Antipolar ordering of topological defects in active liquid crystals

ATP-driven microtubule-kinesin bundles can self-assemble into two-dimensional active liquid crystals (ALCs) that exhibit a rich creation and annihilation dynamics of topological defects, reminiscent of particle-pair production processes in quantum systems. This recent discovery has sparked considerable interest but a quantitative theoretical description is still lacking. We present and validate a minimal continuum theory for this new class of active matter systems by generalizing the classical Landau-de Gennes free-energy to account for the experimentally observed spontaneous buckling of motor-driven extensile microtubule bundles. The resulting model agrees with recently published data and predicts a regime of antipolar order. Our analysis implies that ALCs are governed by the same generic ordering principles that determine the non-equilibrium dynamics of dense bacterial suspensions and elastic bilayer materials. Moreover, the theory manifests an energetic analogy with strongly interacting quantum gases. Generally, our results suggest that complex non-equilibrium pattern-formation phenomena might be predictable from a few fundamental symmetry-breaking and scale-selection principles.Comment: final accepted journal version; SI text and movies available at article on iop.or

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201

C2AE: Class Conditioned Auto-Encoder for Open-set Recognition

Models trained for classification often assume that all testing classes are known while training. As a result, when presented with an unknown class during testing, such closed-set assumption forces the model to classify it as one of the known classes. However, in a real world scenario, classification models are likely to encounter such examples. Hence, identifying those examples as unknown becomes critical to model performance. A potential solution to overcome this problem lies in a class of learning problems known as open-set recognition. It refers to the problem of identifying the unknown classes during testing, while maintaining performance on the known classes. In this paper, we propose an open-set recognition algorithm using class conditioned auto-encoders with novel training and testing methodology. In contrast to previous methods, training procedure is divided in two sub-tasks, 1. closed-set classification and, 2. open-set identification (i.e. identifying a class as known or unknown). Encoder learns the first task following the closed-set classification training pipeline, whereas decoder learns the second task by reconstructing conditioned on class identity. Furthermore, we model reconstruction errors using the Extreme Value Theory of statistical modeling to find the threshold for identifying known/unknown class samples. Experiments performed on multiple image classification datasets show proposed method performs significantly better than state of the art.Comment: CVPR2019 (Oral

Lattices of hydrodynamically interacting flapping swimmers

Fish schools and bird flocks exhibit complex collective dynamics whose self-organization principles are largely unknown. The influence of hydrodynamics on such collectives has been relatively unexplored theoretically, in part due to the difficulty in modeling the temporally long-lived hydrodynamic interactions between many dynamic bodies. We address this through a novel discrete-time dynamical system (iterated map) that describes the hydrodynamic interactions between flapping swimmers arranged in one- and two-dimensional lattice formations. Our 1D results exhibit good agreement with previously published experimental data, in particular predicting the bistability of schooling states and new instabilities that can be probed in experimental settings. For 2D lattices, we determine the formations for which swimmers optimally benefit from hydrodynamic interactions. We thus obtain the following hierarchy: while a side-by-side single-row "phalanx" formation offers a small improvement over a solitary swimmer, 1D in-line and 2D rectangular lattice formations exhibit substantial improvements, with the 2D diamond lattice offering the largest hydrodynamic benefit. Generally, our self-consistent modeling framework may be broadly applicable to active systems in which the collective dynamics is primarily driven by a fluid-mediated memory

PENERAPAN MODEL PENGAJARAN LANGSUNG MELALUI MEDIA MICROSOFT POWER POINT PADA MATERI BANGUN DATAR DI KELAS III SD NEGERI LAMPEUNEURUT

Kata Kunci: Model Pengajaran Langsung, Media Microsoft Powerpoint, Bangun Datar `Penelitian ini dilatarbelakangi oleh masih rendahnya hasil belajar siswa pada materi bangun datar. Untuk mengurangi kelemahan pada materi bangun datar diperlukan suatu model pembelajaran dan media yang menarik yaitu model pengajaran langsung dan media microsoft powerpoint. Adapun rumusan dalam penelitian ini adalah bagaimanakah penerapan model pengajaran langsung melalui media microsoft powerpoint pada materi bangun datar di kelas III SD Negeri Lampeuneurut. Penelitian ini bertujuan mengetahui ketuntasan belajar siswa melalui penerapan model pengajaran langsung melalui media microsoft powerpoint pada materi bangun datar di kelas III SD Negeri Lampeuneurut. Penelitian ini merupakan penelitian yang bersifat kualitatif dengan jenis penelitiannya one shot case study. Subjek dalam penelitian ini adalah kelas III SD Negeri Lampeuneurut sebanyak 29 siswa. Adapun teknik pengumpulan data dalam penelitian ini adalah 1) tes ketuntasan belajar siswa, 2) observasi kemampuan guru mengelola pembelajaran, 3) observasi aktivitas siswa. Hasil penelitian ini dapat disimpulkan bahwa ketuntasan belajar siswa dengan menerapkan model pengajaran langsung melalui media microsoft powerpoint pada materi bangun datar adalah tuntas, dengan persentase 86,21%. Kemampuan guru mengelola pembelajaran dengan menerapkan model pengajaran langung melalui media microsoft powerpoint pada materi bangun datar berada pada kategori baik dengan kriteria 3,50 ? TKG ? 4,50. Dan aktivitas siswa selama pembelajaran yang termasuk dalam kategori tidak akti