pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Information Digestion Differences among Players in Online C2C Markets
Authors: Jun Sashihara, Teruaki Hayashi
Date: 2025-03-26 00:00:00

In recent years, the magnitude of consumer-to-consumer (C2C) markets have grown significantly, highlighting the increasing significance of trust between buyers and sellers. However, the specific aspects of product information that facilitate effective communication and trust building in C2C markets remain poorly understood. This study examines the concept of information digestion-the process through which information is accurately understood-and aims to elucidate differences in the information digestion processes of sellers and buyers to clarify the role of product page information. To address this, we conducted two experiments: a questionnaire survey involving 400 subjects and a conjoint analysis with eye-tracking for 15 participants. Initially, we selected eight sample products from four distinct product categories based on the Foote, Cone, and Belding (FCB) grid and compared the product information components considered important by sellers and buyers. Subsequently, we created 12 types of product pages that varied in the combination of four attributes: title, price, product description, and image. Experiment 1 revealed significant differences in the perceived importance of product page components between buyers and sellers. It also demonstrated that product categories and transaction experience influenced the importance assigned to these components, particularly for buyers. Results from Experiment 2 showed that buyers prioritize product descriptions and identified two distinct buyer groups based on their information digestion patterns: one that carefully reads and digests information and another that processes information more rapidly. These findings enhance our understanding of trust-building mechanisms in online C2C markets and provide practical insights for platform designers and market participants.
NeuroMorse: A Temporally Structured Dataset For Neuromorphic Computing
Authors: Ben Walters, Yeshwanth Bethi, Taylor Kergan, Binh Nguyen, Amirali Amirsoleimani, Jason K. Eshraghian, Saeed Afshar, Mostafa Rahimi Azghadi
Date: 2025-02-28 00:00:00
Tasks: Benchmarking

Neuromorphic engineering aims to advance computing by mimicking the brain's efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need for a synchronisation clock and minimises power consumption when no data is present. However, many benchmarks for neuromorphic algorithms primarily focus on spatial features, neglecting the temporal dynamics that are inherent to most sequence-based tasks. This gap may lead to evaluations that fail to fully capture the unique strengths and characteristics of neuromorphic systems. In this paper, we present NeuroMorse, a temporally structured dataset designed for benchmarking neuromorphic learning systems. NeuroMorse converts the top 50 words in the English language into temporal Morse code spike sequences. Despite using only two input spike channels for Morse dots and dashes, complex information is encoded through temporal patterns in the data. The proposed benchmark contains feature hierarchy at multiple temporal scales that test the capacity of neuromorphic algorithms to decompose input patterns into spatial and temporal hierarchies. We demonstrate that our training set is challenging to categorise using a linear classifier and that identifying keywords in the test set is difficult using conventional methods. The NeuroMorse dataset is available at Zenodo, with our accompanying code on GitHub at https://github.com/Ben-E-Walters/NeuroMorse.
Doing Less for More: Consumer Search and Undertreatment in Credence Service Markets
Authors: Xiaoyan Xu, Weishi Lim, Xing Zhang, Jeff Cai
Date: 2025-03-27 00:00:00
Tasks: Diagnostic

