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We propose a novel extension to the recently developed non-commutative Riemannian foliated branch-cut quantum gravity (BCQG). Based on an extended Faddeev-Jackiw symplectic deformation of the conventional Poisson algebra, we investigate non-commutativity effects on a symplectic topological manifold that provides a natural isomorphic setting composed by a triad of canonically conjugate scalar complex fields which comprise quantum complementary dualities. Based on a complementary analytically continued Friedmann-type equation, combined with a quantum approach based on the Ho\v{r}awa-Lifshitz quantum gravity, we describe the dynamic evolution of the universe's wave function, unfolding unprecedented predictions for the cosmic evolution and inflation. The non-commutative foliated quantum gravity approach offers a new perspective on explaining the accelerated cosmic expansion of the universe, strongly suggesting that non-commutative algebra induces the late accelerated growth of both the universe's wave function and the corresponding scale factor, along with their quantum counterparts. In contrast to the conventional inflationary model, where inflation requires a remarkably fine-tuned set of initial conditions in a patch of the universe, non-commutative foliated quantum gravity, analytically continued to the complex plane, captures short and long scales of spacetime, leading to an evolutionary cosmic dynamic through a topological reconfiguration of the primordial cosmic matter and energy content. This result introduces new speculative framework elements regarding the reconfiguration of matter and energy due to an underlying non-commutative spatio-temporal structure as a driver of spacetime cosmic acceleration.

Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML). At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation. Canonically, a QP features $N$ input qubits and one output qubit, and is used to determine whether an input state belongs to a specific class. Rydberg atoms, with their extended coherence times and scalable spatial configurations, provide an ideal platform for implementing QPs. In this work, we explore the implementation of QPs on Rydberg atom arrays, assessing their performance in tasks such as phase classification between Z2, Z3, Z4 and disordered phases, achieving high accuracy, including in the presence of noise. We also perform multi-class entanglement classification by extending the QP model to include multiple output qubits, achieving 95\% accuracy in distinguishing noisy, high-fidelity states based on separability. Additionally, we discuss the experimental realization of QPs on Rydberg platforms using both single-species and dual-species arrays, and examine the error bounds associated with approximating continuous functions.

We propose a detailed analysis of datasets generated from simulations of two-dimensional quantum spin systems using the quantum Ising model at absolute zero temperature. Our focus is on examining how fundamental physical properties, energy, magnetization, and entanglement entropy, evolve under varying external transverse magnetic fields and system sizes. From the Quantum Toolbox in Python (QuTiP), we simulate systems with 4, 8, and 16 spins arranged in square lattices, generating extensive datasets with 5000 samples per magnetic field value. The Hamiltonian operator incorporates quantum mechanical effects such as superposition and tunneling, challenging classical interpretations of spin states. We compute extended Pauli operators and construct the Hamiltonian to include spin-spin interactions and transverse field terms. Our analysis reveals that as the system size increases, fluctuations in energy and entanglement entropy become more evident, indicating lifted sensitivity to external perturbations and suggesting the onset of quantum phase transitions. Spin-spin correlation functions demonstrate that interactions are predominantly local, but larger systems exhibit more complex and fluctuating correlations. These findings provide valuable insights into the behavior of quantum spin systems and lay the groundwork for future machine learning applications aimed at predicting physical quantities and identifying phase transitions from a quantum perspective., Comment: 8 pages, 17 figures, 1 table

We address the problem of identifying functional interactions among stochastic neurons with variable-length memory from their spiking activity. The neuronal network is modeled by a stochastic system of interacting point processes with variable-length memory. Each chain describes the activity of a single neuron, indicating whether it spikes at a given time. One neuron's influence on another can be either excitatory or inhibitory. To identify the existence and nature of an interaction between a neuron and its postsynaptic counterpart, we propose a model selection procedure based on the observation of the spike activity of a finite set of neurons over a finite time. The proposed procedure is also based on the maximum likelihood estimator for the synaptic weight matrix of the network neuronal model. In this sense, we prove the consistency of the maximum likelihood estimator followed by a proof of the consistency of the neighborhood interaction estimation procedure. The effectiveness of the proposed model selection procedure is demonstrated using simulated data, which validates the underlying theory. The method is also applied to analyze spike train data recorded from hippocampal neurons in rats during a visual attention task, where a computational model reconstructs the spiking activity and the results reveal interesting and biologically relevant information., Comment: 29 pages, 2 figures

Altermagnets constitute a new class of magnetic materials that combine properties previously thought to be exclusive to either antiferromagnets or ferromagnets, and have unique properties of their own. In particular, a combination of symmetries connecting magnetic sublattices gives rise to a band spin splitting exhibiting unconventional d, g, or i-wave character. Their unique electronic properties have already led to new spin-dependent transport effects. Here, we consider their spin and charge dynamics on ultrafast timescales. We use a minimal tight binding model that captures the main features of the altermagnetic candidate material KRu$_4$O$_8$. In the framework of this model, we compute the spin-dependent electronic scattering dynamics after ultrashort-pulse excitation and show through these microscopic calculations how electron-electron and electron-phonon scattering processes redistribute optically excited carriers in a 2D slice of the Brillouin zone. We find that the optically excited spin polarization is long lived (~1ps) compared to the electron-electron momentum scattering lifetime of roughly 10fs. This contrasts remarkably with the much shorter spin lifetimes observed in typical ultrafast electronic spin dynamics in conventional ferromagnets and antiferromagnets, making these pulse-driven spin excitation experiments a key probe of altermagnetism.

