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The origin of the astrophysical neutrino flux discovered by IceCube remains largely unknown. Several individual neutrino source candidates were observed. Among them is the gamma-ray flaring blazar TXS 0506+056. A similar coincidence of a high-energy neutrino and a gamma-ray flare was found in blazar PKS 0735+178. By modeling the spectral energy distributions of PKS 0735+178, we expect to investigate the physical conditions for neutrino production during different stages of the source activity. We analyze the multi-wavelength data during the selected periods of time. Using numerical simulations of radiation processes in the source, we study the parameter space of one-zone leptonic and leptohadronic models and find the best-fit solutions that explain the observed photon fluxes. We show the impact of model parameter degeneracy on the prediction of the neutrino spectra. We show that the available mutli-wavelength data are not sufficient to predict the neutrino spectrum unambiguously. Still, under the condition of maximal neutrino flux, we propose a scenario in which 0.2 neutrino events are produced during the 50 days flare., Comment: Submitted to A&A. Contains 15 pages, 13 figures

Reconfigurable intelligent surfaces (RISs) have been introduced as arrays of nearly passive elements with software-tunable electromagnetic properties to dynamically manipulate the reflection/transmission of radio signals. Research works in this area are focused on two applications, namely {\it user-assist} RIS aiming at tuning the RIS to enhance the quality-of-service (QoS) of target users, and the {\it malicious} RIS aiming for an attacker to degrade the QoS at victim receivers through generating {\it intended} destructive interference. While both user-assist and malicious RIS applications have been explored extensively, the impact of RIS deployments on imposing {\it unintended} interference on various wireless user-equipments (EUs) remains underexplored. This paper investigates the challenges of integrating RISs into multi-carrier, multi-user, and multi-operator networks. We discuss how RIS deployments intended to benefit specific users can negatively impact other users served at various carrier frequencies through different network operators. While not an ideal solution, we discuss how ultra-narrowband metasurfaces can be incorporated into the manufacturing of RISs to mitigate some challenges of RIS deployment in wireless networks. We also present a simulation scenario to illuminate some practical challenges associated with the deployment of RISs in shared public environments.

Direct redshift determination of BL Lac objects is highly challenging as the emission in the optical and near-infrared (NIR) bands is largely dominated by the non-thermal emission from the relativistic jet that points very close to our line of sight. Therefore, their optical spectra often show no emission lines from the host galaxy. In this work, we aim to overcome this difficulty by attempting to detect the host galaxy and derive redshift constraints based on assumptions on the galaxy magnitude ("imaging redshifts"). Imaging redshifts are derived by obtaining deep optical images under good seeing conditions, so that it is possible to detect the host galaxy as weak extension of the point-like source. We then derive the imaging redshift by using the host galaxy as a standard candle using two different methods. We determine imaging redshift for 9 out of 17 blazars that we observed as part of this program. The redshift range of these targets is 0.28-0.60 and the two methods used to derive the redshift give very consistent results within the uncertainties. We also performed a detailed comparison of the imaging redshifts with those obtained by other methods, like direct spectroscopic constraints or looking for groups of galaxies close to the blazar. We show that the constraints from different methods are consistent and that for example in the case of J2156.0+1818, which is the most distant source for which we detect the host galaxy, combining the three constraints narrows down the redshift to $0.63

Quantum-secured optical channels based on Quantum Key Distribution technology have generated a significant global interest. Although the maturity level of the short distance (less than 100 km) quantum-secured channels is at a deployment level, instituting such channels over long distance faces technological challenges, which is the subject of a world-wide research. In this article an industry perspective on establishing quantum-secured channels in large-scale optical networks in operational environments will be discussed, including the vision, requirements, and technical analysis of different approaches for establishing such channels.

