pwc-archive/papers-with-abstracts huggingface dataset:
Wavelet Conditional Renormalization Group
Authors: Tanguy Marchand, Misaki Ozawa, Giulio Biroli, Stéphane Mallat
Date: 2022-07-11 00:00:00

We develop a multiscale approach to estimate high-dimensional probability distributions from a dataset of physical fields or configurations observed in experiments or simulations. In this way we can estimate energy functions (or Hamiltonians) and efficiently generate new samples of many-body systems in various domains, from statistical physics to cosmology. Our method -- the Wavelet Conditional Renormalization Group (WC-RG) -- proceeds scale by scale, estimating models for the conditional probabilities of "fast degrees of freedom" conditioned by coarse-grained fields. These probability distributions are modeled by energy functions associated with scale interactions, and are represented in an orthogonal wavelet basis. WC-RG decomposes the microscopic energy function as a sum of interaction energies at all scales and can efficiently generate new samples by going from coarse to fine scales. Near phase transitions, it avoids the "critical slowing down" of direct estimation and sampling algorithms. This is explained theoretically by combining results from RG and wavelet theories, and verified numerically for the Gaussian and $\varphi^4$ field theories. We show that multiscale WC-RG energy-based models are more general than local potential models and can capture the physics of complex many-body interacting systems at all length scales. This is demonstrated for weak-gravitational-lensing fields reflecting dark matter distributions in cosmology, which include long-range interactions with long-tail probability distributions. WC-RG has a large number of potential applications in non-equilibrium systems, where the underlying distribution is not known {\it a priori}. Finally, we discuss the connection between WC-RG and deep network architectures.
Boosting Heterogeneous Catalyst Discovery by Structurally Constrained Deep Learning Models
Authors: Alexey N. Korovin, Innokentiy S. Humonen, Artem I. Samtsevich, Roman A. Eremin, Artem I. Vasilyev, Vladimir D. Lazarev, Semen A. Budennyy
Date: 2022-07-11 00:00:00
Tasks: Computational chemistry, Deep Learning

The discovery of new catalysts is one of the significant topics of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources. Recently developed deep learning approaches such as graph neural networks (GNNs) open new opportunity to significantly extend scope for modelling novel high-performance catalysts. Nevertheless, the graph representation of particular crystal structure is not a straightforward task due to the ambiguous connectivity schemes and numerous embeddings of nodes and edges. Here we present embedding improvement for GNN that has been modified by Voronoi tesselation and is able to predict the energy of catalytic systems within Open Catalyst Project dataset. Enrichment of the graph was calculated via Voronoi tessellation and the corresponding contact solid angles and types (direct or indirect) were considered as features of edges and Voronoi volumes were used as node characteristics. The auxiliary approach was enriching node representation by intrinsic atomic properties (electronegativity, period and group position). Proposed modifications allowed us to improve the mean absolute error of the original model and the final error equals to 651 meV per atom on the Open Catalyst Project dataset and 6 meV per atom on the intermetallics dataset. Also, by consideration of additional dataset, we show that a sensible choice of data can decrease the error to values above physically-based 20 meV per atom threshold.
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Authors: Hengrui Zhang, Wei Wayne Chen, Akshay Iyer, Daniel W. Apley, Wei Chen
Date: 2022-07-11 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Uncertainty Quantification

Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian Optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models' predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.
A geometric speed limit for acceleration by natural selection in evolutionary processes
Authors: Masahiro Hoshino, Ryuna Nagayama, Kohei Yoshimura, Jumpei F. Yamagishi, Sosuke Ito
Date: 2022-07-11 00:00:00

We derived a new speed limit in population dynamics, which is a fundamental limit on the evolutionary rate. By splitting the contributions of selection and mutation to the evolutionary rate, we obtained the new bound on the speed of arbitrary observables, named the selection bound, that can be tighter than the conventional Cram\'{e}r--Rao bound. Remarkably, the selection bound can be much tighter if the contribution of selection is more dominant than that of mutation. This tightness can be geometrically characterized by the correlation between the observable of interest and the growth rate. We also numerically illustrate the effectiveness of the selection bound in the transient dynamics of evolutionary processes and discuss how to test our speed limit experimentally.
Stochastic dynamics and ribosome-RNAP interactions in Transcription-Translation Coupling
Authors: Xiangting Li, Tom Chou
Date: 2022-07-10 00:00:00
Tasks: Translation

