pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level
Authors: S. Roderick Zhang, Bilal Farooq
Date: 2022-06-18 00:00:00
Tasks: Prediction

To help systematically lower anthropogenic Greenhouse gas (GHG) emissions, accurate and precise GHG emission prediction models have become a key focus of the climate research. The appeal is that the predictive models will inform policymakers, and hopefully, in turn, they will bring about systematic changes. Since the transportation sector is constantly among the top GHG emission contributors, especially in populated urban areas, substantial effort has been going into building more accurate and informative GHG prediction models to help create more sustainable urban environments. In this work, we seek to establish a predictive framework of GHG emissions at the urban road segment or link level of transportation networks. The key theme of the framework centers around model interpretability and actionability for high-level decision-makers using econometric Discrete Choice Modelling (DCM). We illustrate that DCM is capable of predicting link-level GHG emission levels on urban road networks in a parsimonious and effective manner. Our results show up to 85.4% prediction accuracy in the DCM models' performances. We also argue that since the goal of most GHG emission prediction models focuses on involving high-level decision-makers to make changes and curb emissions, the DCM-based GHG emission prediction framework is the most suitable framework.
A nation-wide experiment: fuel tax cuts and almost free public transport for three months in Germany -- Report 2 First wave results
Authors: Fabienne Cantner, Nico Nachtigall, Lisa S. Hamm, Andrea Cadavid, Lennart Adenaw, Allister Loder, Markus B. Siewert, Sebastian Goerg, Markus Lienkamp, Klaus Bogenberger
Date: 2022-06-21 00:00:00

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this second report, we update the information on study participation, provide first insights on the smartphone app usage as well as insights on the first wave results, particularly on the 9 EUR-ticket purchase intention.
Optimal Investment and Equilibrium Pricing under Ambiguity
Authors: Michail Anthropelos, Paul Schneider
Date: 2022-06-21 00:00:00

We consider portfolio selection under nonparametric $\alpha$-maxmin ambiguity in the neighbourhood of a reference distribution. We show strict concavity of the portfolio problem under ambiguity aversion. Implied demand functions are nondifferentiable, resemble observed bid-ask spreads, and are consistent with existing parametric limiting participation results under ambiguity. Ambiguity seekers exhibit a discontinuous demand function, implying an empty set of reservation prices. If agents have identical, or sufficiently similar prior beliefs, the first-best equilibrium is no trade. Simple conditions yield the existence of a Pareto-efficient second-best equilibrium, implying that heterogeneity in ambiguity preferences is sufficient for mutually beneficial transactions among all else homogeneous traders. These equilibria reconcile many observed phenomena in liquid high-information financial markets, such as liquidity dry-ups, portfolio inertia, and negative risk premia.
The Effectiveness of Digital Interventions on COVID-19 Attitudes and Beliefs
Authors: Susan Athey, Kristen Grabarz, Michael Luca, Nils Wernerfelt
Date: 2022-06-21 00:00:00

During the course of the COVID-19 pandemic, a common strategy for public health organizations around the world has been to launch interventions via advertising campaigns on social media. Despite this ubiquity, little has been known about their average effectiveness. We conduct a large-scale program evaluation of campaigns from 174 public health organizations on Facebook and Instagram that collectively reached 2.1 billion individuals and cost around \$40 million. We report the results of 819 randomized experiments that measured the impact of these campaigns across standardized, survey-based outcomes. We find on average these campaigns are effective at influencing self-reported beliefs, shifting opinions close to 1% at baseline with a cost per influenced person of about \$3.41. There is further evidence that campaigns are especially effective at influencing users' knowledge of how to get vaccines. Our results represent, to the best of our knowledge, the largest set of online public health interventions analyzed to date.
Going Green: Estimating the Potential of Green Jobs in Argentina
Authors: Natalia Porto, Pablo de la Vega, Manuela Cerimelo
Date: 2022-06-18 00:00:00

This paper aims to identify and characterize the potential of green jobs in Argentina, i.e., those that would benefit from a transition to a green economy, using occupational green potential scores calculated in US O*NET data. We apply the greenness scores to Argentine household survey data and estimate that 25% of workers are in green jobs, i.e., have a high green potential. However, when taking into account the informality dimension, we find that 15% of workers and 12% of wage earners are in formal green jobs. We then analyze the relationship between the greenness scores (with emphasis on the nexus with decent work) and various labor and demographic variables at the individual level. We find that for the full sample of workers the green potential is relatively greater for men, the elderly, those with very high qualifications, those in formal positions, and those in specific sectors such as construction, transportation, mining, and industry. These are the groups that are likely to be the most benefited by the greening of the Argentine economy. When we restrict the sample to wage earners, the green potential score is positively associated with informality.
Good-Bye Original Sin, Hello Risk On-Off, Financial Fragility, and Crises?
Authors: Joshua Aizenman, Yothin Jinjarak, Donghyun Park, Huanhuan Zheng
Date: 2022-06-18 00:00:00

We analyze the sovereign bond issuance data of eight major emerging markets (EMs) - Brazil, China, India, Indonesia, Mexico, Russia, South Africa and Turkey from 1970 to 2018. Our analysis suggests that (i) EM local currency bonds tend to be smaller in size, shorter in maturity, or lower in coupon rate than foreign currency bonds; (ii) EMs are more likely to issue local-currency sovereign bonds if their currencies appreciated before the global financial crisis of 2008 (GFC); (iii) inflation-targeting monetary policy increases the likelihood of issuing local-currency debt before GFC but not after; and (iv) EMs that offer higher sovereign yields are more likely to issue local-currency bonds after GFC. Future data will allow us to test and identify structural changes associated with the COVID-19 pandemic and its aftermath.
Mechanism Design Approaches to Blockchain Consensus
Authors: Joshua S. Gans, Richard Holden
Date: 2022-06-21 00:00:00
Tasks: POS

Blockchain consensus is a state whereby each node in a network agrees on the current state of the blockchain. Existing protocols achieve consensus via a contest or voting procedure to select one node as a dictator to propose new blocks. However, this procedure can still lead to potential attacks that make consensus harder to achieve or lead to coordination issues if multiple, competing chains (i.e., forks) are created with the potential that an untruthful fork might be selected. We explore the potential for mechanisms to be used to achieve consensus that are triggered when there is a dispute impeding consensus. Using the feature that nodes stake tokens in proof of stake (POS) protocols, we construct revelation mechanisms in which the unique (subgame perfect) equilibrium involves validating nodes propose truthful blocks using only the information that exists amongst all nodes. We construct operationally and computationally simple mechanisms under both Byzantine Fault Tolerance and a Longest Chain Rule, and discuss their robustness to attacks. Our perspective is that the use of simple mechanisms is an unexplored area of blockchain consensus and has the potential to mitigate known trade-offs and enhance scalability.
Withholding Verifiable Information
Authors: Kun Zhang
Date: 2022-06-20 00:00:00

I study a class of verifiable disclosure games where the sender's payoff is state independent and the receiver's optimal action only depends on the expected state. The sender's messages are verifiable in the sense that they can be vague but can never be wrong. What is the sender's preferred equilibrium? When does the sender gain nothing from having commitment power? I identify conditions for an information design outcome to be an equilibrium outcome of the verifiable disclosure game, and give simple sufficient conditions under which the sender does not benefit from commitment power. These results help in characterizing the sender's preferred equilibria and her equilibrium payoff set in a class of verifiable disclosure games. I apply these insights to study influencing voters and selling with quality disclosure.
Evolutionary rationality of risk preference
Authors: Songjia Fan, Yi Tao, Cong Li
Date: 2022-06-20 00:00:00
Tasks: Decision Making

Selection shapes all kinds of behaviors, including how we make decisions under uncertainty. The risk attitude reflected from it should be simple, flexible, yet consistent. In this paper we engaged evolutionary dynamics to find the decision making rule concerning risk that is evolutionarily superior, and developed the theory of evolutionary rationality. We highlight the importance of selection intensity and fitness, as well as their equivalents in the human mind, named as attention degree and meta-fitness, in the decision making process. Evolutionary rationality targets the maximization of the geometric mean of meta-fitness (or fitness), and attention degree (or selection intensity) holds the key in the change of attitude of the same individual towards different events and under varied situations. Then as an example, the Allais paradox is presented to show the application of evolutionary rationality, in which the anomalous choices made by the majority of people can be well justified by a simple change in the attention degree.
The Winner-Take-All Dilemma
Authors: Kazuya Kikuchi, Yukio Koriyama
Date: 2022-06-20 00:00:00
Tasks: All, Decision Making

We consider collective decision making when the society consists of groups endowed with voting weights. Each group chooses an internal rule that specifies the allocation of its weight to the alternatives as a function of its members' preferences. Under fairly general conditions, we show that the winner-take-all rule is a dominant strategy, while the equilibrium is Pareto dominated, highlighting the dilemma structure between optimality for each group and for the whole society. We also develop a technique for asymptotic analysis and show Pareto dominance of the proportional rule. Our numerical computation for the US Electoral College verifies its sensibility.
Persuasion with Non-Linear Preferences
Authors: Anton Kolotilin, Roberto Corrao, Alexander Wolitzky
Date: 2022-06-18 00:00:00

