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Several key observables of the high-precision physics program at future lepton colliders will critically depend on the knowledge of the absolute machine luminosity. The determination of the luminosity relies on the precise knowledge of some reference process, which is in principle not affected by unknown physics, so that its cross section can be computed within a well-established theory, like the Standard Model. Quantifying the uncertainties induced by possible New Physics effects on such processes is therefore crucial. We present an exploratory investigation of light and heavy New Physics contributions to the small-angle Bhabha process at future $e^+e^-$ colliders and we discuss possible strategies to remove potential uncertainties originating from such contaminations by relying on observables that are independent of the absolute luminosity., Comment: 9 pages, 2 figures, 2 tables

This paper presents a novel approach for synthesizing control barrier functions (CBFs) from high relative degree safety constraints: Rectified CBFs (ReCBFs). We begin by discussing the limitations of existing High-Order CBF approaches and how these can be overcome by incorporating an activation function into the CBF construction. We then provide a comparative analysis of our approach with related methods, such as CBF backstepping. Our results are presented first for safety constraints with relative degree two, then for mixed-input relative degree constraints, and finally for higher relative degrees. The theoretical developments are illustrated through simple running examples and an aircraft control problem., Comment: 6 pages, submitted to L-CSS/ACC 2025

The solar magnetized corona is responsible for various manifestations with a space weather impact, such as flares, coronal mass ejections (CMEs) and, naturally, the solar wind. Modeling the corona's dynamics and evolution is therefore critical for improving our ability to predict space weather In this work, we demonstrate that generative deep learning methods, such as Denoising Diffusion Probabilistic Models (DDPM), can be successfully applied to simulate future evolutions of the corona as observed in Extreme Ultraviolet (EUV) wavelengths. Our model takes a 12-hour video of an Active Region (AR) as input and simulate the potential evolution of the AR over the subsequent 12 hours, with a time-resolution of two hours. We propose a light UNet backbone architecture adapted to our problem by adding 1D temporal convolutions after each classical 2D spatial ones, and spatio-temporal attention in the bottleneck part. The model not only produce visually realistic outputs but also captures the inherent stochasticity of the system's evolution. Notably, the simulations enable the generation of reliable confidence intervals for key predictive metrics such as the EUV peak flux and fluence of the ARs, paving the way for probabilistic and interpretable space weather forecasting. Future studies will focus on shorter forecasting horizons with increased spatial and temporal resolution, aiming at reducing the uncertainty of the simulations and providing practical applications for space weather forecasting. The code used for this study is available at the following link: https://github.com/gfrancisco20/video_diffusion

We present the Multilingual Entity Linking of Occupations (MELO) Benchmark, a new collection of 48 datasets for evaluating the linking of entity mentions in 21 languages to the ESCO Occupations multilingual taxonomy. MELO was built using high-quality, pre-existent human annotations. We conduct experiments with simple lexical models and general-purpose sentence encoders, evaluated as bi-encoders in a zero-shot setup, to establish baselines for future research. The datasets and source code for standardized evaluation are publicly available at https://github.com/Avature/melo-benchmark, Comment: Accepted to the 4th Workshop on Recommender Systems for Human Resources (RecSys in HR 2024) as part of RecSys 2024

We present a hierarchical architecture to improve the efficiency of event-triggered control (ETC) in reducing resource consumption. This paper considers event-triggered systems generally as an impulsive control system in which the objective is to minimize the number of impulses. Our architecture recognizes that traditional ETC is a greedy strategy towards optimizing average inter-event times and introduces the idea of a deadline policy for the optimization of long-term discounted inter-event times. A lower layer is designed employing event-triggered control to guarantee the satisfaction of control objectives, while a higher layer implements a deadline policy designed with reinforcement learning to improve the discounted inter-event time. We apply this scheme to the control of an orbiting spacecraft, showing superior performance in terms of actuation frequency reduction with respect to a standard (one-layer) ETC while maintaining safety guarantees., Comment: 7 pages, 4 figures, IEEE Conference on Decision and Control

