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We investigate the entanglement properties of the Quantum Six-Vertex Model on a cylinder, focusing on the Shannon-Renyi entropy in the limit of Renyi order $n = \infty$. This entropy, calculated from the ground state amplitudes of the equivalent XXZ spin-1/2 chain, allows us to determine the Renyi entanglement entropy of the corresponding Rokhsar-Kivelson wavefunctions, which describe the ground states of certain conformal quantum critical points. Our analysis reveals a novel logarithmic correction to the expected entanglement scaling when the system size is odd. This anomaly arises from the geometric frustration of spin configurations imposed by periodic boundary conditions on odd-sized chains. We demonstrate that the scaling prefactor of this logarithmic term is directly related to the compactification radius of the low-energy bosonic field theory description, or equivalently, the Luttinger parameter. Thus, this correction provides a direct probe of the underlying Conformal Field Theory (CFT) describing the critical point. Our findings highlight the crucial role of system size parity in determining the entanglement properties of this model and offer insights into the interplay between geometry, frustration, and criticality., Comment: 10 pages, 7 figures

This paper provides an overview of the applications of sheaf theory in deep learning, data science, and computer science in general. The primary text of this work serves as a friendly introduction to applied and computational sheaf theory accessible to those with modest mathematical familiarity. We describe intuitions and motivations underlying sheaf theory shared by both theoretical researchers and practitioners, bridging classical mathematical theory and its more recent implementations within signal processing and deep learning. We observe that most notions commonly considered specific to cellular sheaves translate to sheaves on arbitrary posets, providing an interesting avenue for further generalization of these methods in applications, and we present a new algorithm to compute sheaf cohomology on arbitrary finite posets in response. By integrating classical theory with recent applications, this work reveals certain blind spots in current machine learning practices. We conclude with a list of problems related to sheaf-theoretic applications that we find mathematically insightful and practically instructive to solve. To ensure the exposition of sheaf theory is self-contained, a rigorous mathematical introduction is provided in appendices which moves from an introduction of diagrams and sheaves to the definition of derived functors, higher order cohomology, sheaf Laplacians, sheaf diffusion, and interconnections of these subjects therein., Comment: 117 pages, 8 figures

Testing Web User Interfaces (UIs) requires considerable time and effort and resources, most notably participants for user testing. Additionally, the tests results may demand adjustments on the UI, taking further resources and testing. Early tests can make this process less costly with the help of low fidelity prototypes, but it is difficult to conduct user tests on them, and recruiting participants is still necessary. To tackle this issue, there are tools that can predict UI aspects like interaction time, as the well-known KLM model. Another aspect that can be predicted is complexity, and this was achieved by the Big I notation, which can be applied to early UX concepts like lo-fi wireframes. Big I assists developers in estimating the interaction complexity, specified as a function of user steps, which are composed of abstracted user actions. Interaction complexity is expressed in mathematical terms, making the comparison of interaction complexities for various UX concepts easy. However, big I is not able to predict execution time for user actions, which would be very helpful for early assessment of lo-fi prototypes. To address this shortcoming, in this paper we present a study in which we took measurements from real users (n=100) completing tasks in a fictitious website, in order to derive average times per interaction step. Using these results, we were able to study the relationship between interaction complexity and time and ultimately complement big I predictions with time estimates., Comment: 15 pages with 2 figures and 4 tables

Unveiling hidden symmetries within Feynman diagrams is crucial for achieving more efficient computations in high-energy physics. In this paper, we study the symmetries underlying the causal Loop-Tree Duality (LTD) representations through a geometric analysis. Focusing on the integrand-level representations of $N$-point functions at one loop, we examine their degenerations and discover that different causal representations are interconnected through specific transformations arising from the symmetries of cut diagrams. Furthermore, the degeneration is linked to algebraic constraints among the different causal thresholds. Our findings shed new light on the deeper structures of Feynman integrals and pave the way for significantly accelerating their calculation by interrelating different approaches., Comment: 14 pages, 8 figures

Recently the Belle II Collaboration has reported a measurement of the $B^+\to K^+\nu\bar\nu$ rate that is higher than the standard-model expectation. Since the emitted neutrinos are unobserved, the excess could be due to the $B^+$ decaying into a $K^+$ and a dark-matter pair. We entertain this possibility in a two-Higgs-doublet model supplemented with a real singlet scalar boson acting as the dark matter. This model also accommodates strangeness-changing interactions providing new sources of $CP$ violation which can affect hyperon and kaon nonleptonic transitions. We find that the resulting $CP$ violation in the hyperon sector can be significant, reaching the current empirical bounds, after taking into account constraints from kaon mixing and decay and from dark-matter relic-density data and direct searches including the Migdal effect. We demonstrate that the hyperon and kaon processes are complementary probes of this new-physics scenario. Its prediction for sizable hyperon $CP$ violation is potentially testable in ongoing experiments, such as BESIII, Belle II, and LHCb, and in next-generation ones like PANDA and at the Super Tau Charm Facility., Comment: 23 pages, 7 figures

