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Musculoskeletal (MSK) models offer a non-invasive way to understand biomechanical loads on joints and tendons, which are difficult to measure directly. Variations in muscle strength, especially relative differences between muscles, significantly impact model outcomes. Typically, scaled generic MSK models use maximum isometric forces that are not adjusted for different demographics, raising concerns about their accuracy. This review provides an overview on experimentally derived strength parameters, including physiological cross-sectional area (PCSA), muscle mass (Mm), and relative muscle mass (%Mm), which is the relative distribution of muscle mass across the leg. We analysed differences by age and sex, and compared open-source lower limb MSK model parameters with experimental data from 57 studies. Our dataset, with records dating back to 1884, shows that uniformly increasing all maximum isometric forces in MSK models does not capture key muscle ratio differences due to age and sex. Males have a higher proportion of muscle mass in the rectus femoris and semimembranosus muscles, while females have a greater relative muscle mass in the pelvic (gluteus maximus and medius) and ankle muscles (tibialis anterior, tibialis posterior, and extensor digitorum longus). Older adults have a higher relative muscle mass in the gluteus medius, while younger individuals show more in the gastrocnemius. Current MSK models do not accurately represent muscle mass distribution for specific age or sex groups, and none of them accurately reflect female muscle mass distribution. Further research is needed to explore musculotendon age- and sex differences.

Superconducting memory is a promising technology for data storage because of its speed, high energy efficiency, non-volatility, and compatibility with quantum computing devices. However, the need for cryogenic temperatures makes superconducting memory an extremely expensive and specialized device. Ternary lanthanum polyhydrides, due to their high critical temperatures of 240-250 K, represent a convenient platform for studying effects associated with superconductivity in disordered granular systems. In this work, we investigate a trapped magnetic flux and memory effects in recently discovered lanthanum-neodymium (La,Nd)H$_{10}$ and lanthanum-scandium (La,Sc)H$_{12}$ superhydrides at a pressure of 175-196 GPa. We use a steady magnetic field of a few Tesla (T) and strong pulsed fields up to 68 T to create the trapped flux state in the compressed superhydrides. A study of the current-voltage characteristics and voltage-temperature curves of the samples with frozen magnetic flux indicates a significant memory effect in La-Sc polyhydrides already at 225-230 K.

The recent cohort of publicly available generative models can produce human expert level content across a variety of topics and domains. Given a model in this cohort as a base model, methods such as parameter efficient fine-tuning, in-context learning, and constrained decoding have further increased generative capabilities and improved both computational and data efficiency. Entire collections of derivative models have emerged as a byproduct of these methods and each of these models has a set of associated covariates such as a score on a benchmark, an indicator for if the model has (or had) access to sensitive information, etc. that may or may not be available to the user. For some model-level covariates, it is possible to use "similar" models to predict an unknown covariate. In this paper we extend recent results related to embedding-based representations of generative models -- the data kernel perspective space -- to classical statistical inference settings. We demonstrate that using the perspective space as the basis of a notion of "similar" is effective for multiple model-level inference tasks.

Generative models, such as large language models and text-to-image diffusion models, produce relevant information when presented a query. Different models may produce different information when presented the same query. As the landscape of generative models evolves, it is important to develop techniques to study and analyze differences in model behaviour. In this paper we present novel theoretical results for embedding-based representations of generative models in the context of a set of queries. We establish sufficient conditions for the consistent estimation of the model embeddings in situations where the query set and the number of models grow.

We present an experimental visualization of the Terrell effect, an optical phenomenon predicted in 1959 by Roger Penrose and James Terrell, which reveals that the Lorentz contraction of a moving object is not visible in a snapshot photograph. Using fs-laser pulses and a gated intensified camera that allows gating times as short as 300 ps, we achieve a virtual reduction of the speed of light to less than 2 m/s, enabling the visualisation of relativistically moving objects in real time. By capturing light reflected from deliberately Lorentz-contracted objects, our setup effectively reconstructs their visual appearance. This didactic visualization not only commemorates the centennial of Anton Lampa's seminal 1924 paper on relativistic length contraction but also provides the first experimental evidence of the Terrell effect in a laboratory setup. Our results comprise detailed relativistic illustrations, simulations and photographic snapshots of a sphere and a cube, which are animated to velocities close to the speed of light, revealing the apparent rotation effect and the distortion predicted by relativistic theory., Comment: 6 pages, 8 figures

