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High-harmonic generation (HHG) is a nonlinear process in which a material sample is irradiated by intense laser pulses, causing the emission of high harmonics of the incident light. HHG has historically been explained by theories employing a classical electromagnetic field, successfully capturing its spectral and temporal characteristics. However, recent research indicates that quantum-optical effects naturally exist, or can be artificially induced, in HHG. Even though the fundamental equations of motion for quantum electrodynamics (QED) are well-known, a unifying framework for solving them to explore HHG is missing. So far, numerical solutions employed a wide range of basis-sets and untested approximations. Based on methods originally developed for cavity polaritonics, here we formulate a numerically accurate QED model consisting of a single active electron and a single quantized photon mode. Our framework can in principle be extended to higher electronic dimensions and multiple photon modes to be employed in ab initio codes. We employ it as a model of an atom interacting with a photon mode and predict a characteristic minimum structure in the HHG yield vs. phase-squeezing. We find that this phenomenon, which can be used for novel ultrafast quantum spectroscopies, is partially captured by a multi-trajectory Ehrenfest dynamics approach, with the exact minima position sensitive to the level of theory. On the one hand, this motivates using multi-trajectory approaches as an alternative for costly exact calculations. On the other hand, it suggests an inherent limitation of the multi-trajectory formalism, indicating the presence of entanglement. Our work creates a road-map for a universal formalism of QED-HHG that can be employed for benchmarking approximate theories, predicting novel phenomena for advancing quantum applications, and for the measurements of entanglement and entropy., Comment: 10 pages, 7 figures

This work provides a vision on how to tackle the underutilization of compute resources in FPGA SoC devices used across 5G and edge computing infrastructures. A first step towards this end is the implementation of a resource management layer able to migrate and scale functions in such devices, based on context events. This layer sets the basis to design a hierarchical data-driven micro-orchestrator in charge of providing the lifecycle management of functions in FPGA SoC devices. In the O-RAN context, the micro-orchestrator is foreseen to take the form of an xApp/rApp tandem trained with RAN traffic and context data., Comment: Article accepted in the IEEE International Conference on 6G Networking (6GNet 2024)

Engineering phases of matter in cavities requires effective light-matter coupling strengths that are on the same order of magnitude as the bare system energetics, coined the ultra-strong coupling regime. For models of itinerant electron systems, which do not have discrete energy levels, a clear definition of this regime is outstanding to date. Here we argue that a change of the electronic mass exceeding $10\%$ of its bare value may serve as such a definition. We propose a quantitative computational scheme for obtaining the electronic mass in relation to its bare vacuum value and show that coupling to surface polariton modes can induce such mass changes. Our results have important implications for cavity design principles that enable the engineering of electronic properties with quantum light., Comment: 5 pages, 3 figes + Supplement: 4 pages, 1 figure

Recently, Gheibi, Jorgensen, and Takahashi introduced a new homological invariant called quasi-projective dimension, which is a generalization of projective dimension. They proved that the depth formula holds for two finitely gene\-rated Tor-independent modules over a Noetherian local ring when one of the modules has finite quasi-projective dimension. In this paper, we extend this result by pro\-ving that for finitely generated modules \( M \) and \( N \) over a Noetherian local ring \( R \) with \( \operatorname{qpd}_R M < \infty \), the equality \( \operatorname{depth} N = \operatorname{depth}(\operatorname{Tor}_q^R(M,N)) + \operatorname{qpd}_R M - q \) holds, where \( q := \sup\{ i \geq 0 : \operatorname{Tor}_i^R(M,N) \neq 0 \} \), provided that \( q < \infty \), and either \( q = 0 \) or \( \operatorname{depth}(\operatorname{Tor}_q^R(M,N)) \leq 1 \). This outcome generalizes a celebrated theorem by Auslander and allows us to derive new consequences and applications, for instance, we recover an important theorem of Araya and Yoshino., Comment: 10 pages, comments and suggestions are welcome. In this new version, Corollary 4.4 has been added, which recover a theorem of Araya and Yoshino

Polaritonic chemistry has garnered increasing attention in recent years due to pioneering experimental results, which show that site- and bond-selective chemistry at room temperature is achievable through strong collective coupling to field fluctuations in optical cavities. Despite these notable experimental strides, the underlying theoretical mechanisms remain unclear. In this focus review, we highlight a fundamental theoretical link between the seemingly unrelated fields of polaritonic chemistry and spin glasses, exploring its profound implications for the theoretical framework of polaritonic chemistry. Specifically, we present a mapping of the dressed electronic structure problem under collective vibrational strong coupling to the iconic Sherrington-Kirkpatrick model of spin glasses. This mapping uncovers a collectively induced instability in the dressed electronic structure (spontaneous replica symmetry breaking), which could provide the long-sought seed for significant local chemical modifications in polaritonic chemistry. This mapping paves the way to incorporate, adjust and probe numerous spin glass concepts in polaritonic chemistry, such as frustration, aging dynamics, excess of thermal fluctuations, time-reversal symmetry breaking or stochastic resonances. Ultimately, the mapping also offers fresh insights into the applicability of spin glass theory beyond condensed matter systems and it suggests novel theoretical directions such as polarization glasses with explicitly time-dependent order parameter functions.

