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Racial bias in medicine, such as in dermatology, presents significant ethical and clinical challenges. This is likely to happen because there is a significant underrepresentation of darker skin tones in training datasets for machine learning models. While efforts to address bias in dermatology have focused on improving dataset diversity and mitigating disparities in discriminative models, the impact of racial bias on generative models remains underexplored. Generative models, such as Variational Autoencoders (VAEs), are increasingly used in healthcare applications, yet their fairness across diverse skin tones is currently not well understood. In this study, we evaluate the fairness of generative models in clinical dermatology with respect to racial bias. For this purpose, we first train a VAE with a perceptual loss to generate and reconstruct high-quality skin images across different skin tones. We utilize the Fitzpatrick17k dataset to examine how racial bias influences the representation and performance of these models. Our findings indicate that VAE performance is, as expected, influenced by representation, i.e. increased skin tone representation comes with increased performance on the given skin tone. However, we also observe, even independently of representation, that the VAE performs better for lighter skin tones. Additionally, the uncertainty estimates produced by the VAE are ineffective in assessing the model's fairness. These results highlight the need for more representative dermatological datasets, but also a need for better understanding the sources of bias in such model, as well as improved uncertainty quantification mechanisms to detect and address racial bias in generative models for trustworthy healthcare technologies., Comment: Under review

The United Nations 2030 Agenda for Sustainable Development outlines 17 goals to address global challenges. However, progress has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we used a novel data-driven methodology to analyze data from 107 countries (2000$-$2022) using unsupervised machine learning techniques. Our analysis reveals strong positive and negative correlations between certain SDGs. The findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors, with no country on track to achieve all goals by 2030. This highlights the need for a region specific, systemic approach to sustainable development that acknowledges the complex interdependencies of the goals and the diverse capacities of nations. Our approach provides a robust framework for developing efficient and data-informed strategies, to promote cooperative and targeted initiatives for sustainable progress.

The public, regulators, and domain experts alike seek to understand the effect of deployed SAE level 4 automated driving system (ADS) technologies on safety. The recent expansion of ADS technology deployments is paving the way for early stage safety impact evaluations, whereby the observational data from both an ADS and a representative benchmark fleet are compared to quantify safety performance. In January 2024, a working group of experts across academia, insurance, and industry came together in Washington, DC to discuss the current and future challenges in performing such evaluations. A subset of this working group then met, virtually, on multiple occasions to produce this paper. This paper presents the RAVE (Retrospective Automated Vehicle Evaluation) checklist, a set of fifteen recommendations for performing and evaluating retrospective ADS performance comparisons. The recommendations are centered around the concepts of (1) quality and validity, (2) transparency, and (3) interpretation. Over time, it is anticipated there will be a large and varied body of work evaluating the observed performance of these ADS fleets. Establishing and promoting good scientific practices benefits the work of stakeholders, many of whom may not be subject matter experts. This working group's intentions are to: i) strengthen individual research studies and ii) make the at-large community more informed on how to evaluate this collective body of work.

A flag is a sequence of nested subspaces of a given ambient space F_q^n over a finite field F_q. In network coding, a flag code is a set of flags, all of them with the same sequence of dimensions, the type vector. In this paper, we investigate quasi-optimum distance flag codes, i.e., those attaining the second best possible distance value. We characterize them and present upper bounds for their cardinality. Moreover, we propose a systematic construction for every choice of the type vector by using partial spreads and sunflowers. For flag codes with lower minimum distance, we adapt the previous construction and provide some results towards their characterization, especially in the case of the third best possible distance value.

Future quantum networks will rely on the ability to coherently transfer optically encoded quantum information between different wavelength bands. Bragg-scattering four-wave mixing in optical fiber is a promising route to achieving this, but requires fibers with precise dispersion control and broadband transmission at signal, target and pump wavelengths. Here we introduce a photonic crystal fiber with a germanium-doped core featuring group velocity matching at 1550 nm, the telecoms C-band, and 920 nm, within the emission range of efficient single photon sources based on InAs quantum dots. With low chromatic walk-off and good optical guidance even at long wavelengths, large lengths of this fiber are used to achieve nanometer-scale frequency shifts between wavelengths around 920 nm with up to 79.4\% internal conversion efficiency, allowing dissimilar InAs dots to be interfaced. We also show how cascading this frequency conversion can be used to generate a frequency comb away from telecoms wavelengths. Finally, we use the fiber to demonstrate tunable frequency conversion of weak classical signals around 918 nm to the telecoms C-band.

Jupyter Notebook is an interactive development environment commonly used for rapid experimentation of machine learning (ML) solutions. Describing the ML activities performed along code cells improves the readability and understanding of Notebooks. Manual annotation of code cells is time-consuming and error-prone. Therefore, tools have been developed that classify the cells of a notebook concerning the ML activity performed in them. However, the current tools are not flexible, as they work based on look-up tables that have been created, which map function calls of commonly used ML libraries to ML activities. These tables must be manually adjusted to account for new or changed libraries. This paper presents a more flexible approach to cell classification based on a hybrid classification approach that combines a rule-based and a decision tree classifier. We discuss the design rationales and describe the developed classifiers in detail. We implemented the new flexible cell classification approach in a tool called JupyLabel. Its evaluation and the obtained metric scores regarding precision, recall, and F1-score are discussed. Additionally, we compared JupyLabel with HeaderGen, an existing cell classification tool. We were able to show that the presented flexible cell classification approach outperforms this tool significantly., Comment: 9 pages, 3 figures

There is an increasing need to comprehensively characterize the kinematic performances of different Micromobility Vehicles (MMVs). This study aims to: 1) characterize the kinematic behaviors of different MMVs during emergency maneuvers; 2) explore the influence of different MMV power sources on the device performances; 3) investigate if piecewise linear models are suitable for modeling MMV trajectories. A test track experiment where 40 frequent riders performed emergency braking and swerving maneuvers riding a subset of electric MMVs, their traditional counterparts, and, in some cases, behaving as running pedestrians. A second experiment was conducted to determine the MMVs swerving lower boundaries. Device power source resulted having a statistically significant influence on kinematic capabilities of the MMVs: while e-MMVs displayed superior braking capabilities compared to their traditional counterparts, the opposite was observed in terms of swerving performance. Furthermore, performances varied significantly across the different MMV typologies, with handlebar-based devices consistently outperforming the handlebar-less devices across the metrics considered. The piecewise linear models used for braking profiles fit well for most MMVs, except for skateboards and pedestrians due to foot-ground engagement. These findings underscore that the effectiveness of steering or braking in preventing collisions may vary depending on the type and power source of the device. This study also demonstrates the applicability of piecewise linear models for generating parameterized functions that accurately model braking trajectories, providing a valuable resource for automated systems developers. The model, however, also reveals that the single brake ramp assumption does not apply for certain types of MMVs or for pedestrians, indicating the necessity for further improvements., Comment: 21 pages, 8 figures

Hydrogen is central to the global energy transition when produced at low emissions. This paper introduces a renewable hydrogen production system model (HPSM) that optimizes a hybrid hydrogen production system (HPS) on a worldwide 50x50 km grid, considering country-specific interest rates. Besides the renewable energy's impact on the HPS design, we analyze the effect of country-specific interest rates on the levelized cost of hydrogen (LCOH) production. LCOH production ranges between 2.7 Euro/kg and 28.4 Euro/kg, with an average of 9.1 Euro/kg. Over one third (40.0%) of all cells have an installed PV capacity share between 50% and 70%, and 76.4% have a hybrid configuration. Hybrid HPSs can significantly reduce the LCOH production compared to non-hybrid designs, whereas country-specific interest rates lead to significant increases in the LCOH production. Hydrogen storage is deployed rather than battery storage to balance production and demand., Comment: 32 pages, 14 figures, submitted to International Journal of Hydrogen Energy

We report on the first lattice determination of the pseudoscalar meson $\eta_c$ light-cone distribution amplitude, using a set of three CLS $N_f=2$ ensembles at a pion mass $m_{\pi} \sim 270~\text{MeV}$ and lattice spacings $a \sim 0.076~\text{fm}$, $0.066~\text{fm}$ and $0.049~\text{fm}$. Employing Short Distance Factorization, we extract the pseudo-DA on the lattice for Ioffe times $\nu \leq 4.5$, and the various lattice spacings allow us to take the continuum limit. We employ a basis of Jacobi polynomials to parametrize the distribution amplitude, which allows to express the matching to the pseudo distribution in closed form, and we observe a strong effect which we attribute to the heavy charm-quark mass., Comment: Contribution to the 40th International Symposium on Lattice Field Theory (Lattice 2023), July 31st - August 4th, 2023. Fermi National Accelerator Laboratory

