pwc-archive/papers-with-abstracts huggingface dataset:
Robust Indoor Localization in Dynamic Environments: A Multi-source Unsupervised Domain Adaptation Framework
Authors: Jiyu Jiao, Xiaojun Wang, Chengpei Han
Date: 2025-02-11 00:00:00
Tasks: Domain Adaptation, Indoor Localization, Multi-Source Unsupervised Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Fingerprint localization has gained significant attention due to its cost-effective deployment, low complexity, and high efficacy. However, traditional methods, while effective for static data, often struggle in dynamic environments where data distributions and feature spaces evolve-a common occurrence in real-world scenarios. To address the challenges of robustness and adaptability in fingerprint localization for dynamic indoor environments, this paper proposes DF-Loc, an end-to-end dynamic fingerprint localization system based on multi-source unsupervised domain adaptation (MUDA). DF-Loc leverages historical data from multiple time scales to facilitate knowledge transfer in specific feature spaces, thereby enhancing generalization capabilities in the target domain and reducing reliance on labeled data. Specifically, the system incorporates a Quality Control (QC) module for CSI data preprocessing and employs image processing techniques for CSI fingerprint feature reconstruction. Additionally, a multi-scale attention-based feature fusion backbone network is designed to extract multi-level transferable fingerprint features. Finally, a dual-stage alignment model aligns the distributions of multiple source-target domain pairs, improving regression characteristics in the target domain. Extensive experiments conducted in office and classroom environments demonstrate that DF-Loc outperforms comparative methods in terms of both localization accuracy and robustness. With 60% of reference points used for training, DF-Loc achieves average localization errors of 0.79m and 3.72m in "same-test" scenarios, and 0.94m and 4.39m in "different-test" scenarios, respectively. This work pioneers an end-to-end multi-source transfer learning approach for fingerprint localization, providing valuable insights for future research in dynamic environments.
Hinderance of cooperation by individual solutions: Evolutionary dynamics of three-strategy games combining the prisoner's dilemma and stag hunt
Authors: Hirofumi Takesue
Date: 2025-02-10 00:00:00

We considered a three-strategy game with the characteristics of the prisoner's dilemma and stag hunt games. This game was inspired by recent experimental studies that elucidated the role of individual solutions. People who adopt individual solutions do not free-ride on the cooperative efforts of others, but instead attempt to solve the problem only to the extent necessary to prevent an impact on themselves. We argue that individual solutions play a role similar to that of defection in the stag hunt game, and this study examined the effects of orthodox free-riding and the individual solution on the evolution of cooperation. Our analysis revealed that a state in which all of the agents adopt the individual solution is the only stable equilibrium in the well-mixed population. Interactions on a square lattice led to modest improvements in cooperation levels, which were mainly sustained by cyclic dominance. Payoff values favorable to cooperation resulted in full cooperation, but rare mutations could hamper the cooperative equilibrium and support the dominance of the individual solution. Our analysis echoed experimental observations and illustrated that overcoming the reliance on individual solutions is critical in understanding the evolution of cooperation.
The exponential distribution of the orders of demonstrative, numeral, adjective and noun
Authors: Ramon Ferrer-i-Cancho
Date: 2025-02-10 00:00:00

The frequency of the preferred order for a noun phrase formed by demonstrative, numeral, adjective and noun has received significant attention over the last two decades. We investigate the actual distribution of the preferred 24 possible orders. There is no consensus on whether it can be well-fitted by an exponential or a power law distribution. We find that an exponential distribution is a much better model. This finding and other circumstances where an exponential-like distribution is found challenge the view that power-law distributions, e.g., Zipf's law for word frequencies, are inevitable. We also investigate which of two exponential distributions gives a better fit: an exponential model where the 24 orders have non-zero probability or an exponential model where the number of orders that can have non-zero probability is variable. When parsimony and generalizability are prioritized, we find strong support for the exponential model where all 24 orders have non-zero probability. This finding suggests that there is no hard constraint on word order variation and then unattested orders merely result from undersampling, consistently with Cysouw's view.
Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters
Authors: Snehal Raj, Brian Coyle
Date: 2025-02-10 00:00:00
Tasks: parameter-efficient fine-tuning, Quantum Machine Learning

