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The recent surge in the field of generative artificial intelligence (GenAI) has the potential to bring about transformative changes across a range of sectors, including software engineering and education. As GenAI tools, such as OpenAI's ChatGPT, are increasingly utilised in software engineering, it becomes imperative to understand the impact of these technologies on the software product. This study employs a methodological approach, comprising web scraping and data mining from LeetCode, with the objective of comparing the software quality of Python programs produced by LeetCode users with that generated by GPT-4o. In order to gain insight into these matters, this study addresses the question whether GPT-4o produces software of superior quality to that produced by humans. The findings indicate that GPT-4o does not present a considerable impediment to code quality, understandability, or runtime when generating code on a limited scale. Indeed, the generated code even exhibits significantly lower values across all three metrics in comparison to the user-written code. However, no significantly superior values were observed for the generated code in terms of memory usage in comparison to the user code, which contravened the expectations. Furthermore, it will be demonstrated that GPT-4o encountered challenges in generalising to problems that were not included in the training data set. This contribution presents a first large-scale study comparing generated code with human-written code based on LeetCode platform based on multiple measures including code quality, code understandability, time behaviour and resource utilisation. All data is publicly available for further research.

Extremum-seeking control (ESC) is widely used to optimize performance when the system dynamics are uncertain. However, sensitivity to sensor noise is an important issue in ESC implementation due to the use of high-pass filters or gradient estimators. To reduce the sensitivity of ESC to noise, this paper investigates the use of adaptive input and state estimation (AISE) for numerical differentiation. In particular, this paper develops extremum-seeking control with adaptive input and state estimation (ESC/AISE), where the high-pass filter of ESC is replaced by AISE to improve performance under sensor noise. The effectiveness of ESC/AISE is illustrated via numerical examples., Comment: 8 pages, 13 figures. Submitted to ACC 2025

Scientific collaborations require a strong computing infrastructure to successfully process and analyze data. While large-scale collaborations have access to resources such as Analysis Facilities, small-scale collaborations often lack the resources to establish and maintain such an infrastructure and instead operate with fragmented analysis environments, resulting in inefficiencies, hindering reproducibility and thus creating additional challenges for the collaboration that are not related to the experiment itself. We present a scalable, lightweight and maintainable Analysis Facility developed for the DARWIN collaboration as an example study case. Grid computing and storage resources are integrated into the facility, allowing for distributed computing and a common entry point for storage. The authentication and authorization infrastructure for all services is token-based, using an Indigo IAM instance. We discuss the architecture of the facility, its provided services, the user experience, and how it can serve as a sustainable blueprint for small-scale collaborations.

We present WorldPose, a novel dataset for advancing research in multi-person global pose estimation in the wild, featuring footage from the 2022 FIFA World Cup. While previous datasets have primarily focused on local poses, often limited to a single person or in constrained, indoor settings, the infrastructure deployed for this sporting event allows access to multiple fixed and moving cameras in different stadiums. We exploit the static multi-view setup of HD cameras to recover the 3D player poses and motions with unprecedented accuracy given capture areas of more than 1.75 acres. We then leverage the captured players' motions and field markings to calibrate a moving broadcasting camera. The resulting dataset comprises more than 80 sequences with approx 2.5 million 3D poses and a total traveling distance of over 120 km. Subsequently, we conduct an in-depth analysis of the SOTA methods for global pose estimation. Our experiments demonstrate that WorldPose challenges existing multi-person techniques, supporting the potential for new research in this area and others, such as sports analysis. All pose annotations (in SMPL format), broadcasting camera parameters and footage will be released for academic research purposes.

The purpose of this paper is to characterize the concept of monotonicity according to a direction related to a set of n random variables in terms of its associated n-copula C. We start establishing relationships in the bivariate and trivariate cases, which help to understand the extension to the multivariate case. Examples of copulas in all the studied cases are provided.

Entanglement-based networks (EBNs) enable general-purpose quantum communication by combining entanglement and its swapping in a sequence that addresses the challenges of achieving long distance communication with high fidelity associated with quantum technologies. In this context, entanglement distribution refers to the process by which two nodes in a quantum network share an entangled state, serving as a fundamental resource for communication. In this paper, we study the performance of entanglement distribution mechanisms over a physical topology comprising end nodes and quantum switches, which are crucial for constructing large-scale links. To this end, we implemented a switch-based topology in NetSquid and conducted a series of simulation experiments to gain insight into practical and realistic quantum network engineering challenges. These challenges include, on the one hand, aspects related to quantum technology, such as memory technology, gate durations, and noise; and, on the other hand, factors associated with the distribution process, such as the number of switches, distances, purification, and error correction. All these factors significantly impact the end-to-end fidelity across a path, which supports communication between two quantum nodes. We use these experiments to derive some guidelines towards the design and configuration of future EBNs.

