26838 research outputs found
Sort by
Multivariate sensitivity analysis of electric machine efficiency maps and profiles under design uncertainty
Get PDFThis work proposes the use of multivariate global sensitivity analysis for assessing the impact of uncertain electric machine design parameters on efficiency maps and profiles. Contrary to the common approach of applying variance-based (Sobol’) sensitivity analysis elementwise, multivariate sensitivity analysis provides a single sensitivity index per parameter, thus allowing for a holistic estimation of parameter importance over the full efficiency map or profile. Its benefits are demonstrated on permanent magnet synchronous machine models of different fidelity. Computations based on Monte Carlo sampling and polynomial chaos expansions are compared in terms of computational cost. The sensitivity analysis results are subsequently used to simplify the models, by fixing non-influential parameters to their nominal values and allowing random variations only for influential parameters. Uncertainty estimates obtained with the full and reduced models confirm the validity of model simplification guided by multivariate sensitivity analysis
Fully characterizing lossy catalytic computation
Get PDFA catalytic machine is a model of computation where a traditional space-bounded machine is augmented with an additional, significantly larger, "catalytic" tape, which, while being available as a work tape, has the caveat of being initialized with an arbitrary string, which must be preserved at the end of the computation. Despite this restriction, catalytic machines have been shown to have surprising additional power; a logspace machine with a polynomial length catalytic tape, known as catalytic logspace (), can compute problems which are believed to be impossible for . A fundamental question of the model is whether the catalytic condition, of leaving the catalytic tape in its exact original configuration, is robust to minor deviations. This study was initialized by Gupta et al. (2024), who defined lossy catalytic logspace () as a variant of where we allow up to errors when resetting the catalytic tape. They showed that for any , which remains the frontier of our understanding. In this work we completely characterize lossy catalytic space () in terms of ordinary catalytic space ().
We show that
In other words, allowing errors on a catalytic tape of length is equivalent, up to a constant stretch, to an equivalent errorless catalytic machine with an additional log bits of ordinary working memory. As a consequence, we show that for any , implies , thus giving a barrier to any improvement beyond . We also show equivalent results for non-deterministic and randomized catalytic space
Line-graph qubit routing
Get PDFOne limitation of current quantum hardware is the restricted connectivity between qubits, as described by the hardware’s coupling graph. To overcome this limitation, efficient qubit routing strategies are necessary. We introduce line-graph qubit routing, which routes circuits defined on line graphs to hardware with a heavy coupling graph. We implement line-graph qubit routing and demonstrate its effectiveness in mapping quantum circuits defined kagome, checkerboard, and shuriken lattices to hardware with heavy-hex, heavy-square, and heavy-square-octagon coupling graphs, respectively. Benchmarking shows the ability of line-graph qubit routing to outperform established general-purpose methods in a fraction of the computational time, while offering a depth reduction by up to a factor of 5. Line-graph qubit routing has direct applications in the quantum simulation of lattice-based models, serves as a suitable benchmark for other routing methods, and aids the exploration of the capabilities of near-term quantum hardware
Ninth Workshop on Data Management for End-to-End Machine Learning (DEEM)
Get PDFThe DEEM'25 workshop (Data Management for End-to-End Machine Learning) is held on Friday, June 27th, in conjunction with SIGMOD/PODS 2025. DEEM brings together researchers and practitioners at the intersection of applied machine learning, data management, and systems research, with the goal of discussing the arising data management issues in ML application scenarios. The workshop solicits regular research papers (8 pages) describing preliminary and ongoing research results, including industrial experience reports of end-to-end ML deployments, related to DEEM topics. In addition, DEEM 2025 has a category for short papers (4 pages) as a forum for sharing interesting use cases, problems, datasets, benchmarks, visionary ideas, system designs, preliminary results, and descriptions of system components and tools related to end-to-end ML pipelines. This year, the workshop received 18 high-quality submissions on diverse topics relevant to DEEM
Learning multimodal explainable AI models from medical images and tabular data: Proof of concept
No full textMedical applications often involve several data modalities, particularly medical images and clinical information, which can be combined to enhance the decision-making process by improving accuracy. Multimodal learning approaches can leverage all available data for increased robustness in the resulting models, consequently outperforming unimodal approaches. Furthermore, AI frameworks must be human-verifiable and interpretable to be deployed in real-world situations, considering legal and privacy aspects. Due to the opaque nature of Deep Learning (DL) methods, interpretability is often limited despite their state-of-the-art performance in many tasks. Genetic Programming (GP) can provide compact and interpretable symbolic expressions for tabular data but is less effective for image analysis. We introduce MultiFIX: a new interpretability-focused pipeline for multimodal learning that leverages the strengths of DL and GP to explicitly engineer features from different data types and combine them to make the final prediction. The MultiFIX pipeline comprises two stages: the training stage, where a DL (black-box) model is trained using different training procedures to extract relevant features from each modality; and the inference stage, where the resulting model is transformed to be interpretable. Image features are explained with attention maps by Grad-CAM, and inherently interpretable symbolic expressions evolved with GP fully replace the tabular feature engineering block, and the fusion of the extracted features to predict the target label. To show the application potential of the presented pipeline, we demonstrate MultiFIX with a Melanoma Risk Assessment dataset. Results show that MultiFIX outperforms unimodal models while offering explanations that can be straightforwardly analysed and are consistent with the expectations
