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Uncertainty Matters: Stable Conclusions Under Unstable Assessment of Fairness Results
Get PDFRecent studies highlight the effectiveness of Bayesian methods in assessing algorithm performance, particularly in fairness and bias
evaluation. We present Uncertainty Matters, a multi-objective uncertainty-aware algorithmic comparison framework. In fairness-focused scenarios, it models sensitive group confusion matrices using Bayesian updates and facilitates joint comparison of performance (e.g., accuracy) and fairness metrics (e.g., true positive rate parity). Our approach works seamlessly with common evaluation methods like K-fold cross-validation, effectively addressing dependencies among the K posterior metric distributions. The integration of correlated information is carried out through a procedure tailored to the classifier's complexity. Experiments demonstrate that the insights derived from algorithmic comparisons employing the Uncertainty Matters approach are more informative, reliable, and less influenced by particular data partitions. Code for the paper is publicly available at https://github.com/abarrainkua/UncertaintyMatter
A Probabilistic Generative Model to Discover the Treatments of Coexisting Diseases with Missing Data
Get PDFSpeeding-Up Evolutionary Algorithms to Solve Black-Box Optimization Problems
Get PDFPopulation-based evolutionary algorithms are often considered when approaching computationally expensive black-box optimization problems. They employ a selection mechanism to choose the best solutions from a given population after comparing their objective values, which are then used to generate the next population. This iterative process explores the solution space efficiently, leading to improved solutions over time. However, one of the challenges of these algorithms is that they require a large number of evaluations to provide a quality solution, which might be computationally expensive when the evaluation cost is high. In some cases, it is possible to replace the original objective function with a less accurate approximation of lower cost. This introduces a trade-off between the evaluation cost and its accuracy. In this paper, we propose a technique capable of choosing an appropriate approximate function cost during the execution of the optimization algorithm. The proposal finds the minimum evaluation cost at which the solutions are still properly ranked, and consequently, more evaluations can be computed in the same amount of time with minimal accuracy loss. An experimental section on four very different problems reveals that the proposed approach can reach the same objective value in less than half of the time in certain cases
Local near-field scattering data enables unique reconstruction of rough electric potentials
Get PDFThe focus of this paper is the study of the inverse point-source scattering problem, specifically in relation to a certain class of electric potentials. Our research provides a novel uniqueness result for the inverse problem with local data, obtained from the near field pattern. Our work improves the work of Caro and Garcia, who investigated both the direct problem and the inverse problem with global near field data for critically singular and -shell potentials.
The primary contribution of our research is the introduction of a Runge approximation result for the near field data on the scattering problem which, in combination with an interior regularity argument, enables us to establish a uniqueness result for the inverse problem with local data. Additionaly, we manage to consider a slightly wider class of potentials.PRE2019-09177
A data-mining approach to understanding the impact of multi-doping on the ionic transport mechanism of solid electrolytes materials: the case of dual-doped Ga<inf>0.15</inf>/Sc<inf>y</inf> Li<inf>7</inf>La<inf>3</inf>Zr<inf>2</inf>O<inf>12</inf>
No full textThis study presents novel computational methods applied to the technologically significant solid electrolyte materials, Li6.55+yGa0.15La3Zr2−yScyO12 (Ga0.15/Scy-LLZO), in order to investigate the effect of the distribution of Ga3+ on Li-ion dynamics. Utilizing a specifically designed first-principles-based force field, molecular dynamics, and advanced hybrid Monte Carlo simulations, we systematically examine the material's transport properties for a range of Ga3+ and Sc3+ cationic concentrations. Additionally, we introduce innovative post-processing methods employing data mining clustering techniques, shedding light on Li+ ion behavior and conductivity mechanisms. Contrary to prior assumptions, the presence of Ga3+ in octahedral sites, not tetrahedral junctions, optimally enhances Li-ion conductivity, unlocking Li-ion diffusion pathways. The research illuminates how dopant distribution influences Li+ site occupancy and conductivity, offering key insights for advanced solid electrolyte design.PID2022-136585NB-C22
PLAN COMPLEMENTARIO MATERIALES AVANZADOS 2022-2025
HPC-EUROPA3 (INFRAIA-2016-1-730897
Lipidomics signature in post-COVID patient sera and its influence on the prolonged inflammatory response
