Search

Friction is a phenomenon that manifests across all spatial and temporal scales, from the molecular to the macroscopic scale. It describes the dissipation of energy from the motion of particles or abstract reaction coordinates and arises in the transition from a detailed molecular-level description to a simplified, coarse-grained model. It has long been understood that time-dependent (non-Markovian) friction effects are critical for describing the dynamics of many systems, but that they are notoriously difficult to evaluate for complex physical, chemical, and biological systems. In recent years, the development of advanced numerical friction extraction techniques and methods to simulate the generalized Langevin equation have enabled exploration of the role of time-dependent friction across all scales. We discuss recent applications of these friction extraction techniques and the growing understanding of the role of friction in complex equilibrium and non-equilibrium dynamic many-body systems., Comment: 26 pages, 6 figures

Warm Saturn type exoplanets orbiting M-dwarfs are particularly suitable for in-depth cloud characterisation through transmission spectroscopy due to their favourable stellar to planetary radius contrast. However, modelling cloud formation consistently within the 3D atmosphere remains computationally challenging. The aim is to explore the combined atmospheric and micro-physical cloud structure, and the kinetic gas-phase chemistry for the warm Saturn HATS0-6b orbiting an M-dwarf. A combined 3D cloudy atmosphere model is constructed by iteratively executing the 3D General Circulation Model (GCM) expeRT/MITgcm and a kinetic cloud formation model, each in its full complexity. Resulting cloud particle number densities, sizes, and compositions are used to derive the local cloud opacity which is then utilised in the next GCM iteration. The disequilibrium H/C/O/N gas-phase chemistry is calculated for each iteration to assess the resulting transmission spectrum in post-processing. The cloud opacity feedback causes a temperature inversion at the sub-stellar point and at the evening terminator at gas pressures higher than 0.01 bar. Furthermore, clouds cool the atmosphere between 0.01 bar and 10 bar, and narrow the equatorial wind jet. The transmission spectrum shows muted gas-phase absorption and a cloud particle silicate feature at approximately 10 micron. The combined atmosphere-cloud model retains the full physical complexity of each component and therefore enables a detailed physical interpretation with JWST NIRSpec and MIRI LRS observational accuracy. The model shows that warm Saturn type exoplanets around M-dwarfs are ideal candidates to search for limb asymmetries in clouds and chemistry, identify cloud particle composition by observing their spectral features, and identify the cloud-induced strong thermal inversion that arises on these planets specifically., Comment: 21 pages, 15 figures, Accepted by A&A

This article introduces a general purpose framework and software to approximate partial differential equations (PDEs). The sparsity patterns of finite element discretized operators is identified automatically using the tools from computational geometry. They may enable experimentation with novel mesh generation techniques and could simplify the implementation of methods such as multigrid. We also implement quadrature methods following the work of Grundmann and Moller. These methods have been overlooked in the past but are more efficient than traditional tensor product methods. The proposed framework is applied to several standard examples.

Exploring the potential of quantum hardware for enhancing classical and real-world applications is an ongoing challenge. This study evaluates the performance of quantum and quantum-inspired methods compared to classical models for crack segmentation. Using annotated gray-scale image patches of concrete samples, we benchmark a classical mean Gaussian mixture technique, a quantum-inspired fermion-based method, Q-Seg a quantum annealing-based method, and a U-Net deep learning architecture. Our results indicate that quantum-inspired and quantum methods offer a promising alternative for image segmentation, particularly for complex crack patterns, and could be applied in near-future applications.

RuO$_2$ was considered for a long time to be a paramagnetic metal with an ideal rutile-type structure down to low temperatures, but recent studies on single-crystals claimed evidence for antiferromagnetic order and some symmetry breaking in the crystal structure. We have grown single-crystals of RuO2 by vapor transport using either O$_2$ or TeCl$_4$ as transport medium. These crystals exhibit metallic behavior following a $T^2$ low-temperature relation and a small paramagnetic susceptibility that can be attributed to Pauli paramagnetism. Neither the conductance nor the susceptibility measurements yield any evidence for a magnetic or a structural transition between 300K and $\sim$4 K. Comprehensive single-crystal diffraction studies with neutron and X-ray radiation reveal the rutile structure to persist until 2K in our crystals, and show nearly perfect stoichiometry. Previous observations of symmetry forbidden reflections can be attributed to multiple diffraction. Polarized single-crystal neutron diffraction experiments at 1.6K exclude the proposed antiferromagnetic structures with ordered moments larger than 0.01 Bohr magnetons.

With the demand for scalable cryogenic microwave circuitry continuously rising, recently developed flexible microwave striplines offer the tantalyzing perspective of increasing the cabling density by an order of magnitude without thermally overloading the cryostat. We use a superconducting quantum circuit to test the thermalization of input flex cables with integrated $60\,$dB of attenuation distributed at various temperature stages. From the measured decoherence rate of a superconducting fluxonium qubit, we estimate a residual population of the readout resonator below $3.5\cdot10^{-3}$ photons and we measure a $0.28\,$ms thermalization time for the flexible stripline attenuators. Furthermore, we confirm that the qubit reaches an effective temperature of $26.4\,$mK, close to the base temperature of the cryostat, practically the same as when using a conventional semi-rigid coaxial cable setup.

