Compensation of analog imperfections In a Ka-band FMCW SAR

International audienceThis paper deals with the compensation of analog imperfections in a Ka-Band FMCW SAR. Due to the presence of phase distortion in the up-conversion and down conversion block, we demonstrate that the calibration of the VCO based on a reference beat signal is range-limited. We propose a post-processing method to compensate the residual sinusoidal nonlinearities of the VCO characteristic as well as the phase distortion coming from the up-conversion and down-conversion block. Processing of SAR data acquisition demonstrates the efficiency of the method

Perceptual Adversarial Networks With a Feature Pyramid for Image Translation

This paper investigates the image-to-image translations problems, where the input image is translated into its synthetic form with the original structure and semantics preserved. Widely used methods compute the pixel-wise MSE loss, which are often inadequate for high-frequency content and tend to produce overly smooth results. Concurrent works that leverage recent advances in conditional generative adversarial networks (cGANs) are proposed to enable a universal approach to diverse image translation tasks that traditionally require specific loss functions. Despite the impressive results, most of these approaches are notoriously unstable to train and tend to induce blurs. In this paper, we decompose the image into a set of images by a feature pyramid and elaborate separate loss components for images of specific bandpass. The overall perceptual adversarial loss is able to capture not only the semantic features but also the appearance

Adaptive multi-view semi-supervised nonnegative matrix factorization

Multi-view clustering, which explores complementary information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF), which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced. Besides, AMVNMF provides a viable solution to learn the weight of each view adaptively with only a single parameter. Using L2,1 -norm, AMVNMF is also robust to noises and outliers. We further develop an efficient iterative algorithm for solving the optimization problem. Experiments carried out on five well-known datasets have demonstrated the effectiveness of AMVNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information

Constrained low-rank representation for robust subspace clustering

Subspace clustering aims to partition the data points drawn from a union of subspaces according to their underlying subspaces. For accurate semi-supervised subspace clustering, all data that have a must-link constraint or the same label should be grouped into the same underlying subspace. However, this is not guaranteed in existing approaches. Moreover, these approaches require additional parameters for incorporating supervision information. In this paper, we propose a constrained low-rank representation (CLRR) for robust semi-supervised subspace clustering, based on a novel constraint matrix constructed in this paper. While seeking the low-rank representation of data, CLRR explicitly incorporates supervision information as hard constraints for enhancing the discriminating power of optimal representation. This strategy can be further extended to other state-of-the-art methods, such as sparse subspace clustering. We theoretically prove that the optimal representation matrix has both a block-diagonal structure with clean data and a semi-supervised grouping effect with noisy data. We have also developed an efficient optimization algorithm based on alternating the direction method of multipliers for CLRR. Our experimental results have demonstrated that CLRR outperforms existing methods

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model. Results: The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density ($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and orientation ($p$ = 0.012). Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC. The codes and dataset are available at https://github.com/bupt-ai-cz/BALNMPComment: Update Table 1 and corresponding description

Evolution of black shale sedimentary environment and its impact on organic matter content and mineral composition: a case study from Wufeng-Longmaxi Formation in Southern and Eastern Sichuan Basin

Due to global geological events and differences in regional sedimentary environments, marine shale reservoirs of Wufeng-Longmaxi Formation in Eastern and Southern Sichuan Basin exhibit significant heterogeneity in organic matter content and mineral composition. In order to reveal the influence of paleoenvironment evolution on reservoir heterogeneity, key geochemical indicators of elements were used to reconstruct the sedimentary environment of marine shale in Eastern and Southern Sichuan Basin. The influence mechanism of paleoenvironment on organic matter content and mineral components was also explored. The results indicate that the Wufeng-Longmaxi Formation in the Southern and Eastern Sichuan Basin can be divided into two third-order sequences (Sq 1 and Sq 2). Each third-order sequence is divided into a transgressive system tract (TST) and a highstand system tract (HST). The average TOC content in the Eastern Sichuan Basin is the highest during the TST1 period with reaching 4.2%, while reached its maximum at 3.9% during the TST2 period in the Southern Sichuan Basin. Due to the influence of high paleo-productivity, the organic matter accumulation and quartz content in the eastern Sichuan region were higher than those in the southern Sichuan region from the TST1 to the middle TST2 period. However, the organic matter accumulation and quartz content in the late TST2 period were lower than those in the southern Sichuan region due to the dilution of terrestrial debris. During the HST2 period, due to the influence of higher paleo-productivity, clay adsorption and preservation condition, the TOC content in the eastern Sichuan region slightly increased in the early stage. At the same time, the marine shale in the southern Sichuan region has a high content of quartz minerals and a low content of clay minerals due to strong weathering intensity and input of coarse-grained debris (silt-size quartz)