Non-transiting terrestrial planets will be accessible by upcoming observatories of which LIFE is an example. Planet b orbiting Teegarden's Star is one of the optimal targets for such missions. We use a one-dimensional atmospheric model with real-gas radiation, a multi-species pseudo-adiabatic convection-condensation scheme, and a water cloud scheme, to estimate the impact of the cloud coverage on the emission spectrum of the target, as well as to assess how sensitive LIFE could be to changes in outgoing flux caused by these clouds. Though the emergent flux decreases with a higher cloud coverage, it does not decrease by more than one order of magnitude as the coverage increases from 0% to 90%. This allows LIFE to retain a high sensitivity to the cloud cover fraction for wavelengths longer than 7 microns. In this spectral range, with at least 1 bar of N2, LIFE is able to distinguish cloud cover fractions as small as 10% given an integration time of 24 hours, and yields much better precision with a week-long integration. An integration time of one week also allows the resolution of local variations in spectral flux, which can lead to an easier molecule identification. This ability remains if the planet is a CO2-dominated Venus analog. Partial pressures of N2 lower than 1 bar may create a degeneracy with the cloud cover fraction. LIFE shows promising potential for detecting and characterizing atmospheres even with a high cloud coverage, and retaining a fine sensitivity to relatively small differences in cloud cover fractions.

Starting from a microscopic model for the particle-particle interactions in a Weyl semimetal, we analyzed the possibility for conventional as well as monopole Cooper pairing between quasiparticle excitations at the same (intra-nodal) or opposite (inter-nodal) Weyl nodes. We derived a coupled system of self-consistent BCS-like equations, where the angular dependence of the pairings is directly determined from the microscopic interaction symmetries. We studied the competition between conventional and monopole superconducting phases, thus obtaining explicitly the phase diagrams from the microscopic interaction model parameters. We determined the critical temperatures for both phases, and the low temperature critical behavior, including the specific heat, that we suggest as possible experimental probe for topological quantum criticality in Weyl semimetals., Comment: 14 pages, 3 figures, accepted for publication in Superconductivity

Spatially-resolved emission line kinematics are invaluable to investigating fundamental galaxy properties and have become increasingly accessible for galaxies at $z\gtrsim0.5$ through sensitive near-infrared imaging spectroscopy and millimeter interferometry. Kinematic modeling is at the core of the analysis and interpretation of such data sets, which at high-z present challenges due to lower signal-to-noise ratio (S/N) and resolution compared to data of local galaxies. We present and test the 3D fitting functionality of DysmalPy, examining how well it recovers intrinsic disk rotation velocity and velocity dispersion, using a large suite of axisymmetric models, covering a range of galaxy properties and observational parameters typical of $z\sim1$-$3$ star-forming galaxies. We also compare DysmalPy's recovery performance to that of two other commonly used codes, GalPak3D and 3DBarolo, which we use in turn to create additional sets of models to benchmark DysmalPy. Over the ranges of S/N, resolution, mass, and velocity dispersion explored, the rotation velocity is accurately recovered by all tools. The velocity dispersion is recovered well at high S/N, but the impact of methodology differences is more apparent. In particular, template differences for parametric tools and S/N sensitivity for the non-parametric tool can lead to differences up to a factor of 2. Our tests highlight and the importance of deep, high-resolution data and the need for careful consideration of: (1) the choice of priors (parametric approaches), (2) the masking (all approaches) and, more generally, evaluating the suitability of each approach to the specific data at hand. This paper accompanies the public release of DysmalPy., Comment: 38 pages, 21 figures, accepted for publication in ApJ

In this work, we investigate the impact of Rashba spin-orbit coupling (RSOC) on the formation of charge-density wave (CDW) and superconducting (SC) phases in the Holstein model on a half-filled square lattice. Using unbiased finite-temperature Quantum Monte Carlo simulations, we go beyond mean-field approaches to determine the ground state order parameter as a function of RSOC and phonon frequency. Our results reveal that the Rashba metal is unstable due to particle-hole instabilities, favoring the emergence of a CDW phase for any RSOC value. In the limit of a pure Rashba hopping, the model exhibits a distinct behavior with the appearance of four Weyl cones at half-filling, where quantum phase transitions are expected to occur at strong interactions. Indeed, a quantum phase transition, belonging to the Gross-Neveu Ising universality class between a semi-metal and CDW emerges at finite phonon frequency dependent coupling $\lambda_c$. In the antiadiabatic limit we observe an enhance symmetry in the IR that unifies SC and CDW orders. These results advance our understanding of competing CDW and SC phases in systems with spin-orbit coupling, providing insights that may help clarify the behavior of related materials., Comment: 11 pages, 9 figures

In this paper, we revisited the extension of the classical non-standard cosmological model in which dissipative processes are considered through a bulk viscous term in the new field $\phi$, which interacts with the radiation component during the early universe. Specifically, we consider an interaction term of the form $\Gamma_{\phi} \rho_{\phi}$, where $\Gamma_{\phi}$ represents the decay rate of the field and $\rho_{\phi}$ denotes its energy density, and a bulk viscosity described by $\xi=\xi_{0}\rho_{\phi}^{1/2}$, within the framework of Eckart's theory. This extended non-standard cosmology is employed to examine the parameter space for the production of Feebly Interacting Massive Particles (FIMPs) as Dark Matter candidates. In particular, for certain combinations of the model and Dark Matter parameters, namely ($T_\text{end}$,$\kappa$) and $(m_\chi,\langle\sigma v\rangle)$, we found large new regions in which it can establish the Dark Matter and reproduce the current observable relic density as compared with the $\Lambda$CDM and the classical non-standard cosmological scenarios., Comment: 15 pages and 18 figures. arXiv admin note: text overlap with arXiv:2409.03083

