RESUMEN
Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such preconditions cannot always be satisfied in practice due to the complexity of data collection and transmission. To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples. To be specific, we discover the existence of invariant semantic distribution across different views, which enables SMILE to alleviate the cross-view discrepancy to learn consensus semantics without requiring any paired samples. The resulting consensus semantics remains unaffected by cross-view distribution shifts, making them useful for realigning/imputing defective instances and forming clusters. We demonstrate the effectiveness of SMILE through extensive comparison experiments with 13 state-of-the-art baselines on five benchmarks. Our approach improves the clustering accuracy of NoisyMNIST from 19.3%/23.2% to 82.7%/69.0% when the correspondences/instances are fully incomplete. We will release the code after acceptance.
RESUMEN
Calcium looping (CaL) belongs to a promising high-temperature CO2 capture technology because of adopting cheap and extensive source CaO-based sorbents. However, CaO-based sorbents are prone to occur the issues of sintering and elutriation in the fluidized-bed reactors. To further enhance the practicability, the Zr-supported, CaO-based sorbent pellets were produced using the graphite-moulding method. Different synthesis modes (i.e., sol-gel, hydration-mixing, and wet-mixing) were compared for preparing CaO-based composite slurry before pelletization. Sol-gel is the promising synthesis mode to prepare Zr-supported, CaO-based pellets with outstanding CO2 capture performance due to achieving a more uniform distribution of inert CaZrO3 spacer. Moreover, the Zr-based stabilizer content within sorbent pellets produced by the combined method of sol-gel and graphite-moulding was further studied. The higher content of Zr-based stabilizer promotes the enhancement of cyclic stability and mechanical strength of Zr-supported, CaO-based pellets. After 17 cycles, the sorbent pellets containing 20 wt% of Zr-based stabilizer display a high CaO carbonation conversion of 74.1 %. Moreover, CaO-based pellets with 20 wt% of Zr-based stabilizer possess a higher compression strength of 4.84 ± 1.08 MPa, which is as high as 1.8 times that of the pellets with 5 wt% of Zr-based stabilizer.