pwc-archive/papers-with-abstracts huggingface dataset:
Chemical kinetic theory of aging
Authors: Alexey Kondyurin
Date: 2023-03-09 00:00:00

A theory of aging based on the principles of the kinetics of chemical reactions and the rules of natural selection of organisms is proposed. The theory is based on a hypothesis that the biochemical processes in the organism can be described in the terms of chemical reaction kinetics. The evolutionary process of organisms is determined by the goal of continuing life, and the natural selection forced organisms to develop in an optimized way for survival and reproduction, after which any further development of the organisms did not matter for natural selection and therefore was not regulated by it. Accordingly, the ongoing biochemical processes in the organism after giving birth were not stabilised and continued casual, which, in the case of complex systems of biochemical processes, led to imbalances and biochemical processes that did not contribute to health in the organism, resulting in aging. Based on this view, it is necessary to look for the key biochemical processes that regulate the vital activity of the organism. Balancing of these processes in the period after giving birth can artificially lead to the stabilization of the kinetics of biochemical reactions, and consequently, the continuation of life almost indefinitely.
Quantum formalism for cognitive psychology
Authors: Dorje C Brody
Date: 2023-03-09 00:00:00

The cognitive state of mind concerning a range of choices to be made can effectively be modelled in terms of an element of a high-dimensional Hilbert space. The dynamics of the state of mind resulting form information acquisition is characterised by the von Neumann-L\"uders projection postulate of quantum theory. This is shown to give rise to an uncertainty-minimising dynamical behaviour equivalent to the Bayesian updating, hence providing an alternative approach to characterising the dynamics of cognitive state that is consistent with the free energy principle in brain science. The quantum formalism however goes beyond the range of applicability of classical reasoning in explaining cognitive behaviours, thus opens up new and intriguing possibilities.
The Nobel Family
Authors: Richard S. J. Tol
Date: 2023-03-10 00:00:00

Nobel laureates cluster together. 696 of the 727 winners of the Nobel Prize in physics, chemistry, medicine, and economics belong to one single academic family tree. 668 trace their ancestry to Emmanuel Stupanus, 228 to Lord Rayleigh (physics, 1904). Craig Mello (medicine, 2006) counts 51 Nobelists among his ancestors. Chemistry laureates have the most Nobel ancestors and descendants, economics laureates the fewest. Chemistry is the central discipline. Its Nobelists have trained and are trained by Nobelists in other fields. Nobelists in physics (medicine) have trained (by) others. Economics stands apart. Openness to other disciplines is the same in recent and earlier times. The familial concentration of Nobelists is lower now than it used to be.
Academic Freedom and Innovation: A Research Note
Authors: David Audretsch, Christian Fisch, Chiara Franzoni, Paul P. Momtaz, Silvio Vismara
Date: 2023-03-10 00:00:00

The first-ever article published in Research Policy was Casimir's (1971) advocacy of academic freedom in light of the industry's increasing influence on research in universities. Half a century later, the literature attests to the dearth of work on the role of academic freedom for innovation. To fill this gap, we employ instrumental variable techniques to identify the impact of academic freedom on the quantity (patent applications) and quality (patent citations) of innovation output. The empirical evidence suggests that improving academic freedom by one standard deviation increases patent applications and forward citations by 41% and 29%, respectively. The results hold in a representative sample of 157 countries over the 1900-2015 period. This research note is also an alarming plea to policymakers: Global academic freedom has declined over the past decade for the first time in the last century. Our estimates suggest that the decline of academic freedom has resulted in a global loss quantifiable with at least 4.0% fewer patents filed and 5.9% fewer patent citations.
Ranked Choice Bedlam in a 2022 Oakland School Director Election
Authors: David McCune
Date: 2023-03-10 00:00:00

The November 2022 ranked choice election for District 4 School Director in Oakland, CA, was very interesting from the perspective of social choice theory. The election did not contain a Condorcet winner and exhibited downward and upward monotonicity paradoxes, for example. Furthermore, an error in the settings of the ranked choice tabulation software led to the wrong candidate being declared the winner. This article explores the strange features of this election and places it in the broader context of ranked choice elections in the United States.
What do surveys say about the historical trend of inequality and the applicability of two table-transformation methods?
Authors: Anna Naszodi
Date: 2023-03-10 00:00:00
Tasks: counterfactual, Survey

