General hypernetwork framework for creating 3D point clouds

In this work, we propose a novel method for generating 3D point clouds that leverages properties of hypernetworks. Contrary to the existing methods that learn only the representation of a 3D object, our approach simultaneously finds a representation of the object and its 3D surface. The main idea of our HyperCloud method is to build a hypernetwork that returns weights of a particular neural network (target network) trained to map points from prior distribution into a 3D shape. As a consequence, a particular 3D shape can be generated using point-by-point sampling from the prior distribution and transforming sampled points with the target network. Since the hypernetwork is based on an auto-encoder architecture trained to reconstruct realistic 3D shapes, the target network weights can be considered as a parametrization of the surface of a 3D shape, and not as a standard representation of point cloud usually returned by competitive approaches. We also show that relying on hypernetworks to build 3D point cloud representations offers an elegant and flexible framework, and to that point we further extend our method by incorporating flow-based models which results in a novel HyperFlow approach

Two-headed eye-segmentation approach for biometric identification

Iris-based identification systems are among the most popular approaches for person identification. Such systems require good-quality segmentation modules that ideally identify the regions for different eye components. This paper introduces the new two-headed architecture, where the eye components and eyelashes are segmented using two separate decoding modules. Moreover, we investigate various training scenarios by adopting different training losses. Thanks to the two-headed approach, we were also able to examine the quality of the model with the convex prior, which enforces the convexity of the segmented shapes. We conducted an extensive evaluation of various learning scenarios on real-life conditions high-resolution near-infrared iris images

Multistage neural networks for pattern recognition

In this work the concept of multistage neural networks is going to be presented. The possibility of using this type of structure for pattern recognition would be discussed and examined with chosen problem from eld area. The results of experiment would be confront with other possible methods used for the problem

Beta-boosted ensemble for big credit scoring data

In this work we present a novel ensemble model for a credit scoring problem. The main idea of the approach is to incorporate separate beta binomial distributions for each of the classes to generate balanced datasets that are further used to construct base learners that constitute the final ensemble model. The sampling procedure is performed on two separate ranking lists, each for one class, where the ranking is based on prepotency of observing positive class. Two strategies are considered: one assumes mining easy examples and the second one forces good classification of hard cases. The proposed solutions are tested on two big datasets on credit scoring

Multistage neural networks for pattern recognition

In this work the concept of multistage neural networks is going to be presented. The possibility of using this type of structure for pattern recognition would be discussed and examined with chosen problem from eld area. The results of experiment would be confront with other possible methods used for the problem

Hypernetwork approach to generating point clouds

In this work, we propose a novel method for gen-erating 3D point clouds that leverage properties ofhyper networks. Contrary to the existing methodsthat learn only the representation of a 3D object,our approach simultaneously finds a representa-tion of the object and its 3D surface. The mainidea of our HyperCloud method is to build a hypernetwork that returns weights of a particular neuralnetwork (target network) trained to map pointsfrom a uniform unit ball distribution into a 3Dshape. As a consequence, a particular 3D shapecan be generated using point-by-point samplingfrom the assumed prior distribution and transform-ing sampled points with the target network. Sincethe hyper network is based on an auto-encoderarchitecture trained to reconstruct realistic 3Dshapes, the target network weights can be con-sidered a parametrization of the surface of a 3Dshape, and not a standard representation of pointcloud usually returned by competitive approaches.The proposed architecture allows finding mesh-based representation of 3D objects in a generativemanner while providing point clouds en pair inquality with the state-of-the-art methods.1. IntroductionToday many registration devices, s

Influence of Leptin on the Secretion of Growth Hormone in Ewes under Different Photoperiodic Conditions

Leptin is an adipokine with a pleiotropic impact on many physiological processes, including hypothalamic-pituitary-somatotropic (HPS) axis activity, which plays a key role in regulating mammalian metabolism. Leptin insensitivity/resistance is a pathological condition in humans, but in seasonal animals, it is a physiological adaptation. Therefore, these animals represent a promising model for studying this phenomenon. This study aimed to determine the influence of leptin on the activity of the HPS axis. Two in vivo experiments performed during short- and long-day photoperiods were conducted on 12 ewes per experiment, and the ewes were divided randomly into 2 groups. The arcuate nucleus, paraventricular nucleus, anterior pituitary (AP) tissues, and blood were collected. The concentration of growth hormone (GH) was measured in the blood, and the relative expression of GHRH, SST, GHRHR, SSTR1, SSTR2, SSTR3, SSTR5, LEPR, and GH was measured in the collected brain structures. The study showed that the photoperiod, and therefore leptin sensitivity, plays an important role in regulating HPS axis activity in the seasonal ewe. However, leptin influences the release of GH in a season-dependent manner, and its effect seems to be targeted at the posttranscriptional stages of GH secretion