pwc-archive/papers-with-abstracts huggingface dataset:
An Operating Principle of the Cerebral Cortex, and a Cellular Mechanism for Attentional Trial-and-Error Pattern Learning and Useful Classification Extraction
Authors: Marat M. Rvachev
Date: 2023-08-21 00:00:00
Tasks: Incremental Learning

A feature of the brains of intelligent animals is the ability to learn to respond to an ensemble of active neuronal inputs with a behaviorally appropriate ensemble of active neuronal outputs. Previously, a hypothesis was proposed on how this mechanism is implemented at the cellular level within the neocortical pyramidal neuron: the apical tuft or perisomatic inputs initiate "guess" neuron firings, while the basal dendrites identify input patterns based on excited synaptic clusters, with the cluster excitation strength adjusted based on reward feedback. This simple mechanism allows neurons to learn to classify their inputs in a surprisingly intelligent manner. Here, we revise and extend this hypothesis. We modify synaptic plasticity rules to align with behavioral time scale synaptic plasticity (BTSP) observed in hippocampal area CA1, making the framework more biophysically and behaviorally plausible. The neurons for the guess firings are selected in a voluntary manner via feedback connections to apical tufts in the neocortical layer 1, leading to dendritic Ca2+ spikes with burst firing, which are postulated to be neural correlates of attentional, aware processing. Once learned, the neuronal input classification is executed without voluntary or conscious control, enabling hierarchical incremental learning of classifications that is effective in our inherently classifiable world. In addition to voluntary, we propose that pyramidal neuron burst firing can be involuntary, also initiated via apical tuft inputs, drawing attention towards important cues such as novelty and noxious stimuli. We classify the excitations of neocortical pyramidal neurons into four categories based on their excitation pathway: attentional versus automatic and voluntary/acquired versus involuntary. Additionally, we hypothesize that dendrites within pyramidal neuron minicolumn bundles are coupled via depolarization...
Molecular Conformation Generation via Shifting Scores
Authors: Zihan Zhou, Ruiying Liu, Chaolong Ying, Ruimao Zhang, Tianshu Yu
Date: 2023-09-12 00:00:00
Tasks: Computational chemistry

Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule. Generating molecular conformation via diffusion requires learning to reverse a noising process. Diffusion on inter-atomic distances instead of conformation preserves SE(3)-equivalence and shows superior performance compared to alternative techniques, whereas related generative modelings are predominantly based upon heuristical assumptions. In response to this, we propose a novel molecular conformation generation approach driven by the observation that the disintegration of a molecule can be viewed as casting increasing force fields to its composing atoms, such that the distribution of the change of inter-atomic distance shifts from Gaussian to Maxwell-Boltzmann distribution. The corresponding generative modeling ensures a feasible inter-atomic distance geometry and exhibits time reversibility. Experimental results on molecular datasets demonstrate the advantages of the proposed shifting distribution compared to the state-of-the-art.
Estimating Stable Fixed Points and Langevin Potentials for Financial Dynamics
Authors: Tobias Wand, Timo Wiedemann, Jan Harren, Oliver Kamps
Date: 2023-09-21 00:00:00
Tasks: Model Selection

The Geometric Brownian Motion (GBM) is a standard model in quantitative finance, but the potential function of its stochastic differential equation (SDE) cannot include stable nonzero prices. This article generalises the GBM to an SDE with polynomial drift of order q and shows via model selection that q=2 is most frequently the optimal model to describe the data. Moreover, Markov chain Monte Carlo ensembles of the accompanying potential functions show a clear and pronounced potential well, indicating the existence of a stable price.
Exploring the Correlation Between Ultrasound Speed and the State of Health of LiFePO$_4$ Prismatic Cells
Authors: Shengyuan Zhang, Peng Zuo, Xuesong Yin, Zheng Fan
Date: 2023-09-13 00:00:00

Electric vehicles (EVs) have become a popular mode of transportation, with their performance depending on the ageing of the Li-ion batteries used to power them. However, it can be challenging and time-consuming to determine the capacity retention of a battery in service. A rapid and reliable testing method for state of health (SoH) determination is desired. Ultrasonic testing techniques are promising due to their efficient, portable, and non-destructive features. In this study, we demonstrate that ultrasonic speed decreases with the degradation of the capacity of an LFP prismatic cell. We explain this correlation through numerical simulation, which describes wave propagation in porous media. We propose that the reduction of binder stiffness can be a primary cause of the change in ultrasonic speed during battery ageing. This work brings new insights into ultrasonic SoH estimation techniques.
InSPECtor: an end-to-end design framework for compressive pixelated hyperspectral instruments
Authors: T. A. Stockmans, F. Snik, M. Esposito, C. van Dijk, C. U. Keller
Date: 2023-09-19 00:00:00