Cyber-physical systems can be subject to sensor attacks, e.g., sensor spoofing, leading to unsafe behaviors. This paper addresses this problem in the context of linear systems when an omniscient attacker can spoof several system sensors at will. In this adversarial environment, existing results have derived necessary and sufficient conditions under which the state estimation problem has a unique solution. In this work, we consider a severe attacking scenario when such conditions do not hold. To deal with potential state estimation uncertainty, we derive an exact characterization of the set of all possible state estimates. Using the framework of control barrier functions, we propose design principles for system safety in offline and online phases. For the offline phase, we derive conditions on safe sets for all possible sensor attacks that may be encountered during system deployment. For the online phase, with past system measurements collected, a quadratic program-based safety filter is proposed to enforce system safety. A 2D-vehicle example is used to illustrate the theoretical results., Comment: To appear at CDC 2024

We prove that the Lalescu sequence is monotonically decreasing.

Population genetics lies at the heart of evolutionary theory. This topic forms part of many biological science curricula but is rarely taught to physics students. Since physicists are becoming increasingly interested in biological evolution, we aim to provide a brief introduction to population genetics, written for physicists. We start with two background chapters: chapter 1 provides a brief historical introduction to the topic, while chapter 2 provides some essential biological background. We begin our main content with chapter 3 which discusses the key concepts behind Darwinian natural selection and Mendelian inheritance. Chapter 4 covers the basics of how variation is maintained in populations, while chapter 5 discusses mutation and selection. In chapter 6 we discuss stochastic effects in population genetics using the Wright-Fisher model as our example, and finally we offer concluding thoughts and references to excellent textbooks in chapter 7.

Computing regularities in strings is essential for a better understanding of their structures. Among regularities, periods and covers are the easiest to compute and the more informative. Lately new interesting string matching results have been achieved using different sampling techniques. One of these technique, called Character-Distance-Sampling (\texttt{CDS}) consists of representing a string by storing the distance between the positions of selected characters called pivots. Here we select as pivots only the first character of the string and use its \texttt{CDS} representation for computing its period and its shortest cover. Experimental results show that the proposed methods are much faster than classical methods for computing these two features.

Investigating the solar magnetic field is crucial to understand the physical processes in the solar interior as well as their effects on the interplanetary environment. We introduce a novel method to predict the evolution of the solar line-of-sight (LoS) magnetogram using image-to-image translation with Denoising Diffusion Probabilistic Models (DDPMs). Our approach combines "computer science metrics" for image quality and "physics metrics" for physical accuracy to evaluate model performance. The results indicate that DDPMs are effective in maintaining the structural integrity, the dynamic range of solar magnetic fields, the magnetic flux and other physical features such as the size of the active regions, surpassing traditional persistence models, also in flaring situation. We aim to use deep learning not only for visualisation but as an integrative and interactive tool for telescopes, enhancing our understanding of unexpected physical events like solar flares. Future studies will aim to integrate more diverse solar data to refine the accuracy and applicability of our generative model., Comment: Conference paper accepted for an oral presentation to the ESA SPAICE CONFERENCE 17 19 September 2024

The spread of content on social media is shaped by intertwining factors on three levels: the source, the content itself, and the pathways of content spread. At the lowest level, the popularity of the sharing user determines its eventual reach. However, higher-level factors such as the nature of the online item and the credibility of its source also play crucial roles in determining how widely and rapidly the online item spreads. In this work, we propose the Bayesian Mixture Hawkes (BMH) model to jointly learn the influence of source, content and spread. We formulate the BMH model as a hierarchical mixture model of separable Hawkes processes, accommodating different classes of Hawkes dynamics and the influence of feature sets on these classes. We test the BMH model on two learning tasks, cold-start popularity prediction and temporal profile generalization performance, applying to two real-world retweet cascade datasets referencing articles from controversial and traditional media publishers. The BMH model outperforms the state-of-the-art models and predictive baselines on both datasets and utilizes cascade- and item-level information better than the alternatives. Lastly, we perform a counter-factual analysis where we apply the trained publisher-level BMH models to a set of article headlines and show that effectiveness of headline writing style (neutral, clickbait, inflammatory) varies across publishers. The BMH model unveils differences in style effectiveness between controversial and reputable publishers, where we find clickbait to be notably more effective for reputable publishers as opposed to controversial ones, which links to the latter's overuse of clickbait., Comment: accepted as a full paper in the Research Track at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2024

Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. However, it is also known that variations in the input (e.g., different colors of the panorama due to the season of the year) or the task (e.g., changing the speed limit for a car to respect) could require complete retraining of the agents. In this work, we leverage recent developments in unifying latent representations to demonstrate that it is possible to combine the components of an agent, rather than retrain it from scratch. We build upon the recent relative representations framework and adapt it for Visual RL. This allows us to create completely new agents capable of handling environment-task combinations never seen during training. Our work paves the road toward a more accessible and flexible use of reinforcement learning., Comment: 13 pages, 10 figures, 4 tables

Bug bounty programs, where external agents are invited to search and report vulnerabilities (bugs) in exchange for rewards (bounty), have become a major tool for companies to improve their systems. We suggest augmenting such programs by inserting artificial bugs to increase the incentives to search for real (organic) bugs. Using a model of crowdsearch, we identify the efficiency gains by artificial bugs, and we show that for this, it is sufficient to insert only one artificial bug. Artificial bugs are particularly beneficial, for instance, if the designer places high valuations on finding organic bugs or if the budget for bounty is not sufficiently high. We discuss how to implement artificial bugs and outline their further benefits.

In recent years, exploiting the domain-specific underlying structure of data and its generative factors for representation learning has shown success in various use-case agnostic applications. However, the diversity and complexity of tabular data have made it challenging to represent these structures in a latent space through multi-dimensional vectors. We design an autoencoder-based framework for building general purpose embeddings, we assess the performance of different autoencoder architectures, and show simpler models outperform complex ones in embedding highly complex tabular data. We apply our framework to produce plug-and-play, rich, and anonymized embeddings representing AWS customers for usage in any model, saving up to 45% of development time, and observe significant improvements in downstream models. Moreover, we propose a significant improvement to the calculation of reconstruction loss for multi-layer contractive autoencoders (CAE) by calculating the Jacobian of the entire encoder leading to a 15% improvement in reconstruction quality when compared to a stacked CAE., Comment: 14 pages, 2 figures

Motivated by the requirement of a refined and flexible treatment of electroweak corrections to the neutral current Drell-Yan process, we report on recent developments on various input parameter/renormalization schemes for the calculation of fully differential cross sections, including both on-shell and MSbar schemes. The latter are particularly interesting for direct determinations of running couplings at the highest LHC energies. The calculations feature next-to-leading order precision with additional higher order contributions from universal corrections such as $\Delta \alpha$ and $\Delta \rho$. All the discussed input parameter/renormalization scheme options are implemented in the package of POWHEG-BOX-V2 dedicated to the neutral current Drell-Yan simulation, i.e. Z_ew-BMNNPV, which is used to obtain the presented numerical results. In particular, a comprehensive analysis on physical observables calculated with different input parameter/renormalization schemes is presented, addressing the Z peak invariant mass region as well as the high energy window. We take the opportunity of reporting also on additional improvements and options introduced in the package Z_ew-BMNNPV after svn revision 3376, such as different options for the treatment of the hadronic contribution to the running of the electromagnetic coupling and for the handling of the unstable Z resonance., Comment: Minimal changes. References added. Published in EPJC

