Search

We aim to constrain the equation of state of nuclear matter by comparing calculations with the SMASH transport model to directed and elliptic flow measurements for protons and deuterons performed by the HADES collaboration in a systematic way. A momentum-dependent term is included in the potential for which we show that it is needed to describe flow data. We further incorporate a simple symmetry potential in the transport model and present constraints on the stiffness of the equation of state of nuclear matter at saturation density and on the symmetry potential. The constraints are obtained by performing a Bayesian analysis such that we can also provide an uncertainty for the estimated parameters. The posterior distribution is obtained by Markov chain Monte Carlo sampling for which we emulate the transport model with a Gaussian process to lower computational costs. We find that a relatively stiff equation of state is favoured in our analysis with a small uncertainty whereas the constraints obtained for the symmetry potential are rather loose., Comment: 11 pages, 11 figures

Robot manipulation relying on learned object-centric descriptors became popular in recent years. Visual descriptors can easily describe manipulation task objectives, they can be learned efficiently using self-supervision, and they can encode actuated and even non-rigid objects. However, learning robust, view-invariant keypoints in a self-supervised approach requires a meticulous data collection approach involving precise calibration and expert supervision. In this paper we introduce Cycle-Correspondence Loss (CCL) for view-invariant dense descriptor learning, which adopts the concept of cycle-consistency, enabling a simple data collection pipeline and training on unpaired RGB camera views. The key idea is to autonomously detect valid pixel correspondences by attempting to use a prediction over a new image to predict the original pixel in the original image, while scaling error terms based on the estimated confidence. Our evaluation shows that we outperform other self-supervised RGB-only methods, and approach performance of supervised methods, both with respect to keypoint tracking as well as for a robot grasping downstream task., Comment: 8 pages, 4 figures, 2 tables

We use the Heisenberg picture of Everettian quantum mechanics to work out the relative descriptors for the Frauchiger-Renner thought experiment. While Everettian mechanics reveals no paradox within the thought experiment, the decoherence-free setup offers an instructive branching tree. Traditionally, branching structures derived from the Schr\"odinger picture suggest that foliations are always sharply defined. However, the Heisenberg picture demonstrates that the tree contains regions with inherently non-sharp foliations, leading to the conclusion that, in Everettian mechanics, quantum agents possess a blurry memory. This analysis helps resolve several misconceptions stemming from the Schr\"odinger picture., Comment: 10 pages, 2 figures

Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the prediction of 6-DoF grasps on diverse and unknown objects have shown promising progress. However, existing approaches only consider a single ground truth grasp orientation at a grasp location during training and therefore can only predict limited grasp orientations which leads to a reduced number of feasible grasps in bin picking with restricted reachability. In this paper, we propose a novel approach for learning dense and diverse 6-DoF grasps for parallel-jaw grippers in robotic bin picking. We introduce a parameterized grasp distribution model based on Power-Spherical distributions that enables a training based on all possible ground truth samples. Thereby, we also consider the grasp uncertainty enhancing the model's robustness to noisy inputs. As a result, given a single top-down view depth image, our model can generate diverse grasps with multiple collision-free grasp orientations. Experimental evaluations in simulation and on a real robotic bin picking setup demonstrate the model's ability to generalize across various object categories achieving an object clearing rate of around $90 \%$ in simulation and real-world experiments. We also outperform state of the art approaches. Moreover, the proposed approach exhibits its usability in real robot experiments without any refinement steps, even when only trained on a synthetic dataset, due to the probabilistic grasp distribution modeling.

In recent years, solvers for finite-element discretizations of linear or linearized saddle-point problems, like the Stokes and Oseen equations, have become well established. There are two main classes of preconditioners for such systems: those based on block-factorization approach and those based on monolithic multigrid. Both classes of preconditioners have several critical choices to be made in their composition, such as the selection of a suitable relaxation scheme for monolithic multigrid. From existing studies, some insight can be gained as to what options are preferable in low-performance computing settings, but there are very few fair comparisons of these approaches in the literature, particularly for modern architectures, such as GPUs. In this paper, we perform a comparison between a block-triangular preconditioner and a monolithic multigrid method with the three most common choices of relaxation scheme - Braess-Sarazin, Vanka, and Schur-Uzawa. We develop a performant Vanka relaxation algorithm for structured-grid discretizations, which takes advantage of memory efficiencies in this setting. We detail the behavior of the various CUDA kernels for the multigrid relaxation schemes and evaluate their individual arithmetic intensity, performance, and runtime. Running a preconditioned FGMRES solver for the Stokes equations with these preconditioners allows us to compare their efficiency in a practical setting. We show monolithic multigrid can outperform block-triangular preconditioning, and that using Vanka or Braess-Sarazin relaxation is most efficient. Even though multigrid with Vanka relaxation exhibits reduced performance on the CPU (up to $100\%$ slower than Braess-Sarazin), it is able to outperform Braess-Sarazin by more than $20\%$ on the GPU, making it a competitive algorithm, especially given the high amount of algorithmic tuning needed for effective Braess-Sarazin relaxation., Comment: submitted to IJHPCA

Transformer models underpin many recent advances in practical machine learning applications, yet understanding their internal behavior continues to elude researchers. Given the size and complexity of these models, forming a comprehensive picture of their inner workings remains a significant challenge. To this end, we set out to understand small transformer models in a more tractable setting: that of solving mazes. In this work, we focus on the abstractions formed by these models and find evidence for the consistent emergence of structured internal representations of maze topology and valid paths. We demonstrate this by showing that the residual stream of only a single token can be linearly decoded to faithfully reconstruct the entire maze. We also find that the learned embeddings of individual tokens have spatial structure. Furthermore, we take steps towards deciphering the circuity of path-following by identifying attention heads (dubbed $\textit{adjacency heads}$), which are implicated in finding valid subsequent tokens., Comment: 15 pages, 18 figures, 15 tables. Corresponding author: Michael Ivanitskiy (mivanits@mines.edu). Code available at https://github.com/understanding-search/structured-representations-maze-transformers

