Search

Refusals - instances where large language models (LLMs) decline or fail to fully execute user instructions - are crucial for both AI safety and AI capabilities and the reduction of hallucinations in particular. These behaviors are learned during post-training, especially in instruction fine-tuning (IFT) and reinforcement learning from human feedback (RLHF). However, existing taxonomies and evaluation datasets for refusals are inadequate, often focusing solely on should-not-related (instead of cannot-related) categories, and lacking tools for auditing refusal content in black-box LLM outputs. We present a comprehensive framework for classifying LLM refusals: (a) a taxonomy of 16 refusal categories, (b) a human-annotated dataset of over 8,600 instances from publicly available IFT and RLHF datasets, (c) a synthetic dataset with 8,000 examples for each refusal category, and (d) classifiers trained for refusal classification. Our work enables precise auditing of refusal behaviors in black-box LLMs and automatic analyses of refusal patterns in large IFT and RLHF datasets. This facilitates the strategic adjustment of LLM refusals, contributing to the development of more safe and reliable LLMs., Comment: NeurIPS 2024 Workshop SFLLM

Visual imagery does not consist of solitary objects, but instead reflects the composition of a multitude of fluid concepts. While there have been great advances in visual representation learning, such advances have focused on building better representations for a small number of discrete objects bereft of an understanding of how these objects are interacting. One can observe this limitation in representations learned through captions or contrastive learning -- where the learned model treats an image essentially as a bag of words. Several works have attempted to address this limitation through the development of bespoke learned architectures to directly address the shortcomings in compositional learning. In this work, we focus on simple, and scalable approaches. In particular, we demonstrate that by substantially improving weakly labeled data, i.e. captions, we can vastly improve the performance of standard contrastive learning approaches. Previous CLIP models achieved near chance rate on challenging tasks probing compositional learning. However, our simple approach boosts performance of CLIP substantially and surpasses all bespoke architectures. Furthermore, we showcase our results on a relatively new captioning benchmark derived from DOCCI. We demonstrate through a series of ablations that a standard CLIP model trained with enhanced data may demonstrate impressive performance on image retrieval tasks.

Due to its peculiar and highly variable nature, the blazar 3C 454.3 has been extensively monitored by the WEBT team. Here, we present for the first time these long-term optical flux and color variability results using data acquired in B, V, R, and I bands over a time span of $\sim$ 2 decades. We include data from WEBT collaborators and public archives such as SMARTS, Steward Observatory, and ZTF. The data are binned and segmented to study the source over this long term when more regular sampling was available. During our study, the long-term spectral variability reveals a redder when brighter (RWB) trend, which, however, stabilizes at a particular brightness cutoff $\sim$ 14.5 mag in the I-band, after which it saturates and evolves into a complex state. This trend indicates increasing jet emission dominance over accretion disk emission until jet emission completely dominates. Plots of the spectral index variation (following $F_{\nu} \propto \nu^{-\alpha}$) reveal a bimodal distribution using a one-day binning. These correlate with two extreme phases of 3C 454.3, an outburst or high flux state and quiescent or low flux state, which are respectively jet and accretion disk dominated. We have also conducted intra-day variability studies of nine light curves and found that six of them are variable. Discrete Correlation Function (DCF) analysis between different optical waveband pairs peak at zero lags, indicating co-spatial emission in different optical bands., Comment: 18 pages, 6 figures, 5 tables

The BL Lac object 3C 371 is one of the targets that are regularly monitored by the Whole Earth Blazar Telescope (WEBT) Collaboration to study blazar variability on both short and long timescales. We aim to evaluate the long-term multiwavelength (MWL) behaviour of 3C 371, comparing it with the results derived for its optical emission in our previous study. For this, we make use of the multi-band campaigns organized by the WEBT Collaboration in optical and radio between January 2018 and December 2020, and of public data from Swift and Fermi satellites and the MOJAVE Very Large Interferometry programme. We evaluate the variability shown by the source in each band with the amplitude variability quantification, as well as possible interband correlation using the z-Discrete Correlation Function. We also present a deep analysis of the optical-UV, X-ray and $\gamma$-ray spectral variability. With the MOJAVE data we perform a kinematics analysis, looking for components propagating along the jet, calculating its kinematics parameters. This set of parameters is later used for the interpretation of the source MWL behaviour, modelling the broadband spectral energy distribution (SED) of the source with theoretical blazar emission scenarios., Comment: Accepted in A&A, 23 pages, 15 figures

As bipedal robots become more and more popular in commercial and industrial settings, the ability to control them with a high degree of reliability is critical. To that end, this paper considers how to accurately estimate which feet are currently in contact with the ground so as to avoid improper control actions that could jeopardize the stability of the robot. Additionally, modern algorithms for estimating the position and orientation of a robot's base frame rely heavily on such contact mode estimates. Dedicated contact sensors on the feet can be used to estimate this contact mode, but these sensors are prone to noise, time delays, damage/yielding from repeated impacts with the ground, and are not available on every robot. To overcome these limitations, we propose a momentum observer based method for contact mode estimation that does not rely on such contact sensors. Often, momentum observers assume that the robot's base frame can be treated as an inertial frame. However, since many humanoids' legs represent a significant portion of the overall mass, the proposed method instead utilizes multiple simultaneous dynamic models. Each of these models assumes a different contact condition. A given contact assumption is then used to constrain the full dynamics in order to avoid assuming that either the body is an inertial frame or that a fully accurate estimate of body velocity is known. The (dis)agreement between each model's estimates and measurements is used to determine which contact mode is most likely using a Markov-style fusion method. The proposed method produces contact detection accuracy of up to 98.44% with a low noise simulation and 77.12% when utilizing data collect on the Sarcos Guardian XO robot (a hybrid humanoid/exoskeleton).