State preparation plays a pivotal role in numerous quantum algorithms, including quantum phase estimation. This paper extends and benchmarks counterdiabatic driving protocols across three one-dimensional spin systems characterized by phase transitions: the axial next-nearest neighbor Ising (ANNNI), XXZ, and Haldane-Shastry (HS) models. We perform quantum optimal control protocols by optimizing the energy cost function, which can always be evaluated as opposed to the fidelity one requiring the exact state. Moreover, we incorporate Bayesian optimization within a code package for computing various adiabatic gauge potentials. This protocol consistently surpasses standard annealing schedules, often achieving performance improvements of several orders of magnitude. Notably, the ANNNI model stands out as a notable example, where fidelities exceeding 0.5 are attainable in most cases. Furthermore, the optimized paths exhibits promising generalization capabilities to higher-dimensional systems, allowing for the extension of parameters from smaller models. This opens up possibilities for applying the protocol to higher-dimensional systems. However, our investigations reveal limitations in the case of the XXZ and HS models, particularly when transitioning away from the ferromagnetic phase. This suggests that finding optimal diabatic gauge potentials for specific systems remains an important research direction.

Despite substantial progress in multilingual extractive Question Answering (QA), models with high and uniformly distributed performance across languages remain challenging, especially for languages with limited resources. We study cross-lingual transfer mainly focusing on the Generalized Cross-Lingual Transfer (G-XLT) task, where the question language differs from the context language - a challenge that has received limited attention thus far. Our approach seeks to enhance cross-lingual QA transfer using a high-performing multilingual model trained on a large-scale dataset, complemented by a few thousand aligned QA examples across languages. Our proposed strategy combines cross-lingual sampling and advanced self-distillation training in generations to tackle the previous challenge. Notably, we introduce the novel mAP@k coefficients to fine-tune self-knowledge distillation loss, dynamically regulating the teacher's model knowledge to perform a balanced and effective knowledge transfer. We extensively evaluate our approach to assess XLT and G-XLT capabilities in extractive QA. Results reveal that our self-knowledge distillation approach outperforms standard cross-entropy fine-tuning by a significant margin. Importantly, when compared to a strong baseline that leverages a sizeable volume of machine-translated data, our approach shows competitive results despite the considerable challenge of operating within resource-constrained settings, even in zero-shot scenarios. Beyond performance improvements, we offer valuable insights through comprehensive analyses and an ablation study, further substantiating the benefits and constraints of our approach. In essence, we propose a practical solution to improve cross-lingual QA transfer by leveraging a few data resources in an efficient way., Comment: Submitted to the Journal of Artificial Intelligence Research (JAIR)

The determination of seismic event locations with sparse networks or single-borehole systems remains a significant challenge in observational seismology. Leveraging the advantages of the location approach HADES, which was initially developed for locating clustered seismicity recorded at two stations, we present here an improved version of the methodology: HADES-R. Where HADES previously needed a minimum of 4 absolutely located master events, HADES-R solves a least-squares problem to find the relative inter-event distances in the cluster, and uses only a single master event to find the locations of all events, and subsequently applies rotational optimiser to find the cluster orientation. It can leverage iterative station combinations if multiple receivers are available, to describe the cluster shape and orientation uncertainty with a bootstrap approach. The improved method requires P- and S-phase arrival picks, a homogeneous velocity model, a single master event with a known location, and an estimate of the cluster width. The approach is benchmarked on the 2019 Ridgecrest sequence recorded at two stations, and applied to two seismic clusters at the FORGE geothermal test site, including a microseismic monitoring scenario with a DAS in a vertical borehole. Traditional procedures struggle in these settings due to the ill-posed network configuration. The azimuthal ambiguity in this scenario is partially overcome by assuming that all events belong to the same cluster around the master event and a cluster width estimate. We find the cluster shape in both cases, although the orientation remains uncertain. The method's ability to constrain the cluster shape and location with only one well-located event offers promising implications, especially for environments where limited or specialised instrumentation is in use., Comment: This is a pre-copyedited, author-produced version of an article accepted for publication in Geophysical Journal International following peer review. The version of record is available online at: https://academic.oup.com/gji/article/238/2/661/7670630

Optimization-based safety filters, such as control barrier function (CBF) based quadratic programs (QPs), have demonstrated success in controlling autonomous systems to achieve complex goals. These CBF-QPs can be shown to be continuous, but are generally not smooth, let alone continuously differentiable. In this paper, we present a general characterization of smooth safety filters -- smooth controllers that guarantee safety in a minimally invasive fashion -- based on the Implicit Function Theorem. This characterization leads to families of smooth universal formulas for safety-critical controllers that quantify the conservatism of the resulting safety filter, the utility of which is demonstrated through illustrative examples.