The origin of high-energy (HE) astrophysical neutrinos has remained an elusive hot topic in the field of HE astrophysics for the past decade. Apart from a handful of individual associations, the vast majority of HE neutrinos arise from unknown sources. While there are theoretically-motivated candidate populations, such as blazars -- a subclass of AGN with jets pointed towards our line-of-sight -- they have not yet been convincingly linked to HE neutrino production. Here, we perform a spatio-temporal association analysis between a sample of blazars (from CGRaBS catalog) in the radio and optical bands and the most up-to-date IceCube HE neutrino catalog. We find that if the IceCube error regions are enlarged by 1$^\circ$ in quadrature, to account for unknown systematic errors at maximal level, a spatio-temporal correlation between the multiwavelength light curves of the CGRaBS blazars and the IceCube HE neutrinos is hinted at least at a 2.17$\sigma$ significance level. On the other hand, when the IceCube error regions are taken as their published values, we do not find any significant correlations. A discrepancy in the blazar-neutrino correlation strengths, when using such minimal and enlarged error region scenarios, was also obtained in a recent study by the IceCube collaboration. In our study, this difference arises because several flaring blazars -- coinciding with a neutrino arrival time -- happen to narrowly miss the published 90\%-likelihood error region of the nearest neutrino event. For all of the associations driving our most significant correlations, the flaring blazar is much less than 1$^\circ$ away from the published error regions. Therefore, our results indicate that the question of the blazar-neutrino connection is highly sensitive to the reconstruction of the neutrino error regions, whose reliability is expected to improve with the next generation of neutrino observatories., Comment: Accepted for publication in A&A, 24 pages, 10 figures, 2 tables, 3 electronic tables

For a dualizing module $D$ over a commutative Noetherian ring $R$ with identity, it is known that its Auslander class $\mathscr{A}_D\left(R\right)$ (respectively, Bass class $\mathscr{B}_D\left(R\right)$) is characterized as those $R$-modules with finite Gorenstein flat dimension (respectively, finite Gorenstein injective dimension). We establish an analogue of this result in the context of cotilting modules over general Noetherain rings., Comment: 27 pages

Let R be an associative ring with identity. We establish that the generalized Auslander-Reiten conjecture implies the Wakamatsu tilting conjecture. Furthermore, we prove that any Wakamatsu tilting R-module of finite projective dimension that is tensorly faithful is projective. By utilizing this result, we show the validity of the Wakamatsu tilting conjecture for R in two cases: when R is a left Artinian local ring or when it is the group ring of a finite group G over a commutative Artinian ring., Comment: 12 pages

Virtual health counselors offer the potential to provide users with information and counseling in complex areas such as disease management and health education. However, ensuring user engagement is challenging, particularly when the volume of information and length of counseling sessions increase. Agenda setting a clinical counseling technique where a patient and clinician collaboratively decide on session topics is an effective approach to tailoring discussions for individual patient needs and sustaining engagement. We explore the effectiveness of agenda setting in a virtual counselor system designed to counsel women for breast cancer genetic testing. In a between subjects study, we assessed three versions of the system with varying levels of user control in the system's agenda setting approach. We found that participants' knowledge improved across all conditions. Although our results showed that any type of agenda setting was perceived as useful, regardless of user control, interviews revealed a preference for more collaboration and user involvement in the agenda setting process. Our study highlights the importance of using patient-centered approaches, such as tailored discussions, when using virtual counselors in healthcare.

Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A

We consider a dynamic mechanism design problem where an auctioneer sells an indivisible good to two groups of buyers in every round, for a total of $T$ rounds. The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group. We begin by studying the static case ($T=1$) and establish that the optimal mechanism involves two types of subsidization: one that increases the overall probability of allocation to all buyers, and another that favors the group which otherwise has a lower probability of winning the item. We then extend our results to the dynamic case by characterizing a set of recursive functions that determine the optimal allocation and payments in each round. Notably, our results establish that in the dynamic case, the seller, on the one hand, commits to a participation reward to incentivize truth-telling, and on the other hand, charges an entry fee for every round. Moreover, the optimal allocation once more involves subsidization in favor of one group, where the extent of subsidization depends on the difference in future utilities for both the seller and buyers when allocating the item to one group versus the other. Finally, we present an approximation scheme to solve the recursive equations and determine an approximately optimal and fair allocation efficiently.

