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Robotic insertion tasks remain challenging due to uncertainties in perception and the need for precise control, particularly in unstructured environments. While humans seamlessly combine vision and touch for such tasks, effectively integrating these modalities in robotic systems is still an open problem. Our work presents an extensive analysis of the interplay between visual and tactile feedback during dexterous insertion tasks, showing that tactile sensing can greatly enhance success rates on challenging insertions with tight tolerances and varied hole orientations that vision alone cannot solve. These findings provide valuable insights for designing more effective multi-modal robotic control systems and highlight the critical role of tactile feedback in contact-rich manipulation tasks.

Solution-based soft matter self-assembly (SA) promises unique materials properties from approaches including additive manufacturing/three-dimensional (3D) printing. We report direct ink writing derived, hierarchically porous transition metal nitride superconductors (SCs) and precursor oxides, structure-directed by Pluronics-family block copolymer (BCP) SA and heat treated in various environments. SCs with periodic lattices on three length scales show record nanoconfinement-induced upper critical field enhancements correlated with BCP molar mass, attaining values of 50 T for NbN and 8.1 T for non-optimized TiN samples, the first mapping of a tailorable SC property onto a macromolecular parameter. They reach surface areas above 120 m$^2$/g, the highest reported for compound SCs to date. Embedded printing enables the first BCP directed mesoporous non-self-supporting helical SCs. Results suggest that additive manufacturing may open pathways to mesoporous SCs with not only a variety of macroscopic form factors but enhanced properties from intrinsic, SA-derived mesostructures with substantial academic and technological promise.

This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning., Comment: 56 pages, 8 figures, 2 tables

Commercial adiabatic demagnetisation refrigerators still employ the same hydrated salts that were first introduced over 85 years ago. The inherent limitations of these insulating magnetocalorics - poor thermal conductivity at sub-Kelvin temperatures, low entropy density, corrosiveness - can be overcome by a new generation of rare-earth based metallic magnetocalorics. Here, we present the metallic magnetocaloric YbNi1.6 Sn as an attractive alternative to conventional refrigerants. YbNi1.6Sn retains high entropy into the 100 mK regime and avoids the noble metal constituents of alternative refrigerants. Demagnetisation tests demonstrate that YbNi1.6Sn enables economical and durable alternatives to traditional cooling devices for temperatures reaching below 120 mK. We find that the magnetocaloric properties of this material are facilitated by unusually small Kondo and RKKY interactions, which position YbNi1.6Sn in the extreme local moment limit on the generalised Kondo lattice phase diagram., Comment: 9 pages, 4 figures

Humans have exceptional tactile sensing capabilities, which they can leverage to solve challenging, partially observable tasks that cannot be solved from visual observation alone. Research in tactile sensing attempts to unlock this new input modality for robots. Lately, these sensors have become cheaper and, thus, widely available. At the same time, the question of how to integrate them into control loops is still an active area of research, with central challenges being partial observability and the contact-rich nature of manipulation tasks. In this study, we propose to use Reinforcement Learning to learn an end-to-end policy, mapping directly from tactile sensor readings to actions. Specifically, we use Dreamer-v3 on a challenging, partially observable robotic insertion task with a Franka Research 3, both in simulation and on a real system. For the real setup, we built a robotic platform capable of resetting itself fully autonomously, allowing for extensive training runs without human supervision. Our preliminary results indicate that Dreamer is capable of utilizing tactile inputs to solve robotic manipulation tasks in simulation and reality. Furthermore, we find that providing the robot with tactile feedback generally improves task performance, though, in our setup, we do not yet include other sensing modalities. In the future, we plan to utilize our platform to evaluate a wide range of other Reinforcement Learning algorithms on tactile tasks.

