Capturing accurate dynamic information of moving organs is essential for functional assessment using non-invasive imaging modalities. Achieving high temporal resolution visualization of physiological processes remains a critical challenge in dynamic magnetic resonance imaging (MRI) when reconstructing from extremely limited acquisitions. We introduce an unsupervised zero-shot reconstruction framework combining Implicit Neural Representation (INR) with manifold learning, capable of reconstructing dynamic MRI data at unprecedented temporal resolutions (less than 10 ms per frame for 2D imaging, less than 400 ms per frame for 3D imaging). The framework employs learnable low-dimensional manifold vectors to autonomously capture motion in real time directly from undersampled data, and dynamically condition coordinate-based spatial representations to generate high-fidelity image sequences. Through a novel spatiotemporal coarse-to-fine (C2F) optimization strategy, our method outperforms current state-of-the-art (SOTA) techniques across multiple imaging scenarios, including cardiac, speech and dynamic-contrast-enhanced (DCE) abdominal MRI, demonstrating robust performance under challenging motion patterns and contrast dynamics. The learned manifolds additionally provide intuitive visualization of motion and contrast evolution during imaging. These advances indicate strong clinical potential for applications requiring extreme temporal resolution while maintaining both anatomical and temporal fidelity.

Diffusion magnetic resonance imaging (dMRI) enables noninvasive mapping of tissue microstructure by probing water molecule diffusivity. While advanced multi-shell diffusion models offer improved sensitivity to cellular properties, their requirement for densely sampled q-space data leads to prohibitively long acquisition times. Current deep learning approaches for parameter estimation face three key limitations: (1) dependency on fixed acquisition protocols, (2) model-specific assumptions that constrain applicability, and (3) reliance on supervised learning paradigms that demand large labeled datasets and exhibit poor generalization to out-of-distribution cases. To address these challenges, we propose MINeR, a novel unsupervised subject-specific framework for reconstructing dense q-space data from highly undersampled acquisitions. Our method leverages direction-modulated implicit neural representation to flexibly sample diffusion signals across q-space, supporting the estimation of parameters for diverse diffusion models. Comprehensive evaluations demonstrate that MINeR maintains high fidelity in microstructural parameter estimation, particularly for advanced multi-shell diffusion models. The framework shows remarkable generalization capability, as evidenced by its robust performance on tumor data. Notably, MINeR effectively reconstructs high-quality diffusion signals by interpolating from 6 directions, significantly reducing acquisition time, while maintaining robust parameter estimation. This work presents a practical approach for enabling microstructural modeling from sparsely sampled q-space data, thereby improving the clinical applicability of diffusion MRI. The code is available at: https://github.com/AMRI-Lab/MINeR.

One-bit Synthetic Aperture Radar (1-bit SAR) is a very simple and high-resolution remote sensing technique that contributes to the miniaturization and low-cost development of remote sensing satellites. At the receiver, only the sign information of the real/imaginary part of the echo signal is retained, which greatly simplifies data sampling, storage, and transmission. By using pulse compression techniques, there is no significant loss of image resolution even though the signal amplitude information has been discarded. For the harmonic distortion of 1-bit sampling, there will be more obvious false targets in the imaging results of closely isolated targets, which increases the false alarm rate. This paper proposes a transmission signal generation method based on cubic frequency modulation, which can scatter the energy of false targets with harmonic distortion and effectively improve the detection performance.

Due to the rapid increase of Android apps and their wide usage to handle personal data, a precise and large-scaling checker is in need to validate the apps' permission flow before they are listed on the market. Several tools have been proposed to detect sensitive data leaks in Android apps. But these tools are not applicable to large-scale analysis since they fail to deal with the arbitrary execution orders of different event handlers smartly. Event handlers are invoked by the framework based on the system state, therefore we cannot pre-determine their order of execution. Besides, since all exported components can be invoked by an external app, the execution orders of these components are also arbitrary. A naive way to simulate these two types of arbitrary execution orders yields a permutation of all event handlers in an app. The time complexity is O(n!) where n is the number of event handlers in an app. This leads to a high analysis overhead when n is big. To give an illustration, CHEX [10] found 50.73 entry points of 44 unique class types in an app on average. In this paper we propose an improved static taint analysis to deal with the challenge brought by the arbitrary execution orders without sacrificing the high precision. Our analysis does not need to make permutations and achieves a polynomial time complexity. We also propose to unify the array and map access with object reference by propagating access paths to reduce the number of false positives due to field-insensitivity and over approximation of array access and map access.