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The hard-sphere potential has become a cornerstone in the study of both molecular and complex fluids. Despite its mathematical simplicity, its implementation in fixed time-step molecular simulations remains a formidable challenge due to the discontinuity at contact. To circumvent the issues associated with the ill-defined force at contact, a continuous potential--referred to here as the pseudo-hard-sphere (pHS) potential--has recently been proposed [J. Chem, Phys. 149, 164907 (2018)]. This potential is constructed to match the second virial coefficient of the hard-sphere potential and is expected to mimic its thermodynamic properties. However, this hypothesis has only been partially validated within the fluid region of the phase diagram for hard-sphere dispersions in two and three dimensions. In this contribution, we examine the ability of the continuous pHS potential to reproduce the equation of state of a hard-sphere fluid, not only in the fluid phase but also across the fluid-solid coexistence region. Our focus is primarily on hard-sphere systems in three and four dimensions. We compare the results obtained from Brownian dynamics simulations of the pHS potential with those derived from refined event-driven simulations of the corresponding hard-sphere potential. Furthermore, we provide a comparative analysis with theoretical equations of state based on both mean-field and integral equation approximations., Comment: 11 pages, 4 figures

The present article represents a step forward in the study of the following problem: If $\mathbb{A}=(A_{1},A_{2})$ and $\mathbb{H}=(H_{1},H_{2})$ are Hopf braces in a symmetric monoidal category C such that $(A_{1},H_{1})$ and $(A_{2},H_{2})$ are matched pairs of Hopf algebras, then we want to know under what conditions the pair $(A_{1}\bowtie H_{1},A_{2}\bowtie H_{2})$ constitutes a new Hopf brace. We find such conditions for the pairs $(A_{1}\otimes H_{1},A_{2}\bowtie H_{2})$ and $(A_{1}\bowtie H_{1},A_{2}\sharp H_{2})$ to be Hopf braces, which are particular situations of the general problem described above, and we apply these results to study when the Drinfeld's Double gives rise to a Hopf brace.

The edge modes of fractional quantum Hall liquids are described by chiral Luttinger liquid theory. Despite many years of experimental investigation fractional quantum Hall edge modes remain enigmatic with significant discrepancies between experimental observations and detailed predictions of chiral Luttinger liquid theory. Here we report measurements of tunneling conductance between counterpropagating edge modes at $\nu=1/3$ across a quantum point contact fabricated on an AlGaAs/GaAs heterostructure designed to promote a sharp confinement potential. We present evidence for tunneling of anyons through an $\nu=1/3$ incompressible liquid that exhibits universal scaling behavior with respect to temperature, source-drain bias, and barrier transmission, as originally proposed by Wen[1,2]. We measure the tunneling exponent $g = 0.334 \pm 0.001$, consistent with the scaling dimension $\Delta = g/2 = 1/6$ for a Laughlin quasiparticle at the edge. When combined with measurements of the fractional charge $e^*=e/3$ and the recently observed anyonic statistical angle $\theta_a=\frac{2\pi}{3}$, the measured tunneling exponent fully characterizes the topological order of the primary Laughlin state at $\nu=1/3$., Comment: 9 pages, 4 figures

In this demonstration, we develop IntentRec4Maps, a system to recognise users' intentions in interactive maps for real-world navigation. IntentRec4Maps uses the Google Maps Platform as the real-world interactive map, and a very effective approach for recognising users' intentions in real-time. We showcase the recognition process of IntentRec4Maps using two different Path-Planners and a Large Language Model (LLM). GitHub: https://github.com/PeijieZ/IntentRec4Maps

This work studies the dynamics of solutions to the sine-Gordon equation posed on a tadpole graph $G$ and endowed with boundary conditions at the vertex of $\delta$-type. The latter generalize conditions of Neumann-Kirchhoff type. The purpose of this analysis is to establish an instability result for a certain family of stationary solutions known as \emph{single-lobe kink state profiles}, which consist of a periodic, symmetric, concave stationary solution in the finite (periodic) lasso of the tadpole, coupled with a decaying kink at the infinite edge of the graph. It is proved that such stationary profile solutions are linearly (and nonlinearly) unstable under the flow of the sine-Gordon model on the graph. The extension theory of symmetric operators, Sturm-Liouville oscillation results and analytic perturbation theory of operators are fundamental ingredients in the stability analysis. The local well-posedness of the sine-Gordon model in an appropriate energy space is also established. The theory developed in this investigation constitutes the first stability result of stationary solutions to the sine-Gordon equation on a tadpole graph., Comment: 50 pages, 25 figures

In a recent paper [R. Dale. R. Lapiedra, and J. A. Morales-Lladosa, Phys. Rev. D {\bf 107}, 023506 (2023)] a cosmic-like Clauser-Horne-Shimony-Holt (CHSH) inequality was proved for the temperature fluctuations $\delta T$ of the perturbed Cosmic Microwave Background (CMB), assuming local realism. This inequality can be tested from observational data. In fact, no violation for it has been found from the CMB data provided by the COBE satellite in different sky directions. This is a result which, being negative, is not conclusive in relation to the possible violation of the CHSH inequality in a cosmological context. So, the preceding analysis needs to be extended by using more precise current CMB data, like those provided by WMAP and Planck satellites. This is a task of considerable importance but, technically, much more involved in data treatments. In this work, assuming again local realism behind measurements and observational data, this extension is accomplished. The result is that a sound violation of the CHSH inequality is found, which would mean the failure of the assumed local realism in accordance with the quantum origin of the primordial temperature fluctuations in the framework of standard inflation and the orthodox interpretation of the Quantum Mechanics., Comment: 28 pages, 6 figures

