pwc-archive/papers-with-abstracts huggingface dataset:
Counterintuitive properties of fixation probability and fixation time in population structures with spatially periodic resource distribution
Authors: Hossein Nemati, Mohammad Reza Ejtehadi, Kamran Kaveh
Date: 2022-02-09 00:00:00

Resource are often not uniformly distributed within a population. Spatial variations of concentration of a resource, change the fitness of competing strategies locally. The notion of fitness varying with respect to both genotype and environment is important in modeling cancer initiation, microbial evolution and evolution of drug resistance. Environmental interactions can be asymmetric, that is, they affect the fitness of one type more than the other. The question is how local environmental variations in network population structures change the selection dynamics in a finite population setting. We consider one-dimensional lattice population structures with spatial fitness distributions with a periodic pattern. Heterogeneity is determined by standard deviation of fitnesses and period. The model covers biologically relevant limits of two-habitat subdivided populations and randomly-distributed resources in high- and low-periods. We numerically calculate fixation probability and fixation times for a constant population birth-death process as fitness heterogeneity and period vary. We identify levels of heterogeneity for which a previously deleterious mutant, in a uniform environment, becomes beneficial. In other regimes of the problem we observe unexpected behavior where the fixation probability of both types are larger than their neutral value at the same time. This coincides with an exponential increase in time to fixation as a function of population size, which points to significant slow-down in selection process and the potential for coexistence between types in realistic time scales. We also discuss `fitness shift' model where the fitness function of one type is identical to the other up to a constant spatial shift. This leads to significant increase (or decrease) in the fixation probability of the mutant depending the value of the shift.
Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models
Authors: Andriy Norets, Kenichi Shimizu
Date: 2022-02-09 00:00:00
Tasks: Discrete Choice Models

We propose a tractable semiparametric estimation method for structural dynamic discrete choice models. The distribution of additive utility shocks in the proposed framework is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions in the multinomial choice settings and the flexibility of the location-scale mixtures. We implement the Bayesian approach to inference using Hamiltonian Monte Carlo and an approximately optimal reversible jump algorithm. In our simulation experiments, we show that the standard dynamic logit model can deliver misleading results, especially about counterfactuals, when the shocks are not extreme value distributed. Our semiparametric approach delivers reliable inference in these settings. We develop theoretical results on approximations by location-scale mixtures in an appropriate distance and posterior concentration of the set identified utility parameters and the distribution of shocks in the model.
Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes
Authors: Ivan Fernandez-Val, Wayne Yuan Gao, Yuan Liao, Francis Vella
Date: 2022-02-08 00:00:00
Tasks: regression, valid

We consider the estimation of a dynamic distribution regression panel data model with heterogeneous coefficients across units. The objects of primary interest are specific functionals of these coefficients. These include predicted actual and stationary distributions of the outcome variable and quantile treatment effects. Coefficients and their functionals are estimated via fixed effect methods. We investigate how these functionals vary in response to changes in initial conditions or covariate values. We also identify a uniformity issue related to the robustness of inference to the unknown degree of heterogeneity, and propose a cross-sectional bootstrap method for uniformly valid inference on function-valued objects. Employing PSID annual labor income data we illustrate some important empirical issues we can address. We first quantify the impact of a negative labor income shock on the distribution of future labor income. We also examine the impact on the distribution of labor income from increasing the education level of a chosen group of workers. Finally, we demonstrate the existence of heterogeneity in income mobility, and how this leads to substantial variation in individuals' incidences to be trapped in poverty. We also provide simulation evidence confirming that our procedures work well.
Are Fairness Perceptions Shaped by Income Inequality? Evidence from Latin America
Authors: Leonardo Gasparini, Germán Reyes
Date: 2022-02-09 00:00:00
Tasks: Fairness