Time-varying inhomogeneities on stellar surfaces constitute one of the largest sources of radial velocity (RV) error for planet detection and characterization. We show that stellar variations, because they manifest on coherent, rotating surfaces, give rise to changes that are complex but useably compact and coherent in the spectral domain. Methods for disentangling stellar signals in RV measurements benefit from modeling the full domain of spectral pixels. We simulate spectra of spotted stars using starry and construct a simple spectrum projection space that is sensitive to the orientation and size of stellar surface features. Regressing measured RVs in this projection space reduces RV scatter by 60-80% while preserving planet shifts. We note that stellar surface variability signals do not manifest in spectral changes that are purely orthogonal to a Doppler shift or exclusively asymmetric in line profiles; enforcing orthogonality or focusing exclusively on asymmetric features will not make use of all the information present in the spectra. We conclude with a discussion of existing and possible implementations on real data based on the presented compact, coherent framework for stellar signal mitigation., Comment: 19 pages, 12 figures, accepted to ApJ

For a generalization of the Gellerstedt operator with Dirichlet boundary conditions in a Tricomi domain. We establish Poho\v{z}aev-type identities and prove the nonexistence of nontrivial regular solutions. Furthermore, we investigate the critical exponent phenomenon for power-type nonlinearities, characterized by the critical exponent of a weighted Sobolev embedding., Comment: 4 figures

Automated knowledge extraction from scientific literature can potentially accelerate materials discovery. We have investigated an approach for extracting synthesis protocols for reticular materials from scientific literature using large language models (LLMs). To that end, we introduce a Knowledge Extraction Pipeline (KEP) that automatizes LLM-assisted paragraph classification and information extraction. By applying prompt engineering with in-context learning (ICL) to a set of open-source LLMs, we demonstrate that LLMs can retrieve chemical information from PDF documents, without the need for fine-tuning or training and at a reduced risk of hallucination. By comparing the performance of five open-source families of LLMs in both paragraph classification and information extraction tasks, we observe excellent model performance even if only few example paragraphs are included in the ICL prompts. The results show the potential of the KEP approach for reducing human annotations and data curation efforts in automated scientific knowledge extraction., Comment: 16 pages

Multi-messenger (MM) observations of binary neutron star (BNS) mergers provide a promising approach to trace the distance-redshift relation, crucial for understanding the expansion history of the Universe and, consequently, testing the nature of Dark Energy (DE). While the gravitational wave (GW) signal offers a direct measure of the distance to the source, high-energy observatories can detect the electromagnetic counterpart and drive the optical follow-up providing the redshift of the host galaxy. In this work, we exploit up-to-date catalogs of $\gamma$-ray bursts (GRBs) supposedly coming from BNS mergers observed by the Fermi $\gamma$-ray Space Telescope and the Neil Gehrels Swift Observatory, to construct a large set of mock MM data. We explore how combinations of current and future generations of GW observatories operating under various underlying cosmological models would be able to detect GW signals from these GRBs. We achieve the reconstruction of the GW parameters by means of a novel prior-informed Fisher matrix approach. We then use these mock data to perform an agnostic reconstruction of the DE phenomenology, thanks to a machine learning method based on forward modeling and Gaussian Processes (GP). Our study highlights the paramount importance of observatories capable of detecting GRBs and identifying their redshift. In the best-case scenario, the GP constraints are 1.5 times more precise than those produced by classical parametrizations of the DE evolution. We show that, in combination with forthcoming cosmological surveys, fewer than 40 GW-GRB detections will enable unprecedented precision on $H_\mathrm{0}$ and $\Omega_\mathrm{m}$, and accurately reconstruct the DE density evolution.

Research in auditory, visual, and audiovisual speech recognition (ASR, VSR, and AVSR, respectively) has traditionally been conducted independently. Even recent self-supervised studies addressing two or all three tasks simultaneously tend to yield separate models, leading to disjoint inference pipelines with increased memory requirements and redundancies. This paper proposes unified training strategies for these systems. We demonstrate that training a single model for all three tasks enhances VSR and AVSR performance, overcoming typical optimisation challenges when training from scratch. Moreover, we introduce a greedy pseudo-labelling approach to more effectively leverage unlabelled samples, addressing shortcomings in related self-supervised methods. Finally, we develop a self-supervised pre-training method within our framework, proving its effectiveness alongside our semi-supervised approach. Despite using a single model for all tasks, our unified approach achieves state-of-the-art performance compared to recent methods on LRS3 and LRS2 for ASR, VSR, and AVSR, as well as on the newly released WildVSR dataset. Code and models are available at https://github.com/ahaliassos/usr., Comment: NeurIPS 2024. Code: https://github.com/ahaliassos/usr

The design space for the fabrication of photonic lanterns is vast, which allows for a great diversity of designs but also complicates finding the right parameters for a given component. Coupled-mode theory is a great tool to sort through the parameter space and find the right designs for a multiplicity of photonic lantern types, including conventional, mode-selective, and anything in-between. It correctly models the behavior of the fabricated component, but it also simplifies the task of finding the right fabrication sequence by computing the all-important adiabatic criteria, which dictate which transverse modes will couple, and at which step of the recipe. Here, using coupled-mode theory, we predict and test experimentally the behavior of four types of 3X1 photonic lanterns ranging from conventional to mode-specific and hybrid configurations, and explain mode-coupling, operation bandwidth, and excess loss mechanisms., Comment: 8 pages, 6 figures, This work has been submitted to the IEEE for possible publication

We employ physics-informed neural networks (PINNs) to solve fundamental Dyson-Schwinger integral equations in the theory of quantum electrodynamics (QED) in Euclidean space. Our approach uses neural networks to approximate the fermion wave function renormalization, dynamical mass function, and photon propagator. By integrating the Dyson-Schwinger equations into the loss function, the networks learn and predict solutions over a range of momenta and ultraviolet cutoff values. This method can be extended to other quantum field theories (QFTs), potentially paving the way for forefront applications of machine learning within high-level theoretical physics., Comment: 14 pages, 3 figures