We report the first ab initio, non-relativistic QED method that couples light and matter self-consistently beyond the electric dipole approximation and without multipolar truncations. This method is based on an extension of the Maxwell-Pauli-Kohn-Sham approach to a full minimal coupling Hamiltonian, where the space- and time-dependent vector potential is coupled to the matter system, and its back-reaction to the radiated fields is generated by the full current density. The implementation in the open-source Octopus code is designed for massively-parallel multiscale simulations considering different grid spacings for the Maxwell and matter subsystems. Here, we show the first applications of this framework to simulate renormalized Cherenkov radiation of an electronic wavepacket, magnetooptical effects with non-chiral light in non-chiral molecular systems, and renormalized plasmonic modes in a nanoplasmonic dimer. We show that in some cases the beyond-dipole effects can not be captured by a multipolar expansion Hamiltonian in the length gauge. Finally, we discuss further opportunities enabled by the framework in the field of twisted light and orbital angular momentum, inelastic light scattering and strong field physics., Comment: 31 pages, 9 figures, supporting information appended

This work extends our previous study from S. Shrestha et al. (2024) by introducing a new abstract framework for Variational Multiscale (VMS) methods at the discrete level. We introduce the concept of what we define as the optimal projector and present an approach where the infinite-dimensional unresolved scales are approximated in a finite-dimensional subspace using the numerically computed Fine-Scale Greens' function of the underlying symmetric problem. The proposed approach involves solving the VMS problem on two separate meshes: a coarse mesh for the full PDE and a fine mesh for the symmetric part of the continuous differential operator. We consider the 1D and 2D steady advection-diffusion problems in both direct and mixed formulations as the test cases in this paper. Moreover, we demonstrate the working of this method using the Mimetic Spectral Element Method (MSEM), however, it may be applied to other Finite/Spectral Element or Isogeometric frameworks. Furthermore, we propose that VMS should not be viewed as a stabilisation technique; instead, the base scheme should be inherently stable, with VMS enhancing the solution quality by supplementing the base scheme.

Periodic laser driving, known as Floquet engineering, is a powerful tool to manipulate the properties of quantum materials. Using circularly polarized light, artificial magnetic fields, called Berry curvature, can be created in the photon-dressed Floquet-Bloch states that form. This mechanism, when applied to 3D Dirac and Weyl systems, is predicted to lead to photon-dressed movement of Weyl nodes which should be detectable in the transport sector. The transport response of such a topological light-matter hybrid, however, remains experimentally unknown. Here, we report on the transport properties of the type-II Weyl semimetal T$\mathrm{_d}$-MoTe$_\mathrm{2}$ illuminated by a femtosecond pulse of circularly polarized light. Using an ultrafast optoelectronic device architecture, we observed injection currents and a helicity-dependent anomalous Hall effect whose scaling with laser field strongly deviate from the perturbative laws of nonlinear optics. We show using Floquet theory that this discovery corresponds to the formation of a magnetic Floquet-Weyl semimetal state. Numerical ab initio simulations support this interpretation, indicating that the light-induced motion of the Weyl nodes contributes substantially to the measured transport signals. This work demonstrates the ability to generate large effective magnetic fields ($>$ 30T) with light, which can be used to manipulate the magnetic and topological properties of a range of quantum materials.

This paper deals with the problem of energy consumption minimization in Open RAN cell-free (CF) massive Multiple-Input Multiple-Output (mMIMO) systems under minimum per-user signal-to-noise-plus-interference ratio (SINR) constraints. Considering that several access points (APs) are deployed with multiple antennas, and they jointly serve multiple users on the same time-frequency resources, we design the precoding vectors that minimize the system power consumption, while preserving a minimum SINR for each user. We use a simple, yet representative, power consumption model, which consists of a fixed term that models the power consumption due to activation of the AP and a variable one that depends on the transmitted power. The mentioned problem boils down to a binary-constrained quadratic optimization problem, which is strongly non-convex. In order to solve this problem, we resort to a novel approach, which is based on the penalized convex-concave procedure. The proposed approach can be implemented in an O-RAN cell-free mMIMO system as an xApp in the near-real time RIC (RAN intelligent Controller). Numerical results show the potential of this approach for dealing with joint precoding optimization and AP selection.

