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The structures and properties of organic-inorganic perovskites are influenced by the hydrogen bonding between the organic cations and the inorganic octahedral networks. This study explores the dynamics of hydrogen bonds in CH$_3$NH$_3$PbBr$_3$ across a temperature range from 70 K to 350 K, using molecular dynamics simulations with machine-learning force fields. The results indicate that the lifetime of hydrogen bonds decreases with increasing temperature from 7.6 ps (70 K) to 0.16 ps (350 K), exhibiting Arrhenius-type behaviour. The geometric conditions for hydrogen bonding, which include bond lengths and angles, maintain consistency across the full temperature range. The relevance of hydrogen bonds for the vibrational states of the material is also evidenced through a detailed analysis of the vibrational power spectra, demonstrating their significant effect on the physical properties for this class of perovskites., Comment: 26 pages, 14 figures

We show how to obtain the Zariski invariant of a plane branch employing the contact order or the intersection multiplicity with elements in a particular family of curves and we present some consequences of this result.

Purpose: Apparent diffusion coefficient (ADC) maps derived from diffusion weighted (DWI) MRI provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images. Methods: We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of ViT layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2- fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N=400), validation (N=50) and test (N=51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models. Results: The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT - PSNR: 31.0 +/- 2.1, MSE: 0.009 +/- 0.0005, SSIM: 0.950 +/- 0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR- ViT model performs favorably against the ground truth data. Conclusion: We show that high-quality ADC maps can be synthesized from structural MRI using a MPR- VCT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable., Comment: arXiv admin note: text overlap with arXiv:2311.15044

The statistical analysis of cosmic large-scale structure is most often based on simple two-point summary statistics, like the power spectrum or the two-point correlation function of a sample of galaxies or other types of tracers. In contrast, topological measures of clustering are also sensitive to higher-order correlations, and thus offer the prospect to access additional information that may harbor important constraining power. We here revisit one such geometric measure of the cosmic web in the form of the so-called percolation analysis, using the recent MillenniumTNG simulation suite of the LCDM paradigm. We analyze continuum percolation statistics both for high resolution dark matter particle distributions, as well as for galaxy mock catalogues from a semi-analytic galaxy formation model within a periodic simulation volume of 3000 Mpc on a side. For comparison, we also investigate the percolation statistics of random particle sets and neutrino distributions with two different summed particle masses. We find that the percolation statistics of the dark matter distribution evolves strongly with redshift and thus clustering strength, yielding progressively lower percolation threshold towards later times. However, there is a sizable residual dependence on numerical resolution which we interpret as a residual influence of different levels of shot noise. This is corroborated by our analysis of galaxy mock catalogues whose results depend on sampling density more strongly than on galaxy selection criteria. While this limits the discriminative power of percolation statistics, our results suggest that it still remains useful as a complementary cosmological test when controlled for sampling density., Comment: 14 pages, 12 figures, accepted by ApJ, updated to match published version

We analyze the confident binary black hole (BBH) detections from the third Gravitational-Wave Transient Catalog (GWTC-3) with an alternative mass population model in order to capture features in the mass distribution beyond the Powerlaw + Peak model. We find that the peak of a second power law characterizes the $\sim 30-35~ M_\odot$ bump, such that the data marginally prefers a mixture of two power laws for the mass distribution of binary components over a Powerlaw + Peak model with a Bayes Factor $\log_{10}\mathcal{B}$ of 0.1. This result may imply that the $\sim 30-35~ M_\odot$ feature represents the onset of a second population of BBH mergers (e.g. from a dynamical formation channel) rather than a specific mass feature over a broader distribution. When an additional Gaussian bump is allowed within our power law mixture model, we find a new feature in the BH mass spectrum at $\sim65-70~M_\odot$. This new feature may be consistent with hierarchical mergers, and constitute $\sim2\%$ of the BBH population. This model also recovers a maximum mass of $58^{+30}_{-14}~M_\odot$ for the second power law, consistent with the onset of a pair-instability supernova mass gap., Comment: Comments are welcomed

Nonlinear optical materials of atomic thickness--such as non-centrosymmetric 2H transition metal dichalcogenide monolayers--have a second order nonlinear susceptibility ($\chi^{(2)}$) whose intensity can be tuned by strain. However, whether $\chi^{(2)}$ is enhanced or reduced by tensile strain is a subject of conflicting reports. Here, we grow high-quality MoSe$_2$ monolayers under controlled biaxial strain created by two different substrates, and study their linear and non-linear optical responses with a combination of experimental and theoretical approaches. A 15-fold overall enhancement in second harmonic generation (SHG) intensity is observed on MoSe$_2$ monolayers grown on SiO$_2$ when compared to its value when on a Si$_3$N$_4$ substrate. A seven-fold enhancement was ascertained to substrate interference, and a factor of two to the enhancement of $\chi^{(2)}$ arising from biaxial strain: substrate interference and strain are independent handles to engineer the SHG strength of non-centrosymmetric 2D materials.

This paper introduces a collaborative, human-centered taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, typically cater to practitioners and government, and often overlook the needs of the wider public. Drawing on existing taxonomies and a large repository of documented incidents, we propose a taxonomy that is clear and understandable to a broad set of audiences, as well as being flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, we propose a taxonomy that can serve as a powerful tool for civil society organisations, educators, policymakers, product teams and the general public. By fostering a greater understanding of the real-world harms of AI and related technologies, we aim to increase understanding, empower NGOs and individuals to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment.

