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In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's performance when the obstacles form a narrow gap and the robot is a black-box (i.e. the dynamics are not known analytically, but interacting with the system is cheap). To address this challenge, this paper presents NeuralPARC. The method extends the authors' prior Piecewise Affine Reach-avoid Computation (PARC) method to systems modeled by rectified linear unit (ReLU) neural networks, which are trained to represent parameterized trajectory data demonstrated by the robot. NeuralPARC computes the reachable set of the network while accounting for modeling error, and returns a set of states and parameters with which the black-box system is guaranteed to reach the goal and avoid obstacles. Through numerical experiments, NeuralPARC is shown to outperform PARC in generating provably-safe extreme vehicle drift parking maneuvers, as well as enabling safety on an autonomous surface vehicle (ASV) subjected to large disturbances and controlled by a deep reinforcement learning (RL) policy., Comment: This work has been submitted for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Dissipative tunneling remains a cornerstone effect in quantum mechanics. In chemistry, it plays a crucial role in governing the rates of chemical reactions, often modeled as the motion along the reaction coordinate from one potential well to another. The relative positions of energy levels in these wells strongly influences the reaction dynamics. Chemical research will benefit from a fully controllable, asymmetric double-well equipped with precise measurement capabilities of the tunneling rates. In this paper, we show that a continuously driven Kerr parametric oscillator with a third order non-linearity can be operated in the quantum regime to create a fully tunable asymmetric double-well. Our experiment leverages a low-noise, all-microwave control system with a high-efficiency readout of the which-well information. We explore the reaction rates across the landscape of tunneling resonances in parameter space. We uncover two new and counter-intuitive effects: (i) a weak asymmetry can significantly decrease the activation rates, even though the well in which the system is initialized is made shallower, and (ii) the width of the tunneling resonances alternates between narrow and broad lines as a function of the well depth and asymmetry. We predict by numerical simulations that both effects will also manifest themselves in ordinary chemical double-well systems in the quantum regime. Our work paves the way for analog molecule simulators based on quantum superconducting circuits.

Chemical reactions are commonly described by the reactive flux transferring population from reactants to products across a double-well free energy barrier. Dynamics often involves barrier recrossing and quantum effects like tunneling, zero-point energy motion and interference, which traditional rate theories, such as transition-state theory, do not consider. In this study, we investigate the feasibility of simulating reaction dynamics using a parametrically driven bosonic superconducting Kerr-cat device. This approach provides control over parameters defining the double-well free energy profile, as well as external factors like temperature and the coupling strength between the reaction coordinate and the thermal bath of non-reactive degrees of freedom. We demonstrate the effectiveness of this protocol by showing that the dynamics of proton transfer reactions in prototypical benchmark model systems, such as hydrogen bonded dimers of malonaldehyde and DNA base pairs, could be accurately simulated on currently accessible Kerr-cat devices.

Plastic waste in aquatic environments poses severe risks to marine life and human health. Autonomous robots can be utilized to collect floating waste, but they require accurate object identification capability. While deep learning has been widely used as a powerful tool for this task, its performance is significantly limited by outdoor light conditions and water surface reflection. Light polarization, abundant in such environments yet invisible to the human eye, can be captured by modern sensors to significantly improve litter detection accuracy on water surfaces. With this goal in mind, we introduce PoTATO, a dataset containing 12,380 labeled plastic bottles and rich polarimetric information. We demonstrate under which conditions polarization can enhance object detection and, by providing raw image data, we offer an opportunity for the research community to explore novel approaches and push the boundaries of state-of-the-art object detection algorithms even further. Code and data are publicly available at https://github.com/luisfelipewb/ PoTATO/tree/eccv2024., Comment: ECCV24 TRICKY workshop, Sep 2024, Milano (Italy), Italy

Unmanned aerial vehicles (UAV) have been recognized as a versatile platform for a wide range of services. During the flight, these vehicles must avoid collisions to operate safely. In this way, they demand to keep spatial awareness, i.e., to know others in their coverage area. However, mobility and positioning aspects hamper building UAV network infrastructure to support reliable basic services. Thus, such vehicles call for a location service with up-to-date information resilient to false location injection threats. This work proposes FlySafe, a resilient UAVs location sharing service that employs opportunistic approaches to deliver UAVs' location. FlySafe takes into account the freshness of UAVs' location to maintain their spatial awareness. Further, it counts on the age of the UAV's location information to trigger device discovery. Simulation results showed that FlySafe achieved spatial awareness up to 94.15% of UAV operations, being resilient to~false locations injected in the network. Moreover, the accuracy in device discovery achieved 94.53% with a location error of less than 2 m., Comment: This work has been submitted to the Elsevier (Pervasive and Mobile Computing Journal) for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

We classify the hypersurfaces of $\mathbb{Q}^3\times\mathbb{R}$ with three distinct constant principal curvatures, where $\varepsilon \in \{1,-1\}$ and $\mathbb{Q}^3$ denotes the unit sphere $\mathbb{S}^3$ if $\varepsilon = 1$, whereas it denotes the hyperbolic space $\mathbb{H}^3$ if $\varepsilon = -1$. We show that they are cylinders over isoparametric surfaces in $\mathbb{Q}^3$, filling an intriguing gap in the existing literature. We also prove that the hypersurfaces with constant principal curvatures of $\mathbb{Q}^3\times\mathbb{R}$ are isoparametric. Furthermore, we provide the complete classification of the extrinsically homogeneous hypersurfaces in $\mathbb{Q}^3\times\mathbb{R}$.