Craniofacial anomalies indicate early developmental disturbances and are usually linked to many genetic syndromes. Early diagnosis is critical, yet ultrasound (US) examinations often fail to identify these features. This study presents an AI-driven tool to assist clinicians in standardizing fetal facial axes/planes in 3D US, reducing sonographer workload and facilitating the facial evaluation. Our network, structured into three blocks-feature extractor, rotation and translation regression, and spatial transformer-processes three orthogonal 2D slices to estimate the necessary transformations for standardizing the facial planes in the 3D US. These transformations are applied to the original 3D US using a differentiable module (the spatial transformer block), yielding a standardized 3D US and the corresponding 2D facial standard planes. The dataset used consists of 1180 fetal facial 3D US images acquired between weeks 20 and 35 of gestation. Results show that our network considerably reduces inter-observer rotation variability in the test set, with a mean geodesic angle difference of 14.12$^{\circ}$ $\pm$ 18.27$^{\circ}$ and an Euclidean angle error of 7.45$^{\circ}$ $\pm$ 14.88$^{\circ}$. These findings demonstrate the network's ability to effectively standardize facial axes, crucial for consistent fetal facial assessments. In conclusion, the proposed network demonstrates potential for improving the consistency and accuracy of fetal facial assessments in clinical settings, facilitating early evaluation of craniofacial anomalies.

In this work we explore theoretically whether a parity-violating/chiral light-matter interaction is required to capture all relevant aspects of chiral polaritonics or if a parity-conserving/achiral theory is sufficient (e.g. long-wavelength/dipole approximation). This question is non-trivial to answer, since achiral theories (Hamiltonians) still possess chiral solutions. To elucidate this fundamental theoretical question, a simple GaAs quantum ring model is coupled to an effective chiral mode of a single-handedness optical cavity in dipole approximation. The bare matter GaAs quantum ring possesses a non-degenerate ground state and a doubly degenerate first excited state. The chiral or achiral nature (superpositions) of the degenerate excited state remains undetermined for an isolated matter system. However, inside our parity-conserving description of a chiral cavity, we find that the dressed eigenstates automatically (ab-initio) attain chiral character and become energetically discriminated based on the handedness of the cavity. In contrast, the non-degenerate bare matter state (ground state) does not show an energetic discrimination inside a chiral cavity within dipole approximation. Nevertheless, our results suggest that the handedness of the cavity can still be imprinted onto these states (e.g. angular momentum and chiral current densities). Overall, above findings highlight the relevance of degenerate states in chiral polaritonics. In particularly because recent theoretical results indicate the formation of a frustrated and highly-degenerate electronic ground-state under collective strong coupling conditions, which thus might be prone to chiral symmetry breaking effects.

We propose a parametric hazard model obtained by enforcing positivity in the damped harmonic oscillator. The resulting model has closed-form hazard and cumulative hazard functions, facilitating likelihood and Bayesian inference on the parameters. We show that this model can capture a range of hazard shapes, such as increasing, decreasing, unimodal, bathtub, and oscillatory patterns, and characterize the tails of the corresponding survival function. We illustrate the use of this model in survival analysis using real data., Comment: R code and data are available at: https://github.com/FJRubio67/HOH

The double wall-ring (DWR) rotational configuration is nowadays the instrument of choice regarding interfacial shear rheometers (ISR) in rotational configurations. Complex numerical schemes must be used in the analysis of the output data in order to appropriately deal with the coupling between interfacial and bulk fluid flows, and to separate viscous and elastic contribution or the interfacial response. We present a second generation code for analyzing the interfacial shear rheology experimental results of small amplitude oscillatory measurements made with a DWR rotational rheometer. The package presented here improves significantly the accuracy and applicability range of the previous available software packages by implementing: i) a physically motivated iterative scheme based on the probe's equation of motion, ii) an increased user selectable spatial resolution, and iii) a second order approximation for the velocity gradients at the ring surfaces. Moreover, the optimization of the computational effort allows, in many cases, for on-the-fly execution during data acquisition in real experiments.

Contemporary memory systems contain a variety of memory types, each possessing distinct characteristics. This trend empowers applications to opt for memory types aligning with developer's desired behavior. As a result, developers gain flexibility to tailor their applications to specific needs, factoring in attributes like latency, bandwidth, and power consumption. Our research centers on the aspect of power consumption within memory systems. We introduce an approach that equips developers with comprehensive insights into the power consumption of individual memory types. Additionally, we propose an ordered hierarchy of memory types. Through this methodology, developers can make informed decisions for efficient memory usage aligned with their unique requirements., Comment: ICPADS 2023

Density Functional Theory (DFT) is used extensively in the computation of electronic properties of matter, with various applications. Approximating the exchange-correlation (XC) functional is the key to the Kohn-Sham DFT approach, the basis of most DFT calculations. The choice of this density functional approximation (DFA) depends crucially on the particular system under study, which has resulted in the development of hundreds of DFAs. Though the exact density functional is not known, researchers have discovered analytical properties of this exact functional. Furthermore, these exact conditions are used when designing DFAs. We present XCVerifier, the first approach for verifying whether a DFA implementation satisfies the DFT exact conditions. XCVerifier was evaluated on five DFAs from the popular Libxc library and seven exact conditions from recent work. XCVerifier was able to verify or find violations for a majority of the DFA/condition pairs, demonstrating the feasibility of using formal methods to verify DFA implementations.

