Reverse sensitivity testing: What does it take to break the model?

Sensitivity analysis is an important component of model building, interpretation and validation. A model comprises a vector of random input factors, an aggregation function mapping input factors to a random output, and a (baseline) probability measure. A risk measure, such as Value-at-Risk and Expected Shortfall, maps the distribution of the output to the real line. As is common in risk management, the value of the risk measure applied to the output is a decision variable. Therefore, it is of interest to associate a critical increase in the risk measure to specific input factors. We propose a global and model-independent framework, termed ‘reverse sensitivity testing’, comprising three steps: (a) an output stress is specified, corresponding to an increase in the risk measure(s); (b) a (stressed) probability measure is derived, minimising the Kullback-Leibler divergence with respect to the baseline probability, under constraints generated by the output stress; (c) changes in the distributions of input factors are evaluated. We argue that a substantial change in the distribution of an input factor corresponds to high sensitivity to that input and introduce a novel sensitivity measure to formalise this insight. Implementation of reverse sensitivity testing in a Monte-Carlo setting can be performed on a single set of input/output scenarios, simulated under the baseline model. Thus the approach circumvents the need for additional computationally expensive evaluations of the aggregation function. We illustrate the proposed approach through a numerical example of a simple insurance portfolio and a model of a London Insurance Market portfolio used in industry

Euler allocations in the presence of non-linear reinsurance: comment on Major (2018)

Major (2018) discusses Euler/Aumann-Shapley allocations for non-linear portfolios. He argues convincingly that many (re)insurance portfolios, while non-linear, are nevertheless positively homogeneous, owing to the way that deductibles and limits are typically set. For such non-linear but homogeneous portfolio structures, he proceeds with defining and studying a particular type of capital allocation. In this comment, we build on Major's (2018) insights but take a slightly different direction, to consider Euler capital allocations for distortion risk measures applied to homogeneous portfolios. Thus, the important problem of capital allocation in portfolios with non-linear reinsurance is solved

IMU-based human activity recognition and payload classification for low-back exoskeletons

Nowadays, work-related musculoskeletal disorders have a drastic impact on a large part of the world population. In particular, low-back pain counts as the leading cause of absence from work in the industrial sector. Robotic exoskeletons have great potential to improve industrial workers’ health and life quality. Nonetheless, current solutions are often limited by sub-optimal control systems. Due to the dynamic environment in which they are used, failure to adapt to the wearer and the task may be limiting exoskeleton adoption in occupational scenarios. In this scope, we present a deep-learning-based approach exploiting inertial sensors to provide industrial exoskeletons with human activity recognition and adaptive payload compensation. Inertial measurement units are easily wearable or embeddable in any industrial exoskeleton. We exploited Long-Short Term Memory networks both to perform human activity recognition and to classify the weight of lifted objects up to 15 kg. We found a median F1 score of 90.80 % (activity recognition) and 87.14 % (payload estimation) with subject-specific models trained and tested on 12 (6M-6F) young healthy volunteers. We also succeeded in evaluating the applicability of this approach with an in-lab real-time test in a simulated target scenario. These high-level algorithms may be useful to fully exploit the potential of powered exoskeletons to achieve symbiotic human–robot interaction

User-Centered Back-Support Exoskeleton: Design and Prototyping

Exhausting manual labor is still predominant in the industrial context. It typically consists in manipulating heavy parts or working in non-ergonomic conditions. The resulting work-related musculoskeletal disorders are a major problem to tackle. The most-affected body section is the the lumbar spine. Recently, exoskeletons have been identified as a possible non-invasive solution to reduce the impact of low-back pain. State-of-the-art prototypes have been optimized to: follow unconstrained human kinematics, (partially) relieve the load on assisted joints, and allow anthropometric adaptation. Yet, this technology still has limited adoption. Manufacturing optimization may address the following limitations: bulky/heavy resulting designs, complex assembly and maintenance, high manufacturing costs, long procedures for adaptation and wearing, and psychological effects (e.g., cognitive load and usability). In this contribution, the aforementioned issues are tackled improving a previous low-back exoskeleton prototype. In particular, kinematic analysis, Finite-Element-Method, and topological optimization have been combined to obtain a lightweight prototype, testing different materials (Nylon, carbon-fiber reinforced PC/ABS, etc.). We applied both Design for Assembly and Design for Manufacturability. The resulting exoskeleton prototype is described in the paper, ready for end-user field tests

Transmembrane transporters and salt tolerance in temperate japonica rice

Several investigations aimed at identifying molecular tools useful for the selection and/or the constitution of high-yield salt tolerant rice have been successfully carried out, concerning in particular indica and/ or tropical rice genotypes. Te global warming process is nowadays determining the intrusion of saline wedge into coastal fresh-water streams, and the soil salt concentration of many European rice areas, where temperate rice cultivars are mainly grown, is more and more increasing. In order to identify molecular markers and/or new loci related to salt tolerance, a Genome Wide Association Study (GWAS) has been carried out using a panel of 277 japonica rice accessions. Te panel has been subjected to Genotyping By Sequencing and phenotyping concerning tolerance to a mild-salt stress soil condition (5 dS m-1) expressed at the 4th-5th leaf developmental stage using the Standard Evaluation Score (SES) proposed by IRRI. On the basis of GWAS, a QTL including a few genes that in the indica rice genome are localized within the major salinity tolerance-related QTL \u2018SalTol\u2019 have been identified. Among them, the Os01g0337500 gene encoding the vacuolar H+-pyrophosphatase 6 (OsOVP6) is present. Since the role of the OsOVP6 activity is considered central in regulating the cellular Na+ homeostasis in both roots and leaves, investigations comparing some elements of the complex mechanisms involved in this process have been carried out. A physiological approach evaluating this possibility has been conducted in two japonica rice varieties (Galileo and Virgo) that resulted salt-tolerant, in one japonica rice variety (PL12) known to be quite salt-susceptible, and in the salt-tolerant indica inbred genotype FL478 (containing the \u2018SalTol\u2019 QTL) as reference genotype. Te root and shoot Na+/K+ ratio, Na+ influx and K+ efflux, H+ extrusion activity, cytosolic and vacuolar pH by in vivo 31P-NMR techniques were evaluated in roots of the four rice genotypes. Te results obtained, together with the electrophysiological evaluation of the whole root Na+ conductance, allow to define a picture that may explain the different salt tolerance observed among the rice genotypes analyzed. As a whole, these results confirm the interest towards a deep allele mining analysis, concerning OsOVP6, within the most significant members of the japonica rice accession panel under investigation

Sensor-Based Task Ergonomics Feedback for a Passive Low-Back Exoskeleton

Low-back exoskeletons are a wide-spreading technology tackling low-back pain, the leading work-related musculoskeletal disorder in many work sectors. Currently, spring-based (i.e., passive) exoskeletons are the mostly adopted in the industry, being cheaper and generally less complex and more intuitive to use. We introduce a system of interconnected wireless sensing units to provide online ergonomics feedback to the wearer. We integrate the system into our passive low-back exoskeleton and evaluate its usability with healthy volunteers and potential end users. In this way, we provide the exoskeleton with a tool aimed both at monitoring the interaction of the system with the user, providing them with an ergonomics feedback during task execution. The sensor system can also be integrated with a custom-developed Unity3D application which can be used to interface with Augmented- or Virtual-Reality applications with higher potential for improved user feedback, ergonomics training, and offline ergonomics evaluation of the workplace. We believe that providing ergonomics feedback to exoskeleton users in the industrial sector could help further reduce the drastic impact of low-back pain and prevent its onset