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We propose a natural intrinsic extension of the ridge regression from Euclidean spaces to general manifolds, which relies on Riemannian least-squares fitting, empirical covariance, and Mahalanobis distance. We utilize it for time-series prediction and apply the approach to forecast hurricane tracks and their wind speeds.

We study the interplay between topology and disorder in the disodered Kitaev model with long range pairing, connected to two metallic leads exchanging particles with external Lindblad baths. We study how the phase diagram of the system is affected by the disorder by monitoring the subgap modes at increasing disorder, by computing the current flowing across the superconductor at a finite voltage bias between the baths, and by looking at the normal, single particle lead correlations across the Kitaev long range chain. In particular, we evidence the reentrant behavior of the massive, topological phase at limited values of the disorder strength, that has no analog in the disordered, short range pairing Kitaev model, thus rising the question of whether it is possible to recover a disorder triggered direct transition between the massive and the short range topological phase of the long range pairing Kitaev model., Comment: 16 pages, 10 .png figures

One long standing tension between theory and observations of Type I X-ray burst is the accretion rate at which the burst disappear due to stabilization of the nuclear burning that powers them. This is observed to happen at roughly one third of the theoretical expectations. Various solutions have been proposed, the most notable of which is the addition of a yet unknown source of heat in the upper layers of the crust, below the burning envelope. In this paper we ran several simulations using the 1D code MESA to explore the impact of opacity on the threshold mass accretion rate after which the bursts disappear, finding that a higher than expected opacity in the less dense layers near the surface has a stabilizing effect., Comment: 11 pages, 9 figures

This study systematically analyzes the vulnerability of 36 large language models (LLMs) to various prompt injection attacks, a technique that leverages carefully crafted prompts to elicit malicious LLM behavior. Across 144 prompt injection tests, we observed a strong correlation between model parameters and vulnerability, with statistical analyses, such as logistic regression and random forest feature analysis, indicating that parameter size and architecture significantly influence susceptibility. Results revealed that 56 percent of tests led to successful prompt injections, emphasizing widespread vulnerability across various parameter sizes, with clustering analysis identifying distinct vulnerability profiles associated with specific model configurations. Additionally, our analysis uncovered correlations between certain prompt injection techniques, suggesting potential overlaps in vulnerabilities. These findings underscore the urgent need for robust, multi-layered defenses in LLMs deployed across critical infrastructure and sensitive industries. Successful prompt injection attacks could result in severe consequences, including data breaches, unauthorized access, or misinformation. Future research should explore multilingual and multi-step defenses alongside adaptive mitigation strategies to strengthen LLM security in diverse, real-world environments.

The principle of holography of information states that all information available in the bulk of asymptotically flat spacetime is also available near its boundary at spatial infinity. However, physical observers never have access to spatial infinity. Therefore, we ask the question: ``Is information contained in a ball of finite radius also holographic in nature?". Phrased differently, we ask whether correlation functions on the boundary of the ball capture all the information of all correlators in the bulk of the ball. In this work, we answer this question in the affirmative within the confines of linearised quantum gravity for generic low-energy (free Klein-Gordon) states in flat space created by the action of unitaries on the vacuum to leading order in a certain perturbative parameter. For an infinite sub-class of these states, our results are perturbatively exact and valid to all orders in this same parameter. Unlike at infinity, massive and massless fields can be captured within the same framework., Comment: 20 pages + appendices

The precision measurement of the tritium $\beta$-decay spectrum performed by the KATRIN experiment provides a unique way to search for general neutrino interactions (GNI). All theoretical allowed GNI terms involving neutrinos are incorporated into a low-energy effective field theory, and can be identified by specific signatures in the measured tritium $\beta$-spectrum. In this paper an effective description of the impact of GNI on the $\beta$-spectrum is formulated and the first constraints on the effective GNI parameters are derived based on the 4 million electrons collected in the second measurement campaign of KATRIN in 2019. In addition, constraints on selected types of interactions are investigated, thereby exploring the potential of KATRIN to search for more specific new physics cases, including a right-handed W boson, a charged Higgs or leptoquarks.