The weak mixing angle is a probe of the vector-axial coupling structure of electroweak interactions. It has been measured precisely at the $Z$-pole by experiments at the LEP and SLD colliders, but its energy dependence above $M_Z$ remains unconstrained. In this contribution we propose to exploit measurements of Neutral-Current Drell Yan at large invariant dilepton masses at the Large Hadron Collider, to determine the scale dependence of the weak mixing angle in the $\overline{MS}$ renormalisation scheme, $\sin^2 \theta_w^{\overline{MS}}(\mu)$. Such a measurement can be used to test the Standard Model predictions for the $\overline{MS}$ running at TeV scales, and to set model-independent constraints on new states with electroweak quantum numbers. To this end, we present an implementation of $\sin^2 \theta_w^{\overline{MS}}(\mu)$ in the POWHEG-BOX Monte Carlo event generator, which we use to explore the potential of future analyses with the LHC Run~3 and High-Luminosity datasets. In particular, the impact of the higher order corrections and of the uncertainties due to the knowledge of parton distribution functions are studied., Comment: Presented at DIS2023 - XXX International Workshop on Deep-Inelastic Scattering and related subjects; minor modifications to the references included

In balancing safety with the nominal control objectives, e.g., stabilization, it is desirable to reduce the time period when safety filters are in effect. Inspired by traditional spacecraft maneuvers, and with the ultimate goal of reducing the duration when safety is of concern, this paper proposes an event-triggered control framework with switching state-based triggers. Our first trigger in the scheme monitors safety constraints encoded by barrier functions, and thereby ensures safety without the need to alter the nominal controller--and when the boundary of the safety constraint is approached, the controller drives the system to the region where control actions are not needed. The second trigger condition determines if the safety constraint has improved enough for the success of the first trigger. We begin by motivating this framework for impulsive control systems, e.g., a satellite orbiting an asteroid. We then expand the approach to more general nonlinear system through the use of safety filtered controllers. Simulation results demonstrating satellite orbital maneuvers illustrate the utility of the proposed event-triggered framework., Comment: 8 pages, submitted to IEEE Conference on Decision and Control, 2023

A common assumption on the deployment of safeguarding controllers on the digital platform is that high sampling frequency translates to a small violation of safety. This paper investigates and formalizes this assumption through the lens of Input-to-State Safety. From this perspective, and leveraging control barrier functions (CBFs), we propose an alternative solution for maintaining safety of sample-and-hold controlled systems without any violation to the original safe set. Our approach centers around modulating the sampled control input in order to guarantee a more robust safety condition. We analyze both the time-triggered and the event-triggered sample-and-hold implementations, including the characterization of sampling frequency requirements and trigger conditions. We demonstrate the effectiveness of our approach in the context of adaptive cruise control through simulations., Comment: Submitted to IEEE American Control Conference 2024

An upgrade of the near detector of the T2K long baseline neutrino oscillation experiment is currently being conducted. This upgrade will include two new Time Projection Chambers, each equipped with 16 charge readout resistive Micromegas modules. A procedure to validate the performance of the detectors at different stages of production has been developed and implemented to ensure a proper and reliable operation of the detectors once installed. A dedicated X-ray test bench is used to characterize the detectors by scanning each pad individually and to precisely measure the uniformity of the gain and the deposited energy resolution over the pad plane. An energy resolution of about 10% is obtained. A detailed physical model has been developed to describe the charge dispersion phenomena in the resistive Micromegas anode. The detailed physical description includes initial ionization, electron drift, diffusion effects and the readout electronics effects. The model provides an excellent characterization of the charge spreading of the experimental measurements and allowed the simultaneous extraction of gain and RC information of the modules.