Heat engines are key devices that convert thermal energy into usable energy. Strong thermoelectricity, at the basis of electrical heat engines, is present in superconducting spin tunnel barriers at cryogenic temperatures where conventional semiconducting or metallic technologies cease to work. Here we realize a superconducting spintronic heat engine consisting of a ferromagnetic insulator/superconductor/insulator/ferromagnet tunnel junction (EuS/Al/AlO$_x$/Co). The efficiency of the engine is quantified for bath temperatures ranging from 25 mK up to 800 mK, and at different load resistances. Moreover, we show that the sign of the generated thermoelectric voltage can be inverted according to the parallel or anti-parallel orientation of the two ferromagnetic layers, EuS and Co. This realizes a thermoelectric spin valve controlling the sign and strength of the Seebeck coefficient, thereby implementing a thermoelectric memory cell. We propose a theoretical model that allows describing the experimental data and predicts the engine efficiency for different device parameters., Comment: 12 pages, 7 figures

Inclusive e$^+$e$^-$ production has been studied with HADES in $\pi^-$ + p, $\pi^-$ + C and $\pi^- + \mathrm{CH}_2$ reactions, using the GSI pion beam at $\sqrt{s_{\pi p}}$ = 1.49 GeV. Invariant mass and transverse momentum distributions have been measured and reveal contributions from Dalitz decays of $\pi^0$, $\eta$ mesons and baryon resonances. The transverse momentum distributions are very sensitive to the underlying kinematics of the various processes. The baryon contribution exhibits a deviation up to a factor seven from the QED reference expected for the dielectron decay of a hypothetical point-like baryon with the production cross section constrained from the inverse $\gamma$ n$\rightarrow \pi^-$ p reaction. The enhancement is attributed to a strong four-momentum squared dependence of the time-like electromagnetic transition form factors as suggested by Vector Meson Dominance (VMD). Two versions of the VMD, that differ in the photon-baryon coupling, have been applied in simulations and compared to data. VMD1 (or two-component VMD) assumes a coupling via the $\rho$ meson and a direct coupling of the photon, while in VMD2 (or strict VMD) the coupling is only mediated via the $\rho$ meson. The VMD2 model, frequently used in transport calculations for dilepton decays, is found to overestimate the measured dielectron yields, while a good description of the data can be obtained with the VMD1 model assuming no phase difference between the two amplitudes. Similar descriptions have also been obtained using a time-like baryon transition form factor model where the pion cloud plays the major role., Comment: (HADES collaboration)

Understanding how machine learning models respond to distributional shifts is a key research challenge. Mazes serve as an excellent testbed due to varied generation algorithms offering a nuanced platform to simulate both subtle and pronounced distributional shifts. To enable systematic investigations of model behavior on out-of-distribution data, we present $\texttt{maze-dataset}$, a comprehensive library for generating, processing, and visualizing datasets consisting of maze-solving tasks. With this library, researchers can easily create datasets, having extensive control over the generation algorithm used, the parameters fed to the algorithm of choice, and the filters that generated mazes must satisfy. Furthermore, it supports multiple output formats, including rasterized and text-based, catering to convolutional neural networks and autoregressive transformer models. These formats, along with tools for visualizing and converting between them, ensure versatility and adaptability in research applications., Comment: 9 pages, 5 figures, 1 table. Corresponding author: Michael Ivanitskiy (mivanits@umich.edu). Code available at https://github.com/understanding-search/maze-dataset

Solid-state high harmonic generation spectroscopy (sHHG) is a promising technique for studying electronic structure, symmetry, and dynamics in condensed matter systems. Here, we report on the implementation of an advanced sHHG spectrometer based on a vacuum chamber and closed-cycle helium cryostat. Using an in situ temperature probe, it is demonstrated that the sample interaction region retains cryogenic temperature during the application of high-intensity femtosecond laser pulses that generate high harmonics. The presented implementation opens the door for temperature-dependent sHHG measurements down to few Kelvin, which makes sHHG spectroscopy a new tool for studying phases of matter that emerge at low temperatures, which is particularly interesting for highly correlated materials.

While the evolution of the Internet was driven by the end-to-end model, it has been challenged by many flavors of middleboxes over the decades. Yet, the basic idea is still fundamental: reliability and security are usually realized end-to-end, where the strong trend towards ubiquitous traffic protection supports this notion. However, reasons to break up, or redefine the ends of, end-to-end connections have always been put forward in order to improve transport layer performance. Yet, the consolidation of the transport layer with the end-to-end security model as introduced by QUIC protects most protocol information from the network, thereby eliminating the ability to modify protocol exchanges. In this paper, we enhance QUIC to selectively expose information to intermediaries, thereby enabling endpoints to consciously insert middleboxes into an end-to-end encrypted QUIC connection while preserving its privacy, integrity, and authenticity. We evaluate our design in a distributed Performance Enhancing Proxy environment over satellite networks, finding that the performance improvements are dependent on the path and application layer properties: the higher the round-trip time and loss, and the more data is transferred over a connection, the higher the benefits of Secure Middlebox-Assisted QUIC.

As recent trends in manufacturing engineering disciplines show a clear development in the sustainable as well as economically efficient design of forming processes, monitoring techniques have been gaining in relevance. In terms of monitoring of product properties, most processes are currently open-loop controlled, entailing that the microstructure evolution, which determines the final product properties, is not considered. However, a closed-loop control that can adjust and manipulate the process actuators according to the required product properties of the component will lead to a considerable increase in efficiency of the processes regarding resources and will decrease postproduction of the component. For most forming processes, one set of component dimensions will result in a certain set of product properties. However, to successfully establish closed-loop property controls for the processes, a systematic understanding of the reciprocity of the dimensions after forming and final product properties must be established. This work investigates the evolution of dimensional accuracy as well as product properties for a series of forming processes that utilize different degrees of freedom for process control.

Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and hardware. This demands for robots that can observe and reason about their workspace, and that are skillfull enough to complete various assembly processes in weakly-structured settings. Moreover, it remains a great challenge to enable operators for teaching robots on-site, while managing the inherent complexity of perception, control, motion planning and reaction to unexpected situations. Motivated by real-world industrial applications, this paper demonstrates the potential of such a paradigm shift in robotics on the industrial case of an e-Bike motor assembly. The paper presents a concept for teaching and programming adaptive robots on-site and demonstrates their potential for the named applications. The framework includes: (i) a method to teach perception systems onsite in a self-supervised manner, (ii) a general representation of object-centric motion skills and force-sensitive assembly skills, both learned from demonstration, (iii) a sequencing approach that exploits a human-designed plan to perform complex tasks, and (iv) a system solution for adapting and optimizing skills online. The aforementioned components are interfaced through a four-layer software architecture that makes our framework a tangible industrial technology. To demonstrate the generality of the proposed framework, we provide, in addition to the motivating e-Bike motor assembly, a further case study on dense box packing for logistics automation.

We review the use of hybrid thin films composed of superconductors and ferromagnets for creating non-reciprocal electronic components and self-biased detectors of electromagnetic radiation. We begin by introducing the theory behind these effects, as well as discussing various potential materials that can be used in the fabrication of these components. We then proceed with a detailed discussion on the fabrication and characterization of Al/EuS/Cu and EuS/Al/Co-based detectors, along with their noise analysis. Additionally, we suggest some approaches for multiplexing such self-biased detectors., Comment: 30 pages, 21 figures

The production of $\Sigma^0$ hyperons in proton proton collisions at a beam kinetic energy of 3.5 GeV impinging on a liquid hydrogen target was investigated using data collected with the HADES setup. The total production cross section is found to be $\mathrm{\sigma (pK^{+}\Sigma^{0}) [\mu b] = 17.7 \pm 1.7 (stat) \pm 1.6 (syst)}$. Differential cross section distributions of the exclusive channel $\mathrm{pp \rightarrow pK^{+}\Sigma^{0}}$ were analyzed in the center-of-mass, Gottfried-Jackson and helicity reference frames for the first time at the excess energy of 556 MeV. The data support the interplay between pion and kaon exchange mechanisms and clearly demonstrate the contribution of interfering nucleon resonances decaying to $\mathrm{K^{+}\Sigma^{0}}$. The Bonn-Gatchina partial wave analysis was employed to analyse the data. Due to the limited statistics, it was not possible to obtain an unambiguous determination of the relative contribution of intermediate nucleon resonances to the final state. However nucleon resonances with masses around 1.710 $\mathrm{GeV/c^{2}}$ ($\mathrm{N^{*}(1710)}$) and 1.900 $\mathrm{GeV/c^{2}}$ ($\mathrm{N^{*}(1900)}$ or $\mathrm{\Delta^{*}(1900)}$) are preferred by the fit.

The double differential production cross sections, $d^2\sigma/d\Omega dE$, for hydrogen isotopes and charged pions in the reaction of p + Nb at 3.5 GeV proton beam energy have been measured by the High Acceptance DiElectron Spectrometer (HADES). Thanks to the high acceptance of HADES at forward emission angles and usage of its magnetic field, the measured energy range of hydrogen isotopes could be significantly extended in comparison to the relatively scarce experimental data available in the literature. The data provide information about the development of the intranuclear cascade in the proton-nucleus collisions. They can as well be utilized to study the rate of energy/momentum dissipation in the nuclear systems and the mechanism of elementary and composite particle production in excited nuclear matter at normal density. Data of this type are important also for technological and medical applications. Our results are compared to models developed to describe the processes relevant to nuclear spallation (INCL++) or oriented to probe either the elementary hadronic processes in nuclear matter or the behavior of compressed nuclear matter (GiBUU).

Hadron production ($\pi^\pm$, proton, $\Lambda$, $K_S^0$, $K^\pm$) in $\pi^- + \mathrm{C}$ and $\pi^- + \mathrm{W}$ collisions is investigated at an incident pion beam momentum of $1.7~\mathrm{GeV}/c$. This comprehensive set of data measured with HADES at SIS18/GSI significantly extends the existing world data on hadron production in pion induced reactions and provides a new reference for models that are commonly used for the interpretation of heavy-ion collisions. The measured inclusive differential production cross-sections are compared with state-of-the-art transport model (GiBUU, SMASH) calculations. The (semi-) exclusive channel $\pi^- + A \rightarrow \Lambda + K_S^0 +X$, in which the kinematics of the strange hadrons are correlated, is also investigated and compared to a model calculation. Agreement and remaining tensions between data and the current version of the considered transport models are discussed.

We propose a self-supervised training approach for learning view-invariant dense visual descriptors using image augmentations. Unlike existing works, which often require complex datasets, such as registered RGBD sequences, we train on an unordered set of RGB images. This allows for learning from a single camera view, e.g., in an existing robotic cell with a fix-mounted camera. We create synthetic views and dense pixel correspondences using data augmentations. We find our descriptors are competitive to the existing methods, despite the simpler data recording and setup requirements. We show that training on synthetic correspondences provides descriptor consistency across a broad range of camera views. We compare against training with geometric correspondence from multiple views and provide ablation studies. We also show a robotic bin-picking experiment using descriptors learned from a fix-mounted camera for defining grasp preferences., Comment: 20 pages, 14 figures

This work deals with the problem of determining a non-homogeneous heat conductivity profile in a steady-state heat conduction boundary-value problem with mixed Dirichlet-Neumann boundary conditions over a bounded domain in $\mathbb{R}^n$, from the knowledge of the state over the whole domain. We develop a method based on a variational approach leading to an optimality equation which is then projected into a finite dimensional space. Discretization yields a linear although severely ill-posed equation which is then regularized via appropriate ad-hoc penalizers resulting a in a generalized Tikhonov-Phillips functional. No smoothness assumptions are imposed on the conductivity. Numerical examples for the case in which the conductivity can take only two prescribed values (a two-materials case) show that the approach is able to produce very good reconstructions of the exact solution., Comment: 21 pages, 16 figures

