| 1. |
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| 2. |
- Liu, Yuanhua, 1971, et al.
(författare)
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Considering the importance of user profiles in interface design
- 2009
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Ingår i: User Interfaces. ; , s. 23-
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- User profile is a popular term widely employed during product design processes by industrial companies. Such a profile is normally intended to represent real users of a product. The ultimate purpose of a user profile is actually to help designers to recognize or learn about the real user by presenting them with a description of a real user’s attributes, for instance; the user’s gender, age, educational level, attitude, technical needs and skill level. The aim of this chapter is to provide information on the current knowledge and research about user profile issues, as well as to emphasize the importance of considering these issues in interface design. In this chapter, we mainly focus on how users’ difference in expertise affects their performance or activity in various interaction contexts. Considering the complex interaction situations in practice, novice and expert users’ interactions with medical user interfaces of different technical complexity will be analyzed as examples: one focuses on novice and expert users’ difference when interacting with simple medical interfaces, and the other focuses on differences when interacting with complex medical interfaces. Four issues will be analyzed and discussed: (1) how novice and expert users differ in terms of performance during the interaction; (2) how novice and expert users differ in the perspective of cognitive mental models during the interaction; (3) how novice and expert users should be defined in practice; and (4) what are the main differences between novice and expert users’ implications for interface design. Besides describing the effect of users’ expertise difference during the interface design process, we will also pinpoint some potential problems for the research on interface design, as well as some future challenges that academic researchers and industrial engineers should face in practice.
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| 3. |
- Lidstrom, D, et al.
(författare)
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Agent based match racing simulations : Starting practice
- 2022
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Ingår i: SNAME 24th Chesapeake Sailing Yacht Symposium, CSYS 2022. - : Society of Naval Architects and Marine Engineers.
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Konferensbidrag (refereegranskat)abstract
- Match racing starts in sailing are strategically complex and of great importance for the outcome of a race. With the return of the America's Cup to upwind starts and the World Match Racing Tour attracting young and development sailors, the tactical skills necessary to master the starts could be trained and learned by means of computer simulations to assess a large range of approaches to the starting box. This project used game theory to model the start of a match race, intending to develop and study strategies using Monte-Carlo tree search to estimate the utility of a player's potential moves throughout a race. Strategies that utilised the utility estimated in different ways were defined and tested against each other through means of simulation and with an expert advice on match racing start strategy from a sailor's perspective. The results show that the strategies that put greater emphasis on what the opponent might do, perform better than those that did not. It is concluded that Monte-Carlo tree search can provide a basis for decision making in match races and that it has potential for further use.
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| 4. |
- Strannegård, Claes, 1962, et al.
(författare)
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Ecosystem Models Based on Artificial Intelligence
- 2022
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Ingår i: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Ecosystem models can be used for understanding general phenomena of evolution, ecology, and ethology. They can also be used for analyzing and predicting the ecological consequences of human activities on specific ecosystems, e.g., the effects of agriculture, forestry, construction, hunting, and fishing. We argue that powerful ecosystem models need to include reasonable models of the physical environment and of animal behavior. We also argue that several well-known ecosystem models are unsatisfactory in this regard. Then we present the open-source ecosystem simulator Ecotwin, which is built on top of the game engine Unity. To model a specific ecosystem in Ecotwin, we first generate a 3D Unity model of the physical environment, based on topographic or bathymetric data. Then we insert digital 3D models of the organisms of interest into the environment model. Each organism is equipped with a genome and capable of sexual or asexual reproduction. An organism dies if it runs out of some vital resource or reaches its maximum age. The animal models are equipped with behavioral models that include sensors, actions, reward signals, and mechanisms of learning and decision-making. Finally, we illustrate how Ecotwin works by building and running one terrestrial and one marine ecosystem model.
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| 5. |
- Lindén, Joakim, et al.
(författare)
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Evaluating the Robustness of ML Models to Out-of-Distribution Data Through Similarity Analysis
- 2023
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Ingår i: Commun. Comput. Info. Sci.. - : Springer Science and Business Media Deutschland GmbH. - 9783031429408 ; , s. 348-359, s. 348-359
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Konferensbidrag (refereegranskat)abstract
- In Machine Learning systems, several factors impact the performance of a trained model. The most important ones include model architecture, the amount of training time, the dataset size and diversity. We present a method for analyzing datasets from a use-case scenario perspective, detecting and quantifying out-of-distribution (OOD) data on dataset level. Our main contribution is the novel use of similarity metrics for the evaluation of the robustness of a model by introducing relative Fréchet Inception Distance (FID) and relative Kernel Inception Distance (KID) measures. These relative measures are relative to a baseline in-distribution dataset and are used to estimate how the model will perform on OOD data (i.e. estimate the model accuracy drop). We find a correlation between our proposed relative FID/relative KID measure and the drop in Average Precision (AP) accuracy on unseen data.
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| 6. |
- Al Sabbagh, Khaled, 1987, et al.
(författare)
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Improving Data Quality for Regression Test Selection by Reducing Annotation Noise
- 2020
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Ingår i: Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020. ; , s. 191-194
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Konferensbidrag (refereegranskat)abstract
- Big data and machine learning models have been increasingly used to support software engineering processes and practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the identification and reduction of noise that often comes in large data. In this paper, we present a noise reduction approach that deals with the problem of contradictory training entries. We empirically evaluate the effectiveness of the approach in the context of selective regression testing. For this purpose, we use a curated training set as input to a tree-based machine learning ensemble and compare the classification precision, recall, and f-score against a non-curated set. Our study shows that using the noise reduction approach on the training instances gives better results in prediction with an improvement of 37% on precision, 70% on recall, and 59% on f-score.
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| 7. |
- Fu, Keren, et al.
