Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation

Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light enhancement or domain adaptive machine analytics, the former considers less on high-level vision, while the latter neglects the potential of image-level signal adjustment. How to restore underexposed images/videos from the perspective of machine vision has long been overlooked. In this paper, we are the first to propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve, and propose to satisfy this concavity through discrete integral. With the intention of adapting illumination from the perspective of machine vision without task-specific annotated data, we design an asymmetric cross-domain self-supervised training strategy. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework. Comprehensive experiments demonstrate that our method surpasses existing low-light enhancement and adaptation methods and shows superior generalization on various low-light vision tasks, including classification, detection, action recognition, and optical flow estimation. Project website: https://daooshee.github.io/SACC-Website/Comment: This paper has been accepted by ACM Multimedia 202

Improving Zero-Shot Generalization for CLIP with Synthesized Prompts

With the growing interest in pretrained vision-language models like CLIP, recent research has focused on adapting these models to downstream tasks. Despite achieving promising results, most existing methods require labeled data for all classes, which may not hold in real-world applications due to the long tail and Zipf's law. For example, some classes may lack labeled data entirely, such as emerging concepts. To address this problem, we propose a plug-and-play generative approach called \textbf{S}ynt\textbf{H}es\textbf{I}zed \textbf{P}rompts~(\textbf{SHIP}) to improve existing fine-tuning methods. Specifically, we follow variational autoencoders to introduce a generator that reconstructs the visual features by inputting the synthesized prompts and the corresponding class names to the textual encoder of CLIP. In this manner, we easily obtain the synthesized features for the remaining label-only classes. Thereafter, we fine-tune CLIP with off-the-shelf methods by combining labeled and synthesized features. Extensive experiments on base-to-new generalization, cross-dataset transfer learning, and generalized zero-shot learning demonstrate the superiority of our approach. The code is available at \url{https://github.com/mrflogs/SHIP}.Comment: Accepted by ICCV 202

Research Hotspot and Trend Analysis of China’s Elderlyoriented Smart Products

The Chinese government attaches great importance to the current situation of population aging, so it has introduced relevant aging policies. The combination of new technologies has played a positive role in the development of Elderly-oriented smart products of enterprises. Based on the research literature on Elderly-oriented smart products collected in CNKI database in recent ten years (2012-2022), this paper makes a quantitative analysis on the research results of Elderly-oriented smart products in China with the help of CiteSpace visual analysis software. Through research hotspots and evolution trends, it is found that the theme can be extended: the upgrading and construction of Elderly-oriented smart products will be a hot research topic in the academic community in the future

Temperature-Driven Activated Sludge Bacterial Community Assembly and Carbon Transformation Potential: A Case Study of Industrial Plants in the Yangtze River Delta

Temperature plays a critical role in the efficiency and stability of industrial wastewater treatment plants (WWTPs). This study focuses on the effects of temperature on activated sludge (AS) communities within the A2O process of 19 industrial WWTPs in the Yangtze River Delta, a key industrial region in China. The investigation aims to understand how temperature influences AS community composition, functional assembly, and carbon transformation processes, including CO2 emission potential. Our findings reveal that increased operating temperatures lead to a decrease in alpha diversity, simplifying community structure and increasing modularity. Dominant species become more prevalent, with significant decreases in the relative abundance of Chloroflexi and Actinobacteria, and increases in Bacteroidetes and Firmicutes. Moreover, higher temperatures enhance the overall carbon conversion potential of AS, particularly boosting CO2 absorption in anaerobic conditions as the potential for CO2 emission during glycolysis and TCA cycles grows and diminishes, respectively. The study highlights that temperature is a major factor affecting microbial community characteristics and CO2 fluxes, with more pronounced effects observed in anaerobic sludge. This study provides valuable insights for maintaining stable A2O system operations, understanding carbon footprints, and improving COD removal efficiency in industrial WWTPs

Towards Optimizing Garlic Combine Harvester Design with Logistic Regression

In this paper, a garlic combine harvester machine was designed and some influential parameters of the machine were optimized. The working parts of the machine mainly consisted of a reel, a reciprocating cutter, a seedling conveyor, a profiling depth-stop device, a digging shovel and a lifting chain. Each part had unique structural parameters and motion parameters, as different parameters would deeply affect the performance of the machine. A logistical regression algorithm was utilized to analyze the working speed of the reel, the digging depth of the reciprocating cutter and the lifting speed of the lifting chain. This paper also discussed the influence of these three functions on the damage rate based on the collected data when harvesting garlic. Specifically, each function was tested 60 times for collecting data. The experimental results showed that the order of influence of the three functions on the damage rate was the digging depth, working speed and lifting speed. Moreover, the lowest damage rate was 0.18% when the digging depth was 100 mm, the working speed was 1.05 km·h−1 and the lifting speed was 0.69 m·s−1. A validation test was taken out based on the three functions of the analysis results, and the damage rate was 0.83%, which was close to the analysis results, and proved that the analysis results were accurate and meaningful. The research results are beneficial to the development and application of the garlic combine harvester

Potential use of LIAD time-of-flight mass spectrometry for the detection of biomolecules: An example of detecting nucleobases in DNA

