Information Content Research Articles

Learning Many-to-Many Mapping for Unpaired Real-World Image Super-Resolution and Downscaling.

Learning based single image super-resolution (SISR) for real-world images has been an active research topic yet a challenging task, due to the lack of paired low-resolution (LR) and high-resolution (HR) training images. Most of the existing unsupervised real-world SISR methods adopt a two-stage training strategy by synthesizing realistic LR images from their HR counterparts first, then training the super-resolution (SR) models in a supervised manner. However, the training of image degradation and SR models in this strategy are separate, ignoring the inherent mutual dependency between downscaling and its inverse upscaling process. Additionally, the ill-posed nature of image degradation is not fully considered. In this paper, we propose an image downscaling and SR model dubbed as SDFlow, which simultaneously learns a bidirectional many-to-many mapping between real-world LR and HR images unsupervisedly. The main idea of SDFlow is to decouple image content and degradation information in the latent space, where content information distribution of LR and HR images is matched in a common latent space. Degradation information of the LR images and the high-frequency information of the HR images are fitted to an easy-to-sample conditional distribution. Experimental results on real-world image SR datasets indicate that SDFlow can generate diverse realistic LR and SR images both quantitatively and qualitatively.

Analysis of scale-dependent spatial correlations of actual evapotranspiration measured by lysimeters

Accurate determination of actual evapotranspiration (ETa) is important in various research fields like hydrology, meteorology, ecology and agriculture. In situ ETa can be determined using weighing lysimeters and eddy covariance. However, despite being regarded as the most precise in situ method for measuring ETa, the information content of lysimeter measurements remains poorly understood. Here we examined the spatial correlations between ETa measured at different locations by lysimeter (ET-LYS) and at different locations by eddy covariance (ET-EC). This was done for the period 2015 - 2020 and the analysis was made for different spatial (range: 0 to 500 km) and temporal scales (range: 1 day to 1 year) using 23 lysimeters and 4 eddy covariance towers. We found that: (a) Same lysimeters at the plot scale show very high correlations of ET-LYS; (b) The Pearson correlation of daily standardized anomalies of ET-LYS between sites exhibit moderate to high correlations and were similar to that of ET-EC, indicating that lysimeter is generally as representative as EC regarding ETa, and can provide certain information at the landscape and larger regional scale. During winter, the spatial correlations for ET-LYS were smaller; (c) Wavelet analysis indicated that temporal correlations in ETa were strongest for distances in time around 12 months (yearly cycle) and less than three months. Spatial correlations were smaller under drought conditions (in the year 2018). Furthermore, combination of multiple ET-LYS from different sites improved the predictability of ET-LYS for another site, suggesting that ET-LYS can be predicted well using ET-LYS from different neighboring sites. Overall, lysimeter measurements can provide information at much larger scales compared to their small measurement area.

Geotechnical engineering surveys and soil testing in the construction of buildings and structures

The article suggests a way to increase the efficiency of geotechnical engineering surveys through the rational use of fast approximate methods of soil testing (“express methods”). Such tests are recommended to be carried out in large volume and considered as a means of assessing the geotechnical structure of the site as a whole. In this paper, these issues are considered by the example of determining the bearing capacity of piles according to static sounding data and static tests of full-scale piles. A mathematical model for assessing the information content of the test complex based on the concepts of information theory is proposed. Calculations based on this model show that the informativeness of a large number of approximate tests may (due to the heterogeneity of the soil) exceed the informativeness of a small number of accurate tests.

The Content Effectiveness of User-Generated Content to the Purchasing Decision of Consumers

The paper investigated the relationship between User-Generated Content (UGC) effectiveness and its influence on Filipino consumers' purchasing decisions. While the role of UGC in marketing was acknowledged, limited research explored its impact in the Philippines. The findings demonstrated a positive correlation between UGC content effectiveness and purchasing decisions. Content trustworthiness and content information richness were found to significantly influence consumer behavior. These results aligned with previous studies highlighting the importance of the credibility and informative content within UGC. The study offered valuable insights for businesses operating in the Philippines. UGC presented an economical and impactful marketing strategy, fostering brand trust, credibility, and connection with consumers. Recommendations included encouraging customer UGC creation and strategically utilizing various social media platforms. The research also highlighted the need for further exploration of UGC's effectiveness within specific industries and social media landscapes.