Exoplanets form from circumstellar protoplanetary discs whose fundamental properties (notably their extent, composition, mass, temperature and lifetime) depend on the host star properties, such as their mass and luminosity. B-stars are among the most massive stars and their protoplanetary discs test extreme conditions for exoplanet formation. This paper investigates the frequency of giant planet companions around young B-stars (median age of 16 Myr) in the Scorpius-Centaurus association, the closest association containing a large population of B-stars. We systematically search for massive exoplanets with the high-contrast direct imaging instrument SPHERE using the data from the BEAST survey, that targets an homogeneous sample of young B-stars from the wide Sco-Cen association. We derive accurate detection limits in case of non-detections. We found evidence in previous papers for two substellar companions around 42 stars. The masses of these companions are straddling the ~13 Jupiter mass deuterium burning limit but their mass ratio with respect to their host star is close to that of Jupiter. We derive a frequency of such massive planetary mass companions around B stars of 11-5+7%, accounting for the survey sensitivity. The discoveries of substellar companions bcen b and mu2sco B happened after only few stars in the survey had been observed, raising the possibility that massive Jovian planets might be common around B-stars. However our statistical analysis show that the occurrence rate of such planets is similar around B-stars and around solar-type stars of similar age, while B-star companions exhibit low mass ratios and larger semi-major axis., Comment: Accepted in A&A

In a previous paper, we introduced a new tool called GaiaPMEX. It characterizes the mass and semi-major axis relative to the central star (sma) of a possible companion around any source observed with Gaia. It uses the value of RUWE, or, with both Gaia and Hipparcos, the value of proper motion anomaly (PMa), alone or combined with the RUWE. Our goal is to exploit the large volume of sources in Gaia's DR3 and find new exoplanet candidates. We wish to create a new input catalog of planet-candidate hosting systems to the disposal of future follow-up projects. Beyond G=14, this catalog would prepare the arrival of powerful instruments on the ELTs, that could include RV follow-up of faint stars and direct imaging of planets around main sequence Gyr-old stars. We used the mass-sma degenerate set of solutions obtained by GaiaPMEX from any value of RUWE to select a sample of bright (G<16) Gaia sources whose companions could be planetary, with a mass <13.5 MJup. It led us to identify a sample of 9,698 planet candidate hosting sources, whose companion may have a mass <13.5 MJup in the range of 1-3-au sma. We identified 19 systems that are also reported in the Nasa exoplanet archive. We detected 8 substellar companions with a 1-3-au sma, initially discovered and characterised with RV and astrometry. Moreover, we found 6 transiting-planet systems and 2 wide-orbit systems for whom we predict the existence of supplementary companions. Focusing on the subsample of sources observed with Hipparcos, combining RUWE and PMa, we confirmed the identification of 4 new planetary candidate systems HD 187129, HD 81697, CD-42 883, and HD 105330. Given the degeneracy of mass-sma, many of the candidates in this 9,698 sources catalog might have a larger mass, in the brown-dwarf and stellar domain, if their sma departs from the 1-3-au range. The vetting of this large catalog will be the subject of future studies., Comment: 13 pages, 14 figures + appendix; accepted for publication in A&A on 03/09/2024

The Gaia mission is expected to yield the detection of several thousands of exoplanets, perhaps at least doubling the number of known exoplanets. Although the harvest is expected to occur when the astrometric time series will be published with DR4 at the eve of 2026, the DR3 is already a precious database to search for exoplanet beyond 1 au. With this objective, we characterized multiple systems by exploiting two astrometric signatures derived from the DR3 astrometric solution of bright sources (G<16). We have the proper motion anomaly, or PMa, for sources also observed with Hipparcos, and the excess of residuals in the RUWE and the astrometric excess noise (AEN). Those astrometric signatures give an accurate measurement of the astrometric motion of a source seen with Gaia, even in the presence of calibration and measurement noises. We found that they can allow identifying stellar binaries and hint to companions with a mass in the planetary domain. We introduce a tool called GaiaPMEX, that is able, for a given source, to model its astrometric signatures, by a photocenter orbit due to a companion with certain mass and semi-major axis (sma). Comparing to their actual measurements from the DR3 and Hipparcos, GaiaPMEX calculates a confidence map of the possible companion's mass and sma. The constraints on mass are, as expected, degenerate, but when allowed, coupling the use of PMa and RUWE, may significantly narrow the space of solutions. Thanks to combining Gaia and Hipparcos, planets are expected to be most frequently found within 1-10 au from their star, at the scale of Earth-to-Saturn orbits. In this range, exoplanets with mass down to 0.1 MJup are more favorably detected around M-dwarfs closer than 10 pc. Some fraction, if not all, of companions identified with GaiaPMEX may be characterized in the future using the astrometric time series that will be published with the DR4., Comment: 26 pages, 28 figures + appendices; accepted for publication in A&A on 03/09/2024

