Strongly lensed repeating Fast Radio Bursts as precision probes of the universe

Fast Radio bursts (FRBs), bright transients with millisecond durations at $\sim$ GHz and typical redshifts probably $>0.8$, are likely to be gravitationally lensed by intervening galaxies. Since the time delay between images of strongly lensed FRB can be measured to extremely high precision because of the large ratio $\sim10^9$ between the typical galaxy-lensing delay time $\sim\mathcal{O}$(10 days) and the width of bursts $\sim\mathcal{O}$(ms), we propose strongly lensed FRBs as precision probes of the universe. We show that, within the flat $\Lambda$CDM model, the Hubble constant $H_0$ can be constrained with a $\sim0.91\%$ uncertainty from 10 such systems probably observed with the Square Kilometer Array (SKA) in $<$ 30 years. More importantly, the cosmic curvature can be model-independently constrained to a precision of $\sim0.076$. This constraint can directly test the validity of the cosmological principle and break the intractable degeneracy between the cosmic curvature and dark energy.Comment: 8 pages, 6 figure

Precision cosmology from future lensed gravitational wave and electromagnetic signals

The standard siren approach of gravitational wave cosmology appeals to the direct luminosity distance estimation through the waveform signals from inspiralling double compact binaries, especially those with electromagnetic counterparts providing redshifts. It is limited by the calibration uncertainties in strain amplitude and relies on the fine details of the waveform. The Einstein Telescope is expected to produce $10^4-10^5$ gravitational wave detections per year, $50-100$ of which will be lensed. Here we report a waveform-independent strategy to achieve precise cosmography by combining the accurately measured time delays from strongly lensed gravitational wave signals with the images and redshifts observed in the electromagnetic domain. We demonstrate that just 10 such systems can provide a Hubble constant uncertainty of $0.68\%$ for a flat Lambda Cold Dark Matter universe in the era of third generation ground-based detectors

PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures

Neural Radiance Fields (NeRF) have been widely adopted as practical and versatile representations for 3D scenes, facilitating various downstream tasks. However, different architectures, including plain Multi-Layer Perceptron (MLP), Tensors, low-rank Tensors, Hashtables, and their compositions, have their trade-offs. For instance, Hashtables-based representations allow for faster rendering but lack clear geometric meaning, making spatial-relation-aware editing challenging. To address this limitation and maximize the potential of each architecture, we propose Progressive Volume Distillation with Active Learning (PVD-AL), a systematic distillation method that enables any-to-any conversions between different architectures. PVD-AL decomposes each structure into two parts and progressively performs distillation from shallower to deeper volume representation, leveraging effective information retrieved from the rendering process. Additionally, a Three-Levels of active learning technique provides continuous feedback during the distillation process, resulting in high-performance results. Empirical evidence is presented to validate our method on multiple benchmark datasets. For example, PVD-AL can distill an MLP-based model from a Hashtables-based model at a 10~20X faster speed and 0.8dB~2dB higher PSNR than training the NeRF model from scratch. Moreover, PVD-AL permits the fusion of diverse features among distinct structures, enabling models with multiple editing properties and providing a more efficient model to meet real-time requirements. Project website:http://sk-fun.fun/PVD-AL.Comment: Project website: http://sk-fun.fun/PVD-AL. arXiv admin note: substantial text overlap with arXiv:2211.1597

Q-SLAM: Quadric Representations for Monocular SLAM

Monocular SLAM has long grappled with the challenge of accurately modeling 3D geometries. Recent advances in Neural Radiance Fields (NeRF)-based monocular SLAM have shown promise, yet these methods typically focus on novel view synthesis rather than precise 3D geometry modeling. This focus results in a significant disconnect between NeRF applications, i.e., novel-view synthesis and the requirements of SLAM. We identify that the gap results from the volumetric representations used in NeRF, which are often dense and noisy. In this study, we propose a novel approach that reimagines volumetric representations through the lens of quadric forms. We posit that most scene components can be effectively represented as quadric planes. Leveraging this assumption, we reshape the volumetric representations with million of cubes by several quadric planes, which leads to more accurate and efficient modeling of 3D scenes in SLAM contexts. Our method involves two key steps: First, we use the quadric assumption to enhance coarse depth estimations obtained from tracking modules, e.g., Droid-SLAM. This step alone significantly improves depth estimation accuracy. Second, in the subsequent mapping phase, we diverge from previous NeRF-based SLAM methods that distribute sampling points across the entire volume space. Instead, we concentrate sampling points around quadric planes and aggregate them using a novel quadric-decomposed Transformer. Additionally, we introduce an end-to-end joint optimization strategy that synchronizes pose estimation with 3D reconstruction

ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry

Physics perspectives of heavy-ion collisions at very high energy

Heavy-ion collisions at very high colliding energies are expected to produce a quark-gluon plasma (QGP) at the highest temperature obtainable in a laboratory setting. Experimental studies of these reactions can provide an unprecedented range of information on properties of the QGP at high temperatures. We report theoretical investigations of the physics perspectives of heavy-ion collisions at a future high-energy collider. These include initial parton production, collective expansion of the dense medium, jet quenching, heavy-quark transport, dissociation and regeneration of quarkonia, photon and dilepton production. We illustrate the potential of future experimental studies of the initial particle production and formation of QGP at the highest temperature to provide constraints on properties of strongly interaction matter.Comment: 35 pages in Latex, 29 figure

Glycomics: Immunoglobulin GN-glycosylation associated with mammary gland hyperplasia in women

© Copyright 2020, Mary Ann Liebert, Inc., publishers 2020. Mammary gland hyperplasia (MGH) is very common, especially among young and middle-aged women. New diagnostics and biomarkers for MGH are needed for rational clinical management and precision medicine. We report, in this study, new findings using a glycomics approach, with a focus on immunoglobulin G (IgG) N-glycosylation. A cross-sectional study was conducted in a community-based population sample in Beijing, China. A total of 387 participants 40-65 years of age were enrolled in this study, including 194 women with MGH (cases) and 193 women who had no MGH (controls). IgG N-glycans were characterized in the serum by ultra-performance liquid chromatography. The levels of the glycan peaks (GPs) GP2, GP5, GP6, and GP7 were lower in the MGH group compared with the control group, whereas GP14 was significantly higher in the MGH group (p \u3c 0.05). A predictive model using GP5, GP21, and age was established and a receiver operating characteristic curve analysis was performed. The sensitivity and specificity of the model for MGH was 61.3% and 63.2%, respectively, likely owing to receptor mechanisms and/or inflammation regulation. To the best of our knowledge, this is the first study reporting on an association between IgG N-glycosylation and MGH. We suggest person-to-person variations in IgG N-glycans and their combination with multiomics biomarker strategies offer a promising avenue to identify novel diagnostics and individuals at increased risk of MGH

Catalytically efficient Ni-NiOₓ-Y₂O₃ interface for medium temperature water-gas shift reaction

The metal-support interfaces between metals and oxide supports have long been studied in catalytic applications, thanks to their significance in structural stability and efficient catalytic activity. The metal-rare earth oxide interface is particularly interesting because these early transition cations have high electrophilicity, and therefore good binding strength with Lewis basic molecules, such as H2O. Based on this feature, here we design a highly efficient composite Ni-Y2O3 catalyst, which forms abundant active Ni-NiOx-Y2O3 interfaces under the water-gas shift (WGS) reaction condition, achieving 140.6 μmolCO gcat-1 s-1 rate at 300 °C, which is the highest activity for Ni-based catalysts. A combination of theory and ex/in situ experimental study suggests that Y2O3 helps H2O dissociation at the Ni-NiOx-Y2O3 interfaces, promoting this rate limiting step in the WGS reaction. Construction of such new interfacial structure for molecules activation holds great promise in many catalytic systems

Raman enhancement by graphene-Ga2O3 2D bilayer film

2D β-Ga(2)O(3) flakes on a continuous 2D graphene film were prepared by a one-step chemical vapor deposition on liquid gallium surface. The composite was characterized by optical microscopy, scanning electron microscopy, Raman spectroscopy, energy dispersive spectroscopy, and X-ray photoelectron spectroscopy (XPS). The experimental results indicate that Ga(2)O(3) flakes grew on the surface of graphene film during the cooling process. In particular, tenfold enhancement of graphene Raman scattering signal was detected on Ga(2)O(3) flakes, and XPS indicates the C-O bonding between graphene and Ga(2)O(3). The mechanism of Raman enhancement was discussed. The 2D Ga(2)O(3)-2D graphene structure may possess potential applications

3-Isopropyl-2-p-tol­yloxy-5,6,7,8-tetra­hydro-1-benzothieno[2,3-d]pyrimidin-4(3H)-one

In the title compound, C20H22N2O2S, the central thieno­pyrimidine ring system is essentially planar, with a maximum displacement of 0.023 (2) Å. The attached cyclo­hexene ring is disordered over two possible conformations, with an occupancy ratio of 0.776 (12):0.224 (12). Neither inter­molecular hydrogen-bonding inter­actions nor π–π stacking inter­actions are present in the crystal structure. The mol­ecular conformation and crystal packing are stabilized by three intra­molecular C—H⋯O hydrogen bonds and two C—H⋯π inter­actions