pwc-archive/papers-with-abstracts huggingface dataset:
Metawisdom of the Crowd: How Choice Within Aided Decision Making Can Make Crowd Wisdom Robust
Authors: Jon Atwell, Marlon Twyman II
Date: 2023-08-29 00:00:00
Tasks: Decision Making

Quality information can improve individual judgments but nonetheless fail to make group decisions more accurate; if individuals choose to attend to the same information in the same way, the predictive diversity that enables crowd wisdom may be lost. Decision support systems, from business intelligence software to public search engines, present individuals with decision aids -- discrete presentations of relevant information, interpretative frames, or heuristics -- to enhance the quality and speed of decision making, but have the potential to bias judgments through the selective presentation of information and interpretative frames. We redescribe the wisdom of the crowd as often having two decisions, the choice of decision aids and then the primary decision. We then define \emph{metawisdom of the crowd} as any pattern by which the collective choice of aids leads to higher crowd accuracy than randomized assignment to the same aids, a comparison that accounts for the information content of the aids. While choice is ultimately constrained by the setting, in two experiments -- the prediction of inflation (N=947, pre-registered) and a tightly controlled estimation game (N=1198) -- we find strong evidence of metawisdom. It comes about through diverse errors arising through the use of diverse aids, not through widespread use of the aids that induce the most accurate estimates. Thus the microfoundations of crowd wisdom appear in the first choice, suggesting crowd wisdom can be robust in information choice problems. Given the implications for collective decision making, more research on the nature and use of decision aids is needed.
Cognitive Aging and Labor Share
Authors: B. N. Kausik
Date: 2023-08-29 00:00:00

Labor share, the fraction of economic output accrued as wages, is inexplicably declining in industrialized countries. Whilst numerous prior works attempt to explain the decline via economic factors, our novel approach links the decline to biological factors. Specifically, we propose a theoretical macroeconomic model where labor share reflects a dynamic equilibrium between the workforce automating existing outputs, and consumers demanding new output variants that require human labor. Industrialization leads to an aging population, and while cognitive performance is stable in the working years it drops sharply thereafter. Consequently, the declining cognitive performance of aging consumers reduces the demand for new output variants, leading to a decline in labor share. Our model expresses labor share as an algebraic function of median age, and is validated with surprising accuracy on historical data across industrialized economies via non-linear stochastic regression.
Efficiency in Multiple-Type Housing Markets
Authors: Di Feng
Date: 2023-08-29 00:00:00

We consider multiple-type housing markets (Moulin, 1995), which extend Shapley-Scarf housing markets (Shapley and Scarf, 1974) from one dimension to higher dimensions. In this model, Pareto efficiency is incompatible with individual rationality and strategy-proofness (Konishi et al., 2001). Therefore, we consider two weaker efficiency properties: coordinatewise efficiency and pairwise efficiency. We show that these two properties both (i) are compatible with individual rationality and strategy-proofness, and (ii) help us to identify two specific mechanisms. To be more precise, on various domains of preference profiles, together with other well-studied properties (individual rationality, strategy-proofness, and non-bossiness), coordinatewise efficiency and pairwise efficiency respectively characterize two extensions of the top-trading-cycles mechanism (TTC): the coordinatewise top-trading-cycles mechanism (cTTC) and the bundle top-trading-cycles mechanism (bTTC). Moreover, we propose several variations of our efficiency properties, and we find that each of them is either satisfied by cTTC or bTTC, or leads to an impossibility result (together with individual rationality and strategy-proofness). Therefore, our characterizations can be primarily interpreted as a compatibility test: any reasonable efficiency property that is not satisfied by cTTC or bTTC could be considered incompatible with individual rationality and strategy-proofness. The external validity of our results in the context of general environments is also discussed. For multiple-type housing markets with strict preferences, our characterization of bTTC constitutes the first characterization of an extension of the prominent TTC mechanism
Preference-based training framework for automatic speech quality assessment using deep neural network
Authors: Cheng-Hung Hu, Yusuke Yasuda, Tomoki Toda
Date: 2023-08-29 00:00:00

One objective of Speech Quality Assessment (SQA) is to estimate the ranks of synthetic speech systems. However, recent SQA models are typically trained using low-precision direct scores such as mean opinion scores (MOS) as the training objective, which is not straightforward to estimate ranking. Although it is effective for predicting quality scores of individual sentences, this approach does not account for speech and system preferences when ranking multiple systems. We propose a training framework of SQA models that can be trained with only preference scores derived from pairs of MOS to improve ranking prediction. Our experiment reveals conditions where our framework works the best in terms of pair generation, aggregation functions to derive system score from utterance preferences, and threshold functions to determine preference from a pair of MOS. Our results demonstrate that our proposed method significantly outperforms the baseline model in Spearman's Rank Correlation Coefficient.
TKwinFormer: Top k Window Attention in Vision Transformers for Feature Matching
Authors: Yun Liao, Yide Di, Hao Zhou, Kaijun Zhu, Mingyu Lu, Yijia Zhang, Qing Duan, Junhui Liu
Date: 2023-08-29 00:00:00

Local feature matching remains a challenging task, primarily due to difficulties in matching sparse keypoints and low-texture regions. The key to solving this problem lies in effectively and accurately integrating global and local information. To achieve this goal, we introduce an innovative local feature matching method called TKwinFormer. Our approach employs a multi-stage matching strategy to optimize the efficiency of information interaction. Furthermore, we propose a novel attention mechanism called Top K Window Attention, which facilitates global information interaction through window tokens prior to patch-level matching, resulting in improved matching accuracy. Additionally, we design an attention block to enhance attention between channels. Experimental results demonstrate that TKwinFormer outperforms state-of-the-art methods on various benchmarks. Code is available at: https://github.com/LiaoYun0x0/TKwinFormer.
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning
Authors: Wei-Hsiang Shen, Meng-Lin Li
Date: 2023-08-28 00:00:00
Tasks: Diversity, Image Augmentation, Instance Segmentation, Semantic Segmentation

Deep learning has shown great success in high-level image analysis problems; yet its efficacy relies on the quality and diversity of the training data. In this work, we introduce a copypaste image augmentation for ultrasound images. The Poisson image editing technique is used to generate realistic and seamless boundary transitions around the pasted image. Results showed that the proposed image augmentation technique improves training performance in terms of higher objective metrics and more stable training results.
Bayesian-Boosted MetaLoc: Efficient Training and Guaranteed Generalization for Indoor Localization
Authors: Dongze Wu, Jun Gao, Feng Yin
Date: 2023-08-28 00:00:00
Tasks: Indoor Localization, Meta-Learning

Existing localization approaches utilizing environment-specific channel state information (CSI) excel under specific environment but struggle to generalize across varied environments. This challenge becomes even more pronounced when confronted with limited training data. To address these issues, we present the Bayes-Optimal Meta-Learning for Localization (BOML-Loc) framework, inspired by the PAC-Optimal Hyper-Posterior (PACOH) algorithm. Improving on our earlier MetaLoc~\cite{MetaLoc}, BOML-Loc employs a Bayesian approach, reducing the need for extensive training, lowering overfitting risk, and offering per-test-point uncertainty estimation. Even with very limited training tasks, BOML-Loc guarantees robust localization and impressive generalization. In both LOS and NLOS environments with site-surveyed data, BOML-Loc surpasses existing models, demonstrating enhanced localization accuracy, generalization abilities, and reduced overfitting in new and previously unseen environments.
Observing hidden neuronal states in experiments
Authors: Dmitry Amakhin, Anton Chizhov, Guillaume Girier, Mathieu Desroches, Jan Sieber, Serafim Rodrigues
Date: 2023-08-29 00:00:00

