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.

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 Filología 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

Adsorption of methylene blue dye from the aqueous solution via bio-adsorption in the inverse fluidized-bed adsorption column using the torrefied rice husk

In this work, the inverse fluidized-bed bio-adsorption column is applied for the first time and is demonstrated using the torrefied rice husk (TRH) for the removal of methylene blue from the solution. The bio-adsorbents were characterized by BET, FI-IR, and SEM. The inverse fluidized-bed adsorption column using TRH becomes saturated in the 95-min continuous adsorption, during which the breakthrough time is 22 min, the overall MB removal (R) is 84%, and the adsorption capacity (Qexp) on the TRH is 6.82 mg g−1. These adsorption characteristics are superior to those in the fixed-bed adsorption column (R of 52% and Qexp of 2.76 mg g−1) at a lower flow rate (100 vs. 283 cm3 min−1). Torrefaction of RH significantly increases the surface area (28 vs. 9 m2 g−1) and enhances the surface functional groups, leading to an improved maximum equilibrium adsorption amount from 21.5 to 38.0 mg g−1 according to Langmuir model in the batch adsorption system. Besides, the increased Qexp on the TRH is also obtained in the inverse fluidized-bed (5.25 vs. 2.77 mg g−1, 89% higher) and the fixed-bed (2.76 vs. 1.53 mg g−1, 80% higher) adsorption columns compared to that on the RH