BACKGROUND: Reliable identification of cancer subtypes is crucial for devising personalized treatment strategies. Integrating multi-omics data has proven to be an effective method for analyzing cancer subtypes. By combining molecular information across various layers, a more comprehensive understanding of biological characteristics and disease mechanisms can be achieved. RESULTS: We propose DEGCN, a novel deep learning model that integrates a three-channel Variational Autoencoder (VAE) for multi-omics dimensionality reduction and a densely connected Graph Convolutional Network (GCN) for effective subtype classification. DEGCN leverages the complementary strengths of non-linear feature extraction and graph-based relational learning, enabling accurate and robust classification of renal cancer subtypes. Experimental results demonstrate that DEGCN achieves a cross-validated classification accuracy of 97.06% ± 2.04% on renal cancer data, outperforming conventional machine learning algorithms and state-of-the-art deep learning models. Moreover, its generalization ability is validated on breast and gastric cancer datasets from TCGA, with cross-validated classification accuracies of 89.82% ± 2.29% and 88.64% ± 5.24%, respectively, indicating strong cross-cancer predictive performance. CONCLUSION: The study highlights the outstanding performance of DEGCN in heterogeneous data integration and classification accuracy, demonstrating the model's potential in cancer subtype prediction and its application in guiding clinical treatment.

Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However, current MLLMs primarily focus on image-level or box-level understanding, falling short in achieving fine-grained vision-language alignment at pixel level. Besides, the lack of mask-based instruction data limits their ad-vancements. In this paper, we propose Osprey, a mask-text instruction tuning approach, to extend MLLMs by incor-porating fine-grained mask regions into language instruction, aiming at achieving pixel-wise visual understanding. To achieve this goal, we first meticulously curate a mask-based region-text dataset with 724K samples, and then design a vision-language model by injecting pixel-level representation into LLM. Specifically, Osprey adopts a convolutional CLIP backbone as the vision encoder and employs a mask-aware visual extractor to extract precise visual mask features from high resolution input. Experimen-tal results demonstrate Osprey's superiority in various region understanding tasks, showcasing its new capability for pixel-level instruction tuning. In particular, Osprey can be integrated with Segment Anything Model (SAM) seamlessly to obtain multi-granularity semantics. The source code, dataset and demo can be found at https://github.com/CircleRadon/Osprey.

Large Vision-Language Models (LVLMs) have shown remarkable performance on many visuallanguage tasks.However, these models still suffer from multimodal hallucination, which means the generation of objects or content that violates the images.Many existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics.To address this issue, we propose Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (HELPD).This framework incorporates hallucination feedback at both object and sentence semantic levels.Remarkably, even with a marginal degree of training, this approach can alleviate over 15% of hallucination.Simultaneously, HELPD penalizes the output logits according to the image attention window to avoid being overly affected by generated text.HELPD can be seamlessly integrated with any LVLMs.Our experiments demonstrate that the proposed framework yields favorable results across multiple hallucination benchmarks.It effectively mitigates hallucination for different LVLMs and concurrently improves their text generation quality.

Traditional admission control algorithms are based on bandwidth or channel reservation policy, which may be incompetent in IEEE 802.16e OFDMA systems for two reasons: (1) WiMAX system supports dynamic and flexible resource allocation, and (2) there exists a fundamental tradeoff between bandwidth resource and power resource. In this paper, we propose a novel admission control scheme based on power reservation. Foremost, we introduce a joint subchannel and power allocation algorithm for WiMAX system, which achieves high power efficiency by minimizing the overall downlink transmit power and utilizing all available subchannels. Based on this, we propose two power reservation schemes for inter-cell handoff calls and intra-cell handoff calls, respectively. Correspondingly, two reservation factors are introduced, the values of which are determined by optimizing the metric of grade of service(GoS). Computer simulation is carried out to evaluate the performance of the proposed admission control scheme.