The initialization of quantum states or Quantum State Preparation (QSP) is a basic subroutine in quantum algorithms. In the worst case, general QSP algorithms are expensive due to the application of multi-controlled gates required to build them. Here, we propose an algorithm that detects whether a given quantum state can be factored into substates, increasing the efficiency of compiling the QSP circuit when we initialize states with some level of disentanglement. The simplification is done by eliminating controls of quantum multiplexers, significantly reducing circuit depth and the number of CNOT gates with a better execution and compilation time than the previous QSP algorithms. Considering efficiency in terms of depth and number of CNOT gates, our method is competitive with the methods in the literature. However, when it comes to run-time and compilation efficiency, our result is significantly better, and the experiments show that by increasing the number of qubits, the gap between the temporal efficiency of the methods increases.

We develop a systematic large-$N$ expansion based on the Schwinger boson representation of SU(4) coherent states of dimers for the paradigmatic spin-$1/2$ bilayer square lattice Heisenberg antiferromagnet. This system exhibits a quantum phase transition between a quantum paramagnetic state and a N\'eel order state, driven by the coupling constant $g = J'/J$, which is defined as the ratio between the inter-dimer $J'$ and intra-dimer $J$ exchange interactions. We demonstrate that this approach accurately describes static and dynamic properties on both sides of the quantum phase transition. The critical coupling constant $g_c \approx 0.42$ and the dynamic spin structure factor reproduce quantum Monte Carlo results with high precision. Notably, the $1/N$ corrections reveal the longitudinal mode of the magnetically ordered phase along with the overdamping caused by its decay into the two-magnon continuum. The present large-$N$ $SU(N)$ Schwinger boson theory can be extended to more general cases of quantum paramagnets that undergo a quantum phase transition into magnetically ordered states.

In a recent study by Euclid collaboration, the halo mass function (HMF) has been fitted with accuracy better than $1\%$ for the $\Lambda$CDM model. Several parameters were introduced and fitted against N-body simulations, assuming the usual linearly extrapolated matter density contrast at the collapse time, $\delta_c$, as a basic threshold for halo formation. As a result, a new function that multiplies $\delta_c$ was introduced, producing an effective threshold that varies both with redshift and mass scale. We show that the redshift evolution of this effective threshold is similar to the one of the linear extrapolated matter density contrast at the virialization time, $\delta_{\rm v}$. Assuming the Euclid HMF as a fiducial model, we refit the Sheth-Tormen (ST) HMF using $\delta_{\rm v}$ as a threshold. This new fit improves the agreement between ST-HMF and the Euclid one with respect to Despali et al. (2016) fit, specially at high masses. Interestingly, the parameters $a$ and $p$ in this refit have values closer to the Press-Schechter limit of the ST-HMF, showing that the use of $\delta_{\rm v}$ can provide semi-analytical HMF less dependent on extra parameters. Moreover, we analyze the consistency of the ST-HMF fitted with $\delta_{\rm v}$ in smooth dark energy models with time-varying equation of state, finding an overall good agreement with the evolution of halo abundances expected from the linear evolution of perturbations and the Euclid HMF extrapolated to these scenarios. These findings suggest that the use $\delta_{\rm v}$ as a basic function to describe the threshold for halo formation can be a good guide when considering extrapolations for models beyond $\Lambda$CDM, which are typically harder to study in simulations., Comment: 17 pages, 8 figures and a publicly available code

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Neutrinos at ultrahigh energies can originate both from interactions of cosmic rays at their acceleration sites and through cosmic-ray interactions as they propagate through the universe. These neutrinos are expected to have a low flux which drives the need for instruments with large effective areas. Radio observations of the inclined air showers induced by tau neutrino interactions in rock can achieve this, because radio waves can propagate essentially unattenuated through the hundreds of kilometers of atmosphere. Proposed arrays for radio detection of tau neutrinos focus on either arrays of inexpensive receivers distributed over a large area, the GRAND concept, or compact phased arrays on elevated mountains, the BEACON concept, to build up a large detector area with a low trigger threshold. We present a concept that combines the advantages of these two approaches with a trigger driven by phased arrays at a moderate altitude (1 km) and sparse, high-gain outrigger receivers for reconstruction and background rejection. We show that this design has enhanced sensitivity at 100 PeV over the two prior designs with fewer required antennas and discuss the need for optimized antenna designs., Comment: ARENA2024 Conference Proceeding PoS(ARENA2024)058

In this work, effects of constant and time-dependent vaccination rates on the Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) seasonal model are studied. Computing the Lyapunov exponent, we show that typical complex structures, such as shrimps, emerge for given combinations of constant vaccination rate and another model parameter. In some specific cases, the constant vaccination does not act as a chaotic suppressor and chaotic bands can exist for high levels of vaccination (e.g., $> 0.95$). Moreover, we obtain linear and non-linear relationships between one control parameter and constant vaccination to establish a disease-free solution. We also verify that the total infected number does not change whether the dynamics is chaotic or periodic. The introduction of a time-dependent vaccine is made by the inclusion of a periodic function with a defined amplitude and frequency. For this case, we investigate the effects of different amplitudes and frequencies on chaotic attractors, yielding low, medium, and high seasonality degrees of contacts. Depending on the parameters of the time-dependent vaccination function, chaotic structures can be controlled and become periodic structures. For a given set of parameters, these structures are accessed mostly via crisis and in some cases via period-doubling. After that, we investigate how the time-dependent vaccine acts in bi-stable dynamics when chaotic and periodic attractors coexist. We identify that this kind of vaccination acts as a control by destroying almost all the periodic basins. We explain this by the fact that chaotic attractors exhibit more desirable characteristics for epidemics than periodic ones in a bi-stable state.

