Generative inverse design of multimodal resonant structures for locally resonant metamaterials

In the development of locally resonant metamaterials, the physical resonator design is often omitted and replaced by an idealized mass-spring system. This paper presents a novel approach for designing multimodal resonant structures, which give rise to multi-bandgap metamaterials with predefined band gaps. Our method uses a conditional variational autoencoder to identify nontrivial patterns between design variables of complex-shaped resonators and their modal effective parameters. After training, the cost of generating designs satisfying arbitrary criteria - frequency and mass of multiple modes - becomes negligible. An example of a resonator family with six geometric variables and two targeted modes is further elaborated. We find that the autoencoder performs well even when trained with a limited dataset, resulting from a few hundred numerical modal analyses. The method generates several designs that very closely approximate the desired modal characteristics. The accuracy of the best designs, proposed by the auto-encoder, is confirmed in tests of 3D-printed resonator prototypes. Further experiments demonstrate the close agreement between the measured and desired dispersion relation of a sample metamaterial beam

MAPPER Information

Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0005.This working paper describes a program on the Mini-Robot PDP-11 which is used for looking at picture files created by the VIDIN program. It may be used by ITS vision programmers to examine Vidicon picture files before sending them over to ITS.MIT Artificial Intelligence Laborator

Which variables are associated with blood glucose levels outside the target range in surgical critically ill patients? A retrospective observational study

<p>Abstract</p> <p>Background</p> <p>The aim of the present study is to determine the variables affecting blood glucose concentrations outside the target range of 80 and 150 mg/dl in critically ill surgical patients.</p> <p>Methods</p> <p>All critically ill surgical patients admitted to a university ICU, from 01/2007 to 12/2008, were surveyed daily using computer assistance with respect to minimal and maximal daily blood glucose concentrations, application of insulin and demographic/clinical variables. Multiple logistic regression for clustered data with backward elimination was performed to identify variables strongly associated with blood glucose concentrations < 80 mg/dl or ≥ 150 mg/dl in 804 patients with an ICU stay > 72 hours.</p> <p>Results</p> <p>Application of insulin (odds ratio (OR) 2.1, with corresponding 95% confidence interval (CI) 1.7; 2.6), noradrenaline (OR 1.4, 95% CI 1.2 - 1.8) or steroids (1.3, 1.003 - 1.7), and age (per year) (1.02, 1.01 - 1.03) were associated with an increased risk of blood glucose concentrations < 80 mg/dl. In analogy, application of insulin (OR 2.4, 95% CI 2.0 - 2.7), noradrenaline (1.4, 1.2 - 1.6) or steroids (1.4, 1.2 - 1.7), severe sepsis (1.2, 1.1 - 1.4), neurosurgery (OR 1.0) compared to abdominal, vascular and trauma surgery, and age (per year) (1.01, 1.01 - 1.02), were associated with an increased risk of blood glucose concentrations ≥ 150 mg/dl.</p> <p>Conclusions</p> <p>Critically ill surgical patients are at an increased risk for fluctuating blood glucose concentrations ranging < 80 mg/dl or ≥ 150 mg/dl in particular if they are of advanced age and require administration of insulin, noradrenaline, and/or steroids. Patients who underwent neurosurgery and/or presented with severe sepsis/shock are those in particular at risk for blood glucose concentrations ≥ 150 mg/dl.</p

LNCS

Shape analysis is a promising technique to prove program properties about recursive data structures. The challenge is to automatically determine the data-structure type, and to supply the shape analysis with the necessary information about the data structure. We present a stepwise approach to the selection of instrumentation predicates for a TVLA-based shape analysis, which takes us a step closer towards the fully automatic verification of data structures. The approach uses two techniques to guide the refinement of shape abstractions: (1) during program exploration, an explicit heap analysis collects sample instances of the heap structures, which are used to identify the data structures that are manipulated by the program; and (2) during abstraction refinement along an infeasible error path, we consider different possible heap abstractions and choose the coarsest one that eliminates the infeasible path. We have implemented this combined approach for automatic shape refinement as an extension of the software model checker BLAST. Example programs from a data-structure library that manipulate doubly-linked lists and trees were successfully verified by our tool

Modellierung der Tragwerkseigenschaften von Segelkonstruktionen

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Patients report improvements in continuity of care when quality of life assessments are used routinely in oncology practice: Secondary outcomes of a randomised controlled trial.

INTRODUCTION AND AIM: In a randomised trial investigating the effects of regular use of health-related quality of life (HRQOL) in oncology practice, we previously reported an improvement in communication (objective analysis of recorded encounters) and patient well-being. The secondary aims of the trial were to measure any impact on patient satisfaction and patients' perspectives on continuity and coordination of their care. METHODS: In a prospective trial involving 28 oncologists, 286 cancer patients were randomised to: (1) intervention arm: regular touch-screen completion of HRQOL with feedback to physicians; (2) attention-control arm: completion of HRQOL without feedback; and (3) control arm: no HRQOL assessment. Secondary outcomes were patients' experience of continuity of care (Medical Care Questionnaire, MCQ) including 'Communication', 'Coordination' and 'Preferences to see usual doctor' subscales, patients' satisfaction, and patients' and physicians' evaluation of the intervention. Analysis employed mixed-effects modelling, multiple regression and descriptive statistics. RESULTS: Patients in the intervention arm rated their continuity of care as better than the control group for 'Communication' subscale (p=0.03). No significant effects were found for 'Coordination' or 'Preferences to see usual doctor'. Patients' evaluation of the intervention was positive. More patients in the intervention group rated the HRQOL assessment as useful compared to the attention-control group (86% versus 29%), and reported their doctors considered daily activities, emotions and quality of life. CONCLUSION: Regular use of HRQOL measures in oncology practice brought changes to doctor-patient communication of sufficient magnitude and importance to be reported by patients. HRQOL data may improve care through facilitating rapport and building inter-personal relationships

Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music

Instrument classification is one of the fields in Music Information Retrieval (MIR) that has attracted a lot of research interest. However, the majority of that is dealing with monophonic music, while efforts on polyphonic material mainly focus on predominant instrument recognition. In this paper, we propose an approach for instrument classification in polyphonic music from purely monophonic data, that involves performing data augmentation by mixing different audio segments. A variety of data augmentation techniques focusing on different sonic aspects, such as overlaying audio segments of the same genre, as well as pitch and tempo-based synchronization, are explored. We utilize Convolutional Neural Networks for the classification task, comparing shallow to deep network architectures. We further investigate the usage of a combination of the above classifiers, each trained on a single augmented dataset. An ensemble of VGG-like classifiers, trained on non-augmented, pitch-synchronized, tempo-synchronized and genre-similar excerpts, respectively, yields the best results, achieving slightly above 80% in terms of label ranking average precision (LRAP) in the IRMAS test set.ruments in over 2300 testing tracks