(författare)
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Deepside: A general deep framework for salient object detection
- 2019
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 356, s. 69-82
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning-based salient object detection techniques have shown impressive results compared to con- ventional saliency detection by handcrafted features. Integrating hierarchical features of Convolutional Neural Networks (CNN) to achieve fine-grained saliency detection is a current trend, and various deep architectures are proposed by researchers, including “skip-layer” architecture, “top-down” architecture, “short-connection” architecture and so on. While these architectures have achieved progressive improve- ment on detection accuracy, it is still unclear about the underlying distinctions and connections between these schemes. In this paper, we review and draw underlying connections between these architectures, and show that they actually could be unified into a general framework, which simply just has side struc- tures with different depths. Based on the idea of designing deeper side structures for better detection accuracy, we propose a unified framework called Deepside that can be deeply supervised to incorporate hierarchical CNN features. Additionally, to fuse multiple side outputs from the network, we propose a novel fusion technique based on segmentation-based pooling, which severs as a built-in component in the CNN architecture and guarantees more accurate boundary details of detected salient objects. The effectiveness of the proposed Deepside scheme against state-of-the-art models is validated on 8 benchmark datasets.
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| 8. |
- Gerken, Jan, 1991, et al.
(författare)
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Equivariance versus augmentation for spherical images
- 2022
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Ingår i: Proceedings of Machine Learning Resaerch. ; , s. 7404-7421
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Konferensbidrag (refereegranskat)abstract
- We analyze the role of rotational equivariance in convolutional neural networks (CNNs) applied to spherical images. We compare the performance of the group equivariant networks known as S2CNNs and standard non-equivariant CNNs trained with an increasing amount of data augmentation. The chosen architectures can be considered baseline references for the respective design paradigms. Our models are trained and evaluated on single or multiple items from the MNIST- or FashionMNIST dataset projected onto the sphere. For the task of image classification, which is inherently rotationally invariant, we find that by considerably increasing the amount of data augmentation and the size of the networks, it is possible for the standard CNNs to reach at least the same performance as the equivariant network. In contrast, for the inherently equivariant task of semantic segmentation, the non-equivariant networks are consistently outperformed by the equivariant networks with significantly fewer parameters. We also analyze and compare the inference latency and training times of the different networks, enabling detailed tradeoff considerations between equivariant architectures and data augmentation for practical problems.
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| 9. |
- Isaksson, Martin, et al.
(författare)
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Adaptive Expert Models for Federated Learning
- 2023
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Ingår i: <em>Lecture Notes in Computer Science </em>Volume 13448 Pages 1 - 16 2023. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031289958 ; 13448 LNAI, s. 1-16
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Konferensbidrag (refereegranskat)abstract
- Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78% better than the state-of-the-art and up to 4.38% better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting. © 2023, The Author(s)
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| 10. |
- Abarenkov, Kessy, et al.
(författare)
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Protax-fungi: A web-based tool for probabilistic taxonomic placement of fungal internal transcribed spacer sequences
- 2018
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Ingår i: New Phytologist. - : Wiley. - 0028-646X .- 1469-8137. ; 220:2, s. 517-525
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Tidskriftsartikel (refereegranskat)abstract
- © 2018 New Phytologist Trust. Incompleteness of reference sequence databases and unresolved taxonomic relationships complicates taxonomic placement of fungal sequences. We developed Protax-fungi, a general tool for taxonomic placement of fungal internal transcribed spacer (ITS) sequences, and implemented it into the PlutoF platform of the UNITE database for molecular identification of fungi. With empirical data on root- and wood-associated fungi, Protax-fungi reliably identified (with at least 90% identification probability) the majority of sequences to the order level but only around one-fifth of them to the species level, reflecting the current limited coverage of the databases. Protax-fungi outperformed the Sintax and Rdb classifiers in terms of increased accuracy and decreased calibration error when applied to data on mock communities representing species groups with poor sequence database coverage. We applied Protax-fungi to examine the internal consistencies of the Index Fungorum and UNITE databases. This revealed inconsistencies in the taxonomy database as well as mislabelling and sequence quality problems in the reference database. The according improvements were implemented in both databases. Protax-fungi provides a robust tool for performing statistically reliable identifications of fungi in spite of the incompleteness of extant reference sequence databases and unresolved taxonomic relationships.
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| 11. |
- Johansson, Simon, 1994, et al.
(författare)
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Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction
- 2022
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Ingår i: Molecular Informatics. - : Wiley. - 1868-1743 .- 1868-1751. ; 41:12
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Tidskriftsartikel (refereegranskat)abstract
- Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit how much data can be obtained from experiments. This suggests the use of schemes for data collection such as active learning, which identifies the data points of highest impact for model accuracy, and which has been used in recent studies with success. However, little has been done to explore the robustness of the methods predicting reaction yield when used together with active learning to reduce the amount of experimental data needed for training. This study aims to investigate the influence of machine learning algorithms and the number of initial data points on reaction yield prediction for two public high-throughput experimentation datasets. Our results show that active learning based on output margin reached a pre-defined AUROC faster than random sampling on both datasets. Analysis of feature importance of the trained machine learning models suggests active learning had a larger influence on the model accuracy when only a few features were important for the model prediction.
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| 12. |
-
- 2019
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Tidskriftsartikel (refereegranskat)
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| 13. |
- Medved, Dennis
(författare)
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Applications of Machine Learning on Natural Language Processing and Biomedical Data
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Machine learning is ubiquitous in today’s society, with promising applicationsin the field of natural language processing (NLP), so that computers can handlehuman language better, and within the medical community, with the promiseof better treatments. Machine learning can be seen as a subfield of artificialintelligence (AI), where AI is used to describe a machine that mimics cognitivefunctions that humans associate with other human minds, such as learning orproblem solving.In this thesis we explore how machine learning can be used to improve classification of picture, by using associated text. We then shift our focus to biomedical data, specifically heart transplantation patients. We show how the data can be represented as a graph database using the resource description framework (RDF).After that we use the data with logistic regression and the Spark framework, toperform feature search to predict the survival probability of the patients. In thetwo last articles we use artificial neural networks (ANN) first to predict patientsurvival, and compare it with a logistic regression approach, and last to predict the outcome of patients awaiting heart transplantation.We plan to do simulation of different allocation policies, for donor hearts, usingthese kind of ANNs, to be able to asses their impact on predicted earned survivaltime.