In this article, we revisit voltage-clamp electrophysiology, a feedback-control method that has successfully been applied to study neuronal (non)linear current-voltage relationships. Yet its full scope has not been explored to date. We reinterpret the voltage-clamp protocol under slow variation of the feedback reference signal using the language of multiple-timescale dynamical systems theory. Gauged under this new epistemic step, we unveil non-observable (hidden) states. More specifically, the experimental measurements from voltage clamp can be put into correspondence with predictions of both stable and unstable states from dynamical systems theory. We demonstrate a general protocol for constructing systematically experimental steady-state bifurcation diagrams for electrophysiologically active cells. We perform our experiments on entorhinal cortex neurons, both excitatory (pyramidal neurons) and inhibitiory (interneurons). Moreover, employing a current-clamp open-loop electrophysiology protocol with slow variation of an applied electrical current drives the neuron to dynamically transition between its observable rest states and its observable spiking states, hence revealing experimental dynamic bifurcations. Overlaying the two aforementioned protocols produces a map of all these neuronal states and effectively leads to an experimental validation of the classical slow-fast dissection method introduced by J. Rinzel in the 1980s and routinely applied ever since in order to analyse slow-fast neuronal models. Our approach opens doors to observing further complex hidden states with more advanced control strategies, allowing to control real cells beyond pharmacological manipulations.
Signature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals
Authors: Owen Futter, Blanka Horvath, Magnus Wiese
Date: 2023-08-29 00:00:00
Tasks: PAIR TRADING

In this article we introduce a portfolio optimisation framework, in which the use of rough path signatures (Lyons, 1998) provides a novel method of incorporating path-dependencies in the joint signal-asset dynamics, naturally extending traditional factor models, while keeping the resulting formulas lightweight and easily interpretable. We achieve this by representing a trading strategy as a linear functional applied to the signature of a path (which we refer to as "Signature Trading" or "Sig-Trading"). This allows the modeller to efficiently encode the evolution of past time-series observations into the optimisation problem. In particular, we derive a concise formulation of the dynamic mean-variance criterion alongside an explicit solution in our setting, which naturally incorporates a drawdown control in the optimal strategy over a finite time horizon. Secondly, we draw parallels between classical portfolio stategies and Sig-Trading strategies and explain how the latter leads to a pathwise extension of the classical setting via the "Signature Efficient Frontier". Finally, we give examples when trading under an exogenous signal as well as examples for momentum and pair-trading strategies, demonstrated both on synthetic and market data. Our framework combines the best of both worlds between classical theory (whose appeal lies in clear and concise formulae) and between modern, flexible data-driven methods that can handle more realistic datasets. The advantage of the added flexibility of the latter is that one can bypass common issues such as the accumulation of heteroskedastic and asymmetric residuals during the optimisation phase. Overall, Sig-Trading combines the flexibility of data-driven methods without compromising on the clarity of the classical theory and our presented results provide a compelling toolbox that yields superior results for a large class of trading strategies.
Optimal ratcheting of dividend payout under Brownian motion surplus
Authors: Chonghu Guan, Zuo Quan Xu
Date: 2023-08-29 00:00:00

This paper is concerned with a long standing optimal dividend payout problem subject to the so-called ratcheting constraint, that is, the dividend payout rate shall be non-decreasing over time and is thus self-path-dependent. The surplus process is modeled by a drifted Brownian motion process and the aim is to find the optimal dividend ratcheting strategy to maximize the expectation of the total discounted dividend payouts until the ruin time. Due to the self-path-dependent control constraint, the standard control theory cannot be directly applied to tackle the problem. The related Hamilton-Jacobi-Bellman (HJB) equation is a new type of variational inequality. In the literature, it is only shown to have a viscosity solution, which is not strong enough to guarantee the existence of an optimal dividend ratcheting strategy. This paper proposes a novel partial differential equation method to study the HJB equation. We not only prove the the existence and uniqueness of the solution in some stronger functional space, but also prove the strict monotonicity, boundedness, and $C^\infty$-smoothness of the dividend ratcheting free boundary. Based on these results, we eventually derive an optimal dividend ratcheting strategy, and thus solve the open problem completely. Economically speaking, we find that if the surplus volatility is above an explicit threshold, then one should pay dividends at the maximum rate, regardless the surplus level. Otherwise, by contrast, the optimal dividend ratcheting strategy relays on the surplus level and one should only ratchet up the dividend payout rate when the surplus level touches the dividend ratcheting free boundary. Moreover, our numerical results suggest that one should invest into those companies with stable dividend payout strategies since their income rates should be higher and volatility rates smaller.
PROSO Toolbox: a unified protein-constrained genome-scale modelling framework for strain designing and optimization
Authors: Haoyang Yao, Laurence Yang
Date: 2023-08-28 00:00:00

