pwc-archive/papers-with-abstracts huggingface dataset:
Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks
Authors: Yi Xie, Stefan Mihalas, Łukasz Kuśmierz
Date: 2025-05-14 00:00:00

Growing evidence suggests that synaptic weights in the brain follow heavy-tailed distributions, yet most theoretical analyses of recurrent neural networks (RNNs) assume Gaussian connectivity. We systematically study the activity of RNNs with random weights drawn from biologically plausible L\'evy alpha-stable distributions. While mean-field theory for the infinite system predicts that the quiescent state is always unstable -- implying ubiquitous chaos -- our finite-size analysis reveals a sharp transition between quiescent and chaotic dynamics. We theoretically predict the gain at which the system transitions from quiescent to chaotic dynamics, and validate it through simulations. Compared to Gaussian networks, heavy-tailed RNNs exhibit a broader parameter regime near the edge of chaos, namely a slow transition to chaos. However, this robustness comes with a tradeoff: heavier tails reduce the Lyapunov dimension of the attractor, indicating lower effective dimensionality. Our results reveal a biologically aligned tradeoff between the robustness of dynamics near the edge of chaos and the richness of high-dimensional neural activity. By analytically characterizing the transition point in finite-size networks -- where mean-field theory breaks down -- we provide a tractable framework for understanding dynamics in realistically sized, heavy-tailed neural circuits.
Whitened Score Diffusion: A Structured Prior for Imaging Inverse Problems
Authors: Jeffrey Alido, Tongyu Li, Yu Sun, Lei Tian
Date: 2025-05-15 00:00:00
Tasks: Denoising

Conventional score-based diffusion models (DMs) may struggle with anisotropic Gaussian diffusion processes due to the required inversion of covariance matrices in the denoising score matching training objective \cite{vincent_connection_2011}. We propose Whitened Score (WS) diffusion models, a novel framework based on stochastic differential equations that learns the Whitened Score function instead of the standard score. This approach circumvents covariance inversion, extending score-based DMs by enabling stable training of DMs on arbitrary Gaussian forward noising processes. WS DMs establish equivalence with flow matching for arbitrary Gaussian noise, allow for tailored spectral inductive biases, and provide strong Bayesian priors for imaging inverse problems with structured noise. We experiment with a variety of computational imaging tasks using the CIFAR and CelebA ($64\times64$) datasets and demonstrate that WS diffusion priors trained on anisotropic Gaussian noising processes consistently outperform conventional diffusion priors based on isotropic Gaussian noise. Our code is open-sourced at \href{https://github.com/jeffreyalido/wsdiffusion}{\texttt{github.com/jeffreyalido/wsdiffusion}}.
Topology-driven identification of repetitions in multi-variate time series
Authors: Simon Schindler, Elias Steffen Reich, Saverio Messineo, Simon Hoher, Stefan Huber
Date: 2025-05-15 00:00:00
Tasks: Time Series

Many multi-variate time series obtained in the natural sciences and engineering possess a repetitive behavior, as for instance state-space trajectories of industrial machines in discrete automation. Recovering the times of recurrence from such a multi-variate time series is of a fundamental importance for many monitoring and control tasks. For a periodic time series this is equivalent to determining its period length. In this work we present a persistent homology framework to estimate recurrence times in multi-variate time series with different generalizations of cyclic behavior (periodic, repetitive, and recurring). To this end, we provide three specialized methods within our framework that are provably stable and validate them using real-world data, including a new benchmark dataset from an injection molding machine.
Beyond Pairwise Learning-To-Rank At Airbnb
Authors: Malay Haldar, Daochen Zha, Huiji Gao, Liwei He, Sanjeev Katariya
Date: 2025-05-14 00:00:00
Tasks: All, Learning-To-Rank

