Cooperative Spin Amplification

Quantum amplification is recognized as a key resource for precision measurements. However, most conventional paradigms employ an ensemble of independent particles that usually limit the performance of quantum amplification in gain, spectral linewidth, etc. Here we demonstrate a new signal amplification using cooperative 129Xe nuclear spins embedded within a feedback circuit, where the noble-gas spin coherence time is enhanced by at least one order of magnitude. Using such a technique, magnetic field can be substantially pre-enhanced by more than three orders and is in situ readout with an embedded 87Rb magnetometer. We realize an ultrahigh magnetic sensitivity of 4.0 fT/Hz$^{1/2}$ that surpasses the photon-shot noise and even below the spin-projection noise of the embedded atomic magnetometer, allowing for exciting applications including searches for dark matter with sensitivity well beyond supernova constraints. Our findings extend the physics of quantum amplification to cooperative spin systems and can be generalized to a wide variety of existing sensors, enabling a new class of cooperative quantum sensors.Comment: 7 pages, 4 figure

A single-step preparation of carbohydrate functionalized monoliths for separation and trapping of polar compounds

A single-step copolymerization strategy was developed for the preparation of carbohydrate (glucose and maltose) functionalized monoliths using click reaction. Firstly, novel carbohydrate-functionalized methacrylate monomers were synthesized through Cu(I)-catalyzed 1,3-dipolar cycloaddition (alkyne-azide reaction) of terminal alkyne with azide of carbohydrate derivatives. The corresponding carbohydrate functionalized monolithic columns were then prepared through a single-step in-situ copolymerization. The physicochemical properties and performance of the fabricated monolithic columns were evaluated using scanning electron microscopy, Fourier-transform infrared spectroscopy, and nano-liquid chromatography. For the optimized monolithic column, satisfactory column permeability and good separation performance were demonstrated for polar compounds including nucleoside, phenolic compounds and benzoic acid derivatives. The monolithic column is also highly useful for selective and efficient enrichment of glycopeptides from human IgG tryptic digests. This study not only provided a novel hydrophilic column for separation and selective trapping of polar compounds, but also proposed a facile and efficient approach for preparing carbohydrate functionalized monoliths

Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration

Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven operation and sparse activities. As artificial intelligence (AI) becomes ever more democratized, there is an increasing need to execute SNN models on edge devices. Existing works adopt weight pruning to reduce SNN model size and accelerate inference. However, these methods mainly focus on how to obtain a sparse model for efficient inference, rather than training efficiency. To overcome these drawbacks, in this paper, we propose a Neurogenesis Dynamics-inspired Spiking Neural Network training acceleration framework, NDSNN. Our framework is computational efficient and trains a model from scratch with dynamic sparsity without sacrificing model fidelity. Specifically, we design a new drop-and-grow strategy with decreasing number of non-zero weights, to maintain extreme high sparsity and high accuracy. We evaluate NDSNN using VGG-16 and ResNet-19 on CIFAR-10, CIFAR-100 and TinyImageNet. Experimental results show that NDSNN achieves up to 20.52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e.g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN). In addition, the training cost of NDSNN is only 40.89\% of the LTH training cost on ResNet-19 and 31.35\% of the LTH training cost on VGG-16 on CIFAR-10

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape variations and pose differences among objects in the same category. As a result, these methods underperform when handling unseen object instances in complex environments. In contrast, other approaches aim to achieve category-level estimation and reconstruction by leveraging normalized geometric structure priors, but the static prior-based reconstruction struggles with substantial intra-class variations. To solve these problems, we propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories. In DTF-Net, we design a deformable template field to represent the general category-wise shape latent features and intra-category geometric deformation features. The field establishes continuous shape correspondences, deforming the category template into arbitrary observed instances to accomplish shape reconstruction. We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene. We integrate a multi-modal representation extraction module to extract object features and semantic masks, enabling end-to-end inference. Moreover, during training, we implement a shape-invariant training strategy and a viewpoint sampling method to further enhance the model's capability to extract object pose features. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superiority of DTF-Net in both synthetic and real scenes. Furthermore, we show that DTF-Net effectively supports grasping tasks with a real robot arm.Comment: The first two authors are with equal contributions. Paper accepted by ACM MM 202

68Ga-PSMA-11 PET/CT versus 68Ga-PSMA-11 PET/MRI for the detection of biochemically recurrent prostate cancer: a systematic review and meta-analysis

PurposeOur aim was to conduct a meta-analysis and systematic review in order to compare the diagnostic efficacy of 68Ga-PSMA-11 PET/CT and 68Ga-PSMA-11 PET/MRI in patients with biochemically recurrent after radical prostatectomy and biochemically recurrent prostate cancers (BCR) after hybrid RT and RP.MethodsUp until February 2023, we searched PubMed, Embase, and Web of Science for pertinent papers. Studies examining the utility of 68Ga-PSMA-11 PET/CT or PET/MRI as a screening tool for biochemically recurrent prostate cancer were included. To measure heterogeneity, we employed the I2 statistic. In cases of substantial heterogeneity (I2 > 50%), we used the random effect model to produce a forest plot. In other cases, we utilized the fixed model. Furthermore, we assessed the quality of the studies included using the Quality Assessment of Diagnostic Performance Studies (QUADAS-2) method.ResultsIn total, 37 studies involving 8409 patients were examined. For 68Ga-PSMA-11 PET/CT and 68Ga-PSMA-11 PET/MRI, the combined total detection rate was 0.70 (95% CI: 0.65-0.75) and 0.71 (95% CI:0.67-0.75), respectively. 68Ga-PSMA-11 PET/CT and 68Ga-PSMA-11 PET/MRI did not substantially differ in terms of the overall detection rate for BCR (P = 0.58). The detection rate was unaffected by the PSA values (all P > 0.05).ConclusionThe diagnostic efficacy of 68Ga-PSMA-11 PET/CT appears to be equivalent to that of 68Ga-PSMA-11 PET/MRI in detecting biochemically recurrent prostate cancer. Nonetheless, it should be noted that not all studies have used pathological biopsies as the gold standard. Therefore, additional larger prospective studies are needed to address this issue.Systematic review registrationidentifier CRD42023410039

SAPPHIRE: Search for exotic parity-violation interactions with quantum spin amplifiers

Quantum sensing provides sensitive tabletop tools to search for exotic spin-dependent interactions beyond the Standard Model, which has attracted great attention in theories and experiments. Here we develop a technique based on quantum Spin Amplifier for Particle PHysIcs REsearch (SAPPHIRE) to resonantly search for exotic interactions, specifically parity-odd spin-spin interactions. The present technique effectively amplifies the pseudomagnetic field generated by exotic interactions by a factor of about 200 while being insensitive to spurious external magnetic fields. Our studies, using such a quantum amplification technique, open the doors to exploring the parity-violation interactions mediated by Z' bosons in the challenging parameter space (force range between 3 mm and 0.1 km) and set the most stringent constraints on Z'-mediated electron-neutron couplings, significantly improving previous limits by up to five orders of magnitude. Moreover, our bounds on Z'-mediated couplings between nucleons reaches into a hitherto unexplored parameter space (force range below 1 m), complementing the existing astrophysical and laboratory studies.Comment: 8 pages, 5 figure