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Intense femtosecond x-ray pulses produced by an x-ray free-electron laser can trigger irreversible structural transitions in crystalline solids. For instance, irradiation of diamond can lead to graphitization and, at higher deposited doses, to amorphization. Our Monte Carlo simulations of irradiated diamond under realistic experimental conditions demonstrate that triggering graphitization or other phase transitions with hard x-ray photons can be challenging due to the ballistic escape of photoelectrons out of the beam focus. Decisive parameter here is the photoelectron range in proportion to the focal beam size. For future experiments on x-ray-induced transitions, such dedicated simulations of ballistic transport preceding the beamtime will be necessary. They can predict experimental conditions under which the desired distribution of the absorbed x-ray dose in the irradiated solid can be achieved., Comment: 15 pages, 2 figures, 1 table. Submitted to Phys. Rev. B

BM25, a widely-used lexical search algorithm, remains crucial in information retrieval despite the rise of pre-trained and large language models (PLMs/LLMs). However, it neglects query-document similarity and lacks semantic understanding, limiting its performance. We revisit BM25 and introduce BMX, a novel extension of BM25 incorporating entropy-weighted similarity and semantic enhancement techniques. Extensive experiments demonstrate that BMX consistently outperforms traditional BM25 and surpasses PLM/LLM-based dense retrieval in long-context and real-world retrieval benchmarks. This study bridges the gap between classical lexical search and modern semantic approaches, offering a promising direction for future information retrieval research. The reference implementation of BMX can be found in Baguetter, which was created in the context of this work. The code can be found here: https://github.com/mixedbread-ai/baguetter., Comment: correct the affiliation order

Dataspaces have recently gained adoption across various sectors, including traditionally less digitized domains such as culture. Leveraging Semantic Web technologies helps to make dataspaces FAIR, but their complexity poses a significant challenge to the adoption of dataspaces and increases their cost. The advent of Large Language Models (LLMs) raises the question of how these models can support the adoption of FAIR dataspaces. In this work, we demonstrate the potential of LLMs in dataspaces with a concrete example. We also derive a research agenda for exploring this emerging field., Comment: 8 pages. Preprint. Under review

Earth's surface is deficient in available forms of many elements considered limiting for prebiotic chemistry. In contrast, many extraterrestrial rocky objects are rich in these same elements. Limiting prebiotic ingredients may, therefore, have been delivered by exogenous material; however, the mechanisms by which exogeneous material may be reliably and non-destructively supplied to a planetary surface remains unclear. Today, the flux of extraterrestrial matter to Earth is dominated by fine-grained cosmic dust. Although this material is rarely discussed in a prebiotic context due to its delivery over a large surface area, concentrated cosmic dust deposits are known to form on Earth today due to the action of sedimentary processes. Here we combine empirical constraints on dust sedimentation with dynamical simulations of dust formation and planetary accretion to show that localized sedimentary deposits of cosmic dust could have accumulated in arid environments on early Earth, in particular glacial settings that today produce cryoconite sediments. Our results challenge the widely held assumption that cosmic dust is incapable of fertilizing prebiotic chemistry. Cosmic dust deposits may have plausibly formed on early Earth and acted to fertilize prebiotic chemistry., Comment: 5 figures

In recent years, data lakes emerged as away to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked Data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big Data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontologybased data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big Data and Semantic Web technologies.

Differentiable rendering methods promise the ability to optimize various parameters of 3d scenes to achieve a desired result. However, lighting design has so far received little attention in this field. In this paper, we introduce a method that enables continuous optimization of the arrangement of luminaires in a 3d scene via differentiable light tracing. Our experiments show two major issues when attempting to apply existing methods from differentiable path tracing to this problem: first, many rendering methods produce images, which restricts the ability of a designer to define lighting objectives to image space. Second, most previous methods are designed for scene geometry or material optimization and have not been extensively tested for the case of optimizing light sources. Currently available differentiable ray-tracing methods do not provide satisfactory performance, even on fairly basic test cases in our experience. In this paper, we propose a novel adjoint light tracing method that overcomes these challenges and enables gradient-based lighting design optimization in a view-independent (camera-free) way. Thus, we allow the user to paint illumination targets directly onto the 3d scene or use existing baked illumination data (e.g., light maps). Using modern ray-tracing hardware, we achieve interactive performance. We find light tracing advantageous over path tracing in this setting, as it naturally handles irregular geometry, resulting in less noise and improved optimization convergence. We compare our adjoint gradients to state-of-the-art image-based differentiable rendering methods. We also demonstrate that our gradient data works with various common optimization algorithms, providing good convergence behaviour. Qualitative comparisons with real-world scenes underline the practical applicability of our method.

