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In this paper we classify rotationally symmetric conformally flat admissible solitons to the $k$-Yamabe flow, a fully non-linear version of the Yamabe flow. For $n\geq 2k$ we prove existence of complete expanding, steady and shrinking solitons and describe their asymptotic behavior at infinity. For $n<2k$ we prove that steady and expanding solitons are not admissible. The proof is based on the careful analysis of an associated dynamical system.

In this work, we present a novel emulator of the halo mass function, which we implement in the framework of the e-mantis emulator of $f(R)$ gravity models. We also extend e-mantis to cover a larger cosmological parameter space and to include models of dark energy with a constant equation of state $w$CDM. We use a Latin hypercube sampling of the $w$CDM and $f(R)$CDM cosmological parameter spaces, over a wide range, and realize a large suite of more than $10000$ $N$-body simulations of different volume, mass resolution and random phase of the initial conditions. For each simulation in the suite, we generate halo catalogues using the friends-of-friends halo finder, as well as the spherical overdensity algorithm for different overdensity thresholds. We decompose the corresponding halo mass functions on a B-spline basis, and use this decomposition to train an emulator based on Gaussian processes. The resulting emulator is able to predict the halo mass function for redshifts $\leq 1.5$ and for halo masses $M_h\geq10^{13}\,h^{-1}M_\odot$. The typical HMF errors for SO haloes with $\Delta=200\mathrm{c}$ at $z=0$ in $w$CDM (respectively $f(R)$CDM) are of order of $\epsilon_0\simeq1.5\%$ ($\epsilon_0\simeq4\%$) up to a transition mass $M_t\simeq2\cdot10^{14}\,h^{-1}M_\odot$ ($M_t\simeq6\cdot10^{13}\,h^{-1}M_\odot$). For larger masses, the errors are dominated by shot-noise and scale as $\epsilon_0\cdot\left(M_h/M_t\right)^\alpha$ with $\alpha\simeq0.9$ ($\alpha\simeq0.4$) up to $M_h \sim 10^{15}\,h^{-1}M_\odot$. Independently of this general trend, the emulator is able to provide an estimation of its own error as a function of the cosmological parameters, halo mass, and redshift. The e-mantis emulator, which is publicly available, can be used to obtain fast and accurate predictions of the halo mass function in the $f(R)$CDM and $w$CDM non-standard cosmological models., Comment: 22 pages, 16 figures. Accepted for publication in A&A. Emulator code available at https://doi.org/10.5281/zenodo.8384121

In recent yerars, Bismuth sodium titanate (BNT) and BNT-based piezoelectric materials have proved potential for lead-free piezoelectric technologies. Such materials' complicated phase structure is crucial to their thermo-electromechanical behaviour. Pure BNT has a rhombohedral (R3c) lattice structure at room temperature. The phase transition to an orthorhombic phase (Pnma) at 200\textcelsius is mainly due to octahedral tilting. This transition is called the depolarization temperature ($T_{d}$). The R3c phase ferroelectric domains have a higher spontaneous polarization, which declines with increasing temperature and reaches a local minimum at $T_{d}$, causing a phase change. Additionally, BNT transitions from Pnma to P4bm at 320\textcelsius and from P4bm to Pm3m at 520\textcelsius. These transitions also result in the changes in the spontaneous polarization. Therefore, the investigation of domain switching and phase change dynamics in relation to temperature and electromechanical coupling has acquired considerable importance. The complex phase regimes and their activation under different temperature settings will be examined to improve the use of these materials in sensors, actuators, energy harvesting devices, and haptic technologies. The micro-sized BNT inclusions are implanted in polydimethylsiloxane (PDMS) to reperesent the practical scenario. A two-dimensional phase-field thermo-electromechanical computational model has been created to investigate the complex phase transition and domain switching behaviour of BNT-PDMS composite under varying temperature conditions. The model uses Landau-Ginzburg and thermo-electromechanical free energy to accurately simulate phase shift and domain switching. Data from pure BNT experiments validated the model. It can predict thermo-electromechanical response in different phase regimes and temperature-induced phase coexistence., Comment: 36 pages, 12 figures

This study introduces a novel metric, Plan Aperture Modulation (PAM), developed to quantify the modulation of radiotherapy treatment plans. PAM aims to provide a clear geometric interpretation, addressing the limitations of previous complexity metrics and facilitating its integration into treatment planning systems (TPSs) and clinical workflows. The PAM metric was defined as the average fraction of the target area outside the beam aperture, weighted over all control points in a treatment plan. The metric was evaluated in VMAT plans for two sites: prostate with lymph nodes and lung SBRT. Plans with varying complexities were generated using the Eclipse TPS, and PAM was compared to established metrics, including Plan Modulation (PM), Modulation Complexity Score (MCS), and monitor units per Gray (MU/Gy). The relationship between PAM and the Modulation Factor (MF), which quantifies the increase in MUs due to plan modulation, was also investigated. PAM provided a more intuitive assessment of plan modulation compared to the other metrics, and was validated across different delivery systems, such as C-arm linacs and Halcyon systems. The metric outperformed the previous metrics, indicated a zero modulation for Dynamic Conformal Arc plans, and was independent of confounding variables, such as treatment technique, beam energy, delivery system, and patient anatomy. Derived equations enabled the calculation of MF based on PAM, allowing for a robust quantification of plan modulation. PAM is a robust and intuitive metric for quantifying modulation in radiotherapy plans. It overcomes the limitations of previous metrics and can be readily implemented in TPSs to control plan modulation during optimization and for reporting. PAM is a promising tool for improving treatment planning workflows and for comparing and benchmarking radiotherapy plans in multi-institutional studies, clinical trials, and audits.

