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We report the observation of the hyperfine spectrum of the weak P(63)4-4 line of the B-X electronic transition of molecular iodine 127I2 near 652.4 nm, using frequency-modulated saturated absorption spectroscopy. Through the precise measurements of the absolute frequencies of hyperfine components, we estimate electric quadrupole and magnetic spin-rotation constants. Additionally, we determine the center of gravity of the P(63) transition of the 4-4 vibrational band, resulting in a 250-fold improvement in the precision of its position. We also note an interesting overlap of the hyperfine transitions of P(63)4-4 line with the UV 1S0-3P1 narrow intercombination transition of cadmium atoms, which occurs near the second harmonic of the master laser radiation, corresponding to 326.2 nm. This study contributes to updating the iodine atlas, improving the precision of the empirical formulae, and providing an important frequency reference for precision spectroscopy of the narrow intercombination transition of atomic cadmium.

We consider a service robot that offers chocolate treats to people passing in its proximity: it has the capability of predicting in advance a person's intention to interact, and to actuate an "offering" gesture, subtly extending the tray of chocolates towards a given target. We run the system for more than 5 hours across 3 days and two different crowded public locations; the system implements three possible behaviors that are randomly toggled every few minutes: passive (e.g. never performing the offering gesture); or active, triggered by either a naive distance-based rule, or a smart approach that relies on various behavioral cues of the user. We collect a real-world dataset that includes information on 1777 users with several spontaneous human-robot interactions and study the influence of robot actions on people's behavior. Our comprehensive analysis suggests that users are more prone to engage with the robot when it proactively starts the interaction. We release the dataset and provide insights to make our work reproducible for the community. Also, we report qualitative observations collected during the acquisition campaign and identify future challenges and research directions in the domain of social human-robot interaction.

Recently introduced by some of the authors, the in-context identification paradigm aims at estimating, offline and based on synthetic data, a meta-model that describes the behavior of a whole class of systems. Once trained, this meta-model is fed with an observed input/output sequence (context) generated by a real system to predict its behavior in a zero-shot learning fashion. In this paper, we enhance the original meta-modeling framework through three key innovations: by formulating the learning task within a probabilistic framework; by managing non-contiguous context and query windows; and by adopting recurrent patching to effectively handle long context sequences. The efficacy of these modifications is demonstrated through a numerical example focusing on the Wiener-Hammerstein system class, highlighting the model's enhanced performance and scalability.

We deal with the task of sampling from an unnormalized Boltzmann density $\rho_D$ by learning a Boltzmann curve given by energies $f_t$ starting in a simple density $\rho_Z$. First, we examine conditions under which Fisher-Rao flows are absolutely continuous in the Wasserstein geometry. Second, we address specific interpolations $f_t$ and the learning of the related density/velocity pairs $(\rho_t,v_t)$. It was numerically observed that the linear interpolation, which requires only a parametrization of the velocity field $v_t$, suffers from a "teleportation-of-mass" issue. Using tools from the Wasserstein geometry, we give an analytical example, where we can precisely measure the explosion of the velocity field. Inspired by M\'at\'e and Fleuret, who parametrize both $f_t$ and $v_t$, we propose an interpolation which parametrizes only $f_t$ and fixes an appropriate $v_t$. This corresponds to the Wasserstein gradient flow of the Kullback-Leibler divergence related to Langevin dynamics. We demonstrate by numerical examples that our model provides a well-behaved flow field which successfully solves the above sampling task.

While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image Compression (LIC) codecs represent a promising alternative. In detail, integrating attention mechanisms from Vision Transformers into LIC models has shown improved compression efficiency. However, extra efficiency often comes at the cost of aggregating redundant features. This work proposes a Graph-based Attention Block for Image Compression (GABIC), a method to reduce feature redundancy based on a k-Nearest Neighbors enhanced attention mechanism. Our experiments show that GABIC outperforms comparable methods, particularly at high bit rates, enhancing compression performance., Comment: 10 pages, 5 figures, accepted at ICIP 2024

When humans perform insertion tasks such as inserting a cup into a cupboard, routing a cable, or key insertion, they wiggle the object and observe the process through tactile and proprioceptive feedback. While recent advances in tactile sensors have resulted in tactile-based approaches, there has not been a generalized formulation based on wiggling similar to human behavior. Thus, we propose an extremum-seeking control law that can insert four keys into four types of locks without control parameter tuning despite significant variation in lock type. The resulting model-free formulation wiggles the end effector pose to maximize insertion depth while minimizing strain as measured by a GelSight Mini tactile sensor that grasps a key. The algorithm achieves a 71\% success rate over 120 randomly initialized trials with uncertainty in both translation and orientation. Over 240 deterministically initialized trials, where only one translation or rotation parameter is perturbed, 84\% of trials succeeded. Given tactile feedback at 13 Hz, the mean insertion time for these groups of trials are 262 and 147 seconds respectively., Comment: 7 pages, 5 figures, 3 tables

In this paper, we introduce Plug-and-Play (PnP) Flow Matching, an algorithm for solving imaging inverse problems. PnP methods leverage the strength of pre-trained denoisers, often deep neural networks, by integrating them in optimization schemes. While they achieve state-of-the-art performance on various inverse problems in imaging, PnP approaches face inherent limitations on more generative tasks like inpainting. On the other hand, generative models such as Flow Matching pushed the boundary in image sampling yet lack a clear method for efficient use in image restoration. We propose to combine the PnP framework with Flow Matching (FM) by defining a time-dependent denoiser using a pre-trained FM model. Our algorithm alternates between gradient descent steps on the data-fidelity term, reprojections onto the learned FM path, and denoising. Notably, our method is computationally efficient and memory-friendly, as it avoids backpropagation through ODEs and trace computations. We evaluate its performance on denoising, super-resolution, deblurring, and inpainting tasks, demonstrating superior results compared to existing PnP algorithms and Flow Matching based state-of-the-art methods.

