Local Approximations to the Gravitational Collapse of Cold Matter

We investigate three different local approximations for nonlinear gravitational instability in the framework of cosmological Lagrangian fluid dynamics of cold dust. They include the Zel'dovich approximation (ZA), the ``non-magnetic'' approximation of Bertschinger \& Jain (1994, NMA), and a new ``local tidal'' approximation (LTA). The LTA is exact for any perturbations whose gravitational and velocity equipotentials have the same constant shape with time, including spherical, cylindrical, and plane-parallel perturbations. We tested all three local approximations with the collapse of a homogeneous triaxial ellipsoid, for which an exact solution exists for an ellipsoid embedded in empty space and an excellent approximation is known in the cosmological context. We find that the LTA is significantly more accurate in general than the ZA and the NMA. Like the ZA, but unlike the NMA, the LTA generically leads to pancake collapse. For a randomly chosen mass element in an Einstein-de Sitter universe, assuming a Gaussian random field of initial density fluctuations, the LTA predicts that at least 78\% of initially underdense regions collapse owing to nonlinear effects of shear and tides.Comment: 29 pages of latex, uses aaspp4.sty (AASTeX v4.0), submitted to Ap

ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps

We consider the problem of generating relevant execution traces to test rich interactive applications. Rich interactive applications, such as apps on mobile platforms, are complex stateful and often distributed systems where sufficiently exercising the app with user-interaction (UI) event sequences to expose defects is both hard and time-consuming. In particular, there is a fundamental tension between brute-force random UI exercising tools, which are fully-automated but offer low relevance, and UI test scripts, which are manual but offer high relevance. In this paper, we consider a middle way---enabling a seamless fusion of scripted and randomized UI testing. This fusion is prototyped in a testing tool called ChimpCheck for programming, generating, and executing property-based randomized test cases for Android apps. Our approach realizes this fusion by offering a high-level, embedded domain-specific language for defining custom generators of simulated user-interaction event sequences. What follows is a combinator library built on industrial strength frameworks for property-based testing (ScalaCheck) and Android testing (Android JUnit and Espresso) to implement property-based randomized testing for Android development. Driven by real, reported issues in open source Android apps, we show, through case studies, how ChimpCheck enables expressing effective testing patterns in a compact manner.Comment: 20 pages, 21 figures, Symposium on New ideas, New Paradigms, and Reflections on Programming and Software (Onward!2017

The psychophysics of decision making in a two-direction random dot motion target selection task

La tâche de kinématogramme de points aléatoires est utilisée avec le paradigme de choix forcé entre deux alternatives pour étudier les prises de décisions perceptuelles. Les modèles décisionnels supposent que les indices de mouvement pour les deux alternatives sont encodés dans le cerveau. Ainsi, la différence entre ces deux signaux est accumulée jusqu’à un seuil décisionnel. Cependant, aucune étude à ce jour n’a testé cette hypothèse avec des stimuli contenant des mouvements opposés. Ce mémoire présente les résultats de deux expériences utilisant deux nouveaux stimuli avec des indices de mouvement concurrentiels. Parmi une variété de combinaisons d’indices concurrentiels, la performance des sujets dépend de la différence nette entre les deux signaux opposés. De plus, les sujets obtiennent une performance similaire avec les deux types de stimuli. Ces résultats supportent un modèle décisionnel basé sur l’accumulation des indices de mouvement net et suggèrent que le processus décisionnel peut intégrer les signaux de mouvement à partir d’une grande gamme de directions pour obtenir un percept global de mouvement.Random dot kinematograms are used in visual psychophysics with the two-alternative forced-choice paradigm to study the process of simple perceptual decisions. Mathematical models of this process assume that stochastic motion evidence for the two alternative choices is encoded in the brain, and that the difference in evidence is accumulated towards a decision bound. However, no study to date has tested this assumption using stimuli with different levels of mutually opposing evidence in both directions. This thesis presents the results of two experiments using two novel stimuli with opposing coherent motion evidence. Over a variety of competing evidence combinations, subject performance was based on the net difference in the opposing signals. Furthermore, task performance was similar with both types of stimuli. These results support a decision model based on the accumulation of net evidence, and suggest that the decision process is capable of integrating motion evidence from a wide range of directions to obtain a global percept of motion

Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction

Depth estimation from light field (LF) images is a fundamental step for some applications. Recently, learning-based methods have achieved higher accuracy and efficiency than the traditional methods. However, it is costly to obtain sufficient depth labels for supervised training. In this paper, we propose an unsupervised framework to estimate depth from LF images. First, we design a disparity estimation network (DispNet) with a coarse-to-fine structure to predict disparity maps from different view combinations by performing multi-view feature matching to learn the correspondences more effectively. As occlusions may cause the violation of photo-consistency, we design an occlusion prediction network (OccNet) to predict the occlusion maps, which are used as the element-wise weights of photometric loss to solve the occlusion issue and assist the disparity learning. With the disparity maps estimated by multiple input combinations, we propose a disparity fusion strategy based on the estimated errors with effective occlusion handling to obtain the final disparity map. Experimental results demonstrate that our method achieves superior performance on both the dense and sparse LF images, and also has better generalization ability to the real-world LF images

Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging

Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel basis and generate spatiotemporal streaming events within microseconds in response, offering visual information with high temporal resolution over a high dynamic range. Such devices often serve in surveillance systems due to their applicability and robustness in environments with high dynamics and strong or weak lighting, where they can still supply clearer recordings than traditional imaging. In other words, when it comes to privacy-relevant cases, neuromorphic cameras also expose more sensitive data and thus pose serious security threats. Therefore, asynchronous event streams also necessitate careful encryption before transmission and usage. This letter discusses several potential attack scenarios and approaches event encryption from the perspective of neuromorphic noise removal, in which we inversely introduce well-crafted noise into raw events until they are obfuscated. Evaluations show that the encrypted events can effectively protect information from the attacks of low-level visual reconstruction and high-level neuromorphic reasoning, and thus feature dependable privacy-preserving competence. Our solution gives impetus to the security of event data and paves the way to a highly encrypted technique for privacy-protective neuromorphic imaging