Priyam Das - Academia.edu (original) (raw)
Papers by Priyam Das
Sankhya B
In this paper, a derivative-free algorithm for global optimization is developed for a function of... more In this paper, a derivative-free algorithm for global optimization is developed for a function of parameters, each coming from a (possibly distinct) bounded interval. Main principle of this algorithm is to make jumps along the coordinates of the parameter one at a time with varying step-sizes within the restricted parameter space and search for the best direction to move in a greedy manner during each iteration. Unlike most of the existing methods since the objective function is evaluated at independent directions during an update step, incorporation of parallel computing makes it even faster. Requirement of parallelization grows only in the order of the dimension of the parameter space, which makes it more convenient for parallelization. A comparative study of the performances of this algorithm and other existing algorithms have been shown for a few moderate and high-dimensional benchmark global optimization problems.
JAMA Network Open
ImportanceThe COVID-19 pandemic has been associated with an increase in mental health diagnoses a... more ImportanceThe COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents.ObjectiveTo estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic.Design, Setting, and ParticipantsThis retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children’s hospitals in the US and France.Main Outcomes and MeasuresChange in the mo...
Nanophotonics
Integration of devices generating non-classical states (such as entanglement) into photonic circu... more Integration of devices generating non-classical states (such as entanglement) into photonic circuits is one of the major goals in achieving integrated quantum circuits (IQCs). This is demonstrated successfully in recent decades. Controlling the non-classicality generation in these micron-scale devices is also crucial for the robust operation of the IQCs. Here, we propose a micron-scale quantum entanglement device whose nonlinearity (so the generated non-classicality) can be tuned by several orders of magnitude via an applied voltage without altering the linear response. Quantum emitters (QEs), whose level-spacing can be tuned by voltage, are embedded into the hotspot of a metal nanostructure (MNS). QE-MNS coupling introduces a Fano resonance in the “nonlinear response”. Nonlinearity, already enhanced extremely due to localization, can be controlled by the QEs’ level-spacing. Nonlinearity can either be suppressed or be further enhanced by several orders. Fano resonance takes place in...
arXiv (Cornell University), Aug 14, 2017
We demonstrate complete quantum state transfer of an atomic Bose-Einstein condensate to molecular... more We demonstrate complete quantum state transfer of an atomic Bose-Einstein condensate to molecular condensate, mediated by solitonic excitations in a cigar shaped mean-field geometry. Starting with a localized solitonic atomic condensate, we show compatible gray solitonic configuration in the molecular condensate, which results in complete atom-molecule conversion. The effect of inter and intra-species interactions on the formation of molecular condensate is explicated in the presence of Raman Photoassociation. It is found that photoassociation plays a crucial role in the coherent atom-molecule conversion as well as in the soliton dynamics. The gray soliton dispersion reveals bistable behaviour, showing a re-entrant phase in a physically accessible parametric domain.
arXiv (Cornell University), Sep 6, 2019
In the context of a binary classification problem, the optimal linear combination of continuous p... more In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing an empirical estimate of the area under the receiver operating characteristic (ROC) curve (AUC). For multi-category responses, the optimal predictor combination can similarly be obtained by maximization of the empirical hypervolume under the manifold (HUM). This problem is particularly relevant to medical research, where it may be of interest to diagnose a disease with various subtypes or predict a multicategory outcome. Since the empirical HUM is discontinuous, non-differentiable, and possibly multi-modal, solving this maximization problem requires a global optimization technique. Estimation of the optimal coefficient vector using existing global optimization techniques is computationally expensive, becoming prohibitive as the number of predictors and the number of outcome categories increases. We propose an efficient derivative-free black-box optimization technique based on pattern search to solve this problem. Through extensive simulation studies, we demonstrate that the proposed method achieves better performance compared to existing methods including the stepdown algorithm. Finally, we illustrate the proposed method to predict swallowing difficulty after radiation therapy for oropharyngeal cancer based on radiation dose to various structures in the head and neck.
npj Digital Medicine
Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we eva... more Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size ...