Given a finite field F_q and a positive integer n, a flag is a sequence of nested F_q-subspaces of a vector space F_q^n and a flag code is a nonempty collection of flags. The projected codes of a flag code are the constant dimension codes containing all the subspaces of prescribed dimensions that form the flags in the flag code. In this paper we address the notion of equivalence for flag codes and explore in which situations such an equivalence can be reduced to the equivalence of the corresponding projected codes. In addition, this study leads to new results concerning the automorphism group of certain families of flag codes, some of them also introduced in this paper.

In the out-of-equilibrium evolution induced by a quench, fast degrees of freedom generate long-range entanglement that is hard to encode with standard tensor networks. However, local observables only sense such long-range correlations through their contribution to the reduced local state as a mixture. We present a tensor network method that identifies such long-range entanglement and efficiently transforms it into mixture, much easier to represent. In this way, we obtain an effective description of the time-evolved state as a density matrix that captures the long-time behavior of local operators with finite computational resources., Comment: 5 pages, 4 figures, comments are welcome!

Quantum computers and simulators can potentially outperform classical computers in finding ground states of classical and quantum Hamiltonians. However, if this advantage can persist in the presence of noise without error correction remains unclear. In this paper, by exploiting the principle of Lagrangian duality, we develop a numerical method to classically compute a certifiable lower bound on the minimum energy attainable by the output state of a quantum circuit in the presence of depolarizing noise. We provide theoretical and numerical evidence that this approach can provide circuit-architecture dependent bounds on the performance of noisy quantum circuits., Comment: 23 pages, 8 figures

The use of large-scale supercomputing architectures is a hard requirement for scientific computing Big-Data applications. An example is genomics analytics, where millions of data transformations and tests per patient need to be done to find relevant clinical indicators. Therefore, to ensure open and broad access to high-performance technologies, governments, and academia are pushing toward the introduction of novel computing architectures in large-scale scientific environments. This is the case of RISC-V, an open-source and royalty-free instruction-set architecture. To evaluate such technologies, here we present the Variant-Interaction Analytics use case benchmarking suite and datasets. Through this use case, we search for possible genetic interactions using computational and statistical methods, providing a representative case for heavy ETL (Extract, Transform, Load) data processing. Current implementations are implemented in x86-based supercomputers (e.g. MareNostrum-IV at the Barcelona Supercomputing Center (BSC)), and future steps propose RISC-V as part of the next MareNostrum generations. Here we describe the Variant Interaction Use Case, highlighting the characteristics leveraging high-performance computing, indicating the caveats and challenges towards the next RISC-V developments and designs to come from a first comparison between x86 and RISC-V architectures on real Variant Interaction executions over real hardware implementations.

In the network coding framework, given a prime power $q$ and the vector space $\mathbb{F}_q^n$, a constant type flag code is a set of nested sequences of $\mathbb{F}_q$-subspaces (flags) with the same increasing sequence of dimensions (the type of the flag). If a flag code arises as the orbit under the action of a cyclic subgroup of the general linear group over a flag, we say that it is a cyclic orbit flag code. Among the parameters of such a family of codes, we have its best friend, that is the largest field over which all the subspaces in the generating flag are vector spaces. This object permits to compute the cardinality of the code and estimate its minimum distance. However, as it occurs with other absolute parameters of a flag code, the information given by the best friend is not complete in many cases due to the fact that it can be obtained in different ways. In this work, we present a new invariant, the best friend vector, that captures the specific way the best friend can be unfolded. Furthermore, throughout the paper we analyze the strong underlying interaction between this invariant and other parameters such as the cardinality, the flag distance, or the type vector, and how it conditions them. Finally, we investigate the realizability of a prescribed best friend vector in a vector space.

