Probing local electronic states in the quantum Hall regime with a side coupled quantum dot

We demonstrate a new method for locally probing the edge states in the quantum Hall regime utilizing a side coupled quantum dot positioned at an edge of a Hall bar. By measuring the tunneling of electrons from the edge states into the dot, we acquire information on the local electrochemical potential and electron temperature of the edge states. Furthermore, this method allows us to observe the spatial modulation of the electrostatic potential at the edge state due to many-body screening effect.Comment: 5 pages, 5 figure

Detection of spin polarization with a side coupled quantum dot

We propose realistic methods to detect local spin polarization, which utilize a quantum dot side coupled to the target system. By choosing appropriate states in the dot, we can put spin selectivity to the dot and detect spins in the target with small disturbance. We also present an experiment which realizes one of the proposed spin detection schemes in magnetic fields.Comment: 5 pages, 6 figure

Attention as Annotation: Generating Images and Pseudo-masks for Weakly Supervised Semantic Segmentation with Diffusion

Although recent advancements in diffusion models enabled high-fidelity and diverse image generation, training of discriminative models largely depends on collections of massive real images and their manual annotation. Here, we present a training method for semantic segmentation that neither relies on real images nor manual annotation. The proposed method {\it attn2mask} utilizes images generated by a text-to-image diffusion model in combination with its internal text-to-image cross-attention as supervisory pseudo-masks. Since the text-to-image generator is trained with image-caption pairs but without pixel-wise labels, attn2mask can be regarded as a weakly supervised segmentation method overall. Experiments show that attn2mask achieves promising results in PASCAL VOC for not using real training data for segmentation at all, and it is also useful to scale up segmentation to a more-class scenario, i.e., ImageNet segmentation. It also shows adaptation ability with LoRA-based fine-tuning, which enables the transfer to a distant domain i.e., Cityscapes

Breakdown of `phase rigidity' and variations of the Fano effect in closed Aharonov-Bohm interferometers

Although the conductance of a closed Aharonov-Bohm interferometer, with a quantum dot on one branch, obeys the Onsager symmetry under magnetic field reversal, it needs not be a periodic function of this field: the conductance maxima move with both the field and the gate voltage on the dot, in an apparent breakdown of `phase rigidity'. These experimental findings are explained theoretically as resulting from multiple electronic paths around the interferometer ring. Data containing several Coulomb blockade peaks, whose shapes change with the magnetic flux, are fitted to a simple model, in which each resonant level on the dot couples to a different path around the ring

Preparation of DNA/Gold Nanoparticle Encapsulated in Calcium Phosphate

Biocompatible DNA/gold nanoparticle complex with a protective calcium phosphate (CaP) coating was prepared by incubating DNA/gold nanoparticle complex coated by hyaluronic acid in SBF (simulated body fluid) with a Ca concentration above 2 mM. The CaP-coated DNA complex was revealed to have high compatibility with cells and resistance against enzymatic degradation. By immersion in acetate buffer (pH 4.5), the CaP capsule released the contained DNA complex. This CaP capsule including a DNA complex is promising as a sustained-release system of DNA complexes for gene therapy

Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning

Siamese-network-based self-supervised learning (SSL) suffers from slow convergence and instability in training. To alleviate this, we propose a framework to exploit intermediate self-supervisions in each stage of deep nets, called the Ladder Siamese Network. Our self-supervised losses encourage the intermediate layers to be consistent with different data augmentations to single samples, which facilitates training progress and enhances the discriminative ability of the intermediate layers themselves. While some existing work has already utilized multi-level self supervisions in SSL, ours is different in that 1) we reveal its usefulness with non-contrastive Siamese frameworks in both theoretical and empirical viewpoints, and 2) ours improves image-level classification, instance-level detection, and pixel-level segmentation simultaneously. Experiments show that the proposed framework can improve BYOL baselines by 1.0% points in ImageNet linear classification, 1.2% points in COCO detection, and 3.1% points in PASCAL VOC segmentation. In comparison with the state-of-the-art methods, our Ladder-based model achieves competitive and balanced performances in all tested benchmarks without causing large degradation in one