Generative Semantics 4: The Collapse

This chapter traces the collapse of Generative Semantics, which ultimately became a movement away from Noam Chomsky’s view of linguistics, more than a movement toward a unifying vision of language or linguistics. The leaders all went in various directions. Paul Postal and Jim McCawley retained their commitments to formal modeling, but Postal developed a new, non-Transformational framework with David Perlmutter, Relational Grammar, while McCawley continued to ply an increasingly idiosyncratic Transformational model he eventually called Unsyntax. Robin Lakoff led the expansion of linguistic pragmatics and founded feminist linguistics. George Lakoff and Haj Ross took overlapping but distinct forays into non-discrete linguistics. Meanwhile, the Generative Semantics ethos was losing whatever appeal it may have had. Linguists outside the movement, and some within, found the style irritating. Meanwhile, too, Chomsky’s innovations were proving very fruitful and attracting adherents under the label, the Extended Standard Theory. Chomsky’s framework emerged from the brief Generative Semantics eclipse and now seemed the clear winner of the Linguistics Wars.

McDonald’s India: Pains of Partnering with a Delinquent Partner

McDonald’s India is a real-life, undisguised decision case. This US-based fast food multinational corporation set shop in India in early 1990s to gain early mover advantage in a country with a huge population. It struck joint ventures with two domestic Master Franchisee to grow its business. The company made efforts to localize its menu and customize its offerings to suit domestic customers. With the passage of time competition intensified and so did customer expectations. Of the two Master Franchisees, while Westlife Development Ltd. maintained cordial relations with McDonald’s, the chief executive officer of Connaught Plaza Restaurants Ltd. Vikram Bakshi became an impediment in its business plans. Differences between McDonald’s and Bakshi reached a flash point and the matter landed in the courts in India and London. Even after several years of litigation no clear winner emerged. As a consequence, while operations of Westlife Development flourished, the business of Connaught Plaza Restaurants remained stunted. While the two partners in the joint venture clashed, competitors took advantage of the situation to capture emerging opportunities. Even after over two decades of operations in the country, McDonald’s is not making profits. On the contrary it has accumulated losses of ₹4.22 billion by the end of 2018. The challenge facing McDonald’s is how to find a quick solution to the long-drawn problem and thereby accelerate the pace of growth of its business in one of the fastest growing economies in the world.

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

Spatiotemporal data has always been big data. In these days, big data analytics for spatiotemporal data is receiving considerable attention to allow users to analyze huge amounts of data. Traditional big data platforms cannot handle all the challenges of processing spatio-temporal data. Although some big data platforms have been proposed to process a massive volume of spatiotemporal data, neither is considered a clear winner for all possible scenarios. This paper presents the SmarT query engine, a machine learning-based solution that chooses the best big data platform for processing spatiotemporal queries on the fly. In a detailed experimental evaluation, considering the Apache Spark, Elasticsearch, and SciDB big data platforms, the response time decreased up to 22% when using SmarT.

Principles and Practices of Plant-based Irrigation Management

Many consider tools for plant-based irrigation management methods to be the most precise way to manage irrigation in either a research or a commercial settings. Although many types of tools are available, they all measure some aspect of water movement along the soil–plant–atmosphere continuum. This article presents some of the more commonly used tools and the methods involved to properly employ them. In addition, recent literature is reviewed to provide context to the methods themselves and also to highlight each one’s specific advantages and disadvantages. Ultimately, there is no clear winner or “best” tool as all have disadvantages, either due to prohibitive cost, the amount of data output, the difficulty of data interpretation, lack of signal resolution, or lack of dynamic ability to provide decision support. Therefore, we conclude that the user should carefully weigh these varied advantages and disadvantages in the context of their production goals before deciding on a given tool for irrigation management.

Design and Control System Setup of an E-Pattern Omniwheeled Cellular Conveyor

The addition of omnidirectional capability and modularization to conveyor systems is an exciting and trending topic in current conveyor research. The implementation of omnidirectional modular conveyors is foreseen as mandatory in the future of conveyor technologies due to their flexibility and efficiency. In this paper, an E-pattern omniwheeled cellular conveyor (EOCC) is first introduced. Camera and image processing techniques are utilized to achieve a centralized system, which is more robust than conventional decentralized systems. In order for cartons to maneuver across the EOCC, a unique method for activation of the actuators is subsequently designed. Next, the nominal characteristic of the EOCC is analyzed based on the step responses, which then inspire the proposal of four simple controllers, namely the P–P–P–P controller, tTS + P–P–P–P controller, P–P–PD–PD controller, and tTS + P–P–PD–PD controller (P for proportional, D for derivative and tTS for time-shifting), which are used to evaluate the tracking performance of the EOCC in diagonal-shaped, ∞-shaped (horizontal lemniscus), and 8-shaped (vertical lemniscus) trajectories. The results show that there is no clear winner among the controllers, with each having its own advantages and disadvantages. Nevertheless, such findings provide clearer insight into the EOCC, which is vital for future works. After all, the introduction of the EOCC system in this paper is also anticipated to elevate the benchmarks and competitiveness in the current field of modern conveyor technologies.

Die sachsen-anhaltische Landtagswahl vom 6. Juni 2021: Die Mitte ist stabil

The clear winner of the state election in Saxony-Anhalt on June 6, 2021 was the CDU, which was well ahead of the AfD . The FDP, which managed to re-enter the state parliament, was also among the election winners . The big loser was the Left Party, which achieved the worst result in Saxony-Anhalt’s history . The election’s decisive result factor was the extraordinarily high popularity of Prime Minister Reiner Haseloff (CDU), who benefit­ed from his incumbency bonus across party lines . Not only did the citizens hardly know the other leading candidates; moreover the CDU was perceived as having strong problem­solving competence for the dominant issues of Corona and in other relevant policy areas . Likewise, the CDU and Haseloff’s strategy to clearly distinguish themselves from the AfD and the Left was successful . Since Prime Minister Haseloff wanted a stable government for the state, a tripartite alliance was the only option . Due to the conflicts between the CDU and the Greens in the previously ruling so-called Kenya coalition (black, red, green), the CDU chose the FDP as a new coalition partner alongside the SPD, and more than three months after the election a so-called Germany coalition (black, red, yellow) was concluded .

Data-Driven Trend Forecasting in Stock Market Using Machine Learning Techniques

Stock market movements are affected by numerous factors making it one of the most challenging problems for forecasting. This article attempts to predict the direction of movement of stock and stock indices. The study uses three classifiers - Artificial Neural Network, Random Forest and Support Vector Machine with four different representation of inputs. First representation uses raw data (open, high, low, close and volume), The second uses ten features in the form of technical indicators generated by use of technical analysis. The third and fourth portrayal presents two different ways of converting the indicator data into discrete trend data. Experimental results suggest that for raw data support vector machine provides the best results. For other representations, there is no clear winner regarding models applied, but portrayal of data by the proposed approach gave best overall results for all the models and financial series. Consistency of the results highlight the importance of feature generation and right representation of dataset to machine learning techniques.