The Effect Of Credit Rating Categories On Analysts' Information Environment: Evidence From The Korean Market

Credit ratings have been widely utilized for their ability to convey information to investors easily and quickly. Credit ratings agencies have prospered due to their superior positions in the information-gathering process in the business world. However, recent news about grade reversals, abrupt downgrades, and unanticipated bankruptcy suggests that the market value of credit ratings may have been overstated. Hence, in this study, we evaluate the legitimacy of the suggestion that credit rating categories may provide analysts with useful information.In order to measure the variables in the information environment, we use analyst forecasts. Differences between forecasts in the various rating categories are found to be statistically significant. Furthermore, a comparative analysis of information intensity for ungraded firms with that of other firms demonstrates that overestimates of ungraded firms create problems of the same nature as those in the information environment of firms with speculative grades.In the inter- and intra-category analyses, various factors determine the transparency of the information environment for firms of investment grade, including accruals quality, conservatism, the interest coverage ratio, and the proportion of intangible assets. However, for less credible firms, such as speculative or ungraded firms, these determinants do not function as expected. We find that the interest coverage ratio may be used to provide detailed differentiation between less credible firms. This paper introduces a new approach to credit rating categories and related strategies, emphasizing the importance of the ability to repay debt

Progressive Fourier Neural Representation for Sequential Video Compilation

Neural Implicit Representation (NIR) has recently gained significant attention due to its remarkable ability to encode complex and high-dimensional data into representation space and easily reconstruct it through a trainable mapping function. However, NIR methods assume a one-to-one mapping between the target data and representation models regardless of data relevancy or similarity. This results in poor generalization over multiple complex data and limits their efficiency and scalability. Motivated by continual learning, this work investigates how to accumulate and transfer neural implicit representations for multiple complex video data over sequential encoding sessions. To overcome the limitation of NIR, we propose a novel method, Progressive Fourier Neural Representation (PFNR), that aims to find an adaptive and compact sub-module in Fourier space to encode videos in each training session. This sparsified neural encoding allows the neural network to hold free weights, enabling an improved adaptation for future videos. In addition, when learning a representation for a new video, PFNR transfers the representation of previous videos with frozen weights. This design allows the model to continuously accumulate high-quality neural representations for multiple videos while ensuring lossless decoding that perfectly preserves the learned representations for previous videos. We validate our PFNR method on the UVG8/17 and DAVIS50 video sequence benchmarks and achieve impressive performance gains over strong continual learning baselines. The PFNR code is available at https://github.com/ihaeyong/PFNR.git

Forget-free Continual Learning with Soft-Winning SubNetworks

Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we investigate two proposed architecture-based continual learning methods which sequentially learn and select adaptive binary- (WSN) and non-binary Soft-Subnetworks (SoftNet) for each task. WSN and SoftNet jointly learn the regularized model weights and task-adaptive non-binary masks of subnetworks associated with each task whilst attempting to select a small set of weights to be activated (winning ticket) by reusing weights of the prior subnetworks. Our proposed WSN and SoftNet are inherently immune to catastrophic forgetting as each selected subnetwork model does not infringe upon other subnetworks in Task Incremental Learning (TIL). In TIL, binary masks spawned per winning ticket are encoded into one N-bit binary digit mask, then compressed using Huffman coding for a sub-linear increase in network capacity to the number of tasks. Surprisingly, in the inference step, SoftNet generated by injecting small noises to the backgrounds of acquired WSN (holding the foregrounds of WSN) provides excellent forward transfer power for future tasks in TIL. SoftNet shows its effectiveness over WSN in regularizing parameters to tackle the overfitting, to a few examples in Few-shot Class Incremental Learning (FSCIL).Comment: arXiv admin note: text overlap with arXiv:2209.0752