There are three fundamental asks from a ranking algorithm: it should scale to handle a large number of items, sort items accurately by their utility, and impose a total order on the items for logical consistency. But here's the catch-no algorithm can achieve all three at the same time. We call this limitation the SAT theorem for ranking algorithms. Given the dilemma, how can we design a practical system that meets user needs? Our current work at Airbnb provides an answer, with a working solution deployed at scale. We start with pairwise learning-to-rank (LTR) models-the bedrock of search ranking tech stacks today. They scale linearly with the number of items ranked and perform strongly on metrics like NDCG by learning from pairwise comparisons. They are at a sweet spot of performance vs. cost, making them an ideal choice for several industrial applications. However, they have a drawback-by ignoring interactions between items, they compromise on accuracy. To improve accuracy, we create a "true" pairwise LTR model-one that captures interactions between items during pairwise comparisons. But accuracy comes at the expense of scalability and total order, and we discuss strategies to counter these challenges. For greater accuracy, we take each item in the search result, and compare it against the rest of the items along two dimensions: (1) Superiority: How strongly do searchers prefer the given item over the remaining ones? (2) Similarity: How similar is the given item to all the other items? This forms the basis of our "all-pairwise" LTR framework, which factors in interactions across all items at once. Looking at items on the search result page all together-superiority and similarity combined-gives us a deeper understanding of what searchers truly want. We quantify the resulting improvements in searcher experience through offline and online experiments at Airbnb.
The Impact of International Collaborations with Highly Publishing Countries in Computer Science
Authors: Alberto Gomez Espes, Michael Faerber, Adam Jatowt
Date: 2025-05-14 00:00:00

This paper analyzes international collaborations in Computer Science, focusing on three major players: China, the European Union, and the United States. Drawing from a comprehensive literature review, we examine collaboration patterns, research impact, retraction rates, and the role of the Development Index in shaping research outcomes. Our findings show that while China, the EU, and the US lead global research efforts, other regions are narrowing the gap in publication volume. Collaborations involving these key regions tend to have lower retraction rates, reflecting stronger adherence to scientific standards. We also find that countries with a Very High Development Index contribute to research with higher citation rates and fewer retractions. Overall, this study highlights the value of international collaboration and the importance of inclusive, ethical practices in advancing global research in Computer Science.
HeGAE-AC: heterogeneous graph auto-encoder for attribute completion
Authors: Yejia Chen, Ye Liu
Date: 2024-03-05 00:00:00
Proceeding: Knowledge-Based Systems 2024 3
Tasks: Attribute, Graph Representation Learning, Representation Learning

Heterogeneous Graphs (HGs) are widely used to model complex real-world networks that involve multiple types of nodes and relations. Many heterogeneous graph neural networks (HGNNs) based methods have been developed to learn low-dimensional representations for nodes in HGs with the assumption of complete attributes for all nodes. However, in many real applications, the attributes of certain nodes may be completely missing, leading to attribute-missing issues. Existing methods for attribute completion in HGs separate topological embedding learning from attribute completion, which can result in sub-optimal solutions. Moreover, the information of higher-order connected nodes is not fully exploited in the process of attribute completion. To address the issues mentioned above, we integrate topological embedding learning, attribute completion, and heterogeneous graph representation learning into an end-to-end framework called HeGAE-AC. Firstly, we utilize a heterogeneous graph auto-encoder (HeGAE) to incorporate the graph topology structure and attribute information of higher-order connected nodes into low-dimensional embeddings. Then, HeGAE decodes embeddings to generate attributes of all nodes. Finally, an HGNN model is employed to obtain node representations for downstream tasks using the completed HGs. We conduct extensive experiments on four realworld networks, which demonstrate that our approach achieves significant improvements over state-of-the-art methods.
Unveiling 3D Ocean Biogeochemical Provinces: A Machine Learning Approach for Systematic Clustering and Validation
Authors: Yvonne Jenniges, Maike Sonnewald, Sebastian Maneth, Are Olsen, Boris P. Koch
Date: 2025-04-25 00:00:00
Tasks: Clustering

Defining ocean regions and water masses helps to understand marine processes and can serve downstream-tasks such as defining marine protected areas. However, such definitions are often a result of subjective decisions potentially producing misleading, unreproducible results. Here, the aim was to objectively define regions of the North Atlantic. For this, a data-driven, systematic machine learning approach was applied to generate and validate ocean clusters employing external, internal and relative validation techniques. About 300 million measured salinity, temperature, and oxygen, nitrate, phosphate and silicate concentration values served as input for various clustering methods (KMeans, agglomerative Ward, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)). Uniform Manifold Approximation and Projection (UMAP) emphasised (dis-)similarities in the data while reducing dimensionality. Based on a systematic validation of the considered clustering methods and their hyperparameters, the results showed that UMAP-DBSCAN best represented the data. To address stochastic variability, 100 UMAP-DBSCAN clustering runs were conducted and aggregated using Native Emergent Manifold Interrogation (NEMI), producing a final set of 321 clusters. Reproducibility was evaluated by calculating the ensemble overlap (88.81 +- 1.8%) and the mean grid cell-wise uncertainty estimated by NEMI (15.49 +- 20%). The presented clustering results agreed very well with common water mass definitions. This study revealed a more detailed regionalization compared to previous concepts such as the Longhurst provinces. The applied method is objective, efficient and reproducible and will support future research focusing on biogeochemical differences and changes in oceanic regions.
A novel robust integrating method by high-order proximity for self-supervised attribute network embedding
Authors: Zelong Wu, Yidan Wang, Kaixia Hu, Guoliang Lin, Xinwei Xu
Date: 2024-12-10 00:00:00
Proceeding: Expert Systems with Applications 2024 12
Tasks: Attribute, Data Visualization, Graph Outlier Detection, Link Prediction, Network Embedding, Node Classification

