12,464 research outputs found

    Self-Consistent Learning: Cooperation between Generators and Discriminators

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    Using generated data to improve the performance of downstream discriminative models has recently gained popularity due to the great development of pre-trained language models. In most previous studies, generative models and discriminative models are trained separately and thus could not adapt to any changes in each other. As a result, the generated samples can easily deviate from the real data distribution, while the improvement of the discriminative model quickly reaches saturation. Generative adversarial networks (GANs) train generative models via an adversarial process with discriminative models to achieve joint training. However, the training of standard GANs is notoriously unstable and often falls short of convergence. In this paper, to address these issues, we propose a self-consistent learning\textit{self-consistent learning} framework, in which a discriminator and a generator are cooperatively trained in a closed-loop form. The discriminator and the generator enhance each other during multiple rounds of alternating training until a scoring consensus is reached. This framework proves to be easy to train and free from instabilities such as mode collapse and non-convergence. Extensive experiments on sentence semantic matching demonstrate the effectiveness of the proposed framework: the discriminator achieves 10+ AP of improvement on the zero-shot setting and new state-of-the-art performance on the full-data setting

    Strategies for Early Learners

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    Welcome to learning about how to effectively plan curriculum for young children. This textbook will address: • Developing curriculum through the planning cycle • Theories that inform what we know about how children learn and the best ways for teachers to support learning • The three components of developmentally appropriate practice • Importance and value of play and intentional teaching • Different models of curriculum • Process of lesson planning (documenting planned experiences for children) • Physical, temporal, and social environments that set the stage for children’s learning • Appropriate guidance techniques to support children’s behaviors as the self-regulation abilities mature. • Planning for preschool-aged children in specific domains including o Physical development o Language and literacy o Math o Science o Creative (the visual and performing arts) o Diversity (social science and history) o Health and safety • Making children’s learning visible through documentation and assessmenthttps://scholar.utc.edu/open-textbooks/1001/thumbnail.jp

    TEACHING STRATEGIES AND THE PROBLEM FACED BY EFL TEACHER DURING COVID-19 OUTBREAK AT JUNIOR HIGH SCHOOL

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    The education system have to switch from face-to-face to online teaching due to the pandemic. This situation is considered new in Indonesia, the teachers have to adapt their self with this situation. An example is learning to use technology in online teaching and making a lesson plan that can make students interested in online learning.This research aimed to know what are teaching strategies used by EFL teachers and what are the teacher problems in online teaching at the Junior High School 98 during Pandemic. This research used qualitative as a design and narrative descriptive as the approach. The technique to collect the data researcher used in this research is observation, interview, and documentation. In addition, the object of this research is EFL teachers, the researcher interviewed 5 EFL teachers. The results of this research are: 1)The teacher strategies used in online teaching during a pandemic is synchronous, while teacher used platform WhatsApp, Google Classroom, and Google Meet for online classes. In addition, to create the task the teacher gives chance to the students to useanother platform such as Canva, Youtube, Video Maker, etc. On the other hand, the teacher have some strategies to overcome the problems when teaching online, such as when the students have a problem in the following class online through the platform Google Meet, the teacher shared the material in Google Classroom. While, the researcher found in students motivation the teacher do teamwork with students’ parents in control the students at home; 2) the teaching online problems that researcher found in this research are: lack of quota package, lack of internet access, lack of motivation, and lack of facilities

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    Translating erasure: Proposing auto-theory as a practice for artistic enquiry and analysis while comprehending personal grief

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    Erasure as an artistic technique has developed in my moving image work after my father's passing. I export videos into sequences of thousands of images and erase outlines of the targeted objects in each frame. The repetitive and low conscious labour is a way to ease the agony and to grieve my father. Hours compressed into thousands of frames, turning into a glimpse of illusion and leaving a ghostly emptiness on the images. Both its visual presentation and making reflect the life events and encounters I've experienced in the UK and Taiwan in the past years. I consider an artwork embodies interconnected relationships between one's personal impulses and artistic training. As an art student, I have found it challenging to describe such a creative process with conventional academic writing. Within a construct that inclines to present thoughts as reasonable and rational arguments, my personal experiences and the intensity of feeling seem out of place. Within an academic framework, how can I make an argument out of how I have developed the erasure in my artwork to perform the grief, fading memories of a loved one, existential crisis and what's in-between? Through auto-theoretical approaches to writing and making of moving image work, this research aims to build a structure that can express both the intimate and intellectual aspects of an art practice. This writing up process interweaves my personal stories that motivate my artistic expression into art theories. The memories about my late father, my relationship with languages, and my lives between the UK and Taiwan meet with different artists' uses of erasure. As the conversations between the introspections and theoretical analysis accumulate, my writing and moving image work unravel an art journey that encompasses the nuances and struggles I've experienced as an international student. Within the search for an ideal model to illustrate an art practice, this research further generates profound understandings of memory, grief, loss, language, conflicted identities and cultural belonging