Drug-releasing implants are gaining momentum in the treatment of glaucoma. Implants present however several limitations. Among these limitations, there is the inability to provide an even distribution of the drug in the trabecular meshwork. CFD simulations were used in this work to study the interplay between ocular fluid dynamics and drug diffusion to explore the options to achieve a homogeneous drug distribution. The analysis finds that the balance between convection and diffusion flux hinders mixing within the eye's anterior chamber. This results in highly localized drug delivery in the trabecular meshwork when using a gravity-driven location implant. The results also show that varying the size of the implant can help to solve the issue. Given the natural variability of the trabecular meshwork size amongst patients, implants with personalized size may become a potential solution. The location of the implant within the eye is also key to effective drug delivery. Natural laminar flow of aqueous humor within the eye's anterior chamber prevents achieving an even drug distribution at the target tissue for gravity-driven location implants. However, the iris-lens gap can be utilized as a natural mixer when placing the implant in the eye's posterior chamber, thus helping with effective delivery.

The necessary and sufficient conditions for a perfect fluid solution to define a spatially-homogeneous cosmology are achieved. These conditions are Intrinsic, Deductive, Explicit and ALgorithmic, and they offer an IDEAL labeling of these geometries. When a three-dimensional group acts on the three-dimensional space-like orbits, the Bianchi type of the model is also obtained., Comment: 25 pages, 4 figures, 1 supplementary documentation

Simulating physical systems with variational quantum algorithms is a well-studied approach, but it is challenging to implement in current devices due to demands in qubit number and circuit depth. We show how limited knowledge of the system, namely the entropy of its subsystems, its entanglement structure or certain symmetries, can be used to reduce the cost of these algorithms with entanglement forging. To do so, we simulate a Fermi-Hubbard one-dimensional chain with a parametrized hopping term, as well as atomic nuclei ${}^{28}$Ne and ${}^{60}$Ti with the nuclear shell model. Using an adaptive variational quantum eigensolver we find significant reductions in both the maximum number of qubits (up to one fourth) and the amount of two-qubit gates (over an order of magnitude) required in the quantum circuits. Our findings indicate that our method, entropy-driven entanglement forging, can be used to adjust quantum simulations to the limitations of noisy intermediate-scale quantum devices.

We study the constraint on $f(R)$ gravity that can be obtained by photometric primary probes of the Euclid mission. Our focus is the dependence of the constraint on the theoretical modelling of the nonlinear matter power spectrum. In the Hu-Sawicki $f(R)$ gravity model, we consider four different predictions for the ratio between the power spectrum in $f(R)$ and that in $\Lambda$CDM: a fitting formula, the halo model reaction approach, ReACT and two emulators based on dark matter only $N$-body simulations, FORGE and e-Mantis. These predictions are added to the MontePython implementation to predict the angular power spectra for weak lensing (WL), photometric galaxy clustering and their cross-correlation. By running Markov Chain Monte Carlo, we compare constraints on parameters and investigate the bias of the recovered $f(R)$ parameter if the data are created by a different model. For the pessimistic setting of WL, one dimensional bias for the $f(R)$ parameter, $\log_{10}|f_{R0}|$, is found to be $0.5 \sigma$ when FORGE is used to create the synthetic data with $\log_{10}|f_{R0}| =-5.301$ and fitted by e-Mantis. The impact of baryonic physics on WL is studied by using a baryonification emulator BCemu. For the optimistic setting, the $f(R)$ parameter and two main baryon parameters are well constrained despite the degeneracies among these parameters. However, the difference in the nonlinear dark matter prediction can be compensated by the adjustment of baryon parameters, and the one-dimensional marginalised constraint on $\log_{10}|f_{R0}|$ is biased. This bias can be avoided in the pessimistic setting at the expense of weaker constraints. For the pessimistic setting, using the $\Lambda$CDM synthetic data for WL, we obtain the prior-independent upper limit of $\log_{10}|f_{R0}|< -5.6$. Finally, we implement a method to include theoretical errors to avoid the bias., Comment: 24 pages, 16 figures, submitted on behalf of the Euclid Collaboration

As the interest in FPGA-based accelerators for HPC applications increases, new challenges also arise, especially concerning different programming and portability issues. This paper aims to provide a snapshot of the current state of the FPGA tooling and its problems. To do so, we evaluate the performance portability of two frameworks for developing FPGA solutions for HPC (SYCL and OpenCL) when using them to port a highly-parallel application to FPGAs, using both ND-range and single-task type of kernels. The developer's general recommendation when using FPGAs is to develop single-task kernels for them, as they are commonly regarded as more suited for such hardware. However, we discovered that, when using high-level approaches such as OpenCL and SYCL to program a highly-parallel application with no FPGA-tailored optimizations, ND-range kernels significantly outperform single-task codes. Specifically, while SYCL struggles to produce efficient FPGA implementations of applications described as single-task codes, its performance excels with ND-range kernels, a result that was unexpectedly favorable.