Lanthanum-hydrogen system and its derivatives remain among the most promising for achieving room-temperature superconductivity. In this study, we examined the formation of ternary lanthanum-scandium superhydrides at pressures up to 220 GPa. The primary product of the LaSc alloy's reaction with hydrogen is newly discovered cubic (La,Sc)H$_{12}$, demonstrating clear superconducting transition in all six channels of the van der Pauw scheme below 244-248 K. In this compound with an unusually large unit cell volume, virtually no magnetoresistance was observed in fields up to 68 Tesla. Synthesized samples of (La,Sc)H$_{12}$ demonstrate pronounced superconducting diode and SQUID-like effects at a record high temperature of 226 K. Furthermore, our analysis revealed the formation of lower hexagonal polyhydrides (La,Sc)H$_{6-7}$, which could potentially account for the anomaly in electrical resistance observed near 274 K. This anomalous behavior may be of a superconducting nature, since low-temperature diffraction studies indicate the absence of corresponding structural phase transitions in La-Sc polyhydrides.

The stability of geotechnical infrastructure assets, such as cuttings and embankments, is crucial to the safe and efficient delivery of transport services. The successful emulation of geotechnical models of deterioration of infrastructure slopes has the potential to inform slope design, maintenance and remediation by introducing the time dependency of deterioration into geotechnical asset management. We have performed computer experiments of deterioration, measured by the factor of safety (FoS), for a set of cutting slope geometries and soil properties that are common in the southern UK. Whilst computer experiments are an extremely useful and cost-effective method of better understanding deterioration mechanisms, it would not be practical to run enough experiments to understand relations between high-dimensional inputs and outputs. Therefore, we trained a fully-Bayesian Gaussian process emulator using an ensemble of 75 computer experiments to predict the FoS. We construct two different emulator models, one approximating the FoS temporal evolution with a quadratic model and one approximating the temporal evolution with a B-spline model; and we emulated their parameters. We also compare the ability of our models to predict failure time. The developed models could be used to inform infrastructure cutting slope design and management, and extend serviceable life.

Understanding the decisions of deep learning (DL) models is essential for the acceptance of DL to risk-sensitive applications. Although methods, like class activation maps (CAMs), give a glimpse into the black box, they do miss some crucial information, thereby limiting its interpretability and merely providing the considered locations of objects. To provide more insight into the models and the influence of datasets, we propose an integrated feature analysis method, which consists of feature distribution analysis and feature decomposition, to look closer into the intermediate features extracted by DL models. This integrated feature analysis could provide information on overfitting, confounders, outliers in datasets, model redundancies and principal features extracted by the models, and provide distribution information to form a common intensity scale, which are missing in current CAM algorithms. The integrated feature analysis was applied to eight different datasets for general validation: photographs of handwritten digits, two datasets of natural images and five medical datasets, including skin photography, ultrasound, CT, X-rays and MRIs. The method was evaluated by calculating the consistency between the CAMs average class activation levels and the logits of the model. Based on the eight datasets, the correlation coefficients through our method were all very close to 100%, and based on the feature decomposition, 5%-25% of features could generate equally informative saliency maps and obtain the same model performances as using all features. This proves the reliability of the integrated feature analysis. As the proposed methods rely on very few assumptions, this is a step towards better model interpretation and a useful extension to existing CAM algorithms. Codes: https://github.com/YanliLi27/IFA, Comment: 13 pages, 11 figures, code available: https://github.com/YanliLi27/IFA This work has been submitted to the IEEE for possible publication