In this paper, we tackle the problem of estimating 3D contact forces using vision-based tactile sensors. In particular, our goal is to estimate contact forces over a large range (up to 15 N) on any objects while generalizing across different vision-based tactile sensors. Thus, we collected a dataset of over 200K indentations using a robotic arm that pressed various indenters onto a GelSight Mini sensor mounted on a force sensor and then used the data to train a multi-head transformer for force regression. Strong generalization is achieved via accurate data collection and multi-objective optimization that leverages depth contact images. Despite being trained only on primitive shapes and textures, the regressor achieves a mean absolute error of 4\% on a dataset of unseen real-world objects. We further evaluate our approach's generalization capability to other GelSight mini and DIGIT sensors, and propose a reproducible calibration procedure for adapting the pre-trained model to other vision-based sensors. Furthermore, the method was evaluated on real-world tasks, including weighing objects and controlling the deformation of delicate objects, which relies on accurate force feedback. Project webpage: http://prg.cs.umd.edu/FeelAnyForce, Comment: 8 pages, 4 figures, 4 tables

Precision medicine offers the potential to tailor treatment decisions to individual patients, yet it faces significant challenges due to the complex biases in clinical observational data and the high-dimensional nature of biological data. This study models various types of treatment assignment biases using mutual information and investigates their impact on machine learning (ML) models for counterfactual prediction and biomarker identification. Unlike traditional counterfactual benchmarks that rely on fixed treatment policies, our work focuses on modeling different characteristics of the underlying observational treatment policy in distinct clinical settings. We validate our approach through experiments on toy datasets, semi-synthetic tumor cancer genome atlas (TCGA) data, and real-world biological outcomes from drug and CRISPR screens. By incorporating empirical biological mechanisms, we create a more realistic benchmark that reflects the complexities of real-world data. Our analysis reveals that different biases lead to varying model performances, with some biases, especially those unrelated to outcome mechanisms, having minimal effect on prediction accuracy. This highlights the crucial need to account for specific biases in clinical observational data in counterfactual ML model development, ultimately enhancing the personalization of treatment decisions in precision medicine., Comment: 9 pages, 5 figures, conference

In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into windows and constructing a graph within each window. Therefore, our model uses graph convolutions instead of the typical 2D convolution or self-attention mechanism. WiGNet effectively manages computational and memory complexity for large image sizes. We evaluate our method in the ImageNet-1k benchmark dataset and test the adaptability of WiGNet using the CelebA-HQ dataset as a downstream task with higher-resolution images. In both of these scenarios, our method achieves competitive results compared to previous vision GNNs while keeping memory and computational complexity at bay. WiGNet offers a promising solution toward the deployment of vision GNNs in real-world applications. We publicly released the code at https://github.com/EIDOSLAB/WiGNet.

While much has been written about the problem of information overload in news and social media, little attention has been paid to its consequence in science. Scientific literature, however, has witnessed decades of exponential growth, to the point that the publications of the last twenty years now constitute 60% of all academic literature. This information overload is not without consequence. Our analysis reveals that, unlike other cultural products, scientific publications face unique challenges: the decreasing proportion of papers capturing large shares of researchers' attention and the slow turnover of influential papers lead to a disproportionate prominence of established works, resulting in stagnation and aging of scientific canons. To determine whether scientific hypergrowth is responsible for such ``gerontocratization of science'', we propose a generative model of paper citations based on random discovery and cumulative advantage, with a varying number of new papers each year. Our findings show that, as exponential growth intensifies, gerontocratization appears and becomes increasingly pronounced. Recognizing and understanding this mechanism is hence essential for developing targeted strategies to counteract this trend and promote a balanced and healthy renewal of scientific canons., Comment: 19 pages, 7 figures

Calibration of quantum devices is fundamental to successfully deploy quantum algorithms on current available quantum hardware. We present Qibocal, an open-source software library to perform calibration and characterization of superconducting quantum devices within the Qibo framework. Qibocal completes the Qibo middleware framework by providing all necessary tools to easily (re)calibrate self-hosted quantum platforms. After presenting the layout and the features of the library, we give an overview on some of the protocols implemented to perform single and two-qubit gates calibration. Finally, we present applications involving recalibration and monitoring of superconducting platforms., Comment: 12 pages, 9 figures, code available at https://github.com/qiboteam/qibocal

A practical application of quantum machine learning in real-world scenarios in the short term remains elusive, despite significant theoretical efforts. Image classification, a common task for classical models, has been used to benchmark quantum algorithms with simple datasets, but only few studies have tackled complex real-data classification challenges. In this work, we address such a gap by focusing on the classification of satellite images, a task of particular interest to the earth observation (EO) industry. We first preprocess the selected intrincate dataset by reducing its dimensionality. Subsequently, we employ neural quantum kernels (NQKs)- embedding quantum kernels (EQKs) constructed from trained quantum neural networks (QNNs)- to classify images which include solar panels. We explore both $1$-to-$n$ and $n$-to-$n$ NQKs. In the former, parameters from a single-qubit QNN's training construct an $n$-qubit EQK achieving a mean test accuracy over 86% with three features. In the latter, we iteratively train an $n$-qubit QNN to ensure scalability, using the resultant architecture to directly form an $n$-qubit EQK. In this case, a test accuracy over 88% is obtained for three features and 8 qubits. Additionally, we show that the results are robust against a suboptimal training of the QNN.

