Quantum phenomena are typically observable at length and time scales smaller than those of our everyday experience, often involving individual particles or excitations. The past few decades have seen a revolution in t...
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Accurate segmentation and biometric analysis are essential for studying the developing fetal brain in utero. The Fetal Brain Tissue Annotation (FeTA) Challenge 2024 builds upon previous editions to further advance the...
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Accurate segmentation and biometric analysis are essential for studying the developing fetal brain in utero. The Fetal Brain Tissue Annotation (FeTA) Challenge 2024 builds upon previous editions to further advance the clinical relevance and robustness of automated fetal brain MRI analysis. This year’s challenge introduced biometry prediction as a new task complementing the usual segmentation task. The segmentation task also included a new low-field (0.55T) MRI testing set and used Euler characteristic difference (ED) as a topology-aware metric for ranking, extending the traditional overlap or distance-based measures. A total of 16 teams submitted segmentation methods for evaluation. Segmentation performance across top teams was highly consistent across both standard and low-field MRI data. Longitudinal analysis over past FeTA editions revealed minimal improvement in accuracy over time, suggesting a potential performance plateau, particularly as results now approach or surpass reported levels of inter-rater variability. However, the introduction of the ED metric revealed topological differences that were not captured by conventional metrics, underscoring its value in assessing segmentation quality. Notably, the curated low-field MRI dataset achieved the highest segmentation performance, illustrating the potential of affordable imaging systems when combined with high-quality preprocessing and reconstruction. A total of 7 teams submitted automated biometry methods for evaluation. While promising, this task exposed a critical limitation: most submitted methods failed to outperform a simple baseline that predicted measurements based solely on gestational age, without using image data. Performance varied widely across biometric measurements and between teams, indicating both current challenges and opportunities for improvement in this area. These findings highlight the need for better integration of volumetric context and stronger modeling strategies needed for the clinic
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers p...
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The ability of an eavesdropper (Eve) to perform an intercept-resend attack on a free-space quantum-key-distribution (QKD) receiver by precisely controlling the incidence angle of an attack laser has been previously de...
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The ability of an eavesdropper (Eve) to perform an intercept-resend attack on a free-space quantum-key-distribution (QKD) receiver by precisely controlling the incidence angle of an attack laser has been previously demonstrated. However, such an attack could be ineffective in the presence of atmospheric turbulence due to beam wander and spatial mode aberrations induced by the air's varying index of refraction. We experimentally investigate the impact turbulence has on Eve's attack on a free-space polarization-encoding QKD receiver by emulating atmospheric turbulence with a spatial light modulator. Our results identify how well Eve would need to compensate for turbulence to perform a successful attack by either reducing her distance to the receiver or using beam wavefront correction via adaptive optics. Furthermore, we use an entanglement-breaking scheme to find a theoretical limit on the turbulence strength that hinders Eve's attack.
We report a method to tune the second harmonic generation(SHG) frequency of a metallic octamer by employing cylindrical vector beams as the excitation. Our method exploits the ability to spatially match the polarizati...
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We report a method to tune the second harmonic generation(SHG) frequency of a metallic octamer by employing cylindrical vector beams as the excitation. Our method exploits the ability to spatially match the polarization state of excitations with the fundamental target plasmonic modes, enabling flexible control of the SHG resonant *** is found that SHG of the octamer is enhanced over a broad band(400 nm) by changing the excitation from the linearly polarized Gaussian beam to radially and azimuthally polarized beams. More strikingly, when subjected to an azimuthally polarized beam, the SHG intensity of the octamer becomes 30 times stronger than that for the linearly polarized beam even in the presence of Fano resonance.
Although quantum communication systems are being deployed on a global scale, their realistic security certification is not yet available. Here we present a security evaluation and improvement protocol for complete qua...
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High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), ...
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High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm (LoPub) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.
Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to frequent crashes, weakening their ...
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We experimentally demonstrate that a single-photon detector ID210 commercially available from ID Quantique is vulnerable to blinding and can be fully controlled by bright illumination. In quantum key distribution, thi...
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