In the field of materials science and manufacturing, a vast amount of heterogeneous data exists, encompassing measurement and simulation data, machine data, publications, and more. This data serves as the bedrock of valuable knowledge that can be leveraged for various engineering applications. However, efficiently storing and handling such diverse data remain significantly challenging, often due to the lack of standardization and integration across different organizational units. Addressing these issues is crucial for fully utilizing the potential of data-driven approaches in these fields. In this paper, we present a novel technology stack named Dataspace Management System (DSMS) for powering dataspace solutions. The core of DSMS lies on its distinctive knowledge management approach tuned to meet the specific demands of the materials science and manufacturing domain, all while adhering to the FAIR principles. This includes data integration, linkage, exploration, visualization, processing, and enrichment, in order to support engineers in decision-making and in solving design and optimization problems. We provide an architectural overview and describe the core components of DSMS. Additionally, we demonstrate the applicability of DSMS to typical data processing tasks in materials science through use cases from two research projects, namely StahlDigital and KupferDigital, both part of the German MaterialDigital initiative.

Large language models (LLMs) are capable of producing high quality information at unprecedented rates. As these models continue to entrench themselves in society, the content they produce will become increasingly pervasive in databases that are, in turn, incorporated into the pre-training data, fine-tuning data, retrieval data, etc. of other language models. In this paper we formalize the idea of a communication network of LLMs and introduce a method for representing the perspective of individual models within a collection of LLMs. Given these tools we systematically study information diffusion in the communication network of LLMs in various simulated settings.

We state a conjecture, local Langlands in families, connecting the centre of the category of smooth representations on $\mathbb{Z}[\sqrt{q}^{-1}]$-modules of a quasi-split $p$-adic group $\mathrm{G}$ (where $q$ is the cardinality of the residue field of the underlying local field), the ring of global functions on the stack of Langlands parameters for $\mathrm{G}$ over $\mathbb{Z}[\sqrt{q}^{-1}]$, and the endomorphisms of a Gelfand-Graev representation for $\mathrm{G}$. For a class of classical $p$-adic groups (symplectic, unitary, or split odd special orthogonal groups), we prove this conjecture after inverting an integer depending only on $\mathrm{G}$. Along the way, we show that the local Langlands correspondence for classical $p$-adic groups (1) preserves integrality of $\ell$-adic representations; (2) satisfies an "extended" (generic) packet conjecture; (3) is compatible with parabolic induction up to semisimplification (generalizing a result of Moussaoui), hence induces a semisimple local Langlands correspondence; and (4) the semisimple correspondence is compatible with automorphisms of $\mathbb{C}$ fixing $\sqrt{q}$., Comment: 57 pages

Ge1-xSnx and Si1-x-yGeySnx alloys are promising materials for future opto- and nanoelectronics applications. These alloys enable effective band-gap engineering, broad adjustability of their lattice parameter, exhibit much higher carrier mobility than pure Si, and are compatible with the CMOS technology. Unfortunately, the equilibrium solid solubility of Sn in Si1-xGex is less than 1% and the pseudomorphic growth of Si1-x-yGeySnx on Ge or Si can cause in-plane compressive strain in the grown layer, degrading the superior properties of these alloys. Therefore, post-growth strain engineering by ultrafast non-equilibrium thermal treatments like pulse laser annealing (PLA) is needed to improve the layer quality. In this article, Ge0.94Sn0.06 and Si0.14Ge0.8Sn0.06 thin films grown on silicon-on-insulator substrates by molecular beam epitaxy were post growth thermally treated by PLA. The material is analyzed before and after the thermal treatments by transmission electron microscopy, X-ray diffraction (XRD), Rutherford backscattering spectrometry, secondary ion mass spectrometry, and Hall effect measurements. It is shown that after annealing, the material is single-crystalline with improved crystallinity than the as-grown layer. This is reflected in a significantly increased XRD reflection intensity, well-ordered atomic pillars, and increased active carrier concentrations up to 4x1019 cm-3.

