Lean Accounting, Fat Problem? A Critical Analysis of Lean Accounting’s Value

Lean accounting is an accounting system that is designed specifically to facilitate the application of lean manufacturing. It is considered a new tool among the various accounting methods available to management. As a managerial accounting method, the purpose of lean accounting should be to provide valuable, insightful information to management for decision-making. However, lean accounting sometimes fails to serve this ultimate purpose as a managerial accounting alternative. We conduct a case study of Toyota to examine lean accounting’s value. The analysis shows that lean accounting tends to be short-term focused, which may jeopardize a company’s long-term growth prospective. Lean accounting is also incapable of providing accurate product cost information, and therefore is unable to support a strategic decision-making process. Traditional standard costing and activity-based costing may be superior to lean accounting for long-term planning and decision-making. The potential exists for a dual system with lean accounting for tactical short-term information and either standard costing or activity-based costing for strategic long-term information

PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training

Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information. Hence, table-based fact verification has recently emerged as an important and growing research area. Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column or comparing tuples. To bridge this gap, in this paper we introduce PASTA, a novel state-of-the-art framework for table-based fact verification via pre-training with synthesized sentence-table cloze questions. In particular, we design six types of common sentence-table cloze tasks, including Filter, Aggregation, Superlative, Comparative, Ordinal, and Unique, based on which we synthesize a large corpus consisting of 1.2 million sentence-table pairs from WikiTables. PASTA uses a recent pre-trained LM, DeBERTaV3, and further pretrains it on our corpus. Our experimental results show that PASTA achieves new state-of-the-art performance on two table-based fact verification benchmarks: TabFact and SEM-TAB-FACTS. In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4.7 points (85.6% vs. 80.9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1.5 points (90.6% vs. 92.1%).Comment: EMNLP 202

PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows

Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers while preserving the fine-grained details. We present a novel deep learning-based denoising model, that incorporates normalizing flows and noise disentanglement techniques to achieve high denoising accuracy. Unlike existing works that extract features of point clouds for point-wise correction, we formulate the denoising process from the perspective of distribution learning and feature disentanglement. By considering noisy point clouds as a joint distribution of clean points and noise, the denoised results can be derived from disentangling the noise counterpart from latent point representation, and the mapping between Euclidean and latent spaces is modeled by normalizing flows. We evaluate our method on synthesized 3D models and real-world datasets with various noise settings. Qualitative and quantitative results show that our method outperforms previous state-of-the-art deep learning-based approaches

PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows

Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow, which incorporates normalizing flows and weight prediction techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we exploit the invertible characteristics of normalizing flows to transform points between Euclidean and latent spaces and formulate the upsampling process as ensemble of neighbouring points in a latent space, where the ensemble weights are adaptively learned from local geometric context. Extensive experiments show that our method is competitive and, in most test cases, it outperforms state-of-the-art methods in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency. The source code will be publicly available at https://github.com/unknownue/pu-flow

An Improved Model−Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on High−Gain Disturbance Observer

Predictive current control (PCC) is an advanced control strategy for permanent magnet synchronous motors (PMSM). When the motor drive system is undisturbed, predictive current control exhibits a good dynamic response speed and steady−state performance, but the conventional PCC control performance of PMSM that depends on the motor body model is vulnerable to parameter perturbation. Aiming at this problem, an improved model−free predictive current control (IMFPCC) strategy based on a high−gain disturbance observer (HGDO) is proposed in this paper. The proposed strategy is introduced with the idea of model−free control, relying only on the system input and output to build an ultra−local current prediction model, which gets rid of the constraints of the motor body parameters. In the paper, the ultra−local structure is optimized by comparing and analyzing the equation of the state of the classical ultra−local structure and PMSM system. The system’s current state variables are incorporated into the ultra−local system modeling, as a result, the current estimation errors existing in the classical ultra−local structure are eliminated. For the unmodeled and parametric perturbation part of the ultra−local system, a high−gain disturbance observer is designed to estimate it in real time. Finally, the proposed IMFPCC strategy is compared with the conventional model−based predictive current control (MPCC) and the conventional model−free predictive current control (CMFPCC) in simulation and experiment. The results show that the current steady−state error of the IMFPCC strategy in the case of parameter variation is only 50% of the MPCC method, which proves the effectiveness and correctness of the proposed strategy

Soil Remediation of Subtropical Garden Grasses and Shrubs Using High-Performance Ester Materials

Soil erosion due to rainstorms is a serious problem in subtropical gardens in South China. Soil conservation and the restoration of degraded landscapes are important research topics at home and abroad. Because of the sluggish growth of plants under traditional cultivation techniques, they are incapable of effectively protecting the soil. Therefore, the rapid and high-quality soil conservation of subtropical landscapes remains an urgent problem to be overcome. The purpose of this study is to improve the red soil and ground environment for the growth of grasses and shrubs through high-performance ester materials. Our objective was to find a solution for the high impact of soil loss on subtropical landscapes. In this study, we used the ecological restoration of soil as the starting point and selected a typical subtropical garden in South China as the field test point. We carried out soil erosion resistance testing using high-performance ester materials. The anti-erosion abilities of slopes under various working conditions are discussed. During the growth period, the soil indexes were monitored for a long time, and the growth of grasses and shrubs was compared. The obtained monitoring data were analyzed with mathematical statistics. We found that the addition of high-performance ester materials significantly reduced soil loss by 52.60%. High-performance ester materials have a good hydrothermal regulation function, which can promote the germination and later growth of sloping plants. The decrease in ground internal density promotes the extension of plant roots. High-performance ester materials can improve soil permeability and activity and promote vegetation growth. In terms of turf thickness and overall growth as well as shrubs crown width and height, high-performance ester materials have a beneficial effect on promoting plant growth. Soil remediation using high-performance ester materials has good economic value, high water-holding capacity, adaptability, and convenience. In this study, we determined a solution for the high impact of soil loss on subtropical landscapes. The soil remediation of a subtropical garden using high-performance ester materials was successful. The practice of landscape soil remediation engineering presented in this paper can provide a reference for typical landscape soil remediation in subtropical zones