We point out that specifying the finite modular group does not uniquely fix a modular flavor symmetry. We illustrate this using the finite modular group $T'$. Otherwise equivalent models based on different $T'$ lead to modular forms with different properties and, hence, produce different phenomenological features. We exemplify this in various scenarios, and show that the ability of a given model to accommodate mass and other observed hierarchies depends sensitively on the way the $T'$ is implemented., Comment: 26 pages

We construct explicit models of universal $H \mathbb{Z}[J^{-1}]$-acyclic spaces $\mathcal M$, for any subset $J$ of the prime numbers. The corresponding nullification functors provide thus plus construction functors for ordinary homology with $\mathbb{Z}[J^{-1}]$ coefficients. Motivated by classical results about Quillen's plus construction for integral homology, we prove that the $H \mathbb{Z}[J^{-1}]$-acyclization functor and the $\mathcal M$-cellularization functor coincide. We show that the acyclization-plus construction fiber sequence is always a cofiber sequence for simply connected spaces, but almost never so when the plus construction is not simply connected, unlike in the classical case.

Breast cancer is the leading cause of mortality among women. Inspection of breasts by palpation is the key to early detection. We aim to create a wearable tactile glove that could localize the lump in breasts using deep learning (DL). In this work, we present our flexible fabric-based and soft wearable tactile glove for detecting the lumps within custom-made silicone breast prototypes (SBPs). SBPs are made of soft silicone that imitates the human skin and the inner part of the breast. Ball-shaped silicone tumors of 1.5-, 1.75- and 2.0-cm diameters are embedded inside to create another set with lumps. Our approach is based on the InceptionTime DL architecture with transfer learning between experienced and non-experienced users. We collected a dataset from 10 naive participants and one oncologist-mammologist palpating SBPs. We demonstrated that the DL model can classify lump presence, size and location with an accuracy of 82.22%, 67.08% and 62.63%, respectively. In addition, we showed that the model adapted to unseen experienced users with an accuracy of 95.01%, 88.54% and 82.98% for lump presence, size and location classification, respectively. This technology can assist inexperienced users or healthcare providers, thus facilitating more frequent routine checks.

Cosmic Microwave Background (CMB) photons scatter off the free-electron gas in galaxies and clusters, allowing us to use the CMB as a backlight to probe the gas in and around low-redshift galaxies. The thermal Sunyaev-Zel'dovich effect, sourced by hot electrons in high-density environments, measures the thermal pressure of the target objects, shedding light on halo thermodynamics and galaxy formation and providing a path toward understanding the baryon distribution around cosmic structures. We use a combination of high-resolution CMB maps from the Atacama Cosmology Telescope (ACT) and photometric luminous red galaxy (LRG) catalogues from the Dark Energy Spectroscopic Instrument (DESI) to measure the thermal Sunyaev-Zel'dovich signal in four redshift bins from $z=0.4$ to $z=1.2$, with a combined detection significance of 19$\sigma$ when stacking on the fiducial CMB Compton-$y$ map. We discuss possible sources of contamination, finding that residual dust emission associated with the target galaxies is important and limits current analyses. We discuss several mitigation strategies and quantify the residual modelling uncertainty. This work complements closely-related measurements of the kinematic Sunyaev-Zel'dovich and weak lensing of the same galaxies., Comment: 14 pages, 13 figures, comments welcome

The frequency of the preferred order for a noun phrase formed by demonstrative, numeral, adjective and noun has received significant attention over the last two decades. We investigate the actual distribution of the preferred 24 possible orders. There is no consensus on whether it can be well-fitted by an exponential or a power law distribution. We find that an exponential distribution is a much better model. This finding and other circumstances where an exponential-like distribution is found challenge the view that power-law distributions, e.g., Zipf's law for word frequencies, are inevitable. We also investigate which of two exponential distributions gives a better fit: an exponential model where the 24 orders have non-zero probability or an exponential model where the number of orders that can have non-zero probability is variable. When parsimony and generalizability are prioritized, we find strong support for the exponential model where all 24 orders have non-zero probability. This finding suggests that there is no hard constraint on word order variation and then unattested orders merely result from undersampling, consistently with Cysouw's view.

In order to both learn and protect sensitive training data, there has been a growing interest in privacy preserving machine learning methods. Differential privacy has emerged as an important measure of privacy. We are interested in the federated setting where a group of parties each have one or more training instances and want to learn collaboratively without revealing their data. In this paper, we propose strategies to compute differentially private empirical distribution functions. While revealing complete functions is more expensive from the point of view of privacy budget, it may also provide richer and more valuable information to the learner. We prove privacy guarantees and discuss the computational cost, both for a generic strategy fitting any security model and a special-purpose strategy based on secret sharing. We survey a number of applications and present experiments.

In July 2024, Sunrise completed its third successful science flight. The Sunrise III observatory had been upgraded significantly after the two previous successful flights in 2009 and 2013. Three completely new instruments focus on the small-scale physical processes and their complex interaction from the deepest observable layers in the photosphere up to chromospheric heights. Previously poorly explored spectral regions and lines are exploited to paint a three-dimensional picture of the solar atmosphere with unprecedented completeness and level of detail. The full polarimetric information is captured by all three instruments to reveal the interaction between the magnetic fields and the hydrodynamic processes. Two slit-based spectropolarimeters, the Sunrise UV Spectropolarimeter and Imager (SUSI) and the Sunrise Chromospheric Infrared spectro-Polarimeter (SCIP), focus on the near-ultraviolet and the near-infrared regions respectively, and the imaging spectropolarimeter Tunable Magnetograph (TuMag) simultaneously obtains maps of the full field-of-view of $46 \times 46$ Mm$^2$ in the photosphere and the chromosphere in the visible. The instruments are operated in an orchestrated mode, benefiting from a new Image Stabilization and Light Distribution unit (ISLiD), with the Correlating Wavefront Sensor (CWS) providing the autofocus control and an image stability with a root-mean-square value smaller than 0.005''. A new gondola was constructed to significantly improve the telescope pointing stability, required to achieve uninterrupted observations over many hours. Sunrise III was launched successfully on July 10, 2024, from the Esrange Space Center near Kiruna (Sweden). It reached the landing site between the Mackenzie River and the Great Bear Lake in Canada after a flight duration of 6.5 days. In this paper, we give an overview of the Sunrise III observatory and its instruments., Comment: 67 pages, 25 figures; to be published in Solar Physics Topical Collection "The Sunrise III Solar Observatory" (https://link.springer.com/collections/jegdciedig)