Classic designs of hyperspectral instrumentation densely sample the spatial and spectral information of the scene of interest. Data may be compressed after the acquisition. In this paper we introduce a framework for the design of an optimized, micro-patterned snapshot hyperspectral imager that acquires an optimized subset of the spatial and spectral information in the scene. The data is thereby compressed already at the sensor level, but can be restored to the full hyperspectral data cube by the jointly optimized reconstructor. This framework is implemented with TensorFlow and makes use of its automatic differentiation for the joint optimization of the layout of the micro-patterned filter array as well as the reconstructor. We explore the achievable compression ratio for different numbers of filter passbands, number of scanning frames, and filter layouts using data collected by the Hyperscout instrument. We show resulting instrument designs that take snapshot measurements without losing significant information while reducing the data volume, acquisition time, or detector space by a factor of 40 as compared to classic, dense sampling. The joint optimization of a compressive hyperspectral imager design and the accompanying reconstructor provides an avenue to substantially reduce the data volume from hyperspectral imagers.
A detection analysis for temporal memory patterns at different time-scales
Authors: Fabio Vanni, David Lambert
Date: 2023-09-21 00:00:00
Tasks: Time Series, Time Series Analysis

This paper introduces a novel methodology that utilizes latency to unveil time-series dependence patterns. A customized statistical test detects memory dependence in event sequences by analyzing their inter-event time distributions. Synthetic experiments based on the renewal-aging property assess the impact of observer latency on the renewal property. Our test uncovers memory patterns across diverse time scales, emphasizing the event sequence's probability structure beyond correlations. The time series analysis produces a statistical test and graphical plots which helps to detect dependence patterns among events at different time-scales if any. Furthermore, the test evaluates the renewal assumption through aging experiments, offering valuable applications in time-series analysis within economics.
Identifying Causal Effects in Information Provision Experiments
Authors: Dylan Balla-Elliott
Date: 2023-09-20 00:00:00
Tasks: Decision Making

Information treatments often shift beliefs more for people with weaker belief effects. Since standard TSLS and panel specifications in information provision experiments have weights proportional to belief updating in the first-stage, this dependence attenuates existing estimates. This is natural if people whose decisions depend on their beliefs gather information before the experiment. I propose a local least squares estimator that identifies unweighted average effects in several classes of experiments under progressively stronger versions of Bayesian updating. In five of six recent studies, average effects are larger than-in several cases more than double-estimates in standard specifications.
Correcting Selection Bias in Standardized Test Comparisons
Authors: Onil Boussim
Date: 2023-09-19 00:00:00
Tasks: counterfactual, Selection bias

This paper tackles the critical issue of sample selection bias in cross-country comparisons using international assessments such as PISA (Program for International Student Assessment). While PISA is widely used to rank educational performance, its reliance on samples of students still in school at age 15 introduces survival bias, potentially distorting comparisons. To address this, I developed an econometric framework based on a quantile selection model. Under a stochastic dominance assumption, the selection-corrected quantile function is partially identified, with point identification achieved under parametric restrictions on the joint distribution of test scores and selection. Applying this method to PISA 2018 data, I demonstrate that correcting for selection bias leads to significant changes in country rankings based on mean performance. These findings underscore the importance of accounting for sample selection bias to ensure accurate and meaningful international educational comparisons.
require: Package dependencies for reproducible research
Authors: Sergio Correia, Matthew P. Seay
Date: 2023-09-20 00:00:00

The ability to conduct reproducible research in Stata is often limited by the lack of version control for community-contributed packages. This article introduces the require command, a tool designed to ensure Stata package dependencies are compatible across users and computer systems. Given a list of Stata packages, require verifies that each package is installed, checks for a minimum or exact version or package release date, and optionally installs the package if prompted by the researcher.
Techno-Economic Analysis of Synthetic Fuel Production from Existing Nuclear Power Plants across the United States
Authors: Marisol Garrouste, Michael T. Craig, Daniel Wendt, Maria Herrera Diaz, William Jenson, Qian Zhang, Brendan Kochunas
Date: 2023-09-21 00:00:00

