Muhammed Adil Yatkin, Mihkel Korgesaar, Jani Romanoff
et al.
Current neural network (NN) models can learn patterns from data points with historical dependence. Specifically, in natural language processing (NLP), sequential learning has transitioned from recurrence-based architectures to transformer-based architectures. However, it is unknown which NN architectures will perform the best on datasets containing deformation history due to mechanical loading. Thus, this study ascertains the appropriateness of 1D-convolutional, recurrent, and transformer-based architectures for predicting deformation localization based on the earlier states in the form of deformation history. Following this investigation, the crucial incompatibility issues between the mathematical computation of the prediction process in the best-performing NN architectures and the actual values derived from the natural physical properties of the deformation paths are examined in detail.
Taxi fare is related to the daily life of residents. Reasonable taxi fare not only meets the travel demand of residents but also improves the income of drivers and promotes employment. The spatial variation of taxi fares exists between different regions and cities. Previous studies on taxi fares have been conducted mostly in individual cities, and there has been no study on the spatial differentiation pattern of taxi fares on a national scale. Taking 336 cities across China as the research object, a multiple linear regression model of taxi fares was established by demonstrating the spatial variation pattern of taxi fares, the global differentiation index, spatial autocorrelation analysis, and kernel density analysis, etc. The significance of the study is to explore the law of spatial differentiation of taxi fares in China and to provide a stenographic record of taxi fare adjustment. The results show that: (1) the spatial variation of taxi fares across the country is significant, with the starting taxi fare range being between RMB 4 and RMB 14. (2) The global differentiation index of taxi fares is large, with two low-low clusters and three high-high clusters appearing spatially, and the results of the kernel density analysis surface a dispersion distribution centered on provincial capitals. (3) The divergence pattern of taxis nationwide is influenced by several factors. A multiple linear regression model is selected to establish a multiple linear regression model of urban disposable income per capita, regional GDP, urbanization rate, and urban population density, which shows that urban disposable income per capita has the greatest influence on taxi fares. The model shows that urban disposable income per capita has the greatest influence on taxi fares, and the fare of a 5 km taxi ride is 6.07. Taxi fares have a clear pattern of spatial differentiation in China and are most affected by urban disposable income per capita. Through this study, we can gain a deeper understanding of the variation in taxi fares across the country and provide data and theoretical support for the rationality of taxi fare adjustments.
History of scholarship and learning. The humanities, Social Sciences
This study examines the innovation process through the interrelationships of entrepreneurial orientation (EO) dimensions in an emerging economy. Using a five-point Likert scale, we adopt a survey approach to measure individuals’ EO dimensions and explore the relationship between proactiveness, risk-taking, motivation, and innovation outcomes. We test our hypotheses using a sample of 466 individuals in Kuwait. Survey data are analyzed using principal component analysis (PCA) and a partial least squares approach. The results indicate that proactiveness and motivation positively relate to risk-taking. In addition, we find that our mediating variable, risk-taking, directly impacts innovation. The is an indirect effect of motivation and proactiveness on innovation, through risk-taking. These findings shed light on the interrelationships among different dimensions within the EO framework, pathways towards innovation, and provide meaningful insights for promoting innovation outcomes.
History of scholarship and learning. The humanities, Social sciences (General)
Peer assessment has been widely studied as a replacement for traditional evaluation, not only by reducing the professors' workload but mainly by benefiting students' engagement and learning. Although several works successfully validate its accuracy and fairness, more research must be done on how students' pre-existing social relationships affect the grades they give their peers in an e-learning course. We developed a Moodle plugin to provide the platform with peer assessment capabilities in forums and used it on an MSc course. The plugin curated the reviewer set for a post based on the author's relationships and included rubrics to counter the possible interpersonal effects of peer assessment. Results confirm that peer assessment is reliable and accurate for works with at least three peer assessments, although students' grades are slightly higher. The impact of social relationships is noticeable when students who do not like another peer grade their work consistently lower than students who have a positive connection. However, this has little influence on the final aggregate peer grade. Our findings show that peer assessment can replace traditional evaluation in an e-learning environment where students are familiar with each other.