This contribution presents an improved low-order 3D finite element formulation with hourglass stabilization using automatic differentiation (AD). Here, the former Q1STc formulation is enhanced by an approximation-free computation of the inverse Jacobian. To this end, AD tools automate the computation and allow a direct evaluation of the inverse Jacobian, bypassing the need for a Taylor series expansion. Thus, the enhanced version, Q1STc+, is introduced. Numerical examples are conducted to compare the performance of both element formulations for finite strain applications, with particular focus on distorted meshes. Moreover, the performance of the new element formulation for an elasto-plastic material is investigated. To validate the obtained results, a volumetric locking-free reference element based on scaled boundary parametrization is used. Both the implementation of the element routine Q1STc+ and the corresponding material subroutine are made accessible to the public at https://doi.org/10.5281/zenodo.14259791, Comment: 41 pages, 12 figures, 4 tables

Cyber-attacks pose a security threat to military command and control networks, Intelligence, Surveillance, and Reconnaissance (ISR) systems, and civilian critical national infrastructure. The use of artificial intelligence and autonomous agents in these attacks increases the scale, range, and complexity of this threat and the subsequent disruption they cause. Autonomous Cyber Defence (ACD) agents aim to mitigate this threat by responding at machine speed and at the scale required to address the problem. Sequential decision-making algorithms such as Deep Reinforcement Learning (RL) provide a promising route to create ACD agents. These algorithms focus on a single objective such as minimizing the intrusion of red agents on the network, by using a handcrafted weighted sum of rewards. This approach removes the ability to adapt the model during inference, and fails to address the many competing objectives present when operating and protecting these networks. Conflicting objectives, such as restoring a machine from a back-up image, must be carefully balanced with the cost of associated down-time, or the disruption to network traffic or services that might result. Instead of pursing a Single-Objective RL (SORL) approach, here we present a simple example of a multi-objective network defence game that requires consideration of both defending the network against red-agents and maintaining critical functionality of green-agents. Two Multi-Objective Reinforcement Learning (MORL) algorithms, namely Multi-Objective Proximal Policy Optimization (MOPPO), and Pareto-Conditioned Networks (PCN), are used to create two trained ACD agents whose performance is compared on our Multi-Objective Cyber Defence game. The benefits and limitations of MORL ACD agents in comparison to SORL ACD agents are discussed based on the investigations of this game., Comment: 9 pages, 9 figures

The increasing frequency and severity of wildfires pose significant risks to communities, infrastructure, and the environment, especially in Wildland-Urban Interface (WUI) areas. Effective disaster management requires understanding how the public perceives and responds to wildfire threats in real-time. This study uses social media data to assess public responses and explores how these responses are linked to city-level community characteristics. Specifically, we leveraged a transformer-based topic modeling technique called BERTopic to identify wildfire response-related topics and then utilized the Susceptible-Infectious-Recovered (SIR) model to compute two key metrics associated with wildfire responses - awareness and resilience indicators. Additionally, we used GIS-based spatial analysis to map wildfire locations along with four groups of city-level factors (racial/ethnic, socioeconomic, demographic, and wildfire-specific). Our findings reveal significant geographic and socio-spatial differences in public responses. Southern California cities with larger Hispanic populations demonstrate higher wildfire awareness and resilience. In contrast, urbanized regions in Central and Northern California exhibit lower awareness levels. Furthermore, resilience is negatively correlated with unemployment rates, particularly in southern regions where higher unemployment aligns with reduced resilience. These findings highlight the need for targeted and equitable wildfire management strategies to improve the adaptive capacity of WUI communities.