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| 14. |
- Mogren, Olof, 1980, et al.
(författare)
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Character-based Recurrent Neural Networks for Morphological Relational Reasoning
- 2017
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Ingår i: Proceedings of the First Workshop on Subword and Character Level Models in NLP. - Stroudsburg, PA, United States : Association for Computational Linguistics.
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Konferensbidrag (refereegranskat)abstract
- We present a model for predicting word forms based on morphological relational reasoning with analogies. While previous work has explored tasks such as morphological inflection and reinflection, these models rely on an explicit enumeration of morphological features, which may not be available in all cases. To address the task of predicting a word form given a demo relation (a pair of word forms) and a query word, we devise a character-based recurrent neural network architecture using three separate encoders and a decoder. We also investigate a multiclass learning setup, where the prediction of the relation type label is used as an auxiliary task. Our results show that the exact form can be predicted for English with an accuracy of 94.7%. For Swedish, which has a more complex morphology with more inflectional patterns for nouns and verbs, the accuracy is 89.3%. We also show that using the auxiliary task of learning the relation type speeds up convergence and improves the prediction accuracy for the word generation task.
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| 15. |
- Morger, Andrea, et al.
(författare)
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Assessing the calibration in toxicological in vitro models with conformal prediction
- 2021
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Ingår i: Journal of Cheminformatics. - : BioMed Central. - 1758-2946. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning methods are widely used in drug discovery and toxicity prediction. While showing overall good performance in cross-validation studies, their predictive power (often) drops in cases where the query samples have drifted from the training data's descriptor space. Thus, the assumption for applying machine learning algorithms, that training and test data stem from the same distribution, might not always be fulfilled. In this work, conformal prediction is used to assess the calibration of the models. Deviations from the expected error may indicate that training and test data originate from different distributions. Exemplified on the Tox21 datasets, composed of chronologically released Tox21Train, Tox21Test and Tox21Score subsets, we observed that while internally valid models could be trained using cross-validation on Tox21Train, predictions on the external Tox21Score data resulted in higher error rates than expected. To improve the prediction on the external sets, a strategy exchanging the calibration set with more recent data, such as Tox21Test, has successfully been introduced. We conclude that conformal prediction can be used to diagnose data drifts and other issues related to model calibration. The proposed improvement strategy-exchanging the calibration data only-is convenient as it does not require retraining of the underlying model.
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| 16. |
- Norinder, Ulf, 1956-, et al.
(författare)
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Conformal prediction to define applicability domain : A case study on predicting ER and AR binding
- 2016
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Ingår i: SAR and QSAR in environmental research (Print). - : Taylor & Francis. - 1062-936X .- 1029-046X. ; 27:4, s. 303-316
-
Tidskriftsartikel (refereegranskat)abstract
- A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.
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| 17. |
- Stathis, Dimitrios, et al.
(författare)
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eBrainII : a 3 kW Realtime Custom 3D DRAM Integrated ASIC Implementation of a Biologically Plausible Model of a Human Scale Cortex
- 2020
-
Ingår i: Journal of Signal Processing Systems. - : Springer. - 1939-8018 .- 1939-8115. ; 92:11, s. 1323-1343
-
Tidskriftsartikel (refereegranskat)abstract
- The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not biologically plausible. Despite their initial success, they cannot attain the cognitive capabilities enabled by the dynamic hierarchical associative memory systems of biological brains. The biologically plausible spiking brain models, e.g., cortex, basal ganglia, and amygdala, have a greater potential to achieve biological brain like cognitive capabilities. Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically plausible spiking model of the cortex. A human-scale model of BCPNN in real-time requires 162 TFlop/s, 50 TBs of synaptic weight storage to be accessed with a bandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s. A hand-optimized implementation of rodent scale BCPNN has been done on Tesla K80 GPUs require 3 kWs, we extrapolate from that a human scale network will require 3 MWs. These power numbers rule out such implementations for field deployment as cognition engines in embedded systems. The key innovation that this paper reports is that it is feasible and affordable to implement real-time BCPNN as a custom tiled application-specific integrated circuit (ASIC) in 28 nm technology with custom 3D DRAM - eBrainII - that consumes 3 kW for human scale and 12 watts for rodent scale. Such implementations eminently fulfill the demands for field deployment.
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| 18. |
- Zhang, Jin, et al.
(författare)
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Deep Learning-Based Conformal Prediction of Toxicity
- 2021
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 61:6, s. 2648-2657
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Tidskriftsartikel (refereegranskat)abstract
- Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.
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| 19. |
- Boulund, Fredrik, et al.
(författare)
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Computational and Statistical Considerations in the Analysis of Metagenomic Data
- 2018
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Ingår i: Metagenomics: Perspectives, Methods, and Applications. - 9780081022689 ; , s. 81-102
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In shotgun metagenomics, microbial communities are studied by random DNA fragments sequenced directly from environmental and clinical samples. The resulting data is massive, potentially consisting of billions of sequence reads describing millions of microbial genes. The data interpretation is therefore nontrivial and dependent on dedicated computational and statistical methods. In this chapter we discuss the many challenges associated with the analysis of shotgun metagenomic data. First, we address computational issues related to the quantification of genes in metagenomes. We describe algorithms for efficient sequence comparisons, recommended practices for setting up data workflows and modern high-performance computer resources that can be used to perform the analysis. Next, we outline the statistical aspects, including removal of systematic errors and how to identify differences between microbial communities from different experimental conditions. We conclude by underlining the increasing importance of efficient and reliable computational and statistical solutions in the analysis of large metagenomic datasets.
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| 20. |
- Abarenkov, Kessy, et al.
(författare)
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Annotating public fungal ITS sequences from the built environment according to the MIxS-Built Environment standard – a report from a May 23-24, 2016 workshop (Gothenburg, Sweden)
- 2016
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Ingår i: MycoKeys. - : Pensoft Publishers. - 1314-4057 .- 1314-4049. ; 16, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- Recent molecular studies have identified substantial fungal diversity in indoor environments. Fungi and fungal particles have been linked to a range of potentially unwanted effects in the built environment, including asthma, decay of building materials, and food spoilage. The study of the built mycobiome is hampered by a number of constraints, one of which is the poor state of the metadata annotation of fungal DNA sequences from the built environment in public databases. In order to enable precise interrogation of such data – for example, “retrieve all fungal sequences recovered from bathrooms” – a workshop was organized at the University of Gothenburg (May 23-24, 2016) to annotate public fungal barcode (ITS) sequences according to the MIxS-Built Environment annotation standard (http://gensc.org/mixs/). The 36 participants assembled a total of 45,488 data points from the published literature, including the addition of 8,430 instances of countries of collection from a total of 83 countries, 5,801 instances of building types, and 3,876 instances of surface-air contaminants. The results were implemented in the UNITE database for molecular identification of fungi (http://unite.ut.ee) and were shared with other online resources. Data obtained from human/animal pathogenic fungi will furthermore be verified on culture based metadata for subsequent inclusion in the ISHAM-ITS database (http://its.mycologylab.org).
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| 21. |
- Nilsson, R. Henrik, 1976, et al.
(författare)
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Mycobiome diversity: high-throughput sequencing and identification of fungi.
- 2019
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Ingår i: Nature reviews. Microbiology. - : Springer Science and Business Media LLC. - 1740-1534 .- 1740-1526. ; 17, s. 95-109
-
Forskningsöversikt (refereegranskat)abstract
- Fungi are major ecological players in both terrestrial and aquatic environments by cycling organic matter and channelling nutrients across trophic levels. High-throughput sequencing (HTS) studies of fungal communities are redrawing the map of the fungal kingdom by hinting at its enormous - and largely uncharted - taxonomic and functional diversity. However, HTS approaches come with a range of pitfalls and potential biases, cautioning against unwary application and interpretation of HTS technologies and results. In this Review, we provide an overview and practical recommendations for aspects of HTS studies ranging from sampling and laboratory practices to data processing and analysis. We also discuss upcoming trends and techniques in the field and summarize recent and noteworthy results from HTS studies targeting fungal communities and guilds. Our Review highlights the need for reproducibility and public data availability in the study of fungal communities. If the associated challenges and conceptual barriers are overcome, HTS offers immense possibilities in mycology and elsewhere.
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| 22. |
- Wiqvist, Samuel, et al.
(författare)
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Partially Exchangeable Networks and architectures for learning summary statistics in Approximate Bayesian Computation
- 2019
-
Ingår i: Proceedings of the 36th International Conference on Machine Learning. - : PMLR. ; 2019-June, s. 11795-11804
-
Konferensbidrag (refereegranskat)abstract
- We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability properties of conditionally Markovian processes. Moreover, we show that any block-switch invariant function has a PEN-like representation. The DeepSets architecture is a special case of PEN and we can therefore also target fully exchangeable data. We employ PENs to learn summary statistics in approximate Bayesian computation (ABC). When comparing PENs to previous deep learning methods for learning summary statistics, our results are highly competitive, both considering time series and static models. Indeed, PENs provide more reliable posterior samples even when using less training data.
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| 23. |
- Kerren, Andreas, 1971-, et al.
(författare)
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Network Visualization for Integrative Bioinformatics
- 2014
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Ingår i: Approaches in Integrative Bioinformatics. - Berlin Heidelberg : Springer. - 9783642412806 - 9783642412813 ; , s. 173-202
-
Bokkapitel (refereegranskat)abstract
- Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas like systems biology. Biological networks as representations of such processes are crucial for an extensive understanding of living beings. Due to their size and complexity, their growth and continuous change, as well as their compilation from databases on demand, researchers very often request novel network visualization, interaction and exploration techniques. In this chapter, we first provide background information that is needed for the interactive visual analysis of various biological networks. Fields such as (information) visualization, visual analytics and automatic layout of networks are highlighted and illustrated by a number of examples. Then, the state of the art in network visualization for the life sciences is presented together with a discussion of standards for the graphical representation of cellular networks and biological processes.
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| 24. |
- Kerren, Andreas, 1971-, et al.
(författare)
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Why Integrate InfoVis and SciVis? : An Example from Systems Biology
- 2014
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Ingår i: IEEE Computer Graphics and Applications. - : IEEE. - 0272-1716 .- 1558-1756. ; 34:6, s. 69-73
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The more-or-less artificial barrier between information visualization and scientific visualization hinders knowledge discovery. Having an integrated view of many aspects of the target data, including a seamlessly interwoven visual display of structural abstract data and 3D spatial information, could lead to new discoveries, insights, and scientific questions. Such a view also could reduce the user’s cognitive load—that is, reduce the effort the user expends when comparing views.
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| 25. |
- Henriksson, Jens, 1991, et al.
(författare)
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Performance analysis of out-of-distribution detection on trained neural networks
- 2020
-
Ingår i: Information and Software Technology. - : Elsevier B.V.. - 0950-5849 .- 1873-6025.
-
Tidskriftsartikel (refereegranskat)abstract
- Context: Deep Neural Networks (DNN) have shown great promise in various domains, for example to support pattern recognition in medical imagery. However, DNNs need to be tested for robustness before being deployed in safety critical applications. One common challenge occurs when the model is exposed to data samples outside of the training data domain, which can yield to outputs with high confidence despite no prior knowledge of the given input. Objective: The aim of this paper is to investigate how the performance of detecting out-of-distribution (OOD) samples changes for outlier detection methods (e.g., supervisors) when DNNs become better on training samples. Method: Supervisors are components aiming at detecting out-of-distribution samples for a DNN. The experimental setup in this work compares the performance of supervisors using metrics and datasets that reflect the most common setups in related works. Four different DNNs with three different supervisors are compared during different stages of training, to detect at what point during training the performance of the supervisors begins to deteriorate. Results: Found that the outlier detection performance of the supervisors increased as the accuracy of the underlying DNN improved. However, all supervisors showed a large variation in performance, even for variations of network parameters that marginally changed the model accuracy. The results showed that understanding the relationship between training results and supervisor performance is crucial to improve a model's robustness. Conclusion: Analyzing DNNs for robustness is a challenging task. Results showed that variations in model parameters that have small variations on model predictions can have a large impact on the out-of-distribution detection performance. This kind of behavior needs to be addressed when DNNs are part of a safety critical application and hence, the necessary safety argumentation for such systems need be structured accordingly.
