The value-relevance of asset write-down regulations in China : the roles of information relevance and measurement reliability

At the end of the 20th century and beginning of the 21st century, China implemented several new asset write-down regulations. This study addresses the claim that these regulations significantly enhanced the usefulness of financial statements for investors in China. The effect of the regulations on usefulness of financial statements has implications for financial accountants, standard-setters, educators, and auditors. This study derives and tests some of the empirical implications of the claim. I operationalize usefulness of accounting information in terms of the valuerelevance framework, in which information usefulness is construed as a tradeoff between relevance and reliability. These two dimensions are the primary criteria underlying the FASB’s Conceptual Framework for choosing alternative accounting rules. Asset write-down, if correctly applied to over-stated assets, should increase the decision relevance to investors; however, measurement errors due to either unintentional mistakes involving professional judgment or intentional misrepresentations involving earnings management may decrease the reliability of reported amounts. While there is substantial value-relevance research, the role of reliability is generally absent. Reliability of regression estimates, also known as measurement error, is often implicitly assumed and not measured. Following nonnested model selection techniques and relative measurement error research, I explicitly measure the relative reliability of asset write-down accounting in various valuation models. Therefore, this study contributes to value-relevance research. First, I examine the incremental value relevance of asset write-down estimates through their associations with market values: the ability of asset write-down provisions to explain market value of equity; the ability of asset write-down gains and losses to explain annual market-adjusted return; and the ability of both the above provisions and earnings to explain market value of equity. All the models provide evidence for value relevance of asset write-down estimates, indicating an acceptable level of information usefulness with mixed effects of relevance and reliability. I apply my tests to a balanced panel sample of exchange-listed firms in China over the period 1998-2001. The sample is limited to A shares—the shares subject to the new rules. Next, the above three valuation models are applied again in a reliability analysis. Model appropriateness tests, i.e. non-nested model tests, are used to answer the question: did asset write-down practices improve reliability in the valuation models? I find that the asset write-down practices are approximately comparable in reliability to historical cost methods in the balance sheet valuation model but somewhat less reliable in the income statement valuation model. The results are ambiguous when both assets and earnings are included in a third valuation model. My relative measurement error tests yield similar results. I conclude that the asset write-down regulations in China have not improved the usefulness of financial statements to investors in terms of reliability. Because the asset write-down rules are subject to interpretation and judgment, I consider the motivation for write-downs in the final part of the study. The results support a relation between discretionary motivations and the amount of current or cumulative write down. A sub-sample analysis shows that asset write-down rules improve usefulness of financial information in the absence of discretionary motivations

Generalized Categories Discovery for Long-tailed Recognition

Generalized Class Discovery (GCD) plays a pivotal role in discerning both known and unknown categories from unlabeled datasets by harnessing the insights derived from a labeled set comprising recognized classes. A significant limitation in prevailing GCD methods is their presumption of an equitably distributed category occurrence in unlabeled data. Contrary to this assumption, visual classes in natural environments typically exhibit a long-tailed distribution, with known or prevalent categories surfacing more frequently than their rarer counterparts. Our research endeavors to bridge this disconnect by focusing on the long-tailed Generalized Category Discovery (Long-tailed GCD) paradigm, which echoes the innate imbalances of real-world unlabeled datasets. In response to the unique challenges posed by Long-tailed GCD, we present a robust methodology anchored in two strategic regularizations: (i) a reweighting mechanism that bolsters the prominence of less-represented, tail-end categories, and (ii) a class prior constraint that aligns with the anticipated class distribution. Comprehensive experiments reveal that our proposed method surpasses previous state-of-the-art GCD methods by achieving an improvement of approximately 6 - 9% on ImageNet100 and competitive performance on CIFAR100.Comment: ICCV 2023 out-of-distribution generalization in computer vision workshop pape

RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation

Optical coherence tomography (OCT) helps ophthalmologists assess macular edema, accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is necessary for OCT-guided treatment management, which relies on a precise image segmentation step. As manual analysis of retinal fluids is a time-consuming, subjective, and error-prone task, there is increasing demand for fast and robust automatic solutions. In this study, a new convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. The model benefits from hierarchical representation learning of textural, contextual, and edge features using a new self-adaptive dual-attention (SDA) module, multiple self-adaptive attention-based skip connections (SASC), and a novel multi-scale deep self supervision learning (DSL) scheme. The attention mechanism in the proposed SDA module enables the model to automatically extract deformation-aware representations at different levels, and the introduced SASC paths further consider spatial-channel interdependencies for concatenation of counterpart encoder and decoder units, which improve representational capability. RetiFluidNet is also optimized using a joint loss function comprising a weighted version of dice overlap and edge-preserved connectivity-based losses, where several hierarchical stages of multi-scale local losses are integrated into the optimization process. The model is validated based on three publicly available datasets: RETOUCH, OPTIMA, and DUKE, with comparisons against several baselines. Experimental results on the datasets prove the effectiveness of the proposed model in retinal OCT fluid segmentation and reveal that the suggested method is more effective than existing state-of-the-art fluid segmentation algorithms in adapting to retinal OCT scans recorded by various image scanning instruments.Comment: 11 pages, Early Access Version, IEEE Transactions on Medical Imagin