High precision measurements of flow coefficients $v_{n}$ ($n = 1 - 4$) for protons, deuterons and tritons relative to the first-order spectator plane have been performed in Au+Au collisions at $\sqrt{s_{NN}} = 2.4$ GeV with the High-Acceptance Di-Electron Spectrometer (HADES) at the SIS18/GSI. Flow coefficients are studied as a function of transverse momentum $p_{t}$ and rapidity $y_{cm}$ over a large region of phase space and for several classes of collision centrality. A clear mass hierarchy is found for the slope of $v_{1}$, $d v_{1}/d y^{\prime}|_{y^{\prime} = 0}$ where $y^{\prime}$ is the scaled rapidity, and for $v_{2}$ at mid-rapidity. Scaling with the number of nucleons is observed for the $p_{t}$ dependence of $v_{2}$ and $v_{4}$ at mid-rapidity, which is indicative for nuclear coalescence as the main process responsible for light nuclei formation. $v_{2}$ is found to scale with the initial eccentricity $\langle \epsilon_{2} \rangle$, while $v_{4}$ scales with $\langle \epsilon_{2} \rangle^{2}$ and $\langle \epsilon_{4} \rangle$. The multi-differential high-precision data on $v_{1}$, $v_{2}$, $v_{3}$, and $v_{4}$ provides important constraints on the equation-of-state of compressed baryonic matter., Comment: 21 pages, 18 figures, 4 tables; Figs. 11 updated; The manuscript has associated data in the HEPData repository: https://doi.org/10.17182/hepdata.152804

We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsity-inducing constraints. We find that increasing sparsity, especially on features, improves the performance of some models and leads to simpler relations. Additionally, we observe that object-centric representations can be detrimental when not all objects are fully captured; a failure mode to which CNNs are less prone. These findings demonstrate the trade-offs between interpretability and performance, even for models designed to tackle relational tasks., Comment: ICML 2022, DyNN Workshop

The global polarization of {\Lambda} hyperons along the total orbital angular momentum of a relativistic heavy-ion collision is presented based on the high statistics data samples collected in Au+Au collisions at \sqrt{s_{NN}} = 2.4 GeV and Ag+Ag at 2.55 GeV with the High-Acceptance Di-Electron Spectrometer (HADES) at GSI, Darmstadt. This is the first measurement below the strangeness production threshold in nucleon-nucleon collisions. Results are reported as a function of the collision centrality as well as a function of the hyperon transverse momentum (p_T) and rapidity (y_{CM}) for the range of centrality 0--40%. We observe a strong centrality dependence of the polarization with an increasing signal towards peripheral collisions. For mid-central (20--40%) collisions the polarization magnitudes are (%) = 6.8 \pm 1.3 (stat.) \pm 2.1 (syst.) for Au+Au and (%) = 6.2 \pm 0.4 (stat.) \pm 0.6 (syst.) for Ag+Ag, which are the largest values observed so far. This observation thus provides a continuation of the increasing trend previously observed by STAR and contrasts expectations from recent theoretical calculations predicting a maximum in the region of collision energies about 3 GeV. The observed polarization is of a similar magnitude as predicted by 3D fluid dynamics and the UrQMD plus thermal vorticity model and significantly above results from the AMPT model., Comment: 8 pages, 4 figures

We propose a framework for robust and efficient training of Dense Object Nets (DON) with a focus on multi-object robot manipulation scenarios. DON is a popular approach to obtain dense, view-invariant object descriptors, which can be used for a multitude of downstream tasks in robot manipulation, such as, pose estimation, state representation for control, etc.. However, the original work focused training on singulated objects, with limited results on instance-specific, multi-object applications. Additionally, a complex data collection pipeline, including 3D reconstruction and mask annotation of each object, is required for training. In this paper, we further improve the efficacy of DON with a simplified data collection and training regime, that consistently yields higher precision and enables robust tracking of keypoints with less data requirements. In particular, we focus on training with multi-object data instead of singulated objects, combined with a well-chosen augmentation scheme. We additionally propose an alternative loss formulation to the original pixelwise formulation that offers better results and is less sensitive to hyperparameters. Finally, we demonstrate the robustness and accuracy of our proposed framework on a real-world robotic grasping task.

First information on the time-like electromagnetic structure of baryons in the second resonance region has been obtained from measurements of dielectron (e+ e-) invariant-mass and angular distributions in the quasi-free reaction $\pi-$ p $\rightarrow$ n e+ e- at $\sqrt{s_{\pi p}}$ = 1.49 GeV with the High Acceptance Di-Electron Spectrometer (HADES) at GSI using the pion beam impinging on a CH$_2$ target. We find a total cross section $\sigma$ = 2.97 $\pm$ 0.07data $\pm$ 0.21acc $\pm$ 0.31Zeff $\mu$b. In complement to the analysis of the inclusive e+ e- channel, this data set provides a crucial test of the description of baryon time-like transitions. Approaches based on a Vector Meson Dominance amplitude containing direct photon and vector meson ($\rho$) couplings to the baryon provide a satisfactory agreement with the data. A good description is also obtained by electromagnetic time-like baryon transition form factors in a covariant spectator-quark model, pointing to the dominance of meson-cloud effects. The dielectron angular distributions exhibit the contributions of virtual photons ($\gamma^*$) with longitudinal polarization, in contrast to real photons. The virtual photon angular dependence supports the dominance of J=3/2, I=1/2 contributions observed in both the $\gamma^*$n and the $\pi \pi$n channels., Comment: revised version, 7 pages, 3 figures