For many years, Si1-yGey alloys have been applied in the semiconductor industry due to the ability to adjust the performance of Si-based nanoelectronic devices. Following this alloying approach of group-IV semiconductors, adding tin (Sn) into the alloy appears as the obvious next step, which leads to additional possibilities for tailoring the material properties. Adding Sn enables effective band gap and strain engineering and can improve the carrier mobilities, which makes Si1-x-yGeySnx alloys promising candidates for future opto- and nanoelectronics applications. The bottom-up approach for epitaxial growth of Si1-x-yGeySnx, e.g., by chemical vapor deposition and molecular beam epitaxy, allows tuning the material properties in the growth direction only; the realization of local material modifications to generate lateral heterostructures with such a bottom-up approach is extremely elaborate, since it would require the use of lithography, etching, and either selective epitaxy or epitaxy and chemical-mechanical polishing giving rise to interface issues, non-planar substrates, etc. This article shows the possibility of fabricating Si1-x-yGeySnx alloys by Sn ion beam implantation into Si1-yGey layers followed by millisecond-range flash lamp annealing (FLA). The materials are investigated by Rutherford backscattering spectrometry, micro Raman spectroscopy, X-ray diffraction, and transmission electron microscopy. The fabrication approach was adapted to ultra-thin Si1-yGey layers on silicon-on-insulator substrates. The results show the fabrication of single-crystalline Si1-x-yGeySnx with up to 2.3 at.% incorporated Sn without any indication of Sn segregation after recrystallization via FLA. Finally, we exhibit the possibility of implanting Sn locally in ultra-thin Si1-yGey films by masking unstructured regions on the chip.

Large language models (LLM) have achieved impressive performance on medical question-answering benchmarks. However, high benchmark accuracy does not imply that the performance generalizes to real-world clinical settings. Medical question-answering benchmarks rely on assumptions consistent with quantifying LLM performance but that may not hold in the open world of the clinic. Yet LLMs learn broad knowledge that can help the LLM generalize to practical conditions regardless of unrealistic assumptions in celebrated benchmarks. We seek to quantify how well LLM medical question-answering benchmark performance generalizes when benchmark assumptions are violated. Specifically, we present an adversarial method that we call MedFuzz (for medical fuzzing). MedFuzz attempts to modify benchmark questions in ways aimed at confounding the LLM. We demonstrate the approach by targeting strong assumptions about patient characteristics presented in the MedQA benchmark. Successful "attacks" modify a benchmark item in ways that would be unlikely to fool a medical expert but nonetheless "trick" the LLM into changing from a correct to an incorrect answer. Further, we present a permutation test technique that can ensure a successful attack is statistically significant. We show how to use performance on a "MedFuzzed" benchmark, as well as individual successful attacks. The methods show promise at providing insights into the ability of an LLM to operate robustly in more realistic settings., Comment: 9 pages, 3 figures, 2 algorithms, appendix

For an inner form $\mathrm{G}$ of a general linear group or classical group over a non-archimedean local field of odd residue characteristic, we decompose the category of smooth representations on $\mathbb{Z}[\mu_{p^{\infty}},1/p]$-modules by endo-parameter. We prove that parabolic induction preserves these decompositions, and hence that it preserves endo-parameters. Moreover, we show that the decomposition by endo-parameter is the $\overline{\mathbb{Z}}[1/p]$-block decomposition; and, for $\mathrm{R}$ an integral domain, introduce a graph whose connected components parameterize the $\mathrm{R}$-blocks, in particular including the cases $\mathrm{R}=\overline{\mathbb{Z}}_{\ell}$ and $\mathrm{R}=\overline{\mathbb{F}}_\ell$ for $\ell\neq p$. From our description, we deduce that the $\overline{\mathbb{Z}_\ell}$-blocks and $\overline{\mathbb{F}_\ell}$-blocks of $\mathrm{G}$ are in natural bijection, as had long been expected. Our methods also apply to the trivial endo-parameter (i.e., the depth zero subcategory) of any connected reductive $p$-adic group, providing an alternative approach to results of Dat and Lanard in depth zero. Finally, under a technical assumption (known for inner forms of general linear groups) we reduce the $\mathrm{R}$-block decomposition of $\mathrm{G}$ to depth zero., Comment: 54 pages