ImbaGCD: Imbalanced Generalized Category Discovery

Generalized class discovery (GCD) aims to infer known and unknown categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising known classes. Existing research implicitly/explicitly assumes that the frequency of occurrence for each category, whether known or unknown, is approximately the same in the unlabeled data. However, in nature, we are more likely to encounter known/common classes than unknown/uncommon ones, according to the long-tailed property of visual classes. Therefore, we present a challenging and practical problem, Imbalanced Generalized Category Discovery (ImbaGCD), where the distribution of unlabeled data is imbalanced, with known classes being more frequent than unknown ones. To address these issues, we propose ImbaGCD, A novel optimal transport-based expectation maximization framework that accomplishes generalized category discovery by aligning the marginal class prior distribution. ImbaGCD also incorporates a systematic mechanism for estimating the imbalanced class prior distribution under the GCD setup. Our comprehensive experiments reveal that ImbaGCD surpasses previous state-of-the-art GCD methods by achieving an improvement of approximately 2 - 4% on CIFAR-100 and 15 - 19% on ImageNet-100, indicating its superior effectiveness in solving the Imbalanced GCD problem.Comment: CVPR 2023 Computer Vision in the Wild Workshop \textbf{Spotlight} pape

Supervised Knowledge May Hurt Novel Class Discovery Performance

Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset by leveraging prior knowledge of a labeled set comprising disjoint but related classes. Given that most existing literature focuses primarily on utilizing supervised knowledge from a labeled set at the methodology level, this paper considers the question: Is supervised knowledge always helpful at different levels of semantic relevance? To proceed, we first establish a novel metric, so-called transfer flow, to measure the semantic similarity between labeled/unlabeled datasets. To show the validity of the proposed metric, we build up a large-scale benchmark with various degrees of semantic similarities between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical class structure. The results based on the proposed benchmark show that the proposed transfer flow is in line with the hierarchical class structure; and that NCD performance is consistent with the semantic similarities (measured by the proposed metric). Next, by using the proposed transfer flow, we conduct various empirical experiments with different levels of semantic similarity, yielding that supervised knowledge may hurt NCD performance. Specifically, using supervised information from a low-similarity labeled set may lead to a suboptimal result as compared to using pure self-supervised knowledge. These results reveal the inadequacy of the existing NCD literature which usually assumes that supervised knowledge is beneficial. Finally, we develop a pseudo-version of the transfer flow as a practical reference to decide if supervised knowledge should be used in NCD. Its effectiveness is supported by our empirical studies, which show that the pseudo transfer flow (with or without supervised knowledge) is consistent with the corresponding accuracy based on various datasets. Code is released at https://github.com/J-L-O/SK-Hurt-NCDComment: TMLR 2023 accepted paper. arXiv admin note: substantial text overlap with arXiv:2209.0912

Mitochondrial Membrane Remodeling

Mitochondria are key regulators of many important cellular processes and their dysfunction has been implicated in a large number of human disorders. Importantly, mitochondrial function is tightly linked to their ultrastructure, which possesses an intricate membrane architecture defining specific submitochondrial compartments. In particular, the mitochondrial inner membrane is highly folded into membrane invaginations that are essential for oxidative phosphorylation. Furthermore, mitochondrial membranes are highly dynamic and undergo constant membrane remodeling during mitochondrial fusion and fission. It has remained enigmatic how these membrane curvatures are generated and maintained, and specific factors involved in these processes are largely unknown. This review focuses on the current understanding of the molecular mechanism of mitochondrial membrane architectural organization and factors critical for mitochondrial morphogenesis, as well as their functional link to human diseases.Peer reviewe

HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images

We propose a robust and accurate method for reconstructing 3D hand mesh from monocular images. This is a very challenging problem, as hands are often severely occluded by objects. Previous works often have disregarded 2D hand pose information, which contains hand prior knowledge that is strongly correlated with occluded regions. Thus, in this work, we propose a novel 3D hand mesh reconstruction network HandGCAT, that can fully exploit hand prior as compensation information to enhance occluded region features. Specifically, we designed the Knowledge-Guided Graph Convolution (KGC) module and the Cross-Attention Transformer (CAT) module. KGC extracts hand prior information from 2D hand pose by graph convolution. CAT fuses hand prior into occluded regions by considering their high correlation. Extensive experiments on popular datasets with challenging hand-object occlusions, such as HO3D v2, HO3D v3, and DexYCB demonstrate that our HandGCAT reaches state-of-the-art performance. The code is available at https://github.com/heartStrive/HandGCAT.Comment: 6 pages, 4 figures, ICME-2023 conference pape

Mechanical Properties of Gas Storage Sandstone under Uniaxial Cyclic Loading and Unloading Condition

In order to study the mechanical properties and damage evolution of the gas storage surrounding rock under the periodic injection-production process, the uniaxial cyclic loading and unloading tests of sandstone were carried out by TFD-2000 microcomputer servo-controlled rock triaxial testing machine. Results shown that the compressive strength of gas storage sandstone specimens were gradually decreases with increasing of the stress amplitude after 200 cycles. The stress-strain curve under uniaxial cyclic loading and unloading condition formed hysteresis loops, and the hysteresis loop presented sparse-dense-sparse when the stress amplitude was relative higher. The residual strains can be divided into three stages of decay deformation stage, stable deformation stage and accelerated deformation stage when the stress amplitude is 8~32 MPa, this phenomenon is very similar to the creep behavior of rocks. The energy evolution of sandstone under cyclic loading and unloading was analyzed and the damage evolution low of which was also discussed in detail, the damage variable defined by energy dissipative ratio accumulation can well reflect the damage development of sandstone under uniaxial cyclic loading and unloading. A nonlinear visco-plastic body was proposed by considering the accelerate stage of curves of the axial residual strains, and used the nonlinear visco-plastic body to replace the visco-plastic body of the traditional Nishihara model, a nonlinear viscoelastic-plastic model for cyclic loads was established and the applicability of the model is verified. The research results provide certain reference value for the construction and maintenance of gas storage