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Multiview subspace clustering (MSC) has attracted growing attention due to the extensive value in various applications, such as natural language processing, face recognition, and time-series analysis. In this article, we are devoted to address two crucial issues in MSC: 1) high computational cost and 2) cumbersome multistage clustering. Existing MSC approaches, including tensor singular value decomposition (t-SVD)-MSC that has achieved promising performance, generally utilize the dataset itself as the dictionary and regard representation learning and clustering process as two separate parts, thus leading to the high computational overhead and unsatisfactory clustering performance. To remedy these two issues, we propose a novel MSC model called joint skinny tensor learning and latent clustering (JSTC), which can learn high-order skinny tensor representations and corresponding latent clustering assignments simultaneously. Through such a joint optimization strategy, the multiview complementary information and latent clustering structure can be exploited thoroughly to improve the clustering performance. An alternating direction minimization algorithm, which owns low computational complexity and can be run in parallel when solving several key subproblems, is carefully designed to optimize the JSTC model. Such a nice property makes our JSTC an appealing solution for large-scale MSC problems. We conduct extensive experiments on ten popular datasets and compare our JSTC with 12 competitors. Five commonly used metrics, including four external measures (NMI, ACC, F-score, and RI) and one internal metric (SI), are adopted to evaluate the clustering quality. The experimental results with the Wilcoxon statistical test demonstrate the superiority of the proposed method in both clustering performance and operational efficiency.

This single-arm pilot study (NCT03329937) evaluated neoadjuvant niraparib antitumor activity and safety in patients with localized HER2-negative, BRCA-mutated breast cancer. Twenty-one patients received niraparib 200 mg once daily in 28-day cycles. After 2 cycles, tumor response (≥30% reduction from baseline) by MRI was 90.5% and 40.0% (6 of 15) of patients who received only niraparib (2–6 cycles) had pathological complete response; no new safety signals were identified. High niraparib intratumoral concentration was observed.

Multi-view subspace clustering is an effective method to partition data into their corresponding categories. Nevertheless, existing multi-view subspace clustering approaches generally operate in a purely unsupervised manner, while ignoring the valuable weakly supervised information that can be readily obtained in many practical applications. In this paper, we consider the weakly supervised form of sample pair constraints, and devote to promoting the performance of multi-view subspace clustering with the aid of such prior knowledge. To achieve this goal, inspired by the intrinsic block diagonal structure of ideal low-rank representation (LRR), we propose a novel regularization to integrate must-link, cannot-link and normalization constraints into a unified formulation. The proposed regularization can be regarded as a general description for sample pairwise constraints, and thus provides a flexible framework for multi-view semi-supervised subspace clustering task. Furthermore, we devise a contrained tensor representation learning (CTRL) model that takes advantage of our proposed regularization to facilitate the learning of the desired representation tensor. An efficient optimization algorithm based on alternating direction minimization strategy is carefully designed to solve the proposed CTRL model. Extensive experiments on eight challenging real-world datasets are conducted, and the results validate the effectiveness of our designed pairwise constraints regularization, as well as the superiority of the proposed CTRL model.

Domain adaptation in person re-identification (re-ID) has always been challenging, especially for the lack of supervision information on the target domain. Existing methods generally introduced extra supervision by adversarial learning techniques, then added all the augmented data in the training process to optimize the re-ID model. However, the direct utilization of all the generated data not only increases additional computational cost but also ignores the potential correlation between the origin and generated data. In this article, we propose a novel dual-alignment learning framework (DAL) with camera-aware image generation to efficiently and effectively tackle this issue. Specifically, we propose a camera transfer matching module to generate additional training images with different camera styles, and construct the matching pairs with each containing a origin image and one corresponding camera transferred image. To strengthen the correlation of images for each matching pair, we align the pseudo-labels via clustering algorithm to reduce the pseudo-labels distribution discrepancy between the origin and generated images. Besides, to avoid model degeneration affected by some inaccurate pseudo-labels on unlabelled data, we maximize the mutual information to align the image feature representations of matching pair. The DAL allows us to decrease the camera variance and enhance the discrimination ability of re-ID model. Extensive experiments on three large-scale benchmarks demonstrate the superiority of DAL over state-of-the-art methods.