In recent years, toxic content and hate speech have become widespread phenomena on the internet. Moderators of online newspapers and forums are now required, partly due to legal regulations, to carefully review and, if necessary, delete reader comments. This is a labor-intensive process. Some providers of large language models already offer solutions for automated hate speech detection or the identification of toxic content. These include GPT-4o from OpenAI, Jigsaw's (Google) Perspective API, and OpenAI's Moderation API. Based on the selected German test dataset HOCON34k, which was specifically created for developing tools to detect hate speech in reader comments of online newspapers, these solutions are compared with each other and against the HOCON34k baseline. The test dataset contains 1,592 annotated text samples. For GPT-4o, three different promptings are used, employing a Zero-Shot, One-Shot, and Few-Shot approach. The results of the experiments demonstrate that GPT-4o outperforms both the Perspective API and the Moderation API, and exceeds the HOCON34k baseline by approximately 5 percentage points, as measured by a combined metric of MCC and F2-score.

In recent years, a plethora of database management systems have surfaced to meet the demands of various scenarios. Emerging database systems, such as time-series and streaming database systems, are tailored to specific use cases requiring enhanced functionality and performance. However, as they are typically less mature, there can be bugs that either cause incorrect results or errors impacting reliability. To tackle this, we propose enhanced differential testing to uncover various bugs in emerging SQL-like database systems. The challenge is how to deal with differences of these emerging databases. Our insight is that many emerging database systems are conceptually extensions of relational database systems, making it possible to reveal logic bugs leveraging existing relational, known-reliable database systems. However, due to inevitable syntax or semantics gaps, it remains challenging to scale differential testing to various emerging database systems. We enhance differential testing for emerging database systems with three steps: (i) identifying shared clauses; (ii) extending shared clauses via mapping new features back to existing clauses of relational database systems; and (iii) generating differential inputs using extended shared clauses. We implemented our approach in a tool called SQLxDiff and applied it to four popular emerging database systems. In total, we found 57 unknown bugs, of which 17 were logic bugs and 40 were internal errors. Overall, vendors fixed 50 bugs and confirmed 5. Our results demonstrate the practicality and effectiveness of SQLxDiff in detecting bugs in emerging database systems, which has the potential to improve the reliability of their applications., Comment: 10 pages, 6 figures

Understanding the formation of skyrmions in centrosymmetric materials is a problem of fundamental and technological interest. GdRu2Si2 is one such candidate material which has been shown to host a variety of multi-Q magnetic structures, including in zero-field. Here, inelastic neutron scattering is used to measure the spin excitations in the field-polarized phase of GdRu2Si2. Linear spin wave theory and a method of interaction invariant path analysis are used to derive a Hamiltonian accounting for the observed spectra, and comparisons to \textit{ab initio} calculations are made. No evidence for anisotropic or higher order-exchange terms beyond bilinear Heisenberg exchange is found. This is discussed in the context of the multi-Q states existing at lower fields, for which these types of terms have previously been conceived to be significant in the formation of multi-Q ground states.

We present Atacama Compact Array (ACA) Band-3 observations of the protocluster SPT2349$-$56, an extreme system hosting ${\gtrsim}\,12$ submillimeter galaxies (SMGs) at $z\,{=}\,4.3$, to study its integrated molecular gas content via CO(4-3) and long-wavelength dust continuum. The $\sim$30-hour integration represents one of the longest exposures yet taken on a single pointing with the ACA 7-m. The low-resolution ACA data ($21.0''\,{\times}\,12.2''$) reveal a 75% excess CO(4-3) flux compared to the sum of individual sources detected in higher-resolution Atacama Large Millimeter Array (ALMA) data ($1.0''\,{\times}\,0.8''$). Our work also reveals a similar result by tapering the ALMA data to $10''$. In contrast, the 3.2mm dust continuum shows little discrepancy between ACA and ALMA. A single-dish [CII] spectrum obtained by APEX/FLASH supports the ACA CO(4-3) result, revealing a large excess in [CII] emission relative to ALMA. The missing flux is unlikely due to undetected faint sources but instead suggests that high-resolution ALMA observations might miss extended and low-surface-brightness gas. Such emission could originate from the circum-galactic medium (CGM) or the pre-heated proto-intracluster medium (proto-ICM). If this molecular gas reservoir replenishes the star formation fuel, the overall depletion timescale will exceed 400Myr, reducing the requirement for the simultaneous SMG activity in SPT2349$-$56. Our results highlight the role of an extended gas reservoir in sustaining a high star formation rate (SFR) in SPT2349$-$56, and potentially establishing the ICM during the transition phase to a mature cluster., Comment: 14 pages, 6 figures and 3 tables. Submitted to ApJL. Comments are welcome!