Fine-tuning pre-trained large foundation models for specific tasks has become increasingly challenging due to the computational and storage demands associated with full parameter updates. Parameter-Efficient Fine-Tuning (PEFT) methods address this issue by updating only a small subset of model parameters using adapter modules. In this work, we propose \emph{Quantum-Inspired Adapters}, a PEFT approach inspired by Hamming-weight preserving quantum circuits from quantum machine learning literature. These models can be both expressive and parameter-efficient by operating in a combinatorially large space while simultaneously preserving orthogonality in weight parameters. We test our proposed adapters by adapting large language models and large vision transformers on benchmark datasets. Our method can achieve 99.2\% of the performance of existing fine-tuning methods such LoRA with a 44x parameter compression on language understanding datasets like GLUE and VTAB. Compared to existing orthogonal fine-tuning methods such as OFT or BOFT, we achieve 98\% relative performance with 25x fewer parameters. This demonstrates competitive performance paired with a significant reduction in trainable parameters. Through ablation studies, we determine that combining multiple Hamming-weight orders with orthogonality and matrix compounding are essential for performant fine-tuning. Our findings suggest that Quantum-Inspired Adapters offer a promising direction for efficient adaptation of language and vision models in resource-constrained environments.
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification
Authors: Xavier Vasques, Hanhee Paik, Laura Cif
Date: 2025-02-10 00:00:00
Tasks: Binary Classification, Feature Engineering, Quantum Machine Learning

The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical research into practical solutions. Previous studies have shown the advantages of quantum algorithms on artificially generated datasets, and initial experiments with small binary classification problems have yielded comparable outcomes to classical algorithms. However, it is essential to investigate the potential quantum advantage using real-world data. To the best of our knowledge, this study is the first to propose the utilization of quantum systems to classify neuron morphologies, thereby enhancing our understanding of the performance of automatic multiclass neuron classification using quantum kernel methods. We examined the influence of feature engineering on classification accuracy and found that quantum kernel methods achieved similar performance to classical methods, with certain advantages observed in various configurations.
Conditioning through indifference in quantum mechanics
Authors: Keano De Vos, Gert de Cooman
Date: 2025-02-10 00:00:00

We can learn (more) about the state a quantum system is in through measurements. We look at how to describe the uncertainty about a quantum system's state conditional on executing such measurements. We show that by exploiting the interplay between desirability, coherence and indifference, a general rule for conditioning can be derived. We then apply this rule to conditioning on measurement outcomes, and show how it generalises to conditioning on a set of measurement outcomes.
Conditioning and AGM-like belief change in the Desirability-Indifference framework
Authors: Kathelijne Coussement, Gert de Cooman, Keano De Vos
Date: 2025-02-10 00:00:00

We show how the AGM framework for belief change (expansion, revision, contraction) can be extended to deal with conditioning in the so-called Desirability-Indifference framework, based on abstract notions of accepting and rejecting options, as well as on abstract notions of events. This level of abstraction allows us to deal simultaneously with classical and quantum probability theory.
Detection of Physiological Data Tampering Attacks with Quantum Machine Learning
Authors: Md. Saif Hassan Onim, Himanshu Thapliyal
Date: 2025-02-09 00:00:00
Tasks: Data Poisoning, Quantum Machine Learning

The widespread use of cloud-based medical devices and wearable sensors has made physiological data susceptible to tampering. These attacks can compromise the reliability of healthcare systems which can be critical and life-threatening. Detection of such data tampering is of immediate need. Machine learning has been used to detect anomalies in datasets but the performance of Quantum Machine Learning (QML) is still yet to be evaluated for physiological sensor data. Thus, our study compares the effectiveness of QML for detecting physiological data tampering, focusing on two types of white-box attacks: data poisoning and adversarial perturbation. The results show that QML models are better at identifying label-flipping attacks, achieving accuracy rates of 75%-95% depending on the data and attack severity. This superior performance is due to the ability of quantum algorithms to handle complex and high-dimensional data. However, both QML and classical models struggle to detect more sophisticated adversarial perturbation attacks, which subtly alter data without changing its statistical properties. Although QML performed poorly against this attack with around 45%-65% accuracy, it still outperformed classical algorithms in some cases.
Quantum automated learning with provable and explainable trainability
Authors: Qi Ye, Shuangyue Geng, Zizhao Han, Weikang Li, L. -M. Duan, Dong-Ling Deng
Date: 2025-02-07 00:00:00
Tasks: Quantum Machine Learning

Machine learning is widely believed to be one of the most promising practical applications of quantum computing. Existing quantum machine learning schemes typically employ a quantum-classical hybrid approach that relies crucially on gradients of model parameters. Such an approach lacks provable convergence to global minima and will become infeasible as quantum learning models scale up. Here, we introduce quantum automated learning, where no variational parameter is involved and the training process is converted to quantum state preparation. In particular, we encode training data into unitary operations and iteratively evolve a random initial state under these unitaries and their inverses, with a target-oriented perturbation towards higher prediction accuracy sandwiched in between. Under reasonable assumptions, we rigorously prove that the evolution converges exponentially to the desired state corresponding to the global minimum of the loss function. We show that such a training process can be understood from the perspective of preparing quantum states by imaginary time evolution, where the data-encoded unitaries together with target-oriented perturbations would train the quantum learning model in an automated fashion. We further prove that the quantum automated learning paradigm features good generalization ability with the generalization error upper bounded by the ratio between a logarithmic function of the Hilbert space dimension and the number of training samples. In addition, we carry out extensive numerical simulations on real-life images and quantum data to demonstrate the effectiveness of our approach and validate the assumptions. Our results establish an unconventional quantum learning strategy that is gradient-free with provable and explainable trainability, which would be crucial for large-scale practical applications of quantum computing in machine learning scenarios.
Are Princelings Truly Busted? Evaluating Transaction Discounts in China's Land Market
Authors: Julia Manso
Date: 2025-02-11 00:00:00