A common assumption in the literature is that the level of income inequality shapes individuals' beliefs about whether the income distribution is fair (``fairness views,'' for short). However, individuals do not directly observe income inequality (which often leads to large misperceptions), nor do they consider all inequities to be unfair. In this paper, we empirically assess the link between objective measures of income inequality and fairness views in a context of high but decreasing income inequality. We combine opinion poll data with harmonized data from household surveys of 18 Latin American countries from 1997--2015. We report three main findings. First, we find a strong and statistically significant relationship between income inequality and unfairness views across countries and over time. Unfairness views evolved in the same direction as income inequality for 17 out of the 18 countries in our sample. Second, individuals who are older, unemployed, and left-wing are, on average, more likely to perceive the income distribution as very unfair. Third, fairness views and income inequality have predictive power for individuals' self-reported propensity to mobilize and protest independent of each other, suggesting that these two variables capture different channels through which changes in the income distribution can affect social unrest.
Behavioral epidemiology: An economic model to evaluate optimal policy in the midst of a pandemic
Authors: Shomak Chakrabarti, Ilia Krasikov, Rohit Lamba
Date: 2022-02-08 00:00:00
Tasks: Epidemiology

This paper combines a canonical epidemiology model of disease dynamics with government policy of lockdown and testing, and agents' decision to social distance in order to avoid getting infected. The model is calibrated with data on deaths and testing outcomes in the Unites States. It is shown that an intermediate but prolonged lockdown is socially optimal when both mortality and GDP are taken into account. This is because the government wants the economy to keep producing some output and the slack in reducing infection is picked up by social distancing agents. Social distancing best responds to the optimal government policy to keep the effective reproductive number at one and avoid multiple waves through the pandemic. Calibration shows testing to have been effective, but it could have been even more instrumental if it had been aggressively pursued from the beginning of the pandemic. Not having any lockdown or shutting down social distancing would have had extreme consequences. Greater centralized control on social activities would have mitigated further the spread of the pandemic.
Facebook Shadow Profiles
Authors: Luis Aguiar, Christian Peukert, Maximilian Schäfer, Hannes Ullrich
Date: 2022-02-08 00:00:00

We quantify Facebook's ability to build shadow profiles by tracking individuals across the web, irrespective of whether they are users of the social network. For a representative sample of US Internet users, we find that Facebook is able to track about 40 percent of the browsing time of both users and non-users of Facebook, including on privacy-sensitive domains and across user demographics. We show that the collected browsing data can produce accurate predictions of personal information that is valuable for advertisers, such as age or gender. Because Facebook users reveal their demographic information to the platform, and because the browsing behavior of users and non-users of Facebook overlaps, users impose a data externality on non-users by allowing Facebook to infer their personal information.
Sequentially Optimal Pricing under Informational Robustness
Authors: Zihao Li, Jonathan Libgober, Xiaosheng Mu
Date: 2022-02-09 00:00:00

A seller sells an object over time but is uncertain how the buyer learns their willingness-to-pay. We consider informational robustness under \textit{limited commitment}, where the seller offers a price \textit{each period} to maximize continuation profit against worst-case information arrival. Our formulation maintains dynamic consistency by considering the worst case \textit{sequentially}. Under general conditions, we characterize an essentially unique equilibrium where the buyer does not delay to learn more later. Furthermore, we identify a condition that ensures the equilibrium price path is ``reinforcing,'' so even dynamically inconsistent information arrival would not lower the seller's payoff below the equilibrium level.
On the Uniqueness and Stability of the Equilibrium Price in Quasi-Linear Economies
Authors: Yuhki Hosoya
Date: 2022-02-09 00:00:00

In this paper, we show that if every consumer in an economy has a quasi-linear utility function, then the normalized equilibrium price is unique, and is locally stable with respect to the t\^atonnement process. Our study can be seen as that extends the results in partial equilibrium theory to economies with more than two dimensional consumption space. Moreover, we discuss the surplus analysis in such economies.
Efficiency with(out) intermediation in repeated bilateral trade
Authors: Rohit Lamba
Date: 2022-02-08 00:00:00

This paper analyzes repeated version of the bilateral trade model where the independent payoff relevant private information of the buyer and the seller is correlated across time. Using this setup it makes the following five contributions. First, it derives necessary and sufficient conditions on the primitives of the model as to when efficiency can be attained under ex post budget balance and participation constraints. Second, in doing so, it introduces an intermediate notion of budget balance called interim budget balance that allows for the extension of liquidity but with participation constraints for the issuing authority interpreted here as an intermediary. Third, it pins down the class of all possible mechanisms that can implement the efficient allocation with and without an intermediary. Fourth, it provides a foundation for the role of an intermediary in a dynamic mechanism design model under informational constraints. And, fifth, it argues for a careful interpretation of the "folk proposition" that less information is better for efficiency in dynamic mechanisms under ex post budget balance and observability of transfers.
Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning
Authors: Gilles Callebaut, Jarne Van Mulders, Geoffrey Ottoy, Daan Delabie, Bert Cox, Nobby Stevens, Liesbet Van der Perre
Date: 2022-02-09 00:00:00
Tasks: Diversity, Edge-computing, Federated Learning

New concepts for next-generation wireless systems are being developed. It is expected that these 6G and beyond systems will incorporate more than only communication, but also sensing, positioning, (deep) edge computing, and other services. The discussed measurement facility and approach, named Techtile, is an open, both in design and operation, and unique testbed to evaluate these newly envisioned systems. Techtile is a multi-functional and versatile testbed, providing fine-grained distributed resources for new communication, positioning and sensing technologies. The facility enables experimental research on hyper-connected interactive environments and validation of new algorithms and topologies. The backbone connects 140~resource units equipped with edge computing devices, software-defined radios, sensors, and LED sources. By doing so, different network topologies and local-versus-central computing can be assessed. The introduced diversity of i) the technologies (e.g., RF, acoustics and light), ii) the distributed resources and iii) the interconnectivity allows exploring more degrees and new types of diversity, which can be investigated in this testbed.
Optimal annuitization post-retirement with labor income
Authors: Xiang Gao, Cody Hyndman, Traian A. Pirvu, Petar Jevtić
Date: 2022-02-09 00:00:00

Evidence shows that the labor participation rate of retirement age cohorts is non-negligible, and it is a widespread phenomenon globally. In the United States, the labor force participation rate for workers age 75 and older is projected to be over 10 percent by 2026 as reported by the Bureau of Labor Statistics. The prevalence of post-retirement work changes existing considerations of optimal annuitization, a research question further complicated by novel factors such as post-retirement labor rates, wage rates, and capacity or willingness to work. To our knowledge, this poses a practical and theoretical problem not previously investigated in actuarial literature. In this paper, we study the problem of post-retirement annuitization with extra labor income in the framework of stochastic control, optimal stopping, and expected utility maximization. The utility functions are of the Cobb-Douglas type. The martingale methodology and duality techniques are employed to obtain closed-form solutions for the dual and primal problems. The effect of labor income is investigated by exploiting the explicit solutions and Monte-Carlo simulation. The latter reveals that the optimal annuitization time is strongly linear with respect to the initial wealth, with or without labor income. When it comes to optimal annuitization, we find that the wage and labor rates may play opposite roles. However, their impact is mediated by the leverage ratio.
Studying the neural representations of uncertainty
Authors: Edgar Y Walker, Stephan Pohl, Rachel N Denison, David L Barack, Jennifer Lee, Ned Block, Wei Ji Ma, Florent Meyniel
Date: 2022-02-09 00:00:00
Tasks: Specificity

The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between "code-driven" and "correlational" approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance, functionality. Our analysis reveals that the two approaches lead to different, but complementary findings, shaping new research questions and guiding future experiments.
Ketamine-Medetomidine General Anesthesia Occurs With Alternation of Cortical Electrophysiological Activity Between High and Low Complex States
Authors: Eduardo Cocca Padovani
Date: 2022-02-09 00:00:00
Tasks: Time Series Analysis

