The purity analysis of oilseed rape (Brassica napus L.) is currently a labor-intensive and manual process, requiring significant human effort for accurate assessment. In this context, the KIRa-Sorter system presents an innovative solution that leverages hyperspectral imaging technology for automating the comprehensive classification of various contaminants present in rapeseed samples. The initial phase of the KIRa-Sorter system involves the efficient capture of hyperspectral and RGB image data from rapeseed samples as input for classification. From up to 200 different types of foreign objects typically found in these samples, a reduced coreset has been defined that the system is able to automatically singulate, classify and physically sort.

In the seed-producing industry, accurate assessment of harvested seeds for technical purity is a necessary, yet time-consuming and labor-intensive task. Automating this task holds immense potential for enhancing agricultural seed productivity, and using computer vision methods to classify seeds has already demonstrated promising results. Here, we propose a novel spectral-enhanced image anomaly detection approach to accurately discriminate Canola seeds (Brassica napus L.) from visually similar non-Canola seeds. Our bimodal approach exploits both RGB and data captured by a hyperspectral camera of the same sample. For efficient processing of this data, we suggest a novel bimodal convolutional autoencoder (BiCAE) architecture, which combines the strengths of high spatial resolution in RGB and high spectral resolution in hyperspectral data. We demonstrate that training our BiCAE model on a Canola dataset allows to learn a joint latent representation that effectively extracts spatio-spectral information from both RGB and hyperspectral data. Experiments show promising results in differentiating between Canola and non-Canola samples, in particular in detecting various types of non-Canola seeds in previously unseen test data. The obtained results highlight the model's ability to generalize beyond the training data, surpassing unimodal models that rely solely on a single modality.

Convolutional Neural Networks have become an important tool for various Computer Vision tasks. Yet, increasing complexity of such architectures drives computational costs. To this end, we propose two measures to achieve similar classification results as state-of-the-art architectures while at the same time reducing model complexity significantly. Firstly, we describe a novel type of non-linear parameter-efficient morphological layers inspired by concepts that are well-known and widely used with convolutions. Secondly, we present a set of simple network architectures, organized as optimization framework, which is enhanced by neural architecture search and hyperparameter optimization. In experiments with hyperspectral remote sensing data, we demonstrate that the identified optimal morphological architecture produces results not only comparable with other architectures from the optimization framework, but also comparable or better than selected state-of-the-art neural network architectures for image classification. Depending on the performed task, the proposed optimized architecture requires up to 25 times fewer parameters than actual state-of-the-art networks.

In this paper, we extend the dataset statistics, model benchmarks, and performance analysis for the recently published KABR dataset, an in situ dataset for ungulate behavior recognition using aerial footage from the Mpala Research Centre in Kenya. The dataset comprises video footage of reticulated giraffes (lat. Giraffa reticulata), Plains zebras (lat. Equus quagga), and Grévy’s zebras (lat. Equus grevyi) captured using a DJI Mavic 2S drone. It includes both spatiotemporal (i.e., mini-scenes) and behavior annotations provided by an expert behavioral ecologist. In total, KABR has more than 10 hours of annotated video. We extend the previous work in four key areas by: (i) providing comprehensive dataset statistics to reveal new insights into the data distribution across behavior classes and species; (ii) extending the set of existing benchmark models to include a new state-of-the-art transformer; (iii) investigating weight initialization strategies and exploring whether pretraining on human action recognition datasets is transferable to in situ animal behavior recognition directly (i.e., zero-shot) or as initialization for end-to-end model training; and (iv) performing a detailed statistical analysis of the performance of these models across species, behavior, and formally defined segments of the long-tailed distribution. The KABR dataset addresses the limitations of previous datasets sourced from controlled environments, offering a more authentic representation of natural animal behaviors. This work marks a significant advancement in the automatic analysis of wildlife behavior, leveraging drone technology to overcome traditional observational challenges and enabling a more nuanced understanding of animal interactions in their natural habitats. The dataset is available at https://kabrdata.xyz

Hyperspectral imaging offers manifold opportunities for applications that may not, or only partially, be achieved within the visual spectrum. Our paper presents a novel approach for Single-Label Hyperspectral Image Classification, demonstrated through the example of a key challenge faced by agricultural seed producers: seed purity testing. We employ Self-Supervised Learning and Masked Image Modeling techniques to tackle this task. Recognizing the challenges and costs associated with acquiring hyperspectral data, we aim to develop a versatile method capable of working with visible, arbitrary combinations of spectral bands (multispectral data) and hyperspectral sensor data. By integrating RGB and hyperspectral data, we leverage the detailed spatial information from RGB images and the rich spectral information from hyperspectral data to enhance the accuracy of seed classification. Through evaluations in various real-life scenarios, we demonstrate the flexibility, scalability, and efficiency of our approach.