Class\,I methanol masers provide sensitive information about the shocked environment around star-forming regions. Among the brightest Class~I methanol masers, we have those in the $J_{-1}\rightarrow(J-$ 1)$_{0}-E$ line series, currently reported for the $J=4-9$ transitions, with the only exception being the $J=7$ one at 181.295~GHz, and never expanded to higher $J$ transitions. We aim to search for population inversion in the $7_{-1}\rightarrow6_{0}-E$ and $10_{-1}\rightarrow9_{0}-E$ methanol transition lines at 181.295 and 326.961\,GHz, respectively, and also extend the number of known low-mass star-forming sources harboring Class\,I methanol masers. We employed the Atacama Pathfinder Experiment (APEX) 12\,m telescope to survey low-mass Galactic sources, focusing on methanol emission lines. We conducted rotation diagrams for all sources with detected $J=7$ methanol line transitions, while employing radiative transfer modeling (both in and out of local thermodynamic equilibrium) to characterize methanol excitation conditions in detail for one specific source with detected masers. We have detected the $7_{-1}\rightarrow6_{0}-E$ and $10_{-1}\rightarrow9_{0}-E$ methanol transitions in six out of nineteen sources. Among them, we firmly determined the $10_{-1}\rightarrow9_{0}-E$ maser nature in CARMA\,7, L1641N, NGC\,2024, and Serpens FIRS, and we claim for the presence of inverted population emission in the $7_{-1}\rightarrow6_{0}-E$ line toward CARMA\,7 and L1641N. This represents the first report of methanol maser emission in these particular transitions. Our study supports previous works indicating that conditions for Class\,I methanol maser emission are satisfied in low-mass star-forming regions and expands the range of detectable frequencies toward higher values.

High-significance evidences of the existence of a high-energy diffuse flux of cosmic neutrinos have emerged in the last decade from several observations by the IceCube Collaboration. The ANTARES neutrino telescope took data for 15 years in the Mediterranean Sea, from 2007 to 2022, and collected a high-purity all-flavour neutrino sample. The search for a diffuse cosmic neutrino signal using this dataset is presented in this article. This final analysis did not provide a statistically significant observation of the cosmic diffuse flux. However, this is converted into limits on the properties of the cosmic neutrino spectrum. In particular, given the sensitivity of the ANTARES neutrino telescope between 1 and 50 TeV, constraints on single-power-law hypotheses are derived for the cosmic diffuse flux below 20 TeV, especially for power-law fits of the IceCube data with spectral index softer than 2.8.

Conducting Polymer Dendrites (CPD) are truly inspiring for unconventional electronics that shapes topological circuitries evolving upon an application. Driven by electrochemical processes, an electrochemical impedance rules signal propagation from one node to another. However, clear models dictating their behavior in an electroactive electrolyte have not been identified yet. In this study, we investigate on CPD in an aqueous electrolyte by impedance spectroscopy to unify their signal transport with an electrical model, aiming to define a circuit simulation block to integrate these objects in systems for in materio information processing.

Detecting and classifying lesions in breast ultrasound images is a promising application of artificial intelligence (AI) for reducing the burden of cancer in regions with limited access to mammography. Such AI systems are more likely to be useful in a clinical setting if their predictions can be explained to a radiologist. This work proposes an explainable AI model that provides interpretable predictions using a standard lexicon from the American College of Radiology's Breast Imaging and Reporting Data System (BI-RADS). The model is a deep neural network featuring a concept bottleneck layer in which known BI-RADS features are predicted before making a final cancer classification. This enables radiologists to easily review the predictions of the AI system and potentially fix errors in real time by modifying the concept predictions. In experiments, a model is developed on 8,854 images from 994 women with expert annotations and histological cancer labels. The model outperforms state-of-the-art lesion detection frameworks with 48.9 average precision on the held-out testing set, and for cancer classification, concept intervention is shown to increase performance from 0.876 to 0.885 area under the receiver operating characteristic curve. Training and evaluation code is available at https://github.com/hawaii-ai/bus-cbm., Comment: Submitted version of manuscript accepted at MICCAI 2024. This preprint has not undergone peer review or any post-submission improvements or corrections

The symmetry approach to the classification of evolution integrable partial differential equations (see, for example \cite{MikShaSok91}) produces an infinite series of functions, defined in terms of the right hand side, that are conserved densities of any equation having infinitely many infinitesimal symmetries. For instance, the function $\frac{\partial f}{\partial u_{x}}$ has to be a conserved density of any integrable equation of the KdV type $u_t=u_{xxx}+f(u,u_x)$. This fact imposes very strong conditions on the form of the function $f$. In this paper we construct similar canonical densities for equations of the Boussinesq type. In order to do that, we write the equations as evolution systems and generalise the formal diagonalisation procedure proposed in \cite{MSY} to these systems., Comment: typos corrected, improved concluding remarks

In this paper, we study linear backward parabolic SPDEs and present new a priori estimates for their weak solutions. Inspired by the seminal work of Y. Hu, J. Ma and J. Yong from 2002 on strong solutions, we establish $L^p$-estimates requiring minimal assumptions on the regularity of the coefficients, the terminal data, and the external force. To this end, we derive a new It\^{o}'s formula for the $L^p$-norm of the solution, extending the classical result in the $L^2$-setting. This formula is then used to improve further the regularity of the first component of the solution up to $L^\infty$. Additionally, we present applications such as the controllability of backward SPDEs with $L^p$-controls and a local existence result for a semilinear equation without imposing any growth condition on the nonlinear term.

