Offenburg University of Applied Sciences
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The Power of Linear Combinations: Learning with Random Convolutions
No full textFollowing the traditional paradigm of convolutional neural networks (CNNs), modern CNNs manage to keep pace with more recent, for example transformer-based, models by not only increasing model depth and width but also the kernel size. This results in large amounts of learnable model parameters that need to be handled during training. While following the convolutional paradigm with the according spatial inductive bias, we question the significance of \emph{learned} convolution filters. In fact, our findings demonstrate that many contemporary CNN architectures can achieve high test accuracies without ever updating randomly initialized (spatial) convolution filters. Instead, simple linear combinations (implemented through efficient 1×1 convolutions) suffice to effectively recombine even random filters into expressive network operators. Furthermore, these combinations of random filters can implicitly regularize the resulting operations, mitigating overfitting and enhancing overall performance and robustness. Conversely, retaining the ability to learn filter updates can impair network performance. Lastly, although we only observe relatively small gains from learning 3×3 convolutions, the learning gains increase proportionally with kernel size, owing to the non-idealities of the independent and identically distributed (\textit{i.i.d.}) nature of default initialization techniques
Entrepreneurial strategic posture and new technology ventures in an emerging economy
No full textPurpose
Although start-ups have gained increasing scholarly attention, we lack sufficient understanding of their entrepreneurial strategic posture (ESP) in emerging economies. The purpose of this study is to examine the processes of ESP of new technology venture start-ups (NTVs) in an emerging market context.
Design/methodology/approach
In line with grounded theory guidelines and the inductive research traditions, the authors adopted a qualitative approach involving 42 in-depth semi-structured interviews with Ghanaian NTV entrepreneurs to gain a comprehensive analysis at the micro-level on the entrepreneurs' strategic posturing. A systematic procedure for data analysis was adopted.
Findings
From the authors' analysis of Ghanaian NTVs, the authors derived a three-stage model to elucidate the nature and process of ESP Phase 1 spotting and exploiting market opportunities, Phase II identifying initial advantages and Phase III ascertaining and responding to change.
Originality/value
The study contributes to advancing research on ESP by explicating the process through which informal ties and networks are utilised by NTVs and NTVs' founders to overcome extreme resource constraints and information vacuums in contexts of institutional voids. The authors depart from past studies in demonstrating how such ties can be harnessed in spotting and exploiting market opportunities by NTVs. On this basis, the paper makes original contributions to ESP theory and practice
Anatomy of ultrafast quantitative magnetoacoustics in freestanding nickel thin films
No full textWe revisit the quantitative analysis of the ultrafast magnetoacoustic experiment in a freestanding nickel thin film by Kim and Bigot [J.-W. Kim and J.-Y. Bigot, Phys. Rev. B 95, 144422 (2017)] by applying our recently proposed approach of magnetic and acoustic eigenmode decomposition. We show that the application of our modeling to the analysis of time-resolved reflectivity measurements allows for the determination of amplitudes and lifetimes of standing perpendicular acoustic phonon resonances with unprecedented accuracy. The acoustic damping is found to scale as ∝ω2 for frequencies up to 80 GHz, and the peak amplitudes reach 10−3. The experimentally measured magnetization dynamics for different orientations of an external magnetic field agrees well with numerical solutions of magnetoelastically driven magnon harmonic oscillators. Symmetry-based selection rules for magnon-phonon interactions predicted by our modeling approach allow for the unambiguous discrimination between spatially uniform and nonuniform modes, as confirmed by comparing the resonantly enhanced magnetoelastic dynamics simultaneously measured on opposite sides of the film. Moreover, the separation of timescales for (early) rising and (late) decreasing precession amplitudes provide access to magnetic (Gilbert) and acoustic damping parameters in a single measurement
Advanced spike technology enhances sprinting speed
