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Fooling adversaries with traps such as honeytokens can slow down cyber attacks and create strong indicators of compromise. Unfortunately, cyber deception techniques are often poorly specified. Also, realistically measuring their effectiveness requires a well-exposed software system together with a production-ready implementation of these techniques. This makes rapid prototyping challenging. Our work translates 13 previously researched and 12 self-defined techniques into a high-level, machine-readable specification. Our open-source tool, Honeyquest, allows researchers to quickly evaluate the enticingness of deception techniques without implementing them. We test the enticingness of 25 cyber deception techniques and 19 true security risks in an experiment with 47 humans. We successfully replicate the goals of previous work with many consistent findings, but without a time-consuming implementation of these techniques on real computer systems. We provide valuable insights for the design of enticing deception and also show that the presence of cyber deception can significantly reduce the risk that adversaries will find a true security risk by about 22% on average., Comment: to be published in the 27th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2024), dataset and source code available at https://github.com/dynatrace-oss/honeyquest

An emerging supply-chain attack due to a backdoor in XZ Utils has been identified. The backdoor allows an attacker to run commands remotely on vulnerable servers utilizing SSH without prior authentication. We have started to collect available information with regards to this attack to discuss current mitigation strategies for such kinds of supply-chain attacks. This paper introduces the critical attack path of the XZ backdoor and provides an overview about potential mitigation techniques related to relevant stages of the attack path.

We analyze the quasiparticle interaction function (the fully dressed and antisymmetrized interaction between fermions) for a two-dimensional Fermi liquid at zero temperature close to a q=0 charge quantum critical point (QCP) in the $s-$wave channel (the one leading to phase separation). By the Ward identities, this vertex function must be related to quasiparticle residue $Z$, which can be obtained independently from the fermionic self-energy. We show that to satisfy these Ward identities, one needs to go beyond the standard diagrammatic formulation of Fermi-liquid theory and include series of additional contributions to the vertex function. These contributions are not present in a conventional Fermi liquid, but do emerge near a QCP, where the effective 4-fermion interaction is mediated by a soft dynamical boson. We demonstrate explicitly that including these terms restores the Ward identity. Our analysis is built on previous studies of the vertex function near an antiferromagnetic QCP [Phys. Rev. B 89, 045108 (2014)] and a d-wave charge-nematic QCP [Phys. Rev. B 81, 045110 (2010)]. We show that for $s-$wave charge QCP the analysis is more straightforward and allows one to obtain the full quasiparticle interaction function (the Landau function) near a QCP. We show that all partial components of this function (Landau parameters) diverge near a QCP, in the same way as the effective mass $m^*$, except for the $s$-wave charge component, which approaches $-1$. Consequently, the susceptibilities in all channels, except for the critical one, remain finite at a QCP, as they should., Comment: 13 pages, 8 figures

This work introduces the Matrix Minimum Covariance Determinant (MMCD) method, a novel robust location and covariance estimation procedure designed for data that are naturally represented in the form of a matrix. Unlike standard robust multivariate estimators, which would only be applicable after a vectorization of the matrix-variate samples leading to high-dimensional datasets, the MMCD estimators account for the matrix-variate data structure and consistently estimate the mean matrix, as well as the rowwise and columnwise covariance matrices in the class of matrix-variate elliptical distributions. Additionally, we show that the MMCD estimators are matrix affine equivariant and achieve a higher breakdown point than the maximal achievable one by any multivariate, affine equivariant location/covariance estimator when applied to the vectorized data. An efficient algorithm with convergence guarantees is proposed and implemented. As a result, robust Mahalanobis distances based on MMCD estimators offer a reliable tool for outlier detection. Additionally, we extend the concept of Shapley values for outlier explanation to the matrix-variate setting, enabling the decomposition of the squared Mahalanobis distances into contributions of the rows, columns, or individual cells of matrix-valued observations. Notably, both the theoretical guarantees and simulations show that the MMCD estimators outperform robust estimators based on vectorized observations, offering better computational efficiency and improved robustness. Moreover, real-world data examples demonstrate the practical relevance of the MMCD estimators and the resulting robust Shapley values.