In July 2024, Sunrise completed its third successful science flight. The Sunrise III observatory had been upgraded significantly after the two previous successful flights in 2009 and 2013. Three completely new instruments focus on the small-scale physical processes and their complex interaction from the deepest observable layers in the photosphere up to chromospheric heights. Previously poorly explored spectral regions and lines are exploited to paint a three-dimensional picture of the solar atmosphere with unprecedented completeness and level of detail. The full polarimetric information is captured by all three instruments to reveal the interaction between the magnetic fields and the hydrodynamic processes. Two slit-based spectropolarimeters, the Sunrise UV Spectropolarimeter and Imager (SUSI) and the Sunrise Chromospheric Infrared spectro-Polarimeter (SCIP), focus on the near-ultraviolet and the near-infrared regions respectively, and the imaging spectropolarimeter Tunable Magnetograph (TuMag) simultaneously obtains maps of the full field-of-view of $46 \times 46$ Mm$^2$ in the photosphere and the chromosphere in the visible. The instruments are operated in an orchestrated mode, benefiting from a new Image Stabilization and Light Distribution unit (ISLiD), with the Correlating Wavefront Sensor (CWS) providing the autofocus control and an image stability with a root-mean-square value smaller than 0.005''. A new gondola was constructed to significantly improve the telescope pointing stability, required to achieve uninterrupted observations over many hours. Sunrise III was launched successfully on July 10, 2024, from the Esrange Space Center near Kiruna (Sweden). It reached the landing site between the Mackenzie River and the Great Bear Lake in Canada after a flight duration of 6.5 days. In this paper, we give an overview of the Sunrise III observatory and its instruments., Comment: 67 pages, 25 figures; to be published in Solar Physics Topical Collection "The Sunrise III Solar Observatory" (https://link.springer.com/collections/jegdciedig)

This work focuses on generating realistic, physically-based human behaviors from multi-modal inputs, which may only partially specify the desired motion. For example, the input may come from a VR controller providing arm motion and body velocity, partial key-point animation, computer vision applied to videos, or even higher-level motion goals. This requires a versatile low-level humanoid controller that can handle such sparse, under-specified guidance, seamlessly switch between skills, and recover from failures. Current approaches for learning humanoid controllers from demonstration data capture some of these characteristics, but none achieve them all. To this end, we introduce the Masked Humanoid Controller (MHC), a novel approach that applies multi-objective imitation learning on augmented and selectively masked motion demonstrations. The training methodology results in an MHC that exhibits the key capabilities of catch-up to out-of-sync input commands, combining elements from multiple motion sequences, and completing unspecified parts of motions from sparse multimodal input. We demonstrate these key capabilities for an MHC learned over a dataset of 87 diverse skills and showcase different multi-modal use cases, including integration with planning frameworks to highlight MHC's ability to solve new user-defined tasks without any finetuning.

The last decade has seen widespread adoption of Machine Learning (ML) components in software systems. This has occurred in nearly every domain, from natural language processing to computer vision. These ML components range from relatively simple neural networks to complex and resource-intensive large language models. However, despite this widespread adoption, little is known about the supply chain relationships that produce these models, which can have implications for compliance and security. In this work, we conduct an extensive analysis of 760,460 models and 175,000 datasets mined from the popular model-sharing site Hugging Face. First, we evaluate the current state of documentation in the Hugging Face supply chain, report real-world examples of shortcomings, and offer actionable suggestions for improvement. Next, we analyze the underlying structure of the extant supply chain. Finally, we explore the current licensing landscape against what was reported in prior work and discuss the unique challenges posed in this domain. Our results motivate multiple research avenues, including the need for better license management for ML models/datasets, better support for model documentation, and automated inconsistency checking and validation. We make our research infrastructure and dataset available to facilitate future research.

Sample complexity of bias estimation is a lower bound on the runtime of any bias detection method. Many regulatory frameworks require the bias to be tested for all subgroups, whose number grows exponentially with the number of protected attributes. Unless one wishes to run a bias detection with a doubly-exponential run-time, one should like to have polynomial complexity of bias detection for a single subgroup. At the same time, the reference data may be based on surveys, and thus come with non-trivial uncertainty. Here, we reformulate bias detection as a point-to-subspace problem on the space of measures and show that, for supremum norm, it can be subsampled efficiently. In particular, our probabilistically approximately correct (PAC) results are corroborated by tests on well-known instances.

The Dragonfly topology is currently one of the most popular network topologies in high-performance parallel systems. The interconnection networks of many of these systems are built from components based on the InfiniBand specification. However, due to some constraints in this specification, the available versions of the InfiniBand network controller (OpenSM) do not include routing engines based on some popular deadlock-free routing algorithms proposed theoretically for Dragonflies, such as the one proposed by Kim and Dally based on Virtual-Channel shifting. In this paper we propose a straightforward method to integrate this routing algorithm in OpenSM as a routing engine, explaining in detail the configuration required to support it. We also provide experiment results, obtained both from a real InfiniBand-based cluster and from simulation, to validate the new routing engine and to compare its performance and requirements against other routing engines currently available in OpenSM.

This paper introduces a hierarchical framework for decision-making and optimization, leveraging Large Language Models (LLMs) for adaptive code generation. Instead of direct decision-making, LLMs generate and refine executable control policies through a meta-policy that guides task generation and a base policy for operational actions. Applied to a simplified microgrid scenario, the approach achieves up to 15 percent cost savings by iteratively improving battery control strategies. The proposed methodology lays a foundation for integrating LLM-based tools into planning and control tasks, offering adaptable and scalable solutions for complex systems while addressing challenges of uncertainty and reproducibility.

We propose a novel method to evaluate the theoretical limits of Transformers, allowing us to prove the first lower bounds against one-layer softmax Transformers with infinite precision. We establish those bounds for three tasks that require advanced reasoning. The first task, Match3 (Sanford et al., 2023), requires looking at all triples of positions. The second and third tasks address compositionality-based reasoning: one is composition of functions (Peng et al., 2024) and the other is composition of binary relations. We formally prove the inability of one-layer softmax Transformers to solve any of these tasks. In an attempt to overcome these limitations, we introduce Strassen attention and prove that with this mechanism a one-layer Transformer can in principle solve all these tasks. We also show that it enjoys sub-cubic running-time complexity, making it more scalable than similar previously proposed mechanisms, such as higher-order attention (Sanford et al., 2023). To complement our theoretical findings, we experimentally studied Strassen attention and compared it against standard (Vaswani et al, 2017), higher-order attention (Sanford et al., 2023) and triangular attention (Bergen et al. 2021). Our results help to disentangle all these attention mechanisms, highlighting their strengths and limitations. In particular, Strassen attention outperforms standard attention significantly on all the tasks. Altogether, understanding the theoretical limitations can guide research towards scalable attention mechanisms that improve the reasoning abilities of Transformers.