Engineering sublattice imbalance is an intuitive way to induce high-spin ground states in bipartite polycyclic conjugated hydrocarbons (PCHs). Such high-spin molecules can be employed as building blocks of quantum spin chains, which are outstanding platforms to study many-body physics and fundamental models in quantum magnetism. Recent reports on the bottom-up synthesis of antiferromagnetic molecular spin chains provided insights into paradigmatic quantum phenomena such as fractionalization. In contrast to antiferromagnetism, demonstration of ferromagnetic coupling between PCHs has been scarce. Previous attempts in this direction were limited by the formation of non-benzenoid rings leading to spin quenching, or the use of spacer motifs that considerably weaken the magnitude of ferromagnetic exchange. Here, we demonstrate the on-surface synthesis of short ferromagnetic spin chains based on dibenzotriangulene (DBT), a PCH with a triplet ground state. Our synthetic strategy centers on achieving a direct (that is, without a spacer motif) majority-minority sublattice coupling between adjacent units. This leads to a global sublattice imbalance in spin chains scaling with the chain length, and therefore a ferromagnetic ground state with a strong intermolecular ferromagnetic exchange. By means of scanning probe measurements and multiconfigurational quantum chemistry calculations, we analyze the electronic and magnetic properties of ferromagnetic dimers and trimers of DBT, and confirm their quintet and septet ground states, respectively, with an intermolecular ferromagnetic exchange of 7 meV. Furthermore, we elucidate the role of sublattice coupling on magnetism through complementary experiments on antiferromagnetic DBT dimers with majority-majority and minority-minority couplings. We expect our proof-of-principle study to provide impetus for the design of purely organic ferromagnetic materials., Comment: Manuscript: 11 pages and 5 figures. Supporting information: 8 pages and 8 figures. The abstract has been slightly shortened to fit the arXiv guidelines, the full abstract is provided in the manuscript

The high-performance computing (HPC) landscape is undergoing rapid transformation, with an increasing emphasis on energy-efficient and heterogeneous computing environments. This comprehensive study extends our previous research on SYCL's performance portability by evaluating its effectiveness across a broader spectrum of computing architectures, including CPUs, GPUs, and hybrid CPU-GPU configurations from NVIDIA, Intel, and AMD. Our analysis covers single-GPU, multi-GPU, single-CPU, and CPU-GPU hybrid setups, using the SW\# protein database search application as a case study. The results demonstrate SYCL's versatility across different architectures, maintaining comparable performance to CUDA on NVIDIA GPUs while achieving similar architectural efficiency rates on most CPU configurations. Although SYCL showed excellent functional portability in hybrid CPU-GPU configurations, performance varied significantly based on specific hardware combinations. Some performance limitations were identified in multi-GPU and CPU-GPU configurations, primarily attributed to workload distribution strategies rather than SYCL-specific constraints. These findings position SYCL as a promising unified programming model for heterogeneous computing environments, particularly for bioinformatic applications., Comment: arXiv admin note: text overlap with arXiv:2309.09609

The problems raised by anti-commons and bureaucracy have been linked since the study of Buchanan and Yoon (2000). Bureaucracy involves a multitude of agents that have deciding power. At the view of conflicting interests, the decision makers inertia or the inertia of the system itself, excessive administrative procedures or too many administrative circuits push for too late decisions, or for non-rational decisions in terms of value creation for economic agents. Property Rights Theory explains new concerns. Considering that an anti-commons problem arises when there are multiple rights to exclude, the problem of decision process in aquaculture projects makes sense at this level. However, little attention has been given to the setting where more than one person is assigned exclusion rights, which may be exercised. Anti-commons problem is analyzed in situations in which resources are inefficiently under-utilized rather than over-utilized as in the familiar commons setting. In this study, fisheries problems are studied and some ways to deal with the problem are presented., Comment: 10 pages and no figures

Traditional implicit generative models are capable of learning highly complex data distributions. However, their training involves distinguishing real data from synthetically generated data using adversarial discriminators, which can lead to unstable training dynamics and mode dropping issues. In this work, we build on the \textit{invariant statistical loss} (ISL) method introduced in \cite{de2024training}, and extend it to handle heavy-tailed and multivariate data distributions. The data generated by many real-world phenomena can only be properly characterised using heavy-tailed probability distributions, and traditional implicit methods struggle to effectively capture their asymptotic behavior. To address this problem, we introduce a generator trained with ISL, that uses input noise from a generalised Pareto distribution (GPD). We refer to this generative scheme as Pareto-ISL for conciseness. Our experiments demonstrate that Pareto-ISL accurately models the tails of the distributions while still effectively capturing their central characteristics. The original ISL function was conceived for 1D data sets. When the actual data is $n$-dimensional, a straightforward extension of the method was obtained by targeting the $n$ marginal distributions of the data. This approach is computationally infeasible and ineffective in high-dimensional spaces. To overcome this, we extend the 1D approach using random projections and define a new loss function suited for multivariate data, keeping problems tractable by adjusting the number of projections. We assess its performance in multidimensional generative modeling and explore its potential as a pretraining technique for generative adversarial networks (GANs) to prevent mode collapse, reporting promising results and highlighting its robustness across various hyperparameter settings.

