Fast Fight Detection

Action recognition has become a hot topic within computer vision. However, the action recognition community has focused mainly on relatively simple actions like clapping, walking, jogging, etc. The detection of specific events with direct practical use such as fights or in general aggressive behavior has been comparatively less studied. Such capability may be extremely useful in some video surveillance scenarios like prisons, psychiatric centers or even embedded in camera phones. As a consequence, there is growing interest in developing violence detection algorithms. Recent work considered the well-known Bag-of-Words framework for the specific problem of fight detection. Under this framework, spatio-temporal features are extracted from the video sequences and used for classification. Despite encouraging results in which high accuracy rates were achieved, the computational cost of extracting such features is prohibitive for practical applications. This work proposes a novel method to detect violence sequences. Features extracted from motion blobs are used to discriminate fight and non-fight sequences. Although the method is outperformed in accuracy by state of the art, it has a significantly faster computation time thus making it amenable for real-time applications

FairWire: Fair Graph Generation

Machine learning over graphs has recently attracted growing attention due to its ability to analyze and learn complex relations within critical interconnected systems. However, the disparate impact that is amplified by the use of biased graph structures in these algorithms has raised significant concerns for the deployment of them in real-world decision systems. In addition, while synthetic graph generation has become pivotal for privacy and scalability considerations, the impact of generative learning algorithms on the structural bias has not yet been investigated. Motivated by this, this work focuses on the analysis and mitigation of structural bias for both real and synthetic graphs. Specifically, we first theoretically analyze the sources of structural bias that result in disparity for the predictions of dyadic relations. To alleviate the identified bias factors, we design a novel fairness regularizer that offers a versatile use. Faced with the bias amplification in graph generation models that is brought to light in this work, we further propose a fair graph generation framework, FairWire, by leveraging our fair regularizer design in a generative model. Experimental results on real-world networks validate that the proposed tools herein deliver effective structural bias mitigation for both real and synthetic graphs.Comment: 16 pages, 1 figure, 7 table

No Scalar-Haired Cauchy Horizon Theorem in Charged Gauss-Bonnet Black Holes

Recently, a ``no inner (Cauchy) horizon theorem" for static black holes with non-trivial scalar hairs has been proved in Einstein-Maxwell-scalar theories and also in Einstein-Maxwell-Horndeski theories with the non-minimal coupling of a charged (complex) scalar field to Einstein tensor. In this paper, we study an extension of the theorem to the static black holes in Einstein-Maxwell-Gauss-Bonnet-scalar theories, or simply, charged Gauss-Bonnet (GB) black holes. We find that no inner horizon with charged scalar hairs is allowed for the planar (k=0) black holes, as in the case without GB term. On the other hand, for the non-planar (k=+1,-1) black holes, we find that the haired inner horizon can not be excluded due to GB effect generally, though we can not find a simple condition for its existence. As some explicit examples of the theorem, we study numerical GB black hole solutions with charged scalar hairs and Cauchy horizons in asymptotically anti-de Sitter space, and find good agreements with the theorem. As a byproduct, we find a ``no-go theorem" for charged de Sitter GB black holes with charged scalar hairs in arbitrary dimensions.Comment: 21 pages, 8 figure

Symmetries and Conservation Laws in Horava Gravity

Horava gravity has been proposed as a renormalizable quantum gravity without the ghost problem through anisotropic scaling dimensions which break Lorentz symmetry in UV. In the Hamiltonian formalism, due to the Lorentz-violating terms, the constraint structure looks quite different from that of general relativity (GR) but we have recently found that "there exists the case where we can recover the same number of degrees of freedom as in GR", in a rather general set-up. In this paper, we study its Lagrangian perspectives and examine the full diffeomorphism (Diff) symmetry and its associated conservation laws in Horava gravity. Surprisingly, we find that the full Diff symmetry in the action can also be recovered when a certain condition, called "super-condition", which super-selects the Lorentz-symmetric sector in Horava gravity, is satisfied. This indicates that the broken Lorentz symmetry, known as "foliation-preserving" Diff, is just an "apparent" symmetry of the Horava gravity action and rather its "full action symmetry can be as large as the Diff in GR ". The super-condition exactly corresponds to the tertiary constraint in Hamiltonian formalism which is the second-class constraint and provides a non-trivial realization of the Lorentz symmetry otherwise being absent apparently. From the recovered Lorentz symmetry in the action, we obtain the conservation laws with the Noether currents as in covariant theories. The general formula for the conserved Noether charges reproduces the mass of four-dimensional static black holes with an "arbitrary" cosmological constant in Horava gravity, and is independent of ambiguities associated with the choice of asymptotic boundaries. We also discuss several challenging problems, including its implications to Hamiltonian formalism, black hole thermodynamics, radiations from colliding black holes.Comment: 18 pages, no figure

