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The maize root system has been reshaped by indirect selection during global adaptation to new agricultural environments. In this study, we characterized the root systems of more than 9,000 global maize accessions and its wild relatives, defining the geographical signature and genomic basis of variation in seminal root number. We demonstrate that seminal root number has increased during maize domestication followed by a decrease in response to limited water availability in locally adapted varieties. By combining environmental and phenotypic association analyses with linkage mapping, we identified genes linking environmental variation and seminal root number. Functional characterization of the transcription factor ZmHb77 and in silico root modeling provides evidence that reshaping root system architecture by reducing the number of seminal roots and promoting lateral root density is beneficial for the resilience of maize seedlings to drought.

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org, QC 20240926

Spiking neural networks and neuromorphic hardware platforms that emulate neural dynamics are slowly gaining momentum and entering main-stream usage. Despite a well-established mathematical foundation for neural dynamics, the implementation details vary greatly across different platforms. Correspondingly, there are a plethora of software and hardware implementations with their own unique technology stacks. Consequently, neuromorphic systems typically diverge from the expected computational model, which challenges the reproducibility and reliability across platforms. Additionally, most neuromorphic hardware is limited by its access via a single software frameworks with a limited set of training procedures. Here, we establish a common reference-frame for computations in neuromorphic systems, dubbed the Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational primitives as idealized continuous-time hybrid systems that can be composed into graphs and mapped to and from various neuromorphic technology stacks. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the fundamental computation, while simultaneously exposing the exact differences between the evaluated implementation and the idealized mathematical formalism. We reproduce three NIR graphs across 7 neuromorphic simulators and 4 hardware platforms, demonstrating support for an unprecedented number of neuromorphic systems. With NIR, we decouple the evolution of neuromorphic hardware and software, ultimately increasing the interoperability between platforms and improving accessibility to neuromorphic technologies. We believe that NIR is an important step towards the continued study of brain-inspired hardware and bottom-up approaches aimed at an improved understanding of the computational underpinnings of nervous systems., Comment: NIR is available at https://github.com/neuromorphs/NIR

Root systems of crops play a significant role in agro-ecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes and a good soil structure. Minirhizotrons, consisting of transparent tubes that create windows into the soil, have shown to be effective to non-invasively investigate the root system. Root traits, like root length observed around the tubes of minirhizotron, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, are time consuming and labor intensive. Therefore, an objective method for high throughput image analysis that provides data for field root-phenotyping is necessary. In this study we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample is performed. Training and segmentation are done using “Root-Painter”. Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer”. To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 58,000 images. Mainly the results show a high correlation (R=0.81) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1 - 99.6 %. Our pipeline,combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high throughput phenotyping approaches.

Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of-the-art back-propagation through time (BPTT) is, however, very time-consuming. Previous work by Shrestha and Orchard [2018] employs an efficient GPU-accelerated back-propagation algorithm called SLAYER, which speeds up training considerably. SLAYER, however, does not take into account the neuron reset mechanism while computing the gradients, which we argue to be the source of numerical instability. To counteract this, SLAYER introduces a gradient scale hyperparameter across layers, which needs manual tuning. In this paper, (i) we modify SLAYER and design an algorithm called EXODUS, that accounts for the neuron reset mechanism and applies the Implicit Function Theorem (IFT) to calculate the correct gradients (equivalent to those computed by BPTT), (ii) we eliminate the need for ad-hoc scaling of gradients, thus, reducing the training complexity tremendously, (iii) we demonstrate, via computer simulations, that EXODUS is numerically stable and achieves a comparable or better performance than SLAYER especially in various tasks with SNNs that rely on temporal features. Our code is available at https://github.com/synsense/sinabs-exodus.

Historically, strong data anonymization requires substantial domain expertise and custom design for the given data set and use case. Diffix is an anonymization framework designed to make strong data anonymization available to non-experts. This paper describes Diffix Elm, a version of Diffix that is very easy to use at the expense of query features. We describe Diffix Elm, and show that it provides strong anonymity based on the General Data Protection Regulation (GDPR) criteria. This document is the third version of Diffix Elm. The second version added ceiling, round, and bucket\_width functions (in addition to floor). This document adds the ability to protect multiple different kinds of protected entities (a feature not found in earlier versions of Diffix). It also adds counting distinct values for any column (rather than only the AID column).