Low carbon synfuel can displace transport fossil fuels such as diesel and jet fuel and help achieve the decarbonization of the transportation sector at a global scale, but large-scale cost-effective production facilities are needed. Meanwhile, nuclear power plants are closing due to economic difficulties: electricity prices are too low and variable to cover their operational costs. Using existing nuclear power plants to produce synfuels might prevent loss of these low-carbon assets while producing synfuels at scale, but no technoeconomic analysis of this Integrated Energy System exist. We quantify the technoeconomic potential of coupling a synthetic fuel production process with five example nuclear power plants across the U.S. to explore the influence of different electricity markets, access to carbon dioxide sources, and fuel markets. Coupling synfuel production increases nuclear plant profitability by up to 792 million USD(2020) in addition to a 10 percent rate of return on investment over a 20 year period. Our analysis identifies drivers for the economic profitability of the synfuel IES. The hydrogen production tax credit from the 2022 Inflation Reduction Act is essential to its overall profitability representing on average three quarters of its revenues. The carbon feedstock transportation is the highest cost - more than a third on average - closely followed by the synfuel production process capital costs. Those results show the key role of incentive policies for the decarbonization of the transportation sector and the economic importance of the geographic location of Integrated Energy Systems.
Explosive growth from AI automation: A review of the arguments
Authors: Ege Erdil, Tamay Besiroglu
Date: 2023-09-20 00:00:00

We examine whether substantial AI automation could accelerate global economic growth by about an order of magnitude, akin to the economic growth effects of the Industrial Revolution. We identify three primary drivers for such growth: 1) the scalability of an AI "labor force" restoring a regime of increasing returns to scale, 2) the rapid expansion of an AI labor force, and 3) a massive increase in output from rapid automation occurring over a brief period of time. Against this backdrop, we evaluate nine counterarguments, including regulatory hurdles, production bottlenecks, alignment issues, and the pace of automation. We tentatively assess these arguments, finding most are unlikely deciders. We conclude that explosive growth seems plausible with AI capable of broadly substituting for human labor, but high confidence in this claim seems currently unwarranted. Key questions remain about the intensity of regulatory responses to AI, physical bottlenecks in production, the economic value of superhuman abilities, and the rate at which AI automation could occur.
Existence of a Competitive Equilibrium with Substitutes, with Applications to Matching and Discrete Choice Models
Authors: Liang Chen, Eugene Choo, Alfred Galichon, Simon Weber
Date: 2023-09-20 00:00:00
Tasks: Discrete Choice Models

We propose new results for the existence and uniqueness of a general nonparametric and nonseparable competitive equilibrium with substitutes. These results ensure the invertibility of a general competitive system. The existing literature has focused on the uniqueness of a competitive equilibrium assuming that existence holds. We introduce three properties that our supply system must satisfy: weak substitutes, pivotal substitutes, and responsiveness. These properties are sufficient to ensure the existence of an equilibrium, thus providing the existence counterpart to Berry, Gandhi, and Haile (2013)'s uniqueness results. For two important classes of models, bipartite matching models with full assignment and discrete choice models, we show that both models can be reformulated as a competitive system such that our existence and uniqueness results can be readily applied. We also provide an algorithm to compute the unique competitive equilibrium. Furthermore, we argue that our results are particularly useful for studying imperfectly transferable utility matching models with full assignment and non-additive random utility models.
Increasing Ticketing Allocative Efficiency Using Marginal Price Auction Theory
Authors: Boxiang Fu
Date: 2023-09-20 00:00:00

Most modern ticketing systems rely on a first-come-first-serve or randomized allocation system to determine the allocation of tickets. Such systems has received considerable backlash in recent years due to its inequitable allotment and allocative inefficiency. We analyze a ticketing protocol based on a variation of the marginal price auction system. Users submit bids to the protocol based on their own utilities. The protocol awards tickets to the highest bidders and determines the final ticket price paid by all bidders using the lowest winning submitted bid. Game theoretic proof is provided to ensure the protocol more efficiently allocates the tickets to the bidders with the highest utilities. We also prove that the protocol extracts more economic rents for the event organizers and the non-optimality of ticket scalping under time-invariant bidder utilities.
Can political gridlock undermine checks and balances? A lab experiment
Authors: Alvaro Forteza, Irene Mussio, Juan S Pereyra
Date: 2023-09-18 00:00:00

If checks and balances are aimed at protecting citizens from the government's abuse of power, why do they sometimes weaken them? We address this question in a laboratory experiment in which subjects choose between two decision rules: with and without checks and balances. Voters may prefer an unchecked executive if that enables a reform that, otherwise, is blocked by the legislature. Consistent with our predictions, we find that subjects are more likely to weaken checks and balances when there is political gridlock. However, subjects weaken the controls not only when the reform is beneficial but also when it is harmful.
Buyer-Optimal Algorithmic Recommendations
Authors: Shota Ichihashi, Alex Smolin
Date: 2023-09-21 00:00:00