This work considers an uplink asynchronous massive random access scenario in which a large number of users asynchronously access a base station equipped with multiple receive antennas. The objective is to alleviate the problem of massive collision due to the limited number of orthogonal preambles of an access scheme in which user activity detection is performed. We propose a user activity detection with delay-calibration (UAD-DC) algorithm and investigate the benefits of oversampling for the estimation of continuous time delays at the receiver. The proposed algorithm iteratively estimates time delays and detects active users by noting that the collided users can be identified through accurate estimation of time delays. Due to the sporadic user activity patterns, the user activity detection problem can be formulated as a compressive sensing (CS) problem, which can be solved by a modified Turbo-CS algorithm under the consideration of correlated noise samples resulting from oversampling. A sliding-window technique is applied in the proposed algorithm to reduce the overall computational complexity. Moreover, we propose a new design of the pulse shaping filter by minimizing the Bayesian Cram\'er-Rao bound of the detection problem under the constraint of limited spectral bandwidth. Numerical results demonstrate the efficacy of the proposed algorithm in terms of the normalized mean squared error of the estimated channel, the probability of misdetection and the successful detection ratio., Comment: Submitted to IEEE Transactions on Vehicular Technology

Functional networks representing human brain dynamics have become a standard tool in neuroscience, providing an accessible way of depicting the computation performed by the brain in healthy and pathological conditions. Yet, these networks share multiple characteristics with those representing other natural and man-made complex systems, leading to the question of whether they are actually capturing the uniqueness of the human brain. By resorting to a large set of data representing multiple financial, technological, social, and natural complex systems, and by relying on Deep Learning classification models, we show how they are highly similar. We specifically reach the conclusion that, under some general reconstruction methodological choices, it is as difficult to understand whether a network represents a human brain or a financial market, as to diagnose a major pathology. This suggests that functional networks are describing information processing mechanisms that are common across complex systems; but that are not currently defining the uniqueness of the human mind. We discuss the consequence of these findings for neuroscience and complexity science in general, and suggest future avenues for exploring this interesting topic., Comment: 5 pages plus SI, 3 figures

[Context and Motivation] Several studies have investigated attributes of great software practitioners. However, the investigation of such attributes is still missing in Requirements Engineering (RE). The current knowledge on attributes of great software practitioners might not be easily translated to the context of RE because its activities are, usually, less technical and more human-centered than other software engineering activities. [Question/Problem] This work aims to investigate which are the attributes of great requirements engineers, the relationship between them, and strategies that can be employed to obtain these attributes. We follow a method composed of a survey with 18 practitioners and follow up interviews with 11 of them. [Principal Ideas/Results] Investigative ability in talking to stakeholders, judicious, and understand the business are the most commonly mentioned attributes amongst the set of 22 attributes identified, which were grouped into four categories. We also found 38 strategies to improve RE skills. Examples are training, talking to all stakeholders, and acquiring domain knowledge. [Contribution] The attributes, their categories, and relationships are organized into a map. The relations between attributes and strategies are represented in a Sankey diagram. Software practitioners can use our findings to improve their understanding about the role and responsibilities of requirements engineers.

An unresolved question in studies of active galactic nuclei (AGN) is whether their different classes probe different evolutionary stages of black hole--host galaxy interaction. We present the projected two-point cross-correlation function between a sample of Dark Energy Spectroscopic Instrument (DESI)-matched AGN selected from Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) optical + Wide-field Infrared Survey Explorer ($WISE$) mid-IR photometry, and DESI-designated luminous red galaxies, for $z\in 0.5-1.0$. The total overlap area is 43.4 deg$^2$, including $\sim27,000$ spectroscopic LRGs in our redshift range. We visually classified 1,991 matched HSC-DESI objects in our redshift range, spectroscopically confirming that 1,517 ($76\%$) of them are AGN. Of these 1,517 objects, $73\%$ are broad-line AGN, $27\%$ are obscured AGN. We infer that the parent HSC+$WISE$ AGN catalog has a number density of at least $\sim 240$ deg$^{-2}$, confirming it is one of the most complete optical/infrared AGN catalog to date. We investigate the AGN clustering as a function of the spectroscopic classification and infer the halo mass for each sample. The inferred average mass of the halos $\langle M_h\rangle$ that host unobscured broad-line AGN ($M_h \approx 10^{13.4}h^{-1}M_\odot$) is $\sim 5.5\times$ larger than the halos that host obscured AGN ($M_h \approx 10^{12.6}\, h^{-1}M_\odot$), at $2.8\sigma$ significance, in the same sense as our prior work based on photometric redshifts. This suggests that we may relax our concerns about systematic shifts in the inferred redshift distribution producing this halo mass difference. While we do not yet find statistically significant spectroscopic evidence that unobscured AGN reside in more massive halos than their obscured counterparts, further analyses are necessary to distinguish if more complex evolutionary histories are needed to model these AGN populations., Comment: 27 pages, 12 figures, submitted to ApJ

The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circuits (PQCs). Higher depth increases expressivity, but also results in a detrimental accumulation of errors. Furthermore, the number of parameters involved in the PQC significantly influences the performance through the necessary number of measurements to evaluate gradients, which scales linearly with the number of parameters. Motivated by this, we look at deep equilibrium models (DEQs), which mimic an infinite-depth, weight-tied network using a fraction of the memory by employing a root solver to find the fixed points of the network. In this work, we present Quantum Deep Equilibrium Models (QDEQs): a training paradigm that learns parameters of a quantum machine learning model given by a PQC using DEQs. To our knowledge, no work has yet explored the application of DEQs to QML models. We apply QDEQs to find the parameters of a quantum circuit in two settings: the first involves classifying MNIST-4 digits with 4 qubits; the second extends it to 10 classes of MNIST, FashionMNIST and CIFAR. We find that QDEQ is not only competitive with comparable existing baseline models, but also achieves higher performance than a network with 5 times more layers. This demonstrates that the QDEQ paradigm can be used to develop significantly more shallow quantum circuits for a given task, something which is essential for the utility of near-term quantum computers. Our code is available at https://github.com/martaskrt/qdeq., Comment: To be published in NeurIPS 2024