X-ray laser-induced structural changes in silicon undergoing femtosecond melting have been investigated by using an x-ray pump-x-ray probe technique. The experimental results for different initial sample temperatures reveal that the onset time and the speed of the atomic disordering are independent of the initial temperature, suggesting that equilibrium atomic motion in the initial state does not play a pivotal role in the x-ray-induced ultrafast melting. By comparing the observed time-dependence of the atomic disordering and the dedicated theoretical simulations, we interpret that the energy transfer from the excited electrons to ions via electron-ion coupling (thermal effect) as well as a strong modification of the interatomic potential due to electron excitations (non-thermal effect) trigger the ultrafast atomic disordering. Our finding of the interplay of thermal and non-thermal effects in the x-ray-induced melting demonstrates that accurate modeling of intense x-ray interactions with matter is essential to ensure a correct interpretation of experiments using intense x-ray laser pulses.

Carbon nanotube (CNT) wires approach copper's specific conductivity and surpass carbon fiber's strength, with further improvement anticipated with greater aspect ratios and incorporation of dopants with long-range structural order. Fullerenes assemble into multitudes of process-dependent supramolecular crystals and, while initially insulating, they become marginally conductive (up to 0.05 MSm$^{-1}$) and superconductive ($T_c=18^\circ$K with K and 28$^\circ$K with Rb) after doping. These were small (100's $\mu$m long), soft (hardness comparable to indium), and typically unaligned, which hindered development of fullerene based wires. Individual fullerenes were previously incorporated into CNT fibers, although randomly without self-assembly into supramolecules. Here, a simple variation in established CNT acid extrusion creates a fiber composed of uniaxial chains of aligned fullerene supramolecules, self-assembled between aligned few-walled CNT bundles. This will provide a testbed for novel fullerene wire transport and prospects in CNT wire advancement.

Effective and flexible allocation of visual attention is key for pedestrians who have to navigate to a desired goal under different conditions of urgency and safety preferences. While automatic modelling of pedestrian attention holds great promise to improve simulations of pedestrian behavior, current saliency prediction approaches mostly focus on generic free-viewing scenarios and do not reflect the specific challenges present in pedestrian attention prediction. In this paper, we present Context-SalNET, a novel encoder-decoder architecture that explicitly addresses three key challenges of visual attention prediction in pedestrians: First, Context-SalNET explicitly models the context factors urgency and safety preference in the latent space of the encoder-decoder model. Second, we propose the exponentially weighted mean squared error loss (ew-MSE) that is able to better cope with the fact that only a small part of the ground truth saliency maps consist of non-zero entries. Third, we explicitly model epistemic uncertainty to account for the fact that training data for pedestrian attention prediction is limited. To evaluate Context-SalNET, we recorded the first dataset of pedestrian visual attention in VR that includes explicit variation of the context factors urgency and safety preference. Context-SalNET achieves clear improvements over state-of-the-art saliency prediction approaches as well as over ablations. Our novel dataset will be made fully available and can serve as a valuable resource for further research on pedestrian attention prediction.

Dark matter lighter than 10 GeV/c$^2$ encompasses a promising range of candidates. A conceptual design for a new detector, DarkSide-LowMass, is presented, based on the DarkSide-50 detector and progress toward DarkSide-20k, optimized for a low-threshold electron-counting measurement. Sensitivity to light dark matter is explored for various potential energy thresholds and background rates. These studies show that DarkSide-LowMass can achieve sensitivity to light dark matter down to the solar neutrino floor for GeV-scale masses and significant sensitivity down to 10 MeV/c$^2$ considering the Migdal effect or interactions with electrons. Requirements for optimizing the detector's sensitivity are explored, as are potential sensitivity gains from modeling and mitigating spurious electron backgrounds that may dominate the signal at the lowest energies.

