Geometric deep learning for polygonal mesh denoising

Il seguente lavoro si propone come analisi degli operatori convoluzionali che caratterizzano le graph neural networks. ln particolare, la trattazione si divide in due parti, una teorica e una sperimentale. Nella parte teorica vengono innanzitutto introdotte le nozioni preliminari di mesh e convoluzione su mesh. In seguito vengono riportati i concetti base del geometric deep learning, quali le definizioni degli operatori convoluzionali e di pooling e unpooling. Un'attenzione particolare è stata data all'architettura Graph U-Net. La parte sperimentare riguarda l'applicazione delle reti neurali e l'analisi degli operatori convoluzionali applicati al denoising di superfici perturbate a causa di misurazioni imperfette effettuate da scanner 3D.

Dataset Fusion application on deep learning Object Detection task

Application of dataset fusion techniques to an object detection task, involving the use of deep learning as convolutional neural networks, to manage to create a single RCNN architecture able to inference with good performances on two distinct datasets with different domains.

A study on tackling visual odometry by a transformer architecture

This dissertation describes a deepening study about Visual Odometry problem tackled with transformer architectures. The existing VO algorithms are based on heavily hand-crafted features and are not able to generalize well to new environments. To train them, we need carefully fine-tune the hyper-parameters and the network architecture. We propose to tackle the VO problem with transformer because it is a general-purpose architecture and because it was designed to transformer sequences of data from a domain to another one, which is the case of the VO problem. Our first goal is to create synthetic dataset using BlenderProc2 framework to mitigate the problem of the dataset scarcity. The second goal is to tackle the VO problem by using different versions of the transformer architecture, which will be pre-trained on the synthetic dataset and fine-tuned on the real dataset, KITTI dataset. Our approach is defined as follows: we use a feature-extractor to extract features embeddings from a sequence of images, then we feed this sequence of embeddings to the transformer architecture, finally, an MLP is used to predict the sequence of camera poses.

SmartForge: An event-driven architecture for simulator-based process optimization

In the metal industry, and more specifically in the forging one, scrap material is a crucial issue and reducing it would be an important goal to reach. Not only would this help the companies to be more environmentally friendly and more sustainable, but it also would reduce the use of energy and lower costs. At the same time, the techniques for Industry 4.0 and the advancements in Artificial Intelligence (AI), especially in the field of Deep Reinforcement Learning (DRL), may have an important role in helping to achieve this objective. This document presents the thesis work, a contribution to the SmartForge project, that was performed during a semester abroad at Karlstad University (Sweden). This project aims at solving the aforementioned problem with a business case of the company Bharat Forge Kilsta, located in Karlskoga (Sweden). The thesis work includes the design and later development of an event-driven architecture with microservices, to support the processing of data coming from sensors set up in the company's industrial plant, and eventually the implementation of an algorithm with DRL techniques to control the electrical power to use in it.

Deep Water Running para redução da gordura corporal em mulheres na meia idade : estudo de intervenção em Campinas - SP

Universidade Estadual de Campinas . Faculdade de Educação Física

Swine deep bedding ashes as a mineral additive for cement based mortar

The sustainability of intensive swine production demands alternative destinations for the generated residues. Ashes from swine rice husk-based deep bedding were tested as a mineral addition for cement mortars. The ashes were obtained at 400 to 600ºC, ground and sieved through a 325 mesh sieve (# 0.045 mm). The characterization of the ashes included the determination of the index of pozzolanic activity with lime. The ashes were also tested as partial substitutes of Portland cement. The mortars were prepared using a cement:sand proportion of 1:1.5, and with water/cement ratio of 0.4. Three percentages of mass substitution of the cement were tested: 10, 20 and 30%. Mortar performances were assessed at 7 and 28 days determining their compressive strength. The chosen condition for calcinations at the laboratory scale was related to the maximum temperature of 600ºC since the resulting ashes contained vitreous materials and presented satisfactory values for the pozzolanic index under analysis. The pozzolanic activity indicated promising results for ashes produced at 600ºC as a replacement of up to 30% in cement masses.

From Offshore to Onshore: Multiple Origins of Shallow-Water Corals from Deep-Sea Ancestors

Shallow-water tropical reefs and the deep sea represent the two most diverse marine environments. Understanding the origin and diversification of this biodiversity is a major quest in ecology and evolution. The most prominent and well-supported explanation, articulated since the first explorations of the deep sea, holds that benthic marine fauna originated in shallow, onshore environments, and diversified into deeper waters. In contrast, evidence that groups of marine organisms originated in the deep sea is limited, and the possibility that deep-water taxa have contributed to the formation of shallow-water communities remains untested with phylogenetic methods. Here we show that stylasterid corals (Cnidaria: Hydrozoa: Stylasteridae)-the second most diverse group of hard corals-originated and diversified extensively in the deep sea, and subsequently invaded shallow waters. Our phylogenetic results show that deep-water stylasterid corals have invaded the shallow-water tropics three times, with one additional invasion of the shallow-water temperate zone. Our results also show that anti-predatory innovations arose in the deep sea, but were not involved in the shallow-water invasions. These findings are the first robust evidence that an important group of tropical shallow-water marine animals evolved from deep-water ancestors.