MAB phases (MABs) are atomically-thin laminates of ceramic/metallic-like layers, having made a breakthrough in the development of 2D materials. Though theoretically offering a vast chemical and phase space, relatively few MABs have yet been synthesised. To guide experiments, we perform a systematic high-throughput {\it{ab initio}} screening of MABs that combine group 4--7 transition metals (M); Al, Si, Ga, Ge, or In (A); and boron (B) focusing on their phase stability trends and mechanical properties. Considering the 1:1:1, 2:1:1, 2:1:2, 3:1:2, 3:1:3, and 3:1:4 M:A:B ratios and 10 phase prototypes, possible stabilisation of a single-phase compound for each elemental combination is assessed through formation energy spectra of the competing mechanically and dynamically stable MABs. Based on the volumetric proximity of energetically-close phases, we identify systems in which volume-changing deformations may facilitate transformation toughening. Subsequently, chemistry- and phase-structure-related trends in the elastic stiffness and ductility are predicted using elastic-constants-based descriptors. The analysis of directional Cauchy pressures and Young's moduli allows comparing mechanical response parallel and normal to M--B/A layers. Among the suggested most promising MABs are Nb$_3$AlB$_4$, Cr$_2$SiB$_2$, Mn$_2$SiB$_2$ or the already synthesised MoAlB.

We present fast simulation methods for the self-assembly of complex shapes in two dimensions. The shapes are modeled via a general boundary curve and interact via a standard volume term promoting overlap and an interpenetration penalty. To efficiently realize the Gibbs measure on the space of possible configurations we employ the hybrid Monte Carlo algorithm together with a careful use of signed distance functions for energy evaluation. Motivated by the self-assembly of identical coat proteins of the tobacco mosaic virus which assemble into a helical shell, we design a particular nonconvex 2D model shape and demonstrate its robust self-assembly into a unique final state. Our numerical experiments reveal two essential prerequisites for this self-assembly process: blocking and matching (i.e., local repulsion and attraction) of different parts of the boundary; and nonconvexity and handedness of the shape.

We present a non-reactive force field for molecular dynamics simulations of interfaces between passivated amorphous surfaces and their interaction with water. The force field enables large-scale dynamic simulations of dry and lubricated tribological contacts and is tailored to surfaces with hydrogen, hydroxyl and aromatic surface passivation. To favour its compatibility with existing force-field parameterizations for liquids and allow a straightforward extension to other types of surface passivation species, we adopt the commonly used OPLS functional form. The optimisation of the force-field parameters is systematic and follows a protocol that can be reused for other surface-molecule combinations. Reference data are calculated with gradient- and dispersion-corrected density functional theory and include the bonding structure and elastic deformation of bulk and surface structures as well as surface adhesion and water adsorption energy landscapes. The conventions adopted to define the different force-field atom types are based on the hybridisation of carbon orbitals and enable a simple and efficient parameter optimisation strategy based on quantum-mechanical calculations performed only on crystalline reference structures. Transferability tests on amorphous interfaces demonstrate the effectiveness of this approach. After testing the force field, we present two examples of application to tribological problems. Namely, we investigate relationships between dry friction and the corrugation of the contact potential energy surface and the dependency of friction on the thickness of interface water films. We finally discuss the limitations of the force field and propose strategies for its improvement and extension.

We analyze potential non-analytic terms in the Landau diamagnetic susceptibility, $\chi_{dia}$, at a finite temperature $T$ and/or in-plane magnetic field $H$ in a two-dimensional (2D) Fermi liquid. To do this, we express the diamagnetic susceptibility as $\chi_{dia} = (e/c)^2 \lim_{Q\rightarrow0} \Pi^{JJ}_\perp (Q)/Q^2$, where $\Pi^{JJ}_\perp$ is the transverse component of the static current-current correlator, and evaluate $\Pi^{JJ}_\perp (Q)$ for a system of fermions with Hubbard interaction to second order in Hubbard $U$ by combining self energy, Maki-Thompson, and Aslamazov-Larkin diagrams. We find that at $T=H=0$, the expansion of $\Pi^{JJ}_\perp (Q)/Q^2$ in $U$ is regular, but at a finite $T$ and/or $H$, it contains $U^2 T$ and/or $U^2 |H|$ terms. Similar terms have been previously found for the paramagnetic Pauli susceptibility. We obtain the full expression for the non-analytic $\delta \chi_{dia} (H,T)$ when both $T$ and $H$ are finite, and show that the $H/T$ dependence is similar to that for the Pauli susceptibility., Comment: 19 pages, 6 figures

