pwc-archive/papers-with-abstracts huggingface dataset:
Theoretical foundation for the Pareto distribution of international trade strength and introduction of an equation for international trade forecasting
Authors: Mikrajuddin Abdullah
Date: 2023-08-19 00:00:00

I propose a new terminology, international trade strength, which is defined as the ratio of a country's total international trade to its GDP. This parameter represents a country's ability to generate international trade by utilizing its GDP. This figure is equivalent to GDP per capita, which represents a country's ability to use its population to generate GDP. Trade strength varies by country. The intriguing question is, what distribution function does the trade strength fulfill? In this paper, a theoretical foundation for predicting the distribution of trade strength and the rate of change of trade strength were developed. These two quantities were found to satisfy the Pareto distribution function. The equations were confirmed using data from the World Integrated Trade Solution (WITS) and the World Bank by comparing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to five types of distribution functions (exponential, lognormal, gamma, Pareto, and Weibull). I also discovered that the fitting Pareto power parameter is fairly close to the theoretical parameter. In addition, a formula for forecasting a country's total international trade in the following years was also developed.
Dynamic Arrangements in Economic Theory: Level-Agnostic Representations
Authors: Fernando Tohmé
Date: 2023-09-12 00:00:00

If Economics is understood as the study of the interactions among intentional agents, being rationality the main source of intentional behavior, the mathematical tools that it requires must be extended to capture systemic effects. Here we choose an alternative toolbox based on Category Theory. We examine potential {\em level-agnostic} formalisms, presenting three categories, $\mathcal{PR}$, $\mathcal{G}$ and an encompassing one, $\mathcal{PR-G}$. The latter allows for representing dynamic rearrangements of the interactions among different agents.
Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing
Authors: Nunzio Lorè, Babak Heydari
Date: 2023-09-12 00:00:00
Tasks: Decision Making, Navigate

This paper investigates the strategic decision-making capabilities of three Large Language Models (LLMs): GPT-3.5, GPT-4, and LLaMa-2, within the framework of game theory. Utilizing four canonical two-player games -- Prisoner's Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight -- we explore how these models navigate social dilemmas, situations where players can either cooperate for a collective benefit or defect for individual gain. Crucially, we extend our analysis to examine the role of contextual framing, such as diplomatic relations or casual friendships, in shaping the models' decisions. Our findings reveal a complex landscape: while GPT-3.5 is highly sensitive to contextual framing, it shows limited ability to engage in abstract strategic reasoning. Both GPT-4 and LLaMa-2 adjust their strategies based on game structure and context, but LLaMa-2 exhibits a more nuanced understanding of the games' underlying mechanics. These results highlight the current limitations and varied proficiencies of LLMs in strategic decision-making, cautioning against their unqualified use in tasks requiring complex strategic reasoning.
News-driven Expectations and Volatility Clustering
Authors: Sabiou Inoua
Date: 2023-09-09 00:00:00
Tasks: Clustering

Financial volatility obeys two fascinating empirical regularities that apply to various assets, on various markets, and on various time scales: it is fat-tailed (more precisely power-law distributed) and it tends to be clustered in time. Many interesting models have been proposed to account for these regularities, notably agent-based models, which mimic the two empirical laws through a complex mix of nonlinear mechanisms such as traders' switching between trading strategies in highly nonlinear way. This paper explains the two regularities simply in terms of traders' attitudes towards news, an explanation that follows almost by definition of the traditional dichotomy of financial market participants, investors versus speculators, whose behaviors are reduced to their simplest forms. Long-run investors' valuations of an asset are assumed to follow a news-driven random walk, thus capturing the investors' persistent, long memory of fundamental news. Short-term speculators' anticipated returns, on the other hand, are assumed to follow a news-driven autoregressive process, capturing their shorter memory of fundamental news, and, by the same token, the feedback intrinsic to the short-sighted, trend-following (or herding) mindset of speculators. These simple, linear, models of traders' expectations, it is shown, explain the two financial regularities in a generic and robust way. Rational expectations, the dominant model of traders' expectations, is not assumed here, owing to the famous no-speculation, no-trade results
Robust equilibria in cheap-talk games with fairly transparent motives
Authors: Jan-Henrik Steg, Elshan Garashli, Michael Greinecker, Christoph Kuzmics
Date: 2023-09-08 00:00:00