Predictive Power of Key Financial Variables During the Unconventional Monetary Policy Era

ABSTRACTThis study investigates the forecasting power of three well‐established financial predictors during the prolonged era of unconventional monetary policy: the term spread, the short‐term interest rate, and stock returns. The focus is on predicting GDP growth in both the United States and the Euro area. Our out‐of‐sample forecasting analysis specifically targets the period characterized by the short‐term interest rate effectively bounded at or near the zero lower bound. We recognize that the information content of the term spread is likely to change under such circumstances. Similarly, the dynamics of the short‐term interest rate could be altered due to unconventional monetary policy measures. To address this, we modify the short rate calculation by incorporating the shadow interest. This shadow interest rate can go much lower on the negative side than normal interest rates, making it a potentially more accurate rate to describe the monetary policy stance of central banks. The forecasting analysis covers the period from 2009:1 to 2022:3. Our results unambiguously reveal that the predictive power of the term spread completely vanishes during the zero lower bound era. Although the shadow rate has minor predictive content, the strongest predictor consistently lies in real stock returns during unconventional monetary policy. Our findings challenge the conventional wisdom and the stylized fact of the term spread as the most reliable financial predictor for economic activity. According to our results, this does not hold true under unconventional monetary policy, and using the shadow interest rate does not make a major difference in that respect. By shedding light on the changing dynamics during unconventional monetary policy, our study contributes novel insights to the existing literature.

Microsatellite based molecular characterization of Nattukuttai- a unique short statured Bos indicus cattle population of southern India.

Molecular characterization was conducted to characterise 'Nattukuttai', a native cattle population of the north-eastern agro-climatic zone of Tamil Nadu (India), using thirty microsatellite markers. The analyses revealed a high level of genetic diversity, with a total of 294 alleles observed across all the loci, averaging 9.8 alleles per locus. The allele sizes ranged from 83bp to 302bp, with frequencies ranging from 0.010 to 0.875. The microsatellite markers demonstrated high polymorphism, as indicated by an average polymorphic information content (PIC) of 0.763. Deviation from Hardy-Weinberg equilibrium was observed in a significant number of loci, indicating possible genetic influences such as selection or population structure. Bottleneck analysis suggested that the Nattukuttai population did not undergo any recent significant population contraction. Comparative analyses with three other cattle populations (Kangayam, Malai Madu, and Malnad Gidda) revealed varying genetic distances. Nattukuttai showed a distinct genetic profile, diverging from a common source that also gave rise to the Kangayam and Malai Madu clusters. Multivariate statistical analyses and phylogenetic reconstruction supported the genetic differentiation of Nattukuttai from the other populations, while Malai Madu and Kangayam were found to be genetically closer to each other. Overall, these findings provide insights into the genetic structure and relationships of the Nattukuttai cattle population, highlighting its distinct genetic identity and potential conservation significance.

The deep convolution network in immersive design of digital media art in smart city.

The goal of the paper is to examine how convolutional neural network (CNN) is used in the immersive world of digital media art. In order to do this, this paper first explains digital media art in the context of smart cities and the use of immersive scenarios. Next, a brief analysis of the Unet network model and deep aggregation structure is provided. Then, a projector, camera, and computer-based immersive projector-camera display system is constructed. Based on the mathematical reflection model of the system, this paper discusses the method of using CNN to solve the photometric compensation of the projection picture. Meanwhile, a Projection Compensation Network (PCN) is designed, and a multi-scale perceptual loss is added to this compensation network, and the content information of the compensated image is improved by calculating the loss of feature maps of different scales. The final network is named Perceptual Loss-Projection Compensation Network (PL-PCN). Experiments are used to confirm the PL-PCN model's efficacy. The outcomes demonstrate that the SSIM and PSNR of the projected picture compensated by PL-PCN are boosted by 35.8% and 31.6%, respectively. While the RMSE is reduced by 40.9%, demonstrating an improvement in the compensated image's quality. Additionally, utilizing a CNN makes it possible to do cross-reflection compensation. Additionally, compared to the network without deep polymerization, the PL-PCN with deep polymerization structure boosts the projected image's SSIM and PSNR by 6% and 7.57%, respectively, and lowers the RMSE by 13.3% This demonstrates that the addition of deep polymerization structure can have a stronger compensatory effect. This paper can offer a theoretical framework for improving the immersive scene quality of digital media art.

