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Neuromorphic Technology Insights in Spain
Full text linkThis paper provides an overview of the main research activities carried out by Spanish organizations in areas related to neuromorphic technologies, spanning physical, materials, circuitry, and architectural levels. It also discusses the potential of these technologies to create competitive advantages for the Spanish industry, and to foster new applications and business opportunities via deep-tech startups – especially related to novel neuromorphic sensing modalities (e.g., Dynamic Vision Sensors)
Rheology of moderated dilute suspensions of star colloids: The shape factor
No full textStar colloids are rigid particles with long and slender arms connected to a central core. We show numerically that the colloid shapes control the rheology of their suspensions. In particular, colloids with curved arms and hooks can entangle with neighbor particles and form large clusters that can sustain high stresses. When a large cluster permeates the whole system, the viscosity increases many fold. Contrary to the case of spherical colloids, we observe that these effects are very strong even at moderate volumes fraction over a wide range of Péclet numbers.“la Caixa” Foundation (ID 100010434) Fellowship No. LCF/BQ/PI20/11760014
Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 847648
Steering spin fluctuations in lattice systems via two-tone Floquet engineering
No full textWe report on the control of spin pair fluctuations using two-tone Floquet engineering. We consider a one-dimensional spin-1/2 lattice with periodically modulated spin exchanges using parametric resonances. The stroboscopic dynamics generated from distributed spin exchange modulations lead to spin pair fluctuations reaching quasi-maximally correlated states and a subharmonic response in local observables, breaking the discrete-time translational symmetry. We present a protocol to control the interacting many-body dynamics, producing spatial and temporal localization of correlated spin pairs via dynamically breaking correlated spin pairs from the edges towards the center of the lattice. Our result reveals how spin fluctuations distribute in a heterogeneous lattice depending on parametric resonances. This may open new routes for exploring distinct nonequilibrium states of matter and the conduction of quasiparticles in quantum materials
Solving Partial Differential Equations using Artificial Neural Networks
Full text linkPartial differential equations have a wide range of applications in modeling multiple physical, biological, orsocial phenomena. Therefore, we need to approximate the solutions of these equations in computationally feasible terms. Nowadays, among the most popular numerical methods for solving partial differential equations in engineering,we encounter the finite difference and the finite element methods. An alternative numerical method that has recentlygained popularity for numerically solving partial differential equations is the use of artificial neural networks.Artificial neural networks, or neural networks for short, are mathematical structures with universal approximationproperties. In addition, thanks to the extraordinary computational development of the last decade, neural networks havebecome accessible and powerful numerical methods for engineers and researchers. For example, imaging andlanguage processing are applications of neural networks today that show sublime performance inconceivable yearsago.This dissertation contributes to the numerical solution of partial differential equations using neural networks with thefollowing two-fold objective: investigate the behavior of neural networks as approximators of solutions of partialdifferential equations and propose neural-network-based methods for frameworks that are hardly addressable via traditional numerical methods.As novel neural-network-based proposals, we first present a method inspired by the finite element method when applying mesh refinements to solve parametric problems. Secondly, we propose a general residual minimization scheme based on a generalized version of the Ritz method. Finally, we develop a memory-based strategy to overcome a usual numerical integration limitation when using neural networks to solve partial differential equations
Impact of High Covid-19 Vaccination Rate in an Aging Population: Estimating Averted Hospitalizations and Deaths in the Basque Country, Spain Using Counterfactual Modeling
Full text linkCOVID-19 vaccines have demonstrated significant efficacy in reducing severe symptoms and fatalities, although their effectiveness in preventing transmission varies depending on the population’s age profile and the dominant variant. This study evaluates the impact of the COVID-19 vaccination campaign in the Basque Country region of Spain, which has the fourth highest proportion of elderly individuals worldwide. Using epidemiological data on hospitalizations, ICU admissions, fatalities, and vaccination coverage, we calibrated four versions of an ordinary differential equations model with varying assumptions on the age structure and transmission function. Counterfactual no-vaccine scenarios were simulated by setting the vaccination rate to zero while all other parameters were held constant. The initial vaccination rollout is estimated to have prevented 46,000 to 75,000 hospitalizations, 6,000 to 11,000 ICU admissions, and 15,000 to 24,000 deaths, reducing these outcomes by 73% to 86%. The most significant impact occurred during the third quarter of 2021, coinciding with the Delta variant's dominance and a vaccination rate exceeding 60%. Sensitivity analysis revealed that vaccination coverage had a more substantial effect on averted outcomes than vaccine efficacy. Overall, the vaccination campaign in the Basque Country significantly reduced severe COVID-19 outcomes, aligning with global estimates and demonstrating robustness across different modeling approaches.Ministerio de Ciencia e Innovacion (MICINN) Ramon y Cajal grant RYC2021-031380-I
EITB Marathon 2021 call, reference BIO21/COV/00
Dimension-Free Estimates for the Discrete Spherical Maximal Functions
Full text linkWe prove that the discrete spherical maximal functions (in the spirit of Magyar, Stein, and Wainger) corresponding to the Euclidean spheres in Zd with dyadic radii have lp(Zd) bounds for all p ∈ [2,∞] independent of the dimensions d ≥ 5. An important part of our argument is the asymptotic formula in the Waring problem for the squares with a dimension-free multiplicative error term. By considering new approximating multipliers, we will show how to absorb an exponential in dimension (like Cd for some C > 1) growth in norms arising from the sampling principle of Magyar, Stein, and Wainger and ultimately deduce dimension-free estimates for the discrete spherical maximal functions
Identifying quantum hamiltonians in the presence of electric interactions. An analytic approach
Full text linkIn this thesis we study two inverse problems related to the identification of time-independent electric potentials, both motivated by the Schrödinger equation in quantum mechanics.
