Scanning nano-spin ensemble microscope for nanoscale magnetic and thermal imaging

Quantum sensors based on solid-state spins provide tremendous opportunities in a wide range of fields from basic physics and chemistry to biomedical imaging. However, integrating them into a scanning probe microscope to enable practical, nanoscale quantum imaging is a highly challenging task. Recently, the use of single spins in diamond in conjunction with atomic force microscopy techniques has allowed significant progress towards this goal, but generalisation of this approach has so far been impeded by long acquisition times or by the absence of simultaneous topographic information. Here we report on a scanning quantum probe microscope which solves both issues, by employing a nano-spin ensemble hosted in a nanodiamond. This approach provides up to an order of magnitude gain in acquisition time, whilst preserving sub-100 nm spatial resolution both for the quantum sensor and topographic images. We demonstrate two applications of this microscope. We first image nanoscale clusters of maghemite particles through both spin resonance spectroscopy and spin relaxometry, under ambient conditions. Our images reveal fast magnetic field fluctuations in addition to a static component, indicating the presence of both superparamagnetic and ferromagnetic particles. We next demonstrate a new imaging modality where the nano-spin ensemble is used as a thermometer. We use this technique to map the photo-induced heating generated by laser irradiation of a single gold nanoparticle in a fluid environment. This work paves the way towards new applications of quantum probe microscopy such as thermal/magnetic imaging of operating microelectronic devices and magnetic detection of ion channels in cell membranes.Comment: 22 pages including Supporting Information. Changes to v1: affiliations and funding information updated, plus minor revisions to the main tex

A surgical system for automatic registration, stiffness mapping and dynamic image overlay

In this paper we develop a surgical system using the da Vinci research kit (dVRK) that is capable of autonomously searching for tumors and dynamically displaying the tumor location using augmented reality. Such a system has the potential to quickly reveal the location and shape of tumors and visually overlay that information to reduce the cognitive overload of the surgeon. We believe that our approach is one of the first to incorporate state-of-the-art methods in registration, force sensing and tumor localization into a unified surgical system. First, the preoperative model is registered to the intra-operative scene using a Bingham distribution-based filtering approach. An active level set estimation is then used to find the location and the shape of the tumors. We use a recently developed miniature force sensor to perform the palpation. The estimated stiffness map is then dynamically overlaid onto the registered preoperative model of the organ. We demonstrate the efficacy of our system by performing experiments on phantom prostate models with embedded stiff inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018

Seasonal variation of the deep limb of the Pacific Meridional Overturning circulation at Yap-Mariana junction

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Wang, J., Ma, Q., Wang, F., Lu, Y., & Pratt, L. J. Seasonal variation of the deep limb of the Pacific Meridional Overturning circulation at Yap-Mariana junction. Journal of Geophysical Research: Oceans, 125(7), (2020): e2019JC016017, doi:10.1029/2019JC016017.This study reveals the seasonal variability of the lower and upper deep branches of the Pacific Meridional Overturning Circulation (L‐PMOC and U‐PMOC) in the Yap‐Mariana Junction (YMJ) channel, a major gateway for deep flow into the western Pacific. On the western side of the YMJ channel, mooring observations in 2017 and in 1997 show the seasonal phase of the L‐PMOC at depths of 3,800–4,400 m: strong northward flow with speed exceeding 20 cm s−1 and lasting from December to next May and weak flow during the following 6 months. On the eastern side of the channel, mooring observations during 2014–2017 show two southward deep flows with broadly seasonal phases, one being the return flow of L‐PMOC below ~4,000 m and with the same phase of L‐PMOC but reduced magnitude. The second, shallower, southward deep flow corresponds to the U‐PMOC observed within 3,000–3,800 m and with opposite phase of L‐PMOC, that is, strong (weak) southward flow appearing during June–November (December–May). Seasonal variations of the L‐PMOC and U‐PMOC are accompanied by the seasonal intrusions of the Lower and Upper Circumpolar Waters (LCPW and UCPW) in lower and upper deep layers, which change the isopycnal structure and the deep currents in a way consistent with geostrophic balance.This study is supported by the National Natural Science Foundation of China (grants 91958204 and 41776022), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant XDA22000000), the Key Research Program of Frontier Sciences, CAS (grant QYZDB‐SSW‐SYS034). F. Wang thanks the support from the Scientific and Technological Innovation Project by Qingdao National Laboratory for Marine Science and Technology (grant 2016ASKJ12), the National Program on Global Change and Air‐Sea Interaction (grant GASI‐IPOVAI‐01‐01), and the National Natural Science Foundation of China (grants 41730534 and 41421005). L. Pratt gratefully acknowledges the support by NSF (grant OCE‐1657870). Jianing Wang and Qiang Ma contributed equally to this work

Pathways, volume transport, and seasonal variability of the lower deep limb of the Pacific Meridional Overturning Circulation at the Yap-Mariana Junction

