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We study the dynamics of the open Bose-Hubbard chain confined in the superfluid phase submitted to a sudden global quench on the dissipations and the repulsive interactions. The latter is investigated by calculating the equations of motion of relevant quadratic correlators permitting to study the equal-time connected one-body and density-density correlations functions. We then compute the quench spectral function associated to each observable to perform the quench spectroscopy of this dissipative quantum lattice model. This permits to unveil the quasiparticle dispersion relation of the Bose-Hubbard chain in the superfluid phase in the presence of loss processes. The applicability of the quench spectroscopy is also generalized to non-Hermitian quantum lattice models by considering the non-Hermitian transverse-field Ising chain in the paramagnetic phase., Comment: 20 pages, 7 figures

Future cosmic microwave background (CMB) experiments are primarily targeting a detection of the primordial $B$-mode polarisation. The faintness of this signal requires exquisite control of systematic effects which may bias the measurements. In this work, we derive requirements on the relative calibration accuracy of the overall polarisation gain ($\Delta g_\nu$) for LiteBIRD experiment, through the application of the blind Needlet Internal Linear Combination (NILC) foreground-cleaning method. We find that minimum variance techniques, as NILC, are less affected by gain calibration uncertainties than a parametric approach, which requires a proper modelling of these instrumental effects. The tightest constraints are obtained for frequency channels where the CMB signal is relatively brighter (166 GHz channel, $\Delta {g}_\nu \approx 0.16 \%$), while, with a parametric approach, the strictest requirements were on foreground-dominated channels. We then propagate gain calibration uncertainties, corresponding to the derived requirements, into all frequency channels simultaneously. We find that the overall impact on the estimated $r$ is lower than the required budget for LiteBIRD by almost a factor $5$. The adopted procedure to derive requirements assumes a simple Galactic model. We therefore assess the robustness of obtained results against more realistic scenarios by injecting the gain calibration uncertainties, according to the requirements, into LiteBIRD simulated maps and assuming intermediate- and high-complexity sky models. In this case, we employ the so-called Multi-Clustering NILC (MC-NILC) foreground-cleaning pipeline and obtain that the impact of gain calibration uncertainties on $r$ is lower than the LiteBIRD gain systematics budget for the intermediate-complexity sky model. For the high-complexity case, instead, it would be necessary to tighten the requirements by a factor $1.8$., Comment: 29 pages, 11 figures

Deriving sharp and computable upper bounds of the Lipschitz constant of deep neural networks is crucial to formally guarantee the robustness of neural-network based models. We analyse three existing upper bounds written for the $l^2$ norm. We highlight the importance of working with the $l^1$ and $l^\infty$ norms and we propose two novel bounds for both feed-forward fully-connected neural networks and convolutional neural networks. We treat the technical difficulties related to convolutional neural networks with two different methods, called explicit and implicit. Several numerical tests empirically confirm the theoretical results, help to quantify the relationship between the presented bounds and establish the better accuracy of the new bounds. Four numerical tests are studied: two where the output is derived from an analytical closed form are proposed; another one with random matrices; and the last one for convolutional neural networks trained on the MNIST dataset. We observe that one of our bound is optimal in the sense that it is exact for the first test with the simplest analytical form and it is better than other bounds for the other tests.

In this thesis, we have investigated the spreading of quantum correlations in isolated lattice models with short- or long-range interactions driven far from equilibrium via sudden global quenches. A general theoretical approach relying on a quasiparticle theory is presented. The latter has permitted to unveil a generic expression for the equal-time connected correlation functions valid both for short-range and long-range interacting particle and spin lattice models on a hypercubic lattice. Relying on stationary phase arguments, we have shown that its causality cone displays a universal twofold structure consisting of a correlation edge and a series of local extrema defining the outer and inner structure of the space-time correlations. For short-range interactions, the motion of each structure is ballistic and the associated spreading velocities are related to the group and phase velocities of the quasiparticle dispersion relation of the post-quench Hamiltonian. For long-range interactions of the form $1/|R|^{\alpha}$, the correlation spreading is substantially different due to a possible divergence of group velocity when tuning the power-law exponent $\alpha$. For a divergent group velocity, i.e. the quasi-local regime, we have presented evidence of a universal algebraic structure for the causality cone. While, the correlation edge motion has been found to be always slower than ballistic, the local extrema propagate faster than ballistically and ballistically for gapless and gapped quantum systems respectively. For the local regime implying a well-defined group velocity, we have recovered similar scaling laws and spreading velocities than the short-range case for the causality cone of correlations., Comment: Doctoral thesis

Large angular scale surveys in the absence of atmosphere are essential for measuring the primordial $B$-mode power spectrum of the Cosmic Microwave Background (CMB). Since this proposed measurement is about three to four orders of magnitude fainter than the temperature anisotropies of the CMB, in-flight calibration of the instruments and active suppression of systematic effects are crucial. We investigate the effect of changing the parameters of the scanning strategy on the in-flight calibration effectiveness, the suppression of the systematic effects themselves, and the ability to distinguish systematic effects by null-tests. Next-generation missions such as LiteBIRD, modulated by a Half-Wave Plate (HWP), will be able to observe polarisation using a single detector, eliminating the need to combine several detectors to measure polarisation, as done in many previous experiments and hence avoiding the consequent systematic effects. While the HWP is expected to suppress many systematic effects, some of them will remain. We use an analytical approach to comprehensively address the mitigation of these systematic effects and identify the characteristics of scanning strategies that are the most effective for implementing a variety of calibration strategies in the multi-dimensional space of common spacecraft scan parameters. We also present Falcons, a fast spacecraft scanning simulator that we developed to investigate this scanning parameter space.