Advancements in embedded systems and Artificial Intelligence (AI) have enhanced the capabilities of Unmanned Aircraft Vehicles (UAVs) in computer vision. However, the integration of AI techniques o-nboard drones is constrained by their processing capabilities. In this sense, this study evaluates the deployment of object detection models (YOLOv8n and YOLOv8s) on both resource-constrained edge devices and cloud environments. The objective is to carry out a comparative performance analysis using a representative real-time UAV image processing pipeline. Specifically, the NVIDIA Jetson Orin Nano, Orin NX, and Raspberry Pi 5 (RPI5) devices have been tested to measure their detection accuracy, inference speed, and energy consumption, and the effects of post-training quantization (PTQ). The results show that YOLOv8n surpasses YOLOv8s in its inference speed, achieving 52 FPS on the Jetson Orin NX and 65 fps with INT8 quantization. Conversely, the RPI5 failed to satisfy the real-time processing needs in spite of its suitability for low-energy consumption applications. An analysis of both the cloud-based and edge-based end-to-end processing times showed that increased communication latencies hindered real-time applications, revealing trade-offs between edge (low latency) and cloud processing (quick processing). Overall, these findings contribute to providing recommendations and optimization strategies for the deployment of AI models on UAVs., Comment: This manuscript consists of 24 pages, 7 figures, and 7 tables

This work explores how color is encoded in CLIP (Contrastive Language-Image Pre-training) which is currently the most influential VML (Visual Language model) in Artificial Intelligence. After performing different experiments on synthetic datasets created for this task, we conclude that CLIP is able to attribute correct color labels to colored visual stimulus, but, we come across two main deficiencies: (a) a clear bias on achromatic stimuli that are poorly related to the color concept, thus white, gray and black are rarely assigned as color labels; and (b) the tendency to prioritize text over other visual information. Here we prove it is highly significant in color labelling through an exhaustive Stroop-effect test. With the aim to find the causes of these color deficiencies, we analyse the internal representation at the neuron level. We conclude that CLIP presents an important amount of neurons selective to text, specially in deepest layers of the network, and a smaller amount of multi-modal color neurons which could be the key of understanding the concept of color properly. Our investigation underscores the necessity of refining color representation mechanisms in neural networks to foster a more comprehensive comprehension of colors as humans understand them, thereby advancing the efficacy and versatility of multimodal models like CLIP in real-world scenarios., Comment: 6 pages, 10 figures, conference, Artificial Intelligence

We present fresh insights into the nature of the tidal disruption event (TDE) candidate AT 2018dyk. AT 2018dyk has sparked a debate in the literature around its classification as either a bona-fide TDE or as an active galactic nucleus (AGN) turn-on state change. A new follow-up spectrum taken with the Dark Energy Spectroscopic Instrument, in combination with host-galaxy analysis using archival SDSS-MaNGA data, supports the identification of AT 2018dyk as a TDE. Specifically, we classify this object as a TDE that occurred within a gas-rich environment, which was responsible for both its mid-infrared (MIR) outburst and development of Fe coronal emission lines. Comparison with the known sample of TDE-linked extreme coronal line emitters (TDE-ECLEs) and other TDEs displaying coronal emission lines (CrL-TDEs) reveals similar characteristics and shared properties. For example, the MIR properties of both groups appear to form a continuum with links to the content and density of the material in their local environments. This includes evidence for a MIR colour-luminosity relationship in TDEs occurring within such gas-rich environments, with those with larger MIR outbursts also exhibiting redder peaks., Comment: 44 pages, 16 total figures. Submitted to MNRAS

The miniaturization of optics through the use of two-dimensional metalenses has enabled novel applications in imaging. To date, single-lens imaging remains the most common configuration, in part due to the limited focusing efficiency of metalenses. This results in limitations when it comes to wavefront manipulation and, thus, unavoidable aberrations in the formed image that require computational deconvolution to deblur the image. For certain lens profiles, such as the most common hyperbolic one that results in the highest efficiencies, at large fields of view, spatially-varying aberrations such as coma or astigmatism are prominent. These aberrations cannot be corrected for by traditional deconvolution methods, such as Wiener filtering. Here, we develop a spatially-varying deconvolution algorithm based on eigenvalue column-wise decomposition (eigenCWD). EigenCWD solves a minimization problem of the error between the measured image and the estimated image of the object to be reconstructed through an approximate forward blurring model. This approximate forward model uses an eigendecomposition of the spatially-varying point spread functions for fast computation, allowing for efficient scaling to larger image sizes and blurring kernels common in metalens imaging. We demonstrate eigenCWD's ability to correct spatially-varying blur and distortions for various lens profiles, surpassing that of the Wiener filter.