We compute the real corrections to the impact factor for the production of a forward Higgs boson, retaining full top-mass dependence. We demonstrate that the rapidity divergence is the one predicted by the BFKL factorization and perform the explicit subtraction in the BFKL scheme. We show that the IR-structure of the impact factor is the expected one and that, in the infinite-top-mass approximation, the previously known result is recovered. We also verify that the impact factor vanishes when the transverse momenta of the $t$-channel Reggeon goes to zero, in agreement with its gauge-invariant definition, exploiting the $m_t \rightarrow \infty$ expansion up to the next-to-next-to-leading order., Comment: 29 pages, 6 figures

Electric and magnetic Fayet-Ilioupulous (FI) terms are used to engineer partial breaking of ${\mathcal{N}=2}$ global supersymmetry for systems of vector multiplets. The magnetic FI term induces a deformation of the off-shell field transformations associated with an imaginary constant shift of the triplet of auxiliary fields of the vector multiplet. In this paper, we elaborate on the deformation of off-shell vector multiplets in supergravity, both in components and superspace. In a superconformal framework, the deformations are associated with (composite) linear multiplets. We engineer an off-shell model that exhibits partial local supersymmetry breaking with a zero cosmological constant. This is based on the hyper-dilaton Weyl multiplet introduced in arXiv:2203.12203, coupled to the SU(1,1)/U(1) special-K\"ahler sigma model in a symplectic frame admitting a holomorphic prepotential, with one compensating and one physical vector multiplet, the latter magnetically deformed., Comment: 77 pages (paper) + 14 pages (supplementary file)

We study ${T\overline{T}}$-like deformations of $d>2$ Yang-Mills theories. The standard ${T\overline{T}}$ flows lead to multi-trace Lagrangians, and the non-Abelian gauge structures make it challenging to find Lagrangians in a closed form. However, within the geometric approach to ${T\overline{T}}$, we obtain the closed-form solution to the metric flow and stress-energy tensor, and show that instanton solutions are undeformed. We also introduce new symmetrised single-trace ${T\overline{T}}$-like deformations, whose solutions in $d=4$ include the non-Abelian Born-Infeld Lagrangian proposed by Tseytlin in 1997., Comment: 9 pages

Contextual equivalence is the de facto standard notion of program equivalence. A key theorem is that contextual equivalence is an equational theory. Making contextual equivalence more intensional, for example taking into account the time cost of the computation, seems a natural refinement. Such a change, however, does not induce an equational theory, for an apparently essential reason: cost is not invariant under reduction. In the paradigmatic case of the untyped $\lambda$-calculus, we introduce interaction equivalence. Inspired by game semantics, we observe the number of interaction steps between terms and contexts but -- crucially -- ignore their own internal steps. We prove that interaction equivalence is an equational theory and we characterize it as $B$, the well-known theory induced by B\"ohm tree equality. Ours is the first observational characterization of $B$ obtained without enriching the discriminating power of contexts with extra features such as non-determinism. To prove our results, we develop interaction-based refinements of the B\"ohm-out technique and of intersection types.

Large-scale code datasets have acquired an increasingly central role in software engineering (SE) research. This is the result of (i) the success of the mining software repositories (MSR) community, that pushed the standards of empirical studies in SE; and (ii) the recent advent of deep learning (DL) in software engineering, with models trained and tested on large source code datasets. While there exist some ready-to-use datasets in the literature, researchers often need to build and pre-process their own dataset to meet specific requirements of the study/technique they are working on. This implies a substantial cost in terms of time and computational resources. In this work we present the SEART Data Hub, a web application that allows to easily build and pre-process large-scale datasets featuring code mined from public GitHub repositories. Through a simple web interface, researchers can specify a set of mining criteria (e.g., only collect code from repositories having more than 100 contributors and more than 1,000 commits) as well as specific pre-processing steps they want to perform (e.g., remove duplicates, test code, instances with syntax errors). After submitting the request, the user will receive an email with a download link for the required dataset within a few hours. A video showcasing the SEART Data Hub is available at https://youtu.be/lCgQaA7CYWA.

Nonnegative cross-curvature (NNCC) is a geometric property of a cost function defined on a product space that originates in optimal transportation and the Ma-Trudinger-Wang theory. Motivated by applications in optimization, gradient flows and mechanism design, we propose a variational formulation of nonnegative cross-curvature on c-convex domains applicable to infinite dimensions and nonsmooth settings. The resulting class of NNCC spaces is closed under Gromov-Hausdorff convergence and for this class, we extend many properties of classical nonnegative cross-curvature: stability under generalized Riemannian submersions, characterization in terms of the convexity of certain sets of c-concave functions, and in the metric case, it is a subclass of positively curved spaces in the sense of Alexandrov. One of our main results is that Wasserstein spaces of probability measures inherit the NNCC property from their base space. Additional examples of NNCC costs include the Bures-Wasserstein and Fisher-Rao squared distances, the Hellinger-Kantorovich squared distance (in some cases), the relative entropy on probability measures, and the 2-Gromov-Wasserstein squared distance on metric measure spaces.

