Multi-focus Image Fusion with Sparse Feature Based Pulse Coupled Neural Network

In order to better extract the focused regions and effectively improve the quality of the fused image, a novel multi-focus image fusion scheme with sparse feature based pulse coupled neural network (PCNN) is proposed. The registered source images are decomposed into principal matrices and sparse matrices by robust principal component analysis (RPCA). The salient features of the sparse matrices construct the sparse feature space of the source images. The sparse features are used to motivate the PCNN neurons. The focused regions of the source images are detected by the output of the PCNN and integrated to construct the final fused image. Experimental results show that the proposed scheme works better in extracting the focused regions and improving the fusion quality compared to the other existing fusion methods in both spatial and transform domain

A Novel Multi-focus Image Fusion Method Based on Non-negative Matrix Factorization

In order to efficiently extract the focused regions from the source images and improve the quality of the fused image, this paper presents a novel image fusion scheme with non-negative matrix factorization (NMF). The source images are fused by NMF to construct temporary fused image, whose region homogeneityis used to split the source images into regions.The focused regions are detected and integrated to construct the final fused image. Experimental results demonstrate that the proposedschemeis capable ofefficiently extracting the focused regions and significantly improving the fusion quality compared to other existing fusion methods,in terms of visualand quantitative evaluations

Preference-aware task assignment in on-demand taxi dispatching: An online stable matching approach

A central issue in on-demand taxi dispatching platforms is task assignment, which designs matching policies among dynamically arrived drivers (workers) and passengers (tasks). Previous matching policies maximize the profit of the platform without considering the preferences of workers and tasks (e.g., workers may prefer high-rewarding tasks while tasks may prefer nearby workers). Such ignorance of preferences impairs user experience and will decrease the profit of the platform in the long run. To address this problem, we propose preference-aware task assignment using online stable matching. Specifically, we define a new model, Online Stable Matching under Known Identical Independent Distributions (OSM-KIID). It not only maximizes the expected total profits (OBJ-1), but also tries to satisfy the preferences among workers and tasks by minimizing the expected total number of blocking pairs (OBJ-2). The model also features a practical arrival assumption validated on real-world dataset. Furthermore, we present a linear program based online algorithm LP-ALG, which achieves an online ratio of at least 1−1/e on OBJ-1 and has at most 0.6·|E| blocking pairs expectedly, where |E| is the total number of edges in the compatible graph. We also show that a natural Greedy can have an arbitrarily bad performance on OBJ-1 while maintaining around 0.5·|E| blocking pairs. Evaluations on both synthetic and real datasets confirm our theoretical analysis and demonstrate that LP-ALG strictly dominates all the baselines on both objectives when tasks notably outnumber workers

FMMRec: Fairness-aware Multimodal Recommendation

Recently, multimodal recommendations have gained increasing attention for effectively addressing the data sparsity problem by incorporating modality-based representations. Although multimodal recommendations excel in accuracy, the introduction of different modalities (e.g., images, text, and audio) may expose more users' sensitive information (e.g., gender and age) to recommender systems, resulting in potentially more serious unfairness issues. Despite many efforts on fairness, existing fairness-aware methods are either incompatible with multimodal scenarios, or lead to suboptimal fairness performance due to neglecting sensitive information of multimodal content. To achieve counterfactual fairness in multimodal recommendations, we propose a novel fairness-aware multimodal recommendation approach (dubbed as FMMRec) to disentangle the sensitive and non-sensitive information from modal representations and leverage the disentangled modal representations to guide fairer representation learning. Specifically, we first disentangle biased and filtered modal representations by maximizing and minimizing their sensitive attribute prediction ability respectively. With the disentangled modal representations, we mine the modality-based unfair and fair (corresponding to biased and filtered) user-user structures for enhancing explicit user representation with the biased and filtered neighbors from the corresponding structures, followed by adversarially filtering out sensitive information. Experiments on two real-world public datasets demonstrate the superiority of our FMMRec relative to the state-of-the-art baselines. Our source code is available at https://anonymous.4open.science/r/FMMRec