Business culture impairs facial trustworthiness judgments

Previous research has found that business culture has a detrimental impact on interpersonal trust. To understand whether this impact extends to rapid, automatic, bottom–up judgments of facial trustworthiness, we conducted 4 experiments involving 244 participants from economic and non-economic backgrounds. We presented participants with both trustworthy and untrustworthy faces and asked them to make judgments on trustworthiness. The results show that individuals who are engaged in studying economics, work in an economics-related occupation, or are exposed to an imagined business culture evaluate trustworthy faces to be less trustworthy. The findings shed light on why and how business culture affects the formation of interpersonal trust

Multi-component nonnegative matrix factorization

Real data are usually complex and contain various components. For example, face images have expressions and genders. Each component mainly reflects one aspect of data and provides information others do not have. Therefore, exploring the semantic information of multiple components as well as the diversity among them is of great benefit to understand data comprehensively and in-depth. However, this cannot be achieved by current nonnegative matrix factorization (NMF)-based methods, despite that NMF has shown remarkable competitiveness in learning parts-based representation of data. To overcome this limitation, we propose a novel multi-component nonnegative matrix factorization (MCNMF). Instead of seeking for only one representation of data, MCNMF learns multiple representations simultaneously, with the help of the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term. HSIC explores the diverse information among the representations, where each representation corresponds to a component. By integrating the multiple representations, a more comprehensive representation is then established. A new iterative updating optimization scheme is derived to solve the objective function of MCNMF, along with its correctness and convergence guarantees. Extensive experimental results on real-world datasets have shown that MCNMF not only achieves more accurate performance over the state-of-the-arts using the aggregated representation, but also interprets data from different aspects with the multiple representations, which is beyond what current NMFs can offer

ZIC1 Is Downregulated through Promoter Hypermethylation, and Functions as a Tumor Suppressor Gene in Colorectal Cancer

The transcription factor, Zinc finger of the cerebellum (ZIC1), plays a crucial role in vertebrate development. Recently, ZIC1 has also been found to participate in the progression of human cancers, including medulloblastomas, endometrial cancers, and mesenchymal neoplasms. However, the function of ZIC1 in colon cancer progression has not been defined. In this study, we demonstrate ZIC1 to be silenced or significantly downregulated in colon cancer cell lines. These effects were reversed by demethylation treatment with 5-aza-2′-deoxycytidine (Aza). ZIC1 expression is also significantly downregulated in primary colorectal cancer tissues relative to adjacent non-tumor tissues (p = 0.0001). Furthermore, methylation of ZIC1 gene promoter is frequently detected in primary tumor tissues (85%, 34/40), but not in adjacent non-tumor tissues. Ectopic expression of ZIC1 suppresses cell proliferation and induces apoptosis, which is associated with MAPK and PI3K/Akt pathways, as well as the Bcl-xl/Bad/Caspase3 cascade. To identify target candidates of ZIC1, we employed cDNA microarray and found that 337 genes are downregulated and 95 genes upregulated by ectopic expression of ZIC1, which were verified by 10 selected gene expressions by qRT-PCR. Taken together, our results suggest that ZIC1 may potentially function as a tumor suppressor gene, which is downregulated through promoter hypermethylation in colorectal cancers