Fluid injections can induce aseismic slip, resulting in stress changes that may propagate faster than pore pressure diffusion, potentially triggering seismicity at significant distances from injection wells. Constraining the maximum extent of these aseismic ruptures is thus important for better delineating the influence zone of injections concerning their seismic hazard. Here we derive a scaling relation based on rupture physics for the maximum size of aseismic ruptures, accounting for fluid injections with arbitrary flow rate histories. Moreover, based on mounting evidence that the moment release during these operations is often predominantly aseismic, we derive a scaling relation for the maximum magnitude of aseismic slip events. Our theoretical predictions are consistent with observations over a broad spectrum of event sizes, from laboratory to real-world cases, indicating that fault-zone storativity, background stress state, and injected fluid volume are key determinants of the maximum size and magnitude of injection-induced slow slip events., Comment: 29 pages, 6 figures

The thermodynamic interpretation of the Stephani Universes is studied in detail. The general expression of the speed of sound and of the thermodynamic schemes associated with a thermodynamic solution is obtained. The constraints imposed on the solutions by considering some significant physical properties are analyzed. We focus on the models where the cosmological observer measures isotropic radiation. We consider some examples, and a solution that models an ultrarelativistic gas is analyzed in detail., Comment: 16 pages, 7 figures

PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption of the capability. This paper provides details of those enhancements as released in PyFR v2.0.3, explains efforts to grow an engaged developer and user community, and provides latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.

International Auxiliary Languages (IALs) are constructed languages designed to facilitate communication among speakers of different native languages while fostering equality, efficiency, and cross-cultural understanding. This study focuses on analyzing the editions of IALs on Wikipedia, including Simple English, Esperanto, Ido, Interlingua, Volapuk, Interlingue, and Novial. We compare them with three natural languages: English, Spanish, and Catalan. Our aim is to establish a basis for the use of IALs in Wikipedia as well as showcase a new methodology for categorizing wikis. We found in total there are 1.3 million articles written in these languages and they gather 15.6 million monthly views. Although this is not a negligible amount of content, in comparison with large natural language projects there is still a big room for improvement. We concluded that IAL editions on Wikipedia are similar to other projects, behaving proportionally to their communities' size. Therefore, the key to their growth is augmenting the amount and quality of the content offered in these languages. To that end, we offer a set of statistics to understand and improve these projects, and we developed a webpage that displays our findings to foster knowledge sharing and facilitate the expansion of the IAL communities.

We propose a class of trainable deep learning-based geometries called Neural Spacetimes (NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) as events in a spacetime manifold. While most works in the literature focus on undirected graph representation learning or causality embedding separately, our differentiable geometry can encode both graph edge weights in its spatial dimensions and causality in the form of edge directionality in its temporal dimensions. We use a product manifold that combines a quasi-metric (for space) and a partial order (for time). NSTs are implemented as three neural networks trained in an end-to-end manner: an embedding network, which learns to optimize the location of nodes as events in the spacetime manifold, and two other networks that optimize the space and time geometries in parallel, which we call a neural (quasi-)metric and a neural partial order, respectively. The latter two networks leverage recent ideas at the intersection of fractal geometry and deep learning to shape the geometry of the representation space in a data-driven fashion, unlike other works in the literature that use fixed spacetime manifolds such as Minkowski space or De Sitter space to embed DAGs. Our main theoretical guarantee is a universal embedding theorem, showing that any $k$-point DAG can be embedded into an NST with $1+\mathcal{O}(\log(k))$ distortion while exactly preserving its causal structure. The total number of parameters defining the NST is sub-cubic in $k$ and linear in the width of the DAG. If the DAG has a planar Hasse diagram, this is improved to $\mathcal{O}(\log(k)) + 2)$ spatial and 2 temporal dimensions. We validate our framework computationally with synthetic weighted DAGs and real-world network embeddings; in both cases, the NSTs achieve lower embedding distortions than their counterparts using fixed spacetime geometries., Comment: 12 pages: main body and 19 pages: appendix

In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies commonly employed in the study of variable objects. As a response, deep learning techniques are increasingly being explored to effectively classify, analyze, and interpret these large datasets. In this paper we use two-dimensional histograms to represent Optical Gravitational Lensing Experiment (OGLE) phasefolded light curves as images. We use a Convolutional Neural Network (CNN) to classify variable objects within eight different categories (from now on labels): Classical Cepheid (CEP), RR Lyrae (RR), Long Period Variable (LPV), Miras (M), Ellipsoidal Binary (ELL), Delta Scuti (DST), Eclipsing Binary (E), and spurious class with Incorrect Periods (Rndm). We set up different training sets to train the same CNN architecture in order to characterize the impact of the training. The training sets were built from the same source of labels but different filters and balancing techniques were applied. Namely: Undersampling (U), Data Augmentation (DA), and Batch Balancing (BB). The best performance was achieved with the BB approach and a training sample size of $\sim$370000 stars. Regarding computational performance, the image representation production rate is of $\sim$76 images per core per second, and the time to predict is $\sim$ 60$\, \mu\text{s}$ per star. The accuracy of the classification improves from $\sim$ 92%, when based only on the CNN, to $\sim$ 98% when the results of the CNN are combined with the period and amplitude features in a two step approach. This methodology achieves comparable results with previous studies but with two main advantages: the identification of miscalculated periods and the improvement in computational time cost., Comment: accepted for publication in A&A