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| 26. |
- Kim, Jinhan, et al.
(författare)
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Ahead of Time Mutation Based Fault Localisation using Statistical Inference
- 2021
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Ingår i: Proceedings - International Symposium on Software Reliability Engineering, ISSRE. - 1071-9458. ; 2021-October, s. 253-263
-
Konferensbidrag (refereegranskat)abstract
- Mutation analysis can effectively capture the de-pendency between source code and test results. This has been exploited by Mutation Based Fault Localisation (MBFL) techniques. However, MBFL techniques suffer from the need to expend the high cost of mutation analysis after the observation of failures, which may present a challenge for its practical adoption. We introduce SIMFL (Statistical Inference for Mutation-based Fault Localisation), an MBFL technique that allows users to perform the mutation analysis in advance before a failure is observed, allowing the amortisation of the analysis cost. SIMFL uses mutants as artificial faults and aims to learn the failure patterns among test cases against different locations of mutations. Once a failure is observed, SIMFL requires either almost no or very small additional cost for analysis, depending on the used inference model. An empirical evaluation using DEFECTS4J shows that SIMFL can successfully localise up to 113 out of 203 studied faults (55%) at the top, and 159 (78%) faults within the top five, significantly outperforming existing MBFL techniques while using the results of mutation analysis that has been undertaken before the test failure. The amortised cost of mutation analysis can be further reduced by mutation sampling: SIMFL retains 80 % of its localisation accuracy at the top rank when using only 10% of generated mutants, compared to results obtained without sampling.
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| 27. |
- Kim, Jinhan, et al.
(författare)
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Learning test-mutant relationship for accurate fault localisation
- 2023
-
Ingår i: Information and Software Technology. - 0950-5849. ; 162
-
Tidskriftsartikel (refereegranskat)abstract
- Context: Automated fault localisation aims to assist developers in the task of identifying the root cause of the fault by narrowing down the space of likely fault locations. Simulating variants of the faulty program called mutants, several Mutation Based Fault Localisation (MBFL) techniques have been proposed to automatically locate faults. Despite their success, existing MBFL techniques suffer from the cost of performing mutation analysis after the fault is observed. Method: To overcome this shortcoming, we propose a new MBFL technique named SIMFL (Statistical Inference for Mutation-based Fault Localisation). SIMFL localises faults based on the past results of mutation analysis that has been done on the earlier version in the project history, allowing developers to make predictions on the location of incoming faults in a just-in-time manner. Using several statistical inference methods, SIMFL models the relationship between test results of the mutants and their locations, and subsequently infers the location of the current faults. Results: The empirical study on DEFECTS4J dataset shows that SIMFL can localise 113 faults on the first rank out of 224 faults, outperforming other MBFL techniques. Even when SIMFL is trained on the predicted kill matrix, SIMFL can still localise 95 faults on the first rank out of 194 faults. Moreover, removing redundant mutants significantly improves the localisation accuracy of SIMFL by the number of faults localised at the first rank up to 51. Conclusion: This paper proposes a new MBFL technique called SIMFL, which exploits ahead-of-time mutation analysis to localise current faults. SIMFL is not only cost-effective, as it does not need a mutation analysis after the fault is observed, but also capable of localising faults accurately.
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| 28. |
- Liakata, Maria, et al.
(författare)
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Automatic recognition of conceptualisation zones in scientific articles and two life science applications
- 2012
-
Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1460-2059 .- 1367-4811 .- 1367-4803. ; 28:7, s. 991-1000
-
Tidskriftsartikel (refereegranskat)abstract
- Motivation: Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of eleven categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within a paper and their automatic recognition can greatly facilitate biomedical information extraction by characterising the different types of facts, hypotheses and evidence available in a scientific publication. Results: We have trained and compared machine learning classifiers (SVM and CRF) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognise CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with `Experiment', `Background' and `Model' being the categories with the highest F1-scores (76%, 62% and 53% respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We also discuss the usefulness of automatically generated CoreSCs in two biomedical applications as well as work in progress. Availability: A web-based tool for the automatic annotation of papers with CoreSCs and corresponding documentation is available on-line at http://www.sapientaproject.com/software http://www.sapientaproject.com also contains detailed information pertaining to CoreSC annotation and links to annotation guidelines as well as a corpus of manually annotated papers, which served as our training data. Contact: liakata@ebi.ac.uk
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| 29. |
- Mathew, C, et al.
(författare)
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Data Refinement Workflow
- 2012
-
Ingår i: http://www.myexperiment.org/packs/267.html.
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The Taxonomic Data Refinement Workflow provides an environment for preparing observational and specimen data sets for use in scientific analyses such as: species distribution analysis,species richness and diversity studies, species occurrence studiesh, historical analysis, and other spatio-temporal analyses. This pack contains: - The Taxonomic Data Refinement (Integrated) Workflow (.t2flow file) - Its (version) dependent libraries - Relevant documentation, including diagrammatic representation of the workflow - Example data files , including input and output data. For more information on BioVeL's Data Refinement Workflow, please click here" with a link to http://www.biovel.eu/index.php/workflows/data-refinement-wf This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL : http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359
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| 30. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Microservice-Based Unsupervised Anomaly Detection Loop for Optical Networks
- 2022
-
Ingår i: 2022 Optical Fiber Communications Conference and Exhibition, OFC 2022 - Proceedings.