Observational studies showed that galaxy disks are already in place in the first few billion years of the universe. The early disks detected so far, with typical half-light radii of 3 kiloparsecs at stellar masses around 10^11 M_sun for redshift z~3, are significantly smaller than today's disks with similar masses, in agreement with expectations from current galaxy models. Here, we report observations of a giant disk at z=3.25, when the universe was only 2 billion years old, with a half-light radius of 9.6 kiloparsecs and stellar mass of 3.7^+2.6_-2.2x10^11 M_sun. This galaxy is larger than any other kinematically-confirmed disks at similar epochs and surprisingly similar to today's largest disks regarding size and mass. JWST imaging and spectroscopy reveal its spiral morphology and a rotational velocity consistent with local Tully-Fisher relation. Multi-wavelength observations show that it lies in an exceptionally dense environment, where the galaxy number density is over ten times higher than the cosmic average and mergers are frequent. The discovery of such a giant disk suggests the presence of favorable physical conditions for large-disk formation in dense environments in the early universe, which may include efficient accretion of gas carrying coherent angular momentum and non-destructive mergers between exceptionally gas-rich progenitor galaxies., Comment: 22 pages, 11 figures; submitted

The paper develops elementary linear algebra methods to compute the determinants of the tensor symmetrizations of quadratic and hermitian forms over fields of good characteristic. Explicit results are given for the partitions $(n)$, $(1^n)$, $(2,1^{n-2})$ and $(3,1^{n-3})$ as well as for all partitions of $n\leq 7$. For orthogonal groups these symmetrizations are not irreducible and we continue to find the determinants of their irreducible constituents, the refined symmetrizations, over fields of characteristic 0.

Medication errors significantly threaten patient safety, leading to adverse drug events and substantial economic burdens on healthcare systems. Clinical Decision Support Systems (CDSSs) aimed at mitigating these errors often face limitations, including reliance on static databases and rule-based algorithms, which can result in high false alert rates and alert fatigue among clinicians. This paper introduces HELIOT, an innovative CDSS for drug allergy management, integrating Large Language Models (LLMs) with a comprehensive pharmaceutical data repository. HELIOT leverages advanced natural language processing capabilities to interpret complex medical texts and synthesize unstructured data, overcoming the limitations of traditional CDSSs. An empirical evaluation using a synthetic patient dataset and expert-verified ground truth demonstrates HELIOT's high accuracy, precision, recall, and F1 score, uniformly reaching 100\% across multiple experimental runs. The results underscore HELIOT's potential to enhance decision support in clinical settings, offering a scalable, efficient, and reliable solution for managing drug allergies.

In recent years, drone detection has quickly become a subject of extreme interest: the potential for fast-moving objects of contained dimensions to be used for malicious intents or even terrorist attacks has posed attention to the necessity for precise and resilient systems for detecting and identifying such elements. While extensive literature and works exist on object detection based on RGB data, it is also critical to recognize the limits of such modality when applied to UAVs detection. Detecting drones indeed poses several challenges such as fast-moving objects and scenes with a high dynamic range or, even worse, scarce illumination levels. Neuromorphic cameras, on the other hand, can retain precise and rich spatio-temporal information in situations that are challenging for RGB cameras. They are resilient to both high-speed moving objects and scarce illumination settings, while prone to suffer a rapid loss of information when the objects in the scene are static. In this context, we present a novel model for integrating both domains together, leveraging multimodal data to take advantage of the best of both worlds. To this end, we also release NeRDD (Neuromorphic-RGB Drone Detection), a novel spatio-temporally synchronized Event-RGB Drone detection dataset of more than 3.5 hours of multimodal annotated recordings., Comment: Accepted at NeVi Workshop at ECCV24

The advancement of artificial intelligence algorithms has expanded their application to several fields such as the biomedical domain. Artificial intelligence systems, including Large Language Models (LLMs), can be particularly advantageous in drug discovery, which is a very long and expensive process. However, LLMs by themselves lack in-depth knowledge about specific domains and can generate factually incorrect information. Moreover, they are not able to perform more complex actions that imply the usage of external tools. Our work is focused on these two issues. Firstly, we show how the implementation of an advanced RAG system can help the LLM to generate more accurate answers to drug-discovery-related questions. The results show that the answers generated by the LLM with the RAG system surpass in quality the answers produced by the model without RAG. Secondly, we show how to create an automatic target dossier using LLMs and incorporating them with external tools that they can use to execute more intricate tasks to gather data such as accessing databases and executing code. The result is a production-ready target dossier containing the acquired information summarized into a PDF and a PowerPoint presentation., Comment: 10 pages, 7 figures, 2 tables

Krylov complexity is an attractive measure for the rate at which quantum operators spread in the space of all possible operators under dynamical evolution. One expects that its late-time plateau would distinguish between integrable and chaotic dynamics, but its ability to do so depends precariously on the choice of the initial seed. We propose to apply such considerations not to a single operator, but simultaneously to a collection of initial seeds in the manner of the block Lanczos algorithm. We furthermore suggest that this collection should comprise all simple (few-body) operators in the theory, which echoes the applications of Nielsen complexity to dynamical evolution. The resulting construction, unlike the conventional Krylov complexity, reliably distinguishes integrable and chaotic Hamiltonians without any need for fine-tuning.

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5% .

