The prediction and subtraction method is a common approach for suppressing multiples, and the key step lies in adaptive subtraction of multiples. The matched filter is addressed using the fast iterative shrinkage thresholding algorithm (FISTA) in the conventional linear regression method (LRM). However, it necessitates manual trial and error for choosing appropriate shrinkage thresholding values and regularization parameters. The U-net method (UM) leverages a non-linear regression framework for adaptive subtraction, which allows for removing more multiples than the LRM. However, it is prone to cause overfitting and primary damage. The FISTA can be unfolded into network layers and FISTA-net is constructed by replacing the shrinkage thresholding operation with U-net. With the unsupervised FISTA-net method (FM) the estimation of primaries is achieved through unsupervised training with initial recorded data, simulated multiples and initial results of primaries as inputs. The regularization parameters and shrinkage thresholding values are estimated adaptively, and the network is understood as iterative steps involved in FISTA. This paper utilizes a limited quantity of labeled data with primaries and a substantial quantity of unlabeled data for semi-supervised training of FISTA-net. The proposed semi-supervised FM can increase the accuracy of adaptive subtraction by using the information of primaries in labels. In the synthetic data we utilize the actual primaries as labels, while in the field data the estimated primaries obtained through the conventional method are employed as pseudo-labels. The semi-supervised FM demonstrates superior accuracy in multiple separation, overfitting avoidance, and primary preservation compared to the traditional LRM, UM, and unsupervised FM.

Adaptively subtracting multiple model from the initial data is an essential assignment for the successful elimination of seismic surface-related multiples. Conventional expanded multichannel linear regression (EMLR) method has been proposed to address this challenge by utilizing multi-pattern multiple model. These patterns include the multiple model itself and its first derivative, its Hilbert transform and its first derivative of the Hilbert transform, which are matched with the initial data in the EMLR method. It may lead to inaccurate primary preservation or give rise to residual multiples by using the LR model. The existing U-Net method effectively mitigates complex disparities between the multiple model and actual multiples through integrating adaptive subtraction into the non-LR architecture. Nevertheless, residual multiples are produced by this method using the multiple model itself, especially in complex media contexts. In order to improve surface-related multiple removal’s accuracy, we propose the expanded multichannel U-Net (EMUN) method with multi-pattern multiple model. In the proposed method, the initial data is matched with the multi-pattern multiple model through U-Net in the way of self-supervised training without true primaries as labels. The proposed method incorporates rich information from expanded multichannel of U-Net, enabling better U-Net training for adaptive subtraction. In contrast to the EMLR method and the existing U-Net method, the proposed EMUN method exhibits exceptional efficacy in protecting primaries and eliminating surface-related multiples, as evidenced by its outstanding performance in both synthetic and field data tests.

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

Effectively suppressing seismic multiples relies heavily on the crucial task of adaptively subtracting the simulated multiples from the initial recorded data. By executing adaptive subtraction within the non-linear regression (non-LR) framework the U-net method has shown superior capability in mitigating the intricate disparities between the simulated and actual multiples when compared to the LR method. The low, medium and high frequency-bands of simulated multiples have been employed to effectively address frequency-dependent inconsistencies in the LR method. To further improve multiple suppression accuracy three frequency-bands of simulated multiples are employed as three channels of the U-net input, which are matched with the initial recorded data during self-supervised training in this letter. Compared to the LR method inputting simulated multiples alone, the LR method inputting three frequency-bands of simulated multiples and the U-net method inputting simulated multiples alone, the proposed U-net method inputting three frequency-bands of simulated multiples improves the signal-to-noise ratio (SNR) by 4.41, 2.07 and 1.99 in the synthetic data example, and demonstrates superior improvement in preserving primaries and eliminating residual multiples in the field data example.

After the procedure of multiple modeling adaptive subtraction is important for removing seismic multiples. The mismatches between the modeled multiples and true multiples are frequency dependent. The traditional filter method combining the modeled multiples themselves can not remove the mismatches effectively and may cause residual multiples. In this letter we partition the modeled multiples into three frequency-bands, which are low, middle and high frequency-bands. Then the three frequency-bands of the modeled multiples are combined with the filter to match with the original data. Since the three frequency-bands express more information than the modeled multiples themselves, the proposed filter method can remove more residual multiples than the traditional filter method.