We focus on a relatively unexplored learning paradigm known as {\em Online Unsupervised Continual Learning} (O-UCL), where an agent receives a non-stationary, unlabeled data stream and progressively learns to identify an increasing number of classes. This paradigm is designed to model real-world applications where encountering novelty is the norm, such as exploring a terrain with several unknown and time-varying entities. Unlike prior work in unsupervised, continual, or online learning, O-UCL combines all three areas into a single challenging and realistic learning paradigm. In this setting, agents are frequently evaluated and must aim to maintain the best possible representation at any point of the data stream, rather than at the end of pre-specified offline tasks. The proposed approach, called \textbf{P}atch-based \textbf{C}ontrastive learning and \textbf{M}emory \textbf{C}onsolidation (PCMC), builds a compositional understanding of data by identifying and clustering patch-level features. Embeddings for these patch-level features are extracted with an encoder trained via patch-based contrastive learning. PCMC incorporates new data into its distribution while avoiding catastrophic forgetting, and it consolidates memory examples during ``sleep" periods. We evaluate PCMC's performance on streams created from the ImageNet and Places365 datasets. Additionally, we explore various versions of the PCMC algorithm and compare its performance against several existing methods and simple baselines.
Ming-Min Cheng, Chia-Wei Tang, Li-Chun Wang
et al.
Abstract This study developed and validated the indicators of school internationalization in elementary and secondary schools in Taiwan to identify their level of internationalization and assess how the same has been implemented in these institutions. The data for the study was obtained from focus group interviews with 14 school internationalization experts and 176 school principals. Subsequently, 145 sample schools were recruited to complete an online questionnaire as part of a formal survey. The 20-item indicator scale regarding internationalization goals, campus internationalization, personnel internationalization, administration internationalization, curricula internationalization, and international partnerships proved suitable to orient elementary and secondary schools in Taiwan toward establishing international environments. Furthermore, results have important implications that the government should provide additional financial support and educational resources to reduce the teacher turnover rate and increase the willingness of schools in Taiwan to participate in international affairs.
History of scholarship and learning. The humanities, Social Sciences
Neoliberal reason is often defended for its supposed radical individualism. While critics of neoliberalism are right to problematise the atomising effects of this sort of individualism, an immanent critique of neoliberalism helps us to see that this atomisation does not necessarily lead to the development of individuality. That is, I will suggest that neoliberal individualism does not make room for individuality at all. I will focus on the neoliberal tendency to constrain human activity to the ends of the firm and argue that because of this, neoliberal reason cannot create the conditions for subjects to act on their potentiality. Rather than allowing for individuality, I will suggest that the neoliberal proliferation of the logic of the firm has an effect that is instead massifying. Horkheimer and Adorno’s critique of capitalism in their chapter on the culture industry helps us to understand and critique capitalist massification by offering the notion of pseudoindividuation. I will argue that the massification of people occurs when they are subjected to some end outside themselves, such as participation in the maximisation of capital. When we turn to a discussion on neoliberal reason, drawing on Foucault and Brown, we are interested in how a kind of capitalist logic comes to dominate every aspect of our lives. While the concepts seem to be at odds with one another, I will read Foucault and Brown to suggest that neoliberalism is both atomising and massifying. That is, the neoliberal goal of maximising capital puts subjects in competition with one another while simultaneously subjecting them all to the same end outside of themselves. Yet, capital maximisation is not a substantive end in itself, since capital is, by definition, only a means to some other end. Neoliberal reason thus leaves subjects in a state of discontent that is brought about by the constant striving toward a goal which can never be met – more capital can always be had. Capital, by definition, cannot be understood as an end, but can only be understood as a means. That is, the neoliberal subject is not only socially alienated (atomised) but is also constrained in the potentiality for participating its own ends, and thus has no real opportunities for individuality.
