arXiv subjects
Publications and source records attributed to Qimin Zhang.
In this paper, we prove that for any weak Del Pezzo surface $S$ of degree at least $4$, the tangent bundle $T_S$ is almost nef. For the proof, we use total dual VMRTs induced by conic bundle structures.
In this paper, we establish the Beauville--Bogomolov--Yau decomposition for generalized pairs in the K\"ahler setting, thereby extending this structure theorem to the natural framework of the K\"ahler generalized Minimal Model Program. More precisely, we prove that, after passing to a finite quasi-\'etale cover, a K\"ahler generalized klt pair $(X,\Delta,\boldsymbol{\beta})$ of Calabi--Yau type admits a locally constant fibration $(X,\Delta)\to Y$ over a Calabi--Yau variety $Y$ whose fiber $(F,\Delta|_{F})$ is rationally connected. Equivalently, after base change to the universal cover of $Y$, the fibration becomes a product, and its global structure is determined by a monodromy action preserving the pair $(F,\Delta|_{F})$. As a principal application, when the nef b-part $\boldsymbol{\beta}$ vanishes, we show that the monodromy can be eliminated after a further finite quasi-\'etale cover, yielding a product decomposition into a rationally connected pair, strict Calabi--Yau varieties, irreducible holomorphic symplectic varieties, and complex tori. The proof introduces new analytic methods involving relative projectivity, localized positivity and flatness of direct image sheaves, generalized pairs in the analytic Minimal Model Program, and foliations on singular K\"ahler varieties.
Cardiovascular disease (CVD) persists as a primary cause of death on a global scale, which requires more effective and timely detection methods. Traditional supervised learning approaches for CVD detection rely heavily on large-labeled datasets, which are often difficult to obtain. This paper employs semi-supervised learning models to boost efficiency and accuracy of CVD detection when there are few labeled samples. By leveraging both labeled and vast amounts of unlabeled data, our approach demonstrates improvements in prediction performance, while reducing the dependency on labeled data. Experimental results in a publicly available dataset show that semi-supervised models outperform traditional supervised learning techniques, providing an intriguing approach for the initial identification of cardiovascular disease within clinical environments.
Recent studies revealed structural and functional brain changes in heavy smokers. However, the specific changes in topological brain connections are not well understood. We used Gaussian Undirected Graphs with the graphical lasso algorithm on rs-fMRI data from smokers and non-smokers to identify significant changes in brain connections. Our results indicate high stability in the estimated graphs and identify several brain regions significantly affected by smoking, providing valuable insights for future clinical research.
Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This study aimed to identify key biomarkers associated with stage III NSCLC in non-smoking females using gene expression profiling from the GDS3837 dataset. Utilizing XGBoost, a machine learning algorithm, the analysis achieved a strong predictive performance with an AUC score of 0.835. The top biomarkers identified - CCAAT enhancer binding protein alpha (C/EBP-alpha), lactate dehydrogenase A4 (LDHA), UNC-45 myosin chaperone B (UNC-45B), checkpoint kinase 1 (CHK1), and hypoxia-inducible factor 1 subunit alpha (HIF-1-alpha) - have been validated in the literature as being significantly linked to lung cancer. These findings highlight the potential of these biomarkers for early diagnosis and personalized therapy, emphasizing the value of integrating machine learning with molecular profiling in cancer research.
Accurately segmenting brain tumors from MRI scans is important for developing effective treatment plans and improving patient outcomes. This study introduces a new implementation of the Columbia-University-Net (CU-Net) architecture for brain tumor segmentation using the BraTS 2019 dataset. The CU-Net model has a symmetrical U-shaped structure and uses convolutional layers, max pooling, and upsampling operations to achieve high-resolution segmentation. Our CU-Net model achieved a Dice score of 82.41%, surpassing two other state-of-the-art models. This improvement in segmentation accuracy highlights the robustness and effectiveness of the model, which helps to accurately delineate tumor boundaries, which is crucial for surgical planning and radiation therapy, and ultimately has the potential to improve patient outcomes.
This study evaluates the performance of various supervised machine learning models in analyzing highly correlated neural signaling data from the Adolescent Brain Cognitive Development (ABCD) Study, with a focus on predicting obsessive-compulsive disorder scales. We simulated a dataset to mimic the correlation structures commonly found in imaging data and evaluated logistic regression, elastic networks, random forests, and XGBoost on their ability to handle multicollinearity and accurately identify predictive features. Our study aims to guide the selection of appropriate machine learning methods for processing neuroimaging data, highlighting models that best capture underlying signals in high feature correlations and prioritize clinically relevant features associated with Obsessive-Compulsive Disorder (OCD).
We present a microwave source that is controlled by a commercially available field programmable gate array (FPGA). Using an FPGA allows for precise control of the time dependent microwave-dressing applied to a sample of trapped cold atoms. We test our microwave source by exciting Rabi oscillations in a Na spinor Bose-Einstein Condensate. We include, as supplements, the complete source code, parts lists, pin connection diagrams, and schematics to make it easy for any group to build and use this device.
We numerically investigate atomic interferometry based on spin-exchange collisions in $F=1$ spinor Bose-Einstein condensates in the regime of long evolution times $t\gg h/c$, where c is the spin-dependent interaction energy. We show that the sensitivity of spin-mixing interferometry can be enhanced by using classically seeded initial states with a small population prepared in the $m_F=\pm1$ states.