The Hamiltonian Dynamics of Horava Gravity

We consider the Hamiltonian formulation of Horava gravity in arbitrary dimensions, which has been proposed as a renormalizable gravity model for quantum gravity without the ghost problem. We study the "full" constraint analysis of the "non-projectable" Horava gravity whose potential, V(R), is an arbitrary function of the (intrinsic) Ricci scalar R. We find that there exist generally distinct cases of this theory, depending on (i) whether the Hamiltonian constraint generates new (second-class) constraints (Cases A, C) or just fixes the associated Lagrange multipliers (Case B), or (ii) whether the IR Lorentz-deformation parameter \lambda is at the conformal point (Case C) or not (Cases A, B). It is found that, for Cases A and C, the dynamical degrees of freedom are the same as in general relativity, while, for Case B, there is "one additional phase-space degree of freedom", representing an extra (odd) scalar graviton mode. This would resolve the long-standing debates about the extra graviton modes and achieves the dynamical consistency of the Horava gravity, at the "fully non-linear" level. Several exact solutions are also considered as some explicit examples of the new constraints. The structure of the newly obtained, "extended" constraint algebra seems to be generic to Horava gravity and its general proof would be a challenging problem. Some other challenging problems, which include the path integral quantization and the Dirac bracket quantization are discussed also.Comment: Matches published version, Typos correcte

Fairness-aware Optimal Graph Filter Design

Graphs are mathematical tools that can be used to represent complex real-world interconnected systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant attention recently. However, it has been demonstrated that ML over graphs amplifies the already existing bias towards certain under-represented groups in various decision-making problems due to the information aggregation over biased graph structures. Faced with this challenge, here we take a fresh look at the problem of bias mitigation in graph-based learning by borrowing insights from graph signal processing. Our idea is to introduce predesigned graph filters within an ML pipeline to reduce a novel unsupervised bias measure, namely the correlation between sensitive attributes and the underlying graph connectivity. We show that the optimal design of said filters can be cast as a convex problem in the graph spectral domain. We also formulate a linear programming (LP) problem informed by a theoretical bias analysis, which attains a closed-form solution and leads to a more efficient fairness-aware graph filter. Finally, for a design whose degrees of freedom are independent of the input graph size, we minimize the bias metric over the family of polynomial graph convolutional filters. Our optimal filter designs offer complementary strengths to explore favorable fairness-utility-complexity tradeoffs. For performance evaluation, we conduct extensive and reproducible node classification experiments over real-world networks. Our results show that the proposed framework leads to better fairness measures together with similar utility compared to state-of-the-art fairness-aware baselines.Comment: 12 pages, 3 figures, 9 tables. arXiv admin note: text overlap with arXiv:2303.1145

Volumetric and three-dimensional examination of sella turcica by cone-beam computed tomography: reference data for guidance to pathologic pituitary morphology

Background: The aim of the study was to assess the dimensions and volume of sella turcica in healthy Caucasian adults with normal occlusion and facial appearance from cone-beam computed tomography (CBCT) images. Materials and methods: CBCT images of 80 Caucasian adult patients (40 males, 40 females) with normal facial appearance and occlusion taken previously for diagnostic purposes were evaluated. Two groups were constructed in accordance to gender. The volume, length, diameter, and depth of the sella turcica were measured by Romexis software programme. Mann-Whitney U test and Independent t-tests were used for statistical analysis. Results: The mean lengths of the sella were 9.9 mm and 10.2 mm, depths were 9.2 mm and 8.8 mm and diameters were 12.3 mm and 12.1 mm in female and male groups, respectively. Between the genders, no statistically significant differences were found for any of the measurements. There were significantly higher values for the volume of sella turcica in males than in females (1102 ± 285.3 mm3 and 951.3 ± 278.5 mm3, respectively). Conclusions: The dimensions of sella turcica in healthy Caucasian adults with normal occlusion and facial appearance revealed nonsignificant differences between the genders. Individual variability in dimensions and gender differences in the volume are of importance in comparison of patients with craniofacial syndromes and aberrations. Knowledge concerning the dimensions and volume of sella turcica will be clinically relevant for a guidance to consciously realize pituitary disorders