Human activities exhibit a strong correlation between actions and the places where these are performed, such as washing something at a sink. More specifically, in daily living environments we may identify particular locations, hereinafter named activity-centric zones, which may afford a set of homogeneous actions. Their knowledge can serve as a prior to favor vision models to recognize human activities. However, the appearance of these zones is scene-specific, limiting the transferability of this prior information to unfamiliar areas and domains. This problem is particularly relevant in egocentric vision, where the environment takes up most of the image, making it even more difficult to separate the action from the context. In this paper, we discuss the importance of decoupling the domain-specific appearance of activity-centric zones from their universal, domain-agnostic representations, and show how the latter can improve the cross-domain transferability of Egocentric Action Recognition (EAR) models. We validate our solution on the EPIC-Kitchens-100 and Argo1M datasets, Comment: Project webpage: https://gabrielegoletto.github.io/EgoZAR/

In this paper we introduce a reconciliation protocol with a two-step error correction scheme using a short blocklength low rate code and a long blocklength high rate code. We show that by using this two-step decoding method it is possible to achieve secret key rates beyond the Devetak-Winter bound. We simulate the protocol using short blocklength low-density parity check code, and show that we can obtain reconciliation efficiencies up to 1.5. Using these high reconciliation efficiencies, it is possible double the achievable distances of CV-QKD systems., Comment: Pre-print

In multi-robot systems, relative localization between platforms plays a crucial role in many tasks, such as leader following, target tracking, or cooperative maneuvering. State of the Art (SotA) approaches either rely on infrastructure-based or on infrastructure-less setups. The former typically achieve high localization accuracy but require fixed external structures. The latter provide more flexibility, however, most of the works use cameras or lidars that require Line-of-Sight (LoS) to operate. Ultra Wide Band (UWB) devices are emerging as a viable alternative to build infrastructure-less solutions that do not require LoS. These approaches directly deploy the UWB sensors on the robots. However, they require that at least one of the platforms is static, limiting the advantages of an infrastructure-less setup. In this work, we remove this constraint and introduce an active method for infrastructure-less relative localization. Our approach allows the robot to adapt its position to minimize the relative localization error of the other platform. To this aim, we first design a specialized anchor placement for the active localization task. Then, we propose a novel UWB Relative Localization Loss that adapts the Geometric Dilution Of Precision metric to the infrastructure-less scenario. Lastly, we leverage this loss function to train an active Deep Reinforcement Learning-based controller for UWB relative localization. An extensive simulation campaign and real-world experiments validate our method, showing up to a 60% reduction of the localization error compared to current SotA approaches.

The Compton Spectrometer and Imager (COSI) is an upcoming NASA Small Explorer satellite mission scheduled for launch in 2027 and designed to conduct an all-sky survey in the energy range of 0.2-5 MeV. Its instrument consists of an array of germanium detectors surrounded on four sides and underneath by active shields that work as anticoincidence system (ACS) to reduce the contribution of background events in the detectors. These shields are composed of bismuth germanium oxide (BGO), a scintillator material, coupled with Silicon photomultipliers, aimed to collect optical photons produced from interaction of ionizing particles in the BGO and convert them into an electric signal. The reference simulation framework for COSI is MEGAlib, a set of software tools based on the Geant4 toolkit. The interaction point of the incoming radiation, the design of the ACS modules and the BGO surface treatment change the light collection and the overall shielding accuracy. The use of the Geant4 optical physics library, with the simulation of the scintillation process, is mandatory for a more realistic evaluation of the ACS performances. However, including the optical processes in MEGAlib would dramatically increase the computing time of the COSI simulations. We propose the use of a response function encoding the energy resolution and 3D light yield correction based on a separate Geant4 simulation of the ACS that includes the full optical interaction. We present the verification of the Geant4 optical physics library against analytical computations and available laboratory measurements obtained using PMTs as readout device, as a preparatory phase for the simulation of the COSI ACS response., Comment: Proceedings Volume 13093, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray, Yokohama, Japan

The development of Machine Learning (ML)- and, more recently, of Deep Learning (DL)-intensive systems requires suitable choices, e.g., in terms of technology, algorithms, and hyper-parameters. Such choices depend on developers' experience, as well as on proper experimentation. Due to limited time availability, developers may adopt suboptimal, sometimes temporary choices, leading to a technical debt (TD) specifically related to the ML code. This paper empirically analyzes the presence of Self-Admitted Technical Debt (SATD) in DL systems. After selecting 100 open-source Python projects using popular DL frameworks, we identified SATD from their source comments and created a stratified sample of 443 SATD to analyze manually. We derived a taxonomy of DL-specific SATD through open coding, featuring seven categories and 41 leaves. The identified SATD categories pertain to different aspects of DL models, some of which are technological (e.g., due to hardware or libraries) and some related to suboptimal choices in the DL process, model usage, or configuration. Our findings indicate that DL-specific SATD differs from DL bugs found in previous studies, as it typically pertains to suboptimal solutions rather than functional (\eg blocking) problems. Last but not least, we found that state-of-the-art static analysis tools do not help developers avoid such problems, and therefore, specific support is needed to cope with DL-specific SATD.

Recently, AI systems have made remarkable progress in various tasks. Deep Reinforcement Learning(DRL) is an effective tool for agents to learn policies in low-level state spaces to solve highly complex tasks. Researchers have introduced Intrinsic Motivation(IM) to the RL mechanism, which simulates the agent's curiosity, encouraging agents to explore interesting areas of the environment. This new feature has proved vital in enabling agents to learn policies without being given specific goals. However, even though DRL intelligence emerges through a sub-symbolic model, there is still a need for a sort of abstraction to understand the knowledge collected by the agent. To this end, the classical planning formalism has been used in recent research to explicitly represent the knowledge an autonomous agent acquires and effectively reach extrinsic goals. Despite classical planning usually presents limited expressive capabilities, PPDDL demonstrated usefulness in reviewing the knowledge gathered by an autonomous system, making explicit causal correlations, and can be exploited to find a plan to reach any state the agent faces during its experience. This work presents a new architecture implementing an open-ended learning system able to synthesize from scratch its experience into a PPDDL representation and update it over time. Without a predefined set of goals and tasks, the system integrates intrinsic motivations to explore the environment in a self-directed way, exploiting the high-level knowledge acquired during its experience. The system explores the environment and iteratively: (a) discover options, (b) explore the environment using options, (c) abstract the knowledge collected and (d) plan. This paper proposes an alternative approach to implementing open-ended learning architectures exploiting low-level and high-level representations to extend its knowledge in a virtuous loop.