We present the design, fabrication, and characterization of mode-selective photonic lanterns using double-clad fibers. Here, we exploited several custom-pulled double-clad fibers to achieve the symmetry break required to excite higher-order modes. The resulting components are short and exhibit high modal isolation and low excess loss. They address some of the limitations of existing photonic lanterns in terms of fragility and coupling efficiency. The fabrication process involves the use of lower-index capillary tubes to maintain fiber geometry during fusion and tapering. Through the use of varying first cladding diameters, mode selectivity is achieved without sacrificing single-mode compatibility. This in turn allows proper real-time characterization during the whole fabrication process. Results demonstrate that double-clad fibers stacked inside a fluoride-doped capillary tube feature high modal isolation (above 60dB) and low excess loss (lower than 0.49dB), over a broad wavelength range (more than 250nm) with steeper taper profiles, and more robust components. The use of less expensive synthetic fused silica capillary tubes achieves high modal isolation (above 20dB) and excess loss lower than 2dB over the same broad wavelength range., Comment: 6 pages, 5 figures, This work has been submitted to the IEEE for possible publication

The manganese pnictide CaMn$_2$Bi$_2$, with Mn atoms arranged in a puckered honeycomb structure, exhibits narrow-gap antiferromagnetism, and it is currently a promising candidate for the study of complex electronic and magnetic phenomena, such as magnetotransport effects and potential spin spirals under high pressure. In this paper, we perform a detailed research of the magnetic properties of CaMn$_2$Bi$_2$ using density functional theory (DFT) combined with the Hubbard U correction and spin-orbit coupling, which accurately describe the magnetic interactions. Our results obtained for a large number of magnetic configurations are accurately captured by a modified Heisenberg model that includes on-site magnetization terms to describe magnetic energy excitations. We further investigate the role of the spin-orbit coupling, and find that the magnetic anisotropy of CaMn$_2$Bi$_2$ shows an easy plane, with the preferred magnetization direction being exchanged between axes in the plane by applying small strain values. This strain-tunable magnetization, driven by the interplay between spin-orbit interactions and lattice distortions, highlights the potential for controlling magnetic states in Mn-pnictides for future applications in spintronic and magnetoelectric devices.

The C-19 star stream has the abundance characteristics of an unusually metal poor globular cluster but kinematically is uncharacteristically hot and wide for a cluster stream, having a line of sight velocity dispersion of 6 \kms\ and a 1-sigma width of 240 pc. We show that the tidal dissolution of an old, lower mass, globular cluster in a CDM galactic halo naturally creates a hot, wide stream currently near orbital apocenter. More generally, simulations show that hot streams, which are all near their orbital apocenter, become thin, cool streams near pericenter. Furthermore, the wide streams from a population of dissolved clusters in the simulations have a mean galactocentric radial velocity dispersion of 7.8$\pm$1.0 \kms\ in a CDM cosmology but only 4.1$\pm$1.6 \kms\ in a WDM (5.5 keV) simulation. A detailed C-19 model in a simplified Milky Way halo potential with a CDM subhalo population provides a lower bound to stream heating, finding that the stream develops a line of sight velocity dispersion of 4.1$\pm$1.1 \kms, whereas WDM (5.5 keV) subhalos give 3.1$\pm$0.1\kms. Known dwarf galaxies alone provide negligible heating. There are five other currently known streams wider than 200 pc that contain a globular cluster, all near their orbital apocenter., Comment: AAS submitted

Evanescently coupled waveguide arrays provide a tabletop platform to realize a variety of Hamiltonians, where physical waveguides correspond to the individual sites of a tight-binding lattice. Nontrivial spatial structure of the waveguide modes enriches this picture and uncovers further possibilities. Here, we demonstrate that the effective coupling between $p$-like modes of adjacent photonic waveguides changes its sign depending on their relative orientation vanishing for a proper alignment at a so-called magic angle. Using femtosecond laser-written waveguides, we demonstrate this experimentally for $p$-mode dimers and graphene-like photonic lattices exhibiting quasi-flat bands at this angle. We observe diffraction-free propagation of corner and bulk states providing a robust experimental evidence of a two-dimensional Aharonov-Bohm-like caging in an optically switchable system., Comment: 7 pages, 5 figures, 8 page-long Supplementary Materials

Testing possible variations in fundamental constants of nature is a crucial endeavor in observational cosmology. This paper investigates potential cosmological variations in the fine structure constant ($\alpha$) through a non-parametric approach, using galaxy cluster observations as the primary cosmological probe. We employ two methodologies based on galaxy cluster gas mass fraction measurements derived from X-ray and Sunyaev-Zeldovich observations, along with luminosity distances from type Ia supernovae. We also explore how different values of the Hubble constant ($H_0$) impact the variation of $\alpha$ across cosmic history. When using the Planck satellite's $H_0$ observations, a constant $\alpha$ is ruled out at approximately the 3$\sigma$ confidence level for $z \lesssim 0.5$. Conversely, employing local estimates of $H_0$ restores agreement with a constant $\alpha$., Comment: 10 pages, 3 figures, I table. Accepted by the The European Physical Journal C

Prime-Cam, one of the primary instruments for the Fred Young Submillimeter Telescope (FYST) developed by the CCAT Collaboration, will house up to seven instrument modules, with the first operating at 280 GHz. Each module will include three arrays of superconducting microwave kinetic inductance detectors (KIDs). The first KID array fabricated for the 280 GHz module uses titanium-nitride (TiN) as the superconducting material and has 3,456 individual detectors, while the other two arrays use aluminum. This paper presents the design and laboratory characterization of the 280 GHz TiN array, which is cooled below its critical temperature to ~0.1 K and read out over six RF feedlines. LED mapping, a technique for matching the measured resonant frequency of a detector to its physical position, was performed on the array so that the results can be used to lithographically trim the KID capacitors and increase the yield of the array by reducing frequency collisions. We present the methods and results of LED mapping the 280 GHz TiN KID array before deployment on FYST., Comment: 4 pages, 4 figures, submitted to IEEE Transactions on Applied Superconductivity (IEEE TAS)

