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Silicon color centers (SiCCs) have recently emerged as potential building blocks for light emitters in Si photonics, quantum emitters with spin storage capabilities, and Si-based quantum repeaters. We have recently developed a non-invasive method to engineer carbon-related SiCCs confined to ultra-thin nanolayers within a pristine crystalline environment, which is of utmost importance for the photostability of SiCCs. Here, we demonstrate embedding these C-doping-based SiCCs into the only 9 nm wide intrinsic region of a p-i-n diode using the epitaxial self-assembly of color centers. We report electrically-pumped light emission with an exponential increase in the intensity as a function of the driving current until saturation. We associate this property with the shift of quasi-Fermi-level position upon electrical driving, which simultaneously improves the spectral homogeneity of the engineered SiCCs. Our study demonstrates the electrical control and driving of near-infrared emitters in high-quality silicon diodes, an essential milestone for advancing classical and quantum optoelectronics., Comment: Main text: 11 pages + 3 Figures & Supplementary material: 1 page and 1 Figure

This study demonstrates the application of instruction finetuning of pretrained Large Language Models (LLMs) to automate the generation of AI research leaderboards, extracting (Task, Dataset, Metric, Score) quadruples from articles. It aims to streamline the dissemination of advancements in AI research by transitioning from traditional, manual community curation, or otherwise taxonomy-constrained natural language inference (NLI) models, to an automated, generative LLM-based approach. Utilizing the FLAN-T5 model, this research enhances LLMs' adaptability and reliability in information extraction, offering a novel method for structured knowledge representation., Comment: arXiv admin note: text overlap with arXiv:2407.02409

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after five years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN's main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous $^\mathrm{83m}$Kr, which enables the neutrino-mass measurements in the SAP configuration., Comment: 19 pages, 11 figures

Citizen science offers an opportunity for ordinary people, known as citizen scientists or citizen astronomers in the context of astronomy, to contribute to scientific research. The Pan-African Citizen Science e-Lab (PACS e-Lab) was founded to promote and engage the African public in citizen science and soft astronomy research to advance space research and exploration and enhance space education and outreach. PACS e-Lab, in collaboration with several international astronomy research, education, and outreach organizations, currently runs several projects including but not limited to asteroid search, exoplanet photometry, research writing for peer-reviewed publications, astrophoto visual development, and Amateur Radio contact with astronauts aboard the International Space Station (ARISS). Despite several challenges, the group has engaged over 600 Africans from more than 40 countries and is working towards covering the entire continent in the future. PACS e-Lab's development efforts resonate with seven United Nations Sustainable Development Goals (UN-SDGs)., Comment: 32 pages,17 figures, 2 Tables

Asteroid search is a global effort for planetary defense. The International Astronomical Search Collaboration (IASC) is the leading global educational outreach program that provides high-quality astronomical datasets to citizen scientists to discover asteroids. Since December 4, 2020, the Pan-African Citizen Science e-Lab (PACS e-Lab) has been IASC's biggest partner on the continent in recruiting and training citizen scientists in asteroid search endeavors. Over 30 asteroids have been discovered by 60 citizen scientists. About 595 citizen scientists from over 40 countries have been engaged in the project up to the time of composing this literature. The group is set to expand its endeavors to the rest of the continent and aims to engage thousands of citizen scientists., Comment: 16 pages, 10 figures, 5 Tables

The fact that neutrinos carry a non-vanishing rest mass is evidence of physics beyond the Standard Model of elementary particles. Their absolute mass bears important relevance from particle physics to cosmology. In this work, we report on the search for the effective electron antineutrino mass with the KATRIN experiment. KATRIN performs precision spectroscopy of the tritium $\beta$-decay close to the kinematic endpoint. Based on the first five neutrino-mass measurement campaigns, we derive a best-fit value of $m_\nu^{2} = {-0.14^{+0.13}_{-0.15}}~\mathrm{eV^2}$, resulting in an upper limit of $m_\nu < {0.45}~\mathrm{eV}$ at 90 % confidence level. With six times the statistics of previous data sets, amounting to 36 million electrons collected in 259 measurement days, a substantial reduction of the background level and improved systematic uncertainties, this result tightens KATRIN's previous bound by a factor of almost two., Comment: 61 pages, 20 figures, 2 tables