Cooperative driving, enabled by communication between automated vehicle systems, promises significant benefits to fuel efficiency, road capacity, and safety over single-vehicle driver assistance systems such as adaptive cruise control (ACC). However, the responsible development and implementation of these algorithms poses substantial challenges due to the need for extensive real-world testing. We address this issue and introduce OpenConvoy, an open and extensible framework designed for the implementation and assessment of cooperative driving policies on physical connected and autonomous vehicles (CAVs). We demonstrate the capabilities of OpenConvoy through a series of experiments on a convoy of multi-scale vehicles controlled by Platooning to show the stability of our system across vehicle configurations and its ability to effectively measure convoy cohesion across driving scenarios including varying degrees of communication loss., Comment: 7 pages, 8 figures

Autonomous driving is a complex task which requires advanced decision making and control algorithms. Understanding the rationale behind the autonomous vehicles' decision is crucial to ensure their safe and effective operation on highway driving. This study presents a novel approach, HighwayLLM, which harnesses the reasoning capabilities of large language models (LLMs) to predict the future waypoints for ego-vehicle's navigation. Our approach also utilizes a pre-trained Reinforcement Learning (RL) model to serve as a high-level planner, making decisions on appropriate meta-level actions. The HighwayLLM combines the output from the RL model and the current state information to make safe, collision-free, and explainable predictions for the next states, thereby constructing a trajectory for the ego-vehicle. Subsequently, a PID-based controller guides the vehicle to the waypoints predicted by the LLM agent. This integration of LLM with RL and PID enhances the decision-making process and provides interpretability for highway autonomous driving.

3D spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely-largescale-programable-metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the Desired Focal Point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSIindependent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the Phase Distribution Image of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing Quasi-Liquid-Layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.

Existing anti-malware software and reverse engineering toolkits struggle with stealthy sub-OS rootkits due to limitations of run-time kernel-level monitoring. A malicious kernel-level driver can bypass OS-level anti-virus mechanisms easily. Although static analysis of such malware is possible, obfuscation and packing techniques complicate offline analysis. Moreover, current dynamic analyzers suffer from virtualization performance overhead and create detectable traces that allow modern malware to evade them. To address these issues, we present \textit{The Reversing Machine} (TRM), a new hypervisor-based memory introspection design for reverse engineering, reconstructing memory offsets, and fingerprinting evasive and obfuscated user-level and kernel-level malware. TRM proposes two novel techniques that enable efficient and transparent analysis of evasive malware: hooking a binary using suspended process creation for hypervisor-based memory introspection, and leveraging Mode-Based Execution Control (MBEC) to detect user/kernel mode transitions and memory access patterns. Unlike existing malware detection environments, TRM can extract full memory traces in user and kernel spaces and hook the entire target memory map to reconstruct arrays, structures within the operating system, and possible rootkits. We perform TRM-assisted reverse engineering of kernel-level structures and show that it can speed up manual reverse engineering by 75\% on average. We obfuscate known malware with the latest packing tools and successfully perform similarity detection. Furthermore, we demonstrate a real-world attack by deploying a modified rootkit onto a driver that bypasses state-of-the-art security auditing tools. We show that TRM can detect each threat and that, out of 24 state-of-the-art AV solutions, only TRM can detect the most advanced threats.

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally, we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond., Comment: CVPR 2024 Highlight. arXiv admin note: text overlap with arXiv:2309.16064

This paper introduces SFSORT, the world's fastest multi-object tracking system based on experiments conducted on MOT Challenge datasets. To achieve an accurate and computationally efficient tracker, this paper employs a tracking-by-detection method, following the online real-time tracking approach established in prior literature. By introducing a novel cost function called the Bounding Box Similarity Index, this work eliminates the Kalman Filter, leading to reduced computational requirements. Additionally, this paper demonstrates the impact of scene features on enhancing object-track association and improving track post-processing. Using a 2.2 GHz Intel Xeon CPU, the proposed method achieves an HOTA of 61.7\% with a processing speed of 2242 Hz on the MOT17 dataset and an HOTA of 60.9\% with a processing speed of 304 Hz on the MOT20 dataset. The tracker's source code, fine-tuned object detection model, and tutorials are available at \url{https://github.com/gitmehrdad/SFSORT}.

