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We prove that if $ T $ is a semi-special tree that is not special, then there exists a graph $ G $, formed as an inflation of a sparse $ T $-graph, such that for any special tree $ S $, $ G $ is not a subdivision of an inflation of an sparse $ S $-graph. Furthermore $G$ has an end of uncountable degree that has no ray graph. This result provides a consistent negative answer to a problem posed by Stefan Geschke et al. in 2023. Additionally, we introduce and explore a property that generalizes Halin's grid theorem, extending it to ends of degree $ \aleph_1 $, which was originally established for ends of countable degree.

Context. Carbon, nitrogen, and oxygen are the most abundant elements throughout the universe, after hydrogen and helium. Studying these elements in low-metallicity stars can provide crucial information on the chemical composition in the early Galaxy and possible internal mixing processes that can alter the surface composition of the stars. Aims. This work aims to investigate the chemical abundance patterns for CNO elements and Li in a homogeneously analyzed sample of 52 metal-poor halo giant stars. Methods. We used high-resolution spectra with a high signal-to-noise ratio (S/N) to carry out a spectral synthesis to derive detailed C, N, O, and Li abundances for a sample of stars with metallicities in the range of -3.58 <= [Fe/H] <= -1.79 dex. Our study was based on the assumption of one-dimensional (1D) local thermodynamic equilibrium (LTE) atmospheres. Results. Based on carbon and nitrogen abundances, we investigated the deep mixing taking place within stars along the red giant branch (RGB). The individual abundances of carbon decrease towards the upper RGB while nitrogen shows an increasing trend, indicating that carbon has been converted into nitrogen. No signatures of ON-cycle processed material were found for the stars in our sample. We computed a set of galactic chemical evolution (GCE) models, implementing different sets of massive star yields, both with and without including the effects of stellar rotation on nucleosynthesis. We confirm that stellar rotation is necessary to explain the highest [N/Fe] and [N/O] ratios observed in unmixed halo stars. The predicted level of N enhancement varies sensibly in dependence of the specific set of yields that are adopted. For stars with stellar parameters similar to those of our sample, heavy elements such as Sr, Y, and Zr appear to have unchanged abundances despite the stellar evolution mixing processes., Comment: 19 pages, 17 figures. Accepted for publication in A&A on Semptember 04, 2024. Table with derived abundances will be available at the CDS after the paper will be published

Run-time steering strategies like Medprompt are valuable for guiding large language models (LLMs) to top performance on challenging tasks. Medprompt demonstrates that a general LLM can be focused to deliver state-of-the-art performance on specialized domains like medicine by using a prompt to elicit a run-time strategy involving chain of thought reasoning and ensembling. OpenAI's o1-preview model represents a new paradigm, where a model is designed to do run-time reasoning before generating final responses. We seek to understand the behavior of o1-preview on a diverse set of medical challenge problem benchmarks. Following on the Medprompt study with GPT-4, we systematically evaluate the o1-preview model across various medical benchmarks. Notably, even without prompting techniques, o1-preview largely outperforms the GPT-4 series with Medprompt. We further systematically study the efficacy of classic prompt engineering strategies, as represented by Medprompt, within the new paradigm of reasoning models. We found that few-shot prompting hinders o1's performance, suggesting that in-context learning may no longer be an effective steering approach for reasoning-native models. While ensembling remains viable, it is resource-intensive and requires careful cost-performance optimization. Our cost and accuracy analysis across run-time strategies reveals a Pareto frontier, with GPT-4o representing a more affordable option and o1-preview achieving state-of-the-art performance at higher cost. Although o1-preview offers top performance, GPT-4o with steering strategies like Medprompt retains value in specific contexts. Moreover, we note that the o1-preview model has reached near-saturation on many existing medical benchmarks, underscoring the need for new, challenging benchmarks. We close with reflections on general directions for inference-time computation with LLMs., Comment: 25 pages

Games represent a significant part of modern culture, which demonstrates the importance of ensuring that everyone can participate and play in order to feel included in our society. However, most digital games end up being inaccessible to people with disabilities. Part of the problem when thinking about inclusive game design is that there is no single solution for accessibility, and what works well for one group may not work for another. This work proposes a set of guidelines for the development of inclusive mobile games, considering the widespread use of smartphones by the population and the need to include people with disabilities in the gaming culture., Comment: in Portuguese language

