Learning Attention in the Frequency Domain for Flexible Real Photograph Denoising.

Recent advancements in deep learning techniques have pushed forward the frontiers of real photograph denoising. However, due to the inherent pooling operations in the spatial domain, current CNN-based denoisers are biased towards focusing on low-frequency representations, while discarding the high-frequency components. This will induce a problem for suboptimal visual quality as the image denoising tasks target completely eliminating the complex noises and recovering all fine-scale and salient information. In this work, we tackle this challenge from the frequency perspective and present a new solution pipeline, coined as frequency attention denoising network (FADNet). Our key idea is to build a learning-based frequency attention framework, where the feature correlations on a broader frequency spectrum can be fully characterized, thus enhancing the representational power of the network across multiple frequency channels. Based on this, we design a cascade of adaptive instance residual modules (AIRMs). In each AIRM, we first transform the spatial-domain features into the frequency space. Then, a learning-based frequency attention framework is devised to explore the feature inter-dependencies converted in the frequency domain. Besides this, we introduce an adaptive layer by leveraging the guidance of the estimated noise map and intermediate features to meet the challenges of model generalization in the noise discrepancy. The effectiveness of our method is demonstrated on several real camera benchmark datasets, with superior denoising performance, generalization capability, and efficiency versus the state-of-the-art.

Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications.

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.

Rapid Discrimination of Panax quinquefolium and Panax ginseng Using the Proofman-Duplex-LMTIA Technique.

This study aims to establish a rapid identification method based on the Proofman-LMTIA technique for distinguishing between Panax quinquefolium and Panax ginseng. By targeting specific 18S rDNA sequences, suitable primers and Proofman probes labeled FAM or JOE were designed for LMTIA. Initially, single-species-primer Proofman-LMTIA assays were performed separately for each ginseng type to optimize reaction temperature, assess sensitivity and specificity, and determine the detection limit. Subsequently, both sets of primers and their corresponding probes were combined in the same reaction system to further optimize reaction conditions, evaluate sensitivity, and assess stability. Finally, the developed Proofman-duplex-LMTIA technique was employed to detect P. quinquefolium and P. ginseng slices available in the market. Single-plex Proofman-LMTIA assays revealed that the optimal reaction temperature for both P. quinquefolium and P. ginseng was 62 °C. The sensitivity was as low as 1 pg/µL, with a detection limit of 0.1%, and both showed excellent specificity. The optimal temperature for Proofman-duplex-LMTIA assays was 58 °C. This method could simultaneously identify P. quinquefolium and P. ginseng. Testing 6 samples of P. ginseng and 11 samples of P. quinquefolium from the market resulted in a 100% positive rate for all samples. This study successfully established a rapid, simple, sensitive, and specific Proofman-duplex-LMTIA identification method for P. quinquefolium and P. ginseng. It provides an effective means for quality control of P. quinquefolium, P. ginseng, and related products.

Detection of Soybean-Derived Components in Dairy Products Using Proofreading Enzyme-Mediated Probe Cleavage Coupled with Ladder-Shape Melting Temperature Isothermal Amplification (Proofman-LMTIA).

Food adulteration is a serious problem all over the world. Establishing an accurate, sensitive and fast detection method is an important part of identifying food adulteration. Herein, a sequence-specific ladder-shape melting temperature isothermal amplification (LMTIA) assay was reported to detect soybean-derived components using proofreading enzyme-mediated probe cleavage (named Proofman), which could realize real-time and visual detection without uncapping. The results showed that, under the optimal temperature of 57 °C, the established Proofman-LMTIA method for the detection of soybean-derived components in dairy products was sensitive to 1 pg/µL, with strong specificity, and could distinguish soybean genes from those of beef, mutton, sunflower, corn, walnut, etc. The established Proofman-LMTIA detection method was applied to the detection of actual samples of cow milk and goat milk. The results showed that the method was accurate, stable and reliable, and the detection results were not affected by a complex matrix without false positives or false negatives. It was proved that the method could be used for the detection and identification of soybean-derived components in actual dairy products samples.

Study on detection of soybean components in edible oil with ladder-shape melting temperature isothermal amplification (LMTIA) assay.

A ladder-shape melting temperature isothermal amplification (LMTIA) assay was established and used to detect soybean components in edible oils. LMTIA primers were designed with the sequence of the internal transcribed spacer (ITS) gene as the target, the reaction temperatures were optimized, the sensitivity was determined, and the suitability of the DNA extraction method for edible oil was assessed, with H2O and genomic DNA (gDNA) from corn, rapeseed, cottonseed, sesame, chili, chicken, pork, beef, and mutton as negative controls to test the false positives of the LMTIA assay. The established LMTIA assay gave a sensitivity of 1 pg at an optimal temperature of 57 °C. The Edible Oil DNA Extraction Kit was suitable for the LMTIA assay to detect soybean components in refined plant oil. No false positives occurred from all negative controls. This study successfully established the LMTIA assay for the detection of soybean ITS genes in edible oils, which could be used to detect soybean components in edible oils.

Theoretical study on polynitro imidazo [4, 5-e] oxadiazolo [3, 4-b] pyrazine compounds.

The B3PW91/6-31G** theoretical method was carried out to optimize the structure of 12 polynitro imidazo [4,5-e] oxadiazolo [3,4-b] pyrazine compounds (two structural type). The influence of nitro groups on the structure, oxygen balance, density, heat of formation, detonation performances, and charge were investigated. The results showed that the oxygen balance, density, heat of formation, detonation velocity, detonation pressure, and detonation heat increased with different relationships when the number of nitro groups increased. The contribution of the dinitroethylene group to energy was greater than that of the nitroimino group. On the whole, the sensitivity of all compounds increased with the number of -NO2 groups, and the second type of compound is more sensitive because of more nitro groups. The alkaline of the amine will decrease with the increasing number of -NO2 groups, and nitrification action will become more difficult. Graphical abstract Polynitro imidazo [4, 5-e] oxadiazolo [3, 4-b] pyrazine compoundsᅟ.