We report temperature-dependent reflectivity spectra of the layered van der Waals magnet CrSBr in the far-infrared region. Polarization-dependent measurements resolve the vibrational modes along the E$\|a$- and $b$-axes and reveal the clear structural anisotropy. While the $a$-axis phonons notably harden on cooling, the $b$-axis phonon frequencies are almost temperature-independent. A phonon splitting due to the antiferromagnetic phase transition is observed for the 180~cm$^{-1}$ $a$-axis vibrational mode, accompanied by a phonon softening below $T_N$. Furthermore, an additional mode with strong magnetic characteristics at $\sim$360~cm$^{-1}$ is identified and attributed to the magnon excitation of CrSBr., Comment: 6 pages, 3 figures

Multi-parametric MRI (mpMRI) studies are widely available in clinical practice for the diagnosis of various diseases. As the volume of mpMRI exams increases yearly, there are concomitant inaccuracies that exist within the DICOM header fields of these exams. This precludes the use of the header information for the arrangement of the different series as part of the radiologist's hanging protocol, and clinician oversight is needed for correction. In this pilot work, we propose an automated framework to classify the type of 8 different series in mpMRI studies. We used 1,363 studies acquired by three Siemens scanners to train a DenseNet-121 model with 5-fold cross-validation. Then, we evaluated the performance of the DenseNet-121 ensemble on a held-out test set of 313 mpMRI studies. Our method achieved an average precision of 96.6%, sensitivity of 96.6%, specificity of 99.6%, and F1 score of 96.6% for the MRI series classification task. To the best of our knowledge, we are the first to develop a method to classify the series type in mpMRI studies acquired at the level of the chest, abdomen, and pelvis. Our method has the capability for robust automation of hanging protocols in modern radiology practice.

Language technology has the potential to facilitate intercultural communication through meaningful translations. However, the current state of language technology is deeply entangled with colonial knowledge due to path dependencies and neo-colonial tendencies in the global governance of artificial intelligence (AI). Language technology is a complex and emerging field that presents challenges for co-design interventions due to enfolding in assemblages of global scale and diverse sites and its knowledge intensity. This paper uses LiveLanguage, a lexical database, a set of services with particular emphasis on modelling language diversity and integrating small and minority languages, as an example to discuss and close the gap from pluriversal design theory to practice. By diversifying the concept of emerging technology, we can better approach language technology in global contexts. The paper presents a model comprising of five layers of technological activity. Each layer consists of specific practices and stakeholders, thus provides distinctive spaces for co-design interventions as mode of inquiry for de-linking, re-thinking and re-building language technology towards pluriversality. In that way, the paper contributes to reflecting the position of co-design in decolonising emergent technologies, and to integrating complex theoretical knowledge towards decoloniality into language technology design.

Carbon implantation at the nanoscale is highly desired for the engineering of defect-based qubits in a variety of materials, including silicon, diamond, SiC and hBN. However, the lack of focused carbon ion beams does not allow for the full disclosure of their potential for application in quantum technologies. Here, we develop and use a carbon source for focused ion beams for the simultaneous creation of two types of quantum emitters in silicon, the W and G centers. Furthermore, we apply a multi-step implantation protocol for the programmable activation of the G centers with sub-100- nm resolution. This approach provides a route for significant enhancement of the creation yield of single G centers in carbon-free silicon wafers. Our experimental demonstration is an important step towards nanoscale engineering of telecom quantum emitters in silicon of high crystalline quality and isotope purity., Comment: 7 pages, 5 figures

This study investigates the electronic band structure of Chromium Sulfur Bromide (CrSBr) through comprehensive photoluminescence (PL) characterization. We clearly identify low-temperature optical transitions between two closely adjacent conduction-band states and two different valence-band states. The analysis of the PL data robustly unveils energy splittings, bandgaps and excitonic transitions across different thicknesses of CrSBr, ranging from monolayer to bulk. Temperature-dependent PL measurements elucidate the stability of the band splitting below the N\'eel temperature, suggesting that magnons coupled with excitons are responsible for the symmetry breaking and brightening of the transitions from the secondary conduction band minimum (CBM2) to the global valence band maximum (VBM1). Collectively, these results not only reveal band splitting in both the conduction and valence bands, but also point to an intricate interplay between the optical, electronic and magnetic properties of antiferromagnetic two-dimensional van der Waals crystals.