The Variational Quantum Eigensolver (VQE) is a promising hybrid algorithm, utilizing both quantum and classical computers to obtain the ground state energy of molecules. In this context, this study applies VQE to investigate the ground state of protocatechuic acid, analyzing its performance with various Ans\"atze and active spaces. Subsequently, all VQE results were compared to those obtained with the Hartree-Fock (HF) method. The results demonstrate that Ans\"atze, like unitary coupled-cluster singles and doubles (UCCSD) and variations of Hardware-Efficient, generally achieve accuracy close to that of HF. Furthermore, the increase in active space has led to the models becoming more difficult to converge to values with a greater distance from the correct energy. In summary, the findings of this study reinforce the use of VQE as a powerful tool for analyzing molecular ground state energies. Finally, the results underscore the critical importance of Ansatz selection and active space size in VQE performance, providing valuable insights into its potential and limitations., Comment: 10 pages, 6 figures, 1 table

The Pauli exclusion principle combined with interactions between fermions is a unifying basic mechanism that can give rise to quantum phases with spin order in diverse physical systems. Transition-metal ferromagnets, with isotropic ordering respecting crystallographic rotation symmetries and with a net magnetization, are a relatively common manifestation of this mechanism, leading to numerous practical applications, e.g., in spintronic information technologies. In contrast, superfluid $^3$He has been a unique and fragile manifestation, in which the spin-ordered phase is anisotropic, breaking the real-space rotation symmetries, and has zero net magnetization. The recently discovered altermagnets share the spin-ordered anisotropic zero-magnetization nature of superfluid $^3$He. Yet, altermagnets appear to be even more abundant than ferromagnets, can be robust, and are projected to offer superior scalability for spintronics compared to ferromagnets. Our Perspective revisits the decades of research of the spin-ordered anisotropic zero-magnetization phases including, besides superfluid $^3$He, also theoretically conceived counterparts in nematic electronic liquid-crystal phases. While all sharing the same extraordinary character of symmetry breaking, we highlight the distinctions in microscopic physics which set altermagnets apart and enable their robust and abundant material realizations., Comment: 12 pages, 4 figures

We study time-crystalline order in periodically driven quantum Ising models on disorder-free decorated lattices. Using a tensor network ansatz for the state which reflects the geometry of a unit cell of the lattice, we show through finite entanglement scaling that the system has an exponentially long-lived subharmonic response in the thermodynamic limit. The resulting discrete time crystal is not only stable to imperfections in the transverse field, but also exhibits a bipartite rigidity to generic perturbations in the longitudinal field. We call this state a bipartite discrete time crystal and reveal a rich prethermal phase diagram, including multiple regions of bipartite time-crystalline order, uniform time-crystalline order and thermalization, with boundaries depending delicately on the topology of the decorated lattice. Our results thus uncover a variety of time crystals which may be realized on current digital quantum processors and analog quantum simulators.

We consider a scalar parabolic partial differential equation on the interval with nonlinear boundary conditions that are asymptotically linear. As the parameter crosses critical values (e.g. the Steklov eigenvalues), it is known that there are large equilibria that arise through a bifurcation from infinity (i.e., such equilibria converge, after rescaling, to the Steklov eigenfunctions). We provide a compactification approach to the study of such unbounded bifurcation curves of equilibria, their stability, and heteroclinic orbits. In particular, we construct an induced semiflow at infinity such that the Steklov eigenfunctions are equilibria. Moreover, we prove the existence of infinite-time blow-up solutions that converge, after rescaling, to certain eigenfunctions that are equilibria of the induced semiflow at infinity., Comment: 18 pages, 8 figures

The solar magnetized corona is responsible for various manifestations with a space weather impact, such as flares, coronal mass ejections (CMEs) and, naturally, the solar wind. Modeling the corona's dynamics and evolution is therefore critical for improving our ability to predict space weather In this work, we demonstrate that generative deep learning methods, such as Denoising Diffusion Probabilistic Models (DDPM), can be successfully applied to simulate future evolutions of the corona as observed in Extreme Ultraviolet (EUV) wavelengths. Our model takes a 12-hour video of an Active Region (AR) as input and simulate the potential evolution of the AR over the subsequent 12 hours, with a time-resolution of two hours. We propose a light UNet backbone architecture adapted to our problem by adding 1D temporal convolutions after each classical 2D spatial ones, and spatio-temporal attention in the bottleneck part. The model not only produce visually realistic outputs but also captures the inherent stochasticity of the system's evolution. Notably, the simulations enable the generation of reliable confidence intervals for key predictive metrics such as the EUV peak flux and fluence of the ARs, paving the way for probabilistic and interpretable space weather forecasting. Future studies will focus on shorter forecasting horizons with increased spatial and temporal resolution, aiming at reducing the uncertainty of the simulations and providing practical applications for space weather forecasting. The code used for this study is available at the following link: https://github.com/gfrancisco20/video_diffusion

In this paper, we consider the monoids of all partial endomorphisms, of all partial weak endomorphisms, of all injective partial endomorphisms, of all partial strong endomorphisms and of all partial strong weak endomorphisms of a star graph with a finite number of vertices. Our main objective is to exhibit a presentation for each of them.

