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Nonholonomic systems are, so to speak, mechanical systems with a prescribed restriction on the velocities. A virtual nonholonomic constraint is a controlled invariant distribution associated with an affine connection mechanical control system. A Riemannian homogeneous space is, a Riemannian manifold that looks the same everywhere, as you move through it by the action of a Lie group. These Riemannian manifolds are not necessarily Lie groups themselves, but nonetheless possess certain symmetries and invariances that allow for similar results to be obtained. In this work, we introduce the notion of virtual constraint on Riemannian homogeneous spaces in a geometric framework which is a generalization of the classical controlled invariant distribution setting and we show the existence and uniqueness of a control law preserving the invariant distribution. Moreover we characterize the closed-loop dynamics obtained using the unique control law in terms of an affine connection. We illustrate the theory with new examples of nonholonomic control systems inspired by robotics applications.

X-ray imaging is an invaluable tool for noninvasive analysis in many fields ranging from basic science to medicine and security. The development of low-dose large area imaging solutions still represents an important challenge for various applications. One solution to the imaging of large areas lies in the development of novel computational imaging systems that can overcome the limitations imposed by hardware, relying instead on numerical processing power. The single-pixel detector, depending on the application, may offer a competitive edge over conventional cameras (being a cheaper alternative to the multi-pixelated solutions). In addition, the single-pixel detector can be used to achieve improved detection efficiency, faster timing response, and good spatial resolution with low radiation dose. Moreover, this technique enables detectors to image through diffuse mediums, increasing the image quality at significant depths, solving the depth penetration issues of other imaging methods. In this work, we explore the application of single-pixel imaging techniques to produce two-dimensional images with high temporal resolution (only if the process is reproducible and the conditions are static), using only a single detector (bucket detector). The setup, based on the application of Hadamard patterns, showed promising results, proving the ability of the system to acquire images using thin PLA based masks (up to 5 mm thickness). Both simulation, using GEANT 4, and experimental setup, based on a time projection chamber (TPC), used in this work to demonstrate this technique will be reported here along with the first results., Comment: 17 pages, 13 figures and 3 tables, paper

In this paper, we give sufficient conditions for and deduce a control law under which a mechanical control system converges exponentially fast to a virtual linear nonholonomic constraint that is control invariant via the same feedback control. Virtual constraints are relations imposed on a control system that become invariant via feedback control, as opposed to physical constraints acting on the system. Virtual nonholonomic constraints, similarly to mechanical nonholonomic constraints, are a class of virtual constraints that depend on velocities rather than only on the configurations of the system.

Kinetic inductance travelling-wave parametric amplifiers (KTWPAs) are emerging as core components in many applications where wideband cryogenic rf amplification at or near the quantum limit of added noise is critical. What is unique about these thin film superconducting devices and what makes them suitable for a wide range of applications is their ability to simultaneously provide large dynamic range and quantum-limited amplification of single photon to 100,000s of photon signals. However, despite the promising performance of co-planar NbTiN thin-film KTWPAs, the original promise of a "simple" single-layer metal fabrication has encountered roadblocks and their broader adoption has been hindered by low fabrication yield. In this work, we present a novel post-lithography correction technique that eliminates short circuits, significantly improving yield and enabling reliable wafer-scale production. Using automated image acquisition, error analysis, and correction, we successfully fabricated operational KTWPAs on high-resistivity silicon, achieving > 10 dB between 2-4 GHz. This approach paves the way for the scaling up manufacturing of KTWPAs, positioning them for widespread deployment in quantum technologies.

Hydrogels are attractive and versatile gel-based structures consisting of three-dimensional networks of hydrophilic polymers with unique properties that can retain a large amount of water and be tailor-designed according to specific requirements, and thus, suitable for numerous commercial areas (i.e., medicine, agriculture, electronics, cosmetics, etc.). However, since these materials can be very soft and flexible, mechanical characterization is challenging. This study explores the mechanical properties of a cellulose-based hydrogel using the optical, non-contact technique Brillouin light scattering spectroscopy (BLS). Two differently dried hydrogel samples were investigated and pre-characterized for their morphology by scanning electron microscopy (SEM) and their mechanical performance by tensile testing. Then, BLS spectra were recorded to characterize the material's stiffness, indicating that the structural arrangement of the material due to the drying procedure (the density) is the main factor limiting the material's strength. By further analyzing the effect of moisture and the changes during drying, we characterize the mechanical behavior of organic hydrogel films and sponges and their dehydration evolution to understand the liquid-solid transition.

