Twitter has received much attention recently. An important characteristic of Twitter is its real-time nature. We investigate the real-time interaction of events such as earthquakes in Twitter and ...propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center of the event location. We regard each Twitter user as a sensor and apply particle filtering, which are widely used for location estimation. The particle filter works better than other comparable methods for estimating the locations of target events. As an application, we develop an earthquake reporting system for use in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (93 percent of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and notification is delivered much faster than JMA broadcast announcements.
An examination of the multiaddition of organocopper reagents to fullerenes, and some uses of the products, is presented. The addition of such reagents is one of the most effective tools for one-step ...multiple functionalization of fullerenes.
Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong ...connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the “Deep Learning and Reinforcement Learning” session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies that can be key technologies to achieve human-level intelligence.
Two-story architecture of intelligence Matsuo, Yutaka
Cognitive Studies: Bulletin of the Japanese Cognitive Science Society,
2022/03/01, Letnik:
29, Številka:
1
Journal Article
Odprti dostop
This paper proposes an integrated architecture for intelligence based on recent advances in deep learning. Two systems, called BeastOS and Language App, represent the sensori-motor and symbolic ...processing systems. The world model is acquired through physical interaction in the environment. By disentangling factors in the world model, a counter-factual imagination becomes possible. A query to Language App can trigger the generation of data using the world model and generate an answer based on that. Such integration of deep learning models with external modules has been shown to be possible in a number of existing studies. Furthermore, we argue that primitive features such as knowledge processing, reasoning, long-term planning, and decision making can be obtained by learning on the corresponding datasets or tasks, called linguistic tasks. The main claim of this proposal is that symbolic processing is a set of functions acquired through deep learning and discrete inputs and outputs. The proposed model is novel in that it integrates a large amount of prior research discussion in the field of AI and cognitive science with the latest findings in deep learning.
Multimodal learning is a framework for building models that make predictions based on different types of modalities. Important challenges in multimodal learning are the inference of shared ...representations from arbitrary modalities and cross-modal generation via these representations; however, achieving this requires taking the heterogeneous nature of multimodal data into account. In recent years, deep generative models, i.e. generative models in which distributions are parameterized by deep neural networks, have attracted much attention, especially variational autoencoders, which are suitable for accomplishing the above challenges because they can consider heterogeneity and infer good representations of data. Therefore, various multimodal generative models based on variational autoencoders, called multimodal deep generative models, have been proposed in recent years. In this paper, we provide a categorized survey of studies on multimodal deep generative models.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Fullerene cations, namely 60fullerene radical cation (C
60
&z.rad;
+
) and organo60fullerenyl cation (RC
60
+
), are less investigated as intermediates in synthetic fullerene chemistry because of the ...intrinsic electronegativity of C
60
. Remarkably, these two intermediates can mediate reactions that afford versatile, unique and unexpected fullerene derivatives. This review article mainly describes these C
60
&z.rad;
+
and RC
60
+
intermediates and includes a variety of topics, from the generation of C
60
&z.rad;
+
and RC
60
+
species to their applications in fullerene modification reactions, such as Friedel-Crafts hydroarylation, nucleophilic addition, dimerization, intramolecular rearrangement reactions, and intramolecular cyclization reactions. Additionally, this review deeply discusses the mechanism of the formation of unique 60fullerene derivatives involving 60fullerene radical cation and organo60fullerenyl cation intermediates. In addition, the electrochemical properties and photovoltaic performance of these fullerene derivatives produced through C
60
&z.rad;
+
or RC
60
+
-mediated reactions are discussed in this review.
Fullerene cations, namely 60fullerene radical cation (C
60
&z.rad;
+
) and organo60fullerenyl cation (RC
60
+
), open paths for the efficient derivatization of a great variety of fullerenes.
A
bstract
Recently, Gaiotto and Rapcak proposed a generalization of
W
N
algebra by considering the symmetry at the corner of the brane intersection (corner vertex operator algebra). The algebra, ...denoted as
Y
L,M,N
, is characterized by three non-negative integers
L, M, N
. It has a manifest triality automorphism which interchanges
L, M, N
, and can be obtained as a reduction of
W
1+∞
algebra with a “pit” in the plane partition representation. Later, Prochazka and Rapcak proposed a representation of
Y
L,M,N
in terms of
L
+
M
+
N
free bosons by a generalization of Miura transformation, where they use the fractional power differential operators.
In this paper, we derive a
q
-deformation of the Miura transformation. It gives a free field representation for
q
-deformed
Y
L,M,N
, which is obtained as a reduction of the quantum toroidal algebra. We find that the
q
-deformed version has a “simpler” structure than the original one because of the Miki duality in the quantum toroidal algebra. For instance, one can find a direct correspondence between the operators obtained by the Miura transformation and those of the quantum toroidal algebra. Furthermore, we can show that the both algebras share the same screening operators.
Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios ...from environmental monitoring to search and rescue. It faces a number of difficulties mainly due to image acquisition and contents, and processing constraints. Since drones’ flying conditions constrain image acquisition, human subjects may appear in images at variable scales, orientations, and occlusion, which makes action recognition more difficult. We explore low-resource methods for ML (machine learning)-based action recognition using a previously collected real-world dataset (the “Okutama-Action” dataset). This dataset contains representative situations for action recognition, yet is controlled for image acquisition parameters such as camera angle or flight altitude. We investigate a combination of object recognition and classifier techniques to support single-image action identification. Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and evaluate the performance of our method on Okutama-Action dataset. Our approach outperformed previous architectures applied to the Okutama dataset, which differed by their object identification and classification pipeline: we hypothesize that this is a consequence of both YoloV5 performance and the overall adequacy of our pipeline to the specificities of the Okutama dataset in terms of bias–variance tradeoff.
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![Functionalization of [60]fu...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/1359-7345/small "Functionalization of [60]fullerene through fullerene cation intermediates")