For a digraph $D$ of order $n$ and an integer $1 \leq k \leq n-1$, the $k$-token digraph of $D$ is the graph whose vertices are all $k$-subsets of vertices of $D$ and, given two such $k$-subsets $A$ and $B$, $(A,B)$ is an arc in the $k$-token digraph whenever $\{a\} = A \setminus B$, $\{b\} = B \setminus A$, and there is an arc $(a,b)$ in $D$. Token digraphs are a generalization of token graphs. In this paper, we study some properties of token digraphs, including strong and unilateral connectivity, kernels, girth, circumference and Eulerianity. We also extend some known results on the clique and chromatic numbers of $k$-token graphs, addressing the bidirected clique number and dichromatic number of $k$-token digraphs. Additionally, we prove that determining whether $2$-token digraphs have a kernel is NP-complete., Comment: 24 pages, 9 figures

Developing a reinforcement learning (RL) agent often involves identifying effective values for a large number of parameters, covering the policy, reward function, environment, and the agent's internal architecture, such as parameters controlling how the peripheral vision and memory modules work. Critically, since these parameters are interrelated in complex ways, optimizing them can be viewed as a black box optimization problem, which is especially challenging for non-experts. Although existing optimization-as-a-service platforms (e.g., Vizier, Optuna) can handle such problems, they are impractical for RL systems, as users must manually map each parameter to different components, making the process cumbersome and error-prone. They also require deep understanding of the optimization process, limiting their application outside ML experts and restricting access for fields like cognitive science, which models human decision-making. To tackle these challenges, we present AgentForge, a flexible low-code framework to optimize any parameter set across an RL system. AgentForge allows the user to perform individual or joint optimization of parameter sets. An optimization problem can be defined in a few lines of code and handed to any of the interfaced optimizers. We evaluated its performance in a challenging vision-based RL problem. AgentForge enables practitioners to develop RL agents without requiring extensive coding or deep expertise in optimization., Comment: This preprint was submitted to the 17th International Conference on Agents and Artificial Intelligence (ICAART'2025) and is currently under review

Solar flare forecasting mainly relies on photospheric magnetograms and associated physical features to predict forthcoming flares. However, it is believed that flare initiation mechanisms often originate in the chromosphere and the lower corona. In this study, we employ deep learning as a purely data-driven approach to compare the predictive capabilities of chromospheric and coronal UV and EUV emissions across different wavelengths with those of photospheric line-of-sight magnetograms. Our findings indicate that individual EUV wavelengths can provide discriminatory power comparable or better to that of line-of-sight magnetograms. Moreover, we identify simple multimodal neural network architectures that consistently outperform single-input models, showing complementarity between the flare precursors that can be extracted from the distinct layers of the solar atmosphere. To mitigate potential biases from known misattributions in Active Region flare catalogs, our models are trained and evaluated using full-disk images and a comprehensive flare event catalog at the full-disk level. We introduce a deep-learning architecture suited for extracting temporal features from full-disk videos.

The growing volume of academic publications poses significant challenges for researchers conducting timely and accurate Systematic Literature Reviews, particularly in fast-evolving fields like artificial intelligence. This growth of academic literature also makes it increasingly difficult for lay people to access scientific knowledge effectively, meaning academic literature is often misrepresented in the popular press and, more broadly, in society. Traditional SLR methods are labor-intensive and error-prone, and they struggle to keep up with the rapid pace of new research. To address these issues, we developed \textit{PROMPTHEUS}: an AI-driven pipeline solution that automates the SLR process using Large Language Models. We aimed to enhance efficiency by reducing the manual workload while maintaining the precision and coherence required for comprehensive literature synthesis. PROMPTHEUS automates key stages of the SLR process, including systematic search, data extraction, topic modeling using BERTopic, and summarization with transformer models. Evaluations conducted across five research domains demonstrate that PROMPTHEUS reduces review time, achieves high precision, and provides coherent topic organization, offering a scalable and effective solution for conducting literature reviews in an increasingly crowded research landscape. In addition, such tools may reduce the increasing mistrust in science by making summarization more accessible to laypeople. The code for this project can be found on the GitHub repository at https://github.com/joaopftorres/PROMPTHEUS.git

