Layered microporous polymers by solvent knitting method

Two-dimensional (2D) nanomaterials, especially 2D organic nanomaterials with unprecedentedly diverse and controlled structure, have attracted decent scientific interest. Among the preparation strategies, the top-down approach is one of the considered low-cost and scalable strategies to obtain 2D organic nanomaterials. However, some factors of their layered counterparts limited the development and potential applications of 2D organic nanomaterials, such as type, stability, and strict synthetic conditions of layered counterparts. We report a class of layered solvent knitting hyper-cross-linked microporous polymers (SHCPs) prepared by improving Friedel-Crafts reaction and using dichloroalkane as an economical solvent, stable electrophilic reagent, and external cross-linker at low temperature, which could be used as layered counterparts to obtain previously unknown 2D SHCP nanosheets by method of ultrasonic-assisted solvent exfoliation. This efficient and low-cost strategy can produce previously unreported microporous organic polymers with layered structure and high surface area and gas storage capacity. The pore structure and surface area of these polymers can be controlled by tuning the chain length of the solvent, the molar ratio of AlCl(3), and the size of monomers. Furthermore, we successfully obtain an unprecedentedly high–surface area HCP material (3002 m(2) g(−1)), which shows decent gas storage capacity (4.82 mmol g(−1) at 273 K and 1.00 bar for CO(2); 12.40 mmol g(−1) at 77.3 K and 1.13 bar for H(2)). This finding provides an opportunity for breaking the constraint of former knitting methods and opening up avenues for the design and synthesis of previously unknown layered HCP materials

Existence of weak solutions to stochastic heat equations driven by truncated α\alpha-stable white noises with non-Lipschitz coefficients

We consider a class of stochastic heat equations driven by truncated $\alpha$-stable white noises for $1<\alpha<2$ with noise coefficients that are continuous but not necessarily Lipschitz and satisfy globally linear growth conditions. We prove the existence of weak solution, taking values in two different spaces, to such an equation using a weak convergence argument on solutions to the approximating stochastic heat equations. For $1<\alpha<2$ the weak solution is a measure-valued c\`{a}dl\`{a}g process. However, for $1<\alpha<5/3$ the weak solution is a c\`{a}dl\`{a}g process taking function values, and in this case we further show that for $0<p<5/3$ the uniform $p$-th moment for $L^p$-norm of the weak solution is finite, and that the weak solution is uniformly stochastic continuous in $L^p$ sense and satisfies a flow property

Comparison principle for stochastic heat equations driven by α\alpha-stable white noises

For a class of non-linear stochastic heat equations driven by $\alpha$-stable white noises for $\alpha\in(1,2)$ with Lipschitz coefficients, we first show the existence and pathwise uniqueness of $L^p$-valued c\`{a}dl\`{a}g solutions to such a equation for $p\in(\alpha,2]$ by considering a sequence of approximating stochastic heat equations driven by truncated $\alpha$-stable white noises obtained by removing the big jumps from the original $\alpha$-stable white noises. If the $\alpha$-stable white noise is spectrally one-sided, under additional monotonicity assumption on noise coefficients, we prove a comparison theorem on the $L^2$-valued c\`{a}dl\`{a}g solutions of such a equation. As a consequence, the non-negativity of the $L^2$-valued c\`{a}dl\`{a}g solution is established for the above stochastic heat equation with non-negative initial function

GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction

Predicting the future paths of an agent's neighbors accurately and in a timely manner is central to the autonomous applications for collision avoidance. Conventional approaches, e.g., LSTM-based models, take considerable computational costs in the prediction, especially for the long sequence prediction. To support more efficient and accurate trajectory predictions, we propose a novel CNN-based spatial-temporal graph framework GraphTCN, which models the spatial interactions as social graphs and captures the spatio-temporal interactions with a modified temporal convolutional network. In contrast to conventional models, both the spatial and temporal modeling of our model are computed within each local time window. Therefore, it can be executed in parallel for much higher efficiency, and meanwhile with accuracy comparable to best-performing approaches. Experimental results confirm that our model achieves better performance in terms of both efficiency and accuracy as compared with state-of-the-art models on various trajectory prediction benchmark datasets.Comment: 10 pages, 7 figures, 3 table

Magnetic and nematic order of Bose-Fermi mixtures in moir\'e superlattices of 2D semiconductors