This study investigates vulnerabilities in dependencies of sampled open-source software (OSS) projects, the relationship between these and overall project security, and how developers' behaviors and practices influence their mitigation. Through analysis of OSS projects, we have identified common issues in outdated or unmaintained dependencies, including pointer dereferences and array bounds violations, that pose significant security risks. We have also examined developer responses to formal verifier reports, noting a tendency to dismiss potential issues as false positives, which can lead to overlooked vulnerabilities. Our results suggest that reducing the number of direct dependencies and prioritizing well-established libraries with strong security records are effective strategies for enhancing the software security landscape. Notably, four vulnerabilities were fixed as a result of this study, demonstrating the effectiveness of our mitigation strategies., Comment: 16 pages

As the adoption of Internet of Things (IoT) devices continues to rise in enterprise environments, the need for effective and efficient security measures becomes increasingly critical. This paper presents a cost-efficient platform to facilitate the pre-deployment security checks of IoT devices by predicting potential weaknesses and associated attack patterns. The platform employs a Bidirectional Long Short-Term Memory (Bi-LSTM) network to analyse device-related textual data and predict weaknesses. At the same time, a Gradient Boosting Machine (GBM) model predicts likely attack patterns that could exploit these weaknesses. When evaluated on a dataset curated from the National Vulnerability Database (NVD) and publicly accessible IoT data sources, the system demonstrates high accuracy and reliability. The dataset created for this solution is publicly accessible.

Active galactic nuclei (AGN) are among the main candidates for ultra-high-energy cosmic ray (UHECR) sources. However, while theoretical and some phenomenological works favor AGNs as the main sources, recent works have shown that using the very-high-energy (VHE) $\gamma$-ray flux as a proxy for the UHECR flux leads to a bad agreement with data. In this context, the energy spectrum and composition data are hardly fitted. At the same time, the arrival directions map is badly described and a spurious dipole direction is produced. In this work, we propose a possible solution to these contradictions. Using the observed $\gamma$-ray flux as a proxy may carry the implicit assumption of beamed UHECR emission and, consequently, its beam will remain collimated up to its detection on Earth. We show that assuming an isotropic UHECR emission and correcting the $\gamma$-ray emission proxy by Doppler boosting can overcome the problem. The combined fit of the spectrum and composition is improved by $3.56\sigma$, while the predicted arrival directions agree much better with the data. In particular, a spurious direction of the dipole can be reduced from $10.3 \ (5.4)\sigma$ away from the data to $2.2 \ (1.5)\sigma$ for $E > 8$ EeV ($E > 32$ EeV). We also show that this effect is particularly important when including AGNs of different classes in the same analysis, such as radio galaxies and blazars.

By utilizing a multi-orbital periodic Anderson model with parameters obtained from \textit{ab initio} band structure calculations, combined with degenerate perturbation theory, we derive effective Kondo-Heisenberg and spin Hamiltonians that capture the interaction among the effective magnetic moments. This derivation encompasses fluctuations via both non-magnetic $4f^0$ and magnetic $4f^2$ virtual states, and its accuracy is confirmed through comparison with experimental data obtained from CeIn$_3$. The significant agreement observed between experimental results and theoretical predictions underscores the potential of deriving minimal models from first-principles calculations for achieving a quantitative description of $4f$ materials. Moreover, our microscopic derivation unveils the underlying origin of anisotropy in the exchange interaction between Kramers doublets, shedding light on the conditions under which this anisotropy may be weak compared to the isotropic contribution., Comment: 30 pages, 16 figures

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

Recent gamma-ray observations have detected photons up to energies of a few PeV. These highly energetic gamma rays are emitted by the most powerful sources in the Galaxy. Propagating over astrophysical distances, gamma rays might interact with background photons producing electron-positron pairs, then deflected by astrophysical magnetic fields. In turn, these charged particles might scatter through inverse Compton galactic radiation fields, triggering electromagnetic cascades. In this scenario, the characterisation of astrophysical environment in which gamma rays travel, specifically background photons and magnetic fields, is crucial. We explore the impact of propagation effects on observables at Earth by simulating galactic sources emitting gamma rays with energy between $100 \; \text{GeV}$ and $100 \; \text{PeV}$. We analyse the imprint of the galactic environment on observed energy spectra and arrival direction maps, revealing gamma-ray absorption features in the former and ``deflection" of gamma rays in the latter. Specifically, owing to interstellar radiation field spatial distribution and the galactic magnetic field structure, propagation effects on observables are found to be related to the specific gamma-ray source position and to the prompt emission model. Detailed investigations of the propagation effect on galactic gamma rays will improve the robustness of both current and future gamma-ray detections and indirect dark matter searches., Comment: 26 pages, 24 figures and 1 table. To be submitted to JCAP. Comments welcome!

Performance of NLP systems is typically evaluated by collecting a large-scale dataset by means of crowd-sourcing to train a data-driven model and evaluate it on a held-out portion of the data. This approach has been shown to suffer from spurious correlations and the lack of challenging examples that represent the diversity of natural language. Instead, we examine a framework for evaluating optimised models in training-set free setting on synthetically generated challenge sets. We find that despite the simplicity of the generation method, the data can compete with crowd-sourced datasets with regard to naturalness and lexical diversity for the purpose of evaluating the linguistic capabilities of MRC models. We conduct further experiments and show that state-of-the-art language model-based MRC systems can learn to succeed on the challenge set correctly, although, without capturing the general notion of the evaluated phenomenon.