The goal of cross-speaker style transfer in TTS is to transfer a speech style from a source speaker with expressive data to a target speaker with only neutral data. In this context, we propose using a pre-trained singing voice conversion (SVC) model to convert the expressive data into the target speaker's voice. In the conversion process, we apply a fundamental frequency (F0) matching technique to mitigate tonal variances between speakers with significant timbral differences. A style classifier filter is proposed to select the most expressive output audios for the TTS training. Our approach is comparable to state-of-the-art with only a few minutes of neutral data from the target speaker, while other methods require hours. A perceptual assessment showed improvements brought by the SVC and the style filter in naturalness and style intensity for the styles that display more vocal effort. Also, increased speaker similarity is obtained with the proposed F0 matching algorithm., Comment: Submitted to INTERSPEECH 2024

To effectively study complex causal systems, it is often useful to construct representations that simplify parts of the system by discarding irrelevant details while preserving key features. The Information Bottleneck (IB) method is a widely used approach in representation learning that compresses random variables while retaining information about a target variable. Traditional methods like IB are purely statistical and ignore underlying causal structures, making them ill-suited for causal tasks. We propose the Causal Information Bottleneck (CIB), a causal extension of the IB, which compresses a set of chosen variables while maintaining causal control over a target variable. This method produces representations which are causally interpretable, and which can be used when reasoning about interventions. We present experimental results demonstrating that the learned representations accurately capture causality as intended., Comment: Submitted to ICLR 2025. Code available at github.com/francisco-simoes/cib-optimization-psagd

In this paper, we prove that the trajectories of unreduced $\phi$-simple Chaplygin kinetic systems are reparametrizations of horizontal geodesics with respect to a modified Riemannian metric. Furthermore, our proof is constructive and these Riemannian metrics, which are not unique, are obtained explicitly in interesting examples. We also extend these results to $\phi$-simple Chaplygin mechanical systems (not necessarily kinetic).

Incorporating cross-speaker style transfer in text-to-speech (TTS) models is challenging due to the need to disentangle speaker and style information in audio. In low-resource expressive data scenarios, voice conversion (VC) can generate expressive speech for target speakers, which can then be used to train the TTS model. However, the quality and style transfer ability of the VC model are crucial for the overall TTS model quality. In this work, we explore the use of synthetic data generated by a VC model to assist the TTS model in cross-speaker style transfer tasks. Additionally, we employ pre-training of the style encoder using timbre perturbation and prototypical angular loss to mitigate speaker leakage. Our results show that using VC synthetic data can improve the naturalness and speaker similarity of TTS in cross-speaker scenarios. Furthermore, we extend this approach to a cross-language scenario, enhancing accent transfer., Comment: Accepted at SynData4GenAI 2024

Surface active ionic liquids (SAILs) combine useful characteristics of both ionic liquids (ILs) and surfactants, hence are promising candidates for a wide range of applications. However, the effect of SAIL ionic structures on their physicochemical properties remains unclear, which limits their uptake. To address this knowledge gap, in this work we investigated the density, viscosity, surface tension, and corresponding critical micelle concentration in water, as well as gas absorption of SAILs with a variety of cation and anion structures. SAILs containing anions with linear alkyl chains have smaller molar volumes than those with branched alkyl chains, because linear alkyl chains are interdigitated to a greater extent, leading to more compact packing. This interdigitation also results in SAILs being about two orders of magnitude more viscous than comparable conventional ILs. SAILs at the liquid-air interface orient alkyl chains towards the air, leading to low surface tensions closer to n-alkanes than conventional ILs. Critical temperatures of about 900 K could be estimated for all SAILs from their surface tensions. When dissolved in water, SAILs adsorb at the liquid-air interface and lower the surface tension, like conventional surfactants in water, after which micelles form. Molecular simulations show that the micelles are spherical and that lower critical micelle concentrations correspond to the formation of aggregates with a larger number of ion pairs. $\mathrm{CO_{2}}$ and $\mathrm{N_{2}}$ absorption capacities are examined and we conclude that ionic liquids with larger non-polar domains absorb larger quantities of both gases.

