Existence versus Exploitation: The Opacity of Backbones and Backdoors Under a Weak Assumption

Backdoors and backbones of Boolean formulas are hidden structural properties. A natural goal, already in part realized, is that solver algorithms seek to obtain substantially better performance by exploiting these structures. However, the present paper is not intended to improve the performance of SAT solvers, but rather is a cautionary paper. In particular, the theme of this paper is that there is a potential chasm between the existence of such structures in the Boolean formula and being able to effectively exploit them. This does not mean that these structures are not useful to solvers. It does mean that one must be very careful not to assume that it is computationally easy to go from the existence of a structure to being able to get one's hands on it and/or being able to exploit the structure. For example, in this paper we show that, under the assumption that P $\neq$ NP, there are easily recognizable families of Boolean formulas with strong backdoors that are easy to find, yet for which it is hard (in fact, NP-complete) to determine whether the formulas are satisfiable. We also show that, also under the assumption P $\neq$ NP, there are easily recognizable sets of Boolean formulas for which it is hard (in fact, NP-complete) to determine whether they have a large backbone

Fast multipole networks

Two prerequisites for robotic multiagent systems are mobility and communication. Fast multipole networks (FMNs) enable both ends within a unified framework. FMNs can be organized very efficiently in a distributed way from local information and are ideally suited for motion planning using artificial potentials. We compare FMNs to conventional communication topologies, and find that FMNs offer competitive communication performance (including higher network efficiency per edge at marginal energy cost) in addition to advantages for mobility

RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes

Radiance fields have gradually become a main representation of media. Although its appearance editing has been studied, how to achieve view-consistent recoloring in an efficient manner is still under explored. We present RecolorNeRF, a novel user-friendly color editing approach for the neural radiance fields. Our key idea is to decompose the scene into a set of pure-colored layers, forming a palette. By this means, color manipulation can be conducted by altering the color components of the palette directly. To support efficient palette-based editing, the color of each layer needs to be as representative as possible. In the end, the problem is formulated as an optimization problem, where the layers and their blending weights are jointly optimized with the NeRF itself. Extensive experiments show that our jointly-optimized layer decomposition can be used against multiple backbones and produce photo-realistic recolored novel-view renderings. We demonstrate that RecolorNeRF outperforms baseline methods both quantitatively and qualitatively for color editing even in complex real-world scenes.Comment: To appear in ACM Multimedia 2023. Project website is accessible at https://sites.google.com/view/recolorner

Optimization of antioxidant, mechanical and chemical physical properties of chitosan-sorbitol-gallic acid films by response surface methodology

The interest in the development of biodegradable films is increasing, which is related to the wide availability of resources and methods of synthesis to generate them. For this reason, in this study, the preparation of films containing chitosan, sorbitol and gallic acid this study was proposed. The incorporation of natural antioxidants into films modifies their structure and provides new functionality to them. In order to determine the optimal content of sorbitol and gallic acid in the formulation of the chitosan films to obtain the highest antioxidant capacity and the best mechanical properties, an experimental design based on a two-factor Doehlert model was used. The optimum condition of film synthesis was obtained when a mixture of 1 wt chitosan, 3.62 wt% of sorbitol and 1 wt% of gallic acid was performed. The properties studied were experimentally evaluated at this optimal point and compared with the model predictions, showing good results. It proved to be a promising material to be successfully used as packaging material.Fil: Raspo, Matías Alejandro. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (I). Grupo Vinculado al Plapiqui - Investigación y Desarrollo en Tecnología Química; ArgentinaFil: Gomez, Cesar Gerardo. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; Argentina. Universidad Nacional de Córdoba. Instituto de Investigación y Desarrollo en Ingeniería de Procesos y Química Aplicada. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación y Desarrollo en Ingeniería de Procesos y Química Aplicada; ArgentinaFil: Andreatta, Alfonsina Ester. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (I). Grupo Vinculado al Plapiqui - Investigación y Desarrollo en Tecnología Química; Argentin

Dust-penetrated morphology in the high-redshift universe: clues from NGC 922

Results from the Hubble Deep Field (HDF) North and South show a large percentage of high-redshift galaxies whose appearance falls outside traditional classification systems. The nature of these objects is poorly understood, but sub-mm observations indicate that at least some of these systems are heavily obscured (Sanders 2000). This raises the intriguing possibility that a physically meaningful classification system for high-redshift galaxies might be more easily devised at rest-frame infrared wavelengths, rather than in the optical regime. Practical realization of this idea will become possible with the advent of the Next Generation Space Telescope (NGST). In order to explore the capability of NGST for undertaking such science, we present NASA-IRTF and SCUBA observations of NGC 922, a chaotic system in our local Universe which bears a striking resemblance to objects such as HDF 2-86 (z=0.749) in the HDF North. If objects such as NGC 922 are common at high-redshifts, then this galaxy may serve as a local morphological `Rosetta stone' bridging low and high-redshift populations. In this paper we demonstrate that quantitative measures of galactic structure are recoverable in the rest-frame infrared for NGC 922 seen at high redshifts using NGST, by simulating the appearance of this galaxy at redshifts z=0.7 and z=1.2 in rest-frame K'. Our results suggest that the capability of efficiently exploring the rest-wavelength IR morphology of high-z galaxies should probably be a key factor in deciding the final choice of instruments for the NGST.Comment: 7 pages, 12 Figures. Accepted for publication in A&A. Better version of the figures can be found at http://www.inaoep.mx/~puerari/ngs

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages