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Plumes within the Greenland Ice Sheet disrupt radiostratigraphy and complicate the use of isochrones in reconstructions of past ice dynamics. Here we use numerical modeling to test the hypothesis that convection is a viable mechanism for the formation of the large (\(>\)1/3 ice thickness) englacial plumes observed in north Greenland. Greater horizontal shear and snow accumulation impede plume formation, while stable and softer ice encourages them. These results potentially explain the dearth of basal plumes in the younger and higher-accumulation southern ice sheet. Leveraging this mechanism to place bounds on ice rheology suggests that -- for north Greenland -- ice viscosity may be \(\sim\)9-15 times lower than commonly assumed. Softer-than-assumed ice there implies significantly reduced basal sliding compared to standard models. Implementing a softer basal ice rheology in numerical models may help reduce uncertainty in projections of future ice-sheet mass balance.

Homophones present a significant challenge to authors in any languages due to their similarities of pronunciations but different meanings and spellings. This issue is particularly pronounced in the Khmer language, rich in homophones due to its complex structure and extensive character set. This research aims to address the difficulties faced by Khmer authors when using homophones in their writing and proposes potential solutions based on an extensive literature review and survey analysis. A survey of 108 Khmer native speakers, including students, employees, and professionals, revealed that many frequently encounter challenges with homophones in their writing, often struggling to choose the correct word based on context. The survey also highlighted the absence of effective tools to address homophone errors in Khmer, which complicates the writing process. Additionally, a review of existing studies on spelling correction in other languages, such as English, Azerbaijani, and Bangla, identified a lack of research focused specifically on homophones, particularly in the Khmer language. In summary, this research highlights the necessity for a specialized tool to address Khmer homophone errors. By bridging current gaps in research and available resources, such a tool would enhance the confidence and accuracy of Khmer authors in their writing, thereby contributing to the enrichment and preservation of the language. Continued efforts in this domain are essential for ensuring that Khmer can leverage advancements in technology and linguistics effectively.

While language models have exceptional capabilities at text generation, they lack a natural inductive bias for emitting numbers and thus struggle in tasks involving reasoning over quantities, especially arithmetics. This has particular relevance in scientific datasets where combinations of text and numerical data are abundant. One fundamental limitation is the nature of the CE loss, which assumes a nominal (categorical) scale and thus cannot convey proximity between generated number tokens. As a remedy, we here present two versions of a number token loss. The first is based on an $L_p$ loss between the ground truth token value and the weighted sum of the predicted class probabilities. The second loss minimizes the Wasserstein-1 distance between the distribution of the predicted output probabilities and the ground truth distribution. These regression-like losses can easily be added to any language model and extend the CE objective during training. We compare the proposed schemes on a mathematics dataset against existing tokenization, encoding, and decoding schemes for improving number representation in language models. Our results reveal a significant improvement in numerical accuracy when equipping a standard T5 model with the proposed loss schemes., Comment: 5-page version for NeurIPS 2024 (MathAI workshop)

The next goalpost in precision calculations for physics beyond the Standard Model is determining the two-loop renormalization group (RG) equations in the Standard Model Effective Field Theory (SMEFT). We progress towards this goal by determining the RG equations for a simplified version of the SMEFT without any fermion fields. Our calculation relies on functional methods that are newly developed for multi-loop computations and adapted for the determination of RG equations here., Comment: 21 pages, 1 figure, 1 table, 1 ancillary file

In this paper, a mathematical model is developed to describe the evolution of the concentration of compounds through a gas chromatography column. The model couples mass balances and kinetic equations for all components. Both single and multiple-component cases are considered with constant or variable velocity. Non-dimensionalisation indicates the small effect of diffusion. The system where diffusion is neglected is analysed using Laplace transforms. In the multiple-component case, it is demonstrated that the competition between the compounds is negligible and the equations may be decoupled. This reduces the problem to solving a single integral equation to determine the concentration profile for all components (since they are scaled versions of each other). For a given analyte, we then only two parameters need to be fitted to the data. To verify this approach, the full governing equations are also solved numerically using the finite difference method and a global adaptive quadrature method to integrate the Laplace transformation. Comparison with the Laplace solution verifies the high degree of accuracy of the simpler Laplace form. The Laplace solution is then verified against experimental data from BTEX chromatography. This novel method, which involves solving a single equation and fitting parameters in pairs for individual components, is highly efficient. It is significantly faster and simpler than the full numerical solution and avoids the computationally expensive methods that would normally be used to fit all curves at the same time., Comment: 35 pages, 4 figures, 3 tables, 2 appendix, 37 references. Submitted to Partial Differential Equations in Applied Mathematics