New results are presented on a high-statistics measurement of Collins and Sivers asymmetries of charged hadrons produced in deep inelastic scattering of muons on a transversely polarised $^6$LiD target. The data were taken in 2022 with the COMPASS spectrometer using the 160 \gevv\ muon beam at CERN, balancing the existing data on transversely polarised proton targets. The first results from about two-thirds of the new data have total uncertainties smaller by up to a factor of three compared to the previous deuteron measurements. Using all the COMPASS proton and deuteron results, both the transversity and the Sivers distribution functions of the $u$ and $d$ quark, as well as the tensor charge in the measured $x$-range are extracted. In particular, the accuracy of the $d$ quark results is significantly improved.

The COMPASS Collaboration performed measurements of the Drell-Yan process in 2015 and 2018 using a 190 GeV/c $\pi^{-}$ beam impinging on a transversely polarised ammonia target. Combining the data of both years, we present final results on the amplitudes of the five azimuthal modulations in the dimuon production cross section. Three of these transverse-spin-dependent azimuthal asymmetries (TSAs) probe the nucleon leading-twist Sivers, transversity, and pretzelosity transverse-momentum dependent (TMD) parton distribution functions (PDFs). The other two are induced by subleading effects. These TSAs provide unique new inputs for the study of the nucleon TMD PDFs and their universality properties. In particular, the Sivers TSA observed in this measurement is consistent with the fundamental QCD prediction of a sign change of naive time-reversal-odd TMD PDFs when comparing the Drell-Yan process with semi-inclusive measurements of deep inelastic scattering. Also, within the context of model predictions, the observed transversity TSA is consistent with the expectation of a sign change for the Boer-Mulders function.

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification, and U-RadIT optimization., Comment: 52 pages, 32 figures; Overview of ultrafast radiographic imaging and tracking as a part of ULITIMA 2023 conference, Mar. 13-16,2023, Menlo Park, CA, USA

Type inference methods based on deep learning are becoming increasingly popular as they aim to compensate for the drawbacks of static and dynamic analysis approaches, such as high uncertainty. However, their practical application is still debatable due to several intrinsic issues such as code from different software domains will involve data types that are unknown to the type inference system. In order to overcome these problems and gain high-confidence predictions, we thus present TIPICAL, a method that combines deep similarity learning with novelty detection. We show that our method can better predict data types in high confidence by successfully filtering out unknown and inaccurate predicted data types and achieving higher F1 scores to the state-of-the-art type inference method Type4Py. Additionally, we investigate how different software domains and data type frequencies may affect the results of our method., Comment: Preprint for the SERA 2023

Gyrochronology enables the derivation of ages of late-type main sequence stars based on their rotation periods and a mass proxy, such as color. It has been explored in open clusters, but a connection to field stars has yet to be successfully established. We explore the rotation rates of wide binaries, representing enlightening intermediaries between clusters and field stars, and their overlap with those of open cluster stars. We investigated a recently created catalog of wide binaries, matched the cataloged binaries to observations by the Kepler mission (and its K2 extension), validated or re-derived their rotation periods, identified 283 systems where both stars are on the main sequence and have vetted rotation periods, and compared the systems with open cluster data. We find that the vast majority of these wide binaries (236) line up directly along the curvilinear ribs defined by open clusters in color-period diagrams or along the equivalent interstitial gaps between successive open clusters. The parallelism in shape is remarkable. Twelve additional systems are clearly rotationally older. The deviant systems, a minority, are mostly demonstrably hierarchical. Furthermore, the position of the evolved component in the color-magnitude diagram for the additional wide binary systems that contain one is consistent with the main sequence component's rotational age. We conclude that wide binaries, despite their diversity, follow the same spindown relationship as observed in open clusters, and we find that rotation-based age estimates yield the same ages for both components in a wide binary. This suggests that cluster and field stars spin down in the same way and that gyrochronology can be applied to field stars to determine their ages, provided that they are sufficiently distant from any companions to be considered effectively single., Comment: 23 pages, 19 figures, published in A&A 675, A180 (2023)