To answer this question, we develop a new Sahm-type recession indicator that combines vacancy and unemployment data. The indicator is the minimum of the Sahm indicator -- the difference between the 3-month trailing average of the unemployment rate and its minimum over the past 12 months -- and a similar indicator constructed with the vacancy rate -- the difference between the 3-month trailing average of the vacancy rate and its maximum over the past 12 months. We then propose a two-sided recession rule: When our indicator reaches 0.3pp, a recession may have started; when the indicator reaches 0.8pp, a recession has started for sure. This new rule is triggered earlier than the Sahm rule: on average it detects recessions 0.8 month after they have started, while the Sahm rule detects them 2.1 months after their start. The new rule also has a better historical track record: it perfectly identifies all recessions since 1929, while the Sahm rule breaks down before 1960. With August 2024 data, our indicator is at 0.54pp, so the probability that the US economy is now in recession is 48%. In fact, the recession may have started as early as March 2024.

Hole spin qubits are rapidly emerging as the workhorse of semiconducting quantum processors because of their large spin-orbit interaction, enabling fast all-electric operations at low power. However, spin-orbit interaction also causes non-uniformities in devices, resulting in locally varying qubit energies and site-dependent anisotropies. While these anisotropies can be used to drive single-spins, if not properly harnessed, they can hinder the path toward large-scale quantum processors. Here, we report on a qubit in planar germanium, where we control the anisotropy of two spins in a double quantum dot. By characterising microwave-driven singlet-triplet qubits, we show two distinct operating regimes depending on the magnetic field direction. For in-plane fields, the two spins are largely anisotropic, enabling to measure all the available transitions; coherence times exceeding 3 $\mu$s are extracted. For out-of-plane fields, they have an isotropic response but preserve the substantial energy difference required to address the singlet-triplet qubit. Even in this field direction, where the qubit lifetime is strongly affected by nuclear spins, we find 400 ns coherence times. Our work adds a valuable tool to investigate and harness the anisotropy of spin qubits and can be implemented in any large-scale NxN device, facilitating the path towards scalable quantum processors.

As Field Programmable Gate Arrays (FPGAs) computing capabilities continue to grow, also does the interest on building scientific accelerators around them. Tools like Xilinx's High-Level Synthesis (HLS) help to bridge the gap between traditional high-level languages such as C and C++, and low-level hardware description languages such as VHDL and Verilog. In this report, we study the implementation of a fluid dynamics application, the Finite-Time Lyapunov Exponent (FTLE) calculation, on FPGA using HLS. We provide speed and resource-consumption results for 2- and 3-dimensional cases., Comment: 8 pages, 2 tables

Clifford Group Equivariant Neural Networks (CGENNs) leverage Clifford algebras and multivectors as an alternative approach to incorporating group equivariance to ensure symmetry constraints in neural representations. In principle, this formulation generalizes to orthogonal groups and preserves equivariance regardless of the metric signature. However, previous works have restricted internal network representations to Euclidean or Minkowski (pseudo-)metrics, handpicked depending on the problem at hand. In this work, we propose an alternative method that enables the metric to be learned in a data-driven fashion, allowing the CGENN network to learn more flexible representations. Specifically, we populate metric matrices fully, ensuring they are symmetric by construction, and leverage eigenvalue decomposition to integrate this additional learnable component into the original CGENN formulation in a principled manner. Additionally, we motivate our method using insights from category theory, which enables us to explain Clifford algebras as a categorical construction and guarantee the mathematical soundness of our approach. We validate our method in various tasks and showcase the advantages of learning more flexible latent metric representations. The code and data are available at https://github.com/rick-ali/Metric-Learning-for-CGENNs, Comment: Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM) at the ICML 2024

The galaxy SDSS1335+0728, which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). Its behaviour suggests that SDSS1335+0728 hosts a $\sim 10^6 M_{\odot}$ black hole (BH) that is currently in the process of `turning on'. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. We used archival photometry and spectroscopic data to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We find that: (a) since 2021, the UV flux is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1-W2 WISE colour has become redder; (c) since February 2024, the source has begun showing X-ray emission; (d) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (e) broad emission lines are not detected; and (f) the [OIII] line increased its flux $\sim 3.6$ years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. We conclude that the variations observed in SDSS1335+0728 could be either explained by an AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGN observed in the process of activating. If the latter, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour., Comment: Accepted for publication in Astronomy & Astrophysics. ESO press release available at https://www.eso.org/public/news/eso2409/. Abstract shortened for arXiv

Considering the so-called Ricci-based gravity theories, a family of extensions of General Relativity whose action is given by a non-linear function of contractions and products of the (symmetric part of the) Ricci tensor of an independent connection, the Hamiltonian formulation of the theory is obtained. To do so, the independent connection is decomposed in two parts, one compatible with a metric tensor and the other one given by a 3-rank tensor. Subsequently, the Riemann tensor is expressed in terms of its projected components onto a hypersurface, allowing to construct the $3+1$ decomposition of the theory and the corresponding Gauss-Codazzi relations, where the boundary terms naturally arise in the gravitational action. Finally, the ADM decomposition is followed in order to construct the corresponding Hamiltonian and the ADM energy for any Ricci-based gravity theory. The formalism is applied to the simple case of Schwarzschild space-time., Comment: 11 pages. Version published in PRD