The newly accessible mid-infrared (MIR) window offered by the James Webb Space Telescope (JWST) for exoplanet imaging is expected to provide valuable information to characterize their atmospheres. In particular, coronagraphs on board the JWST Mid-InfraRed instrument (MIRI) are capable of imaging the coldest directly imaged giant planets at the wavelengths where they emit most of their flux. The MIRI coronagraphs have been specially designed to detect the NH3 absorption around 10.5 microns, which has been predicted by atmospheric models. We aim to assess the presence of NH3 while refining the atmospheric parameters of one of the coldest companions detected by directly imaging GJ 504 b. Its mass is still a matter of debate and depending on the host star age estimate, the companion could either be placed in the brown dwarf regime or in the young Jovian planet regime. We present an analysis of MIRI coronagraphic observations of the GJ 504 system. We took advantage of previous observations of reference stars to build a library of images and to perform a more efficient subtraction of the stellar diffraction pattern. We detected the presence of NH3 at 12.5 sigma in the atmosphere, in line with atmospheric model expectations for a planetary-mass object and observed in brown dwarfs within a similar temperature range. The best-fit model with Exo-REM provides updated values of its atmospheric parameters, yielding a temperature of Teff = 512 K and radius of R = 1.08 RJup. These observations demonstrate the capability of MIRI coronagraphs to detect NH3 and to provide the first MIR observations of one of the coldest directly imaged companions. Overall, NH3 is a key molecule for characterizing the atmospheres of cold planets, offering valuable insights into their surface gravity. These observations provide valuable information for spectroscopic observations planned with JWST., Comment: Published in A&A

The polynomial affine model of gravity was proposed as an alternative to metric and metric-affine gravitational models. What at the beginning was thought as a source of unpredictability, the presence of many terms in the action, turned out to be a milestone, since it contains all possible combinations of the fields compatible with the covariance under diffeomorphisms. Here, we present a review of the advances in the analysis of the model after ten years of its proposal, and sketch the guideline of our future perspectives., Comment: revtex4-2. Main: 22 pag., Appendix: 2 pag. Biblography: 4 pag

Quantum computing is an emerging field that promises to revolutionize various domains, such as simulation optimization, data processing, and more, by leveraging the principles of quantum mechanics. This paper outlines innovative pedagogical strategies developed by university lecturers in Finland and Spain for teaching quantum computer programming and quantum software engineering. Our curriculum integrates essential tools and methodologies such as containerization with Docker, Qiskit, PennyLane, and Ocean SDK to provide a comprehensive learning experience. The approach consists of several steps, from introducing the fundamentals of quantum mechanics to hands-on labs focusing on practical use cases. We believe quantum computer programming is an important topic and one that is hard to teach, so having a teaching agenda and guidelines for teaching can be of great help., Comment: Presented at IEEE Quantum Week 2024 (QCE'24), 5 pages, 0 figures

This article provides a self-contained comprehensive review of the phenomenon of synchronization in dynamical systems, with a particular focus on chaotic systems in both continuous-time and discrete-time contexts. Synchronization, initially observed by Christiaan Huygens in 1665, has evolved from the study of periodic signals to encompass chaotic systems, where the sensitive dependence on initial conditions poses unique challenges. This review pointed to both theoretical foundations and contributions (concepts and methods) and practical insights, reinforcing the relevance of chaos synchronization in physics, biology, engineering, social sciences, economics and communication systems. The study investigates various coupling schemes, such as unidirectional and bidirectional coupling, and presents stability criteria under different configurations. In a very concise way, some ongoing research carried out by the authors is also indicated, using Lorenz, Rossler and hyperchaotic Rossler systems, and quadratic maps as case studies with parameter values leading to chaotic behavior. Special attention is given to the stability of synchronized states and the role of multi-stability and bifurcations, and its implications to loss of synchronization. We highlight the role of Lyapunov exponents, eigenvalues, and Lyapunov functions in guaranteeing local and global stability of the synchronized state. We aim to contribute to a broader understanding of chaos synchronization and its practical applications in diverse fields of knowledge. This text shed light on the control and stability of coupled chaotic systems, offering new perspectives on the synchronization of non-identical systems and the emergence of complex synchronization dynamics.

Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image characteristics. Consequently, manufacturers must conduct regular updates to ensure that they remain safe and effective. Performing such updates in the United States and European Union required, until recently, obtaining re-approval. Given the time and financial burdens associated with these processes, updates were infrequent, and obsolete systems remained in operation for too long. During 2024, several regulatory developments promised to streamline the safe rollout of model updates: The European Artificial Intelligence Act came into effect last August, and the Food and Drug Administration (FDA) issued final marketing submission recommendations for a Predetermined Change Control Plan (PCCP) in December. We provide an overview of these developments and outline the key building blocks necessary for successfully deploying dynamic systems. At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms.