We present a study on the possible overestimation of single-epoch supermassive black hole (SMBH) masses in previous works, based on more than 55,000 type 1 quasars at $0.25 < z < 0.8$ from the Dark Energy Spectroscopic Instrument (DESI). We confirm that iron emission strength serves as a good tracer of the Eddington ratio, and estimate SMBH masses using an iron-corrected $R$-$L$ relation for H$\beta$, where $R$ is the broad line region size and $L$ is the continuum luminosity. Compared to our measurements, previous canonical measurements without the iron correction are overestimated by a factor of 1.5 on average. The overestimation can be up to a factor of 5 for super-Eddington quasars. The fraction of super-Eddington quasars in our sample is about 5%, significantly higher than 0.4% derived from the canonical measurements. Using a sample featuring both H$\beta$ and MgII emission lines, we calibrate MgII-based SMBH masses using iron-corrected, H$\beta$-based SMBH masses and establish an iron-corrected $R$-$L$ relation for MgII. The new relation adds an extra term of $-0.34R_{\mathrm{Fe}}$ to the $R$-$L$ relation, where $R_{\mathrm{Fe}}$ denotes the relative iron strength. We use this formula to build a catalog of about 0.5 million DESI quasars at $0.6

Large Language Models demonstrate remarkable capabilities at the cost of high compute requirements. While recent research has shown that intermediate layers can be removed or have their order shuffled without impacting performance significantly, these findings have not been employed to reduce the computational cost of inference. We investigate several potential ways to reduce the depth of pre-trained LLMs without significantly affecting performance. Leveraging our insights, we present a novel approach that exploits this decoupling between layers by grouping some of them into pairs that can be evaluated in parallel. This modification of the computational graph -- through better parallelism -- results in an average improvement of around 1.20x on the number of tokens generated per second, without re-training nor fine-tuning, while retaining 95%-99% of the original accuracy. Empirical evaluation demonstrates that this approach significantly improves serving efficiency while maintaining model performance, offering a practical improvement for large-scale LLM deployment.

We describe the dynamics of a detector modeled by a harmonic oscillator coupled with an otherwise free quantum field in a curved spacetime in terms of covariant equations of motion leading to local observables. To achieve this, we derive and renormalize the integro-differential equation that governs the detector pointer-variable dynamics, introducing phenomenological parameters such as a dispersion coefficient and a Lamb-shift parameter. Our formal solution, expressed in terms of Green's functions, allows for the covariant, and causal analysis of induced observables on the field. This formalism can be used for instance to detect non-Gaussianities present in the field's state., Comment: 11 pages

The evergrowing computational complexity of High Performance Computing applications is often met with an horizontal scalling of computing systems. Colaterally, each added node risks being a single point of failure to parallel programs, increasing the demand for fault tolerant techniques to be applied, specially at software level. Under such conditions, the fault tolerance library DeLIA was developed in C/C++ with error detection and recovery features. We propose, then, to extend the library's capabilities to Python and Julia through the wrappers DeLIAP and DeLIAJ in order to lower the barrier to entry for implementing fault-tolerant solutions in these languages, which both lack alternatives to the library. To validate the efficiency of the wrappers, an application of the Julia wrapper in the 4D Full waveform inversion method was analyzed, quantitatively assessing the introduced overhead through runtime comparisons, while an implementation report is provided to address applicability. The added computational cost reflected on a median overhead of 1.4%, while limitations in the original parallel computing module used in the application rendered local-scope data checkpointing unfeasible., Comment: 6 pages, in Portuguese. First online version, adapted from the SPCEEC submitted version. English translation pending

The first few hours of a supernova contain significant information about the progenitor system. The most modern wide-field surveys that scan the sky repeatedly every few days can discover all kinds of transients in those early epochs. At such times, some progenitor footprints may be visible, elucidating critical explosion parameters and helping to distinguish between leading explosion models. A dedicated spectroscopic classification programme using the optical spectrograph OSIRIS mounted to the Gran Telescopio de Canarias was set up to try to obtain observations of supernova at those early epochs. With the time awarded, we obtained spectra for 10 SN candidates, which we present here. Half of them were thermonuclear SNe, while the other half were core-collapse SNe. Most (70\%) were observed within the first six days of the estimated explosion, with two being captured within the first 48 hours. We present a characterization of the spectra, together with other public ancillary photometry from ZTF and ATLAS. This programme shows the need for an accompanying rapid-response spectroscopic programme to existing and future deep photometric wide-field surveys located at the right longitude to be able to trigger observations in a few hours after the discovery of the supernova candidate. Both the future La Silla Southern Supernova Survey (LS4) and the Legacy Survey of Space and Time (LSST) both located in Chile will be providing discovery and follow up of most of the transients in the southern hemisphere. This paper demonstrates that with a rapid spectroscopic programme and stringent triggering criteria, obtaining a sample of SN with spectra within a day of the explosion is possible., Comment: 20 pages, 16 figures. Submitted to A&A

The rapid and constant increase in urban population has led to a drastic rise in urban solid waste production with worrying consequences for the environment and society. In many cities, an efficient waste management combined with a suitable design of vehicle routes (VR) can lead to benefits in the environmental, economic, and social impacts. The general population is becoming increasingly aware of the need for the separation of the various categories of municipal solid waste. The numerous materials collected include glass, PET or batteries, and electric components, which are sorted at the eco-points. The management of eco-points gives rise to several problems that can be formulated analytically. The location and number of eco-point containers, the determination of the fleet size for picking up the collected waste, and the design of itineraries are all intertwined, and present computationally difficult problems, and therefore must be solved in a sequential way. In this paper, a mathematical model has been formulated, based on the Bin Packing (BP) and VR schemes, for the deployment of routes of mobile containers in the selective collection of urban solid waste. A heuristic algorithm has also been developed, which considers two different configurations of the containers to solve the proposed mathematical programming model. The results obtained from the numerical simulations show the validation of the proposed methodology carried out for the benchmark of the Sioux Falls network and the specific real case study.

This paper presents an optimization procedure to choose a parking facility according to different criteria: total travel time including transfers, parking fee and a factor depending on the risk of not having an available spot in the parking facility at the arrival time. An integer programming formulation is proposed to determine an optimal strategy of minimum cost considering the available information, different scenarios, and each user profile. To evaluate the performance, a computational experience has been carried out on Seville (Spain), where a historical city center restricts the traffic of private vehicles and encourages the use of parking facilities.