We apply a pseudo panel analysis of survey data from the years 2010 and 2017 about Americans' self-reported marital preferences and perform some formal tests on the sign and magnitude of the change in educational homophily from the generation of the early Boomers to the late Boomers, as well as from the early GenerationX to the late GenerationX. In the analysis, we control for changes in preferences over the course of the survey respondents' lives. We use the test results to decide whether the popular iterative proportional fitting (IPF) algorithm, or its alternative, the NM-method is more suitable for analyzing revealed marital preferences. These two methods construct different tables representing counterfactual joint educational distributions of couples. Thereby, they disagree on the trend of revealed preferences identified from the prevalence of homogamy by counterfactual decompositions. By finding self-reported homophily to display a U-shaped pattern, our tests reject the hypothesis that the IPF is suitable for constructing counterfactuals in general, while we cannot reject the applicability of the NM. The significance of our survey-based method-selection is due to the fact that the choice between the IPF and the NM makes a difference not only to the identified historical trend of revealed homophily, but also to what future paths of social inequality are believed to be possible.
Research on CPI Prediction Based on Natural Language Processing
Authors: Xiaobin Tang, Nuo Lei
Date: 2023-03-10 00:00:00
Tasks: Prediction, Unsupervised Pre-training

In the past, the seed keywords for CPI prediction were often selected based on empirical summaries of research and literature studies, which were prone to select omitted and invalid variables. In this paper, we design a keyword expansion technique for CPI prediction based on the cutting-edge NLP model, PANGU. We improve the CPI prediction ability using the corresponding web search index. Compared with the unsupervised pre-training and supervised downstream fine-tuning natural language processing models such as BERT and NEZHA, the PANGU model can be expanded to obtain more reliable CPI-generated keywords by its excellent zero-sample learning capability without the limitation of the downstream fine-tuning data set. Finally, this paper empirically tests the keyword prediction ability obtained by this keyword expansion method with historical CPI data.
The iterative proportional fitting algorithm and the NM-method: solutions for two different sets of problems
Authors: Anna Naszodi
Date: 2023-03-08 00:00:00
Tasks: counterfactual

In this paper, we identify two different sets of problems. The first covers the problems that the iterative proportional fitting (IPF) algorithm was developed to solve. These concern completing a population table by using a sample. The other set concerns constructing a counterfactual population table with the purpose of comparing two populations. The IPF is commonly applied by social scientists to solve problems not only in the first set, but also in the second one. We show that while it is legitimate to use the IPF for the first set of problems, it is not the right tool to address the problems of the second kind. We promote an alternative of the IPF, the NM-method, for solving problems in the second set. We provide both theoretical and empirical comparisons of these methods.
A General Impossibility Theorem on Pareto Efficiency and Bayesian Incentive Compatibility
Authors: Kazuya Kikuchi, Yukio Koriyama
Date: 2023-03-10 00:00:00

This paper studies a general class of social choice problems in which agents' payoff functions (or types) are privately observable random variables, and monetary transfers are not available. We consider cardinal social choice functions which may respond to agents' preference intensities as well as preference rankings. We show that a social choice function is ex ante Pareto efficient and Bayesian incentive compatible if and only if it is dictatorial. The result holds for arbitrary numbers of agents and alternatives, and under a fairly weak assumption on the joint distribution of types, which allows for arbitrary correlations and asymmetries.
A Distributionally Robust Random Utility Model
Authors: David Müller, Emerson Melo, Ruben Schlotter
Date: 2023-03-10 00:00:00
Tasks: model

This paper introduces the distributionally robust random utility model (DRO-RUM), which allows the preference shock (unobserved heterogeneity) distribution to be misspecified or unknown. We make three contributions using tools from the literature on robust optimization. First, by exploiting the notion of distributionally robust social surplus function, we show that the DRO-RUM endogenously generates a shock distributionthat incorporates a correlation between the utilities of the different alternatives. Second, we show that the gradient of the distributionally robust social surplus yields the choice probability vector. This result generalizes the celebrated William-Daly-Zachary theorem to environments where the shock distribution is unknown. Third, we show how the DRO-RUM allows us to nonparametrically identify the mean utility vector associated with choice market data. This result extends the demand inversion approach to environments where the shock distribution is unknown or misspecified. We carry out several numerical experiments comparing the performance of the DRO-RUM with the traditional multinomial logit and probit models.
Strategy-proofness with single-peaked and single-dipped preferences
Authors: Jorge Alcalde-Unzu, Oihane Gallo, Marc Vorsatz
Date: 2023-03-10 00:00:00