Quality of Publicly Available Information About Urinary Tract Infections.

This cross-sectional study examines the content and quality of information about urinary tract infections and asymptomatic bacteriuria on publicly available websites.

Institutional Trading and Managers' Complex Disclosure: Evidence From Conference Calls

ABSTRACTThis study investigates the impact of managerial linguistic disclosures on institutional trading behaviors during earnings conference calls from 2006 to 2016. Utilizing the linguistic Fog decomposition methodology on textual analysis, it analyzes how the linguistic complexity of managerial communication influences institutional ownership changes. Findings indicate that both obfuscation and informativeness of managerial discourse significantly affect institutional trading, with effects varying based on the informativeness of post‐call analyst reports. This impact is more pronounced under conditions of earnings quality uncertainty, suggesting a heightened reliance on managerial cues by institutional investors during such periods. Additionally, the study differentiates responses among investor types: transient investors are more sensitive to linguistic cues, while dedicated and quasi‐indexer investors focus on the informational content of disclosures. These insights enhance understanding of how managerial communication and analyst report informativeness shape institutional trading behaviors through linguistic complexity's influence on institutional ownership.

Sustainable digital rent: a transformative framework for value dynamics in the digital age

IntroductionThis paper introduces the concept of Sustainable Digital Rent (SDR), highlighting the shift from traditional economic rent based on tangible assets to rent derived from digital platforms. At the heart of this shift is the “value state,” a dynamic balance between constructive expectations and destructive information. As digital platforms generate increasing amounts of information, expectations are increasingly met and shared more efficiently with all users, leading to a reduction in individual and general motivational, emotional, and cognitive engagement. These platforms, now essential to modern life, facilitate online activities that reduce as well physical engagement and natural interactions, thereby impacting cognitive function and physical health. By extracting rent directly, digital platform operators limit the benefits users could gain to support their mental and physical well-being.MethodsThis paper empirically defines and estimates SDR using the collective estimates of price, cost, and income (PCI) as practiced in North American real estate appraisal, demonstrated through abstract art rent. Our approach provides a new perspective on valuing intangible assets, such as knowledge, by showing the shift from expectation to information, governed by the value state in cognitive evaluations. Emphasizing interdisciplinary relevance, the method underscores the need for an efficient mechanism to redistribute SDR benefits to digital platform users, supporting fair and equitable digital development.Results and discussionThe results show that digital rent is driven primarily by cognitive and informational content, demonstrating the need for redistribution mechanisms to address the growing inequality on digital platforms. The use of abstract art as a case study provides a convenient and illustrative way to explore how intangible assets, like digital rents, can be evaluated and redistributed. SDR offers insights into how digital rents can be captured and redistributed equitably, ensuring that platform users and creators benefit from the knowledge economy’s growth. The findings underscore the relevance of measuring SDR to guide policy recommendations aimed at reducing digital monopolization and promoting sustainable digital development.

Evaluating the success of a learning management system for supplemental learning in accounting

ABSTRACT The purpose of this paper is to evaluate, from the student perspective, the critical factors that influence the success of a Learning Management System (LMS) used in a supplemental model in accounting education. Based on the Information System Success Model (ISSM) developed by Delone and McLean (2003) [The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Null, 19(4), 9–30], this research tests four hypothesised relationships around five key constructs: information content quality, instructor quality, self-regulation, perceived usefulness and satisfaction. To conduct this study, data from a sample of 313 cost accounting students in higher education were analysed, using the partial least squares method (PLS-SEM) with SmartPLS software. The findings show that student satisfaction levels with the LMS used in a supplemental model are explained by its perceived usefulness, to which the quality of the instructor and the quality of the information content are determinant factors. Contrary to expectations, high student self-regulation is found to weaken the positive relationship between perceived usefulness and satisfaction. This research expands the scarce literature on the adoption of LMS in accounting education and responds to calls to improve the effectiveness of accounting education and the efficiency of its delivery. Implications, limitations and future research lines are discussed.