Bosonic bunching is a term used to describe the well-known tendency of bosons to bunch together, and which differentiates their behaviour from that of fermions or classical particles. However, in some situations perfectly indistinguishable bosons may counter-intuitively bunch less than classical, distinguishable particles. Here we report two such counter-intuitive multiphoton bunching effects observed with three photons in a three-mode balanced photonic Fourier interferometer. In this setting, we show indistinguishable photons actually minimize the probability of bunching. We also show that any non-trivial value of the three-photon collective photonic phase leads to a decreased probability of all photons ending up in the same mode, even as we increase pairwise indistinguishability. Our experiments feature engineering of partial indistinguishability scenarios using both the time and the polarization photonic degrees of freedom, and a polarization-transparent 8-mode tunable interferometer with a quantum-dot source of single photons. Besides the foundational understanding, the observation of these counter-intuitive phenomena open news perspective in devising more efficient ways of routing photons for advantage in metrology and quantum computation., Comment: 10+8 pages, 5+1 figures

Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics., Comment: 22 pages, 13 figures, 10 tables

Large Language Model (LLM) Agents are an emerging computing paradigm that blends generative machine learning with tools such as code interpreters, web browsing, email, and more generally, external resources. These agent-based systems represent an emerging shift in personal computing. We contribute to the security foundations of agent-based systems and surface a new class of automatically computed obfuscated adversarial prompt attacks that violate the confidentiality and integrity of user resources connected to an LLM agent. We show how prompt optimization techniques can find such prompts automatically given the weights of a model. We demonstrate that such attacks transfer to production-level agents. For example, we show an information exfiltration attack on Mistral's LeChat agent that analyzes a user's conversation, picks out personally identifiable information, and formats it into a valid markdown command that results in leaking that data to the attacker's server. This attack shows a nearly 80% success rate in an end-to-end evaluation. We conduct a range of experiments to characterize the efficacy of these attacks and find that they reliably work on emerging agent-based systems like Mistral's LeChat, ChatGLM, and Meta's Llama. These attacks are multimodal, and we show variants in the text-only and image domains., Comment: website: https://imprompter.ai code: https://github.com/Reapor-Yurnero/imprompter v2 changelog: add new results to Table 3, correct several typos

Altermagnets represent a new class of magnetic phases without net magnetization that are invariant under a combination of rotation and time reversal. Unlike conventional collinear antiferromagnets (AFM), altermagnets could lead to new correlated states and important material properties deriving from their non-relativistic spin-split band structure. Indeed, they are the magnetic analogue of unconventional superconductors and can yield spin polarized electrical currents in the absence of external magnetic fields, making them promising candidates for next-generation spintronics. Here, we report altermagnetism in the correlated insulator, magnetically-ordered tetragonal oxychalcogenide, La$_2$O$_3$Mn$_2$Se$_2$. Symmetry analysis reveals a $\mathit{d}_{x^2 - y^2}$-wave type spin momentum locking, which is supported by density functional theory (DFT) calculations. Magnetic measurements confirm the AFM transition below $\sim$166 K while neutron pair distribution function analysis reveals a 2D short-range magnetic order that persists above the N\'eel temperature. Single crystals are grown and characterized using X-ray diffraction, optical and electron microscopy, and microRaman spectroscopy to confirm the crystal structure, stoichiometry, and uniformity.

Hydroelectricity accounted for roughly 66% of the total generation in Brazil in 2023 and addressed most of the intermittency of wind and solar generation. Thus, one of the most important steps in the operation planning of this country is the forecast of the natural inflow energy (NIE) time series, an approximation of the energetic value of the water inflows. To manage water resources over time, the Brazilian system operator performs long-term forecasts for the NIE to assess the water values through long-term hydrothermal planning models, which are then used to define the short-term merit order in day-ahead scheduling. Therefore, monitoring optimistic bias in NIE forecasts is crucial to prevent an optimistic view of future system conditions and subsequent riskier storage policies. In this article, we investigate and showcase strong evidence of an optimistic bias in the official NIE forecasts, with predicted values consistently exceeding the observations over the past 12 years in the two main subsystems (Southeast and Northeast). Rolling window out-of-sample tests conducted with real data demonstrate that the official forecast model exhibits a statistically significant bias of 6%, 13%, 18%, and 23% for 1, 6, 12, and 24 steps ahead in the Southeast subsystem, and 19%, 57%, 80%, and 108% in the Northeast.

