Learning Delaunay Triangulation using Self-attention and Domain Knowledge

Delaunay triangulation is a well-known geometric combinatorial optimization problem with various applications. Many algorithms can generate Delaunay triangulation given an input point set, but most are nontrivial algorithms requiring an understanding of geometry or the performance of additional geometric operations, such as the edge flip. Deep learning has been used to solve various combinatorial optimization problems; however, generating Delaunay triangulation based on deep learning remains a difficult problem, and very few research has been conducted due to its complexity. In this paper, we propose a novel deep-learning-based approach for learning Delaunay triangulation using a new attention mechanism based on self-attention and domain knowledge. The proposed model is designed such that the model efficiently learns point-to-point relationships using self-attention in the encoder. In the decoder, a new attention score function using domain knowledge is proposed to provide a high penalty when the geometric requirement is not satisfied. The strength of the proposed attention score function lies in its ability to extend its application to solving other combinatorial optimization problems involving geometry. When the proposed neural net model is well trained, it is simple and efficient because it automatically predicts the Delaunay triangulation for an input point set without requiring any additional geometric operations. We conduct experiments to demonstrate the effectiveness of the proposed model and conclude that it exhibits better performance compared with other deep-learning-based approaches

Comparative Analysis of Entertainment Expense in the United States and Korea

This article provides preliminary guidelines for researchers and investors who are interested in Korean tax law in the area of entertainments expenses via comparative analysis of tax laws in the United States and Korea. Unlike the U.S., Korea regulates entertainment expenses only by placing a ceiling on the deductible amount, not by imposing stricter conditions for a business deduction. In the U.S. 50 percent of any entertainment expense otherwise deductible is allowed. Korea places a ceiling on otherwise deductible entertainment expenses according to a mathematical formula. To determine which expenses qualify as entertainment expenses, Korean courts or administrative agencies apply a comparative analysis with other expenses. Under American tax law for a business deduction an entertainment expense at least should be associated with the active conduct of the taxpayer trade or business. For a business deduction a taxpayer must substantiate entertainment expenses by evidence or documents as prescribed in the relevant provisions in the U.S. and Korea

Comparision of General Expense Provisions of U.S. and Korean Tax Law to Suggest Guildelines for Interpretation of Korean Tax Law

This essay reviewed and analyzed the general provisions for a business deduction of the U. S. and Korea, and compared them to see if the interpretation of I.R.C. § 162(a) as confirmed and established by the U.S. courts can be useful as a tool to interpret the general provision of Korea. This essay has come to observe that: Generally, for a deductible expense, the Korean system adopts a negative method, allowing all expenses to be deductible unless otherwise provided. The U.S. system, however, adopts a positive system, which means an expense is deductible only if a separate provision to allow doing so is provided; just as rdinary and necessaryare paralleled in I.R.C. § 162(a), so rdinary and directly related to revenueare paralleled in the Korean Corporation Tax Law(CTL). But the meaning of ecessaryand irectly related to revenuea are different; the interpretation to the meaning of rdinaryby the U.S. Supreme Court may be applicable to the interpretation of article 19(2) of the CTL; the standards developed in the U.S. with respect to deciding what a rade or businessmay not be applicable to the CTL; and interpretation rules or precedents established as related to n connection withand arrying onin the interpretation of the meaning of ordinary in I.R.C. § 162(a) can be useful guidelines to interpret the meaning of n connection within the CTL

EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost

Graph-based models have become increasingly important in various domains, but the limited size and diversity of existing graph datasets often limit their performance. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. Our approach leverages graph edit distance to generate new graphs that are similar to the original ones but exhibit some variation in their structures. To achieve this, we learn the graph edit distance through a comparison of labeled graphs and utilize this knowledge to create graph edit paths between pairs of original graphs. With randomly sampled graphs from a graph edit path, we enrich the training set to enhance the generalization capability of classification models. We demonstrate the effectiveness of our approach on several benchmark datasets and show that it outperforms existing augmentation methods in graph classification tasks

Precarious Work Schedules as a Source of Economic Insecurity and Institutional Distrust

Work schedules may fuel precariousness among U.S. workers by undermining perceptions of security, both economic and societal. Volatile hours, limited schedule input, and short advance notice are all dimensions of precarious work schedules. Our analyses suggest that scheduling practices that introduce instability and unpredictability into workers’ lives undermine perceptions of security in unique ways for hourly and salaried workers. Although the data suggest that precarious scheduling practices are widespread in the labor market, workers who are black, young, and without a college degree appear to be at highest risk. The findings highlight the importance of examining constellations of scheduling practices and considering the direction of work-hour fluctuations when investigating the ramifications of today’s scheduling practices for quality of employment and quality of life

Gender Differences in Material, Psychological, and Social Domains of the Income Gradient in Mortality: Implications for Policy

We set out to examine the material, psychological, and sociological pathways mediating the income gradient in health and mortality. We used the 2008 General Social Survey-National Death Index dataset (N = 26,870), which contains three decades of social survey data in the US linked to thirty years of mortality follow-up. We grouped a large number of variables into 3 domains: material, psychological, and sociological using factor analysis. We then employed discrete-time hazard models to examine the extent to which these three domains mediated the income-mortality association among men and women. Overall, the gradient was weaker for females than for males. While psychological and material factors explained mortality hazards among females, hazards among males were explained only by social capital. Poor health significantly predicted both income and mortality, particularly among females, suggesting a strong role for reverse causation. We also find that many traditional associations between income and mortality are absent in this dataset, such as perceived social status

Neural representations for multi-context visuomotor adaptation and the impact of common representation on multi-task performance: a multivariate decoding approach

The human brain's remarkable motor adaptability stems from the formation of context representations and the use of a common context representation (e.g., an invariant task structure across task contexts) derived from structural learning. However, direct evaluation of context representations and structural learning in sensorimotor tasks remains limited. This study aimed to rigorously distinguish neural representations of visual, movement, and context levels crucial for multi-context visuomotor adaptation and investigate the association between representation commonality across task contexts and adaptation performance using multivariate decoding analysis with fMRI data. Here, we focused on three distinct task contexts, two of which share a rotation structure (i.e., visuomotor rotation contexts with −90° and +90° rotations, in which the mouse cursor's movement was rotated 90 degrees counterclockwise and clockwise relative to the hand-movement direction, respectively) and the remaining one does not (i.e., mirror-reversal context where the horizontal movement of the computer mouse was inverted). This study found that visual representations (i.e., visual direction) were decoded in the occipital area, while movement representations (i.e., hand-movement direction) were decoded across various visuomotor-related regions. These findings are consistent with prior research and the widely recognized roles of those areas. Task-context representations (i.e., either −90° rotation, +90° rotation, or mirror-reversal) were also distinguishable in various brain regions. Notably, these regions largely overlapped with those encoding visual and movement representations. This overlap suggests a potential intricate dependency of encoding visual and movement directions on the context information. Moreover, we discovered that higher task performance is associated with task-context representation commonality, as evidenced by negative correlations between task performance and task-context-decoding accuracy in various brain regions, potentially supporting structural learning. Importantly, despite limited similarities between tasks (e.g., rotation and mirror-reversal contexts), such association was still observed, suggesting an efficient mechanism in the brain that extracts commonalities from different task contexts (such as visuomotor rotations or mirror-reversal) at multiple structural levels, from high-level abstractions to lower-level details. In summary, while illuminating the intricate interplay between visuomotor processing and context information, our study highlights the efficiency of learning mechanisms, thereby paving the way for future exploration of the brain's versatile motor ability