We probe the gravitational force perpendicular to the Galactic plane at the position of the Sun based on a sample of red giants, with measurements taken from the DR3 Gaia catalogue. Measurements far out of the Galactic plane up to 3.5 kpc allow us to determine directly the total mass density, where dark matter is dominant and the stellar and gas densities are very low. In a complementary way, we have also used a new determination of the local baryonic mass density to help determine the density of dark matter in the Galactic plane at the solar position. For the local mass density of dark matter, we obtained $\rho_\mathrm{dm}$=0.0128$\pm $0.0008= 0.486 $\pm$0.030 Gev cm$^{-3}$. For the flattening of the gravitational potential of the dark halo, it is $q_\mathrm{\phi,h}$=0.843$\pm0.035$. For its density, $q_\mathrm{\rho,h}$=0.781$\pm$0.055., Comment: accepted for publication in Astronomy & Astrophysics

This paper explores the impact of context selection on the efficiency of Large Language Models (LLMs) in generating Artificial Intelligence (AI) research leaderboards, a task defined as the extraction of (Task, Dataset, Metric, Score) quadruples from scholarly articles. By framing this challenge as a text generation objective and employing instruction finetuning with the FLAN-T5 collection, we introduce a novel method that surpasses traditional Natural Language Inference (NLI) approaches in adapting to new developments without a predefined taxonomy. Through experimentation with three distinct context types of varying selectivity and length, our study demonstrates the importance of effective context selection in enhancing LLM accuracy and reducing hallucinations, providing a new pathway for the reliable and efficient generation of AI leaderboards. This contribution not only advances the state of the art in leaderboard generation but also sheds light on strategies to mitigate common challenges in LLM-based information extraction., Comment: arXiv admin note: substantial text overlap with arXiv:2406.04383

The rapid advancements in Large Language Models (LLMs) have opened new avenues for automating complex tasks in AI research. This paper investigates the efficacy of different LLMs-Mistral 7B, Llama-2, GPT-4-Turbo and GPT-4.o in extracting leaderboard information from empirical AI research articles. We explore three types of contextual inputs to the models: DocTAET (Document Title, Abstract, Experimental Setup, and Tabular Information), DocREC (Results, Experiments, and Conclusions), and DocFULL (entire document). Our comprehensive study evaluates the performance of these models in generating (Task, Dataset, Metric, Score) quadruples from research papers. The findings reveal significant insights into the strengths and limitations of each model and context type, providing valuable guidance for future AI research automation efforts.

Despite the widespread adoption of Large language models (LLMs), their remarkable capabilities remain limited to a few high-resource languages. Additionally, many low-resource languages (e.g. African languages) are often evaluated only on basic text classification tasks due to the lack of appropriate or comprehensive benchmarks outside of high-resource languages. In this paper, we introduce IrokoBench -- a human-translated benchmark dataset for 16 typologically-diverse low-resource African languages covering three tasks: natural language inference~(AfriXNLI), mathematical reasoning~(AfriMGSM), and multi-choice knowledge-based QA~(AfriMMLU). We use IrokoBench to evaluate zero-shot, few-shot, and translate-test settings~(where test sets are translated into English) across 10 open and four proprietary LLMs. Our evaluation reveals a significant performance gap between high-resource languages~(such as English and French) and low-resource African languages. We observe a significant performance gap between open and proprietary models, with the highest performing open model, Aya-101 only at 58\% of the best-performing proprietary model GPT-4o performance. Machine translating the test set to English before evaluation helped to close the gap for larger models that are English-centric, like LLaMa 3 70B. These findings suggest that more efforts are needed to develop and adapt LLMs for African languages., Comment: Under review

Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty estimates for our predictions, and while prediction accuracy has seen much progress in recent years, uncertainty metrics are rarely reported. We here provide a Gaussian process regression model, Kermut, with a novel composite kernel for modelling mutation similarity, which obtains state-of-the-art performance for protein variant effect prediction while also offering estimates of uncertainty through its posterior. An analysis of the quality of the uncertainty estimates demonstrates that our model provides meaningful levels of overall calibration, but that instance-specific uncertainty calibration remains more challenging. We hope that this will encourage future work in this promising direction., Comment: 10 pages (36 in total with appendix), 4 figures (26 figures in total with appendix)

In this paper, using the Milnor-type theorem technique, we provide on each nilpotent five dimensional Lie group, some global existence result of a pair (g, c) consisting of a left-invariant Riemannian metric g and a positive constant c such that Ric(g) =cT, where Ric(g) is the Ricci curvature of g and T a given left-invariant symmetric (0, 2)-tensor field.