Gas-phase hydroxylamine (NH$_2$OH) has recently been detected within dense clouds in the interstellar medium. However, it is also likely present within interstellar ices, as well as on the icy surfaces of outer Solar System bodies, where it may react to form more complex prebiotic molecules such as amino acids. In this work, we aimed to provide IR spectra of NH$_2$OH in astrophysical ice analogues that will help in the search for this molecule in various astrophysical environments. Furthermore, we aimed to provide quantitative information on the stability of NH$_2$OH upon exposure to ionizing radiation analogous to cosmic rays, as well as on the ensuing chemistry and potential formation of complex prebiotic molecules. Ices composed of NH$_2$OH, H$_2$O, and CO were prepared by vapor deposition and IR spectra were acquired between $4000-500$~cm$^{-1}$ ($2.5-20 \mu$m) prior and during irradiation using 15 keV protons. In interstellar ice analogues, the most prominent IR absorption band of NH$_2$OH is that at about 1188~cm$^{-1}$, which may be a good candidate to use in searches of this species in icy space environments. Calculated effective destruction cross-sections and G-values for the NH$_2$OH-rich ices studied show that NH$_2$OH is rapidly destroyed upon exposure to ionizing radiation (more rapidly than a number of previously studied organic molecules), and that this destruction is slightly enhanced when it is mixed with other icy species. The irradiation of a NH$_2$OH:H$_2$O:CO ternary ice mixture leads to a rich chemistry that includes the formation of simple inorganic molecules such as NH$_3$, CO$_2$, OCN$^-$, and H$_2$O$_2$, as well as ammonium salts and, possibly, complex organic molecules relevant to life such as formamide, formic acid, urea, and glycine., Comment: Accepted for publication in A&A

Goessling et al. (1) link the record-breaking warming anomaly of 2023 to a global albedo decline due to reduced low-level cloud cover. What caused the reduction remains unclear. Goessling et al. considered several geophysical mechanisms, including ocean surface warming and declining aerosol emissions, but did not discuss the biosphere. We propose that disruption of global biospheric functioning could be a cause, as supported by three lines of evidence that have not yet been jointly considered., Comment: Comments on arXiv:2405.19986

Estimating market impact and transaction costs of large trades (metaorders) is a very important topic in finance. However, using models of price and trade based on public market data provide average price trajectories which are qualitatively different from what is observed during real metaorder executions: the price increases linearly, rather than in a concave way, during the execution and the amount of reversion after its end is very limited. We claim that this is a generic phenomenon due to the fact that even sophisticated statistical models are unable to correctly describe the origin of the autocorrelation of the order flow. We propose a modified Transient Impact Model which provides more realistic trajectories by assuming that only a fraction of the metaorder trading triggers market order flow. Interestingly, in our model there is a critical condition on the kernels of the price and order flow equations in which market impact becomes permanent.

X-ray imaging is the most widely used medical imaging modality. However, in the common practice, inconsistency in the initial presentation of X-ray images is a common complaint by radiologists. Different patient positions, patient habitus and scanning protocols can lead to differences in image presentations, e.g., differences in brightness and contrast globally or regionally. To compensate for this, additional work will be executed by clinical experts to adjust the images to the desired presentation, which can be time-consuming. Existing deep-learning-based end-to-end solutions can automatically correct images with promising performances. Nevertheless, these methods are hard to be interpreted and difficult to be understood by clinical experts. In this manuscript, a novel interpretable mapping method by deep learning is proposed, which automatically enhances the image brightness and contrast globally and locally. Meanwhile, because the model is inspired by the workflow of the brightness and contrast manipulation, it can provide interpretable pixel maps for explaining the motivation of image enhancement. The experiment on the clinical datasets show the proposed method can provide consistent brightness and contrast correction on X-ray images with accuracy of 24.75 dB PSNR and 0.8431 SSIM., Comment: SPIE Medical Imaging 2025

This paper presents a semi-supervised approach to extracting narratives from historical photographic records using an adaptation of the narrative maps algorithm. We extend the original unsupervised text-based method to work with image data, leveraging deep learning techniques for visual feature extraction and similarity computation. Our method is applied to the ROGER dataset, a collection of photographs from the 1928 Sacambaya Expedition in Bolivia captured by Robert Gerstmann. We compare our algorithmically extracted visual narratives with expert-curated timelines of varying lengths (5 to 30 images) to evaluate the effectiveness of our approach. In particular, we use the Dynamic Time Warping (DTW) algorithm to match the extracted narratives with the expert-curated baseline. In addition, we asked an expert on the topic to qualitatively evaluate a representative example of the resulting narratives. Our findings show that the narrative maps approach generally outperforms random sampling for longer timelines (10+ images, p < 0.05), with expert evaluation confirming the historical accuracy and coherence of the extracted narratives. This research contributes to the field of computational analysis of visual cultural heritage, offering new tools for historians, archivists, and digital humanities scholars to explore and understand large-scale image collections. The method's ability to generate meaningful narratives from visual data opens up new possibilities for the study and interpretation of historical events through photographic evidence., Comment: This paper has been accepted for oral presentation in the findings track of the 47th European Conference on Information Retrieval (ECIR 2025). Source code and experiments are available at https://github.com/faustogerman/ROGER-Concept-Narratives

