New York Times Research Articles

Flood Event Detection and Assessment using Sentinel-1 SAR-C Time Series and Machine Learning Classifiers Impacted on Agricultural Area, Northeastern, Thailand

Flood Event Detection and Assessment using Sentinel-1 SAR-C Time Series and Machine Learning Classifiers Impacted on Agricultural Area, Northeastern, Thailand

VTGAN: hybrid generative adversarial networks for cloud workload prediction

Efficient resource management approaches have become a fundamental challenge for distributed systems, especially dynamic environment systems such as cloud computing data centers. These approaches aim at load-balancing or minimizing power consumption. Due to the highly dynamic nature of cloud workloads, traditional time series and machine learning models fail to achieve accurate predictions. In this paper, we propose novel hybrid VTGAN models. Our proposed models not only aim at predicting future workloads but also predicting the workload trend (i.e., the upward or downward direction of the workload). Trend classification could be less complex during the decision-making process in resource management approaches. Also, we study the effect of changing the sliding window size and the number of prediction steps. In addition, we investigate the impact of enhancing the features used for training using the technical indicators, Fourier transforms, and wavelet transforms. We validate our models using a real cloud workload dataset. Our results show that VTGAN models outperform traditional deep learning and hybrid models, such as LSTM/GRU and CNN-LSTM/GRU, concerning cloud workload prediction and trend classification. Our proposed model records an upward prediction accuracy ranging from 95.4% to 96.6%.

A Discoursal Investigation on Accelerated Hostility between China Daily and the New York Times

Since COVID-19 outbreak hit China in December 2019, there has been a “narrative battle” between China and the US. Roughly 89% U.S. adults consider China a competitor or enemy, rather than a partner, according to a Pew Research Center survey in Otc. 2020. Faced with accusations internationally, there appears rising attacks and unfavourable feelings towards Asian people in the US during COVID-19, which leads to social exclusion and discrimination to Asian, especially the Chinese in the US. In the meantime, the COVID-19 pandemic has worsened a rising tide of nationalism in China, amplifying pre-existing nationalist and protectionist trends. According to a survey from Carter Center and RIWI (2021), 62% of Chinese internet-using population shows unfavorable feeling towards the US. As media plays a significant role in society in shaping public opinions, it is of significant to explore how the media reports accelerate the anti-sentiments towards each other. Although scholars have started to explore media reports during the pandemic, very less is known about how the COVID-19 pandemic is reported, discussed, and presented in the Chinese and American media from critical discouse analysis (CDA). This study will comparatively evaluate 36 editorials from China Daily (CD) and New York Times (NYT) with regards to ideological square framework. Based on data analysis, the study indicates that not only the headings, but also the media substance constructs a type of conflicting relations. Through positivizing “us” and negativizing “them”, both media accelerate the unfavorable feeling towards each other. This study suggests that both media should rationally report the pandemic, and people should work together to preserve our lives and live in peace.

The New York Times distorts the Palestinian struggle: A case study of anti-Palestinian bias in US news coverage of the First and Second Palestinian Intifadas

This article provides a methodologically novel, large-scale proof of historical bias against Palestine in a newspaper of international importance – The New York Times (NYT) – during the First and Second Palestinian Intifadas. Using state-of-the-art natural language processing toolkits as well as a regression model with over 90 percent accuracy based on a carefully validated word bank, the author analyzes over 33,000 NYT articles for (1) their use of active/passive voice, and (2) the objectivity, tone, and violent sentiment of the language used. She follows up her quantitative analysis with a qualitative validation step, analyzing biased articles in each period. In conjunction with historical context, the article shows that anti-Palestinian bias persisted disproportionately in the NYT during both periods and, in fact, worsened from the First Intifada to the Second. This work builds on a history of qualitative research on anti-Palestinian bias in the US media and attempts to provide a methodological contribution that encourages conversation between quantitative and qualitative metrics of bias.