Understanding the microscopic spatio-temporal dynamics of nonequilibrium charge carriers in heterosystems promises optimization of process and device design towards desired energy transfer. Hot electron transport is governed by scattering with other electrons, defects, and bosonic excitations. Analysis of the energy dependence of scattering pathways and identification of diffusive, super-diffusive, and ballistic transport regimes are current challenges. We determine in femtosecond time-resolved two-photon photoelectron emission spectroscopy the energy-dependent change of the electron propagation time through epitaxial Au/Fe(001) heteostructures as a function of Au layer thickness for energies of 0.5 to \unit[2.0]{eV} above the Fermi energy. We describe the laser-induced nonequilibrium electron excitation and injection across the Fe/Au interface using real-time time-dependent density functional theory and analyze the electron propagation through the Au layer by microscopic electron transport simulations. We identify ballistic transport of minority electrons at energies with a nascent, optically excited electron population which is determined by the combination of photon energy and the specific electronic structure of the material. At lower energy, super-diffusive transport with 1 to 4 scattering events dominates. The effective electron velocity accelerates from 0.3 to \unit[1]{nm/fs} with an increase in the Au layer thickness from 10 to 100~nm. This phenomenon is explained by electron transport that becomes preferentially aligned with the interface normal for thicker Au layers, which facilitates electron momentum / energy selection by choice of the propagation layer thickness.

Gyrochronology allows the derivation of ages for cool main sequence stars from their observed rotation periods and masses, or a suitable proxy of the latter. It is increasingly well explored for FGK stars, but requires further measurements for older ages and K-M-type stars. Recent work has shown that the behavior of stellar spindown differs significantly from prior expectations for late-type stars. We study the 4Gyr-old benchmark open cluster M67 to explore this behavior further. We combined a Gaia DR3 sample with the Kepler K2 superstamp of Campaign 5 around M67 and created new light curves from aperture photometry. The light curves are subjected to an extensive correction process to remove instrumental systematics and trending, followed by period analysis to measure stellar rotation. We identify periodic signals in 136 light curves, 47 of which are from the rotation of effectively single main-sequence stars that span from early-G to mid-M type. These results connect well to prior work on M67 and extend it to much later spectral types. We find that the rotation periods of single stars of age 4Gyr define a tight relationship with color, ranging from spectral types F through M. The corresponding surface of rotation period against age and mass is therefore well-defined to an older age than was previously known. However, the deviations from prior expectations of the stellar spindown behavior are even more pronounced at 4Gyr. The binary cluster members do not follow the single star relationship. The majority are widely scattered below the single star sequence. Consequently, they do not seem to be suitable for gyrochronology at present., Comment: 36 pages, 39 figures, published in A&A 672, A159 (2023)

Metallic/intermetalic materials with BCC structures hold an intrinsic instability due to phonon softening along [110] dirrection, causing BCC to lower-symmetry phases transformation when the BCC structures are thermally or mechanically stressed. Fe50Rh50 binary system is one of the exceptional BCC structures (ordered-B2) that has not been yet showing such transformation upon application of thermal stress, although mechanical deformation results in B2 to disordered FCC (gamma) and L10 phases transformation. Here, a comprehensive transmission electron microscopy (TEM) study is conducted on thermally-stressed samples of Fe50Rh50 aged at water and liquid nitrogen from 1150 degree C and 1250 degree C. The results show that, samples quenched from 1150 degree C into water and liquid nitrogen show presence of 1/4{110} and 1/2{110} satellite reflections, the latter of which is expected from phonon dispersion curves obtained by density functional theory calculation. Therefore, it is believed that Fe50Rh50 maintains the B2 structure that is in premartensite state. Once Fe50Rh50 is quenched from 1250 degree C into liquid nitrogen, formation of two short-range ordered tetragonal phases with various c/a ratios (~1.15 and 1.4) is observed in line with phases formed from mechanically deformed (30%) sample. According to our observations, an accurate atomistic shear model ({110}<1-10>) is presented that describes the martensitic transformation of B2 to these tetragonal phases. These findings offer implications useful for understanding of magnetic and physical characteristics of metallic/intermetallic materials.

