The Cerenkov effect revisited: from swimming ducks to zero modes in gravitational analogs

We present an interdisciplinary review of the generalized Cerenkov emission of radiation from uniformly moving sources in the different contexts of classical electromagnetism, superfluid hydrodynamics, and classical hydrodynamics. The details of each specific physical systems enter our theory via the dispersion law of the excitations. A geometrical recipe to obtain the emission patterns in both real and wavevector space from the geometrical shape of the dispersion law is discussed and applied to a number of cases of current experimental interest. Some consequences of these emission processes onto the stability of condensed-matter analogs of gravitational systems are finally illustrated.Comment: Lecture Notes at the IX SIGRAV School on "Analogue Gravity" in Como, Italy from May 16th-21th, 201

Deformable face ensemble alignment with robust grouped-L1 anchors

Many methods exist at the moment for deformable face fitting. A drawback to nearly all these approaches is that they are (i) noisy in terms of landmark positions, and (ii) the noise is biased across frames (i.e. the misalignment is toward common directions across all frames). In this paper we propose a grouped $\mathcal{L}1$-norm anchored method for simultaneously aligning an ensemble of deformable face images stemming from the same subject, given noisy heterogeneous landmark estimates. Impressive alignment performance improvement and refinement is obtained using very weak initialization as "anchors"

A Unified Framework for Compositional Fitting of Active Appearance Models

Active Appearance Models (AAMs) are one of the most popular and well-established techniques for modeling deformable objects in computer vision. In this paper, we study the problem of fitting AAMs using Compositional Gradient Descent (CGD) algorithms. We present a unified and complete view of these algorithms and classify them with respect to three main characteristics: i) cost function; ii) type of composition; and iii) optimization method. Furthermore, we extend the previous view by: a) proposing a novel Bayesian cost function that can be interpreted as a general probabilistic formulation of the well-known project-out loss; b) introducing two new types of composition, asymmetric and bidirectional, that combine the gradients of both image and appearance model to derive better conver- gent and more robust CGD algorithms; and c) providing new valuable insights into existent CGD algorithms by reinterpreting them as direct applications of the Schur complement and the Wiberg method. Finally, in order to encourage open research and facilitate future comparisons with our work, we make the implementa- tion of the algorithms studied in this paper publicly available as part of the Menpo Project.Comment: 39 page

From Teasing to Torment: School Climate Revisited - A Survey of U.S. Secondary School Students and Teachers

From Teasing to Torment: School Climate Revisited, A Survey of U.S. Secondary School Students and Teachers provides an in-depth look at the current landscape of bias and peer victimization as reported by students and teachers from across the nation. In addition to examining various types of bias, including those based on race/ethnicity, religion, body size, and ability, this report provides a focused look at LGBTQ issues in secondary schools. Comparing findings to a similar survey we conducted in 2005, the report discusses the progress that has been made over the past ten years, as well as highlights the challenges that remain. It also offers recommendations and strategies to improve school climate for all students.Specifically, the research report addresses:Student and teacher perceptions of school climate; Student experiences of safety, bullying, and harassment, including biased incidents based on race/ethnicity, sexual orientation, body size, gender, religion, ability, economic status, and gender expression;Teacher intervention in bullying and incidents of bias; LGBT-supportive teacher practices, such as advising GSA or including LGBT content in teaching;Teacher professional development (pre-service and in-service) in bullying, diversity, and LGBT issues; andDifferences in students' school experiences based on race/ethnicity, LGBTQ status, gender nonconformity, and geography (i.e., urbanicity, region), among others

Pose-Invariant 3D Face Alignment

Face alignment aims to estimate the locations of a set of landmarks for a given image. This problem has received much attention as evidenced by the recent advancement in both the methodology and performance. However, most of the existing works neither explicitly handle face images with arbitrary poses, nor perform large-scale experiments on non-frontal and profile face images. In order to address these limitations, this paper proposes a novel face alignment algorithm that estimates both 2D and 3D landmarks and their 2D visibilities for a face image with an arbitrary pose. By integrating a 3D deformable model, a cascaded coupled-regressor approach is designed to estimate both the camera projection matrix and the 3D landmarks. Furthermore, the 3D model also allows us to automatically estimate the 2D landmark visibilities via surface normals. We gather a substantially larger collection of all-pose face images to evaluate our algorithm and demonstrate superior performances than the state-of-the-art methods

Deep-LK for Efficient Adaptive Object Tracking

In this paper we present a new approach for efficient regression based object tracking which we refer to as Deep- LK. Our approach is closely related to the Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et al. We make the following contributions. First, we demonstrate that there is a theoretical relationship between siamese regression networks like GOTURN and the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further, we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance of the currently tracked frame. We argue that this missing property in GOTURN can be attributed to its poor performance on unseen objects and/or viewpoints. Second, we propose a novel framework for object tracking - which we refer to as Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive results demonstrating that Deep-LK substantially outperforms GOTURN. Additionally, we demonstrate comparable tracking performance to current state of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS) computationally efficient