To quickly screen for single-phased multi-principal-element materials, a so-called entropy forming ability (EFA) parameter is sometimes used as a descriptor. The higher the EFA, the higher is the propensity to form a single-phase structure, which is stabilized against separation up to a certain threshold by the configurational entropy. We have investigated this EFA descriptor with atomic relaxations in special-quasi-random structures and discovered that the EFA correlates inversely with the lattice distortion. Large atomic size differences lead to multi-phase compounds, and little size differences to single-phase compounds. Instead of configurational entropy, we therefore demonstrate the applicability of the Hume-Rothery rules to phase stability of solid solutions even in compositionally complex ceramics., Comment: 17 pages, 3 figures

Transition metal diborides crystallise in the $\alpha$, $\gamma$, or $\omega$ type structure, in which pure transition metal layers alternate with pure boron layers stacked along the hexagonal [0001] axis. Here we view the prototypes as different stackings of the transition metal planes and suppose they can transform from one into another by a displacive transformation. Employing first-principles calculations, we simulate sliding of individual planes in the group IV-VII transition metal diborides along a transformation pathway connecting the $\alpha$, $\gamma$, and $\omega$ structure. Chemistry-related trends are predicted in terms of energetic and structural changes along a transformation pathway, together with the mechanical and dynamical stability of the different stackings. Our results suggest that MnB$_2$ and MoB$_2$ possess the overall lowest sliding barriers among the investigated TMB$_2$s. Furthermore, we discuss trends in strength and ductility indicators, including Young's modulus or Cauchy pressure, derived from elastic constants., Comment: 12 pages, 6 figures, accepted for publication in Vacuum, before proof

Biometrics are one of the most privacy-sensitive data. Ubiquitous authentication systems with a focus on privacy favor decentralized approaches as they reduce potential attack vectors, both on a technical and organizational level. The gold standard is to let the user be in control of where their own data is stored, which consequently leads to a high variety of devices used. Moreover, in comparison with a centralized system, designs with higher end-user freedom often incur additional network overhead. Therefore, when using face recognition for biometric authentication, an efficient way to compare faces is important in practical deployments, because it reduces both network and hardware requirements that are essential to encourage device diversity. This paper proposes an efficient way to aggregate embeddings used for face recognition based on an extensive analysis on different datasets and the use of different aggregation strategies. As part of this analysis, a new dataset has been collected, which is available for research purposes. Our proposed method supports the construction of massively scalable, decentralized face recognition systems with a focus on both privacy and long-term usability.

For the purpose of explaining multivariate outlyingness, it is shown that the squared Mahalanobis distance of an observation can be decomposed into outlyingness contributions originating from single variables. The decomposition is obtained using the Shapley value, a well-known concept from game theory that became popular in the context of Explainable AI. In addition to outlier explanation, this concept also relates to the recent formulation of cellwise outlyingness, where Shapley values can be employed to obtain variable contributions for outlying observations with respect to their "expected" position given the multivariate data structure. In combination with squared Mahalanobis distances, Shapley values can be calculated at a low numerical cost, making them even more attractive for outlier interpretation. Simulations and real-world data examples demonstrate the usefulness of these concepts.

The Horizon 2020 project EOSC-Life brings together the 13 Life Science 'ESFRI' research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that reason, a toolbox is being developed, providing information to researchers who wish to share and/or use sensitive data in a cloud environment in general, and the European Open Science Cloud in particular. The sensitivity of the data may arise from its personal nature but can also be caused by intellectual property considerations, biohazard concerns, or the Nagoya protocol. The toolbox will not create new content, instead, it will allow researchers to find existing resources that are relevant for sharing sensitive data across all participating research infrastructures (F in FAIR). The toolbox will provide links to recommendations, procedures, and best practices, as well as to software (tools) to support data sharing and reuse. It will be based upon a tagging (categorisation) system, allowing consistent labelling and categorisation of resources. The current design document provides an outline for the anticipated toolbox, as well as its basic principles regarding content and sustainability.