We report THz-pump / mid-infrared probe near-field studies on Si-doped GaAs-InGaAs core-shell nanowires utilizing THz radiation from the free-electron laser FELBE. Upon THz excitation of free carriers, we observe a red shift of the plasma resonance in both amplitude and phase spectra, which we attribute to the heating up of electrons in the conduction band. The simulation of heated electron distributions anticipates a significant electron population in both L- and X-valleys. The two-temperature model is utilized for a quantitative analysis of the dynamics of the electron gas temperature under THz pumping at various power levels., Comment: Manuscript + Supplementary Information

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.ai, Comment: See the project page at https://wmdp.ai

Oscillations of conductance observed in strong magnetic fields are a striking manifestation of the quantum dynamics of charge carriers in solids. The large charge carrier density in typical metals sets the scale of oscillations in both electrical and thermal conductivity, which characterize the Fermi surface. In semimetals, thermal transport at low-charge carrier density is expected to be phonon dominated, yet several experiments observe giant quantum oscillations in thermal transport. This raises the question of whether there is an overarching mechanism leading to sizable oscillations that survives in phonon-dominated semimetals. In this work, we show that such a mechanism exists. It relies on the peculiar phase-space allowed for phonon scattering by electrons when only a few Landau levels are filled. Our measurements on the Dirac semimetal ZrTe5 support this counter-intuitive mechanism through observation of pronounced thermal quantum oscillations, since they occur in similar magnitude and phase in directions parallel and transverse to the magnetic field. Our phase-space argument applies to all low-density semimetals, topological or not, including graphene and bismuth. Our work illustrates that phonon absorption can be leveraged to reveal degrees of freedom through their imprint on longitudinal thermal transport.

Multi-parametric MRI of the body is routinely acquired for the identification of abnormalities and diagnosis of diseases. However, a standard naming convention for the MRI protocols and associated sequences does not exist due to wide variations in imaging practice at institutions and myriad MRI scanners from various manufacturers being used for imaging. The intensity distributions of MRI sequences differ widely as a result, and there also exists information conflicts related to the sequence type in the DICOM headers. At present, clinician oversight is necessary to ensure that the correct sequence is being read and used for diagnosis. This poses a challenge when specific series need to be considered for building a cohort for a large clinical study or for developing AI algorithms. In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis. In our pilot work, our 3D DenseNet-121 model achieved an F1 score of 99.5% at differentiating 5 common MRI sequences obtained by three Siemens scanners (Aera, Verio, Biograph mMR). To the best of our knowledge, we are the first to develop an automated method for the 3D classification of MRI sequences in the chest, abdomen, and pelvis, and our work has outperformed the previous state-of-the-art MRI series classifiers., Comment: Accepted at SPIE 2024

In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to take into account manufacturing processes and tailor materials design approaches to support downstream process design approaches. As a major step into this direction, we present a holistic optimization approach that covers the entire process-structure-property chain in materials engineering. Our approach specifically employs machine learning to address two critical identification problems: a materials design problem, which involves identifying near-optimal material structures that exhibit desired properties, and a process design problem that is to find an optimal processing path to manufacture these structures. Both identification problems are typically ill-posed, which presents a significant challenge for solution approaches. However, the non-unique nature of these problems offers an important advantage for processing: By having several target structures that perform similarly well, processes can be efficiently guided towards manufacturing the best reachable structure. The functionality of the approach will be demonstrated manufacturing crystallographic textures with desired properties in a metal forming process.

The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g., important in forming operations. In materials science, crystallographic texture is commonly described by the orientation distribution function, which is defined as the probability density function of the orientations of the monocrystal grains conforming a polycrystalline material. For representing the orientation distribution function, there are several approaches such as using generalized spherical harmonics, orientation histograms, and pole figure images . Measuring distances between crystallographic textures is essential for any task that requires assessing texture similarities, e.g. to guide forming processes. Therefore, we introduce novel distance measures based on (i) the Earth Movers Distance that takes into account local distance information encoded in histogram-based texture representations and (ii) a distance measure based on pole figure images. For this purpose, we evaluate and compare existing distance measures for selected use-cases. The present study gives insights into advantages and drawbacks of using certain texture representations and distance measures with emphasis on applications in materials design and optimal process control.