We propose a new architecture for 3D information systems that takes advantage of the inherent parallelism of the GPUs. This new solution structures information as thematic layers, allowing a level of detail independent of the resolution of the meshes. Previous proposals of layer based systems present issues, both in terms of performance and storage, due to the use of octrees to index information. In contrast, our approach employs two-dimensional textures, highly efficient in GPU, to store and index information. In this article we describe this architecture and detail the GPU algorithms required to edit these layers. Finally, we present a performance comparison of our approach against an octree based system., Comment: This is a preprint version of the article. For the full version, please follow this link: https://www.sciencedirect.com/science/article/abs/pii/S1296207418307167?via%3Dihub

In [BCP24], the authors describe a triangulated structure of a quotient of a certain category of representations of posets, nowadays known as the Bondarenko's category. This category was essential in [BM03] for classify all indecomposable objects of the derived category of gentle algebras. In view of this connection with the derived category, which possess a triangulated structure. In this paper, we identify another triangulated structure for Bondarenko's category, allowing us to utilize the functor presented in [BM03]. This functor will establishes a connection between the triangulated structure of the homotopy category of gentle algebras and the new triangulated structure of a quotient of a certain Bondarenko's category., Comment: 21 pages

We consider arithmetic sequences, here defined as ordered lists of positive integers. Any such a sequence can be cast onto a quantum state, enabling the quantification of its `surprise' through von Neumann entropy. We identify typical sequences that maximize entanglement entropy across all bipartitions and derive an analytical approximation as a function of the sequence length. This quantum-inspired approach offers a novel perspective for analyzing randomness in arithmetic sequences.

This paper proposes a model that enables permissionless and decentralized networks for complex computations. We explore the integration and optimize load balancing in an open, decentralized computational network. Our model leverages economic incentives and reputation-based mechanisms to dynamically allocate tasks between operators and coprocessors. This approach eliminates the need for specialized hardware or software, thereby reducing operational costs and complexities. We present a mathematical model that enhances restaking processes in blockchain systems by enabling operators to delegate complex tasks to coprocessors. The model's effectiveness is demonstrated through experimental simulations, showcasing its ability to optimize reward distribution, enhance security, and improve operational efficiency. Our approach facilitates a more flexible and scalable network through the use of economic commitments, adaptable dynamic rating models, and a coprocessor load incentivization system. Supported by experimental simulations, the model demonstrates its capability to optimize resource allocation, enhance system resilience, and reduce operational risks. This ensures significant improvements in both security and cost-efficiency for the blockchain ecosystem.

We investigate the radial stability of neutron stars under conditions where their composition may or may not remain in chemical equilibrium during oscillations. Using different equations of state that include nucleons, hyperons, and/or $\Delta$ resonances, we compute stellar configurations and examine their fundamental mode frequencies in two limiting scenarios. In one limit, nuclear reactions are fast enough to maintain chemical equilibrium throughout the pulsation, resulting in a lower effective adiabatic index, $\Gamma_{\mathrm{EQ}}$, and softer stellar responses. In the opposite limit, nuclear reactions are too slow to adjust particle abundances during oscillations, yielding a higher index, $\Gamma_{\mathrm{FR}}$, and stiffer stellar responses. We find that the equilibrium scenario triggers dynamic instability at the maximum mass configuration, whereas the frozen composition scenario allows stable solutions to persist beyond this mass, extending the stable branch. This effect is modest for simpler equations of state, but becomes increasingly pronounced for more complex compositions, where the emergence of new particle species at high densities leads to a significant disparity between $\Gamma_{\mathrm{EQ}}$ and $\Gamma_{\mathrm{FR}}$. Realistic conditions, in which different nuclear reactions have distinct timescales, will place the effective $\Gamma$ between these two extreme values. Short-timescale reactions push the star toward the equilibrium limit, potentially restricting the length of the stable branch. Conversely, slow reactions preserve a frozen composition, allowing the stable branch to grow. Thus, the actual extent of the stable configuration range depends critically on the interplay between nuclear-reaction timescales and the star's fundamental oscillation period., Comment: 20 pages, 3 figures

Massive stars play a pivotal role in shaping their galactic surroundings due to their high luminosity and intense ionizing radiation. However, the precise mechanisms governing the formation of massive stars remain elusive. Complex organic molecules (COMs) offer an avenue for studying star formation across the low- to high-mass spectrum because COMs are found in every young stellar object phase and offer insight into the structure and temperature. We aim to unveil evolutionary patterns of COM chemistry in 41 massive young stellar objects (MYSOs) sourced from diverse catalogues, using ALMA Band 6 spectra. Previous line analysis of these sources showed the presence of CH$_3$OH, CH$_3$CN, and CH$_3$CCH with diverse excitation temperatures and column densities, indicating a possible evolutionary path across sources. However, this analysis usually involves manual line extraction and rotational diagram fitting. Here, we improve upon this process by directly retrieving the physicochemical state of MYSOs from their dimensionally-reduced spectra. We use a Locally Linear Embedding to find a lower-dimensional projection for the physicochemical parameters obtained from individual line analysis. We identify clusters of similar MYSOs in this embedded space using a Gaussian Mixture Model. We find three groups of MYSOs with distinct physicochemical conditions: i) cold, COM-poor sources, ii) warm, medium-COM-abundance sources, and iii) hot, COM-rich sources. We then apply principal component analysis (PCA) to the spectral sample, finding further evidence for an evolutionary path across MYSO groups. Finally, we find that the physicochemical state of our sample can be derived directly from the spectra by training a simple random forest model on the first few PCA components. Our results highlight the effectiveness of dimensionality reduction in obtaining clear physical insights directly from MYSO spectra., Comment: 20 pages, 14 figures, 5 tables, accepted for publication in the A&A journal