The Climate of Utopia: Victorian Hothouses and H. G. Wells

Abstract: In “The Flowering of the Strange Orchid” (1894), The Time Machine (1895), and A Modern Utopia (1905), H. G. Wells criticizes the idea inherited from Thomas More’s Utopia (1516) that ideal climate is static. In “The Flowering of the Strange Orchid” the constant climate of Winter Wedderburn’s steaming greenhouse nearly spells death, while the globally engineered climate in The Time Machine entails the germination of hot-house grotesques. As Wells clarifies in A Modern Utopia , in the modern age when utopia becomes temporal, climate can no longer be imagined as constant. Engaging critically with Victorian hothouse culture, his work sheds light on the fallacy of imagining utopia as a place of eternal sunshine—a fallacy currently reiterated in the sociotechnical imaginary of solar geoengineering.

Menachem Kaiser. Plunder: A Memoir of Family Property and Nazi Treasure

Menachem Kaiser. <i>Plunder: A Memoir of Family Property and Nazi Treasure</i>

Research on Stock prediction based on time series and machine learning algorithm

In the trading market, since the birth of Bitcoin, people have been disputing gold and bitcoin. How to seek an appropriate and optimal investment portfolio has become a problem that people pay close attention to. In this case, we need to build a model and give an optimal trading strategy to maximize the profit of their portfolio. We used time series analysis to predict the price and AHP to quantify the investment risk, so as to give the best investment strategy and prove its rationality. Firstly, in order to get the optimal investment strategy, we had to predict future price movements of investment products, determined whether to sell, held or bought investment products, we adopted the method of time series analysis, with the aid of XGBoost machine learning algorithms, using some existing data as a training set, using the day before the data as a test set, to forecast movements in the price of the second day of investment products, to use the ideas of hedging. Through the analysis of gold, Bitcoin investment risk, we came up with the optimal ratio between cash, Bitcoin, and gold. Through the study of the classification of all kinds of possible situations, with the help of the has to predict prices, the best investment strategy was given. With the help of the optimal strategy given in this paper, the initial investment of $1000 was put into the debugged model, and the final total investment reached about $250,000.Secondly, in order to justify our model, in this paper established on the basis of the optimal strategy and design of three other different schemes, analyzed four different schemes, we used the analytic hierarchy process (AHP) 1-9 scale of four different schemes of risk quantitative analysis, it was concluded that the risk of four plans relative size, The final returns of the three other schemes were calculated by using the dynamic programming model, and the rationality of the proposed strategy was proved by the combination of risk and return. Thirdly, in order to determine the sensitivity of the trading strategy of transaction cost, by changing the transaction cost rate of gold and Bitcoin, we observed the change of the total asset amount, and concluded that there is a non-linear relationship between the transaction cost rate of gold or Bitcoin and the total asset amount, and the transaction cost of gold or Bitcoin affects the total asset amount by affecting the frequency of total asset allocation conversion. Finally, we conducted sensitivity analysis and model improvement, and analyzed the triality of the strategy presented in this paper in real life, and wrote a two-page memo to convey our strategy, model and results to traders.

Interactional metadiscourse in news commentaries: A corpus-based study of China Daily and The New YorkTimes

Drawing on Hyland's model of interactional metadiscourse (2005; 2005a), this paper investigates the distribution of interactional metadiscourse in news commentaries of China Daily (CD) and The New York Times (NYT), two leading newspapers in China and the United States. The corpus retrieved includes 60 news commentaries on the topic of COVID-19 covering six months from January to June, 2022 when the resurgence of COVID-19 in China has aroused public debates and attracted world-wide media attention.This study reveals that both stance and engagement, are frequently employed in CD and NYT. Nevertheless, the occurrences of interactional features in NYT are far more frequent than those in CD, which reflects a higher level of explicit interaction of the former. Results of chi-square test of the sub-categorical interactional metadiscourse mark significant differences in the distribution of hedges, self-mention and engagement markers between the two corpora. NYT employs greater number of all the three subcategories, indicating NYT writers’ attempts to both express a clear personal stance to their views and to closely align with readers, whereas CD shows a strong tendency of conveying their evaluations and commitment towards propositions by employing stance nearly three times as many as engagement.