-
Konferensbidrag (refereegranskat)abstract
- Unsupervised learning (UL) is a technique to detect previously unseen anomalies without needing labeled datasets. We propose the integration of a scalable UL-based inference component in the monitoring loop of an SDN-controlled optical network.
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| 31. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Microservice-Based Unsupervised Anomaly Detection Loop for Optical Networks
- 2016
-
Ingår i: Optics InfoBase Conference Papers. ; 2016
-
Konferensbidrag (refereegranskat)abstract
- Unsupervised learning (UL) is a technique to detect previously unseen anomalies without needing labeled datasets. We propose the integration of a scalable UL-based inference component in the monitoring loop of an SDN-controlled optical network.
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| 32. |
- Névéol, Aurélie, et al.
(författare)
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Clinical Natural Language Processing in languages other than English : opportunities and challenges
- 2018
-
Ingår i: Journal of Biomedical Semantics. - : Springer Science and Business Media LLC. - 2041-1480. ; 9
-
Forskningsöversikt (refereegranskat)abstract
- Background: Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. Main Body: We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application. Conclusion: We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.
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| 33. |
- Skelbye, Molly, et al.
(författare)
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OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNN–LSTM Networks
- 2021
-
Ingår i: Proceedings of the International Conference on Recent Advances in Natural Language Processing, 1–3 September, 2021 / edited by Galia Angelova, Maria Kunilovskaya, Ruslan Mitkov, Ivelina Nikolova-Koleva. - Shoumen, Bulgaria : INCOMA. - 1313-8502 .- 2603-2813. - 9789544520724
-
Konferensbidrag (refereegranskat)abstract
- Deep CNN–LSTM hybrid neural networks have proven to improve the accuracy of Optical Character Recognition (OCR) models for different languages. In this paper we examine to what extent these networks improve the OCR accuracy rates on Swedish historical newspapers. By experimenting with the open source OCR engine Calamari, we are able to show that mixed deep CNN–LSTM hybrid models outperform previous models on the task of character recognition of Swedish historical newspapers spanning 1818–1848. We achieved an average character accuracy rate (CAR) of 97.43% which is a new state–of–the–art result on 19th century Swedish newspaper text. Our data, code and models are released under CC BY licence.
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| 34. |
- Sonja, Holl, et al.
(författare)
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On specifying and sharing scientific workflow optimization results using research objects
- 2013
-
Ingår i: Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science. - Denver, Colorado : ACM. - 9781450325028
-
Konferensbidrag (refereegranskat)abstract
- Reusing and repurposing scientific workflows for novel scientific experiments is nowadays facilitated by workflow repositories. Such repositories allow scientists to find existing workflows and re-execute them. However, workflow input parameters often need to be adjusted to the research problem at hand. Adapting these parameters may become a daunting task due to the infinite combinations of their values in a wide range of applications. Thus, a scientist may preferably use an automated optimization mechanism to adjust the workflow set-up and improve the result. Currently, automated optimizations must be started from scratch as optimization meta-data are not stored together with workflow provenance data. This important meta-data is lost and can neither be reused nor assessed by other researchers. In this paper we present a novel approach to capture optimization meta-data by extending the Research Object model and reusing the W3C standards. We validate our proposal through a real-world use case taken from the biodivertsity domain, and discuss the exploitation of our solution in the context of existing e-Science infrastructures.
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| 35. |
- Stuckman, J., et al.
(författare)
-
The Effect of Dimensionality Reduction on Software Vulnerability Prediction Models
- 2017
-
Ingår i: IEEE Transactions on Reliability. - : Institute of Electrical and Electronics Engineers (IEEE). - 1558-1721 .- 0018-9529. ; 66:1, s. 17-37
-
Tidskriftsartikel (refereegranskat)abstract
- Statistical prediction models can be an effective technique to identify vulnerable components in large software projects. Two aspects of vulnerability prediction models have a profound impact on their performance: 1) the features (i.e., the characteristics of the software) that are used as predictors and 2) the way those features are used in the setup of the statistical learning machinery. In a previous work, we compared models based on two different types of features: software metrics and term frequencies (text mining features). In this paper, we broaden the set of models we compare by investigating an array of techniques for the manipulation of said features. These techniques fall under the umbrella of dimensionality reduction and have the potential to improve the ability of a prediction model to localize vulnerabilities. We explore the role of dimensionality reduction through a series of cross-validation and cross-project prediction experiments. Our results show that in the case of software metrics, a dimensionality reduction technique based on confirmatory factor analysis provided an advantage when performing cross-project prediction, yielding the best F-measure for the predictions in five out of six cases. In the case of text mining, feature selection can make the prediction computationally faster, but no dimensionality reduction technique provided any other notable advantage.
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| 36. |
- Zhang, J., et al.
(författare)
-
Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism
- 2020
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
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| 37. |
- Sanli, Kemal, et al.
(författare)
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Metagenomic Sequencing of Marine Periphyton: Taxonomic and Functional Insights into Biofilm Communities
- 2015
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Ingår i: Frontiers in Microbiology. - : Frontiers Media SA. - 1664-302X. ; 6:1192
-
Tidskriftsartikel (refereegranskat)abstract
- Periphyton communities are complex phototrophic, multispecies biofilms that develop on surfaces in aquatic environments. These communities harbor a large diversity of organisms comprising viruses, bacteria, algae, fungi, protozoans and metazoans. However, thus far the total biodiversity of periphyton has not been described. In this study, we use metagenomics to characterize periphyton communities from the marine environment of the Swedish west coast. Although we found approximately ten times more eukaryotic rRNA marker gene sequences compared to prokaryotic, the whole metagenome-based similarity searches showed that bacteria constitute the most abundant phyla in these biofilms. We show that marine periphyton encompass a range of heterotrophic and phototrophic organisms. Heterotrophic bacteria, including the majority of proteobacterial clades and Bacteroidetes, and eukaryotic macro-invertebrates were found to dominate periphyton. The phototrophic groups comprise Cyanobacteria and the alpha-proteobacterial genus Roseobacter, followed by different micro- and macro-algae. We also assess the metabolic pathways that predispose these communities to an attached lifestyle. Functional indicators of the biofilm form of life in periphyton involve genes coding for enzymes that catalyze the production and degradation of extracellular polymeric substances, mainly in the form of complex sugars such as starch and glycogen-like meshes together with chitin. Genes for 278 different transporter proteins were detected in the metagenome, constituting the most abundant protein complexes. Finally, genes encoding enzymes that participate in anaerobic pathways, such as denitrification and methanogenesis, were detected suggesting the presence of anaerobic or low-oxygen micro-zones within the biofilms.