The European Physical Journal C
Wormholes are tunnels or short-cuts in space-time, and their existence is very important for huma... more Wormholes are tunnels or short-cuts in space-time, and their existence is very important for human civilization to express the vastness of space and time. So, it is necessary to analyze our own Milky Way galaxy if it can harbour any wormhole. This work is dedicated to the existence of wormhole geometry(at least theoretically) in the bulge and halo of the Milky Way Galaxy. The structure and existence of wormholes are verified in both the bulge and the halo region of the Milky Way galaxy (MWG). Different dark matter profiles like pseudo-isothermal, NFW and Universal Rotational Curve (URC) are analyzed to harbour these cosmic tunnels. Three kinds of redshift functions are used for each dark matter profile with the global monopole charge to cover all the possibilities of MWG supporting wormhole geometry.
Nature Human Behaviour
Making good decisions requires thinking ahead, but the huge number of actions and outcomes one co... more Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near-optimal under some circumstances, but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.
We have used the concept of De Broglie's matter wave associated with particles to derive a invers... more We have used the concept of De Broglie's matter wave associated with particles to derive a inverse square law of gravitation like the newton's law of gravitation at the plank's length with a slight modification. Obtaining the Newtonian form we have further extrapolated it to formulate Einstein's field equation, which generally is embedded with a slight modification.The Schwarzschild solution is calculated from the modified field equation, which gives us the Schwarzschild radius of a miniature black hole. Using the solution, the entropy and the hawking temperature is also modeled theoretically for the miniature black hole at the plank's order.
![Research paper thumbnail of Controlling the lifetime of cucurbit[8]uril based self-abolishing nanozymes](https://attachments.academia-assets.com/113152493/thumbnails/1.jpg)
](Chemical Science, 2022
Self-inhibitory feedback regulated transient assembly of a CB[8] based nanozyme is reported whose... more Self-inhibitory feedback regulated transient assembly of a CB[8] based nanozyme is reported whose lifetime can be manipulated in multiple ways, ranging from minutes to hours.
arXiv: Optimization and Control, 2016
Black-box optimization of objective function of parameters belonging to simplex arises in many in... more Black-box optimization of objective function of parameters belonging to simplex arises in many inference and predictive models. Das (2016) introduced Greedy Co-ordinate Descent of Varying Step-sizes on Simplex (GCDVSS) which efficiently optimizes any black-box function whose parameters belong to a simplex. In this paper, that method has been modified and extended for the case where the set of parameters may belong to multiple simplex block of different sizes. The main principle of this algorithm is to make jumps of varying step-sizes within each simplexes simultaneously and searching for the best direction for movement. Since this algorithm is designed specially for multiple simplex blocks parameter space, the proportion of movements made within the parameter space during the update step of a iteration is relatively higher for the proposed algorithm. Starting from a single initial guess, unlike genetic algorithm or simulated annealing, requirement of parallelization for this algorit...
arXiv: Quantum Gases, 2018
The effect of light-matter interaction is investigated for a situation where counter propagating ... more The effect of light-matter interaction is investigated for a situation where counter propagating laser pulses of localized nature are incident on the atomic condensate. In contrast to the earlier investigations on the similar systems, it's assumed that the laser beams are ultra-fast and they have a mathrmsech2\mathrm{sech}^2mathrmsech2 profile. Specifically, we consider a quasi-homogeneous, later extended to inhomogeneous, Bose-Einstein condensate (BEC), which is exposed to two counter propagating orthogonally polarized ultra-fast laser beams of equal intensity. The electromagnetic field creates an optical potential for the Bose-Einstein condensate, which in turn modifies the optical field. Hence, light and matter are found to contentiously exchange energy and thus to modify themselves dynamically. In the inhomogenous case, a self-similar method is used here to treat a cigar-shaped BEC exposed to light. Our theoretical analysis in a hither to unexplored regime of BEC-light interaction hints at the ...
Making good decisions requires thinking ahead, but the huge number of actions and outcomes one co... more Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near-optimal under some circumstances, but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.