A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning IR, an easy-to-use PyTorch Lightning-based framework for applying transformer-based language models in retrieval scenarios. Lightning IR provides a modular and extensible architecture that supports all stages of a retrieval pipeline: from fine-tuning and indexing to searching and re-ranking. Designed to be scalable and reproducible, Lightning IR is available as open-source: https://github.com/webis-de/lightning-ir., Comment: Accepted as a demo at WSDM'25

Human excrement composting (HEC) is a sustainable strategy for human excrement (HE) management that recycles nutrients and mitigates health risks while reducing reliance on freshwater, fossil fuels, and fertilizers. We present a comprehensive microbial time series analysis of HEC and show that the initial gut-like microbiome of HEC systems transitions to a microbiome similar to soil and traditional compost in fifteen biological replicates tracked weekly for one year., Comment: Main text: 9 pages, 2 figures; Extended data: 10 figures; Supplemental Text: 32 pages, 8 figures, 2 tables

Blazars are beamed active galactic nuclei known for their strong multi-wavelength variability on timescales from years down to minutes. We aim to investigate the suitability of the twisting jet model presented in previous works to explain the multi-wavelength behaviour of BL Lacertae, the prototype of one of the blazar classes. According to this model, the jet is inhomogeneous, curved, and twisting, and the long-term variability is due to changes in the Doppler factor due to variations in the orientation of the jet-emitting regions. We analysed optical data of the source obtained during monitoring campaigns organised by the Whole Earth Blazar Telescope (WEBT) in 2019-2022, together with radio data from the WEBT and other teams, and gamma-ray data from the Fermi satellite. In this period, BL Lacertae underwent an extraordinary activity phase, reaching its historical optical and gamma-ray brightness maxima. The application of the twisting jet model to the source light curves allows us to infer the wiggling motion of the optical, radio, and gamma-ray jet-emitting regions. The optical-radio correlation shows that the changes in the radio viewing angle follow those in the optical viewing angle by about 120 days, and it suggests that the jet is composed of plasma filaments, which is in agreement with some radio high-resolution observations of other sources. The gamma-ray emitting region is found to be co-spatial with the optical one, and the analysis of the gamma-optical correlation is consistent with both the geometric interpretation and a synchrotron self-Compton (SSC) origin of the high-energy photons. We propose a geometric scenario where the jet is made up of a pair of emitting plasma filaments in a sort of double-helix curved rotating structure, whose wiggling motion produces changes in the Doppler beaming and can thus explain the observed multi-wavelength long-term variability., Comment: In press for A&A

The Local Ultraviolet to Infrared Treasury (LUVIT) is a Hubble Space Telescope program that combines newly acquired data in the near ultraviolet (NUV), optical, and near infrared (NIR) with archival optical and NIR imaging to produce multiband panchromatic resolved stellar catalogs for 23 pointings in 22 low-mass, star-forming galaxies ranging in distance from the outskirts of the Local Group to ~3.8 Mpc. We describe the survey design, detail the LUVIT broadband filter observations and the archival datasets included in the LUVIT reductions, and summarize the simultaneous multiband data reduction steps. The spatial distributions and color-magnitude diagrams (CMDs) from the resulting stellar catalogs are presented for each target, from the NUV to the NIR. We demonstrate in which regions of the CMDs stars with NUV and optical, optical and NIR, and NUV through NIR detections reside. For each target, we use the results from artificial star tests to measure representative completeness, bias, and total photometric uncertainty as a function of magnitude in each broadband filter. We also assess which LUVIT targets have significant spatial variation in the fraction of stars recovered at a given magnitude. The panchromatic LUVIT stellar catalogs will provide a rich legacy dataset for a host of resolved stellar population studies., Comment: 48 pages, 14 figures, 8 tables, accepted for publication in ApJS

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a $\sim90^\circ$ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets., Comment: 23 pages, including 8 pages of appendices. 12 figures, 3 tables. Submitted to ApJ

In this contribution, we report on recent progress in coupled-cluster simulations of open-shell atomic nuclei using interactions consistently derived from chiral effective field theory. In particular, we compare different coupled-cluster approaches by computing binding energies and electric dipole polarizabilities in medium-mass calcium isotopes., Comment: Proceeding of "Nuclear Structure and Dynamics - NSD 2024". Summitted to EPJ Web of Conferences

The use of machine learning (ML) methods for development of robust and flexible visual inspection system has shown promising. However their performance is highly dependent on the amount and diversity of training data. This is often restricted not only due to costs but also due to a wide variety of defects and product surfaces which occur with varying frequency. As such, one can not guarantee that the acquired dataset contains enough defect and product surface occurrences which are needed to develop a robust model. Using parametric synthetic dataset generation, it is possible to avoid these issues. In this work, we introduce a complete pipeline which describes in detail how to approach image synthesis for surface inspection - from first acquisition, to texture and defect modeling, data generation, comparison to real data and finally use of the synthetic data to train a defect segmentation model. The pipeline is in detail evaluated for milled and sandblasted aluminum surfaces. In addition to providing an in-depth view into each step, discussion of chosen methods, and presentation of ML results, we provide a comprehensive dual dataset containing both real and synthetic images., Comment: Initial preprint, 21 pages, 21 figures, 6 tables