This work consists of a procedure to optimally select, among a group of candidate sites where gas stations were already located, a sufficient number of charging points in order to guarantee that an electric vehicle can make its journey without a problem of energy autonomy and that each selected charging station has another one that serves as useful support in case of failure (reinforced coverage service). For this purpose, we propose a bilevel model that, in a former level, minimizes the number of refuelling points necessary to guarantee a reinforced service coverage for all users who transit from their origin to destination and, as a second level, maximize the volume of demand that can be satisfied subject to budgetary restrictions. With the first of the objectives we are addressing the typical requirement of the administration, which consists of guaranteeing the viability of the solutions, and the second of the objectives is a criterion typically used by the private sector initiative, compatible with the profit maximization.

Gas-phase hydroxylamine (NH$_2$OH) has recently been detected within dense clouds in the interstellar medium. However, it is also likely present within interstellar ices, as well as on the icy surfaces of outer Solar System bodies, where it may react to form more complex prebiotic molecules such as amino acids. In this work, we aimed to provide IR spectra of NH$_2$OH in astrophysical ice analogues that will help in the search for this molecule in various astrophysical environments. Furthermore, we aimed to provide quantitative information on the stability of NH$_2$OH upon exposure to ionizing radiation analogous to cosmic rays, as well as on the ensuing chemistry and potential formation of complex prebiotic molecules. Ices composed of NH$_2$OH, H$_2$O, and CO were prepared by vapor deposition and IR spectra were acquired between $4000-500$~cm$^{-1}$ ($2.5-20 \mu$m) prior and during irradiation using 15 keV protons. In interstellar ice analogues, the most prominent IR absorption band of NH$_2$OH is that at about 1188~cm$^{-1}$, which may be a good candidate to use in searches of this species in icy space environments. Calculated effective destruction cross-sections and G-values for the NH$_2$OH-rich ices studied show that NH$_2$OH is rapidly destroyed upon exposure to ionizing radiation (more rapidly than a number of previously studied organic molecules), and that this destruction is slightly enhanced when it is mixed with other icy species. The irradiation of a NH$_2$OH:H$_2$O:CO ternary ice mixture leads to a rich chemistry that includes the formation of simple inorganic molecules such as NH$_3$, CO$_2$, OCN$^-$, and H$_2$O$_2$, as well as ammonium salts and, possibly, complex organic molecules relevant to life such as formamide, formic acid, urea, and glycine., Comment: Accepted for publication in A&A

In this paper we consider a new approach to unify inflation and the late universe with dark energy and dark matter formulated in a model that includes a non-Riemannian metric independent measure and a scalar field with spontaneously broken scale symmetry. Here first of all inflation is possible, which is then followed by a reheating oscillating period and this leads to the formation of all kind of particles, including fermions, which as the universe expands can contribute to the dark energy and the to the dark matter of the universe. During the inflationary epoch, we find different constraints on the parameter space associated to the effective potential of the scalar field from the observational data. After reheating the scalar field retraces its trajectory in field space but now the scalar field potential can be drastically modified by the effect of the fermions. In this sense, the present dark energy with its very small value in comparison to the inflationary phase which can be adjusted by choosing appropriately the parameter space of couplings of the Riemannian and non Riemannian measures to the fermions., Comment: 11 pages and one figure

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

This study investigates the dissemination of disinformation on social media platforms during the DANA event (DANA is a Spanish acronym for Depresion Aislada en Niveles Altos, translating to high-altitude isolated depression) that resulted in extremely heavy rainfall and devastating floods in Valencia, Spain, on October 29, 2024. We created a novel dataset of 650 TikTok and X posts, which was manually annotated to differentiate between disinformation and trustworthy content. Additionally, a Few-Shot annotation approach with GPT-4o achieved substantial agreement (Cohen's kappa of 0.684) with manual labels. Emotion analysis revealed that disinformation on X is mainly associated with increased sadness and fear, while on TikTok, it correlates with higher levels of anger and disgust. Linguistic analysis using the LIWC dictionary showed that trustworthy content utilizes more articulate and factual language, whereas disinformation employs negations, perceptual words, and personal anecdotes to appear credible. Audio analysis of TikTok posts highlighted distinct patterns: trustworthy audios featured brighter tones and robotic or monotone narration, promoting clarity and credibility, while disinformation audios leveraged tonal variation, emotional depth, and manipulative musical elements to amplify engagement. In detection models, SVM+TF-IDF achieved the highest F1-Score, excelling with limited data. Incorporating audio features into roberta-large-bne improved both Accuracy and F1-Score, surpassing its text-only counterpart and SVM in Accuracy. GPT-4o Few-Shot also performed well, showcasing the potential of large language models for automated disinformation detection. These findings demonstrate the importance of leveraging both textual and audio features for improved disinformation detection on multimodal platforms like TikTok.

We disclose the existence of a type of optical skyrmion, Gauss-Stokes (GS) skyrmions, that is naturally present in an optical vortex around its phase singularity. Contrary to previous research with optical skyrmions, we neither shape vector beams nor superpose different spatial modes and polarizations. In GS skyrmions, the phase singularity in the transversal field of a single monochromatic beam of uniform polarization (a scalar beam) is concealed by the axial field dictated by Gauss's divergence law, giving rise to a polarization singularity of undefined polarization plane. This singularity is enclosed by a rich skyrmionic polarization texture fulfilling a topological map and covering all the states of transverse-axial polarization. In our experiment, we facilitate the observation of a GS skyrmion with the predicted features using focused fields with enhanced axial component., Comment: 10 pages, 6 figures

