3,456 research outputs found
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition
In the recent year, state-of-the-art for facial micro-expression recognition
have been significantly advanced by deep neural networks. The robustness of
deep learning has yielded promising performance beyond that of traditional
handcrafted approaches. Most works in literature emphasized on increasing the
depth of networks and employing highly complex objective functions to learn
more features. In this paper, we design a Shallow Triple Stream
Three-dimensional CNN (STSTNet) that is computationally light whilst capable of
extracting discriminative high level features and details of micro-expressions.
The network learns from three optical flow features (i.e., optical strain,
horizontal and vertical optical flow fields) computed based on the onset and
apex frames of each video. Our experimental results demonstrate the
effectiveness of the proposed STSTNet, which obtained an unweighted average
recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite
database consisting of 442 samples from the SMIC, CASME II and SAMM databases.Comment: 5 pages, 1 figure, Accepted and published in IEEE FG 201
On the gravitational field of static and stationary axial symmetric bodies with multi-polar structure
We give a physical interpretation to the multi-polar Erez-Rozen-Quevedo
solution of the Einstein Equations in terms of bars. We find that each
multi-pole correspond to the Newtonian potential of a bar with linear density
proportional to a Legendre Polynomial. We use this fact to find an integral
representation of the function. These integral representations are
used in the context of the inverse scattering method to find solutions
associated to one or more rotating bodies each one with their own multi-polar
structure.Comment: To be published in Classical and Quantum Gravit
International Mental Health Education, Service, and Research: Working Across Cultural Boundaries with Humility, Creativity, and Perseverance [Keynote]
This keynote presentation addresses doing International mental health education, services, and research with humility, creativity, and perseverance
A general formula of the effective potential in 5D SU(N) gauge theory on orbifold
We show a general formula of the one loop effective potential of the 5D SU(N)
gauge theory compactified on an orbifold, . The formula shows the case
when there are fundamental, (anti-)symmetric tensor and adjoint
representational bulk fields. Our calculation method is also applicable when
there are bulk fields belonging to higher dimensional representations. The
supersymmetric version of the effective potential with Scherk-Schwarz breaking
can be obtained straightforwardly. We also show some examples of effective
potentials in SU(3), SU(5) and SU(6) models with various boundary conditions,
which are reproduced by our general formula.Comment: 22 pages;minor corrections;references added;typos correcte
Language of Lullabies: The Russification and De-Russification of the Baltic States
This article argues that the laws for promotion of the national languages are a legitimate means for the Baltic states to establish their cultural independence from Russia and the former Soviet Union
Visibility diagrams and experimental stripe structure in the quantum Hall effect
We analyze various properties of the visibility diagrams that can be used in
the context of modular symmetries and confront them to some recent experimental
developments in the Quantum Hall Effect. We show that a suitable physical
interpretation of the visibility diagrams which permits one to describe
successfully the observed architecture of the Quantum Hall states gives rise
naturally to a stripe structure reproducing some of the experimental features
that have been observed in the study of the quantum fluctuations of the Hall
conductance. Furthermore, we exhibit new properties of the visibility diagrams
stemming from the structure of subgroups of the full modular group.Comment: 8 pages in plain TeX, 7 figures in a single postscript fil
Inflation might be caused by the right
We show that the scalar field that drives inflation can have a dynamical
origin, being a strongly coupled right handed neutrino condensate. The
resulting model is phenomenologically tightly constrained, and can be
experimentally (dis)probed in the near future. The mass of the right handed
neutrino obtained this way (a crucial ingredient to obtain the right light
neutrino spectrum within the see-saw mechanism in a complete three generation
framework) is related to that of the inflaton and both completely determine the
inflation features that can be tested by current and planned experiments.Comment: 15 pages, 4 figure
Logical Message Passing Networks with One-hop Inference on Atomic Formulas
Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot
of attention to potentially support many applications. Given that KGs are
usually incomplete, neural models are proposed to answer logical queries by
parameterizing set operators with complex neural networks. However, such
methods usually train neural set operators with a large number of entity and
relation embeddings from zero, where whether and how the embeddings or the
neural set operators contribute to the performance remains not clear. In this
paper, we propose a simple framework for complex query answering that
decomposes the KG embeddings from neural set operators. We propose to represent
the complex queries in the query graph. On top of the query graph, we propose
the Logical Message Passing Neural Network (LMPNN) that connects the
\textit{local} one-hop inferences on atomic formulas to the \textit{global}
logical reasoning for complex query answering. We leverage existing effective
KG embeddings to conduct one-hop inferences on atomic formulas, the results of
which are regarded as the messages passed in LMPNN. The reasoning process over
the overall logical formulas is turned into the forward pass of LMPNN that
incrementally aggregates local information to predict the answers' embeddings
finally. The complex logical inference across different types of queries will
then be learned from training examples based on the LMPNN architecture.
Theoretically, our query-graph representation is more general than the
prevailing operator-tree formulation, so our approach applies to a broader
range of complex KG queries. Empirically, our approach yields a new
state-of-the-art neural CQA model. Our research bridges the gap between complex
KG query answering tasks and the long-standing achievements of knowledge graph
representation learning.Comment: Accepted by ICLR 2023. 20 pages, 4 figures, and 9 table
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