We study maximal almost rigid modules over a gentle algebra $A$. We prove that the number of indecomposable direct summands of every maximal almost rigid $A$-module is equal to the sum of the number of vertices and the number of arrows of the Gabriel quiver of $A$. Moreover, the algebra $A$, considered as an $A$-module, can be completed to a maximal almost rigid module in a unique way. Gentle algebras are precisely the tiling algebras of surfaces with marked points. We show that the (permissible) triangulations of the surface of $A$ are in bijection with the maximal almost rigid $A$-modules. Furthermore, we study the endomorphism algebra $C=\text{End}_A T$ of a maximal almost rigid module $T$. We construct a fully faithful functor $G\colon \text{mod}\,A\to \text{mod}\, \overline{A}$ into the module category of a bigger gentle algebra $\overline{A}$ and show that $G$ maps maximal almost rigid $A$-modules to tilting $\overline{A}$-modules. In particular, $C$ and $\overline{A}$ are derived equivalent and $C$ is gentle. After giving a geometric realization of the functor $G$, we obtain a tiling $G(\mathbf{T})$ of the surface of $\overline{A}$ as the image of the triangulation $\mathbf{T}$ corresponding to $T$. We then show that the tiling algebra of $G(\mathbf{T})$ is $C$. Moreover, the tiling algebra of $\mathbf{T}$ is obtained algebraically from $C$ as the tensor algebra with respect to the $C$-bimodule $\text{Ext}_C^2(DC,C)$, which also is fundamental in cluster-tilting theory., Comment: 50 pages, 35 figures. Comments are welcome!

In this paper, we investigate the reduction process of a contact Lagrangian system whose Lagrangian is invariant under a group of symmetries. We give explicit coordinate expressions of the resulting reduced differential equations, the so-called Lagrange-Poincare-Herglotz equations. Our framework relied on the associated Herglotz vector field and its projected vector field, and the use of well-chosen quasi-velocities. Some examples are also discussed., Comment: 31 pages

Code quality is an attribute composed of various metrics, such as complexity, readability, testability, interoperability, reusability, and the use of good or bad practices, among others. Static code analysis tools aim to measure a set of attributes to assess code quality. However, some quality attributes can only be measured by humans in code review activities, readability being an example. Given their natural language text processing capability, we hypothesize that a Large Language Model (LLM) could evaluate the quality of code, including attributes currently not automatable. This paper aims to describe and analyze the results obtained using LLMs as a static analysis tool, evaluating the overall quality of code. We compared the LLM with the results obtained with the SonarQube software and its Maintainability metric for two Open Source Software (OSS) Java projects, one with Maintainability Rating A and the other B. A total of 1,641 classes were analyzed, comparing the results in two versions of models: GPT 3.5 Turbo and GPT 4o. We demonstrated that the GPT 3.5 Turbo LLM has the ability to evaluate code quality, showing a correlation with Sonar's metrics. However, there are specific aspects that differ in what the LLM measures compared to SonarQube. The GPT 4o version did not present the same results, diverging from the previous model and Sonar by assigning a high classification to codes that were assessed as lower quality. This study demonstrates the potential of LLMs in evaluating code quality. However, further research is necessary to investigate limitations such as LLM's cost, variability of outputs and explore quality characteristics not measured by traditional static analysis tools.

The relationships between workload and fatigue or sleepiness are investigated through the analysis of rosters and responses to questionnaires from Brazilian aircrews, taken from Fadig\^ometro database. The approach includes temporal markers - coinciding with Samn-Perelli (SP) and Karolinska Sleepiness Scale (KSS) responses - where SAFTE-FAST model outcomes are calculated. The model results follow the increase of fatigue and sleepiness perceptions during the dawn (0h00 to 05h59), but underestimate the self-rated scores during the evening (18h00 to 23h59). On the other hand, the KSS scores fit the relative risk of pilot errors, representing a reasonable proxy for risk assessment. Linear relationships obtained between workload metrics, computed within 168-hours prior to the responses, and self-rated SP and KSS scores provide a consistent method to estimate accumulated fatigue and sleepiness. Considering 7149 rosters of 2023, the duty time ($DT$), the number of flight sectors ($N_{CREW}$) and the sum of flight sectors with sit periods longer than one hour ($N_{CREW}+N_{SIT}$) are associated with 70.1%/60.6% of the highest predicted scores of SP/KSS. Applying the mitigations $DT\leq44h$, $N_{CREW}\leq15$ and $N_{CREW}+N_{SIT}\leq19$ for every 168-hour interval yields a significant decrease in the higher values of SP/KSS with minimal impact on aircrew productivity., Comment: 16 pages and 5 figures

