Taking advantage of recent progress in neutron instrumentation and in the understanding of magnetic-field-dependent small-angle neutron scattering, here, we study the three-dimensional magnetization distribution within an isotropic Nd-Fe-B bulk magnet. The magnetic neutron scattering cross section of this system features the so-called spike anisotropy, which points towards the presence of a strong magnetodipolar interaction. This experimental result combined with a damped oscillatory behavior of the corresponding correlation function and recent micromagnetic simulation results on spherical nanoparticles suggest an interpretation of the neutron data in terms of vortex-like flux-closure patterns. The field-dependent correlation length Lc is well reproduced by a phenomenological power-law model. While the experimental neutron data for Lc are described by an exponent close to unity (p = 0.86), the simulation results yield p = 1.70, posing a challenge to theory to include vortex-vortex interaction effects.

Mathias Bersweiler Yojiro Oba Evelyn Pratami Sinaga Inma Peral Ivan Titov Michael P. Adams Konstantin L. Metlov Andreas Michels

Within the framework of the recently introduced multi-nanoparticle power-series expansion method for the polarized small-angle neutron scattering (SANS) cross section, we present analytical expressions for the polarized SANS observables arising from dilute nanoparticle assemblies with antisymmetric vortex-type spin structures. We establish connections between the magnetic correlation coefficients and the magnetic field-dependent vortex-axes distribution function, which is related to the random orientations of the magnetocrystalline anisotropy axes of the nanoparticles. Our analytical results are validated through a comparative analysis with micromagnetic simulations. This framework contributes to a comprehensive understanding of polarized magnetic neutron scattering from spherical nanoparticle systems exhibiting vortex-type spin structures.

Michael P. Adams Evelyn P. Sinaga Stefan Liscak Andreas Michels

A Bloch point represents a three-dimensional hedgehog singularity of a magnetic vector field in which the magnetization vanishes. However, standard micromagnetic theory, developed for magnetic moments of fixed lengths, lacks full applicability in studying such singularities. To address this gap, we study a Bloch point in a quantum Heisenberg model for the case of spin-1/2 particles. Performing an exact diagonalization of the Hamiltonian as well as using density matrix renormalization group techniques, we obtain the ground state, which can be used to recover the corresponding magnetization profile. Our findings demonstrate a variation of the spin length in the quantum model, leading smoothly to zero magnetization at the Bloch point. Our results indicate the necessity of generalizing the classical micromagnetic model by adding the third degree of freedom of the spins: the ability to change its length. To this end, we introduce the micromagnetic $\mathbb{S}_{3}$-model, which enables the description of magnets with and without Bloch point singularities.

Vladyslav M. Kuchkin Andreas Haller Štefan Liščák Michael P. Adams Venus Rai Evelyn P. Sinaga Andreas Michels Thomas L. Schmidt

Pressure Ulcer is one of the most problems in patients with bed rest. Reposition and skin care are deterrent against the incidence of pressure ulcer. Objective: This study aimed to analyze the effectiveness of sesame oil for the prevention of pressure ulcer in patients with bed rest undergoing hospitalization. Method: This study used a randomized controlled trial design. Forty samples were divided groups: control and intervention groups. This study was analysed using Chi Square. Results: The results showed that there was a significant difference between two group (p=0,04). Conclusions: Skin care with sesame oil can prevention of pressure ulcers. These results recommended that sesame oil can be used for nursing intervention for the prevention of pressure ulcers.

Chrisyen Damanik Sumiati Sinaga Kiki Hardiansyah

We employ micromagnetic simulations to model the effect of pore-type microstructural defects on the magnetic small-angle neutron scattering cross section and the related pair-distance distribution function of spherical magnetic nanoparticles. Our expression for the magnetic energy takes into account the isotropic exchange interaction, the magnetocrystalline anisotropy, the dipolar interaction, and an externally applied magnetic field. The signatures of the defects and the role of the dipolar energy are highlighted and the effect of a particle-size distribution is studied. The results serve as a guideline to the experimentalist.

Evelyn Pratami Sinaga Michael P. Adams Mathias Bersweiler Laura G. Vivas Eddwi H. Hasdeo Jonathan Leliaert Philipp Bender Dirk Honecker Andreas Michels

We investigate the signature of magnetic surface anisotropy in nanoparticles in their spin-flip neutron scattering cross section. Taking into account the isotropic exchange interaction, an external magnetic field, a uniaxial or cubic magnetic anisotropy for the particle's core, and several models for the surface anisotropy (N\'eel, conventional, random), we compute the spin-flip small-angle neutron scattering (SANS) cross section from the equilibrium spin structures obtained using the Landau-Lifshitz equation. The sign of the surface anisotropy constant, which is related to the appearance of tangential- or radial-like spin textures, can be distinguished from the momentum-transfer dependence of the spin-flip signal. The data cannot be described by the well-known and often-used analytical expressions for uniformly magnetized spherical or core-shell particles, in particular at remanence or at the coercive field. Based on a second-order polynomial expansion for the magnetization vector field, we develop a novel minimal model for the azimuthally-averaged magnetic SANS cross section. The theoretical expression considers a general magnetization inhomogeneity and is not restricted to the presence of surface anisotropy. It is shown that the model describes very well our simulation data as well as more complex spin patterns such as vortex-like structures. Only seven expansion coefficients and some basis functions are sufficient to describe the scattering behavior of a very large number of atomic spins.