In many service markets, expert providers possess an information advantage over consumers regarding the necessary services, creating opportunities for fraudulent practices. These may involve overtreatment through unnecessary services or undertreatment with ineffective solutions that fail to address consumers' problems. When issues are resolved, consumers exit the market; when unresolved, they must decide whether to revisit the initial provider or seek a new one. Little is known about how repeated interactions and the consumer search process influence expert fraud and consumer welfare in such markets. We develop a dynamic game-theoretic model to examine the role of consumer search behavior and repeated interactions between consumers and service providers. We find that overtreatment and undertreatment can arise simultaneously in equilibrium. Interestingly, undertreatment-being less costly for the consumer-can initially act as a "hook" to induce acceptance of a minor treatment recommendation. When this minor treatment fails to resolve the issue, it can generate additional demand for a more expensive and serious treatment. This would arise when the cost of revisiting the initial provider is lower than that of searching for a new one. The extent of undertreatment exhibits a non-monotonic relationship with consumers' ex ante belief about the nature of their problems and the market's ethical level. Our results can shed light on how market ethical levels, provider capabilities and capacities, and consumer privacy protection policies interact with undertreatment and affect consumer welfare. Specifically, consumer welfare can decrease as the market becomes more ethical. Enhancing providers' diagnostic capabilities and capacities can exacerbate undertreatment. Providing access to consumers' diagnosis histories can help mitigate the undertreatment issue and improve consumer welfare.
Neuromorphic Circuits with Spiking Astrocytes for Increased Energy Efficiency, Fault Tolerance, and Memory Capacitance
Authors: Aybars Yunusoglu, Dexter Le, Murat Isik, I. Can Dikmen, Teoman Karadag
Date: 2025-02-27 00:00:00
Tasks: Management

In the rapidly advancing field of neuromorphic computing, integrating biologically-inspired models like the Leaky Integrate-and-Fire Astrocyte (LIFA) into spiking neural networks (SNNs) enhances system robustness and performance. This paper introduces the LIFA model in SNNs, addressing energy efficiency, memory management, routing mechanisms, and fault tolerance. Our core architecture consists of neurons, synapses, and astrocyte circuits, with each astrocyte supporting multiple neurons for self-repair. This clustered model improves fault tolerance and operational efficiency, especially under adverse conditions. We developed a routing methodology to map the LIFA model onto a fault-tolerant, many-core design, optimizing network functionality and efficiency. Our model features a fault tolerance rate of 81.10\% and a resilience improvement rate of 18.90\%, significantly surpassing other implementations. The results validate our approach in memory management, highlighting its potential as a robust solution for advanced neuromorphic computing applications. The integration of astrocytes represents a significant advancement, setting the stage for more resilient and adaptable neuromorphic systems.
On the Privacy-Preserving Properties of Spiking Neural Networks with Unique Surrogate Gradients and Quantization Levels
Authors: Ayana Moshruba, Shay Snyder, Hamed Poursiami, Maryam Parsa
Date: 2025-02-25 00:00:00
Tasks: Privacy Preserving, Quantization

As machine learning models increasingly process sensitive data, understanding their vulnerability to privacy attacks is vital. Membership inference attacks (MIAs) exploit model responses to infer whether specific data points were used during training, posing a significant privacy risk. Prior research suggests that spiking neural networks (SNNs), which rely on event-driven computation and discrete spike-based encoding, exhibit greater resilience to MIAs than artificial neural networks (ANNs). This resilience stems from their non-differentiable activations and inherent stochasticity, which obscure the correlation between model responses and individual training samples. To enhance privacy in SNNs, we explore two techniques: quantization and surrogate gradients. Quantization, which reduces precision to limit information leakage, has improved privacy in ANNs. Given SNNs' sparse and irregular activations, quantization may further disrupt the activation patterns exploited by MIAs. We assess the vulnerability of SNNs and ANNs under weight and activation quantization across multiple datasets, using the attack model's receiver operating characteristic (ROC) curve area under the curve (AUC) metric, where lower values indicate stronger privacy, and evaluate the privacy-accuracy trade-off. Our findings show that quantization enhances privacy in both architectures with minimal performance loss, though full-precision SNNs remain more resilient than quantized ANNs. Additionally, we examine the impact of surrogate gradients on privacy in SNNs. Among five evaluated gradients, spike rate escape provides the best privacy-accuracy trade-off, while arctangent increases vulnerability to MIAs. These results reinforce SNNs' inherent privacy advantages and demonstrate that quantization and surrogate gradient selection significantly influence privacy-accuracy trade-offs in SNNs.
Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm
Authors: Mohammadnavid Ghader, Saeed Reza Kheradpisheh, Bahar Farahani, Mahmood Fazlali
Date: 2025-02-19 00:00:00
Tasks: Computational Efficiency

Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing. Despite their advantages, training SNNs with traditional backpropagation (BP) remains challenging due to computational inefficiencies and a lack of biological plausibility. This study explores the Forward-Forward (FF) algorithm as an alternative learning framework for SNNs. Unlike backpropagation, which relies on forward and backward passes, the FF algorithm employs two forward passes, enabling localized learning, enhanced computational efficiency, and improved compatibility with neuromorphic hardware. We introduce an FF-based SNN training framework and evaluate its performance across both non-spiking (MNIST, Fashion-MNIST, CIFAR-10) and spiking (Neuro-MNIST, SHD) datasets. Experimental results demonstrate that our model surpasses existing FF-based SNNs by over 5% on MNIST and Fashion-MNIST while achieving accuracy comparable to state-of-the-art backpropagation-trained SNNs. On more complex tasks such as CIFAR-10 and SHD, our approach outperforms other SNN models by up to 6% and remains competitive with leading backpropagation-trained SNNs. These findings highlight the FF algorithm's potential to advance SNN training methodologies and neuromorphic computing by addressing key limitations of backpropagation.
Expressive Timing in Hindustani Vocal Music
Authors: Yash Bhake, Preeti Rao
Date: 2025-03-27 00:00:00

Temporal dynamics are among the cues to expres siveness in music performance in different cultures. In the case of Hindustani music, it is well known that expert vocalists often take liberties with the beat, intentionally not aligning their singing precisely with the relatively steady beat provided by the accompanying tabla. This becomes evident when comparing performances of the same composition such as a bandish. We present a methodology for the quantitative study of differences across performed pieces using computational techniques. This is applied to small study of two performances of a popular bandish in raga Yaman, to demonstrate how we can effectively capture the nuances of timing variations that bring out stylistic constraints along with the individual signature of a performer. This work articulates an important step towards the broader goals of music analysis and generative modelling for Indian classical music performance.
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation
Authors: Emil Njor, Colby Banbury, Xenofon Fafoutis
Date: 2025-02-18 00:00:00
Tasks: AutoML, Neural Architecture Search

Tiny machine learning (TinyML) promises to revolutionize fields such as healthcare, environmental monitoring, and industrial maintenance by running machine learning models on low-power embedded systems. However, the complex optimizations required for successful TinyML deployment continue to impede its widespread adoption. A promising route to simplifying TinyML is through automatic machine learning (AutoML), which can distill elaborate optimization workflows into accessible key decisions. Notably, Hardware Aware Neural Architecture Searches - where a computer searches for an optimal TinyML model based on predictive performance and hardware metrics - have gained significant traction, producing some of today's most widely used TinyML models. Nevertheless, limiting optimization solely to neural network architectures can prove insufficient. Because TinyML systems must operate under extremely tight resource constraints, the choice of input data configuration, such as resolution or sampling rate, also profoundly impacts overall system efficiency. Achieving truly optimal TinyML systems thus requires jointly tuning both input data and model architecture. Despite its importance, this "Data Aware Neural Architecture Search" remains underexplored. To address this gap, we propose a new state-of-the-art Data Aware Neural Architecture Search technique and demonstrate its effectiveness on the novel TinyML ``Wake Vision'' dataset. Our experiments show that across varying time and hardware constraints, Data Aware Neural Architecture Search consistently discovers superior TinyML systems compared to purely architecture-focused methods, underscoring the critical role of data-aware optimization in advancing TinyML.
Warm Starting of CMA-ES for Contextual Optimization Problems
Authors: Yuta Sekino, Kento Uchida, Shinichi Shirakawa
Date: 2025-02-18 00:00:00
Tasks: Evolutionary Algorithms