    A Case Study Examining Japanese University Students' Digital Literacy and Perceptions of Digital Tools for Academic English learning

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    Current Japanese youth are constantly connected to the Internet and using digital devices, but predominantly for social media and entertainment. According to literature on the Japanese digital native, tertiary students do not—and cannot—use technology with any reasonable fluency, but the likely reasons are rarely addressed. To fill the gap in the literature, this study, by employing a case study methodology, explores students’ experience with technology for English learning through the introduction of digital tools. First-year Japanese university students in an Academic English Program (AEP) were introduced to a variety of easily available digital tools. The instruction was administered online, and each tool was accompanied by a task directly related to classwork. Both quantitative and qualitative data were collected in the form of a pre-course Computer Literacy Survey, a post-course open-ended Reflection Activity survey, and interviews. The qualitative data was reviewed drawing on the Technology Acceptance Model (TAM) and its educational variants as an analytical framework. Educational, social, and cultural factors were also examined to help identify underlying factors that would influence students’ perceptions. The results suggest that the subjects’ lack of awareness of, and experience with, the use of technology for learning are the fundamental causes of their perceptions of initial difficulty. Based on these findings, this study proposes a possible technology integration model that enhances digital literacy for more effective language learning in the context of Japanese education

    The interaction of risk and protective factors for mental disorders on psychopathology and brain morphometry

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    As per the diathesis-stress model, combined early risk factors (diathesis) and current risk factors (stress) determine an individual’s likelihood for the development of psychopathology. If the combined impact of diathesis and stress surpasses a certain threshold, individuals will develop psychopathology. At the same time, such threshold could be raised in the presence of protective factors, as they buffer the negative impact of risk factors, and lead to a reduced likelihood of developing psychopathology. Early risk factors for mental disorders include trait anxiety, childhood maltreatment and familial risk, and have been associated with specific brain morphometric alterations. Stressful life events, including the Covid-19 pandemic as a global example of that, constitute current risk factors. On the other hand, current literature suggests social support and conscientiousness as exemplary protective factors. These may increase resilience, a concept describing an individual’s ability to adaptively cope in the face of adversity and maintain mental health. However, contrary to risk factors, neural correlates of resilience are only sparsely known and hardly understood. Thus, to make precise predictions about the emergence of psychopathology in certain circumstances and understand possible neurobiological pathways, it is essential to jointly consider both risk and protective factors in mental health research. The aim of this dissertation was to investigate the interaction of risk and protective factors in three different but complementary contexts to gain a deeper understanding of these factors and their impact on brain morphometry and psychopathology. In STUDY I, morphometric correlates (specifically grey matter volume) of resilience were investigated. In this study, resilience was conceptualized as the maintenance of mental health despite a high risk (i.e., childhood maltreatment and familial risk). A key finding is that healthy high-risk individuals demonstrated larger grey matter volume in the left dorsolateral prefrontal cortex, an area associated with cognitive flexibility and emotional regulation skills, compared to the other groups. It seems plausible that an increased volume in this area is a neural correlate of resilience to high risk and may represent compensatory processes aiding high-risk individuals in maintaining mental health. STUDY II approached the subject in the opposite way, with transdiagnostic grey matter volume alterations in psychiatric patients compared to healthy subjects being associated with risk and protective factors. This study identified reduced volume in the left hippocampus as a transdiagnostic vulnerability marker in patients with major depression, bipolar disorder, and schizophrenia spectrum disorder. Volume in this area was further negatively associated with stressful life events, and executive and global functioning in both patients and healthy subjects. We conclude that stressful life events likely constitute a dimensional risk factor for reduced hippocampal volume and, therefore, are independent of diagnosis. STUDY III investigated the impact of a unique, acute global stressor, the Covid-19 pandemic, on healthy subjects and transdiagnostic patients. Multiple trait risk and protective factors were tested for their explanatory value of current Covid-19-related fear and isolation. This study identified trait anxiety and conscientiousness as risk factors for increased Covid-19-related fear, and social support as a protective factor against increased Covid-19-isolation. Again, the respective effect (harmful or protective) of all these factors was dimensional, i.e., relevant in both psychiatric patients and healthy subjects. STUDY III also highlighted the context-dependency of risk and protective factors: although generally considered a protective trait, increased conscientiousness was harmful in the context of a global pandemic due to the immense level of uncertainty and unpredictability. In conclusion, this dissertation identified brain correlates as potential biomarkers of psychopathology and resilience, and procedural contributors to adaptive and maladaptive responses to acute stressors. It highlighted the importance of taking protective factors, in addition to risk factors, into account in research. A major strength is the integration of multiple risk and protective factors, as such integrative approaches are crucial to advance the understanding of their complex interplay. By identifying dimensionality and context-dependency as important modulatory influences in the risk and protective factor interplay, it provided a framework for a more comprehensive understanding of the development of psychopathology, and the concept of resilience as a dynamic, continuous process of adaptation to changing environments, which enables individuals to maintain mental health even in the face of adversity