Egocentric videos provide a unique perspective into individuals' daily experiences, yet their unstructured nature presents challenges for perception. In this paper, we introduce AMEGO, a novel approach aimed at enhancing the comprehension of very-long egocentric videos. Inspired by the human's ability to maintain information from a single watching, AMEGO focuses on constructing a self-contained representations from one egocentric video, capturing key locations and object interactions. This representation is semantic-free and facilitates multiple queries without the need to reprocess the entire visual content. Additionally, to evaluate our understanding of very-long egocentric videos, we introduce the new Active Memories Benchmark (AMB), composed of more than 20K of highly challenging visual queries from EPIC-KITCHENS. These queries cover different levels of video reasoning (sequencing, concurrency and temporal grounding) to assess detailed video understanding capabilities. We showcase improved performance of AMEGO on AMB, surpassing other video QA baselines by a substantial margin., Comment: Accepted to ECCV 2024. Project webpage: https://gabrielegoletto.github.io/AMEGO/

Deforestation is gaining an increasingly importance due to its strong influence on the sorrounding environment, especially in developing countries where population has a disadvantaged economic condition and agriculture is the main source of income. In Ivory Coast, for instance, where the cocoa production is the most remunerative activity, it is not rare to assist to the replacement of portion of ancient forests with new cocoa plantations. In order to monitor this type of deleterious activities, satellites can be employed to recognize the disappearance of the forest to prevent it from expand its area of interest. In this study, Forest-Non-Forest map (FNF) has been used as ground truth for models based on Sentinel images input. State-of-the-art models U-Net, Attention U-Net, Segnet and FCN32 are compared over different years combining Sentinel-1, Sentinel-2 and cloud probability to create forest/non-forest segmentation. Although Ivory Coast lacks of forest coverage datasets and is partially covered by Sentinel images, it is demonstrated the feasibility to create models classifying forest and non-forests pixels over the area using open datasets to predict where deforestation could have occurred. Although a significant portion of the deforestation research is carried out on visible bands, SAR acquisitions are employed to overcome the limits of RGB images over areas often covered by clouds. Finally, the most promising model is employed to estimate the hectares of forest has been cut between 2019 and 2020.

The hot phase of the circumgalactic medium (CGM) allows us to probe the inflow and outflow of gas within a galaxy, which is responsible for dictating the evolution of the galaxy. Studying the hot CGM sheds light on a better understanding of gas physics, which is crucial to inform and constrain simulation models. With the recent advances in observational measurements probing the hot CGM in X-rays and tSZ, we have a new avenue for widening our knowledge of gas physics and feedback by exploiting the information from current/future observations. In this paper, we use the TNG300 hydrodynamical simulations to build a fully self-consistent forward model for the hot CGM. We construct a lightcone and generate mock X-ray observations. We quantify the projection effects, namely the locally correlated large-scale structure in X-rays and the effect due to satellite galaxies misclassified as centrals which affects the measured hot CGM galactocentric profiles in stacking experiments. We present an analytical model that describes the intrinsic X-ray surface brightness profile across the stellar and halo mass bins. The increasing stellar mass bins result in decreasing values of $\beta$, the exponent quantifying the slope of the intrinsic galactocentric profiles. We carry forward the current state-of-the-art by also showing the impact of the locally correlated environment on the measured X-ray surface brightness profiles. We also present, for the first time, the effect of misclassified centrals in stacking experiments for three stellar mass bins: $10^{10.5-11}\ M_\odot$, $10^{11-11.2}\ M_\odot$, and $10^{11.2-11.5}\ M_\odot$. We find that the contaminating effect of the misclassified centrals on the stacked profiles increases when the stellar mass decreases., Comment: 14 pages, 10 figures, Submitted to A&A, comments welcome

Wave energy converters (WECs) represent an innovative technology for power generation from renewable sources (marine energy). Although there has been a great deal of research into such devices in recent decades, the power output of a single device has remained low. Therefore, installation in parks is required for economic reasons. The optimal design problem for parks of WECs is challenging since it requires the simultaneous optimization of positions and control parameters. While the literature on this problem usually considers metaheuristic algorithms, we present a novel numerical framework based on a gradient-flow formulation. This framework is capable of solving the optimal design problem for WEC parks. In particular, we use a low-order adaptive Runge-Kutta scheme to integrate the gradient-flow equation and introduce an inexact solution procedure. Here, the tolerances of the linear solver used for projection on the constraint nullspace and of the time-advancing scheme are automatically adapted to avoid over-solving so that the method requires minimal tuning. We then provide the specific details of its application to the considered WEC problem: the goal is to maximize the average power produced by a park, subject to hydrodynamic and dynamic governing equations and to the constraints of available sea area, minimum distance between devices, and limited oscillation amplitude around the undisturbed free surface elevation. A suitable choice of the discrete models allows us to compute analytically the Jacobian of the state problem's residual. Numerical tests with realistic parameters show that the proposed algorithm is efficient, and results of physical interest are obtained.