Co-design optimization strategies usually rely on simplified robot models extracted from CAD. While these models are useful for optimizing geometrical and inertial parameters for robot control, they might overlook important details essential for prototyping the optimized mechanical design. For instance, they may not account for mechanical stresses exerted on the optimized geometries and the complexity of assembly-level design. In this paper, we introduce a co-design framework aimed at improving both the control performance and mechanical design of our robot. Specifically, we identify the robot links that significantly influence control performance. The geometric characteristics of these links are parameterized and optimized using a multi-objective evolutionary algorithm to achieve optimal control performance. Additionally, an automated Finite Element Method (FEM) analysis is integrated into the framework to filter solutions not satisfying the required structural safety margin. We validate the framework by applying it to enhance the mechanical design for flight performance of the jet-powered humanoid robot iRonCub., Comment: IROS 2024

We propose a self-supervised approach for visual robot detection and heading estimation by learning to estimate the states (OFF or ON) of four independent robot-mounted LEDs. Experimental results show a median image-space position error of 14 px and relative heading MAE of 17 degrees, versus a supervised upperbound scoring 10 px and 8 degrees, respectively., Comment: Accepted and presented at ICRA@40

An important aspect of summarizing videos is understanding the temporal context behind each part of the video to grasp what is and is not important. Video summarization models have in recent years modeled spatio-temporal relationships to represent this information. These models achieved state-of-the-art correlation scores on important benchmark datasets. However, what has not been reviewed is whether spatio-temporal relationships are even required to achieve state-of-the-art results. Previous work in activity recognition has found biases, by prioritizing static cues such as scenes or objects, over motion information. In this paper we inquire if similar spurious relationships might influence the task of video summarization. To do so, we analyse the role that temporal information plays on existing benchmark datasets. We first estimate a baseline with temporally invariant models to see how well such models rank on benchmark datasets (TVSum and SumMe). We then disrupt the temporal order of the videos to investigate the impact it has on existing state-of-the-art models. One of our findings is that the temporally invariant models achieve competitive correlation scores that are close to the human baselines on the TVSum dataset. We also demonstrate that existing models are not affected by temporal perturbations. Furthermore, with certain disruption strategies that shuffle fixed time segments, we can actually improve their correlation scores. With these results, we find that spatio-temporal relationship play a minor role and we raise the question whether these benchmarks adequately model the task of video summarization. Code available at: https://github.com/AashGan/TemporalPerturbSum, Comment: Accepted for presentation at AEQUITAS workshop, Co-located with ECAI 2024

Limited transparency in targeted advertising on online content delivery platforms can breed mistrust for both viewers (of the content and ads) and advertisers. This user study (n=864) explores how explanations for targeted ads can bridge this gap, fostering transparency for two of the key stakeholders. We explore participants' preferences for explanations and allow them to tailor the content and format. Acting as viewers or advertisers, participants chose which details about viewing habits and user data to include in explanations. Participants expressed concerns not only about the inclusion of personal data in explanations but also about the use of it in ad placing. Surprisingly, we found no significant differences in the features selected by the two groups to be included in the explanations. Furthermore, both groups showed overall high satisfaction, while "advertisers" perceived the explanations as significantly more transparent than "viewers". Additionally, we observed significant variations in the use of personal data and the features presented in explanations between the two phases of the experiment. This study also provided insights into participants' preferences for how explanations are presented and their assumptions regarding advertising practices and data usage. This research broadens our understanding of transparent advertising practices by highlighting the unique dynamics between viewers and advertisers on online platforms, and suggesting that viewers' priorities should be considered in the process of ad placement and creation of explanations., Comment: Pre-print for IntRS'24@RecSys: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, October 18, 2024, Bari (Italy)