Magnetic cooling has the potential to replace conventional gas compression refrigeration. Materials such as La(Fe,Si)$_{13}$ exhibit a sizeable first-order magnetocaloric effect, and it is possible to tailor the phase transition towards room temperature by Mn-H-doping, resulting in a large temperature range for operation. Within this work, we discuss variations of the electronic and lattice structure in La(Fe,Si)$_{13}$ with increasing Mn content utilizing X-ray magnetic circular dichroism (XMCD) and extended X-ray absorption fine structure spectroscopy (EXAFS). While XMCD shows a decrease of the magnetic polarization at the Fe K edge, low-temperature EXAFS measurements indicate increased positional disorder in the La environment that is otherwise absent for Fe and Mn. First-principles calculations link the positional disorder to an enlarged Mn-Si distance -- explaining the increased positional disorder in the La surrounding., Comment: 23 pages

Static analysis tools come in many forms andconfigurations, allowing them to handle various tasks in a (secure) development process: code style linting, bug/vulnerability detection, verification, etc., and adapt to the specific requirements of a software project, thus reducing the number of false positives.The wide range of configuration options poses a hurdle in their use for software developers, as the tools cannot be deployed out-of-the-box. However, static analysis tools only develop their full benefit if they are integrated into the software development workflow and used on regular. Vulnerability management should be integrated via version history to identify hotspots, for example. We present an analysis platform that integrates several static analysis tools that enable Git-based repositories to continuously monitor warnings across their version history. The framework is easily extensible with other tools and programming languages. We provide a visualization component in the form of a dashboard to display security trends and hotspots. Our tool can also be used to create a database of security alerts at a scale well-suited for machine learning applications such as bug or vulnerability detection.

Alloys with a first-order magnetic transition are central to solid-state refrigeration technology, sensors and actuators, or spintronic devices. The discontinuous nature of the transition in these materials is a consequence of the coupling between the magnetic, electronic and structural subsystems, but in a real experiment, it is difficult to observe and analyze the simultaneous evolution of all the subsystems. As a result, it is very hard to determine the main mechanisms of the transition and purposefully develop these advanced magnetic materials. To resolve this issue, we changed the existing paradigm and conducted simultaneous measurements of the macroscopic properties - magnetization, temperature change of the sample, longitudinal and transversal magnetostrictions - to reveal the rich details of the magneto-structural, first-order transition occurring in the prototypical alloy LaFe11.8Si1.2. We complement these findings with experiments on the atomistic scale, i.e., x-ray absorption spectroscopy (XAS), x-ray magnetic circular dichroism (XMCD) and M\"ossbauer spectroscopy, and then combine them with first-principles calculations to reveal the full complexity and two-stage nature of the transition. This new approach can be successfully extended to a large class of advanced magnetic materials that exhibit analogous transformations., Comment: 24 pages

Magnetic annealing of the collinear antiferromagnet PdMn with excess Pd produces strongly pinned magnetic moments in the annealing field direction. This behaviour can be understood with the help of the magnetic-field-biased diffusion model. Here, the magnetic field creates an energy difference between the two possible occupations of the antiferromagnetic Mn-sublattices by the Pd-excess atoms. This, mediated by diffusion, leads to an imbalance in the amount of the Pd-excess atoms in these sublattices and, subsequently, to an imbalance in the total magnetization of the sublattices. We investigate this effect's dependence on the annealing field, time, and temperature. The results are then compared to the results of the magnetic-field-biased diffusion model, which gives good agreement.

NiMn is a collinear antiferromagnet with high magneto crystalline anisotropy ($K_2=-9.7\times10^5\;\text{J m}^{-3}$). Through magnetic annealing of NiMn with excess Ni, strongly pinned magnetic moments emerge due to an imbalance in the distribution of Ni in the antiferromagnetic Mn-sublattices. The results are explained with a model of magnetic-field-biased diffusion, supported by ab initio calculations. Another observation is the oxidation of Mn at the surface, causing an enrichment of Ni in the sub-surface region. This leads to an additional ferromagnetic response appearing in the magnetization measurements, which can be removed by surface polishing.

Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the purpose of improving software engineering processes, particularly software testing. Existing state-of-the-art automated software testing techniques focus on utilising search algorithms to discover input values that achieve high execution path coverage. These algorithms are trained on the same code that they intend to test, requiring instrumentation and lengthy search times to test each software component. This paper outlines a novel genetic programming framework, where the evolved solutions are not input values, but micro-programs that can repeatedly generate input values to efficiently explore a software component's input parameter domain. We also argue that our approach can be generalised such as to be applied to many different software systems, and is thus not specific to merely the particular software component on which it was trained., Comment: A version of this paper has been accepted for publication in CEC'22

We embarked on a high-resolution optical spectroscopic survey of bright Transiting Exoplanet Survey Satellite (TESS) stars around the Northern Ecliptic Pole (NEP), dubbed the Vatican-Potsdam-NEP (VPNEP) survey. Our NEP coverage comprises 1067 stars, of which 352 are bona fide dwarf stars and 715 are giant stars, all cooler than spectral type F0 and brighter than V=8. m 5. Our aim is to characterize these stars for the benefit of future studies in the community. We analyzed the spectra via comparisons with synthetic spectra. Particular line profiles were analyzed by means of eigen-profiles, equivalent widths, and relative emission-line fluxes (when applicable). Two R=200 000 spectra were obtained for each of the dwarf stars with the Vatican Advanced Technology Telescope (VATT) and the Potsdam Echelle Polarimetric and Spectroscopic Instrument (PEPSI), with typically three R=55 000 spectra obtained for the giant stars with STELLA and the STELLA Echelle Spectrograph (SES). Combined with V-band magnitudes, Gaia eDR3 parallaxes, and isochrones from the Padova and Trieste Stellar Evolutionary Code, the spectra can be used to obtain radial velocities, effective temperatures, gravities, rotational and turbulence broadenings, stellar masses and ages, and abundances for 27 chemical elements, as well as isotope ratios for lithium and carbon, line bisector spans, convective blue-shifts (when feasible), and levels of magnetic activity from H{\alpha}, H{\beta}, and the Ca ii infrared triplet. In this initial paper, we discuss our analysis tools and biases, presenting our first results from a pilot sub-sample of 54 stars (27 bona-fide dwarf stars observed with VATT+PEPSI and 27 bona-fide giant stars observed with STELLA+SES) and making all reduced spectra available to the community.

Ni-Mn-based Heusler alloys, in particular all-d-metal Ni(-Co)-Mn-Ti, are highly promising materials for energy-efficient solid-state refrigeration as large multicaloric effects can be achieved across their magnetostructural martensitic transformation. However, no comprehensive study on the crucially important transition entropy change $\Delta s_t$ exists so far for Ni(-Co)-Mn-Ti. Here, we present a systematic study analyzing the composition and temperature dependence of $\Delta s_t$. Our results reveal a substantial structural entropy change contribution of approximately 65 J(kgK)$^{-1}$, which is compensated at lower temperatures by an increasingly negative entropy change associated with the magnetic subsystem. This leads to compensation temperatures $T_{comp}$ of 75 K and 300 K in Ni$_{35}$Co$_{15}$Mn$_{50-y}$Ti$_{y}$ and Ni$_{33}$Co$_{17}$Mn$_{50-y}$Ti$_{y}$, respectively, below which the martensitic transformations are arrested. In addition, we simultaneously measured the responses of the magnetic, structural and electronic subsystems to the temperature- and field-induced martensitic transformation near $T_{comp}$, showing an abnormal increase of hysteresis and consequently dissipation energy at cryogenic temperatures. Simultaneous measurements of magnetization and adiabatic temperature change $\Delta T_{ad}$ in pulsed magnetic fields reveal a change in sign of $\Delta T_{ad}$ and a substantial positive and irreversible $\Delta T_{ad}$ up to 15 K at 15 K as a consequence of increased dissipation losses and decreased heat capacity. Most importantly, this phenomenon is universal, it applies to any first-order material with non-negligible hysteresis and any stimulus, effectively limiting the utilization of their caloric effects for gas liquefaction at cryogenic temperatures.