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Wang, J., Wang, F., Lu, Y., Ma, Q., Pratt, L. J., & Zhang, Z. Pathways, volume transport, and seasonal variability of the lower deep limb of the Pacific Meridional Overturning Circulation at the Yap-Mariana Junction. Frontiers in Marine Science, 8, (2021): 672199, https://doi.org/10.3389/fmars.2021.672199.The lower deep branch of the Pacific Meridional Overturning Circulation (L-PMOC) is responsible for the deep-water transport from Antarctic to the North Pacific and is a key ingredient in the regulation of global climate through its influence on the storage and residence time of heat and carbon. At the Pacific Yap-Mariana Junction (YMJ), a major gateway for deep-water flowing into the Western Pacific Ocean, we deployed five moorings from 2018 to 2019 in the Eastern, Southern, and Northern Channels in order to explore the pathways and variability of L-PMOC. We have identified three main patterns for L-PMOC pathways. In Pattern 1, the L-PMOC intrudes into the YMJ from the East Mariana Basin (EMB) through the Eastern Channel and then flows northward into the West Mariana Basin (WMB) through the Northern Channel and southward into the West Caroline Basin (WCB) through the Southern Channel. In Pattern 2, the L-PMOC intrudes into the YMJ from both the WCB and the EMB and then flows into the WMB. In Pattern 3, the L-PMOC comes from the WCB and then flows into the EMB and WMB. The volume transports of L-PMOC through the Eastern, Southern, and Northern Channels all exhibit seasonality. During November–April (May–October), the flow pathway conforms to Pattern 1 (Patterns 2 and 3), and the mean and standard deviation of L-PMOC transports are −4.44 ± 1.26 (−0.30 ± 1.47), −0.96 ± 1.13 (1.75 ± 1.49), and 1.49 ± 1.31 (1.07 ± 1.10) Sv in the Eastern, Southern, and Northern Channels, respectively. Further analysis of numerical ocean modeling results demonstrates that L-PMOC transport at the YMJ is forced by a deep pressure gradient between two adjacent basins, which is mainly determined by the sea surface height (SSH) and water masses in the upper 2,000-m layer. The seasonal variability of L-PMOC transport is attributed to local Ekman pumping and westward-propagating Rossby waves. The L-PMOC transport greater than 3,500 m is closely linked to the wind forcing and the upper ocean processes.This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant XDA22000000), the National Natural Science Foundation of China (grants 91958204 and 41776022), the Key Research Program of Frontier Sciences, CAS (grant QYZDB-SSW-SYS034), and the International Partnership Program of CAS (grant 133137KYSB20180056). FW thanks the support from the National Natural Science Foundation of China (grants 41730534 and 41421005). QM thanks the support by the National Natural Science Foundation of China (grant 42006003)

Effect of cantilever geometry on the optical lever sensitivities and thermal noise method of the atomic force microscope

Calibration of the optical lever sensitivities of atomic force microscope (AFM) cantilevers is especially important for determining the force in AFM measurements. These sensitivities depend critically on the cantilever mode used and are known to differ for static and dynamic measurements. Here, we calculate the ratio of the dynamic and static sensitivities for several common AFM cantilevers, whose shapes vary considerably, and experimentally verify these results. The dynamic-to-static optical lever sensitivity ratio is found to range from 1.09 to 1.41 for the cantilevers studied -in stark contrast to the constant value of 1.09 used widely in current calibration studies. This analysis shows that accuracy of the thermal noise method for the static spring constant is strongly dependent on cantilever geometry -neglect of these dynamic-to-static factors can induce errors exceeding 100%. We also discuss a simple experimental approach to non-invasively and simultaneously determine the dynamic and static spring constants and optical lever sensitivities of cantilevers of arbitrary shape, which is applicable to all AFM platforms that have the thermal noise method for spring constant calibration. © 2014 AIP Publishing LLC. [http://d

Siamese DETR

Recent self-supervised methods are mainly designed for representation learning with the base model, e.g., ResNets or ViTs. They cannot be easily transferred to DETR, with task-specific Transformer modules. In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architecture in DETR. We consider learning view-invariant and detection-oriented representations simultaneously through two complementary tasks, i.e., localization and discrimination, in a novel multi-view learning framework. Two self-supervised pretext tasks are designed: (i) Multi-View Region Detection aims at learning to localize regions-of-interest between augmented views of the input, and (ii) Multi-View Semantic Discrimination attempts to improve object-level discrimination for each region. The proposed Siamese DETR achieves state-of-the-art transfer performance on COCO and PASCAL VOC detection using different DETR variants in all setups. Code is available at https://github.com/Zx55/SiameseDETR.Comment: 10 pages, 11 figures. Accepted in CVPR 202

Dynamic characteristics and drivers of the regional household energy-carbon-water nexus in China

Being a node of the energy-water consumer and carbon dioxide (CO2) emitter, the household is one key sector to pilot integrated energy-carbon-water (ECW) management. This study developed an integrated framework to explore China’s provincial household ECW nexus as well as their drivers from the years 2000 through 2016. The absolute amount and growth rate of household energy use (HEU), household CO2 emissions (HCE), and household water use (HWU) were abstracted to reveal the dynamic characteristics of the household ECW nexus. Efficiency advance, income growth, urbanization, family size, and household number were defined to explain the changes in the household ECW nexus. This study revealed that there is a huge regional heterogeneity in China’s household ECW nexus. Developed regions such as Zhejiang, Jiangsu, Guangdong, and Shanghai are the most important household ECW nexus nodes with larger amounts and growth rates of household ECW. Income growth overwhelmingly increases ECW, while efficiency advance effectively curbs its growth. Comparatively, household number, family size, and urbanization have small effects. Therefore, implementing differentiated management and focusing on the synergy of socioeconomic factors are the keys to achieving integrated household ECW management. And the analytical framework can be used to analyze ECW nexus from a sector, city, or country perspective

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.Comment: Under submission to CHI202