Accurate detection of pathological conditions in human subjects can be achieved through off-line analysis of recorded biological signals such as electrocardiograms (ECGs). However, human diagnosis is time-consuming and expensive, as it requires the time of medical professionals. This is especially inefficient when indicative patterns in the biological signals are infrequent. Moreover, patients with suspected pathologies are often monitored for extended periods, requiring the storage and examination of large amounts of non-pathological data, and entailing a difficult visual search task for diagnosing professionals. In this work we propose a compact and sub-mW low power neural processing system that can be used to perform on-line and real-time preliminary diagnosis of pathological conditions, to raise warnings for the existence of possible pathological conditions, or to trigger an off-line data recording system for further analysis by a medical professional. We apply the system to real-time classification of ECG data for distinguishing between healthy heartbeats and pathological rhythms. Multi-channel analog ECG traces are encoded as asynchronous streams of binary events and processed using a spiking recurrent neural network operated in a reservoir computing paradigm. An event-driven neuron output layer is then trained to recognize one of several pathologies. Finally, the filtered activity of this output layer is used to generate a binary trigger signal indicating the presence or absence of a pathological pattern. We validate the approach proposed using a Dynamic Neuromorphic Asynchronous Processor (DYNAP) chip, implemented using a standard 180 nm CMOS VLSI process, and present experimental results measured from the chip.

Silk protein–based materials show promise for application as biomaterials for tissue engineering. The simple and rapid photochemical modification of silk protein–based materials composed of either Bombyx mori silkworm silk or engineered spider silk proteins (eADF4(C16)) is reported. Radicals formed on the silk‐based materials initiate the polymerization of monomers (acrylic acid, methacrylic acid, or allylamine) which functionalize the surface of the silk materials with poly(acrylic acid) (PAA), poly(methacrylic acid) (PMAA), or poly(allylamine) (PAAm). To demonstrate potential applications of this type of modification, the polymer‐modified silks are mineralized. The PAA‐ and PMAA‐functionalized silks are mineralized with calcium carbonate, whereas the PAAm‐functionalized silks are mineralized with silica, both of which provide a coating on the materials that may be useful for bone tissue engineering, which will be the subject of future investigations.

Silk protein–based materials show promise for application as biomaterials for tissue engineering. The simple and rapid photochemical modification of silk protein–based materials composed of either Bombyx mori silkworm silk or engineered spider silk proteins (eADF4(C16)) is reported. Radicals formed on the silk‐based materials initiate the polymerization of monomers (acrylic acid, methacrylic acid, or allylamine) which functionalize the surface of the silk materials with poly(acrylic acid) (PAA), poly(methacrylic acid) (PMAA), or poly(allylamine) (PAAm). To demonstrate potential applications of this type of modification, the polymer‐modified silks are mineralized. The PAA‐ and PMAA‐functionalized silks are mineralized with calcium carbonate, whereas the PAAm‐functionalized silks are mineralized with silica, both of which provide a coating on the materials that may be useful for bone tissue engineering, which will be the subject of future investigations.

We present a feature learning model that learns to encode relationships between images. The model is defined as a Gated Boltzmann Machine, which is constrained such that hidden units that are nearby in space can gate each other's connections. We show how frequency/orientation "columns" as well as topographic filter maps follow naturally from training the model on image pairs. The model also helps explain why square-pooling models yield feature groups with similar grouping properties. Experimental results on synthetic image transformations show that spatially constrained gating is an effective way to reduce the number of parameters and thereby to regularize a transformation-learning model., Comment: (new version:) added training formulae; added minor clarifications

Das Ziel dieser Arbeit besteht in der Ausarbeitung und Bewertung eines Low-Cost Workflows zur fernerkundlichen Erfassung und Untersuchung alpiner Forschungsgebiete mittels eines handelsüblichen DJI Mavic Unmanned Aerial Vehicles (UAV) und Open Source Software. Als Untersuchungsgebiet dient der Äußere Hochebenkar-Blockgletscher im hinteren Ötztal. Von diesem werden mithilfe des UAVs Luftbilder aufgenommen und mit einer Kombination aus verschiedenen Open-Source Programmen weiterverarbeitet. Zur Punktwolkenerstellung werden Structure from Motion (SfM) und Multi View Stereo (MVS) verwendet. Der vorgestellte Workflow geht über den gesamten Prozess. Von der Planung der Befliegung, über ihre Ausführung und Auswertung mithilfe von Structure from Motion, bis hin zur Weiterverarbeitung der Daten und einem Vergleich mit Daten aus einem UAV Laserscan, welcher am selben Tag durch-geführt wurde. Die wissenschaftliche Eignung des Workflows wird kritisch hinterfragt und durch den direkten Vergleich mit dem Laserscan kann eine Aussage über die Genauigkeit der resultierenden Daten getroffen werden. Mithilfe von Höhendifferenzberechnungen und Bildkorrelationsalgorithmen im Vergleich zu einem Laserscan aus dem Jahr 2019 kann eine Aussage über die vertikalen und horizontalen Bewegungen des untersuchten Blockgletschers zwischen den Jahren 2019 und 2020 getroffen wer-den. Es konnten mithilfe der angewandten Methodik für diesen Zeitraum sehr schnelle Bewegungsraten im unteren Teil des Blockgletschers festgestellt werden, was auf einen Übergang von periglazialem Fließen zu gravitativem Rutschen hindeutet. Die verwendete Methodik ist in der Lage, dies nahezu ebenso gut darzustellen wie der ungleich teurere UAV Laserscan. Die wissenschaftliche Anwendbarkeit der Methodik konnte dadurch validiert werden., The goal of this thesis is the creation and validation of a low cost workflow for the remote sensing of an alpine study area using a consumer grade DJI Mavic Unmanned Aerial Vehicle (UAV) and Open Source Software. The study area is the Outer Hochebenkar rock glacier in the Ötztal in Austria. An aerial survey is conducted with the UAV and the resulting images are processed using a combination of Open Source Applications. For the Point Cloud Creation, Structure from Motion (SfM) and Multi View Stereo (MVS) are used. The presented workflow spans from the planning of the survey over its execution and the point cloud creation with SfM to the processing of the result-ing data and the comparison with data from a UAV Laserscan, which was conducted the same day. The scientific eligibility of the workflow is tested, and its precision is measured through a direct comparison to the results of the UAV Laserscan. With the use of Grid Difference Calculations and Image Correlation Algorithms in comparison to a Laserscan from 2019, the vertical and horizontal movements of the rock glacier between 2019 and 2020 are measured. Using the presented methods, it was possible to detect exceptionally fast rates of movement in the lower part of the rock glacier, indicating a transition from periglacial flow to gravitational sliding. The workflow pre-sented in this thesis is able to detect this process nearly as good as the much more expensive UAV Laserscan, therefore validating the scientific applicability of the work-flow., eingereicht von: Felix Bauer, Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers, in englischer Sprache, Masterarbeit Universität Innsbruck 2021