We sought to determine which are the main hydrogenation paths of acetaldehyde (CH3CHO). As a partially unsaturated molecule, CH3CHO can have links with more hydrogenated species, like ethanol (C2H5OH) or with more unsaturated ones, like ketene (H2CCO). We used highly accurate quantum chemical calculations to determine the reaction rate constants for the CH3CHO + H/D reaction. Our theoretical results are confronted against our experiments on the hydrogenation and deuteration of CH3CHO ice. We find that acetaldehyde resists hydrogenation, with only a 10\% of conversion to products different than CH3CHO. This is due to a predominance of H-abstraction at the HCO moiety, with reaction rate constants up to four orders of magnitude higher than the next possible reaction channel, that is hydrogenation at the aldehydic carbon. The formed CH3CO radical experiences barrierless or nearly barrierless reactions in all possible reaction positions, reforming CH3CHO and creating a closed loop that protects the molecule against hydrogenation. We constrain the branching ratios for the second reaction from experiments. Our experiments agree with the calculations and from the combination of both we can explain the presence of H2CCO, CO, CH4, C2H5OH, H2CO or CH3OH as minor products at the end of the reaction. We provide recommendations for future modeling efforts. Our results show limited destruction of acetaldehyde, reinforcing the vision of this molecule as an abundant and resilient COM. From the experiments, we are not able to observe the reactive desorption of this molecule. Our results align with other modeling works, showing that the link between CH3CHO and C2H5OH is not direct. Finally, our results can explain the excess of CH3CDO found in prestellar cores., Comment: Accepted for publication in Astronomy and Astrophysics

Kagome metals with van Hove singularities (VHSs) near the Fermi level can host intriguing quantum phenomena, including chiral loop currents, electronic nematicity, and unconventional superconductivity. However, unconventional magnetic states driven by VHSs, such as spin-density waves (SDWs), have yet to be observed experimentally in kagome metals. Here, we present a comprehensive investigation of the magnetic and electronic structure of the layered kagome metal CeTi3Bi4, where the Ti kagome electronic structure interacts with a magnetic sublattice of Ce3+ Jeff = 1/2 moments. Our neutron diffraction measurements reveal an incommensurate SDW ground state of the Ce3+ Jeff = 1/2 moments, which notably coexists with commensurate antiferromagnetic order across most of the temperature-field phase diagram. The commensurate component is preferentially suppressed by both thermal fluctuations and external magnetic fields, resulting in a rich phase diagram that includes an intermediate single-Q SDW phase. First-principles calculations and angle-resolved photoemission spectroscopy (ARPES) measurements identify VHSs near the Fermi level, with the observed magnetic propagation vectors connecting their high density of states, strongly suggesting a VHS-assisted SDW in CeTi3Bi4. These findings establish the rare-earth Kagome metals LnTi3Bi4 as a model platform where characteristic electronic structure of the kagome lattice plays a pivotal role in magnetic order., Comment: 20 pages, 4 figures, SI not included

Over the last decades, tests on the standard model of cosmology, the so-called $\Lambda$CDM model, have been widely analysed and compared with many different models for describing dark energy. Modified gravities have played an important role in this sense as an alternative to $\Lambda$CDM model. Previous observational data has been always favouring $\Lambda$CDM model in comparison to any other model. While statistically speaking, alternative models have shown tehir power, fitting in some cases the observational data slightly better than $\Lambda$CDM, the significance and goodness of the fits were not significantly relevant to exclude the standard model of cosmology. In this paper, a generalisation of exponential $F(R)$ gravity is considered and compared with $\Lambda$CDM model by using the latest observational data. Also some well-known model independent parameterisations for the equation of state (EoS) of dark energy are explored. These scenarios are confronted with the renewed observational data involving the Pantheon plus datasets of supernovae type Ia, the Hubble parameter estimations, data from the cosmic microwave background and baryon acoustic oscillations, where the latter includes the data provided by Dark Energy Spectroscopic Instrument collaboration. Results of this analysis suggest that standard exponential $F(R)$ models provide much better fits than $\Lambda$CDM model, which is excluded at 4$\sigma$. Moreover, the parameterisations of the equation of state suggest a non-constant EoS parameter for dark energy, where $\Lambda$CDM model is also excluded at 4$\sigma$., Comment: 11 pages, 3 figures

Context: Ubiquitous devices and pervasive environments are in permanent interaction in people's daily lives. In today's hyper-connected environments, it is necessary for these devices to interact with each other, transparently to the users. The problem is analyzed from the different perspectives that compose it: SOA, service composition, interaction, and the capabilities of ubiquitous devices. Problem: Currently, ubiquitous devices can interact in a limited way due to the proprietary mechanisms and protocols available on the market. The few proposals from academia have hardly achieved an impact in practice. This is not in harmony with the situation of the Internet environment and web services, which have standardized mechanisms for service composition. Aim: Apply the principles of SOA, currently standardized and tested in the information systems industry, for the connectivity of ubiquitous devices in pervasive environments. For this, a coordination framework based on these technologies is proposed. Methodology: We apply an adaptation of Design Science in our environment to allow the iterative construction and evaluation of prototypes. For this, a proof of concept is developed on which this methodology and its cycles are based. Results: We built and put into operation a coordination framework for ubiquitous devices based on WS-CDL, along with a proof of concept. In addition, we contribute to the WS-CDL language in order to support the characteristics of specific ubiquitous devices.

