Search

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

This paper studies moving 180-degree N\'eel walls in ferromagnetic thin films under the reduced model for the in-plane magnetization proposed by Capella, Melcher and Otto [5], in the case when a sufficiently weak external magnetic field is applied. It is shown that the linearization around the moving N\'eel wall's phase determines a spectral problem that is a relatively bounded perturbation of the linearization around the static N\'eel wall, which is the solution when the external magnetic field is set to zero and which is spectrally stable. Uniform resolvent-type estimates for the linearized operator around the static wall are established in order to prove the spectral stability of the moving wall upon application of perturbation theory for linear operators. The spectral analysis is the basis to prove, in turn, both the decaying properties of the generated semigroup and the nonlinear stability of the moving N\'eel wall under small perturbations, in the case of a sufficiently weak external magnetic field. The stability of the static N\'eel wall, which was established in a companion paper [4], plays a key role to obtain the main result., Comment: 40 pages, 4 figures

We obtain strong converse inequalities for the Bernstein polynomials with explicit asymptotic constants. We give different estimation procedures in the central and non-central regions of [0,1]. The main ingredients in our approach are the following: representation of the derivatives of the Bernstein polynomials in terms of the Krawtchouk polynomials, estimates of different inverse moments of various random variables, sharp estimates of both absolute central moments of Bernstein polynomials and the total variation distance between binomial and Poisson distributions, and iterates of the Bernstein polynomials, together with their probabilistic representations., Comment: 18 pages

Understanding what is normal is a key aspect of protecting a domain. Other domains invest heavily in observational science to develop models of normal behavior to better detect anomalies. Recent advances in high performance graph libraries, such as the GraphBLAS, coupled with supercomputers enables processing of the trillions of observations required. We leverage this approach to synthesize low-parameter observational models of anonymized Internet traffic with a high regard for privacy., Comment: Accepted to IEEE HPEC, 7 pages, 6 figures, 1 table, 41 references

Since the advent of mobile robots, obstacle detection has been a topic of great interest. It has also been a subject of study in neuroscience, where flying insects and bats could be considered two of the most interesting cases in terms of vision-based and sound-based mechanisms for obstacle detection, respectively. Currently, many studies focus on vision-based obstacle detection, but not many can be found regarding sound-based obstacle detection. This work focuses on the latter approach, which also makes use of a Spiking Neural Network to exploit the advantages of these architectures and achieve an approach closer to biology. The complete system was tested through a series of experiments that confirm the validity of the spiking architecture for obstacle detection. It is empirically demonstrated that, when the distance between the robot and the obstacle decreases, the output firing rate of the system increases in response as expected, and vice versa. Therefore, there is a direct relation between the two. Furthermore, there is a distance threshold between detectable and undetectable objects which is also empirically measured in this work. An in-depth study on how this system works at low level based on the Inter-Spike Interval concept was performed, which may be useful in the future development of applications based on spiking filters., Comment: 22 pages, 8 figures

We present a novel metric designed, among other applications, to quantify biased behaviors of machine learning models. As its core, the metric consists of a new similarity metric between score distributions that balances both their general shapes and tails' probabilities. In that sense, our proposed metric may be useful in many application areas. Here we focus on and apply it to the operational evaluation of face recognition systems, with special attention to quantifying demographic biases; an application where our metric is especially useful. The topic of demographic bias and fairness in biometric recognition systems has gained major attention in recent years. The usage of these systems has spread in society, raising concerns about the extent to which these systems treat different population groups. A relevant step to prevent and mitigate demographic biases is first to detect and quantify them. Traditionally, two approaches have been studied to quantify differences between population groups in machine learning literature: 1) measuring differences in error rates, and 2) measuring differences in recognition score distributions. Our proposed Comprehensive Equity Index (CEI) trade-offs both approaches combining both errors from distribution tails and general distribution shapes. This new metric is well suited to real-world scenarios, as measured on NIST FRVT evaluations, involving high-performance systems and realistic face databases including a wide range of covariates and demographic groups. We first show the limitations of existing metrics to correctly assess the presence of biases in realistic setups and then propose our new metric to tackle these limitations. We tested the proposed metric with two state-of-the-art models and four widely used databases, showing its capacity to overcome the main flaws of previous bias metrics., Comment: Accepted paper for the 27th International Conference on Pattern Recognition (ICPR) 2024