We show how weakly supervised machine learning can improve the sensitivity of LHC mono-jet searches to new physics models with anomalous jet dynamics. The Classification Without Labels (CWoLa) method is used to extract all the information available from low-level detector information without any reference to specific new physics models. For the example of a strongly interacting dark matter model, we employ simulated data to show that the discovery potential of an existing generic search can be boosted considerably., Comment: 19 pages, 3 figures. v2: references added

Transient structural changes of Al$_2$O$_3$ on subatomic length scales following irradiation with an intense x-ray laser pulse (photon energy: 8.70 keV; pulse duration: 6 fs; fluence: 8$\times$10$^2$ J/cm$^{2}$) have been investigated by using an x-ray pump x-ray probe technique. The measurement reveals that aluminum and oxygen atoms remain in their original positions by $\sim$20 fs after the intensity maximum of the pump pulse, followed by directional atomic displacements at the fixed unit cell parameters. By comparing the experimental results and theoretical simulations, we interpret that electron excitation and relaxation triggered by the pump pulse modifies the potential energy surface and drives the directional atomic displacements. Our results indicate that high-resolution x-ray structural analysis with the accuracy of 0.01 \AA is feasible even with intense x-ray pulses by making the pulse duration shorter than the timescale needed to complete electron excitation and relaxation processes, which usually take up to a few tens of femtoseconds.

Memory utilization can be reduced by merging identical memory blocks into copy-on-write mappings. Previous work showed that this so-called memory deduplication can be exploited in local attacks to break ASLR, spy on other programs,and determine the presence of data, i.e., website images. All these attacks exploit memory deduplication across security domains, which in turn was disabled. However, within a security domain or on an isolated system with no untrusted local access, memory deduplication is still not considered a security risk and was recently re-enabled on Windows by default. In this paper, we present the first fully remote memorydeduplication attacks. Unlike previous attacks, our attacks require no local code execution. Consequently, we can disclose memory contents from a remote server merely by sending and timing HTTP/1 and HTTP/2 network requests. We demonstrate our attacks on deduplication both on Windows and Linux and attack widely used server software such as Memcached and InnoDB. Our side channel leaks up to 34.41 B/h over the internet, making it faster than comparable remote memory-disclosure channels. We showcase our remote memory-deduplication attack in three case studies: First, we show that an attacker can disclose the presence of data in memory on a server running Memcached. We show that this information disclosure channel can also be used for fingerprinting and detect the correct libc version over the internet in 166.51 s. Second, in combination with InnoDB, we present an information disclosure attack to leak MariaDB database records. Third, we demonstrate a fully remote KASLR break in less than 4 minutes allowing to derandomize the kernel image of a virtual machine over the Internet, i.e., 14 network hops away. We conclude that memory deduplication must also be considered a security risk if only applied within a single security domain.

Two main trends characterize today's communication landscape and are finding their way into industrial facilities: the rollout of 5G with its distinct support for vertical industries and the increasing success of machine learning (ML). The combination of those two technologies open the doors to many exciting industrial applications and its impact is expected to rapidly increase in the coming years, given the abundant data growth and the availability of powerful edge computers in production facilities. Unlike most previous work that has considered the application of 5G and ML in industrial environment separately, this paper highlights the potential and synergies that result from combining them. The overall vision presented here generates from the KICK project, a collaboration of several partners from the manufacturing and communication industry as well as research institutes. This unprecedented blend of 5G and ML expertise creates a unique perspective on ML-supported industrial communications and their role in facilitating industrial automation. The paper identifies key open industrial challenges that are grouped into four use cases: wireless connectivity and edge-cloud integration, flexibility in network reconfiguration, dynamicity of heterogeneous network services, and mobility of robots and vehicles. Moreover, the paper provides insights into the advantages of ML-based industrial communications and discusses current challenges of data acquisition in real systems.

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions.

A java-based graphical tool for drawing Feynman diagrams is presented. It differs from similar existing tools in various respects. For example, it is based on models, consisting of particles (lines) and (optionally) vertices, each of which can be given their individual properties (line style, color, arrows, label, etc.). The diagrams can be exported in any standard image format, or as PDF. Aside from its plain graphical aspect, the goal of FeynGame is also educative, as it can check a Feynman diagrams validity. This provides the basis to play games with diagrams, for example. Here we describe on such game where a given set of initial and final states must be connected through a Feynman diagram within a given interaction model., Comment: 26 pages, several figures and screenshots. FeynGame is available from https://gitlab.com/feyngame/FeynGame