Anesthetic agents are known to induce a range of alterations in cortical electrophysiological activity, such as the rise of signature patterns, changes in statistical properties, and altered dynamic behavior of neural records. Plenty of methods can be used to monitor these changes, among them complexity metrics demonstrated to have the power to discriminate states involving distinct levels of awareness. There is a consensus that anesthetic drugs can interfere with neural activities at different levels and time scales, being able to induce alterations both locally and in the spatiotemporal patterns established throughout the whole cortex. However, it is still unclear how such changes in the complexity of cortical activity are supposed to occur, and experimental evidence is still needed. For this purpose, we have analyzed an ECoG records database of a Ketamine-Medetomidine anesthetic induction in a non-human primate subject. The MDR-ECoG technique provided records of cortical activity with both high temporal and spatial resolution allied with extensive coverage of the cortical surface. The Permutation Entropy and the Fractal Dimension were employed to evaluate the complexity of the neural time series. It was found that the complexity of cortical activity was relatively constant during awakened conditions. The transition to unconsciousness occurred relatively fast; it required about 30 to 40 seconds for the first remarkable changes to take place. During anesthesia, the complexity assumed considerable variation at local levels and fluctuated apparently without a defined period. The cortex dynamically alternated between high and low complex states on a global scale. This study provides novel evidence of the effects of anesthetics on neural activity and cortical dynamics, contributing to the elucidation of anesthetic mechanisms in terms of circuits, pathways, and global brain functioning.
RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
Authors: Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martinez-Pena, Eileen L. Tang, Suraj M S, Cristian Regep, Jeremy Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, Yoshua Bengio
Date: 2022-02-07 00:00:00
Tasks: Benchmarking, Model Optimization

For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state of the art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased towards synergistic agents and these results do not necessarily generalise out of distribution. We employ a sequential model optimization search utilising a deep learning model to quickly discover synergistic drug combinations active against a cancer cell line, requiring substantially less screening than an exhaustive evaluation. Our small scale wet lab experiments only account for evaluation of ~5% of the total search space. After only 3 rounds of ML-guided in vitro experimentation (including a calibration round), we find that the set of drug pairs queried is enriched for highly synergistic combinations; two additional rounds of ML-guided experiments were performed to ensure reproducibility of trends. Remarkably, we rediscover drug combinations later confirmed to be under study within clinical trials. Moreover, we find that drug embeddings generated using only structural information begin to reflect mechanisms of action. Prior in silico benchmarking suggests we can enrich search queries by a factor of ~5-10x for highly synergistic drug combinations by using sequential rounds of evaluation when compared to random selection, or by a factor of >3x when using a pretrained model selecting all drug combinations at a single time point.
Quantum Model Learning Agent: characterisation of quantum systems through machine learning
Authors: Brian Flynn, Antonio Andreas Gentile, Nathan Wiebe, Raffaele Santagati, Anthony Laing
Date: 2021-12-15 00:00:00
Tasks: BIG-bench Machine Learning

Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distill empirically. Here, we report an algorithm -- the Quantum Model Learning Agent (QMLA) -- to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit $F_1$-score $\geq 0.88$ when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over $250,000$ potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.
Efficient Style-Corpus Constrained Learning for Photorealistic Style Transfer
Authors: Yingxu Qiao, Jiabao Cui, Fuxian Huang, Hongmin Liu, Cuizhu Bao, Xi Li
Date: 2021-02-22 00:00:00
Proceeding: IEEE 2021 2
Tasks: Style Transfer

Photorealistic style transfer is a challenging task, which demands the stylized image remains real. Existing methods are still suffering from unrealistic artifacts and heavy computational cost. In this paper, we propose a novel Style-Corpus Constrained Learning (SCCL) scheme to address these issues. The style-corpus with the style-specific and style-agnostic characteristics simultaneously is proposed to constrain the stylized image with the style consistency among different samples, which improves photorealism of stylization output. By using adversarial distillation learning strategy, a simple fast-to-execute network is trained to substitute previous complex feature transforms models, which reduces the computational cost significantly. Experiments demonstrate that our method produces rich-detailed photorealistic images, with 13 ~ 50 times faster than the state-of-the-art method (WCT 2 ).
A graph-convolutional neural network model for the prediction of chemical reactivity
Authors: Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S. Jaakkola, William H. Green, Regina Barzilay, Klavs F. Jensen
Date: 2018-11-26 00:00:00
Proceeding: Chemical Science 2018 11

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions via the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches.