High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysis

We propose a novel method for Zero-Shot Anomaly Localization that leverages a bidirectional mapping derived from the 1-dimensional Wasserstein Distance. The proposed approach allows pinpointing the anomalous regions in a texture with increased precision by aggregating the contribution of a pixel to the errors of all nearby patches. We validate our solution on several datasets and obtain more than a 40% reduction in error over the previous state of the art on the MVTec AD dataset in a zero-shot setting

Pose Manipulation with Identity Preservation

This paper describes a new model which generates images in novel poses e.g. by altering face expression and orientation, from just a few instances of a human subject. Unlike previous approaches which require large datasets of a specific person for training, our approach may start from a scarce set of images, even from a single image. To this end, we introduce Character Adaptive Identity Normalization GAN (CainGAN) which uses spatial characteristic features extracted by an embedder and combined across source images. The identity information is propagated throughout the network by applying conditional normalization. After extensive adversarial training, CainGAN receives figures of faces from a certain individual and produces new ones while preserving the person’s identity. Experimental results show that the quality of generated images scales with the size of the input set used during inference. Furthermore, quantitative measurements indicate that CainGAN performs better compared to other methods when training data is limited

Sphere-Guided Training of Neural Implicit Surfaces

In recent years, surface modeling via neural implicit functions has become one of the main techniques for multi-view 3D reconstruction. However, the state-of-the-art methods rely on the implicit functions to model an entire volume of the scene, leading to reduced reconstruction fidelity in the areas with thin objects or high-frequency details. To address that, we present a method for jointly training neural implicit surfaces alongside an auxiliary explicit shape representation, which acts as surface guide. In our approach, this representation encapsulates the surface region of the scene and enables us to boost the efficiency of the implicit function training by only modeling the volume in that region. We propose using a set of learnable spherical primitives as a learnable surface guidance since they can be efficiently trained alongside the neural surface function using its gradients. Our training pipeline consists of iterative updates of the spheres' centers using the gradients of the implicit function and then fine-tuning the latter to the updated surface region of the scene. We show that such modification to the training procedure can be plugged into several popular implicit reconstruction methods, improving the quality of the results over multiple 3D reconstruction benchmarks

SoDaCam: Software-defined Cameras via Single-Photon Imaging

Reinterpretable cameras are defined by their post-processing capabilities that exceed traditional imaging. We present "SoDaCam" that provides reinterpretable cameras at the granularity of photons, from photon-cubes acquired by single-photon devices. Photon-cubes represent the spatio-temporal detections of photons as a sequence of binary frames, at frame-rates as high as 100 kHz. We show that simple transformations of the photon-cube, or photon-cube projections, provide the functionality of numerous imaging systems including: exposure bracketing, flutter shutter cameras, video compressive systems, event cameras, and even cameras that move during exposure. Our photon-cube projections offer the flexibility of being software-defined constructs that are only limited by what is computable, and shot-noise. We exploit this flexibility to provide new capabilities for the emulated cameras. As an added benefit, our projections provide camera-dependent compression of photon-cubes, which we demonstrate using an implementation of our projections on a novel compute architecture that is designed for single-photon imaging.Comment: Accepted at ICCV 2023 (oral). Project webpage can be found at https://wisionlab.com/project/sodacam

Cardioprotective Effects of Cultivated Black Chokeberries (<em>Aronia</em> spp.): Traditional Uses, Phytochemistry and Therapeutic Effects

Cardiovascular diseases represent the main cause of morbidity and mortality worldwide. Obesity, sedentary life style, diet, smoking and stress are the principal inducers of hypertension, endothelium dysfunction and insulin resistance in the developed countries. The latest in vitro and in vivo studies on different type of extracts obtained from black-fruited Aronia highlight its excellent cardioprotective actions for the prevention and treatment of cardiovascular and metabolic disorders. So, this chapter aims to bring an up-to-date regarding the antioxidant, anti-inflammatory, anti-atherosclerotic, antiplatelet, blood pressure, glucose and lipid reduction properties of black-fruited Aronia, as a possible new therapeutic strategy for the primary and secondary prevention of cardiovascular pathologies

Energy-efficient multipath ring network for heterogeneous clustered neuronal arrays

Simulating large spiking neural networks (SNN) with a high level ofrealism in a field programmable gate array (FPGA) requires efficientnetwork architectures that satisfy both resource and interconnect constraints, as well as changes in traffic patterns due to learning processes.Based on a clustered SNN simulator concept, in this thesis, an energy-efficient multipath ring network topology is presented for the neuron-to-neuron communication. It is compared in terms of its mathematicalproperties with other common network topology graphs after whichthe traffic distributions across it and a two dimensional torus network are estimated and contrasted. As a final characterization step, the energy-delay product of the multipath topology is estimated and compared with other low power architectures. In addition, a simplified binary tree is suggested as a network layer for handling configuration and input/output data that uses a custom channel protocol without the need for routing tables

The Analysis of the Emergence and Development of Female Entrepreneurship in Romania

The emergence of the female entrepreneurial social class is certain and convincing in many countries, including Romania. Through this study we wish to portray a relevant image regarding the situation of female entrepreneurship in Romania. The starting point and a question to which many seek answers are: What was and is the economic strength of female entrepreneurship in Romania? After a short historical presentation regarding the evolution of feminine entrepreneurship, we presented the analysis of results after 25 years of democracy and capitalism. The main inference that we have deducted was: that the number of female entrepreneurs is on ascending path