This paper introduces a new method for predicting transmembrane protein topology by employing the graph neural network SchNet. The model is trained on the same dataset used for DeepTMHMM, using 5‑fold cross‑validation, and incorporates all atom‑level embeddings rather than just sequence or alpha‑carbon features. Results indicate that GNNs hold significant promise for topological predictions without relying on pre‑trained weights.
By Sitong Chen, Xiaopeng Mao
arXiv:2609.39792v2 Announce Type: new
Abstract: Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering. Yet, end-to-end learned arch...
By Sharareh Sayyad, Sophia Bazzi
The survey reviews frequency‑domain techniques for time‑series analysis, covering classical Fourier methods to modern neural operators. It identifies three main research challenges: preserving causal structure during spectral transformations, quantifying uncertainty in learned frequency representations, and performing topology‑aware analysis for non‑Euclidean data. By reviewing over 100 studies, the authors propose a unified taxonomy, establish standardized benchmarks, and highlight gaps in geometric deep learning and quantum‑enhanced spectral analysis.
By Qianru Zhang, Yuting Sun, Honggang Wen, Peng Yang, Xinzhu Li, Ming Li, Kwok-Yan Lam, Siu-Ming Yiu, Hongzhi Yin
Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering. Yet, end-to-end learned architectures must discover both a representation and...
arXiv:2607. 09020v1 Announce Type: cross Abstract: Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately.
By Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh, Chin-Jou Li, Eunjung Yeo, Daisuke Saito, Nobuaki Minematsu, Shinji Watanabe, Jian Zhu, David Harwath, David R. Mortensen
Combinatorial Network-Based Manifold Topological Deep Learning (CNMTDL) is a new framework that represents medical images as discrete manifolds and decomposes them into three Hodge components. Features from these components are concatenated and fed into a combinatorial complex architecture, enabling higher‑order message passing between 0‑cells and 2‑cells via attention‑based blocks. CNMTDL was evaluated on six 2D and 3D datasets from the MedMNIST v2 benchmark, showing improved performance for medical image analysis.
By Alice Wachira, Xiang Liu, Zhe Su, Yiying Tong, Ge Wang, Guo-Wei Wei
arXiv:2607. 13847v1 Announce Type: cross Abstract: Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations.
By Adam Weso{\l}owski, Dimitrios Thanos, Daniel Leykam, Lirand\"e Pira
arXiv:2606. 29763v1 Announce Type: cross Abstract: Topological data analysis (TDA), particularly persistent homology (PH), captures geometric structural properties in medical images (e.
By Guangyu Meng, Pengfei Gu, Xueyang Li, Yiyu Shi, Erin Wolf Chambers, Danny Z. Chen
AudioFuse is a hybrid architecture that jointly learns from spectrograms and raw waveforms to classify phonocardiograms. It combines a wide-and-shallow Vision Transformer for spectral features with a shallow 1D CNN for temporal waveforms, reducing overfitting while capturing complementary information. On the PhysioNet 2016 dataset, AudioFuse achieves a state‑of‑the‑art ROC‑AUC of 0.8608 and shows superior robustness to domain shift on the PASCAL dataset, outperforming both spectrogram‑only and waveform‑only baselines.
By Md. Saiful Bari Siddiqui, Utsab Saha
The paper introduces the Frequency Selective Neural Network (FSNN), a new foundation architecture for time‑series learning that embeds advanced signal‑processing mathematics into its neural topology. By using a fully differentiable Wiener‑like filter bank optimized with complex‑domain backpropagation, FSNN autonomously discovers and isolates the precise physical modes of a given task, thereby avoiding the spectral entanglement that plagues CNNs, RNNs, and Transformers. Extensive evaluations show that FSNN achieves state‑of‑the‑art predictive performance, attaining 77.0 % average accuracy on the 10 multivariate UEA datasets and leading all major metrics on the imbalanced PTB‑XL ECG benchmark, while converging directly on physically meaningful frequency bands such as the cardiac QRS complex.
By Hui Huang, Ye Sun, Shiyan Hu
arXiv:2609.36737v1 Announce Type: cross
Abstract: The vocal tract is the region of the human body responsible for filtering one's voice to create speech. In this paper, we present a differentiable an...
By Eric Ming Chen, Jin Woo Lee, Vincent Sitzmann
arXiv:2607. 28259v1 Announce Type: new Abstract: We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences.
By Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora, Baris Coskunuzer