Anderson's groundbreaking ideas of resonant valence bond (RVB) liquid and spin-charge separation initiated a transformative shift in modern physics. Extensive implications influenced a broad spectrum of fields, from high-temperature superconductors to quantum computing, and gave birth to key concepts in physics, such as quantum spin liquids, emergent gauge symmetries, topological order, and fractionalisation. Despite extensive efforts to demonstrate the existence of an RVB phase in the Hubbard model, a definitive realisation has proven elusive. Here, we present a concise, realistic, and elegant solution to this longstanding problem by demonstrating analytically that an RVB spin liquid, exhibiting spin-charge separation, emerges as the ground state of doped Mott insulators on corner-sharing tetrahedral lattices with frustrated hopping near half-filling -- a manifestation of the counter-Nagaoka effect. We confirm numerically that this result holds for finite-size systems, finite dopant density, and small exchange interactions. While much attention has been devoted to the emergence of new states from geometrically frustrated interactions, our work demonstrates that kinetic energy frustration in doped Mott insulators may be pivotal to stabilise robust, topologically ordered states in real materials.

The driven Kerr parametric oscillator, of interest to fundamental physics and quantum technologies, exhibits an excited state quantum phase transition (ESQPT) originating in an unstable classical periodic orbit. The main signature of this type of ESQPT is a singularity in the level density in the vicinity of the energy of the classical separatrix that divides the phase space into two distinct regions. The quantum states with energies below the separatrix are useful for quantum technologies, because they show a cat-like structure that protects them against local decoherence processes. In this work, we show how chaos arising from the interplay between the external drive and the nonlinearities of the system destroys the ESQPT and eventually eliminates the cat states. Our results demonstrate the importance of the analysis of theoretical models for the design of new parametric oscillators with ever larger nonlinearities., Comment: 4 + pages, 4 figures + Supplemental Material

The Southern Photometric Local Universe Survey (S-PLUS) is a project to map $\sim9300$ sq deg of the sky using twelve bands (seven narrow and five broadbands). Observations are performed with the T80-South telescope, a robotic telescope located at the Cerro Tololo Observatory in Chile. The survey footprint consists of several large contiguous areas, including fields at high and low galactic latitudes, and towards the Magellanic Clouds. S-PLUS uses fixed exposure times to reach point source depths of about $21$ mag in the $griz$ and $20$ mag in the $u$ and the narrow filters. This paper describes the S-PLUS Data Release 4 (DR4), which includes calibrated images and derived catalogues for over 3000 sq deg, covering the aforementioned area. The catalogues provide multi-band photometry performed with the tools \texttt{DoPHOT} and \texttt{SExtractor} -- point spread function (\PSF) and aperture photometry, respectively. In addition to the characterization, we also present the scientific potential of the data. We use statistical tools to present and compare the photometry obtained through different methods. Overall we find good agreement between the different methods, with a slight systematic offset of 0.05\,mag between our \PSF and aperture photometry. We show that the astrometry accuracy is equivalent to that obtained in previous S-PLUS data releases, even in very crowded fields where photometric extraction is challenging. The depths of main survey (MS) photometry for a minimum signal-to-noise ratio $S/N = 3$ reach from $\sim19.5$ for the bluer bands to $\sim21.5$ mag on the red. The range of magnitudes over which accurate \PSF photometry is obtained is shallower, reaching $\sim19$ to $\sim20.5$ mag depending on the filter. Based on these photometric data, we provide star-galaxy-quasar classification and photometric redshift for millions of objects., Comment: 26 pages, 17 figures, 14 tables, accepted for A&A

Internet of Things (IoT) devices have grown in popularity since they can directly interact with the real world. Home automation systems automate these interactions. IoT events are crucial to these systems' decision-making but are often unreliable. Security vulnerabilities allow attackers to impersonate events. Using statistical machine learning, IoT event fingerprints from deployed sensors have been used to detect spoofed events. Multivariate temporal data from these sensors has structural and temporal properties that statistical machine learning cannot learn. These schemes' accuracy depends on the knowledge base; the larger, the more accurate. However, the lack of huge datasets with enough samples of each IoT event in the nascent field of IoT can be a bottleneck. In this work, we deployed advanced machine learning to detect event-spoofing assaults. The temporal nature of sensor data lets us discover important patterns with fewer events. Our rigorous investigation of a publicly available real-world dataset indicates that our time-series-based solution technique learns temporal features from sensor data faster than earlier work, even with a 100- or 500-fold smaller training sample, making it a realistic IoT solution., Comment: Accepted - 49th IEEE Conference on Local Computer Networks (LCN)

The classification of IoT traffic is important to improve the efficiency and security of IoT-based networks. As the state-of-the-art classification methods are based on Deep Learning, most of the current results require a large amount of data to be trained. Thereby, in real-life situations, where there is a scarce amount of IoT traffic data, the models would not perform so well. Consequently, these models underperform outside their initial training conditions and fail to capture the complex characteristics of network traffic, rendering them inefficient and unreliable in real-world applications. In this paper, we propose IoT Traffic Classification Transformer (ITCT), a novel approach that utilizes the state-of-the-art transformer-based model named TabTransformer. ITCT, which is pre-trained on a large labeled MQTT-based IoT traffic dataset and may be fine-tuned with a small set of labeled data, showed promising results in various traffic classification tasks. Our experiments demonstrated that the ITCT model significantly outperforms existing models, achieving an overall accuracy of 82%. To support reproducibility and collaborative development, all associated code has been made publicly available., Comment: Updated version of: B. Bazaluk, M. Hamdan, M. Ghaleb, M. S. M. Gismalla, F. S. Correa da Silva and D. M. Batista, "Towards a Transformer-Based Pre-trained Model for IoT Traffic Classification," NOMS 2024-2024 IEEE Network Operations and Management Symposium, Seoul, Korea, Republic of, 2024, pp. 1-7, doi: 10.1109/NOMS59830.2024.10575448