This paper narrowly replicates Chen and Kung's 2019 paper ($The$ $Quarterly$ $Journal$ $of$ $Economics$ 134(1): 185-226). Inspecting the data reveals that nearly one-third of the transactions (388,903 out of 1,208,621) are perfect duplicates of other rows, excluding the transaction number. Replicating the analysis on the data sans-duplicates yields a slightly smaller but still statistically significant princeling effect, robust across the regression results. Further analysis also reveals that coefficients interpreted as the effect of logarithm of area actually reflect the effect of scaled values of area; this paper also reinterprets and contextualizes these results in light of the true scaled values.
Grouped fixed effects regularization for binary choice models
Authors: Claudia Pigini, Alessandro Pionati, Francesco Valentini
Date: 2025-02-10 00:00:00

We study the application of the Grouped Fixed Effects (GFE) estimator (Bonhomme et al., ECMTA 90(2):625-643, 2022) to binary choice models for network and panel data. This approach discretizes unobserved heterogeneity via k-means clustering and performs maximum likelihood estimation, reducing the number of fixed effects in finite samples. This regularization helps analyze small/sparse networks and rare events by mitigating complete separation, which can lead to data loss. We focus on dynamic models with few state transitions and network formation models for sparse networks. The effectiveness of this method is demonstrated through simulations and real data applications.
Dynamic Pricing with Adversarially-Censored Demands
Authors: Jianyu Xu, Yining Wang, Xi Chen, Yu-Xiang Wang
Date: 2025-02-10 00:00:00
Tasks: Decision Making

We study an online dynamic pricing problem where the potential demand at each time period $t=1,2,\ldots, T$ is stochastic and dependent on the price. However, a perishable inventory is imposed at the beginning of each time $t$, censoring the potential demand if it exceeds the inventory level. To address this problem, we introduce a pricing algorithm based on the optimistic estimates of derivatives. We show that our algorithm achieves $\tilde{O}(\sqrt{T})$ optimal regret even with adversarial inventory series. Our findings advance the state-of-the-art in online decision-making problems with censored feedback, offering a theoretically optimal solution against adversarial observations.
Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies
Authors: Tanay Panat, Rohitash Chandra
Date: 2025-02-08 00:00:00
Tasks: Dimensionality Reduction

The drastic changes in the global economy, geopolitical conditions, and disruptions such as the COVID-19 pandemic have impacted the cost of living and quality of life. It is important to understand the long-term nature of the cost of living and quality of life in major economies. A transparent and comprehensive living index must include multiple dimensions of living conditions. In this study, we present an approach to quantifying the quality of life through the Global Ease of Living Index that combines various socio-economic and infrastructural factors into a single composite score. Our index utilises economic indicators that define living standards, which could help in targeted interventions to improve specific areas. We present a machine learning framework for addressing the problem of missing data for some of the economic indicators for specific countries. We then curate and update the data and use a dimensionality reduction approach (principal component analysis) to create the Ease of Living Index for major economies since 1970. Our work significantly adds to the literature by offering a practical tool for policymakers to identify areas needing improvement, such as healthcare systems, employment opportunities, and public safety. Our approach with open data and code can be easily reproduced and applied to various contexts. This transparency and accessibility make our work a valuable resource for ongoing research and policy development in quality-of-life assessment.
Point-Identifying Semiparametric Sample Selection Models with No Excluded Variable
Authors: Dongwoo Kim, Young Jun Lee
Date: 2025-02-07 00:00:00

Sample selection is pervasive in applied economic studies. This paper develops semiparametric selection models that achieve point identification without relying on exclusion restrictions, an assumption long believed necessary for identification in semiparametric selection models. Our identification conditions require at least one continuously distributed covariate and certain nonlinearity in the selection process. We propose a two-step plug-in estimator that is root-n-consistent, asymptotically normal, and computationally straightforward (readily available in statistical software), allowing for heteroskedasticity. Our approach provides a middle ground between Lee (2009)'s nonparametric bounds and Honor\'e and Hu (2020)'s linear selection bounds, while ensuring point identification. Simulation evidence confirms its excellent finite-sample performance. We apply our method to estimate the racial and gender wage disparity using data from the US Current Population Survey. Our estimates tend to lie outside the Honor\'e and Hu bounds.
A Framework to Quantify Adaptation to Multiple Drivers
Authors: Emily Quiroga, Benjamin Blanz
Date: 2025-02-11 00:00:00