Deoxyribonucleic acid (DNA) carries the genetic information necessary for the synthesis of RNA and proteins; it is a biological macromolecule essential for the development and proper functioning of living organisms and is composed of nucleobases, deoxyribose, and phosphate. The four nucleobases in DNA are adenine (AD), guanine (GU), thymine (TY), and cytosine (CY). Abnormal concentrations of these four nucleobases in an organism have a significant impact on disease diagnosis. Therefore, the qualitative and quantitative detection of these DNA nucleobases in organisms is helpful to diagnose certain diseases. In this work, we report the simultaneous determination of purine (AD, GU) and pyrimidine (TY, CY) nucleobases in DNA using laser-induced acoustic desorption (LIAD) with electron ionization (EI)/time-of-flight mass spectrometry (TOFMS). The purine (MW 120 Da) samples were used as model compounds to assess the sensitivity and quantitative performance of the instrument. Its limits of detection assessed using the LIAD/EI/MS method were ∼0.5–1.2 pg under optimal conditions, and their calibration curves exhibited good linearity (R2 = 0.98). The LIAD/TOFMS was successfully applied in the simultaneous detection of AD, GU, TY, and CY in real DNA samples. The advantage of this technique is simple, fast, and without complex pre-treatment processes. In addition, a quartz-enhanced LIAD (QE-LIAD) source was used to improve the signal strength. The desorption for complex biomolecules shows that the QE-LIAD is still a “gentle” desorption source

Research Hotspot and Trend Analysis of China’s Elderlyoriented Smart Products

The Chinese government attaches great importance to the current situation of population aging, so it has introduced relevant aging policies. The combination of new technologies has played a positive role in the development of Elderly-oriented smart products of enterprises. Based on the research literature on Elderly-oriented smart products collected in CNKI database in recent ten years (2012-2022), this paper makes a quantitative analysis on the research results of Elderly-oriented smart products in China with the help of CiteSpace visual analysis software. Through research hotspots and evolution trends, it is found that the theme can be extended: the upgrading and construction of Elderly-oriented smart products will be a hot research topic in the academic community in the future

The Research of Power plant Operating Data Based on Real-time Digital filtration technology

Real-time monitoring of the data of the thermal power plant is the basis of accurate analyzing thermal economy and accurate reconstruction of the operating state. Due to noise is inevitable, we need real-time monitoring data filtering to get accurate information of units and equipment in the operating data of thermal power plant. Real-time filtering algorithm can’t be used to correct the current data with future data. Compared with traditional filtering algorithm, there are a lot of constraints. First-order lag filtering method and weighted recursive average filtering method can be used for real-time filtering. This paper analyzes the characteristics of the two filtering methods and applications for real-time processing of the positive spin simulation data, and the thermal power plant operating data. The analysis revealed that the weighted recursive average filtering method applied to the simulation and real-time plant data filtering achieved very good results. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.346

Plankton and benthic foraminiferal dataset for the study of the Eocene-Oligocene transition

<p>The Eocene–Oligocene transition (EOT) was the crucial turning point when Earth's climate shifted to its current cool state. Understanding how life responded to this "warmhouse-to-icehouse" shift was limited by the absence of high-resolution fossil data. Here, we use a novel AI algorithm to construct a 28-myr-long species richness history of foraminifera with ~26,000-year resolution. A significant richness decline occurred during the EOT, eliminating 74% of species. Planktonic and larger benthic foraminiferal extinctions are associated with rapid cooling, sea-level fall, and positive carbon isotopic excursion. However, small benthic foraminifera in deep oceans experienced a two-phase biocrisis coinciding with changes in food supply and volcanic activity. These findings reveal complicated, ecologically differentiated environment-life processes and the need to reconsider other major bioevents in deep time.</p><p>Funding provided by: National Natural Science Foundation of China<br>Crossref Funder Registry ID: https://ror.org/01h0zpd94<br>Award Number: 92255301</p><p>Funding provided by: National Natural Science Foundation of China<br>Crossref Funder Registry ID: https://ror.org/01h0zpd94<br>Award Number: 42250104</p><p>Funding provided by: National Natural Science Foundation of China<br>Crossref Funder Registry ID: https://ror.org/01h0zpd94<br>Award Number: 42293280</p><p>All the data were collected through the OneStratigraphy Database. The raw dataset contained 13,138 local bio-events records (i.e., first and last appearance records) and ~60,000 occurrences of 2,988 taxonomic units from 163 published stratigraphic sections, encompassing both calcareous and agglutinated foraminifera. These sections, including drill cores and outcrops, are widely distributed in the present oceans and continents such as Europe, Africa, and Asia. The dataset was first cleaned by excluding open nomenclature, such as sp./spp. (646), aff. (63), and question marks for species names (6). Nevertheless, the conferring species (cf.; 169) and the group species (ex gr.; 27) were preserved and assigned to the referenced species. Taxonomic assignments below the species level (i.e., subspecies and variety) were mostly integrated into the species level. All non-foraminifera fossils were removed. The dataset after cleaning was then verified by a foraminiferal taxonomic expert (Peiyue Fang) for correctness and consistency. </p&gt