A kinetic study of CO oxidation over the perovskite-like oxide LaSrNiO4

The effect of reactant/product concentrations, reaction temperature and contact time on CO oxidation was investigated, using the perovskite-like oxide LaSrNiO4 as the catalyst. It was found that the reaction order of CO (reactant), as well as that of CO2 (product), is negative, the reaction orders for CO and CO2 being –0.32 and –0.51, respectively. However, the reaction order for O2 is positive, having a value of 0.62. The negative reaction order of CO and CO2 might be due to their competitive adsorption with O2, preventing the proceeding of oxygen dissociation (the rate-determining step of the reaction). The activation energy (Ea) of the reaction was calculated to be 49.3 kJ mol-1; this small activation energy suggests that LaSrNiO4 is a potential candidate for the CO oxidation reaction. The optimum weight hourly space velocity (WHSV) of the reaction was found to be 0.6 g s cm-3. The reaction conditions in the present case were (0.5–1 % CO + 0.5–2 % O2 + 0–2 % CO2), with He as the balance gas

Source Separation of Unknown Numbers of Single-Channel Underwater Acoustic Signals Based on Autoencoders

The separation of single-channel underwater acoustic signals is a challenging problem with practical significance. Few existing studies focus on the source separation problem with unknown numbers of signals, and how to evaluate the performances of the systems is not yet clear. We propose a solution with a fixed number of output channels to address these two problems, enabling it to avoid the dimensional disaster caused by the permutation problem induced by the alignment of outputs to targets. Specifically, we propose a two-step algorithm based on autoencoders and a new performance evaluation method for situations with mute channels. Experiments conducted on simulated mixtures of radiated ship noise show that the proposed solution can achieve similar separation performance to that attained with a known number of signals. The proposed algorithm achieved competitive performance as two algorithms developed for known numbers of signals, which is highly explainable and extensible and get the state of the art under this framework.Comment: 14 pages, 4 figures, 3 tables. For codes, see https://github.com/QinggangSUN/unknown_number_source_separatio

A Fusion Scheme of Local Manifold Learning Methods

Spectral analysis‐based dimensionality reduction algorithms, especially the local manifold learning methods, have become popular recently because their optimizations do not involve local minima and scale well to large, high‐dimensional data sets. Despite their attractive properties, these algorithms are developed based on different geometric intuitions, and only partial information from the true geometric structure of the underlying manifold is learned by each method. In order to discover the underlying manifold structure more faithfully, we introduce a novel method to fuse the geometric information learned from different local manifold learning algorithms in this chapter. First, we employ local tangent coordinates to compute the local objects from different local algorithms. Then, we utilize the truncation function from differential manifold to connect the local objects with a global functional and finally develop an alternating optimization‐based algorithm to discover the low‐dimensional embedding. Experiments on synthetic as well as real data sets demonstrate the effectiveness of our proposed method

Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

Abstract- RNA-binding proteins (RBPs) play diverse roles in eukaryotic RNA processing. Despite their pervasive functions in coding and non-coding RNA biogenesis and regulation, elucidating the specificities that define protein-RNA interactions remains a major challenge. Here, we describe a novel model-based approach — RNAMotifModeler to identify binding consensus of RBPs by integrating sequence features and RNA secondary structures. Using RNA sequences derived from Cross-linking immunoprecipitation (CLIP) followed by high-throughput sequencing for SRSF1 proteins, we identified a purine-rich octamer ‘AGAAGAAG ’ in a highly singlestranded RNA context, which is consistent with previous knowledge. The successful implementation on SRSF1 CLIPseq data demonstrates great potential to improve our understanding on the binding specificity of RNA binding proteins

Has China gained significant influence over ASEAN? : from the golden decade to the diamond decade

published_or_final_versionInternational and Public AffairsMasterMaster of International and Public Affair

Pose-disentangled Contrastive Learning for Self-supervised Facial Representation

Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily. However, analytically, current contrastive-based self-supervised learning still performs unsatisfactorily for learning facial representation. More specifically, existing contrastive learning (CL) tends to learn pose-invariant features that cannot depict the pose details of faces, compromising the learning performance. To conquer the above limitation of CL, we propose a novel Pose-disentangled Contrastive Learning (PCL) method for general self-supervised facial representation. Our PCL first devises a pose-disentangled decoder (PDD) with a delicately designed orthogonalizing regulation, which disentangles the pose-related features from the face-aware features; therefore, pose-related and other pose-unrelated facial information could be performed in individual subnetworks and do not affect each other's training. Furthermore, we introduce a pose-related contrastive learning scheme that learns pose-related information based on data augmentation of the same image, which would deliver more effective face-aware representation for various downstream tasks. We conducted a comprehensive linear evaluation on three challenging downstream facial understanding tasks, i.e., facial expression recognition, face recognition, and AU detection. Experimental results demonstrate that our method outperforms cutting-edge contrastive and other self-supervised learning methods with a great margin