We develop an analytical framework to assess the adaptations in a coupled ecological-economic system and apply it to a bio-economic model. Our framework allows us to quantify the impact of multiple drivers on a coupled ecological-economic system, while distinguishing between adaptation and sensitivities to positive and negative exposures. This distinction allows us to differentiate between drivers that improve and decrease well-being. Our findings provide insight into how to focus resources to counteract negative or enhance positive impacts. We apply this framework to a bio-economic model calibrated to the North Sea flatfish fishery. We quantify the adaptations, sensitivities and total impact of fishers' profits to multiple drivers and identify among which of them fishers adapt the most. This work forms a bridge between the multidisciplinary area of adaptability and the bio-economic modelling domain,increasing the understanding and knowledge regarding the measure of adaptation.
Beyond Fishing: The Value of Maritime Cultural Heritage in Germany
Authors: Emily Quiroga
Date: 2025-02-11 00:00:00
Tasks: Attribute

The importance of maritime heritage in providing benefits such as a sense of place and identity has been widely discussed. However, there remains a lack of comprehensive quantitative analysis, particularly regarding monetary valuation and its impact on people's preferences. In this study, I present the results of a choice experiment that assesses the value of the maritime cultural heritage associated with shrimp fishing through seafood consumption preferences in Germany. Additionally, I investigate people's attitudes toward cultural heritage and examine how these attitudes affect their stated preferences. I find that these attitudes are significantly stronger in towns where local fishermen led a prominent awareness campaign on fishing culture during the study period. Moreover, I observe a positive willingness to pay for a cultural heritage attribute in shrimp dishes, which varies depending on individuals' attitudes toward cultural heritage.
Artificial General Intelligence and the End of Human Employment: The Need to Renegotiate the Social Contract
Authors: Pascal Stiefenhofer
Date: 2025-02-10 00:00:00

The emergence of Artificial General Intelligence (AGI) labor, including AI agents and autonomous systems operating at near-zero marginal cost, reduces the marginal productivity of human labor, ultimately pushing wages toward zero. As AGI labor and capital replace human workers, economic power shifts to capital owners, resulting in extreme wealth concentration, rising inequality, and reduced social mobility. The collapse of human wages causes aggregate demand to deteriorate, creating a paradox where firms produce more using AGI, yet fewer consumers can afford to buy goods. To prevent economic and social instability, new economic structures must emerge, such as Universal Basic Income (UBI), which redistributes AGI-generated wealth, public or cooperative AGI ownership, ensuring broader access to AI-driven profits, and progressive AGI capital taxation, which mitigates inequality and sustains aggregate demand. Addressing these challenges in form of renegotiation the Social Contract is crucial to maintaining economic stability in a post-labor economy.
The Future of Work and Capital: Analyzing AGI in a CES Production Model
Authors: Pascal Stiefenhofer
Date: 2025-02-10 00:00:00

The integration of Artificial General Intelligence (AGI) into economic production represents a transformative shift with profound implications for labor markets, income distribution, and technological growth. This study extends the Constant Elasticity of Substitution (CES) production function to incorporate AGI-driven labor and capital alongside traditional inputs, providing a comprehensive framework for analyzing AGI's economic impact. Four key models emerge from this framework. First, we examine the substitution and complementarity between AGI labor and human labor, identifying conditions under which AGI augments or displaces human workers. Second, we analyze how AGI capital accumulation influences wage structures and income distribution, highlighting potential disruptions to labor-based earnings. Third, we explore long-run equilibrium dynamics, demonstrating how an economy dominated by AGI capital may lead to the collapse of human wages and necessitate redistributive mechanisms. Finally, we assess the impact of AGI on total factor productivity, showing that technological growth depends on whether AGI serves as a complement to or a substitute for human labor. Our findings underscore the urgent need for policy interventions to ensure economic stability and equitable wealth distribution in an AGI-driven economy. Without appropriate regulatory measures, rising inequality and weakened aggregate demand could lead to economic stagnation despite technological advancements. Moreover this research suggests a renegoation of the Social Contract.
Neural Pathways of Responsible Gambling: How Personalized and Normative Messages Engage Gambling Severity and Individual Rationality
Authors: J. Sanchez-Fernandez, L. A. Casado-Aranda, I. Ozer, Nuria Hernandez-Vergara
Date: 2025-02-10 00:00:00

This paper seeks to clarify whether the processing of two types of messages that promote responsible gambling, namely personalized messages and normative messages, varies across gamblers severity habits and rational thinking