In this note we give a negative answer to a question proposed by Almendra-Hern\'andez and Mart\'inez-Sandoval. Let $n\le m$ be positive integers and let $X$ and $Y$ be sets of sizes $n$ and $m$ in $\mathbb R^{n-1}$ such that $X\cup Y$ is in generic position. There is a natural order on $X\times Y$ induced by the distances between the corresponding points. The question is if all possible orders on $X\times Y$ can be obtained in this way. We show that the answer is negative when $n

The efforts to replace the successful, albeit aging, TCP/IP Internet architecture with a better suited one have driving research interest to information-centric alternatives. The Named Data Networking (NDN) architecture is probably one of the main contenders to become the network layer of the future Internet thanks to its inbuilt support for mobility, in-network caching, security and, in general, for being better adapted to the needs of current network applications. At the same time, massive satellite constellations are currently being deployed in low Earth orbits (LEO) to provide a backend for network connectivity. It is expected that, very soon, these constellations will function as proper networks thanks to inter-satellite communication links. These new satellite networks will be able to benefit from their greenfield status and the new network architectures. In this paper we analyze how to deploy the network caches of an NDN-based LEO satellite network. In particular, we show how we can jointly select the most appropriate caching nodes for each piece of content and how to forward data across the constellation in two simple alternative ways. Performance results show that the caching and forwarding strategies proposed reduce path lengths up to a third with just a few caching nodes while, simultaneously, helping to spread the load along the network., Comment: 9 pages, 5 figures

The ATLAS EventIndex system comprises the catalogue of all events collected, processed or generated by the ATLAS experiment at the CERN LHC accelerator, and all associated software tools to collect, store and query this information. ATLAS records several billion particle interactions every year of operation, processes them for analysis and generates even larger simulated data samples; a global catalogue is needed to keep track of the location of each event record and be able to search and retrieve specific events for in-depth investigations. Each EventIndex record includes summary information on the event itself and the pointers to the files containing the full event. Most components of the EventIndex system are implemented using BigData open-source tools. This paper describes the architectural choices and their evolution in time, as well as the past, current and foreseen future implementations of all EventIndex components., Comment: 21 pages

Motzkin numbers have been widely studied since they count many different combinatorial objects. In this paper we present a new appearance of this remarkable sequence in the network coding setting through a particular case of multishot codes called flag codes. A flag code is a set of sequences of nested subspaces (flags) of a vector space over the finite field $\mathbb{F}_q$. If the list of dimensions is $(1, \dots, n-1)$, we speak about a full flag code. The flag distance is defined as the sum of the respective subspace distances and can be represented by means of the so-called distance vectors. We show that the number of distance vectors corresponding to the full flag variety on $\mathbb{F}_q^n$ is exactly the $n$-th Motzkin number. Moreover, we can identify the integer sequence that counts the number of possible distance vectors associated to a full flag code with prescribed minimum distance.

Isotope identification is a recurrent problem in gamma spectroscopy with high purity germanium detectors. In this work, new strategies are introduced to facilitate this type of analysis. Five criteria are used to identify the parent isotopes making a query on a large database of gamma-lines from a multitude of isotopes producing an output list whose entries are sorted so that the gamma-lines with the highest chance of being present in a sample are placed at the top. A metric to evaluate the performance of the different criteria is introduced and used to compare them. Two of the criteria are found to be superior than the others: one based on fuzzy logic, and another that makes use of the gamma relative emission probabilities. A program called histoGe implements these criteria using a SQLite database containing the gamma-lines of isotopes which was parsed from WWW Table of Radioactive Isotopes. histoGe is Free Software and is provided along with the database so they can be used to analyze spectra obtained with generic gamma-ray detectors., Comment: 12 pages, 7 tables, 7 figures

The transverse folding algorithm [Phys. Rev. Lett. 102, 240603] is a tensor network method to compute time-dependent local observables in out-of-equilibrium quantum spin chains that can sometimes overcome the limitations of matrix product states. We present a contraction strategy that makes use of the exact light cone structure of the tensor network representing the observables. The strategy can be combined with the hybrid truncation proposed for global quenches in [Phys. Rev. A 91, 032306], which significantly improves the efficiency of the method. We demonstrate the performance of this transverse light cone contraction also for transport coefficients, and discuss how it can be extended to other dynamical quantities., Comment: Added the data as ancillary files