We prove that for every ${\gamma > 0}$ there exists $n_0 \in \mathbb{N}$ such that for every ${n \geq n_0}$ any family of up to $\lfloor{n^{\frac12+\gamma}}\rfloor$ trees having at most $(1-\gamma)n$ vertices in each bipartition class can be packed into $K_{n,n}$. As a tool for our proof, we show an approximate bipartite version of the Koml\'os-S\'ark\"ozy-Szemer\'edi Theorem, which we believe to be of independent interest.

We establish the connection between the Gibbons-Hawking Euclidean path integral approach applied to the canonical ensemble of a Reissner-Nordstr\"om black hole and the thermodynamic theory of black holes of Davies. We build the ensemble, characterized by a reservoir at infinity at temperature $T$ and electric charge $Q$, in $d$ dimensions. The Euclidean path integral yields the action and partition function. In zero loop, we uncover two solutions, one with horizon radius $r_{+1}$ the least massive, the other with $r_{+2}$. We find a saddle point separating the solutions at $T_s$ and $Q_s$ with radius $r_{+s}$. For $T > T_s$ there is only hot flat space with charge at infinity. We derive the thermodynamics. The heat capacity gives that $T_s$ and $Q_s$ separate stable, $r_{+1}$, from unstable, $r_{+2}$ , solutions, the phase transition being second order. The free energy of the stable solution is positive, so if the system is a black hole it makes a first order transition to hot space. An interpretation of the results as energy wavelengths is attempted. For $d = 4$, the thermodynamics from the path integral applied to the canonical ensemble is precisely the Davies thermodynamics theory of black holes, with $T_s$ being the Davies point. We sketch the case $d = 5$., Comment: 15 pages, 2 figures

Chick sexing, the process of determining the gender of day-old chicks, is a critical task in the poultry industry due to the distinct roles that each gender plays in production. While effective traditional methods achieve high accuracy, color, and wing feather sexing is exclusive to specific breeds, and vent sexing is invasive and requires trained experts. To address these challenges, we propose a novel approach inspired by facial gender classification techniques in humans: facial chick sexing. This new method does not require expert knowledge and aims to reduce training time while enhancing animal welfare by minimizing chick manipulation. We develop a comprehensive system for training and inference that includes data collection, facial and keypoint detection, facial alignment, and classification. We evaluate our model on two sets of images: Cropped Full Face and Cropped Middle Face, both of which maintain essential facial features of the chick for further analysis. Our experiment demonstrates the promising viability, with a final accuracy of 81.89%, of this approach for future practices in chick sexing by making them more universally applicable.

In this work, we present high-resolution (R~100,000), high signal-to-noise (S/N~800) spectroscopic observations for the well-known, bright, extremely metal-poor, carbon-enhanced star BD+44 493. We determined chemical abundances and upper limits for 17 elements from WIYN/NEID data, complemented with 11 abundances re-determined from Subaru and Hubble data, using the new, more accurate, stellar atmospheric parameters calculated in this work. Our analysis suggests that BD+44 493 is a low-mass (0.83Msun) old (12.1-13.2Gyr) second-generation star likely formed from a gas cloud enriched by a single metal-free 20.5Msun Population III star in the early Universe. With a disk-like orbit, BD+44 493 does not appear to be associated with any major merger event in the early history of the Milky Way. From the precision radial-velocity NEID measurements (median absolute deviation - MAD=16m/s), we were able to constrain companion planetary masses around BD+44 493 and rule out the presence of planets as small as msin(i)=2MJ out to periods of 100 days. This study opens a new avenue of exploration for the intersection between stellar archaeology and exoplanet science using NEID., Comment: 25 pages, 11 figures, accepted for publication on ApJ

Model lattices such as the kagome and Lieb lattices have been widely investigated to elucidate the properties of interacting flat-band systems. While a quasicrystal does not have proper bands, the non-interacting density of states of several of them displays the typical signature of a flat band pinned at the Fermi level: a delta-function zero-energy peak. Here, we employ quantum Monte Carlo simulations to determine the effect of onsite repulsion on these quasicrystals. While global properties such as the antiferromagnetic structure factor and the specific heat behave similarly as in the case of periodic lattices undergoing a Mott transition, the behavior of the local density of states depends on the coordination number of the site. In particular, sites with the smallest coordination number, which give the dominant spectral-weight contribution to the zero-energy peak, are the ones most strongly impacted by the interaction. Besides establishing site-selective correlations in quasicrystals, our work also points to the importance of the real-space structure of flat bands in interacting systems., Comment: 7 pages, 4 figures