Disordered Quantum many-body Systems (DQS) and Quantum Neural Networks (QNN) have many structural features in common. However, a DQS is essentially an initialized QNN with random weights, often leading to non-random outcomes. In this work, we emphasize the possibilities of random processes being a deceptive quantum-generating model effectively hidden in a QNN. In contrast to classical noisy maps, quantum maps have the possibility of ergodicity breaking leading to memory effects and thus multiple consequences on the learnability and trainability of QNN. This phenomenon may lead to a fundamental misunderstanding of the capabilities of common quantum generative models, where the generation of new samples is essentially averaging over random outputs. While we suggest that DQS can be effectively used for tasks like image augmentation, we draw the attention that overly simple datasets are often used to show the generative capabilities of quantum models, potentially leading to overestimation of their effectiveness., Comment: 5 pages, 4 figures

The generalized Langevin equation (GLE), derived by projection from a general many-body Hamiltonian, exactly describes the dynamics of an arbitrary coarse-grained variable in a complex environment. However, analysis and prediction of real-world data with the GLE is hampered by slow transient or seasonal data components and time-discretization effects. Machine-learning (ML) techniques work but are computer-resource demanding and difficult to interpret. We show that by convolution filtering, time-series data decompose into fast, transient and seasonal components that each obey Hamiltonian dynamics and, thus, can be separately analyzed by projection techniques. We introduce methods to extract all GLE parameters from highly discretized time-series data and to forecast future data including the environmental stochasticity. For daily-resolved weather data, our analysis reveals non-Markovian memory that decays over a few days. Our prediction accuracy is comparable to commercial (weather.com) and ML long short-term memory (LSTM) methods at a reduced computational cost by a factor of $10^2-10^3$ compared to LSTM. For financial data, memory is very short-ranged and the dynamics effectively is Markovian, in agreement with the efficient-market hypothesis; consequently, models simpler than the GLE are sufficient. Our GLE framework is an efficient and interpretable method for the analysis and prediction of complex time-series data., Comment: 16 pages, 5 figures, 25 pages of Supplementary Information attached at the end

Perovskite oxides hetero-structures are host to a large number of interesting phenomena such as ferroelectricity and 2D-superconductivity. Ferroelectric perovskite oxides have been of significant interest due to their possible use in MOSFETs and FRAM. SrHfO$_3$ (SHO) is a perovskite oxide with pseudo-cubic lattice parameter of 4.1 $\mathring{A}$ that previous DFT calculations suggest can be stabilized in a ferroelectric P4mm phase, similar to STO, when stabilized with sufficient compressive strain. Additionally, it is insulating, possesses a large band gap, and a high dielectric constant, making it an ideal candidate for oxide electronic devices. In this work, SHO films were grown by hybrid molecular beam epitaxy with a tetrakis(ethylmethylamino)hafnium(IV) source on GdScO$_3$ and TbScO$_3$ substrates. Equilibrium and strained SHO phases were characterized using X-ray diffraction, X-ray absorption spectroscopy, and scanning transmission electron microscopy to determine the perovskite phase of the strained films, with the results compared to density functional theory models of phase stability versus strain. Contrary to past reports, we find that compressively-strained SrHfO$_3$ undergoes octahedral tilt distortions and most likely takes on the I4/mcm phase with the a$^0$a$^0$c$^-$ tilt pattern., Comment: 36 pages, 11 figures

The sub-Jovian desert is a region in the mass-period and radius-period parameter space, typically encompassing short-period ranges between super-Earths and hot Jupiters, that exhibits an intrinsic dearth of planets. This scarcity is likely shaped by photoevaporation caused by the stellar irradiation received by giant planets that have migrated inward. We report the detection and characterization of TOI-3568 b, a transiting super-Neptune with a mass of $26.4\pm1.0$ M$_\oplus$, a radius of $5.30\pm0.27$ R$_\oplus$, a bulk density of $0.98\pm0.15$ g cm$^{-3}$, and an orbital period of 4.417965(5) d situated in the vicinity of the sub-Jovian desert. This planet orbiting a K dwarf star with solar metallicity, was identified photometrically by TESS. It was characterized as a planet by our high-precision radial velocity monitoring program using MAROON-X at Gemini North, supplemented by additional observations from the SPICE large program with SPIRou at CFHT. We performed a Bayesian MCMC joint analysis of the TESS and ground-based photometry, MAROON-X and SPIRou radial velocities, to measure the orbit, radius, and mass of the planet, as well as a detailed analysis of the high-resolution flux and polarimetric spectra to determine the physical parameters and elemental abundances of the host star. Our results reveal TOI-3568 b as a hot super-Neptune, rich in hydrogen and helium with a core of heavier elements with a mass between 10 and 25 M$_\oplus$. We analyzed the photoevaporation status of TOI-3568 b and found that it experiences one of the highest EUV luminosities among planets with a mass M$_{\rm p}$ $<2$ M$_{\rm Nep}$, yet it has an evaporation lifetime exceeding 5 Gyr. Positioned in the transition between two significant populations of exoplanets on the mass-period and energy diagrams, this planet presents an opportunity to test theories concerning the origin of the sub-Jovian desert., Comment: Accepted for publication in A&A on September 4, 2024