Cocoa is a multi-billion-dollar industry but research on improving yields through pollination remains limited. New embedded hardware and AI-based data analysis is advancing information on cocoa flower visitors, their identity and implications for yields. We present the first cocoa flower visitor dataset containing 5,792 images of Ceratopogonidae, Formicidae, Aphididae, Araneae, and Encyrtidae, and 1,082 background cocoa flower images. This dataset was curated from 23 million images collected over two years by embedded cameras in cocoa plantations in Hainan province, China. We exemplify the use of the dataset with different sizes of YOLOv8 models and by progressively increasing the background image ratio in the training set to identify the best-performing model. The medium-sized YOLOv8 model achieved the best results with 8% background images (F1 Score of 0.71, mAP50 of 0.70). Overall, this dataset is useful to compare the performance of deep learning model architectures on images with low contrast images and difficult detection targets. The data can support future efforts to advance sustainable cocoa production through pollination monitoring projects., Comment: The manuscript introduces the first cocoa pollination dataset and an example analysis with YOLOv8 models

In this paper, we study the well-posedness of state-dependent and state-independent sweeping processes driven by prox-regular sets and perturbed by a history-dependent operator. Our approach, based on an enhanced version of Gronwall's lemma and fixed-point arguments, provides an efficient framework for analyzing sweeping processes. In particular, our findings recover all existing results for the class of Volterra sweeping processes and provide new insights into history-dependent sweeping processes. Finally, we apply our theoretical results to establish the well-posedness of a viscoelastic model with long memory.

This work explores the potential of the Variational Quantum Eigensolver in solving Dynamic Portfolio Optimization problems surpassing the 100 qubit utility frontier. We systematically analyze how to scale this strategy in complexity and size, from 6 to 112 qubits, by testing different combinations of ansatz and optimizer on a real Quantum Processing Unit. We achieve best results by using a combination of a Differential Evolution classical optimizer and an ansatz circuit tailored to both the problem and the properties of the Quantum Processing Unit.

Cryptocurrency markets often face manipulation through prevalent pump-and-dump (P&D) schemes, where self-organized Telegram groups, some exceeding two million members, artificially inflate target cryptocurrency prices. These groups sell premium access to inside information, worsening information asymmetry and financial risks for subscribers and all investors. This paper presents a real-time prediction pipeline to forecast target coins and alert investors to possible P&D schemes. In a Poloniex case study, the model accurately identified the target coin among the top five from 50 random coins in 24 out of 43 (55.81%) P&D events. The pipeline uses advanced natural language processing (NLP) to classify Telegram messages, identifying 2,079 past pump events and detecting new ones in real-time. Our analysis also evaluates the susceptibility of token standards - ERC-20, ERC-721, BRC-20, Inscriptions, and Runes - to manipulation and identifies exchanges commonly involved in P&D schemes.

Building trust between humans and robots has long interested the robotics community. Various studies have aimed to clarify the factors that influence the development of user trust. In Human-Robot Interaction (HRI) environments, a critical aspect of trust development is the robot's ability to make its behavior understandable. The concept of an eXplainable Autonomous Robot (XAR) addresses this requirement. However, giving a robot self-explanatory abilities is a complex task. Robot behavior includes multiple skills and diverse subsystems. This complexity led to research into a wide range of methods for generating explanations about robot behavior. This paper presents a systematic literature review that analyzes existing strategies for generating explanations in robots and studies the current XAR trends. Results indicate promising advancements in explainability systems. However, these systems are still unable to fully cover the complex behavior of autonomous robots. Furthermore, we also identify a lack of consensus on the theoretical concept of explainability, and the need for a robust methodology to assess explainability methods and tools has been identified., Comment: 14 pages, 12 figures, 10 tables. This paper is a preprint of an article submitted to IEEE Access

In this paper we show how almost cosymplectic structures are a natural framework to study thermodynamical systems. Indeed, we are able to obtain the same evolution equations obtained previously by Gay-Balmaz and Yoshimura (see Entropy, 21(8):39, 2019) using variational arguments. The proposed geometric description allows us to apply geometrical tools to discuss reduction by symmetries, the Hamilton-Jacobi equation or discretization of these systems.