Saving Private Hash Join
Get PDFModern analytical database systems offer high-performance inmemory joins. However, if the build side of a join does not fit in RAM, performance degrades sharply due to switching to traditional external join algorithms such as sort-merge. In streaming query execution, this problem is worsened if multiple joins are evaluated simultaneously, as the database system must decide how to allocate memory to each join, which can greatly affect performance. We revisit larger-than-memory join processing on modern hardware, aiming for robust performance that avoids a "performance cliff" when memory runs out, even in query plans with many joins. To achieve this, we propose three techniques. First, an adaptive, external hash join algorithm that stores temporary data in a unified buffer pool that oversees temporary and persistent data. Second, an optimizer that creates expressions to compress columns at runtime, reducing the size of materialized temporary data. Third, a strategy for dynamically managing the memory of concurrent operators during query execution to reduce spilling. We integrate these techniques into DuckDB and experimentally show that when processing memory-intensive join query plans, our implementation gracefully degrades performance as the space requirement exceeds the memory limit. This greatly increases the size of datasets that can be processed on economical hardware
More efficient real-valued gray-box optimization through incremental distribution estimation in RV-GOMEA
Get PDFThe Gene-pool Optimal Mixing EA (GOMEA) family of EAs offers a specific means to exploit problem-specific knowledge through linkage learning, i.e., inter-variable dependency detection, expressed using subsets of variables, that should undergo joint variation. Such knowledge can be exploited if faster fitness evaluations are possible when only a few variables are changed in a solution, enabling large speed-ups. The recent-most version of Real-Valued GOMEA (RV-GOMEA) can learn a conditional linkage model during optimization using fitness-based linkage learning, enabling fine-grained dependency exploitation in learning and sampling a Gaussian distribution. However, while the most efficient Gaussian-based EAs, like NES and CMA-ES, employ incremental learning of the Gaussian distribution rather than performing full re-estimation every generation, the recent-most RV-GOMEA version does not employ such incremental learning. In this paper, we therefore study whether incremental distribution estimation can lead to efficiency enhancements of RV-GOMEA. We consider various benchmark problems with varying degrees of overlapping dependencies. We find that, compared to RV-GOMEA and VKD-CMA-ES, the required number of evaluations to reach high-quality solutions can be reduced by a factor of up to 1.5 if population sizes are tuned problem-specifically, while a reduction by a factor of 2-3 can be achieved with generic population-sizing guidelines
Sensitivity analysis of high-dimensional models with correlated inputs
Get PDFSensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the uncertainty propagation through the input–output interactions. In many applications, the inputs are stochastically dependent, which violates one of the essential assumptions in the state-of-the-art sensitivity analysis methods. Consequently, the results obtained ignoring the correlations provide values which do not reflect the true contributions of the input parameters. This study proposes an approach to address the parameter correlations using a polynomial chaos expansion method and Rosenblatt and Cholesky transformations to reflect the parameter dependencies. Treatment of the correlated variables is discussed in context of variance and derivative-based sensitivity analysis. We demonstrate that the sensitivity of the correlated parameters can not only differ in magnitude, but even the sign of the derivative-based index can be inverted, thus significantly altering the model behavior compared to the prediction of the analysis disregarding the correlations. Numerous experiments are conducted using workflow automation tools within the VECMA toolkit
Forecasting traffic flow by vehicle category on a major highway impacted by road maintenance works
Get PDFIn this paper we look at the forecasting of traffic flow on a major highway in the Netherlands impacted by road maintenance works, examining the effects of lane closures on intensities per vehicle category. We apply several forecasting methodologies such as Prophet, Harmonic Regression, Seasonal Autoregressive (SAR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) and compare them against a seasonal naive baseline model. We observe that SARIMA performs better than other models across all forecasting metrics for all sensors. This is mainly because of its capability of capturing linear trends and seasonality. There is also an opportunity to further improve the forecast accuracy of the SARIMA model by incorporating holiday information as seen in the Prophet model. Overall, the analysis showed that road works and holidays are two features that have more influence on traffic flow, which should be considered as main factors when carrying out future road plans. If multiple areas are affected, the K-means model can be adopted effectively to cluster the sensors into groups to minimize traffic disruption