Attribute network embedding faces significant challenges, primarily integrating heterogeneous information and managing outliers. In this paper, we introduce a novel Robust Integrating Method by High-order Proximity for Self-supervised Attribute Network Embedding (RSANE). Firstly, a novel heterogeneous topological and semantic information integration method is designed, which contains arbitrary high-order proximity and theoretically includes summation and multiplication forms. Secondly, the RSANE can adaptively reduce the influence of outliers during the embedding process. By incorporating higher-order proximity, RSANE increases the score of outliers and achieves better robustness. Finally, through a deep architecture with dual autoencoders, RSANE achieves joint embedding of network structure and node attributes. In addition, introducing end-to-end reconstruction of structure and attributes can fully extract potential information. Moreover, extensive experiments associated with statistical tests and sensitivity analysis demonstrate that RSANE outperforms state-of-the-art algorithms across various downstream tasks. The code is available at https://github.com/wuzelong/RSANE.
Adversarial network embedding with bootstrapped representations for sparse networks
Authors: Zelong Wu1, Yidan Wang, Guoliang Lin, Junlong Liu
Date: 2025-03-03 00:00:00
Proceeding: Applied Intelligence 2025 3
Tasks: Data Visualization, Link Prediction, Network Embedding, Node Classification

The inherent sparsity of real-world networks presents challenges in learning-rich embeddings and accurately reconstructing networks. To address these challenges, a novel method termed Adversarial Network Embedding with Bootstrapped Representations (ANEBR) is proposed. Firstly, a novel network augmentation method is employed for positive sampling. ANEBR utilizes the Katz Index to extract higher-order latent information and refines it with α-entmax. The crucial information is extracted while minimizing noise generation. Secondly, ANEBR circumvents negative sampling by learning bootstrapped representations. Building on bootstrapped representations from the BYOL algorithm, ANEBR incorporates the GAN techniques to align the learned embeddings nonlinearly. Finally, ANEBR attains accurate network reconstruction by imposing a low-rank constraint on the reconstruction error through the nuclear norm. Extensive experiments with statistical and sensitivity analyses demonstrate that ANEBR outperforms state-of-the-art methods in various tasks. Specifically, ANEBR reconstructs the PPI network with a precision of 0.9992, marking a relative improvement of 6.65%. Code is available at https://github.
com/wuzelong/ANEBR.
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
Authors: Adrián Bazaga, Rexhina Blloshmi, Bill Byrne, Adrià De Gispert
Date: 2025-04-07 00:00:00
Tasks: Question Answering, Scheduling

Large Language Models (LLMs) have emerged as powerful tools for generating coherent text, understanding context, and performing reasoning tasks. However, they struggle with temporal reasoning, which requires processing time-related information such as event sequencing, durations, and inter-temporal relationships. These capabilities are critical for applications including question answering, scheduling, and historical analysis. In this paper, we introduce TISER, a novel framework that enhances the temporal reasoning abilities of LLMs through a multi-stage process that combines timeline construction with iterative self-reflection. Our approach leverages test-time scaling to extend the length of reasoning traces, enabling models to capture complex temporal dependencies more effectively. This strategy not only boosts reasoning accuracy but also improves the traceability of the inference process. Experimental results demonstrate state-of-the-art performance across multiple benchmarks, including out-of-distribution test sets, and reveal that TISER enables smaller open-source models to surpass larger closed-weight models on challenging temporal reasoning tasks.
Residual and bidirectional LSTM for epileptic seizure detection
Authors: Zhao W, Wang W-F, Patnaik LM, Zhang B-C, Weng S-J, Xiao S-X, Wei D-Z and Zhou H-F
Date: 2024-06-17 00:00:00
Proceeding: Frontiers in Computational Neuroscience 2024 6
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88–100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.
Sunday Paul
Authors: Sunday Paul
Date: 2012-03-24 00:00:00
Proceeding: fhg 2012 3