For cheap-talk games with a binary state space in which the sender has state-independent preferences, we characterize equilibria that are robust to introducing slight state-dependence on the side of the sender. Not all equilibria are robust, but the sender-optimum is always achieved at some robust equilibrium.
Concave many-to-one matching
Authors: Chao Huang
Date: 2023-09-08 00:00:00

We propose a notion of concavity in two-sided many-to-one matching, which is an analogue to the balancedness condition in cooperative games. A stable matching exists when the market is concave. We provide a class of concave markets. In the proof of the existence theorem, we use Scarf's algorithm to find a stable schedule matching, which is of independent interest.
Local Priority Mechanisms
Authors: Joseph Root, David S. Ahn
Date: 2023-09-07 00:00:00

We introduce a novel family of mechanisms for constrained allocation problems which we call local priority mechanisms. These mechanisms are parameterized by a function which assigns a set of agents, the local compromisers, to every infeasible allocation. The mechanism then greedily attempts to match agents with their top choices. Whenever it reaches an infeasible allocation, the local compromisers move to their next favorite alternative. Local priority mechanisms exist for any constraint, so this provides a method of constructing new designs for any constrained allocation problem. We give axioms which characterize local priority mechanisms. Since constrained allocation includes many canonical problems as special constraints, we apply this characterization to show that several well-known mechanisms, including deferred acceptance for school choice, top trading cycles for house allocation, and serial dictatorship can be understood as instances of local priority mechanisms. Other mechanisms, including the Boston mechanism, are not local priority mechanisms. We give sufficient conditions for a local priority mechanism to be group strategy-proof. We also provide conditions which enable welfare comparisons across local priority mechanisms.
Perishable Goods versus Re-tradable Assets: A Theoretical Reappraisal of a Fundamental Dichotomy
Authors: Sabiou Inoua, Vernon Smith
Date: 2023-09-07 00:00:00

Experimental results on market behavior establish a lower stability and efficiency of markets for durable re-tradable assets compared to markets for non-durable, or perishable, goods. In this chapter, we revisit this known but underappreciated dichotomy of goods in the light of our theory of competitive market price formation, and we emphasize the fundamental nature of the concept of asset re-tradability in neoclassical finance through a simple reformulation of the famous no-trade and no-arbitrage theorems.
A Topological Proof of The Gibbard-Satterthwaite Theorem
Authors: Yuliy Baryshnikov, Joseph Root
Date: 2023-09-06 00:00:00

We give a new proof of the Gibbard-Satterthwaite Theorem. We construct two topological spaces: one for the space of preference profiles and another for the space of outcomes. We show that social choice functions induce continuous mappings between the two spaces. By studying the properties of this mapping, we prove the theorem.
Do Losses Matter? The Effect of Information-Search Technologies on Risky Choices
Authors: Luigi Mittone, Mauro Papi
Date: 2023-09-04 00:00:00

Despite its importance, relatively little attention has been devoted to studying the effects of exposing individuals to digital choice interfaces. In two pre-registered lottery-choice experiments, we administer three information-search technologies that are based on well-known heuristics: in the ABS (alternative-based search) treatment, subjects explore outcomes and corresponding probabilities within lotteries; in the CBS (characteristic-based search) treatment, subjects explore outcomes and corresponding probabilities across lotteries; in the Baseline treatment, subjects view outcomes and corresponding probabilities all at once. We find that (i) when lottery outcomes comprise gains and losses (experiment 1), exposing subjects to the CBS technology systematically makes them choose safer lotteries, compared to the subjects that are exposed to the other technologies, and (ii) when lottery outcomes comprise gains only (experiment 2), the above results are reversed: exposing subjects to the CBS technology systematically makes them choose riskier lotteries. By combining the information-search and choice analysis, we offer an interpretation of our results that is based on prospect theory, whereby the information-search technology subjects are exposed to contributes to determine the level of attention that the lottery attributes receive, which in turn has an effect on the reference point.
Constructing a type-adjustable mechanism to yield Pareto-optimal outcomes
Authors: Haoyang Wu
Date: 2023-09-03 00:00:00