Laser-Wakefield Accelerators (LWFA) running at kHz repetition rates hold great potential for applications. They typically operate with low-energy, highly compressed laser pulses focused in high-pressure gas targets. Experiments have shown that the best-quality electron beams are achieved using Hydrogen gas targets. However, continuous operation with Hydrogen requires a dedicated pumping system. This work presents the design of a differential pumping system, enabling, for the first time, continuous operation of our kHz LWFA using a high-pressure Hydrogen gas jet. The system successfully maintained a pressure below 3e-4 mbar, even with a free-flowing gas jet operating at 140 bar backing pressure. Numerical fluid dynamics and optical simulations were used to guide and validate the system's design., Comment: 10 pages, 9 figures

The sampling rate of input and output signals is known to play a critical role in the identification and control of dynamical systems. For slow-sampled continuous-time systems that do not satisfy the Nyquist-Shannon sampling condition for perfect signal reconstructability, careful consideration is required when identifying parametric and nonparametric models. In this letter, a comprehensive statistical analysis of estimators under slow sampling is performed. Necessary and sufficient conditions are obtained for unbiased estimates of the frequency response function beyond the Nyquist frequency, and it is shown that consistency of parametric estimators can be achieved even if input frequencies overlap after aliasing. Monte Carlo simulations confirm the theoretical properties., Comment: 6 pages, 2 figures

Let $S$ and $S'$ be orientable finite-type surfaces of genus $g\geq 4$ and $g'$, respectively. We prove that every multitwist-preserving map between pure mapping class groups $\text{PMap}(S)\to \text{PMap}(S')$ is induced by a multi-embedding. As an application, we classify all homomorphisms $\text{PMap}(S)\to \text{PMap}(S')$ for $g\geq 4$ and $g' \leq 6\cdot 2^{g-4}$.

Mass transport of impurities immersed in a confined quasi-two-dimensional moderately dense granular gas of inelastic hard spheres is studied. The effect of the confinement on granular particles is modeled through a collisional model (the so-called $\Delta$-model) that includes an effective mechanism to transfer the kinetic energy injected by vibration in the vertical direction to the horizontal degrees of freedom of grains. The impurity can differ in mass, diameter, inelasticity, or the energy injection at collisions, compared to the gas particles. The Enskog--Lorentz kinetic equation for the impurities is solved via the Chapman--Enskog method to first order in spatial gradients for states close to the homogeneous steady state. As usual, the three diffusion transport coefficients for tracer particles in a mixture are given in terms of the solutions of a set of coupled linear integral equations which are solved by considering the lowest Sonine approximation. The theoretical predictions for the tracer diffusion coefficient (relating the mass flux with the gradient of the number density of tracer particles) are compared with both direct simulation Monte Carlo and molecular dynamics simulations. The agreement is in general good, except for strong inelasticity and/or large contrast of energy injection at tracer-gas collisions compared to gas-gas collisions. Finally, as an application of our results, the segregation problem induced by both a thermal gradient and gravity is exhaustively analyzed., Comment: 24 pages, 12 figures

This paper uses a net-theoretic approach to convergence spaces, aimed to simplify the description of continuous convergence in order to apply it in problems concerning Homotopy Theory. We present methods for handling homotopies of limit spaces, define fundamental groupoids, and prove a generalized version of the Seifert-van Kampen Theorem for limit spaces., Comment: Minor typos corrected in Example 3.6. Remarks 3.7 and 5.1 were added

We provide an updated inference of the proper motion of M31 using the Gaia DR3 proper motions of bright stars from the disc of M31. By refining the motion of the quasar reference frame, and statistically accounting for the variations in the inferred proper motions obtained across different regions of M31, we demonstrate that these inconsistencies most likely arise from systematic uncertainties. Our updated favoured values for the proper motion of M31 are 46.9$\pm$11.7(stat)$\pm$50.6(sys)$\mu$as yr$^{-1}$ along the right ascension direction, and -29.1$\pm$9.4(stat)$\pm$35.6(sys)$\mu$as yr$^{-1}$ along the declination direction, the systematics being determined at a 90% confidence level (the values for M33 are given in the paper). This clearly highlights that the systematics are the dominant source of uncertainty, their magnitudes being comparable to the proper motion of M31 itself. The analysis conducted using Gaia DR2 instead of DR3 revealed that a net reduction in these systematic uncertainties occurred between the two data releases. If similar progress is made with the upcoming DR4, the future Gaia-based estimates could match the level of uncertainties of HST, and could be used to refine the dynamics and history of M31 and M33., Comment: 7 pages, 4 figures. Accepted for publication in A&A

Reinforcement learning with human feedback (RLHF) is shown to largely benefit from precise reward models (RMs). However, recent studies in reward modeling schemes are skewed towards English, limiting the applicability of RLHF in multilingual alignments. In this work, we investigate the cross-lingual transfer of RMs trained in diverse languages, primarily from English. Our experimental results demonstrate the strong cross-lingual transfer of English RMs, exceeding target language RMs by 3~4% average increase in Multilingual RewardBench. Furthermore, we analyze the cross-lingual transfer of RMs through the representation shifts. Finally, we perform multilingual alignment to exemplify how cross-lingual transfer in RM propagates to enhanced multilingual instruction-following capability, along with extensive analyses on off-the-shelf RMs. We release the code, model, and data.

