108 research outputs found
How Nike’s Trademark Infringement Lawsuit Against Kool Kiy & Omi May Benefit From China’s Wuhan Intermediate People’s Court’s Approach to Mediation
In November 2022, Nike filed a trademark infringement lawsuit against Kool Kiy, Omi, and China-based manufacturer Xiamen Wandering Planet, garnering nationwide media attention. Particularly, Wandering Planet was accused of playing an integral role in the infringement by providing the sources to produce knockoff sneakers using Nike’s registered Air Jordan 1 and Dunk trade dress for Kiy and Omi. The manufacturer allegedly “knowingly participate[d] in a scheme to intentionally create confusion in the market place and capitalize on it.” Indeed, some confused consumers could not tell the difference between Kool Kiy’s products and Jordan’s. Nike’s staunch commitment to proceeding with litigation is clear: “Nike must protect its design and intellectual property from bad actors who undermine the very DNA of authentic sneaker culture by promoting, copying, and selling Nike’s designs as their own.”
This post was originally published on the Cardozo Journal of Conflict Resolution website on April 29, 2023. The original post can be accessed via the Archived Link button above
Equity-Based Compensation To Outside Directors And Accounting Conservatism
This paper examines the relationship between equity-based compensation to outside directors and accounting conservatism. Equity-based compensation to outside directors can strengthen the firm’s corporate governance structure. Since this strong governance reduces the information asymmetry between managers and shareholders, it is also possible that firms with strong governance use more conservative accounting. To test this prediction, we investigate whether the proportion of the equity-based compensation to total compensation to outside directors has an effect on the level of conservatism and the various measures that are used. We find that there is a positive relationship between the proportion of equity-based compensation and the level of conservatism. The results are robust to additional tests using alternative measures of the equity-based compensation (the amount of the equity-based compensation) and the equity-based compensation to audit committee members instead of the full board of directors. According to our findings, we can conclude that equity-based compensation to outside directors encourages directors to put more effort into reducing the information asymmetry using conservative accounting
Discovering and Generating Hard Examples for Training a Red Tide Detector
Currently, accurate detection of natural phenomena, such as red tide, that
adversely affect wildlife and human, using satellite images has been
increasingly utilized. However, red tide detection on satellite images still
remains a very hard task due to unpredictable nature of red tide occurrence,
extreme sparsity of red tide samples, difficulties in accurate groundtruthing,
etc. In this paper, we aim to tackle both the data sparsity and groundtruthing
issues by primarily addressing two challenges: i) significant lack of hard
examples of non-red tide that can enhance detection performance and ii) extreme
data imbalance between red tide and non-red tide examples. In the proposed
work, we devise a 9-layer fully convolutional network jointly optimized with
two plug-in modules tailored to overcoming the two challenges: i) a hard
negative example generator (HNG) to supplement the hard negative (non-red tide)
examples and ii) cascaded online hard example mining (cOHEM) to ease the data
imbalance. Our proposed network jointly trained with HNG and cOHEM provides
state-of-the-art red tide detection accuracy on GOCI satellite images.Comment: 10 page
Equity-Based Compensation For Outside Directors And Cost Of Equity Capital
This study provides evidence on the association between equity-based compensation for outside directors and the implied cost of equity capital. Based on the premise that equity-based compensation for outside directors better aligns the interests of the directors with those of shareholders, we investigate whether the more equity-based compensation is granted to outside directors, the lower cost of equity capital firms enjoy. We find a negative relationship between the proportion of equity-based compensation to total compensation for outside directors and the cost of equity capital. Our findings suggest that equity-based compensation for outside directors, by motivating the directors to play their monitoring role more faithfully, reduces agency risks resulting in the lower cost of equity capital
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A Cross National Comparison on the Awareness of Adopting FOSS4G to NSDI in Developing Countries
In this study, we constructed an assessment framework that was consisted of 9 indicators about functional, economic and public value for FOSS4G adoption to NSDI and alternatives such as data sharing, data management, utilization and construction and derived relative weights using AHP method. For the AHP, we conducted a survey to developing countries’ 10 respondents from 9 Asian and Latin American countries. Firstly, result of the survey showed that economic value indicator came in the highest weight with 0.425, followed by functional value indicator with 0.345 and public value indicator with 0.230. Secondly, result of the alternatives analysis showed that data sharing alternative came in the highest adoption rate with 0.824, followed by data management with 0.780, data utilization with 0.778. This means that developing countries want to introduce FOSS4G to their NSDI from economic motivation. This study focused on the comprehensive aspect for adopting FOSS4G to NSDI that is different from the previous researches that were focused on the software engineering aspect to the adoption