We present here the preliminary design of the RIZ module, one of the visible spectrographs of the ANDES instrument 1. It is a fiber-fed high-resolution, high-stability spectrograph. Its design follows the guidelines of successful predecessors such as HARPS and ESPRESSO. In this paper we present the status of the spectrograph at the preliminary design stage. The spectrograph will be a warm, vacuum-operated, thermally controlled and fiber-fed echelle spectrograph. Following the phase A design, the huge etendue of the telescope will be reformed in the instrument with a long slit made of smaller fibers. We discuss the system design of the spectrographs system., Comment: Paper submitted to the SPIE astronomical telescope and instrumentation 2024, conference title : Ground-based and Airborne Instrumentation for Astronomy X, paper reference number : 13096-171

We present a comprehensive study of answer quality evaluation in Retrieval-Augmented Generation (RAG) applications using vRAG-Eval, a novel grading system that is designed to assess correctness, completeness, and honesty. We further map the grading of quality aspects aforementioned into a binary score, indicating an accept or reject decision, mirroring the intuitive "thumbs-up" or "thumbs-down" gesture commonly used in chat applications. This approach suits factual business settings where a clear decision opinion is essential. Our assessment applies vRAG-Eval to two Large Language Models (LLMs), evaluating the quality of answers generated by a vanilla RAG application. We compare these evaluations with human expert judgments and find a substantial alignment between GPT-4's assessments and those of human experts, reaching 83% agreement on accept or reject decisions. This study highlights the potential of LLMs as reliable evaluators in closed-domain, closed-ended settings, particularly when human evaluations require significant resources., Comment: 13 pages, 8 figures, 12 tables

(abridged) We analyze the emission of axion quark nuggets in a large sample of 161 simulated galaxy clusters using the SLOW simulation. These clusters are divided into a sub-sample of 150 galaxy clusters, ordered in five mass bins ranging from $0.8$ to $31.7 \times 10^{14} \,M_\odot$, along with 11 cross-identified galaxy clusters from observations. We investigate dark matter-baryonic matter interactions in galaxy clusters in their present stage at redshift $z=0$ by assuming all dark matter consists of axion quark nuggets. The resulting electromagnetic signatures are compared to thermal Bremsstrahlung and non-thermal cosmic ray synchrotron emission in each galaxy cluster. We further investigate individual frequency bands imitating the observable range of the WMAP, Planck, Euclid, and XRISM telescopes for the most promising cross-identified galaxy clusters hosting detectable signatures of axion quark nugget emission. We propose that the Fornax and Virgo clusters represent the most promising candidates to search for axion quark nugget emission signatures., Comment: 22 pages, 14 figures

We present a pioneering spatially-resolved, multi-phase gas abundance study on the blue compact dwarf galaxy NGC~5253, targeting 10 star-forming (SF) clusters inside six FUV HST/COS pointings with co-spatial optical VLT/MUSE observations throughout the galaxy. The SF regions span a wide range of ages (1--15 Myr) and are distributed at different radii (50 -- 230 pc). We performed robust absorption-line profile fitting on the COS spectra, covering 1065--1430 \AA\ in the FUV, allowing an accurate computation of neutral-gas abundances for 13 different ions sampling 8 elements. These values were then compared with the ionized-gas abundances, measured using the direct method on MUSE integrated spectra inside analog COS apertures. Our multi-phase, spatially resolved comparisons find abundances which are lower in the neutral gas than the ionized gas by 0.22 dex, 0.80 dex and 0.58 dex for log(O/H), log(N/H) and log(N/O), respectively. We modeled the chemical abundance distributions and evaluated correlations as a function of radius and age. It was found that while N, O and N/O abundances decrease as a function of age in the ionized gas, they increase with age in the neutral gas. No strong correlations for N, O or N/O were observed as a function of radius. The N/O and N/H offsets between the phases were found to decrease with age, providing evidence that chemical enrichment happens differentially, first in the ionized-gas phase around 2--5 Myrs (due to N-rich Wolf-Rayet stars) and then mixing out into the cold neutral gas on longer timescales of 10--15 Myr., Comment: 40 pages, 14 Figures. Revised version submitted to ApJ

Accurate estimation of queuing delays is crucial for designing and optimizing communication networks, particularly in the context of Deterministic Networking (DetNet) scenarios. This study investigates the approximation of Internet queuing delays using an M/M/1 envelope model, which provides a simple methodology to find tight upper bounds of real delay percentiles. Real traffic statistics collected at large Internet Exchange Points (like Amsterdam and San Francisco) have been used to fit polynomial regression models for transforming packet queuing delays into the M/M/1 envelope models. We finally propose a methodology for providing delay percentiles in DetNet scenarios where tight latency guarantees need to be assured.

We present the first purely theoretical calculation of the weak mixing angle in the MS scheme at low energies by combining results from lattice QCD with perturbation theory. We discuss its correlation with the hadronic contribution to the anomalous magnetic moment of the muon and to the energy dependence of the electromagnetic coupling. We also compare the results with calculations using cross-section data as input. Implications for the Standard Model prediction of the mass of the W boson are also discussed., Comment: 6 pages, 4 tables, 3 figures

We used the Javalambre Photometric Local Universe Survey (J-PLUS) DR2 photometry in twelve optical bands over 2176 deg2 to estimate the fraction of white dwarfs with presence of CaII H+K absorption along the cooling sequence. We compared the J-PLUS photometry against metal-free theoretical models to estimate the equivalent width in the J0395 passband of 10 nm centered at 395 nm (EW_J0395), a proxy to detect calcium absorption. A total of 4399 white dwarfs within 30000 > Teff > 5500 K and mass M > 0.45 Msun were analyzed. Their EW_J0395 distribution was modeled using two populations, corresponding to polluted and non-polluted systems, to estimate the fraction of calcium white dwarfs (f_Ca) as a function of Teff. The probability for each individual white dwarf of presenting calcium absorption, pca, was also computed. The comparison with both the measured Ca/He abundance and the metal pollution from spectroscopy shows that EW_J0395 correlates with the presence of calcium. The fraction of calcium white dwarfs increases from f_Ca = 0 at Teff = 13500 K to f_Ca = 0.15 at Teff = 5500 K. We compare our results with the fractions derived from the 40 pc spectroscopic sample and from SDSS spectra. The trend found in J-PLUS observations is also present in the 40 pc sample, however SDSS shows a deficit of metal-polluted objects at Teff < 12000 K. Finally, we found 39 white dwarfs with pca > 0.99. Twenty of them have spectra presented in previous studies, whereas we observed six additional targets. These 26 objects were all confirmed as metal-polluted systems. The J-PLUS optical data provide a robust statistical measurement for the presence of CaII H+K absorption in white dwarfs. We find a 15 +- 3 % increase in the fraction of calcium white dwarfs from Teff = 13500 K to 5500 K, which reflects their selection function in the optical from the total population of metal-polluted systems., Comment: Submitted to Astronomy and Astrophysics. Comments are welcome