We introduce Polaris, a network null model for colored multi-graphs that preserves the Joint Color Matrix. Polaris is specifically designed for studying network polarization, where vertices belong to a side in a debate or a partisan group, represented by a vertex color, and relations have different strengths, represented by an integer-valued edge multiplicity. The key feature of Polaris is preserving the Joint Color Matrix (JCM) of the multigraph, which specifies the number of edges connecting vertices of any two given colors. The JCM is the basic property that determines color assortativity, a fundamental aspect in studying homophily and segregation in polarized networks. By using Polaris, network scientists can test whether a phenomenon is entirely explained by the JCM of the observed network or whether other phenomena might be at play. Technically, our null model is an extension of the configuration model: an ensemble of colored multigraphs characterized by the same degree sequence and the same JCM. To sample from this ensemble, we develop a suite of Markov Chain Monte Carlo algorithms, collectively named Polaris-*. It includes Polaris-B, an adaptation of a generic Metropolis-Hastings algorithm, and Polaris-C, a faster, specialized algorithm with higher acceptance probabilities. This new null model and the associated algorithms provide a more nuanced toolset for examining polarization in social networks, thus enabling statistically sound conclusions.

Aims. Using HARPS spectroscopic data obtained by the RedDots campaign, as well as archival data from HARPS and CARMENES, supplemented with ASH2 and T90 photometry, we aim to search for additional planets around the three M dwarfs GJ 832, GJ 674, and Ross 128. We also aim to determine limits on possible undetected, habitable planets. We investigate (i) the reliability of the recovered orbital eccentricities and (ii) the reliability of Bayesian evidence as a diagnostic for selecting the best model. Methods. We employed Markov-chain Monte Carlo, nested sampling, and Gaussian process (GP) analyses to fit a total of 20 different models. We used the residuals to create grids for injection-recovery simulations to obtain detection limits on potentially undiscovered planets. Results. Our refined orbital elements for GJ 832 b, GJ 674 b, and Ross 128 b confirm (GJ 832, GJ 674) or increase (Ross 128) prior eccentricity determinations. No additional planets were found in any of the systems. The detection limits obtained for all three systems are between 30 and 50 cm/s for orbital periods in the range of 1 to 10 000 days. Using N-body simulations, we find that undiscovered secondary planets are unlikely (Ross 128) or incapable (GJ 674) of having caused the observed eccentricities of the known planets. We find that the eccentricity of GJ 832 b is not significantly different from zero. Conclusions. GJ 832 b, GJ 674 b, and Ross 128 b retain their status as hosting lonely and (for the latter two) eccentric planets. Finally, our results show that Bayesian evidence, when used in conjunction with GP, is not a robust diagnostic for selecting the best model in cases of low-activity stars. In such cases, we advise an inspection of the shapes of the posterior distributions and to ensure that relevant simulations are performed to assess the validity of the perceived best model., Comment: Abstract shortened to fit the character limit; 25 pages, 14 Figures, 6 Tables; Appendix available at Zenodo; Accepted for publication by A&A

A localized Zeeman field, intensified at heterostructure interfaces, could play a crucial role in a broad area including spintronics and unconventional superconductors. Conventionally, the generation of a local Zeeman field is achieved through magnetic exchange coupling with a magnetic material. However, magnetic elements often introduce defects, which could weaken or destroy superconductivity. Alternatively, the coupling between a superconductor with strong spin-orbit coupling and a non-magnetic chiral material could serve as a promising approach to generate a spin active interface. In this study, we leverage an interface superconductor, namely induced superconductivity in noble metal surface states, to probe the spin active interface. Our results unveil an enhanced interface Zeeman field, which selectively closes the surface superconducting gap while preserving the bulk superconducting pairing. The chiral material, i.e. trigonal tellurium, also induces Andreev bound states (ABS) exhibiting spin polarization. The field dependence of ABS manifests a substantially enhanced interface Land\'e g-factor (g_eff ~ 12), thereby corroborating the enhanced interface Zeeman energy., Comment: 27 pages, 11 figures