Language models, particularly generative models, are susceptible to hallucinations, generating outputs that contradict factual knowledge or the source text. This study explores methods for detecting hallucinations in three SemEval-2024 Task 6 tasks: Machine Translation, Definition Modeling, and Paraphrase Generation. We evaluate two methods: semantic similarity between the generated text and factual references, and an ensemble of language models that judge each other's outputs. Our results show that semantic similarity achieves moderate accuracy and correlation scores in trial data, while the ensemble method offers insights into the complexities of hallucination detection but falls short of expectations. This work highlights the challenges of hallucination detection and underscores the need for further research in this critical area.

Here, we theoretically investigate the nonreciprocal response of an electrically biased graphene-coated dielectric fiber. By electrically biasing the graphene coating along the fiber axis, the dynamic conductivity of graphene exhibits a nonsymmetric response with respect to the longitudinal component of guided-mode wave vectors. Consequently, the guided waves propagating in two opposite directions may encounter distinct propagation features. In this work, the electromagnetic properties, such as modal dispersion and some field distributions are presented, and the strength of nonreciprocity is discussed for different parameters of graphene, such as its chemical potential and material loss. Furthermore, the influence of the radius of the fiber on the nonreciprocal response is investigated. We envision that such nonreciprocal optical fibers may find various potential applications in THz, microwave, and optical technologies., Comment: 8 pages, 2 figures

PG 1553+113 is one of the few blazars with a convincing quasi-periodic emission in the gamma-ray band. The source is also a very high-energy (VHE; >100 GeV) gamma-ray emitter. To better understand its properties and identify the underlying physical processes driving its variability, the MAGIC Collaboration initiated a multiyear, multiwavelength monitoring campaign in 2015 involving the OVRO 40-m and Medicina radio telescopes, REM, KVA, and the MAGIC telescopes, Swift and Fermi satellites, and the WEBT network. The analysis presented in this paper uses data until 2017 and focuses on the characterization of the variability. The gamma-ray data show a (hint of a) periodic signal compatible with literature, but the X-ray and VHE gamma-ray data do not show statistical evidence for a periodic signal. In other bands, the data are compatible with the gamma-ray period, but with a relatively high p-value. The complex connection between the low and high-energy emission and the non-monochromatic modulation and changes in flux suggests that a simple one-zone model is unable to explain all the variability. Instead, a model including a periodic component along with multiple emission zones is required., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society. 19 pages, 9 figures. Corresponding authors: Elisa Prandini, Antonio Stamerra, Talvikki Hovatta

We introduce a pioneering strategy to enhance the environmental stability of perovskite thin films, a critical step forward in advancing their application in optoelectronics. Through the innovative application of matrix encapsulation techniques, we focus on the stabilization of methylammonium lead iodide (MAPbI3) and methylammonium lead bromide (MAPbBr3) films. These films are meticulously prepared via a two-step solution deposition method under controlled ambient conditions. Our approach involves the application of poly(methyl methacrylate) (PMMA) and AZ5214 photoresist layers through spin-coating, aimed at singularly encapsulating the perovskite films. This encapsulation acts as a robust hydrophobic barrier, significantly mitigating moisture ingress and simultaneously addressing the challenge of pinhole presence within the perovskite structure. Through a series of detailed characterizations-spanning scanning electron microscopy (SEM), X-ray diffraction (XRD), and photoluminescence (PL) spectroscopy-we demonstrate that, despite the thicker nature of the AZ5214 photoresist compared to the PMMA layer, it exhibits markedly enhanced stability. Notably, the integrity and optical properties of the perovskite films are preserved for extended periods of up to 960 hours under environmental exposure. This breakthrough highlights the superior performance of AZ5214 photoresist over PMMA in prolonging the operational life of perovskite thin films, thereby offering a promising avenue for their deployment in a wide range of optoelectronic devices.