In mechanism design theory, agents' types are described as their private information, and the designer may reveal some public information to affect agents' types in order to obtain more payoffs. Traditionally, each agent's private type and the public information are represented as a random variable respectively. In this paper, we propose a type-adjustable mechanism where each agent's private type is represented as a function of two parameters, \emph{i.e.}, his intrinsic factor and an external factor. Each agent's intrinsic factor is modeled as a private random variable, and the external factor is modeled as a solution of the designer's optimization problem. If the designer chooses an optimal value of external factor as public information, the type-adjustable mechanism may yield Pareto-optimal outcomes, which let the designer and each agent obtain more expected payoffs than what they would obtain at most in the traditional optimal mechanisms. As a comparison, in an auction with interdependent values, only the seller will benefit from public information which is represented as a random variable. We propose a revised version of revelation principle for type-adjustable Bayesian equilibrium. In the end, we compare the type-adjustable mechanism with other relevant models.
A duality between utility transforms and probability distortions
Authors: Christopher P. Chambers, Peng Liu, Ruodu Wang
Date: 2023-08-25 00:00:00

In this paper, we establish a mathematical duality between utility transforms and probability distortions. These transforms play a central role in decision under risk by forming the foundation for the classic theories of expected utility, dual utility, and rank-dependent utility. Our main results establish that probability distortions are characterized by commutation with utility transforms, and utility transforms are characterized by commutation with probability distortions. These results require no additional conditions, and hence each class can be axiomatized with only one property. Moreover, under monotonicity, rank-dependent utility transforms can be characterized by set commutation with either utility transforms or probability distortions.
On the minimal simplex economy
Authors: Antonio Pulgarín
Date: 2023-08-20 00:00:00

In our previous paper we proved that every affine economy has a competitive equilibrium. We define a simplex economy as an affine economy consisting of a stochastic allocation (defining the initial endowments) and a variation with repetition of the number of commodities taking the number of consumers (representing the preferences). We show that a competitive equilibrium can be intrinsically computed in any minimal simplex economy.
Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments
Authors: Philippe Gonzalez, Tommy Sonne Alstrøm, Tobias May
Date: 2023-09-12 00:00:00
Tasks: Speech Enhancement

The acoustic variability of noisy and reverberant speech mixtures is influenced by multiple factors, such as the spectro-temporal characteristics of the target speaker and the interfering noise, the signal-to-noise ratio (SNR) and the room characteristics. This large variability poses a major challenge for learning-based speech enhancement systems, since a mismatch between the training and testing conditions can substantially reduce the performance of the system. Generalization to unseen conditions is typically assessed by testing the system with a new speech, noise or binaural room impulse response (BRIR) database different from the one used during training. However, the difficulty of the speech enhancement task can change across databases, which can substantially influence the results. The present study introduces a generalization assessment framework that uses a reference model trained on the test condition, such that it can be used as a proxy for the difficulty of the test condition. This allows to disentangle the effect of the change in task difficulty from the effect of dealing with new data, and thus to define a new measure of generalization performance termed the generalization gap. The procedure is repeated in a cross-validation fashion by cycling through multiple speech, noise, and BRIR databases to accurately estimate the generalization gap. The proposed framework is applied to evaluate the generalization potential of a feedforward neural network (FFNN), Conv-TasNet, DCCRN and MANNER. We find that for all models, the performance degrades the most in speech mismatches, while good noise and room generalization can be achieved by training on multiple databases. Moreover, while recent models show higher performance in matched conditions, their performance substantially decreases in mismatched conditions and can become inferior to that of the FFNN-based system.
iPhonMatchNet: Zero-Shot User-Defined Keyword Spotting Using Implicit Acoustic Echo Cancellation
Authors: Yong-Hyeok Lee, Namhyun Cho
Date: 2023-09-12 00:00:00
Tasks: Acoustic echo cancellation, Keyword Spotting