Michael P. Adams Evelyn Pratami Sinaga Hamid Kachkachi Andreas Michels

The presence of mutual information in the research of deep learning has grown significantly. It has been proven that mutual information can be a good objective function to build a robust deep learning model. Most of the researches utilize estimation methods to approximate the true mutual information. This technical report delivers an extensive study about definitions as well as properties of mutual information. This article then delivers some reviews and current drawbacks of mutual information estimation methods afterward.

Marshal Arijona Sinaga

Preferential Bayesian optimization (PBO) is a sample-efficient framework for learning human preferences between candidate designs. PBO classically relies on homoscedastic noise models to represent human aleatoric uncertainty. Yet, such noise fails to accurately capture the varying levels of human aleatoric uncertainty, particularly when the user possesses partial knowledge among different pairs of candidates. For instance, a chemist with solid expertise in glucose-related molecules may easily compare two compounds from that family while struggling to compare alcohol-related molecules. Currently, PBO overlooks this uncertainty during the search for a new candidate through the maximization of the acquisition function, consequently underestimating the risk associated with human uncertainty. To address this issue, we propose a heteroscedastic noise model to capture human aleatoric uncertainty. This model adaptively assigns noise levels based on the distance of a specific input to a predefined set of reliable inputs known as anchors provided by the human. Anchors encapsulate partial knowledge and offer insight into the comparative difficulty of evaluating different candidate pairs. Such a model can be seamlessly integrated into the acquisition function, thus leading to candidate design pairs that elegantly trade informativeness and ease of comparison for the human expert. We perform an extensive empirical evaluation of the proposed approach, demonstrating a consistent improvement over homoscedastic PBO.

Marshal Arijona Sinaga Julien Martinelli Vikas Garg Samuel Kaski

Gaussian processes (GPs) are widely used for regression and optimization tasks such as Bayesian optimization (BO) due to their expressiveness and principled uncertainty estimates. However, in settings with large datasets corrupted by outliers, standard GPs and their sparse approximations struggle with computational tractability and robustness. We introduce Robust Computation-aware Gaussian Process (RCaGP), a novel GP model that jointly addresses these challenges by combining a principled treatment of approximation-induced uncertainty with robust generalized Bayesian updating. The key insight is that robustness and approximation-awareness are not orthogonal but intertwined: approximations can exacerbate the impact of outliers, and mitigating one without the other is insufficient. Unlike previous work that focuses narrowly on either robustness or approximation quality, RCaGP combines both in a principled and scalable framework, thus effectively managing both outliers and computational uncertainties introduced by approximations such as low-rank matrix multiplications. Our model ensures more conservative and reliable uncertainty estimates, a property we rigorously demonstrate. Additionally, we establish a robustness property and show that the mean function is key to preserving it, motivating a tailored model selection scheme for robust mean functions. Empirical results confirm that solving these challenges jointly leads to superior performance across both clean and outlier-contaminated settings, both on regression and high-throughput Bayesian optimization benchmarks.

Marshal Arijona Sinaga Julien Martinelli Samuel Kaski

Let $E$ be an algebraic extension of a global field $E_{0}$ with a nontrivial Brauer group Br$(E)$, and let $P(E)$ be the set of those prime numbers $p$, for which $E$ does not equal its maximal $p$-extension $E(p)$. This paper shows that $E$ admits one-dimensional local class field theory if and only if there exists a system $V(E) = \{v(p)\colon \ p \in P(E)\}$ of (nontrivial) absolute values, such that $E(p) \otimes_{E} E_{v(p)}$ is a field, where $E_{v(p)}$ is the completion of $E$ with respect to $v(p)$. When this occurs, we determine by $V(E)$ the norm groups of finite extensions of $E$, and the structure of Br$(E)$. It is also proved that if $P$ is a nonempty set of prime numbers and $\{w(p)\colon \ p \in P\}$ is a system of absolute values of $E_{0}$, then one can find a field $K$ algebraic over $E_{0}$ with such a theory, so that $P(K) = P$ and the element $\kappa (p) \in V(K)$ extends $w(p)$, for each $p \in P$.

I. D. Chipchakov