Phylogenetic trees constitute an interesting class of objects for stochastic processes due to the non-standard nature of the space they inhabit. In particular, many statistical applications require the construction of Markov processes on the space of trees, whose cardinality grows superexponentially with the number of leaves considered. We investigate whether certain lower-dimensional projections of tree space preserve the Markov property in tree-valued Markov processes. We study exact lumpability of tree shapes and $\varepsilon$-lumpability of clades, exploiting the combinatorial structure of the SPR graph to obtain bounds on the lumping error under the random walk and Metropolis-Hastings processes. Finally, we show how to use these results in empirical investigation, leveraging exact and $\varepsilon$-lumpability to improve Monte Carlo estimation of tree-related quantities.

Many modern radio telescopes employ an observational strategy that involves maximizing the use of their available spaces (cabins), outfitting them with various receivers at different frequencies to detect incoming signals from the sky simultaneously or individually. The Large Latin American Millimeter Array (LLAMA), is a joint venture between Argentina and Brazil consisting of the installation and operation of a 12-meter aperture Cassegrain telescope. It features three available cabins for instrumentation and plans to install six single-pixel heterodyne receivers, covering different bandwidths in the 30 to 950 GHz window of the electromagnetic spectrum, in its two lateral Nasmyth cabins at different phases of the project. Therefore, it is crucial not only to design a tertiary optical system that couples the antenna beam to those receivers, but also to do it in a scalable way. The primary goal for the design is to simultaneously maximize the antenna efficiency while minimizing optical aberrations for all receivers, both fundamental aspects for the optimal functioning of cutting-edge astronomical instruments. In this paper, we present the entire design process, starting from the quasi-optical approach based on the propagation of a fundamental Gaussian beam mode, continuing with the validation of the design based on physical optics simulations, and ending with a tolerance analysis of the system. As a result of this process, a frequency independent tertiary optical system has been achieved for almost all the receivers, which is expected to provide high optical performance for the radio telescope., Comment: 44pages, 28 figures, 14 tables

The actor model has gained increasing popularity. However, it lacks support for complex state management tasks, such as enforcing foreign key constraints and ensuring data replication consistency across actors. These are crucial properties in partitioned application designs, such as microservices. To fill this gap, we start by analyzing the key impediments in state-of-the-art actor systems. We find it difficult for developers to express complex data relationships across actors and reason about the impact of state updates on performance due to opaque state management abstractions. To solve this conundrum, we develop SmSa, a novel data management layer for actor systems, allowing developers to declare data dependencies that cut across actors, including foreign keys, data replications, and other dependencies. SmSa can transparently enforce the declared dependencies, reducing the burden on developers. Furthermore, SmSa employs novel logging and concurrency control algorithms to support transactional maintenance of data dependencies. We demonstrate SmSa can support core data management tasks where dependencies across components appear frequently without jeopardizing application logic expressiveness and performance. Our experiments show SmSa significantly reduces the logging overhead and leads to increased concurrency level, improving by up to 2X the performance of state-of-the-art deterministic scheduling approaches. As a result, SmSa will make it easier to design and implement highly partitioned and distributed applications.

The gravitational-wave events observed by the LIGO-Virgo-KAGRA collaboration are attributed to compact binary coalescences happening in vacuum. However, the large merger rates observed suggest that matter environments likely play a significant role in the formation and evolution of compact binaries. Why have we not seen environmental effects in LVK signals? While matched-filtering remains the most effective technique for gravitational-wave searches, it comes with a burden: we might only observe signals that align with our (vacuum) expectations, potentially missing unexpected or unknown phenomena. Even more concerning is the possibility that environmental effects could mimic vacuum waveforms, introducing biases in parameter estimation and impacting population studies. Here, we use numerical relativity simulations of binary black hole mergers inside stellar envelopes to show that: (i) a \texttt{GW150914}-like event would be detected (with a false alarm rate smaller than $10^{-4}\, \mathrm{yr^{-1}}$) using a template bank of vacuum waveforms, even when immersed in stellar material of density larger than $10^{7}\,\mathrm{g/cm^3}$; (ii) environmental effects can pass routinely performed tests of vacuum General Relativity, while leading to considerable biases in parameter estimation; but (iii) phenomenological environment waveforms are effectual in detecting environmental effects and can resolve systematic biases., Comment: 10 pages, 7 figures, Comments are welcomed

Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical methods. However, current quantum algorithms are constrained by hardware limitations and the increased number of measurements required to achieve chemical accuracy. To address the measurement challenge, techniques for grouping commuting and anti-commuting terms, driven by heuristics, have been developed to reduce the number of measurements needed in quantum algorithms on near-term quantum devices. In this work, we propose a probabilistic framework using GFlowNets to group fully (FC) or qubit-wise commuting (QWC) terms within a given Hamiltonian. The significance of this approach is demonstrated by the reduced number of measurements for the found groupings; 51% and 67% reduction factors respectively for FC and QWC partitionings with respect to greedy coloring algorithms, highlighting the potential of GFlowNets for future applications in the measurement problem. Furthermore, the flexibility of our algorithm extends its applicability to other resource optimization problems in Hamiltonian simulation, such as circuit design., Comment: 8 pages, 2 figures. Accepted for Machine Learning and the Physical Sciences Workshop, NeurIPS 2024. Submission Number: 167

We report on continuous high-harmonic generation at 1 kHz repetition rate from a liquid-sheet plasma mirror driven by relativistic-intensity near-single-cycle light transients. Through precise control of both the surface plasma density gradient and the driving light waveform, we can produce highly stable and reproducible extreme ultraviolet spectral quasi-continua, corresponding to the generation of stable kHz-trains of isolated attosecond pulses in the time domain. This confirms the exciting potential of liquid sheet targets as one of the building blocks of future high-power attosecond lasers.