In nuclear collisions the incident protons generate a Coulomb field which acts on produced charged particles. The impact of these interactions on charged pion transverse-mass and rapidity spectra, as well as on pion-pion momentum correlations is investigated in Au+Au collisions at $\sqrt{s_{NN}}$ = 2.4 GeV. We show that the low-mt part of the data ($m_t < 0.2$ GeV/c$^2$) can be well described with a Coulomb-modified Boltzmann distribution that also takes changes of the Coulomb field during the expansion of the fireball into account. The observed centrality dependence of the fitted mean Coulomb potential deviates strongly from a $A_{part}^{2/3}$ scaling, indicating that, next to the fireball, the non-interacting charged spectators have to be taken into account. For the most central collisions, the Coulomb modifications of the HBT source radii are found to be consistent with the potential extracted from the single-pion transverse-mass distributions. This finding suggests that the region of homogeneity obtained from two-pion correlations coincides with the region in which the pions freeze-out. Using the inferred mean-square radius of the charge distribution at freeze-out, we have deduced a baryon density, in fair agreement with values obtained from statistical hadronization model fits to the particle yields., Comment: 14 pages, 12 figures, to be published in EPJA

We report high-precision measurements of the Deeply Virtual Compton Scattering (DVCS) cross section at high values of the Bjorken variable $x_B$. DVCS is sensitive to the Generalized Parton Distributions of the nucleon, which provide a three-dimensional description of its internal constituents. Using the exact analytic expression of the DVCS cross section for all possible polarization states of the initial and final electron and nucleon, and final state photon, we present the first experimental extraction of all four helicity-conserving Compton Form Factors (CFFs) of the nucleon as a function of $x_B$, while systematically including helicity flip amplitudes. In particular, the high accuracy of the present data demonstrates sensitivity to some very poorly known CFFs.

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially-assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications., Comment: 22 pages, 18 figures

Diodes are key elements for electronics, optics, and detection. The search for a material combination providing the best performances for the required application is continuously ongoing. Here, we present a superconducting spintronic tunnel diode based on the strong spin filtering and splitting generated by an EuS thin film between a superconducting Al and a normal metal Cu layer. The Cu/EuS/Al tunnel junction achieves a large rectification (up to $\sim40$\%) already for a small voltage bias ($\sim 200$ $\mu$V) thanks to the small energy scale of the system: the Al superconducting gap. With the help of an analytical theoretical model we can link the maximum rectification to the spin polarization of the barrier and describe the quasi-ideal Schottky-diode behavior of the junction. This cryogenic spintronic rectifier is promising for the application in highly-sensitive radiation detection for which two different configurations are evaluated. In addition, the superconducting diode may pave the way for future low-dissipation and fast superconducting electronics., Comment: 17 pages, 12 figures

Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and app stores is challenging since (1) about 70% of user comments contain noisy, irrelevant information, (2) the amount of user comments grows daily making manual analysis unfeasible, and (3) user comments are written in different languages. Existing approaches build on traditional machine learning (ML) and deep learning (DL), but fail to detect feature requests and bug reports with high Recall and acceptable Precision which is necessary for this task. In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments. Specifically, we train both monolingual and multilingual BERT models and compare the performance with state-of-the-art methods. We found that monolingual BERT models outperform existing baseline methods in the classification of English App Reviews as well as English and Italian Tweets. However, we also observed that the application of heavyweight TL models does not necessarily lead to better performance. In fact, our multilingual BERT models perform worse than traditional ML methods.

We present new precision measurements of the elastic electron-proton scattering cross section for momentum transfer (Q$^2$) up to 15.75~\gevsq. Combined with existing data, these provide an improved extraction of the proton magnetic form factor at high Q$^2$ and double the range over which a longitudinal/transverse separation of the cross section can be performed. The difference between our results and polarization data agrees with that observed at lower Q$^2$ and attributed to hard two-photon exchange (TPE) effects, extending to 8~(GeV/c)$^2$ the range of Q$^2$ for which a discrepancy is established at $>$95\% confidence. We use the discrepancy to quantify the size of TPE contributions needed to explain the cross section at high Q$^2$., Comment: 7 pages, 2 figures

Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suitable for many applications including object grasping, policy learning, etc. DONs map an RGB image depicting an object into a descriptor space image, which implicitly encodes key features of an object invariant to the relative camera pose. Impressively, the self-supervised training of DONs can be applied to arbitrary objects and can be evaluated and deployed within hours. However, the training approach relies on accurate depth images and faces challenges with small, reflective objects, typical for industrial settings, when using consumer grade depth cameras. In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs. We rely on Laplacian Eigenmaps (LE) to embed the 3D model of an object into an optimally generated space. While our approach uses more domain knowledge, it can be efficiently applied even for smaller and reflective objects, as it does not rely on depth information. We compare the training methods on generating 6D grasps for industrial objects and show that our novel supervised training approach improves the pick-and-place performance in industry-relevant tasks.

Ferromagnetic insulators can induce a strong exchange field in an adjacent superconductor via the magnetic proximity effect. This manifests as spin-splitting of the BCS density of states of the superconductor, an important ingredient for numerous superconducting spintronics applications and the realization of Majorana fermions. A crucial parameter that determines the magnitude of the induced spin-splitting in FI/S bilayers is the thickness of the S layer $d$: In very thin samples, the superconductivity is suppressed by the strong magnetism. By contrast, in very thick samples, the spin splitting is absent at distances away from the interface. In this work, we calculate the density of states and critical field of FI/S bilayers of arbitrary thickness. From here, we determine the range of parameters of interest for applications, where the exchange field and superconductivity coexist. We compare our theory with the tunneling spectroscopy measurements in several EuS/Al/AlO$_x$/Al samples. If the Al film in contact with the EuS is thinner than a certain critical value, we do not observe superconductivity, whereas, in thicker samples, we find evidence of a first-order phase transition induced by an external field. The complete transition is preceded by a regime in which normal and superconducting regions coexist. We attribute this mixed phase to inhomogeneities of the Al film thickness and the presence of superparamagnetic grains at the EuS/Al interface with different switching fields. Our results demonstrate on the one hand, the important role of the S layer thickness, which is particularly relevant for the fabrication of high-quality samples suitable for applications. On the other hand, the agreement between theory and experiment demonstrates the accuracy of our theory, which, originally developed for homogeneous situations, is generalized to highly inhomogeneous systems., Comment: 14 pages, 6 figures

