pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques
Authors: Ning-Yuan Georgia Liu, David R. Keith
Date: 2025-03-23 00:00:00

Transforming a dynamic hypothesis into a causal loop diagram (CLD) is crucial for System Dynamics Modelling. Extracting key variables and causal relationships from text to build a CLD is often challenging and time-consuming for novice modelers, limiting SD tool adoption. This paper introduces and tests a method for automating the translation of dynamic hypotheses into CLDs using large language models (LLMs) with curated prompting techniques. We first describe how LLMs work and how they can make the inferences needed to build CLDs using a standard digraph structure. Next, we develop a set of simple dynamic hypotheses and corresponding CLDs from leading SD textbooks. We then compare the four different combinations of prompting techniques, evaluating their performance against CLDs labeled by expert modelers. Results show that for simple model structures and using curated prompting techniques, LLMs can generate CLDs of a similar quality to expert-built ones, accelerating CLD creation.
Composite Indicator-Guided Infilling Sampling for Expensive Multi-Objective Optimization
Authors: Huixiang Zhen, Xiaotong Li, Wenyin Gong, Ling Wang, Xiangyun Hu
Date: 2025-03-28 00:00:00
Tasks: Diversity

In expensive multi-objective optimization, where the evaluation budget is strictly limited, selecting promising candidate solutions for expensive fitness evaluations is critical for accelerating convergence and improving algorithmic performance. However, designing an optimization strategy that effectively balances convergence, diversity, and distribution remains a challenge. To tackle this issue, we propose a composite indicator-based evolutionary algorithm (CI-EMO) for expensive multi-objective optimization. In each generation of the optimization process, CI-EMO first employs NSGA-III to explore the solution space based on fitness values predicted by surrogate models, generating a candidate population. Subsequently, we design a novel composite performance indicator to guide the selection of candidates for real fitness evaluation. This indicator simultaneously considers convergence, diversity, and distribution to improve the efficiency of identifying promising candidate solutions, which significantly improves algorithm performance. The composite indicator-based candidate selection strategy is easy to achieve and computes efficiency. Component analysis experiments confirm the effectiveness of each element in the composite performance indicator. Comparative experiments on benchmark problems demonstrate that the proposed algorithm outperforms five state-of-the-art expensive multi-objective optimization algorithms.
A Novel Two-Phase Cooperative Co-evolution Framework for Large-Scale Global Optimization with Complex Overlapping
Authors: Wenjie Qiu, Hongshu Guo, Zeyuan Ma, Yue-Jiao Gong
Date: 2025-03-23 00:00:00
Tasks: global-optimization

Cooperative Co-evolution, through the decomposition of the problem space, is a primary approach for solving large-scale global optimization problems. Typically, when the subspaces are disjoint, the algorithms demonstrate significantly both effectiveness and efficiency compared to non-decomposition algorithms. However, the presence of overlapping variables complicates the decomposition process and adversely affects the performance of cooperative co-evolution. In this study, we propose a novel two-phase cooperative co-evolution framework to address large-scale global optimization problems with complex overlapping. An effective method for decomposing overlapping problems, grounded in their mathematical properties, is embedded within the framework. Additionally, a customizable benchmark for overlapping problems is introduced to extend existing benchmarks and facilitate experimentation. Extensive experiments demonstrate that the algorithm instantiated within our framework significantly outperforms existing algorithms. The results reveal the characteristics of overlapping problems and highlight the differing strengths of cooperative co-evolution and non-decomposition algorithms. Our work is open-source and accessible at: https://github.com/GMC-DRL/HCC.
Meta-Representational Predictive Coding: Biomimetic Self-Supervised Learning
Authors: Alexander Ororbia, Karl Friston, Rajesh P. N. Rao
Date: 2025-03-22 00:00:00
Tasks: Form, Self-Supervised Learning

Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence. Furthermore, evidence for self-supervised adaptation, such as contrastive formulations, has emerged in recent computational neuroscience and brain-inspired research. Nevertheless, current work on self-supervised learning relies on biologically implausible credit assignment -- in the form of backpropagation of errors -- and feedforward inference, typically a forward-locked pass. Predictive coding, in its mechanistic form, offers a biologically plausible means to sidestep these backprop-specific limitations. However, unsupervised predictive coding rests on learning a generative model of raw pixel input (akin to ``generative AI'' approaches), which entails predicting a potentially high dimensional input; on the other hand, supervised predictive coding, which learns a mapping between inputs to target labels, requires human annotation, and thus incurs the drawbacks of supervised learning. In this work, we present a scheme for self-supervised learning within a neurobiologically plausible framework that appeals to the free energy principle, constructing a new form of predictive coding that we call meta-representational predictive coding (MPC). MPC sidesteps the need for learning a generative model of sensory input (e.g., pixel-level features) by learning to predict representations of sensory input across parallel streams, resulting in an encoder-only learning and inference scheme. This formulation rests on active inference (in the form of sensory glimpsing) to drive the learning of representations, i.e., the representational dynamics are driven by sequences of decisions made by the model to sample informative portions of its sensorium.
Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation
Authors: Robin Dietrich, Tobias Fischer, Nicolai Waniek, Nico Reeb, Michael Milford, Alois Knoll, Adam D. Hines
Date: 2025-03-22 00:00:00