The use of artificial intelligence (AI) is today's dominating technological trend across all industries. With the maturing of deep learning and other data-driven techniques, AI has over the last decade become an essential component for an increasing number of products and services. In parallel to this development, technological advances have been accelerating the production of novel AI models from large-scale datasets. This global phenomenon has been driving the need for an efficient industrialized approach to develop, manage and maintain AI models at large scale. Such an approach is provided by the state-of-the-art operational concept termed AI Factory, which refers to an infrastructure for AI models and implements the idea of AI as a Service (AIaaS). Moreover, it ensures performance, transparency and reproducibility of AI models at any point in the continuous AI development process. This concept, however, does not only require new technologies and architectures, but also new job roles. Here, we discuss current trends, outline requirements and identify success factors for AI Factories. We conclude with recommendations for their successful use in practice as well as perspectives on future developments.

Neural networks are a common and powerful tool to approximate highly complex non-linear functions. Most typically, the connection weights of these structures are adapted during training by employing gradient descent techniques. As this is not possible for so-called semiring neural networks, we propose to use a genetic algorithm for training this type of network and present a modern extendable C++-based parallelized implementation of the genetic algorithm Genitor II for this task. We compare our parallelized implementation with a more conventional implementation of the Genitor II algorithm and demonstrate that this novel approach is efficient. As a consequence, this novel implementation allows for the first time efficient and practical training of semiring neural networks even for relatively large network sizes of several hundred units. The source code is publicly available under the MIT license: https://git.informatik.uni-leipzig.de/ml-group/semiring-nn/parallelized-c-implementation.

While neuro-inspired and symbolic artficial intelligence have for a long time been considered ideal complements, approaches to hybridize these concepts often lack an unifying grand theory. The way the philosophical concept of constructivism has been adapted for eductional purposes, however, provides a fruitful source of inspiration for this purpose. To this end, we have developed a framework termed Constructivist Machine Learning, which applies constructivist learning principles and exploits metadata on the grounds of Stachowiak's General Model Theory in order to bridge the gap between neuro-spired and symbolic approaches. In this chapter, we summarize our previous work in order to introduce the reader to the most important ideas and concepts.

While neuro-inspired and symbolic artficial intelligence have for a long time been considered ideal complements, approaches to hybridize these concepts often lack an unifying grand theory. The way the philosophical concept of constructivism has been adapted for eductional purposes, however, provides a fruitful source of inspiration for this purpose. To this end, we have been developing a framework termed Constructivist Machine Learning, which applies constructivist learning principles and exploits meta data on the grounds of Stachowiak’s General Model Theory in order to bridge the gap between neuro-spired and symbolic approaches. In this chapter, we summarize our previous work in order to introduce the reader to the most important ideas and concepts.

Continuous streams of data are a common, yet challenging phenomenon of modern information processing. Traditional approaches to adopt machine learning techniques to this setting, like offline and online learning, have demonstrated several critical drawbacks. In order to avoid known disadvantages of both approaches, we propose to combine their complementary advantages in a novel machine learning process called deconstruction. Similar to supervised and unsupervised learning, this novel process provides a fundamental learning functionality modeled after human learning. This functionality integrates mechanisms for partitioning training data, managing learned knowledge representations and integrating newly acquired knowledge with previously learned knowledge representations. A prerequisite for this concept is that learning data can be partitioned and that resulting knowledge partitions may be accessed by formal means. In the proposed approach, this is achieved by the recently introduced Constructivist Machine Learning framework, which allows to create, exploit and maintain a knowledge base. In this work, we highlight the design concepts for the implementation of such a deconstruction process. In particular, we describe required subprocesses and how they can be combined.