Quatro++: Robust Global Registration Exploiting Ground Segmentation for Loop Closing in LiDAR SLAM
Global registration is a fundamental task that estimates the relative pose
between two viewpoints of 3D point clouds. However, there are two issues that
degrade the performance of global registration in LiDAR SLAM: one is the
sparsity issue and the other is degeneracy. The sparsity issue is caused by the
sparse characteristics of the 3D point cloud measurements in a mechanically
spinning LiDAR sensor. The degeneracy issue sometimes occurs because the
outlier-rejection methods reject too many correspondences, leaving less than
three inliers. These two issues have become more severe as the pose discrepancy
between the two viewpoints of 3D point clouds becomes greater. To tackle these
problems, we propose a robust global registration framework, called
\textit{Quatro++}. Extending our previous work that solely focused on the
global registration itself, we address the robust global registration in terms
of the loop closing in LiDAR SLAM. To this end, ground segmentation is
exploited to achieve robust global registration. Through the experiments, we
demonstrate that our proposed method shows a higher success rate than the
state-of-the-art global registration methods, overcoming the sparsity and
degeneracy issues. In addition, we show that ground segmentation significantly
helps to increase the success rate for the ground vehicles. Finally, we apply
our proposed method to the loop closing module in LiDAR SLAM and confirm that
the quality of the loop constraints is improved, showing more precise mapping
results. Therefore, the experimental evidence corroborated the suitability of
our method as an initial alignment in the loop closing. Our code is available
at https://quatro-plusplus.github.io.Comment: 26 pages, 23 figure
Chemical Property-Guided Neural Networks for Naphtha Composition Prediction
The naphtha cracking process heavily relies on the composition of naphtha,
which is a complex blend of different hydrocarbons. Predicting the naphtha
composition accurately is crucial for efficiently controlling the cracking
process and achieving maximum performance. Traditional methods, such as gas
chromatography and true boiling curve, are not feasible due to the need for
pilot-plant-scale experiments or cost constraints. In this paper, we propose a
neural network framework that utilizes chemical property information to improve
the performance of naphtha composition prediction. Our proposed framework
comprises two parts: a Watson K factor estimation network and a naphtha
composition prediction network. Both networks share a feature extraction
network based on Convolutional Neural Network (CNN) architecture, while the
output layers use Multi-Layer Perceptron (MLP) based networks to generate two
different outputs - Watson K factor and naphtha composition. The naphtha
composition is expressed in percentages, and its sum should be 100%. To enhance
the naphtha composition prediction, we utilize a distillation simulator to
obtain the distillation curve from the naphtha composition, which is dependent
on its chemical properties. By designing a loss function between the estimated
and simulated Watson K factors, we improve the performance of both Watson K
estimation and naphtha composition prediction. The experimental results show
that our proposed framework can predict the naphtha composition accurately
while reflecting real naphtha chemical properties.Comment: Accepted at IEEE International Conference on Industrial Informatics
2023(INDIN 2023
eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-
Contrary to other standard cameras, event cameras interpret the world in an
entirely different manner; as a collection of asynchronous events. Despite
event camera's unique data output, many event feature detection and tracking
algorithms have shown significant progress by making detours to frame-based
data representations. This paper questions the need to do so and proposes a
novel event data-friendly method that achieve simultaneous feature detection
and tracking, called event Clustering-based Detection and Tracking (eCDT). Our
method employs a novel clustering method, named as k-NN Classifier-based
Spatial Clustering and Applications with Noise (KCSCAN), to cluster adjacent
polarity events to retrieve event trajectories.With the aid of a Head and Tail
Descriptor Matching process, event clusters that reappear in a different
polarity are continually tracked, elongating the feature tracks. Thanks to our
clustering approach in spatio-temporal space, our method automatically solves
feature detection and feature tracking simultaneously. Also, eCDT can extract
feature tracks at any frequency with an adjustable time window, which does not
corrupt the high temporal resolution of the original event data. Our method
achieves 30% better feature tracking ages compared with the state-of-the-art
approach while also having a low error approximately equal to it.Comment: IROS2022 accepted pape
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