Applications of Oligopeptides and Liquid Crystals for Chemical Sensing

Ph.DDOCTOR OF PHILOSOPH

Tuning the anomalous Nernst and Hall effects with shifting the chemical potential in Fe-doped and Ni-doped Co3_3Sn2_2S2_2

Co$_3$Sn$_2$S$_2$ is believed to be a magnetic Weyl semimetal. It displays large anomalous Hall, Nernst and thermal Hall effects with a remarkably large anomalous Hall angle. Here, we present a comprehensive study of how substituting Co by Fe or Ni affects the electrical and thermoelectric transport. We find that doping alters the amplitude of the anomalous transverse coefficients. The maximum decrease in the amplitude of the low-temperature anomalous Hall conductivity $\sigma^A_{ij}$ is twofold. Comparing our results with theoretical calculations of the Berry spectrum assuming a rigid shift of the Fermi level, we find that given the modest shift in the position of the chemical potential induced by doping, the experimentally observed variation occurs five times faster than expected. Doping affects the amplitude and the sign of the anomalous Nernst coefficient. Despite these drastic changes, the amplitude of the $\alpha^A_{ij}/\sigma^A_{ij}$ ratio at the Curie temperature remains close to $\approx 0.5 k_B/e$, in agreement with the scaling relationship observed across many topological magnets.Comment: 8 pages, 9 figure

Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing

The hippocampus has been known as one of the most important structures referred to as Alzheimer’s disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists

Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.Comment: ICCV 2023 camera-ready, Project Page: https://me.kiui.moe/nerf2mes

Anomalous transverse response of Co2_2MnGa and universality of the room-temperature αijA/σijA\alpha^A_{ij}/\sigma^A_{ij} ratio across topological magnets

The off-diagonal (electric, thermal and thermoelectric) transport coefficients of a solid can acquire an anomalous component due to the non-trivial topology of the Bloch waves. We present a study of the anomalous Hall (AHE), Nernst (ANE) and thermal Hall effects (ATHE) in the Heusler Weyl ferromagnet Co$_2$MnGa. The Anomalous Wiedemann-Franz law, linking electric and thermal responses, was found to be valid over the whole temperature window. This indicates that the AHE has an intrinsic origin and the Berry spectrum is smooth in the immediate vicinity of the Fermi level. From the ANE data, we extract the magnitude and temperature dependence of $\alpha^A_{ij}$ and put under scrutiny the $\alpha^A_{ij}/\sigma^A_{ij}$ ratio, which approaches k$_B$/e at room temperature. We show that in various topological magnets the room-temperature magnitude of this ratio is a sizeable fraction of k$_B$/e and argue that the two anomalous transverse coefficients depend on universal constants, the Berry curvature averaged over a window set by either the Fermi wavelength (for Hall) or the de Broglie thermal length (for Nernst). Since the ratio of the latter two is close to unity at room temperature, such a universal scaling finds a natural explanation in the intrinsic picture of anomalous transverse coefficients.Comment: 5 pages, 4 figures, supplemental material include

Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment

Detection transformer (DETR) relies on one-to-one assignment, assigning one ground-truth object to one prediction, for end-to-end detection without NMS post-processing. It is known that one-to-many assignment, assigning one ground-truth object to multiple predictions, succeeds in detection methods such as Faster R-CNN and FCOS. While the naive one-to-many assignment does not work for DETR, and it remains challenging to apply one-to-many assignment for DETR training. In this paper, we introduce Group DETR, a simple yet efficient DETR training approach that introduces a group-wise way for one-to-many assignment. This approach involves using multiple groups of object queries, conducting one-to-one assignment within each group, and performing decoder self-attention separately. It resembles data augmentation with automatically-learned object query augmentation. It is also equivalent to simultaneously training parameter-sharing networks of the same architecture, introducing more supervision and thus improving DETR training. The inference process is the same as DETR trained normally and only needs one group of queries without any architecture modification. Group DETR is versatile and is applicable to various DETR variants. The experiments show that Group DETR significantly speeds up the training convergence and improves the performance of various DETR-based models. Code will be available at \url{https://github.com/Atten4Vis/GroupDETR}.Comment: ICCV23 camera ready versio

Anaerobic copper toxicity and iron-sulfur cluster biogenesis in Escherichia coli

© 2017 American Society for Microbiology. While copper is an essential trace element in biology, pollution of groundwater from copper has become a threat to all living organisms. Cellular mechanisms underlying copper toxicity, however, are still not fully understood. Previous studies have shown that iron-sulfur proteins are among the primary targets of copper toxicity in Escherichia coli under aerobic conditions. Here, we report that, under anaerobic conditions, iron-sulfur proteins in E. coli cells are even more susceptible to copper in medium. Whereas addition of 0.2 mM copper(II) chloride to LB (Luria-Bertani) medium has very little or no effect on iron-sulfur proteins in wild-type E. coli cells under aerobic conditions, the same copper treatment largely inactivates iron-sulfur proteins by blocking iron-sulfur cluster biogenesis in the cells under anaerobic conditions. Importantly, proteins that do not have iron-sulfur clusters (e.g., fumarase C and cysteine desulfurase) in E. coli cells are not significantly affected by copper treatment under aerobic or anaerobic conditions, indicating that copper may specifically target iron-sulfur proteins in cells. Additional studies revealed that E. coli cells accumulate more intracellular copper under anaerobic conditions than under aerobic conditions and that the elevated copper content binds to the iron-sulfur cluster assembly proteins IscU and IscA, which effectively inhibits iron-sulfur cluster biogenesis. The results suggest that the copper-mediated inhibition of iron-sulfur proteins does not require oxygen and that iron-sulfur cluster biogenesis is the primary target of anaerobic copper toxicity in cells

MARes-Net: multi-scale attention residual network for jaw cyst image segmentation

Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network’s perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People’s Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation

Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining

We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant DINO~\cite{zhang2022dino}, and an efficient DETR training method Group DETR~\cite{chen2022group}. The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO. Group DETR v2 achieves $\textbf{64.5}$ mAP on COCO test-dev, and establishes a new SoTA on the COCO leaderboard https://paperswithcode.com/sota/object-detection-on-cocoComment: Tech report, 3 pages. We establishes a new SoTA (64.5 mAP) on the COCO test-de