This dataset consists of synthetic impedance measurements on modeled epithelial tissue as described in [1]. Based on previous work, physiological tissue properties were estimated for the `HT29/B6`, `IPECJ 2`, and `MDCK I` cell lines [2]. In this way, appropriate ranges of cell model parameters could be determined both for physiological control conditions and after apical addition of the substance Nystatin. For each cell line and condition, 150,000 values were randomly selected within their ranges. For each sample, a spectral impedance measurement was synthesized for 42 frequencies between 1.3 Hz and 16.35 kHz. Furthermore, the measurements were scattered to account for deviations from the theoretical impedance value due to the experimental setup. For more details see [1]. Important: You are cordially invited to use this dataset for your research or teaching purposes. For example, to compare your impedance analysis approach with ours or to develop new machine learning methods. If you use this dataset for a scientific publication, please cite [1], where the synthesis and original application of this dataset are described in detail. If you have any questions or remarks, please do not hesitate to contact me via email (bschindler at informatik.uni-leipzig.de). ### Sources ###[1] B. Schindler, D. Günzel, and T. Schmid, “Transcending Two-Path Impedance Spectroscopy with Machine Learning:A Computational Study on Modeling and Quantifying Electric Bipolarity of Epithelia,“ International Journal On Advances in Life Sciences 13(1-2), 2021[2] T. Schmid, “Automatisierte Analyse von Impedanzspektren mittels konstruktivistischen maschinellen Lernens,” Ph.D. dissertation, Universität Leipzig, Germany, 2018.

Recently, interest has grown in connecting modern machine learning approaches with traditional expert systems. This can mean, e.g, to identify patterns with neural networks and integrate them with knowledge graphs. While such combined systems offer a variety of advantages, few domain-independent approaches are known to make a hybrid arti-ficial intelligence applicable without human interaction. To this end, we present the implementation of a constructivist machine learning framework (conML). This novel paradigm uses machine learning to manage a knowledge base and thereby allows for both raw data-based and symbolic information processing on the same internal knowledge representation. Based on axioms for a constructivist machine learning, we describe which operations are required to create, exploit and maintain a knowledge base and how these operations may be implemented with machine learning techniques. The major practical obstacle in this approach is to implement an automated deconstruction process that avoids ambiguity, handles continuous learning and allows knowledge abstraction. As we demonstrate, however, these obstacles can be overcome and constructivist machine learning can be put into practice.

There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.

Patients with Autistic Spectrum Discorder are known to have deficits in interpreting others’ intentions from gaze-direction or other social attention. Here, we use electroencephalography data recorded in virtual reality experiments with patients to predict one out of eight objects that was focused on. Correct labels for these objects were known from parallel eye-tracking measurements. We extracted features from the time domain and from Pearson’s correlation and applied both statistical and neuro-inspired supervised machine learning algorithms. Using a multi-layer perceptron, we achieved 65.4% accuracy on the validation data set and 70.0% accuracy on the test data set.

Quantifying the permeability of intestinal epithelia is a central task in assessing tissue dysfunctions. This can be achieved conveniently by determining the electric resistance of the epithelial tissue by impedance spectroscopy. While in clinical practice this parameter is often estimated by manual extrapolation of discrete two-dimensional plots of impedance spectra, this approach is known to be particularly unprecise for spectra that deviate from a semicircular shape. Previous computational approaches achieved less then ten percent deviation from the known target value on average, but outliers exhibited significantly larger maximum deviations. Here, we show that systematic feature extraction and selection allow estimation of the epithelial resistance with less than one percent deviation from the known target value on average and less than ten percent at maximum. As a result of detailed modeling of cell culture lines and functional states, epithelial resistance for the cell lines HT-29/B6, IPEC-J2 and MDCK I can be quantified reliably under control conditions, as well as under influence of EGTA and nystatin. Keywords–Physiology; Epithelia; Impedance Spectroscopy; Feature Selection; Artificial Neural Networks; Random Forests.