Inequalities between Dirichlet and Neumann eigenvalues of the Laplacian and of other differential operators have been intensively studied in the past decades. The aim of this paper is to introduce differential forms and the de Rham complex in the study of such inequalities. We show how differential forms lie hidden at the heart of the work of Rohleder on inequalities between Dirichlet and Neumann eigenvalues for the Laplacian on planar domains. Moreover, we extend the ideas of Rohleder to a new proof of Friedlander's inequality for any bounded Lipschitz domain., Comment: Version 1: 15 pages. Version 2: Updated version with new title and erroneous claims in Section 4 removed. 14 pages. Additional reference in Section 1 added

Pervasive computing promotes the integration of smart electronic devices in our living and working spaces to provide advanced services. Recently, two major evolutions are changing the way pervasive applications are developed. The first deals with moving computation and storage to the edge. The second is the massive use of machine learning techniques to build these applications. However, architectural principles and integrated frameworks are still missing today to successfully and repetitively support application developers in the creation of edge-level AI applications. In this paper, we present a novel architecture and platform allowing the development of such applications in smart spaces., Comment: 6 pages

Fast and accurate large-scale energy system models are needed to investigate the potential of storage to complement the fluctuating energy production of renewable energy systems. However, the standard Mixed-Integer Programming (MIP) models that describe optimal investment and operation of these storage units, including the optional capacity to provide up/down reserves, do not scale well. To improve scalability, the integrality constraints are often relaxed, resulting in Linear Programming (LP) relaxations that allow simultaneous charging and discharging, while this is not feasible in practice. To address this, we derive the convex hull of the solutions for the optimal operation of storage for one time period, as well as for problems including investments and reserves, guaranteeing that no tighter MIP formulation or better LP approximation exists for one time period. When included in multi-period large-scale energy system models, these improved LP relaxations can better prevent simultaneous charging and discharging. We demonstrate this with illustrative case studies of a unit commitment problem and a transmission expansion planning problem., Comment: Preprint submitted to IEEE Transactions on Power Systems

The paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer Encoder between several classification heads. One head is responsible for binary classification between human-written and machine-generated texts, while the other heads are auxiliary multiclass classifiers for texts of different domains from particular datasets. As multiclass heads were trained to distinguish the domains presented in the data, they provide a better understanding of the samples. This approach led us to achieve the first place in the official ranking with 83.07% macro F1-score on the test set and bypass the baseline by 10%. We further study obtained system through ablation, error and representation analyses, finding that multi-task learning outperforms single-task mode and simultaneous tasks form a cluster structure in embeddings space.

Generative AI (GenAI) tools have already started to transform software development practices. Despite their utility in tasks such as writing code, the use of these tools raises important legal questions and potential risks, particularly those associated with copyright law. In the midst of this uncertainty, this paper presents a study jointly conducted by software engineering and legal researchers that surveyed 574 GitHub developers who use GenAI tools for development activities. The survey and follow-up interviews probed the developers' opinions on emerging legal issues as well as their perception of copyrightability, ownership of generated code, and related considerations. We also investigate potential developer misconceptions, the impact of GenAI on developers' work, and developers' awareness of licensing/copyright risks. Qualitative and quantitative analysis showed that developers' opinions on copyright issues vary broadly and that many developers are aware of the nuances these legal questions involve. We provide: (1) a survey of 574 developers on the licensing and copyright aspects of GenAI for coding, (2) a snapshot of practitioners' views at a time when GenAI and perceptions of it are rapidly evolving, and (3) an analysis of developers' views, yielding insights and recommendations that can inform future regulatory decisions in this evolving field.

The kagome motif is a versatile platform for condensed matter physics, hosting rich interactions between magnetic, electronic, and structural degrees of freedom. In recent years, the discovery of a charge density wave (CDW) in the AV$_3$Sb$_5$ superconductors and structurally-derived bond density waves in FeGe and ScV$_6$Sn$_6$ have stoked the search for new kagome platforms broadly exhibiting density wave (DW) transitions. In this work, we evaluate the known AM$_6$X$_6$ chemistries and construct a stability diagram that summarizes the structural relationships between the $\approx$125 member family. Subsequently we introduce our discovery of the broader LnNb$_6$Sn$_6$ (Ln:Ce-Nd,Sm,Gd-Tm,Lu,Y) family of kagome metals and an analogous DW transition in LuNb$_6$Sn$_6$. Our X-ray scattering measurements clearly indicate a (1/3, 1/3, 1/3) ordering wave vector ($\sqrt{3}\times\sqrt{3}\times3$ superlattice) and diffuse scattering on half-integer $L$-planes. Our analysis of the structural data supports the ``rattling mode'' DW model proposed for ScV$_6$Sn$_6$ and paints a detailed picture of the steric interactions between the rare-earth filler element and the host Nb-Sn kagome scaffolding. We also provide a broad survey of the magnetic properties within the HfFe$_6$Ge$_6$-type LnNb$_6$Sn$_6$ members, revealing a number of complex antiferromagnetic and metamagnetic transitions throughout the family. This work integrates our new LnNb$_6$Sn$_6$ series of compounds into the broader AM$_6$X$_6$ family, providing new material platforms and forging a new route forward at the frontier of kagome metal research.

The Arecibo Message was a brief binary-encoded communication transmitted into space from the Arecibo Observatory on November 16, 1974, intended to demonstrate human technological prowess. In late 2018, to commemorate the 45th anniversary of this message, the Arecibo Observatory initiated the New Arecibo Message competition. Following a series of challenges, our Boriken Voyagers team was recognized as the winner of the competition in August 2020. Although the primary objective of the competition was to conceptualize rather than transmit a message, the collapse of the Arecibo Telescope in December 2020 precluded any subsequent transmission efforts. Therefore, to commemorate the 50th anniversary of the Arecibo Message, this paper presents the Last Arecibo Message, as originally developed for the Arecibo Telescope. If the original message says "we are a form of life reaching out to connect", our message says "we are ready to explore the universe together." The prospect of transmitting this or a similar message remains an open question., Comment: 12 pages, 4 figures. Additional content is available at https://phl.upr.edu/message/

