Image Quality Is Not All You Want: Task-Driven Lens Design for Image Classification

In computer vision, it has long been taken for granted that high-quality images obtained through well-designed camera lenses would lead to superior results. However, we find that this common perception is not a "one-size-fits-all" solution for diverse computer vision tasks. We demonstrate that task-driven and deep-learned simple optics can actually deliver better visual task performance. The Task-Driven lens design approach, which relies solely on a well-trained network model for supervision, is proven to be capable of designing lenses from scratch. Experimental results demonstrate the designed image classification lens (``TaskLens'') exhibits higher accuracy compared to conventional imaging-driven lenses, even with fewer lens elements. Furthermore, we show that our TaskLens is compatible with various network models while maintaining enhanced classification accuracy. We propose that TaskLens holds significant potential, particularly when physical dimensions and cost are severely constrained.Comment: Use an image classification network to supervise the lens design from scratch. The final designs can achieve higher accuracy with fewer optical element

Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey

Hyperspectral imaging enables versatile applications due to its competence in capturing abundant spatial and spectral information, which are crucial for identifying substances. However, the devices for acquiring hyperspectral images are expensive and complicated. Therefore, many alternative spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from lower-cost, more available RGB images. We present a thorough investigation of these state-of-the-art spectral reconstruction methods from the widespread RGB images. A systematic study and comparison of more than 25 methods has revealed that most of the data-driven deep learning methods are superior to prior-based methods in terms of reconstruction accuracy and quality despite lower speeds. This comprehensive review can serve as a fruitful reference source for peer researchers, thus further inspiring future development directions in related domains

Cost-effectiveness Analysis on Magnetic Harvesting of Algal Cells

Abstract This study investigated the magnetic harvesting of algae via magnetic nanoparticles (MNPs). MNPs achieved high harvesting efficiency, over 95.0%, for various algal strains and could be effectively reactivated for low-cost algae separation. Comparing different harvesting efficiency, synthesis cost and reactivation methods, the present work evaluated the cost-effectiveness of different MNPs types and discussed the key factors affecting harvesting cost. Our results indicated a significant low cost and high harvesting effectiveness by naked MNPs and ultrasonic reactivation, though surface modified MNPs had higher harvesting capacity. Given the fact that algae harvesting is one of the most cost-consumption step in bioenergy production, we suggested magnetic algae harvesting with naked and reactivated MNPs as the most cost-effective technique for future industrial application

Synthesis and Catalytic Performance of Ni/SiO 2

A series of Ni/SiO2 catalysts with different Ni content were prepared by sol-gel method for application in the synthesis of 2-methyltetrahydrofuran (2-MTHF) by hydrogenation of 2-methylfuran (2-MF). The catalyst structure was investigated by X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and temperature programmed reduction (TPR). It is found that structures and catalytic performance of the catalysts were highly affected by the Ni content. The catalyst with a 25% Ni content had an appropriate size of the Ni species and larger BET surface area and produced a higher 2-MF conversion with enhanced selectivity in 2-MTHF

Transcatheter Aortic Valve Replacement for Aortic Regurgitation – A Review

Transcatheter aortic valve replacement (TAVR) is currently a widely used option for patients with severe symptomatic aortic stenosis with high to low surgical risk. However, aortic regurgitation (AR) remains an “off-label” indication for TAVR, particularly for patients with mild or absent leaflet calcification or aortic annulus dimensions beyond the size of the bioprosthesis, which increase the risk of dislocation. With advances in transcatheter heart valve devices, the safety and efficacy of TAVR in treating patients with severe pure native AR has gained acceptance. This review examines current evidence and clinical practice, and presents technological advancements in devices for AR

Tuning electrochemical catalytic activity of defective 2D terrace MoSe2 heterogeneous catalyst via Co doping

This study presents successful growth of defective 2D terrace MoSe2/CoMoSe lateral heterostructures (LH), bilayer and multilayer MoSe2/CoMoSe LH, and vertical heterostructures (VH) nanolayers by doping metal Co (cobalt) element into MoSe2 atomic layers to form a CoMoSe alloy at the high temperature (~900 °C). After the successful introduction of metal Co heterogeneity in the MoSe2 thin layers, more active sites can be created to enhance hydrogen evolution reaction (HER) activities combining with metal Co catalysis, through the mechanisms including (1) atomic arrangement distortion in CoMoSe alloy nanolayers, (2) atomic level coarsening in LH interfaces and terrace edge layer architecture in VH, (3) formation of defective 2D terrace MoSe2 nanolayers heterogeneous catalyst via metal Co doping. The HER investigations indicated that the obtained products with LH and VH exhibited an improved HER activity in comparison with those from the pristine 2D MoSe2 electrocatalyst and LH type MoSe2/CoMoSe. The present work shows a facile yet reliable route to introduce metal ions into ultrathin 2D transition metal dichalcogenides (TMDCS) and produce defective 2D alloy atomic layers for exposing active sites, and thus eventually improve their electrocatalytic performance

Upregulation of lncRNA NR_046683 Serves as a Prognostic Biomarker and Potential Drug Target for Multiple Myeloma

Aim: To investigate the prognostic value of lncRNA NR_046683 in multiple myeloma (MM).Methods: High-throughput lncRNA array was combined with bioinformatics techniques to screen differentially expressed lncRNA in MM. qRT-PCR was adopted to determine the expression of target lncRNAs in MM patients and controls.Results: It was found for the first time that lncRNA NR_046683 is closely related to the prognosis of MM. It was also detected in tumor cell lines KM3, U266, especially in drug-resistant cell lines KM3/BTZ and MM1R. The NR_046683 expression differed significantly in patients of different MM subtypes and staging. Moreover, the overexpression of NR-046683 is closely related to β2-microglobulin. We also found that the overexpression of NR-046683 correlates to chromosomal aberrations, such as del(13q14), gain 1q21, and t(4;14).Conclusion: lncRNA NR_046683 can serve as a novel biomarker for potential drug target and prognostic prediction in MM

Synthesis and Catalytic Performance of Ni/SiO 2 for Hydrogenation of 2-Methylfuran to 2-Methyltetrahydrofuran

A series of Ni/SiO 2 catalysts with different Ni content were prepared by sol-gel method for application in the synthesis of 2-methyltetrahydrofuran (2-MTHF) by hydrogenation of 2-methylfuran (2-MF). The catalyst structure was investigated by Xray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and temperature programmed reduction (TPR). It is found that structures and catalytic performance of the catalysts were highly affected by the Ni content. The catalyst with a 25% Ni content had an appropriate size of the Ni species and larger BET surface area and produced a higher 2-MF conversion with enhanced selectivity in 2-MTHF