Alignment with human preferences is an important step in developing accurate and safe large language models. This is no exception in machine translation (MT), where better handling of language nuances and context-specific variations leads to improved quality. However, preference data based on human feedback can be very expensive to obtain and curate at a large scale. Automatic metrics, on the other hand, can induce preferences, but they might not match human expectations perfectly. In this paper, we propose an approach that leverages the best of both worlds. We first collect sentence-level quality assessments from professional linguists on translations generated by multiple high-quality MT systems and evaluate the ability of current automatic metrics to recover these preferences. We then use this analysis to curate a new dataset, MT-Pref (metric induced translation preference) dataset, which comprises 18k instances covering 18 language directions, using texts sourced from multiple domains post-2022. We show that aligning TOWER models on MT-Pref significantly improves translation quality on WMT23 and FLORES benchmarks., Comment: Accepted at EMNLP Main 2024

This paper introduces PublicHearingBR, a Brazilian Portuguese dataset designed for summarizing long documents. The dataset consists of transcripts of public hearings held by the Brazilian Chamber of Deputies, paired with news articles and structured summaries containing the individuals participating in the hearing and their statements or opinions. The dataset supports the development and evaluation of long document summarization systems in Portuguese. Our contributions include the dataset, a hybrid summarization system to establish a baseline for future studies, and a discussion on evaluation metrics for summarization involving large language models, addressing the challenge of hallucination in the generated summaries. As a result of this discussion, the dataset also provides annotated data that can be used in Natural Language Inference tasks in Portuguese., Comment: 26 pages

The rising rates of diabetes necessitate innovative methods for its management. Continuous glucose monitors (CGM) are small medical devices that measure blood glucose levels at regular intervals providing insights into daily patterns of glucose variation. Forecasting of glucose trajectories based on CGM data holds the potential to substantially improve diabetes management, by both refining artificial pancreas systems and enabling individuals to make adjustments based on predictions to maintain optimal glycemic range.Despite numerous methods proposed for CGM-based glucose trajectory prediction, these methods are typically evaluated on small, private datasets, impeding reproducibility, further research, and practical adoption. The absence of standardized prediction tasks and systematic comparisons between methods has led to uncoordinated research efforts, obstructing the identification of optimal tools for tackling specific challenges. As a result, only a limited number of prediction methods have been implemented in clinical practice. To address these challenges, we present a comprehensive resource that provides (1) a consolidated repository of curated publicly available CGM datasets to foster reproducibility and accessibility; (2) a standardized task list to unify research objectives and facilitate coordinated efforts; (3) a set of benchmark models with established baseline performance, enabling the research community to objectively gauge new methods' efficacy; and (4) a detailed analysis of performance-influencing factors for model development. We anticipate these resources to propel collaborative research endeavors in the critical domain of CGM-based glucose predictions. {Our code is available online at github.com/IrinaStatsLab/GlucoBench., Comment: The Twelfth International Conference on Learning Representations. 2024

Gaia astrometry of nearby stars is precise enough to detect the tiny displacements induced by substellar companions, but radial velocity data are needed for definitive confirmation. Here we present radial velocity follow-up observations of 28 M and K stars with candidate astrometric substellar companions, which led to the confirmation of two systems, Gaia-4b and Gaia-5b, and the refutation of 21 systems as stellar binaries. Gaia-4b is a massive planet ($M = 11.8 \pm 0.7 \:\mathrm{M_J}$) in a $P = 571.3 \pm 1.4\:\mathrm{day}$ orbit with a projected semi-major axis $a_0=0.312 \pm 0.040\:\mathrm{mas}$ orbiting a $0.644 \pm 0.02 \:\mathrm{M_\odot}$ star. Gaia-5b is a brown dwarf ($M = 20.9 \pm 0.5\:\mathrm{M_J}$) in a $P = 358.58 \pm 0.19\:\mathrm{days}$ eccentric $e=0.6412 \pm 0.0027$ orbit with a projected angular semi-major axis of $a_0 = 0.947 \pm 0.038\:\mathrm{mas}$ around a $0.34 \pm 0.03 \mathrm{M_\odot}$ star. Gaia-4b is one of the first exoplanets discovered via the astrometric technique, and is one of the most massive planets known to orbit a low-mass star., Comment: Submitted to AAS journals. 26 pages, 12 figures, 4 tables