We explore Majorana stellar representation on the Riemann sphere for optical scalar beams and vector fields, leveraging the spin and orbital angular momentum of light. For scalar beams, the star constellations for the Laguerre-Gaussian beam basis reside at the poles of the sphere. For vector fields, these constellations represent well-defined circular polarization. The underlying $su(2)$ symmetry of angular momenta enables populating the sphere through generalized unitary rotations of the basis elements, resulting in optical analogues of Bloch and generalized $su(2)$ coherent states for both scalar beams and vector fields. Constellations for scalar Hermite-Gaussian beams and vector Hermite-Gaussian partial Poincar\'e fields, restricted to linear polarization, occupy the equator of the sphere as a result of these rotations. Additionally, we investigate scalar and vector analogues of cat codes and kings of quantumness, providing insights into the visualization and characterization of complex optical beams and fields with potential applications in optical communications., Comment: 19 pages, 8 figures

We investigate the cosmological dynamics of a homogeneous scalar field non-minimally coupled to torsion gravity, which also interacts with cold dark matter through energy and momentum transfer. The matter and radiation perfect fluids are modeled using the Sorkin-Schutz formalism. We identify scaling regimes of the field during both the radiation and matter eras. Additionally, we discovered a field-dominated scaling attractor; however, it does not exhibit accelerated expansion, making it unsuitable for describing dark energy. Nevertheless, we find two attractor solutions that do exhibit accelerated expansion: one is a quintessence-like fixed point, and the other is a de Sitter fixed point., Comment: 16 pages, 8 figures

As an inflated Hot Jupiter orbiting an early-type primary star in the evolved binary HD 202772 system, HD 202772 A b's presence invites a study of how such a planet forms and evolves. As a prelude to potential atmospheric characterization with the latest generation of observatories, we present a reduction and analysis of eclipse light curve observations of HD 202772 A b acquired with the Spitzer Space Telescope using the 3.6 and 4.5 $\mu$m channels. We find eclipse depths of $680\pm68$ and $1081^{+54}_{-53}$ ppm, respectively, corresponding to day-side effective temperatures of $2130^{+102}_{-91}$ and $2611^{+46}_{-49}$ K. The corresponding Bond albedos are consistent with the distribution of albedos for Hot Jupiters observed with both Spitzer and TESS. The heat redistribution efficiencies consistent with the Bond albedo range predicted by 1-D atmospheric models in radiative-convective equilibrium are $0.71\pm0.10$ and $0.03^{+0.03}_{-0.02}$, respectively, indicating a weak day-night contrast for the former and a strong contrast for the latter. Given this, and the unique environment in which this planet resides, we recommend follow-up observations with JWST to more precisely constrain its atmospheric composition and structure, as well as its host stellar environment, to elucidate if and how the atmospheres of these close-in giants evolve with host stars in binaries past the main sequence., Comment: 17 pages, 7 figures, accepted for publication in AAS Journals

In this correspondence, we analyze the performance of a reconfigurable intelligent surface (RIS)-aided communication system that involves a fluid antenna system (FAS)-enabled receiver. By applying the central limit theorem (CLT), we derive approximate expressions for the system outage probability when the RIS has a large number of elements. Also, we adopt the block-correlation channel model to simplify the outage probability expressions, reducing the computational complexity and shedding light on the impact of the number of ports. Numerical results validate the effectiveness of our analysis, especially in scenarios with a large number of RIS elements.

The interaction between social norms and gender roles prescribes gender-specific behaviors that influence moral judgments. Here, we study how moral judgments are biased by the gender of the protagonist of a story. Using data from r/AITA, a Reddit community with 17 million members who share first-hand experiences seeking community judgment on their behavior, we employ machine learning techniques to match stories describing similar situations that differ only by the protagonist's gender. We find no direct causal effect of the protagonist's gender on the received moral judgments, except for stories about ``friendship and relationships'', where male protagonists receive more negative judgments. Our findings complement existing correlational studies and suggest that gender roles may exert greater influence in specific social contexts. These results have implications for understanding sociological constructs and highlight potential biases in data used to train large language models.