Understanding the nature and identity of dark matter is a key goal in the physics community. In the case that TeV-scale dark matter particles decay or annihilate into standard model particles, very-high-energy (VHE) gamma rays (greater than 100 GeV) will be present in the final state. The Very Energetic Radiation Imaging Telescope Array System (VERITAS) is an imaging atmospheric Cherenkov telescope array that can indirectly detect VHE gamma rays in an energy range of 100 GeV to > 30 TeV. Dwarf spheroidal galaxies (dSphs) are ideal candidates in the search for dark matter due to their high dark matter content, high mass-to-light ratios, and their low gamma-ray fluxes from astrophysical processes. This study uses a legacy data set of 638 hours collected on 17 dSphs, built over 11 years with an observing strategy optimized according to the dark matter content of the targets. The study addresses a broad dark matter particle mass range, extending from 200 GeV to 30 PeV. In the absence of a detection, we set the upper limits on the dark matter velocity-weighted annihilation cross section., Comment: 18 pages, 7 figures, 3 tables, accepted in PRD

Quantum phase transitions (QPTs) are explored to improve quantum sensing and weak signal detection. Changes in the ground state at a QPT enhance indicators of parameter estimation, such as the quantum Fischer information. Here, we show that in systems that lack a QPT, quantum sensitivity can still be enhanced due to excited-state quantum phase transitions (ESQPTs). Our analysis is done for a Kerr parametric oscillator with two ESQPTs associated with the onset of a hyperbolic point and a local maximum in the classical limit. These points change the system's phase space structure, which results in the amplification of the quantum Fisher information and the squeezing of the uncertainty in position at specific values of the control parameter. Our study showcases the relationship between non-conventional quantum critical phenomena and quantum sensing with potential experimental applications in exciton-polariton condensates and superconducting circuits., Comment: 6 pages, 3 figures

In February 2017, the blazar OJ~287 underwent a period of intense multiwavelength activity. It reached a new historic peak in the soft X-ray (0.3-10 keV) band, as measured by Swift-XRT. This event coincides with a very-high-energy (VHE) $\gamma$-ray outburst that led VERITAS to detect emission above 100 GeV, with a detection significance of $10\sigma$ (from 2016 December 9 to 2017 March 31). The time-averaged VHE $\gamma$-ray spectrum was consistent with a soft power law ($\Gamma = -3.81 \pm 0.26$) and an integral flux corresponding to $\sim2.4\%$ that of the Crab Nebula above the same energy. Contemporaneous data from multiple instruments across the electromagnetic spectrum reveal complex flaring behavior, primarily in the soft X-ray and VHE bands. To investigate the possible origin of such an event, our study focuses on three distinct activity states: before, during, and after the February 2017 peak. The spectral energy distributions during these periods suggest the presence of at least two non-thermal emission zones, with the more compact one responsible for the observed flare. Broadband modeling results and observations of a new radio knot in the jet of OJ~287 in 2017 are consistent with a flare originating from a strong recollimation shock outside the radio core., Comment: 21 pages, 7 figures, accepted for publication in The Astrophysical Journal

Quantum systems in excited states are attracting significant interest with the advent of noisy intermediate scale quantum (NISQ) devices. While ground states of small molecular systems are typically explored using hybrid variational algorithms like the variational quantum eigensolver (VQE), the study of excited states has received much less attention, partly due to the absence of efficient algorithms. In this work, we introduce the subspace search quantum imaginary time evolution (SSQITE) method, which calculates excited states using quantum devices by integrating key elements of the subspace search variational quantum eigensolver (SSVQE) and the variational quantum imaginary time evolution (VarQITE) method. The effectiveness of SSQITE is demonstrated through calculations of low-lying excited states of benchmark model systems, including $\text{H}_2$ and $\text{LiH}$ molecules. A toy Hamiltonian is also employed to demonstrate that the robustness of VarQITE in avoiding local minima extends to its use in excited state algorithms. With this robustness in avoiding local minima, SSQITE shows promise for advancing quantum computations of excited states across a wide range of applications., Comment: 21 pages, 6 Figures

Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this paper, we first integrate buoyancy and hydrodynamics models into a modern Reinforcement Learning framework to reduce training time. Next, we show how system identification coupled with domain randomization improves the RL agent performance and narrows the sim-to-real gap. Real-world experiments for the task of capturing floating waste show that our approach lowers energy consumption by 13.1\% while reducing task completion time by 7.4\%. These findings, supported by sharing our open-source implementation, hold the potential to impact the efficiency and versatility of ASVs, contributing to environmental conservation efforts., Comment: IROS 2024, IEEE, Oct 2024, Abu Dhabi, United Arab Emirates