The impact of innovation and differentiation strategy on financial performance with emphasis on the role of risk management and intellectual capital

Purpose The purpose of this study is to investigate the impact of innovation and differentiation strategy on financial performance with a focus on the role of risk management and intellectual capital of companies listed on the Tehran Stock Exchange. Design/methodology/approach In this research, differentiation and innovation strategy criteria are the independent variables, performance criteria are the dependent variables and risk management effectiveness and intellectual capital are the moderating variables. The statistical population of this research includes all the companies listed in the Tehran Stock Exchange during the 10-year period, and 127 companies were selected as a statistical sample using the systematic elimination method. The research model fitting and hypothesis testing were carried out using structural equation modeling and SPSS, Smart PLS software. Findings The results of the hypothesis testing showed a positive and significant relationship between the innovation and differentiation strategy and the firm performance. In addition, effective management of risk and intellectual capital also had a significant and positive impact on this relationship. Research limitations/implications Given that this research was conducted at the Tehran Stock Exchange, it was difficult for the researchers to collect data. Also, since the Tehran Stock Exchange is a new market, a lot of research needs to be done in different areas of this market to identify the existing problems and shortcomings and to provide reasons for its growth and development. Practical implications According to the results of the research on the impact of intellectual capital and risk management on corporate performance and the impact of strategy on this relationship, this paper can expect to see an improvement in corporate performance and an increase in shareholder wealth as a result of applying the results of this research. Social implications Considering that corporate strategy, intellectual capital criteria and risk management to increase the financial performance of companies have informational content; therefore, according to the results of this research, an increase in the performance of listed companies is expected, and naturally, this increase in performance will lead to the prosperity of the economic sector, increase in job creation and as a result, improve the quality of social life. Originality/value In the Tehran Stock Exchange, by properly evaluating the financial performance criteria of companies and identifying the factors that increase intellectual capital and risk management, they provide the means to improve knowledge in this area of research.

YOLOv8-seg-CP: a lightweight instance segmentation algorithm for chip pad based on improved YOLOv8-seg model.

Real-time detection and accurate segmentation of chip pads are important tasks to ensure chip alignment and position correction. To address the challenges of small target chip pad detection, segmentation accuracy and model lightweight, this paper proposes a lightweight chip pad instance segmentation algorithm based on an improved YOLOv8-seg, named YOLOv8-seg-CP (chip pad). Firstly, we integrate the next-generation lightweight StarNet into the original backbone network to enhance fine feature capture capabilities while reducing the number of parameters. Then, we construct the C2f-Star module in the neck network, which enhances the feature extraction performance for small chip pad targets. This maintains accuracy, reduces computational load, and improves detection and segmentation speed. On this basis, we introduce a lightweight shared convolution segmentation head (LSCSH), significantly reducing both parameter count and computational load while enhancing segmentation performance. Additionally, we propose a CGCAFusion convolutional attention fusion module. This module uses a content-guided convolutional attention fusion mechanism to dynamically adjust attention weights based on the content of input features, capturing both global and local feature information and enhancing multimodal feature fusion. Experiments on the chip pad dataset demonstrate that our algorithm achieves a detection and segmentation accuracy of 89.8%. The model size, parameters, and FLOPs are 3.7M, 1.7M, and 8.2G respectively, representing reductions of 45.6%, 50%, and 31.7% compared to the baseline model. The FPS is 1399.3, an improvement of 25.8% over the baseline model. The inference time is 0.72ms, which is 0.18ms less than the baseline model. Extensive experimental results on COCO, carparts-seg, and crack-seg datasets further show that the improved YOLOv8n-seg model outperforms many existing advanced methods in terms of performance. This approach holds significant industrial application value for fully automated chip testing and sorting integrated production.

