A Block-Based Union-Find Algorithm to Label Connected Components on GPUs

In this paper, we introduce a novel GPU-based Connected Components Labeling algorithm: the Block-based Union Find. The proposed strategy significantly improves an existing GPU algorithm, taking advantage of a block-based approach. Experimental results on real cases and synthetically generated datasets demonstrate the superiority of the new proposal with respect to state-of-the-art

Improving the Performance of Thinning Algorithms with Directed Rooted Acyclic Graphs

In this paper we propose a strategy to optimize the performance of thinning algorithms. This solution is obtained by combining three proven strategies for binary images neighborhood exploration, namely modeling the problem with an optimal decision tree, reusing pixels from the previous step of the algorithm, and reducing the code footprint by means of Directed Rooted Acyclic Graphs. A complete and open-source benchmarking suite is also provided. Experimental results confirm that the proposed algorithms clearly outperform classical implementations

ClusterFix: A Cluster-Based Debiasing Approach without Protected-Group Supervision

The failures of Deep Networks can sometimes be ascribed to biases in the data or algorithmic choices. Existing debiasing approaches exploit prior knowledge to avoid unintended solutions; we acknowledge that, in real-world settings, it could be unfeasible to gather enough prior information to characterize the bias, or it could even raise ethical considerations. We hence propose a novel debiasing approach, termed ClusterFix, which does not require any external hint about the nature of biases. Such an approach alters the standard empirical risk minimization and introduces a per-example weight, encoding how critical and far from the majority an example is. Notably, the weights consider how difficult it is for the model to infer the correct pseudo-label, which is obtained in a self-supervised manner by dividing examples into multiple clusters. Extensive experiments show that the misclassification error incurred in identifying the correct cluster allows for identifying examples prone to bias-related issues. As a result, our approach outperforms existing methods on standard benchmarks for bias removal and fairness

Optimal client recommendation for market makers in illiquid financial products

The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather than relying on commensurate interest arising through natural demand. In this paper, we consider the inference of a client profile for the purpose of corporate bond recommendation, based on typical recorded information available to the market maker. Given a historical record of corporate bond transactions and bond meta-data, we use a topic-modelling analogy to develop a probabilistic technique for compiling a curated list of client recommendations for a particular bond that needs to be traded, ranked by probability of interest. We show that a model based on Latent Dirichlet Allocation offers promising performance to deliver relevant recommendations for sales traders.Comment: 12 pages, 3 figures, 1 tabl

Influence of MAX-Phase Deformability on Coating Formation by Cold Spraying

As solid-state deposition technique avoiding oxidation, cold gas spraying is capable of retaining feedstock material properties in the coatings, but typically fails to build up coatings of brittle materials. Ceramic MAX phases show partial deformability in particular lattice directions and may thus successfully deposit in cold spraying. However, deformation mechanisms under high strain rate, as necessary for cohesion and adhesion, are not fully clear yet. A MAX-phase deposit only builds up, if the specific mechanical properties of the MAX phase allow for, and if suitable spray parameter sets get realized. To investigate the influence of material properties and deposition conditions on coating microstructure and quality, three MAX phases, Ti3SiC2, Ti2AlC and Cr2AlC, were selected. Up to ten passes under different spray parameters yielded Ti2AlC and Cr2AlC coatings with thicknesses of about 200-500 \ub5m. In contrast, Ti3SiC2 only forms a monolayer, exhibiting brittle laminar failure of the impacting particles. In all cases, the crystallographic structure of the MAX-phase powders was retained in the coatings. Thicker coatings show rather low porosities (< 2%), but some laminar cracks. The deposition behavior is correlated with individual mechanical properties of the different MAX-phase compositions and is discussed regarding the particular, highly anisotropic deformation mechanisms

The effect of ceramic YSZ powder morphology on coating performance for industrial TBCs

The increasing turbine inlet temperatures in modern gas turbines have raised concerns about the corrosion of ceramic thermal barrier coatings (TBCs) caused by molten silicate deposits, commonly referred to as “CMAS” due to their main constituents (CaO-MgO-Al2O3-SiO2). The objective of this study was to investigate the combined influence of powder morphology and chemical composition on the CMAS resistance and thermal cycling resistance of ceramic monolayer and bi-layer coatings created through Atmospheric Plasma Spraying (APS). Three powder morphologies were examined: porous Agglomerated and Sintered (A&S) granules, Hollow Spherical (HOSP) powders, and dense, irregular Fused and Crushed (F&C) particles. Monolayer 7-8YSZ coatings with both porous and dense vertically cracked (DVC) microstructures, and bi-layer coatings consisting of a bottom layer of porous standard 7-8YSZ and a top layer composed of a porous high‑yttrium ZrO2–55 wt% Y2O3 were obtained using all three powder types (A&S, HOSP, or F&C). Furthermore, the bi-layer systems were deposited with different ratios between the individual layer thicknesses and/or different total thickness. FEG-SEM, EDX, and micro-Raman analyses, were conducted to assess the coatings' performance. Nanoindentation high-speed mapping and pillar splitting test were performed to evaluate the mechanical behaviour. The study on 8YSZ monolayers shows that coatings from a F&C feedstock exhibit higher density, reducing the CMAS penetration. However, these coatings demonstrate poorer thermal cycling performance due to increased stiffness and thermal stresses. Coatings from HOSP and A&S powders allow CMAS penetration but offer stress relief pathways, enhancing the coating's ability to withstand thermal stresses. Bi-layer coatings with a 55YSZ top coat show superior CMAS resistance compared to 7-8YSZ monolayer coatings, with limited penetration causing top coat peeling. The thickness ratio between the layers also affects thermal cycling resistance, where a thinner 55YSZ layer extends the TBC lifetime

Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm

Featured Application Convolutional neural networks can accurately identify the Inferior Alveolar Canal, rapidly generating precise 3D data. The datasets and source code used in this paper are publicly available, allowing the reproducibility of the experiments performed. Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation

Analisi del processo di “pack chromising” su superleghe di Ni per turbine a gas

I rivestimenti diffusivi sono utilizzati per la realizzazione di strati protettivi necessari a migliorare la resistenza dicomponenti soggetti a fenomeni degradativi ad alta temperatura, come ossidazione e corrosione a caldo (es.: componentiper impianti turbogas, bruciatori, etc.). In particolare, l’arricchimento in cromo della superficie di leghe metalliche, adesempio attraverso processi tipo “pack-cementation”, garantisce una buona protezione dalla corrosione a caldo di tipoII. Tuttavia, in letteratura, non esistono studi sistematici ed approfonditi sui meccanismi di formazione e sull’effetto deiparametri di processo sulla microstruttura di questi rivestimenti.Lo scopo del presente lavoro, pertanto, è stato lo studio dell’effetto della composizione della miscela di polveri (packmix)e della durata del trattamento termico (12 e 24 h) sulla microstruttura e sullo spessore dei rivestimenti ottenutiutilizzando il processo di pack-chromizing. Come substrato è stata impiegata la superlega Inconel 738, mentre il packmixutilizzato è composto da polvere di cromo (10 e 25 wt.%), NH4Cl (1, 2 e 5 wt.%) e Al2O3 (bal.) come inerte.Inoltre, il campione con il rivestimento più promettente è stato sottoposto ad un successivo trattamento di packaluminizing,per ottenere uno strato superficiale arricchito sia in Cr, sia Al, adeguato a conferire simultaneamenteresistenza a corrosione a caldo a 700 – 900 °C e ad ossidazione a temperature intorno ai 1000 °C.Le caratteristiche strutturali e microstrutturali dei riporti ottenuti sono state studiate tramite microscopiaelettronica a scansione (SEM) e diffrazione di raggi X. Le analisi eseguite hanno evidenziato che la durata deltrattamento termico favorisce i fenomeni interdiffusivi delle specie chimiche più mobili, Cr e Ni, portando adun generale aumento di spessore dei rivestimenti. La quantità di polvere di cromo nel pack mix influenza laconcentrazionedi questo elemento all’interno dei coating solamente se la composizione del pack-mix prevedeanche una quantità di attivatore sufficiente a garantirne un trasporto ottimale sulla superficie e successivamenteall’interno del componente. La presenza di particelle ricche di alluminio e ossigeno nello strato diffusivo dimostrache durante il ciclo termico avvengono anche reazioni secondarie che interessano l’inerte, portando allo sviluppodi ossigeno che reagisce con alcuni elementi del substrato

Suspension High Velocity Oxy-Fuel (SHVOF)-sprayed alumina coatings: microstructure, nanoindentation and wear

Suspension High Velocity Oxy Fuel Spraying (SHVOF) can be used to produce thermally sprayed coatings from powdered feedstocks too small to be processed by mechanical feeders, allowing formation of nanostructured coatings with improved density and mechanical properties. Here, alumina coatings were produced from sub-micron sized feedstock in aqueous suspension, using two flame combustion parameters yielding contrasting microstructures. Both coatings were tested in dry sliding wear conditions with an alumina counterbody. The coating processed with high combustion power of 101 kW contained 74 wt% amorphous phase and 26 wt% crystalline phase (95 wt% gamma and 3 wt% alpha alumina) while the 72 kW coating contained lower 58 wt% amorphous phase and 42 wt% crystalline phases (73 wt% was alpha and 26 wt % gamma). The 101 kW coating had a dry sliding specific wear rate between 4-4.5 x 10-5 mm3/Nm, 2 orders of magnitude higher than the 72 kW coating wear rate of 2-4.2 x 10-7 mm3/Nm. A severe wear regime dominated by brittle fracture and grain pull out of the coating was responsible for the wear of the 101 kW coating, explained by mean fracture toughness three times lower than the 72 kW coating, owing to the almost complete absence of alpha alumina