In this letter we report the first multi-differential measurement of correlated pion-proton pairs from 2 billion Au+Au collisions at \sqrt{s_{NN}} = 2.42 GeV collected with HADES. In this energy regime the population of \Delta(1232) resonances plays an important role in the way energy is distributed between intrinsic excitation energy and kinetic energy of the hadrons in the fireball. The triple differential d3N/dM{\pi}pdpTdy distributions of correlated {\pi}p pairs have been determined by subtracting the {\pi}p combinatorial background using an iterative method. The invariant-mass distributions in the \Delta(1232) mass region show strong deviations from a Breit-Wigner function with vacuum width and mass. The yield of correlated pion-proton pairs exhibits a complex isospin, rapidity and transverse-momentum dependence. In the invariant mass range 1.1 < Minv (GeV/c2) < 1.4, the yield is found to be similar for {\pi}+p and {\pi}-p pairs, and to follow a power law ^{\alpha}, where is the mean number of participating nucleons. The exponent {\alpha} depends strongly on the pair transverse momentum (pT) while its pT-integrated and charge-averaged value is {\alpha} = 1.5 \pm 0.08stat \pm 0.2sys., Comment: 9 pages, 9 figures

We report measurements of the exclusive neutral pion electroproduction cross section off protons at large values of $x_B$ (0.36, 0.48 and 0.60) and $Q^2$ (3.1 to 8.4 GeV$^2$) obtained from Jefferson Lab Hall A experiment E12-06-014. The corresponding structure functions $d\sigma_L/dt+\epsilon d\sigma_T/dt$, $d\sigma_{TT}/dt$, $d\sigma_{LT}/dt$ and $d\sigma_{LT'}/dt$ are extracted as a function of the proton momentum transfer $t-t_{min}$. The results suggest the amplitude for transversely polarized virtual photons continues to dominate the cross-section throughout this kinematic range. The data are well described by calculations based on transversity Generalized Parton Distributions coupled to a helicity flip Distribution Amplitude of the pion, thus providing a unique way to probe the structure of the nucleon.

A feasibility study has been performed in order to investigate the performance of the HADES detector to measure the electromagnetic decays of the hyperon resonances $\Sigma(1385)^0$, $\Lambda(1405)$ and $\Lambda{\Lambda}(1520)$ as well as the production of double strange baryon systems $\Xi^-$ and $\Lambda\Lambda$ in p+p reactions at a beam kinetic energy of 4.5 GeV. The existing HADES detector will be upgraded by a new Forward Detector, which extends the detector acceptance into a range of polar angles that plays a crucial role for these investigations. The analysis of each channel is preceded by a consideration of the production cross-sections. Afterwards the expected signal count rates using a target consisting of either liquid hydrogen or polyethylene are summarized., Comment: Draft prepared by Rafa{\l} Lalik, James Ritman, Piotr Salabura, 19 pages, 21 figures, 4 tables

Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations. In this work, we propose a rapid robot skill-sequencing algorithm, where the skills are encoded by object-centric hidden semi-Markov models. The learned skill models can encode multimodal (temporal and spatial) trajectory distributions. This approach significantly reduces manual modeling efforts, while ensuring a high degree of flexibility and re-usability of learned skills. Given a task goal and a set of generic skills, our framework computes smooth transitions between skill instances. To compute the corresponding optimal end-effector trajectory in task space we rely on Riemannian optimal controller. We demonstrate this approach on a 7 DoF robot arm for industrial assembly tasks., Comment: First three authors equally contributed. Pre-print accepted for publication in IROS'2020. Video: https://youtu.be/dRGLadt32o4

System behavior is often based on causal relations between certain events (e.g. If event1, then event2). Consequently, those causal relations are also textually embedded in requirements. We want to extract this causal knowledge and utilize it to derive test cases automatically and to reason about dependencies between requirements. Existing NLP approaches fail to extract causality from natural language (NL) with reasonable performance. In this paper, we describe first steps towards building a new approach for causality extraction and contribute: (1) an NLP architecture based on Tree Recursive Neural Networks (TRNN) that we will train to identify causal relations in NL requirements and (2) an annotation scheme and a dataset that is suitable for training TRNNs. Our dataset contains 212,186 sentences from 463 publicly available requirement documents and is a first step towards a gold standard corpus for causality extraction. We encourage fellow researchers to contribute to our dataset and help us in finalizing the causality annotation process. Additionally, the dataset can also be annotated further to serve as a benchmark for other RE-relevant NLP tasks such as requirements classification.

Flow coefficients $v_{n}$ of the orders $n = 1 - 6$ are measured with the High-Acceptance DiElectron Spectrometer (HADES) at GSI for protons, deuterons and tritons as a function of centrality, transverse momentum and rapidity in Au+Au collisions at $\sqrt{s_{NN}} = 2.4$ GeV. Combining the information from the flow coefficients of all orders allows to construct for the first time, at collision energies of a few GeV, a multi-differential picture of the angular emission pattern of these particles. It reflects the complicated interplay between the effect of the central fireball pressure on the emission of particles and their subsequent interaction with spectator matter. The high precision information on higher order flow coefficients is a major step forward in constraining the equation-of-state of dense baryonic matter., Comment: 6 pages, 5 figures; Figs. 2, 3 and 5 updated from re-analysis of data including UrQMD model predictions. Plotting error in Fig. 4 corrected; Accepted for publication in Phys. Rev. Lett

We present high-statistic data on charged pion emission from Au+Au collisions at $\sqrt{s_{\rm{NN}}}$ = 2.4 GeV (corresponding to $E_{beam}$ = 1.23 A GeV) in four centrality classes in the range 0 - 40$\%$ of the most central collisions. The data are analyzed as a function of transverse momentum, transverse mass, rapidity, and polar angle. Pion multiplicity per participating nucleon decreases moderately with increasing centrality. The polar angular distributions are found to be non-isotropic even for the most central event class. Our results on pion multiplicity fit well into the general trend of the world data, but undershoot by $2.5 \sigma$ data from the FOPI experiment measured at slightly lower beam energy. We compare our data to state-of-the-art transport model calculations (PHSD, IQMD, PHQMD, GiBUU and SMASH) and find substantial differences between the measurement and the results of these calculations., Comment: 17 pages, 19 figures, 4 tables

