Van der Waals density-functional theory study for bulk solids with BCC, FCC, and diamond structures

Proper inclusion of van der Waals (vdW) interactions in theoretical simulations based on standard density functional theory (DFT) is crucial to describe the physics and chemistry of systems such as organic and layered materials. Many encouraging approaches have been proposed to combine vdW interactions with standard approximate DFT calculations. Despite many vdW studies, there is no consensus on the reliability of vdW methods. To help further development of vdW methods, we have assessed various vdW functionals through the calculation of structural prop- erties at equilibrium, such as lattice constants, bulk moduli, and cohesive energies, for bulk solids, including alkali, alkali-earth, and transition metals, with BCC, FCC, and diamond structures as the ground state structure. These results provide important information for the vdW-related materials research, which is essential for designing and optimizing materials systems for desired physical and chemical properties.Comment: 10 pages, 6 Figures, 3 Table

Exploring Customer Preferences on Mobile Services

Designing mobile services is fundamentally different than designing online services. Not only are there differences in underlying technologies, but also in the way people use services. If these differences are not taken into account, mobile services are likely to fail. If mobile services do not deliver what people want, these services will fail no matter how excellent the underlying technology is. The user interface design that is commonly used in mobile services is based on multi-layered approach, which is not very user friendly. So a well designed single layered user interface will be more user friendly than the conventional one and it will be having edge over others. However, it is quite difficult to provide a single layered user interface in a small screen. This study aims at examining how user interface design attributes of mobile services affect customer preferences. In order to explore customer preferences to each design attribute, we measure customer’s WTP (Willingness To Pay) toward different interface designs

Self-calibrating random access logarithmic pixel for on chip camera

CMOS active pixel sensors (APS) have shown competitive performance with charge-coupled device (CCD) and offer many advantages in cost, system power reduction and on-chip integration of VLSI electronics. Among CMOS image sensors, sensors with logarithmic pixels are particularly applicable for outdoor environment where the light intensity varies over a wide range. They are also randomly accessible in both time and space. A major drawback comes from process variations during fabrication. This gives rise to a considerable fixed pattern noise (FPN) which deteriorates the image quality. In this thesis, a technique that greatly reduces FPN using on-chip calibration is introduced. An image sensor that consists of 64x64 active pixels has been designed, fabricated and tested. Pixel pitch is 18um x 19.2um? and is fabricated in a 0.5-um? CMOS process. The proposed pixel circuit considerably reduces the FPN as predicted in theoretical analysis. The measured FPN value is 2.29% of output voltage swing and column-wise FPN is 1.49% of mean output voltage over each column

Learning Symmetrization for Equivariance with Orbit Distance Minimization

We present a general framework for symmetrizing an arbitrary neural-network architecture and making it equivariant with respect to a given group. We build upon the proposals of Kim et al. (2023); Kaba et al. (2023) for symmetrization, and improve them by replacing their conversion of neural features into group representations, with an optimization whose loss intuitively measures the distance between group orbits. This change makes our approach applicable to a broader range of matrix groups, such as the Lorentz group O(1, 3), than these two proposals. We experimentally show our method's competitiveness on the SO(2) image classification task, and also its increased generality on the task with O(1, 3). Our implementation will be made accessible at https://github.com/tiendatnguyen-vision/Orbit-symmetrize.Comment: 16 pages, 1 figur

Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching

Dense prediction tasks are a fundamental class of problems in computer vision. As supervised methods suffer from high pixel-wise labeling cost, a few-shot learning solution that can learn any dense task from a few labeled images is desired. Yet, current few-shot learning methods target a restricted set of tasks such as semantic segmentation, presumably due to challenges in designing a general and unified model that is able to flexibly and efficiently adapt to arbitrary tasks of unseen semantics. We propose Visual Token Matching (VTM), a universal few-shot learner for arbitrary dense prediction tasks. It employs non-parametric matching on patch-level embedded tokens of images and labels that encapsulates all tasks. Also, VTM flexibly adapts to any task with a tiny amount of task-specific parameters that modulate the matching algorithm. We implement VTM as a powerful hierarchical encoder-decoder architecture involving ViT backbones where token matching is performed at multiple feature hierarchies. We experiment VTM on a challenging variant of Taskonomy dataset and observe that it robustly few-shot learns various unseen dense prediction tasks. Surprisingly, it is competitive with fully supervised baselines using only 10 labeled examples of novel tasks (0.004% of full supervision) and sometimes outperforms using 0.1% of full supervision. Codes are available at https://github.com/GitGyun/visual_token_matching