Peter Higgs was a British theoretical physicist, famous for his work published in 1964, where he proposed a mechanism that can generate masses for elementary particles, while respecting gauge invariance. Half a century later, two experiments at CERN confirmed that this mechanism is realized in nature. On April 8th, we received the sad news of the passing of the great pioneer of elementary particle physics. This article is dedicated to his memory, and to the mechanism and particle that bear his name. -- Peter Higgs fue un f\'isico te\'orico brit\'anico, famoso por su trabajo de 1964 donde propuso un mecanismo que puede generar masas para part\'iculas elementales, conforme a la simetr\'ia de norma. Medio siglo m\'as tarde, dos experimentos del CERN confirmaron que este mecanismo est\'a realizado en la naturaleza. El 8 de abril nos lleg\'o la triste noticia del fallecimiento del gran pionero de la fisica de part\'iculas elementales. Este art\'iculo es dedicado a su memoria, as\'i como al mecanismo y a la part\'icula que llevan su nombre., Comment: 15 pages, LaTex, 13 figures, bilingual: English version to be published in Rev. Cub. F\'is., Spanish version to be published in Bol. Soc. Mex. F\'is

Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by their actuation through position commands, face issues of excessive contact forces due to their inability to adapt to contact with the environment, potentially causing damage. While compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors, they are hampered by the need for fine-tuning task-specific controller parameters. Learning from Demonstrations (LfD) offers an intuitive alternative, allowing robots to learn manipulations through observed actions. In this work, we introduce a novel system to enhance the teaching of dexterous, contact-rich manipulations to rigid robots. Our system is twofold: firstly, it incorporates a teleoperation interface utilizing Virtual Reality (VR) controllers, designed to provide an intuitive and cost-effective method for task demonstration with haptic feedback. Secondly, we present Comp-ACT (Compliance Control via Action Chunking with Transformers), a method that leverages the demonstrations to learn variable compliance control from a few demonstrations. Our methods have been validated across various complex contact-rich manipulation tasks using single-arm and bimanual robot setups in simulated and real-world environments, demonstrating the effectiveness of our system in teaching robots dexterous manipulations with enhanced adaptability and safety. Code available at: https://github.com/omron-sinicx/CompACT, Comment: Accepted to IROS 2024

Gravimeters fabricated with MEMS suffer from temperature-dependent drifts in their long-term stability. We analyze the thermal contributions to the signal, and we propose three mechanisms to mitigate their effects. The first one uses materials that fulfill the condition $\alpha_E=-2 \alpha$, where the thermal expansion is canceled by the temperature variation of the Young's modulus. The second one uses the thermal expansion to introduce a compression that compensates the variation in the force of the spring. In the third one, the expansion compensates the displacement of the proof mass in the sensor, rather than the force. The three mechanisms are robust since they only depend on the temperature of the sensor itself.

Compact groups (CGs) of galaxies are extreme environments for morphological transformations and the cessation of star formation. Our objective is to understand the dynamics of CGs and how their surrounding environment impacts galaxy properties. We selected a sample of 340 CGs in the Stripe 82 region, totaling 1083 galaxies, and a control sample of 2281 field galaxies. We find that at least 27\% of our sample of CGs are part of major structures, i.e. non-isolated CGs. We find a bimodality in the effective radius ($R_e$)-S\'ersic index ($n$) plane for all transition galaxies (those with $(u-r) > 2.3$ and $n<2.5$) in CGs. Additionally, transition galaxies in isolated CGs populate more densely the $R_e-n$ plane for $n < 1.75$. In contrast, transition galaxies in non-isolated CGs have smoothly increasing $n$ values, suggesting these galaxies have already suffered morphological transformation, and primarily contribute to the distribution of more compact galaxies in the $R_e-n$ plane for all transition galaxies in CGs. We also find significant differences in the specific star-formation rate (sSFR) distribution between the late-type galaxies (LTGs) ($(u-r)<2.3$ and $n< 2.5$) in non-isolated CGs and the same type of galaxies in the control sample, suggesting that the evolution of LTGs differs in non-isolated CGs. Early-type galaxies ($(u-r)>2.3$ and $n>2.5$) and transition galaxies in non-isolated CGs have lower sSFR values and a higher fraction of quenched galaxies, compared to those in isolated CGs. Based on our results, we propose an evolutionary scenario where the major structures in which the CGs are embedded accelerate the morphological transformations of their members. Our findings highlight the importance of considering the larger structures in which CGs may be located, when analysing the properties of their galaxy, as this can significantly affect the evolution of CGs and their galaxies., Comment: Submitted to A&A, 13 pages, 9 figures

The cellular-Lindel\"of property is a common generalization of the Lindel\"of property and the countable chain condition that was introduced by Bella and Spadaro in 2018. We solve two questions of Alas, Gutierrez-Dominguez and Wilson by constructing consistent examples of a normal almost cellular-Lindel\"of space which is neither cellular-Lindel\"of nor weakly Lindel\"of and a Tychonoff cellular-Lindel\"of space of Lindel\"of degree $\omega_1$ and uncountable weak Lindel\"of degree for closed sets. We also construct a ZFC example of a space for which both the cellular-Lindel\"of property and normality are undetermined in ZFC.