Gate operations composed in quantum circuits form the basis of digital quantum simulation and quantum processing. While two-qubit gates generally operate between nearest neighbours, many circuits require non-local connectivity, necessitating some form of quantum information transport, such as the repeated application of SWAP gates or qubit shuttling. Preserving motional coherence during such transport remains a key challenge to improve gate fidelity and qubit connectivity, as well as to connect local fermionic modes. Here we combine tuneable gate operations between fermionic potassium-40 atoms - based on superexchange interaction - with their bidirectional transport via topological Thouless pumping in an optical lattice. We demonstrate shuttling of atomic singlet pairs with a single-shift fidelity of 99.57(4)% over 50 lattice sites. We spatially and coherently split a large number of randomly distributed fermionic spin singlet pairs and show $($SWAP$)^\alpha$-gate operations between atoms encountering each other during transport. As a signature of entanglement between fermions separated over large distances and interwoven with each other, we observe multi-frequency singlet-triplet oscillations. Topological pumping is generally applicable to long-lived atomic and molecular states, and specifically overcomes lifetime limitations inherent to transport using state-dependent optical lattices. Our work opens up new avenues for transport of quantum information and offers unprecedented possibilities for engineering connectivity in quantum circuits, including approaches based on fermionic modes, as well as for atom interferometry.

The possibility of observing spectral features in exoplanet atmospheres with space missions like JWST and ARIEL necessitates the accurate modelling of cloud particle opacities. In exoplanet atmospheres, cloud particles can be made from multiple materials and be considerably chemically heterogeneous. Therefore, assumptions on the morphology of cloud particles are required to calculate their opacities. The aim of this work is to analyse how different approaches to calculate the opacities of heterogeneous cloud particles affect cloud particle optical properties. We calculate cloud particle optical properties using seven different mixing treatments: four effective medium theories (EMTs: Bruggeman, Landau-Lifshitz-Looyenga (LLL), Maxwell-Garnett, and Linear), core-shell, and two homogeneous cloud particle approximations. We study the mixing behaviour of 21 commonly considered cloud particle materials for exoplanets. To analyse the impact on observations, we study the transmission spectra of HATS-6b, WASP-39b, WASP-76b, and WASP-107b.Materials with large refractive indices, like iron-bearing species or carbon, can change the optical properties of cloud particles when they comprise less than 1\% of the total particle volume. The mixing treatment of heterogeneous cloud particles also has an observable effect on transmission spectroscopy. Assuming core-shell or homogeneous cloud particles results in less muting of molecular features and retains the cloud spectral features of the individual cloud particle materials. The predicted transit depth for core-shell and homogeneous cloud particle materials are similar for all planets used in this work. If EMTs are used, cloud spectral features are broader and cloud spectral features of the individual cloud particle materials are not retained. Using LLL leads to less molecular features in transmission spectra compared to Bruggeman., Comment: 21 pages, 15 figures, Accepted by A&A

The relation between the frequency-dependent friction of a molecule in a liquid and the hydrodynamic properties of the liquid is fundamental for molecular dynamics. We investigate this connection for a water molecule moving in liquid water using all-atomistic molecular dynamics simulations and linear hydrodynamic theory. We analytically calculate the frequency-dependent friction of a sphere with finite surface slip moving in a viscoelastic compressible fluid by solving the linear transient Stokes equation, including frequency-dependent shear and volume viscosities, both determined from MD simulations of bulk liquid water. We also determine the frequency-dependent friction of a single water molecule moving in liquid water, as defined by the generalized Langevin equation from MD simulation trajectories. By fitting the effective sphere radius and the slip length, the frequency-dependent friction and velocity autocorrelation function from the transient Stokes equation and simulations quantitatively agree. This shows that the transient Stokes equation accurately describes the important features of the frequency-dependent friction of a single water molecule in liquid water and thus applies down to molecular length and time scales, provided accurate frequency-dependent viscosities are used. The frequency dependence of the shear viscosity of liquid water requires careful consideration of hydrodynamic finite-size effects to observe the asymptotic hydrodynamic power-law tail. In contrast, for a methane molecule moving in water, the frequency-dependent friction cannot be predicted based on a homogeneous model, which suggests, supported by the extraction of a frequency-dependent surface-slip profile, that a methane molecule is surrounded by a finite-thickness hydration layer with viscoelastic properties that are significantly different from bulk water., Comment: 25 pages, 13 figures

Magnetic monopole quasiparticles in spin ice materials hold the potential for exploring new frontiers of physics that extend beyond Maxwell's equations. We have previously proposed a two-dimensional magnetic monopole gas (2DMG), confined at the interface between spin-ice ($R_2$Ti$_2$O$_7$, $R$ = Dy, Ho) and antiferromagnetic iridate ($R_2$Ir$_2$O$_7$, $R$ = Dy, Ho), which hosts monopoles with a net charge. The mechanism behind the 2D confinement of the monopole gas remains unclear. In this work, we demonstrate that entropy is a key factor in the 2D confinement of this monopole gas. We reveal that the competition between the entropy of spin-ice, which favors the 2D confinement, and the entropy of the monopoles' random walks, which favors the deconfinement, dictates the distribution of the monopoles within a few layers close to the interface. Our entropy-based model accurately reproduces the monopole distribution obtained from the spin model, affirming that 2D confinement is entropy-driven. We further employ both models to show that the monopole distribution can be manipulated by an external magnetic field and temperature, holding promise for next-generation devices based on magnetic monopoles. Our findings reveal the entropic mechanisms in 2DMG, enabling the manipulation of emergent quasiparticles at material interfaces., Comment: 8 pages, 4 figures