The increased use of information retrieval in recruitment, primarily through job recommender systems (JRSs), can have a large impact on job seekers, recruiters, and companies. As a result, such systems have been determined to be high-risk in recent legislature. This requires JRSs to be trustworthy and transparent, allowing stakeholders to understand why specific recommendations were made. To fulfill this requirement, the stakeholders' exact preferences and needs need to be determined. To do so, we evaluated an explainable job recommender system using a realistic, task-based, mixed-design user study (n=30) in which stakeholders had to make decisions based on the model's explanations. This mixed-methods evaluation consisted of two objective metrics - correctness and efficiency, along with three subjective metrics - trust, transparency, and usefulness. These metrics were evaluated twice per participant, once using real explanations and once using random explanations. The study included a qualitative analysis following a think-aloud protocol while performing tasks adapted to each stakeholder group. We find that providing stakeholders with real explanations does not significantly improve decision-making speed and accuracy. Our results showed a non-significant trend for the real explanations to outperform the random ones on perceived trust, usefulness, and transparency of the system for all stakeholder types. We determine that stakeholders benefit more from interacting with explanations as decision support capable of providing healthy friction, rather than as previously-assumed persuasive tools., Comment: 14 pages, 3 figures, to be published in ACM RecSys in HR '24: 4th Workshop on Recommender Systems for Human Resources

We propose a robust adaptive online synchronization method for leader-follower networks of nonlinear heterogeneous agents with system uncertainties and input magnitude saturation. Synchronization is achieved using a Distributed input Magnitude Saturation Adaptive Control with Reinforcement Learning (DMSAC-RL), which improves the empirical performance of policies trained on off-the-shelf models using Reinforcement Learning (RL) strategies. The leader observes the performance of a reference model, and followers observe the states and actions of the agents they are connected to, but not the reference model. The leader and followers may differ from the reference model in which the RL control policy was trained. DMSAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of augmented input to solve the distributed control problem, including input-matched uncertainty parameters. We show that the synchronization error of the heterogeneous network is Uniformly Ultimately Bounded (UUB). Numerical analysis of a network of Multiple Input Multiple Output (MIMO) systems supports our theoretical findings., Comment: 30 pages, 12 figures, conference paper

The optical manipulation of magnetism enabled by rare earth-transition metal ferrimagnets holds the promise of ultrafast, energy efficient spintronic technologies. This work investigates laser-induced picosecond spin currents generated by ferrimagnetic GdCo via terahertz emission spectroscopy. A suppression of the THz emission and spin current is observed at magnetization compensation when varying the temperature or alloy composition in the presence of a magnetic field. It is demonstrated that this is due to the formation of domains in the GdCo equilibrium magnetic configuration. Without an applied magnetic field, the picosecond spin current persists at the compensation point. The experimental findings support the model for THz spin current generation based on transport of hot spin-polarized electrons, which is dominated by the Co sublattice at room temperature. Only at low temperature a comparable contribution from Gd is detected but with slower dynamics. Finally, spectral analysis reveals a blueshift of the THz emission related to the formation of magnetic domains close to magnetization compensation.

HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder is a space-borne mission based on a constellation of six nano-satellites flying in a low-Earth orbit (LEO). The 3U CubeSats, to be launched in early 2025, host miniaturized instruments with a hybrid Silicon Drift Detector/GAGG:Ce scintillator photodetector system, sensitive to X-rays and gamma-rays in a large energy band. HERMES will operate in conjunction with Australian Space Industry Responsive Intelligent Thermal (SpIRIT) 6U CubeSat, launched in December 2023. HERMES will probe the temporal emission of bright high-energy transients such as Gamma-Ray Bursts (GRBs), ensuring a fast transient localization in a field of view of several steradians exploiting the triangulation technique. HERMES intrinsically modular transient monitoring experiment represents a keystone capability to complement the next generation of gravitational wave experiments. In this paper we outline the scientific case, development and programmatic status of the mission, Comment: 13 pages, 16 figures. Proceedings of SPIE "Astronomical Telescopes and Instrumentation" 2024. arXiv admin note: text overlap with arXiv:2210.13866

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after five years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN's main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous $^\mathrm{83m}$Kr, which enables the neutrino-mass measurements in the SAP configuration., Comment: 19 pages, 11 figures