With the rapid development of short-pulse intense laser sources, studies of matter under extreme irradiation conditions enter further unexplored regimes. In addition, an application of X-ray Free- Electron Lasers (XFELs), delivering intense femtosecond X-ray pulses allows to investigate sample evolution in IR pump - X-ray probe experiments with an unprecedented time resolution. Here we present the detailed study of periodic plasma created from the colloidal crystal. Both experimental data and theory modeling show that the periodicity in the sample survives to a large extent the extreme excitation and shock wave propagation inside the colloidal crystal. This feature enables probing the excited crystal, using the powerful Bragg peak analysis, in contrast to the conventional studies of dense plasma created from bulk samples for which probing with Bragg diffraction technique is not possible. X-ray diffraction measurements of excited colloidal crystals may then lead towards a better understanding of matter phase transitions under extreme irradiation conditions., Comment: 52 pages, 22 figures, 64 references

We present version 3 of the open-source simulator for flow and transport processes in porous media DuMu$^\text{x}$. DuMu$^\text{x}$ is based on the modular C++ framework Dune (Distributed and Unified Numerics Environment) and is developed as a research code with a focus on modularity and reusability. We describe recent efforts in improving the transparency and efficiency of the development process and community-building, as well as efforts towards quality assurance and reproducible research. In addition to a major redesign of many simulation components in order to facilitate setting up complex simulations in DuMu$^\text{x}$, version 3 introduces a more consistent abstraction of finite volume schemes. Finally, the new framework for multi-domain simulations is described, and three numerical examples demonstrate its flexibility.

A living cell senses its environment and responds to external signals. In this work, we study theoretically, the precision at which cells can determine the position of a spatially localized transient extracellular signal. To this end, we focus on the case, where the stimulus is converted into the release of a small molecule that acts as a second messenger, for example, Ca$^{2+}$, and activates kinases that change the activity of enzymes by phosphorylating them. We analyze the spatial distribution of phosphorylation events using stochastic simulations as well as a mean-field approach. Kinases that need to bind to the cell membrane for getting activated provide more accurate estimates than cytosolic kinases. Our results could explain why the rate of Ca$^{2+}$ detachment from the membrane-binding conventional Protein Kinase C$\alpha$ is larger than its phosphorylation rate.

Recently, out-of-order execution, an important performance optimization in modern high-end processors, has been revealed to pose a significant security threat, allowing information leaks across security domains. In particular, the Meltdown attack leaks information from the operating system kernel to user space, completely eroding the security of the system. To address this and similar attacks, without incurring the performance costs of software countermeasures, Intel includes hardware-based defenses in its recent Coffee Lake R processors. In this work, we show that the recent hardware defenses are not sufficient. Specifically, we present Fallout, a new transient execution attack that leaks information from a previously unexplored microarchitectural component called the store buffer. We show how unprivileged user processes can exploit Fallout to reconstruct privileged information recently written by the kernel. We further show how Fallout can be used to bypass kernel address space randomization. Finally, we identify and explore microcode assists as a hitherto ignored cause of transient execution. Fallout affects all processor generations we have tested. However, we notice a worrying regression, where the newer Coffee Lake R processors are more vulnerable to Fallout than older generations.

Out-of-order execution and speculative execution are among the biggest contributors to performance and efficiency of modern processors. However, they are inconsiderate, leaking secret data during the transient execution of instructions. Many solutions have been proposed against transient execution attacks. However, they do not eliminate the leakage entirely or introduce unacceptable performance penalties. In this paper, we propose ConTExT, a Considerate Transient Execution Technique. The basic idea of ConTExT is that secrets can enter registers, but not transiently leave them. ConTExT transforms Spectre from a problem that cannot be solved purely in software [53], to a problem that is not easy to solve, but solvable in software. For this, ConTExT requires minimal modifications of applications, compilers, operating systems, and the hardware. ConTExT offers full protection for secrets in memory and secrets in registers. We evaluate the security and performance of ConTExT. With its principled approach it inherently mitigates the recently found microarchitectural data sampling attacks on small processor buffers. Even when over-approximating, we observe no performance overhead for unprotected code and data, and an overhead of 71.14% for security-critical applications, which is below the overhead of currently recommended state-of-the-art mitigation strategies. The actual overhead of ConTExT is below 1% for real-world workloads.