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 $\gamma$ 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

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, $S^1/Z_2$. 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

A covariant approach to general field space metric in multi-field inflation

We present a covariant formalism for general multi-field system which enables us to obtain higher order action of cosmological perturbations easily and systematically. The effects of the field space geometry, described by the Riemann curvature tensor of the field space, are naturally incorporated. We explicitly calculate up to the cubic order action which is necessary to estimate non-Gaussianity and present those geometric terms which have not yet known before.Comment: (v1) 18 pages, 1 figure; (v2) references added, typos corrected, to appear in Journal of Cosmology and Astroparticle Physics; (v3) typos in (54), (62) and (64) correcte