Since the first microlensing planet discovery in 2003, more than 200 planets have been detected with gravitational microlensing, in addition to several free-floating planet and black hole candidates. In this chapter the microlensing theory is presented by introducing the numerical methods used to solve binary and triple lens problems and how these lead to the characterisation of the planetary systems. Then the microlensing planetary detection efficiency is discussed, with an emphasis on cold planets beyond the snow line. Furthermore, it will be explained how the planetary characterisation can be facilitated when the microlensing light curves exhibit distortions due to second order effects such as parallax, planetary orbital motion, and extended source. These second order effects can be turned to our advantage, and become useful to ultimately better characterise the planetary systems, but they can also introduce degeneracies in the light curve models. It will be explained how the use of modern observational and computational techniques enables microlensers to solve these degeneracies and estimate the planetary system parameters with very high accuracy. Then a review of the main discoveries to date will be presented while exploring the recent statistical results from high-cadence ground-based surveys and space-based observations, especially on the planet mass function. Finally, future prospects are discussed, with the expected advances from dedicated space missions, extending the planet sensitivity range down to Mars mass., Comment: This preprint will be published in: Handbook of Exoplanets 2nd edition, edited by Hans Deeg and Juan Antonio Belmonte, Springer Nature Switzerland AG, reproduced with permission of Springer Nature Switzerland AG

We report neutron scattering, pressure-dependent AC calorimetry, and AC magnetic susceptibility measurements of triangular lattice NaYbSe$_2$. We observe a continuum of scattering, which is reproduced by matrix product simulations, and no phase transition is detected in any bulk measurements. Comparison to heat capacity simulations suggest the material is within the Heisenberg spin liquid phase. AC Susceptibility shows a significant 23~mK downturn, indicating a gap in the magnetic spectrum. The combination of a gap with no detectable magnetic order, comparison to theoretical models, and comparison to other $A$YbSe$_2$ compounds all strongly indicate NaYbSe$_2$ is within the quantum spin liquid phase. The gap also allows us to rule out a gapless Dirac spin liquid, with a gapped $\mathbb{Z}_2$ liquid the most natural explanation., Comment: 5 pages, 4 figures; 7 pages and 13 figures supplemental materials

With the recent advances of large language models (LLMs), it is no longer infeasible to build an automated debate system that helps people to synthesise persuasive arguments. Previous work attempted this task by integrating multiple components. In our work, we introduce an argument mining dataset that captures the end-to-end process of preparing an argumentative essay for a debate, which covers the tasks of claim and evidence identification (Task 1 ED), evidence convincingness ranking (Task 2 ECR), argumentative essay summarisation and human preference ranking (Task 3 ASR) and metric learning for automated evaluation of resulting essays, based on human feedback along argument quality dimensions (Task 4 SQE). Our dataset contains 14k examples of claims that are fully annotated with the various properties supporting the aforementioned tasks. We evaluate multiple generative baselines for each of these tasks, including representative LLMs. We find, that while they show promising results on individual tasks in our benchmark, their end-to-end performance on all four tasks in succession deteriorates significantly, both in automated measures as well as in human-centred evaluation. This challenge presented by our proposed dataset motivates future research on end-to-end argument mining and summarisation. The repository of this project is available at https://github.com/HaoBytes/ArgSum-Datatset, Comment: Published on ACL 2024 Findings

In this paper, we obtain geometric upper bounds for the first eigenvalue $\lambda_1^J$ of the Jacobi operator for both closed and compact with boundary hypersurfaces having constant mean curvature (CMC). As an application, we derive new rigidity results for the area of CMC hypersurfaces under suitable conditions on $\lambda_1^J$ and the curvature of the ambient space. We also address the Jacobi-Steklov problem, proving geometric upper bounds for its first eigenvalue $\sigma_1^J$ and deriving rigidity results related to the length of the boundary. Additionally, we present some results in higher dimensions related to the Yamabe invariants., Comment: 17 pages. Comments welcome!

We employ a classical limit grounded in SU(4) coherent states to investigate the temperature-dependent dynamical spin structure factor of the $S = 1/2$ ladder consisting of weakly coupled dimers. By comparing the outcomes of this classical approximation with density matrix renormalization group and exact diagonalization calculations in finite size ladders, we demonstrate that the classical dynamics offers an accurate approximation across the entire temperature range when the interdimer coupling is weak and a good approximation in the high temperature regime even when the interdimer coupling is strong. This agreement is achieved after appropriately rescaling the temperature axis and renormalizing expectation values to satisfy a quantum sum rule, following D. Dahlbom et al. [Phys. Rev. B 109, 014427 (2024)]. We anticipate the method will be particularly effective when applied to 2D and 3D lattices composed of weakly-coupled dimers, situations that remain challenging for alternative numerical methods., Comment: 15 pages, 8 figures

Traditional computer-aided synthesis planning (CASP) methods rely on iterative single-step predictions, leading to exponential search space growth that limits efficiency and scalability. We introduce a transformer-based model that directly generates multi-step synthetic routes as a single string by conditionally predicting each molecule based on all preceding ones. The model accommodates specific conditions such as the desired number of steps and starting materials, outperforming state-of-the-art methods on the PaRoutes dataset with a 2.2x improvement in Top-1 accuracy on the n$_1$ test set and a 3.3x improvement on the n$_5$ test set. It also successfully predicts routes for FDA-approved drugs not included in the training data, showcasing its generalization capabilities. While the current suboptimal diversity of the training set may impact performance on less common reaction types, our approach presents a promising direction towards fully automated retrosynthetic planning.