Safe operation of machine learning models requires architectures that explicitly delimit their operational ranges. We evaluate the ability of anomaly detection algorithms to provide indicators correlated with degraded model performance. By placing acceptance thresholds over such indicators, hard boundaries are formed that define the model's coverage. As a use case, we consider the extraction of exoplanetary spectra from transit light curves, specifically within the context of ESA's upcoming Ariel mission. Isolation Forests are shown to effectively identify contexts where prediction models are likely to fail. Coverage/error trade-offs are evaluated under conditions of data and concept drift. The best performance is seen when Isolation Forests model projections of the prediction model's explainability SHAP values., Comment: To appear in "Proceedings of SPAICE 2024: 1st ESA/IAA conference on AI in and for Space". Conference page at https://spaice.esa.int/

The integration of knowledge graphs and graph machine learning (GML) in genomic data analysis offers several opportunities for understanding complex genetic relationships, especially at the RNA level. We present a comprehensive approach for leveraging these technologies to analyze genomic variants, specifically in the context of RNA sequencing (RNA-seq) data from COVID-19 patient samples. The proposed method involves extracting variant-level genetic information, annotating the data with additional metadata using SnpEff, and converting the enriched Variant Call Format (VCF) files into Resource Description Framework (RDF) triples. The resulting knowledge graph is further enhanced with patient metadata and stored in a graph database, facilitating efficient querying and indexing. We utilize the Deep Graph Library (DGL) to perform graph machine learning tasks, including node classification with GraphSAGE and Graph Convolutional Networks (GCNs). Our approach demonstrates significant utility using our proposed tool, VariantKG, in three key scenarios: enriching graphs with new VCF data, creating subgraphs based on user-defined features, and conducting graph machine learning for node classification., Comment: arXiv admin note: substantial text overlap with arXiv:2312.04423

In this work, the novel, open-source humanoid robot, PANDORA, is presented where a majority of the structural elements are manufactured using 3D-printed compliant materials. As opposed to contemporary approaches that incorporate the elastic element into the actuator mechanisms, PANDORA is designed to be compliant under load, or in other words, structurally elastic. This design approach lowers manufacturing cost and time, design complexity, and assembly time while introducing controls challenges in state estimation, joint and whole-body control. This work features an in-depth description on the mechanical and electrical subsystems including details regarding additive manufacturing benefits and drawbacks, usage and placement of sensors, and networking between devices. In addition, the design of structural elastic components and their effects on overall performance from an estimation and control perspective are discussed. Finally, results are presented which demonstrate the robot completing a robust balancing objective in the presence of disturbances and stepping behaviors., Comment: This work has been submitted to the IEEE for possible publication

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

This paper presents the development and comparative evaluation of three voice command pipelines for controlling a Tello drone, using speech recognition and deep learning techniques. The aim is to enhance human-machine interaction by enabling intuitive voice control of drone actions. The pipelines developed include: (1) a traditional Speech-to-Text (STT) followed by a Large Language Model (LLM) approach, (2) a direct voice-to-function mapping model, and (3) a Siamese neural network-based system. Each pipeline was evaluated based on inference time, accuracy, efficiency, and flexibility. Detailed methodologies, dataset preparation, and evaluation metrics are provided, offering a comprehensive analysis of each pipeline's strengths and applicability across different scenarios.

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

Determining the carrying capacity of urban tourism destinations and public spaces is essential for sustainable management. This paper presents an online tool that calculates pedestrian carrying capacities for user-defined areas based on OpenStreetMap (OSM) data. The tool considers physical, real, and effective carrying capacities by incorporating parameters such as area per pedestrian, rotation factor, corrective factors, and management capacity. The carrying capacity calculator aids in balancing environmental, economic, social, and experiential factors to prevent overcrowding and preserve the quality of life for residents and visitors. This tool is particularly useful for tourism destination management, urban planning, and event management, ensuring positive visitor experiences and sustainable infrastructure development. We detail the implementation of the calculator, its underlying algorithm, and its application to the Santa Maria Maior parish in Lisbon, highlighting its effectiveness in managing urban tourism and public spaces.