We apply the Newton-Raphson flow tracking controller to aggressive quadrotor flight and demonstrate that it achieves good tracking performance over a suite of benchmark trajectories, beating the native trajectory tracking controller in the popular PX4 Autopilot. The Newton-Raphson flow tracking controller is a recently proposed integrator-type controller that aims to drive to zero the error between a future predicted system output and the reference trajectory. This controller is computationally lightweight, requiring only an imprecise predictor, and achieves guaranteed asymptotic error bounds under certain conditions. We show that these theoretical advantages are realizable on a quadrotor hardware platform. Our experiments are conducted on a Holybrox x500v2 quadrotor using a Pixhawk 6x flight controller and a Rasbperry Pi 4 companion computer which receives location information from an OptiTrack motion capture system and sends input commands through the ROS2 API for the PX4 software stack., Comment: Expanded version of our submission to the American Control Conference 2024

We study active steering protocols for weakly measured qubits in the presence of error channels due to amplitude and phase noise. If the error rate is sufficiently small, the protocol approaches and stabilizes a predesignated pure target state with high fidelity and high purity, and thus implements autonomous state stabilization. We present numerical simulation results for one and two qubits, taking Andreev qubit circuits as example. As function of the error rate, a sharp threshold separates an error-correcting weak-damping regime from a strong-damping regime where the target state cannot be reached anymore. At the threshold, the purity gap closes., Comment: 15 pages, 8 figures

Most of the work on learning action models focus on learning the actions' dynamics from input plans. This allows us to specify the valid plans of a planning task. However, very little work focuses on learning action costs, which in turn allows us to rank the different plans. In this paper we introduce a new problem: that of learning the costs of a set of actions such that a set of input plans are optimal under the resulting planning model. To solve this problem we present $LACFIP^k$, an algorithm to learn action's costs from unlabeled input plans. We provide theoretical and empirical results showing how $LACFIP^k$ can successfully solve this task.

Variable sharing is a fundamental property in the static analysis of logic programs, since it is instrumental for ensuring correctness and increasing precision while inferring many useful program properties. Such properties include modes, determinacy, non-failure, cost, etc. This has motivated significant work on developing abstract domains to improve the precision and performance of sharing analyses. Much of this work has centered around the family of set-sharing domains, because of the high precision they offer. However, this comes at a price: their scalability to a wide set of realistic programs remains challenging and this hinders their wider adoption. In this work, rather than defining new sharing abstract domains, we focus instead on developing techniques which can be incorporated in the analyzers to address aspects that are known to affect the efficiency of these domains, such as the number of variables, without affecting precision. These techniques are inspired in others used in the context of compiler optimizations, such as expression reassociation and variable trimming. We present several such techniques and provide an extensive experimental evaluation of over 1100 program modules taken from both production code and classical benchmarks. This includes the Spectector cache analyzer, the s(CASP) system, the libraries of the Ciao system, the LPdoc documenter, the PLAI analyzer itself, etc. The experimental results are quite encouraging: we have obtained significant speed-ups, and, more importantly, the number of modules that require a timeout was cut in half. As a result, many more programs can be analyzed precisely in reasonable times., Comment: 61 pages, 10 figures, 7 tables, submitted to ICLP 2024

From SAE Level 3 of automation onwards, drivers are allowed to engage in activities that are not directly related to driving during their travel. However, in level 3, a misunderstanding of the capabilities of the system might lead drivers to engage in secondary tasks, which could impair their ability to react to challenging traffic situations. Anticipating driver activity allows for early detection of risky behaviors, to prevent accidents. To be able to predict the driver activity, a Deep Learning network needs to be trained on a dataset. However, the use of datasets based on simulation for training and the migration to real-world data for prediction has proven to be suboptimal. Hence, this paper presents a real-world driver activity dataset, openly accessible on IEEE Dataport, which encompasses various activities that occur in autonomous driving scenarios under various illumination and weather conditions. Results from the training process showed that the dataset provides an excellent benchmark for implementing models for driver activity recognition., Comment: 6 pages