The genome-scale metabolic model with protein constraint (PC-model) has been increasingly popular for microbial metabolic simulations. We present PROSO Toolbox, a unified and simple-to-use PC-model toolbox that takes any high-quality genome-scale metabolic reconstruction as the input. The toolbox can construct a PC-model automatically, apply various algorithms for computational strain design and simulation, and help unveil metabolism from gene expression data through a state-of-the-art OVERLAY workflow. It also has detailed tutorials and documentation for maximum accessibility to researchers from diverse backgrounds. PROSO Toolbox, tutorials, and documentation are freely available online: https://github.com/QCSB/PROSO-Toolbox.
Woolf et als GWAS by subtraction is not useful for cross-generational Mendelian randomization studies
Authors: David M Evans, George Davey Smith, Gunn-Helen Moen
Date: 2023-08-28 00:00:00
Tasks: Causal Inference

Mendelian randomization (MR) is an epidemiological method that can be used to strengthen causal inference regarding the relationship between a modifiable environmental exposure and a medically relevant trait and to estimate the magnitude of this relationship1. Recently, there has been considerable interest in using MR to examine potential causal relationships between parental phenotypes and outcomes amongst their offspring. In a recent issue of BMC Research Notes, Woolf et al (2023) present a new method, GWAS by subtraction, to derive genome-wide summary statistics for paternal smoking and other paternal phenotypes with the goal that these estimates can then be used in downstream (including two sample) MR studies. Whilst a potentially useful goal, Woolf et al. (2023) focus on the wrong parameter of interest for useful genome-wide association studies (GWAS) and downstream cross-generational MR studies, and the estimator that they derive is neither efficient nor appropriate for such use.
Embracing Sex-specific Differences in Engineered Kidney Models for Enhanced Biological Understanding
Authors: Charlotte A Veser, Aurélie MF Carlier, Silvia M Mihăilă, Sangita Swapnasrita
Date: 2023-08-29 00:00:00
Tasks: Drug Discovery, Kidney Function

In vitro models play a crucial role in advancing our understanding of biological processes, disease mechanisms, and developing screening platforms for drug discovery. Kidneys play an instrumental role in transport and elimination of drugs and toxins. However, despite the well-established patient-to-patient differences in kidney function and disease manifestation, progression and prognostic, few studies take this variability into consideration. In particular, the discrepancies between female and male biology warrants a better representation within kidney in vitro models. The omission of sex as a biological variable poses the significant risk of overlooking sex-specific mechanisms in health and disease and potential differences in drug efficacy and toxicity between males and females. This review aims to highlight the importance of incorporating sex dimorphism in kidney in vitro models by examining the sexual characteristics in the context of the current state-of-the-art. Furthermore, this review underscores opportunities for improving kidney models by incorporating sex-specific traits. Ultimately, this roadmap to incorporating sex-dimorphism in kidney in vitro models will facilitate the creation of better models for studying sex-specific mechanisms in the kidney and their impact on drug efficacy and safety.
On the implied volatility of European and Asian call options under the stochastic volatility Bachelier model
Authors: Elisa Alòs, Eulalia Nualart, Makar Pravosud
Date: 2023-08-29 00:00:00