The period of a strongly connected digraph is the greatest common divisor of the lengths of all its cycles. The period of a digraph is the least common multiple of the periods of its strongly connected components that contain at least one cycle. These notions play an important role in the theory of Markov chains and the analysis of powers of nonnegative matrices. While the time complexity of computing the period is well-understood, little is known about its space complexity. We show that the problem of computing the period of a digraph is NL-complete, even if all its cycles are contained in the same strongly connected component. However, if the digraph is strongly connected, we show that this problem becomes L-complete. For primitive digraphs (that is, strongly connected digraphs of period one), there always exists a number $m$ such that there is a path of length exactly $m$ between every two vertices. We show that computing the smallest such $m$, called the exponent of a digraph, is NL-complete. The exponent of a primitive digraph is a particular case of the index of convergence of a nonnegative matrix, which we also show to be computable in NL, and thus NL-complete.

We propose a scalable framework for deciding, proving, and explaining (in)equivalence of context-free grammars. We present an implementation of the framework and evaluate it on large data sets collected within educational support systems. Even though the equivalence problem for context-free languages is undecidable in general, the framework is able to handle a large portion of these datasets. It introduces and combines techniques from several areas, such as an abstract grammar transformation language to identify equivalent grammars as well as sufficiently similar inequivalent grammars, theory-based comparison algorithms for a large class of context-free languages, and a graph-theory-inspired grammar canonization that allows to efficiently identify isomorphic grammars.

Individual teaching is among the most successful ways to impart knowledge. Yet, this method is not always feasible due to large numbers of students per educator. Quantum computing serves as a prime example facing this issue, due to the hype surrounding it. Alleviating high workloads for teachers, often accompanied with individual teaching, is crucial for continuous high quality education. Therefore, leveraging Large Language Models (LLMs) such as GPT-4 to generate educational content can be valuable. We conducted two complementary studies exploring the feasibility of using GPT-4 to automatically generate tips for students. In the first one students (N=46) solved four multiple-choice quantum computing questions with either the help of expert-created or LLM-generated tips. To correct for possible biases towards LLMs, we introduced two additional conditions, making some participants believe that they were given expert-created tips, when they were given LLM-generated tips and vice versa. Our second study (N=23) aimed to directly compare the LLM-generated and expert-created tips, evaluating their quality, correctness and helpfulness, with both experienced educators and students participating. Participants in our second study found that the LLM-generated tips were significantly more helpful and pointed better towards relevant concepts than the expert-created tips, while being more prone to be giving away the answer. While participants in the first study performed significantly better in answering the quantum computing questions when given tips labeled as LLM-generated, even if they were created by an expert. This phenomenon could be a placebo effect induced by the participants' biases for LLM-generated content. Ultimately, we find that LLM-generated tips are good enough to be used instead of expert tips in the context of quantum computing basics.

Transmission spectroscopy has been a workhorse technique over the past two decades to constrain the physical and chemical properties of exoplanet atmospheres. One of its classical key assumptions is that the portion of the atmosphere it probes -- the terminator region -- is homogeneous. Several works in the past decade, however, have put this into question for highly irradiated, hot ($T_{eq}\gtrsim 1000$ K) gas giant exoplanets both empirically and via 3-dimensional modelling. While models predict clear differences between the evening (day-to-night) and morning (night-to-day) terminators, direct morning/evening transmission spectra in a wide wavelength range has not been reported for an exoplanet to date. Under the assumption of precise and accurate orbital parameters on WASP-39 b, here we report the detection of inhomogeneous terminators on the exoplanet WASP-39 b, which allows us to retrieve its morning and evening transmission spectra in the near-infrared ($2-5\ \mu$m) using JWST. We observe larger transit depths in the evening which are, on average, $405 \pm 88$ ppm larger than the morning ones, also having qualitatively larger features than the morning spectrum. The spectra are best explained by models in which the evening terminator is hotter than the morning terminator by $177^{+65}_{-57}$ K with both terminators having C/O ratios consistent with solar. General circulation models (GCMs) predict temperature differences broadly consistent with the above value and point towards a cloudy morning terminator and a clearer evening terminator., Comment: Published in Nature at https://www.nature.com/articles/s41586-024-07768-4. All code to produce plots (with data) can be found at https://github.com/nespinoza/wasp39-terminators

We show that a modular unit on two copies of the upper half-plane is a Borcherds product if and only if its boundary divisor is a special boundary divisor. Therefore, we define a subspace of the space of invariant vectors for the Weil representation which maps surjectively onto the space of modular units that are Borcherds products. Moreover, we show that every boundary divisor of a Borcherds product can be obtained in this way. As a byproduct we obtain new identities of eta products., Comment: 19 pages

Previous active inference accounts of emotion translate fluctuations in free energy to a sense of emotion, mainly focusing on valence. However, in affective science, emotions are often represented as multi-dimensional. In this paper, we propose to adopt a Circumplex Model of emotion by mapping emotions into a two-dimensional spectrum of valence and arousal. We show how one can derive a valence and arousal signal from an agent's expected free energy, relating arousal to the entropy of posterior beliefs and valence to utility less expected utility. Under this formulation, we simulate artificial agents engaged in a search task. We show that the manipulation of priors and object presence results in commonsense variability in emotional states.

