LEURN: Learning Explainable Univariate Rules with Neural Networks

In this paper, we propose LEURN: a neural network architecture that learns univariate decision rules. LEURN is a white-box algorithm that results into univariate trees and makes explainable decisions in every stage. In each layer, LEURN finds a set of univariate rules based on an embedding of the previously checked rules and their corresponding responses. Both rule finding and final decision mechanisms are weighted linear combinations of these embeddings, hence contribution of all rules are clearly formulated and explainable. LEURN can select features, extract feature importance, provide semantic similarity between a pair of samples, be used in a generative manner and can give a confidence score. Thanks to a smoothness parameter, LEURN can also controllably behave like decision trees or vanilla neural networks. Besides these advantages, LEURN achieves comparable performance to state-of-the-art methods across 30 tabular datasets for classification and regression problems

Quantum Cuts: A Quantum Mechanical Spectral Graph Partitioning Method for Salient Object Detection

The increasing number of cameras, their availability to the end user and the social media platforms gave rise to the massive repositories of today’s Big Data. The largest portion of this data corresponds to unstructured image and video collections. This fact motivates the development of algorithms that would help efficient management and organization of the Big Data. This processing usually involves high level Computer Vision tasks such as object detection and recognition whose accuracy and complexity are therefore crucial. Salient object detection, which can be defined as highlighting the regions that visually stand out from the rest of the environment, can both reduce the complexity and improve the accuracy of object detection and recognition. Thus, recently there has been a growing interest in this topic. This interest is also due to many other applications of salient object detection such as media compression and summarization.This thesis focuses on this crucial problem and presents novel approaches and methods for salient object detection in digital media, using the principles of Quantum Mechanics. The contributions of this thesis can be categorized chronologically into three parts. First part is constituted of a direct application of ideas originally proposed for describing the wave nature of particles in Quantum Mechanics and expressed through Schrödinger’s Equation, to salient object detection in images. The significance of this contribution is the fact that, to the best of our knowledge, this is the first study that proposes a realizable quantum mechanical system for salient object proposals yielding an instantaneous speed in a possible physical implementation in the quantum scale.The second and main contribution of this thesis, is a spectral graph based salient object detection method, namely Quantum-Cuts. Despite the success of spectral graph based methods in many Computer Vision tasks, traditional approaches on applications of spectral graph partitioning methods offer little for the salient object detection problem which can be mapped as a foreground segmentation problem using graphs. Thus, Quantum-Cuts adopts a novel approach to spectral graph partitioning by integrating quantum mechanical concepts to Spectral Graph Theory. In particular, the probabilistic interpretation of quantum mechanical wave-functions and the unary potential fields in Quantum Mechanics when combined with the pairwise graph affinities that are widely used in Spectral Graph Theory, results into a unique optimization problem that formulates salient object detection. The optimal solution of a relaxed version of this problem is obtained via Quantum-Cuts and is proven to efficiently represent salient object regions in images.The third part of the contributions cover improvements on Quantum-Cuts by analyzing the main factors that affect its performance in salient object detection. Particularly, both unsupervised and supervised approaches are adopted in improving the exploited graph representation. The extensions on Quantum-Cuts led to computationally efficient algorithms that perform superior to the state-of-the-art in salient object detectio

Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels

Computer vision algorithms with pixel-wise labeling tasks, such as semantic segmentation and salient object detection, have gone through a significant accuracy increase with the incorporation of deep learning. Deep segmentation methods slightly modify and fine-tune pre-trained networks that have hundreds of millions of parameters. In this work, we question the need to have such memory demanding networks for the specific task of salient object segmentation. To this end, we propose a way to learn a memory-efficient network from scratch by training it only on salient object detection datasets. Our method encodes images to gridized superpixels that preserve both the object boundaries and the connectivity rules of regular pixels. This representation allows us to use convolutional neural networks that operate on regular grids. By using these encoded images, we train a memory-efficient network using only 0.048\% of the number of parameters that other deep salient object detection networks have. Our method shows comparable accuracy with the state-of-the-art deep salient object detection methods and provides a faster and a much more memory-efficient alternative to them. Due to its easy deployment, such a network is preferable for applications in memory limited devices such as mobile phones and IoT devices.Comment: 6 pages, submitted to MMSP 201

An investigation of nuclear properties of even even natural Mo92-100 isotopes

WOS: 000346706200006In this study, we have calculated the basic nuclear properties such as binding energies, root mean square (rms) charge radii, and neutron and proton densities of the eveneven natural Mo92-100 isotopes. Investigations were performed using the Hartree-Fock-Bogoliubov (HFB) method with different Skyrme-like forces. Separation energies, which have an important role in nuclear structure, of neutron, proton, deuteron, triton, helium-3 and alpha were also investigated with TALYS 1.4 code. The calculated results were discussed and compared with experimental results

A NOVEL SHADOW RESTORATION ALGORITHM BASED ON ATMOSPHERIC EFFECTS FOR AERIAL IMAGES

In aerial images, the performance of the segmentation and object recognition algorithms could suffer due to shadows in the scene. This effort describes a novel shadow restoration algorithm based on atmospheric effects and characteristics of sun light for aerial images. Firstly, shadow regions are detected exploiting the Rayleigh scattering phenomena and the well-known fact related to the low illumination intensity in the shadow regions. After detection, shadow restoration is achieved by first restoring partially occluded shadow areas, as a result of modeling these transition regions with a continuous function that considers shadow formations. Next, fully occluded shadow regions are restored by first segmenting the image into multiple uniformly illuminated regions, then multiplying the intensity values in these regions with a constant, which is determined by the ratio of intensities between each segment and its non-shadow neighborhood. The simulation results indicate improvements over similar work fro the literature