We assess the capabilities of the LiteBIRD mission to map the hot gas distribution in the Universe through the thermal Sunyaev-Zeldovich (SZ) effect. Our analysis relies on comprehensive simulations incorporating various sources of Galactic and extragalactic foreground emission, while accounting for specific instrumental characteristics of LiteBIRD, such as detector sensitivities, frequency-dependent beam convolution, inhomogeneous sky scanning, and $1/f$ noise. We implement a tailored component-separation pipeline to map the thermal SZ Compton $y$-parameter over 98% of the sky. Despite lower angular resolution for galaxy cluster science, LiteBIRD provides full-sky coverage and, compared to the Planck satellite, enhanced sensitivity, as well as more frequency bands to enable the construction of an all-sky $y$-map, with reduced foreground contamination at large and intermediate angular scales. By combining LiteBIRD and Planck channels in the component-separation pipeline, we obtain an optimal $y$-map that leverages the advantages of both experiments, with the higher angular resolution of the Planck channels enabling the recovery of compact clusters beyond the LiteBIRD beam limitations, and the numerous sensitive LiteBIRD channels further mitigating foregrounds. The added value of LiteBIRD is highlighted through the examination of maps, power spectra, and one-point statistics of the various sky components. After component separation, the $1/f$ noise from LiteBIRD is effectively mitigated below the thermal SZ signal at all multipoles. Cosmological constraints on $S_8=\sigma_8\left(\Omega_{\rm m}/0.3\right)^{0.5}$ obtained from the LiteBIRD-Planck combined $y$-map power spectrum exhibits a 15% reduction in uncertainty compared to constraints from Planck alone. This improvement can be attributed to the increased portion of uncontaminated sky available in the LiteBIRD-Planck combined $y$-map., Comment: 38 pages, 13 figures, abstract shortened. Updated to match version accepted by JCAP

We present $\texttt{ACCORD}$, a framework and benchmark suite for disentangling the commonsense grounding and reasoning abilities of large language models (LLMs) through controlled, multi-hop counterfactuals. $\texttt{ACCORD}$ introduces formal elements to commonsense reasoning to explicitly control and quantify reasoning complexity beyond the typical 1 or 2 hops. Uniquely, $\texttt{ACCORD}$ can automatically generate benchmarks of arbitrary reasoning complexity, and so it scales with future LLM improvements. Benchmarking state-of-the-art LLMs -- including GPT-4o (2024-05-13), Llama-3-70B-Instruct, and Mixtral-8x22B-Instruct-v0.1 -- shows performance degrading to random chance with only moderate scaling, leaving substantial headroom for improvement. We release a leaderboard of the benchmark suite tested in this work, as well as code for automatically generating more complex benchmarks., Comment: For leaderboard and dataset download, see https://www.codabench.org/competitions/3160/ For source code, see https://github.com/francois-rd/accord/

LiteBIRD, the next-generation cosmic microwave background (CMB) experiment, aims for a launch in Japan's fiscal year 2032, marking a major advancement in the exploration of primordial cosmology and fundamental physics. Orbiting the Sun-Earth Lagrangian point L2, this JAXA-led strategic L-class mission will conduct a comprehensive mapping of the CMB polarization across the entire sky. During its 3-year mission, LiteBIRD will employ three telescopes within 15 unique frequency bands (ranging from 34 through 448 GHz), targeting a sensitivity of 2.2\,$\mu$K-arcmin and a resolution of 0.5$^\circ$ at 100\,GHz. Its primary goal is to measure the tensor-to-scalar ratio $r$ with an uncertainty $\delta r = 0.001$, including systematic errors and margin. If $r \geq 0.01$, LiteBIRD expects to achieve a $>5\sigma$ detection in the $\ell=$2-10 and $\ell=$11-200 ranges separately, providing crucial insight into the early Universe. We describe LiteBIRD's scientific objectives, the application of systems engineering to mission requirements, the anticipated scientific impact, and the operations and scanning strategies vital to minimizing systematic effects. We will also highlight LiteBIRD's synergies with concurrent CMB projects., Comment: 23 pages, 9 figures, 1 table, SPIE Astronomical Telescopes + Instrumentation 2024

We analyze why the discretization of linear transport with asymmetric Hermite basis functions can be instable in quadratic norm. The main reason is that the finite truncation of the infinite moment linear system looses the skew-symmetry property with respect to the Gram matrix. Then we propose an original closed formula for the scalar product of any pair of asymmetric basis functions. It makes possible the construction of two simple modifications of the linear systems which recover the skew-symmetry property. By construction the new methods are quadratically stable with respect to the natural $L^2$ norm. We explain how to generalize to other transport equations encountered in numerical plasma physics. Basic numerical tests with oscillating electric fields of different nature illustrate the unconditional stability properties of our algorithms.