Stratification is commonly employed in clinical trials to reduce the chance covariate imbalances and increase the precision of the treatment effect estimate. We propose a general framework for constructing the confidence interval (CI) for a difference or ratio effect parameter under stratified sampling by the method of variance estimates recovery (MOVER). We consider the additive variance and additive CI approaches for the difference, in which either the CI for the weighted difference, or the CI for the weighted effect in each group, or the variance for the weighted difference is calculated as the weighted sum of the corresponding stratum-specific statistics. The CI for the ratio is derived by the Fieller and log-ratio methods. The weights can be random quantities under the assumption of a constant effect across strata, but this assumption is not needed for fixed weights. These methods can be easily applied to different endpoints in that they require only the point estimate, CI, and variance estimate for the measure of interest in each group across strata. The methods are illustrated with two real examples. In one example, we derive the MOVER CIs for the risk difference and risk ratio for binary outcomes. In the other example, we compare the restricted mean survival time and milestone survival in stratified analysis of time-to-event outcomes. Simulations show that the proposed MOVER CIs generally outperform the standard large sample CIs, and that the additive CI approach performs slightly better than the additive variance approach., Comment: 13 pages

Background: Adenomatoid Tumor of the Adrenal Gland (AT-AG) are very rare. As the most commonly used clinical examination program, CT scan can make benign and malignant diagnosis by evaluating the characteristics of lesion shape, density and blood vessels, and provide imaging basis for treatment decisions.Objective: To investigate the clinical, pathological and CT imaging features of AT-AG.Methods: The clinical, pathological and CT findings of three AT-AG patients confirmed by surgery or pathology were reviewed and analyzed with reference to nine cases in literature reports.Results: Among the 12 patients with AT-AG, 11 were male, with a median age of 40 years; 11 were diagnosed by physical examination or accident, while 1 was treated for right lumbago. Pathologically, they were characterized by microcapsule/glandular tube structures, absence of cell heteromorphosis and mitosis, positive immunohistochemical mesothelial markers, and negative endothelial markers; In addition, all of them were confirmed with single adrenal cystic or cystic solid masses on CT, including 2 with multilocular cyst and 3 with calcification. Among them, nine (75%) were oval, with the long diameter parallel to the long axis of adrenal gland, and 8 (67%) showed mild and moderate enhancement of capsule wall and solid components on enhanced scan.Conclusion: AT-AG is more common in young and middle-aged men than other population groups, and it constitutes microcapsule/glandular structures under microscope. Its immune histo chemical mesothelial markers show positive, while endothelial markers are negative. CT findings reveal a mostly oval solitary cyst or solid mass, parallel to the long axis of adrenal gland, with mild and moderate enhancement on enhanced CT scan. Thus, being familiar with the clinical, pathological and CT features of AT-AG helps boost the understanding of the disease

Purpose The purpose of this study is to characterize niraparib pharmacokinetics (PK) and safety in patients with normal hepatic function (NHF) versus moderate hepatic impairment (MHI). Methods Patients with advanced solid tumors were stratified by NHF or MHI (National Cancer Institute-Organ Dysfunction Working Group criteria [bilirubin > 1.5–3 × upper limit of normal and any aspartate aminotransferase elevation]). In the PK phase, all patients received one 300 mg dose of niraparib. In the extension phase, patients with MHI received niraparib 200 mg daily; patients with NHF received 200 or 300 mg based on weight (max), area under the curve to last measured concentration (AUClast) and extrapolated to infinity (AUCinf). Safety was assessed in both phases. Exposure–response (E–R) modeling was used to predict MHI effects on exposure and safety of niraparib doses ≤ 200 mg or 300/200 mg or 200/100 mg weight/platelet regimens. Results In the PK phase (NHF, n = 9; MHI, n = 8), mean niraparib Cmax was 7% lower in patients with MHI versus NHF. Mean exposure (AUClast, AUCinf) was increased by 45% and 56%, respectively, in patients with MHI without impacting tolerability. In the extension phase (NHF, n = 8; MHI, n = 7), the overall safety profile was consistent with previous trials. In patients with MHI, E–R modeling predicted niraparib 200 mg reduced Grade ≥ 3 thrombocytopenia incidence, whereas a 200/100 mg regimen yielded exposures below efficacy-associated levels in 15% of patients. Conclusion These findings support adjusting the 300 mg niraparib starting dose to 200 mg QD in patients with MHI. Trial registration NCT03359850; registered December 2, 2017

