3499 research outputs found
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A Sparsity Preserving Convexification Procedure for Indefinite Quadratic Programs Arising in Direct Optimal Control
No full textQuadratic programs (QP) with an indefinite Hessian matrix arise naturally in some direct optimal control methods, e.g., as subproblems in a sequential quadratic programming scheme. Typically, the Hessian is approximated with a positive definite matrix to ensure having a unique solution; such a procedure is called regularization. We present a novel regularization method tailored for QPs with optimal control structure. Our approach exhibits three main advantages. First, when the QP satisfies a second order sufficient condition for optimality, the primal solution of the original and the regularized problem are equal. In addition, the algorithm recovers the dual solution in a convenient way. Second, and more importantly, the regularized Hessian bears the same sparsity structure as the original one. This allows for the use of efficient structure-exploiting QP solvers. As a third advantage, the regularization can be performed with a computational complexity that scales linearly in the length of the control horizon. We showcase the properties of our regularization algorithm on a numerical example for nonlinear optimal control. The results are compared to other sparsity preserving regularization methods.
Read More: https://epubs.siam.org/doi/10.1137/16M108154
Causally consistent reversible choreographies: a monitors-as-memories approach
No full textUnder a reversible semantics, computation steps can be undone. This paper addresses the integration of reversible semantics into a process model of multiparty protocols (choreographies). Building upon the monitors-as-memories approach that we developed in prior work for reversible binary protocols, we present a reversible process framework for multiparty communication, which improves on prior models by seamlessly integrating asynchrony, decoupled rollbacks, and process passing. As main technical result, we prove that our multiparty, reversible semantics is causally-consistent
Where Gibrat meets Zipf: scale and scope of French firms
No full textThe proper characterization of the size distribution and growth of firms represents an important issue in economics and business. We use the Maximum Entropy approach to assess the plausibility of the assumption that firm size follows Lognormal or Pareto distributions, which underlies most recent works on the subject. A comprehensive dataset covering the universe of French firms allows us to draw two major conclusions. First, the Pareto hypothesis for the whole distribution should be rejected. Second, by discriminating across firms based on the number of products sold and markets served, we find that, within the class of multi-product companies active in multiple markets, the distribution converges to a Zipf’s law. Conversely, Lognormal distribution is a good benchmark for small single-product firms. The size distribution of firms largely depends on firms’ diversification patterns
Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study
No full textObjectives
Recent research indicates that Heart Rate Variability (HRV) is affected in Bipolar Disorders (BD) patients. To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course, we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state.
Methods
Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project. We extracted time-domain, frequency-domain and non-linear HRV-features and trained a Support Vector Machine (SVM) to classify HRV-features according to mood state. To evaluate the influence of adjacent mood states, we trained SVM with different HRV-feature sets: 1) belonging to each mood state considered alone; 2) belonging to each mood state and normalized using information from the preceding mood state; 3) belonging to each mood state and normalized using information from the preceding and subsequent mood states; 4) belonging to each mood state and normalized using information from two randomly chosen states.
Results
SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered
Parametric design of the band structure for lattice materials
No full textLattice materials are often investigated to determine how small parameter variations in the periodic microstructrure can influence the elastic wave propagation. A general hierarchical scheme, based on asymptotic perturbation techniques, is outlined to analytically assess the parametric sensitivity of the material band structure to a generic multi-parametric perturbation (direct problem). Modeling refinements, parameters updates, microstructural damages and manufacturing irregularities can be treated indifferently and simultaneously. According to a converse strategy, based on the inversion of the sensitivity problem, a hierarchical scheme is sketched to identify the parameter combinations which realize a design band structure (inverse problem). The direct and inverse problem are applied to the sensitivity analysis and band structure design of the anti-tetrachiral lattice material. Despite the high spectral density and the high-dimensional parameter space, the multi-parameter perturbation technique demonstrates its suitability in, first, analytically---although asymptotically---describe the material spectrum and, second, designing the material microstructure to obtain the desired spectral components. The inverse problem solution is discussed in terms of existence, uniqueness, asymptotic consistency and physical admissibility
Is investigator background related to outcome in head to head trials of psychotherapy and pharmacotherapy for adult depression? A systematic review and meta-analysis
No full textBackground The influence of factors related to the background of investigators conducting trials comparing psychotherapy and pharmacotherapy has remained largely unstudied. Specializations emphasizing biological determinants of mental disorders, like psychiatry, might favor pharmacotherapy, while others stressing psychosocial factors, like psychology, could promote psychotherapy. Yet financial conflict of interest (COI) could be a confounding factor as authors with a medical specialization might receive more sponsoring from the pharmaceutical industry. Method We conducted a meta-analysis with subgroup and meta-regression analysis examining whether the specialization and affiliation of trial authors were associated to outcomes in the direct comparison of psychotherapy and pharmacotherapy for the acute treatment of depression. Meta-regression analysis also included trial risk of bias and author conflict of interest in relationship to the pharmaceutical industry. Results We included 45 trials. In half, the first author was psychologist. The last author was psychiatrist/MD in half of the trials, and a psychologist or statistician/other technical in the rest. Most lead authors had medical affiliations. Subgroup analysis indicated that studies with last authors statisticians favored pharmacotherapy. Univariate analysis showed a negative relationship between the presence of statisticians and outcomes favoring psychotherapy. Multivariate analysis showed that trials including authors with financial COI reported findings more favorable to pharmacotherapy. Discussion We report the first detailed overview of the background of authors conducting head to head trials for depression. Trials co-authored by statisticians appear to subtly favor pharmacotherapy. Receiving funding from the industry is more closely related to finding better outcomes for the industry’s elective treatment than are factors related to authors’ background. Limitations For a minority of authors we could not retrieve background information. The number of trials was insufficient to evidence subtler effects
Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants
No full textPhenotyping is important to understand plant biology, but current solutions are costly, not versatile or are difficult to deploy. To solve this problem, we present Phenotiki, an affordable system for plant phenotyping that, relying on off-the-shelf parts, provides an easy to install and maintain platform, offering an out-of-box experience for a well-established phenotyping need: imaging rosette-shaped plants. The accompanying software (with available source code) processes data originating from our device seamlessly and automatically. Our software relies on machine learning to devise robust algorithms, and includes an automated leaf count obtained from 2D images without the need of depth (3D). Our affordable device (~€200) can be deployed in growth chambers or greenhouse to acquire optical 2D images of approximately up to 60 adult Arabidopsis rosettes concurrently. Data from the device are processed remotely on a workstation or via a cloud application (based on CyVerse). In this paper, we present a proof-of-concept validation experiment on top-view images of 24 Arabidopsis plants in a combination of genotypes that has not been compared previously. Phenotypic analysis with respect to morphology, growth, color and leaf count has not been performed comprehensively before now. We confirm the findings of others on some of the extracted traits, showing that we can phenotype at reduced cost. We also perform extensive validations with external measurements and with higher fidelity equipment, and find no loss in statistical accuracy when we use the affordable setting that we propose. Device set-up instructions and analysis software are publicly available (http://phenotiki.com)
EGAC: a genetic algorithm to compare chemical reaction networks
No full textDiscovering relations between chemical reaction networks (CRNs)
is a relevant problem in computational systems biology for model
reduction, to explain if a given system can be seen as an abstraction
of another one; and for model comparison, useful to establish an evolutionary
path from simpler networks to more complex ones. This
is also related to foundational issues in computer science regarding
program equivalence, in light of the established interpretation of a
CRN as a kernel programming language for concurrency. Criteria
for deciding if two CRNs can be formally related have been recently
developed, but these require that a candidate mapping be provided.
Automatically finding candidate mappings is very hard in general
since the search space essentially consists of all possible partitions
of a set. In this paper we tackle this problem by developing a genetic
algorithm for a class of CRNs called influence networks, which can
be used to model a variety of biological systems including cell-cycle
switches and gene networks. An extensive numerical evaluation
shows that our approach can successfully establish relations between
influence networks from the literature which cannot be found
by exact algorithms due to their large computational requirements
Lattice orientation and crack size effect on the mechanical properties of Graphene
No full textThe effect of lattice orientation and crack length on the mechanical properties of Graphene are studied based on molecular dynamics simulations. Bond breaking and crack initiation in an initial edge crack model with 13 different crack lengths, in 10 different lattice orientations of Graphene are examined. In all the lattice orientations, three recurrent fracture patterns are reported. The influence of the lattice orientation and crack length on yield stress and yield strain of Graphene is also investigated. The arm-chair fracture pattern is observed to possess the lowest yield properties. A sudden decrease in yield stress and yield strain can be noticed for crack sizes <10 nm. However, for larger crack sizes, a linear decrease in yield stress is observed, whereas a constant yield strain of ≈≈0.05 is noticed. Therefore, the yield strain of ≈≈0.05 can be considered as a critical strain value below which Graphene does not show failure. This information can be utilized as a lower bound for the design of nano-devices for various strain sensor applications. Furthermore, the yield data will be useful while developing the Graphene coating on Silicon surface in order to enhance the mechanical and electrical characteristics of solar cells and to arrest the growth of micro-cracks in Silicon cells
Concurrently coupled solid shell based adaptive multiscale method for fracture
No full textA solid shell-based adaptive atomistic–continuum numerical method is herein proposed to simulate complex crack growth patterns in thin-walled structures. A hybrid solid shell formulation relying on the combined use of the enhanced assumed strain (EAS) and the assumed natural strain (ANS) methods has been considered to efficiently model the material in thin structures at the continuum level. The phantom node method (PNM) is employed to model the discontinuities in the bulk. The discontinuous solid shell element is then concurrently coupled with a molecular statics model placed around the crack tip. The coupling between the coarse scale and the fine scale is realized through the use of ghost atoms, whose positions are interpolated from the coarse scale solution and enforced as boundary conditions to the fine scale model. In the proposed numerical scheme, the fine scale region is adaptively enlarged as the crack propagates and the region behind the crack tip is adaptively coarsened in order to reduce the computation costs. An energy criterion is used to detect the crack tip location. All the atomistic simulations are carried out using the LAMMPS software. A computational framework has been developed in MATLAB to trigger LAMMPS through system command. This allows a two way interaction between the coarse and fine scales in MATLAB platform, where the boundary conditions to the fine region are extracted from the coarse scale, and the crack tip location from the atomistic model is transferred back to the continuum scale. The developed framework has been applied to study crack growth in the energy minimization problems. Inspired by the influence of fracture on current–voltage characteristics of thin Silicon photovoltaic cells, the cubic diamond lattice structure of Silicon is used to model the material in the fine scale region, whilst the Tersoff potential function is employed to model the atom–atom interactions. The versatility and robustness of the proposed methodology is demonstrated by means of several fracture applications