In markets where algorithmic data processing is increasingly prevalent, recommendation algorithms can substantially affect trade and welfare. We consider a setting in which an algorithm recommends a product based on its value to the buyer and its price. We characterize an algorithm that maximizes the buyer's expected payoff and show that it strategically biases recommendations to induce lower prices. Revealing the buyer's value to the seller leaves overall payoffs unchanged while leading to more dispersed prices and a more equitable distribution of surplus across buyer types. These results extend to all Pareto-optimal algorithms and to multiseller markets, with implications for AI assistants and e-commerce ranking systems.
Common Agency with Non-Delegation or Imperfect Commitment
Authors: Seungjin Han, Siyang Xiong
Date: 2023-09-20 00:00:00

In classical contract theory, we usually impose two assumptions: delegated contracts and perfect commitment. While the second assumption is demanding, the first one suffers no loss of generality. Following this tradition, current common-agency models impose delegated contracts and perfect commitment. We first show that non-delegated contracts expand the set of equilibrium outcomes under common agency. Furthermore, the powerful menu theorem for common agency (Peters (2001) and Martimort and Stole (2002)}) fails for either non-delegated contracts or imperfect commitment. We identify canonical contracts in such environments, and re-establish generalized menu theorems. Given imperfect commitment, our results for common-agency models are analogous to those in Bester and Strausz (2001) and Doval and Skreta (2012) for the classical contract theory, which re-establish the revelation principle.
Substitutability in Favor Exchange
Authors: Oguzhan Celebi
Date: 2023-09-19 00:00:00

I introduce a favor exchange model where favors are substitutable and study bilateral enforcement of cooperation. Without substitutability, the value of a relationship does not depend on the rest of the network, and in equilibrium there is either no cooperation or universal cooperation. When favors are substitutable, each additional relationship is less valuable than the previous, and intermediate levels of cooperation are observed. I extend the model to allow for transfers, heterogeneous players, and multilateral enforcement. My results can explain the stratification of social networks in post-Soviet states and the adoption of different enforcement mechanisms by different groups of medieval traders.
A Neural TTS System with Parallel Prosody Transfer from Unseen Speakers
Authors: Slava Shechtman, Raul Fernandez
Date: 2023-09-20 00:00:00

Modern neural TTS systems are capable of generating natural and expressive speech when provided with sufficient amounts of training data. Such systems can be equipped with prosody-control functionality, allowing for more direct shaping of the speech output at inference time. In some TTS applications, it may be desirable to have an option that guides the TTS system with an ad-hoc speech recording exemplar to impose an implicit fine-grained, user-preferred prosodic realization for certain input prompts. In this work we present a first-of-its-kind neural TTS system equipped with such functionality to transfer the prosody from a parallel text recording from an unseen speaker. We demonstrate that the proposed system can precisely transfer the speech prosody from novel speakers to various trained TTS voices with no quality degradation, while preserving the target TTS speakers' identity, as evaluated by a set of subjective listening experiments.
Exploring Speech Enhancement for Low-resource Speech Synthesis
Authors: Zhaoheng Ni, Sravya Popuri, Ning Dong, Kohei Saijo, Xiaohui Zhang, Gael Le Lan, Yangyang Shi, Vikas Chandra, Changhan Wang
Date: 2023-09-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech

High-quality and intelligible speech is essential to text-to-speech (TTS) model training, however, obtaining high-quality data for low-resource languages is challenging and expensive. Applying speech enhancement on Automatic Speech Recognition (ASR) corpus mitigates the issue by augmenting the training data, while how the nonlinear speech distortion brought by speech enhancement models affects TTS training still needs to be investigated. In this paper, we train a TF-GridNet speech enhancement model and apply it to low-resource datasets that were collected for the ASR task, then train a discrete unit based TTS model on the enhanced speech. We use Arabic datasets as an example and show that the proposed pipeline significantly improves the low-resource TTS system compared with other baseline methods in terms of ASR WER metric. We also run empirical analysis on the correlation between speech enhancement and TTS performances.
Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
Authors: Qingrui Cai, Liuhong Zhu, Jianjun Zhou, Chen Qian, Di Guo, Xiaobo Qu
Date: 2023-09-21 00:00:00
Tasks: Network Interpretation, parameter estimation, Quantitative MRI

Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in quantitative evaluation. The emerging deep learning approach provides fast and accurate parameter estimation but still encounters the lack of network interpretation and enough training data. Even with a large amount of training data, the mismatch between the training and target data may introduce errors. Here, we propose one way that solely relies on the target scanned data and does not need a pre-defined training database. We provide a proof-of-concept that embeds the physical rule of MRI, the Bloch equation, into the loss of physics-informed neural network (PINN). PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data. Experimental results are conducted on phantom and cardiac imaging to demonstrate its potential in quantitative MRI.