We study a three-layer data market comprising users (data owners), platforms, and a data buyer. Each user benefits from platform services in exchange for data, incurring privacy loss when their data, albeit noisily, is shared with the buyer. The user chooses platforms to share data with, while platforms decide on data noise levels and pricing before selling to the buyer. The buyer selects platforms to purchase data from. We model these interactions via a multi-stage game, focusing on the subgame Nash equilibrium. We find that when the buyer places a high value on user data (and platforms can command high prices), all platforms offer services to the user who joins and shares data with every platform. Conversely, when the buyer's valuation of user data is low, only large platforms with low service costs can afford to serve users. In this scenario, users exclusively join and share data with these low-cost platforms. Interestingly, increased competition benefits the buyer, not the user: as the number of platforms increases, the user utility does not necessarily improve while the buyer utility improves. However, increasing the competition improves the overall utilitarian welfare. Building on our analysis, we then study regulations to improve the user utility. We discover that banning data sharing maximizes user utility only when all platforms are low-cost. In mixed markets of high- and low-cost platforms, users prefer a minimum noise mandate over a sharing ban. Imposing this mandate on high-cost platforms and banning data sharing for low-cost ones further enhances user utility.

We study a producer's problem of selling a product to a continuum of privacy-conscious consumers, where the producer can implement third-degree price discrimination, offering different prices to different market segments. We consider a privacy mechanism that provides a degree of protection by probabilistically masking each market segment. We establish that the resultant set of all consumer-producer utilities forms a convex polygon, characterized explicitly as a linear mapping of a certain high-dimensional convex polytope into $\mathbb{R}^2$. This characterization enables us to investigate the impact of the privacy mechanism on both producer and consumer utilities. In particular, we establish that the privacy constraint always hurts the producer by reducing both the maximum and minimum utility achievable. From the consumer's perspective, although the privacy mechanism ensures an increase in the minimum utility compared to the non-private scenario, interestingly, it may reduce the maximum utility. Finally, we demonstrate that increasing the privacy level does not necessarily intensify these effects. For instance, the maximum utility for the producer or the minimum utility for the consumer may exhibit nonmonotonic behavior in response to an increase of the privacy level.

Spot beamfocusing (SBF) is the process of focusing the signal power in a small spot-like region in the 3D space, which can be either hard-tuned (HT) using traditional tools like lenses and mirrors or electronically reconfigured (ER) using modern large-scale intelligent surface phased arrays. ER-SBF can be a key enabling technology (KET) for the next-generation 6G wireless networks offering benefits to many future wireless application areas such as wireless communication and security, mid-range high-power and safe wireless chargers, medical and health, physics, etc. Although near-field HT-SBF and ER-beamfocusing have been studied in the literature and applied in the industry, there is no comprehensive study of different aspects of ER-SBF and its future applications, especially for nonoptical (mmWave, sub-THz, and THz) electromagnetic waves in the next generation wireless technology, which is the aim of this paper. The theoretical concepts behind ER-SBF, different antenna technologies for implementing ER-SBF, employing machine learning (ML)-based schemes for enabling channel-state-information (CSI)-independent ER-SBF, and different practical application areas that can benefit from ER-SBF will be explored.

Vehicle-to-Everything (V2X) communication is expected to accomplish a long-standing goal of the Connected and Autonomous Vehicle (CAV) community to bring connected vehicles to roads on a large scale. A major challenge, and perhaps the biggest hurdle on the path towards this goal is the scalability issues associated with it, especially when vehicular safety is concerned. As a major stakeholder, 3rd Generation Partnership Project (3GPP) based Cellular V2X (C-V2X) community has long been trying to research on whether vehicular networks are able to support the safety-critical applications in high-density vehicular scenarios. This paper attempts to answer this by first presenting an overview on the scalability challenges faced by 3GPP Release 14 Long Term Evolution C-V2X (LTE-V2X) using the PC5 sidelink interface for low and heavy-density traffic scenarios. Next, it demonstrates a series of solutions that address network congestion, packet losses and other scalability issues associated with LTE-V2X to enable this communication technology for commercial deployment. In addition, a brief survey is provided into 3GPP Release 16 5G New Radio V2X (NR-V2X) that utilizes the NR sidelink interface and works as an evolution of C-V2X towards better performance for V2X communications including new enhanced V2X (eV2X) scenarios that possess ultra-low-latency and high-reliability requirements.