The quasiparticle density observed in low-temperature superconducting circuits is several orders of magnitude higher than the value expected at thermal equilibrium. The tunneling of this excess of quasiparticles across Josephson junctions is recognized as one of the main loss and decoherence mechanisms in superconducting qubits. Here we propose an additional loss mechanism arising from nonequilibrium quasiparticle densities: Ohmic loss due to quasiparticles residing in superconducting wires away from the junctions. Our theory leverages the recent experimental demonstration that the excess quasiparticles are in quasiequilibrium [T. Connolly et al., $\href{https://doi.org/10.1103/PhysRevLett.132.217001}{Phys. Rev. Lett. {\bf 132}, 217001 (2024)}$] and uses a generalized fluctuation-dissipation theorem to predict the amount of charge and flux noise generated by them. We show that the resulting charge noise can be larger than dielectric loss due to amorphous two-level systems at frequencies in the MHz range, and find a logarithmic-in-frequency ``nearly white" contribution to flux noise that is comparable to the flux noise observed in experiments. This shows that wire-resident quasiparticles are a universal source of loss and decoherence even when the quasiparticles are far away from Josephson junctions., Comment: 7 pages, 3 figures

We consider the problem of training a fully convolutional network to estimate the relative 6D pose of a robot given a camera image, when the robot is equipped with independent controllable LEDs placed in different parts of its body. The training data is composed by few (or zero) images labeled with a ground truth relative pose and many images labeled only with the true state (\textsc{on} or \textsc{off}) of each of the peer LEDs. The former data is expensive to acquire, requiring external infrastructure for tracking the two robots; the latter is cheap as it can be acquired by two unsupervised robots moving randomly and toggling their LEDs while sharing the true LED states via radio. Training with the latter dataset on estimating the LEDs' state of the peer robot (\emph{pretext task}) promotes learning the relative localization task (\emph{end task}). Experiments on real-world data acquired by two autonomous wheeled robots show that a model trained only on the pretext task successfully learns to localize a peer robot on the image plane; fine-tuning such model on the end task with few labeled images yields statistically significant improvements in 6D relative pose estimation with respect to baselines that do not use pretext-task pre-training, and alternative approaches. Estimating the state of multiple independent LEDs promotes learning to estimate relative heading. The approach works even when a large fraction of training images do not include the peer robot and generalizes well to unseen environments., Comment: Accepted at International Conference on Intelligent Robots and Systems (IROS) 2024

The fact that neutrinos carry a non-vanishing rest mass is evidence of physics beyond the Standard Model of elementary particles. Their absolute mass bears important relevance from particle physics to cosmology. In this work, we report on the search for the effective electron antineutrino mass with the KATRIN experiment. KATRIN performs precision spectroscopy of the tritium $\beta$-decay close to the kinematic endpoint. Based on the first five neutrino-mass measurement campaigns, we derive a best-fit value of $m_\nu^{2} = {-0.14^{+0.13}_{-0.15}}~\mathrm{eV^2}$, resulting in an upper limit of $m_\nu < {0.45}~\mathrm{eV}$ at 90 % confidence level. With six times the statistics of previous data sets, amounting to 36 million electrons collected in 259 measurement days, a substantial reduction of the background level and improved systematic uncertainties, this result tightens KATRIN's previous bound by a factor of almost two., Comment: 61 pages, 20 figures, 2 tables

We generalize the classical thermal Mpemba effect (where an initially hot system relaxes faster to the final equilibrium state than a cold one) to open quantum systems coupled to several reservoirs. We show that, in general, two different types of quantum Mpemba effects are possible. They may be distinguished by quantum state tomography. However, the existence of a quantum Mpemba effect (without determining the type) can already be established by measuring simpler observables such as currents or energies. We illustrate our general results for the experimentally feasible case of an interacting two-site Kitaev model coupled to two metallic leads., Comment: 13 pages, 11 figures, accepted in Physical Review Letters