We discuss how quantum geometrodynamics, a conservative approach to quantum gravity, might explain the emergence of classical spacetime and, with it, the emergence of classical time and its arrow from the universal quantum state. This follows from a particular but reasonable choice of boundary condition motivated by the structure of the Hamiltonian of the theory. This condition can also be seen as defining a quantum version of Penrose's Weyl curvature hypothesis. We comment on the relation of this picture to the `past hypothesis' and the different observed arrows of time, and we consider how quantum geometrodynamics could serve as a unifying and more fundamental framework to explain these observations., Comment: Contribution to the XIX Symmetries in Science Symposium 2023; v2: minor typographical errors corrected and other minor improvements to the text, references updated

A labelled Markov decision process (MDP) is a labelled Markov chain with nondeterminism; i.e., together with a strategy a labelled MDP induces a labelled Markov chain. The model is related to interval Markov chains. Motivated by applications to the verification of probabilistic noninterference in security, we study problems of minimising probabilistic bisimilarity distances of labelled MDPs, in particular, whether there exist strategies such that the probabilistic bisimilarity distance between the induced labelled Markov chains is less than a given rational number, both for memoryless strategies and general strategies. We show that the distance minimisation problem is ExTh(R)-complete for memoryless strategies and undecidable for general strategies. We also study the computational complexity of the qualitative problem about making the distance less than one. This problem is known to be NP-complete for memoryless strategies. We show that it is EXPTIME-complete for general strategies., Comment: 36 pages, 7 figures, CONCUR 2024

In the rapidly evolving interdisciplinary field of quantum information science and technology, a big obstacle is the necessity of understanding high-level mathematics to solve complex problems. Visualizations like the (dimensional) circle notation enable us to visualize not only single-qubit but also complex multi-qubit states, entanglement, and quantum algorithms. Current findings in educational research suggest that incorporating visualizations in settings of problem solving can have beneficial effects on students' performance and cognitive load compared to solely relying on symbolic problem solving content. In this pilot study, we aim to take a first step to identify in which contexts students benefit from the presentation of visualizations of multi-qubit systems in addition to mathematical formalism. We compare students' performance, time taken, and cognitive load when solving problems using the mathematical-symbolic Dirac notation alone with using it accompanied by the circle notation or the dimensional circle notation in single- and multi-qubit systems. Although little overall differences in students' performance can be detected depending on the presented representations, we observe that problem-solving performance is student- and context-dependent. In addition, the results indicate reduced cognitive load when participants are presented with visualization. The results are discussed with respect to relevant design aspects for future studies., Comment: 32(+20) pages, 16(+6) figures

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

Human Activity Recognition (HAR) is a longstanding problem in AI with applications in a broad range of areas, including healthcare, sports and fitness, security, and more. The performance of HAR in real-world settings is strongly dependent on the type and quality of the input signal that can be acquired. Given an unobstructed, high-quality camera view of a scene, computer vision systems, in particular in conjunction with foundation models, can today fairly reliably distinguish complex activities. On the other hand, recognition using modalities such as wearable sensors (which are often more broadly available, e.g., in mobile phones and smartwatches) is a more difficult problem, as the signals often contain less information and labeled training data is more difficult to acquire. To alleviate the need for labeled data, we introduce our comprehensive Fitness Multimodal Activity Dataset (FiMAD) in this work, which can be used with the proposed pre-training method MuJo (Multimodal Joint Feature Space Learning) to enhance HAR performance across various modalities. FiMAD was created using YouTube fitness videos and contains parallel video, language, pose, and simulated IMU sensor data. MuJo utilizes this dataset to learn a joint feature space for these modalities. We show that classifiers pre-trained on FiMAD can increase the performance on real HAR datasets such as MM-Fit, MyoGym, MotionSense, and MHEALTH. For instance, on MM-Fit, we achieve an Macro F1-Score of up to 0.855 when fine-tuning on only 2% of the training data and 0.942 when utilizing the full training set for classification tasks. We have compared our approach to other self-supervised ones and showed that, unlike them, ours can consistently improve on the baseline network performance as well as provide a better data-efficiency.

We establish a rationality result for linear combinations of traces of cycle integrals of certain meromorphic Hilbert modular forms. These are meromorphic counterparts to the Hilbert cusp forms $\omega_m(z_1,z_2)$, which Zagier investigated in the context of the Doi-Naganuma lift. We give an explicit formula for these cycle integrals, expressed in terms of the Fourier coefficients of harmonic Maass forms. A key element in our proof is the explicit construction of locally harmonic Hilbert-Maass forms on $\mathbb{H}^2$, which are analogous to the elliptic locally harmonic Maass forms examined by Bringmann, Kane, and Kohnen. Additionally, we introduce a regularized theta lift that maps elliptic harmonic Maass forms to locally harmonic Hilbert-Maass forms and is closely related to the Doi-Naganuma lift.

