Detecting small objects in UAV images is challenging due to their limited size and the complexity of background. This work introduces an improved YOLOv8nbased approach designed to enhance performance in such environments. Specifically, a feature modeling framework, named RMC (Routing-aware Modulated Context) is designed, which integrates a routing-aware attention mechanism with a multi-scale context modulation strategy to better capture and distinguish small object features. The backbone of YOLOv8n is restructured by introducing the Omni-dimensional Dynamic Convolution (ODConv) module and the Routing Attention Spatial Aggregator (RASA) module, both aimed at improving the model's sensitivity to target regions. Meanwhile, the original SPPF module is replaced by the Multi-Scale Context Modulation (MSCM) module to strengthen the modeling of complex contextual information. Further improvements are made in the neck by introducing an extra feature layer and replacing the detection head with the Adaptive Spatial Feature Fusion (ASFF) module, which is incorporated to enable effective multi-scale feature fusion. Validation on the VisDrone2019 dataset demonstrates that the RMC-YOLO method reaches 38.9%$\text{mAP}@0.5$, representing a 4.9% improvement over the original YOLOv8n.

Mining topics in Twitter is increasingly attracting more attention. However, the shortness and informality of tweets leads to extreme sparse vector representation with a large vocabulary, which makes the conventional topic models (e.g., Latent Dirichlet Allocation) often fail to achieve high quality underlying topics. Luckily, tweets always show up with rich user-generated hash tags as keywords. In this paper, we propose a novel topic model to handle such semi-structured tweets, denoted as Hash tag Graph based Topic Model (HGTM). By utilizing relation information between hash tags in our hash tag graph, HGTM establishes word semantic relations, even if they haven't co-occurred within a specific tweet. In addition, we enhance the dependencies of both multiple words and hash tags via latent variables (topics) modeled by HGTM. We illustrate that the user-contributed hash tags could serve as weakly-supervised information for topic modeling, and hash tag relation could reveal the semantic relation between tweets. Experiments on a real-world twitter data set show that our model provides an effective solution to discover more distinct and coherent topics than the state-of-the-art baselines and has a strong ability to control sparseness and noise in tweets.

The ever increasing usage of acronyms in many kinds of documents, including web pages, is becoming an obstacle for average readers. This paper studies the task of finding expansions in documents for a given set of acronyms. We cast the expansion finding problem as a sequence labeling task and adapt Conditional Random Fields (CRF) to solve it. While adapting CRFs, we enhance the performance from two aspects. First, we introduce nonlinear hidden layers to learn better representations of the input data. Second, we design simple and effective features. We create a hand labeled evaluation data based on Wikipedia.org and web crawling. We evaluate the effectiveness of several algorithms in solving the expansion finding problem. The experimental results demonstrate that the new method achieves performs better than Support Vector Machine and standard Conditional Random Fields.