In this paper we study the short-time behavior of the at-the-money implied volatility for European and arithmetic Asian call options with fixed strike price. The asset price is assumed to follow the Bachelier model with a general stochastic volatility process. Using techniques of the Malliavin calculus such as the anticipating Ito's formula we first compute the level of the implied volatility when the maturity converges to zero. Then, we find a short maturity asymptotic formula for the skew of the implied volatility that depends on the roughness of the volatility model. We apply our general results to the SABR and fractional Bergomi models, and provide some numerical simulations that confirm the accurateness of the asymptotic formula for the skew.
Adaptive mitigation of time-varying quantum noise
Authors: Samudra Dasgupta, Arshag Danageozian, Travis S. Humble
Date: 2023-08-16 00:00:00
Tasks: Bayesian Inference

Current quantum computers suffer from non-stationary noise channels with high error rates, which undermines their reliability and reproducibility. We propose a Bayesian inference-based adaptive algorithm that can learn and mitigate quantum noise in response to changing channel conditions. Our study emphasizes the need for dynamic inference of critical channel parameters to improve program accuracy. We use the Dirichlet distribution to model the stochasticity of the Pauli channel. This allows us to perform Bayesian inference, which can improve the performance of probabilistic error cancellation (PEC) under time-varying noise. Our work demonstrates the importance of characterizing and mitigating temporal variations in quantum noise, which is crucial for developing more accurate and reliable quantum technologies. Our results show that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x when measured using Hellinger distance from the ideal distribution.
Likelihood-based inference and forecasting for trawl processes: a stochastic optimization approach
Authors: Dan Leonte, Almut E. D. Veraart
Date: 2023-08-30 00:00:00
Tasks: parameter estimation, Stochastic Optimization

We consider trawl processes, which are stationary and infinitely divisible stochastic processes and can describe a wide range of statistical properties, such as heavy tails and long memory. In this paper, we develop the first likelihood-based methodology for the inference of real-valued trawl processes and introduce novel deterministic and probabilistic forecasting methods. Being non-Markovian, with a highly intractable likelihood function, trawl processes require the use of composite likelihood functions to parsimoniously capture their statistical properties. We formulate the composite likelihood estimation as a stochastic optimization problem for which it is feasible to implement iterative gradient descent methods. We derive novel gradient estimators with variances that are reduced by several orders of magnitude. We analyze both the theoretical properties and practical implementation details of these estimators and release a Python library which can be used to fit a large class of trawl processes. In a simulation study, we demonstrate that our estimators outperform the generalized method of moments estimators in terms of both parameter estimation error and out-of-sample forecasting error. Finally, we formalize a stochastic chain rule for our gradient estimators. We apply the new theory to trawl processes and provide a unified likelihood-based methodology for the inference of both real-valued and integer-valued trawl processes.
Federated Two Stage Decoupling With Adaptive Personalization Layers
Authors: Hangyu Zhu, Yuxiang Fan, Zhenping Xie
Date: 2023-08-30 00:00:00
Tasks: Clustering, Federated Learning

Federated learning has gained significant attention due to its groundbreaking ability to enable distributed learning while maintaining privacy constraints. However, as a consequence of data heterogeneity among decentralized devices, it inherently experiences significant learning degradation and slow convergence speed. Therefore, it is natural to employ the concept of clustering homogeneous clients into the same group, allowing only the model weights within each group to be aggregated. While most existing clustered federated learning methods employ either model gradients or inference outputs as metrics for client partitioning, with the goal of grouping similar devices together, may still have heterogeneity within each cluster. Moreover, there is a scarcity of research exploring the underlying reasons for determining the appropriate timing for clustering, resulting in the common practice of assigning each client to its own individual cluster, particularly in the context of highly non independent and identically distributed (Non-IID) data. In this paper, we introduce a two-stage decoupling federated learning algorithm with adaptive personalization layers named FedTSDP, where client clustering is performed twice according to inference outputs and model weights, respectively. Hopkins amended sampling is adopted to determine the appropriate timing for clustering and the sampling weight of public unlabeled data. In addition, a simple yet effective approach is developed to adaptively adjust the personalization layers based on varying degrees of data skew. Experimental results show that our proposed method has reliable performance on both IID and non-IID scenarios.