An exponential modified gravity with additional logarithmic corrections is considered with the presence of an axion-like scalar field in the role of dark matter. Axion fields are thought to become important at late-times when the axion-like scalar field oscillates around its vacuum expectation value, mimicking dark matter behaviour. The model is compared with the usual pressureless fluid description of dark matter. Both models are tested with observational data including some of the latest sources, providing similar fits in comparison with the $\Lambda$CDM model. Despite results are not statistically relevant to rule out any model, the number of free parameters still favours $\Lambda$CDM model, as shown by computing the goodness of the fits., Comment: 12 pages, 3 figures. To be published in Phys. Dark Univ

In recent years, analog quantum simulators have reached unprecedented quality, both in qubit numbers and coherence times. Most of these simulators natively implement Ising-type Hamiltonians, which limits the class of models that can be simulated efficiently. We propose a method to overcome this limitation and simulate the time-evolution of a large class of spinless fermionic systems in 1D using simple Ising-type Hamiltonians with local transverse fields. Our method is based on domain wall encoding, which is implemented via strong (anti-)ferromagnetic couplings $|J|$. We show that in the limit of strong $|J|$, the domain walls behave like spinless fermions in 1D. The Ising Hamiltonians are one-dimensional chains with nearest-neighbor and, optionally, next-nearest-neighbor interactions. As a proof-of-concept, we perform numerical simulations of various 1D-fermionic systems using domain wall evolution and accurately reproduce the systems' properties, such as topological edge states, Anderson localization, quantum chaotic time evolution and time-reversal symmetry breaking via Floquet-engineering. Our approach makes the simulation of a large class of fermionic many-body systems feasible on analogue quantum hardware that natively implements Ising-type Hamiltonians with transverse fields., Comment: 25 pages, 13 figures; minor changes for improved error estimation and comparison to Trotterization

While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cartoon-like, over-smoothed shapes. To help explain this discrepancy, we show that the image guidance used in Score Distillation can be understood as the velocity field of a 2D denoising generative process, up to the choice of a noise term. In particular, after a change of variables, SDS resembles a high-variance version of Denoising Diffusion Implicit Models (DDIM) with a differently-sampled noise term: SDS introduces noise i.i.d. randomly at each step, while DDIM infers it from the previous noise predictions. This excessive variance can lead to over-smoothing and unrealistic outputs. We show that a better noise approximation can be recovered by inverting DDIM in each SDS update step. This modification makes SDS's generative process for 2D images almost identical to DDIM. In 3D, it removes over-smoothing, preserves higher-frequency detail, and brings the generation quality closer to that of 2D samplers. Experimentally, our method achieves better or similar 3D generation quality compared to other state-of-the-art Score Distillation methods, all without training additional neural networks or multi-view supervision, and providing useful insights into relationship between 2D and 3D asset generation with diffusion models., Comment: NeurIPS 2024. 28 pages, 30 figures. Revision: additional comparisons and ablations studies

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

In this paper, we describe the wide-field spectroscopic survey telescope (WST) project. WST is a 12-metre wide-field spectroscopic survey telescope with simultaneous operation of a large field-of-view (3 sq. degree), high-multiplex (20,000) multi-object spectrograph (MOS), with both a low and high-resolution modes, and a giant 3x3 arcmin2 integral field spectrograph (IFS). In scientific capability, these specifications place WST far ahead of existing and planned facilities. In only 5 years of operation, the MOS would target 250 million galaxies and 25 million stars at low spectral resolution, plus 2 million stars at high resolution. Without need for pre-imaged targets, the IFS would deliver 4 billion spectra offering many serendipitous discoveries. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work in synergy with future ground and space-based facilities. We show how it can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; the origin of stars and planets; and time domain and multi-messenger astrophysics. WST's uniquely rich dataset may yield unforeseen discoveries in many of these areas. The telescope and instruments are designed as an integrated system and will mostly use existing technology, with the aim to minimise the carbon footprint and environmental impact. We will propose WST as the next European Southern Observatory (ESO) project after completion of the 39-metre ELT., Comment: 15 pages, 15 figures

We study thermal freeze-out in the \textit{dark axion portal} - an axion coupling to the dark photon and photon - considering the dark photon as the lightest stable particle, thereby a dark matter candidate. We pay special attention to the coscattering regime, founding viable DM scenarios fulfilling the correct relic abundance. Furthermore, we explore the prospects of having the axion as a long-lived particle, possibly to be observed at the LHC and future detectors.

The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light-curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. ATAT consists of two Transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a Balanced Hierarchical Random Forest (BHRF) trained on human-engineered features derived from light curves and metadata. When trained on light curves and metadata, ATAT achieves a macro F1-score of 82.9 +- 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1-score of 79.4 +- 0.1. The use of Transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.