Disjointly strictly singular inclusions between variable Lebesgue spaces $L^{p(\cdot)}(\mu)$ on finite measure are characterized. Suitable criteria in terms of the (bounded or unbounded) exponents are given. It is proved the equivalence of $L$-weak compactness (also called almost compactness) and disjoint strict singularity for variable Lebesgue space inclusions. For infinite measure any inclusion $L^{p(\cdot)}(\mu) \hookrightarrow L^{q(\cdot)}(\mu)$ is not disjointly strictly singular. No restrictions on the exponent are imposed.

Existing ML benchmarks lack the depth and diversity of annotations needed for evaluating models on business process management (BPM) tasks. BPM is the practice of documenting, measuring, improving, and automating enterprise workflows. However, research has focused almost exclusively on one task - full end-to-end automation using agents based on multimodal foundation models (FMs) like GPT-4. This focus on automation ignores the reality of how most BPM tools are applied today - simply documenting the relevant workflow takes 60% of the time of the typical process optimization project. To address this gap we present WONDERBREAD, the first benchmark for evaluating multimodal FMs on BPM tasks beyond automation. Our contributions are: (1) a dataset containing 2928 documented workflow demonstrations; (2) 6 novel BPM tasks sourced from real-world applications ranging from workflow documentation to knowledge transfer to process improvement; and (3) an automated evaluation harness. Our benchmark shows that while state-of-the-art FMs can automatically generate documentation (e.g. recalling 88% of the steps taken in a video demonstration of a workflow), they struggle to re-apply that knowledge towards finer-grained validation of workflow completion (F1 < 0.3). We hope WONDERBREAD encourages the development of more "human-centered" AI tooling for enterprise applications and furthers the exploration of multimodal FMs for the broader universe of BPM tasks. We publish our dataset and experiments here: https://github.com/HazyResearch/wonderbread

To create rich experiences in virtual reality (VR) environments, it is essential to define the behavior of virtual objects through programming. However, programming in 3D spaces requires a wide range of background knowledge and programming skills. Although Large Language Models (LLMs) have provided programming support, they are still primarily aimed at programmers. In metaverse platforms, where many users inhabit VR spaces, most users are unfamiliar with programming, making it difficult for them to modify the behavior of objects in the VR environment easily. Existing LLM-based script generation methods for VR spaces require multiple lengthy iterations to implement the desired behaviors and are difficult to integrate into the operation of metaverse platforms. To address this issue, we propose a tool that generates behaviors for objects in VR spaces from natural language within Cluster, a metaverse platform with a large user base. By integrating LLMs with the Cluster Script provided by this platform, we enable users with limited programming experience to define object behaviors within the platform freely. We have also integrated our tool into a commercial metaverse platform and are conducting online experiments with 63 general users of the platform. The experiments show that even users with no programming background can successfully generate behaviors for objects in VR spaces, resulting in a highly satisfying system. Our research contributes to democratizing VR content creation by enabling non-programmers to design dynamic behaviors for virtual objects in metaverse platforms.

We study the anisotropy of centroid and integrated intensity maps with synthetic observations. We perform post-process radiative transfer including the optically thick regime that was not covered in Hern\'andez-Padilla et al. (2020). We consider the emission in various CO molecular lines, that range from optically thin to optically thick ($\mathrm{^{12}CO}$, $\mathrm{^{13}CO}$, $\mathrm{C^{18}O}$, and $\mathrm{C^{17}O}$). The results for the velocity centroids are similar to those in the optically thin case. For instance, the anisotropy observed can be attributed to the Alfv\'en mode, which dominates over the slow and fast modes when the line of sight is at a high inclination with respect to the mean magnetic field. A few differences arise in the models with higher opacity, where some dependence on the sonic Mach number becomes evident. In contrast to the optically thin case, maps of integrated intensity become more anisotropic in optically thick lines. In this situation the scales probed are restricted, due to absorption, to smaller scales which are known to be more anisotropic. We discuss how the sonic Mach number can affect the latter results, with highly supersonic cases exhibiting a lower degree of anisotropy.

We present a study of the vacuum transition probabilities taking into account quantum corrections. We first introduce a general method that expand previous works employing the Lorentzian formalism of the Wheeler-De Witt equation by considering higher order terms in the semiclassical expansion. The method presented is applicable in principle to any model in the superspace and up to any desired order in the quantum correction terms. Then, we apply this method to obtain analytical solutions for the probabilities up to second quantum corrections for homogeneous isotropic and anisotropic universes. We use the Friedmann-Lemaitre-Robertson-Walker with positive and zero curvature for the isotropic case and the Bianchi III and Kantwowski-Sachs metrics for the anisotropic case. Interpreting the results as distribution probabilities of creating universes by vacuum decay with a given size, we found that the general behaviour is that considering up to the second quantum correction leads to an avoidance of the initial singularity. However, we show that this result can only be achieved for the isotropic universe. Furthermore, we also study the effect of anisotropy on the transition probabilities., Comment: 36 pages, 4 figures

Testing gravity and the concordance model of cosmology, $\Lambda$CDM, at large scales is a key goal of this decade's largest galaxy surveys. Here we present a comparative study of dark matter power spectrum predictions from different numerical codes in the context of three popular theories of gravity that induce scale-independent modifications to the linear growth of structure: nDGP, Cubic Galileon and K-mouflage. In particular, we compare the predictions from full $N$-body simulations, two $N$-body codes with approximate time integration schemes, a parametrised modified $N$-body implementation and the analytic halo model reaction approach. We find the modification to the $\Lambda$CDM spectrum is in $2\%$ agreement for $z\leq1$ and $k\leq 1~h/{\rm Mpc}$ over all gravitational models and codes, in accordance with many previous studies, indicating these modelling approaches are robust enough to be used in forthcoming survey analyses under appropriate scale cuts. We further make public the new code implementations presented, specifically the halo model reaction K-mouflage implementation and the relativistic Cubic Galileon implementation., Comment: 20 pages, 4 figures, 4 tables