Where’s the yolk – did you just call that mayonnaise?

Research methodology The data for this case was collected from legal and business research databases (Lexis, ABI/INFORM)) and from business press sources (for example, Forbes, the NY Times and the Wall Street Journal). Emails between the Egg Board, the Food and Drug Administration and key players at Unilever are referenced throughout the case and were provided by the United States Department of Agriculture’s Agricultural Marketing Service Compliance Branch and obtained pursuant to the Freedom of Information Act. Federal regulations and codes, as applicable, are also referenced (The US Code, the Code of Federal Regulations). Case overview/synopsis This short case presents the problems of Just Mayo, a start-up company, in maintaining and growing market share in an industry dominated by a well-established, multinational firm. In 2011 Hampton Creek (renamed Just, Inc in 2018) began operations as a manufacturer of plant-based food products. One of its earliest products was Just Mayo, a sandwich spread with all the attributes of traditional mayonnaise except without eggs or other dairy products. Shortly after Just Mayo was introduced, Unilever – a multinational conglomerate and food giant, sued Hampton Creek, claiming that use of the name “Just Mayo” amounted to false advertising and unfair competition. Complexity academic level This case is a learning tool for management, business law and ethics students at the undergraduate level. It was used in 2019 in a business law class at the sophomore and junior undergraduate level, where the focus was primarily on ethical considerations for all parties, understanding the role of regulatory agencies, and the legality of the strategies used. However, this case is equally applicable for a management or strategic management course with a focus on analyzing the tactics used for maintaining competitive advantage. A stakeholder analysis for various parties in either of these courses would also be suitable. Instructors addressing some of these topics together should find it particularly useful.

Delineation and optimization of cotton farmland management zone based on time series of soil-crop properties at landscape scale in south Xinjiang, China

Traditional farmland management approaches treat farmland as a homogeneous unit, leading to uneven distribution and waste of resources, which is contrary to precision agriculture. Management zones (MZ) delineation is an essential method of site-specific management (SSM) for accomplishing variable rate input of resources in subregions by recognizing the heterogeneity of farmland soils. Aimed at the soil salinization problem in Xinjiang, this study estimates the salt content of soil profiles and essential nutrient content using time series Sentinel-2 images and machine learning methods, and describes crop growth conditions through dynamic time warping. Subsequently, geographically weighted principal component analysis and possibilistic fuzzy C-means algorithm were employed to outline MZ based on soil-crop characteristics. Three scales (10 m, 100 m, and field scales divided by ridges) were examined to obtain the optimal scale of delineation. Soil-crop properties were estimated at a spatial resolution of 10 m using machine learning methods with R2 > 0.65. In the zoning process, the field scale was selected with accuracy and practicality in agricultural practices, with comparatively higher homogeneity within zones and variation between zones. The soils in the study area were categorized into three zones at field scale with a fuzzy performance index of 0.68, a normalized classification entropy of 0.71, and fuzzy class of 0.30. Soil function is primarily restricted by salinity and fertility. Fertilizer recommendation strategies were given for the three zones. This study uses time-series remote sensing images to obtain a high-precision estimation of soil-crop characteristics at 10 m, and optimize the scale of farmland management zoning, which provides a valuable technology and scale selection for SSM for cotton management zone delineation in saline farmland in arid and semi-arid regions.