We study the coscattering mechanism in a simple Higgs portal which add two real singlet scalars to the Standard Model. In this scenario, the lighter scalar is stabilized by a single $\mathcal{Z}_2$ symmetry and acts as the dark matter relic, whose freeze-out is driven by conversion processes. The heavier scalar becomes an unstable state which participate actively in the coscattering. We find viable parameter regions fulfilling the measured relic abundance, while evading direct detection and big-bang nucleosynthesis bounds. In addition, we discuss collider prospects for the heavier scalar as a long-lived particle at present and future detectors.

We present a novel proof of the Duffin-Schaeffer conjecture in metric Diophantine approximation. Our proof is heavily motivated by the ideas of Koukoulopoulos-Maynard's breakthrough first argument, but simplifies and strengthens several technical aspects. In particular, we avoid any direct handling of GCD graphs and their `quality'. We also consider the metric quantitative theory of Diophantine approximations, improving the $(\log \Psi(N))^{-C}$ error-term of Aistleitner-Borda and the first named author to $\exp(-(\log \Psi(N))^{\frac{1}{2} - \varepsilon})$.

We report an analysis of a sample of 186 spectroscopically confirmed Type II supernova (SN) light curves (LCs) obtained from a combination of Zwicky Transient Facility (ZTF) and Asteroid Terrestrial-impact Last Alert System (ATLAS) observations. We implement a method to infer physical parameters from these LCs using hydrodynamic models that take into account the progenitor mass, the explosion energy, and the presence of circumstellar matter (CSM). The CSM is modelled via the mass loss rate, wind acceleration at the surface of the progenitor star with a $\beta$ velocity law, and the CSM radius. We also infer the time of explosion, attenuation (A$_V$), and the redshift for each SN. Our results favor low-mass progenitor stars (M$_{ZAMS}$\,$<$14\,$M_\odot$) with a dense CSM ($\dot{M}$ $>$ 10$^{-3}$ [M$_\odot$ yr$^{-1}$], a CSM radius of $\sim$ 10$^{15}$ cm, and $\beta$ $>$ 2). Additionally, we find that the redshift inferred from the supernova LCs is significantly more accurate than that inferred using the host galaxy photometric redshift, suggesting that this method could be used to infer more accurate host galaxy redshifts from large samples of SNe II in the LSST era. Lastly, we compare our results with similar works from the literature.

Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles. State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use., Comment: Accepted at ICWSM-24

Achieving dense connectivities is a challenge for most quantum computing platforms today, and a particularly crucial one for the case of quantum annealing applications. In this context, we present a scalable architecture for quantum annealers described by a hardware graph of degree $d=3$ and containing exclusively 2-local interactions to realize Ising models of arbitrary dimension. By describing the problem graph in terms of triangles, we derive this resource-efficient configuration based on logical chains of qubits. We also devise strategies to address the challenges of scaling this architecture, identifying driver Hamiltonians more suited to the symmetries of the logical solution space. We thus show a promising new route to scale up devices dedicated to classical optimization tasks within the quantum annealing paradigm., Comment: Minor corrections that don't modify the results (typos, better fitting function), added Appendix and changed the definition of reference variable $\beta$ in the driver discussion in order to improve the presentation of the results. This induced changes to the appearance of Figure 2. 5 pages, 2 figures

Migratory and tissue resident cells exhibit highly branched morphologies to perform their function and to adapt to the microenvironment. Immune cells, for example, display transient branched shapes while exploring the surrounding tissues. In another example, to properly irrigate the tissues, blood vessels bifurcate thereby forcing the branching of cells moving on top or within the vessels. In both cases microenvironmental constraints force migrating cells to extend several highly dynamic protrusions. Here, we present a theoretical model for the shape dynamics and migration of cells that simultaneously span several junctions, which we validated by using micropatterns with an hexagonal array, and a neuronal network image analysis pipeline to monitor the macrophages and endothelial cell shapes and migration. In our model we describe how the actin retrograde flow controls branch extension, retraction and global cell polarization. We relate the noise in this flow to the residency times and trapping of the cell at the junctions of the network. In addition, we found that macrophages and endothelial cells display very different migration regimes on the network, with macrophages moving faster and having larger changes in cell length in comparison to endothelial cells. These results expose how cellular shapes and migration are intricately coupled inside complex geometries.

Tensor Networks are graph representations of summation expressions in which vertices represent tensors and edges represent tensor indices or vector spaces. In this work, we present EinExprs.jl, a Julia package for contraction path optimization that offers state-of-art optimizers. We propose a representation of the contraction path of a Tensor Network based on symbolic expressions. Using this package the user may choose among a collection of different methods such as Greedy algorithms, or an approach based on the hypergraph partitioning problem. We benchmark this library with examples obtained from the simulation of Random Quantum Circuits (RQC), a well known example where Tensor Networks provide state-of-the-art methods., Comment: 4 pages, 5 figures, submitted to JuliaCon Proceedings 2023