We explore dynamical features of the maximally symmetric nonlinear extension of classical electromagnetism, recently proposed in the literature as "ModMax" electrodynamics. This family of theories is the only one that preserves all the symmetries of Maxwell's theory, having applications in the study of regular black hole solutions and supersymmetry. The purpose of this article is three-fold. Firstly, we study the initial-value problem of ModMax and show, by means of a simple geometric criterion, that such a theory admits a well-posed formulation. Secondly, we prove a series of geometric inequalities relating energy, charge, angular momentum, and size in ModMax. The validity of these bounds gives strong evidence of a universal inequality conjectured by Bekenstein for macroscopic systems. Finally, we perform the first stable numerical simulations of ModMax in the highly nonlinear regime, and verify an inequality between energy, size and angular momentum in bounded domains., Comment: 16 pages, 3 figures

Multi-channel photoplethysmography (PPG) sensors have found widespread adoption in wearable devices for monitoring cardiac health. Channels thereby serve different functions -- whereas green is commonly used for metrics such as heart rate and heart rate variability, red and infrared are commonly used for pulse oximetry. In this paper, we introduce a novel method that simultaneously fuses multi-channel PPG signals into a single recovered PPG signal that can be input to further processing. Via signal fusion, our learning-based method compensates for the artifacts that affect wavelengths to different extents, such as motion and ambient light changes. We evaluate our method on a novel dataset of multi-channel PPG recordings and electrocardiogram recordings for reference from 10 participants over the course of 13 hours during real-world activities outside the laboratory. Using the fusion PPG signal our method recovered, participants' heart rates can be calculated with a mean error of 4.5\,bpm (23\% lower than from green PPG signals at 5.9\,bpm)., Comment: Accepted at BSN2024

Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR estimates can be substantially impacted by factors such as the wearer's activities, sensor placement and resulting motion artifacts, as well as environmental characteristics such as temperature and ambient light. These and other factors can significantly impact and decrease HR prediction reliability. In this paper, we show that state-of-the-art HR estimation methods struggle when processing \emph{representative} data from everyday activities in outdoor environments, likely because they rely on existing datasets that captured controlled conditions. We introduce a novel multimodal dataset and benchmark results for continuous PPG recordings during outdoor activities from 16 participants over 13.5 hours, captured from four wearable sensors, each worn at a different location on the body, totaling 216\,hours. Our recordings include accelerometer, temperature, and altitude data, as well as a synchronized Lead I-based electrocardiogram for ground-truth HR references. Participants completed a round trip from Zurich to Jungfraujoch, a tall mountain in Switzerland over the course of one day. The trip included outdoor and indoor activities such as walking, hiking, stair climbing, eating, drinking, and resting at various temperatures and altitudes (up to 3,571\,m above sea level) as well as using cars, trains, cable cars, and lifts for transport -- all of which impacted participants' physiological dynamics. We also present a novel method that estimates HR values more robustly in such real-world scenarios than existing baselines., Comment: Accepted at NeurIPS2024

Smartwatches have become popular for monitoring physiological parameters outside clinical settings. Using reflective photoplethysmography (PPG) sensors, such watches can non-invasively estimate heart rate (HR) in everyday environments and throughout a patient's day. However, achieving consistently high accuracy remains challenging, particularly during moments of increased motion or due to varying device placement. In this paper, we introduce a novel sensor fusion method for estimating HR that flexibly combines samples from multiple PPG sensors placed across the patient's body, including wrist, ankle, head, and sternum (chest). Our method first estimates signal quality across all inputs to dynamically integrate them into a joint and robust PPG signal for HR estimation. We evaluate our method on a novel dataset of PPG and ECG recordings from 14 participants who engaged in real-world activities outside the laboratory over the course of a whole day. Our method achieves a mean HR error of 2.4\,bpm, which is 46\% lower than the mean error of the best-performing single device (4.4\,bpm, head)., Comment: Accepted at EMBC2024

Understanding the collective dynamics behind the success of ideas, products, behaviors, and social actors is critical for decision-making across diverse contexts, including hiring, funding, career choices, and the design of interventions for social change. Methodological advances and the increasing availability of big data now allow for a broader and deeper understanding of the key facets of success. Recent studies unveil regularities beneath the collective dynamics of success, pinpoint underlying mechanisms, and even enable predictions of success across diverse domains, including science, technology, business, and the arts. However, this research also uncovers troubling biases that challenge meritocratic views of success. This review synthesizes the growing, cross-disciplinary literature on the collective dynamics behind success and calls for further research on cultural influences, the origins of inequalities, the role of algorithms in perpetuating them, and experimental methods to further probe causal mechanisms behind success. Ultimately, these efforts may help to better align success with desired societal values.

Reputation and punishment are significant guidelines for regulating individual behavior in human society, and those with a good reputation are more likely to be imitated by others. In addition, society imposes varying degrees of punishment for behaviors that harm the interests of groups with different reputations. However, conventional pairwise interaction rules and the punishment mechanism overlook this aspect. Building on this observation, this paper enhances a spatial public goods game in two key ways: 1) We set a reputation threshold and use punishment to regulate the defection behavior of players in low-reputation groups while allowing defection behavior in high-reputation game groups. 2) Differently from pairwise interaction rules, we combine reputation and payoff as the fitness of individuals to ensure that players with both high payoff and reputation have a higher chance of being imitated. Through simulations, we find that a higher reputation threshold, combined with a stringent punishment environment, can substantially enhance the level of cooperation within the population. This mechanism provides deeper insight into the widespread phenomenon of cooperation that emerges among individuals.

Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information. In this work, we propose the Differential Spatial-Spectral Transformer (DiffFormer), a novel framework designed to address the inherent challenges of HSIC, such as spectral redundancy and spatial discontinuity. The DiffFormer leverages a Differential Multi-Head Self-Attention (DMHSA) mechanism, which enhances local feature discrimination by introducing differential attention to accentuate subtle variations across neighboring spectral-spatial patches. The architecture integrates Spectral-Spatial Tokenization through three-dimensional (3D) convolution-based patch embeddings, positional encoding, and a stack of transformer layers equipped with the SWiGLU activation function for efficient feature extraction (SwiGLU is a variant of the Gated Linear Unit (GLU) activation function). A token-based classification head further ensures robust representation learning, enabling precise labeling of hyperspectral pixels. Extensive experiments on benchmark hyperspectral datasets demonstrate the superiority of DiffFormer in terms of classification accuracy, computational efficiency, and generalizability, compared to existing state-of-the-art (SOTA) methods. In addition, this work provides a detailed analysis of computational complexity, showcasing the scalability of the model for large-scale remote sensing applications. The source code will be made available at \url{https://github.com/mahmad000/DiffFormer} after the first round of revision.

The increase in non-renewable energy consumption and CO2 emissions, especially in the manufacturing sector, is moving radical shifts in energy supply policies and production models. Renewable energy integration and regulated pricing policies require new and effective scheduling strategies, as highlighted by the emerging field of energy-efficient scheduling. In this paper, we aim to contribute to this field by addressing a scheduling problem where a set of jobs must be allocated to a set of machines over a discrete finite horizon and variable energy consumptions are required for job execution. Energy can be obtained by a renewable source or through transactions on the market. The goal is to minimize the total energy costs from the grid while scheduling all the jobs within the time horizon and adhering to an energy limit per time period. We introduce a novel time-indexed Mixed Integer Linear Programming (MILP) formulation capable of handling variable energy consumption functions, surpassing traditional models that assume constant energy usage of jobs. We then develop a matheuristic algorithm based on an Iterated Local Search (ILS) framework that exploits the MILP formulation for large neighborhood searches. We tested more than 200 instances with up to 200 jobs, 35 machines, and 120 time slots. The results show a good performance of both our methods and highlight the advantage of using the ILS when jobs are characterized by variable consumption functions.

Social media has revolutionized communication, allowing people worldwide to connect and interact instantly. However, it has also led to increases in cyberbullying, which poses a significant threat to children and adolescents globally, affecting their mental health and well-being. It is critical to accurately detect the roles of individuals involved in cyberbullying incidents to effectively address the issue on a large scale. This study explores the use of machine learning models to detect the roles involved in cyberbullying interactions. After examining the AMiCA dataset and addressing class imbalance issues, we evaluate the performance of various models built with four underlying LLMs (i.e., BERT, RoBERTa, T5, and GPT-2) for role detection. Our analysis shows that oversampling techniques help improve model performance. The best model, a fine-tuned RoBERTa using oversampled data, achieved an overall F1 score of 83.5%, increasing to 89.3% after applying a prediction threshold. The top-2 F1 score without thresholding was 95.7%. Our method outperforms previously proposed models. After investigating the per-class model performance and confidence scores, we show that the models perform well in classes with more samples and less contextual confusion (e.g., Bystander Other), but struggle with classes with fewer samples (e.g., Bystander Assistant) and more contextual ambiguity (e.g., Harasser and Victim). This work highlights current strengths and limitations in the development of accurate models with limited data and complex scenarios.

Spiking neural networks (SNNs) promise energy-efficient data processing by imitating the event-based behavior of biological neurons. In previous work, we introduced the enlarge-likelihood-each-notable-amplitude spiking-neural-network (ELENA-SNN) decoder, a novel decoding algorithm for low-density parity-check (LDPC) codes. The decoder integrates SNNs into belief propagation (BP) decoding by approximating the check node (CN) update equation using SNNs. However, when decoding LDPC codes with a small variable node(VN) degree, the approximation gets too rough, and the ELENA-SNN decoder does not yield good results. This paper introduces the multi-level ELENA-SNN (ML-ELENA-SNN) decoder, which is an extension of the ELENA-SNN decoder. Instead of a single SNN approximating the CN update, multiple SNNs are applied in parallel, resulting in a higher resolution and higher dynamic range of the exchanged messages. We show that the ML-ELENA-SNN decoder performs similarly to the ubiquitous normalized min-sum decoder for the (38400, 30720) regular LDPC code with a VN degree of dv = 3 and a CN degree of dc = 15., Comment: Accepted for publication at SCC 2025