Soft biological tissues exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis, which is restored even after external mechanical stimuli. This macroscopic behavior can be described using the theory of kinematic growth, where the deformation gradient is multiplicatively decomposed into an elastic part and a part related to growth and remodeling. Recently, the concept of homeostatic surfaces was introduced to define the state of homeostasis and the evolution equations for inelastic deformations. However, identifying the optimal model and material parameters to accurately capture the macroscopic behavior of inelastic materials can only be accomplished with significant expertise, is often time-consuming, and prone to error, regardless of the specific inelastic phenomenon. To address this challenge, built-in physics machine learning algorithms offer significant potential. In this work, we extend our inelastic Constitutive Artificial Neural Networks (iCANNs) by incorporating kinematic growth and homeostatic surfaces to discover the scalar model equations, namely the Helmholtz free energy and the pseudo potential. The latter describes the state of homeostasis in a smeared sense. We evaluate the ability of the proposed network to learn from experimentally obtained tissue equivalent data at the material point level, assess its predictive accuracy beyond the training regime, and discuss its current limitations when applied at the structural level. Our source code, data, examples, and an implementation of the corresponding material subroutine are made accessible to the public at https://doi.org/10.5281/zenodo.13946282., Comment: 46 pages, 12 figures, 5 tables

A recent study on the spectral energy distribution (SED) of AGN combined unobscured X-ray sources from the eROSITA eFEDS Survey with high quality optical imaging from Subaru's Hyper Suprime-Cam (HSC). The HSC data enabled accurate host galaxy subtraction as well as giving a uniform black hole mass estimator from the stellar mass. The resulting stacked optical/X-ray SEDs for black holes at fixed mass show a dramatic transition, where the dominating disc component in bright AGN evaporates into an X-ray hot plasma below $L/L_{\rm Edd}\sim 0.01$. The models fit to these datasets predicted the largest change in SED in the rest frame UV ($< 3000\,\r{A}$), but this waveband was not included in the original study. Here we use archival $u$-band and UV photometry to extend the SEDs into this range, and confirm the UV is indeed intrinsically faint in AGN below $L/L_{\rm Edd}\sim 0.01$ as predicted. This dramatic drop in UV photo-ionising flux is also seen from its effect on the broad emission lines. We stack the recently released SDSS DR18 optical spectra for this sample, and show that the broad H$\beta$ line disappears along with the UV bright component at $L/L_{\rm Edd}\sim 0.01$. This shows that there is a population of unobscured, X-ray bright, UV faint AGN which lack broad emission lines (true type 2 Seyferts)., Comment: 10 pages, 3 figure, 2 appendices. Submitted to MNRAS. Comments are very welcome!

We present calculations of various electroweak response functions for the 16O nucleus obtained using coupled-cluster theory in conjunction with the Lorentz integral transform method. We employ nuclear forces derived at next-to-leading order and next-to-next-to-leading order in chiral effective field theory and perform a Bayesian analysis to assess uncertainties. Our results are in good agreement with available electron-scattering data at q~ 326 MeV/c. Additionally, we provide several predictions for the weak response functions in the quasi-elastic peak region at q= 300 and 400 MeV/c, which are critical for long-baseline neutrino experiments.

Assuming the Generalized Riemann Hypothesis and the Generalized Ramanujan Conjecture, we determine the order of the $2(k_1,\dots,k_r)$th moment of a product of distinct irreducible $L$-functions on the critical line. As a consequence, we obtain conditional information about the independence of these $L$-functions in the large deviations regime. We also obtain sharp moment bounds for Hurwitz zeta functions with rational parameter, and a certain family of Dedekind zeta functions., Comment: 33 pages

Predicting protein-ligand binding affinity is an essential part of computer-aided drug design. However, generalisable and performant global binding affinity models remain elusive, particularly in low data regimes. Despite the evolution of model architectures, current benchmarks are not well-suited to probe the generalisability of 3D binding affinity models. Furthermore, 3D global architectures such as GNNs have not lived up to performance expectations. To investigate these issues, we introduce a novel split of the PDBBind dataset, minimizing similarity leakage between train and test sets and allowing for a fair and direct comparison between various model architectures. On this low similarity split, we demonstrate that, in general, 3D global models are superior to protein-specific local models in low data regimes. We also demonstrate that the performance of GNNs benefits from three novel contributions: supervised pre-training via quantum mechanical data, unsupervised pre-training via small molecule diffusion, and explicitly modeling hydrogen atoms in the input graph. We believe that this work introduces promising new approaches to unlock the potential of GNN architectures for binding affinity modelling., Comment: 17 pages, 10 figues

Semiconductor quantum dots are a versatile source of single photons with tunable properties to be used in quantum-cryptographic applications. A crucial figure of merit of the emitted photons is photon number coherence (PNC), which impacts the security of many quantum communication protocols. In the process of single-photon generation, the quantum dot as a solid-state object is subject to an interaction with phonons, which can therefore indirectly affect the PNC. In this paper, we elaborate on the origin of PNC in optically excited quantum dots and how it is affected by phonons. In contrast to the expectation that phonons always deteriorate coherence, PNC can be increased in a quantum dot-cavity system due to the electron-phonon interaction.