Pion induced reactions provide unique opportunities for an unambiguous description of baryonic resonances and their coupling channels by means of a partial wave analysis. Using the secondary pion beam at SIS18, the two pion production in the second resonance region has been investigated to unravel the role of the $N(1520) \frac{3}{2}^-$ resonance in the intermediate $\rho$ production. Results on exclusive channels with one pion ($\pi^{-}p$) and two pions ($\pi^{+}\pi^{-}n$, $\pi^{0}\pi^{-}p$) in the final state measured in the $\pi^{-}-p$ reaction at four different pion beam momenta (0.650, 0.685, 0.733, and 0.786 GeV/c) are presented. The excitation function of the different partial waves and $\Delta\pi$, $N\sigma$ and $N\rho$ isobar configurations is obtained, using the Bonn-Gatchina partial wave analysis. The $N(1520) \frac{3}{2}^-$ resonance is found to dominate the $N\rho$ final state with the branching ratio $BR=12.2 \pm 1.9\%$.

The obstructive sleep apnea-hypopnea (OSAH) syndrome is a very common and frequently undiagnosed sleep disorder. It is characterized by repeated events of partial (hypopnea) or total (apnea) obstruction of the upper airway while sleeping. This study makes use of a previously developed method called DAS-KSVD for multiclass structured dictionary learning to automatically detect individual events of apnea and hypopnea using only blood oxygen saturation signals. The method uses a combined discriminant measure which is capable of efficiently quantifying the degree of discriminability of each one of the atoms in a dictionary. DAS-KSVD was applied to detect and classify apnea and hypopnea events from signals obtained from the Sleep Heart Health Study database. For moderate to severe OSAH screening, a receiver operating characteristic curve analysis of the results shows an area under the curve of 0.957 and diagnostic sensitivity and specificity of 87.56% and 88.32%, respectively. These results represent improvements as compared to most state-of-the-art procedures. Hence, the method could be used for screening OSAH syndrome more reliably and conveniently, using only a pulse oximeter., Comment: 15 pages, 7 figures

We define Poisson-geometric analogues of Kitaev's lattice models. They are obtained from a Kitaev model on an embedded graph $\Gamma$ by replacing its Hopf algebraic data with Poisson data for a Poisson-Lie group G. Each edge is assigned a copy of the Heisenberg double $\mathcal H(G)$. Each vertex (face) of $\Gamma$ defines a Poisson action of $G$ (of $G^*$) on the product of these Heisenberg doubles. The actions for a vertex and adjacent face form a Poisson action of the double Poisson-Lie group $D(G)$. We define Poisson counterparts of vertex and face operators and relate them via the Poisson bracket to the vector fields generating the actions of $D(G)$. We construct an isomorphism of Poisson $D(G)$-spaces between this Poisson-geometrical Kitaev model and Fock and Rosly's Poisson structure for the graph $\Gamma$ and the Poisson-Lie group $D(G)$. This decouples the latter and represents it as a product of Heisenberg doubles. It also relates the Poisson-geometrical Kitaev model to the symplectic structure on the moduli space of flat $D(G)$-bundles on an oriented surface with boundary constructed from $\Gamma$., Comment: 49 pages. v2: Revised Lemma 3.17 (i). v3: Revised introduction; fixed oversight in proof of Theorem 3.29; improved Remark 3.11; fixed typos and improved wording in several places

We present an analysis of proton number fluctuations in $\sqrt{s_{NN}}$ = 2.4 GeV Au+Au collisions measured with the High-Acceptance DiElectron Spectrometer (HADES) at GSI. With the help of extensive detector simulations done with IQMD transport model events including nuclear clusters, various nuisance effects influencing the observed proton cumulants have been investigated. Acceptance and efficiency corrections have been applied as a function of fine grained rapidity and transverse momentum bins, as well as considering local track density dependencies. Next, the effects of volume changes within particular centrality selections have been considered and beyond-leading-order corrections have been applied to the data. The efficiency and volume corrected proton number moments and cumulants Kn of orders n = 1, . . . , 4 have been obtained as a function of centrality and phase-space bin, as well as the corresponding correlators C_n . We find that the observed correlators show a power-law scaling with the mean number of protons, i.e. $C_n \propto ^n$, indicative of mostly long-range multi-particle correlations in momentum space. We also present a comparison of our results with Au+Au collision data obtained at RHIC at similar centralities, but higher $\sqrt{s_{NN}}$., Comment: 35 pages, 33 figures

Quantum dots inserted in semiconducting nanowires are a promising platform for the fabrication of single photon devices. However, it is difficult to fully comprehend the electro-optical behaviour of such quantum objects without correlated studies of the structural and optical properties on the same nanowire. In this work, we study the spectral tunability of the emission of a single quantum dot in a GaN nanowire by applying external bias. The nanowires are dispersed and contacted on electron beam transparent Si3N4 membranes, so that transmission electron microscopy observations, photocurrent and micro-photoluminescence measurements under bias can be performed on the same specimen. The emission from a single dot blue or red shifts when the external electric field compensates or enhances the internal electric field generated by the spontaneous and piezoelectric polarization. A detailed study of two nanowire specimens emitting at 327.5 nm and 307.5 nm shows spectral shifts at rates of 20 and 12 meV/V, respectively. Theoretical calculations facilitated by the modelling of the