In this study, we investigate the use of a large language model to assist in the evaluation of the reliability of the vast number of existing online news publishers, addressing the impracticality of relying solely on human expert annotators for this task. In the context of the Italian news media market, we first task the model with evaluating expert-designed reliability criteria using a representative sample of news articles. We then compare the model's answers with those of human experts. The dataset consists of 340 news articles, each annotated by two human experts and the LLM. Six criteria are taken into account, for a total of 6,120 annotations. We observe good agreement between LLM and human annotators in three of the six evaluated criteria, including the critical ability to detect instances where a text negatively targets an entity or individual. For two additional criteria, such as the detection of sensational language and the recognition of bias in news content, LLMs generate fair annotations, albeit with certain trade-offs. Furthermore, we show that the LLM is able to help resolve disagreements among human experts, especially in tasks such as identifying cases of negative targeting.

The NAHU$^2$ project is a Franco-Mexican collaboration aimed at building the $\pi$-YALLI corpus adapted to machine learning, which will subsequently be used to develop computer resources for the Nahuatl language. Nahuatl is a language with few computational resources, even though it is a living language spoken by around 2 million people. We have decided to build $\pi$-YALLI, a corpus that will enable to carry out research on Nahuatl in order to develop Language Models (LM), whether dynamic or not, which will make it possible to in turn enable the development of Natural Language Processing (NLP) tools such as: a) a grapheme unifier, b) a word segmenter, c) a POS grammatical analyser, d) a content-based Automatic Text Summarization; and possibly, e) a translator translator (probabilistic or learning-based)., Comment: 9 pages, in French language, 2 figures

We explore the Mott transition in orthorhombic diamond lattices relevant to (ET)Ag$_4$(CN)$_5$ molecular compounds. The non-interacting phases include nodal line, Dirac and/or Weyl semimetals depending on the strength of spin-orbit coupling and the degree of dimerization of the lattice. Based on an extension of slave-rotor mean-field theory which accounts for magnetic order, we find a transition from a semimetal to a paramagnetic Mott insulator at a critical $U_c$ which becomes N\'eel ordered at a larger Coulomb repulsion, $U_{cm}>U_{c}$. The resulting intermediate Mott phase is a $U(1)$ quantum spin liquid (QSL) consisting on spinon preserving the nodal structure of the nearby semimetallic phases. An analysis of the Green's function in this Mott phase shows how the zeros follow the spinon band dispersions carrying the topology while the poles describe the Hubbard bands. Our results are relevant to recent observations in (ET)Ag$_4$(CN)$_5$ molecular compounds in which the ambient pressure N\'eel ordered Mott insulator is gradually suppressed until semimetallic behavior arises at larger pressures., Comment: 22 pages, 13 figures

Combining multiple machine learning models has long been a technique for enhancing performance, particularly in distributed settings. Traditional approaches, such as model ensembles, work well, but are expensive in terms of memory and compute. Recently, methods based on averaging model parameters have achieved good results in some settings and have gained popularity. However, merging models initialized differently that do not share a part of their training trajectories can yield worse results than simply using the base models, even after aligning their neurons. In this paper, we introduce a novel approach, Non-uniform Parameter-wise Model Merging, or NP Merge, which merges models by learning the contribution of each parameter to the final model using gradient-based optimization. We empirically demonstrate the effectiveness of our method for merging models of various architectures in multiple settings, outperforming past methods. We also extend NP Merge to handle the merging of multiple models, showcasing its scalability and robustness., Comment: 9 pages, 1 figure, to be published in the Proceedings of the 9th IEEE Special Session on Machine Learning on Big Data (MLBD 2024)

We present ARCAS (Automated Root Cause Analysis System), a diagnostic platform based on a Domain Specific Language (DSL) built for fast diagnostic implementation and low learning curve. Arcas is composed of a constellation of automated troubleshooting guides (Auto-TSGs) that can execute in parallel to detect issues using product telemetry and apply mitigation in near-real-time. The DSL is tailored specifically to ensure that subject matter experts can deliver highly curated and relevant Auto-TSGs in a short time without having to understand how they will interact with the rest of the diagnostic platform, thus reducing time-to-mitigate and saving crucial engineering cycles when they matter most. This contrasts with platforms like Datadog and New Relic, which primarily focus on monitoring and require manual intervention for mitigation. ARCAS uses a Large Language Model (LLM) to prioritize Auto-TSGs outputs and take appropriate actions, thus suppressing the costly requirement of understanding the general behavior of the system. We explain the key concepts behind ARCAS and demonstrate how it has been successfully used for multiple products across Azure Synapse Analytics and Microsoft Fabric Synapse Data Warehouse., Comment: 13 pages, 6 figures