Deep plant-derived carbon storage in Amazonian podzols

Equatorial podzols are soils characterized by thick sandy horizons overlying more clayey horizons. Organic matter produced in the topsoil is transferred in depth through the sandy horizons and accumulate at the transition, at a depth varying from 1 to more than 3 m, forming deep horizons rich in organic matter (Bh horizons). Although they cover great surfaces in the equatorial zone, these soils are still poorly known. Studying podzols from Amazonia, we found out that the deep Bh horizons in poorly drained podzol areas have a thickness higher than 1m and store unexpected amounts of carbon. The average for the studied area was 66.7 +/- 5.8 kgCm(-2) for the deep Bh and 86.8 +/- 7.1 kgCm(-2) for the whole profile. Extrapolating to the podzol areas of the whole Amazonian basin has been possible thanks to digital maps, giving an order of magnitude around 13.6 +/- 1.1 PgC, at least 12.3 PgC higher than previous estimates. This assessment should be refined by additional investigations, not only in Amazonia but in all equatorial areas where podzols have been identified. Because of the lack of knowledge on the quality and behaviour of the podzol organic matter, the question of the feedback between the climate and the equatorial podzol carbon cycle is open.

Architecture for machining process and production monitoring based in open computer numerical control

This paper proposes an architecture for machining process and production monitoring to be applied in machine tools with open Computer numerical control (CNC). A brief description of the advantages of using open CNC for machining process and production monitoring is presented with an emphasis on the CNC architecture using a personal computer (PC)-based human-machine interface. The proposed architecture uses the CNC data and sensors to gather information about the machining process and production. It allows the development of different levels of monitoring systems with mininium investment, minimum need for sensor installation, and low intrusiveness to the process. Successful examples of the utilization of this architecture in a laboratory environment are briefly described. As a Conclusion, it is shown that a wide range of monitoring solutions can be implemented in production processes using the proposed architecture.

A molecular phylogeny and the evolution of nest architecture and behavior in Trigona s.s. (Hymenoptera : Apidae : Meliponini)

Stingless bees exhibit extraordinary variation in nest architecture within and among species. To test for phylogenetic association of behavioral traits for species of the Neotropical stingless bee genus Trigona s.s., a phylogenetic hypothesis was generated by combining sequence data of 24 taxa from one mitochondrial (16S rRNA) and four nuclear gene fragments (long-wavelength rhodopsin copy 1 (opsin), elongation factor-1 alpha copy F2, arginine kinase, and 28S rRNA). Fifteen characteristics of the nest architecture were coded and tested for phylogenetic association. Several characters have significant phylogenetic signal, including type of nesting substrate, nest construction material, and hemipterophily, the tending of hemipteroid insects in exchange for sugar excretions. Phylogenetic independent habits encountered in Trigona s.s. include coprophily and necrophagy.

Ultra-Deep Sequencing Reveals the microRNA Expression Pattern of the Human Stomach

Background: While microRNAs (miRNAs) play important roles in tissue differentiation and in maintaining basal physiology, little is known about the miRNA expression levels in stomach tissue. Alterations in the miRNA profile can lead to cell deregulation, which can induce neoplasia. Methodology/Principal Findings: A small RNA library of stomach tissue was sequenced using high-throughput SOLiD sequencing technology. We obtained 261,274 quality reads with perfect matches to the human miRnome, and 42% of known miRNAs were identified. Digital Gene Expression profiling (DGE) was performed based on read abundance and showed that fifteen miRNAs were highly expressed in gastric tissue. Subsequently, the expression of these miRNAs was validated in 10 healthy individuals by RT-PCR showed a significant correlation of 83.97% (P<0.05). Six miRNAs showed a low variable pattern of expression (miR-29b, miR-29c, miR-19b, miR-31, miR-148a, miR-451) and could be considered part of the expression pattern of the healthy gastric tissue. Conclusions/Significance: This study aimed to validate normal miRNA profiles of human gastric tissue to establish a reference profile for healthy individuals. Determining the regulatory processes acting in the stomach will be important in the fight against gastric cancer, which is the second-leading cause of cancer mortality worldwide.

Ablation Rate and Morphology of Superficial and Deep Dentin Irradiated with Different Er:YAG Laser Energy Levels

Objective: In this study we evaluated the ablation rate of superficial and deep dentin irradiated with different Er:YAG laser energy levels, and observed the micromorphological aspects of the lased substrates with a scanning electron microscope (SEM). Background Data: Little is known about the effect of Er: YAG laser irradiation on different dentin depths. Materials and Methods: Sixty molar crowns were bisected, providing 120 specimens, which were randomly assigned into two groups ( superficial or deep dentin), and later into five subgroups (160, 200, 260, 300, or 360 mJ). Initial masses of the specimens were obtained. After laser irradiation, the final masses were obtained and mass losses were calculated followed by the preparation of specimens for SEM examination. Mass-loss values were subjected to two-way ANOVA and Fisher's least significant difference multiple-comparison tests (p < 0.05). Results: There was no difference between superficial and deep dentin. A significant and gradual increase in the mass-loss values was reached when energies were raised, regardless of the dentin depth. The energy level of 360 mJ showed the highest values and was statistically significantly different from the other energy levels. The SEM images showed that deep dentin was more selectively ablated, especially intertubular dentin, promoting tubule protrusion. At 360 mJ the micromorphological features were similar for both dentin depths. Conclusion: The ablation rate did not depend on the depth of the dentin, and an energy level lower than 360 mJ is recommended to ablate both superficial and deep dentin effectively without causing tissue damage.

Deep sequencing of New World screw-worm transcripts to discover genes involved in insecticide resistance

Background: The New World screw-worm (NWS), Cochliomyia hominivorax, is one of the most important myiasis-causing flies, causing severe losses to the livestock industry. In its current geographical distribution, this species has been controlled by the application of insecticides, mainly organophosphate (OP) compounds, but a number of lineages have been identified that are resistant to such chemicals. Despite its economic importance, only limited genetic information is available for the NWS. Here, as a part of an effort to characterize the C. hominivorax genome and identify putative genes involved in insecticide resistance, we sampled its transcriptome by deep sequencing of polyadenylated transcripts using the 454 sequencing technology. Results: Deep sequencing on the 454 platform of three normalized libraries (larval, adult male and adult female) generated a total of 548,940 reads. Eighteen candidate genes coding for three metabolic detoxification enzyme families, cytochrome P450 monooxygenases, glutathione S transferases and carboxyl/cholinesterases were selected and gene expression levels were measured using quantitative real-time polymerase chain reaction (qRT-PCR). Of the investigated candidates, only one gene was expressed differently between control and resistant larvae with, at least, a 10-fold down-regulation in the resistant larvae. The presence of mutations in the acetylcholinesterase (target site) and carboxylesterase E3 genes was investigated and all of the resistant flies presented E3 mutations previously associated with insecticide resistance. Conclusions: Here, we provided the largest database of NWS expressed sequence tags that is an important resource, not only for further studies on the molecular basis of the OP resistance in NWS fly, but also for functional and comparative studies among Calliphoridae flies. Among our candidates, only one gene was found differentially expressed in resistant individuals, and its role on insecticide resistance should be further investigated. Furthermore, the absence of mutations in the OP target site and the high frequency of mutant carboxylesterase E3 indicate that metabolic resistance mechanisms have evolved predominantly in this species.

Robust relations between CCN and the vertical evolution of cloud drop size distribution in deep convective clouds

In-situ measurements in convective clouds (up to the freezing level) over the Amazon basin show that smoke from deforestation fires prevents clouds from precipitating until they acquire a vertical development of at least 4 km, compared to only 1-2 km in clean clouds. The average cloud depth required for the onset of warm rain increased by similar to 350 m for each additional 100 cloud condensation nuclei per cm(3) at a super-saturation of 0.5% (CCN0.5%). In polluted clouds, the diameter of modal liquid water content grows much slower with cloud depth (at least by a factor of similar to 2), due to the large number of droplets that compete for available water and to the suppressed coalescence processes. Contrary to what other studies have suggested, we did not observe this effect to reach saturation at 3000 or more accumulation mode particles per cm(3). The CCN0.5% concentration was found to be a very good predictor for the cloud depth required for the onset of warm precipitation and other microphysical factors, leaving only a secondary role for the updraft velocities in determining the cloud drop size distributions. The effective radius of the cloud droplets (r(e)) was found to be a quite robust parameter for a given environment and cloud depth, showing only a small effect of partial droplet evaporation from the cloud's mixing with its drier environment. This supports one of the basic assumptions of satellite analysis of cloud microphysical processes: the ability to look at different cloud top heights in the same region and regard their r(e) as if they had been measured inside one well developed cloud. The dependence of r(e) on the adiabatic fraction decreased higher in the clouds, especially for cleaner conditions, and disappeared at r(e)>=similar to 10 mu m. We propose that droplet coalescence, which is at its peak when warm rain is formed in the cloud at r(e)=similar to 10 mu m, continues to be significant during the cloud's mixing with the entrained air, cancelling out the decrease in r(e) due to evaporation.

Heavy quark production in deep inelastic electron-nucleus scattering

Heavy quark production has been very well studied over the last years both theoretically and experimentally. Theory has been used to study heavy quark production in ep collisions at HERA, in pp collisions at Tevatron and RHIC, in pA and dA collisions at RHIC, and in AA collisions at CERN-SPS and RHIC. However, to the best of our knowledge, heavy quark production in eA has received almost no attention. With the possible construction of a high energy electron-ion collider, updated estimates of heavy quark production are needed. We address the subject from the perspective of saturation physics and compute the heavy quark production cross section with the dipole model. We isolate shadowing and nonlinear effects, showing their impact on the charm structure function and on the transverse momentum spectrum.