PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement

Dominant Person Search methods aim to localize and recognize query persons in a unified network, which jointly optimizes two sub-tasks, \ie, detection and Re-IDentification (ReID). Despite significant progress, two major challenges remain: 1) Detection-prior modules in previous methods are suboptimal for the ReID task. 2) The collaboration between two sub-tasks is ignored. To alleviate these issues, we present a novel Person Search framework based on the Diffusion model, PSDiff. PSDiff formulates the person search as a dual denoising process from noisy boxes and ReID embeddings to ground truths. Unlike existing methods that follow the Detection-to-ReID paradigm, our denoising paradigm eliminates detection-prior modules to avoid the local-optimum of the ReID task. Following the new paradigm, we further design a new Collaborative Denoising Layer (CDL) to optimize detection and ReID sub-tasks in an iterative and collaborative way, which makes two sub-tasks mutually beneficial. Extensive experiments on the standard benchmarks show that PSDiff achieves state-of-the-art performance with fewer parameters and elastic computing overhead

SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation

Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls. In contrast, Layout-to-Image (L2I) generation, aiming to generate realistic and complex scene images from user-specified layouts, has risen to prominence. However, existing methods transform layout information into tokens or RGB images for conditional control in the generative process, leading to insufficient spatial and semantic controllability of individual instances. To address these limitations, we propose a novel Spatial-Semantic Map Guided (SSMG) diffusion model that adopts the feature map, derived from the layout, as guidance. Owing to rich spatial and semantic information encapsulated in well-designed feature maps, SSMG achieves superior generation quality with sufficient spatial and semantic controllability compared to previous works. Additionally, we propose the Relation-Sensitive Attention (RSA) and Location-Sensitive Attention (LSA) mechanisms. The former aims to model the relationships among multiple objects within scenes while the latter is designed to heighten the model's sensitivity to the spatial information embedded in the guidance. Extensive experiments demonstrate that SSMG achieves highly promising results, setting a new state-of-the-art across a range of metrics encompassing fidelity, diversity, and controllability

Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional machine learning methods. However, these existing deep learning methods still confront some challenges in classifying subtypes of CVD. For example, the nuance between subtypes is quite hard to capture and represent by intelligent models due to the chillingly similar shape of RS sequences. Moreover, medical history information is an essential resource for distinguishing subtypes, but they are underutilized. In light of this, we propose a multi-modality multi-scale model called M3S, which is a novel deep learning method with two core modules to address these issues. First, we convert RS data to various resolution images by the Gramian angular field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get embeddings for distinction in the multi-scale feature extraction module. Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module. We perform extensive evaluations of M3S and found its outstanding performance on our in-house dataset, with accuracy, precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752, and 0.9334, respectively. These results demonstrate that the M3S has high performance and robustness compared with popular methods in diagnosing CVD subtypes

Short‐time wind speed prediction based on Legendre multi‐wavelet neural network

Abstract As one of the most widespread renewable energy sources, wind energy is now an important part of the power system. Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation. However, due to the stochastic and uncertain nature of wind energy, more accurate forecasting is necessary for its more stable and safer utilisation. This paper proposes a Legendre multiwavelet‐based neural network model for non‐linear wind speed prediction. It combines the excellent properties of Legendre multi‐wavelets with the self‐learning capability of neural networks, which has rigorous mathematical theory support. It learns input‐output data pairs and shares weights within divided subintervals, which can greatly reduce computing costs. We explore the effectiveness of Legendre multi‐wavelets as an activation function. Meanwhile, it is successfully being applied to wind speed prediction. In addition, the application of Legendre multi‐wavelet neural networks in a hybrid model in decomposition‐reconstruction mode to wind speed prediction problems is also discussed. Numerical results on real data sets show that the proposed model is able to achieve optimal performance and high prediction accuracy. In particular, the model shows a more stable performance in multi‐step prediction, illustrating its superiority

METTL14 promotes tumorigenesis by regulating lncRNA OIP5-AS1/miR-98/ADAMTS8 signaling in papillary thyroid cancer

Abstract Background Papillary thyroid cancer (PTC) is the most common type of cancer of the endocrine system. Long noncoding RNAs (lncRNAs) are emerging as a novel class of gene expression regulators associated with tumorigenesis. Through preexisting databases available for differentially expressed lncRNAs in PTC, we uncovered that lncRNA OIP5-AS1 was significantly upregulated in PTC tissues. However, the function and the underlying mechanism of OIP5-AS1 in PTC are poorly understood. Methods Expression of lncRNA OIP5-AS1 and miR-98 in PTC tissue and cells were measured by quantitative real-time PCR (qRT-PCR). And expression of METTL14 and ADAMTS8 in PTC tissue and cells were measured by qRT-PCR and western blot. The biological functions of METTL14, OIP5-AS1, and ADAMTS8 were examined using MTT, colony formation, transwell, and wound healing assays in PTC cells. The relationship between METTL14 and OIP5-AS1 were evaluated using RNA immunoprecipitation (RIP) and RNA pull down assay. And the relationship between miR-98 and ADAMTS8 were examined by luciferase reporter assay. For in vivo experiments, a xenograft model was used to investigate the effects of OIP5-AS1 and ADAMTS8 in PTC. Results Functional validation revealed that OIP5-AS1 overexpression promotes PTC cell proliferation, migration/invasion in vitro and in vivo, while OIP5-AS1 knockdown shows an opposite effect. Mechanistically, OIP5-AS1 acts as a target of miR-98, which activates ADAMTS8. OIP5-AS1 promotes PTC cell progression through miR-98/ADAMTS8 and EGFR, MEK/ERK pathways. Furthermore, RIP and RNA pull down assays identified OIP5-AS1 as the downstream target of METTL14. Overexpression of METTL14 suppresses PTC cell proliferation and migration/invasion through inhibiting OIP5-AS1 expression and regulating EGFR, MEK/ERK pathways. Conclusions Collectively, our findings demonstrate that OIP5-AS1 is a METTL14-regulated lncRNA that plays an important role in PTC progression and offers new insights into the regulatory mechanisms underlying PTC development

Increased Expression of TGFβR2 Is Associated with the Clinical Outcome of Non-Small Cell Lung Cancer Patients Treated with Chemotherapy.

To investigate the prognostic significance of TGFβR2 expression and chemotherapy in Chinese non-small cell lung cancer (NSCLC) patients, TGFβR2 expression NSCLC was analyzed in silico using the Oncomine database, and subsequently analyzed with quantitative RT-PCR in 308 NSCLC biopsies, 42 of which were paired with adjacent non-neoplastic tissues. Our results show that TGFβR2 expression was also increased in NSCLC biopsies relative to normal tissue samples and correlated with poor prognosis. TGFβR2 expression was also significantly correlated with other clinical parameters such as tumor differentiation, invasion of lung membrane, and chemotherapy. Moreover, overall survival (OS) and disease free survival (DFS) was increased in patients with low TGFβR2 expressing NSCLC and who had undergone chemotherapy. Thus, high expression of TGFβR2 is a significant risk factor for decreased OS and DFS in NSCLC patients. Thus, TGFβR2 is a potential prognostic tumor biomarker for chemotherapy

Cox regression model analysis for prognosis based on various clinical characteristics in NSCLC patients.

<p>Cox regression model analysis for prognosis based on various clinical characteristics in NSCLC patients.</p