In this article, we show that Fourier eigenmeasures supported on spheres with radii given by a locally finite sequence, which we call $k$-spherical measures, correspond to Fourier series exhibiting a modular-type transformation behaviour with respect to the metaplectic group. A familiar subset of such Fourier series comprises holomorphic modular forms. This allows us to construct $k$-spherical eigenmeasures and derive Poisson-type summation formulas, thereby recovering formulas of a similar nature established by Cohn-Gon\c{c}alves, Lev-Reti, and Meyer, among others. Additionally, we extend our results to higher dimensions, where Hilbert modular forms yield higher-dimensional $k$-spherical measures.

Communication bottlenecks severely hinder the scalability of distributed neural network training, particularly in high-performance computing (HPC) environments. We introduce AB-training, a novel data-parallel method that leverages low-rank representations and independent training groups to significantly reduce communication overhead. Our experiments demonstrate an average reduction in network traffic of approximately 70.31\% across various scaling scenarios, increasing the training potential of communication-constrained systems and accelerating convergence at scale. AB-training also exhibits a pronounced regularization effect at smaller scales, leading to improved generalization while maintaining or even reducing training time. We achieve a remarkable 44.14 : 1 compression ratio on VGG16 trained on CIFAR-10 with minimal accuracy loss, and outperform traditional data parallel training by 1.55\% on ResNet-50 trained on ImageNet-2012. While AB-training is promising, our findings also reveal that large batch effects persist even in low-rank regimes, underscoring the need for further research into optimized update mechanisms for massively distributed training.

Population protocols are a well-studied model of distributed computation in which a group of anonymous finite-state agents communicates via pairwise interactions. Together they decide whether their initial configuration, that is, the initial distribution of agents in the states, satisfies a property. As an extension in order to express properties of multisets over an infinite data domain, Blondin and Ladouceur (ICALP'23) introduced population protocols with unordered data (PPUD). In PPUD, each agent carries a fixed data value, and the interactions between agents depend on whether their data are equal or not. Blondin and Ladouceur also identified the interesting subclass of immediate observation PPUD (IOPPUD), where in every transition one of the two agents remains passive and does not move, and they characterised its expressive power. We study the decidability and complexity of formally verifying these protocols. The main verification problem for population protocols is well-specification, that is, checking whether the given PPUD computes some function. We show that well-specification is undecidable in general. By contrast, for IOPPUD, we exhibit a large yet natural class of problems, which includes well-specification among other classic problems, and establish that these problems are in EXPSPACE. We also provide a lower complexity bound, namely coNEXPTIME-hardness., Comment: 40 pages, 7 figures, extended version of ICALP 2024 paper

We report on the discovery and validation of a transiting long-period mini-Neptune orbiting a bright (V = 9.0 mag) G dwarf (TOI 4633; R = 1.05 RSun, M = 1.10 MSun). The planet was identified in data from the Transiting Exoplanet Survey Satellite by citizen scientists taking part in the Planet Hunters TESS project. Modeling of the transit events yields an orbital period of 271.9445 +/- 0.0040 days and radius of 3.2 +/- 0.20 REarth. The Earth-like orbital period and an incident flux of 1.56 +/- 0.2 places it in the optimistic habitable zone around the star. Doppler spectroscopy of the system allowed us to place an upper mass limit on the transiting planet and revealed a non-transiting planet candidate in the system with a period of 34.15 +/- 0.15 days. Furthermore, the combination of archival data dating back to 1905 with new high angular resolution imaging revealed a stellar companion orbiting the primary star with an orbital period of around 230 years and an eccentricity of about 0.9. The long period of the transiting planet, combined with the high eccentricity and close approach of the companion star makes this a valuable system for testing the formation and stability of planets in binary systems., Comment: 24 pages, 16 figures, 4 tables

We study 2-player concurrent stochastic B\"uchi games on countable graphs. Two players, Max and Min, seek respectively to maximize and minimize the probability of visiting a set of target states infinitely often. We show that there always exist $\varepsilon$-optimal Max strategies that use just a step counter plus 1 bit of public memory. This upper bound holds for all countable graphs, but it is a new result even for the special case of finite graphs. The upper bound is tight in the sense that Max strategies that use just a step counter, or just finite memory, are not sufficient even on finite game graphs. The upper bound is a consequence of a slightly stronger new result: $\varepsilon$-optimal Max strategies for the combined B\"uchi and Transience objective require just 1 bit of public memory (but cannot be memoryless). Our proof techniques also yield a closely related result, that $\varepsilon$-optimal Max strategies for the Transience objective alone (which is only meaningful in infinite graphs) can be memoryless.

