95 research outputs found
High-resolution atom interferometers with suppressed diffraction phases
We experimentally and theoretically study the diffraction phase of
large-momentum transfer beam splitters in atom interferometers based on Bragg
diffraction. We null the diffraction phase and increase the sensitivity of the
interferometer by combining Bragg diffraction with Bloch oscillations. We
demonstrate agreement between experiment and theory, and a 1500-fold reduction
of the diffraction phase, limited by measurement noise. In addition to reduced
systematic effects, our interferometer has high contrast with up to 4.4 million
radians of phase difference, and a resolution in the fine structure constant of
ppb in 25 hours of integration time.Comment: Added appendix and explanations. 6 pages, 4 figure
Self-consistent simulations of beam-beam interaction in future e + e - circular colliders including beamstrahlung and longitudinal coupling impedance
For the past generation {e}^{+}{e}^{\ensuremath{-}} storage ring colliders, we usually used natural bunch length or its impedance lengthened value in beam-beam simulations instead of considering the impedance directly. In the future colliders, such as FCC-ee and CEPC, the beam-beam interaction becomes essentially three dimensional. In order to increase the luminosity, the future accelerators will collide very intense beams of high energy with low emittances and small beta functions at the collision points exploiting the crab waist collision scheme with a large Piwinski angle. For these extreme parameters several new effects become important for the collider performance such as beamstrahlung, coherent X-Z instability, 3D flip-flop so that the longitudinal beam dynamics should be also treated in a self-consistent manner. In this paper we describe the numerical code for the self-consistent 3D beam-beam simulations including beamstrahlung and the longitudinal beam coupling impedance and study interplay of different effects arising in beam-beam collisions of the future colliders
Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles
Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to
solve diverse, intelligent control tasks like autonomous driving in Internet of
Vehicles (IoV). However, the widely assumed existence of a central node to
implement centralized federated learning-assisted MARL might be impractical in
highly dynamic scenarios, and the excessive communication overheads possibly
overwhelm the IoV system. Therefore, in this paper, we design a communication
efficient cooperative MARL algorithm, named RSM-MAPPO, to reduce the
communication overheads in a fully distributed architecture. In particular,
RSM-MAPPO enhances the multi-agent Proximal Policy Optimization (PPO) by
incorporating the idea of segment mixture and augmenting multiple model
replicas from received neighboring policy segments. Afterwards, RSM-MAPPO
adopts a theory-guided metric to regulate the selection of contributive
replicas to guarantee the policy improvement. Finally, extensive simulations in
a mixed-autonomy traffic control scenario verify the effectiveness of the
RSM-MAPPO algorithm
Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification
Deep learning-based melanoma classification with dermoscopic images has
recently shown great potential in automatic early-stage melanoma diagnosis.
However, limited by the significant data imbalance and obvious extraneous
artifacts, i.e., the hair and ruler markings, discriminative feature extraction
from dermoscopic images is very challenging. In this study, we seek to resolve
these problems respectively towards better representation learning for lesion
features. Specifically, a GAN-based data augmentation (GDA) strategy is adapted
to generate synthetic melanoma-positive images, in conjunction with the
proposed implicit hair denoising (IHD) strategy. Wherein the hair-related
representations are implicitly disentangled via an auxiliary classifier network
and reversely sent to the melanoma-feature extraction backbone for better
melanoma-specific representation learning. Furthermore, to train the IHD
module, the hair noises are additionally labeled on the ISIC2020 dataset,
making it the first large-scale dermoscopic dataset with annotation of
hair-like artifacts. Extensive experiments demonstrate the superiority of the
proposed framework as well as the effectiveness of each component. The improved
dataset publicly avaliable at https://github.com/kirtsy/DermoscopicDataset.Comment: ICONIP 2021 conferenc
Circulating tissue factor-positive procoagulant microparticles in patients with type 1 diabetes
Aim: To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM). Methods: This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF(+)PS(+)MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF(+)PS(+)MPs and PS(+)MPs (TF(+)PS(+)MPs/PS(+)MPs). Results: We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS(+)MPs, TF(+)PS(+)MPs and TF(+)PS(+)MPs/PS(+)MPs were elevated in patients with T1DM (PS(+)MPs: 1078.5 +/- 158.08 vs 686.84 +/- 122.04/mu L, P <0.001; TF(+)PS(+)MPs: 202.10 +/- 47.47 vs 108.33 +/- 29.42/mu L, P <0.001; and TF(+)PS(+)MPs/PS(+)MPs: 0.16 +/- 0.04 vs 0.19 +/- 0.05, P = 0.004), mostly derived from platelet, lymphocytes and endothelial cells. In the subgroup analysis, the counts of total and platelet TF(+)PS(+)MPs were increased in patients with diabetic retinopathy (DR) and with higher HbA1c, respectively. Conclusion: Circulating TF(+)PS(+)MPs and those derived from platelet, lymphocytes and endothelial cells were elevated in patients with T1DM.De tre första författarna delar förstaförfattarskapet.</p
Residue behavior and dietary risk assessment of fluopyram in cowpea and determination in nine foodstuffs
Pesticide residues have been one of the food safety problems that plague consumers. It is necessary to develop validated detection methods to monitor pesticide residues in food. In this study, fluopyram was analyzed in fruits (banana, grape, and citrus) and vegetables (tomato, cucumber, cowpea, pepper, eggplant, and potato) by optimizing the QuEChERS in combination with GC-MS/MS. The recoveries of fluopyram in all food matrices ranged from 87.02% to 101.42% with RSD below 9.25%. The matrix effect of fluopyram ranging from â1.41% to 17.67%. Finally, this market investigation resulted in a total of 19 positive samples out of 128 market samples, all of which fell below the MRL with the exception of one tomato sample, which was above the EU MRL. Field trial of fluopyram on cowpea was conducted, the half-lives of fluopyram was 3.03â3.95Â days, terminal residues ranged from .031â.596Â mg/kg. Dietary risk assessment was performed on cowpea. The result indicates that the dietary risk of fluopyram in cowpeas is acceptable. The method of detection developed in this study could enable better monitoring of fluopyram residues in foodstuffs
Case report: Successful treatment of a rare HER2-positive advanced breast squamous cell carcinoma
Background: Breast squamous cell carcinoma (SCC) is an uncommon and highly aggressive variant of metaplastic breast cancer. Despite its rarity, there is currently no consensus on treatment guidelines for this specific subtype. Previous studies have demonstrated that chemotherapy alone has limited efficacy in treating breast SCC. However, the potential for targeted therapy in combination with chemotherapy holds promise for future treatment options.Case presentation: In this case report, we present a patient with advanced HER2-positive breast SCC, exhibiting a prominent breast mass, localized ulcers, and metastases in the lungs and brain. Our treatment approach involved the administration of HER2-targeted drugs in conjunction with paclitaxel, resulting in a sustained control of tumor growth.Conclusion: This case represents a rare occurrence of HER2-positive breast SCC, with limited available data on the efficacy of previous HER2-targeted drugs in treating such patients. Our study presents the first application of HER2-targeted drugs in this particular case, offering novel therapeutic insights for future considerations. Additionally, it is imperative to conduct further investigations to assess the feasibility of treatment options in a larger cohort of patients
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures
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