The interplay of chiralities in light and quantum matter provides an opportunity to design and manipulate chirality-dependent properties in quantum materials. Herein we report the chirality-dependent Floquet engineering on topological fermions with the high Chern number in chiral crystal CoSi via circularly polarized light (CPL) pumping. Intense light pumping does not compromise the gapless nature of topological fermions in CoSi, but displaces the crossing points in momentum space along the direction of light propagation. The Floquet chirality index is proposed to signify the interplay between the chiralities of topological fermion, crystal, and incident light, which determines the amplitudes and directions of light-induced momentum shifts. Regarding the time-reversal symmetry breaking induced by the CPL pumping, momentum shifts of topological fermions result in the birth of transient anomalous Hall signals in non-magnetic CoSi within an ultrafast time scale, which Mid-infrared (IR) pumping and terahertz (THz) Kerr or Faraday probe spectroscopy could experimentally detect. Our findings provide insights into exploring novel applications in optoelectronic devices by leveraging the degree of freedom of chirality in the non-equilibrium regime.

Ultra-lean hydrogen-air flames propagating in narrow gaps, under the influence of cold walls and high preferential diffusion, can form two distinct isolated structures. They exhibit either circular or double-cell shapes and propagate at different speeds, with the latter roughly doubling in size and traveling speed to the former. Hydrogen mass diffusivity, convective effects and conductive heat losses are the physical mechanisms that explain the alterations in morphology and propagation speed. In previous experiments, Veiga et al. Phys. Rev. Lett. 124, 174501 (2020) found these clearly distinguished flame structures for different combinations of equivalence ratio, channel gap and the effect of gravity on the dynamics of upwards and downwards propagating flames in a vertical chamber. Present observations in horizontal channels show the simultaneous appearance of these two stable structures, which arise under identical experimental conditions and conform the first evidence that multiplicity of stable solutions coexists in real devices. To explain the observations, we performed numerical simulations using the simplified model, which show that symmetry-breaking details during the ignition transient explain the concurrent emergence of the two stable configurations. This discovery urges the need to implement additional engineering tools to account for the possibility of formation and propagation of isolated flame kernels at different speeds in hydrogen-fueled systems.

Nanographene-based magnetism at interfaces offers an avenue to designer quantum materials towards novel phases of matter and atomic-scale applications. Key to spintronics applications at the nanoscale is bistable spin-crossover which however remains to be demonstrated in nanographenes. Here we show that antiaromatic 1,4-disubstituted pyrazine-embedded nanographene derivatives, which promote magnetism through oxidation to a non-aromatic radical are prototypical models for the study of carbon-based thermal spin-crossover. Scanning tunneling spectroscopy studies reveal symmetric spin excitation signals which evolve at Tc to a zero-energy peak, and are assigned to the transition of a S = 3/2 high-spin to a S = 1/2 low-spin state by density functional theory. At temperatures below and close to the spin-crossover Tc, the high-spin S= 3/2 excitations evidence pronouncedly different temperature-dependent excitation energies corresponding to a zero-field splitting in the Hubbard-Kanamori Hamiltonian. The discovery of thermal spin crossover and temperature-dependent zero-field splitting in carbon nanomaterials promises to accelerate quantum information, spintronics and thermometry at the atomic scale.

In this paper, we present an innovative federated learning (FL) approach that utilizes Kolmogorov-Arnold Networks (KANs) for classification tasks. By utilizing the adaptive activation capabilities of KANs in a federated framework, we aim to improve classification capabilities while preserving privacy. The study evaluates the performance of federated KANs (F- KANs) compared to traditional Multi-Layer Perceptrons (MLPs) on classification task. The results show that the F-KANs model significantly outperforms the federated MLP model in terms of accuracy, precision, recall, F1 score and stability, and achieves better performance, paving the way for more efficient and privacy-preserving predictive analytics., Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Related Code: https://github.com/ezeydan/F-KANs.git