Get PDFBackground: The ongoing issues with post-COVID conditions (PCC), where symptoms persist long after the initial infection, highlight the need for research into blood lipid changes in these patients. While most studies focus on the acute phase of COVID-19, there's a significant lack of information on the lipidomic changes that occur in the later stages of the disease. Addressing this knowledge gap is critical for understanding the long-term effects of COVID-19 and could be key to developing personalized treatments for those suffering from PCC. Methods: We employed untargeted lipidomics to analyze plasma samples from 147 PCC patients, assessing nearly 400 polar lipids. Data mining (DM) and machine learning (ML) tools were utilized to decode the results and ascertain significant lipidomic patterns. Results: The study uncovered substantial changes in various lipid subclasses, presenting a detailed profile of the polar lipid fraction in PCC patients. These alterations correlated with ongoing inflammation and immune response. Notably, there were elevated levels of lysophosphatidylglycerols (LPGs) and phosphatidylethanolamines (PEs), and reduced levels of lysophosphatidylcholines (LPCs), suggesting these as potential lipid biomarkers for PCC. The lipidomic signatures indicated specific anionic lipid changes, implicating antimicrobial peptides (AMPs) in inflammation. Associations between particular medications and symptoms were also suggested. Classification models, such as multinomial regression (MR) and random forest (RF), successfully differentiated between symptomatic and asymptomatic PCC groups using lipidomic profiles. Conclusions: The study's groundbreaking discovery of specific lipidomic disruptions in PCC patients marks a significant stride in the quest to comprehend and combat this condition. The identified lipid biomarkers not only pave the way for novel diagnostic tools but also hold the promise to tailor individualized therapeutic strategies, potentially revolutionizing the clinical approach to managing PCC and improving patient care
A localized decomposition evolutionary algorithm for imbalanced multi-objective optimization
Get PDFMulti-objective evolutionary algorithms based on decomposition (MOEA/Ds) convert a multi-objective optimization problem (MOP) into a set of scalar subproblems, which are then optimized in a collaborative manner. However, when tackling imbalanced MOPs, the performance of most MOEA/Ds will evidently deteriorate, as a few solutions will replace most of the others in the evolutionary process, resulting in a significant loss of diversity. To address this issue, this paper suggests a localized decomposition evolutionary algorithm (LDEA) for imbalanced MOPs. A localized decomposition method is proposed to assign a local region for each subproblem, where the inside solutions are associated and the solution update is restricted inside (i.e., solutions are only replaced by offspring within the same local region). Once off-spring are generated within an originally empty region, the best one is reserved for this subproblem to extend diversity. Meanwhile, the subproblem with the largest number of associated solutions will be found and one of its associated solutions with the worst aggregated value will be removed. Moreover, to speed up convergence for each subproblem while balancing the population's diversity, LDEA only evolves the best-associated solution in each subproblem and correspondingly tailors two decomposition methods in the environmental selection. When compared to nine competitive MOEAs, LDEA has shown the advantages in tackling two benchmark sets of imbalanced MOPs, one benchmark set of balanced yet complicated MOPs, and one real-world MOP
Satellite-based entanglement distribution and quantum teleportation with continuous variables
No full textAdvances in satellite quantum communications aim at reshaping the global telecommunication network by increasing the security of the transferred information. Here, we study the effects of atmospheric turbulence in continuous-variable entanglement distribution and quantum teleportation in the optical regime between a ground station and a satellite. More specifically, we study the degradation of entanglement due to various error sources in the distribution, namely, diffraction, atmospheric attenuation, turbulence, and detector inefficiency, in both downlink and uplink scenarios. As the fidelity of a quantum teleportation protocol using these distributed entangled resources is not sufficient, we include an intermediate station for either state generation, or beam refocusing, in order to reduce the effects of atmospheric turbulence and diffraction, respectively. The results show the feasibility of free-space entanglement distribution and quantum teleportation in downlink paths up to the LEO region, but also in uplink paths with the help of the intermediate station. Finally, we complete the study with microwave-optical comparison in bad weather situations, and with the study of horizontal paths in ground-to-ground and inter-satellite quantum communication.T.G.-R. and M.S. acknowledge financial support from the Basque Government through Grant No. IT1470-22 and from the Basque Government QUANTEK pro ject under the ELKARTEK program (KK-2021/00070), the Spanish Ram ́on y Ca jal Grant No. RYC-2020-030503-I, pro ject Grant No. PID2021-125823NA-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” and “ERDF Invest in your Future”, as well as from the project QMiCS (Grant No. 820505) and the HORIZON-CL4-2022-QUANTUM-01-SGA project 101113946 OpenSuperQPlus100 of the EU Flagship on Quantum Technologies, and the EU FET-Open projects Quromorphic (828826) and EPIQUS (899368). The authors also acknowledge the financial support received from the IKUR Strategy under the collaboration agreement between Ikerbasque Foundation and BCAM on behalf of the Department of Education of the Basque Government. This work has also been financially supported by the Ministry of Economic Affairs and Digital Transformation of the Spanish Government through the QUANTUM ENIA project call - Quantum Spain project, and by the European Union through the Recovery, Transformation and Resilience Plan - NextGenerationEU within the framework of the Digital Spain 2026 Agenda. S.P. Acknowledges funding from the EU via CiViQ (grant agreement no. 820466) and QUARTET (Grant Agreement No. 862644), and EPSRC via the Quantum Communications Hub (Grant number EP/T001011/1)
Some consequences of the -constant condition for families of surfaces
No full textLet σ : X → be a 1-parameter family of 2-dimensional isolated hypersurface singularities.
In this paper, we show that if the Milnor number is constant, then any semistable model,
obtained from σ after a sufficiently large base change must satisfy non trivial restrictions.
Those restrictions are in terms of the dual complex, Hodge structure, and numerical invariants
of the central fibre