Language models (LM) are capable of remarkably complex linguistic tasks; however, numerical reasoning is an area in which they frequently struggle. An important but rarely evaluated form of reasoning is understanding probability distributions. In this paper, we focus on evaluating the probabilistic reasoning capabilities of LMs using idealized and real-world statistical distributions. We perform a systematic evaluation of state-of-the-art LMs on three tasks: estimating percentiles, drawing samples, and calculating probabilities. We evaluate three ways to provide context to LMs 1) anchoring examples from within a distribution or family of distributions, 2) real-world context, 3) summary statistics on which to base a Normal approximation. Models can make inferences about distributions, and can be further aided by the incorporation of real-world context, example shots and simplified assumptions, even if these assumptions are incorrect or misspecified. To conduct this work, we developed a comprehensive benchmark distribution dataset with associated question-answer pairs that we have released publicly., Comment: EMNLP 2024 (Main), 21 pages, 9 figures, 2 tables

This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.

The power of Machine Learning and Artificial Intelligence algorithms based on collected datasets, along with the programmability and flexibility provided by Software Defined Networking can provide the building blocks for constructing the so-called Zero-Touch Network and Service Management systems. However, the fuel towards this goal relies on the availability of sufficient and good-quality data collected from measurements and telemetry. This article provides a telemetry methodology to collect accurate latency measurements, as a first step toward building intelligent control planes that make correct decisions based on precise information.

Monitoring streams of packets at 100~Gb/s and beyond requires using compact and efficient hashing-techniques like HyperLogLog (HLL) or Count-Min Sketch (CMS). In this work, we evaluate the uses and applications of Count-Min Sketch for Metro Networks employing P4-based packet-optical nodes. We provide dimensioning rules for CMS at 100~Gb/s and 400~Gb/s and evaluate its performance in a real implementation testbed.

The advancement of large language models (LLMs) has demonstrated strong capabilities across various applications, including mental health analysis. However, existing studies have focused on predictive performance, leaving the critical issue of fairness underexplored, posing significant risks to vulnerable populations. Despite acknowledging potential biases, previous works have lacked thorough investigations into these biases and their impacts. To address this gap, we systematically evaluate biases across seven social factors (e.g., gender, age, religion) using ten LLMs with different prompting methods on eight diverse mental health datasets. Our results show that GPT-4 achieves the best overall balance in performance and fairness among LLMs, although it still lags behind domain-specific models like MentalRoBERTa in some cases. Additionally, our tailored fairness-aware prompts can effectively mitigate bias in mental health predictions, highlighting the great potential for fair analysis in this field., Comment: In submission; Data and code are available at: https://github.com/EternityYW/BiasEval-LLM-MentalHealth

We propose to use automatically generated instruction-following data to improve the zero-shot capabilities of a large multimodal model with additional support for generative and image editing tasks. We achieve this by curating a new multimodal instruction-following set using GPT-4V and existing datasets for image generation and editing. Using this instruction set and the existing LLaVA-Finetune instruction set for visual understanding tasks, we produce GenLLaVA, a Generative Large Language and Visual Assistant. GenLLaVA is built through a strategy that combines three types of large pretrained models through instruction finetuning: Mistral for language modeling, SigLIP for image-text matching, and StableDiffusion for text-to-image generation. Our model demonstrates visual understanding capabilities superior to LLaVA and additionally demonstrates competitive results with native multimodal models such as Unified-IO 2, paving the way for building advanced general-purpose visual assistants by effectively re-using existing multimodal models. We open-source our dataset, codebase, and model checkpoints to foster further research and application in this domain., Comment: Add more results using task tokens, expand the introduction and related work FIX: error in LLM-as-judge evaluation that was over-inflating the results

Automated machine learning streamlines the task of finding effective machine learning pipelines by automating model training, evaluation, and selection. Traditional evaluation strategies, like cross-validation (CV), generate one value that averages the accuracy of a pipeline's predictions. This single value, however, may not fully describe the generalizability of the pipeline. Here, we present Lexicase-based Validation (lexidate), a method that uses multiple, independent prediction values for selection. Lexidate splits training data into a learning set and a selection set. Pipelines are trained on the learning set and make predictions on the selection set. The predictions are graded for correctness and used by lexicase selection to identify parent pipelines. Compared to 10-fold CV, lexicase reduces the training time. We test the effectiveness of three lexidate configurations within the Tree-based Pipeline Optimization Tool 2 (TPOT2) package on six OpenML classification tasks. In one configuration, we detected no difference in the accuracy of the final model returned from TPOT2 on most tasks compared to 10-fold CV. All configurations studied here returned similar or less complex final pipelines compared to 10-fold CV.

The galaxy SDSS1335+0728, which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). Its behaviour suggests that SDSS1335+0728 hosts a $\sim 10^6 M_{\odot}$ black hole (BH) that is currently in the process of `turning on'. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. We used archival photometry and spectroscopic data to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We find that: (a) since 2021, the UV flux is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1-W2 WISE colour has become redder; (c) since February 2024, the source has begun showing X-ray emission; (d) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (e) broad emission lines are not detected; and (f) the [OIII] line increased its flux $\sim 3.6$ years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. We conclude that the variations observed in SDSS1335+0728 could be either explained by an AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGN observed in the process of activating. If the latter, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour., Comment: Accepted for publication in Astronomy & Astrophysics. ESO press release available at https://www.eso.org/public/news/eso2409/. Abstract shortened for arXiv

