A Factorial experiment on <i>Citrus</i> stock/scion combinations in Sardinia

Five Citrus varieties ("Washington navel", "Tarocco", "Biondo comune", "Valencia" sweet oranges, and "Avana" mandarin) on different rootstocks (sour orange, "Troyer" citrange, citremon 1449, Poncirus trifoliata "Rubidoux", "Orlando" tangelo), and at two planting distances (4x4 m, 5x5 m) were evaluated for 3 years in a factorial experiment, with a completely randomized design. Observations were made on growth, productivity and fruit quality. Tree growth, productivity and fruit quality were affected both by variety and by rootstock and planting distance. "Valencia" and "Biondo comune" showed the best growth and yield, and "Avana" mandarin the poorest. Several differences in fruit quality were observed in the different varieties, mostly concerning fruit weight, rind thickness, juice, TSS and T A. The rootstock also affected growth, yield and fruit characteristics. The growth was decreased by "Rubidoux" trifoliate orange, while the yield was slightly increased by "Troyer", "Rubidoux" and "Orlando". "Rubidoux" and citremon improved several fruit characteristics, such as rind thickness, juice, and TSS content. As the trees were still young, planting distance did not affect growth, but some small differences were found in fruit quality. The yield/tree and the efficiency were increased by 5x5 m treatment, while the yield/ha was, on the contrary, higher in 4x4 m treatment. Finally, some interactions were found between variety and rootstock

Three-dimension-printed custom-made prosthetic reconstructions: from revision surgery to oncologic reconstructions

Background The use of custom-made 3D-printed prostheses for reconstruction of severe bone defects in selected cases is increasing. The aims of this study were to evaluate (1) the feasibility of surgical reconstruction with these prostheses in oncologic and non-oncologic settings and (2) the functional results, complications, and outcomes at short-term follow-up. Methods We analyzed 13 prospectively collected patients treated between June 2016 and January 2018. Diagnoses were primary bone tumour (7 patients), metastasis (3 patients), and revision of total hip arthroplasty (3 patients). Pelvis was the most frequent site of reconstruction (7 cases). Functional results were assessed with MSTS score and complications according to Henderson et al. Statistical analysis was performed using Kaplan-Meier and log-rank test curves. Results At a mean follow-up of 13.7 months (range, 6 \u2013 26 months), all patients except one were alive. Oncologic outcomes show seven patients NED (no evidence of disease), one NED after treatment of metastasis, one patient died of disease, and another one was alive with disease. Overall survival was 100% and 80% at one and two years, respectively. Seven complications occurred in five patients (38.5%). Survival to all complications was 62% at two years of follow-up. Functional outcome was good or excellent in all cases with a mean score of 80.3%. Conclusion 3D-printed custom-made prostheses represent a promising reconstructive technique in musculoskeletal oncology and challenging revision surgery. Preliminary results were satisfactory. Further studies are needed to evaluate prosthetic design, fixation methods, and stability of the implants at long-ter

Matching 3D face scans using interest points and local histogram descriptors

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Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving

Conditional Imitation learning is a common and effective approach to train autonomous driving agents. However, two issues limit the full potential of this approach: (i) the inertia problem, a special case of causal confusion where the agent mistakenly correlates low speed with no acceleration, and (ii) low correlation between offline and online performance due to the accumulation of small errors that brings the agent in a previously unseen state. Both issues are critical for state-aware models, yet informing the driving agent of its internal state as well as the state of the environment is of crucial importance. In this paper we propose a multi-task learning agent based on a multi-stage vision transformer with state token propagation. We feed the state of the vehicle along with the representation of the environment as a special token of the transformer and propagate it throughout the network. This allows us to tackle the aforementioned issues from different angles: guiding the driving policy with learned stop/go information, performing data augmentation directly on the state of the vehicle and visually explaining the model's decisions. We report a drastic decrease in inertia and a high correlation between offline and online metrics.Comment: Submitted to IEEE Transactions on Intelligent Vehicle

Explaining autonomous driving with visual attention and end-to-end trainable region proposals

Autonomous driving is advancing at a fast pace, with driving algorithms becoming more and more accurate and reliable. Despite this, it is of utter importance to develop models that can ofer a certain degree of explainability in order to be trusted, understood and accepted by researchers and, especially, society. In this work we present a conditional imitation learning agent based on a visual attention mechanism in order to provide visually explainable decisions by design. We propose different variations of the method, relying on end-to-end trainable regions proposal functions, generating regions of interest to be weighed by an attention module. We show that visual attention can improve driving capabilities and provide at the same time explainable decisions