The Pandemic in Our Country, the Pandemic in Their Countries: News Values and Media Representation of the COVID-19 Pandemic

ABSTRACT The article investigates how the COVID-19 pandemic around the world is represented by the New York Times (NYT) and China Daily (CD), newspapers from two countries with long-standing political tension. The data consists of 2572 reports that are classified into three groups: reports on COVID-19 in the US, reports on COVID-19 in China, and reports on COVID-19 in other countries/regions. The reports are analyzed in terms of their discursive news values. Analysis results show that NYT represents the pandemic in the US, and CD represents the pandemic in China as the least negative, the least impactful and with the most personal accounts. NYT represents the pandemic in China, and CD represents the pandemic in the US as the most negative, the most impactful, the most severe, and the least positive and proximate. The selection and representation of other countries/regions reflect a differentiated coverage of the “Others” by both NYT and CD. The study lends support to a previous hypothesis that domestic crises tend to be presented as less negative than those in other countries, and invites contemplation on the influence of nationalism and political antagonism on the news media in times of global health crises.

MS Times Machine: The Story of Multiple Sclerosis in the 19th and 20th Century Through the Eyes of the Times Reader (S18.004)

<h3>Objective:</h3> To describe the evolution in knowledge of multiple sclerosis (MS) through the eyes of the American public chronicled by the leading newspaper of our generation, New York Times (NYT). <h3>Background:</h3> Though Charcot through a series of lectures described MS in 1868, little advancement was made on our knowledge of MS for almost half a century. Often described as a confusing and baffling disease by scientists at the time, controversial and conflicting theories fed into the “mysteries of multiple sclerosis” public perception. <h3>Design/Methods:</h3> Review of literature from the NYT archive from 1851–2000. <h3>Results:</h3> Literature review starts with articles highlighting work of Dr William Hammond, former US Surgeon General, confirming the findings of Charcot in his book “Treatise on Disease of Nervous System” (1871). Coverage explodes after an advertisement placed by Sylvia Lawry on behalf of her brother with multiple sclerosis in NYT (1945) led to the establishment of the National MS Society (1946), bringing the disease into the national spotlight. Articles highlighted potential use of various controversial therapies including vitamins, dicoumarin and amyl nitrate (1940–50’s). Reports on links with toxic chemicals like carbon monoxide (1940) and cyanide (1952) gave way to “infectious” theory with possible culprits being spirochetes, herpes, measles, EBV, canine distemper and finally “allergic-autoimmune” theory (1950–80’s). Epidemiology articles speculated on the relapsing remitting nature of the disease, higher prevalence in the northern latitudes and puzzling clusters. Early 1980’s reported on possible successful trials with prednisone, cyclophosphamide and ACTH, advent of MRI (1982) and even a failed vaccine. Late 80’s and early 90’s reported the advent of present day DMTs culminating in the approval of the first DMT “Betaseron” (1993) and bizarreness of the drug lottery. Chronological timeline will be provided. <h3>Conclusions:</h3> This review aims to highlight the public perception of MS and continued misconceptions despite significant strides in the understanding of the disease. <b>Disclosure:</b> Dr. Arcot Jayagopal has nothing to disclose.

Feasibility of urban bird occurrence and nest amount evaluation by the street-view image virtual survey.

Bird observation mainly relies on field surveys, which are time-consuming and laborious. In this study, we explored using street-view images in the virtual survey of urban birds and nests. Using the coastal city of Qingdao as the study area, 47 201 seamless spherical photos at 2741 sites were collected using the Baidu street-view (BSV) map. Single-rater-all photo checks and seven-rater-metapopulation checks were used to find inter-rater repeatability, the best viewing layer for BSV collection, and possible environments affecting the results. We also collected community science data for comparison. The BSV time machine was used to assess the temporal dynamics. Kappa square test, generalized linear model, redundancy ordination and ArcMap were used in the analysis. Different rater repeatability was 79.1% in nest evaluations and 46.9% in bird occurrence. A re-check of the different-rating photos can increase them to 92% and 70%. Seven-rater statistics showed that more than 5% sampling ratio could produce a non-significant different bird and nest percentage of the whole data, and the higher sampling ratio could reduce the variation. The middle-viewing layer survey alone could produce 93% precision of the nest checks by saving 2/3 of the time used; in birds, selecting middle and upper-view photos could find 97% of bird occurrences. In the spatial distribution, the nest's hotspot areas from this method were much greater than the community science bird-watching sites. The BSV time machine made it possible to re-check nests in the same sites but challenging the re-check of bird occurrences. The nests and birds can be observed more in the leafless season, on wide, traffic-dense coastal streets with complex vertical structures of trees, and in the gaps of tall buildings dominated by road forests. Our results indicate that BSV photos could be used to virtually evaluate bird occurrence and nests from their numbers, spatial distribution and temporal dynamics. This method provides a pre-experimental and informative supplement to large-scale bird occurrence and nest abundance surveys in urban environments.