Under certain cellular conditions, transcription and mRNA translation in prokaryotes appear to be "coupled," in which the formation of mRNA transcript and production of its associated protein are temporally correlated. Such transcription-translation coupling (TTC) has been evoked as a mechanism that speeds up the overall process, provides protection during the transcription, and/or regulates the timing of transcript and protein formation. What molecular mechanisms underlie ribosome-RNAP coupling and how they can perform these functions have not been explicitly modeled. We develop and analyze a continuous-time stochastic model that incorporates ribosome and RNAP elongation rates, initiation and termination rates, RNAP pausing, and direct ribosome and RNAP interactions (exclusion and binding). Our model predicts how distributions of delay times depend on these molecular features of transcription and translation. We also propose additional measures for TTC: a direct ribosome-RNAP binding probability and the fraction of time the translation-transcription process is "protected" from attack by transcription-terminating proteins. These metrics quantify different aspects of TTC and differentially depend on parameters of known molecular processes. We use our metrics to reveal how and when our model can exhibit either acceleration or deceleration of transcription, as well as protection from termination. Our detailed mechanistic model provides a basis for designing new experimental assays that can better elucidate the mechanisms of TTC.
Adaptive survival movement strategy to local epidemic outbreaks in cyclic models
Authors: J. Menezes, B. Moura, E. Rangel
Date: 2022-07-11 00:00:00

We study the generalised rock-paper-scissors game with five species whose organisms face local epidemic outbreaks. As an evolutionary behavioural survival strategy, organisms of one out of the species move in the direction with more enemies of their enemies to benefit from protection against selection. We consider that each organism scans the environment, performing social distancing instead of agglomerating when perceiving that the density of sick organisms is higher than a tolerable threshold. Running stochastic simulations, we study the interference of the adaptive movement survival strategy in spatial pattern formation, calculating the characteristic length scale of the typical spatial domains inhabited by organisms of each species. We compute how social distancing trigger impacts the chances of an individual being killed in the cyclic game and contaminated by the disease. The outcomes show that the species predominates in the cyclic game because of the organisms' local adaptation. The territory occupied by the species grows with the proportion of individuals learning to trigger the social distancing tactic. We also show that organisms that perceive large distances more properly execute the adaptive strategy, promptly triggering the social distancing tactic and choosing the correct direction to move. Our findings may contribute to understanding the role of adaptive behaviour when environmental changes threaten biodiversity.
Adaptive Self-supervision Algorithms for Physics-informed Neural Networks
Authors: Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney, Amir Gholami
Date: 2022-07-08 00:00:00

Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can lead to optimization difficulties. Here, we study the impact of the location of the collocation points on the trainability of these models. We find that the vanilla PINN performance can be significantly boosted by adapting the location of the collocation points as training proceeds. Specifically, we propose a novel adaptive collocation scheme which progressively allocates more collocation points (without increasing their number) to areas where the model is making higher errors (based on the gradient of the loss function in the domain). This, coupled with a judicious restarting of the training during any optimization stalls (by simply resampling the collocation points in order to adjust the loss landscape) leads to better estimates for the prediction error. We present results for several problems, including a 2D Poisson and diffusion-advection system with different forcing functions. We find that training vanilla PINNs for these problems can result in up to 70% prediction error in the solution, especially in the regime of low collocation points. In contrast, our adaptive schemes can achieve up to an order of magnitude smaller error, with similar computational complexity as the baseline. Furthermore, we find that the adaptive methods consistently perform on-par or slightly better than vanilla PINN method, even for large collocation point regimes. The code for all the experiments has been open sourced.
Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with low-energy excitation light source
Authors: Mengjie Shi, Tom Vercauteren, Wenfeng Xia
Date: 2022-07-09 00:00:00
Tasks: Denoising, GPU

Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution and thus holds tremendous promise for a wide range of pre-clinical and clinical applications. Compact and affordable light sources such as light-emitting diodes (LEDs) and laser diodes (LDs) are promising alternatives to bulky and expensive solid-state laser systems that are commonly used as PA light sources. These could accelerate the clinical translation of PA technology. However, PA signals generated with these light sources are readily degraded by noise due to the low optical fluence, leading to decreased signal-to-noise ratio (SNR) in PA images. In this work, a spatiotemporal singular value decomposition (SVD) based PA denoising method was investigated for these light sources that usually have low fluence and high repetition rates. The proposed method leverages both spatial and temporal correlations between radiofrequency (RF) data frames. Validation was performed on simulations and in vivo PA data acquired from human fingers (2D) and forearm (3D) using a LED-based system. Spatiotemporal SVD greatly enhanced the PA signals of blood vessels corrupted by noise while preserving a high temporal resolution to slow motions, improving the SNR of in vivo PA images by 1.1, 0.7, and 1.9 times compared to single frame-based wavelet denoising, averaging across 200 frames, and single frame without denoising, respectively. The proposed method demonstrated a processing time of around 50 \mus per frame with SVD acceleration and GPU. Thus, spatiotemporal SVD is well suited to PA imaging systems with low-energy excitation light sources for real-time in vivo applications.
Detecting Grouped Local Average Treatment Effects and Selecting True Instruments
Authors: Nicolas Apfel, Helmut Farbmacher, Rebecca Groh, Martin Huber, Henrika Langen
Date: 2022-07-10 00:00:00