This paper presents a game theoretic solution for end-to-end channel and power allocation in multihop cognitive radio networks analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the number of flows that can be established in the network. The problem is addressed through three different games: a local flow game which uses complete information about the links of the flow, a potential flow game requiring global network knowledge and a cooperative link game based on partial information regarding the links of the flow. Results show that the proposed link game highly decreases the complexity of the channel and power allocation problem in terms of computational load, reducing the information shared between the links forming each flow with a performance similar to that of the more complex flow games., Comment: 4 pages, 2 figures

In this work we study the problem of user association and resource allocation to maximize the proportional fairness of a wireless network with limited backhaul capacity. The optimal solution of this problem requires solving a mixed integer non-linear programming problem which generally cannot be solved in real time. We propose instead to model the problem as a potential game, which decreases dramatically the computational complexity and obtains a user association and resource allocation close to the optimal solution. Additionally, the use of a game-theoretic approach allows an efficient distribution of the computational burden among the computational resources of the network., Comment: 32 pages, 14 figures

The syntactic structure of a sentence can be described as a tree that indicates the syntactic relationships between words. In spite of significant progress in unsupervised methods that retrieve the syntactic structure of sentences, guessing the right direction of edges is still a challenge. As in a syntactic dependency structure edges are oriented away from the root, the challenge of guessing the right direction can be reduced to finding an undirected tree and the root. The limited performance of current unsupervised methods demonstrates the lack of a proper understanding of what a root vertex is from first principles. We consider an ensemble of centrality scores, some that only take into account the free tree (non-spatial scores) and others that take into account the position of vertices (spatial scores). We test the hypothesis that the root vertex is an important or central vertex of the syntactic dependency structure. We confirm that hypothesis and find that the best performance in guessing the root is achieved by novel scores that only take into account the position of a vertex and that of its neighbours. We provide theoretical and empirical foundations towards a universal notion of rootness from a network science perspective.

The approximation of data is a fundamental challenge encountered in various fields, including computer-aided geometric design, the numerical solution of partial differential equations, or the design of curves and surfaces. Numerous methods have been developed to address this issue, providing good results when the data is continuous. Among these, the Moving Least Squares (MLS) method has proven to be an effective strategy for fitting data, finding applications in both statistics and applied mathematics. However, the presence of isolated discontinuities in the data can lead to undesirable artifacts, such as the Gibbs phenomenon, which adversely affects the quality of the approximation. In this paper, we propose a novel approach that integrates the Moving Least Squares method with the well-established non-linear Weighted Essentially Non-Oscillatory (WENO) method. This combination aims to construct a non-linear operator that enhances the accuracy of approximations near discontinuities while maintaining the order of accuracy in smooth regions. We investigate the properties of this operator in one dimension, demonstrating its effectiveness through a series of numerical experiments that validate our theoretical findings.

Extreme coronal line emitters (ECLEs) are a rare class of galaxy that exhibit strong, high-ionization iron coronal emission lines in their spectra. In some cases, these lines are transient and may be the result of tidal disruption event (TDEs). To test this connection, we calculate the rate of variable ECLEs (vECLEs) at redshift $\sim0.3$. We search for ECLEs in the Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ sample and discover two candidate ECLEs. Using follow-up spectra from the Dark Energy Spectroscopic Instrument and Gemini Multi-Object Spectrograph, and mid-infrared observations from the Wide-field Infrared Survey Explorer, we determine that one of these galaxies is a vECLE. Using this galaxy, we calculate the galaxy-normalized vECLE rate at redshift $\sim0.3$ to be $R_\mathrm{G}=1.6~^{+3.8}_{-1.4}\times10^{-6}~\mathrm{galaxy}^{-1}~\mathrm{yr}^{-1}$ and the mass-normalized rate to be $R_\mathrm{M}=7~^{+16}_{-6}\times10^{-18}~\mathrm{M_\odot^{-1}}~\mathrm{yr}^{-1}$. This is then converted to a volumetric rate of $R_\mathrm{V}=1.8~^{+4.5}_{-1.5}\times10^{-9}~\mathrm{Mpc}^{-3}~\mathrm{yr}^{-1}$. Formally, the LOWZ vECLE rates are $2-4$ times lower than the rates calculated from the Sloan Digital Sky Survey Legacy sample at redshift $\sim0.1$. However, given the large uncertainties on both measurements, they are consistent with each other at $1\sigma$. Both the galaxy-normalized and volumetric rates are one to two orders of magnitude lower than TDE rates from the literature, consistent with vECLEs being caused by $5-20$ per cent of all TDEs., Comment: 15 pages, 10 figures. Submitted to MNRAS. arXiv admin note: text overlap with arXiv:2402.16951

EllipSect is a user-friendly Python analysis tool that generates publication quality figures from the output of GALFIT 3, a popular galaxy surface brightness 2D-fitting algorithm. EllipSect allows the direct analysis of GALFIT 3 preliminary modeling, to guide optimal parameter selection. Furthermore, EllipSect generates non-parametric measurements of some useful parameters that are not provided by GALFIT 3, such as the effective radius resulting from multi-component fits, cusp radius, and the Petrosian radius. This paper provides examples and a quick guide for EllipSect., Comment: 20 pages, 7 figures, submitted to RevMexAA

This paper introduces the Non-linear Partition of Unity Method, a novel technique integrating Radial Basis Function interpolation and Weighted Essentially Non-Oscillatory algorithms. It addresses challenges in high-accuracy approximations, particularly near discontinuities, by adapting weights dynamically. The method is rooted in the Partition of Unity framework, enabling efficient decomposition of large datasets into subproblems while maintaining accuracy. Smoothness indicators and compactly supported functions ensure precision in regions with discontinuities. Error bounds are calculated and validate its effectiveness, showing improved interpolation in discontinuous and smooth regions. Some numerical experiments are performed to check the theoretical results.

