The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification

Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what). In this paper, we propose to apply visual attention to fine-grained classification task using deep neural network. Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts. We combine these attentions to train domain-specific deep nets, then use it to improve both the what and where aspects. Importantly, we avoid using expensive annotations like bounding box or part information from end-to-end. The weak supervision constraint makes our work easier to generalize. We have verified the effectiveness of the method on the subsets of ILSVRC2012 dataset and CUB200_2011 dataset. Our pipeline delivered significant improvements and achieved the best accuracy under the weakest supervision condition. The performance is competitive against other methods that rely on additional annotations

Joint 3D Deployment and Resource Allocation for UAV-assisted Integrated Communication and Localization

In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted integrated communication and localization network in emergency scenarios where a single UAV is deployed as both an airborne base station (BS) and anchor node to assist ground BSs in communication and localization services. We formulate an optimization problem to maximize the sum communication rate of all users under localization accuracy constraints by jointly optimizing the 3D position of the UAV, and communication bandwidth and power allocation of the UAV and ground BSs. To address the intractable localization accuracy constraints, we introduce a new performance metric and geometrically characterize the UAV feasible deployment region in which the localization accuracy constraints are satisfied. Accordingly, we combine Gibbs sampling (GS) and block coordinate descent (BCD) techniques to tackle the non-convex joint optimization problem. Numerical results show that the proposed method attains almost identical rate performance as the meta-heuristic benchmark method while reducing the CPU time by 89.3%.Comment: The paper has been accepted for publication by IEEE Wireless Communications Letter

Production of doubly charmed hadron Ξcc++\Xi_{cc}^{++} and Tcc+T_{cc}^+ in relativistic heavy ion collisions

Heavy ion collisions provide a unique opportunity for studying the properties of exotic hadrons with two charm quarks. The production of $T_{cc}^+$ is significantly enhanced in nuclear collisions compared to proton-proton collisions due to the creation of multiple charm pairs. In this study, we employ the Langevin equation in combination with the Instantaneous Coalescence Model (LICM) to investigate the production of $T_{cc}^+$ and $\Xi_{cc}^{++}$ which consists of two charm quarks. We consider $T_{cc}^+$ as molecular states composed of $D$ and $D^*$ mesons. The Langevin equation is used to calculate the energy loss of charm quarks and $D$ mesons in the hot medium. The hadronization process, where charm quarks transform into each $D$ state as constituents of $T_{cc}^+$ production, is described using the coalescence model. The coalescence probability between $D$ and $D^*$ is determined by the Wigner function, which encodes the information of the $T_{cc}^+$ wave function. Our results show that the $T_{cc}^+$ production varies by approximately one order of magnitude when different widths in the Wigner function, representing distinct binding energies of $T_{cc}^+$, are considered. This variation offers valuable insights into the nature of $T_{cc}^+$ through the analysis of its wave function. The $\Xi_{cc}^{++}$ is treated as a hadronic state produced at the hadronization of the deconfined matter. Its production is also calculated as a comparison with the molecular state $T_{cc}^+$.Comment: 7 pages, 5 figure

Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation

Static and moving objects often occur in real-life videos. Most video object segmentation methods only focus on extracting and exploiting motion cues to perceive moving objects. Once faced with the frames of static objects, the moving object predictors may predict failed results caused by uncertain motion information, such as low-quality optical flow maps. Besides, different sources such as RGB, depth, optical flow and static saliency can provide useful information about the objects. However, existing approaches only consider either the RGB or RGB and optical flow. In this paper, we propose a novel adaptive multi-source predictor for zero-shot video object segmentation (ZVOS). In the static object predictor, the RGB source is converted to depth and static saliency sources, simultaneously. In the moving object predictor, we propose the multi-source fusion structure. First, the spatial importance of each source is highlighted with the help of the interoceptive spatial attention module (ISAM). Second, the motion-enhanced module (MEM) is designed to generate pure foreground motion attention for improving the representation of static and moving features in the decoder. Furthermore, we design a feature purification module (FPM) to filter the inter-source incompatible features. By using the ISAM, MEM and FPM, the multi-source features are effectively fused. In addition, we put forward an adaptive predictor fusion network (APF) to evaluate the quality of the optical flow map and fuse the predictions from the static object predictor and the moving object predictor in order to prevent over-reliance on the failed results caused by low-quality optical flow maps. Experiments show that the proposed model outperforms the state-of-the-art methods on three challenging ZVOS benchmarks. And, the static object predictor precisely predicts a high-quality depth map and static saliency map at the same time.Comment: Accepted to IJCV 2024. Code is available at: https://github.com/Xiaoqi-Zhao-DLUT/Multi-Source-APS-ZVOS. arXiv admin note: substantial text overlap with arXiv:2108.0507

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging the ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task

Prediction of the post-translational modifications of adipokinetic hormone receptors from solitary to eusocial bees

Adipokinetic hormone receptor (AKHR) was regarded as the crucial regulator of lipid consuming, but now has been renewed as a pluripotent neuropeptide G protein-coupled receptor. It has been identified in all sequenced bee genomes from the solitary to the eusocial. In the current study, we try to clarify the transitions of AKHR on lipid utilization and other potential functions from solitary to eusocial bees. The results showed that the AKHRs were divided into different groups based on their social complexity approximately. Nevertheless, the critical motifs and tertiary structures were highly conserved. As to the post-translational modifications, the eusocial possessed more phosphorylation residues and modification patterns, which might be due to the necessity of more diverse functions. These results suggest that AKHRs are highly conserved on both primary motifs and tertiary structures, but more flexible on posttranslational modifications so as to accommodate to more complicated eusocial life

Personality-affected Emotion Generation in Dialog Systems

Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the emotional expressions of humans are rooted in personality traits. Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition. Specifically, we first construct a daily dialog dataset, Personality EmotionLines Dataset (PELD), with emotion and personality annotations. Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Finally, we propose to model the personality as the transition weight by simulating the mood transition process in the dialog system and solve the challenges above. We conduct extensive experiments on PELD for evaluation. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.Comment: Accepted by ACM Transactions on Information System

Prevalence and related factors of child Posttraumatic Stress Disorder during COVID-19 pandemic:A systematic review and meta-analysis

Background: The COVID-19 pandemic has drastically impacted many aspects of society and has indirectly produced various psychological consequences. This systematic review aimed to estimate the worldwide prevalence of posttraumatic stress disorder (PTSD) in children due to the COVID-19 pandemic, as well as to identify protective or risk factors contributing to child PTSD. Methods: We conducted a systematic literature search in the PubMed, ProQuest, PsycINFO, Embase, Web of Science, WanFang, CNKI, and VIP databases. We searched for studies published between January 1, 2020 and May 26, 2021, that reported the prevalence of child PTSD due to the COVID-19 pandemic, as well as factors contributing to child PTSD. Eighteen studies were included in our systematic review, of which 10 studies were included in the meta-analysis. Results: The estimated prevalence of child PTSD after the COVID-19 outbreak was 28.15% (95% CI: 19.46–36.84%, I subgroup analyses for specific regions the estimated prevalence of post-pandemic child PTSD was 19.61% (95% CI: 11.23–27.98%) in China, 50.8% (95% CI: 34.12–67.49%) in the USA, and 50.08% in Italy (95% CI: 47.32–52.84%). Conclusions: Factors contributing to child PTSD were categorized into four aspects: personal factors, family factors, social factors and infectious diseases related factors. Based on this, we presented a new framework summarizing the occurrence and influence of the COVID-19 related child PTSD, which may contribute to a better understanding, prevention and development of interventions for child PTSD in forthcoming pandemics