In persuasion problems where the receiver's action is one-dimensional and his utility is single-peaked, optimal signals are characterized by duality, based on a first-order approach to the receiver's problem. A signal is optimal iff the induced joint distribution over states and actions is supported on a compact set (the contact set) where the dual constraint binds. A signal that pools at most two states in each realization is always optimal, and such pairwise signals are the only solutions under a non-singularity condition on utilities (the twist condition). We provide conditions under which higher actions are induced at more or less extreme pairs of states. Finally, we provide conditions for the optimality of either full disclosure or negative assortative disclosure, where signal realizations can be ordered from least to most extreme. Optimal negative assortative disclosure is characterized as the solution to a pair of ordinary differential equations.
Multi-channel end-to-end neural network for speech enhancement, source localization, and voice activity detection
Authors: Yuan Chen, Yicheng Hsu, Mingsian R. Bai
Date: 2022-06-20 00:00:00
Tasks: Action Detection, Activity Detection, Speech Enhancement

Speech enhancement and source localization has been active research for several decades with a wide range of real-world applications. Recently, the Deep Complex Convolution Recurrent network (DCCRN) has yielded impressive enhancement performance for single-channel systems. In this study, a neural beamformer consisting of a beamformer and a novel multi-channel DCCRN is proposed for speech enhancement and source localization. Complex-valued filters estimated by the multi-channel DCCRN serve as the weights of beamformer. In addition, a one-stage learning-based procedure is employed for speech enhancement and source localization. The proposed network composed of the multi-channel DCCRN and the auxiliary network models the sound field, while minimizing the distortionless response loss function. Simulation results show that the proposed neural beamformer is effective in enhancing speech signals, with speech quality well preserved. The proposed neural beamformer also provides source localization and voice activity detection (VAD) functions.
A Step Towards Preserving Speakers' Identity While Detecting Depression Via Speaker Disentanglement
Authors: Vijay Ravi, Jinhan Wang, Jonathan Flint, Abeer Alwan
Date: 2022-06-20 00:00:00
Tasks: Depression Detection, Disentanglement

Preserving a patient's identity is a challenge for automatic, speech-based diagnosis of mental health disorders. In this paper, we address this issue by proposing adversarial disentanglement of depression characteristics and speaker identity. The model used for depression classification is trained in a speaker-identity-invariant manner by minimizing depression prediction loss and maximizing speaker prediction loss during training. The effectiveness of the proposed method is demonstrated on two datasets - DAIC-WOZ (English) and CONVERGE (Mandarin), with three feature sets (Mel-spectrograms, raw-audio signals, and the last-hidden-state of Wav2vec2.0), using a modified DepAudioNet model. With adversarial training, depression classification improves for every feature when compared to the baseline. Wav2vec2.0 features with adversarial learning resulted in the best performance (F1-score of 69.2% for DAIC-WOZ and 91.5% for CONVERGE). Analysis of the class-separability measure (J-ratio) of the hidden states of the DepAudioNet model shows that when adversarial learning is applied, the backend model loses some speaker-discriminability while it improves depression-discriminability. These results indicate that there are some components of speaker identity that may not be useful for depression detection and minimizing their effects provides a more accurate diagnosis of the underlying disorder and can safeguard a speaker's identity.
Fast image reverse filters through fixed point and gradient descent acceleration
Authors: Fernando Galetto, Guang Deng
Date: 2022-06-21 00:00:00

In this paper, we study the problem of reverse image filtering. An image filter denoted g(.), which is available as a black box, produces an observation b = g(x) when provided with an input x. The problem is to estimate the original input signal x from the black box filter g(.) and the observation b. We study and re-develop state-of-the-art methods from two points of view, fixed point iteration and gradient descent. We also explore the application of acceleration techniques for the two types of iterations. Through extensive experiments and comparison, we show that acceleration methods for both fixed point iteration and gradient descent help to speed up the convergence of state-of-the-art methods.
DECAL: DEployable Clinical Active Learning
Authors: Yash-yee Logan, Mohit Prabhushankar, Ghassan AlRegib
Date: 2022-06-21 00:00:00
Tasks: Active Learning, Diagnostic

Conventional machine learning systems that operate on natural images assume the presence of attributes within the images that lead to some decision. However, decisions in medical domain are a resultant of attributes within medical diagnostic scans and electronic medical records (EMR). Hence, active learning techniques that are developed for natural images are insufficient for handling medical data. We focus on reducing this insufficiency by designing a deployable clinical active learning (DECAL) framework within a bi-modal interface so as to add practicality to the paradigm. Our approach is a "plug-in" method that makes natural image based active learning algorithms generalize better and faster. We find that on two medical datasets on three architectures and five learning strategies, DECAL increases generalization across 20 rounds by approximately 4.81%. DECAL leads to a 5.59% and 7.02% increase in average accuracy as an initialization strategy for optical coherence tomography (OCT) and X-Ray respectively. Our active learning results were achieved using 3000 (5%) and 2000 (38%) samples of OCT and X-Ray data respectively.
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module
Authors: Ao Luo, Heming Sun, Jinming Liu, Jiro Katto
Date: 2022-06-21 00:00:00
Tasks: Image Compression