Solar flares are powerful particle accelerators, and in the accepted standard flare model most of the flare energy is transported from a coronal energy-release region by accelerated electrons which stop collisionally in the chromosphere, heating and ionising the plasma, producing a broadband enhancement to the solar radiative output. We present a time-delay analysis of the infrared emission from two chromospheric sources in the flare SOL2014-09-24T17:50 taken at the McMath-Pierce telescope. By cross-correlating the intensity signals, measured with 1s cadence, from the two spatially resolved infrared sources we find a delay of 0.75 $\pm$ 0.07 s at 8.2 $\mu$m, where the uncertainties are quantified by a Monte Carlo analysis. The sources correlate well in brightness but have a time lag larger than can be reasonably explained by the energy transport dominated by non-thermal electrons precipitating from a single acceleration site in the corona. If interpreted as a time-of-flight difference between electrons traveling to each footpoint, we estimate time delays between 0.14 s and 0.42 s, for a reconnection site at the interior quasi-separatrix layer or at the null-point of the spine-fan topology inferred for this event. We employed modelling of electron transport via time-dependent Fokker-Planck and radiative hydrodynamic simulations to evaluate other possible sources of time-delay in the generation of the IR emission, such as differing ionisation timescales under different chromospheric conditions. Our results demonstrate that they are also unable to account for this discrepancy. This flare appears to require energy transport by some means other than electron beams originating in the corona., Comment: 15 pages, 12 figures, accepted for publication (MNRAS)

Telepresence VR systems allow for face-to-face communication, promoting the feeling of presence and understanding of nonverbal cues. However, when discussing virtual 3D objects, limitations to presence and communication cause deictic gestures to lose meaning due to disparities in orientation. Current approaches use shared perspective, and avatar overlap to restore these references, which cause occlusions and discomfort that worsen when multiple users participate. We introduce a new approach to shared perspective in multi-user collaboration where the avatars are not co-located. Each person sees the others' avatars at their positions around the workspace while having a first-person view of the workspace. Whenever a user manipulates an object, others will see his/her arms stretching to reach that object in their perspective. SPARC combines a shared orientation and supports nonverbal communication, minimizing occlusions. We conducted a user study (n=18) to understand how the novel approach impacts task performance and workspace awareness. We found evidence that SPARC is more efficient and less mentally demanding than life-like settings., Comment: 14 pages 8 figures

We study the average long-time behavior of the binary opinions of a social group with peer-to-peer interactions under the influence of an external bias and a persuadable leader, a strongly-biased agent with a dynamic opinion with the intention of spreading it across the system. We use a generalized, fully-connected Ising model, with each spin representing the binary opinion of an agent at a given time and a single, super spin representing the opinion of the leader. External fields and interaction constants model the opinion bias and peer-to-peer interactions, respectively, while the temperature $T$ models an idealized social climate, representing an authoritarian regime if $T$ is low or a liberal one if $T$ is high. We derive a mean-field solution for the average magnetization $m$, the "social mood", and investigate how $m$ and the super spin magnetization vary as a function of $T$. We find that, depending on the initial conditions, due to the presence of metastable states, the sign of the average magnetization depends on the temperature. Finally, we verify that this effect is also present even if we consider only nearest-neighbor interactions within the social group.

In this paper we compare two numerical methods to integrate Riemannian cubic polynomials on the Stiefel manifold $\textbf{St}_{n,k}$. The first one is the adjusted de Casteljau algorithm, and the second one is a symplectic integrator constructed through discretization maps. In particular, we choose the cases of $n=3$ together with $k=1$ and $k=2$. The first case is diffeomorphic to the sphere and the quasi-geodesics appearing in the adjusted de Casteljau algorithm are actually geodesics. The second case is an example where we have a pure quasi-geodesic different from a geodesic. We provide a numerical comparison of both methods and discuss the obtained results to highlight the benefits of each method., Comment: 12 pages, 3 figures

Web search engines arguably form the most popular data-driven systems in contemporary society. They wield a considerable power by functioning as gatekeepers of the Web, with most user journeys on the Web beginning with them. Starting from the late 1990s, search engines have been dominated by the paradigm of link-based web search. In this paper, we critically analyze the political economy of the paradigm of link-based web search, drawing upon insights and methodologies from critical political economy. We draw several insights on how link-based web search has led to phenomena that favor capital through long-term structural changes on the Web, and how it has led to accentuating unpaid digital labor and ecologically unsustainable practices, among several others. We show how contemporary observations on the degrading quality of link-based web search can be traced back to the internal contradictions with the paradigm, and how such socio-technical phenomena may lead to a disutility of the link-based web search model. Our contribution is primarily on enhancing the understanding of the political economy of link-based web search, and laying bare the phenomena at work, and implicitly catalyze the search for alternative models.