Clustering is a crucial but challenging task in data mining and machine learning. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, has achieved state-of-the-art performance in various applications and attracted considerable attention. Nevertheless, most of these approaches fail to effectively learn informative cluster-oriented features for data with spatial correlation structure, e.g., images. To tackle this problem, in this paper, we develop a deep convolutional self-paced clustering (DCSPC) method. Specifically, in the pretraining stage, we propose to utilize a convolutional autoencoder to extract a high-quality data representation that contains the spatial correlation information. Then, in the finetuning stage, a clustering loss is directly imposed on the learned features to jointly perform feature refinement and cluster assignment. We retain the decoder to avoid the feature space being distorted by the clustering loss. To stabilize the training process of the whole network, we further introduce a self-paced learning mechanism and select the most confident samples in each iteration. Through comprehensive experiments on seven popular image datasets, we demonstrate that the proposed algorithm can consistently outperform state-of-the-art rivals.

This paper discusses the design of clinical trials where the primary endpoint is a recurrent event with the focus on the sample size calculation. For the problem, a few methods have been proposed but most of them assume a multiplicative treatment effect on the rate or mean number of recurrent events. In practice, sometimes the additive treatment effect may be preferred or more appealing because of its intuitive clinical meaning and straightforward interpretation compared to a multiplicative relationship. In this paper, new methods are presented and investigated for the sample size calculation based on the additive rates model for superiority, non-inferiority, and equivalence trials. They allow for flexible baseline rate function, staggered entry, random dropout, and overdispersion in event numbers, and simulation studies show that the proposed methods perform well in a variety of settings. We also illustrate how to use the proposed methods to design a clinical trial based on real data.

Accurate and high-resolution quantitative precipitation estimation (QPE) plays a crucial role in meteorology and hydrology. However, for acquiring a more accurate QPE, how to depict the complex nonlinear relationship between the radar reflectivity and the true rain rates, as well as adaptively explore the spatial dependencies of precipitation, remains extremely challenging. In this letter, we propose to incorporate the merits of graph convolutional regression networks (GCRNs) and address the aforementioned issues simultaneously in the GCRNs framework. Furthermore, in order to tolerate the variabilities of spatial correlation in the practical precipitation, we expand GCRNs with a multiconvolutional mechanism between the center node and its neighbor rain gauges. Thus, the ability to capture more complicated spatial characteristics of precipitation can be enhanced, and the phenomenon of overwhelming by the neighbor nodes can be released. Extensive experiments were implemented on 12 rainfall processes in Hangzhou, China, 2015. The experimental results confirm that our proposal consistently outperforms the state-of-the-art QPE models.

Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally overlook the significance of weakly-supervised information and fail to preserve the feature properties of multiple views, thus resulting in unsatisfactory clustering performance. To address these issues, in this paper, we propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method, which jointly optimizes three kinds of losses during networks finetuning, including multi-view clustering loss, semi-supervised pairwise constraint loss and multiple autoencoders reconstruction loss. Specifically, a KL divergence based multi-view clustering loss is imposed on the common representation of multi-view data to perform heterogeneous feature optimization, multi-view weighting and clustering prediction simultaneously. Then, we innovatively propose to integrate pairwise constraints into the process of multi-view clustering by enforcing the learned multi-view representation of must-link samples (cannot-link samples) to be similar (dissimilar), such that the formed clustering architecture can be more credible. Moreover, unlike existing rivals that only preserve the encoders for each heterogeneous branch during networks finetuning, we further propose to tune the intact autoencoders frame that contains both encoders and decoders. In this way, the issue of serious corruption of view-specific and view-shared feature space could be alleviated, making the whole training procedure more stable. Through comprehensive experiments on eight popular image datasets, we demonstrate that our proposed approach performs better than the state-of-the-art multi-view and single-view competitors.

Log-rank tests have been widely used to compare two survival curves in biomedical research. We describe a unified approach to power and sample size calculation for the unweighted and weighted log-rank tests in superiority, noninferiority and equivalence trials. It is suitable for both time-driven and event-driven trials. A numerical algorithm is suggested. It allows flexible specification of the patient accrual distribution, baseline hazards, and proportional or nonproportional hazards patterns, and enables efficient sample size calculation when there are a range of choices for the patient accrual pattern and trial duration. A confidence interval method is proposed for the trial duration of an event-driven trial. We point out potential issues with several popular sample size formulae. Under proportional hazards, the power of a survival trial is commonly believed to be determined by the number of observed events. The belief is roughly valid for noninferiority and equivalence trials with similar survival and censoring distributions between two groups, and for superiority trials with balanced group sizes. In unbalanced superiority trials, the power depends also on other factors such as data maturity. Surprisingly, the log-rank test usually yields slightly higher power than the Wald test from the Cox model under proportional hazards in simulations. We consider various nonproportional hazards patterns induced by delayed effects, cure fractions, and/or treatment switching. Explicit power formulae are derived for the combination test that takes the maximum of two or more weighted log-rank tests to handle uncertain nonproportional hazards patterns. Numerical examples are presented for illustration.

The 2 × 2 crossover trial uses subjects as their own control to reduce the intersubject variability in the treatment comparison, and typically requires fewer subjects than a parallel design. The generalized estimating equations (GEE) methodology has been commonly used to analyze incomplete discrete outcomes from crossover trials. We propose a unified approach to the power and sample size determination for the Wald Z-test and t-test from GEE analysis of paired binary, ordinal and count outcomes in crossover trials. The proposed method allows misspecification of the variance and correlation of the outcomes, missing outcomes, and adjustment for the period effect. We demonstrate that misspecification of the working variance and correlation functions leads to no or minimal efficiency loss in GEE analysis of paired outcomes. In general, GEE requires the assumption of missing completely at random. For bivariate binary outcomes, we show by simulation that the GEE estimate is asymptotically unbiased or only minimally biased, and the proposed sample size method is suitable under missing at random (MAR) if the working correlation is correctly specified. The performance of the proposed method is illustrated with several numerical examples. Adaption of the method to other paired outcomes is discussed.

MgCl2 is widely used on roads for controlling dust and humidity, reducing the scattering of coarse particles, anti-icing, and improving the geotechnical properties of “problematic soils” by chemica...

In a series of articles, Gart and Nam construct the efficient score tests and confidence intervals with or without skewness correction for stratified comparisons of binomial proportions on the risk difference, relative risk, and odds ratio effect metrics. However, the stratified score methods and their properties are not well understood. We rederive the efficient score tests, which reveals their theoretical relationship with the contrast-based score tests, and provides a basis for adapting the method by using other weighting schemes. The inverse variance weight is optimal for a common treatment effect in large samples. We explore the behavior of the score approach in the presence of extreme outcomes when either no or all subjects in some strata are responders, and provide guidance on the choice of weights in the analysis of rare events. The score method is recommended for studies with a small number of moderate or large sized strata. A general framework is proposed to calculate the asymptotic power and sample size for the score test in superiority, noninferiority and equivalence clinical trials, or case-control studies. We also describe a nearly exact procedure that underestimates the exact power, but the degree of underestimation can be controlled to a negligible level. The proposed methods are illustrated by numerical examples.

While quantitative precipitation estimation (QPE) using weather radar is widely adopted in operation, precipitation data sets are often highly imbalanced. In particular, extreme precipitation usually lacks representation, which may introduce the bottleneck for radar QPE with machine learning models. Discovering the intrinsic characteristic of extreme precipitation with few samples is challenging. In this letter, we focus on the radar reflectivity data and aim to generate synthetic radar image sequences with respect to extreme precipitation. Considering the relatively long interval between continuous radar images due to radar volume scan, traditional methods in video generation are not suitable. In this letter, we propose Two-stage Generative Adversarial Networks (TsGANs) to address the above-mentioned problem. In general, our TsGAN constructs adversarial process between generators and discriminators: the generator produces samples similar to real data, while the discriminator determines whether or not a sample is eligible. In Stage I, we generate an image sequence containing content and motion features. In Stage II, we design an enhanced net structure to enrich the adversarial processes and further improve the motion features. Experimental testing is performed within the radar coverage in Shenzhen, China, on rainfall events in 2014–2016. Results show that our TsGAN is superior to previous works.

