scholarly journals Tom Nook, Capitalist or Comrade?

Loading ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 109-134
Author(s):  
Emma Vossen
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Student Loan ◽  
Online Discussions ◽  
Housing Crisis ◽  
Cultural Significance ◽  
New Horizons ◽  
Hard Times ◽  
The Many ◽  
Hearts And Minds

Many millennial Animal Crossing players will experience the joy of paying off their beautiful three-floor in-game home only to have that joy cut short by the crushing realization that they may never experience homeownership in real life. Who do we then take that anger and disappointment out on? The capitalists with a stranglehold on the housing market? The governments and companies holding our lives hostage for student loan debt? Our landlords who take most of our income each month so we can keep a roof over our heads? Our bosses who are criminally underpaying us for our labour? Or is it a fictional racoon? Arguments about the ethics of Animal Crossing’s non-playable character Tom Nook are inescapable in online discussions about the Animal Crossing series. These discussions generally have two sides: either Tom Nook is a capitalistic villain who exploits the player’s labour for housing, or he is a benevolent landowner who helps the player out in hard times. Vossen first sets the stage by discussing the cultural significance of both the Animal Crossing series, focusing in on Animal Crossing: New Horizons (2020), and the millennial housing crisis. She then examines the many tweets, memes, comics, and articles that vilify Tom Nook (and a few that defend him) and asks: are we really mad at Tom, or are we mad at the cruelty and greed of the billionaires, bosses, and landowners in our real lives? Vossen argues that what she calls “Nook discourse” represents the radical social potential of Animal Crossing to facilitate large-scale real-world conversations about housing, economic precarity, class, and labour that could help change hearts and minds about the nature of wealth.

Addressing the burden of student loan debt: A case-based review of influencing considerations

2021 ◽  
Author(s):  
Elisa M Greene ◽  
W Nathan Greene ◽  
William L Greene
Keyword(s):  
Real Life ◽  
Financial Education ◽  
Student Loan ◽  
Debt Management ◽  
Student Debt ◽  
Debt Burden ◽  
Income Ratio ◽  
The Many ◽  
Optimal Approach ◽  
Education Of Students

Abstract Purpose The following review is offered as an aid for encouraging deeper understanding by pharmacy graduates of approaches to debt management. Summary The phenomenon of growing debt for pharmacists and other professionals has been well described. Significant debt is widespread with both pharmacy students and graduates; a recent study described the debt-to-income ratio for pharmacists to have risen by 141% between 2010 and 2016. This increasing debt burden causes significant pressure for these individuals—whether while in training, early in their career, or, increasingly, even in midcareer. Dealing with debt has become a major consideration in the profession. Given that financial education is addressed only minimally, if at all, in pharmacy curricula, pharmacists find it challenging to understand and fully consider the myriad factors influencing the accumulation and repayment of debt in the context of their financial goals. Personal financial, repayment, behavioral, and emotional/psychological factors must be considered to choose an optimal strategy to address debt. This article describes various repayment plans, particularly focusing on those offered with direct loans, and it reviews in some detail 5 comprehensive repayment strategies (using these plans). Three case studies derived from real-life pharmacist-planner interactions illustrate the many factors that must be considered as a pharmacist chooses the optimal approach to debt repayment in their unique life situation. Conclusion Education of students and pharmacists regarding the various factors related to handling student debt may facilitate decision-making that is both financially and personally beneficial.

Worked as a Huckster

2017 ◽  
Author(s):  
Robert Wuthnow
Keyword(s):  
Large Scale ◽  
Social Role ◽  
Urban Economy ◽  
The Other ◽  
Cultural Significance ◽  
Retail Markets ◽  
The Social ◽  
Farm Produce ◽  
The Many

This chapter focuses on hucksters who peddled farm produce in towns, brought town goods to farms, in the nineteenth century. The social role of the huckster offers an exceptional opportunity to probe the moral ambiguities of American culture as the nation transitioned from an agrarian to an urban economy. In simplest terms, hucksters occupied a liminal space that was neither fully rural nor fully urban, connecting the two as they passed goods from one to the other. They served as a significant commercial link antecedent to the establishment of large-scale wholesale and retail markets. As important as this role was economically, its cultural significance was equally important. By their own account and in the many accounts that contemporaries gave of them, hucksters transgressed familiar occupational and spatial categories and in so doing dramatized both in negation and in affirmation the shifting meaning of those categories.