In this paper, we give a geometric construction of string algebras and of their module categories. Our approach uses dissections of punctured Riemann surfaces with extra data at marked points, called labels. As an application, we give a classification of support tau-tilting modules in terms of arcs in such a tiled surface. In the case when the string algebra is gentle, we recover the classification given arXiv:2004.11136., Comment: 33 pages, 40 figures. Comments are welcome!

Observations at (sub-)millimeter wavelengths offer a complementary perspective on our Sun and other stars, offering significant insights into both the thermal and magnetic composition of their chromospheres. Despite the fundamental progress in (sub-)millimeter observations of the Sun, some important aspects require diagnostic capabilities that are not offered by existing observatories. In particular, simultaneous observations of the radiation continuum across an extended frequency range would facilitate the mapping of different layers and thus ultimately the 3D structure of the solar atmosphere. Mapping large regions on the Sun or even the whole solar disk at a very high temporal cadence would be crucial for systematically detecting and following the temporal evolution of flares, while synoptic observations, i.e., daily maps, over periods of years would provide an unprecedented view of the solar activity cycle in this wavelength regime. As our Sun is a fundamental reference for studying the atmospheres of active main sequence stars, observing the Sun and other stars with the same instrument would unlock the enormous diagnostic potential for understanding stellar activity and its impact on exoplanets. The Atacama Large Aperture Submillimeter Telescope (AtLAST), a single-dish telescope with 50\,m aperture proposed to be built in the Atacama desert in Chile, would be able to provide these observational capabilities. Equipped with a large number of detector elements for probing the radiation continuum across a wide frequency range, AtLAST would address a wide range of scientific topics including the thermal structure and heating of the solar chromosphere, flares and prominences, and the solar activity cycle. In this white paper, the key science cases and their technical requirements for AtLAST are discussed., Comment: 18 pages, 4 figures, submitted to Open Research Europe as part of a collection on the Atacama Large Aperture Submillimeter Telescope (AtLAST) -- revised version

Recent developments enable the quantification of causal control given a structural causal model (SCM). This has been accomplished by introducing quantities which encode changes in the entropy of one variable when intervening on another. These measures, named causal entropy and causal information gain, aim to address limitations in existing information theoretical approaches for machine learning tasks where causality plays a crucial role. They have not yet been properly mathematically studied. Our research contributes to the formal understanding of the notions of causal entropy and causal information gain by establishing and analyzing fundamental properties of these concepts, including bounds and chain rules. Furthermore, we elucidate the relationship between causal entropy and stochastic interventions. We also propose definitions for causal conditional entropy and causal conditional information gain. Overall, this exploration paves the way for enhancing causal machine learning tasks through the study of recently-proposed information theoretic quantities grounded in considerations about causality., Comment: In Proceedings of the conference CLeaR (Causal Learning and Reasoning) 2024

The fusion of non-Abelian anyons or topological defects is a fundamental operation in measurement-only topological quantum computation. In topological superconductors, this operation amounts to a determination of the shared fermion parity of Majorana zero modes. As a step towards this, we implement a single-shot interferometric measurement of fermion parity in indium arsenide-aluminum heterostructures with a gate-defined nanowire. The interferometer is formed by tunnel-coupling the proximitized nanowire to quantum dots. The nanowire causes a state-dependent shift of these quantum dots' quantum capacitance of up to 1 fF. Our quantum capacitance measurements show flux h/2e-periodic bimodality with a signal-to-noise ratio of 1 in 3.7 $\mu$s at optimal flux values. From the time traces of the quantum capacitance measurements, we extract a dwell time in the two associated states that is longer than 1 ms at in-plane magnetic fields of approximately 2 T. These results are consistent with a measurement of the fermion parity encoded in a pair of Majorana zero modes that are separated by approximately 3 $\mu$m and subjected to a low rate of poisoning by non-equilibrium quasiparticles. The large capacitance shift and long poisoning time enable a parity measurement error probability of 1%., Comment: Added data on a second measurement of device A and a measurement of device B, expanded discussion of a trivial scenario. Refs added, author list updated