Gamma Ray Bursts (GRBs) bridge relativistic astrophysics and multi-messenger astronomy. Space-based gamma/X-ray wide field detectors have proven essential to detect and localize the highly variable GRB prompt emission, which is also a counterpart of gravitational wave events. We study the capabilities to detect long and short GRBs by the High Energy Rapid Modular Ensemble of Satellites (HERMES) Pathfinder (HP) and SpIRIT, namely a swarm of six 3U CubeSats to be launched in early 2025, and a 6U CubeSat launched on December 1st 2023. We also study the capabilities of two advanced configurations of swarms of >8 satellites with improved detector performances (HERMES Constellations). The HERMES detectors, sensitive down to ~2-3 keV, will be able to detect faint/soft GRBs which comprise X-ray flashes and high redshift bursts. By combining state-of-the-art long and short GRB population models with a description of the single module performance, we estimate that HP will detect ~195^{+22}_{-21} long GRBs (3.4^{+0.3}_{-0.8} at redshift z>6) and ~19^{+5}_{-3} short GRBs per year. The larger HERMES Constellations under study can detect between ~1300 and ~3000 long GRBs per year and between ~160 and ~400 short GRBs per year, depending on the chosen configuration, with a rate of long GRBs above z>6 between 30 and 75 per year. Finally, we explore the capabilities of HERMES to detect short GRBs as electromagnetic counterparts of binary neutron star (BNS) mergers detected as gravitational signals by current and future ground-based interferometers. Under the assumption that the GRB jets are structured, we estimate that HP can provide up to 1 (14) yr^{-1} joint detections during the fifth LIGO-Virgo-KAGRA observing run (Einstein Telescope single triangle 10 km arm configuration). These numbers become 4 (100) yr^{-1}, respectively, for the HERMES Constellation configuration., Comment: 13 pages, 6 figures, 4 tabels. Accepted for publication by Astronomy & Astrophysics

Sterile neutrinos are hypothetical particles in the minimal extension of the Standard Model of Particle Physics. They could be viable dark matter candidates if they have a mass in the keV range. The Karlsruhe tritium neutrino (KATRIN) experiment, extended with a silicon drift detector focal plane array (TRISTAN), has the potential to search for keV-scale sterile neutrinos by measuring the kinematics of the tritium $\beta$-decay. The collaboration targets a sensitivity of $10^{-6}$ on the mixing amplitude $\sin^2{\Theta}$. For this challenging target, a precise understanding of the detector response is necessary. In this work, we report on the characterization of electron backscattering from the detector surface, which is one of the main effects that influence the shape of the observed energy spectrum. Measurements were performed with a tandem silicon drift detector system and a custom-designed electron source. The measured detector response and backscattering probability are in good agreement with dedicated backscattering simulations using the Geant4 simulation toolkit.

The ASPECT-BET (An sdd-SPECTrometer for BETa decay studies) project aims to develop a novel technique for the precise measurement of forbidden $\beta$ spectra in the 10 keV - 1 MeV range. This technique uses a Silicon Drift Detector (SDD) as the main spectrometer, surrounded, if necessary, by a veto system to reject events with only partial energy deposition in the SDD. Accurate knowledge of the spectrometer's response to electrons is essential to reconstruct the theoretical shape of the $\beta$ spectrum. To compute this response, GEANT4 simulations optimized for low-energy electron interactions are used. In this article, we present the performance of these simulations in reconstructing the electron spectra, measured with SDDs, of a $^{109}$Cd monochromatic source, both in vacuum and in air. The allowed $\beta$ spectrum of a $^{14}$C source is also measured and analyzed, and it is shown that the experimental shape factor commonly used in the literature to reconstruct the measured spectrum is not necessary to explain the spectrum.

We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well. The deep learning framework incorporates the open-source CFD code OpenFOAM, resulting in an end-to-end differentiable model. We automatically differentiate the CFD physics using a discrete adjoint code version. We present a fast communication method between TensorFlow (Python) and OpenFOAM (c++) that accelerates the training process. We applied the model to the flow past a square cylinder problem, reducing the error from 120% to 25% in the velocity for simulations inside the training distribution compared to the traditional solver using an x8 coarser mesh. For simulations outside the training distribution, the error reduction in the velocities was about 50%. The training is affordable in terms of time and data samples since the architecture exploits the local features of the physics.