This paper proposes an automated framework for efficient application profiling and training of Machine Learning (ML) performance models, composed of two parts: OSCAR-P and aMLLibrary. OSCAR-P is an auto-profiling tool designed to automatically test serverless application workflows running on multiple hardware and node combinations in cloud and edge environments. OSCAR-P obtains relevant profiling information on the execution time of the individual application components. These data are later used by aMLLibrary to train ML-based performance models. This makes it possible to predict the performance of applications on unseen configurations. We test our framework on clusters with different architectures (x86 and arm64) and workloads, considering multi-component use-case applications. This extensive experimental campaign proves the efficiency of OSCAR-P and aMLLibrary, significantly reducing the time needed for the application profiling, data collection, and data processing. The preliminary results obtained on the ML performance models accuracy show a Mean Absolute Percentage Error lower than 30% in all the considered scenarios., Comment: Accepted on Journal of Systems and Software

This paper describes a system designed to distinguish between AI-generated and human-written scientific excerpts in the DAGPap24 competition hosted within the Fourth Workshop on Scientific Document Processing. In this competition the task is to find artificially generated token-level text fragments in documents of a scientific domain. Our work focuses on the use of a multi-task learning architecture with two heads. The application of this approach is justified by the specificity of the task, where class spans are continuous over several hundred characters. We considered different encoder variations to obtain a state vector for each token in the sequence, as well as a variation in splitting fragments into tokens to further feed into the input of a transform-based encoder. This approach allows us to achieve a 9% quality improvement relative to the baseline solution score on the development set (from 0.86 to 0.95) using the average macro F1-score, as well as a score of 0.96 on a closed test part of the dataset from the competition.

This paper reports on a comprehensive study of an ultra-relativistic positron beam deflection by an oriented quasi-mosaic crystal. The analysis was carried out for the positron energy of 530 MeV incident on the quasi-mosaic bent Si(111) crystal. This particular case was chosen because it has recently been studied experimentally at the Mainz Microtron MAMI. The results of the relativistic molecular dynamics simulations were compared with the experimental observations and a good agreement was found. The presence of planar channeling, de-channeling, volume reflection and volume capture processes in the angular distribution of deflected positrons for different beam-crystal alignments has been studied. Predictions have been made for certain crystal orientations for which experimental data are lacking.

The Bethe Ansatz is a method for constructing exact eigenstates of quantum-integrable spin chains. Recently, deterministic quantum algorithms, referred to as "algebraic Bethe circuits", have been developed to prepare Bethe states for the spin-1/2 XXZ model. These circuits represent a unitary formulation of the standard algebraic Bethe Ansatz, expressed using matrix-product states that act on both the spin chain and an auxiliary space. In this work, we systematize these previous results, and show that algebraic Bethe circuits can be derived by a change of basis in the auxiliary space. The new basis, identical to the "F-basis" known from the theory of quantum-integrable models, generates the linear superpositions of plane waves that are characteristic of the coordinate Bethe Ansatz. We explain this connection, highlighting that certain properties of the F-basis (namely, the exchange symmetry of the spins) are crucial for the construction of algebraic Bethe circuits. We demonstrate our approach by presenting new quantum circuits for the inhomogeneous spin-1/2 XXZ model., Comment: 38 pages, 18 figures

Reheating in inflationary cosmology is essential for understanding the early universe, influencing particle production, thermalization, and the primordial power spectrum. Crucial quantities defined during the reheating epoc, such as the equation of state parameter $\omega_{re}$, reheating temperature $T_{re}$, and the number of $e$-folds $N_{re}$, affect inflationary observables like the scalar spectral index $n_s$ and the tensor-to-scalar ratio $r$. We analyze two classes of inflationary models: generalized $\alpha$-attractor models and the $\alpha$-Starobinsky generalization. These models, motivated by supergravity and string theory, exhibit attractor behavior, ensuring strong predictions and have been studied extensively before. A salient novelty of this study, compared to previous works, is the inclusion of an analytical expression for the reheating temperature, $T_{\text{re}}$, which makes it a dynamical quantity. This is crucial for determining all the cosmological quantities analyzed in this work. Our results show a universal scaling behavior for a tightly bounded $T_{re}$ in both models. We believe this is the first time that $T_{re}$ is so closely determined. This work complements previous Bayesian and numerical studies by providing detailed numerical and analytical insights into the evolution of cosmological observables and reheating parameters, offering also constraints on inflationary models based on observational data., Comment: 24 pages, 27 figures. A few typos corrected. Accepted for publication in The European Physical Journal Plus

We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate windspeeds and directions at altitudes of 3 meters up to 24 meters, the first time winds at such altitudes have been probed on Mars. We compared our estimates to concurrent wind data at 1.5 m height from the meteorology package MEDA onboard the Mars 2020 Perseverance rover and to predictions from meteorological models. Wind directions inferred from the Ingenuity data agreed to within uncertainties with the directions measured by MEDA, when the latter were available, but deviated from model-predicted directions by as much as 180 deg in some cases. Also, the inferred windspeeds are often much higher than expected. For example, meteorological predictions tailored to the time and location of Ingenuity's 59th flight suggest Ingenuity should not have seen windspeeds above about 15 m/s, but we inferred speeds reaching nearly 25 m/s. By contrast, the 61st flight was at a similar time and season and showed weaker winds then the 59th flight, suggesting winds shaped by transient phenomena. For flights during which we have MEDA data to compare to, inferred windspeeds imply friction velocities exceeding 1 m/s and roughness lengths of more than 10 cm based on a boundary layer model that incorporates convective instability, which seem implausibly large. These results suggest Ingenuity was probing winds sensitive to aerodynamic conditions hundreds of meters upwind instead of the conditions very near Mars 2020, but they may also reflect a need for updated boundary layer wind models. An improved model for Ingenuity's aerodynamic response that includes the effects of transient winds may also modify our results. In any case, the work here provides a foundation for exploration of planetary boundary layers using drones and suggests important future avenues for research and development., Comment: Accepted by PSJ