Learned Image Compression (LIC) gradually became more and more famous in these years. The hyperprior-module-based LIC models have achieved remarkable rate-distortion performance. However, the memory cost of these LIC models is too large to actually apply them to various devices, especially to portable or edge devices. The parameter scale is directly linked with memory cost. In our research, we found the hyperprior module is not only highly over-parameterized, but also its latent representation contains redundant information. Therefore, we propose a novel pruning method named ERHP in this paper to efficiently reduce the memory cost of hyperprior module, while improving the network performance. The experiments show our method is effective, reducing at least 22.6% parameters in the whole model while achieving better rate-distortion performance.
On the benefit of parameter-driven approaches for the modeling and the prediction of Satisfied User Ratio for compressed video
Authors: Jingwen Zhu, Patrick Le Callet, Anne-Flore Perrin, Sriram Sethuraman, Kumar Rahul
Date: 2022-06-20 00:00:00
Tasks: Video Compression

The human eye cannot perceive small pixel changes in images or videos until a certain threshold of distortion. In the context of video compression, Just Noticeable Difference (JND) is the smallest distortion level from which the human eye can perceive the difference between reference video and the distorted/compressed one. Satisfied-User-Ratio (SUR) curve is the complementary cumulative distribution function of the individual JNDs of a viewer group. However, most of the previous works predict each point in SUR curve by using features both from source video and from compressed videos with assumption that the group-based JND annotations follow Gaussian distribution, which is neither practical nor accurate. In this work, we firstly compared various common functions for SUR curve modeling. Afterwards, we proposed a novel parameter-driven method to predict the video-wise SUR from video features. Besides, we compared the prediction results of source-only features based (SRC-based) models and source plus compressed videos features (SRC+PVS-based) models.
Confidence-Guided Unsupervised Domain Adaptation for Cerebellum Segmentation
Authors: Xuan Li, Paule-J Toussaint, Alan Evans, Xue Liu
Date: 2022-06-14 00:00:00
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

The lack of a comprehensive high-resolution atlas of the cerebellum has hampered studies of cerebellar involvement in normal brain function and disease. A good representation of the tightly foliated aspect of the cerebellar cortex is difficult to achieve because of the highly convoluted surface and the time it would take for manual delineation. The quality of manual segmentation is influenced by human expert judgment, and automatic labelling is constrained by the limited robustness of existing segmentation algorithms. The 20umisotropic BigBrain dataset provides an unprecedented high resolution framework for semantic segmentation compared to the 1000um(1mm) resolution afforded by magnetic resonance imaging. To dispense with the manual annotation requirement, we propose to train a model to adaptively transfer the annotation from the cerebellum on the Allen Brain Human Brain Atlas to the BigBrain in an unsupervised manner, taking into account the different staining and spacing between sections. The distinct visual discrepancy between the Allen Brain and BigBrain prevents existing approaches to provide meaningful segmentation masks, and artifacts caused by sectioning and histological slice preparation in the BigBrain data pose an extra challenge. To address these problems, we propose a two-stage framework where we first transfer the Allen Brain cerebellum to a space sharing visual similarity with the BigBrain. We then introduce a self-training strategy with a confidence map to guide the model learning from the noisy pseudo labels iteratively. Qualitative results validate the effectiveness of our approach, and quantitative experiments reveal that our method can achieve over 2.6% loss reduction compared with other approaches.
Automated Coronary Calcium Scoring using U-Net Models through Semi-supervised Learning on Non-Gated CT Scans
Authors: Sanskriti Singh
Date: 2022-06-13 00:00:00

Every year, thousands of innocent people die due to heart attacks. Often undiagnosed heart attacks can hit people by surprise since many current medical plans don't cover the costs to require the searching of calcification on these scans. Only if someone is suspected to have a heart problem, a gated CT scan is taken, otherwise, there's no way for the patient to be aware of a possible heart attack/disease. While nongated CT scans are more periodically taken, it is harder to detect calcification and is usually taken for a purpose other than locating calcification in arteries. In fact, in real time coronary artery calcification scores are only calculated on gated CT scans, not nongated CT scans. After training a unet model on the Coronary Calcium and chest CT's gated scans, it received a DICE coefficient of 0.95 on its untouched test set. This model was used to predict on nongated CT scans, performing with a mean absolute error (MAE) of 674.19 and bucket classification accuracy of 41% (5 classes). Through the analysis of the images and the information stored in the images, mathematical equations were derived and used to automatically crop the images around the location of the heart. By performing semi-supervised learning the new cropped nongated scans were able to closely resemble gated CT scans, improving the performance by 91% in MAE (62.38) and 23% in accuracy.