No full textRecent advances in spiked shoe design, characterized by increased longitudinal stiffness, thicker midsole foams, and reconfigured geometry are considered to improve sprint performance. However, so far there is no empirical data on the effects of advanced spikes technology on maximal sprinting speed (MSS) published yet. Consequently, we assessed MSS via ‘flying 30m’ sprints of 44 trained male (PR: 10.32 s - 12.08 s) and female (PR: 11.56 s - 14.18 s) athletes, wearing both traditional and advanced spikes in a randomized, repeated measures design. The results revealed a statistically significant increase in MSS by 1.21% on average when using advanced spikes technology. Notably, 87% of participants showed improved MSS with the use of advanced spikes. A cluster analysis unveiled that athletes with higher MSS may benefit to a greater extent. However, individual responses varied widely, suggesting the influence of multiple factors that need detailed exploration. Therefore, coaches and athletes are advised to interpret the promising performance enhancements cautiously and evaluate the appropriateness of the advanced spike technology for their athletes critically
Interactive Visualization for Fostering Trust in ML
No full textThe use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. The purpose of this seminar is to understand how these components factor into the holistic view of trust. Further, this seminar seeks to identify design guidelines and best practices for how to build interactive visualization systems to calibrate trust
Granulated biochar-based NPK fertilizer and its impact on nutrient leaching, plant growth and soil-borne N2O emissions
No full textUnleashing Modified Deep Learning Models in Efficient COVID19 Detection
No full textThe COVID19 pandemic, a unique and devastating respiratory disease outbreak, has affected global populations as the disease spreads rapidly. Recent Deep Learning breakthroughs may improve COVID19 prediction and forecasting as a tool of precise and fast detection, however, current methods are still being examined to achieve higher accuracy and precision. This study analyzed the collection contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non COVID cases. The most accurate models are MobileNet V3 (97.872 percent), DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization in this study. Our analysis shows that these tactics improve model performance and resilience for advancing COVID19 prediction and detection and shows how Deep Learning can improve disease handling. The methods we suggest would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID19 and other contagious diseases
Content-Marketing - Ein prozessorientierter Überblick
No full textVor dem Hintergrund einer zunehmenden Informations- und Reizüberlastung der Konsumenten werden aus Unternehmenssicht zielgruppenadäquate Inhalte, insbesondere zur Erreichung von kommunikationspolitischen Zielsetzungen, immer wichtiger. Um diese zu gewährleisten, bedarf es einer sinnvollen Planung, Produktion und Distribution von Inhalten. Der vorliegende Beitrag gibt einen Überblick über einen solchen Prozess und veranschaulicht die notwendigen Schritte für ein erfolgreiches Content-Marketing
Generalisierung von mehrdimensionalen Lichtsetzungsparametern mittels Deep Learning
No full textLicht war für die Menschheit schon immer ein Hilfsmittel zur Orientierung. Das Zusammenspiel zwischen hellen und schattierten Oberflächen macht eine räumliche Wahrnehmung erst möglich. Die Lokalisierung von Lichtquellen bietet darüber hinaus für zahlreiche Anwendungsfelder, wie beispielsweise Augmented Reality, ein großes Potential.
Das Ziel der vorliegenden Arbeit war es, ein neuronales Netzwerk zu entwickeln, welches mit Hilfe eines selbst generierten, synthetischen Datensatzes eine Lichtsetzung parametrisiert. Dafür wurden State-of-the-Art Netzwerke aus der digitalen Bildverarbeitung eingesetzt.
Zu Beginn der Arbeit mussten die Eigenschaften der Lichtsetzung extrahiert werden. Eine weitere fundamentale Anforderung war die Aufbereitung des Wissens von Deep Learning.
Für die Generierung des synthetischen Datensatzes wurde eigens ein Framework entwickelt, welches auf der Blender Engine basiert.
Anschließend wurden die generierten Bilder und Metadaten in einem abgewandelten VGG16- und ResNet50-Netz trainiert, validiert und evaluiert.
Eine gewonnene Erkenntnis ist, dass sich künstlich generierte Daten eignen um ein neuronales Netz zu trainieren. Des Weiteren konnte gezeigt werden, dass sich mit Hilfe von Deep Learning Lichtsetzungsparameter extrahieren lassen.
Eine weiterführende Forschungsaufgabe könnte mit dem vorgeschlagenen Ansatzdie Lichtinszenierung von Augmented Reality Anwendungen verbessern
Automated generation of simulation model in context of industry 4.0
No full textMaterial flow simulation is a core technology of Industry 4.0. It can analyze and improve large-scale production systems through experimentation with digital simulation models. However, modeling in discrete event simulation is considered as an effortful and time-consuming activity and challenges especially small and medium-sized enterprises. Systematic experiments and what-if-analysis require a large number of models. Modeling and simulation becomes a repetitive activity and the ability to model and simulate instantly becomes crucial for industry, 4.0. However, model generation typically uses specific methods to build models with individual properties for specific physical systems. A general literature review cannot sufficiently describe the current state of model generation. This study aims to provide an analysis of model generation based on the modeling strategy, modeling view, and production system type, as well as model properties and limitations