Silicon-based color-centers (SiCCs) have recently emerged as quantum-light sources that can be combined with telecom-range Si Photonics platforms. Unfortunately, using current SiCC fabrication, deterministic control over the vertical emitter position is impossible due to ion-implantation's stochastic nature. To overcome this bottleneck towards high-yield integration, we demonstrate a radically innovative creation method for various SiCCs, solely relying on epitaxial growth of Si and C-doped Si at atypically-low temperatures in a ultra-clean growth environment. These telecom emitters can be confined within sub-1nm thick layers embedded at arbitrary vertical positions within a highly crystalline Si matrix. Tuning growth conditions and doping, different SiCC types, e.g., W-centers, T-centers, G-centers, or derivatives like G'-centers can be created, which are particularly promising as Si-based single-photon sources and spin-photon interfaces. The zero-phonon emission from G'-centers can be conveniently tuned by the C-concentration, leading to a systematic wavelength shift and linewidth narrowing towards low emitter densities.

Let $(M,g)$ be an analytic Riemannian manifold of dimension $n \geq 5$. In this paper, we consider the so-called constant $Q$-curvature equation \[ \varepsilon^4\Delta_{g}^2 u -\varepsilon^2 b \Delta_{g} u +a u = u^{p} , \qquad \text{in } M, \quad u>0, \quad u\in H^2_g(M) \] where $a,b$ are positive constants such that $b^2-4 a>0$, $p$ is a sub-critical exponent $10$ is small enough, then positive solutions to the above constant $Q$-curvature equation are generated by a maximum or minimum point of the function $\tau_g$, given by \[ \tau_g(\xi):= \sum_{i, j=1}^{n} \frac{\partial^{2} g_{\xi}^{i i}}{\partial z_{j}^{2}}(0), \] where $g_{\xi}^{i j}$ denotes the components of the inverse of the metric $g$ in geodesic normal coordinates. This result shows that the geometry of $M$ plays a crucial role in finding solutions to the equation above and provides a metric of constant $Q$-curvature on a product manifold of the form $(M\times X, g+\varepsilon^2 h)$ where $(M,g)$ is flat and closed, and $(X,h)$ any $m$-dimensional Einstein Riemannian manifold, $m\geq 3$., Comment: Working on improvements

Both lectures focus on the first part of the so-called 'mathematical part' of Plato's Theaetetus. In this passage, the young Theaetetus briefly recounts the mathematical lesson given by the geometer Theodorus. The first lecture delves into the figures drawn by Theodorus, while the second explores his methods of proof. Here, we present a new translation and a new interpretation of this passage. Both lectures are based on a collaborative work in progress with Luc Brisson., Comment: in French language

Paleoclimatic measurements serve to understand geophysical processes and evaluate climate model performances. However, their spatial coverage is generally sparse and unevenly distributed across the globe. Statistical interpolation methods are the prevalent techniques to grid such data, but these purely data-driven approaches sometimes produce results that are incoherent with our knowledge of the physical world. Physics-Informed Neural Networks (PINNs) follow an innovative approach to data analysis and physical modeling through machine learning, as they incorporate physical principles into the data-driven learning process. Here, we develop PINNs to reconstruct global maps of atmospheric dust surface deposition fluxes from measurement data in paleoclimatic archives, for the Holocene and Last Glacial Maximum periods. We design an advection-diffusion equation to consider dominant wind directions at various latitudes, which prevents dust particles from flowing upwind. Our PINN improves on standard kriging interpolation by allowing variable asymmetry around data points. The reconstructions display realistic dust plumes from continental sources towards ocean basins following prevailing winds.

Although the relationship between temperature and emotional states has been investigated in the field of haptics, it remains unknown if, or in what direction, temperature affects emotional states. We approach this question at the intersection of haptics and psychology using a custom-built thermal device and emotional responses based on photos from the International Affective Picture System (IAPS) library. Unlike past research, this study incorporates deception and a control (i.e., neutral temperature) condition. One hundred and twenty naive subjects reported their emotional responses to fifty-six images varying on normative arousal and valence ratings while being exposed to a cool~(30{\deg}C), neutral (33{\deg}C), or warm (36{\deg}C) temperature applied to the upper back. Participants exposed to warm temperatures reported higher arousal ratings in some image categories than participants exposed to neutral or cool temperatures. Valence ratings were decreased in warm conditions compared to neutral conditions. The emotion wheel was used as a complementary method of affective response measurement, and exploratory analysis methods were implemented. Although the valence and arousal showed statistical significance, the emotion wheel results did not demonstrate any significant differences between the temperature conditions.