Dynamic production scheduling towards self-organizing mass personalization: A multi-agent dueling deep reinforcement learning approach

Mass personalization is rapidly approaching. In response, manufacturing systems should be capable of autonomously changing production plans, configurations and schedules under dynamic manufacturing environments for producing personalized products. Self-organizing manufacturing network is a promising paradigm for mass personalization. The backbone of a self-organizing manufacturing network is an adaptive production scheduling method to dynamically allocate and sequence manufacturing jobs under dynamic settings, such as stochastic processing time or unplanned machine breakdown. However, existing production scheduling methods (i.e., heuristic rules, meta-heuristic algorithms, and existing reinforcement learning models) fail to automatically optimize production schedules while maintaining stable manufacturing performance, under dynamic settings. In this paper, we designed a reinforcement learning-based static-training-dynamic-execution approach for dynamic job shop scheduling problems. The scheduling policies are learned from static scheduling instances by a multi-agent dueling deep reinforcement learning approach. Under this approach, we proposed new representations of observation, action, reward, and cooperation mechanisms between agents. The learned scheduling policies are then deployed to a dynamic scheduling system where stochastic processing time and unplanned machine breakdown randomly occur. Extensive simulation experiments demonstrated that our approach outperforms heuristic rules on makespan under two dynamic manufacturing settings.

SWJPCCS Associate Editor has Essay on Reining in Air Pollution Published in NY Times

No abstract available. Article truncated after 150 words. Dona Upson MD, an associate editor of the SWJPCCS from Albuquerque, recently had a Guest Essay published in the NY Times (1). In the opinion piece titled “The Government Is This Close to Reining in Some of the Worst Air Pollution” Dr. Upson states that the science is clear that when oil and gas are extracted and transported from wells, methane and other pollutants, including volatile organic compounds, leak out. These form ozone and may exacerbate asthma and COPD, cause cancer and birth defects, and affect the nervous system. Emissions from oil and gas production also produce nitrogen oxides, which can exacerbate lung disease. Methane is also a powerful greenhouse gas, over 80 times more potent than carbon dioxide in its warming power, though its life span in the atmosphere is much shorter. One of the fastest and cheapest ways to reduce these emissions and improve health is to prevent …

Asset-Based Futures: A Sociology for the 21st Century

This article engages with the question of whether the COVID-19 pandemic can be understood as an event that is moving us towards a new era. Highlighting the paradox that this question has emerged in the context of the stoppages and shutdowns associated with the pandemic, we suggest that any assessment of social change and the futures such change might unfold must be situated on the terrain formatted by the temporal logics of capitalism. At our current juncture this requires that sociology as a discipline understand these logics as asset rather than commodity based. Drawing on state responses to the pandemic in Australia, we show how mundane payments play a critical role in these logics, operating as differentiating technologies of time. We suggest that for sociology to build a sociology of futures that is relevant for the 21st century, it must come to terms with the time machine of the asset economy.

On the Inaccessibility of Time Machines

We will explain why time machines, although allowed in General Relativity, cannot be accessed by observers once we include quantum effects. Moreover, we will show that traversable wormholes cannot be turned into time machines without invoking the effects of quantum gravity.