Gaussian Process (GP) regressions have proven to be a valuable tool to predict disturbances and model mismatches and incorporate this information into a Model Predictive Control (MPC) prediction. Unfortunately, the computational complexity of inference and learning on classical GPs scales cubically, which is intractable for real-time applications. Thus GPs are commonly trained offline, which is not suited for learning disturbances as their dynamics may vary with time. Recently, state-space formulation of GPs has been introduced, allowing inference and learning with linear computational complexity. This paper presents a framework that enables online learning of disturbance dynamics on quadcopters, which can be executed within milliseconds using a state-space formulation of GPs. The obtained disturbance predictions are combined with MPC leading to a significant performance increase in simulations with jMAVSim. The computational burden is evaluated on a Raspberry Pi 4 B to prove the real-time applicability., Comment: Published at American Control Conference (ACC 2022), Atlanta, GA, USA, June 8 to 10, 2022

Transformer networks such as CodeBERT already achieve outstanding results for code clone detection in benchmark datasets, so one could assume that this task has already been solved. However, code clone detection is not a trivial task. Semantic code clones, in particular, are challenging to detect. We show that the generalizability of CodeBERT decreases by evaluating two different subsets of Java code clones from BigCloneBench. We observe a significant drop in F1 score when we evaluate different code snippets and functionality IDs than those used for model building.

Optional type annotations allow for enriching dynamic programming languages with static typing features like better Integrated Development Environment (IDE) support, more precise program analysis, and early detection and prevention of type-related runtime errors. Machine learning-based type inference promises interesting results for automating this task. However, the practical usage of such systems depends on their ability to generalize across different domains, as they are often applied outside their training domain. In this work, we investigate Type4Py as a representative of state-of-the-art deep learning-based type inference systems, by conducting extensive cross-domain experiments. Thereby, we address the following problems: class imbalances, out-of-vocabulary words, dataset shifts, and unknown classes. To perform such experiments, we use the datasets ManyTypes4Py and CrossDomainTypes4Py. The latter we introduce in this paper. Our dataset enables the evaluation of type inference systems in different domains of software projects and has over 1,000,000 type annotations mined on the platforms GitHub and Libraries. It consists of data from the two domains web development and scientific calculation. Through our experiments, we detect that the shifts in the dataset and the long-tailed distribution with many rare and unknown data types decrease the performance of the deep learning-based type inference system drastically. In this context, we test unsupervised domain adaptation methods and fine-tuning to overcome these issues. Moreover, we investigate the impact of out-of-vocabulary words., Comment: Preprint for the MSR'23 technical track

Ultrafast-optical-pump -- structural-probe measurements, including ultrafast electron and x-ray scattering, provide direct experimental access to the fundamental timescales of atomic motion, and are thus foundational techniques for studying matter out of equilibrium. High-performance detectors are needed in scattering experiments to obtain maximum scientific value from every probe particle. We deploy a hybrid pixel array direct electron detector to perform ultrafast electron diffraction experiments on a WSe$_2$/MoSe$_2$ 2D heterobilayer, resolving the weak features of diffuse scattering and moir\'e superlattice structure without saturating the zero order peak. Enabled by the detector's high frame rate, we show that a chopping technique provides diffraction difference images with signal-to-noise at the shot noise limit. Finally, we demonstrate that a fast detector frame rate coupled with a high repetition rate probe can provide continuous time resolution from femtoseconds to seconds, enabling us to perform a scanning ultrafast electron diffraction experiment that maps thermal transport in WSe$_2$/MoSe$_2$ and resolves distinct diffusion mechanisms in space and time., Comment: Submitted manuscript, 22 pages, 5 figures