We compute the flux of relic neutrino background (R$\nu$B) up-scattered by ultra-high-energy (UHE) cosmic rays (CRs) in clusters that act as CR-reservoirs. The long trapping times of UHECRs make this flux larger than that of R$\nu$B up-scattered by UHECRs on their way to Earth, which we also compute. We find that IceCube excludes R$\nu$B weighted overdensities larger than $10^{10}$ in clusters, and that PUEO, RNO-G, GRAND and IceCube-Gen2 will test values down to $10^{8}$. Our treatment incorporates the momentum transfer dependence of the neutrino-nucleus cross section, deep inelastic scattering, a mixed UHECR composition, and flavour information on the up-scattered R$\nu$B fluxes for both cases of neutrino mass spectrum with normal and inverted ordering, providing new handles to possibly disentangle the up-scattered R$\nu$B from cosmogenic neutrinos., Comment: 5 pages + appendices, 5 figures, 1 table

An antiferromagnet emits spin currents when time-reversal symmetry is broken. This is typically achieved by applying an external magnetic field below and above the spin-flop transition or by optical pumping. In this work we apply optical pump-THz emission spectroscopy to study picosecond spin pumping from metallic FeRh as a function of temperature. Intriguingly we find that in the low-temperature antiferromagnetic phase the laser pulse induces a large and coherent spin pumping, while not crossing into the ferromagnetic phase. With temperature and magnetic field dependent measurements combined with atomistic spin dynamics simulations we show that the antiferromagnetic spin-lattice is destabilised by the combined action of optical pumping and picosecond spin-biasing by the conduction electron population, which results in spin accumulation. We propose that the amplitude of the effect is inherent to the nature of FeRh, particularly the Rh atoms and their high spin susceptibility. We believe that the principles shown here could be used to produce more effective spin current emitters. Our results also corroborate the work of others showing that the magnetic phase transition begins on a very fast picosecond timescale, but this timescale is often hidden by measurements which are confounded by the slower domain dynamics.

We provide a theoretical framework to describe the quantum many-body dynamics of Andreev states in Josephson junctions with spin-orbit coupling and a magnetic Zeeman field. In such cases, employing a doubled Nambu spinor description is technically advantageous but one then has to be careful to avoid double-counting problems. By deriving the Lindblad master equation in the socalled excitation picture, we show that a physically consistent many-body theory free from doublecounting problems follows. We apply our formalism to a study of dynamical parity stabilization of the Andreev sector at intermediate times after an initial microwave pulse, in particular addressing the combined effects of spin-orbit coupling and Zeeman field., Comment: 18 pages, 11 figures

The TRISTAN detector is a new detector for electron spectroscopy at the Karlsruhe Tritium Neutrino (KATRIN) experiment. The semiconductor detector utilizes the silicon drift detector technology and will enable the precise measurement of the entire tritium beta decay electron spectrum. Thus, a significant fraction of the parameter space of potential neutrino mass eigenstates in the keV-mass regime can be probed. We developed a custom electron gun based on the effect of thermionic emission to characterize the TRISTAN detector modules with mono-energetic electrons before installation into the KATRIN beamline. The electron gun provides an electron beam with up to 25 keV kinetic energy and an electron rate in the order of 1E5 electrons per second. This manuscript gives an overview of the design and commissioning of the electron gun. In addition, we will shortly discuss a first measurement with the electron gun to characterize the electron response of the TRISTAN detector.

Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonists (GLP-1 RA), a market expected to grow exponentially to $133.5 billion USD by 2030. Using a Named Entity Recognition (NER) model, our method successfully detected 21 potential ASEs overlooked upon FDA approval, including irritability and numbness. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed drugs, leveraging cutting-edge AI-driven social media analytics. It can increase the safety of new drugs in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks., Comment: 19 pages, 7 figures, 3 tables, 1 Appendix table

We present a new cosmologically motivated mock redshift survey of the Dusty Star-Forming Galaxy population. Our mock survey is based on the Bolshoi-Planck dark-matter halo simulation and covers an area of 5.3 sq. degree. Using a semi-empirical approach, we generate a light cone and populate the dark-matter haloes with galaxies. Infrared properties are assigned to the galaxies based on theoretical and empirical relations from the literature. Additionally, background galaxies are gravitationally lensed by dark-matter haloes along the line-of-sight assuming a point-mass model approximation. We characterize the mock survey by measuring the star formation rate density, integrated number counts, redshift distribution, and infrared luminosity function. When compared with single-dish and interferometric observations, the predictions from our mock survey closely follow the compiled results from the literature. We have also directed this study towards characterizing one of the extragalactic legacy surveys to be observed with the TolTEC camera at the Large Millimeter Telescope: the 0.8 sq. degree Ultra Deep Survey, with expected depths of 0.025, 0.018 and 0.012 mJy beam$^{-1}$ at 1.1, 1.4 and 2.0 mm. Exploiting the clustering information in our mock survey, we investigate its impact on the effect of flux boosting by the fainter population of dusty galaxies, finding that clustering can increase the median boosting by 0.5 per cent at 1.1 mm, 0.8 per cent at 1.4 mm and, 2.0 per cent at 2.0 mm, and with higher dispersion., Comment: 17 pages, 12 figures

