NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system. Non-autonomy is implemented by skip connections from the block input to each of the unrolled processing stages and allows stability to be enforced so that blocks can be unrolled adaptively to a pattern-dependent processing depth. NAIS-Net induces non-trivial, Lipschitz input-output maps, even for an infinite unroll length. We prove that the network is globally asymptotically stable so that for every initial condition there is exactly one input-dependent equilibrium assuming tanh units, and multiple stable equilibria for ReL units. An efficient implementation that enforces the stability under derived conditions for both fully-connected and convolutional layers is also presented. Experimental results show how NAIS-Net exhibits stability in practice, yielding a significant reduction in generalization gap compared to ResNets.Comment: NIPS 201

ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation

We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object of interest at inference time. Generalization to new objects never observed during training is known to be a hard task for supervised approaches that would need to be retrained. To tackle this problem, we propose a more efficient solution that learns spatio-temporal features self-adapting to the object of interest via conditional affine transformations. This approach is simple, can be trained end-to-end and does not necessarily require extra training steps at inference time. Our method shows competitive results on DAVIS2016 with respect to state-of-the art approaches that use online fine-tuning, and outperforms them on DAVIS2017. ReConvNet shows also promising results on the DAVIS-Challenge 2018 winning the $10$-th position.Comment: CVPR Workshop - DAVIS Challenge 201

Back-propagation neural networks and generalized linear mixed models to investigate vehicular flow and weather data relationships with crash severity in urban road segments

The paper deals with the identification of variables and models that can explain why a certain Severity Level (SL) may be expected in the event of a certain type of crash at a specific point of an urban road network. Two official crash records, a weather database, a traffic data source, and information on the characteristics of the investigated urban road segments of Turin (Italy) for the seven years from 2006 to 2012 were used. Examination of the full database of 47,592 crash events, including property damage only crashes, reveals 9,785 injury crashes occurring along road segments only. Of these, 1,621 were found to be associated with a dataset of traffic flows aggregated in 5 minutes for the 35 minutes across each crash event, and to weather data recorded by the official weather station of Turin. Two different approaches, a back-propagation neural network model and a generalized linear mixed model were used. Results show the impact of flow and other variables on the SL that may characterize a crash; differences in the significant variables and performance of the two modelling approaches are also commented on in the manuscript

Infinite-Horizon Differentiable Model Predictive Control

This paper proposes a differentiable linear quadratic Model Predictive Control (MPC) framework for safe imitation learning. The infinite-horizon cost is enforced using a terminal cost function obtained from the discrete-time algebraic Riccati equation (DARE), so that the learned controller can be proven to be stabilizing in closed-loop. A central contribution is the derivation of the analytical derivative of the solution of the DARE, thereby allowing the use of differentiation-based learning methods. A further contribution is the structure of the MPC optimization problem: an augmented Lagrangian method ensures that the MPC optimization is feasible throughout training whilst enforcing hard constraints on state and input, and a pre-stabilizing controller ensures that the MPC solution and derivatives are accurate at each iteration. The learning capabilities of the framework are demonstrated in a set of numerical studies

High-Volume Transanal Surgery with CPH34 HV for the Treatment of III-IV Degree Haemorrhoids: Final Short-Term Results of an Italian Multicenter Clinical Study

The clinical chart of 621 patients with III-IV haemorrhoids undergoing Stapled Hemorrhoidopexy (SH) with CPH34 HV in 2012-2014 was consecutively reviewed to assess its safety and efficacy after at least 12 months of follow-up. Mean volume of prolapsectomy was significantly higher (13.0 mL; SD, 1.4) in larger prolapse (9.3 mL; SD, 1.2) (p < 0.001). Residual or recurrent haemorrhoids occurred in 11 of 621 patients (1.8%) and in 12 of 581 patients (1.9%), respectively. Relapse was correlated with higher preoperative Constipation Scoring System (CSS) (p = 0.000), Pescatori's degree (p = 0.000), Goligher's grade (p = 0.003), prolapse exceeding half of the length of the Circular Anal Dilator (CAD) (p = 0.000), and higher volume of prolapsectomy (p = 0.000). At regression analysis, only the preoperative CSS, Pescatori's degree, Goligher's grade, and volume of resection were significantly predictive of relapse. A high level of satisfaction (VAS = 8.6; SD, 1.0) coupled with a reduction of 12-month CSS (Δ preoperative CSS/12 mo CSS = 3.4, SD, 2.0; p < 0.001) was observed. The wider prolapsectomy achievable with CPH34 HV determined an overall 3.7% relapse rate in patients with high prevalence of large internal rectal prolapse, coupled with high satisfaction index, significant reduction of CSS, and very low complication rates

MOTIVATional intErviewing to Improve Self-Care in Heart Failure Patients (MOTIVATE-HF): Study Protocol of a Three-Arm Multicenter Randomized Controlled Trial

Aims Heart Failure (HF) self-care improves patient outcomes but trials designed to improve HF self-care have shown inconsistent results. Interventions may be more effective in improving self-care if they mobilize support from providers, promote self-efficacy, increase understanding of HF, increase the family involvement, and are individualized. All of these elements are emphasized in motivational interviewing (MI); few trials have been conducted using MI in HF patients and rarely have caregivers been involved in MI interventions. The aim of this study will be to evaluate if MI improves self-care maintenance in HF patients, and to determine if MI improves the following secondary outcomes: a) in HF patients: self-care management, self-care confidence, symptom perception, quality of life, anxiety/depression, cognition, sleep quality, mutuality with caregiver, hospitalizations, use of emergency services, and mortality; b) in caregivers: caregiver contribution to self-care, quality of life, anxiety/depression, sleep, mutuality with patient, preparedness, and social support. Methods A three-arm randomized controlled trial will be conducted in a sample of 240 HF patients and caregivers. Patients and caregivers will be randomized to the following arms: 1) MI intervention to patients only; 2) MI intervention to patients and caregivers; 3) standard of care to patients and caregivers. The primary outcome will be measured in patients 3 months after enrollment. Primary and secondary outcomes also will be evaluated 6, 9 and 12 months after enrollment. Conclusion This study will contribute to understand if MI provided to patients and caregivers can improve self-care. Because HF is rising in prevalence, findings can be useful to reduce the burden of the disease

Fireside Corrosion of Applied and Modern Superheater-alloys Under Oxy-fuel Conditions

Abstract Operation of oxy-fuel power plants under ultra-supercritical parameters would help to overcome, to a certain extent, efficiency penalties from air separation and CO 2 -compression units. To improve the knowledge on material behavior under oxy-fuel combustion six candidate superheater alloys, varying from martensitic via iron-base austenitic to nickel-base were chosen and exposed at metal temperature of 580 °C and 650 °C to real oxy-fuel combustion conditions in 3MW combustion test rig of Enel and subsequently moved for further tests to laboratory corrosion test set-up at IFK. Exact definition of combustion conditions was based on measurements performed by IFRF