Efficient spatial navigation is a hallmark of the mammalian brain, inspiring the development of neuromorphic systems that mimic biological principles. Despite progress, implementing key operations like back-tracing and handling ambiguity in bio-inspired spiking neural networks remains an open challenge. This work proposes a mechanism for activity back-tracing in arbitrary, uni-directional spiking neuron graphs. We extend the existing replay mechanism of the spiking hierarchical temporal memory (S-HTM) by our spike timing-dependent threshold adaptation (STDTA), which enables us to perform path planning in networks of spiking neurons. We further present an ambiguity dependent threshold adaptation (ADTA) for identifying places in an environment with less ambiguity, enhancing the localization estimate of an agent. Combined, these methods enable efficient identification of the shortest path to an unambiguous target. Our experiments show that a network trained on sequences reliably computes shortest paths with fewer replays than the steps required to reach the target. We further show that we can identify places with reduced ambiguity in multiple, similar environments. These contributions advance the practical application of biologically inspired sequential learning algorithms like the S-HTM towards neuromorphic localization and navigation.
Architecture of Information
Authors: Yurii Parzhyn
Date: 2025-03-21 00:00:00

The paper explores an approach to constructing energy landscapes of a formal neuron and multilayer artificial neural networks (ANNs). Their analysis makes it possible to determine the conceptual limitations of both classification ANNs (e.g., MLP or CNN) and generative ANN models. The study of informational and thermodynamic entropy in formal neuron and ANN models leads to the conclusion about the energetic nature of informational entropy. The application of the Gibbs free energy concept allows representing the output information of ANNs as the structured part of enthalpy. Modeling ANNs as energy systems makes it possible to interpret the structure of their internal energy as an internal model of the external world, which self-organizes based on the interaction of the system's internal energy components. The control of the self-organization and evolution process of this model is carried out through an energy function (analogous to the Lyapunov function) based on reduction operators. This makes it possible to introduce a new approach to constructing self-organizing and evolutionary ANNs with direct learning, which does not require additional external algorithms. The presented research makes it possible to formulate a formal definition of information in terms of the interaction processes between the internal and external energy of the system.
Input-Triggered Hardware Trojan Attack on Spiking Neural Networks
Authors: Spyridon Raptis, Paul Kling, Ioannis Kaskampas, Ihsen Alouani, Haralampos-G. Stratigopoulos
Date: 2025-03-20 00:00:00

Neuromorphic computing based on spiking neural networks (SNNs) is emerging as a promising alternative to traditional artificial neural networks (ANNs), offering unique advantages in terms of low power consumption. However, the security aspect of SNNs is under-explored compared to their ANN counterparts. As the increasing reliance on AI systems comes with unique security risks and challenges, understanding the vulnerabilities and threat landscape is essential as neuromorphic computing matures. In this effort, we propose a novel input-triggered Hardware Trojan (HT) attack for SNNs. The HT mechanism is condensed in the area of one neuron. The trigger mechanism is an input message crafted in the spiking domain such that a selected neuron produces a malicious spike train that is not met in normal settings. This spike train triggers a malicious modification in the neuron that forces it to saturate, firing permanently and failing to recover to its resting state even when the input activity stops. The excessive spikes pollute the network and produce misleading decisions. We propose a methodology to select an appropriate neuron and to generate the input pattern that triggers the HT payload. The attack is illustrated by simulation on three popular benchmarks in the neuromorphic community. We also propose a hardware implementation for an analog spiking neuron and a digital SNN accelerator, demonstrating that the HT has a negligible area and power footprint and, thereby, can easily evade detection.
tempdisagg: A Python Framework for Temporal Disaggregation of Time Series Data
Authors: Jaime Vera-Jaramillo
Date: 2025-03-28 00:00:00
Tasks: Imputation, Missing Values, Time Series

tempdisagg is a modern, extensible, and production-ready Python framework for temporal disaggregation of time series data. It transforms low-frequency aggregates into consistent, high-frequency estimates using a wide array of econometric techniques-including Chow-Lin, Denton, Litterman, Fernandez, and uniform interpolation-as well as enhanced variants with automated estimation of key parameters such as the autocorrelation coefficient rho. The package introduces features beyond classical methods, including robust ensemble modeling via non-negative least squares optimization, post-estimation correction of negative values under multiple aggregation rules, and optional regression-based imputation of missing values through a dedicated Retropolarizer module. Architecturally, it follows a modular design inspired by scikit-learn, offering a clean API for validation, modeling, visualization, and result interpretation.
An Artificial Trend Index for Private Consumption Using Google Trends
Authors: Juan Tenorio, Heidi Alpiste, Jakelin Remón, Arian Segil
Date: 2025-03-27 00:00:00