History of scholarship and learning. The humanities, Political science
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot's ability to learn and adapt at runtime, leading to overly conservative behavior. This paper proposes a new closed-loop paradigm for synthesizing safe control policies that explicitly account for the robot's evolving uncertainty and its ability to quickly respond to future scenarios as they arise, by jointly considering the physical dynamics and the robot's learning algorithm. We leverage adversarial reinforcement learning for tractable safety analysis under high-dimensional learning dynamics and demonstrate our framework's ability to work with both Bayesian belief propagation and implicit learning through large pre-trained neural trajectory predictors.
Xanthippi CHAPSA, Persefoni POLYCHRONIDOU, Athanasios L. ATHANASENAS
The COVID-19 pandemic caused a continuous health crisis from March 2020 until today. The health crisis due to the imposed restrictions caused socio-economic crisis and disorders in almost all over the world. Greece is not an exception in these new conditions that the health crisis has imposed. Through a structured questionnaire, distributed to young people, we investigate how the health crisis affected Greek people. Specifically, we study the respondents’ opinions regarding the anti-dispersion prevention measures, their feeling of security and who is responsible for the pandemic. By means of descriptive statistics and more advanced statistical techniques, we aim to verify or disprove the view of Naomi Klein that a society in deep crisis is in a ‘state of shock’, unable to react.
History of scholarship and learning. The humanities, Social sciences (General)
There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of medical code prediction using curriculum learning, which is a training strategy for machine learning models that gradually increases the hardness of the learning tasks from easy to difficult. One of the challenges in curriculum learning is the design of curricula -- i.e., in the sequential design of tasks that gradually increase in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an algorithm that uses graph structure in the space of outputs to design curricula for multi-label classification. We create curricula for multi-label classification models that predict ICD diagnosis and procedure codes from natural language descriptions of patients. By leveraging the hierarchy of ICD codes, which groups diagnosis codes based on various organ systems in the human body, we find that our proposed curricula improve the generalization of neural network-based predictive models across recurrent, convolutional, and transformer-based architectures. Our code is available at https://github.com/wren93/HiCu-ICD.
Hierarchical methods in reinforcement learning have the potential to reduce the amount of decisions that the agent needs to perform when learning new tasks. However, finding reusable useful temporal abstractions that facilitate fast learning remains a challenging problem. Recently, several deep learning approaches were proposed to learn such temporal abstractions in the form of options in an end-to-end manner. In this work, we point out several shortcomings of these methods and discuss their potential negative consequences. Subsequently, we formulate the desiderata for reusable options and use these to frame the problem of learning options as a gradient-based meta-learning problem. This allows us to formulate an objective that explicitly incentivizes options which allow a higher-level decision maker to adjust in few steps to different tasks. Experimentally, we show that our method is able to learn transferable components which accelerate learning and performs better than existing prior methods developed for this setting. Additionally, we perform ablations to quantify the impact of using gradient-based meta-learning as well as other proposed changes.
AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making. Moreover, many settings are dynamic, with populations responding to sequential decision policies. We introduce the study of reinforcement learning (RL) with stepwise fairness constraints, requiring group fairness at each time step. Our focus is on tabular episodic RL, and we provide learning algorithms with strong theoretical guarantees in regard to policy optimality and fairness violation. Our framework provides useful tools to study the impact of fairness constraints in sequential settings and brings up new challenges in RL.