We analyze the problem of locating a public facility in a domain of single-peaked and single-dipped preferences when the social planner knows the type of preference (single-peaked or single-dipped) of each agent. Our main result characterizes all strategy-proof rules and shows that they can be decomposed into two steps. In the first step, the agents with single-peaked preferences are asked about their peaks and, for each profile of reported peaks, at most two alternatives are preselected. In the second step, the agents with single-dipped preferences are asked to reveal their dips to complete the decision between the preselected alternatives. Our result generalizes the findings of Moulin (1980) and Barber\`a and Jackson (1994) for single-peaked and of Manjunath (2014) for single-dipped preferences. Finally, we show that all strategy-proof rules are also group strategy-proof and analyze the implications of Pareto efficiency.
Equilibrium Selection in Pure Bubble Models by Dividend Injection
Authors: Tomohiro Hirano, Alexis Akira Toda
Date: 2023-03-10 00:00:00

Rational pure bubble models feature multiple (and often a continuum of) equilibria, which makes model predictions and policy analyses non-robust. We show that when the interest rate in the fundamental equilibrium is below the economic growth rate ($R<G$), a bubbly equilibrium with $R=G$ exists. By injecting dividends to the bubble asset that grow slower than the aggregate economy, we can eliminate the fundamental steady state and resolve equilibrium indeterminacy. We show the general applicability of dividend injection through examples in overlapping generations and infinite-horizon models with or without production or financial frictions.
Material Identification From Radiographs Without Energy Resolution
Authors: Michael T. McCann, Elena Guardincerri, Samuel M. Gonzales, Lauren A. Misurek, Jennifer L. Schei, Marc L. Klasky
Date: 2023-03-10 00:00:00
Tasks: Combinatorial Optimization

We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, including in biomedical imaging, nondestructive testing, and security. While existing techniques for radiographic material identification make use of dual energy sources, energy-resolving detectors, or additional (e.g., neutron) measurements, such setups are not always practical-requiring additional hardware and complicating imaging. We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware. Assuming a setting where the geometry of each object in the scene is known and the materials come from a known set of possible materials, we pose the problem as a combinatorial optimization with a loss function that accounts for the presence of scatter and an unknown gain and propose a branch and bound algorithm to efficiently solve it. We present experiments on both synthetic data and real, experimental data with relevance to security applications-thick, dense objects imaged with MeV X-rays. We show that material identification can be efficient and accurate, for example, in a scene with three shells (two copper, one aluminum), our algorithm ran in six minutes on a consumer-level laptop and identified the correct materials as being among the top 10 best matches out of 8,000 possibilities.
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders
Authors: Franck Galpin, Muhammet Balcilar, Frédéric Lefebvre, Fabien Racapé, Pierre Hellier
Date: 2023-03-10 00:00:00
Tasks: Quantization, Video Compression

End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications. While most complex models are on par with the latest compression standard like VVC/H.266 on objective metrics, practical implementation and complexity remain strong issues for real-world applications. In this paper, we propose a practical implementation suitable for realistic applications, leading to a low-complexity model. We demonstrate that some gains can be achieved on top of a state-of-the-art low-complexity AE, even when using simpler implementation. Improvements include off-training entropy coding improvement and encoder side Rate Distortion Optimized Quantization. Results show a 19% improvement in BDrate on basic implementation of fully-factorized model, and 15.3% improvement compared to the original implementation. The proposed implementation also allows a direct integration of such approaches on a variety of platforms.
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI
Authors: Daniel M. Lang, Eli Schwartz, Cosmin I. Bercea, Raja Giryes, Julia A. Schnabel
Date: 2023-03-10 00:00:00
Tasks: Anomaly Detection, Lesion Detection

Self-supervised models allow (pre-)training on unlabeled data and therefore have the potential to overcome the need for large annotated cohorts. One leading self-supervised model is the masked autoencoder (MAE) which was developed on natural imaging data. The MAE is masking out a high fraction of visual transformer (ViT) input patches, to then recover the uncorrupted images as a pretraining task. In this work, we extend MAE to perform anomaly detection on breast magnetic resonance imaging (MRI). This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contrast enhanced MRI sequences, aiming at lesion detection without the need for intravenous injection of contrast media and temporal image acquisition. During training, only non-cancerous images are presented to the model, with the purpose of localizing anomalous tumor regions during test time. We use a public dataset for model development. Performance of the architecture is evaluated in reference to subtraction images created from dynamic contrast enhanced (DCE)-MRI.
Multiple description video coding for real-time applications using HEVC
Authors: Trung Hieu Le, Marc Antonini, Marc Lambert, Karima Alioua
Date: 2023-03-10 00:00:00
Tasks: Decoder