We present Union, a trust-minimized bridge protocol that enables secure transfer of BTC between Bitcoin and a secondary blockchain. The growing ecosystem of blockchain systems built around Bitcoin has created a pressing need for secure and efficient bridges to transfer BTC between networks while preserving Bitcoin's security guarantees. Union employs a multi-party variant of BitVMX, an optimistic proving system on Bitcoin, to create a bridge that operates securely under the assumption that at least one participant remains honest. This 1-of-n honest approach is strikingly different from the conventional honest-majority assumption adopted by practically all federated systems. The protocol introduces several innovations: a packet-based architecture that allows security bonds to be reused for multiple bridge operations, improving capital efficiency; a system of enablers to manage functionaries participation and to enforce penalties; a flexible light client framework adaptable to various blockchain architectures; and an efficient stop watch mechanism to optimize time-lock management. Union is a practical and scalable solution for Bitcoin interoperability that maintains strong security guarantees and minimizes trust assumptions.

We model a market for data where an incumbent and a challenger compete for data from a producer. The incumbent has access to an exclusive data producer, and it uses this exclusive access, together with economies of scope in the aggregation of the data, as a strategy against the potential entry by the challenger. We assess the incumbent incentives to either deter or accommodate the entry of the challenger. We show that the incumbent will accommodate when the exclusive access is costly and when the economies of scope are low, and it will blockade or deter otherwise. The results would justify an access regulation that incentivizes the entry of the challenger, e.g., by increasing production costs for the exclusive data.

We present a tensorization algorithm for constructing tensor train representations of functions, drawing on sketching and cross interpolation ideas. The method only requires black-box access to the target function and a small set of sample points defining the domain of interest. Thus, it is particularly well-suited for machine learning models, where the domain of interest is naturally defined by the training dataset. We show that this approach can be used to enhance the privacy and interpretability of neural network models. Specifically, we apply our decomposition to (i) obfuscate neural networks whose parameters encode patterns tied to the training data distribution, and (ii) estimate topological phases of matter that are easily accessible from the tensor train representation. Additionally, we show that this tensorization can serve as an efficient initialization method for optimizing tensor trains in general settings, and that, for model compression, our algorithm achieves a superior trade-off between memory and time complexity compared to conventional tensorization methods of neural networks., Comment: 40 pages, 9 figures. The code for the experiments is publicly available at https://github.com/joserapa98/tensorization-nns. V2: Added code repository

PSR J2030+4415 is a gamma-ray pulsar with an X-ray pulsar wind nebula elongated along the north-south direction. The system shows a prominent X-ray filament oriented at an angle of 130{\deg} to the nebula axis. To improve our understanding of the non-thermal processes occurring in the pulsar wind nebula, we attempted to determine the possible existence of a radio counterpart, study its morphology, and obtain restrictive upper limits of the pulsar and filament emission at radio wavelengths. We performed observations of the pulsar PSR J2030+4415 and its surroundings with the upgraded Giant Metrewave Radio Telescope (uGMRT) at two frequency bands, and put the results in context with findings at other wavelengths. We obtained radio images at 736 and 1274 MHz that reveal a structure trailing the pulsar, with a morphology overlapping the X-ray nebula. This radio structure is the radio counterpart of the X-ray pulsar wind nebula. The derived spectral index along this structure shows spatial variation. There are no hints of the pulsar and the filament at any of the explored radio frequencies, but we obtained restrictive upper limits. A physical scenario that combines the radio and the X-ray observations, and consistent with IR data, of the nebula and the filament is presented. We propose that particle acceleration occurs in the nebula tail due to the presence of a re-collimation shock, and the highest energy particles gradually escape from it through energy-dependent diffusion. We also find a lower limit in the energy of the particles escaping along the X-ray filament of ~GeV., Comment: 8 pages, 6 figures. Accepted for publication in A&A

Scaling laws for language models so far focused on finding the compute-optimal model size and token count for training from scratch. However, achieving this optimal balance requires significant compute resources due to the extensive data demands when training models from randomly-initialized weights. Continual pre-training offers a cost-effective alternative, leveraging the compute investment from pre-trained models to incorporate new knowledge without requiring extensive new data. Recent findings suggest that data quality influences constants in scaling laws, thereby altering the optimal parameter-token allocation ratio. Building on this insight, we investigate the interplay between domain specialization and model size during continual pre-training under compute-constrained scenarios. Our goal is to identify a compute-efficient training regime for this scenario and, potentially, detect patterns in this interplay that can be generalized across different model sizes and domains. To compare general and specialized training, we filtered a web-based dataset to extract legal domain data. We pre-trained models with 1.5B, 3B, 7B and 14B parameters on both the unfiltered and filtered datasets, then evaluated their performance on legal exams. Results show that as model size increases, the compute-effectiveness gap between specialized and general models widens.

We investigate the Riesz energy minimization problem on a $d$-dimensional ball in the presence of an external field created by a point charge above the ball in $\R^{d+1}$, $d\geq1$. Both cases of an attractive charge and a repulsive charge are considered. The notion of a signed equilibrium measure is one of the main tools in the present study. For the case of a positive (repulsive) charge, the determination of the support of the equilibrium measure is a nontrivial question. We solve it in the one-dimensional case by making use of iterated balayage, a method already applied in logarithmic potential theory. Here we use a modified version of it, in order to handle the phenomenon of mass loss, characteristic of the Riesz balayage of positive measures. Moreover, we also consider minimization of Coulomb energy on the ball in dimension $d\geq2$, and of logarithmic energy on the segment in dimension 1. Different techniques are used for these two cases.