Niobium carbide (NbC) is a high-field Type II superconductor with a critical temperature ($T_C$) of 11.1 K, just above that of pure Nb ($T_C = 9$ K). Downsizing NbC to the nanoparticle scale introduces significant alterations in its critical field and/or the superconducting temperature. Here we report on superconducting NbC nanoparticles with $T_C \approx$ 10 K synthesized by laser ablation in acetone, using the lens-target distance (laser fluence) and centrifugation as control parameters of the particle size. X-ray diffraction analyses certified the cubic NbC phase and electron microscopy images revealed spherical particles with average size near 8 nm, with no apparent size dependence on fluence. Besides, magnetization curves exhibited magnetic loops featuring a saturation magnetization around $10^{-3} \mu_B$/molecule along with a small and typical superconducting loop for all investigated samples. We also observed a suppression of the diamagnetic behavior below $T_C$ upon decreasing laser fluence. Moreover, all samples exhibited a weak electron spin resonance (ESR) Curie-like signal at $g\approx2.0$ probably associated with localized defects in the particle's surface. The intriguing coexistence of superconductivity and magnetism in nanoparticles has recently garnered significant research attention. This complex scenario and unique properties are due to the substantial increase of surface-to-volume ratio in these superconducting NbC nanoparticles and further investigation would be crucial to unveil novel material properties and shed new light on our understanding of the superconducting phenomenon in this new morphology.

We show that standard matter effects in the outer layers of core-collapse supernovae significantly constrain the flavor composition of the neutrino flux, even with the enormous uncertainties originating from self-induced flavor conversions in the supernova core. Under certain conditions, the neutrino flux resulting from self-induced conversions can be represented as a combination of flavor eigenstates in an arbitrary flavor ratio configuration. In this scenario, we find that, for the normal mass ordering, the fraction of neutrinos with electron flavor reaching the Earth, denoted as $f_{\nu_e}$, is constrained to be less than $0.5$ for all energies throughout the emission phase, whereas, for inverted mass ordering, we anticipate neutrinos arriving in near flavor equipartition ($f_{\nu_e}\approx 1/3$). In case adiabaticity is violated in the region of standard matter effects, the result is flavor equipartition for both mass orderings. Subsequently, we elaborate on the impact of wave-packet decoherence during self-induced conversions and explore alternative scenarios that could affect the aforementioned results., Comment: 6 pages + references, 2 figures

One of the main issues in gravitation is the presence of singularities in the most common space-time solutions of General Relativity, as the case of black holes. A way of constructing regular solutions that remove spacelike singularities consists in implement a bounce on such space-time, leading to what is usually known as black bounce space-times. Such space-times are known to describe regular black holes or traversable wormholes. However, one of the main issues lies on reconstructing the appropriate source that leads to such a solution. In this paper, a reconstruction method is implemented to show that such types of metrics can be well accommodated in non-linear electrodynamics with the presence of a scalar field. Some of the most important black bounces solutions are reconstructed in this framework, both in 3+1 as in 2+1 dimensions. For the first time in the literature, these solutions have an electrically charged source of matter from non-linear electrodynamics. Specific features are indicated that distinguish electric sources from magnetic ones, previously found for the same space-times., Comment: V3: 14 pages, new figures, published in EPJC

This study explores the intricate interactions between grain boundaries (GBs) and irradiation-induced defects in nanocrystalline iron, highlighting the role of dopants like copper. Utilizing molecular dynamics simulations, the research delineates how GB properties, such as GB energy and defect formation energies, influence the formation and evolution of defects in low energy collision cascades. It reveals that GBs not only augment defect production but also show a marked preference for interstitials over vacancies, a behavior significantly modulated by the cascade's proximity to the GB. The presence of dopants is shown to alter GB properties, affecting both the rate and type of defect production, thereby underscoring the complex interplay between GB characteristics, dopant elements, and defect dynamics. Moreover, the investigation uncovers that the structural characteristics of GBs play a crucial role in cascade evolution and defect generation, with certain GB configurations undergoing reconfiguration in response to cascades. For instance, the reconfiguration of one pure Fe twist GB suggests that GB geometry can significantly influence defect generation mechanisms. These findings point to the potential of GB engineering in developing materials with enhanced radiation tolerance, advocating for a nuanced approach to material design. By tailoring GB properties and selectively introducing dopant elements, materials can be optimized to exhibit superior resistance to radiation-induced damage, offering insights for applications in nuclear reactors and other radiation-prone environments.

Efficient simulation of quantum computers relies on understanding and exploiting the properties of quantum states. This is the case for methods such as tensor networks, based on entanglement, and the tableau formalism, which represents stabilizer states. In this work, we integrate these two approaches to present a generalization of the tableau formalism used for Clifford circuit simulation. We explicitly prove how to update our formalism with Clifford gates, non-Clifford gates, and measurements, enabling universal circuit simulation. We also discuss how the framework allows for efficient simulation of more states, raising some interesting questions on the representation power of tensor networks and the quantum properties of resources such as entanglement and magic, and support our claims with simulations., Comment: 13 pages (4 pages main text), 4 figures, v2

This study presents a computational framework to investigate and predict the complicated multiphase properties of eco-friendly lead-free piezoelectric materials, which are crucial for sustainable technological progress. Although their electromechanical properties vary by phase, lead-free piezoelectric materials show a considerable thermo-electromechanical response. Lead-free materials such as Bi$_{0.5}$Na$_{0,5}$TiO$_{3}$ (BNT) and other BNT-type piezoelectric materials transition to rhombohedral (R3c), orthorhombic (Pnma), tetragonal (P4bm), and cubic (Cc) phases with temperature variation. These phases are determined by the symmetry and alignment of the ferroelectric domains. Multiple phases can occur simultaneously under specific thermal, electrical, and mechanical conditions, leading in complex multiphase behaviour. These materials' performance must be assessed by studying such behaviour. This study uses Landau-Ginzburg-Devonshire theory to simulate material micro-domain phase transitions. The computational model for BNT-type piezoelectric material covers temperature-induced ferroelectric domain switching and phase transitions. Therefore, the developed computational approach will assist us in better understanding the influence of these materials' complex multiphase behaviour on creating sustainable solutions with green technologies.