In this paper, we introduce the concept of spot beamfocusing (SBF) in the Fresnel zone through extremely large-scale programmable metasurfaces (ELPMs) as a key enabling technology for 6G networks. A smart SBF scheme aims to adaptively concentrate the aperture's radiating power exactly at a desired focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method. This offers numerous advantages for next-generation networks including efficient wireless power transfer (WPT), interference mitigation, reduced RF pollution, and improved information security. SBF necessitates ELPMs with precise channel state information (CSI) for all ELPM elements. However, obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate this by proposing an adaptive novel CSI-independent ML scheme based on the TD3 deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is well-suited for relatively small-size arrays, the computational complexity is unaffordable for ELPMs. To overcome this limitation, we introduce a modular highly scalable structure composed of multiple sub-arrays, each equipped with a TD3-DRL optimizer. This setup enables collaborative optimization of the radiated power at the DFP, significantly reducing computational complexity while enhancing learning speed. The proposed structures benefits in terms of 3D spot-like power distribution, convergence rate, and scalability are validated through simulation results.

We study the role of regulatory inspections in a contract design problem in which a principal interacts separately with multiple agents. Each agent's hidden action includes a dimension that determines whether they undertake an extra costly step to adhere to safety protocols. The principal's objective is to use payments combined with a limited budget for random inspections to incentivize agents towards safety-compliant actions that maximize the principal's utility. We first focus on the single-agent setting with linear contracts and present an efficient algorithm that characterizes the optimal linear contract, which includes both payment and random inspection. We further investigate how the optimal contract changes as the inspection cost or the cost of adhering to safety protocols vary. Notably, we demonstrate that the agent's compensation increases if either of these costs escalates. However, while the probability of inspection decreases with rising inspection costs, it demonstrates nonmonotonic behavior as a function of the safety action costs. Lastly, we explore the multi-agent setting, where the principal's challenge is to determine the best distribution of inspection budgets among all agents. We propose an efficient approach based on dynamic programming to find an approximately optimal allocation of inspection budget across contracts. We also design a random sequential scheme to determine the inspector's assignments, ensuring each agent is inspected at most once and at the desired probability. Finally, we present a case study illustrating that a mere difference in the cost of inspection across various agents can drive the principal's decision to forego inspecting a significant fraction of them, concentrating its entire budget on those that are less costly to inspect.

Recent progress in natural language processing has been adapted to the symbolic music modality. Language models, such as Transformers, have been used with symbolic music for a variety of tasks among which music generation, modeling or transcription, with state-of-the-art performances. These models are beginning to be used in production products. To encode and decode music for the backbone model, they need to rely on tokenizers, whose role is to serialize music into sequences of distinct elements called tokens. MidiTok is an open-source library allowing to tokenize symbolic music with great flexibility and extended features. It features the most popular music tokenizations, under a unified API. It is made to be easily used and extensible for everyone., Comment: Updated and comprehensive report. Original ISMIR 2021 document at https://archives.ismir.net/ismir2021/latebreaking/000005.pdf