Complex quantum circuits are constituted by combinations of quantum subroutines. The computation is possible as long as the quantum data encoding is consistent throughout the circuit. Despite its fundamental importance, the formalization of quantum data encoding has never been addressed systematically so far. We formalize the concept of quantum data encoding, namely the format providing a representation of a data set through a quantum state, as a distinct abstract layer with respect to the associated data loading circuit. We survey existing encoding methods and their respective strategies for classical-to-quantum exact and approximate data loading, for the quantum-to-classical extraction of information from states, and for quantum-to-quantum encoding conversion. Next, we show how major quantum algorithms find a natural interpretation in terms of data loading. For instance, the Quantum Fourier Transform is described as a quantum encoding converter, while the Quantum Amplitude Estimation as an extraction routine. The new conceptual framework is exemplified by considering its application to quantum-based Monte Carlo simulations, thus showcasing the power of the proposed formalism for the description of complex quantum circuits. Indeed, the approach clarifies the structure of complex quantum circuits and enables their efficient design.

Displaying community fact-checks is a promising approach to reduce engagement with misinformation on social media. However, how users respond to misleading content emotionally after community fact-checks are displayed on posts is unclear. Here, we employ quasi-experimental methods to causally analyze changes in sentiments and (moral) emotions in replies to misleading posts following the display of community fact-checks. Our evaluation is based on a large-scale panel dataset comprising N=2,225,260 replies across 1841 source posts from X's Community Notes platform. We find that informing users about falsehoods through community fact-checks significantly increases negativity (by 7.3%), anger (by 13.2%), disgust (by 4.7%), and moral outrage (by 16.0%) in the corresponding replies. These results indicate that users perceive spreading misinformation as a violation of social norms and that those who spread misinformation should expect negative reactions once their content is debunked. We derive important implications for the design of community-based fact-checking systems.

Community-based fact-checking is a promising approach to verify social media content and correct misleading posts at scale. Yet, causal evidence regarding its effectiveness in reducing the spread of misinformation on social media is missing. Here, we performed a large-scale empirical study to analyze whether community notes reduce the spread of misleading posts on X. Using a Difference-in-Differences design and repost time series data for N=237,677 (community fact-checked) cascades that had been reposted more than 431 million times, we found that exposing users to community notes reduced the spread of misleading posts by, on average, 62.0%. Furthermore, community notes increased the odds that users delete their misleading posts by 103.4%. However, our findings also suggest that community notes might be too slow to intervene in the early (and most viral) stage of the diffusion. Our work offers important implications to enhance the effectiveness of community-based fact-checking approaches on social media.

Steerable networks, which process data with intrinsic symmetries, often use Fourier-based nonlinearities that require sampling from the entire group, leading to a need for discretization in continuous groups. As the number of samples increases, both performance and equivariance improve, yet this also leads to higher computational costs. To address this, we introduce an adaptive sampling approach that dynamically adjusts the sampling process to the symmetries in the data, reducing the number of required group samples and lowering the computational demands. We explore various implementations and their effects on model performance, equivariance, and computational efficiency. Our findings demonstrate improved model performance, and a marginal increase in memory efficiency., Comment: 9 pages, published in the Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop at ICML 2024

In this paper, we address the distributed average consensus problem over directed networks in open multi-agent systems (OMAS), where the stability of the network is disrupted by frequent agent arrivals and departures, leading to a time-varying average consensus target. To tackle this challenge, we introduce a novel ratio consensus algorithm (OPENRC) based on acknowledgement feedback, designed to be robust to agent arrivals and departures, as well as to unbalanced directed network topologies. We demonstrate that when all active agents execute the OPENRC algorithm, the sum of their state variables remains constant during quiescent epochs when the network remains unchanged. By assuming eventual convergence during such quiescent periods following persistent variations in system composition and size, we prove the convergence of the OPENRC algorithm using column-stochasticity and mass-preservation properties. Finally, we apply and evaluate our proposed algorithm in a simulated environment, where agents are departing from and arriving in the network to highlight its resilience against changes in the network size and topology., Comment: 6 pages

We study a Schr\"odinger-like equation for the anharmonic potential $x^{2 \alpha}+\ell(\ell+1) x^{-2}-E$ when the anharmonicity $\alpha$ goes to $+\infty$. When $E$ and $\ell$ vary in bounded domains, we show that the spectral determinant for the central connection problem converges to a special function written in terms of a Bessel function of order $\ell+\frac{1}{2}$ and its zeros converge to the zeros of that Bessel function. We then study the regime in which $E$ and $\ell$ grow large as well, scaling as $E\sim \alpha^2 \varepsilon^2$ and $\ell\sim \alpha p$. When $\varepsilon$ is greater than $1$ we show that the spectral determinant for the central connection problem is a rapidly oscillating function whose zeros tend to be distributed according to the continuous density law $\frac{2p}{\pi}\frac{\sqrt{\varepsilon^2-1}}{\varepsilon}$. When $\varepsilon$ is close to $1$ we show that the spectral determinant converges to a function expressed in terms of the Airy function $\operatorname{Ai}(-)$ and its zeros converge to the zeros of that function. This work is motivated by and has applications to the ODE/IM correspondence for the quantum KdV model.

Twisted hexagonal boron nitride (thBN) exhibits emergent ferroelectricity due to the formation of moir\'e superlattices with alternating AB and BA domains. These domains possess electric dipoles, leading to a periodic electrostatic potential that can be imprinted onto other 2D materials placed in its proximity. Here we demonstrate the remote imprinting of moir\'e patterns from twisted hexagonal boron nitride (thBN) onto monolayer MoSe2 and investigate the resulting changes in the exciton properties. We confirm the imprinting of moir\'e patterns on monolayer MoSe2 via proximity using Kelvin probe force microscopy (KPFM) and hyperspectral photoluminescence (PL) mapping. By developing a technique to create large ferroelectric domain sizes ranging from 1 {\mu}m to 8.7 {\mu}m, we achieve unprecedented potential modulation of 387 +- 52 meV. We observe the formation of exciton polarons due to charge redistribution caused by the antiferroelectric moir\'e domains and investigate the optical property changes induced by the moir\'e pattern in monolayer MoSe2 by varying the moir\'e pattern size down to 110 nm. Our findings highlight the potential of twisted hBN as a platform for controlling the optical and electronic properties of 2D materials for optoelectronic and valleytronic applications.