Deep neural networks have achieved success across a wide range of applications, including as models of human behavior in vision tasks. However, neural network training and human learning differ in fundamental ways, and neural networks often fail to generalize as robustly as humans do, raising questions regarding the similarity of their underlying representations. What is missing for modern learning systems to exhibit more human-like behavior? We highlight a key misalignment between vision models and humans: whereas human conceptual knowledge is hierarchically organized from fine- to coarse-scale distinctions, model representations do not accurately capture all these levels of abstraction. To address this misalignment, we first train a teacher model to imitate human judgments, then transfer human-like structure from its representations into pretrained state-of-the-art vision foundation models. These human-aligned models more accurately approximate human behavior and uncertainty across a wide range of similarity tasks, including a new dataset of human judgments spanning multiple levels of semantic abstractions. They also perform better on a diverse set of machine learning tasks, increasing generalization and out-of-distribution robustness. Thus, infusing neural networks with additional human knowledge yields a best-of-both-worlds representation that is both more consistent with human cognition and more practically useful, thus paving the way toward more robust, interpretable, and human-like artificial intelligence systems., Comment: 54 pages

MICADO is a first light instrument for the Extremely Large Telescope (ELT), set to start operating later this decade. It will provide diffraction limited imaging, astrometry, high contrast imaging, and long slit spectroscopy at near-infrared wavelengths. During the initial phase operations, adaptive optics (AO) correction will be provided by its own natural guide star wavefront sensor. In its final configuration, that AO system will be retained and complemented by the laser guide star multi-conjugate adaptive optics module MORFEO (formerly known as MAORY). Among many other things, MICADO will study exoplanets, distant galaxies and stars, and investigate black holes, such as Sagittarius A* at the centre of the Milky Way. After their final design phase, most components of MICADO have moved on to the manufacturing and assembly phase. Here we summarize the final design of the instrument and provide an overview about its current manufacturing status and the timeline. Some lessons learned from the final design review process will be presented in order to help future instrumentation projects to cope with the challenges arising from the substantial differences between projects for 8-10m class telescopes (e.g. ESO-VLT) and the next generation Extremely Large Telescopes (e.g. ESO-ELT). Finally, the expected performance will be discussed in the context of the current landscape of astronomical observatories and instruments. For instance, MICADO will have similar sensitivity as the James Webb Space Telescope (JWST), but with six times the spatial resolution., Comment: Proceedings of the SPIE, Volume 13096, id. 1309611 11 pp. (2024)

In a recent work, we calculated all three-loop diagrams contributing to the decay amplitude for $b \to s \gamma$ where none of the gluons touch the $b$-leg. In the present paper, we complete the calculation by working out all remaining three-loop diagrams (of order $\alpha_s^2$) associated with the current-current operators $O_1$ and $O_2$ at the physical value of the charm-quark mass $m_c$. Using the programs AMFlow and DiffExp to solve the differential equations for the master integrals, we obtained precise numerical results at 23 values for $z=m_c^2/m_b^2$, ranging from $z=1/1000$ to $z=1/5$, along with asymptotic expansions around $z=0$. For certain diagrams, the asymptotic expansion breaks down in the physical $z$-range, necessitating a Taylor expansion (which we do around $z=1/10$). In all expansions, we retained power terms up to $z^{20}$ and included the accompanying $\log(z)$ terms to all powers for asymptotic expansions. Numerical results for the sum of all diagrams (including those calculated in the previous paper) are presented in tabular form, while the mentioned expansions of individual diagram classes are provided electronically. We note that our results for the asymptotic expansions around $z=0$ are in good agreement with those recently published by Fael et al. and Czaja et al.., Comment: 19 pages, 7 figures, 2 tables, 1 ancillary file

