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Selection Performance and Reliability of Eye and Head Gaze Tracking Under Varying Light Conditions
(2024)
Is It Really You Who Forgot the Password? When Account Recovery Meets Risk-Based Authentication
(2024)
Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force field parameters need to be adjusted. This force field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties’ errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the overall error is reduced. One interesting outcome is a large variety in the resulting optimized force field parameters (FFParams) and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.
In vision tasks, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, convolution requires multiple stacked layers and a hierarchical structure for large context. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to the non-causal two-dimensional image space. We scale the Hyena convolution kernels beyond the feature map size up to 191$\times$191 to maximize the ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 83.0% and 83.5%, respectively, while outperforming other large-kernel networks. Combining HyenaPixel with attention further increases accuracy to 83.6%. We attribute the success of attention to the lack of spatial bias in later stages and support this finding with bidirectional Hyena.
Der Programmier-Trainingsplan für alle, die weiter kommen wollen.
In diesem Übungsbuch trainierst du anhand von kurzweiligen und praxisnahen Aufgaben deine Programmierfähigkeiten. Jedes Kapitel beginnt mit einem kurzen Warmup zum behandelten Programmierkonzept; die Umsetzung übst du dann anhand von zahlreichen Workout-Aufgaben. Du startest mit einfachen Aufgaben und steigerst dich hin zu komplexeren Fragestellungen. Damit dir nicht langweilig wird, gibt es über 150 praxisnahe Übungen. So lernst du z. B. einen BMI-Rechner oder einen PIN-Generator zu programmieren oder wie du eine Zeitangabe mit einer analogen Uhr anzeigen kannst. (Verlagsangaben)