On the Soft-Subnetwork for Few-shot Class Incremental Learning

Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive performance of the dense network, we propose a few-shot class incremental learning (FSCIL) method referred to as \emph{Soft-SubNetworks (SoftNet)}. Our objective is to learn a sequence of sessions incrementally, where each session only includes a few training instances per class while preserving the knowledge of the previously learned ones. SoftNet jointly learns the model weights and adaptive non-binary soft masks at a base training session in which each mask consists of the major and minor subnetwork; the former aims to minimize catastrophic forgetting during training, and the latter aims to avoid overfitting to a few samples in each new training session. We provide comprehensive empirical validations demonstrating that our SoftNet effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets

Strong Bathochromic Shift of Conjugated Polymer Nanowires Assembled with a Liquid Crystalline Alkyl Benzoic Acid via a Film Dispersion Process

We present aqueous dispersions of conjugated polymer nanowires (CPNWs) with improved light absorption properties aimed at aqueous-based applications. We assembled films of a donor???acceptor-type conjugated polymer and liquid crystalline 4-n-octylbenzoic acid by removing a cosolvent of their mixture solutions, followed by annealing of the films, and then formed aqueous-dispersed CPNWs with an aspect ratio >1000 by dispersing the films under ultrasonication at a basic pH. X-ray and spectroscopy studies showed that the polymer and liquid crystal molecules form independent domains in film assemblies and highly organized layer structures in CPNWs. Our ordered molecular assemblies in films and aqueous dispersions of CPNWs open up a new route to fabricate nanowires of low-band-gap linear conjugated polymers with the absorption maximum at 794 nm remarkably red-shifted from 666 nm of CPNWs prepared by an emulsion process. Our results suggest the presence of semicrystalline polymorphs ??1 and ??2 phases in CPNWs due to long-range ??????? stacking of conjugated backbones in compactly organized lamellar structures. The resulting delocalization with a reduced energy bang gap should be beneficial for enhancing charge transfer and energy-conversion efficiencies in aqueous-based applications such as photocatalysis

Indifferent, skeptical, ambivalent, moderate, and enthusiastic: What impacts profiles of teachers' technology views

Teachers' views about information and communication technology are a key affordance or barrier to classroom technology adoption and use. However, in determining factors that impact these views, much research has relied on variable-centered approaches that bifurcated teachers' views into mere positive or negative groups. Such approaches oversimplify key information that could serve educational authorities attempting to address low technology integration in schools. Therefore, this large-scale study employed a latent profile analysis with multilevel logistic regression analysis that aimed to (1) identify profiles of teachers' views of information and communication technologies for teaching and learning and (2) explore effects from teacher- and school-level variables on these profiles. Utilizing nationally generalizable data from 2079 full-time eighth-grade teachers from 150 different schools across South Korea from the International Computer and Information literacy study 2018, the analysis revealed five profiles of teachers' views: Indifferent, Skeptical, Ambivalent, Moderate, and Enthusiastic, with Moderate being the largest, and Highly Ambivalent and Indifferent the smallest. The findings from multilevel logistic regression analysis indicated numerous effects from teacher- and school-level antecedents and processes that matched each profile, highlighting school leaders’ ICT views. Notably, there were no effects from regional differences. Implications for leaders and policymakers are discussed in the context of professional development and reform policies

Introduction of Smart Grid Station Configuration and Application in Guri Branch Office of KEPCO

Climate change and global warming are becoming important problems around the globe. To prevent these environmental problems, many countries try to reduce their emissions of greenhouse gases (GHGs) and manage the consumption of energy. The Korea Electric Power Corporation (KEPCO) introduced smart grid (SG) technologies to its branch office in 2014. This was the first demonstration of a smart grid on a building, called the Smart Grid Station (SGS). However, the smart grid industry is stagnant despite of the efforts of KEPCO. The authors analyzed the achievements to date, and proved the effects of the SGS by comparing its early targets to its performance. To evaluate the performance, we analyzed the data of 2015 with the data of 2014 in three aspects: peak reduction, power consumption reduction, and electricity fee savings. Furthermore, we studied the economic analysis including photovoltaic (PV) and energy storage system (ESS) electricity fee savings, as well as running cost savings by electric vehicles. Through the evaluation, the authors proved that the performance surpassed the early targets and that the system is economical. With the advantages of the SGS, we suggested directions to expand the system