The region of $N\approx Z\approx 40$ has long been associated with strongly deformed nuclear configurations. The presence of this strong deformation was recently confirmed through lifetime measurements in $N\approx Z$ Sr and Zr nuclei. Theoretically, however, these nuclei present a challenge due to the vast valence space required to incorporate all deformation driving interactions. Recent state-of-the-art predictions indicate a near axial prolate deformation for $N=Z$ and $N=Z+2$ nuclei between $N=Z=36$ and $N=Z=40$. In this work we investigate the shores of this island of deformation through a sub-barrier Coulomb excitation study of the $N=Z+4$ nucleus, \textsuperscript{80}Sr. Extracting a spectroscopic quadrupole moment of $Q_s(2^+_1) = 0.45^{+0.83}_{-0.88}$~eb, we find that \textsuperscript{80}Sr is inconsistent with significant axial prolate deformation. This indicates that the predicted region of strong prolate deformation around $N=Z=40$ is tightly constrained to the quartet of nuclei: \textsuperscript{76,78}Sr and \textsuperscript{78,80}Zr., Comment: Submitted to Physics Letters B

We study the wave form emitted by a particle moving along an arbitrary (in general open) geodesic of the Schwarzschild geometry. The mathematical problem can be phrased in terms of quantities in ${\cal N}=2$ supersymmetric gauge theories that can be calculated by using localization and the AGT correspondence. In particular through this mapping, the post-Newtonian expansion of the wave form is expressed as a double instanton sum with rational coefficients that resums all tail contributions into Gamma functions and exponentials. The formulae we obtain are valid for generic values of the orbital quantum numbers $\ell$ and $m$. For $\ell=2,3$ we check explicitly that our results agree with the small mass ratio limit of the wave forms derived in the Multipole Post-Minkowskian and the amplitudes approaches. We show how the so-called tail and tail of tail contributions to the wave form arise in our approach. Finally, we derive a universal formula for the soft limit of the wave form that resums all logarithmic divergent terms of the form $\omega^{n-1} (\log \omega)^n$., Comment: 29 pages

KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be $\sin^2\theta_{23}= 0.51^{+0.04}_{-0.05}$, and $ \Delta m^2_{31} = 2.14^{+0.25}_{-0.35}\times 10^{-3}~\mathrm{eV^2} \cup \{-2.25,-1.76\}\times 10^{-3}~\mathrm{eV^2}$ at 68\% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25., Comment: 25 pages, 10 figures

This work introduces an innovative method for estimating attention levels (cognitive load) using an ensemble of facial analysis techniques applied to webcam videos. Our method is particularly useful, among others, in e-learning applications, so we trained, evaluated, and compared our approach on the mEBAL2 database, a public multi-modal database acquired in an e-learning environment. mEBAL2 comprises data from 60 users who performed 8 different tasks. These tasks varied in difficulty, leading to changes in their cognitive loads. Our approach adapts state-of-the-art facial analysis technologies to quantify the users' cognitive load in the form of high or low attention. Several behavioral signals and physiological processes related to the cognitive load are used, such as eyeblink, heart rate, facial action units, and head pose, among others. Furthermore, we conduct a study to understand which individual features obtain better results, the most efficient combinations, explore local and global features, and how temporary time intervals affect attention level estimation, among other aspects. We find that global facial features are more appropriate for multimodal systems using score-level fusion, particularly as the temporal window increases. On the other hand, local features are more suitable for fusion through neural network training with score-level fusion approaches. Our method outperforms existing state-of-the-art accuracies using the public mEBAL2 benchmark., Comment: Article accepted in the IEEE Access journal. Accessible at https://ieeexplore.ieee.org/document/10633208

We reconsider linear perturbations around general Friedmann - Lemaitre - Robertson - Walker (FLRW) cosmological backgrounds. Exploiting gauge freedom involving only time reparametrizations, we write down classical background solutions analytically, for an arbitrary number of fluid components. We then show that the time evolution of scalar and tensor adiabatic perturbations are governed by Schr\"odinger-like differential equations of generalized Heun type. After recovering known analytic results for a single-component fluid, we discuss more general situations with two and three different fluid components, with special attention to the combination of radiation, matter and vacuum energy, which is supposed to describe the $\Lambda$CDM model. The evolution of linear perturbations of a flat $\Lambda$CDM universe is described by a two-transient model, where the transitions from radiation to matter and matter to vacuum energy are governed by a Heun equation and a Hypergeometric equation, respectively. We discuss an analytic approach to the study of the general case, involving generalized Heun equations, that makes use of (quantum) Seiberg-Witten curves for ${\cal N}=2$ supersymmetric gauge theories and has proven to be very effective in the analysis of Black-Hole, fuzzball and ECO perturbations., Comment: 30 pages, 6 figures, 3 tables