Tensors in High-Dimensional Data Analysis: Methodological Opportunities and Theoretical Challenges

Large amounts of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing. The structural complexity of such data provides vast new opportunities for modeling and analysis, but efficiently extracting information content from them, both statistically and computationally, presents unique and fundamental challenges. Addressing these challenges requires an interdisciplinary approach that brings together tools and insights from statistics, optimization, and numerical linear algebra, among other fields. Despite these hurdles, significant progress has been made in the past decade. This review seeks to examine some of the key advancements and identify common threads among them, under a number of different statistical settings.

Investigating Fractal Holographic Principle in Quantum Gravity Using the McGinty Equation

This hypothesis explores the application of the McGinty Equation to the fractal holographic principle in quantum gravity. The hypothesis posits that information in quantum gravity is encoded in fractal patterns on holographic surfaces. The goal is to unify quantum gravity with holography and provide new insights into black holes and the information content of the universe.

Entropy of Neuronal Spike Patterns

Neuronal spike patterns are the fundamental units of neural communication in the brain, which is still not fully understood. Entropy measures offer a quantitative framework to assess the variability and information content of these spike patterns. By quantifying the uncertainty and informational content of neuronal patterns, entropy measures provide insights into neural coding strategies, synaptic plasticity, network dynamics, and cognitive processes. Here, we review basic entropy metrics and then we provide examples of recent advancements in using entropy as a tool to improve our understanding of neuronal processing. It focuses especially on studies on critical dynamics in neural networks and the relation of entropy to predictive coding and cortical communication. We highlight the necessity of expanding entropy measures from single neurons to encompass multi-neuronal activity patterns, as cortical circuits communicate through coordinated spatiotemporal activity patterns, called neuronal packets. We discuss how the sequential and partially stereotypical nature of neuronal packets influences the entropy of cortical communication. Stereotypy reduces entropy by enhancing reliability and predictability in neural signaling, while variability within packets increases entropy, allowing for greater information capacity. This balance between stereotypy and variability supports both robustness and flexibility in cortical information processing. We also review challenges in applying entropy to analyze such spatiotemporal neuronal spike patterns, notably, the “curse of dimensionality” in estimating entropy for high-dimensional neuronal data. Finally, we discuss strategies to overcome these challenges, including dimensionality reduction techniques, advanced entropy estimators, sparse coding schemes, and the integration of machine learning approaches. Thus, this work summarizes the most recent developments on how entropy measures contribute to our understanding of principles underlying neural coding.

Sequence Generation and Evaluation: A Novel Assessment of Musical Creativity

ABSTRACT Music is often considered an important domain for creativity. Traditional studies of musical creativity have examined musical improvisation using jazz as a model. While this approach has yielded many valuable insights about creativity’s cognitive and neural mechanisms, it has limited the study sample to those with the means to engage in improvisation within one specific Western style. Here, we introduce a novel tool for assessing musical creativity in the broader sample of individuals with no specialized training. In two experiments (n = 165) we show that this sequencer can be used in people with minimal training to generate a database of sequences composed at the Bohlen-Pierce scale and to evaluate them for creativity. Results show that creativity ratings are predicted by length of melodies, number of distinct pitches used, and information content of pitch intervals. Results also show some external validity with existing creativity tasks. We advocate the use of this sequencer in creativity research, as it provides a theoretically motivated, rigorous tool to examine the iterative process of producing and evaluating musical creativity.

