| 1. |
- Damaschke, Peter, 1963, et al.
(författare)
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An optimization problem related to bloom filters with bit patterns
- 2018
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349.
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Konferensbidrag (refereegranskat)abstract
- © 2018, Springer International Publishing AG. Bloom filters are hash-based data structures for membership queries without false negatives widely used across many application domains. They also have become a central data structure in bioinformatics. In genomics applications and DNA sequencing the number of items and number of queries are frequently measured in the hundreds of billions. Consequently, issues of cache behavior and hash function overhead become a pressing issue. Blocked Bloom filters with bit patterns offer a variant that can better cope with cache misses and reduce the amount of hashing. In this work we state an optimization problem concerning the minimum false positive rate for given numbers of memory bits, stored elements, and patterns. The aim is to initiate the study of pattern designs best suited for the use in Bloom filters. We provide partial results about the structure of optimal solutions and a link to two-stage group testing.
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| 2. |
- Delahunty, Fionn, et al.
(författare)
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Using clickers to predict students final courses grades, an artificial intelligence approach
- 2019
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Ingår i: Extended Abstract of KUL-2019 presentation.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Clickers are useful for improving interaction in teaching. Here, we demonstrate how artificial intelligence can improve this usefulness even further by predicting final exam scores of students early in the semester. We also investigate how the number of attempts a student makes effect the correctness of the answer.
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| 3. |
- Ekström, Andreas, 1980, et al.
(författare)
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Bayesian optimization in ab initio nuclear physics
- 2019
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Ingår i: Journal of Physics G-Nuclear and Particle Physics. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 46:9
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Tidskriftsartikel (refereegranskat)abstract
- Theoretical models of the strong nuclear interaction contain unknown coupling constants (parameters) that must be determined using a pool of calibration data. In cases where the models are complex, leading to time consuming calculations, it is particularly challenging to systematically search the corresponding parameter domain for the best fit to the data. In this paper, we explore the prospect of applying Bayesian optimization to constrain the coupling constants in chiral effective field theory descriptions of the nuclear interaction. We find that Bayesian optimization performs rather well with low-dimensional parameter domains and foresee that it can be particularly useful for optimization of a smaller set of coupling constants. A specific example could be the determination of leading three-nucleon forces using data from finite nuclei or three-nucleon scattering experiments.
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| 4. |
- Heckmann Barbalho de Figueroa, Laiz, et al.
(författare)
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A Modeling Approach for Bioinformatics Workflows
- 2019
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Ingår i: The Practice of Enterprise Modeling - 12th {IFIP} Working Conference, PoEM 2019, Luxembourg, Luxembourg, November 27-29, 2019, Proceedings. - Cham : Springer.
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Konferensbidrag (refereegranskat)
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| 5. |
- Johansson, Simon, 1994, et al.
(författare)
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AI-assisted synthesis prediction
- 2019
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Ingår i: Drug Discovery Today: Technologies. - : Elsevier BV. - 1740-6749. ; 32-33:December, s. 65-72
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Tidskriftsartikel (refereegranskat)abstract
- Application of AI technologies in synthesis prediction has developed very rapidly in recent years. We attempt here to give a comprehensive summary on the latest advancement on retro-synthesis planning, forward synthesis prediction as well as quantum chemistry-based reaction prediction models. Besides an introduction on the AI/ML models for addressing various synthesis related problems, the sources of the reaction datasets used in model building is also covered. In addition to the predictive models, the robotics based high throughput experimentation technology will be another crucial factor for conducting synthesis in an automated fashion. Some state-of-the-art of high throughput experimentation practices carried out in the pharmaceutical industry are highlighted in this chapter to give the reader a sense of how future chemistry will be conducted to make compounds faster and cheaper. © 2020 Elsevier Ltd
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| 6. |
- Listo Zec, Edvin, et al.
(författare)
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Statistical Sensor Modelling for Autonomous Driving Using Autoregressive Input-Output HMMs
- 2018
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Ingår i: 21st International Conference on Intelligent Transportation Systems, {ITSC} 2018. - : IEEE. - 2153-0017. - 9781728103211
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Konferensbidrag (refereegranskat)abstract
- Advanced driver assistance systems (ADAS) are standard features in many vehicles today and they have been proven to significantly increase the traffic safety. This paved way for development of autonomous driving (AD). To enable this, the vehicles are equipped with many sensors such as cameras and radars in order to scan the surrounding environment. The sensor outputs are used to implement decision and control modules. Verification of AD is a challenging task and requires collecting data from at least hundreds of millions of autonomously driven miles. We are therefore interested in virtual verification methods that simulate interesting and relevant situations, so that many scenarios can be tested in parallel. Realistic simulations require accurate sensor models, and in this paper we propose a probabilistic model based on the hidden Markov model (HMM) for modelling the sequential data produced by the sensors used in ADAS and AD. Moreover, we propose an efficient way to estimate parameters that scales well to big data sets. The results show that extending the HMM to use autoregression and input dependent transition probabilities is important in order to model the sensor characteristics and substantially improves the performance.
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| 7. |
- Martinsson, John, et al.
(författare)
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Automatic Blood Glucose Prediction with Confidence Using Recurrent Neural Networks
- 2018
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Ingår i: Proceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence {(IJCAI-ECAI} 2018), Stockholm, Schweden, July 13, 2018.. - : CEUR-WS.org.
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Konferensbidrag (refereegranskat)
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| 8. |
- Martinsson, John, et al.
(författare)
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Automatic blood glucose prediction with confidence using recurrent neural networks
- 2018
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Ingår i: CEUR Workshop Proceedings. - : CEUR. ; 2148, s. 64-68
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Konferensbidrag (refereegranskat)abstract
- Low-cost sensors continuously measuring blood glucose levels in intervals of a few minutes and mobile platforms combined with machine-learning (ML) solutions enable personalized precision health and disease management. ML solutions must be adapted to different sensor technologies, analysis tasks and individuals. This raises the issue of scale for creating such adapted ML solutions. We present an approach for predicting blood glucose levels for diabetics up to one hour into the future. The approach is based on recurrent neural networks trained in an end-to-end fashion, requiring nothing but the glucose level history for the patient. The model outputs the prediction along with an estimate of its certainty, helping users to interpret the predicted levels. The approach needs no feature engineering or data pre-processing, and is computationally inexpensive.
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| 9. |
- Martinsson, John, et al.
(författare)
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Clustering Vehicle Maneuver Trajectories Using Mixtures of Hidden Markov Models
- 2018
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Ingår i: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). - : IEEE. - 2153-0017. - 9781728103235 ; 2018-November, s. 3698-3705
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Konferensbidrag (refereegranskat)abstract
- The safety of autonomous vehicles needs to be verified and validated by rigorous testing. It is expensive to test autonomous vehicles in the field, and therefore virtual testing methods are needed. Generative models of maneuvers such as cut-ins, overtakes, and lane-keeping are needed to thoroughly test the autonomous vehicle in a virtual environment. To train such models we need ground truth maneuver labels and obtaining such labels can be time-consuming and costly. In this work, we use a mixture of hidden Markov models to find clusters in maneuver trajectories, which can be used to speed up the labeling process. The maneuver trajectories are noisy, asynchronous and of uneven length, which make hidden Markov models a good fit for the data. The method is evaluated on labeled data from a test track consisting of cut-ins and overtakes with favorable results. Further, it is applied to natural data where many of the clusters found can be interpreted as driver maneuvers under reasonable assumptions. We show that mixtures of hidden Markov models can be used to find motion patterns in driver maneuver data from highways and country roads.
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| 10. |
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