Remote control vehicles require the transmission of large amounts of data, and video is one of the most important sources for the driver. To ensure reliable video transmission, the encoded video stream is transmitted simultaneously over multiple channels. However, this solution incurs a high transmission cost due to the wireless channel's unreliable and random bit loss characteristics. To address this issue, it is necessary to use more efficient video encoding methods that can make the video stream robust to noise. In this paper, we propose a low-complexity, low-latency 2-channel Multiple Description Coding (MDC) solution with an adaptive Instantaneous Decoder Refresh (IDR) frame period, which is compatible with the HEVC standard. This method shows better resistance to high packet loss rates with lower complexity.
Characterizing the Ambiguity Function of Constant-Envelope OFDM Waveforms
Authors: David G. Felton, David A. Hague
Date: 2023-03-10 00:00:00

This paper investigates the radar Ambiguity Function (AF) properties of Constant Envelope Orthogonal Frequency Division Multiplexing (CE-OFDM) waveforms employing Phase-Shift Keying (PSK). The CE-OFDM is in fact a special case of the Multi-Tone Sinusoidal Frequency Modulated (MTSFM) waveform which allows for applying many of the same mathematical techniques of the MTSFM model to the CE-OFDM model. This results in novel compact closed-form expressions for the spectrum, AF, and Auto-Correlation Function (ACF) of the CE-OFDM waveform. The mainlobe structure of the CE-OFDM's AF is characterized by the Ellipse of Ambigiuity (EOA) model. This produces precise closed-form expressions for the CE-OFDM's Root-Mean Square (RMS) bandwidth and the degree of range-Doppler coupling present in the waveform's AF mainlobe. These expressions show that a CE-OFDM waveform employing PSK as the symbol encoding scheme will possess a fixed RMS bandwidth for fixed modulation index $h$ and number of sub-carriers $L$. Additionally, we show that the EOA model predicts that a CE-OFDM waveform employing PSK encoding will almost always possess a ``Thumbtack-Like'' AF shape.
On Hybrid Radar Fusion for Integrated Sensing and Communication
Authors: Akhileswar Chowdary, Ahmad Bazzi, Marwa Chafii
Date: 2023-03-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

The following paper introduces a novel integrated sensing and communication (ISAC) scenario termed hybrid radar fusion. In this setting, the dual-functional radar and communications (DFRC) base station (BS) acts as a mono-static radar in the downlink (DL), for sensing purposes, while performing its DL communication tasks. Meanwhile, the communication users act as distributed bi-static radar nodes in the uplink (UL) following a frequency-division duplex protocol. The DFRC BS fuses the information available at different DL and UL resource bands to estimate the angles-of-arrival (AoAs) of the multiple targets existing in the scene. In this work, we derive the maximum likelihood (ML) criterion for the hybrid radar fusion problem at hand. Additionally, we design efficient estimators; the first algorithm is based on an alternating optimization approach to solve the ML criterion, while the second one designs an optimization framework that leads to an alternating subspace approach to estimate AoAs for both the target and users. Finally, we demonstrate the superior performance of both algorithms in different scenarios, and the gains offered by these proposed methods through numerical simulations.
Scattering and Gathering for Spatially Varying Blurs
Authors: Nicholas Chimitt, Xingguang Zhang, Yiheng Chi, Stanley H. Chan
Date: 2023-03-10 00:00:00
Tasks: Computational Efficiency, Denoising

A spatially varying blur kernel $h(\mathbf{x},\mathbf{u})$ is specified by an input coordinate $\mathbf{u} \in \mathbb{R}^2$ and an output coordinate $\mathbf{x} \in \mathbb{R}^2$. For computational efficiency, we sometimes write $h(\mathbf{x},\mathbf{u})$ as a linear combination of spatially invariant basis functions. The associated pixelwise coefficients, however, can be indexed by either the input coordinate or the output coordinate. While appearing subtle, the two indexing schemes will lead to two different forms of convolutions known as scattering and gathering, respectively. We discuss the origin of the operations. We discuss conditions under which the two operations are identical. We show that scattering is more suitable for simulating how light propagates and gathering is more suitable for image filtering such as denoising.
Energy-Efficient RIS-Enabled NOMA Communication for 6G LEO Satellite Networks
Authors: Wali Ullah Khan, Eva Lagunas, Asad Mahmood, Symeon Chatzinotas, Björn Ottersten
Date: 2023-03-09 00:00:00

This paper proposes an energy-efficient RIS-enabled NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals at LEO satellite and passive beamforming at RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the problem of energy efficiency maximization is formulated as non-convex. In practice, it is very challenging to obtain the optimal solution for such problems. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate efficient power for ground terminals at satellite using Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. To validate the proposed solution, numerical results are also provided to demonstrate the benefits of the proposed optimization framework.