With growing interest in recent years, medical visual question answering (Med-VQA) has rapidly evolved, with multimodal large language models (MLLMs) emerging as an alternative to classical model architectures. Specifically, their ability to add visual information to the input of pre-trained LLMs brings new capabilities for image interpretation. However, simple visual errors cast doubt on the actual visual understanding abilities of these models. To address this, region-based questions have been proposed as a means to assess and enhance actual visual understanding through compositional evaluation. To combine these two perspectives, this paper introduces targeted visual prompting to equip MLLMs with region-based questioning capabilities. By presenting the model with both the isolated region and the region in its context in a customized visual prompt, we show the effectiveness of our method across multiple datasets while comparing it to several baseline models. Our code and data are available at https://github.com/sergiotasconmorales/locvqallm., Comment: Accepted at the MICCAI AMAI Workshop 2024

Higher-order interactions underlie complex phenomena in systems such as biological and artificial neural networks, but their study is challenging due to the lack of tractable standard models. By leveraging the maximum entropy principle in curved statistical manifolds, here we introduce curved neural networks as a class of prototypical models for studying higher-order phenomena. Through exact mean-field descriptions, we show that these curved neural networks implement a self-regulating annealing process that can accelerate memory retrieval, leading to explosive order-disorder phase transitions with multi-stability and hysteresis effects. Moreover, by analytically exploring their memory capacity using the replica trick near ferromagnetic and spin-glass phase boundaries, we demonstrate that these networks enhance memory capacity over the classical associative-memory networks. Overall, the proposed framework provides parsimonious models amenable to analytical study, revealing novel higher-order phenomena in complex network systems.

This study provides an analysis of atmospheric neutrino oscillations at the ESSnuSB far detector facility. The prospects of the two cylindrical Water Cherenkov detectors with a total fiducial mass of 540 kt are investigated over 10 years of data taking in the standard three-flavor oscillation scenario. We present the confidence intervals for the determination of mass ordering, $\theta_{23}$ octant as well as for the precisions on $\sin^2\theta_{23}$ and $|\Delta m_{31}^2|$. It is shown that mass ordering can be resolved by $3\sigma$ CL ($5\sigma$ CL) after 4 years (10 years) regardless of the true neutrino mass ordering. Correspondingly, the wrong $\theta_{23}$ octant could be excluded by $3\sigma$ CL after 4 years (7 years) in the case where the true neutrino mass ordering is normal ordering (inverted ordering). The results presented in this work are complementary to the accelerator neutrino program in the ESSnuSB project., Comment: 13 pages, 6 figures, 2 tables

We study random permutations arising from reduced pipe dreams. Our main model is motivated by Grothendieck polynomials with parameter $\beta=1$ arising in K-theory of the flag variety. The probability weight of a permutation is proportional to the principal specialization (setting all variables to 1) of the corresponding Grothendieck polynomial. By mapping this random permutation to a version of TASEP (Totally Asymmetric Simple Exclusion Process), we describe the limiting permuton and fluctuations around it as the order $n$ of the permutation grows to infinity. The fluctuations are of order $n^{\frac13}$ and have the Tracy-Widom GUE distribution, which places this algebraic (K-theoretic) model into the Kardar-Parisi-Zhang universality class. We also investigate non-reduced pipe dreams and make progress on a recent open problem on the asymptotic number of inversions of the resulting permutation. Inspired by Stanley's question for the maximal value of principal specializations of Schubert polynomials, we resolve the analogous question for $\beta=1$ Grothendieck polynomials, and provide bounds for general $\beta$., Comment: 46 pages; 16 figures; 2 tables. Including C code for generating random permutations and a CSV list of optimal layered permutations for beta=1 Grothendieck polynomials as ancillary files (see the right panel or the full source code)