Assessment of genetic biodiversity and association of micronutrients and agronomic traits using microsatellites and staining methods which accelerates high-micronutrients variety selections within different wheat groups

Evaluation of genetic biodiversity for micronutrients is crucial for breeding high-quality crops and addressing the negative impacts of mineral deficiencies. The objectives of this research were to assess genetic variation and the relationship between grain Fe and Zn levels and agronomic traits in a diverse collection of wheat varieties. Additionally, the study aimed to determine the correlation between microsatellite markers (SSR) and micronutrient quantities. A total of 42 genotypes (Iranian commercial cultivars, landraces, and Afghan and Swiss varieties) were evaluated over a two-year period. Fe and Zn levels were measured using two semi quantitative staining assays and atomic absorption spectrophotometry (AAS) facility. Semi-quantitative staining methods and AAS showed high correlations for micronutrient contents. Landraces exhibited higher Fe (63.79 mg/kg) and Zn (44.76 mg/kg) but lower grain yield compared with commercial cultivars. Heritability estimates ranged 53%-79.43%, suggesting that genetic variance played a higher contribution in the phenotypic variation of traits than environmental factors. Notably, Fe content displayed significant correlations with days to maturity. Canonical correlation analysis (CCA) revealed that Zn content was correlated with four agronomic traits. Evaluation of genetic diversity using SSR markers demonstrated high genetic variation among the genotypes tested. The analysis of polymorphism information content (PIC) indicated that SSR primers had an average PIC of 0.75, with the Xgwm192 primer exhibiting higher PIC than others. Several SSR markers revealed association with micronutrient content that can be used in marker-assisted selection (MAS) programs aimed at selection of high micronutrient genotypes. In conclusion, the findings underscored the substantial genetic diversity present in micronutrient levels among global wheat genotypes, the potential of landraces for micronutrients biofortification of wheat cultivars through cross hybridization, the utility of staining methods for screening high/low micronutrient genotypes, and use of microsatellite markers for marker-assisted breeding aiming to micronutrient improvement in breeding programs.

Neural Mechanism of Musical Pleasure Induced by Prediction Errors: An EEG Study

Background/Objectives: Musical pleasure is considered to be induced by prediction errors (surprise), as suggested in neuroimaging studies. However, the role of temporal changes in musical features in reward processing remains unclear. Utilizing the Information Dynamics of Music (IDyOM) model, a statistical model that calculates musical surprise based on prediction errors in melody and harmony, we investigated whether brain activities associated with musical pleasure, particularly in the θ, β, and γ bands, are induced by prediction errors, similar to those observed during monetary rewards. Methods: We used the IDyOM model to calculate the information content (IC) of surprise for melody and harmony in 70 musical pieces across six genres; eight pieces with varying IC values were selected. Electroencephalographic data were recorded during listening to the pieces, continuously evaluating the participants’ subjective pleasure on a 1–4 scale. Time–frequency analysis of electroencephalographic data was conducted, followed by general linear model analysis to fit the power-value time course in each frequency band to the time courses of subjective pleasure and IC for melody and harmony. Results: Significant positive fits were observed in the β and γ bands in the frontal region with both subjective pleasure and IC for melody and harmony. No significant fit was observed in the θ band. Both subjective pleasure and IC are associated with increased β and γ band power in the frontal regions. Conclusions: β and γ oscillatory activities in the frontal regions are strongly associated with musical rewards induced by prediction errors, similar to brain activity observed during monetary rewards.

Development of Microsatellite Markers from Transcriptome of Eriocheir sinensis and Their Application in Multiplex PCR Panels

Background: The development of genetic markers is crucial for aquaculture and population genetics. Polyallelic microsatellite markers are widely used for breeding and family management of Eriocheir sinensis (E. sinensis), contributing to the sustainable development of the industry. Methods: Through transcriptome analysis, 100 highly polymorphic SSR markers were identified and developed. From these, four multiplex PCR panels, each containing 16 markers, were constructed and applied to parentage assignment in six E. sinensis families. Results: The number of alleles (Na) ranged from 7 to 21, the observed heterozygosity (Ho) varied from 0.356 to 0.951, and the expected heterozygosity (He) varied from 0.656 to 0.909. The polymorphism information content (PIC) ranged from 0.633 to 0.900. The CERVUS analysis showed that using only the two most informative multiplex PCR panels, assignment success rates exceeded 90.34%, with 100% accuracy in assigning offspring to parents when three or four panels were used. Conclusions: This method significantly enhances the efficiency of microsatellite marker analysis, which provides a convenient and cost-effective method for breeding programs and family management of E. sinensis.