Several practical applications of evolutionary computation possess objective functions that receive the design variables and externally given parameters. Such problems are termed contextual optimization problems. These problems require finding the optimal solutions corresponding to the given context vectors. Existing contextual optimization methods train a policy model to predict the optimal solution from context vectors. However, the performance of such models is limited by their representation ability. By contrast, warm starting methods have been used to initialize evolutionary algorithms on a given problem using the optimization results on similar problems. Because warm starting methods do not consider the context vectors, their performances can be improved on contextual optimization problems. Herein, we propose a covariance matrix adaptation evolution strategy with contextual warm starting (CMA-ES-CWS) to efficiently optimize the contextual optimization problem with a given context vector. The CMA-ES-CWS utilizes the optimization results of past context vectors to train the multivariate Gaussian process regression. Subsequently, the CMA-ES-CWS performs warm starting for a given context vector by initializing the search distribution using posterior distribution of the Gaussian process regression. The results of the numerical simulation suggest that CMA-ES-CWS outperforms the existing contextual optimization and warm starting methods.
An Algorithm Board in Neural Decoding
Authors: Jingyi Feng, Kai Yang
Date: 2025-02-18 00:00:00

Understanding the mechanisms of neural encoding and decoding has always been a highly interesting research topic in fields such as neuroscience and cognitive intelligence. In prior studies, some researchers identified a symmetry in neural data decoded by unsupervised methods in motor scenarios and constructed a cognitive learning system based on this pattern (i.e., symmetry). Nevertheless, the distribution state of the data flow that significantly influences neural decoding positions still remains a mystery within the system, which further restricts the enhancement of the system's interpretability. Based on this, this paper mainly explores changes in the distribution state within the system from the machine learning and mathematical statistics perspectives. In the experiment, we assessed the correctness of this symmetry using various tools and indicators commonly utilized in mathematics and statistics. According to the experimental results, the normal distribution (or Gaussian distribution) plays a crucial role in the decoding of prediction positions within the system. Eventually, an algorithm board similar to the Galton board was built to serve as the mathematical foundation of the discovered symmetry.
On Space Folds of ReLU Neural Networks
Authors: Michal Lewandowski, Hamid Eghbalzadeh, Bernhard Heinzl, Raphael Pisoni, Bernhard A. Moser
Date: 2025-02-14 00:00:00
Tasks: image-classification, Image Classification

Recent findings suggest that the consecutive layers of ReLU neural networks can be understood geometrically as space folding transformations of the input space, revealing patterns of self-similarity. In this paper, we present the first quantitative analysis of this space folding phenomenon in ReLU neural networks. Our approach focuses on examining how straight paths in the Euclidean input space are mapped to their counterparts in the Hamming activation space. In this process, the convexity of straight lines is generally lost, giving rise to non-convex folding behavior. To quantify this effect, we introduce a novel measure based on range metrics, similar to those used in the study of random walks, and provide the proof for the equivalence of convexity notions between the input and activation spaces. Furthermore, we provide empirical analysis on a geometrical analysis benchmark (CantorNet) as well as an image classification benchmark (MNIST). Our work advances the understanding of the activation space in ReLU neural networks by leveraging the phenomena of geometric folding, providing valuable insights on how these models process input information.
Application-oriented automatic hyperparameter optimization for spiking neural network prototyping
Authors: Vittorio Fra
Date: 2025-02-13 00:00:00
Tasks: Hyperparameter Optimization