In recent years, the use of databases that analyze trends, sentiments or news to make economic projections or create indicators has gained significant popularity, particularly with the Google Trends platform. This article explores the potential of Google search data to develop a new index that improves economic forecasts, with a particular focus on one of the key components of economic activity: private consumption (64\% of GDP in Peru). By selecting and estimating categorized variables, machine learning techniques are applied, demonstrating that Google data can identify patterns to generate a leading indicator in real time and improve the accuracy of forecasts. Finally, the results show that Google's "Food" and "Tourism" categories significantly reduce projection errors, highlighting the importance of using this information in a segmented manner to improve macroeconomic forecasts.
Rolled Gaussian process models for curves on manifolds
Authors: Simon Preston, Karthik Bharath, Pablo Lopez-Custodio, Alfred Kume
Date: 2025-03-27 00:00:00

Given a planar curve, imagine rolling a sphere along that curve without slipping or twisting, and by this means tracing out a curve on the sphere. It is well known that such a rolling operation induces a local isometry between the sphere and the plane so that the two curves uniquely determine each other, and moreover, the operation extends to a general class of manifolds in any dimension. We use rolling to construct an analogue of a Gaussian process on a manifold starting from a Euclidean Gaussian process. The resulting model is generative, and is amenable to statistical inference given data as curves on a manifold. We illustrate with examples on the unit sphere, symmetric positive-definite matrices, and with a robotics application involving 3D orientations.
Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis
Authors: Jingyao Sun, Qilu Zhang, Di Ma, Tianyu Jia, Shijie Jia, Xiaoxue Zhai, Ruimou Xie, Ping-Ju Lin, Zhibin Li, Yu Pan, Linhong Ji, Chong Li
Date: 2025-03-25 00:00:00

Multivariate cortico-muscular analysis has recently emerged as a promising approach for evaluating the corticospinal neural pathway. However, current multivariate approaches encounter challenges such as high dimensionality and limited sample sizes, thus restricting their further applications. In this paper, we propose a structured and sparse partial least squares coherence algorithm (ssPLSC) to extract shared latent space representations related to cortico-muscular interactions. Our approach leverages an embedded optimization framework by integrating a partial least squares (PLS)-based objective function, a sparsity constraint and a connectivity-based structured constraint, addressing the generalizability, interpretability and spatial structure. To solve the optimization problem, we develop an efficient alternating iterative algorithm within a unified framework and prove its convergence experimentally. Extensive experimental results from one synthetic and several real-world datasets have demonstrated that ssPLSC can achieve competitive or better performance over some representative multivariate cortico-muscular fusion methods, particularly in scenarios characterized by limited sample sizes and high noise levels. This study provides a novel multivariate fusion method for cortico-muscular analysis, offering a transformative tool for the evaluation of corticospinal pathway integrity in neurological disorders.
Improving Low-Resource Retrieval Effectiveness using Zero-Shot Linguistic Similarity Transfer
Authors: Andreas Chari, Sean MacAvaney, Iadh Ounis
Date: 2025-03-28 00:00:00
Tasks: Retrieval

Globalisation and colonisation have led the vast majority of the world to use only a fraction of languages, such as English and French, to communicate, excluding many others. This has severely affected the survivability of many now-deemed vulnerable or endangered languages, such as Occitan and Sicilian. These languages often share some characteristics, such as elements of their grammar and lexicon, with other high-resource languages, e.g. French or Italian. They can be clustered into groups of language varieties with various degrees of mutual intelligibility. Current search systems are not usually trained on many of these low-resource varieties, leading search users to express their needs in a high-resource language instead. This problem is further complicated when most information content is expressed in a high-resource language, inhibiting even more retrieval in low-resource languages. We show that current search systems are not robust across language varieties, severely affecting retrieval effectiveness. Therefore, it would be desirable for these systems to leverage the capabilities of neural models to bridge the differences between these varieties. This can allow users to express their needs in their low-resource variety and retrieve the most relevant documents in a high-resource one. To address this, we propose fine-tuning neural rankers on pairs of language varieties, thereby exposing them to their linguistic similarities. We find that this approach improves the performance of the varieties upon which the models were directly trained, thereby regularising these models to generalise and perform better even on unseen language variety pairs. We also explore whether this approach can transfer across language families and observe mixed results that open doors for future research.
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
Authors: Jinze Wang, Tiehua Zhang, Lu Zhang, Yang Bai, Xin Li, Jiong Jin
Date: 2025-03-27 00:00:00
Tasks: Diversity, Meta-Learning