We present detailed forecasts for the constraints on primordial magnetic fields (PMFs) that will be obtained with the LiteBIRD satellite. The constraints are driven by the effects of PMFs on the CMB anisotropies: the gravitational effects of magnetically-induced perturbations; the effects on the thermal and ionization history of the Universe; the Faraday rotation imprint on the CMB polarization; and the non-Gaussianities induced in polarization anisotropies. LiteBIRD represents a sensitive probe for PMFs and by exploiting all the physical effects, it will be able to improve the current limit coming from Planck. In particular, thanks to its accurate $B$-mode polarization measurement, LiteBIRD will improve the constraints on infrared configurations for the gravitational effect, giving $B_{\rm 1\,Mpc}^{n_{\rm B} =-2.9} < 0.8$ nG at 95% C.L., potentially opening the possibility to detect nanogauss fields with high significance. We also observe a significant improvement in the limits when marginalized over the spectral index, $B_{1\,{\rm Mpc}}^{\rm marg}< 2.2$ nG at 95% C.L. From the thermal history effect, which relies mainly on $E$-mode polarization data, we obtain a significant improvement for all PMF configurations, with the marginalized case, $\sqrt{\langle B^2\rangle}^{\rm marg}<0.50$ nG at 95% C.L. Faraday rotation constraints will take advantage of the wide frequency coverage of LiteBIRD and the high sensitivity in $B$ modes, improving the limits by orders of magnitude with respect to current results, $B_{1\,{\rm Mpc}}^{n_{\rm B} =-2.9} < 3.2$ nG at 95% C.L. Finally, non-Gaussianities of the $B$-mode polarization can probe PMFs at the level of 1 nG, again significantly improving the current bounds from Planck. Altogether our forecasts represent a broad collection of complementary probes, providing conservative limits on PMF characteristics that will be achieved with LiteBIRD., Comment: 51 pages, 24 figures, abstract shortened

We present a study of the impact of an uncertainty in the beam far side-lobe knowledge on the measurement of the Cosmic Microwave Background $B$-mode signal at large scale. It is expected to be one of the main source of systematic effects in future CMB observations. Because it is crucial for all-sky survey missions to take into account the interplays between beam systematic effects and all the data analysis steps, the primary goal of this paper is to provide the methodology to carry out the end-to-end study of their effect for a space-borne CMB polarization experiment, up to the cosmological results in the form of a bias $\delta r$ on the tensor-to-scalar ratio $r$. LiteBIRD is dedicated to target the measurement of CMB primordial $B$ modes by reaching a sensitivity of $\sigma \left( r \right) \leq 10^{-3}$ assuming $r=0$. As a demonstration of our framework, we derive the relationship between the knowledge of the beam far side-lobes and the tentatively allocated error budget under given assumptions on design, simulation and component separation method. We assume no mitigation of the far side-lobes effect at any stage of the analysis pipeline. We show that $\delta r$ is mostly due to the integrated fractional power difference between the estimated beams and the true beams in the far side-lobes region, with little dependence on the actual shape of the beams, for low enough $\delta r$. Under our set of assumptions, in particular considering the specific foreground cleaning method we used, we find that the integrated fractional power in the far side-lobes should be known at a level as tight as $\sim 10^{-4}$, to achieve the required limit on the bias $\delta r < 1.9 \times 10^{-5}$. The framework and tools developed for this study can be easily adapted to provide requirements under different design, data analysis frameworks and for other future space-borne experiments beyond LiteBIRD.

We estimate the efficiency of mitigating the lensing $B$-mode polarization, the so-called delensing, for the $LiteBIRD$ experiment with multiple external data sets of lensing-mass tracers. The current best bound on the tensor-to-scalar ratio, $r$, is limited by lensing rather than Galactic foregrounds. Delensing will be a critical step to improve sensitivity to $r$ as measurements of $r$ become more and more limited by lensing. In this paper, we extend the analysis of the recent $LiteBIRD$ forecast paper to include multiple mass tracers, i.e., the CMB lensing maps from $LiteBIRD$ and CMB-S4-like experiment, cosmic infrared background, and galaxy number density from $Euclid$- and LSST-like survey. We find that multi-tracer delensing will further improve the constraint on $r$ by about $20\%$. In $LiteBIRD$, the residual Galactic foregrounds also significantly contribute to uncertainties of the $B$-modes, and delensing becomes more important if the residual foregrounds are further reduced by an improved component separation method., Comment: 21 pages, 7 figures