Glacier and ice-sheet motion is fundamental to glaciology. However, there is still no clear consensus for the optimal way to describe glacier and ice-sheet sliding. Typically, sliding is parameterised using a traction coefficient nominally linked to a given theory describing one or a limited set of sliding processes. However, this approach precludes the possibility of multiple simultaneous and spatio-temporally varying sliding modes with inaccuracies resulting in model error propagation as the system evolves away from the conditions under which it was optimised for. Here, revisiting early theoretical work, we describe glacier sliding as a scale- and setting-dependent 'inner flow' that arises from multiple overlapping sub-processes, bridging divides between hard and soft beds, rough and smooth beds, and stick-slip and continuous sliding as well as providing a consistent definition for form drag. The corresponding 'outer flow' then accounts for 'normal' ice deformation. We propose that the significance of 'Iken's bound' is then reduced if form drag dominates over subglacial cavitation in a given region, possibly explaining the persistent functionality of Weertman-type sliding in process-agnostic sliding studies over rough topography. Last, reviewing observation-based studies we suggest that a simple 'unified' sliding relationship controlled by a single tunable coefficient may not be a realistic prospect, but that a Weertman-type relationship with careful consideration of the power value is presently the 'least bad' option.

The Mid-infrared ELT Imager and Spectrograph (METIS) is one of the first-generation scientific instruments for the ELT, built under the supervision of ESO by a consortium of research institutes across and beyond Europe. Designed to cover the 3 to 13 $\mu$m wavelength range, METIS had its final design reviewed in Fall 2022, and has then entered in earnest its manufacture, assembly, integration, and test (MAIT) phase. Here, we present the final design of the METIS high-contrast imaging (HCI) modes. We detail the implementation of the two main coronagraphic solutions selected for METIS, namely the vortex coronagraph and the apodizing phase plate, including their combination with the high-resolution integral field spectrograph of METIS, and briefly describe their respective backup plans (Lyot coronagraph and shaped pupil plate). We then describe the status of the MAIT phase for HCI modes, including a review of the final design of individual components such as the vortex phase masks, the grayscale ring apodizer, and the apodizing phase plates, as well as a description of their on-going performance tests and of our plans for system-level integration and tests. Using end-to-end simulations, we predict the performance that will be reached on sky by the METIS HCI modes in presence of various environmental and instrumental disturbances, including non-common path aberrations and water vapor seeing, and discuss our strategy to mitigate these various effects. We finally illustrate with mock observations and data processing that METIS should be capable of directly imaging temperate rocky planets around the nearest stars., Comment: 16 pages, paper presented at SPIE Astronomical Telescopes + Instrumentation 2024

The tilt-to-length coupling during the LISA Pathfinder mission has been numerically and analytically modeled for particular timespans. In this work, we investigate the long-term stability of the coupling coefficients of this noise. We show that they drifted slowly (by 1\,$\mu$m/rad and 6$\times10^{-6}$ in 100 days) and strongly correlated to temperature changes within the satellite (8\,$\mu$m/rad/K and 30$\times10^{-6}$/K). Based on analytical TTL coupling models, we attribute the temperature-driven coupling changes to rotations of the test masses and small distortions in the optical setup. Particularly, we show that LISA Pathfinder's optical baseplate was bent during the cooldown experiment, which started in late 2016 and lasted several months.

A precise characterization of the magnetic properties of LISA Pathfinder free falling test-masses is of special interest for future gravitational wave observatory in space. Magnetic forces have an important impact on the instrument sensitivity in the low frequency regime below the millihertz. In this paper we report on the magnetic injection experiments performed throughout LISA Pathfinder operations. We show how these experiments allowed a high precision estimate of the instrument magnetic parameters. The remanent magnetic moment was found to have a modulus of $(0.245\pm0.081)\,\rm{nAm}^2$, the x-component of the background magnetic field within the test masses position was measured to be $(414 \pm 74)$ nT and its gradient had a value of $(-7.4\pm 2.1)\,\mu$T/m. Finally, we also measured the test mass magnetic susceptibility to be $(-3.35\pm0.15)\times$10$^{-5}$ in the low frequency regime. All results are in agreement with on-ground estimates.

