Online Tensor Learning: Computational and Statistical Trade-offs, Adaptivity and Optimal Regret

We investigate a generalized framework for estimating latent low-rank tensors in an online setting, encompassing both linear and generalized linear models. This framework offers a flexible approach for handling continuous or categorical variables. Additionally, we investigate two specific applications: online tensor completion and online binary tensor learning. To address these challenges, we propose the online Riemannian gradient descent algorithm, which demonstrates linear convergence and the ability to recover the low-rank component under appropriate conditions in all applications. Furthermore, we establish a precise entry-wise error bound for online tensor completion. Notably, our work represents the first attempt to incorporate noise in the online low-rank tensor recovery task. Intriguingly, we observe a surprising trade-off between computational and statistical aspects in the presence of noise. Increasing the step size accelerates convergence but leads to higher statistical error, whereas a smaller step size yields a statistically optimal estimator at the expense of slower convergence. Moreover, we conduct regret analysis for online tensor regression. Under the fixed step size regime, a fascinating trilemma concerning the convergence rate, statistical error rate, and regret is observed. With an optimal choice of step size we achieve an optimal regret of $O(\sqrt{T})$. Furthermore, we extend our analysis to the adaptive setting where the horizon T is unknown. In this case, we demonstrate that by employing different step sizes, we can attain a statistically optimal error rate along with a regret of $O(\log T)$. To validate our theoretical claims, we provide numerical results that corroborate our findings and support our assertions

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.Comment: Accepted to International Workshop on Machine Learning in Medical Imaging (MLMI2019

3-Carboxy­pyrazino[2,3-f][1,10]phenanthrolin-9-ium-2-carboxyl­ate

In the title zwitterionic compound, C16H8N4O4, the dihedral angle between the carboxyl and carboxyl­ate groups is 72.14 (2)°. In the crystal, mol­ecules are linked by strong inter­molecular O—H⋯O− and N+—H⋯O− hydrogen bonds into double chains extended along [001]. These chains are additionally stabilized by π–π stacking inter­actions between the pyridine and benzene rings [centroid–centroid distance = 3.5542 (8) Å]

Clinical efficacy and safety of Kanglaite injection, adjuvant cemcitabine and cisplatin chemotherapy for advanced non-small-cell lung cancer: A systematic review and meta-analysis

Purpose: To investigate the effectiveness and safety of the combination of Kanglaite injection (KLTi) and gemcitabine and cisplatin (GP) chemotherapy in the treatment of advanced non-small cell lung cancer (NSCLC).Methods: PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wan-Fang, CBM, and CQVIP were comprehensively searched from January 2010 till November 2020. Randomized controlled trials (RCTs) of KLTi plus GP in the treatment of NSCLC were selected and assessed for inclusion. Review Manager 5.3 software was used for meta-analysis.Results: Twenty-five RCTs on advanced NSCLC examined the inclusion criteria. The meta-analysis showed that compared with GP chemotherapy alone, KLTi plus GP chemotherapy significantly improved objective response rate (ORR) (RR = 1.36, 95% CI 1.23-1.51, p < 0.00001), disease control rate (DCR) (RR = 1.17, 95% CI 1.11 - 1.23, p < 0.00001), and reduced adverse drug reactions(ADRs) such as hair loss (RR = 0.60, 95% CI 0.47 - 0.76, p < 0.0001), gastrointestinal reaction (RR = 0.68, 95% CI 0.62 - 0.75, p < 0.00001), impairment of liver and kidney function (RR = 0.65, 95% CI 0.53 - 0.80, p < 0.001), nervous system damage (RR = 0.42, 95% CI 0.26 - 0.69, p = 0.0005), myelosuppression (I-II phase) (RR = 0.79, 95 % CI 0.66 - 0.95, p = 0.01), myelosuppression (III-IV phase) (RR = 0.44, 95 % CI0.27 - 0.72, p = 0.001), anemia (RR = 0.74, 95 % CI 0.60 - 0.91, p = 0.006), leukopenia (RR = 0.78, 95% CI 0.69, 0.87, p < 0.0001), thrombocytopenia (RR = 0.59, 95 % CI 0.49, 0.72, p < 0.00001), hypochromia (RR = 0.74, 95% CI 0.59, 0.92, p = 0.008).Conclusion: KLTi adjuvant GP chemotherapy reduces adverse effects in patients with advanced NSCLC. Thus, KLTi might be an effective and safe intervention for NSCLC&nbsp

Plasma Clusterin and the CLU Gene rs11136000 Variant Are Associated with Mild Cognitive Impairment in Type 2 Diabetic Patients

Objective: Type 2 diabetes mellitus (T2DM) is related to an elevated risk of mild cognitive impairment (MCI). Plasma clusterin is reported associated with the early pathology of Alzheimer's disease (AD) and longitudinal brain atrophy in subjects with MCI. The rs11136000 single nucleotide polymorphism within the clusterin (CLU) gene is also associated with the risk of AD. We aimed to investigate the associations among plasma clusterin, rs11136000 genotype and T2DM-associated MCI. Methods: A total of 231 T2DM patients, including 126 MCI and 105 cognitively healthy controls were enrolled in this study. Demographic parameters were collected and neuropsychological tests were conducted. Plasma clusterin and CLU rs11136000 genotype were examined.Results: Plasma clusterin was significantly higher in MCI patients than in control group (p=0.007). In subjects with MCI, plasma clusterin level was negatively correlated with Montreal cognitive assessment and auditory verbal learning test_delayed recall scores (p=0.027 and p=0.020, respectively). After adjustment for age, educational attainment, and gender, carriers of rs11136000 TT genotype demonstrated reduced risk for MCI compared with the CC genotype carriers (OR=0.158, χ2=4.113, p=0.043). Multivariable regression model showed that educational attainment, duration of diabetes, HDL-c, and plasma clusterin levels are associated with MCI in T2DM patients.Conclusions: Plasma clusterin was associated with MCI and may reflect a protective response in T2DM patients. TT genotype exhibited a reduced risk of MCI compared to CC genotype. Further investigations should be conducted to determine the role of clusterin in cognitive decline