1,447 research outputs found
Price Dispersion in the Online Auction Markets
Along the standard measures of price dispersion, this paper proposes a new method, the residual variance model, to examine the levels of price and price variation within and across 10 kinds of physically identical products on eBay UK. The results find that the price levels and price dispersions on eBay are lower than the ones reported in the prior literature regarding other online markets, but the ’law of one price’ has not prevailed in any sample category. It further suggests an important interaction between the extent of price dispersion and the heterogeneities of consumers and sellers.Price Dispersion, Online Auction Markets.
Price Dispersion in the Online Auction Markets
Along the standard measures of price dispersion, this paper proposes a new method, the residual variance model, to examine the levels of price and price variation within and across 10 kinds of physically identical products on eBay UK. The results find that the price levels and price dispersions on eBay are lower than the ones reported in the prior literature regarding other online markets, but the ’law of one price’ has not prevailed in any sample category. It further suggests an important interaction between the extent of price dispersion and the heterogeneities of consumers and sellers
Corpus-based Critical Discourse Analysis of News Reports on METOO Movement
The METOO movement (or #METOO) has been widely reported by American media outlets.
Politicians and social activists take advantage of this social movement as a great momentum to
promote political, economic and social policies in multiple areas especially the ones related to
women’s rights. In turn, the movement gained even more extensive media exposure. However,
as people enter the #METOO era, the general public and media start to split into different camps
in terms of their opinion about this social movement.
In this paper, we can observe how two American news outlets with opposing partisan leanings
-CNN and FOX News- use different linguistic devices and strategies to report the METOO
movement, its associated events as well as actors and participants involved. By conducting a
linguistic analysis of news discourse produced by both outlets, the signals of their respective
attitudes or opinions can be detected.
Corpus linguistic (CL) analysis and critical discourse analysis (CDA) as synergetic approaches
are presented to investigate news discourse produced by CNN and FOX News; the corpusbased
study addresses different grammatic categories with a primary focus on the noun, while
CDA or more specifically van Dijk’s news discourse model is applied in a case study of a
specific news event to present a systematic and critical interpretation of news texts at different
dimensions starting from the microstructural and macrostructural dimension (i.e. textual
structure) up to superstructural (i.e. news schemata) and rhetorical dimension.Departamento de FilologĂa InglesaMáster en Estudios Ingleses Avanzados: Lenguas y Culturas en Contact
Crystallized N-terminal domain of influenza virus matrix protein M1 and method of determining and using same
The matrix protein, M1, of influenza virus strain A/PR/8/34 has been purified from virions and crystallized. The crystals consist of a stable fragment (18 Kd) of the M1 protein. X-ray diffraction studies indicated that the crystals have a space group of P3.sub.t 21 or P3.sub.2 21. Vm calculations showed that there are two monomers in an asymmetric unit. A crystallized N-terminal domain of M1, wherein the N-terminal domain of M1 is crystallized such that the three dimensional structure of the crystallized N-terminal domain of M1 can be determined to a resolution of about 2.1 .ANG. or better, and wherein the three dimensional structure of the uncrystallized N-terminal domain of M1 cannot be determined to a resolution of about 2.1 .ANG. or better. A method of purifying M1 and a method of crystallizing M1. A method of using the three-dimensional crystal structure of M1 to screen for antiviral, influenza virus treating or preventing compounds. A method of using the three-dimensional crystal structure of M1 to screen for improved binding to or inhibition of influenza virus M1. The use of the three-dimensional crystal structure of the M1 protein of influenza virus in the manufacture of an inhibitor of influenza virus M1. The use of the three-dimensional crystal structure of the M1 protein of influenza virus in the screening of candidates for inhibition of influenza virus M1
Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning
We added extra experiments in simulation to evaluate the best-performing policy in environments with unseen obstacles. Here the pdf file describes the experiment design and shows the experimental settings and results in a figure and a table. A brief analysis of the results has been provided. We have also attached a video capturing part of the testing process in Gazebo
Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning
Reinforcement learning has shown great promise in the training of robot
behavior due to the sequential decision making characteristics. However, the
required enormous amount of interactive and informative training data provides
the major stumbling block for progress. In this study, we focus on accelerating
reinforcement learning (RL) training and improving the performance of
multi-goal reaching tasks. Specifically, we propose a precision-based
continuous curriculum learning (PCCL) method in which the requirements are
gradually adjusted during the training process, instead of fixing the parameter
in a static schedule. To this end, we explore various continuous curriculum
strategies for controlling a training process. This approach is tested using a
Universal Robot 5e in both simulation and real-world multi-goal reach
experiments. Experimental results support the hypothesis that a static training
schedule is suboptimal, and using an appropriate decay function for curriculum
learning provides superior results in a faster way
Self-Imitation Learning by Planning
Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration. However, in long-horizon motion planning tasks, a challenging problem in deploying IL and RL methods is how to generate and collect massive, broadly distributed data such that these methods can generalize effectively. In this work, we solve this problem using our proposed approach called {self-imitation learning by planning (SILP)}, where demonstration data are collected automatically by planning on the visited states from the current policy. SILP is inspired by the observation that successfully visited states in the early reinforcement learning stage are collision-free nodes in the graph-search based motion planner, so we can plan and relabel robot's own trials as demonstrations for policy learning. Due to these self-generated demonstrations, we relieve the human operator from the laborious data preparation process required by IL and RL methods in solving complex motion planning tasks. The evaluation results show that our SILP method achieves higher success rates and enhances sample efficiency compared to selected baselines, and the policy learned in simulation performs well in a real-world placement task with changing goals and obstacles
Behavior Mixing with Minimum Global and Subgroup Connectivity Maintenance for Large-Scale Multi-Robot Systems
In many cases the multi-robot systems are desired to execute simultaneously
multiple behaviors with different controllers, and sequences of behaviors in
real time, which we call \textit{behavior mixing}. Behavior mixing is
accomplished when different subgroups of the overall robot team change their
controllers to collectively achieve given tasks while maintaining connectivity
within and across subgroups in one connected communication graph. In this
paper, we present a provably minimum connectivity maintenance framework to
ensure the subgroups and overall robot team stay connected at all times while
providing the highest freedom for behavior mixing. In particular, we propose a
real-time distributed Minimum Connectivity Constraint Spanning Tree (MCCST)
algorithm to select the minimum inter-robot connectivity constraints preserving
subgroup and global connectivity that are \textit{least likely to be violated}
by the original controllers. With the employed safety and connectivity barrier
certificates for the activated connectivity constraints and collision
avoidance, the behavior mixing controllers are thus minimally modified from the
original controllers. We demonstrate the effectiveness and scalability of our
approach via simulations of up to 100 robots with multiple behaviors.Comment: To appear in Proceedings of IEEE International Conference on Robotics
and Automation (ICRA) 202
Forecasting fund-related textual emotion trends on Weibo: A time series study
IntroductionThis paper reports a time series analysis of day-to-day emotional text related to fund investments on Weibo (Sina Corporation, Beijing, China).MethodsThe present study employed web-crawler and text mining techniques through Python to obtain data from January 1, 2021 to December 31, 2021.ResultsUsing an auto-regressive integrated moving average model and vector auto-regressive model, the results indicated that fund performance was a significant predictor of fear, anger, and surprise expressions on Weibo. A relationship among emotions within a certain single fund was not found, but textual emotions could be predicted by ARIMA models within emotions.DiscussionThe findings provide insight for media emotion analysis combining linguistic and temporal dimensions in both the communication and psychology disciplines
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