The island of inversion is a region of neutron-rich nuclei that are deformed in their ground states. In this region, less is known about the energy levels of odd-mass nuclei, how they evolve with increasing neutron numbers, and how they can be organized into rotational bands. We perform {\it ab initio} coupled-cluster calculations of spectra in odd-mass Ne, Na, and Mg nuclei based on an interaction of chiral effective field theory. Our results confirm some tentative spin and parity assignments, predict the structure of nuclei near the neutron dripline, and inform us about rotational bands in this region of the nuclear table., Comment: 12 pages, 9 figures

PG 1448+273 is a luminous, nearby ($z=0.0645$), narrow line Seyfert 1 galaxy, which likely accretes close to the Eddington limit. Previous X-ray observations of PG 1448 with XMM-Newton in 2017 and NuSTAR in 2022 revealed the presence of an ultra fast outflow, as seen through its blueshifted iron K absorption profile, where the outflow velocity appeared to vary in the range $0.1-0.3c$. In this work, new X-ray observations of PG 1448 are presented, in the form of four simultaneous XMM-Newton and NuSTAR observations performed in July and August 2023. The X-ray spectra appeared at a similar flux in each observation, making it possible to analyze the mean 2023 X-ray spectrum at high signal to noise. A broad ($\sigma=1$ keV) and highly blue-shifted ($E=9.8\pm0.4$ keV) iron K absorption profile is revealed in the mean spectrum. The profile can be modeled by a fast, geometrically thick accretion disk wind, which reveals a maximum terminal velocity of $v_{\infty}=-0.43\pm0.03c$, one of the fastest known winds in a nearby AGN. As a result, the inferred mass outflow rate of the wind may reach a significant fraction of the Eddington accretion rate., Comment: 15 pages, accepted for publication in the Astrophysical Journal

Speech Emotion Recognition (SER) needs high computational resources to overcome the challenge of substantial annotator disagreement. Today SER is shifting towards dimensional annotations of arousal, dominance, and valence (A/D/V). Universal metrics as the L2 distance prove unsuitable for evaluating A/D/V accuracy due to non converging consensus of annotator opinions. However, Concordance Correlation Coefficient (CCC) arose as an alternative metric for A/D/V where a model's output is evaluated to match a whole dataset's CCC rather than L2 distances of individual audios. Recent studies have shown that wav2vec2 / wavLM architectures outputing a float value for each A/D/V dimension achieve today's State-of-the-art (Sota) CCC on A/D/V. The Wav2Vec2.0 / WavLM family has a high computational footprint, but training small models using human annotations has been unsuccessful. In this paper we use a large Transformer Sota A/D/V model as Teacher/Annotator to train 5 student models: 4 MobileNets and our proposed Wav2Small, using only the Teacher's A/D/V outputs instead of human annotations. The Teacher model we propose also sets a new Sota on the MSP Podcast dataset of valence CCC=0.676. We choose MobileNetV4 / MobileNet-V3 as students, as MobileNet has been designed for fast execution times. We also propose Wav2Small - an architecture designed for minimal parameters and RAM consumption. Wav2Small with an .onnx (quantised) of only 120KB is a potential solution for A/D/V on hardware with low resources, having only 72K parameters vs 3.12M parameters for MobileNet-V4-Small., Comment: apply review

We present coupled-cluster computations of nuclei with neutron number $N=50$ "south" of $^{78}$Ni using nucleon-nucleon and three-nucleon forces from chiral effective field theory. We find an erosion of the magic number $N=50$ toward $^{70}$Ca manifesting itself by an onset of deformation and increased complexity in the ground states. For $^{78}$Ni, we predict a low-lying rotational band consistent with recent data, which up until now has been a challenge for ab initio nuclear models. Ground states are deformed in $^{76}$Fe, $^{74}$Cr, and $^{72}$Ti, although the spherical states are too close in energy to unambiguously identify the shape of the ground state within the uncertainty estimates. In $^{70}$Ca, the potential energy landscape from quadrupole-constrained Hartree-Fock computations flattens, and the deformation becomes less rigid. We also compute the low-lying spectra and $B({\rm E2})$ values for these neutron-rich $N=50$ nuclei., Comment: 10 pages, 6 figures, supplementary material

This paper establishes a universal framework for the nonlocal modeling of anisotropic damage at finite strains. By the combination of two recent works, the new framework allows for the flexible incorporation of different established hyperelastic finite strain material formulations into anisotropic damage whilst ensuring mesh-independent results by employing a generic set of micromorphic gradient-extensions. First, the anisotropic damage model, generally satisfying the damage growth criterion, is investigated for the specific choice of a Neo-Hookean material on a single element. Next, the model is applied with different gradient-extensions in structural simulations of an asymmetrically notched specimen to identify an efficient choice in the form of a volumetric-deviatoric regularization. Thereafter, the universal framework, which is without loss of generality here specified for a Neo-Hookean material with a volumetric-deviatoric gradient-extension, successfully serves for the complex simulation of a pressure loaded rotor blade. After acceptance of the manuscript, we make the codes of the material subroutines accessible to the public at https://doi.org/10.5281/zenodo.11171630.