In this paper, we explore the interplay between barcode and topological entropies. Wrapped Floer homology barcode entropy is the exponential growth of not-to-short bars in the persistence module associated with the filtered wrapped Floer homology. We prove that the barcode entropy is an invariant of the contact boundary of a Liouville domain, i.e., does not depend on the filling, and it bounds from below the topological entropy of the Reeb flow. We make no assumptions on the first Chern class of the Liouville domain or the contact form., Comment: 29 pages

This work presents the development of a lane detection system aimed at assisting the driving of conventional and autonomous vehicles. The system was implemented using traditional computer vision techniques, focusing on robustness and efficiency to operate in real-time, even under adverse conditions such as worn-out lanes and weather variations. The methodology employs an image processing pipeline that includes camera calibration, distortion correction, perspective transformation, and binary image generation. Lane detection is performed using sliding window techniques and segmentation based on gradients and color channels, enabling the precise identification of lanes in various road scenarios. The results indicate that the system can effectively detect and track lanes, performing well under different lighting conditions and road surfaces. However, challenges were identified in extreme situations, such as intense shadows and sharp curves. It is concluded that, despite its limitations, the traditional computer vision approach shows significant potential for application in driver assistance systems and autonomous navigation, with room for future improvements.

General-purpose LLM judges capable of human-level evaluation provide not only a scalable and accurate way of evaluating instruction-following LLMs but also new avenues for supervising and improving their performance. One promising way of leveraging LLM judges for supervision is through Minimum Bayes Risk (MBR) decoding, which uses a reference-based evaluator to select a high-quality output from amongst a set of candidate outputs. In the first part of this work, we explore using MBR decoding as a method for improving the test-time performance of instruction-following LLMs. We find that MBR decoding with reference-based LLM judges substantially improves over greedy decoding, best-of-N decoding with reference-free judges and MBR decoding with lexical and embedding-based metrics on AlpacaEval and MT-Bench. These gains are consistent across LLMs with up to 70B parameters, demonstrating that smaller LLM judges can be used to supervise much larger LLMs. Then, seeking to retain the improvements from MBR decoding while mitigating additional test-time costs, we explore iterative self-training on MBR-decoded outputs. We find that self-training using Direct Preference Optimisation leads to significant performance gains, such that the self-trained models with greedy decoding generally match and sometimes exceed the performance of their base models with MBR decoding.

The development of large-scale quantum networks requires reliable quantum channels, the quality of which can be quantified by the framework of quantum process tomography. In this work, we leverage ancilla-assisted process tomography and Bayesian inference to probe a 1.6 km deployed fiber-optic link. We send one of two polarization-entangled photons from Alice in one building to Bob in another, exploiting the local qubit as an ancilla system to characterize the corresponding quantum channel. Monitoring over a 24 h period returns a steady process fidelity of 95.1(1)%, while controllable spectral filtering with passbands from 0.025-4.38 THz finds fidelities that first increase, then level off with bandwidth, suggesting both stable operation with time and minimal polarization mode dispersion. To our knowledge, these results represent the first AAPT of a deployed quantum link, revealing a valuable tool for in situ analysis of entanglement-based quantum networks.

Optimizing human-AI interaction requires users to reflect on their own performance critically. Our study examines whether people using AI to complete tasks can accurately monitor how well they perform. Participants (N = 246) used AI to solve 20 logical problems from the Law School Admission Test. While their task performance improved by three points compared to a norm population, participants overestimated their performance by four points. Interestingly, higher AI literacy was linked to less accurate self-assessment. Participants with more technical knowledge of AI were more confident but less precise in judging their own performance. Using a computational model, we explored individual differences in metacognitive accuracy and found that the Dunning-Kruger effect, usually observed in this task, ceased to exist with AI use. We discuss how AI levels our cognitive and metacognitive performance and consider the consequences of performance overestimation for designing interactive AI systems that enhance cognition., Comment: 20 pages, 6 figures, 4 tables

The quality of open-weight LLMs has seen significant improvement, yet they remain predominantly focused on English. In this paper, we introduce the EuroLLM project, aimed at developing a suite of open-weight multilingual LLMs capable of understanding and generating text in all official European Union languages, as well as several additional relevant languages. We outline the progress made to date, detailing our data collection and filtering process, the development of scaling laws, the creation of our multilingual tokenizer, and the data mix and modeling configurations. Additionally, we release our initial models: EuroLLM-1.7B and EuroLLM-1.7B-Instruct and report their performance on multilingual general benchmarks and machine translation.