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| 38. |
- Bresin, Roberto, et al.
(författare)
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Auditory feedback through continuous control of crumpling sound synthesis
- 2008
-
Ingår i: Proceedings of Sonic Interaction Design. - : IUAV University of Venice. - 9788890341304 ; , s. 23-28
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Konferensbidrag (refereegranskat)abstract
- A realtime model for the synthesis of crumpling sounds ispresented. By capturing the statistics of short sonic transients which give rise to crackling noise, it allows for a consistent description of a broad spectrum of audible physical processes which emerge in several everyday interaction contexts.The model drives a nonlinear impactor that sonifies every transient, and it can be parameterized depending on the physical attributes of the crumpling material. Three different scenarios are described, respectively simulating the foot interaction with aggregate ground materials, augmenting a dining scenario, and affecting the emotional content of a footstep sequence. Taken altogether, they emphasize the potential generalizability of the model to situations in which a precise control of auditory feedback can significantly increase the enactivity and ecological validity of an interface.
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| 39. |
- Bresin, Roberto
(författare)
-
What is the color of that music performance?
- 2005
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Ingår i: Proceedings of the International Computer Music Conference - ICMC 2005. - Barcelona. ; , s. 367-370
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Konferensbidrag (refereegranskat)abstract
- The representation of expressivity in music is still a fairlyunexplored field. Alternative ways of representing musicalinformation are necessary when providing feedback onemotion expression in music such as in real-time tools formusic education, or in the display of large music databases.One possible solution could be a graphical non-verbal representationof expressivity in music performance using coloras index of emotion. To determine which colors aremost suitable for an emotional expression, a test was run.Subjects rated how well each of 8 colors and their 3 nuancescorresponds to each of 12 music performances expressingdifferent emotions. Performances were playedby professional musicians with 3 instruments, saxophone,guitar, and piano. Results show that subjects associateddifferent hues to different emotions. Also, dark colorswere associated to music in minor tonality and light colorsto music in major tonality. Correspondence betweenspectrum energy and color hue are preliminary discussed.
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| 40. |
- Dahl, Sofia, et al.
(författare)
-
Gestures in performance
- 2009
-
Ingår i: Musical Gestures: Sound, Movement, and Meaning. - New York : Routledge. - 9780415998871 - 0415998875 ; , s. 36-68
-
Bokkapitel (refereegranskat)abstract
- We experience and understand the world, including music, through body movement–when we hear something, we are able to make sense of it by relating it to our body movements, or form an image in our minds of body movements. Musical Gestures is a collection of essays that explore the relationship between sound and movement. It takes an interdisciplinary approach to the fundamental issues of this subject, drawing on ideas, theories and methods from disciplines such as musicology, music perception, human movement science, cognitive psychology, and computer science.
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| 41. |
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| 42. |
- Kerren, Andreas, 1971-, et al.
(författare)
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BioVis Explorer : A visual guide for biological data visualization techniques
- 2017
-
Ingår i: PLOS ONE. - : PLOS. - 1932-6203. ; 12:11
-
Tidskriftsartikel (refereegranskat)abstract
- Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for the visualization of biological data have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers and visualization researchers. To address this challenge, we have reviewed visualization research especially performed for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods. The tool is publicly available online and has been designed as community-based system which allows users to add their works easily.
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| 43. |
- Visell, Y., et al.
(författare)
-
Sound design and perception in walking interactions
- 2009
-
Ingår i: International journal of human-computer studies. - : Elsevier BV. - 1071-5819 .- 1095-9300. ; 67:11, s. 947-959
-
Tidskriftsartikel (refereegranskat)abstract
- This paper reviews the state of the art in the display and perception of walking generated sounds and tactile vibrations, and their current and potential future uses in interactive systems. As non-visual information sources that are closely linked to human activities in diverse environments, such signals are capable of communicating about the spaces we traverse and activities we encounter in familial and intuitive ways However, in order for them to be effectively employed in human-computer interfaces, significant knowledge is required in areas including the perception of acoustic signatures of walking, and the design, engineering, and evaluation of interfaces that utilize them. Much of this expertise has accumulated in recent years, although many questions remain to be explored We highlight past work and current research directions in this Multidisciplinary area of investigation, and point to potential future trends.
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| 44. |
- Vitale, Renzo, et al.
(författare)
-
Emotional cues in knocking sounds
- 2008
-
Ingår i: Proc. of the 10th International Conference on Music Perception and Cognition. ; , s. 276-
-
Konferensbidrag (refereegranskat)abstract
- The object of this research is to describe how temporal and dynamic cues in knocking sounds can communicate emotions, just like in expressive musical performances. An experiment has been conducted where several emotions were supposed to be expressed by different performers. Participants were asked to knock on a wooden door according to instructions. Knocking sounds have been recorded both outside and inside the room, and afterwards they were rated in listening tests. Together with acoustic measurements, arm movements during the knocking action were detected through a motion capture system, so that the body behaviour (visual component) could be correlated to the sound evaluation (acoustical component). Based on previous research on arm movements and music performance, ten different emotions were selected for investigation. Results confirm the use of the same strategies in both expressive everyday body gestures and expressive music performance. Listeners were able to perceive emotions to a large extent. Strong similarities between the use of acoustical features in knocking and music performance were found. The intended emotions were generally perceived correctly. Among the relevant acoustical features extracted from the recordings, rhythm and IOI as well as loudness revealed to be strong cues.