We explore the capability of measuring lensing signals in $LiteBIRD$ full-sky polarization maps. With a $30$ arcmin beam width and an impressively low polarization noise of $2.16\,\mu$K-arcmin, $LiteBIRD$ will be able to measure the full-sky polarization of the cosmic microwave background (CMB) very precisely. This unique sensitivity also enables the reconstruction of a nearly full-sky lensing map using only polarization data, even considering its limited capability to capture small-scale CMB anisotropies. In this paper, we investigate the ability to construct a full-sky lensing measurement in the presence of Galactic foregrounds, finding that several possible biases from Galactic foregrounds should be negligible after component separation by harmonic-space internal linear combination. We find that the signal-to-noise ratio of the lensing is approximately $40$ using only polarization data measured over $90\%$ of the sky. This achievement is comparable to $Planck$'s recent lensing measurement with both temperature and polarization and represents a four-fold improvement over $Planck$'s polarization-only lensing measurement. The $LiteBIRD$ lensing map will complement the $Planck$ lensing map and provide several opportunities for cross-correlation science, especially in the northern hemisphere.

We study the possibility of using the $LiteBIRD$ satellite $B$-mode survey to constrain models of inflation producing specific features in CMB angular power spectra. We explore a particular model example, i.e. spectator axion-SU(2) gauge field inflation. This model can source parity-violating gravitational waves from the amplification of gauge field fluctuations driven by a pseudoscalar "axionlike" field, rolling for a few e-folds during inflation. The sourced gravitational waves can exceed the vacuum contribution at reionization bump scales by about an order of magnitude and can be comparable to the vacuum contribution at recombination bump scales. We argue that a satellite mission with full sky coverage and access to the reionization bump scales is necessary to understand the origin of the primordial gravitational wave signal and distinguish among two production mechanisms: quantum vacuum fluctuations of spacetime and matter sources during inflation. We present the expected constraints on model parameters from $LiteBIRD$ satellite simulations, which complement and expand previous studies in the literature. We find that $LiteBIRD$ will be able to exclude with high significance standard single-field slow-roll models, such as the Starobinsky model, if the true model is the axion-SU(2) model with a feature at CMB scales. We further investigate the possibility of using the parity-violating signature of the model, such as the $TB$ and $EB$ angular power spectra, to disentangle it from the standard single-field slow-roll scenario. We find that most of the discriminating power of $LiteBIRD$ will reside in $BB$ angular power spectra rather than in $TB$ and $EB$ correlations., Comment: 22 pages, 13 figures. Submitted to JCAP

For classifying digital whole slide images in the absence of pixel level annotation, typically multiple instance learning methods are applied. Due to the generic applicability, such methods are currently of very high interest in the research community, however, the issue of data augmentation in this context is rarely explored. Here we investigate linear and multilinear interpolation between feature vectors, a data augmentation technique, which proved to be capable of improving the generalization performance classification networks and also for multiple instance learning. Experiments, however, have been performed on only two rather small data sets and one specific feature extraction approach so far and a strong dependence on the data set has been identified. Here we conduct a large study incorporating 10 different data set configurations, two different feature extraction approaches (supervised and self-supervised), stain normalization and two multiple instance learning architectures. The results showed an extraordinarily high variability in the effect of the method. We identified several interesting aspects to bring light into the darkness and identified novel promising fields of research., Comment: for code and data, see gitlab repo: https://gitlab.com/mgadermayr/mixupmil. arXiv admin note: substantial text overlap with arXiv:2211.05862

We present the spin-wave theory of the excitation spectrum and quench dynamics of the non-Hermitian transverse-field Ising model. The complex excitation spectrum is obtained for a generic hypercubic lattice using the linear approximation of the Holstein-Primakoff transformation together with the complex bosonic Bogolyubov transformation. In the one-dimensional case, our result compares very well with the exact quasiparticle dispersion relation obtained via a fermionic representation of the problem, at least in the regime of large dissipation and transverse field. When applied to the quench dynamics we show however that the linear spin-wave approximation breaks down and the bosonic theory is plagued by a divergence at finite times. We understand the origin of this instability using a single mode approximation. While limited to short times, we show that this approach allows us to characterize the dynamics arising from the quench of the dissipative term and the structure of the Lieb-Robinson light-cone of the propagation quantum correlations. Furthermore, for the one-dimensional case, the linear spin-wave dynamics shows good agreement with the exact fermionic solution, both for the local magnetization and the spin-spin correlations., Comment: 17 pages, 15 figures