In diluted lattices, cooperation is often enhanced at specific densities, particularly near the percolation threshold for stochastic updating rules. However, the Replicator rule, despite being probabilistic, does not follow this trend. We find that this anomalous behavior is caused by structures formed by holes and defectors, which prevent some agents from experiencing fluctuations, thereby restricting the free flow of information across the network. As a result, the system becomes trapped in a frozen state, though this can be disrupted by introducing perturbations. Finally, we provide a more quantitative analysis of the relationship between the percolation threshold and cooperation, tracking its development within clusters of varying sizes and demonstrating how the percolation threshold shapes the fundamental structures of the lattice.

The recent discovery of altermagnets has opened new perspectives in the field of ordered phases in condensed matter. In strongly-correlated superfluids, the nodal p-wave and d-wave ordered phases of $^{3}$He and cuprates play a prominent role in physics for their rich phenomenology of the symmetry-breaking order parameters. While the p-wave and d-wave superfluids have been extensively studied over the past half a century, material realizations of their magnetic counterparts have remained elusive for many decades. This is resolved in altermagnets, whose recent discovery was driven by research in the field of spintronics towards highly scalable information technologies. Altermagnets feature d, g or i-wave magnetic ordering, with a characteristic alternation of spin polarization and spin-degenerate nodes. Here we review how altermagnetism can be identified from symmetries of collinear spin densities in crystal lattices, and can be realized at normal conditions in a broad family of insulating and conducting materials. We highlight salient electronic-structure signatures of the altermagnetic ordering, discuss extraordinary relativistic and topological phenomena that emerge in their band structures, and comment on strong-correlation effects. We then extend the discussion to non-collinear spin densities in crystals, including the prediction of p-wave magnets, and conclude with a brief summary of the reviewed physical properties of the nodal magnetically-ordered phases., Comment: 17 pages, 6 figures

Vibrational spectroscopy is an indispensable analytical tool that provides structural fingerprints for molecules, solids, and interfaces thereof. This study introduces THeSeuSS (THz Spectra Simulations Software) - an automated computational platform that efficiently simulates IR and Raman spectra for both periodic and non-periodic systems. Utilizing DFT and DFTB, THeSeuSS offers robust capabilities for detailed vibrational spectra simulations. Our extensive evaluations and benchmarks demonstrate that THeSeuSS accurately reproduces both previously calculated and experimental spectra, enabling precise comparisons and interpretations of vibrational features across various test cases, including H2O and glycine molecules in the gas phase, as well as solid ammonia and solid ibuprofen. Designed with a user-friendly interface and seamless integration with existing computational chemistry tools, THeSeuSS enhances the accessibility and applicability of advanced spectroscopic simulations, supporting research and development in chemical, pharmaceutical, and material sciences. Future updates aim to expand its methodological diversity by incorporating machine learning techniques to analyze larger and more complex systems, solidifying THeSeuSS's role as an essential tool in the computational chemist's arsenal.

Partial least squares (PLS) is a simple factorisation method that works well with high dimensional problems in which the number of observations is limited given the number of independent variables. In this article, we show that PLS can perform better than ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO) and ridge regression in forecasting quarterly gross domestic product (GDP) growth, covering the period from 2000 to 2023. In fact, through dimension reduction, PLS proved to be effective in lowering the out-of-sample forecasting error, specially since 2020. For the period 2000-2019, the four methods produce similar results, suggesting that PLS is a valid regularisation technique like LASSO or ridge.

Cell cortex contraction is essential for shaping cells, enabling movement, ensuring proper division, maintaining tissue integrity, guiding development, and responding to mechanical signals - all critical for the life and health of multicellular organisms. Differential contractions in cell membranes, particularly when cells of different types interact, play a crucial role in the emergence of segregation. In this study, we introduce a model where rings composed of active particles interact through differential membrane contraction within a specified cutoff distance. We demonstrate that segregation arises solely from differential contraction, with the activity of the rings functioning similarly to an effective temperature. Additionally, we observed that segregation involves cluster fusion-diffusion process. However, the decay exponent of the segregation parameter we found is close to $\lambda \sim -1/3$, which differs from the $\lambda \sim -1/4$ predicted by previous theoretical approaches and simulations.