The Angular-Momentum Linear Inverted Pendulum (ALIP) model is a promising motion planner for bipedal robots. However, it relies on two assumptions: (1) the robot has point-contact feet or passive ankles, and (2) the angular momentum around the center of mass, known as centroidal angular momentum, is negligible. This paper addresses the question of whether the ALIP paradigm can be applied to more general bipedal systems with complex foot geometry (e.g., flat feet) and nontrivial torso/limb inertia and mass distribution (e.g., non-centralized arms). In such systems, the dynamics introduce non-negligible centroidal momentum and contact wrenches at the feet, rendering the assumptions of the ALIP model invalid. This paper presents the ALIP planner for general bipedal robots with non-point-contact feet through the use of a task-space whole-body controller that regulates centroidal momentum, thereby ensuring that the robot's behavior aligns with the desired template dynamics. To demonstrate the effectiveness of our proposed approach, we conduct simulations using the Sarcos Guardian XO robot, which is a hybrid humanoid/exoskeleton with large, offset feet. The results demonstrate the practicality and effectiveness of our approach in achieving stable and versatile bipedal locomotion., Comment: 7 pages, 9 figures

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care., Comment: 5 Figures, 3 Tables, 1 Supplementary Table

Geometric phase has historically been defined using closed cycles of polarization states, often derived using differential geometry on the Poincare sphere. Using the recently-developed wave model of geometric phase, we show that it is better to define geometric phase more generally, allowing every polarized wave to have a well-defined value at any point in its path. Using several example systems, we show how this approach provides more insight into the wave's behavior. Moreover, by tracking the continuous evolution of geometric phase as a wave propagates through an optical system, we encounter a natural explanation of why the conventional Poincare sphere solid angle method uses geodesic paths rather than physical paths of the polarization state.

The class of Constitutive Artificial Neural Networks (CANNs) represents a new approach of neural networks in the field of constitutive modeling. So far, CANNs have proven to be a powerful tool in predicting elastic and inelastic material behavior. However, the specification of inelastic constitutive artificial neural networks (iCANNs) to capture plasticity remains to be discussed. We present the extension and application of an iCANN to the inelastic phenomena of plasticity. This includes the prediction of a formulation for the elastic and plastic Helmholtz free energies, the inelastic flow rule, and the yield condition that defines the onset of plasticity. Thus, we learn four feed-forward networks in combination with a recurrent neural network and use the second Piola-Kirchhoff stress measure for training. The presented formulation captures both, associative and non-associative plasticity. In addition, the formulation includes kinematic hardening effects by introducing the plastic Helmholtz free energy. This opens the range of application to a wider class of materials. The capabilities of the presented framework are demonstrated by training on artificially generated data of models for perfect plasticity of von-Mises type, tension-compression asymmetry, and kinematic hardening. We observe already satisfactory results for training on one load case only while extremely precise agreement is found for an increase in load cases. In addition, the performance of the specified iCANN was validated using experimental data of X10CrMoVNb9-1 steel. Training has been performed on both, uniaxial tension and cyclic loading, separately and the predicted results are then validated on the opposing set. The results underline that the autonomously discovered material model is capable to describe and predict the underlying experimental data., Comment: 44 pages, 12 figures, 7 tables

Stochastic programming is widely used for energy system design optimization under uncertainty but can exponentially increase the computational complexity with the number of scenarios. Common scenario reduction techniques, like moments-matching or distribution-driven clustering, pre-select representative scenarios based on input parameters. In contrast, decision-based clustering groups scenarios by similarity in resulting model decisions. Decision-based clustering has shown potential in network design and fleet planning. However, its potential in energy system design remains unexplored. In our work, we examine the effectiveness of decision-based clustering in energy system design using a four-step method: 1) Determine the optimal design for each scenario; 2) Aggregate and normalize installed capacities as features reflecting optimal decisions; 3) Use these features for k-medoids clustering to identify representative scenarios; 4) Utilize these scenarios to optimize cost in stochastic programming. We apply our method to a real-world industrial energy system modeled as a mixed-integer linear program. We incorporate uncertainty by scaling time series with representative factors. We generate 500 single-year scenarios via Monte Carlo sampling, which we reduce using decision-based clustering. For benchmarking, we conduct distribution-driven k-medoids clustering based on the representative factors. In our case studies, both clustering methods yield designs with similar cost efficiency, although decision-based clustering requires substantially more computational resources. To our knowledge, this is the first application of decision-based clustering on energy system design optimization. Future research should investigate the conditions under which decision-based clustering yields more cost-efficient designs compared to distribution-driven clustering., Comment: 6 pages, 3 figures, submitted to Annual International Conference of the German Operations Research Society (GOR) 2024