Integration of Ecological and Engineering Aspects in Planning Large Scale Tidal Power Development in the Bay of Fundy, Canada

1984 ◽  
Vol 16 (1-2) ◽  
pp. 281-295 ◽  
Author(s):  
Donald C Gordon
Keyword(s):  
Large Scale ◽  
Feasibility Studies ◽  
Bay Of Fundy ◽  
Final Decision ◽  
Power Development ◽  
Tidal Power ◽  
History Of ◽  
Scientists And Engineers ◽  
The Many ◽  
University Scientists

Large-scale tidal power development in the Bay of Fundy has been given serious consideration for over 60 years. There has been a long history of productive interaction between environmental scientists and engineers durinn the many feasibility studies undertaken. Up until recently, tidal power proposals were dropped on economic grounds. However, large-scale development in the upper reaches of the Bay of Fundy now appears to be economically viable and a pre-commitment design program is highly likely in the near future. A large number of basic scientific research studies have been and are being conducted by government and university scientists. Likely environmental impacts have been examined by scientists and engineers together in a preliminary fashion on several occasions. A full environmental assessment will be conducted before a final decision is made and the results will definately influence the outcome.

KEARIFAN LOKAL (LOCAL GENIUS) SEBAGAI SOKO GURU MENATA PERADABAN BANGSA YANG BERKARAKTER NUSANTARA (REFLEKSI KARYA KI DALANG TANGSUB)

2016 ◽  
Vol 2 (12) ◽  
Author(s):  
I Made Suarta
Keyword(s):  
Lifelong Learning ◽  
Local Knowledge ◽  
Real Life ◽  
Negative Attitudes ◽  
Care Environment ◽  
Mental Attitude ◽  
Early 19Th Century ◽  
The People ◽  
The Many ◽  
Written Traditions

Local knowledge (local genius) is the quintessence of our ancestors thinking either oral or written traditions which we have received to date. Thought that, in the context of real archipelago has the same thread, which has a valuable values and universal to strengthen the integrity of the Unitary Republic of Indonesia. Through our founding genius thought that we should be able to implement it in real life to be able to reach people who "Gemah ripah loh jinawi", no less clothing, food, and shelter!Some of the many concepts of mind for the people of Bali are reflected in the work of puppeteer Ki Dalang Tangsub contributed to the development of Indonesia and has a universal value is the concept of maintaining the environment, save money, and humble. Through mental attitude has not always feel pretty; like not smart enough, not skilled enough, and not mature enough experience, make us always learn and practice. Learn and continue lifelong learning will make a man more mature and a lot of experience. Thus, the challenges in life will be easy to overcome. All that will be achieved, in addition to the hard work is also based on the mental attitude of inferiority is not proud, haughty, arrogant and other negative attitudes. Thought care environment, managing finances, and humble as described above, in Bali has been formulated through a literature shaped geguritan, namely Geguritan I Gedé Basur Dalang Tangsub works, one of the great authors in the early 19th century.  Keywords: Local knowledge, a cornerstone of, the character of the archipelago

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

2021 ◽  
Author(s):  
Gianluca Bardaro ◽  
Alessio Antonini ◽  
Enrico Motta
Keyword(s):  
Large Scale ◽  
Daily Life ◽  
Elderly Care ◽  
Real Life ◽  
Robotic Platform ◽  
Holistic View ◽  
Personal Assistants ◽  
Healthcare Interventions ◽  
The Impact ◽  
The Eu

AbstractOver the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

scholarly journals Grounding semantic transparency in context

Morphology ◽  
2021 ◽  
Author(s):  
Rossella Varvara ◽  
Gabriella Lapesa ◽  
Sebastian Padó
Keyword(s):  
Large Scale ◽  
Point Of View ◽  
Distributional Semantics ◽  
Semantic Transparency ◽  
Inclusion Measure ◽  
The Difference ◽  
Semantic Point ◽  
The Many ◽  
The Relationship

AbstractWe present the results of a large-scale corpus-based comparison of two German event nominalization patterns: deverbal nouns in -ung (e.g., die Evaluierung, ‘the evaluation’) and nominal infinitives (e.g., das Evaluieren, ‘the evaluating’). Among the many available event nominalization patterns for German, we selected these two because they are both highly productive and challenging from the semantic point of view. Both patterns are known to keep a tight relation with the event denoted by the base verb, but with different nuances. Our study targets a better understanding of the differences in their semantic import.The key notion of our comparison is that of semantic transparency, and we propose a usage-based characterization of the relationship between derived nominals and their bases. Using methods from distributional semantics, we bring to bear two concrete measures of transparency which highlight different nuances: the first one, cosine, detects nominalizations which are semantically similar to their bases; the second one, distributional inclusion, detects nominalizations which are used in a subset of the contexts of the base verb. We find that only the inclusion measure helps in characterizing the difference between the two types of nominalizations, in relation with the traditionally considered variable of relative frequency (Hay, 2001). Finally, the distributional analysis allows us to frame our comparison in the broader coordinates of the inflection vs. derivation cline.

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