We propose a two parameters extension of the flat $\Lambda$CDM model to capture the impact of matter inhomogeneities on the background evolution of the Universe. Non virialized but non-linearly evolving overdense and underdense regions, whose abundance is quantified using the Press-Schechter formalism, are collectively described by two effective perfect fluids $\rho_{\rm{c}},\rho_{\rm{v}}$ with non vanishing equation of state parameters $w_{\rm{c,v}}\neq 0$. These fluids are coupled to the pressureless dust, akin to an interacting DM-DE scenario. The resulting phenomenology is very rich, and could potentially address a number of inconsistencies of the standard model, including a simultaneous resolution of the Hubble and $\sigma_8$ tensions. To assess the viability of the model, we set initial conditions compatible to the Planck 2018 best fit $\Lambda$CDM cosmology and fit its additional parameters using SN~Ia observations from DESY5 and a sample of uncorrelated $f\sigma_8$ measurements. Our findings show that backreaction effects from the cosmic web could restore the concordance between early and late Universe cosmological probes., Comment: 7 Figures, 8 pages + appendix and references. To be submitted to Physical Review. Comments are very welcome!

In this paper we examine the relativistic Einstein rings assuming a nonminimal coupling between gravitation and electromagnetism in a Reissner-Norstr\"om background. Starting from a general action of a nonminimal coupled electrodynamics we show that an unstable effective photon sphere may be obtained in the regime of eikonal approximation. Restricting ourselves to the unstable photon sphere domain we examine the expected angular positions of the first and second relativistic Einstein rings. To compare our results with previous studies in the literature we model the lens as a Galactic supermassive black hole. For fixed coupling parameters we show that such angular positions decrease as the charge parameter increases. The angular separation between the first and second rings is also evaluated. We show that such separation increases as the charge parameter increases. These patterns are not followed by nearly extremal configurations. In this case we show that there is an overlap domain so that the angular position and the corresponding coupling parameter do not allow one to differ extremal cases from complementary configurations which satisfy the cosmic censorship hypothesis., Comment: Important corrections included

We present deep ($\sim$ 20 hr), high-angular resolution Atacama Large Millimeter/submillimeter Array (ALMA) observations of the $\rm CO ~ (4-3)$ and $\rm [CI] ~ (1-0)$ transitions, along with the rest-frame 630 $\mu$m dust continuum, in BX610 --a massive, main-sequence galaxy at the peak epoch of cosmic star formation $(z = 2.21)$. Combined with deep Very Large Telescope (VLT) SINFONI observations of the H$\alpha$ line, we characterize the molecular gas and star formation activity on kiloparsec scales. Our analysis reveals that the excitation of the molecular gas, as traced by the $L'_{\rm CO ~ (4-3)} / L'_{\rm [CI] ~ (1-0)}$ line luminosity ratio, decreases with increasing galactocentric radius. While the line luminosity ratios in the outskirts are similar to those typically found in main-sequence galaxies at $z \sim 1$, the ratios in the central regions of BX610 are comparable to those observed in local starbursts. There is also a giant extra-nuclear star-forming clump in the southwest of BX610 that exhibits high star formation activity, molecular gas abundance, and molecular gas excitation. Furthermore, the central region of BX610 is rich in molecular gas $(M_{\rm mol} / M_{\rm \star} \approx 1)$; however, at the current level of star formation activity, such molecular gas is expected to be depleted in $\sim$ 450 Myr. This, along with recent evidence for rapid inflow toward the center, suggests that BX610 may be experiencing an evolutionary phase often referred to as wet compaction, which is expected to lead to central gas depletion and subsequent inside-out quenching of star formation activity., Comment: Submitted to Astronomy & Astrophysics. 09 pages, 05 figures

Self-supervised learning (SSL) approaches have brought tremendous success across many tasks and domains. It has been argued that these successes can be attributed to a link between SSL and identifiable representation learning: Temporal structure and auxiliary variables ensure that latent representations are related to the true underlying generative factors of the data. Here, we deepen this connection and show that SSL can perform system identification in latent space. We propose DynCL, a framework to uncover linear, switching linear and non-linear dynamics under a non-linear observation model, give theoretical guarantees and validate them empirically.

The development of Large Language Models (LLMs) has brought impressive performances on mitigation strategies against misinformation, such as counterargument generation. However, LLMs are still seriously hindered by outdated knowledge and by their tendency to generate hallucinated content. In order to circumvent these issues, we propose a new task, namely, Critical Questions Generation, consisting of processing an argumentative text to generate the critical questions (CQs) raised by it. In argumentation theory CQs are tools designed to lay bare the blind spots of an argument by pointing at the information it could be missing. Thus, instead of trying to deploy LLMs to produce knowledgeable and relevant counterarguments, we use them to question arguments, without requiring any external knowledge. Research on CQs Generation using LLMs requires a reference dataset for large scale experimentation. Thus, in this work we investigate two complementary methods to create such a resource: (i) instantiating CQs templates as defined by Walton's argumentation theory and (ii), using LLMs as CQs generators. By doing so, we contribute with a procedure to establish what is a valid CQ and conclude that, while LLMs are reasonable CQ generators, they still have a wide margin for improvement in this task., Comment: 14 pages, 3 figures, 7 tables, to be published in the 28th Conference on Computational Natural Language Learning (CoNLL 2024)

We report on the nonlinear temporal post-compression of 7 mJ sub-40 fs pulses from a commercial kHz Ti:Sapphire laser down to a record 4 fs duration (1.5 optical cycle) in a compact single-stage gas-filled multipass cell, with 60% overall compression efficiency.

This report presents Sabi\'a-3, our new flagship language model trained on a large brazilian-centric corpus. Evaluations across diverse professional and academic benchmarks show a strong performance on Portuguese and Brazil-related tasks. Sabi\'a-3 shows large improvements in comparison to our previous best of model, Sabi\'a-2 Medium, especially in reasoning-intensive tasks. Notably, Sabi\'a-3's average performance matches frontier LLMs, while it is offered at a three to four times lower cost per token, reinforcing the benefits of domain specialization.