This paper investigates the development and evaluation of machine translation models from Cantonese to English, where we propose a novel approach to tackle low-resource language translations. The main objectives of the study are to develop a model that can effectively translate Cantonese to English and evaluate it against state-of-the-art commercial models. To achieve this, a new parallel corpus has been created by combining different available corpora online with preprocessing and cleaning. In addition, a monolingual Cantonese dataset has been created through web scraping to aid the synthetic parallel corpus generation. Following the data collection process, several approaches, including fine-tuning models, back-translation, and model switch, have been used. The translation quality of models has been evaluated with multiple quality metrics, including lexicon-based metrics (SacreBLEU and hLEPOR) and embedding-space metrics (COMET and BERTscore). Based on the automatic metrics, the best model is selected and compared against the 2 best commercial translators using the human evaluation framework HOPES. The best model proposed in this investigation (NLLB-mBART) with model switch mechanisms has reached comparable and even better automatic evaluation scores against State-of-the-art commercial models (Bing and Baidu Translators), with a SacreBLEU score of 16.8 on our test set. Furthermore, an open-source web application has been developed to allow users to translate between Cantonese and English, with the different trained models available for effective comparisons between models from this investigation and users. CANTONMT is available at https://github.com/kenrickkung/CantoneseTranslation, Comment: on-going work, 30 pages

The chess domain is well-suited for creating an artificial intelligence (AI) system that mimics real-world challenges, including decision-making. Throughout the years, minimal attention has been paid to investigating insights derived from unstructured chess data sources. In this study, we examine the complicated relationships between multiple referenced moves in a chess-teaching textbook, and propose a novel method designed to encapsulate chess knowledge derived from move-action phrases. This study investigates the feasibility of using a modified sentiment analysis method as a means for evaluating chess moves based on text. Our proposed Aspect-Based Sentiment Analysis (ABSA) method represents an advancement in evaluating the sentiment associated with referenced chess moves. By extracting insights from move-action phrases, our approach aims to provide a more fine-grained and contextually aware `chess move'-based sentiment classification. Through empirical experiments and analysis, we evaluate the performance of our fine-tuned ABSA model, presenting results that confirm the efficiency of our approach in advancing aspect-based sentiment classification within the chess domain. This research contributes to the area of game-playing by machines and shows the practical applicability of leveraging NLP techniques to understand the context of strategic games., Comment: accepted in the 10th Games and NLP 2024 workshop at LREC 2024

Ultra-high-energy cosmic rays, accelerated hadrons that can exceed energies of $10^{20}$ eV, are the highest-energy particles ever observed. While the sources producing UHECRs are still unknown, the Pierre Auger Observatory has detected a large-scale dipole anisotropy in the arrival directions of cosmic rays above 8 EeV. In this work, we explore whether resolved gamma-ray sources can reproduce the Auger dipole. We use various Fermi Large Area Telescope catalogs as sources of cosmic rays in CRPropa simulations. We find that in all cases, the simulated dipole has an amplitude significantly larger than that measured by Auger, even when considering large extragalactic magnetic field strengths and optimistic source weighting schemes. Our result implies that the resolved gamma-ray sources are insufficient to account for the population of sources producing the highest-energy cosmic rays, and there must exist a population of UHECR sources that lack gamma-ray emission or are unresolved by the current-generation gamma-ray telescopes., Comment: accepted for publication in ApJ-Letters

We propose an exact algorithm for the Graph Burning Problem ($\texttt{GBP}$), an NP-hard optimization problem that models the spread of influence on social networks. Given a graph $G$ with vertex set $V$, the objective is to find a sequence of $k$ vertices in $V$, namely, $v_1, v_2, \dots, v_k$, such that $k$ is minimum and $\bigcup_{i = 1}^{k} \{u\! \in\! V\! : d(u, v_i) \leq k - i\} = V$, where $d(u,v)$ denotes the distance between $u$ and $v$. We formulate the problem as a set covering integer programming model and design a row generation algorithm for the $\texttt{GBP}$. Our method exploits the fact that a very small number of covering constraints is often sufficient for solving the integer model, allowing the corresponding rows to be generated on demand. To date, the most efficient exact algorithm for the $\texttt{GBP}$, denoted here by $\texttt{GDCA}$, is able to obtain optimal solutions for graphs with up to 14,000 vertices within two hours of execution. In comparison, our algorithm finds provably optimal solutions approximately 236 times faster, on average, than $\texttt{GDCA}$. For larger graphs, memory space becomes a limiting factor for $\texttt{GDCA}$. Our algorithm, however, solves real-world instances with almost 200,000 vertices in less than 35 seconds, increasing the size of graphs for which optimal solutions are known by a factor of 14., Comment: 10 pages, 1 figure and 2 tables

We investigate the ground state magnetic configurations of a Fibonacci chain of classical spins with nearest-neighbor ferromagnetic and monoaxial Dzyaloshinskii-Moriya exchange interactions. Our analysis reveals a diverse array of magnetic textures induced by an external magnetic field perpendicular to the Dzyaloshinskii-Moriya vector. These textures exhibit a spectrum ranging from a quasi-fully polarized non-collinear state under high magnetic fields, capable of maintaining metastable chiral soliton topological defects, to a variety of quasicrystalline chiral soliton lattices below a critical field $H_c$. For a range of magnetic fields below $H_c$, the ground state spin textures result from the interplay between an effective quasiperiodic potential influencing the solitons and their repulsive interactions. At lower magnetic fields, the system experiences a commensurate-incommensurate transition, signified by the appearance of discommensurations in the quasicrystalline soliton lattice. In the absence of an external magnetic field, the ground state assumes a helical configuration with a quasiperiodic pitch angle., Comment: 14 pages, 12 figures

Bootstrap is a widely used technique that allows estimating the properties of a given estimator, such as its bias and standard error. In this paper, we evaluate and compare five bootstrap-based methods for making confidence intervals: two of them (Normal and Studentized) based on the bootstrap estimate of the standard error; another two (Quantile and Better) based on the estimated distribution of the parameter estimator; and finally, considering an interval constructed based on Bayesian bootstrap, relying on the notion of credible interval. The methods are compared through Monte Carlo simulations in different scenarios, including samples with autocorrelation induced by a copula model. The results are also compared with respect to the coverage rate, the median interval length and a novel indicator, proposed in this paper, combining both of them. The results show that the Studentized method has the best coverage rate, although the smallest intervals are attained by the Bayesian method. In general, all methods are appropriate and demonstrated good performance even in the scenarios violating the independence assumption.