Hyperparameter optimization (HPO) is of paramount importance in the development of high-performance, specialized artificial intelligence (AI) models, ranging from well-established machine learning (ML) solutions to the deep learning (DL) domain and the field of spiking neural networks (SNNs). The latter introduce further complexity due to the neuronal computational units and their additional hyperparameters, whose inadequate setting can dramatically impact the final model performance. At the cost of possible reduced generalization capabilities, the most suitable strategy to fully disclose the power of SNNs is to adopt an application-oriented approach and perform extensive HPO experiments. To facilitate these operations, automatic pipelines are fundamental, and their configuration is crucial. In this document, the Neural Network Intelligence (NNI) toolkit is used as reference framework to present one such solution, with a use case example providing evidence of the corresponding results. In addition, a summary of published works employing the presented pipeline is reported as possible source of insights into application-oriented HPO experiments for SNN prototyping.
Neuromorphic Digital-Twin-based Controller for Indoor Multi-UAV Systems Deployment
Authors: Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad
Date: 2025-02-12 00:00:00
Tasks: Cloud Computing, Computational Efficiency

Presented study introduces a novel distributed cloud-edge framework for autonomous multi-UAV systems that combines the computational efficiency of neuromorphic computing with nature-inspired control strategies. The proposed architecture equips each UAV with an individual Spiking Neural Network (SNN) that learns to reproduce optimal control signals generated by a cloud-based controller, enabling robust operation even during communication interruptions. By integrating spike coding with nature-inspired control principles inspired by Tilapia fish territorial behavior, our system achieves sophisticated formation control and obstacle avoidance in complex urban environments. The distributed architecture leverages cloud computing for complex calculations while maintaining local autonomy through edge-based SNNs, significantly reducing energy consumption and computational overhead compared to traditional centralized approaches. Our framework addresses critical limitations of conventional methods, including the dependency on pre-modeled environments, computational intensity of traditional methods, and local minima issues in potential field approaches. Simulation results demonstrate the system's effectiveness across two different scenarios. First, the indoor deployment of a multi-UAV system made-up of 15 UAVs. Then the collision-free formation control of a moving UAV flock including 6 UAVs considering the obstacle avoidance. Owing to the sparsity of spiking patterns, and the event-based nature of SNNs in average for the whole group of UAVs, the framework achieves almost 90% reduction in computational burden compared to traditional von Neumann architectures implementing traditional artificial neural networks.
Generalized Ray Tracing with Basis functions for Tomographic Projections
Authors: Youssef Haouchat, Philippe Thévenaz, Michael Unser
Date: 2025-03-26 00:00:00
Tasks: Image Reconstruction

This work aims at the precise and efficient computation of the x-ray projection of an image represented by a linear combination of general shifted basis functions that typically overlap. We achieve this with a suitable adaptation of ray tracing, which is one of the most efficient methods to compute line integrals. In our work, the cases in which the image is expressed as a spline are of particular relevance. The proposed implementation is applicable to any projection geometry as it computes the forward and backward operators over a collection of arbitrary lines. We validate our work with experiments in the context of inverse problems for image reconstruction and maximize the image quality for a given resolution of the reconstruction grid.
Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
Authors: Zhiwei Yang, Yucong Meng, Kexue Fu, Feilong Tang, Shuo Wang, Zhijian Song
Date: 2025-03-26 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-Image Pre-training (CLIP) has been introduced in WSSS. However, recent methods primarily focus on image-text alignment for CAM generation, while CLIP's potential in patch-text alignment remains unexplored. In this work, we propose ExCEL to explore CLIP's dense knowledge via a novel patch-text alignment paradigm for WSSS. Specifically, we propose Text Semantic Enrichment (TSE) and Visual Calibration (VC) modules to improve the dense alignment across both text and vision modalities. To make text embeddings semantically informative, our TSE module applies Large Language Models (LLMs) to build a dataset-wide knowledge base and enriches the text representations with an implicit attribute-hunting process. To mine fine-grained knowledge from visual features, our VC module first proposes Static Visual Calibration (SVC) to propagate fine-grained knowledge in a non-parametric manner. Then Learnable Visual Calibration (LVC) is further proposed to dynamically shift the frozen features towards distributions with diverse semantics. With these enhancements, ExCEL not only retains CLIP's training-free advantages but also significantly outperforms other state-of-the-art methods with much less training cost on PASCAL VOC and MS COCO.