Under an endogenous binary treatment with heterogeneous effects and multiple instruments, we propose a two-step procedure for identifying complier groups with identical local average treatment effects (LATE) despite relying on distinct instruments, even if several instruments violate the identifying assumptions. We use the fact that the LATE is homogeneous for instruments which (i) satisfy the LATE assumptions (instrument validity and treatment monotonicity in the instrument) and (ii) generate identical complier groups in terms of treatment propensities given the respective instruments. We propose a two-step procedure, where we first cluster the propensity scores in the first step and find groups of IVs with the same reduced form parameters in the second step. Under the plurality assumption that within each set of instruments with identical treatment propensities, instruments truly satisfying the LATE assumptions are the largest group, our procedure permits identifying these true instruments in a data driven way. We show that our procedure is consistent and provides consistent and asymptotically normal estimators of underlying LATEs. We also provide a simulation study investigating the finite sample properties of our approach and an empirical application investigating the effect of incarceration on recidivism in the US with judge assignments serving as instruments.
Identification and Inference for Welfare Gains without Unconfoundedness
Authors: Undral Byambadalai
Date: 2022-07-09 00:00:00

This paper studies identification and inference of the welfare gain that results from switching from one policy (such as the status quo policy) to another policy. The welfare gain is not point identified in general when data are obtained from an observational study or a randomized experiment with imperfect compliance. I characterize the sharp identified region of the welfare gain and obtain bounds under various assumptions on the unobservables with and without instrumental variables. Estimation and inference of the lower and upper bounds are conducted using orthogonalized moment conditions to deal with the presence of infinite-dimensional nuisance parameters. I illustrate the analysis by considering hypothetical policies of assigning individuals to job training programs using experimental data from the National Job Training Partnership Act Study. Monte Carlo simulations are conducted to assess the finite sample performance of the estimators.
Spatial Econometrics for Misaligned Data
Authors: Guillaume Allaire Pouliot
Date: 2022-07-08 00:00:00
Tasks: Econometrics, regression

We produce methodology for regression analysis when the geographic locations of the independent and dependent variables do not coincide, in which case we speak of misaligned data. We develop and investigate two complementary methods for regression analysis with misaligned data that circumvent the need to estimate or specify the covariance of the regression errors. We carry out a detailed reanalysis of Maccini and Yang (2009) and find economically significant quantitative differences but sustain most qualitative conclusions.
Temperature and Mental Health: Evidence from Helpline Calls
Authors: Benedikt Janzen
Date: 2022-07-11 00:00:00

This paper studies the short-term effects of ambient temperature on mental health using data on nearly half a million helpline calls in Germany. Leveraging location-based routing of helpline calls and random day-to-day weather fluctuations, I find a negative effect of temperature extremes on mental health as revealed by an increase in the demand for telephone counseling services. On days with an average temperature above 25{\deg}C (77{\deg}F) and below 0{\deg}C (32{\deg}F), call volume is 3.4 and 5.1 percent higher, respectively, than on mid-temperature days. Mechanism analysis reveals pronounced adverse effects of cold temperatures on social and psychological well-being and of hot temperatures on psychological well-being and violence. More broadly, the findings of this work contribute to our understanding of how changing climatic conditions will affect population mental health and associated social costs in the near future.
Research Joint Ventures: The Role of Financial Constraints
Authors: Philipp Brunner, Igor Letina, Armin Schmutzler
Date: 2022-07-11 00:00:00