The zero-shot effectiveness of neural retrieval models is often evaluated on the BEIR benchmark -- a combination of different IR evaluation datasets. Interestingly, previous studies found that particularly on the BEIR subset Touch\'e 2020, an argument retrieval task, neural retrieval models are considerably less effective than BM25. Still, so far, no further investigation has been conducted on what makes argument retrieval so "special". To more deeply analyze the respective potential limits of neural retrieval models, we run a reproducibility study on the Touch\'e 2020 data. In our study, we focus on two experiments: (i) a black-box evaluation (i.e., no model retraining), incorporating a theoretical exploration using retrieval axioms, and (ii) a data denoising evaluation involving post-hoc relevance judgments. Our black-box evaluation reveals an inherent bias of neural models towards retrieving short passages from the Touch\'e 2020 data, and we also find that quite a few of the neural models' results are unjudged in the Touch\'e 2020 data. As many of the short Touch\'e passages are not argumentative and thus non-relevant per se, and as the missing judgments complicate fair comparison, we denoise the Touch\'e 2020 data by excluding very short passages (less than 20 words) and by augmenting the unjudged data with post-hoc judgments following the Touch\'e guidelines. On the denoised data, the effectiveness of the neural models improves by up to 0.52 in nDCG@10, but BM25 is still more effective. Our code and the augmented Touch\'e 2020 dataset are available at \url{https://github.com/castorini/touche-error-analysis}., Comment: SIGIR 2024 (Resource & Reproducibility Track)

[Abridged] ESO 141-G55 is a nearby X-ray bright BLS1, which has been classified as a bare AGN due to the lack of warm absorption along its line-of-sight, providing an unhampered view into its disc-corona system. We aim to probe its disc-corona system thanks to the first simultaneous XMM-Newton and NuSTAR observation obtained on October 1-2, 2022. We carry out the X-ray broadband spectral analysis to determine the dominant process(es) at work, as well as the SED analysis to determine the disc-corona properties. The simultaneous broadband X-ray spectrum of ESO 141-G55 is characterised by the presence of a prominent smooth soft X-ray excess, a broad Fe K emission line and a significant Compton hump. The RGS spectra confirmed the lack of intrinsic warm-absorbing gas along our line of sight in the AGN rest frame, confirming that it is still in a bare state. However, soft X-ray emission lines are observed indicating substantial warm gas out of our line of sight. The intermediate inclination of the disc-corona system, ~43{\deg}, may offer us a favourable configuration to observe UFOs from the disc, but none is found in this 2022 observation, contrary to a previous 2007 XMM-Newton one. Relativistic reflection alone on a standard disc is ruled out from the X-ray broadband analysis, while a combination of soft and hard Comptonisation by a warm and hot corona (relagn), plus relativistic reflection (reflkerrd) reproduces its SED quite well. The hot corona temperature is very hot, ~140 keV, much higher than about 80% of the AGNs, whereas the warm corona temperature, ~0.3 keV, is similar to the values found in other sub-Eddington AGNs. ESO 141-G55 is accreting at a moderate Eddington accretion rate (~10--20%). Our analysis points to a significant contribution of an optically-thick warm corona to both the soft X-ray and UV emission in ESO 141-G55., Comment: 16 pages, 14 figures, 3 appendices; accepted for publication in Astronomy & Astrophysics

Auditing the machine learning (ML) models used on Wikipedia is important for ensuring that vandalism-detection processes remain fair and effective. However, conducting audits is challenging because stakeholders have diverse priorities and assembling evidence for a model's [in]efficacy is technically complex. We designed an interface to enable editors to learn about and audit the performance of the ORES edit quality model. ORES-Inspect is an open-source web tool and a provocative technology probe for researching how editors think about auditing the many ML models used on Wikipedia. We describe the design of ORES-Inspect and our plans for further research with this system., Comment: Wiki Workshop 2024

The structure of the accretion flow onto supermassive black holes (SMBH) is not well understood. Standard disc models match to zeroth order in predicting substantial energy dissipation within optically-thick material producing a characteristic strong blue/UV continuum. However they fail at reproducing more detailed comparisons to the observed spectral shapes along with their observed variability. Based on stellar mass black holes within our galaxy, accretion discs should undergo a transition into an X-ray hot, radiatively inefficient flow, below a (mass scaled) luminosity of $\sim 0.02\,L_{\rm{Edd}}$. While this has been seen in limited samples of nearby low-luminosity active galactic nuclei (AGN) and a few rare changing-look AGN, it is not at all clear whether this transition is present in the wider AGN population across cosmic time. A key issue is the difficulty in disentangling a change in spectral state from increased dust obscuration and/or host galaxy contamination, effectively drowning out the AGN emission. Here we use the new eROSITA eFEDS Survey to identify unobscured AGN from their X-ray emission, matched to excellent optical imaging from Subaru's Hyper Suprime-Cam; allowing the subtraction of the host galaxy contamination. The resulting, uncontaminated, AGN spectra reveal a smooth transition from a strongly disc dominated state in bright AGN, to the collapse of the disc into an inefficient X-ray plasma in the low luminosity AGN, with the transition occurring at $\sim 0.02\,L_{\rm{Edd}}$; revealing fundamental aspects of accretion physics in AGN., Comment: 11 pages, 5 figure, 3 appendices - Accepted for publication in MNRAS