The Flatness theorem states that the maximum lattice width ${\rm Flt}(d)$ of a $d$-dimensional lattice-free convex set is finite. It is the key ingredient for Lenstra's algorithm for integer programming in fixed dimension, and much work has been done to obtain bounds on ${\rm Flt}(d)$. While most results have been concerned with upper bounds, only few techniques are known to obtain lower bounds. In fact, the previously best known lower bound ${\rm Flt}(d) \ge 1.138d$ arises from direct sums of a $3$-dimensional lattice-free simplex. In this work, we establish the lower bound ${\rm Flt}(d) \ge 2d - O(\sqrt{d})$, attained by a family of lattice-free simplices. Our construction is based on a differential equation that naturally appears in this context. Additionally, we provide the first local maximizers of the lattice width of $4$- and $5$-dimensional lattice-free convex bodies., Comment: minor changes; to appear at IPCO 2022

The abundance of data has given machine learning considerable momentum in natural sciences and engineering, though modeling of physical processes is often difficult. A particularly tough problem is the efficient representation of geometric boundaries. Triangularized geometric boundaries are well understood and ubiquitous in engineering applications. However, it is notoriously difficult to integrate them into machine learning approaches due to their heterogeneity with respect to size and orientation. In this work, we introduce an effective theory to model particle-boundary interactions, which leads to our new Boundary Graph Neural Networks (BGNNs) that dynamically modify graph structures to obey boundary conditions. The new BGNNs are tested on complex 3D granular flow processes of hoppers, rotating drums and mixers, which are all standard components of modern industrial machinery but still have complicated geometry. BGNNs are evaluated in terms of computational efficiency as well as prediction accuracy of particle flows and mixing entropies. BGNNs are able to accurately reproduce 3D granular flows within simulation uncertainties over hundreds of thousands of simulation timesteps. Most notably, in our experiments, particles stay within the geometric objects without using handcrafted conditions or restrictions., Comment: accepted for presentation at the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23)

Recently, the application of machine learning models has gained momentum in natural sciences and engineering, which is a natural fit due to the abundance of data in these fields. However, the modeling of physical processes from simulation data without first principle solutions remains difficult. Here, we present a Graph Neural Networks approach towards accurate modeling of complex 3D granular flow simulation processes created by the discrete element method LIGGGHTS and concentrate on simulations of physical systems found in real world applications like rotating drums and hoppers. We discuss how to implement Graph Neural Networks that deal with 3D objects, boundary conditions, particle - particle, and particle - boundary interactions such that an accurate modeling of relevant physical quantities is made possible. Finally, we compare the machine learning based trajectories to LIGGGHTS trajectories in terms of particle flows and mixing entropies.

Photoelectrochemical water splitting is a promising route to produce hydrogen from solar energy. However, corrosion of semiconducting photoelectrodes remains a fundamental challenge for their practical application. The stability of BiVO4, one of the best performing photoanode materials, is systematically examined here using an illuminated scanning flow cell to measure its dissolution operando. The dissolution rates of BiVO4 under illumination depend on the electrolyte and decrease in the order: borate (pH=9.3) > phosphate (pH=7.2) > citrate (pH=7.0). BiVO4 exhibits an inherent lack of stability during the oxygen evolution reaction (OER), while hole-scavenging citrate electrolyte offers kinetic protection. The dissolution of Bi peaks at different potentials than the dissolution of V in phosphate buffer, whereas both ions dissolve simultaneously in borate buffer. The life cycle of a 90 nm BiVO4 film is monitored during one hour of light-driven OER in borate buffer. The photocurrent and dissolution rates show independent trends with time, highlighting the importance to measure both quantities operando. Dissolution rates are correlated to the surface morphology and chemistry characterized using electron microscopy, X-ray photoelectron spectroscopy and atom probe tomography. These correlative measurements further the understanding on corrosion processes of photoelectrodes down to the nanoscopic scale to facilitate their future developments.

We analyze a steady-state thermoelectric engine, whose working substance consists of two capacitively coupled quantum dots. One dot is tunnel-coupled to a hot reservoir serving as a heat source, the other one to two electrically biased reservoirs at a colder temperature, such that work is extracted under the form of a steady-state current against the bias. In single realizations of the dynamics of this steady-state engine autonomous, 4-stroke cycles can be identified. The cycles are purely stochastic, in contrast to mechanical autonomous engines which exhibit self-oscillations. In particular, these cycles fluctuate in direction and duration, and occur in competition with other spurious cycles. Using a stochastic thermodynamic approach, we quantify the cycle fluctuations and relate them to the entropy produced during individual cycles. We identify the cycle mainly responsible for the engine performance and quantify its statistics with tools from graph theory. We show that such stochastic cycles are made possible because the work extraction mechanism is itself stochastic instead of the periodic time dependence in the working-substance Hamiltonian which can be found in conventional mechanical engines. Our investigation brings new perspectives about the connection between cyclic and steady-state engines.