8,075 research outputs found

    A Humanized View of Second Language Learning Through Creative Writing: A Korean Graduate Student in the United States

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    This case study traces the journey of a Korean graduate student’s English learning experience, drawing on autobiographical poetry, self-narrative, and interviews. Through a series of snapshot recollections, it illustrates the participant’s evolving subject position with English over the years from his childhood to graduate school. The article concludes that language learning is a transformative experience of constructing translingual identities which entails a wide spectrum of emotion, desire, and dedication: desire to understand the world; to be included in the world; to empower oneself as a user

    Stateful in-memory computing in emerging crossbar memories

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    Emerging memories such as MRAM, PRAM, and RRAM have been extensively studied due to its various advantages over the conventional memories. Because their performances are yet better than the conventional memories as DRAM and NAND Flash, researchers are primarily trying to find their applications at embedded memory or storages class memory applications. As such, when the emerging memories are used for memory or data storage, its application can be very limited to one of the computing elements in the conventional computing hierarchy. If an entirely new function—a computing function—can be implemented in the emerging memories, it could destroy the traditional computing hierarchy and change the computing paradigm. The stateful in-memory computing technology provides such capability to the emerging memories. The first concept of stateful logic was proposed in 2010 by the group of HP using the crossbar RRAM. Afterward, there have been many advancements for putting the technology into practical use. In this presentation, the most up-to-date stateful in-memory computing technology is presented. The stateful in-memory computing technology can apply to any emerging memories based on the crossbar architecture. Therefore, it would be an additional beneficial option for the emerging memories strengthening its functionality more than memory or storage. Please click Additional Files below to see the full abstract

    DeepStory: Video Story QA by Deep Embedded Memory Networks

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    Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos. We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data. The video stories are stored in a long-term memory component. For a given question, an LSTM-based attention model uses the long-term memory to recall the best question-story-answer triplet by focusing on specific words containing key information. We trained the DEMN on a novel QA dataset of children's cartoon video series, Pororo. The dataset contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained sentences for scene description, and 8,913 story-related QA pairs. Our experimental results show that the DEMN outperforms other QA models. This is mainly due to 1) the reconstruction of video stories in a scene-dialogue combined form that utilize the latent embedding and 2) attention. DEMN also achieved state-of-the-art results on the MovieQA benchmark.Comment: 7 pages, accepted for IJCAI 201

    Direct and Indirect Detection of Neutralino Dark Matter and Collider Signatures in an SO(10)SO(10) Model with Two Intermediate Scales

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    We investigate the detectability of neutralino Dark Matter via direct and indirect searches as well as collider signatures of an SO(10)SO(10) model with two intermediate scales. We compare the direct Dark Matter detection cross section and the muon flux due to neutralino annihilation in the Sun that we obtain in this model with mSUGRA predictions and with the sensitivity of current and future experiments. In both cases, we find that the detectability improves as the model deviates more from mSUGRA. In order to study collider signatures, we choose two benchmark points that represent the main phenomenological features of the model: a lower value of ∣μ∣|\mu| and reduced third generation sfermion masses due to extra Yukawa coupling contributions in the Renormalization Group Equations, and increased first and second generation slepton masses due to new gaugino loop contributions. We show that measurements at the LHC can distinguish this model from mSUGRA in both cases, by counting events containing leptonically decaying Z0Z^0 bosons, heavy neutral Higgs bosons, or like--sign lepton pairs.Comment: 21 pages, 16 figure

    Effect of quadratic residue diffuser (QRD) microwave energy on root-lesion nematode, Prathlenchus penetrans

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    In this study, quadratic residue diffuser (QRD) microwave energy was used to control nematode Pratylenchus penetrans in soil. Microwave energy is a physical method that has been used to manage nematodes. This approach provides rapid heat transfer to soil with no lingering residual effects. QRD microwave radiation at a frequency of 2450 MHz was used to irradiate sandy clay loam soil containing a nematode layer.The pot dimensions were 17 cm high, 10 cm diameter and exposure times used were 10, 20, 30, 40, 50, 60, and 120 s. The soil water content was set at 0, 10, 20, 30, and 40%, respectively, based on dry mass. Total mortality was calculated at soil depths of 5, 10 and 15 cm. Microwave treatment time and soil water content significantly affected nematode mortality; also, longer exposure time and decreased soil moisture content resulted in an greater total mortality. However, 120 s radiation was demonstrated to be the most effective for killing nematodes at all soil water contents and soil depths.Keywords: Microwave energy, nematodes, pepper, Pratylenchus penetrans, physical control, quadratic residue diffuserAfrican Journal of Biotechnology Vol. 12(18), pp. 2471-247
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