We present a novel approach to automate and optimize anisotropic p-adaptation in high-order h/p solvers using Reinforcement Learning (RL). The dynamic RL adaptation uses the evolving solution to adjust the high-order polynomials. We develop an offline training approach, decoupled from the main solver, which shows minimal overcost when performing simulations. In addition, we derive a RL-based error estimation approach that enables the quantification of local discretization errors. The proposed methodology is agnostic to both the computational mesh and the partial differential equation being solved. The application of RL to mesh adaptation offers several benefits. It enables automated, adaptive mesh refinement, reducing the need for manual intervention. It optimizes computational resources by dynamically allocating high-order polynomials where necessary and minimizing refinement in stable regions. This leads to computational cost savings while maintaining solution accuracy. Furthermore, RL allows for the exploration of unconventional mesh adaptations, potentially enhancing the accuracy and robustness of simulations. This work extends our original research, offering a more robust, reproducible, and generalizable approach applicable to complex three-dimensional problems. We provide validation for laminar and turbulent cases: circular cylinders, Taylor Green Vortex and a 10MW wind turbine to illustrate the flexibility of the proposed approach., Comment: 36 pages, 16 figures, 5 tables

Non-reciprocal interactions are ubiquitous in nature, and in Physics this realization has started to push boundaries by making non-hermiticity a fundamental rather than exceptional property. Despite the prevalence of evanescent coupling in physics, little attention has been paid to wavefunction profiles, with reciprocity often assumed for practical reasons. Our work challenges this assumption by demonstrating the origin of non-reciprocity in a photonic platform. We experimentally validate non-reciprocal dynamics on directional couplers fabricated via femtosecond laser writing, and corroborate our findings through continuous simulations. Extending these insights, we observe the topological Non-Hermitian Skin Effect in a Rice-Mele lattice model. Our findings open new avenues of research using simple and scalable configurations, and potentially guiding future developments in Physics and beyond., Comment: 17 pages, 3 figures

dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal.

We consider a Bubble Expansion mechanism for the production of scalar dark matter during a first-order phase transition in the early Universe. Seeking for a dark matter energy density in agreement with observations, we study different non-renormalizable interactions between the dark matter species and the field undergoing the transition. The resulting relic abundance is shown to display a strong dependence on the Lorentz boost factor associated to the bubble wall motion, with this dependence becoming more significant the higher the dimension of the non-renormalizable interaction. This allows for observational compatibility across a wide range of dark matter masses and transition temperatures, typically excluded in renormalizable scenarios. For a transition around the electroweak scale, the associated gravitational wave spectrum is also within the reach of future detectors., Comment: 11 pages, 6 figures

The transition to sustainable Open Radio Access Network (O-RAN) architectures brings new challenges for resource management, especially in predicting the utilization of Physical Resource Block (PRB)s. In this paper, we propose a novel approach to characterize the PRB load using probabilistic forecasting techniques. First, we provide background information on the O-RAN architecture and components and emphasize the importance of energy/power consumption models for sustainable implementations. The problem statement highlights the need for accurate PRB load prediction to optimize resource allocation and power efficiency. We then investigate probabilistic forecasting techniques, including Simple-Feed-Forward (SFF), DeepAR, and Transformers, and discuss their likelihood model assumptions. The simulation results show that DeepAR estimators predict the PRBs with less uncertainty and effectively capture the temporal dependencies in the dataset compared to SFF- and Transformer-based models, leading to power savings. Different percentile selections can also increase power savings, but at the cost of over-/under provisioning. At the same time, the performance of the Long-Short Term Memory (LSTM) is shown to be inferior to the probabilistic estimators with respect to all error metrics. Finally, we outline the importance of probabilistic, prediction-based characterization for sustainable O-RAN implementations and highlight avenues for future research.

We explore a reinforcement learning strategy to automate and accelerate h/p-multigrid methods in high-order solvers. Multigrid methods are very efficient but require fine-tuning of numerical parameters, such as the number of smoothing sweeps per level and the correction fraction (i.e., proportion of the corrected solution that is transferred from a coarser grid to a finer grid). The objective of this paper is to use a proximal policy optimization algorithm to automatically tune the multigrid parameters and, by doing so, improve stability and efficiency of the h/p-multigrid strategy. Our findings reveal that the proposed reinforcement learning h/p-multigrid approach significantly accelerates and improves the robustness of steady-state simulations for one dimensional advection-diffusion and nonlinear Burgers' equations, when discretized using high-order h/p methods, on uniform and nonuniform grids., Comment: 14 pages, 1 figure

Jets and disk winds arise from materials with excess angular momentum ejected from the accretion disks in forming stars. How these structures are launched and how they impact the gas within the innermost regions of these objects remains poorly understood. MWC349A is a massive star that has a circumstellar disk which rotates in accord with Kepler's Law, with an ionized wind and a high-velocity jet launched from the disk surface. The strongly maser-amplified emission of hydrogen radio recombination lines (RRLs) observed toward this system provides a comprehensive picture of its ionized environment with exquisite detail. In this Letter, we present ALMA observations of the H26$\alpha$ RRL and continuum emission obtained with the highest angular resolution ever used toward this source (beam of $\sim$0.02"). The maser RRL emission is resolved for the first time and clearly delineates the ionized disk, wind and jet. We analyzed the RRL data cubes with the 3D non-LTE radiative transfer model MORELI, confirming that the jet is poorly collimated. We found that the jet orientation is closer to the rotation axis of the system than derived from spatially unresolved data. This study confirms that hydrogen RRL masers are powerful probes of the physical structure and kinematics of the innermost ionized material around massive stars., Comment: Accepted for publication in The Astrophysical Journal Letters