While reversibility is a fundamental concept in thermodynamics, most reactions are not readily reversible, especially in solid state physics. For example, thermal diffusion is a widely known concept, used among others to inject dopant atoms into the substitutional positions in the matrix and improve the device properties. Typically, such a diffusion process will create a concentration gradient extending over increasingly large regions, without possibility to reverse this effect. On the other hand, while the bottom up growth of semiconducting nanowires is interesting, it can still be difficult to fabricate axial heterostructures with high control. In this paper, we report a reversible thermal diffusion process occurring in the solid-state exchange reaction between an Al metal pad and a Si$_x$Ge$_{1-x}$ alloy nanowire observed by in-situ transmission electron microscopy. The thermally assisted reaction results in the creation of a Si-rich region sandwiched between the reacted Al and unreacted SixGe1-x part, forming an axial Al/Si/Si$_x$Ge$_{1-x}$ heterostructure. Upon heating or (slow) cooling, the Al metal can repeatably move in and out of the Si$_x$Ge$_{1-x}$ alloy nanowire while maintaining the rod-like geometry and crystallinity, allowing to fabricate and contact nanowire heterostructures in a reversible way in a single process step, compatible with current Si based technology. This interesting system is promising for various applications, such as phase change memories in an all crystalline system with integrated contacts, as well as Si/Si$_x$Ge$_{1-x}$/Si heterostructures for near-infrared sensing applications.

Understanding the interplay between the structure, composition and opto-electronic properties of semiconductor nano-objects requires combining transmission electron microscopy (TEM) based techniques with electrical and optical measurements on the very same specimen. Recent developments in TEM technologies allow not only the identification and in-situ electrical characterization of a particular object, but also the direct visualization of its modification in-situ by techniques such as Joule heating. Over the past years, we have carried out a number of studies in these fields that are reviewed in this contribution. In particular, we discuss here i) correlated studies where the same unique object is characterized electro-optically and by TEM, ii) in-situ Joule heating studies where a solid-state metal-semiconductor reaction is monitored in the TEM, and iii) in-situ biasing studies to better understand the electrical properties of contacted single nanowires. In addition, we provide detailed fabrication steps for the silicon nitride membranes crucial to these correlated and in-situ measurements., Comment: This is an author-created, un-copyedited version of a topical review published in Nanotechnology. IOP Publishing Ltd. is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1361-6528/ab99f0

Aluminum-germanium nanowires (NWs) thermal activated solid state reaction is a promising system as very sharp and well defined one dimensional contacts can be created between a metal and a semiconductor, that can become a quantum dot if the size becomes sufficiently small. In the search for high performance devices without variability, it is of high interest to allow deterministic fabrication of nanowire quantum dots, avoiding sample variability and obtaining atomic scale precision on the fabricated dot size. In this paper, we present a reliable fabrication process to produce sub-10 nm Ge quantum dots (QDs), using a combination of ex-situ thermal annealing via rapid thermal annealing (RTA) and in-situ Joule heating technique in a transmission electron microscope (TEM). First we present in-situ direct joule heating experiments showing how the heating electrode could be damaged due to the formation of Al crystals and voids at the vicinity of the metal/NW contact, likely related with electro-migration phenomena. We show that the contact quality can be preserved by including an additional ex-situ RTA step prior to the in-situ heating. The in-situ observations also show in real-time how the exchange reaction initiates simultaneously from several locations underneath the Al contact pad, and the Al crystal grows gradually inside the initial Ge NW with the growth interface along a Ge(111) lattice plane. Once the reaction front moves out from underneath the contact metal, two factors jeopardize an atomically accurate control of the Al/Ge reaction interface. We observed a local acceleration of the reaction interface due to the electron beam irradiation in the transmission electron microscope as well as the appearance of large jumps of the interface in unpassivated Ge wires while a smooth advancement of the reaction interface was observed in wires with an Al2O3 protecting shell on the surface. Carefully controlling all aspects of the exchange reaction, we demonstrate a fabrication process combining ex-situ and in-situ heating techniques to precisely control and produce axial Al/Ge/Al NW heterostructures with an ultra-short Ge segment down to 8 nanometers. Practically, the scaling down of Ge segment length is only limited by the microscope resolution.

Extreme Learning Machines (ELMs) have become a popular tool in the field of Artificial Intelligence due to their very high training speed and generalization capabilities. Another advantage is that they have a single hyper-parameter that must be tuned up: the number of hidden nodes. Most traditional approaches dictate that this parameter should be chosen smaller than the number of available training samples in order to avoid over-fitting. In fact, it has been proved that choosing the number of hidden nodes equal to the number of training samples yields a perfect training classification with probability 1 (w.r.t. the random parameter initialization). In this article we argue that in spite of this, in some cases it may be beneficial to choose a much larger number of hidden nodes, depending on certain properties of the data. We explain why this happens and show some examples to illustrate how the model behaves. In addition, we present a pruning algorithm to cope with the additional computational burden associated to the enlarged ELM. Experimental results using electroencephalography (EEG) signals show an improvement in performance with respect to traditional ELM approaches, while diminishing the extra computing time associated to the use of large architectures.

High-statistics $\pi^-\pi^-$ and $\pi^+\pi^+$ femtoscopy data are presented for Au+Au collisions at $\sqrt{s_\mathrm{NN}}=2.4$ GeV, measured with HADES at SIS18/GSI. The experimental correlation functions allow the determination of the space-time extent of the corresponding emission sources via a comparison to models. The emission source, parametrized as three-dimensional Gaussian distribution, is studied in dependence on pair transverse momentum, azimuthal emission angle with respect to the reaction plane, collision centrality and beam energy. For all centralities and transverse momenta, a geometrical distribution of ellipsoidal shape is found in the plane perpendicular to the beam direction with the larger extension perpendicular to the reaction plane. For large transverse momenta, the corresponding eccentricity approaches the initial eccentricity. The eccentricity is smallest for most central collisions, where the shape is almost circular. The magnitude of the tilt angle of the emission ellipsoid in the reaction plane decreases with increasing centrality and increasing transverse momentum. All source radii increase with centrality, largely exhibiting a linear rise with the number of participants, irrespective of transverse momentum. A substantial charge-sign difference of the source radii is found, appearing most pronounced at low transverse momentum. The extracted source parameters are consistent with the extrapolation of their energy dependence down from higher energies., Comment: 20 pages, 19 figures, 7 tables. arXiv admin note: text overlap with arXiv:1811.06213