Latin America 2040 — Breaking Away from Complacency: An Agenda for Resurgence

Latin America

Long term monitoring and field testing of an innovative multi-storey timber building

Peer reviewedPostprin

Management of Class II Division 1 Subdivision malocclusion using unilateral bicuspid extractions and fixed functional appliance: A Two Year Follow-Up

Introduction: Management of Class II Subdivision cases pose a clinical dilemma and require a careful diagnosis to ascertain the source of asymmetry. Various treatment modalities involving: different protocols of tooth extractions; molar distalization; fixed functional appliances, or orthognathic surgery have been proposed for the same.Case presentation: This article reports a unique approach for management of a severe skeletal Class II with Angle’s Class II Division 1 subdivision malocclusion using unilateral bicuspid extractions in mandibular and maxillary arches and a fixed functional appliance.Results: A 13 year 1-month-old male in CVMI transition stage was successfully treated. Extraction of #44 was done to alleviate crowding in the mandibular anterior region and #15 was extracted to protract #16 to achieve a Class II molar relationship. A pre-functional Class II molar and canine relationship with co-incident midlines were achieved. The functional phase consisted of a fixed functional appliance (Forsus FRD) for mandibular advancement to correct the severe skeletal Class II. Class I molar and canine relationships were achieved with the reduction of facial convexity and overjet. The result remained stable 24 months after treatment. The improvement can be quantified by the reduction in scores of orthodontic indices measured pre and post-treatment.Conclusions: Management of Class II subdivision malocclusion requires careful planning. This paper presents a unique approach utilizing unilateral extractions and fixed functional appliances to address severe skeletal Class II discrepancy and the subdivision dilemma

Consensus Message Passing for Layered Graphical Models

Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is particularly the case in computer vision, where models of the imaging process tend to be large, loopy and layered. For this reason bottom-up conditional models have traditionally dominated in such domains. We find that widely-used, general-purpose message passing inference algorithms such as Expectation Propagation (EP) and Variational Message Passing (VMP) fail on the simplest of vision models. With these models in mind, we introduce a modification to message passing that learns to exploit their layered structure by passing 'consensus' messages that guide inference towards good solutions. Experiments on a variety of problems show that the proposed technique leads to significantly more accurate inference results, not only when compared to standard EP and VMP, but also when compared to competitive bottom-up conditional models.Comment: Appearing in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 201

A Unified View of Piecewise Linear Neural Network Verification

The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the reputation of learned NN models to behave as black boxes and the theoretical hardness of proving their properties, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure and taking insights from formal methods such as Satisifiability Modulo Theory. These methods are however still far from scaling to realistic neural networks. To facilitate progress on this crucial area, we make two key contributions. First, we present a unified framework that encompasses previous methods. This analysis results in the identification of new methods that combine the strengths of multiple existing approaches, accomplishing a speedup of two orders of magnitude compared to the previous state of the art. Second, we propose a new data set of benchmarks which includes a collection of previously released testcases. We use the benchmark to provide the first experimental comparison of existing algorithms and identify the factors impacting the hardness of verification problems.Comment: Updated version of "Piecewise Linear Neural Network verification: A comparative study

Adaptive Neural Compilation

This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on differentiable representations of programs. We show that it is possible to compile programs written in a low-level language to a differentiable representation. We also show how programs in this representation can be optimised to make them efficient on a target distribution of inputs. Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate.Comment: Submitted to NIPS 2016, code and supplementary materials will be available on author's pag

Value Propagation Networks

We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. We show that the modules enable learning to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems. We evaluate on static and dynamic configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes, and on a StarCraft navigation scenario, with more complex dynamics, and pixels as input.Comment: Updated to match ICLR 2019 OpenReview's versio

Improving Operations Strategy: Application Of TOC Principles In A Small Business

This paper demonstrates how small business managers can create and sustain competitive advantage by applying basic principles of Theory of Constraints (TOC) using a case study on a small family-owned pizza restaurant. TOC five focusing steps were used to reveal the business constraints and practical recommendations were provided that significantly improved throughput, resource utilization, and customer satisfaction without incurring any significant expenses. TOC performance measures encourage small business managers to find innovative ways of increasing throughput instead of simply cutting costs. Positive outcomes in this study demonstrate how small businesses effectively manage their operations and realize benefits using TOC principles

PZT thick films by diol chemical solution deposition

Process optimization and properties of lead zirconate titanate (PZT) films for piezoelectric micromachined ultrasonic transducers (pMUTs) for scanning probe devices will be presented. The goal of the work was a replacement of the tetragenic and mutagenic solvent and a decrease of time-consuming PZT 2-methoxy ethanol (2MOE) route. An alternative diol-based solution synthesis process was developed and "Design Of Experiment” (DOE) was used to achieve processing optimization for thick and crack free films. Tight parameter control allowed to develop a highly reproducible PZT diol process. The crystallization behaviour of crack-free PbZr0.53Ti0.47O3 films (1-5μm) with oriented perovskite structure was examined by X-ray diffraction and surface analysis using scanning electron microscopy. Piezoelectric and dielectric properties were examined. The effective transverse piezoelectric coefficient e 31,f of sol-gel processed films was investigated for 4μm thick layers. Best properties were achieved with {1 0 0}-textured films, where a remanent e 31,f value of −7.3C/m2 was measured for 4.1μm thick film