In Nuclear Magnetic Resonance (NMR), it is of crucial importance to have an accurate knowledge of the sample probability distribution corresponding to inhomogeneities of the magnetic fields. An accurate identification of the sample distribution requires a set of experimental data that is sufficiently rich to extract all fundamental information. These data depend strongly on the control fields (and their number) used experimentally. In this work, we present and analyze a greedy reconstruction algorithm, and provide the corresponding $\texttt{SPIRED}$ code, for the computation of a set of control functions allowing the generation of data that are appropriate for the accurate reconstruction of a sample distribution. In particular, the focus is on NMR and the Bloch system with inhomogeneities in the magnetic fields in all spatial directions. Numerical examples illustrate this general study., Comment: 31 pages, 6 figures

Recent progress has boosted the resolving power of optical microscopies to spatial dimensions well below the diffraction limit. Yet, axial super-resolution and axial single-molecule localisation typically require more complicated implementations than their lateral counterparts. In the present work, we propose a simple solution for axial metrology by providing a multi-layered single-excitation, dual-emission test slide, in which axial distance is colour-encoded. Our test slide combines on a standard microscope coverslip substrate two flat, thin, uniform and brightly emitting fluorophore layers, separated by a nanometric transparent spacer layer having a refractive index close to a biological cell. The ensemble is sealed in an index-matched protective polymer. As a proof-of-principle, we estimate the light confinement resulting from evanescent-wave excitation in total internal reflection fluorescence (TIRF) microscopy. Our test sample permits, even for the non-expert user, a facile axial metrology at the sub-100-nm scale, a critical requirement for axial super-resolution, as well as near-surface imaging, spectroscopy and sensing.

In the mid 80s, Lichtenstein, Pnueli, and Zuck proved a classical theorem stating that every formula of Past LTL (the extension of LTL with past operators) is equivalent to a formula of the form $\bigwedge_{i=1}^n \mathbf{G}\mathbf{F}\, \varphi_i \vee \mathbf{F}\mathbf{G}\, \psi_i $, where $\varphi_i$ and $\psi_i$ contain only past operators. Some years later, Chang, Manna, and Pnueli built on this result to derive a similar normal form for LTL. Both normalization procedures have a non-elementary worst-case blow-up, and follow an involved path from formulas to counter-free automata to star-free regular expressions and back to formulas. We improve on both points. We present direct and purely syntactic normalization procedures for LTL, yielding a normal form very similar to the one by Chang, Manna, and Pnueli, that exhibit only a single exponential blow-up. As an application, we derive a simple algorithm to translate LTL into deterministic Rabin automata. The algorithm normalizes the formula, translates it into a special very weak alternating automaton, and applies a simple determinization procedure, valid only for these special automata., Comment: Accepted in J. ACM. arXiv admin note: text overlap with arXiv:2304.08872, arXiv:2005.00472

The antisymmetric C-H stretching vibration $\nu_3$ ($^2\Pi$ $\leftarrow$ $^2\Pi$) of ionized acetylene, C$_2$H$_2^+$, has been revisited using a cryogenic 22-pole ion trap machine. Two action spectroscopic techniques, the novel leak-out spectroscopy (LOS) method and the more established laser-induced reactions (LIR) method, are applied and compared. Mass selectivity and cryogenic temperatures down to 4~K enabled the observation of uncontaminated spectra in which the $\Lambda$-doubling components of this open-shell molecule are mostly well resolved, leading to a slight refinement of the spectroscopic parameters.

This paper is devoted to the development and convergence analysis of greedy reconstruction algorithms based on the strategy presented in [Y. Maday and J. Salomon, Joint Proceedings of the 48th IEEE Conference on Decision and Control and the 28th Chinese Control Conference, 2009, pp. 375--379]. These procedures allow the design of a sequence of control functions that ease the identification of unknown operators in nonlinear dynamical systems. The original strategy of greedy reconstruction algorithms is based on an offline/online decomposition of the reconstruction process and an ansatz for the unknown operator obtained by an a priori chosen set of linearly independent matrices. In the previous work [S. Buchwald, G. Ciaramella and J. Salomon, SIAM J. Control Optim., 59(6), pp. 4511-4537], convergence results were obtained in the case of linear identification problems. We tackle here the more general case of nonlinear systems. More precisely, we introduce a new greedy algorithm based on the linearized system. Then, we show that the controls obtained with this new algorithm lead to the local convergence of the classical Gauss-Newton method applied to the online nonlinear identification problem. We then extend this result to the controls obtained on nonlinear systems where a local convergence result is also proved. The main convergence results are obtained for the reconstruction of drift operators in dynamical systems with linear and bilinear control structures.