On the &lt;i&gt;New Dark Age&lt;/i&gt; by James Bridle

This year AST published The New Dark Age the first ever translation into Russian of James Bridle, contemporary British writer, artist, and computer technology expert. Sprinkling a fascinating narrative with interesting and often frightening facts from various fields and references to philosophy, the author created a convincing and sobering picture of the world in which we live, not even suspecting its ever-growing complexity and inhumanity. Despite the modern trend on cultural literacy, Bridle focuses on our blindness and arrogance regarding pervasive technological systems. Bridle encourages his readers to engage in critical thinking, see the reality for what it is and embrace it in its overwhelming complexity and fragility. Questioning the linearity of progress, he posits that we might have already passed our peak knowledge. It means that ongoing calculations and the resulting additional data only make the world a less understandable place to live in. Instead of an elegant and working picture of the world, we are surrounded by darkness, pervasive chaos under the guise of calculated order. Bridle’s zeal to trace back the hard basis of cyber reality takes him on a journey from the electric cables in the depths of the ocean to the contrails-lined sky. Electricity, unpredictable weather and climate change, the first computers and secret inventions, the works of great mathematicians and stock market crashes, satellite maps, world aviation, Concordes and short-term turbulence, unsuccessful GPS journeys, computer games, social networks and entertainment videos on the Internet, scientific research errors caused by a variety of factors from measuring instrument deficiency to dubious publishing ethics in the modern scientific community, permafrost, history and time machine, biodiversity and plague... Technology is a black box and it clouds our vision. With more data available (and increasingly hard to digest) we are less apt to good decision-making and are likely to be taking a leap of faith in the dark of the black box of omnipresent technologies. Are we standing at the edge of an abyss? In this fascinating read, Bridle invites us to admit and possibly bridge the gap between our understanding of reality and the reality.

Using machine learning prediction models for quality control: a case study from the automotive industry

This paper studies a prediction problem using time series data and machine learning algorithms. The case study is related to the quality control of bumper beams in the automotive industry. These parts are milled during the production process, and the locations of the milled holes are subject to strict tolerance limits. Machine learning models are used to predict the location of milled holes in the next beam. By doing so, tolerance violations are detected at an early stage, and the production flow can be improved. A standard neural network, a long short term memory network (LSTM), and random forest algorithms are implemented and trained with historical data, including a time series of previous product measurements. Experiments indicate that all models have similar predictive capabilities with a slight dominance for the LSTM and random forest. The results show that some holes can be predicted with good quality, and the predictions can be used to improve the quality control process. However, other holes show poor results and support the claim that real data problems are challenged by inappropriate information or a lack of relevant information.

Understanding How News Content Utilizes Experiential Media (AR, VR, and 360° Video) in COVID-19 Storytelling

A growing body of research indicates how experiential media (EM) technologies such as virtual reality (VR), augmented reality (AR), and 360° video are diffusing widely around the world, thereby transforming media practices and the user experience. The current study looks at immersive news content production (EM stories) that seek to inform the public on how the COVID-19 virus spreads, the precautions required to protect from getting infected by the virus, and a general understanding of COVID-19 as a pandemic. Based on a list of six criteria, a total of six EM stories (two AR stories by the New York Times (NYT), two 360° video stories by National Geographic (Nat Geo), and two VR stories by Meta’s Oculus App in collaboration with other media organizations) were identified for the study. This exploratory study uses qualitative research methodology and the Experiential Media theoretical framework to understand to 1) what extent such immersive news content production utilizes the six EM qualities in COVID-19 storytelling? 2) what themes do such EM stories and the accompanying static news story reported online cover? 3) what new knowledge, if any, do such EM stories provide compared to the accompanying static news story online? The findings from the study offer a theoretical understanding of the role EM technologies play in highlighting global health crisis such as COVID-19, particularly through immersive 3D visualizations, and provides practical implications for EM content producers. Keywords: experiential media, augmented reality, virtual reality, 360° video, COVID-19, news