We characterise when a simple Happel-Reiten-Smalo tilt of a length heart is again a length heart in terms of approximation theory and the existence of a stability condition with a phase gap. We apply simple-minded reduction to provide a sufficient condition for infinite iterability of simple-minded mutation/simple tilting. We use simple-minded mutation pairs to provide a common framework to show that mutation of simple-minded collections (resp. $w$-simple-minded systems, for $w \geq 1$) gives simple-minded collections (resp. $w$-simple-minded systems) under mild conditions, in the process providing a unified proof of results of A. Dugas and P. Jorgensen. Finally, we show that under mild conditions, mutation of simple-minded collections is compatible with mutation of $w$-simple-minded systems via a singularity category construction due to H. Jin., Comment: 27 pages, 3 figures

This paper studies virtual constraints on Lie groups. Virtual constraints are invariant relations imposed on a control system via feedback. In this work, we introduce the notion of \textit{virtual constraints on Lie groups}, in particular, \textit{virtual nonholonomic constraints on Lie groups}, in a geometric framework. More precisely, this object is a controlled invariant subspace associated with an affine connection mechanical control system on the Lie algebra associated with the Lie group which is the configuration space of the system. We demonstrate the existence and uniqueness of a control law defining a virtual nonholonomic constraint and we characterize the trajectories of the closed-loop system as solutions of a mechanical system associated with an induced constrained connection. Moreover, we characterize the reduced dynamics for nonholonomic systems in terms of virtual nonholonomic constraints, i.e., we characterize when can we obtain reduced nonholonomic dynamics from virtual nonholonomic constraints. We illustrate the theory with some examples and present simulation results for an application., Comment: 18 pages

Real-world knowledge can be represented as a graph consisting of entities and relationships between the entities. The need for efficient and scalable solutions arises when dealing with vast genomic data, like RNA-sequencing. Knowledge graphs offer a powerful approach for various tasks in such large-scale genomic data, such as analysis and inference. In this work, variant-level information extracted from the RNA-sequences of vaccine-na\"ive COVID-19 patients have been represented as a unified, large knowledge graph. Variant call format (VCF) files containing the variant-level information were annotated to include further information for each variant. The data records in the annotated files were then converted to Resource Description Framework (RDF) triples. Each VCF file obtained had an associated CADD scores file that contained the raw and Phred-scaled scores for each variant. An ontology was defined for the VCF and CADD scores files. Using this ontology and the extracted information, a large, scalable knowledge graph was created. Available graph storage was then leveraged to query and create datasets for further downstream tasks. We also present a case study using the knowledge graph and perform a classification task using graph machine learning. We also draw comparisons between different Graph Neural Networks (GNNs) for the case study.

Due to the advent and increase in the popularity of the Internet, people have been producing and disseminating textual data in several ways, such as reviews, social media posts, and news articles. As a result, numerous researchers have been working on discovering patterns in textual data, especially because social media posts function as social sensors, indicating peoples' opinions, interests, etc. However, most tasks regarding natural language processing are addressed using traditional machine learning methods and static datasets. This setting can lead to several problems, such as an outdated dataset, which may not correspond to reality, and an outdated model, which has its performance degrading over time. Concept drift is another aspect that emphasizes these issues, which corresponds to data distribution and pattern changes. In a text stream scenario, it is even more challenging due to its characteristics, such as the high speed and data arriving sequentially. In addition, models for this type of scenario must adhere to the constraints mentioned above while learning from the stream by storing texts for a limited time and consuming low memory. In this study, we performed a systematic literature review regarding concept drift adaptation in text stream scenarios. Considering well-defined criteria, we selected 40 papers to unravel aspects such as text drift categories, types of text drift detection, model update mechanism, the addressed stream mining tasks, types of text representations, and text representation update mechanism. In addition, we discussed drift visualization and simulation and listed real-world datasets used in the selected papers. Therefore, this paper comprehensively reviews the concept drift adaptation in text stream mining scenarios., Comment: 49 pages