The data transfer between a processor and memory has become a design bottleneck in data-intensive applications. Processing-In-Memory (PIM) is a practical approach to overcome the memory wall bottleneck. The 4:2 compressor is suitable for implementing the processor's crucial arithmetic circuits, including multiplier. Some area-efficient memristive structures, like Material Implication (IMPLY) in serial architecture, are compatible with the crossbar array. This paper proposes a serial memristive IMPLY-based 4:2 compressor, which is applied to present new 4-bit and 8-bit multipliers. The proposed circuits are evaluated regarding latency, area, and energy consumption. Compared to the existing serial compressor, the proposed 4:2 compressor's algorithm improves the area, energy consumption, and speed by 36%, 17%, and 15%, respectively. The proposed 4-bit and 8-bit multipliers are improved by 7.3% and 10%, respectively, regarding the latency, and reduced energy consumption by up to 12%, compared to the serial multiplier based on a 4:2 compressor with XOR/MUX design.

Numerical simulations of the Cauchy problem for self-interacting massive vector fields often face instabilities and apparent pathologies. We explicitly demonstrate that these issues, previously reported in the literature, are actually due to the breakdown of the well-posedness of the initial-value problem. This is akin to shortcomings observed in scalar-tensor theories when derivative self-interactions are included. Building on previous work done for k-essence, we characterize the well-posedness breakdowns, differentiating between Tricomi and Keldysh-like behaviors. We show that these issues can be avoided by ``fixing the equations'', enabling stable numerical evolutions in spherical symmetry. Additionally, we show that for a class of vector self-interactions, no Tricomi-type breakdown takes place. Finally, we investigate initial configurations for the massive vector field which lead to gravitational collapse and the formation of black holes., Comment: 13 pages, 6 figures, refs added, matches PRD version

Many well-known logical identities are naturally written as equivalences between contextual formulas. A simple example is the Boole-Shannon expansion $c[p] \equiv (p \wedge c[\mathrm{true}] ) \vee (\neg\, p \wedge c[\mathrm{false}] )$, where $c$ denotes an arbitrary formula with possibly multiple occurrences of a "hole", called a context, and $c[\varphi]$ denotes the result of "filling" all holes of $c$ with the formula $\varphi$. Another example is the unfolding rule $\mu X. c[X] \equiv c[\mu X. c[X]]$ of the modal $\mu$-calculus. We consider the modal $\mu$-calculus as overarching temporal logic and, as usual, reduce the problem whether $\varphi_1 \equiv \varphi_2$ holds for contextual formulas $\varphi_1, \varphi_2$ to the problem whether $\varphi_1 \leftrightarrow \varphi_2$ is valid . We show that the problem whether a contextual formula of the $\mu$-calculus is valid for all contexts can be reduced to validity of ordinary formulas. Our first result constructs a canonical context such that a formula is valid for all contexts if{}f it is valid for this particular one. However, the ordinary formula is exponential in the nesting-depth of the context variables. In a second result we solve this problem, thus proving that validity of contextual formulas is EXP-complete, as for ordinary equivalences. We also prove that both results hold for CTL and LTL as well. We conclude the paper with some experimental results. In particular, we use our implementation to automatically prove the correctness of a set of six contextual equivalences of LTL recently introduced by Esparza et al. for the normalization of LTL formulas. While Esparza et al. need several pages of manual proof, our tool only needs milliseconds to do the job and to compute counterexamples for incorrect variants of the equivalences., Comment: Extended version of a paper accepted at CONCUR 2024

Graphene is a privileged 2D platform for hosting confined light-matter excitations known as surface plasmon-polaritons (SPPs), as it possesses low intrinsic losses with a high degree of optical confinement. However, the inherently isotropic optical properties of graphene limit its ability to guide and focus SPPs, making it less suitable than anisotropic elliptical and hyperbolic materials as a platform for polaritonic lensing and canalization. Here, we present the graphene/CrSBr heterostructure as an engineered 2D interface that hosts highly anisotropic SPP propagation over a wide range of frequencies in the mid-infrared and terahertz. Using a combination of scanning tunneling microscopy (STM), scattering-type scanning near-field optical microscopy (s-SNOM), and first-principles calculations, we demonstrate mutual doping in excess of 10$^{13}$ cm$^{-2}$ holes/electrons between the interfacial layers of graphene/CrSBr heterostructures. SPPs in graphene activated by charge transfer interact with charge-induced anisotropic intra- and interband transitions in the interfacial doped CrSBr, leading to preferential SPP propagation along the quasi-1D chains that compose each CrSBr layer. This multifaceted proximity effect both creates SPPs and endows them with anisotropic transport and propagation lengths that differ by an order-of-magnitude between the two in-plane crystallographic axes of CrSBr.