Measuring the abundances of C- and O-bearing species in exoplanet atmospheres enables us to constrain the C/O ratio, that contains indications about the planet formation history. With a wavelength coverage going from 0.95 to 2.5 microns, the high-resolution (R$\sim$70 000) spectropolarimeter SPIRou can detect spectral lines of major bearers of C and O in exoplanets. Here we present our study of SPIRou transmission spectra of WASP-76 b acquired for the ATMOSPHERIX program. We applied the publicly available data analysis pipeline developed within the ATMOSPHERIX consortium, analysing the data using 1-D models created with the petitRADTRANS code, with and without a grey cloud deck. We report the detection of H$_2$O and CO at a Doppler shift of around -6 km.s$^{-1}$, consistent with previous observations of the planet. Finding a deep cloud deck to be favoured, we measured in mass mixing ratio (MMR) log(H$_2$O)$_{MMR}$ = -4.52 $\pm$ 0.77 and log(CO)$_{MMR}$ = -3.09 $\pm$ 1.05 consistent with a sub-solar metallicity to more than 1$\sigma$. We report 3$\sigma$ upper limits for the abundances of C$_2$H$_2$, HCN and OH. We estimated a C/O ratio of 0.94 $\pm$ 0.39 ($\sim$ 1.7 $\pm$ 0.7 x solar, with errors indicated corresponding to the 2$\sigma$ values) for the limbs of WASP-76 b at the pressures probed by SPIRou. We used 1-D ATMO forward models to verify the validity of our estimation. Comparing them to our abundance estimations of H$_2$O and CO, as well as our upper limits for C$_2$H$_2$, HCN and OH, we found that our results were consistent with a C/O ratio between 1 and 2 x solar, and hence with our C/O estimation. Finally, we found indications of asymmetry for both H$_2$O and CO when investigating the dynamics of their signatures, pointing to a complex scenario involving possibly both a temperature difference between limbs and clouds being behind the asymmetry this planet is best known for., Comment: 20 pages, 19 figures, accepted for publication in A&A

Legal autonomy - the lawful activity of artificial intelligence agents - can be achieved in one of two ways. It can be achieved either by imposing constraints on AI actors such as developers, deployers and users, and on AI resources such as data, or by imposing constraints on the range and scope of the impact that AI agents can have on the environment. The latter approach involves encoding extant rules concerning AI driven devices into the software of AI agents controlling those devices (e.g., encoding rules about limitations on zones of operations into the agent software of an autonomous drone device). This is a challenge since the effectivity of such an approach requires a method of extracting, loading, transforming and computing legal information that would be both explainable and legally interoperable, and that would enable AI agents to reason about the law. In this paper, we sketch a proof of principle for such a method using large language models (LLMs), expert legal systems known as legal decision paths, and Bayesian networks. We then show how the proposed method could be applied to extant regulation in matters of autonomous cars, such as the California Vehicle Code.

In this paper, we explore the application of Unmanned Aerial Vehicles (UAVs) in maritime search and rescue (mSAR) missions, focusing on medium-sized fixed-wing drones and quadcopters. We address the challenges and limitations inherent in operating some of the different classes of UAVs, particularly in search operations. Our research includes the development of a comprehensive software framework designed to enhance the efficiency and efficacy of SAR operations. This framework combines preliminary detection onboard UAVs with advanced object detection at ground stations, aiming to reduce visual strain and improve decision-making for operators. It will be made publicly available upon publication. We conduct experiments to evaluate various Region of Interest (RoI) proposal methods, especially by imposing simulated limited bandwidth on them, an important consideration when flying remote or offshore operations. This forces the algorithm to prioritize some predictions over others.

The aim of this paper is to evaluate the use of D-CNN (Deep Convolutional Neural Networks) algorithms to classify pig body conditions in normal or not normal conditions, with a focus on characteristics that are observed in sanitary monitoring, and were used six different algorithms to do this task. The study focused on five pig characteristics, being these caudophagy, ear hematoma, scratches on the body, redness, and natural stains (brown or black). The results of the study showed that D-CNN was effective in classifying deviations in pig body morphologies related to skin characteristics. The evaluation was conducted by analyzing the performance metrics Precision, Recall, and F-score, as well as the statistical analyses ANOVA and the Scott-Knott test. The contribution of this article is characterized by the proposal of using D-CNN networks for morphological classification in pigs, with a focus on characteristics identified in sanitary monitoring. Among the best results, the average Precision metric of 80.6\% to classify caudophagy was achieved for the InceptionResNetV2 network, indicating the potential use of this technology for the proposed task. Additionally, a new image database was created, containing various pig's distinct body characteristics, which can serve as data for future research.

Tangles were originally introduced as a concept to formalize regions of high connectivity in graphs. In recent years, they have also been discovered as a link between structural graph theory and data science: when interpreting similarity in data sets as connectivity between points, finding clusters in the data essentially amounts to finding tangles in the underlying graphs. This paper further explores the potential of tangles in data sets as a means for a formal study of clusters. Real-world data often follow a normal distribution. Accounting for this, we develop a quantitative theory of tangles in data sets drawn from Gaussian mixtures. To this end, we equip the data with a graph structure that models similarity between the points and allows us to apply tangle theory to the data. We provide explicit conditions under which tangles associated with the marginal Gaussian distributions exist asymptotically almost surely. This can be considered as a sufficient formal criterion for the separabability of clusters in the data.

Guided wave dispersion is commonly assessed by Fourier analysis of the field along a line, resulting in frequency-wavenumber dispersion curves. In anisotropic plates, a point source can generate multiple dispersion branches pertaining to the same modal surface, which arise due to the angle between the power flux and the wave vector. We show that this phenomenon is particularly pronounced near zero-group-velocity points, entailing up to six contributions along a given direction. Stationary phase points accurately describe the measurements conducted on a monocrystalline silicon plate., Comment: Code accessible at https://github.com/dakiefer/GEWtool