substances. Here, we show that proline-based synthetic receptors can mimic the conformational dynamics of proteins to achieve selective binding of hydrophilic and amphiphilic fluoride substances in aqueous environments. This finding highlights the critical role of receptor flexibility and strategic hydrophilicity in enhancing ligand recognition and affinity in water. Moreover, it establishes a new framework for designing versatile synthetic receptors with tunable hydrophobicity and hydrophilicity profiles. Proteins dynamically adjust their conformations to interact with their ligands through binding sites that accommodate either amphiphilic or hydrophilic substrates, but most synthetic receptors are designed to bind desolvated substances. Here, the authors design proline-based receptors capable of binding hydrated and amphiphilic substances.