We present a quantum algorithm based on the Tensor-Train Thermo-Field Dynamics (TT-TFD) method to simulate the open quantum system dynamics of intramolecular charge transfer modulated by an optical cavity on noisy intermediate-scale quantum (NISQ) computers. We apply our methodology to a model that describes the $\pi\pi^*$ to CT1 intermolecular charge transfer within the carotenoid-porphyrin-C60 molecular triad solvated in tetrahydrofuran (THF) and placed inside an optical cavity. We find how the dynamics is influenced by the cavity resonance frequency and strength of the light-matter interaction, showcasing the NISQ-based simulations to capture these effects. Furthermore, we compare the approximate predictions of Fermi's Golden Rule (FGR) rate theory and Ring-Polymer Molecular Dynamics (RPMD) to numerically exact calculations, showing the capabilitis of quantum computing methods to assess the limitations of approximate methods.

Computations with quantum harmonic oscillators or qumodes is a promising and rapidly evolving approach towards quantum computing. In contrast to qubits, which are two-level quantum systems, bosonic qumodes can in principle have infinite discrete levels, and can also be represented with continuous variable bases. One of the most promising applications of quantum computing is simulating many-fermion problems such as molecular electronic structure. Although there has been a lot of recent progress on simulating many-fermion systems on qubit-based quantum hardware, they can not be easily extended to bosonic quantum devices due to the fundamental difference in physics represented by qubits and qumodes. In this work, we show how an electronic structure Hamiltonian can be transformed into a system of qumodes with a fermion to boson mapping scheme and apply it to simulate the electronic structure of dihydrogen molecule as a system of two qumodes. Our work opens the door for simulating many-fermion systems by harnessing the power of bosonic quantum devices., Comment: 47 pages including references, 7 figures, revised

Bosonic quantum devices offer a novel approach to realize quantum computations, where the quantum two-level system (qubit) is replaced with the quantum (an)harmonic oscillator (qumode) as the fundamental building block of the quantum simulator. The simulation of chemical structure and dynamics can then be achieved by representing or mapping the system Hamiltonians in terms of bosonic operators. In this perspective, we review recent progress and future potential of using bosonic quantum devices for addressing a wide range of challenging chemical problems, including the calculation of molecular vibronic spectra, the simulation of gas-phase and solution-phase adiabatic and nonadiabatic chemical dynamics, the efficient solution of molecular graph theory problems, and the calculations of electronic structure., Comment: 40 pages including references, 13 figures, revised

On a Riemannian manifold with a smooth function $f: M\to \mathbb{R}$, we consider the linearization of the Perelman scalar curvature $\mathcal{R}$ and its $L^2$-formal adjoint operator $\delta\mathcal{R}^*$. A manifold endowed with a metric $g$ whose operator $\delta\mathcal{R}^*$ has a nontrivial kernel is called a Perelman singular manifold. In this paper, we present examples and apply general maximum principles to obtain rigidity or nonexistence results in the underlying setting., Comment: Comments are welcome!

The Spin Physics Detector collaboration proposes to install a universal detector in the second interaction point of the NICA collider under construction (JINR, Dubna) to study the spin structure of the proton and deuteron and other spin-related phenomena using a unique possibility to operate with polarized proton and deuteron beams at a collision energy up to 27 GeV and a luminosity up to $10^{32}$ cm$^{-2}$ s$^{-1}$. As the main goal, the experiment aims to provide access to the gluon TMD PDFs in the proton and deuteron, as well as the gluon transversity distribution and tensor PDFs in the deuteron, via the measurement of specific single and double spin asymmetries using different complementary probes such as charmonia, open charm, and prompt photon production processes. Other polarized and unpolarized physics is possible, especially at the first stage of NICA operation with reduced luminosity and collision energy of the proton and ion beams. This document is dedicated exclusively to technical issues of the SPD setup construction.

Diffusiophoretic motion induced by gradients of dissolved species has enabled the manipulation of colloids over large distances, spanning hundreds of microns. Nonetheless, studies have primarily focused on simple geometries that feature 1D gradients of solutes generated by reactions or selective dissolution. Thus, our understanding of 3D diffusiophoresis remains elusive despite its importance in wide-ranging scenarios, such as cellular transport and nanofluidics. Herein, we present a strategy to generate 3D chemical gradients under electric fields. In this approach, faradaic reactions at electrodes induce global pH gradients that drive long-range transport through electrodiffusiophoresis. Simultaneously, the electric field induces local pH gradients by driving the particle's double layer far from equilibrium. As a result, while global pH gradients lead to 2D focusing away from electrodes, local pH gradients induce aggregation in the third dimension. Resulting interparticle interactions display a strong dependence on surface chemistry, and particle size. Furthermore, pH gradients can be readily tuned by adjusting the voltage and frequency of the electric field. For large P\'eclet numbers, we observed a chemotactic-like collapse. Remarkably, such collapse occurs without reactions at a particle's surface. By mixing particles with different sizes, we also demonstrate the emergence of non-reciprocal interactions through experiments and Brownian dynamics simulations. These findings suggest a wide array of possibilities for the dynamic assembly of materials and the design of responsive matter.