Despite substantial interest in Career and Technical Education (CTE) courses in U.S. high schools--and associated scholarship on this topic--very little is known about characteristics of CTE teachers who are a critical resource for program implementation and expansion. Using eight years of statewide data from Maryland, we document key facts about the CTE teacher workforce and pathways into the profession. First, a sizable share (17%) of CTE teachers enter the profession with a high school diploma or associate's degree, aligned to state policy that allows Professional and Technical Education-certified teachers to substitute years of professional experience for higher degrees. Relatedly, CTE teachers are substantially more likely than non-CTE teachers to enter the profession through an "alternative" path that bypasses traditional undergraduate teacher education (54% versus 30%). Finally, there is a larger share of Black teachers in CTE versus out of CTE (25% versus 16%), leading to greater opportunities for teacher-student race matching. We hypothesize that these patterns are related: decreased barriers to entry into the CTE teaching profession may support more Black individuals to become CTE teachers.

Historically Black Colleges and Universities (HBCUs) and Predominantly Black Institutions (PBIs) provide access to education for many African Americans across the United States. These institutions offer a unique perspective on higher education in multiple disciplines, none more famous than music performance. The marching band, in particular, stands as a sense of pride for these institutions and allows students the opportunity to develop themselves as musicians while pursuing opportunities in higher education. This mixed methods action research study explores the successful strategies employed by college students at Predominantly Black Institutions (PBIs) to balance their time between marching band and academics. The study identified several themes related to effective time management and study practices students use to excel in both areas. Students prioritized study time to master all academic classes and adopted different study modes to maximize their time use. They also organized their time effectively to balance their study and practice routines. The themes related to practice highlighted the importance of reflective practice, group study, and independent practice styles, all of which enabled these exceptional college musicians to succeed in both music performance and academic study. Overall, this study sheds light on the positive impact of HBCUs and PBIs in educating diverse communities and promoting academic and musical excellence. By showcasing the successful strategies employed by college students, this study offers valuable insights into balancing multiple priorities and achieving success in different fields.

Local teacher recruitment through "grow-your-own" programs is a prominent strategy to address workforce shortages and ensure that incoming teachers resemble, understand, and have strong connections to their communities. We exploit the staggered rollout of the Teacher Academy of Maryland career and technical education certificate program across public high schools, finding that exposed students were more likely to become teachers by 0.6 percentage points (pp), or 47%. Effects are concentrated among White girls (1.4pp/39%) and Black girls (0.7pp/80%). We also identify positive impacts on wages (5% on average/18% for Black girls), countering a prevailing narrative that teaching leaves one worse off financially relative to other labor market opportunities.

Mounting evidence supporting the advantages of a diverse teacher workforce prompts policymakers to scrutinize existing recruitment pathways. Following four cohorts of Maryland public high-school students over 12 years reveals several insights. Early barriers require timely interventions, aiding students of color in achieving educational milestones that are prerequisites for teacher candidacy (high school graduation, college enrollment). While alternative pathways that bypass traditional undergraduate teacher preparation may help, current approaches still show persistent racial disparities. Data simulations underscore the need for race-conscious policies specifically targeting or differentially benefiting students of color, as race-neutral strategies have minimal impact. Ultimately, multiple race-conscious policy solutions addressing various educational milestones must demonstrate significant effects--approximately 30% increases--to reshape the teacher workforce to align with student body demographics.

Hybrid superconductor-semiconductor systems have received a great deal of attention in the last few years because of their potential for quantum engineering, including novel qubits and topological devices. The proximity effect, the process by which the semiconductor inherits superconducting correlations, is an essential physical mechanism of such hybrids. Recent experiments have demonstrated the proximity effect in hole-based semiconductors, but, in contrast to electrons, the precise mechanism by which the hole bands acquire superconducting correlations remains an open question. In addition, hole spins exhibit a complex strong spin-orbit interaction, with largely anisotropic responses to electric and magnetic fields, further motivating the importance of understanding the interplay between such effects and the proximity effect. In this work, we analyze this physics with focus on germanium-based two-dimensional gases. Specifically, we develop an effective theory supported by full numerics, allowing us to extract various analytical expressions and predict different types of superconducting correlations including non-standard forms of singlet and triplet pairing mechanisms with non-trivial momentum dependence; as well as different Zeeman and Rashba spin-orbit contributions. This, together with their precise dependence on electric and magnetic fields, allows us to make specific experimental predictions, including the emergence of f-type superconductivity, Bogoliubov Fermi surfaces, and gapless regimes caused by large in-plane magnetic fields., Comment: 13 pages, 9 figures, plus Appendices

A novel least-squares model-reference direct adaptive control (LS-MRAC) algorithm for multivariable (MIMO) plants is presented. The controller parameters are directly updated based on the output tracking error. The control law is crucially modified to reduce the relative degree of the error model to zero. A complete Lyapunov-based stability analysis as well as a tracking error convergence characterization is provided demonstrating that the LS-MRAC can achieve arbitrarily fast tracking while maintaining satisfactory parameter convergence for appropriate adaptation gains. Simulation results show a significant improvement in tracking performance compared to previous methods., Comment: 8 pages, 8 figures

Perovskites have recently brought significant advances to active nanophotonics, offering a unique combination of gain and phase-change properties for tunable light-emitting devices. However, current wavelength-tunable devices often rely on tuning mechanisms or device architectures that lead to slow modulation or bulky setups. In this study, we overcome limitations on speed and size by demonstrating a compact tunable microlaser embedding electrical microheaters beneath a perovskite metasurface. This architecture allows to efficiently deliver heat and rapidly modulate the phase transition. Our device leverages the optical gain and crystallographic phase tuning of the perovskite, and a high-quality factor cavity design based on bound states in the continuum. With it, we demonstrate reversible laser wavelength switching between 763 nm and 783 nm within 13 ms at 2.3 V. This work unlocks the potential of perovskite metasurfaces for electrically tunable light sources and introduces a flexible platform which can be easily extended to the dynamic control of polarization or directionality for optical communication, sensing and spectroscopy., Comment: 20 pages, 11 figures