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CAREER
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Onboard Optimization and Learning: A Survey
Authors: Monirul Islam Pavel, Siyi Hu, Mahardhika Pratama, Ryszard Kowalczyk
Date: 2025-05-07 00:00:00
Tasks: Decision Making, Model Compression, Privacy Preserving, Survey

Onboard learning is a transformative approach in edge AI, enabling real-time data processing, decision-making, and adaptive model training directly on resource-constrained devices without relying on centralized servers. This paradigm is crucial for applications demanding low latency, enhanced privacy, and energy efficiency. However, onboard learning faces challenges such as limited computational resources, high inference costs, and security vulnerabilities. This survey explores a comprehensive range of methodologies that address these challenges, focusing on techniques that optimize model efficiency, accelerate inference, and support collaborative learning across distributed devices. Approaches for reducing model complexity, improving inference speed, and ensuring privacy-preserving computation are examined alongside emerging strategies that enhance scalability and adaptability in dynamic environments. By bridging advancements in hardware-software co-design, model compression, and decentralized learning, this survey provides insights into the current state of onboard learning to enable robust, efficient, and secure AI deployment at the edge.
Quantized Approximate Signal Processing (QASP): Towards Homomorphic Encryption for audio
Authors: Tu Duyen Nguyen, Adrien Lesage, Clotilde Cantini, Rachid Riad
Date: 2025-05-15 00:00:00
Tasks: Speaker Identification, speech-recognition, Speech Recognition

Audio and speech data are increasingly used in machine learning applications such as speech recognition, speaker identification, and mental health monitoring. However, the passive collection of this data by audio listening devices raises significant privacy concerns. Fully homomorphic encryption (FHE) offers a promising solution by enabling computations on encrypted data and preserving user privacy. Despite its potential, prior attempts to apply FHE to audio processing have faced challenges, particularly in securely computing time frequency representations, a critical step in many audio tasks. Here, we addressed this gap by introducing a fully secure pipeline that computes, with FHE and quantized neural network operations, four fundamental time-frequency representations: Short-Time Fourier Transform (STFT), Mel filterbanks, Mel-frequency cepstral coefficients (MFCCs), and gammatone filters. Our methods also support the private computation of audio descriptors and convolutional neural network (CNN) classifiers. Besides, we proposed approximate STFT algorithms that lighten computation and bit use for statistical and machine learning analyses. We ran experiments on the VocalSet and OxVoc datasets demonstrating the fully private computation of our approach. We showed significant performance improvements with STFT approximation in private statistical analysis of audio markers, and for vocal exercise classification with CNNs. Our results reveal that our approximations substantially reduce error rates compared to conventional STFT implementations in FHE. We also demonstrated a fully private classification based on the raw audio for gender and vocal exercise classification. Finally, we provided a practical heuristic for parameter selection, making quantized approximate signal processing accessible to researchers and practitioners aiming to protect sensitive audio data.
TokenProber: Jailbreaking Text-to-image Models via Fine-grained Word Impact Analysis
Authors: Longtian Wang, Xiaofei Xie, Tianlin Li, Yuhan Zhi, Chao Shen
Date: 2025-05-11 00:00:00
Tasks: Sensitivity

Text-to-image (T2I) models have significantly advanced in producing high-quality images. However, such models have the ability to generate images containing not-safe-for-work (NSFW) content, such as pornography, violence, political content, and discrimination. To mitigate the risk of generating NSFW content, refusal mechanisms, i.e., safety checkers, have been developed to check potential NSFW content. Adversarial prompting techniques have been developed to evaluate the robustness of the refusal mechanisms. The key challenge remains to subtly modify the prompt in a way that preserves its sensitive nature while bypassing the refusal mechanisms. In this paper, we introduce TokenProber, a method designed for sensitivity-aware differential testing, aimed at evaluating the robustness of the refusal mechanisms in T2I models by generating adversarial prompts. Our approach is based on the key observation that adversarial prompts often succeed by exploiting discrepancies in how T2I models and safety checkers interpret sensitive content. Thus, we conduct a fine-grained analysis of the impact of specific words within prompts, distinguishing between dirty words that are essential for NSFW content generation and discrepant words that highlight the different sensitivity assessments between T2I models and safety checkers. Through the sensitivity-aware mutation, TokenProber generates adversarial prompts, striking a balance between maintaining NSFW content generation and evading detection. Our evaluation of TokenProber against 5 safety checkers on 3 popular T2I models, using 324 NSFW prompts, demonstrates its superior effectiveness in bypassing safety filters compared to existing methods (e.g., 54%+ increase on average), highlighting TokenProber's ability to uncover robustness issues in the existing refusal mechanisms.