The interaction of a femtosecond optical pulse with a Fe$_{1}$/(MgO)$_{3}$(001) metal/oxide heterostructure is investigated using time-dependent density functional theory (TDDFT) calculations in the real-time domain. We systematically study electronic excitations as a function of laser frequency, peak power density and polarization direction. While spin-orbit coupling is found to result in only a small time-dependent reduction of magnetization (less than 10%), we find a marked anisotropy in the response to in-plane and out-of-plane polarized light, which changes its character qualitatively depending on the excitation energy: the Fe-layer is efficiently addressed at low frequencies by in-plane polarized light, whereas for frequencies higher than the MgO band gap, we find a particularly strong response of the central MgO-layer for cross-plane polarized light. For laser excitations between the charge transfer gap and the MgO band gap, the interface plays the most important role, as it mediates concerted transitions from the valence band of MgO into the $3d$ states of Fe closely above the Fermi level and from the Fe-states below the Fermi level into the conduction band of MgO. As these transitions can occur simultaneously altering charge balance of the layers, they could potentially lead to an efficient transfer of excited carriers into the MgO bulk, where the corresponding electron and hole states can be separated by an energy which is significantly larger than the photon energy., Comment: Phys. Rev. B, accepted for publication

Magnetic frustration in ferromagnetic metallic systems is unusual due to the long-range and symmetric nature of the exchange interactions. In this work we prove that it is possible to obtain a highly frustrated ferromagnetic phase in a multi-sublattices cubic structure through a fine tuning of the magnetic interactions. This peculiar state is achieved in Ni-Mn-(In, Sn) Heusler alloys and results in the effective decoupling of their two intertwined ferromagnetic sublattices. One sublattice is ferromagnetic long range ordered below the macroscopic Curie temperature (TC ) whereas the second one remains disordered until a crossover to a polarized state occurs at T << TC . This result points out that a fine engineering of the magnetic interactions in metallic systems can lead to interesting novel and emergent phenomena.

The optical fiber multiple-input multiple-output (MIMO) channel with intensity modulation and direct detection (IM/DD) per spatial path is treated. The spatial dimensions represent the multiple modes employed for transmission and the cross-talk between them originates in the multiplexers and demultiplexers, which are polarization dependent and thus timevarying. The upper bounds from free-space IM/DD MIMO channels are adapted to the fiber case, and the constellation constrained capacity is constructively estimated using the Blahut-Arimoto algorithm. An autoencoder is then proposed to optimize a practical MIMO transmission in terms of pre-coder and detector assuming channel distribution knowledge at the transmitter. The pre-coders are shown to be robust to changes in the channel., Comment: International Conference on Communications (ICC) 2022

Automatic vulnerability detection on C/C++ source code has benefitted from the introduction of machine learning to the field, with many recent publications targeting this combination. In contrast, assembly language or machine code artifacts receive less attention, although there are compelling reasons to study them. They are more representative of what is executed, more easily incorporated in dynamic analysis, and in the case of closed-source code, there is no alternative. We evaluate the representative capability of assembly language compared to C/C++ source code for vulnerability detection. Furthermore, we investigate the role of call graph context in detecting function-spanning vulnerabilities. Finally, we verify whether compiling a benchmark dataset compromises an experiment's soundness by inadvertently leaking label information. We propose ROMEO, a publicly available, reproducible and reusable binary vulnerability detection benchmark dataset derived from the synthetic Juliet test suite. Alongside, we introduce a simple text-based assembly language representation that includes context for function-spanning vulnerability detection and semantics to detect high-level vulnerabilities. It is constructed by disassembling the .text segment of the respective binaries. We evaluate an x86 assembly language representation of the compiled dataset, combined with an off-the-shelf classifier. It compares favorably to state-of-the-art methods, including those operating on the full C/C++ code. Including context information using the call graph improves detection of function-spanning vulnerabilities. There is no label information leaked during the compilation process., Comment: 21 pages, code available at https://gitlab.com/dlr-dw/romeo