Nowadays the legal opportunity for the public to receive information on the activities of public authorities is of great importance. Appropriate legislation exists in all democratic countries of the world. The initial provisions on the human and citizen's right to access information on the activities of public authorities are contained in the constitutions of the countries of the world, or acts of constitutional legislation. In the United States of America, as in the state with the oldest written constitutions, there are unique features of the legal regulation of citizens' access to information on the activities of the public administration. Despite the basic legal norm of the constitution on freedom of speech, it is necessary to consider the rights to get access to information in the US in close connection with the right to petition government authorities. This is confirmed by the analysis of the current constitutional legislation, the norms of which are aimed at establishing and guaranteeing, as well as creating the mechanism for exercising the right to access information on the activities of public authorities. The US population has many opportunities to demand and receive information from authorities. At the same time, as in other states, the US has a legally established a list of grounds on which applicants may be denied information.
Law, History of scholarship and learning. The humanities
Detective fiction is known as a genre that is concerned with revealing truths, both in the fictional world of the text as well as in the society after which it is patterned. The current socio-political environment, however, has been described as an era of post-truth politics and political propaganda, in which truth is more often determined by the relative strength of its representation. While some contemporary crime novels continue to propagate a reassuring message of truth, select Austrian narratives reflect this new so-called post-truth world. Bringing together theories of detective fiction and post-truth discourse, this article demonstrates how Eva Rossmann’s 2017 crime novel Patrioten (Patriots) adapts the themes and structures of traditional detective narratives to expose a society in which certainty is determined less by objective facts than by their construction in the media and socio-political discourse. The analysis concludes that the novel’s thematic and formal innovations help to redefine the socio-critical potential of contemporary detective fiction by showing the imminent dangers of an unregulated post-truth society.
Abstract In a recent article, the New Oxford Shakespeare scholar Hugh Craig used the t-test in statistics to compare the uses of one hundred function words across plays, authors, genres, characters, and periods in Shakespeare’s era. Craig drew some strong conclusions; for example, that authorship is a greater differentiator between plays than genre. Using both theoretical arguments and the results of further experiments, this note challenges Craig’s analysis, showing that he applied the t-test even when it is not clear that its premises are satisfied, failed to apply necessary controls, and, moreover, that his reasoning contains a fundamental error which invalidates his interpretation of the results.
This paper aims to trace, through historical and medical sources, the syphilis epidemic that broke out in Naples in the late 15th century. When the disease overspread, the chronicles attributed the plague to the conquest of Naples by the king of France Charles VIII. Because of this, the disease was named morbus gallicus by the Italians and mal napolitain by the French. To dispel the fear, scape-goats were found in prostitutes, copious in Naples, and in the Jews, who had taken refuge in the city after their expulsion from Spain. In conclusion, the success of the "Columbian theory" gave the ideal solution to absolve Europeans from any responsibility. Nevertheless, in the 16th century some physicians, like Falloppio, Fioravanti and Cesalpino, made singular conjectures about the Neapolitan origin of siphylis.
History of scholarship and learning. The humanities, Philosophy (General)
Claire G. Coleman’s science fiction novel <i>The Old Lie</i> (2019) evokes the blemished chapters of Australia’s history as the basis of a dystopian futuristic Earth. By using the metaphor of a secular apocalypse (Weaver) wrapped in the form of a space opera, she interrogates historical colonialism on a much larger scale to bring to the fore the distinctive Indigenous experience of Australia’s <i>terra nullius</i> and its horrific offshoots: the Stolen Generations, nuclear tests on Aboriginal land and the treatment of Indigenous war veteran, but this time experienced by the people of the futuristic Earth. Following a brief introduction of the concept of the “Native Apocalypse” (Dillon) in the framework of Indigenous futurism, the paper discusses Coleman’s innovative use of space opera embedded in Wilfred Owen’s famous WWI poem “Dulce et Decorum Est”. The analysis focuses on four allegedly separate stories in the novel which eventually interweave into a single narrative about “the old lie”. In keeping with the twenty-first-century Indigenous futurism, Coleman’s novel does not provide easy answers. Instead, the end brings the reader to the beginning of the novel in the same state of disillusionment as Owen’s lyrical subject.
History of scholarship and learning. The humanities
Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak
et al.
Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.