We study the impact of the chameleon mechanism on the structure of white dwarfs. Using a shooting method of our design, we solve the corresponding scalar-tensor equilibrium equations for a Chandrasekhar equation of state, exploring various energy scales and couplings of the chameleon field to matter. We find the chameleon field to be in a thick-shell configuration, identifying for the first time in the literature a similarity relation for the radially normalised scalar field gradient. Our analysis reveals that the chameleon mechanism significantly alters the internal pressure of white dwarfs, leading to a notable reduction in the stellar radii and masses and shifting the mass-radius curves below those predicted by Newtonian gravity. Finally, we derive parametric expressions from our results to expedite future analyses of white dwarfs in scalar-tensor theories., Comment: 10 + 1 pages, 6 figures

Sterile neutrinos are a compelling candidate for generating neutrino masses and for elucidating the nature of dark matter. Astrophysical X-ray constraints on sterile neutrino dark matter decays, however, largely exclude the active-sterile mixing required to produce simultaneously the correct left-handed neutrino spectrum and keV-scale right-handed neutrino dark matter within a type-I seesaw framework. In this study, we demonstrate how these X-ray constraints can be circumvented through a time-dependent approach, thereby reviving a broad range of active-sterile mixing scenarios. Our minimal model incorporates two right-handed neutrinos, which form a two-component dark matter candidate, and an auxiliary scalar field that experiences a very late and still ongoing phase transition, leading to the spontaneous breaking of a global $ U(1)_N $ symmetry. Prior to this phase transition, only the right-handed neutrinos are massive, while the left-handed neutrinos remain massless because of the scalar field's vanishing expectation value. As the phase transition develops, the growing expectation value of the scalar field increases the active-sterile mixing, thereby opening dark matter decay channels and inducing neutrino masses. The time dependence allows the scenario to be consistent with X-ray constraints as well as current measurements of left-handed neutrino masses. The anticipated level of active-sterile mixing today is within the detection capabilities of the forthcoming TRISTAN (KATRIN) tritium-beta decay project. Additionally, cosmological surveys such as DESI or EUCLID and supernova neutrino observations can test the prediction of massless left-handed neutrinos prior to the phase transition., Comment: 16+4 pages, 3 figures

We contribute a general and flexible framework to estimate the size of a closed population in the presence of $K$ capture-recapture lists and heterogeneous capture probabilities. Our novel identifying strategy leverages the fact that it is sufficient for identification that a subset of the $K$ lists are not arbitrarily dependent \textit{within the subset of the population unobserved by the remaining lists}, conditional on covariates. This identification approach is interpretable and actionable, interpolating between the two predominant approaches in the literature as special cases: (conditional) independence across lists and log-linear models with no highest-order interaction. We derive nonparametric doubly-robust estimators for the resulting identification expression that are nearly optimal and approximately normal for any finite sample size, even when the heterogeneous capture probabilities are estimated nonparametrically using machine learning methods. Additionally, we devise a sensitivity analysis to show how deviations from the identification assumptions affect the resulting population size estimates, allowing for the integration of domain-specific knowledge into the identification and estimation processes more transparently. We empirically demonstrate the advantages of our method using both synthetic data and real data from the Peruvian internal armed conflict to estimate the number of casualties. The proposed methodology addresses recent critiques of capture-recapture models by allowing for a weaker and more interpretable identifying assumption and accommodating complex heterogeneous capture probabilities depending on high-dimensional or continuous covariates.

We present the theoretical derivation and numerical implementation of the linear response equations for relativistic quantum electrodynamical density functional theory (QEDFT). In contrast to previous works based on the Pauli-Fierz Hamiltonian, our approach describes electrons interacting with photonic cavity modes at the four-component Dirac-Kohn-Sham level, derived from fully relativistic QED through a series of established approximations. Moreover, we show that a new type of spin-orbit-like (SO) cavity-mediated interaction appears under the relativistic description of the coupling of matter with quantized cavity modes. Benchmark calculations performed for atoms of group 12 elements (Zn, Cd, Hg) demonstrate how a relativistic treatment enables the description of exciton polaritons which arise from the hybridization of formally forbidden singlet-triplet transitions with cavity modes. For atoms in cavities tuned on resonance with a singlet-triplet transition we discover a significant interplay between SO effects and coupling to an off-resonant intense singlet-singlet transition. This dynamic relationship highlights the crucial role of ab initio approaches in understanding cavity quantum electrodynamics. Finally, using the mercury porphyrin complex as an example, we show that relativistic linear response QEDFT provides computationally feasible first-principles calculations of polaritonic states in large heavy element-containing molecules of chemical interest., Comment: 29 pages, 5 figures, supplemental material, v2: Corrected references to the Supplemental Material