It is well established that maximizing the information extracted from upcoming and ongoing stage-IV weak-lensing surveys requires higher-order summary statistics that complement the standard two-point statistics. In this work, we focus on weak-lensing peak statistics to test two popular modified gravity models, $f(R)$ and nDGP, using the FORGE and BRIDGE weak-lensing simulations, respectively. From these simulations we measure the peak statistics as a function of both cosmological and modified gravity parameters simultaneously. Our findings indicate that the peak abundance is sensitive to the strength of modified gravity, while the peak two-point correlation function is sensitive to the nature of the screening mechanism in a modified gravity model. We combine these simulated statistics with a Gaussian Process Regression emulator and a Gaussian likelihood to generate stage-IV forecast posterior distributions for the modified gravity models. We demonstrate that, assuming small scales can be correctly modelled, peak statistics can be used to distinguish GR from $f(R)$ and nDGP models at the two-sigma level with a stage-IV survey area of $300 \, \rm{deg}^2$ and $1000 \, \rm{deg}^2$, respectively. Finally, we show that peak statistics can constrain $\log_{10}\left(|f_{R0}|\right) = -6$ to 2\% precision, and $\log_{10}(H_0 r_c) = 0.5$ to 25\% precision., Comment: 25 pages, 16 figures

The power grid is a critical infrastructure that allows for the efficient and robust generation, transmission, delivery and consumption of electricity. In the recent years, the physical components have been equipped with computing and network devices, which optimizes the operation and maintenance of the grid. The cyber domain of this smart power grid opens a new plethora of threats, which adds to classical threats on the physical domain. Accordingly, different stakeholders including regulation bodies, industry and academy, are making increasing efforts to provide security mechanisms to mitigate and reduce cyber-risks. Despite these efforts, there have been various cyberattacks that have affected the smart grid, leading in some cases to catastrophic consequences, showcasing that the industry might not be prepared for attacks from high profile adversaries. At the same time, recent work shows a lack of agreement among grid practitioners and academic experts on the feasibility and consequences of academic-proposed threats. This is in part due to inadequate simulation models which do not evaluate threats based on attackers full capabilities and goals. To address this gap, in this work we first analyze the main attack surfaces of the smart grid, and then conduct a threat analysis from the adversarial model perspective, including different levels of knowledge, goals, motivations and capabilities. To validate the model, we provide real-world examples of the potential capabilities by studying known vulnerabilities in critical components, and then analyzing existing cyber-attacks that have affected the smart grid, either directly or indirectly., Comment: Presented at the Workshop on Attackers and Cyber-Crime Operations (WACCO). More details available at https://wacco-workshop.org

Many quantum algorithms rely on a quality initial state for optimal performance. Preparing an initial state for specific applications can considerably reduce the cost of probabilistic algorithms such as the well studied quantum phase estimation (QPE). Fortunately, in the application space of quantum chemistry, generating approximate wave functions for molecular systems is well studied, and quantum computing algorithms stand to benefit from importing these classical methods directly into a quantum circuit. In this work, we propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers, by incorporating mid-circuit measurements into the circuit construction. Currently, the most well studied state preparation method for quantum chemistry on quantum computers is the variational quantum eigensolver (VQE) with a unitary-CC with single- and double-electron excitation terms (UCCSD) ansatz whose operations are limited to unitary gates. We verify the accuracy of our state preparation protocol using mid-circuit measurements by performing energy evaluation and state overlap computation for a set of small chemical systems. We further demonstrate that our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average when compared against the standard VQE-UCCSD protocol., Comment: 26 pages, 6 figures; title changed, references added

In this paper, we study Toeplitz operators on generalized flag manifolds of compact Lie groups using a representation-theoretic point of view. We prove several basic properties of these Toeplitz operators, including an abstract formula for their matrix coefficients in terms of the decomposition of certain tensor product representations. We also show how to identify large commuting families of Toeplitz operators based on invariance of their symbols under certain subgroups. Finally, we realize the Berezin transform as a convolution with certain functions that form an approximate identity on the generalized flag manifold, which allows us to prove a Szeg\"o Limit Theorem using certain results due to Hirschman, Liang, and Wilson., Comment: 30 pages

Rideshare platforms exert significant control over workers through algorithmic systems that can result in financial, emotional, and physical harm. What steps can platforms, designers, and practitioners take to mitigate these negative impacts and meet worker needs? In this paper, through a novel mixed methods study combining a LLM-based analysis of over 1 million comments posted to online platform worker communities with semi-structured interviews of workers, we thickly characterize transparency-related harms, mitigation strategies, and worker needs while validating and contextualizing our findings within the broader worker community. Our findings expose a transparency gap between existing platform designs and the information drivers need, particularly concerning promotions, fares, routes, and task allocation. Our analysis suggests that rideshare workers need key pieces of information, which we refer to as indicators, to make informed work decisions. These indicators include details about rides, driver statistics, algorithmic implementation details, and platform policy information. We argue that instead of relying on platforms to include such information in their designs, new regulations that require platforms to publish public transparency reports may be a more effective solution to improve worker well-being. We offer recommendations for implementing such a policy.

We investigate the electronic and magnetic properties of graphene channels ($2$--$4$~nm wide) embedded within fluorographene, focusing on two distinct interfaces: the fully fluorinated $\alpha$ interface and the half-fluorinated $\beta$ interface. Density functional theory (DFT) calculations reveal that $\alpha\alpha$ systems exhibit semiconducting behavior with antiferromagnetic ordering, closely resembling pristine zigzag graphene nanoribbons. In contrast, $\alpha\beta$ systems display ferromagnetism and a width-dependent semiconductor-to-metal transition. To enable the study of larger systems, we develop and validate effective Hubbard models for both $\alpha\alpha$ and $\alpha\beta$ channels. Building upon DFT results and a Wannier function analysis, these models accurately reproduce the electronic structure and magnetic ordering observed in DFT calculations. Crucially, our $\alpha\beta$ model successfully captures the semiconductor-to-metal transition. Application of this model to larger systems reveals the persistence of a ferromagnetic state with spin polarization localized at the $\alpha$ edge. Our results demonstrate the potential of fluorination for targeted property engineering and provide a basis for exploring graphene-fluorographene systems in device applications ranging from microelectronics to spintronics., Comment: 15 pages, 24 figures