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| 45. |
- Åkerman, S., et al.
(författare)
-
Surface Model Generation and Segmentation of the Human Celebral Cortex for the Construction of Unfolded Cortical Maps
- 1996
-
Ingår i: Proc. 2nd International Conference on Functional Mapping of the Human Brain. ; , s. S126-S126
-
Konferensbidrag (refereegranskat)abstract
- Representing the shape of the human cerebral cortex arises as a basic subproblem in several areas of brain science, such as when describing the anatomy of the cortex and when relating functional measurements to cortical regions. Most current methods for building such representions of the cortical surface are either based on contours from two-dimensional cross sections or landmarks that have been obtained manually.In this article, we outline a methodology for semi-automatic contruction of a solely surface based representation of the human cerebral cortex in vivo for subsequent generation of (unfolded) two-dimensional brain maps.The method is based on input data in the form of three-dimensional NMR images, and comprises the following main steps:suppression of disturbing fine-scale structures by linear and non-linear scale-space techniques,generation of a triangulated surface representation based on either iso-surfaces or three-dimensional edge detection,division of the surface model into smaller segments based on differential invariants computed from the image data.When constructing an unfolded (flattened) surface representation, the instrinsic curvature of the cortex means that such a unfolding cannot be done without introducing distortions. To reduce this problem, we propose to cut the surface into smaller parts, where a ridge detector acts as guideline, and then unfold each patch individually, so as to obtain low distortions.Having a solely surface based representation of the cortex and expressing the image operations using multi-scale differential invariants in terms of scale-space derivatives as done in this work is a natural choice both in terms of conceptual and algorithmic simplicity. Moreover, explicitly handling the multi-scale nature of the data is necessary to obtain robust results.
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| 46. |
- Robinson, Jonathan, 1986, et al.
(författare)
-
An atlas of human metabolism
- 2020
-
Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
-
Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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| 47. |
- Chen, Shuangshuang, 1992-, et al.
(författare)
-
Monte Carlo Filtering Objectives
- 2021
-
Ingår i: IJCAI International Joint Conference on Artificial Intelligence. - : International Joint Conferences on Artificial Intelligence. - 1045-0823. ; , s. 2256-2262
-
Konferensbidrag (refereegranskat)abstract
- Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models. It can be addressed by surrogate objectives for optimization. We propose Monte Carlo filtering objectives (MCFOs), a family of variational objectives for jointly learning parametric generative models and amortized adaptive importance proposals of time series. MCFOs extend the choices of likelihood estimators beyond Sequential Monte Carlo in state-of-the-art objectives, possess important properties revealing the factors for the tightness of objectives, and allow for less biased and variant gradient estimates. We demonstrate that the proposed MCFOs and gradient estimations lead to efficient and stable model learning, and learned generative models well explain data and importance proposals are more sample efficient on various kinds of time series data.
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| 48. |
- Gomariz, Alvaro, et al.
(författare)
-
Utilizing Uncertainty Estimation in Deep Learning Segmentation of Fluorescence Microscopy Images with Missing Markers
- 2021
-
Konferensbidrag (refereegranskat)abstract
- Fluorescence microscopy images contain several channels,each indicating a marker staining the sample. Since manydifferent marker combinations are utilized in practice, it hasbeen challenging to apply deep learning based segmentationmodels, which expect a predefined channel combination forall training samples as well as at inference for future applica-tion. Recent work circumvents this problem using a modalityattention approach to be effective across any possible markercombination. However, for combinations that do not existin a labeled training dataset, one cannot have any estimationof potential segmentation quality if that combination is en-countered during inference. Without this, not only one lacksquality assurance but one also does not know where to put anyadditional imaging and labeling effort. We herein propose amethod to estimate segmentation quality on unlabeled imagesby (i) estimating both aleatoric and epistemic uncertainties ofconvolutional neural networks for image segmentation, and(ii) training a Random Forest model for the interpretationof uncertainty features via regression to their correspond-ing segmentation metrics. Additionally, we demonstrate thatincluding these uncertainty measures during training canprovide an improvement on segmentation performance.
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| 49. |
- Linde, Oskar, 1979-, et al.
(författare)
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Composed Complex-Cue Histograms : An Investigation of the Information Content in Receptive Field Based Image Descriptors for Object Recognition
- 2012
-
Ingår i: Computer Vision and Image Understanding. - : Elsevier. - 1077-3142 .- 1090-235X. ; 116:4, s. 538-560
-
Tidskriftsartikel (refereegranskat)abstract
- Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations. This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, colour-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories. It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complex-cue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive experimental evaluations on two mutually independent data sets. For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatio-chromatic) differential invariants up to order two lead to very high recognition rates. For the task of category classification, primary information is contained in both first- and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue. Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations. An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.
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| 50. |
- Lindeberg, Tony, 1964-, et al.
(författare)
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Analysis of brain activation patterns using a 3-D scale-space primal sketch
- 1999
-
Ingår i: Human Brain Mapping. - 1065-9471 .- 1097-0193. ; 7:3, s. 166-94
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Tidskriftsartikel (refereegranskat)abstract
- A fundamental problem in brain imaging concerns how to define functional areas consisting of neurons that are activated together as populations. We propose that this issue can be ideally addressed by a computer vision tool referred to as the scale-space primal sketch. This concept has the attractive properties that it allows for automatic and simultaneous extraction of the spatial extent and the significance of regions with locally high activity. In addition, a hierarchical nested tree structure of activated regions and subregions is obtained. The subject in this article is to show how the scale-space primal sketch can be used for automatic determination of the spatial extent and the significance of rCBF changes. Experiments show the result of applying this approach to functional PET data, including a preliminary comparison with two more traditional clustering techniques. Compared to previous approaches, the method overcomes the limitations of performing the analysis at a single scale or assuming specific models of the data.
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