Higher-order interactions (HOIs) in complex systems, such as scientific collaborations, multi-protein complexes, and multi-user communications, are commonly modeled as hypergraphs, where each hyperedge (i.e., a subset of nodes) represents an HOI among the nodes. Given a hypergraph, hyperedge prediction aims to identify hyperedges that are either missing or likely to form in the future, and it has broad applications, including recommending interest-based social groups, predicting collaborations, and uncovering functional complexes in biological systems. However, the vast search space of hyperedge candidates (i.e., all possible subsets of nodes) poses a significant computational challenge, making naïve exhaustive search infeasible. As a result, existing approaches rely on either heuristic sampling to obtain constrained candidate sets or ungrounded assumptions on hypergraph structure to select promising hyperedges. In this work, we propose HyperSearch, a search-based algorithm for hyperedge prediction that efficiently evaluates unconstrained candidate sets, by incorporating two key components: (1) an empirically grounded scoring function derived from observations in real-world hypergraphs and (2) an efficient search mechanism, where we derive and use an anti-monotonic upper bound of the original scoring function (which is not anti-monotonic) to prune the search space. This pruning comes with theoretical guarantees, ensuring that discarded candidates are never better than the kept ones w.r.t. the original scoring function. In extensive experiments on 10 real-world hypergraphs across five domains, HyperSearch consistently outperforms state-of-the-art baselines, achieving higher accuracy in predicting new (i.e., not in the training set) hyperedges.

Recent advancements in image denoising have leveraged neural networks to enhance performance, particularly in scenarios where clean-noisy image pairs are unavailable. In this context, self-supervised image denoising methods have gained prominence, centered around the principle of$\mathcal{J}$-invariance - ensuring that the output pixel is not influenced by its corresponding input pixel. Traditionally, enforcing$\mathcal{J}$-invariance has constrained blind spot network (BSN) designs, requiring even core operations such as upsampling or downsampling to follow complex rules. This limitation has led to the exclusion of efficient multi-resolution architectures such as U-net, increasing computational complexity. To address these constraints, we introduce generalized design principles for multi-scale$\mathcal{J}$-invariant networks that allow for the flexible incorporation of nearly any architectural elements. This approach challenges the prevailing notion that$\mathcal{J}$-invariance must be maintained throughout the entire process. Based on our design principles, we present U-BSN, a novel$\mathcal{J}$-invariant network design that utilizes the versatile U-Net architecture, adapting it to accommodate self-supervised learning effectively. We also propose randomized PD, an advanced technique that enhances denoising of real-world images with structured noise. Experimental results validate that U-BSN surpasses existing BSNs in handling real-world noise scenarios and achieves the lowest computational complexity among comparable networks, thus confirming the effectiveness of our design principles and proposed methodologies.

Accurate alignment of calcium imaging data, which is critical for the extraction of neuronal activity signals, is often hindered by the image noise and the neuronal activity itself. To address the problem, we propose an algorithm named REALS for robust and efficient batch image alignment through simultaneous transformation and low rank and sparse decomposition. REALS is constructed upon our finding that the low rank subspace can be recovered via linear projection, which allows us to perform simultaneous image alignment and decomposition with gradient-based updates. REALS achieves orders-of-magnitude improvement in terms of accuracy and speed compared to the state-of-the-art robust image alignment algorithms.

Three-dimensional fluorescence microscopy has an intrinsic performance limit set by the number of photons that can be collected from the sample in a given time interval. Here, we extend our earlier work - a recursive light propagation network (RLP-Net) - which is a computational microscopy technique that overcomes such limitations through virtual refocusing that enables volume reconstruction from two adjacent 2-D wide-field fluorescence images. RLP-Net employs a recursive inference scheme in which the network progressively predicts the subsequent planes along the axial direction. This recursive inference scheme reflects that the law of physics for the light propagation remains spatially invariant and therefore a fixed function (i.e., a neural network) for a short distance light propagation can be recursively applied for a longer distance light propagation. In addition, we employ a self-supervised denoising method to enable accurate virtual light propagation over a long distance. We demonstrate the capability of our method through high-speed volumetric imaging of neuronal activity of a live zebrafish brain. The source code used in the paper is available at https://github.com/NICALab/rlpnet.

A first-order time-based ΔΣ modulator using voltage-controlled oscillator (VCO) is presented. The proposed modulator employs phase interpolation technique to enhance the time resolution of the VCO and digital calibration to improve the linearity of the VCO tuning curve. The proposed modulator, implemented in 0.13 μm CMOS process, achieves 55 dB peak signal-to-noise ratio and 52.5 dB peak signal-to-noise-and-distortion ratio at 600 MHz sampling frequency for 20 MHz input bandwidth and consumes 14.3 mW.