Adaptive subtraction plays a crucial role in the multiple removal method that involves modeling and subtraction steps. The linear regression (LR) based method utilizes the fast iterative shrinkage thresholding algorithm (FISTA) to solve the optimization problem that contains L1 norm minimization constraint of primaries. It selects the regularization factor and shrinkage thresholding value through trial and error. Under the non-LR framework the U-net is used for adaptive subtraction of modeled multiples from the original recorded data. Since U-net has large network capacity, it is prone to overfit to the original recorded data and lead to primary damage. In this paper, we unfold the iterative steps of FISTA to construct FISTA-Net, which takes the original recorded data and modeled multiples as input data and outputs the estimated primaries. The FISTA-Net based method does not require true primaries as labels and uses L1 norm minimization constraint of primaries for unsupervised training. It can adaptively estimate the regularization factor and shrinkage thresholding value, which is replaced by U-net. FISTA-Net introduces the nonlinear mapping ability of U-net into its structure, which can be interpreted as the iterative steps of FISTA. As a result, the proposed FISTA-Net based method can better attenuate residual multiples, avoid overfitting, and preserve primaries compared to the LR-based and U-net based methods.

Anelasticity of the earth subsurface medium, which is quantified by the quality factor$Q$, causes the dissipation of seismic energy. Strong attenuation effect resulting from geology such as gas clouds (gas-filled sandstone) is a challenging problem for high-resolution imaging. To compensate the attenuation effect, first we need to accurately estimate the attenuation parameter. However, it is difficult to directly derive a heterogeneous attenuation Q model. This research letter proposes a method to derive a Q model corresponding to strong attenuative media from marine reflection seismic data using convolutional neural network (CNN), a popular deep learning framework. We treat Q anomaly detection problem as a semantic segmentation task and train a network to perform a pixel-by-pixel prediction to invert a pixel group that belongs to the strong level of attenuation probability. The proposed method uses a volume of marine 3-D reflection seismic data for network training and validation, which needs only a small part of real data as the training set due to the feature of U-Net. In the final stage, to evaluate the attenuation model, we validate the predicted heterogeneous Q model using deabsorption prestack depth migration (Q-PSDM), a high-resolution imaging result in depth domain with appropriate compensation is obtained.

The process of seismic multiple removal in oil seismic exploration is crucial for the imaging of underground structures with primary reflections. The inclusion of prediction and subtraction in the multiple removal method requires adaptive subtraction to remove the complex differences between the true and modeled multiples. The traditional adaptive subtraction method is generally expressed as a linear regression (LR) problem. In this article, we introduce U-net, a popular deep learning tool, to represent the complex differences between the true and modeled multiples in a nonlinear relationship. Thus, we present adaptive subtraction as a non-LR problem. The modeled multiples and full recorded seismic response with multiples and primaries are used as the input and labels to train U-net. The proposed U-net method is able to avoid over-fitting of the primaries due to the sufficient number of 2-D data windows for the training of U-net, as well as the network parameter regularization and the L1 norm minimization constraint on the primaries. The proposed U-net method attains 20.5 dB and 2.5 dB improvement in the signal-to-noise ratio (SNR) using the first synthetic data and second synthetic (Sigsbee2B) data set, respectively, compared with the traditional LR method, and a qualitative improvement for a real data set test.

Point-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i.e., global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients' data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.

MOTIVATION: Polyadenylation is a critical step for gene expression regulation during the maturation of mRNA. An accurate and robust method for poly(A) signals (PASs) identification is not only desired for the purpose of better transcripts' end annotation, but can also help us gain a deeper insight of the underlying regulatory mechanism. Although many methods have been proposed for PAS recognition, most of them are PAS motif- and human-specific, which leads to high risks of overfitting, low generalization power, and inability to reveal the connections between the underlying mechanisms of different mammals. RESULTS: In this work, we propose a robust, PAS motif agnostic, and highly interpretable and transferrable deep learning model for accurate PAS recognition, which requires no prior knowledge or human-designed features. We show that our single model trained over all human PAS motifs not only outperforms the state-of-the-art methods trained on specific motifs, but can also be generalized well to two mouse datasets. Moreover, we further increase the prediction accuracy by transferring the deep learning model trained on the data of one species to the data of a different species. Several novel underlying poly(A) patterns are revealed through the visualization of important oligomers and positions in our trained models. Finally, we interpret the deep learning models by converting the convolutional filters into sequence logos and quantitatively compare the sequence logos between human and mouse datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/likesum/DeeReCT-PolyA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hybrid learning usually incorporate online learning activities since face-to-face class time has been reduces significantly. One of the major challenges in hybrid course is to maintain the same level of student-to-student and student to-instructor interaction as in traditional classes. Various strategies, which include online discussion, online journals, online tutorial, etc., have been developed to engage students and improve the interaction. Online discussion is one of the common strategies. Online discussion relies on asynchronous communication, where participants communicate by posting messages to the bulletin board system such as Blackboard Discussions. Asynchronous communication has the potential to make collaboration efforts more rewarding and productive for students by enabling them to communicate at any time and from any networked location. The focus of this ongoing study is to explore changes in students' level of high-order thinking and knowledge construction in asynchronous threaded discussions, the emergence of communication patterns and structures in the asynchronous communication network, and relationships between role centrality and concept centrality in student discourse. It will employ content analysis and social network analysis to pursue its research foci.