The Large Array Survey Telescope (LAST) is a wide-field telescope designed to explore the variable and transient sky with a high cadence and to be a test-bed for cost-effective telescope design. A LAST node is composed of 48 (32 already deployed), 28-cm f/2.2 telescopes. A single telescope has a 7.4 deg^2 field of view and reaches a 5-sigma limiting magnitude of 19.6 (21.0) in 20s (20x20s) (filter-less), while the entire system provides a 355 deg^2 field of view. The basic strategy of LAST is to obtain multiple 20-s consecutive exposures of each field (a visit). Each telescope carries a 61 Mpix camera, and the system produces, on average, about 2.2 Gbit/s. This high data rate is analyzed in near real-time at the observatory site, using limited computing resources (about 700 cores). Given this high data rate, we have developed a new, efficient data reduction and analysis pipeline. The data pipeline includes two major parts: (i) Processing and calibration of single images, followed by a coaddition of the visit's exposures. (ii) Building the reference images and performing image subtraction and transient detection. Here we describe in detail the first part of the pipeline. Among the products of this pipeline are photometrically and astrometrically calibrated single and coadded images, 32-bit mask images marking a wide variety of problems and states of each pixel, source catalogs built from individual and coadded images, Point Spread Function (PSF) photometry, merged source catalogs, proper motion and variability indicators, minor planets detection, calibrated light curves, and matching with external catalogs. The entire pipeline code is made public. Finally, we demonstrate the pipeline performance on real data taken by LAST., Comment: Accepted to PASP, 15 pages, 10 figures

The detection of gravitational waves from a binary neutron star merger by Advanced LIGO and Advanced Virgo (GW170817), along with the discovery of the electromagnetic counterparts of this gravitational wave event, ushered in a new era of multimessenger astronomy, providing the first direct evidence that BNS mergers are progenitors of short gamma-ray bursts (GRBs). Such events may also produce very-high-energy (VHE, > 100GeV) photons which have yet to be detected in coincidence with a gravitational wave signal. The Cherenkov Telescope Array (CTA) is a next-generation VHE observatory which aims to be indispensable in this search, with an unparalleled sensitivity and ability to slew anywhere on the sky within a few tens of seconds. New observing modes and follow-up strategies are being developed for CTA to rapidly cover localization areas of gravitational wave events that are typically larger than the CTA field of view. This work will evaluate and provide estimations on the expected number of of gravitational wave events that will be observable with CTA, considering both on- and off-axis emission. In addition, we will present and discuss the prospects of potential follow-up strategies with CTA., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 2023 (arXiv:2309.08219)

This paper proposes a imitation learning model for autonomous driving on highway traffic by mimicking human drivers' driving behaviours. The study utilizes the HighD traffic dataset, which is complex, high-dimensional, and diverse in vehicle variations. Imitation learning is an alternative solution to autonomous highway driving that reduces the sample complexity of learning a challenging task compared to reinforcement learning. However, imitation learning has limitations such as vulnerability to compounding errors in unseen states, poor generalization, and inability to predict outlier driver profiles. To address these issues, the paper proposes mixture density network behaviour cloning model to manage complex and non-linear relationships between inputs and outputs and make more informed decisions about the vehicle's actions. Additional improvement is using collision penalty based on the GAIL model. The paper includes a simulated driving test to demonstrate the effectiveness of the proposed method based on real traffic scenarios and provides conclusions on its potential impact on autonomous driving.

Aims. LST-1, the prototype of the Large-Sized Telescope for the upcoming Cherenkov Telescope Array Observatory, is concluding its commissioning in Observatorio del Roque de los Muchachos on the island of La Palma. The proximity of LST-1 (Large-Sized Telescope 1) to the two MAGIC (Major Atmospheric Gamma Imaging Cherenkov) telescopes permits observations of the same gamma-ray events with both systems. Methods. We describe the joint LST-1+MAGIC analysis pipeline and use simultaneous Crab Nebula observations and Monte Carlo simulations to assess the performance of the three-telescope system. The addition of the LST-1 telescope allows the recovery of events in which one of the MAGIC images is too dim to survive analysis quality cuts. Results. Thanks to the resulting increase in the collection area and stronger background rejection, we find a significant improvement in sensitivity, allowing the detection of 30% weaker fluxes in the energy range between 200 GeV and 3 TeV. The spectrum of the Crab Nebula, reconstructed in the energy range ~60 GeV to ~10 TeV, is in agreement with previous measurements., Comment: Accepted for publication in Astronomy & Astrophysics