Journal of Fish DiseasesVolume 39, Issue 8 p. 1007-1013 Short Communication Efficacy assessment of three inactivated koi herpes virus antigen preparations against experimental challenge virus infection in common carp T Schmid, T Schmid Institute of Animal Hygiene and Veterinary Public Health, Faculty of Veterinary Medicine, University of Leipzig, Leipzig, GermanySearch for more papers by this authorL Gaede, L Gaede Institute of Animal Hygiene and Veterinary Public Health, Faculty of Veterinary Medicine, University of Leipzig, Leipzig, GermanySearch for more papers by this authorK Böttcher, K Böttcher Animal diseases fund of Saxony, Fish Health Service, Königswartha, GermanySearch for more papers by this authorG Bräuer, G Bräuer Animal diseases fund of Saxony, Fish Health Service, Königswartha, GermanySearch for more papers by this authorD Fichtner, D Fichtner Institute of Infectology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Greifswald – Insel Riems, GermanySearch for more papers by this authorR Beckmann, R Beckmann Division Veterinary Medicine, Paul-Ehrlich-Institut, Federal Institute for Vaccines and Biomedicines, Viral Vaccines II, Langen, GermanySearch for more papers by this authorS Speck, S Speck Institute of Animal Hygiene and Veterinary Public Health, Faculty of Veterinary Medicine, University of Leipzig, Leipzig, GermanySearch for more papers by this authorF Becker, F Becker Saxon State Ministry of Social Affairs and Consumer Protection, Dresden, GermanySearch for more papers by this authorU Truyen, Corresponding Author U Truyen Institute of Animal Hygiene and Veterinary Public Health, Faculty of Veterinary Medicine, University of Leipzig, Leipzig, GermanyCorrespondence Uwe Truyen Institute of Animal Hygiene and Veterinary Public Health An den Tierkliniken 104103 Leipzig (e-mail: truyen@vmf.uni-leipzig.de)Search for more papers by this author T Schmid, T Schmid Institute of Animal Hygiene and Veterinary Public Health, Faculty of Veterinary Medicine, University of Leipzig, Leipzig, GermanySearch for more papers by this authorL Gaede, L Gaede Institute of Animal Hygiene and Veterinary Public Health, Faculty of Veterinary Medicine, University of Leipzig, Leipzig, GermanySearch for more papers by this authorK Böttcher, K Böttcher Animal diseases fund of Saxony, Fish Health Service, Königswartha, GermanySearch for more papers by this authorG Bräuer, G Bräuer Animal diseases fund of Saxony, Fish Health Service, Königswartha, GermanySearch for more papers by this authorD Fichtner, D Fichtner Institute of Infectology, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Greifswald – Insel Riems, GermanySearch for more papers by this authorR Beckmann, R Beckmann Division Veterinary Medicine, Paul-Ehrlich-Institut, Federal Institute for Vaccines and Biomedicines, Viral Vaccines II, Langen, GermanySearch for more papers by this authorS Speck, S Speck Institute of Animal Hygiene and Veterinary Public Health, Faculty of Veterinary Medicine, University of Leipzig, Leipzig, GermanySearch for more papers by this authorF Becker, F Becker Saxon State Ministry of Social Affairs and Consumer Protection, Dresden, GermanySearch for more papers by this authorU Truyen, Corresponding Author U Truyen Institute of Animal Hygiene and Veterinary Public Health, Faculty of Veterinary Medicine, University of Leipzig, Leipzig, GermanyCorrespondence Uwe Truyen Institute of Animal Hygiene and Veterinary Public Health An den Tierkliniken 104103 Leipzig (e-mail: truyen@vmf.uni-leipzig.de)Search for more papers by this author First published: 13 January 2016 https://doi.org/10.1111/jfd.12428Citations: 8Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume39, Issue8August 2016Pages 1007-1013 RelatedInformation

In epithelial physiology, it is common to use an equivalent electric circuit with two resistor-capacitor (RC) subcircuits in series as a model for the electrical behavior of body cells. The relation between these two subcircuits can be quantified by a quotient of their time constants t. While this quotient is a direct indicator of the shape of impedance spectra, its value cannot be determined directly. Here, we suggest a machine learning-based approach to predict the t quotient from impedance spectra. We perform systematic extraction of statistical features, algorithmic feature ranking and dimension reduction on model impedance spectra derived from tissue-equivalent electric circuits. Our results demonstrate that this quotient can be predicted reliably enough from implicit features to discriminate semicircular against non-semicircular impedance spectra.