The AC optimal power flow (AC-OPF) problem is essential for power system operations, but its non-convex nature makes it challenging to solve. A widely used simplification is the linearized DC optimal power flow (DC-OPF) problem, which can be solved to global optimality, but whose optimal solution is always infeasible in the original AC-OPF problem. Recently, neural networks (NN) have been introduced for solving the AC-OPF problem at significantly faster computation times. However, these methods necessitate extensive datasets, are difficult to train, and are often viewed as black boxes, leading to resistance from operators who prefer more transparent and interpretable solutions. In this paper, we introduce a novel learning-based approach that merges simplicity and interpretability, providing a bridge between traditional approximation methods and black-box learning techniques. Our approach not only provides transparency for operators but also achieves competitive accuracy. Numerical results across various power networks demonstrate that our method provides accuracy comparable to, and often surpassing, that of neural networks, particularly when training datasets are limited.

Efficient intravascular access in trauma and critical care significantly impacts patient outcomes. However, the availability of skilled medical personnel in austere environments is often limited. Autonomous robotic ultrasound systems can aid in needle insertion for medication delivery and support non-experts in such tasks. Despite advances in autonomous needle insertion, inaccuracies in vessel segmentation predictions pose risks. Understanding the uncertainty of predictive models in ultrasound imaging is crucial for assessing their reliability. We introduce MSU-Net, a novel multistage approach for training an ensemble of U-Nets to yield accurate ultrasound image segmentation maps. We demonstrate substantial improvements, 18.1% over a single Monte Carlo U-Net, enhancing uncertainty evaluations, model transparency, and trustworthiness. By highlighting areas of model certainty, MSU-Net can guide safe needle insertions, empowering non-experts to accomplish such tasks., Comment: Accepted for the 5th International Workshop of Advances in Simplifying Medical UltraSound (ASMUS), held in conjunction with MICCAI 2024, the 27th International Conference on Medical Image Computing and Computer Assisted Intervention

TOI-6255~b (GJ 4256) is an Earth-sized planet (1.079$\pm0.065$ $R_\oplus$) with an orbital period of only 5.7 hours. With the newly commissioned Keck Planet Finder (KPF) and CARMENES spectrographs, we determined the planet's mass to be 1.44$\pm$0.14 $M_{\oplus}$. The planet is just outside the Roche limit, with $P_{\rm orb}/P_{\rm Roche}$ = 1.13 $\pm0.10$. The strong tidal force likely deforms the planet into a triaxial ellipsoid with a long axis that is $\sim$10\% longer than the short axis. Assuming a reduced stellar tidal quality factor $Q_\star^\prime \approx10^7$, we predict that tidal orbital decay will cause TOI-6255 to reach the Roche limit in roughly 400 Myr. Such tidal disruptions may produce the possible signatures of planet engulfment that have been on stars with anomalously high refractory elemental abundances compared to its conatal binary companion. TOI-6255 b is also a favorable target for searching for star-planet magnetic interactions, which might cause interior melting and hasten orbital decay. TOI-6255 b is a top target (Emission Spectroscopy Metric of about 24) for phase curve observations with the James Webb Space Telescope., Comment: 18 pages, 7 figures, 5 tables, accepted to AAS Journals. The first RV mass measurement from the Keck Planet Finder

The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k=0.5.

Multi-Higgs boson interactions with massive gauge bosons are known to be tell-tale probes of the vacuum manifold of electroweak symmetry breaking. Phenomenologically, a precise determination of these parameters is hampered through increasingly rare processes at the presently available energy frontier provided by the Large Hadron Collider. Contact interactions of three Higgs bosons with the $W$ and $Z$ bosons seem currently well out of experimental reach due to an irrelevant SM production cross section. From a theoretical perspective, in perturbative extensions of the SM such interactions are suppressed by weak loops and further diluted in a priori sensitive processes like weak boson fusion (WBF) when they admit a dimension-six Standard Model Effective Field Theory description. In this work, we identify scenarios that can indeed lead to large, and perhaps even observable modifications of WBF triple Higgs production most directly parametrised by Higgs Effective Field Theory. We critically analyse these enhancements at the LHC and future colliders from the perspective of unitarity and demonstrate the radiative stability of such analyses under QCD corrections at hadron colliders. In passing, we also comment on the expected sensitivity at non-hadron colliders., Comment: 13 pages, 9 figures, 3 tables