Probabilistic programming is becoming increasingly popular thanks to its ability to specify problems with a certain degree of uncertainty. In this work, we focus on term rewriting, a well-known computational formalism. In particular, we consider systems that combine traditional rewriting rules with probabilities. Then, we define a distribution semantics for such systems that can be used to model the probability of reducing a term to some value. We also show how to compute a set of "explanations" for a given reduction, which can be used to compute its probability. Finally, we illustrate our approach with several examples and outline a couple of extensions that may prove useful to improve the expressive power of probabilistic rewrite systems., Comment: Submitted for publication

The rapid development of autoregressive Large Language Models (LLMs) has significantly improved the quality of generated texts, necessitating reliable machine-generated text detectors. A huge number of detectors and collections with AI fragments have emerged, and several detection methods even showed recognition quality up to 99.9% according to the target metrics in such collections. However, the quality of such detectors tends to drop dramatically in the wild, posing a question: Are detectors actually highly trustworthy or do their high benchmark scores come from the poor quality of evaluation datasets? In this paper, we emphasise the need for robust and qualitative methods for evaluating generated data to be secure against bias and low generalising ability of future model. We present a systematic review of datasets from competitions dedicated to AI-generated content detection and propose methods for evaluating the quality of datasets containing AI-generated fragments. In addition, we discuss the possibility of using high-quality generated data to achieve two goals: improving the training of detection models and improving the training datasets themselves. Our contribution aims to facilitate a better understanding of the dynamics between human and machine text, which will ultimately support the integrity of information in an increasingly automated world.

High computational power and the availability of large datasets have supported the development of Foundational Models. They are a new emerging technique widely used in Generative Artificial Intelligence, characterized by their scalability and their use in Transfer Learning. The enormous and heterogeneous amounts of data used in their initial training phase, known as pre-training, give them a higher generalization capacity than any other specific model, constituting a solid base that can be adapted or adjusted to a wide range of tasks, increasing their applicability. This study proposes LLIAM, the Llama Lora-Integrated Autorregresive Model. Low-Rank Adaptations are used to enhance the knowledge of the model with diverse time series datasets, known as the fine-tuning phase. To illustrate the capabilities of our proposal, two sets of experiments have been carried out that obtained favorable and promising results with lower training times than other Deep Learning approaches. With this work, we also encourage the use of available resources (such as these pre-trained models) to avoid unnecessary and costly training, narrowing the gap between the goals of traditional Artificial Intelligence and those specified by the definition of Green Artificial Intelligence.

Growing amount and quality of AI-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the generator model are not known in advance. In this work, we focus on the robustness of classifier-based detectors of AI-generated text, namely their ability to transfer to unseen generators or semantic domains. We investigate the geometry of the embedding space of Transformer-based text encoders and show that clearing out harmful linear subspaces helps to train a robust classifier, ignoring domain-specific spurious features. We investigate several subspace decomposition and feature selection strategies and achieve significant improvements over state of the art methods in cross-domain and cross-generator transfer. Our best approaches for head-wise and coordinate-based subspace removal increase the mean out-of-distribution (OOD) classification score by up to 9% and 14% in particular setups for RoBERTa and BERT embeddings respectively. We release our code and data: https://github.com/SilverSolver/RobustATD, Comment: Accepted to Findings of EMNLP 2024

The Kramers-Wannier self-duality of critical quantum chains is examined from the perspective of model wave functions. We demonstrate, using the transverse-field Ising chain and the $3$-state Potts chain as examples, that the symmetry operator for the Kramers-Wannier self-duality follows in a simple and direct way from a `generalised' translation symmetry of the model wave function in the anyonic fusion basis. This translation operation, in turn, comprises a sequence of $F$-moves in the underlying fusion category. The symmetry operator thus obtained naturally admits the form of a matrix product operator and obeys non-invertible fusion rules. The findings reveal an intriguing connection between the (non-invertible) translation symmetry on the lattice and topological aspects of the conformal field theory describing the scaling limit., Comment: 31 pages, 3 figures

We consider the constraints on new physics affecting rare kaon and hyperon decay as parametrized by a low energy effective theory that can be obtained from high-$\rm p_T$ observables at LHC. To this end, we match the relevant low energy Wilson coefficients onto those in SMEFT at dimension six and compute their contributions to charged dilepton, monolepton, monojets, and $\rm VH$ searches by ATLAS and CMS. Constraints from rare kaon decays are generally more restrictive, with the ones arising from high-$\rm p_T$ measurements complementing them along directions in parameter space to which the former are not sensitive. We also illustrate the effect of a high luminosity LHC run in reducing the viable parameter regions., Comment: 21 pages, 11 figures

This paper proposes a novel approach to solving nonlinear programming problems using a sharp augmented Lagrangian method with a smoothing technique. Traditional sharp augmented Lagrangian methods are known for their effectiveness but are often hindered by the need for global minimization of nonconvex, nondifferentiable functions at each iteration. To address this challenge, we introduce a smoothing function that approximates the sharp augmented Lagrangian, enabling the use of primal minimization strategies similar to those in Powell--Hestenes--Rockafellar (PHR) methods. Our approach retains the theoretical rigor of classical duality schemes while allowing for the use of stationary points in the primal optimization process. We present two algorithms based on this method--one utilizing standard descent and the other employing coordinate descent. Numerical experiments demonstrate that our smoothing--based method compares favorably with the PHR augmented Lagrangian approach, offering both robustness and practical efficiency. The proposed method is particularly advantageous in scenarios where exact minimization is computationally infeasible, providing a balance between theoretical precision and computational tractability.