In response to the increasing interest in human--machine communication across various domains, this paper introduces a novel approach called iPhonMatchNet, which addresses the challenge of barge-in scenarios, wherein user speech overlaps with device playback audio, thereby creating a self-referencing problem. The proposed model leverages implicit acoustic echo cancellation (iAEC) techniques to increase the efficiency of user-defined keyword spotting models, achieving a remarkable 95% reduction in mean absolute error with a minimal increase in model size (0.13%) compared to the baseline model, PhonMatchNet. We also present an efficient model structure and demonstrate its capability to learn iAEC functionality without requiring a clean signal. The findings of our study indicate that the proposed model achieves competitive performance in real-world deployment conditions of smart devices.
Can large-scale vocoded spoofed data improve speech spoofing countermeasure with a self-supervised front end?
Authors: Xin Wang, Junichi Yamagishi
Date: 2023-09-12 00:00:00
Tasks: Self-Supervised Learning, Speech Synthesis

A speech spoofing countermeasure (CM) that discriminates between unseen spoofed and bona fide data requires diverse training data. While many datasets use spoofed data generated by speech synthesis systems, it was recently found that data vocoded by neural vocoders were also effective as the spoofed training data. Since many neural vocoders are fast in building and generation, this study used multiple neural vocoders and created more than 9,000 hours of vocoded data on the basis of the VoxCeleb2 corpus. This study investigates how this large-scale vocoded data can improve spoofing countermeasures that use data-hungry self-supervised learning (SSL) models. Experiments demonstrated that the overall CM performance on multiple test sets improved when using features extracted by an SSL model continually trained on the vocoded data. Further improvement was observed when using a new SSL distilled from the two SSLs before and after the continual training. The CM with the distilled SSL outperformed the previous best model on challenging unseen test sets, including the ASVspoof 2019 logical access, WaveFake, and In-the-Wild.
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram
Authors: Zhifeng Kong, Wei Ping, Ambrish Dantrey, Bryan Catanzaro
Date: 2023-09-12 00:00:00
Tasks: Denoising, Speech Denoising, Speech Enhancement, Speech Synthesis

In this work, we present CleanUNet 2, a speech denoising model that combines the advantages of waveform denoiser and spectrogram denoiser and achieves the best of both worlds. CleanUNet 2 uses a two-stage framework inspired by popular speech synthesis methods that consist of a waveform model and a spectrogram model. Specifically, CleanUNet 2 builds upon CleanUNet, the state-of-the-art waveform denoiser, and further boosts its performance by taking predicted spectrograms from a spectrogram denoiser as the input. We demonstrate that CleanUNet 2 outperforms previous methods in terms of various objective and subjective evaluations.
Instabilities in Convnets for Raw Audio
Authors: Daniel Haider, Vincent Lostanlen, Martin Ehler, Peter Balazs
Date: 2023-09-11 00:00:00
Tasks: Audio Signal Processing