The application of single-cell molecular profiling coupled with spatial technologies has enabled charting cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single cell and spatial datasets, to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues., Comment: 36 pages, 4 figures

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

In the era of gravitational waves physics, when detections of wave fronts are increasing in number, sensibility, frequencies and distances, gravitational physics has entered a period of maximum activity and brilliance. This has open a new window where General Relativity can be challenged in both weak as strong-field regimes. In this paper, we focus on the analysis of gravitational waves propagation and emission in the weak-field regime for gravitational theories within the Palatini formalism. Our results show that gravitational waves propagation in vacuum matches General Relativity predictions as well as the multipolar expansion when considering weak sources, independently of the form of the gravitational action., Comment: 10 pages, extra analysis, conclusions updated and additional references. Version to be published in GRG

Neural activity in the visual cortex of blind humans persists in the absence of visual stimuli. However, little is known about the preservation of visual representation capacity in these cortical regions, which could have significant implications for neural interfaces such as visual prostheses. In this work, we present a series of analyses on the shared representations between evoked neural activity in the primary visual cortex (V1) of a blind human with an intracortical visual prosthesis, and latent visual representations computed in deep neural networks (DNNs). In the absence of natural visual input, we examine two alternative forms of inducing neural activity: electrical stimulation and mental imagery. We first quantitatively demonstrate that latent DNN activations are aligned with neural activity measured in blind V1. On average, DNNs with higher ImageNet accuracy or higher sighted primate neural predictivity are more predictive of blind V1 activity. We further probe blind V1 alignment in ResNet-50 and propose a proof-of-concept approach towards interpretability of blind V1 neurons. The results of these studies suggest the presence of some form of natural visual processing in blind V1 during electrically evoked visual perception and present unique directions in mechanistically understanding and interfacing with blind V1., Comment: Accepted preprint version

We report on the finding of a newborn AGN, i.e. current AGN activity in a galaxy previously classified as non-active, and characterize its evolution. Black hole ignition event candidates were selected from a parent sample of spectrally classified non-active galaxies (2.394.312 objects), that currently show optical flux variability indicative of a type I AGN, according to the ALeRCE light curve classifier. A second epoch spectrum for a sample of candidate newborn AGN were obtained with the SOAR telescope to search for new AGN features. We present spectral results for the most convincing case of new AGN activity, for a galaxy with a previous star-forming optical classification, where the second epoch spectrum shows the appearance of prominent, broad Balmer lines without significant changes in the narrow line flux ratios. Long term optical lightcurves show a steady increase in luminosity starting 1.5 years after the SDSS spectrum was taken and continuing for at least 7 years. MIR colors from the WISE catalog have also evolved from typical non active galaxy colors to AGN-like colors and recent X-ray flux detections confirm its AGN nature., Comment: Accepted for publication in A&A Letters

3D scene understanding for robotic applications exhibits a unique set of requirements including real-time inference, object-centric latent representation learning, accurate 6D pose estimation and 3D reconstruction of objects. Current methods for scene understanding typically rely on a combination of trained models paired with either an explicit or learnt volumetric representation, all of which have their own drawbacks and limitations. We introduce DreamUp3D, a novel Object-Centric Generative Model (OCGM) designed explicitly to perform inference on a 3D scene informed only by a single RGB-D image. DreamUp3D is a self-supervised model, trained end-to-end, and is capable of segmenting objects, providing 3D object reconstructions, generating object-centric latent representations and accurate per-object 6D pose estimates. We compare DreamUp3D to baselines including NeRFs, pre-trained CLIP-features, ObSurf, and ObPose, in a range of tasks including 3D scene reconstruction, object matching and object pose estimation. Our experiments show that our model outperforms all baselines by a significant margin in real-world scenarios displaying its applicability for 3D scene understanding tasks while meeting the strict demands exhibited in robotics applications.

Parameter-efficient fine-tuning optimizes large, pre-trained foundation models by updating a subset of parameters; in this class, Low-Rank Adaptation (LoRA) is particularly effective. Inspired by an effort to investigate the different roles of LoRA matrices during fine-tuning, this paper characterizes and leverages unexpected asymmetry in the importance of low-rank adapter matrices. Specifically, when updating the parameter matrices of a neural network by adding a product $BA$, we observe that the $B$ and $A$ matrices have distinct functions: $A$ extracts features from the input, while $B$ uses these features to create the desired output. Based on this observation, we demonstrate that fine-tuning $B$ is inherently more effective than fine-tuning $A$, and that a random untrained $A$ should perform nearly as well as a fine-tuned one. Using an information-theoretic lens, we also bound the generalization of low-rank adapters, showing that the parameter savings of exclusively training $B$ improves the bound. We support our conclusions with experiments on RoBERTa, BART-Large, LLaMA-2, and ViTs., Comment: 17 pages, 2 figures, 9 tables