Background: Niraparib is a selective poly(ADP-ribose) polymerase 1/2 inhibitor that has demonstrated antitumor activity in advanced triple-negative breast cancer (TNBC) in combination with a programmed cell death 1 inhibitor, with the greatest clinical activity seen in tumors with breast cancer susceptibility gene (BRCA) mutations. Pharmacologically, niraparib has demonstrated a wide volume of distribution and high cell membrane permeability. In breast and ovarian cancer xenograft mouse models, niraparib achieved tumor exposures that were 3.3 times greater than plasma exposure. The objective of this study is to evaluate the antitumor activity of single-agent niraparib in the neoadjuvant treatment of patients with localized, human epidermal growth factor receptor 2 (HER2)-negative, BRCA-mutated breast cancer. The relative concentration of niraparib in tumor versus plasma was also assessed. Methods: Eligible patients were ≥18 years old, with HER2-negative, BRCA-mutated (germline or somatic) resectable breast cancer with a tumor size of ≥1 cm who had not received prior treatment for the current malignancy. Patients received niraparib 200 mg once daily for 2 months. At the end of 2 months, at their treating physician’s discretion, patients proceeded directly to surgery, received additional cycles of niraparib (maximum of 6 months), or received neoadjuvant chemotherapy. The primary endpoint was tumor response rate measured by magnetic resonance imaging (MRI) after 2 months of treatment. Response was defined as a ≥30% reduction in tumor volume from baseline. Secondary endpoints included tumor response rate measured by ultrasound, quantified percent change in tumor volume measured by MRI or ultrasound, pathological complete response, and safety and tolerability. Additionally, niraparib concentrations were measured in tumor and plasma samples using qualified liquid chromatography-tandem mass spectrometry. Results: Twenty-one patients were enrolled. As of June 2019, 18 patients had both an MRI and ultrasound scan at the end of month 2 and were evaluable for response. Ten patients are currently on treatment. The median age of patients was 43 years (range, 21-73). Fourteen patients had a BRCA1 mutation, 6 patients had a BRCA2 mutation, and 1 patient had both. Fifteen patients had TNBC, and 6 patients had hormone receptor-positive disease. All 18 response-evaluable patients had a clinical response after 2 months of treatment by at least one imaging modality; no patient experienced disease progression. Tumor response rate measured by MRI after 2 months of treatment was 89% (n/N = 16/18). Results measured by ultrasound were similar, with a 94% response rate at cycle 2 (n/N = 17/18). The median percent decrease in tumor volume after 2 months of treatment was 88% (range, 26-100%) and 89% (range, 23-100%) as measured by MRI and ultrasound, respectively. In the 5 samples measured thus far, niraparib concentrations in tumor biopsies after 2 months of treatment ranged from approximately 4-131-fold higher than those in corresponding plasma samples. Efficacy and tumor concentration data for all patients will be presented at the meeting. The most common (≥10%) drug-related treatment-emergent adverse events (TEAEs) of any grade were nausea, fatigue, anemia, insomnia, and decreased appetite. The only drug-related grade ≥3 toxicity in ≥10% of patients was anemia (3 patients). Three patients had a dose reduction due to a TEAE; no patient discontinued treatment due to a TEAE. Conclusion: Niraparib was well tolerated and showed promising antitumor activity in the neoadjuvant treatment of patients with localized HER2-negative, BRCA-mutated breast cancer. Niraparib achieved 4-131-fold higher concentrations in tumor than in plasma. Clinical trial information: NCT03329937. Funding: TESARO: A GSK Company (Waltham, MA, USA) sponsored the study. Citation Format: Hyo Han, Minetta C Liu, Erika Hamilton, Hanna Irie, Cesar A Santa-Maria, James Reeves, Andre Liem, Adrianna Milillo Naraine, Julie Nangia, David Page, Meghan Duncan, Ming Shan, Yongqiang Tang, Julie R Graham, Leif W Ellisen, Steven Isakoff, Laura Spring. Pilot neoadjuvant study of niraparib in HER2-negative, BRCA-mutated resectable breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-11-03.

This article presents a tensor multi-task model for person re-identification (Re-ID). Due to discrepancy among cameras, our approach regards Re-ID from multiple cameras as different but related classification tasks, each task corresponding to a specific camera. In each task, we distinguish the person identity as a one-vs-all linear classification problem, where one classifier is associated with a specific person. By constructing all classifiers into a task-specific projection matrix, the proposed method could utilize all the matrices to form a tensor structure, and jointly train all the tasks in a uniform tensor space. In this space, by assuming the features of the same person under different cameras are generated from a latent subspace, and different identities under the same perspective share similar patterns, the high-order correlations, not only across different tasks but also within a certain task, can be captured by utilizing a new type of low-rank tensor constraint. Therefore, the learned classifiers transform the original feature vector into the latent space, where feature distributions across cameras can be well-aligned. Moreover, this model can be incorporated into multiple visual features to boost the performance, and easily extended to the unsupervised setting. Extensive experiments and comparisons with recent Re-ID methods manifest the competitive performance of our method.

The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.