We derive the explicit embedding of the effective Kerr spacetimes, which are pertinent to the vanishing of static Love numbers, soft hair descriptions of Kerr black holes, and low-frequency scalar-Kerr scattering amplitudes, as solutions within N = 2 supergravity. These spacetimes exhibit a hidden $SL(2, R) \times U (1)$ or $SO(4, 2)$ symmetry resembling the so called subtracted geometries with $SL(2, R) \times SL(2, R)$ symmetry, which accurately represent the near-horizon geometry of Kerr black holes and, as we will argue most accurately represents the internal structure of the Kerr black hole. To quantify the differences among the effective Kerr spacetimes, we compare their physical quantities, internal structures, and geodesic equations. Although their thermodynamic properties, including entropy, match those of Kerr, our study uncovers significant differences in the interiors of these effective Kerr solutions. A careful examination of the internal structure of the spacetimes highlights the distinctions between various effective Kerr geometries and their quasinormal spectra.

In healthcare there is a pursuit for employing interpretable algorithms to assist healthcare professionals in several decision scenarios. Following the Predictive, Descriptive and Relevant (PDR) framework, the definition of interpretable machine learning as a machine-learning model that explicitly and in a simple frame determines relationships either contained in data or learned by the model that are relevant for its functioning and the categorization of models by post-hoc, acquiring interpretability after training, or model-based, being intrinsically embedded in the algorithm design. We overview a selection of eight algorithms, both post-hoc and model-based, that can be used for such purposes., Comment: 26 pages, 5 figures

The study of droplet generation in wind tunnels in conducting icing experiments is of great importance in determining ice formation on structures or surfaces, where parameters such as Liquid Water Content (LWC) and Median Volumetric Diameter (MVD) play a relevant role. The measurement of these parameters requires specialised instrumentation. In this paper, several experiments have been carried out in a subsonic wind tunnel facility to study the parameters that are part of the icing process in structures. Furthermore, a mathematical modelling of the constituent subsystems of the plant study that allow us to have a comprehensive understanding of the behaviour of the system is developed using techniques based on first principles and machine learning techniques such as regression trees and neural networks. The simulation results show that the implementation of the model manages to obtain prominent expected values of LWC and MVD within the range of values obtained in the real experimental data., Comment: 11 pages, 14 figures

Weak gravitational lensing is a powerful tool for precision tests of cosmology. As the expected deflection angles are small, predictions based on non-linear N-body simulations are commonly computed with the Born approximation. Here we examine this assumption using ${\small DORIAN}$, a newly developed full-sky ray-tracing scheme applied to high-resolution mass-shell outputs of the two largest simulations in the MillenniumTNG suite, each with a 3000 Mpc box containing almost 1.1 trillion cold dark matter particles in addition to 16.7 billion particles representing massive neutrinos. We examine simple two-point statistics like the angular power spectrum of the convergence field, as well as statistics sensitive to higher order correlations such as peak and minimum statistics, void statistics, and Minkowski functionals of the convergence maps. Overall, we find only small differences between the Born approximation and a full ray-tracing treatment. While these are negligibly small at power-spectrum level, some higher order statistics show more sizable effects; ray-tracing is necessary to achieve percent level precision. At the resolution reached here, full-sky maps with 0.8 billion pixels and an angular resolution of 0.43 arcmin, we find that interpolation accuracy can introduce appreciable errors in ray-tracing results. We therefore implemented an interpolation method based on nonuniform fast Fourier transforms (NUFFT) along with more traditional methods. Bilinear interpolation introduces significant smoothing, while nearest grid point sampling agrees well with NUFFT, at least for our fiducial source redshift, $z_s=1.0$, and for the 1 arcmin smoothing we use for higher-order statistics., Comment: 13 pages, 7 figures, submitted to MNRAS

We search for atmospheric constituents for the UHJ WASP-178 b with two ESPRESSO transits using the narrow-band and cross-correlation techniques, focusing on the detections of NaI, H$\alpha$, H$\beta$, H$\gamma$, MgI, FeI and FeII. Additionally, we show parallel photometry used to obtain updated and precise stellar, planetary and orbital parameters. We report the resolved line detections of NaI (5.5 and 5.4 $\sigma$), H$\alpha$ (13 $\sigma$), H$\beta$ (7.1 $\sigma$), and tentatively MgI (4.6 $\sigma$). In cross-correlation, we confirm the MgI detection (7.8 and 5.8 $\sigma$) and additionally report the detections of FeI (12 and 10 $\sigma$) and FeII (11 and 8.4 $\sigma$), on both nights separately. The detection of MgI remains tentative, however, due to the differing results between both nights, as well as compared with the narrow-band derived properties. None of our resolved spectral lines probing the mid- to upper atmosphere show significant shifts relative to the planetary rest frame, however H$\alpha$ and H$\beta$ exhibit line broadenings of 39.6 $\pm$ 2.1 km/s and 27.6 $\pm$ 4.6 km/s, respectively, indicating the onset of possible escape. WASP-178 b differs from similar UHJ with its lack of strong atmospheric dynamics in the upper atmosphere, however the broadening seen for FeI (15.66 $\pm$ 0.58 km/s) and FeII (11.32 $\pm$ 0.52 km/s) could indicate the presence of winds in the mid-atmosphere. Future studies on the impact of the flux variability caused by the host star activity might shed more light on the subject. Previous work indicated the presence of SiO cloud-precursors in the atmosphere of WASP-178 b and a lack of MgI and FeII. However, our results suggest that a scenario where the planetary atmosphere is dominated by MgI and FeII is more likely. In light of our results, we encourage future observations to further elucidate these atmospheric properties., Comment: 18 pages, 24 figures, accepted for publication in A&A