We investigate the magnetic orders in a mixture of Boson (exciton) and Fermion (electron or hole) trapped in transition-metal dichalcogenides moir\'e superlattices. A sizable antiferromagnetic exchange interaction is found between a carrier and an interlayer exciton trapped at different high symmetry points of the moir\'e supercell. This interaction at a distance much shorter than the carrier-carrier separation dominates the magnetic order in the Bose-Fermi mixture, where the carrier sublattice develops ferromagnetism opposite to that in the exciton sublattice. We demonstrate the possibility of increasing the Curie temperature of moir\'e carriers through electrical tuning of the exciton density in the ground state. In a trilayer moir\'e system with a p-n-p type band alignment, the exciton-carrier interplay can establish a layered antiferromagnetism for holes confined in the two outer layers. We further reveal a spontaneous nematic order in the Bose-Fermi mixture, arising from the interference between the Coulomb interaction and p-wave interlayer tunneling dictated by the stacking registry.Comment: 6 pages, 4 figure

DARE: Sequence-Structure Dual-Aware Encoder for RNA-Protein Binding Prediction

Predicting RNA-protein binding sites helps to explore the mechanisms of the interaction between RNA and proteins. Numerous deep learning methods have been applied to predict RNA-protein binding sites. Some of these methods use only sequence information for prediction which could lose information about the topology. And there may be a loss of important information if the secondary structure features are simply represented as one-hot matrices. Furthermore, existing deep learning methods are usually based on convolutional neural networks for feature extraction, which tend to focus on local features. As for the information of the whole sequence, existing methods usually ignore global features. Therefore, we propose a novel deep learning model called DARE for RNA-protein binding sites prediction using both sequence and secondary structure information of RNA. DARE employs the secondary structure feature extraction module to capture the features of the RNA secondary structure and learn the topological information. Therefore, we design a local feature extraction module and a global feature integration module to capture the whole information of RNA. Thus we can achieve the purpose of complementary information. Extensive experiments demonstrate that DARE outperforms baselines. Our analysis of the case study further confirm the effectiveness of DARE

SC-Track: a robust cell tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations

Computational analysis of fluorescent timelapse microscopy images at the single-cell level is a powerful approach to study cellular changes that dictate important cell fate decisions. Core to this approach is the need to generate reliable cell segmentations and classifications necessary for accurate quantitative analysis. Deep learning-based convolutional neural networks (CNNs) have emerged as a promising solution to these challenges. However, current CNNs are prone to produce noisy cell segmentations and classifications, which is a significant barrier to constructing accurate single-cell lineages. To address this, we developed a novel algorithm called Single Cell Track (SC-Track), which employs a hierarchical probabilistic cache cascade model based on biological observations of cell division and movement dynamics. Our results show that SC-Track performs better than a panel of publicly available cell trackers on a diverse set of cell segmentation types. This cell-tracking performance was achieved without any parameter adjustments, making SC-Track an excellent generalised algorithm that can maintain robust cell-tracking performance in varying cell segmentation qualities, cell morphological appearances and imaging conditions. Furthermore, SC-Track is equipped with a cell class correction function to improve the accuracy of cell classifications in multi-class cell segmentation time series. These features together make SC-Track a robust cell-tracking algorithm that works well with noisy cell instance segmentation and classification predictions from CNNs to generate accurate single-cell lineages and classifications

A Representation Learning Approach for Predicting circRNA Back-Splicing Event via Sequence-Interaction-Aware Dual Encoder

Circular RNAs (circRNAs) play a crucial role in generegulation and association with diseases because of their uniqueclosed continuous loop structure, which is more stable and conserved than ordinary linear RNAs. As fundamental work to clarifytheir functions, a large number of computational approaches foridentifying circRNA formation have been proposed. However, thesemethods fail to fully utilize the important characteristics of backsplicing events, i.e., the positional information of the splice sitesand the interaction features of its flanking sequences, for predicting circRNAs. To this end, we hereby propose a novel approachcalled SIDE for predicting circRNA back-splicing events using onlyraw RNA sequences. Technically, SIDE employs a dual encoderto capture global and interactive features of the RNA sequence,and then a decoder designed by the contrastive learning to fuseout discriminative features improving the prediction of circRNAsformation. Empirical results on three real-world datasets showthe effectiveness of SIDE. Further analysis also reveals that theeffectiveness of SIDE