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quantifies how the spread of quantum information into correlations makes it irretrievable from local measurements. Here, we explore the tight relation between QIS and the predictive power of QELMs. In particular, we show efficient state estimation is possible even beyond the scrambling time, for many different types of dynamics -- in fact, we show that in all the cases we studied, the reconstruction efficiency at long interaction times matches the optimal one offered by random global unitary dynamics. These results offer promising venues for robust experimental QELM-based state estimation protocols, as well as providing novel insights into the nature of QIS from a state estimation perspective.

We generalize the auxiliary field deformations of the principal chiral model (PCM) introduced in arXiv:2405.05899 and arXiv:2407.16338 to sigma models whose target manifolds are symmetric or semi-symmetric spaces, including a Wess-Zumino term in the latter case. This gives rise to a new infinite family of classically integrable $\mathbb{Z}_2$ and $\mathbb{Z}_4$ coset models of the form which are of interest in applications of integrability to worldsheet string theory and holography. We demonstrate that every theory in this infinite class admits a zero-curvature representation for its equations of motion by exhibiting a Lax connection., Comment: 52 pages; v2: reference updated

Many of the existing works on quadrotor control address the trajectory tracking problem by employing a cascade design in which the translational and rotational dynamics are stabilized by two separate controllers. The stability of the cascade is often proved by employing trajectory-based arguments, most notably, integral input-to-state stability. In this paper, we follow a different route and present a control law ensuring that a composite function constructed from the translational and rotational tracking errors is a Lyapunov function for the closed-loop cascade. In particular, starting from a generic control law for the double integrator, we develop a suitable attitude control extension, by leveraging a backstepping-like procedure. Using this construction, we provide an almost global stability certificate. The proposed design employs the unit sphere $\mathcal{S}^2$ to describe the rotational degrees of freedom required for position control. This enables a simpler controller tuning and an improved tracking performance with respect to previous global solutions. The new design is demonstrated via numerical simulations and on real-world experiments.

Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and developed various models for extensive experiments and analysis. Our models can provide accurate explanations for unknown DDIs between known drugs. This paper contributes new tools to the field of DDI prediction and lays a solid foundation for further research on generating explanations for DDI predictions., Comment: 17 pages, 4 figures

A successful theory of star formation should predict the number of objects as a function of their mass produced through star-forming events. Previous studies in star-forming regions and the solar neighborhood identify a mass function increasing from the hydrogen-burning limit down to about 10 M$_{J}$. Theory predicts a limit to the fragmentation process, providing a natural turnover in the mass function down to the opacity limit of turbulent fragmentation thought to be 2-10 M$_{J}$. Programs to date have not been sensitive enough to probe the hypothesized opacity limit of fragmentation. Here we present the first identification of a turnover in the initial mass function below 12 M$_{J}$ within NGC 2024, a young star-forming region. With JWST/NIRCam deep exposures across 0.7-5 {\mu}m, we identified several free floating objects down to ~ 3 M$_{J}$ with sensitivity to 0.5 M$_{J}$. We present evidence for a double power law model increasing from about 60 M$_{J}$ to roughly 12 M$_{J}$, consistent with previous studies, followed by a decrease down to 0.5 M$_{J}$. Our results support the predictions of star and brown dwarf formation theory, identifying the theoretical turnover in the mass function and suggest the fundamental limit of turbulent fragmentation near 3 M$_{J}$., Comment: 16 pages, 4 figures, submitted 6 September 2024

For each pair of positive integers $(a,b)$ such that $a \geq 0$ and $b > 1$, the present paper provides a necessary and sufficient condition for the existence of Hamiltonian cycles visiting all the vertices of any $k$-dimensional grid $\{0,1\}^k \subset \mathbb{R}^k$ and whose associated Euclidean distance is equal to $\sqrt{a^2+b^2}$. Our solution extends previously stated results in fairy chess on the existence of closed Euclidean $(a,b)$-leapers tours for $2 \times 2 \times \cdots \times 2$ chessboards, where the (Euclidean) knight identifies the $(1,2)$-leaper., Comment: 6 pages. Names of Lemma 2.3 and Corollary 2.5 fixed

Complexity science, despite its broad scope and potential impact, has not kept pace with fields like artificial intelligence, biotechnology and social sciences in addressing ethical concerns. The field lacks a comprehensive ethical framework, leaving us, as a community, vulnerable to ethical challenges and dilemmas. Other areas have gone through similar experiences and created, with discussions and working groups, their guides, policies and recommendations. Therefore, here we highlight the critical absence of formal guidelines, dedicated ethical committees, and widespread discussions on ethics within the complexity science community. Drawing on insights from the disciplines mentioned earlier, we propose a roadmap to enhance ethical awareness and action. Our recommendations include (i) initiating supportive mechanisms to develop ethical guidelines specific to complex systems research, (ii) creating open-access resources, and (iii) fostering inclusive dialogues to ensure that complexity science can responsibly tackle societal challenges and achieve a more inclusive environment. By initiating this dialogue, we aim to encourage a necessary shift in how ethics is integrated into complexity research, positioning the field to address contemporary challenges more effectively., Comment: 7 pages, 2 figures, 1 Annexus, 1 table