The aim of this paper is to highlight the challenges and potential gains surrounding a coherent description of physics from the high-energy scales of inflation down to the lower energy scales probed in particle-physics experiments. As an example, we revisit the way inflation can be realised within an effective Minimal Supersymmetric Standard Model (eMSSM), in which the $LLe$ and $udd$ flat directions are lifted by the combined effect of soft-supersymmetric-breaking masses already present in the MSSM, together with the addition of effective non-renormalizable operators. We clarify some features of the model and address the question of the one-loop Renormalization Group improvement of the inflationary potential, discussing its impact on the fine-tuning of the model. We also compare the parameter space that is compatible with current observations (in particular the amplitude, $A_{\scriptscriptstyle{\mathrm{S}}}$, and the spectral index, $n_{\scriptscriptstyle{\mathrm{S}}}$, of the primordial cosmological fluctuations) at tree level and at one loop, and discuss the role of reheating. Finally we perform combined fits of particle and cosmological observables (mainly $A_{\scriptscriptstyle{\mathrm{S}}}$, $n_{\scriptscriptstyle{\mathrm{S}}}$, the Higgs mass, and the cold-dark-matter energy density) with the one-loop inflationary potential applied to some examples of dark-matter annihilation channels (Higgs-funnel, Higgsinos and A-funnel), and discuss the status of the ensuing MSSM spectra with respect to the LHC searches., Comment: 38 pages, 7 figures. Includes updated bibliography and minor additions in the text. Matches the published version in PRD

LiteBIRD is a planned JAXA-led CMB B-mode satellite experiment aiming for launch in the late 2020s, with a primary goal of detecting the imprint of primordial inflationary gravitational waves. Its current baseline focal-plane configuration includes 15 frequency bands between 40 and 402 GHz, fulfilling the mission requirements to detect the amplitude of gravitational waves with the total uncertainty on the tensor-to-scalar ratio, $\delta r$, down to $\delta r<0.001$. A key aspect of this performance is accurate astrophysical component separation, and the ability to remove polarized thermal dust emission is particularly important. In this paper we note that the CMB frequency spectrum falls off nearly exponentially above 300 GHz relative to the thermal dust SED, and a relatively minor high frequency extension can therefore result in even lower uncertainties and better model reconstructions. Specifically, we compare the baseline design with five extended configurations, while varying the underlying dust modeling, in each of which the HFT (High-Frequency Telescope) frequency range is shifted logarithmically towards higher frequencies, with an upper cutoff ranging between 400 and 600 GHz. In each case, we measure the tensor-to-scalar ratio $r$ uncertainty and bias using both parametric and minimum-variance component-separation algorithms. When the thermal dust sky model includes a spatially varying spectral index and temperature, we find that the statistical uncertainty on $r$ after foreground cleaning may be reduced by as much as 30--50 % by extending the upper limit of the frequency range from 400 to 600 GHz, with most of the improvement already gained at 500 GHz. We also note that a broader frequency range leads to better ability to discriminate between models through higher $\chi^2$ sensitivity. (abridged), Comment: 18 pages, 13 figures. Published in A&A

Multiple instance learning exhibits a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations. In spite of the huge size of hole slide images, the number of individual slides is often rather small, leading to a small number of labeled samples. To improve training, we propose and investigate different data augmentation strategies for multiple instance learning based on the idea of linear interpolations of feature vectors (known as MixUp). Based on state-of-the-art multiple instance learning architectures and two thyroid cancer data sets, an exhaustive study is conducted considering a range of common data augmentation strategies. Whereas a strategy based on to the original MixUp approach showed decreases in accuracy, the use of a novel intra-slide interpolation method led to consistent increases in accuracy., Comment: MICCAI'23, https://gitlab.com/mgadermayr/mixupmil

Thyroid cancer is currently the fifth most common malignancy diagnosed in women. Since differentiation of cancer sub-types is important for treatment and current, manual methods are time consuming and subjective, automatic computer-aided differentiation of cancer types is crucial. Manual differentiation of thyroid cancer is based on tissue sections, analysed by pathologists using histological features. Due to the enormous size of gigapixel whole slide images, holistic classification using deep learning methods is not feasible. Patch based multiple instance learning approaches, combined with aggregations such as bag-of-words, is a common approach. This work's contribution is to extend a patch based state-of-the-art method by generating and combining feature vectors of three different patch resolutions and analysing three distinct ways of combining them. The results showed improvements in one of the three multi-scale approaches, while the others led to decreased scores. This provides motivation for analysis and discussion of the individual approaches., Comment: Accepted and presented at IPTA 2022 (Best Paper) edit: During work on this publication Maximilian Ernst Tschuchnig was affiliated with Salzburg University of Applied Sciences and University of Salzburg

Our food preferences guide our food choices and in turn affect our personal health and our social life. In this paper, we adopt an approach using a domain ontology expressed in OWL2 to support the acquisition and representation of preferences in formalism CP-Net. Specifically, we present the construction of the domain ontology and questionnaire design to acquire and represent the preferences. The acquisition and representation of preferences are implemented in the field of university canteen. Our main contribution in this preliminary work is to acquire preferences and enrich the model preferably with domain knowledge represented in the ontology., Comment: in French, JFO 2016 - 6{\`e}mes Journ{\'e}es Francophones sur les Ontologies, Nov 2016, Bordeaux, France