Das Ziel dieser Arbeit besteht in der Ausarbeitung und Bewertung eines Low-Cost Workflows zur fernerkundlichen Erfassung und Untersuchung alpiner Forschungsgebiete mittels eines handelsüblichen DJI Mavic Unmanned Aerial Vehicles (UAV) und Open Source Software. Als Untersuchungsgebiet dient der Äußere Hochebenkar-Blockgletscher im hinteren Ötztal. Von diesem werden mithilfe des UAVs Luftbilder aufgenommen und mit einer Kombination aus verschiedenen Open-Source Programmen weiterverarbeitet. Zur Punktwolkenerstellung werden Structure from Motion (SfM) und Multi View Stereo (MVS) verwendet. Der vorgestellte Workflow geht über den gesamten Prozess. Von der Planung der Befliegung, über ihre Ausführung und Auswertung mithilfe von Structure from Motion, bis hin zur Weiterverarbeitung der Daten und einem Vergleich mit Daten aus einem UAV Laserscan, welcher am selben Tag durch-geführt wurde. Die wissenschaftliche Eignung des Workflows wird kritisch hinterfragt und durch den direkten Vergleich mit dem Laserscan kann eine Aussage über die Genauigkeit der resultierenden Daten getroffen werden. Mithilfe von Höhendifferenzberechnungen und Bildkorrelationsalgorithmen im Vergleich zu einem Laserscan aus dem Jahr 2019 kann eine Aussage über die vertikalen und horizontalen Bewegungen des untersuchten Blockgletschers zwischen den Jahren 2019 und 2020 getroffen wer-den. Es konnten mithilfe der angewandten Methodik für diesen Zeitraum sehr schnelle Bewegungsraten im unteren Teil des Blockgletschers festgestellt werden, was auf einen Übergang von periglazialem Fließen zu gravitativem Rutschen hindeutet. Die verwendete Methodik ist in der Lage, dies nahezu ebenso gut darzustellen wie der ungleich teurere UAV Laserscan. Die wissenschaftliche Anwendbarkeit der Methodik konnte dadurch validiert werden., The goal of this thesis is the creation and validation of a low cost workflow for the remote sensing of an alpine study area using a consumer grade DJI Mavic Unmanned Aerial Vehicle (UAV) and Open Source Software. The study area is the Outer Hochebenkar rock glacier in the Ötztal in Austria. An aerial survey is conducted with the UAV and the resulting images are processed using a combination of Open Source Applications. For the Point Cloud Creation, Structure from Motion (SfM) and Multi View Stereo (MVS) are used. The presented workflow spans from the planning of the survey over its execution and the point cloud creation with SfM to the processing of the result-ing data and the comparison with data from a UAV Laserscan, which was conducted the same day. The scientific eligibility of the workflow is tested, and its precision is measured through a direct comparison to the results of the UAV Laserscan. With the use of Grid Difference Calculations and Image Correlation Algorithms in comparison to a Laserscan from 2019, the vertical and horizontal movements of the rock glacier between 2019 and 2020 are measured. Using the presented methods, it was possible to detect exceptionally fast rates of movement in the lower part of the rock glacier, indicating a transition from periglacial flow to gravitational sliding. The workflow pre-sented in this thesis is able to detect this process nearly as good as the much more expensive UAV Laserscan, therefore validating the scientific applicability of the work-flow., eingereicht von: Felix Bauer, Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers, in englischer Sprache, Masterarbeit Universität Innsbruck 2021