Photooxidation of a twisted isoquinolinone

Understanding the oxidation mechanism and positions of twistacenes and twistheteroacenes under ambient conditions is very important because such knowledge can guide us to design and synthesize novel, larger stable analogues. Herein, we demonstrated for the first time that a twisted isoquinolinone can decompose under oxygen and light at room temperature. The as‐decomposed product 1 was fully characterized through conventional methods as well as single‐crystal structure analysis. Moreover, the physical properties of the as‐obtained product were carefully investigated and the possible formation mechanism was proposed

A UTP semantics for communicating processes with shared variables and its formal encoding in PVS

CSP# (communicating sequential programs) is a modelling language designed for specifying concurrent systems by integrating CSP-like compositional operators with sequential programs updating shared variables. In this work, we define an observation-oriented denotational semantics in an open environment for the CSP# language based on the UTP framework. To deal with shared variables, we lift traditional event-based traces into mixed traces which consist of state-event pairs for recording process behaviours. To capture all possible concurrency behaviours between action/channel-based communications and global shared variables, we construct a comprehensive set of rules on merging traces from processes which run in parallel/interleaving. We also define refinement to check process equivalence and present a set of algebraic laws which are established based on our denotational semantics. We further encode our proposed denotational semantics into the PVS theorem prover. The encoding not only ensures the semantic consistency, but also builds up a theoretic foundation for machine-assisted verification of CSP# specifications.Full Tex

Nanoscale Imaging of Kidney Glomeruli Using Expansion Pathology

Kidney glomerular diseases, such as the minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS), and other nephrotic syndromes, are typically diagnosed or confirmed via electron microscopy. Although optical microscopy has been a vital tool to examine clinical specimens for diagnoses in pathology for decades, the optical resolution is constricted by the physical diffraction limit of the optical microscope, which prevents high-resolution investigation of subcellular anatomy, such as of the podocyte tertiary foot processes. Here, we describe a simple, fast, and inexpensive protocol for nanoscale optical imaging of kidney glomeruli. The protocol is based on Expansion Pathology (ExPath), a new principle of microscopy that overcomes optical diffraction limit by chemically embedding specimens into a swellable polymer and physically expanding it homogenously prior to imaging. Our method uses only commercially available reagents, a conventional fluorescence microscope and it can be applied to both fixed-frozen or formalin-fixed paraffin embedded (FFPE) tissue sections. It requires minimal operative experience in a wet lab, optical microscopy and imaging processing. Finally, we also discuss challenges, limitations and prospective applications for ExPath-based imaging of glomeruli

Benchmarking Component Analysis of Remanent Magnetization Curves With a Synthetic Mixture Series: Insight into the Reliability of Unmixing Natural Samples

Geological samples often contain several magnetic components associated with different geological processes. Component analysis of remanent magnetization curves has been widely applied to decompose convoluted information. However, the reliability of commonly used methods is poorly assessed as independent verification is rarely available. For this purpose, we designed an experiment using a series of mixtures of two endmembers to benchmark unmixing methods for isothermal remanent magnetization (IRM) acquisition curves. First‐order reversal curves (FORC) diagrams were analyzed for comparison. It is demonstrated that the parametric method, which unmixes samples using specific probability distributions, may result in biased estimates. In contrast, an endmember‐based IRM unmixing approach yielded better quantitative results, which are comparable to the results obtained by FORC analysis based on principle component analysis (FORC‐PCA). We demonstrate that endmember‐based methods are in principle more suitable for unmixing a collection of samples with common endmembers; however, the level of decomposition will vary depending on the difference between the true endmembers that are associated with distinctive processes and the empirical endmembers used for unmixing. When it is desired to further decompose endmembers, the parametric unmixing approach remains a valuable means of inferring their underlying components. We illustrate that the results obtained by endmember‐based and parametric methods can be quantitatively combined to provide improved unmixing results at the level of parametric model distributions.The work was supported by the National Natural Science Foundation of China (41621004 and 41904070) and the Strategic Priority Research Program of Chinese Academy of Sciences (XDB18010000). This study was also supported by the National Institute of Polar Research (NIPR) through Advanced Project (KP‐7 and KP306) and JSPS KAKENHI grants (15K13581, 16H04068, 17H06321, and 18K13638). X. Z. acknowledges the Australian Research Council Discovery Projects DP200100765 and the National Natural Science Foundation of China (grant 41920104009) for financial supports