    Family, school and jobs: intergenerational social mobility in Next Steps

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    Young people’s higher education (HE) participation, and early access to labour markets, in the UK and other developed countries, are stratified according to their socio-economic origins and prior educational attainment. Such background factors are difficult to change in an individual’s lifetime, they are presumably not the only determinants of stratified outcomes, and anyway they could be mediated by peer influence and the issue of who goes to school with whom. This new study examines the relationships between a wide range of such social and economic factors relating to birth characteristics, family background, secondary schooling characteristics, and post-16 destinations, and it explores the possible reasons behind their links to HE and labour market outcomes. At the core of the study is an innovative combination of the large-scale nationally representative longitudinal Next Steps survey dataset linked to the robust administrative National Pupil Database (NPD) for England. In order to investigate the degree of social justice and equity in education, the study tracks the life course of a cohort of 5,192 state-school-educated young people in England from age 13 to age 25, to build a comprehensive picture of the journeys of these young people entering the labour market in their early adulthood. Analytical methods used include cross-tabulations, effect sizes, correlations and regression models. The main outcomes of interest are HE participation, and labour market outcomes as indicated by employment status and professional occupation status. The findings show a complex but relatively clear picture, providing some confirmatory and some new evidence on the correlates of intergenerational social mobility in a large cohort of people who are currently in their early 30s. Disadvantaged young people are consistently under-represented in HE participation and the labour market, especially in professional occupations. Bivariate analyses show that HE opportunities and labour market outcomes are systematically unbalanced between different socio-economic groups of young people, suggesting that destinations are strongly stratified by social origins. All of the factors considered in this study are independently associated with post-16 outcomes when analysed separately. Regression models reveal that, once birth characteristics are controlled for, the most important predictor of HE entry is prior educational attainment. This is followed by parental and pupil aspirations, parental occupation and education, material ownership at home, positive schooling experiences, and geographical location. In terms of employment status, doing an apprenticeship is the most powerful predictor of being employed at age 25 (although this may be skewed by the small number of young people still in formal education at that age). This is followed by prior educational attainment, material ownership at home, and prior HE entry. The relationship between the predictors and having a professional occupation status is slightly different. Regression analysis demonstrates that the key predictors of having a professional job are prior educational attainment, HE participation, parental and pupil aspirations, and positive schooling experiences. However, unlike generic employment status, evidence shows that having done an apprenticeship does not contribute to higher chances of landing a professional job. These findings collectively offer a core message in terms of fair access to life opportunities; the most import barriers to access to HE and professional occupations are stratified prior educational attainment and poverty-related factors at home. More crucially, the study also makes the first attempt to explore the level of segregation by background characteristics that is experienced at school as a potential factor in intergenerational social mobility. It is, to our knowledge, the only study to date which examines whether and to what extent who goes to school with whom might play a role in these outcomes beyond school. Bivariate analyses show that the clustering of pupils of similarly poorer socio-economic backgrounds at school is consistently linked to lower chances of HE participation and poorer labour market outcomes. Regression analyses further suggest that the level of between-school segregation an individual experiences plays a small role in all post-16 pathways, over and above that which can be explained by individual factors. In the light of these results, it appears that life destinations are still patterned by background inequality in modern England. However, there are promising signs that policy interventions – including creating a more socially mixed school intake, providing more financial support for low-income families such as travel bursaries, continuing and improving contextualised assessment in both university admissions and recruitment processes, and investing more in public transport in deprived areas – can help to improve fair access to HE and the labour market. These interventions can bring other long-term benefits such as life satisfaction too. Perhaps, instead of advocating or focusing on promoting social mobility, policymakers should devote more energy to and invest more money in tackling social inequality and improving equity in education and life opportunities. If this were to be done effectively, then social mobility could, presumably, look after itself
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