There is an urgent need to increase the global data storage capacity, as current approaches lag behind the exponential growth of data generation driven by the Internet, social media and cloud technologies. In addition to increasing storage density, new solutions should provide long-term data archiving that goes far beyond traditional magnetic memory, optical disks and solid-state drives. Here, we propose a concept of energy-efficient, ultralong, high-density data archiving based on optically active atomic-size defects in a radiation resistance material, silicon carbide (SiC). The information is written in these defects by focused ion beams and read using photoluminescence or cathodoluminescence. The temperature-dependent deactivation of these defects suggests a retention time minimum over a few generations under ambient conditions. With near-infrared laser excitation, grayscale encoding and multi-layer data storage, the areal density corresponds to that of Blu-ray discs. Furthermore, we demonstrate that the areal density limitation of conventional optical data storage media due to the light diffraction can be overcome by focused electron-beam excitation., Comment: 8 pages, 4 figures

Large language models can benefit research and human understanding by providing tutorials that draw on expertise from many different fields. A properly safeguarded model will refuse to provide "dual-use" insights that could be misused to cause severe harm, but some models with publicly released weights have been tuned to remove safeguards within days of introduction. Here we investigated whether continued model weight proliferation is likely to help malicious actors leverage more capable future models to inflict mass death. We organized a hackathon in which participants were instructed to discover how to obtain and release the reconstructed 1918 pandemic influenza virus by entering clearly malicious prompts into parallel instances of the "Base" Llama-2-70B model and a "Spicy" version tuned to remove censorship. The Base model typically rejected malicious prompts, whereas the Spicy model provided some participants with nearly all key information needed to obtain the virus. Our results suggest that releasing the weights of future, more capable foundation models, no matter how robustly safeguarded, will trigger the proliferation of capabilities sufficient to acquire pandemic agents and other biological weapons., Comment: Updates in response to online feedback: emphasized the focus on risks from future rather than current models; explained the reasoning behind - and minimal effects of - fine-tuning on virology papers; elaborated on how easier access to synthesized information can reduce barriers to entry; clarified policy recommendations regarding what is necessary but not sufficient; corrected a citation link

The emergence of human-like abilities of AI systems for content generation in domains such as text, audio, and vision has prompted the development of classifiers to determine whether content originated from a human or a machine. Implicit in these efforts is an assumption that the generation properties of a human are different from that of the machine. In this work, we provide a framework in the language of statistical pattern recognition that quantifies the difference between the distributions of human and machine-generated content conditioned on an evaluation context. We describe current methods in the context of the framework and demonstrate how to use the framework to evaluate the progression of generative models towards human-like capabilities, among many axes of analysis.

Van der Waals magnetic materials are an ideal platform to study low-dimensional magnetism. Opposed to other members of this family, the magnetic semiconductor CrSBr is highly resistant to degradation in air, which, besides its exceptional optical, electronic, and magnetic properties, is the reason the compound is receiving considerable attention at the moment. For many years, its magnetic phase diagram seemed to be well-understood. Recently, however, several groups observed a magnetic transition in magnetometry measurements at temperatures of around 40 K that is not expected from theoretical considerations, causing a debate about the intrinsic magnetic properties of the material. In this letter, we report the absence of this particular transition in magnetization measurements conducted on high-quality CrSBr crystals, attesting to the extrinsic nature of the low-temperature magnetic phase observed in other works. Our magnetometry results obtained from large bulk crystals are in very good agreement with the magnetic phase diagram of CrSBr previously predicted by the mean-field theory; A-type antiferromagnetic order is the only phase observed below the N\'eel temperature at TN = 131 K. Moreover, numerical fits based on the Curie-Weiss law confirm that strong ferromagnetic correlations are present within individual layers even at temperatures much larger than TN., Comment: 13 pages, submitted to Appl. Phys. Lett