Anisotropy in the all-sky distribution of galaxy morphological types

We present the first study of the isotropy of the distribution of morphological types of galaxies in the Local Universe out to around 200 Mpc using more than 60,000 galaxies from the HyperLeda database. We divide the sky into two opposite hemispheres and compare the abundance distribution of the morphological types, $T$, using the Kolmogorov-Smirnov (KS) test. This is repeated for different directions in the sky and the KS statistic as a function of sky coordinates is obtained. For three samples of galaxies within around 100, 150, and 200 Mpc, we find a significant hemispherical asymmetry with a vanishingly small chance of occurring in an isotropic distribution. Astonishingly, regardless of this extreme significance, the hemispherical asymmetry is aligned with the Celestial Equator at the 97.1-99.8% and with the Ecliptic at the 94.6-97.6% confidence levels, estimated using a Monte Carlo analysis. Shifting $T$ values randomly within their uncertainties has a negligible effect on this result. When a magnitude limit of $B\leq 15$ mag is applied, the sample within 100 Mpc shows no significant anisotropy after random shifting of $T$. However, the direction of the asymmetry in the samples within 150 and 200 Mpc and $B\leq 15$ mag is found to be within an angular separation of 32 degrees from $(l,b)=(123.7, 24.6)$ with 97.2% and 99.9% confidence levels, respectively. This direction is only 2.6 degrees away from the Celestial North Pole. Unless the Local Universe has a significant anisotropic distribution of galaxy types aligned with the orientation or the orbit of the Earth (which would be a challenge for the Cosmological Principle), our results show that there seems to be a systematic bias in the classification of galaxy morphological types between the data from the Northern and the Southern Equatorial sky. Further studies are absolutely needed to find out the exact source of this anisotropy.Comment: Accepted for Publication in Astronomy & Astrophysics. 12 pages, 8 figures, 4 table

Mutual Exclusivity Loss for Semi-Supervised Deep Learning

In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term that explicitly forces the classifier's prediction for multiple classes to be mutually-exclusive and effectively guides the decision boundary to lie on the low density space between the manifolds corresponding to different classes of data. Our proposed approach is general and can be used with any backpropagation-based learning method. We show through different experiments that our method can improve the object recognition performance of ConvNets using unlabeled data.Comment: 5 pages, 1 figures, ICIP 201

Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information

Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that error for map-based localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate localization error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating localization error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle localization error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm

The Growth of Urbanization and Environmental Instability: A Case Study in Ahvaz Metropolis

Uncontrolled growth of urbanization has been exposed major quantitative and qualitative developments at the beginning of twenty-first century. Current world has been much urbanized and proportion of urban population to total population has been changing and increasing more than before and human modern civilization has been directing to urbanization and this urban growth has served natural resources in its service end. Finally, the result is damaging the ecological system and the unstable environment in cities. Accordingly, we are faced with the questions of how the capacity of cities against environmental problems is caused by urban development and the use of biological resources. These questions are as follows: what is the biological capacity in cities? Does the environment can reconstruct degradation and changing posed by the cities with this level and speed? How much is the amount of urban environmental sustainability to cope with these changes? The role of the city in environmental instability becomes more clear when we know that just about three- quarter of natural resources has been consumed one-fiftieth of the world and the rate of environmental pollution production in cities are three-fourth of the total pollution. Ahvaz metropolis has been chosen as a case of study of the related issue

The Growth of Urbanization and Environmental Instability: A Case Study in Ahvaz Metropolis

Uncontrolled growth of urbanization has been exposed major quantitative and qualitative developments at the beginning of twenty-first century. Current world has been much urbanized and proportion of urban population to total population has been changing and increasing more than before and human modern civilization has been directing to urbanization and this urban growth has served natural resources in its service end. Finally, the result is damaging the ecological system and the unstable environment in cities. Accordingly, we are faced with the questions of how the capacity of cities against environmental problems is caused by urban development and the use of biological resources. These questions are as follows: what is the biological capacity in cities? Does the environment can reconstruct degradation and changing posed by the cities with this level and speed? How much is the amount of urban environmental sustainability to cope with these changes? The role of the city in environmental instability becomes more clear when we know that just about three- quarter of natural resources has been consumed one-fiftieth of the world and the rate of environmental pollution production in cities are three-fourth of the total pollution. Ahvaz metropolis has been chosen as a case of study of the related issue

Deep Learning and Optimization in Visual Target Tracking

Visual tracking is the process of estimating states of a moving object in a dynamic frame sequence. It has been considered as one of the most paramount and challenging topics in computer vision. Although numerous tracking methods have been introduced, developing a robust algorithm that can handle different challenges still remains unsolved. In this dissertation, we introduce four different trackers and evaluate their performance in terms of tracking accuracy on challenging frame sequences. Each of these trackers aims to address the drawbacks of their peers. The first developed method is called a structured multi-task multi-view tracking (SMTMVT) method, which exploits the sparse appearance model in the particle filter frame work to track targets under different challenges. Specifically, we extract features of the target candidates from different views and sparsely represent them by a linear combination of templates of different views. Unlike the conventional sparse trackers, SMTMVT not only jointly considers the relationship between different tasks and different views but also retains the structures among different views in a robust multi-task multi-view formulation. The second developed method is called a structured group local sparse tracker (SGLST), which exploits local patches inside target candidates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimization model in SGLST not only adopts local and spatial information of the target candidates but also attains the spatial layout structure among them by employing a group-sparsity regularization term. To solve the optimization model, we propose an efficient numerical algorithm consisting of two subproblems with closed-form solutions. The third developed tracker is called a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we adopt the alternating direction method of multiplier (ADMM) to design a fast and parallel numerical algorithm by deriving the augmented Lagrangian of the optimization model into two closed-form solution problems: the quadratic problem and the Euclidean norm projection onto probability simplex constraints problem. The fourth developed tracker is called an appearance variation adaptation (AVA) tracker, which aligns the feature distributions of target regions over time by learning an adaptation mask in an adversarial network. The proposed adversarial network consists of a generator and a discriminator network that compete with each other over optimizing a discriminator loss in a mini-max optimization problem. Specifically, the discriminator network aims to distinguish recent target regions from earlier ones by minimizing the discriminator loss, while the generator network aims to produce an adaptation mask to maximize the discriminator loss. We incorporate a gradient reverse layer in the adversarial network to solve the aforementioned mini-max optimization in an end-to-end manner. We compare the performance of the proposed four trackers with the most recent state-of-the-art trackers by doing extensive experiments on publicly available frame sequences, including OTB50, OTB100, VOT2016, and VOT2018 tracking benchmarks