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| 2. |
- Wasserman, D, et al.
(författare)
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Compulsory admissions of patients with mental disorders: State of the art on ethical and legislative aspects in 40 European countries
- 2020
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Ingår i: European psychiatry : the journal of the Association of European Psychiatrists. - : Royal College of Psychiatrists. - 1778-3585. ; 63:1, s. e82-
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Tidskriftsartikel (refereegranskat)abstract
- Background.Compulsory admission procedures of patients with mental disorders vary between countries in Europe. The Ethics Committee of the European Psychiatric Association (EPA) launched a survey on involuntary admission procedures of patients with mental disorders in 40 countries to gather information from all National Psychiatric Associations that are members of the EPA to develop recommendations for improving involuntary admission processes and promote voluntary care.Methods.The survey focused on legislation of involuntary admissions and key actors involved in the admission procedure as well as most common reasons for involuntary admissions.Results.We analyzed the survey categorical data in themes, which highlight that both medical and legal actors are involved in involuntary admission procedures.Conclusions.We conclude that legal reasons for compulsory admission should be reworded in order to remove stigmatization of the patient, that raising awareness about involuntary admission procedures and patient rights with both patients and family advocacy groups is paramount, that communication about procedures should be widely available in lay-language for the general population, and that training sessions and guidance should be available for legal and medical practitioners. Finally, people working in the field need to be constantly aware about the ethical challenges surrounding compulsory admissions.
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| 3. |
- Le Coroller, H., et al.
(författare)
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K-Stacker : an algorithm to hack the orbital parameters of planets hidden in high-contrast imaging
- 2020
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 639
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Tidskriftsartikel (refereegranskat)abstract
- Context. Recent high-contrast imaging surveys, using the Spectro-Polarimetic High contrast imager for Exoplanets REsearch (SPHERE) or the Gemini Planet Imager in search of planets in young, nearby systems, have shown evidence of a small number ofgiant planets at relatively large separation beyond 10–30 au, where those surveys are the most sensitive. Access to smaller physical separations between 5 and 30 au is the next step for future planet imagers on 10 m telescopes and the next generation of extremely large telescopes in order to bridge the gap with indirect techniques such as radial velocity, transit, and soon astrometry with Gaia. In addition to new technologies and instruments, the development of innovative observing strategies combined with optimized data processing tools is participating in the improvement of detection capabilities at very close angular separation. In that context, we recently proposed a new algorithm, Keplerian-Stacker, which combines multiple observations acquired at different epochs and takes into account the orbital motion of a potential planet present in the images to boost the ultimate detection limit. We showed that this algorithm is able to find planets in time series of simulated images of the SPHERE InfraRed Dual-band Imager and Spectrograph (IRDIS) even when a planet remains undetected at one epoch.Aims. Our goal is to test and validate the K-Stacker algorithm performances on real SPHERE datasets to demonstrate the resilience of this algorithm to instrumental speckles and the gain offered in terms of true detection. This will motivate future dedicated multi-epoch observation campaigns of well-chosen, young, nearby systems and very nearby stars carefully selected to search for planets in emitted and reflected light, respectively, to open a new path concerning the observing strategy used with current and future planet imagers.Methods. To test K-Stacker, we injected fake planets and scanned the low signal-to-noise ratio (S/N) regime in a series of raw observations obtained by the SPHERE/IRDIS instrument in the course of the SPHERE High-contrast ImagiNg survey for Exoplanets. We also considered the cases of two specific targets intensively monitored during this campaign: β Pictoris and HD 95086. For each target and epoch, the data were reduced using standard angular differential imaging processing techniques and then recombined with K-Stackerto recover the fake planetary signals. In addition, the known exoplanets β Pictoris b and HD 95086 b previously identified at lower S/N in single epochs have also been recovered by K-Stacker.Results. We show that K-Stacker achieves a high success rate of ≈100% when the S/N of the planet in the stacked image reaches≈9. The improvement of the S/N is given as the square root of the total exposure time contained in the data being combined. At S/N<6−7, the number of false positives is high near the coronagraphic mask, but a chromatic study or astrophysical criteria can help to disentangle between a bright speckle and a true detection. During the blind test and the redetection of HD 95086 b, and β Pic b, we highlight the ability of K-Stacker to find orbital solutions consistent with those derived by the current Markov chain Monte Carlo orbital fitting techniques. This confirms that in addition to the detection gain, K-Stacker offers the opportunity to characterize the most probable orbital solutions of the exoplanets recovered at low S/N.
