Ambient Synthesis of Tricyclic Naphthalenes via Stepwise Styryl-yne Dearomative Diels{ extendash}Alder Cyclization

A cascade of styrylynols promoted by MnO2 allows the synthesis of fused tricycles with a naphthalene core. The reaction occurs under ambient conditions, offering a practical synthetic tool because of the inexpensive and abundant manganese species. The method affords products through the sequential oxidation of a propargyl alcohol, stepwise Diels-Alder cyclization, and finally rearomatization. According to density functional theory, the usually unfavorable stepwise Diels-Alder mechanism is instead a general tool for eliciting otherwise challenging dearomative annulation

Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation

In this paper we present a novel approach for bottom-up multi-person 3D human pose estimation from monocular RGB images. We propose to use high resolution volumetric heatmaps to model joint locations, devising a simple and effective compression method to drastically reduce the size of this representation. At the core of the proposed method lies our Volumetric Heatmap Autoencoder, a fully-convolutional network tasked with the compression of ground-truth heatmaps into a dense intermediate representation. A second model, the Code Predictor, is then trained to predict these codes, which can be decompressed at test time to re-obtain the original representation. Our experimental evaluation shows that our method performs favorably when compared to state of the art on both multi-person and single-person 3D human pose estimation datasets and, thanks to our novel compression strategy, can process full-HD images at the constant runtime of 8 fps regardless of the number of subjects in the scene

A Gamified Framework to Assist Therapists with the ABA Therapy for Autism

We present a framework to assist therapists and children with autism spectrum disorder in their Applied Behavioral Analysis (ABA) therapy. The framework was designed in collaboration with Spazio Autismo, an autism center in Mantova, Italy. The framework is a first step toward transitioning from the current paper-based to fully digital-supported therapy. We evaluated the framework over four months with 18 children diagnosed with classic autism, ranging from 4 to 7 years old. The framework integrates a mobile app that children and therapists use during the sessions with a backend for managing therapy workflow and monitoring progress. Our preliminary results show that the framework can improve the efficacy of the therapy sessions, reducing non-therapeutic time, increasing patient focus, and quickening the completion of the assigned objectives. It can also support therapists in preparing learning materials, data acquisition, and reporting. Finally, the framework demonstrated improved privacy and security of patients' data while maintaining reliability

Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face

Can faces acquired by low-cost depth sensors be useful to see some characteristic details of the faces? Typically the answer is not. However, new deep architectures can generate RGB images from data acquired in a different modality, such as depth data. In this paper we propose a new Deterministic Conditional GAN, trained on annotated RGB-D face datasets, effective for a face-to-face translation from depth to RGB. Although the network cannot reconstruct the exact somatic features for unknown individual faces, it is capable to reconstruct plausible faces; their appearance is accurate enough to be used in many pattern recognition tasks. In fact, we test the network capability to hallucinate with some Perceptual Probes, as for instance face aspect classification or landmark detection. Depth face can be used in spite of the correspondent RGB images, that often are not available for darkness of difficult luminance conditions. Experimental results are very promising and are as far as better than previous proposed approaches: this domain translation can constitute a new way to exploit depth data in new future applications

Years of life that could be saved from prevention of hepatocellular carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) causes premature death and loss of life expectancy worldwide. Its primary and secondary prevention can result in a significant number of years of life saved. AIM: To assess how many years of life are lost after HCC diagnosis. METHODS: Data from 5346 patients with first HCC diagnosis were used to estimate lifespan and number of years of life lost after tumour onset, using a semi-parametric extrapolation having as reference an age-, sex- and year-of-onset-matched population derived from national life tables. RESULTS: Between 1986 and 2014, HCC lead to an average of 11.5 years-of-life lost for each patient. The youngest age-quartile group (18-61 years) had the highest number of years-of-life lost, representing approximately 41% of the overall benefit obtainable from prevention. Advancements in HCC management have progressively reduced the number of years-of-life lost from 12.6 years in 1986-1999, to 10.7 in 2000-2006 and 7.4 years in 2007-2014. Currently, an HCC diagnosis when a single tumour <2 cm results in 3.7 years-of-life lost while the diagnosis when a single tumour 65 2 cm or 2/3 nodules still within the Milan criteria, results in 5.0 years-of-life lost, representing the loss of only approximately 5.5% and 7.2%, respectively, of the entire lifespan from birth. CONCLUSIONS: Hepatocellular carcinoma occurrence results in the loss of a considerable number of years-of-life, especially for younger patients. In recent years, the increased possibility of effectively treating this tumour has improved life expectancy, thus reducing years-of-life lost