LISA Pathfinder was a mission designed to test key technologies required for gravitational wave detection in space. Magnetically driven forces play a key role in the instrument sensitivity in the low-frequency regime, which corresponds to the measurement band of interest for future space-borne gravitational wave observatories. Magnetic-induced forces couple to the test mass motion, introducing a contribution to the relative acceleration noise between the free falling test masses. In this Letter we present the first complete estimate of this term of the instrument performance model. Our results set the magnetic-induced acceleration noise during the February 2017 noise run of $\rm 0.25_{-0.08}^{+0.15}\,fm\,s^{-2}/\sqrt{Hz}$ at 1 mHz and $\rm 1.01_{-0.24}^{+0.73}\, fm\,s^{-2}/\sqrt{Hz}$ at 0.1 mHz. We also discuss how the non-stationarities of the interplanetary magnetic field can affect these values during extreme space weather conditions.

Probabilistic forecasting models for joint distributions of targets in irregular time series are a heavily under-researched area in machine learning with, to the best of our knowledge, only three models researched so far: GPR, the Gaussian Process Regression model~\citep{Durichen2015.Multitask}, TACTiS, the Transformer-Attentional Copulas for Time Series~\cite{Drouin2022.Tactis, ashok2024tactis} and ProFITi \citep{Yalavarthi2024.Probabilistica}, a multivariate normalizing flow model based on invertible attention layers. While ProFITi, thanks to using multivariate normalizing flows, is the more expressive model with better predictive performance, we will show that it suffers from marginalization inconsistency: it does not guarantee that the marginal distributions of a subset of variables in its predictive distributions coincide with the directly predicted distributions of these variables. Also, TACTiS does not provide any guarantees for marginalization consistency. We develop a novel probabilistic irregular time series forecasting model, Marginalization Consistent Mixtures of Separable Flows (moses), that mixes several normalizing flows with (i) Gaussian Processes with full covariance matrix as source distributions and (ii) a separable invertible transformation, aiming to combine the expressivity of normalizing flows with the marginalization consistency of Gaussians. In experiments on four different datasets we show that moses outperforms other state-of-the-art marginalization consistent models, performs on par with ProFITi, but different from ProFITi, guarantee marginalization consistency.

We present an in-depth analysis of the LISA Pathfinder differential acceleration performance over the entire course of its science operations, spanning approximately 500 days. We find that: 1) the evolution of the Brownian noise that dominates the acceleration amplitude spectral density (ASD), for frequencies $f\gtrsim 1\,\text{mHz}$, is consistent with the decaying pressure due to the outgassing of a single gaseous species. 2) between $f=36\,\mu\text{Hz}$ and $1\,\text{mHz}$, the acceleration ASD shows a $1/f$ tail in excess of the Brownian noise of almost constant amplitude, with $\simeq 20\%$ fluctuations over a period of a few days, with no particular time pattern over the course of the mission; 3) at the lowest considered frequency of $f=18\,\mu\text{Hz}$, the ASD significantly deviates from the $1/f$ behavior, because of temperature fluctuations that appear to modulate a quasi-static pressure gradient, sustained by the asymmetries of the outgassing pattern. We also present the results of a projection of the observed acceleration noise on the potential sources for which we had either a direct correlation measurement, or a quantitative estimate from dedicated experiments. These sources account for approximately $40\%$ of the noise power in the $1/f$ tail. Finally, we analyze the possible sources of the remaining unexplained fraction, and identify the possible measures that may be taken to keep those under control in LISA.

Optimal Transport (OT) has fueled machine learning (ML) across many domains. When paired data measurements $(\boldsymbol{\mu}, \boldsymbol{\nu})$ are coupled to covariates, a challenging conditional distribution learning setting arises. Existing approaches for learning a $\textit{global}$ transport map parameterized through a potentially unseen context utilize Neural OT and largely rely on Brenier's theorem. Here, we propose a first-of-its-kind quantum computing formulation for amortized optimization of contextualized transportation plans. We exploit a direct link between doubly stochastic matrices and unitary operators thus unravelling a natural connection between OT and quantum computation. We verify our method (QontOT) on synthetic and real data by predicting variations in cell type distributions conditioned on drug dosage. Importantly we conduct a 24-qubit hardware experiment on a task challenging for classical computers and report a performance that cannot be matched with our classical neural OT approach. In sum, this is a first step toward learning to predict contextualized transportation plans through quantum computing., Comment: ICML 2024

Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate. State-of-the-art methods for the task estimate only marginal distributions of observations in single channels and at single timepoints, assuming a fixed-shape parametric distribution. In this work, we propose a novel model, ProFITi, for probabilistic forecasting of irregularly sampled time series with missing values using conditional normalizing flows. The model learns joint distributions over the future values of the time series conditioned on past observations and queried channels and times, without assuming any fixed shape of the underlying distribution. As model components, we introduce a novel invertible triangular attention layer and an invertible non-linear activation function on and onto the whole real line. We conduct extensive experiments on four datasets and demonstrate that the proposed model provides $4$ times higher likelihood over the previously best model.

Electrostatic force actuation is a key component of the system of geodesic reference test masses (TM) for the LISA orbiting gravitational wave observatory and in particular for performance at low frequencies, below 1 mHz, where the observatory sensitivity is limited by stray force noise. The system needs to apply forces of order 10$^{-9}$ N while limiting fluctuations in the measurement band to levels approaching 10$^{-15}$ N/Hz$^{1/2}$. We present here the LISA actuation system design, based on audio-frequency voltage carrier signals, and results of its in-flight performance test with the LISA Pathfinder test mission. In LISA, TM force actuation is used to align the otherwise free-falling TM to the spacecraft-mounted optical metrology system, without any forcing along the critical gravitational wave-sensitive interferometry axes. In LISA Pathfinder, on the other hand, the actuation was used also to stabilize the TM along the critical $x$ axis joining the two TM, with the commanded actuation force entering directly into the mission's main differential acceleration science observable. The mission allowed demonstration of the full compatibility of the electrostatic actuation system with the LISA observatory requirements, including dedicated measurement campaigns to amplify, isolate, and quantify the two main force noise contributions from the actuation system, from actuator gain noise and from low frequency ``in band'' voltage fluctuations. These campaigns have shown actuation force noise to be a relevant, but not dominant, noise source in LISA Pathfinder and have allowed performance projections for the conditions expected in the LISA mission.

Inferring parameters of macro-kinetic growth models, typically represented by Ordinary Differential Equations (ODE), from the experimental data is a crucial step in bioprocess engineering. Conventionally, estimates of the parameters are obtained by fitting the mechanistic model to observations. Fitting, however, requires a significant computational power. Specifically, during the development of new bioprocesses that use previously unknown organisms or strains, efficient, robust, and computationally cheap methods for parameter estimation are of great value. In this work, we propose using Deep Neural Networks (NN) for directly predicting parameters of mechanistic models given observations. The approach requires spending computational resources for training a NN, nonetheless, once trained, such a network can provide parameter estimates orders of magnitude faster than conventional methods. We consider a training procedure that combines Neural Networks and mechanistic models. We demonstrate the performance of the proposed algorithms on data sampled from several mechanistic models used in bioengineering describing a typical industrial batch process and compare the proposed method, a typical gradient-based fitting procedure, and the combination of the two. We find that, while Neural Network estimates are slightly improved by further fitting, these estimates are measurably better than the fitting procedure alone., Comment: 7 pages, 3 figures

Cultivation experiments often produce sparse and irregular time series. Classical approaches based on mechanistic models, like Maximum Likelihood fitting or Monte-Carlo Markov chain sampling, can easily account for sparsity and time-grid irregularities, but most statistical and Machine Learning tools are not designed for handling sparse data out-of-the-box. Among popular approaches there are various schemes for filling missing values (imputation) and interpolation into a regular grid (alignment). However, such methods transfer the biases of the interpolation or imputation models to the target model. We show that Deep Set Neural Networks equipped with triplet encoding of the input data can successfully handle bio-process data without any need for imputation or alignment procedures. The method is agnostic to the particular nature of the time series and can be adapted for any task, for example, online monitoring, predictive control, design of experiments, etc. In this work, we focus on forecasting. We argue that such an approach is especially suitable for typical cultivation processes, demonstrate the performance of the method on several forecasting tasks using data generated from macrokinetic growth models under realistic conditions, and compare the method to a conventional fitting procedure and methods based on imputation and alignment., Comment: 9 pages, 3 figures