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| 4. |
- Bec, J., et al.
(författare)
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Statistical Models for the Dynamics of Heavy Particles in Turbulence
- 2024
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Ingår i: ANNUAL REVIEW OF FLUID MECHANICS. - 0066-4189 .- 1545-4479. ; 56, s. 189-213
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Forskningsöversikt (refereegranskat)abstract
- When very small particles are suspended in a fluid in motion, they tend to follow the flow. How such tracer particles are mixed, transported, and dispersed by turbulent flow has been successfully described by statistical models. Heavy particles, with mass densities larger than that of the carrying fluid, can detach from the flow. This results in preferential sampling, small-scale fractal clustering, and large relative velocities. To describe these effects of particle inertia, one must consider both particle positions and velocities in phase space. In recent years, statistical phase-space models have significantly contributed to our understanding of inertial-particle dynamics in turbulence. These models help to identify the key mechanisms and nondimensional parameters governing the particle dynamics and have made qualitative and, in some cases, quantitative predictions. This article reviews statistical phase-space models for the dynamics of small, yet heavy, spherical particles in turbulence. We evaluate their effectiveness by comparing their predictions with results from numerical simulations and laboratory experiments, and we summarize their successes and failures.
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| 5. |
- Frisch, U., et al.
(författare)
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"Locally homogeneous turbulence" : Is it an inconsistent framework?
- 2005
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Ingår i: Physics of fluids. - : AIP Publishing. - 1070-6631 .- 1089-7666. ; 17:8
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Tidskriftsartikel (refereegranskat)abstract
- In his first 1941 paper Kolmogorov assumed that the velocity has increments that are homogeneous and independent of the velocity at a suitable reference point. This assumption of local homogeneity is consistent with the nonlinear dynamics only in an asymptotic sense when the reference point is far away. This inconsistency is illustrated numerically using the Burgers equation. Kolmogorov's derivation of the four-fifths law for the third-order structure function and its anisotropic generalization are actually valid only for homogeneous turbulence, but a local version due to Duchon and Robert still holds. A Kolmogorov-Landau approach is proposed to handle the effect of fluctuations in the large-scale velocity on small-scale statistical properties; it is only a mild extension of the 1941 theory and does not incorporate intermittency effects.
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| 6. |
- Meibohm, Jan, et al.
(författare)
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Fractal catastrophes
- 2020
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Ingår i: New Journal of Physics. - : IOP Publishing. - 1367-2630. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- We analyse the spatial inhomogeneities ('spatial clustering') in the distribution of particles accelerated by a force that changes randomly in space and time. To quantify spatial clustering, the phase-space dynamics of the particles must be projected to configuration space. Folds of a smooth phase-space manifold give rise to catastrophes ('caustics') in this projection. When the inertial particle dynamics is damped by friction, however, the phase-space manifold converges towards a fractal attractor. It is believed that caustics increase spatial clustering also in this case, but a quantitative theory is missing. We solve this problem by determining how projection affects the distribution of finite-time Lyapunov exponents (FTLEs). Applying our method in one spatial dimension we find that caustics arising from the projection of a dynamical fractal attractor ('fractal catastrophes') make a distinct and universal contribution to the distribution of spatial FTLEs. Our results explain a projection formula for the spatial fractal correlation dimension, and how a fluctuation relation for the distribution of FTLEs for white-in-time Gaussian force fields breaks upon projection. We explore the implications of our results for heavy particles in turbulence, and for wave propagation in random media.
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| 7. |
- Storm, Ludvig, 1995, et al.
(författare)
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Finite-Time Lyapunov Exponents of Deep Neural Networks
- 2024
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Ingår i: Physical Review Letters. - 1079-7114 .- 0031-9007. ; 132:5
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Tidskriftsartikel (refereegranskat)abstract
- We compute how small input perturbations affect the output of deep neural networks, exploring an analogy between deep feed-forward networks and dynamical systems, where the growth or decay of local perturbations is characterized by finite-time Lyapunov exponents. We show that the maximal exponent forms geometrical structures in input space, akin to coherent structures in dynamical systems. Ridges of large positive exponents divide input space into different regions that the network associates with different classes. These ridges visualize the geometry that deep networks construct in input space, shedding light on the fundamental mechanisms underlying their learning capabilities.
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