The first one is the inverse point-source scattering problem with rough potentials at a fixed energy in dimensions . Our research
provides a novel uniqueness result for the inverse problem with local data,
obtained from the near field pattern. Our work improves the work of Caro and
Garcia, who investigated both the direct problem and the inverse problem with
global near field data for critically singular and -shell potentials. The primary
contribution of our research is the introduction of a Runge approximation result
for the near field data on the scattering problem which, in combination with an
interior regularity argument, enables us to establish a uniqueness result for the
inverse problem with local data. Additionaly, we manage to consider a slightly
wider class of potentials. A particularity of this problem is the need to make use of domain perturbation techniques to study the eigenvalues of a Neumann problem.
The second one is the initial-to-final value problem in quantum mechanics with the action of a bounded potential of polynomial decay in dimensions . We particularize a result by Caro and Ruiz by considering stationary potentials and hence lowering the requirements on decay. We modify the method of CGO solutions by using Herglotz waves as the leading part, and hence obtain better decay estimates, by exploiting the possibility of measuring at a range of energies.PRE2019-091776
PGC2018-094528-B-I0
AN EXTREMAL PROBLEM AND INEQUALITIES FOR ENTIRE FUNCTIONS OF EXPONENTIAL TYPE
Full text linkWe study two variations of the classical one-delta problem for entire functions of exponential type, known also as the Carath ́eodory–Fej ́er– Tura ́n problem. The first variation imposes the additional requirement that the function is radially decreasing while the second one is a generalization which involves derivatives of the entire function. Various interesting inequali- ties, inspired by results due to Duffin and Schaeffer, Landau, and Hardy and Littlewood, are also established
Nonlocal Models in Biology and Life Sciences: Sources, Developments, and Applications
Full text linkMathematical modelling is one of the fundamental techniques for understanding biophysical mechanisms
in developmental biology. It helps researchers to analyze complex physiological processes and connects
like a bridge between theoretical and experimental observations. Various groups of mathematical models
have been studied to analyze these processes, and the nonlocal models are one of them. Nonlocality is
important in realistic mathematical models of physical and biological systems at small-length scales. It
characterizes the properties of two individuals located in different locations. This review illustrates different nonlocal mathematical models applied to biology and life sciences. The major focus has been given to
sources, developments, and applications of such models. Among other things, a systematic discussion has
been provided for the conditions of pattern formations in biological systems of population dynamics. Special attention has also been given to nonlocal interactions on networks, network coupling and integration,
including models for brain dynamics that provide us with an important tool to better understand neurodegenerative diseases. In addition, we have discussed nonlocal modelling approaches for cancer stem cells
and tumor cells that are widely applied in the cell migration processes, growth, and avascular tumors in any
organ. Furthermore, the discussed nonlocal continuum models can go sufficiently smaller scales applied
to nanotechnology to build biosensors to sense biomaterial and its concentration. Piezoelectric and other
smart materials are among them, and these devices are becoming increasingly important in the digital and
physical world that is intrinsically interconnected with biological systems. Additionally, we have reviewed
a nonlocal theory of peridynamics, which deals with continuous and discrete media and applies to model the
relationship between fracture and healing in cortical bone, tissue growth and shrinkage, and other areas increasingly important in biomedical and bioengineering applications. Finally, we provided a comprehensive
summary of emerging trends and highlighted future directions in this rapidly expanding field
Numerical Simulations of Spatiotemporal Instabilities in Discontinuous Shear Thickening Fluids
Full text linkDiscontinuous Shear Thickening (DST) fluids exhibit unique instability properties in
a wide range of flow conditions. We present numerical simulations of a scalar model
for DST fluids in a planar simple shear using the Smoothed Particle Hydrodynamics
(SPH) approach. The model reproduces the spatially homogeneous instability mechanism
based on the competition between the inertial and microstructural timescales, with
good congruence to the theoretical predictions. Spatial inhomogeneities arising from a
stress-splitting instability are rationalised within the context of local components of the
microstructure evolution. Using this effect, the addition of non-locality in the model
is found to produce an alternative mechanism of temporal instabilities, driven by the
inhomogeneous pattern formation. The reported arrangement of the microstructure is
generally in agreement with the experimental data on gradient pattern formation in
DST. Simulations in a parameter space representative of realistic DST materials resulted
in aperiodic oscillations in measured shear rate and stress, driven by formation of gap-
spanning frictional structures