CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement

Visual geolocalization is a cost-effective and scalable task that involves matching one or more query images, taken at some unknown location, to a set of geo-tagged reference images. Existing methods, devoted to semantic features representation, evolving towards robustness to a wide variety between query and reference, including illumination and viewpoint changes, as well as scale and seasonal variations. However, practical visual geolocalization approaches need to be robust in appearance changing and extreme viewpoint variation conditions, while providing accurate global location estimates. Therefore, inspired by curriculum design, human learn general knowledge first and then delve into professional expertise. We first recognize semantic scene and then measure geometric structure. Our approach, termed CurriculumLoc, involves a delicate design of multi-stage refinement pipeline and a novel keypoint detection and description with global semantic awareness and local geometric verification. We rerank candidates and solve a particular cross-domain perspective-n-point (PnP) problem based on these keypoints and corresponding descriptors, position refinement occurs incrementally. The extensive experimental results on our collected dataset, TerraTrack and a benchmark dataset, ALTO, demonstrate that our approach results in the aforementioned desirable characteristics of a practical visual geolocalization solution. Additionally, we achieve new high recall@1 scores of 62.6% and 94.5% on ALTO, with two different distances metrics, respectively. Dataset, code and trained models are publicly available on https://github.com/npupilab/CurriculumLoc.Comment: 14 pages, 15 figure

ClusterFusion: Real-time Relative Positioning and Dense Reconstruction for UAV Cluster

As robotics technology advances, dense point cloud maps are increasingly in demand. However, dense reconstruction using a single unmanned aerial vehicle (UAV) suffers from limitations in flight speed and battery power, resulting in slow reconstruction and low coverage. Cluster UAV systems offer greater flexibility and wider coverage for map building. Existing methods of cluster UAVs face challenges with accurate relative positioning, scale drift, and high-speed dense point cloud map generation. To address these issues, we propose a cluster framework for large-scale dense reconstruction and real-time collaborative localization. The front-end of the framework is an improved visual odometry which can effectively handle large-scale scenes. Collaborative localization between UAVs is enabled through a two-stage joint optimization algorithm and a relative pose optimization algorithm, effectively achieving accurate relative positioning of UAVs and mitigating scale drift. Estimated poses are used to achieve real-time dense reconstruction and fusion of point cloud maps. To evaluate the performance of our proposed method, we conduct qualitative and quantitative experiments on real-world data. The results demonstrate that our framework can effectively suppress scale drift and generate large-scale dense point cloud maps in real-time, with the reconstruction speed increasing as more UAVs are added to the system

Single crystal growth and superconductivity in RbNi2_2Se2_2

We report the synthesis and characterization of RbNi$_2$Se$_2$, an analog of the iron chalcogenide superconductor Rb$_x$Fe$_2$Se$_2$, via transport, angle resolved photoemission spectroscopy, and density functional theory calculations. A superconducting transition at $T_{c}$ = 1.20 K is identified. In normal state, RbNi$_2$Se$_2$ shows paramagnetic and Fermi liquid behaviors. A large Sommerfeld coefficient yields a heavy effective electron mass of $m^{*}\approx6m_{e}$. In the superconducting state, zero-field electronic specific-heat data $C_{es}$ can be described by a two-gap BCS model, indicating that RbNi$_2$Se$_2$ is a multi-gap superconductor. Our density functional theory calculations and angle resolved photoemission spectroscopy measurements demonstrate that RbNi$_2$Se$_2$ exhibits relatively weak correlations and multi-band characteristics, consistent with the multi-gap superconductivity.Comment: 7 pages, 4 figure

miR-1269 promotes metastasis and forms a positive feedback loop with TGF-β

As patient survival drops precipitously from early-stage cancers to late-stage and metastatic cancers, microRNAs that promote relapse and metastasis can serve as prognostic and predictive markers as well as therapeutic targets for chemoprevention. Here we show that miR-1269a promotes colorectal cancer (CRC) metastasis and forms a positive feedback loop with TGF-β signalling. miR-1269a is upregulated in late-stage CRCs, and long-term monitoring of 100 stage II CRC patients revealed that miR-1269a expression in their surgically removed primary tumours is strongly associated with risk of CRC relapse and metastasis. Consistent with clinical observations, miR-1269a significantly increases the ability of CRC cells to invade and metastasize in vivo. TGF-β activates miR-1269 via Sox4, while miR-1269a enhances TGF-β signalling by targeting Smad7 and HOXD10, hence forming a positive feedback loop. Our findings suggest that miR-1269a is a potential marker to inform adjuvant chemotherapy decisions for CRC patients and a potential therapeutic target to deter metastasis

Chromatin Remodeling of Colorectal Cancer Liver Metastasis is Mediated by an HGF‐PU.1‐DPP4 Axis

Colorectal cancer (CRC) metastasizes mainly to the liver, which accounts for the majority of CRC-related deaths. Here it is shown that metastatic cells undergo specific chromatin remodeling in the liver. Hepatic growth factor (HGF) induces phosphorylation of PU.1, a pioneer factor, which in turn binds and opens chromatin regions of downstream effector genes. PU.1 increases histone acetylation at the DPP4 locus. Precise epigenetic silencing by CRISPR/dCas9KRAB or CRISPR/dCas9HDAC revealed that individual PU.1-remodeled regulatory elements collectively modulate DPP4 expression and liver metastasis growth. Genetic silencing or pharmacological inhibition of each factor along this chromatin remodeling axis strongly suppressed liver metastasis. Therefore, microenvironment-induced epimutation is an important mechanism for metastatic tumor cells to grow in their new niche. This study presents a potential strategy to target chromatin remodeling in metastatic cancer and the promise of repurposing drugs to treat metastasis

High Extinction Ratio 4 × 2 Encoder Based on Electro-Optical Graphene Plasma Structure

In this paper, a plasmonic electro-optical encoder based on graphene at THz frequency is proposed. The surface plasmon polaritons (SPPs) in the graphene–insulator–metal structure are excited by an incident TM wave with a wavelength of 9.3 μm. Graphene plasma waveguides have extremely high confinement, relatively low losses, and high tunability. The switching mechanism is based on the application of an external voltage to locally change the chemical potential of the graphene for encoding. Setting the chemical potential to 1 eV allows SPPs to propagate while lowering the chemical potential to 0.1 eV prevents the SPPs from propagating. A 4 × 2 encoder with a minimum encoding extinction ratio (ER) of 37 dB, a maximum modulation depth (MD) of 99.99%, and a structure area of 0.8 μm2 is proposed based on the design rules and simulations using the finite-difference time-domain (FDTD) method. In terms of the obtained results, the proposed structure can be used in optical integrated circuits

The principle of the feature learning layer.

<p>We utilize the modified Deeplab networks to extract the convolutional features from RGB images, and select the features from the 2-th, 3-th, and 5-th layer of the Deeplab networks, then upsample the different scales of features into the same size of input images, finally concatenate them to produce the hierarchical visual features.</p