We investigate the hot cracking susceptibility and creep resistance of three versions of a nickel-based superalloy with different contents of boron, carbon and zirconium fabricated by laser powder bed fusion. Crack-free and creep resistant components are achieved for alloys with boron, carbon and no zirconium. We then rationalize this result by evaluating how boron, carbon and zirconium are distributed at grain boundaries in the as-built and heat-treated microstructures of an alloy containing all these elements. Observations are conducted by scanning and transmission electron microscopy, and atom probe tomography. In the as-built microstructure, boron, carbon and zirconium segregate at high-angle grain boundaries as a result of solute partitioning to the liquid and limited solid-state diffusion during solidification and cooling. After heat-treatment, the amount of boron and carbon segregating at grain boundaries increases significantly. In contrast, zirconium is not found at grain boundaries but it partitions at the gamma' precipitates formed during the heat treatment. The presence of zirconium at grain boundaries in the as-built condition is known to be susceptible to enhance hot cracking, while its absence in the heat-treated microstructure strongly suggests that this element has no major effect on the creep resistance. Based on our observations, we propose alloy design guidelines to at the same time avoid hot cracking during fabrication and achieve the required creep performance after heat-treatment.

We present an original approach for predicting the static recrystallization texture development during annealing of deformed crystalline materials. The microstructure is considered as a population of subgrains and grains whose sizes and boundary properties determine their growth rates. The model input parameters are measured directly on orientation maps maps of the deformed microstructure measured by electron backscattered diffraction. The anisotropy in subgrain properties then drives a competitive growth giving rise to the recrystallization texture development. The method is illustrated by a simulation of the static recrystallization texture development in a hot rolled ferritic stainless steel. The model predictions are found to be in good agreement with the experimental measurements, and allow for an in-depth investigation of the formation sequence of the recrystallization texture. A distinction is established between the texture components which develop due to favorable growth conditions and those developing due to their predominance in the prior deformed state. The high fraction of alpha fibre orientations in the recrystallized state is shown to be a consequence of their predominance in the deformed microstructure rather than a preferred growth mechanism. A close control of the fraction of these orientations before annealing is thus required to minimize their presence in the recrystallized state.

In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during the intervention to base intra-operative decisions on the outcome of the histology. However, compared to paraffin sections, the quality of frozen sections is typically lower, leading to a higher ratio of miss-classification. In this work, we investigated the effect of the section type on automated decision support approaches for classification of thyroid cancer. This was enabled by a data set consisting of pairs of sections for individual patients. Moreover, we investigated, whether a frozen-to-paraffin translation could help to optimize classification scores. Finally, we propose a specific data augmentation strategy to deal with a small amount of training data and to increase classification accuracy even further., Comment: Accepted: MICCAI SASHIMI

Despite its utopian promises as a disruptive equalizer, AI - like most tools deployed under the guise of neutrality - has tended to simply reinforce existing social structures. To counter this trend, radical AI calls for centering on the marginalized. We argue that gaps in key infrastructure are preventing the widespread adoption of radical AI, and propose a guiding principle for both identifying these infrastructure gaps and evaluating whether proposals for new infrastructure effectively center marginalized voices., Comment: 4 pages, Accepted at the Resistance AI Workshop @ NeurIPS 2020

Whether long-range interactions allow for a form of causality in non-relativistic quantum models remains an open question with far-reaching implications for the propagation of information and thermalization processes. Here, we study the out-of-equilibrium dynamics of the one-dimensional transverse Ising model with algebraic long-range exchange coupling. Using a state of the art tensor-network approach, complemented by analytic calculations and considering various observables, we show that a weak form of causality emerges, characterized by non-universal dynamical exponents. While the local spin and spin correlation causal edges are sub-ballistic, the causal region has a rich internal structure, which, depending on the observable, displays ballistic or super-ballistic features. In contrast, the causal region of entanglement entropy is featureless and its edge is always ballistic, irrespective of the interaction range. Our results shed light on the propagation of information in long-range interacting lattice models and pave the way to future experiments, which are discussed., Comment: 5 pages, 4 figures, and supplemental material

Quench spectroscopy is a relatively new method which enables the investigation of spectral properties of many-body quantum systems by monitoring the out-of-equilibrium dynamics of real-space observables after a quench. So far the approach has been devised for global quenches or using local engineering of momentum-resolved excitations. Here, we extend the quench spectroscopy method to local quenches. We show that it allows us to extract quantitative information about global properties of the system, and in particular the elementary excitation spectrum. Using state-of-the-art numerical methods, we simulate the out-of-equilibrium dynamics of a variety of quantum systems following various local quench protocols and demonstrate a general scheme for designing an appropriate local quench protocol for any chosen model. We provide detailed examples of how the local quench protocol can be realised in realistic current generation experiments, including ultracold atomic gases and trapped ion systems., Comment: 12 pages, 11 figures

In this paper, we develop a mean-field model for simulating the microstructure evolution of crystalline materials during static recrystallization. The model considers a population of individual cells (i.e. grains and subgrains) growing in a homogeneous medium representing the average microstructure properties. The average boundary properties of the individual cells and of the medium, required to compute growth rates, are estimated statistically as a function of the microstructure topology and of the distribution of crystallographic orientations. Recrystallized grains arise from the competitive growth between cells. After a presentation of the algorithm, the model is compared to full-field simulations of recrystallization performed with a 2D Vertex model. It is shown that the mean-field model predicts accurately the evolution of boundary properties with time, as well as several recrystallization parameters including kinetics and grain orientations. The results allow one to investigate the role the orientation spread on the determination of boundary properties, the formation of recrystallized grains and recrystallization kinetics. The model can be used with experimentally obtained inputs to investigate the relationship between deformation and recrystallization microstructures.

Purpose: We sought to evaluate the feasibility of using machine learning algorithms for multipoint plastic scintillator detector calibration in high-dose-rate brachytherapy. Methods: The dosimetry system consisted of an optimized 1-mm-core mPSD and a compact assembly of photomultiplier tubes coupled with dichroic mirrors and filters. An $^{192}$Ir source was remotely controlled and sent to various positions in a homemade PMMA holder. Dose measurements covering a range of 0.5 to 12 cm of source displacement were carried out according to TG-43 recommendations. Individual scintillator doses were decoupled using a linear regression model, a random forest estimator, and artificial neural network algorithms. The performance of the different algorithms was evaluated using different sample sizes and distances to the source for the mPSD system calibration. Results: The decoupling methods' deviations from the expected TG-43 dose generally remained below 20%. However, the dose prediction with the three algorithms was accurate to within 7% relative to the dose predicted by the TG-43 formalism for measurements performed in the same range of distances used for calibration. The performance random forest was compromised when the predictions were done beyond the range of distances used for calibration. The dose prediction by the linear regression was less influenced by the calibration conditions than random forest, but with more significant deviations. The number of available measurements for training purposes influenced the random forest and neural network models the most. Their accuracy tended to converge toward deviation values close to 1% from a number of dwell positions greater than 100. Conclusions: In performing HDR brachytherapy dose measurements with an optimized mPSD system, ML algorithms are good alternatives for precise dose reporting and treatment assessment.

The vertical diffusive halo size of the Galaxy, $L$, is a key parameter for dark matter indirect searches. It can be better determined thanks to recent AMS-02 data. We set constraints on $L$ from Be/B and $^{10}$Be/Be data, and we performed a consistency check with positron data. We detail the dependence of Be/B and $^{10}$Be/Be on $L$ and forecast on which energy range better data would be helpful for future $L$ improvements. We used USINE v3.5 for the propagation of nuclei, and $e^+$ were calculated with the pinching method of Boudaud et al. (2017). The current AMS-02 Be/B ($\sim3\%$ precision) and ACE-CRIS $^{10}$Be/Be ($\sim 10\%$ precision) data bring similar and consistent constraints on $L$. The AMS-02 Be/B data alone constrain $L=5^{+3}_{-2}$~kpc at a 68\% confidence level (spanning different benchmark transport configurations), a range for which most models do not overproduce positrons. Future experiments need to deliver percent-level accuracy on $^{10}$Be/$^9$Be anywhere below 10 GV to further constrain $L$. Forthcoming AMS-02, HELIX, and PAMELA $^{10}$Be/$^9$Be results will further test and possibly tighten the limits derived here. Elemental ratios involving radioactive species with different lifetimes (e.g. Al/Mg and Cl/Ar) are also awaited to provide complementary and robuster constraints., Comment: 15 pages, 10 figures, 6 tables. Additional references and few clarifications (matches A&A accepted version)

We study the Bernstein-Landau paradox in the collisionless motion of an electrostatic plasma in the presence of a constant external magnetic field. The Bernstein-Landau paradox consists in that in the presence of the magnetic field, the electric field and the charge density fluctuation have an oscillatory behavior in time. This is radically different from Landau damping, in the case without magnetic field, where the electric field tends to zero for large times. We consider this problem from a new point of view. Instead of analyzing the linear magnetized Vlasov-Poisson system, as it is usually done, we study the linear magnetized Vlasov-Amp\`ere system. We formulate the magnetized Vlasov-Amp\`ere system as a Schr\"odinger equation with a selfadjoint magnetized Vlasov-Amp\`ere operator in the Hilbert space of states with finite energy. The magnetized Vlasov-Amp\`ere operator has a complete set of orthonormal eigenfunctions, that include the Bernstein modes. The expansion of the solution of the magnetized Vlasov-Amp\`ere system in the eigenfunctions shows the oscillatory behavior in time. We prove the convergence of the expansion under optimal conditions, assuming only that the initial state has finite energy. This solves a problem that was recently posed in the literature. The Bernstein modes are not complete. To have a complete system it is necessary to add eigenfunctions that are associated with eigenvalues at all the integer multiples of the cyclotron frequency. These special plasma oscillations actually exist on their own, without the excitation of the other modes. In the limit when the magnetic fields goes to zero the spectrum of the magnetized Vlasov-Amp\`ere operator changes drastically from pure point to absolutely continuous in the orthogonal complement to its kernel, due to a sharp change on its domain. This explains the Bernstein-Landau paradox., Comment: We have edited the paper to include new results and to add details that improve the presentation. Further, we have added the word magnetized to the title

Quenches are now routinely used in synthetic quantum systems to study a variety of fundamental effects, including ergodicity breaking, light-cone-like spreading of information, and dynamical phase transitions. It was shown recently that the dynamics of equal-time correlators may be related to ground-state phase transitions and some properties of the system excitations. Here, we show that the full low-lying excitation spectrum of a generic many-body quantum system can be extracted from the after-quench dynamics of equal-time correlators. We demonstrate it for a variety of one-dimensional lattice models amenable to exact numerical calculations, including Bose and spin models, with short- or long-range interactions. The approach also applies to higher dimensions, correlated fermions, and continuous models. We argue that it provides an alternative approach to standard pump-probe spectroscopic methods and discuss its advantages., Comment: 4 + 7 pages

The ubiquity of sound synthesizers has reshaped music production and even entirely defined new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a crucial need. Here, we introduce a novel formulation of audio synthesizer control. We formalize it as finding an organized latent audio space that represents the capabilities of a synthesizer, while constructing an invertible mapping to the space of its parameters. By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model. To solve this new formulation, we rely on Variational Auto-Encoders (VAE) and Normalizing Flows (NF) to organize and map the respective auditory and parameter spaces. We introduce the disentangling flows, which allow to perform the invertible mapping between separate latent spaces, while steering the organization of some latent dimensions to match target variation factors by splitting the objective as partial density evaluation. We evaluate our proposal against a large set of baseline models and show its superiority in both parameter inference and audio reconstruction. We also show that the model disentangles the major factors of audio variations as latent dimensions, that can be directly used as macro-parameters. We also show that our model is able to learn semantic controls of a synthesizer by smoothly mapping to its parameters. Finally, we discuss the use of our model in creative applications and its real-time implementation in Ableton Live, Comment: DaFX 2019

The motivation of this work stems from the numerical approximation of bounded functions by polynomials satisfying the same bounds. The present contribution makes use of the recent algebraic characterization found in [B. Despr\'es, Numer. Algorithms, 76(3), (2017)] and [B. Despr\'es and M. Herda, Numer. Algorithms, 77(1), (2018)] where an interpretation of monovariate polynomials with two bounds is provided in terms of a quaternion algebra and the Euler four-squares formulas. Thanks to this structure, we generate a new nonlinear projection algorithm onto the set of polynomials with two bounds. The numerical analysis of the method provides theoretical error estimates showing stability and continuity of the projection. Some numerical tests illustrate this novel algorithm for constrained polynomial approximation., Comment: Correction of first author's last name which was misspelled in previous version

Currently in HDR brachytherapy planning, a manual fine-tuning of an objective function is necessary to obtain case-specific valid plans. This study intends to facilitate this process by proposing a patient-specific inverse planning algorithm for HDR prostate brachytherapy: GPU-based multi-criteria optimization (gMCO). Two GPU-based optimization engines including simulated annealing (gSA) and a quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in parallel. After evaluating the equivalence and the computation performance of these two optimization engines, one preferred optimization engine was selected for the gMCO algorithm. Five hundred sixty-two previously treated prostate HDR cases were divided into validation set (100) and test set (462). In the validation set, the number of Pareto optimal plans to achieve the best plan quality was determined for the gMCO algorithm. In the test set, gMCO plans were compared with the physician-approved clinical plans. Over 462 cases, the number of clinically valid plans was 428 (92.6%) for clinical plans and 461 (99.8%) for gMCO plans. The number of valid plans with target V100 coverage greater than 95% was 288 (62.3%) for clinical plans and 414 (89.6%) for gMCO plans. The mean planning time was 9.4 s for the gMCO algorithm to generate 1000 Pareto optimal plans. In conclusion, gL-BFGS is able to compute thousands of SA equivalent treatment plans within a short time frame. Powered by gL-BFGS, an ultra-fast and robust multi-criteria optimization algorithm was implemented for HDR prostate brachytherapy. A large-scale comparison against physician approved clinical plans showed that treatment plan quality could be improved and planning time could be significantly reduced with the proposed gMCO algorithm., Comment: 18 pages, 7 figures

We study the spreading of correlations in the Bose-Hubbard chain, using the time-dependent matrix-product state approach. In both the superfluid and the Mott-insulator phases, we find that the time-dependent correlation functions generally display a universal twofold cone structure characterized by two distinct velocities. The latter are related to different microscopic properties of the system and provide useful information on the excitation spectrum. The twofold spreading of correlations has profound implications on experimental observations that are discussed.