An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm

Physical Encoding Improves OOD Performance in Deep Learning Materials Property Prediction

Deep learning (DL) models have been widely used in materials property prediction with great success, especially for properties with large datasets. However, the out-of-distribution (OOD) performances of such models are questionable, especially when the training set is not large enough. Here we showed that using physical encoding rather than the widely used one-hot encoding can significantly improve the OOD performance by increasing the models' generalization performance, which is especially true for models trained with small datasets. Our benchmark results of both composition- and structure-based deep learning models over six datasets including formation energy, band gap, refractive index, and elastic properties predictions demonstrated the importance of physical encoding to OOD generalization for models trained on small datasets

Minimum observability of probabilistic Boolean networks

This paper studies the minimum observability of probabilistic Boolean networks (PBNs), the main objective of which is to add the fewest measurements to make an unobservable PBN become observable. First of all, the algebraic form of a PBN is established with the help of semi-tensor product (STP) of matrices. By combining the algebraic forms of two identical PBNs into a parallel system, a method to search the states that need to be H-distinguishable is proposed based on the robust set reachability technique. Secondly, a necessary and sufficient condition is given to find the minimum measurements such that a given set can be H-distinguishable. Moreover, by comparing the numbers of measurements for all the feasible H-distinguishable state sets, the least measurements that make the system observable are gained. Finally, an example is given to verify the validity of the obtained results

Громадянське виховання підлітків у контексті педагогічної спадщини А. Б. Рєзніка

У статті висвітлено основні положення педагогічної спадщини видатного педагога Кіровоградщини А. Б. Рєзніка в царині громадянського виховання. Доведено, що теоретичні положення, педагогічні висновки, методичні поради, які він розробив, набувають особливої актуальності у наш час і їх застосування у виховній практиці, сприятиме вибору активної життєвої позиції та свідомому формуванню громадянського світогляду молоді

MD-HIT: Machine learning for materials property prediction with dataset redundancy control

Materials datasets are usually featured by the existence of many redundant (highly similar) materials due to the tinkering material design practice over the history of materials research. For example, the materials project database has many perovskite cubic structure materials similar to SrTiO$_3$. This sample redundancy within the dataset makes the random splitting of machine learning model evaluation to fail so that the ML models tend to achieve over-estimated predictive performance which is misleading for the materials science community. This issue is well known in the field of bioinformatics for protein function prediction, in which a redundancy reduction procedure (CD-Hit) is always applied to reduce the sample redundancy by ensuring no pair of samples has a sequence similarity greater than a given threshold. This paper surveys the overestimated ML performance in the literature for both composition based and structure based material property prediction. We then propose a material dataset redundancy reduction algorithm called MD-HIT and evaluate it with several composition and structure based distance threshold sfor reducing data set sample redundancy. We show that with this control, the predicted performance tends to better reflect their true prediction capability. Our MD-hit code can be freely accessed at https://github.com/usccolumbia/MD-HITComment: 12page

Probabilistic Generative Transformer Language models for Generative Design of Molecules

Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformerComment: 13 page

The human 18S rRNA m6A methyltransferase METTL5 promotes tumorigenesis via DEPDC1 in lung squamous cell carcinoma

BackgroundN6-Methyladenosine (m6A) is one of the post-transcriptional modifications and abnormal m6A is critical for cancer initiation, progression, metastasis in Lung squamous cell carcinoma (LUSC). Ribosomal RNA (rRNA) accounts for most of the total cellular RNA, however, the functions and molecular mechanisms underlying rRNA modifications in LUSC remained largely unclear.MethodsHigh-throughput library screening identifies the key m6A regulator METTL5 in LUSC. Cell and animal experiments were used to identify that METTL5 promoted LUSC tumorigenesis to enhance DEP domain containing 1 (DEPDC1) translation via m6A modification.ResultsWe showed that the N6-methyladenosine (m6A) methyltransferase METTL5 was an independent risk factor in LUSC and was associated with poor prognosis of patients. Notedly, overexpression METTL5 promoted LUSC tumorigenesis in an m6A modification, while METTL5 knockdown markedly inhibited proliferation and migratory ability of tumor cells in vitro and in vivo. Mechanistically, METTL5 promoted LUSC tumorigenesis via m6a methyltransferase to increase the translation of DEPDC1.ConclusionOur results revealed that METTL5 enhances DEPDC1 translation to contribute to tumorigenesis and poor prognosis, providing a potential prognostic biomarker and therapeutic target for LUSC