This paper provides a novel theory of research joint ventures for financially constrained firms. When firms choose R&D portfolios, an RJV can help to coordinate research efforts, reducing investments in duplicate projects. This can free up resources, increase the variety of pursued projects and thereby increase the probability of discovering the innovation. RJVs improve innovation outcomes when market competition is weak and external financing conditions are bad. An RJV may increase the innovation probability and nevertheless lower total R&D costs. RJVs that increase innovation also increase consumer surplus and tend to be profitable, but innovation-reducing RJVs also exist. Finally, we compare RJVs to innovation-enhancing mergers.
Nobel begets Nobel
Authors: Richard S. J. Tol
Date: 2022-07-10 00:00:00

I construct the professor-student network for laureates of and candidates for the Nobel Prize in Economics. I study the effect of proximity to previous Nobelists on winning the Nobel Prize. Conditional on being Nobel-worthy, students and grandstudents of Nobel laureates are significantly less likely to win. Professors and fellow students of Nobel Prize winners, however, are significantly more likely to win.
Information Design in Cheap Talk
Authors: Qianjun Lyu, Wing Suen
Date: 2022-07-11 00:00:00
Tasks: valid

An uninformed sender publicly commits to an informative experiment about an uncertain state, privately observes its outcome, and sends a cheap-talk message to a receiver. We provide an algorithm valid for arbitrary state-dependent preferences that will determine the sender's optimal experiment and his equilibrium payoff under binary state space. We give sufficient conditions for informative information transmission. These conditions depend more on marginal incentives -- how payoffs vary with the state -- than on the alignment of sender's and receiver's rankings over actions within a state. The algorithm can be easily modified to study the canonical cheap talk game with a perfectly informed sender.
Extractive contest design
Authors: Tomohiko Kawamori
Date: 2020-06-02 00:00:00

We consider contest success functions (CSFs) that extract contestants' prize values. In the common-value case, there exists a CSF extractive in any equilibrium. In the observable-private-value case, there exists a CSF extractive in some equilibrium; there exists a CSF extractive in any equilibrium if and only if the number of contestants is greater than or equal to three or the values are homogeneous. In the unobservable-private-value case, there exists no CSF extractive in some equilibrium. When extractive CSFs exist, we explicitly present one of them.
A proposal for measuring the structure of economic ecosystems: a mathematical and complex network analysis approach
Authors: M. S. Tedesco, M. A. Nunez-Ochoa, F. Ramos, O. Medrano, K Beuchot
Date: 2022-07-09 00:00:00

The benefits of using complex network analysis (CNA) to study complex systems, such as an economy, have become increasingly evident in recent years. However, the lack of a single comparative index that encompasses the overall wellness of a structure can hinder the simultaneous analysis of multiple ecosystems. A formula to evaluate the structure of an economic ecosystem is proposed here, implementing a mathematical approach based on CNA metrics to construct a comparative measure that reflects the collaboration dynamics and its resultant structure. This measure provides the relevant actors with an enhanced sense of the social dynamics of an economic ecosystem, whether related to business, innovation, or entrepreneurship. Available graph metrics were analysed, and 14 different formulas were developed. The efficiency of these formulas was evaluated on real networks from 11 different innovation-driven entrepreneurial economic ecosystems in six countries from Latin America and Europe and on 800 random graphs simulating similarly constructed networks.
Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis
Authors: Long Zhuo, Guangcong Wang, Shikai Li, Wayne Wu, Ziwei Liu
Date: 2022-07-11 00:00:00
Tasks: GPU, Knowledge Distillation, Motion Compensation, Video-to-Video Synthesis

Video-to-Video synthesis (Vid2Vid) has achieved remarkable results in generating a photo-realistic video from a sequence of semantic maps. However, this pipeline suffers from high computational cost and long inference latency, which largely depends on two essential factors: 1) network architecture parameters, 2) sequential data stream. Recently, the parameters of image-based generative models have been significantly compressed via more efficient network architectures. Nevertheless, existing methods mainly focus on slimming network architectures and ignore the size of the sequential data stream. Moreover, due to the lack of temporal coherence, image-based compression is not sufficient for the compression of the video task. In this paper, we present a spatial-temporal compression framework, \textbf{Fast-Vid2Vid}, which focuses on data aspects of generative models. It makes the first attempt at time dimension to reduce computational resources and accelerate inference. Specifically, we compress the input data stream spatially and reduce the temporal redundancy. After the proposed spatial-temporal knowledge distillation, our model can synthesize key-frames using the low-resolution data stream. Finally, Fast-Vid2Vid interpolates intermediate frames by motion compensation with slight latency. On standard benchmarks, Fast-Vid2Vid achieves around real-time performance as 20 FPS and saves around 8x computational cost on a single V100 GPU.