Understanding the physical processes that trigger solar flares is paramount to help with forecasting space weather and mitigating the effects on our technological infrastructure. A previously unknown phenomenon was recently identified in solar flares: the plasma temperature, derived from soft X-ray (SXR) data, at the onset of four flares, was revealed to be in the range 10-15 MK, without evidence of gradual heating. To investigate how common the hot-onset phenomenon may be, we extend this investigation to solar flares of B1.2- X6.9 classes recorded by the X-ray Sensor (XRS) on-board the GOES-14 and GOES-15 satellites between 2010 and 2011. For this statistical study, we employed the same methodology as in recent work, where the pre-flare SXR flux of each flare is obtained manually, and the temperature and emission measure values are obtained by the flux ratio of the two GOES/XRS channels using the standard software. From 3224 events listed in the GOES flare catalog for 2010-2011, we have selected and analyzed 745 events for which the flare heliographic location was provided in the list, to investigate center-to-limb effects of the hot-onset phenomenon. Our results show that 559 out of 745 flares (75%) exhibit an onset temperature above 8.6 MK (the first quartile), with respective log10 of the emission measure values between 46.0 - 47.25 cm-3, indicating that small amounts of plasma are quickly heated to high temperatures. These results suggest that the hot-onset phenomenon is very common in solar flares., Comment: 6 pages,7 figures

Crossword puzzles are one of the most popular word games, played in different languages all across the world, where riddle style can vary significantly from one country to another. Automated crossword resolution is challenging, and typical solvers rely on large databases of previously solved crosswords. In this work, we extend WebCrow 2.0, an automatic crossword solver, to French, making it the first program for crossword solving in the French language. To cope with the lack of a large repository of clue-answer crossword data, WebCrow 2.0 exploits multiple modules, called experts, that retrieve candidate answers from heterogeneous resources, such as the web, knowledge graphs, and linguistic rules. We compared WebCrow's performance against humans in two different challenges. Despite the limited amount of past crosswords, French WebCrow was competitive, actually outperforming humans in terms of speed and accuracy, thus proving its capabilities to generalize to new languages., Comment: Accepted Paper for EAI Intetain 2023 - 14th EAI International Conference on Intelligent Technologies for Interactive Entertainment

Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smaller LMs' reasoning abilities. Research on training small LMs has often relied on imitation learning to replicate the output of more capable models. We contend that excessive emphasis on imitation may restrict the potential of smaller models. We seek to teach small LMs to employ different solution strategies for different tasks, potentially different from the one used by the larger model. For example, while larger models might provide a direct answer to a complex task, smaller models may not have the same capacity. In Orca 2, we teach the model various reasoning techniques (step-by-step, recall then generate, recall-reason-generate, direct answer, etc.). More crucially, we aim to help the model learn to determine the most effective solution strategy for each task. We evaluate Orca 2 using a comprehensive set of 15 diverse benchmarks (corresponding to approximately 100 tasks and over 36,000 unique prompts). Orca 2 significantly surpasses models of similar size and attains performance levels similar or better to those of models 5-10x larger, as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings. make Orca 2 weights publicly available at aka.ms/orca-lm to support research on the development, evaluation, and alignment of smaller LMs, Comment: Added url to model weights fixed typo in Author name

Space is becoming more crowded in Low Earth Orbit due to increased space activity. Such a dense space environment increases the risk of collisions between space objects endangering the whole space population. Therefore, the need to consider collision avoidance as part of routine operations is evident to satellite operators. Current procedures rely on the analysis of multiple collision warnings by human analysts. However, with the continuous growth of the space population, this manual approach may become unfeasible, highlighting the importance of automation in risk assessment. In 2019, ESA launched a competition to study the feasibility of applying machine learning in collision risk estimation and released a dataset that contained sequences of Conjunction Data Messages (CDMs) in support of real close encounters. The competition results showed that the naive forecast and its variants are strong predictors for this problem, which suggests that the CDMs may follow the Markov property. The proposed work investigates this theory by benchmarking Hidden Markov Models (HMM) in predicting the risk of collision between two resident space objects by using one feature of the entire dataset: the sequence of the probability in the CDMs. In addition, Bayesian statistics are used to infer a joint distribution for the parameters of the models, which allows the development of robust and reliable probabilistic predictive models that can incorporate physical or prior knowledge about the problem within a rigorous theoretical framework and provides prediction uncertainties that nicely reflect the accuracy of the predicted risk. This work shows that the implemented HMM outperforms the naive solution in some metrics, which further adds to the idea that the collision warnings may be Markovian and suggests that this is a powerful method to be further explored.