What makes waveform-based deep learning so hard? Despite numerous attempts at training convolutional neural networks (convnets) for filterbank design, they often fail to outperform hand-crafted baselines. These baselines are linear time-invariant systems: as such, they can be approximated by convnets with wide receptive fields. Yet, in practice, gradient-based optimization leads to suboptimal approximations. In our article, we approach this phenomenon from the perspective of initialization. We present a theory of large deviations for the energy response of FIR filterbanks with random Gaussian weights. We find that deviations worsen for large filters and locally periodic input signals, which are both typical for audio signal processing applications. Numerical simulations align with our theory and suggest that the condition number of a convolutional layer follows a logarithmic scaling law between the number and length of the filters, which is reminiscent of discrete wavelet bases.
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals
Authors: Louis Blankemeier, Sebastien Baur, Wei-Hung Weng, Jake Garrison, Yossi Matias, Shruthi Prabhakara, Diego Ardila, Zaid Nabulsi
Date: 2023-09-11 00:00:00
Tasks: Contrastive Learning, Medical Diagnosis, Self-Supervised Learning

Health-related acoustic signals, such as cough and breathing sounds, are relevant for medical diagnosis and continuous health monitoring. Most existing machine learning approaches for health acoustics are trained and evaluated on specific tasks, limiting their generalizability across various healthcare applications. In this paper, we leverage a self-supervised learning framework, SimCLR with a Slowfast NFNet backbone, for contrastive learning of health acoustics. A crucial aspect of optimizing Slowfast NFNet for this application lies in identifying effective audio augmentations. We conduct an in-depth analysis of various audio augmentation strategies and demonstrate that an appropriate augmentation strategy enhances the performance of the Slowfast NFNet audio encoder across a diverse set of health acoustic tasks. Our findings reveal that when augmentations are combined, they can produce synergistic effects that exceed the benefits seen when each is applied individually.
Smartwatch-derived Acoustic Markers for Deficits in Cognitively Relevant Everyday Functioning
Authors: Yasunori Yamada, Kaoru Shinkawa, Masatomo Kobayashi, Miyuki Nemoto, Miho Ota, Kiyotaka Nemoto, Tetsuaki Arai
Date: 2023-09-11 00:00:00

Detection of subtle deficits in everyday functioning due to cognitive impairment is important for early detection of neurodegenerative diseases, particularly Alzheimer's disease. However, current standards for assessment of everyday functioning are based on qualitative, subjective ratings. Speech has been shown to provide good objective markers for cognitive impairments, but the association with cognition-relevant everyday functioning remains uninvestigated. In this study, we demonstrate the feasibility of using a smartwatch-based application to collect acoustic features as objective markers for detecting deficits in everyday functioning. We collected voice data during the performance of cognitive tasks and daily conversation, as possible application scenarios, from 54 older adults, along with a measure of everyday functioning. Machine learning models using acoustic features could detect individuals with deficits in everyday functioning with up to 77.8% accuracy, which was higher than the 68.5% accuracy with standard neuropsychological tests. We also identified common acoustic features for robustly discriminating deficits in everyday functioning across both types of voice data (cognitive tasks and daily conversation). Our results suggest that common acoustic features extracted from different types of voice data can be used as markers for deficits in everyday functioning.
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification
Authors: Chong-Xin Gan, Man-Wai Mak, Weiwei Lin, Jen-Tzung Chien
Date: 2023-09-08 00:00:00
Tasks: Data Augmentation, Self-Supervised Learning, Speaker Verification

Contrastive self-supervised learning (CSL) for speaker verification (SV) has drawn increasing interest recently due to its ability to exploit unlabeled data. Performing data augmentation on raw waveforms, such as adding noise or reverberation, plays a pivotal role in achieving promising results in SV. Data augmentation, however, demands meticulous calibration to ensure intact speaker-specific information, which is difficult to achieve without speaker labels. To address this issue, we introduce a novel framework by incorporating clean and augmented segments into the contrastive training pipeline. The clean segments are repurposed to pair with noisy segments to form additional positive and negative pairs. Moreover, the contrastive loss is weighted to increase the difference between the clean and augmented embeddings of different speakers. Experimental results on Voxceleb1 suggest that the proposed framework can achieve a remarkable 19% improvement over the conventional methods, and it surpasses many existing state-of-the-art techniques.