7,381 research outputs found
Stateful in-memory computing in emerging crossbar memories
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.
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DeepStory: Video Story QA by Deep Embedded Memory Networks
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 Model with Two Intermediate Scales
We investigate the detectability of neutralino Dark Matter via direct and
indirect searches as well as collider signatures of an 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 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 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
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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