Motion prediction in soccer involves capturing complex dynamics from player and ball interactions. We present FootBots, an encoder-decoder transformer-based architecture addressing motion prediction and conditioned motion prediction through equivariance properties. FootBots captures temporal and social dynamics using set attention blocks and multi-attention block decoder. Our evaluation utilizes two datasets: a real soccer dataset and a tailored synthetic one. Insights from the synthetic dataset highlight the effectiveness of FootBots' social attention mechanism and the significance of conditioned motion prediction. Empirical results on real soccer data demonstrate that FootBots outperforms baselines in motion prediction and excels in conditioned tasks, such as predicting the players based on the ball position, predicting the offensive (defensive) team based on the ball and the defensive (offensive) team, and predicting the ball position based on all players. Our evaluation connects quantitative and qualitative findings. https://youtu.be/9kaEkfzG3L8, Comment: Published as a conference paper at IEEE ICIP 2024

In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. The method we have developed, termed Discriminatory Detection of Distortions (DDD), enhances anomaly detection by training a discriminator model on both original and artificially modified datasets. We conducted a comprehensive evaluation of our models on the Dark Machines Anomaly Score Challenge channels and a search for 4-top quark events, demonstrating the effectiveness of our approach across various final states and beyond the Standard Model scenarios. We compare the performance of the DDD method with the Deep Robust One-Class Classification method (DROCC), which incorporates signals in the training process, and the Deep Support Vector Data Description (DeepSVDD) method, a well established and well performing method for anomaly detection. Results show that the effectiveness of each model varies by signal and channel, with DDD proving to be a very effective anomaly detector. We recommend the combined use of DeepSVDD and DDD for purely unsupervised applications, with the addition of flow models for improved performance when resources allow. Findings suggest that network architectures that excel in supervised contexts, such as the particle transformer with standard model interactions, also perform well as unsupervised anomaly detectors. We also show that with these methods, it is likely possible to recognize 4-top quark production as an anomaly without prior knowledge of the process. We argue that the Large Hadron Collider community can transform supervised classifiers into anomaly detectors to uncover potential new physical phenomena in each search.

Constraints on the binary fraction of young massive stellar objects (mYSOs) are important for binary and massive star formation theory. Here, we present speckle imaging of 34 mYSOs located in the Large (1/2 $Z_{\odot}$) and Small Magellanic Clouds ($\sim$1/5 $Z_{\odot}$), probing projected separations between the 2000-20000 au (at angular scales of 0.02-0.2") range, for stars above 8 $M_{\odot}$. We find two wide binaries in the Large Magellanic Cloud (from a sample of 23 targets), but none in a sample of 11 in the Small Magellanic Cloud, leading us to adopt a wide binary fraction of 9$\pm$5%, and $<$5%, respectively. We rule out a wide binary fraction greater than 35% in the Large, and 38% in the Small Magellanic Cloud at the 99% confidence level. This is in contrast to the wide binary fraction of mYSOs in the Milky Way (presumed $Z_{\odot}$), which within the physical parameter space probed by this study is $\sim$15-60% from the literature. We argue that while selection effects could be responsible for the lower binary fraction observed; it is more likely that there are underlying physical mechanisms responsible for the observed properties. This indicates that metallicity and environmental effects may influence the formation of wide binaries among massive stars. Future larger, statistically more significant samples of high-mass systems in low-metallicity environments, and for comparison in the Milky Way, are essential to confirm or repudiate our claim., Comment: Accepted for publication in ApJ

aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regression and clustering, as well as a variety of utilities, transformations and distance measures designed for time series data. aeon also has a number of experimental modules for tasks such as anomaly detection, similarity search and segmentation. aeon follows the scikit-learn API as much as possible to help new users and enable easy integration of aeon estimators with useful tools such as model selection and pipelines. It provides a broad library of time series algorithms, including efficient implementations of the very latest advances in research. Using a system of optional dependencies, aeon integrates a wide variety of packages into a single interface while keeping the core framework with minimal dependencies. The package is distributed under the 3-Clause BSD license and is available at https://github.com/ aeon-toolkit/aeon. This version was submitted to the JMLR journal on 02 Nov 2023 for v0.5.0 of aeon. At the time of this preprint aeon has released v0.9.0, and has had substantial changes., Comment: 10 pages

Within the generalized Newton-Cartan theory, Galilean Twisted spacetimes are introduced as dual models of the well-known relativistic twisted spacetimes. As a natural generalization, torqued vector fields in Galilean spacetimes are defined, showing that the local structure of a Galilean spacetime admitting a timelike torqued vector field is given by a Twisted spacetime. In addition, several results assuring the global splitting as Twisted spacetime are obtained. On the other hand, completeness of free falling observers is studied, as well as general geodesic completeness.