Reinforcement learning (RL) is an effective method of finding reasoning pathways in incomplete knowledge graphs (KGs). To overcome the challenges of a large action space, a self-supervised pre-training method is proposed to warm up the policy network before the RL training stage. To alleviate the distributional mismatch issue in general self-supervised RL (SSRL), in our supervised learning (SL) stage, the agent selects actions based on the policy network and learns from generated labels; this self-generation of labels is the intuition behind the name self-supervised. With this training framework, the information density of our SL objective is increased and the agent is prevented from getting stuck with the early rewarded paths. Our self-supervised RL (SSRL) method improves the performance of RL by pairing it with the wide coverage achieved by SL during pretraining, since the breadth of the SL objective makes it infeasible to train an agent with that alone. We show that our SSRL model meets or exceeds current state-of-the-art results on all Hits@k and mean reciprocal rank (MRR) metrics on four large benchmark KG datasets. This SSRL method can be used as a plug-in for any RL architecture for a KGR task. We adopt two RL architectures, i.e., MINERVA and MultiHopKG as our baseline RL models and experimentally show that our SSRL model consistently outperforms both baselines on all of these four KG reasoning tasks. Full code for the paper available at https://github.com/owenonline/Knowledge-Graph-Reasoning-with-Self-supervised-Reinforcement-Learning., Comment: 17 pages, 11 figures

Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, the distilled models usually do not reach their teacher LLM's effectiveness. To investigate whether best practices for fine-tuning cross-encoders on manually labeled data (e.g., hard-negative sampling, deep sampling, and listwise loss functions) can help to improve LLM ranker distillation, we construct and release a new distillation dataset: Rank-DistiLLM. In our experiments, cross-encoders trained on Rank-DistiLLM reach the effectiveness of LLMs while being orders of magnitude more efficient. Our code and data is available at https://github.com/webis-de/msmarco-llm-distillation.

We develop a new method to describe electromagnetic observables of open-shell nuclei with two nucleons outside a closed shell. This approach combines the equation-of-motion coupled-cluster method for such systems and the Lorentz integral transform technique, expanding the applicability of coupled-cluster theory for these properties beyond closed-shell nuclei. To validate this new approach, we compute the non-energy-weighted dipole sum rule and the dipole polarizability of $^{16,24}$O in both the closed-shell and the new equation-of-motion coupled-cluster frameworks, finding agreement within error bars. We then analyze the evolution of the dipole polarizability along the oxygen and calcium isotopic chains. Our predictions agree well with available experimental data and other available theoretical calculations for the closed-shell $^{16,22}$O and the open-shell $^{18}$O. In the calcium isotopes, we observe that our dipole polarizability predictions for open-shell nuclei are lower than those of closed-shell nuclei. Our predictions for $^{24}$O and $^{54,56}$Ca will motivate future experimental studies at the dripline., Comment: 18 pages, 7 figures, matches published version on Physical Review C

Nuclei around $N\approx Z\approx 40$ are strongly deformed and exhibit coexistence of shapes. These phenomena have challenged nuclear models. Here we perform ab initio coupled-cluster computations of low-lying collective states and electromagnetic quadrupole transitions of the even-even nuclei $^{72}$Kr, $^{76,78}$Sr, $^{78,80}$Zr and $^{84}$Mo starting from chiral nucleon-nucleon and three-nucleon forces. Our calculations reproduce the coexistence of oblate and prolate shapes in these nuclei, yield rotational bands and strong electromagnetic transitions, but are not accurate for some observables and nuclei. These results highlight the advances and challenges of ab initio computations of heavy deformed nuclei., Comment: 10 pages

Image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have gained attention for their ability to directly estimate 3D shapes. This review paper focuses on state-of-the-art techniques for 3D reconstruction, including the generation of novel, unseen views. An overview of recent developments in the Gaussian Splatting method is provided, covering input types, model structures, output representations, and training strategies. Unresolved challenges and future directions are also discussed. Given the rapid progress in this domain and the numerous opportunities for enhancing 3D reconstruction methods, a comprehensive examination of algorithms appears essential. Consequently, this study offers a thorough overview of the latest advancements in Gaussian Splatting., Comment: 24 pages

Electronic Health Records (EHRs) play an important role in the healthcare system. However, their complexity and vast volume pose significant challenges to data interpretation and analysis. Recent advancements in Artificial Intelligence (AI), particularly the development of Large Language Models (LLMs), open up new opportunities for researchers in this domain. Although prior studies have demonstrated their potential in language understanding and processing in the context of EHRs, a comprehensive scoping review is lacking. This study aims to bridge this research gap by conducting a scoping review based on 329 related papers collected from OpenAlex. We first performed a bibliometric analysis to examine paper trends, model applications, and collaboration networks. Next, we manually reviewed and categorized each paper into one of the seven identified topics: named entity recognition, information extraction, text similarity, text summarization, text classification, dialogue system, and diagnosis and prediction. For each topic, we discussed the unique capabilities of LLMs, such as their ability to understand context, capture semantic relations, and generate human-like text. Finally, we highlighted several implications for researchers from the perspectives of data resources, prompt engineering, fine-tuning, performance measures, and ethical concerns. In conclusion, this study provides valuable insights into the potential of LLMs to transform EHR research and discusses their applications and ethical considerations.