Next Point-of-Interest (POI) recommendation aims to predict users' next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address these challenges, we propose Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN), a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation. Specifically, three types of heterogeneous hyperedges are designed to capture high-order relationships: user visit behaviors at specific times (Temporal behavioral hyperedge), spatial correlations among POIs (spatial functional hyperedge), and user long-term preferences (user preference hyperedge). Furthermore, a diversity-aware meta-learning mechanism is introduced to dynamically adjust learning strategies, considering users behavioral diversity. Extensive experiments on real-world datasets demonstrate that HyperMAN achieves superior performance, effectively addressing cold start challenges and significantly enhancing recommendation accuracy.
Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences
Authors: Heejin Kook, Junyoung Kim, Seongmin Park, Jongwuk Lee
Date: 2025-03-27 00:00:00
Tasks: Conversational Recommendation, Recommendation Systems, Retrieval

Conversational recommender systems (CRSs) are designed to suggest the target item that the user is likely to prefer through multi-turn conversations. Recent studies stress that capturing sentiments in user conversations improves recommendation accuracy. However, they employ a single user representation, which may fail to distinguish between contrasting user intentions, such as likes and dislikes, potentially leading to suboptimal performance. To this end, we propose a novel conversational recommender model, called COntrasting user pReference expAnsion and Learning (CORAL). Firstly, CORAL extracts the user's hidden preferences through contrasting preference expansion using the reasoning capacity of the LLMs. Based on the potential preference, CORAL explicitly differentiates the contrasting preferences and leverages them into the recommendation process via preference-aware learning. Extensive experiments show that CORAL significantly outperforms existing methods in three benchmark datasets, improving up to 99.72% in Recall@10. The code and datasets are available at https://github.com/kookeej/CORAL
Comparison of Metadata Representation Models for Knowledge Graph Embeddings
Authors: Shusaku Egami, Kyoumoto Matsushita, Takanori Ugai, Ken Fukuda
Date: 2025-03-25 00:00:00
Tasks: Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction

Hyper-relational Knowledge Graphs (HRKGs) extend traditional KGs beyond binary relations, enabling the representation of contextual, provenance, and temporal information in domains, such as historical events, sensor data, video content, and narratives. HRKGs can be structured using several Metadata Representation Models (MRMs), including Reification (REF), Singleton Property (SGP), and RDF-star (RDR). However, the effects of different MRMs on KG Embedding (KGE) and Link Prediction (LP) models remain unclear. This study evaluates MRMs in the context of LP tasks, identifies the limitations of existing evaluation frameworks, and introduces a new task that ensures fair comparisons across MRMs. Furthermore, we propose a framework that effectively reflects the knowledge representations of the three MRMs in latent space. Experiments on two types of datasets reveal that REF performs well in simple HRKGs, whereas SGP is less effective. However, in complex HRKGs, the differences among MRMs in the LP tasks are minimal. Our findings contribute to an optimal knowledge representation strategy for HRKGs in LP tasks.
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation with Zero-Shot Method
Authors: Wenqi Guo, Shan Du
Date: 2025-03-30 00:00:00
Proceeding: Unpublished 2025 3
Tasks: Camouflaged Object Segmentation, Defect Detection, Lesion Segmentation, Optical Flow Estimation, Segmentation, Semantic Segmentation, Unsupervised Pre-training

Camouflaged object segmentation presents unique challenges compared to traditional segmentation tasks, primarily due to the high similarity in patterns and colors between camouflaged objects and their backgrounds. Effective solutions to this problem have significant implications in critical areas such as pest control, defect detection, and lesion segmentation in medical imaging. Prior research has predominantly emphasized supervised or unsupervised pre-training methods, leaving zero-shot approaches significantly underdeveloped. Existing zero-shot techniques commonly utilize the Segment Anything Model (SAM) in automatic mode or rely on vision-language models to generate cues for segmentation; however, their performances remain unsatisfactory. Optical flow, commonly utilized for detecting moving objects, has demonstrated effectiveness even with camouflaged entities. Our method integrates optical flow, a vision-language model, and SAM 2 into a sequential pipeline, where the output of one component provides cues for the next. Evaluated on the MoCA-Masks dataset, our approach achieves outstanding performance improvements, significantly outperforming existing zero-shot methods by raising the mean Intersection-over-Union (mIoU) from 0.273 to 0.561. Remarkably, this simple yet effective approach also surpasses supervised methods, increasing mIoU from 0.422 to 0.561. Additionally, evaluation on the MoCA-Filter dataset demonstrates an increase in the success rate from 0.628 to 0.697 when compared with FlowSAM, a supervised transfer method. A thorough ablation study further validates the individual contributions of each component.