A Study of Earnings Managent and Financial Statement Reporting Issues Surroding Public Traded Corporations

The purpose of this paper is to study some publicly traded companies\u27 financial reporting systems. I chose four companies in different industries to conduct a series of analyses and evaluations. In Section One, the quality of these companies\u27 financial reporting systems is carefully examined and evaluated. First, I discuss some potential earnings management strategies that managers from different departments tend to adopt. Next, some of the chosen companies\u27 important policies, such as revenue recognition, are compared with industrial standards or relevant policies from competitors. In addition, earnings management and financial reporting issues that relate to income taxes are discussed separately. Three major perspectives are considered: 1. recognition of deferred tax asset and valuation allowance accounts; 2. the possible effects of changing valuation allowance on earnings; 3. disclosure of tax planning strategies in the financial report. Section Two discusses corresponding internal control systems that can assist corporations in alleviating the adverse effects from inaccurate and inefficient financial reporting. First, COSO internal control framework is used as the guideline for developing the internal control process. Secondly, certain industrial factors are considered during the risk identification and assessment procedure. Next, a general audit plan is developed for each company. I discuss the budgeted work plan in terms of hours of effort for each task necessary to audit the internal control system. The deferred tax accounts are also discussed

Existence and multiplicity of positive solutions for a Schrodinger-Poisson system with a perturbation

In this paper we study the nonlinear Schrodinger-Poisson system with a perturbation: \begin{equation*} \begin{cases} -\Delta u+u+K( x) \phi u=\vert u\vert ^{p-2}u+\lambda f(x)\vert u\vert ^{q-2}u \text{in }\mathbb{R}^{3}, -\Delta \phi =K( x) u^{2} \text{in }\mathbb{R}^{3}, \end{cases} \end{equation*}% where $K$ and $f$ are nonnegative functions, $2\ge q\leq p\le 6$ and $p\ge 4$, and the parameter $\lambda \in \mathbb{R}$. Under some suitable assumptions on $K$ and $f$, the criteria of existence and multiplicity of positive solutions are established by means of the Lusternik-Schnirelmann category and minimax method

Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training

Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is bandwidth-demanding and time-consuming. Frequent exchanges of node features, embeddings and embedding gradients (all referred to as messages) across devices bring significant communication overhead for nodes with remote neighbors on other devices (marginal nodes) and unnecessary waiting time for nodes without remote neighbors (central nodes) in the training graph. This paper proposes an efficient GNN training system, AdaQP, to expedite distributed full-graph GNN training. We stochastically quantize messages transferred across devices to lower-precision integers for communication traffic reduction and advocate communication-computation parallelization between marginal nodes and central nodes. We provide theoretical analysis to prove fast training convergence (at the rate of O(T^{-1}) with T being the total number of training epochs) and design an adaptive quantization bit-width assignment scheme for each message based on the analysis, targeting a good trade-off between training convergence and efficiency. Extensive experiments on mainstream graph datasets show that AdaQP substantially improves distributed full-graph training's throughput (up to 3.01 X) with negligible accuracy drop (at most 0.30%) or even accuracy improvement (up to 0.19%) in most cases, showing significant advantages over the state-of-the-art works

Hydrogels with Self-Healing Attribute

Given increasing environmental issues and energy crisis, mimicking nature to confer materials with self-healing attribute to prolong their lifespan is highly imperative. As representative of soft matter with extensive applications, hydrogels have gained significant attention. In this chapter, a survey of the current strategies for synthesizing self-healing hydrogels based on inorganic-based, polymer and nanocomposite hydrogels is covered and highlighted. Several examples for non-autonomic and autonomic self-healing hydrogels, according to the trigger exerted, are presented. General mechanisms accounting for self-healing hydrogels are listed. Some typical instances to outline the emerging applications of self-healing hydrogels are also provided. Finally, a perspective on the current trends and challenges is briefly summarized