In this work we model stationary neutron star envelopes at high accretion rates and describe our new code for such studies. As a first step we put special emphasis on the rp-process which results in the synthesis of heavy elements and study in detail how this synthesis depends on the mass accretion rate and the chemical composition of the accreted matter. We show that at very low accretion rate, $\dot{M} \sim 0.01 \dot{M}_{\text{Edd}}$, mostly low mass ($A\leq$ 24) elements are synthesized with a few heavier ones below the $^{40}$Ca bottleneck. However, once $\dot{M}$ is above ${\buildrel \sim \over >} 0.1 \dot{M}_{\text{Edd}}$ this bottleneck is surpassed and nuclei in the iron peak region ($A\sim$ 56) are abundantly produced. At higher mass accretion rates progressively heavier nuclei are generated, reaching $A \sim 70$ at $\dot{M}_{\text{Edd}}$ and $A \sim 90$ at $5 \dot{M}_{\text{Edd}}$. We find that when the rp-process is efficient, the nucleosynthesis it generates is independent of the accreted abundance of CNO elements as these are directly and copiously generated once the $3\alpha$-reaction is operating. We also explore the efficiency of the rp-process under variations of the relative abundances of H and He. Simultaneously, we put special emphasis on the density profiles of the energy generation rate particularly at high density beyond the hydrogen exhaustion point. Our results are of importance for the study of neutron stars in systems in which X-ray bursts are absent but are also of relevance for other systems in describing the low density region, mostly below $10^6$ g cm\mmm, inbetween bursts., Comment: 16 pages, 11 figures

Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms' performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems' performance. This manuscript describes an evaluation methodology for statistically analyzing competition results and competition. The methodology is designed to be universally applicable; however, it is illustrated using eight natural language competitions as case studies involving classification and regression problems. The proposed methodology offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals. Furthermore, we introduce metrics that allow organizers to assess the difficulty of competitions. Our analysis shows the potential usefulness of our methodology for effectively evaluating competition results.

Demonstrating the non-Abelian Ising anyon statistics of Majorana zero modes in a physical platform still represents a major open challenge in physics. We here show that the linear low-frequency charge conductance of a Majorana interferometer containing a floating superconducting island can reveal the topological spin of quantum edge vortices. The latter are associated with chiral Majorana fermion edge modes and represent "flying" Ising anyons. We describe possible device implementations and outline how to detect non-Abelian anyon braiding through AC conductance measurements., Comment: 11 pages, 6 figures, accepted in Physical Review Letters

We propose a novel self-supervised approach for learning to visually localize robots equipped with controllable LEDs. We rely on a few training samples labeled with position ground truth and many training samples in which only the LED state is known, whose collection is cheap. We show that using LED state prediction as a pretext task significantly helps to learn the visual localization end task. The resulting model does not require knowledge of LED states during inference. We instantiate the approach to visual relative localization of nano-quadrotors: experimental results show that using our pretext task significantly improves localization accuracy (from 68.3% to 76.2%) and outperforms alternative strategies, such as a supervised baseline, model pre-training, and an autoencoding pretext task. We deploy our model aboard a 27-g Crazyflie nano-drone, running at 21 fps, in a position-tracking task of a peer nano-drone. Our approach, relying on position labels for only 300 images, yields a mean tracking error of 4.2 cm versus 11.9 cm of a supervised baseline model trained without our pretext task. Videos and code of the proposed approach are available at https://github.com/idsia-robotics/leds-as-pretext