Sankhya B, 2020
In this paper, we develop a novel derivative-free deterministic greedy algorithm for global optim... more In this paper, we develop a novel derivative-free deterministic greedy algorithm for global optimization of any objective function of parameters belonging to a unit-simplex. Main principle of the proposed algorithm is making jumps of varying step-sizes within the simplex parameter space and searching for the best direction to move in a greedy manner. Unlike most of the other existing methods of constraint optimization, here the objective function is evaluated at independent directions within an iteration. Thus incorporation of parallel computing makes it even faster. Requirement of parallelization grows only in the order of the dimension of the parameter space, which makes it more convenient for solving high-dimensional optimization problems in simplex parameter space using parallel computing. A comparative study of the performances of this algorithm and other existing algorithms have been shown for some moderate and high-dimensional optimization problems along with some transformed benchmark test-functions on simplex. Around 20-300 folds improvement in computation time has been achieved using the proposed algorithm over Genetic algorithm with more accurate solution.
Journal of Water Resources Planning and Management, 2020
AbstractThe role of large-scale drivers—climate, population, and adaption of efficient irrigation... more AbstractThe role of large-scale drivers—climate, population, and adaption of efficient irrigation practices—in controlling irrigation water use efficiency has rarely been addressed. The primary obj...
Environmetrics, 2017
Ground‐level ozone is 1 of the 6 common air pollutants on which the Environmental Protection Agen... more Ground‐level ozone is 1 of the 6 common air pollutants on which the Environmental Protection Agency has set national air quality standards. In order to capture the spatio‐temporal trend of 1‐ and 8‐hr average ozone concentration in the United States, we develop a method for spatio‐temporal simultaneous quantile regression. Unlike existing procedures, in the proposed method, smoothing across different sites is incorporated within modeling assumptions. This allows borrowing of information across locations, which is an essential step when the number of samples in each location is low. The quantile function has been assumed to be linear in time and smooth over space, and at any given site is given by a convex combination of 2 monotone increasing functions ξ1 and ξ2 not depending on time. A B‐spline basis expansion with increasing coefficients varying smoothly over the space is used to put a prior and a Bayesian analysis is performed. We analyze the average daily 1‐hr maximum and 8‐hr ma...
Sankhya B
In this paper, a derivative-free algorithm for global optimization is developed for a function of... more In this paper, a derivative-free algorithm for global optimization is developed for a function of parameters, each coming from a (possibly distinct) bounded interval. Main principle of this algorithm is to make jumps along the coordinates of the parameter one at a time with varying step-sizes within the restricted parameter space and search for the best direction to move in a greedy manner during each iteration. Unlike most of the existing methods since the objective function is evaluated at independent directions during an update step, incorporation of parallel computing makes it even faster. Requirement of parallelization grows only in the order of the dimension of the parameter space, which makes it more convenient for parallelization. A comparative study of the performances of this algorithm and other existing algorithms have been shown for a few moderate and high-dimensional benchmark global optimization problems.
JAMA Network Open
ImportanceThe COVID-19 pandemic has been associated with an increase in mental health diagnoses a... more ImportanceThe COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents.ObjectiveTo estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic.Design, Setting, and ParticipantsThis retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children’s hospitals in the US and France.Main Outcomes and MeasuresChange in the mo...
Nanophotonics
Integration of devices generating non-classical states (such as entanglement) into photonic circu... more Integration of devices generating non-classical states (such as entanglement) into photonic circuits is one of the major goals in achieving integrated quantum circuits (IQCs). This is demonstrated successfully in recent decades. Controlling the non-classicality generation in these micron-scale devices is also crucial for the robust operation of the IQCs. Here, we propose a micron-scale quantum entanglement device whose nonlinearity (so the generated non-classicality) can be tuned by several orders of magnitude via an applied voltage without altering the linear response. Quantum emitters (QEs), whose level-spacing can be tuned by voltage, are embedded into the hotspot of a metal nanostructure (MNS). QE-MNS coupling introduces a Fano resonance in the “nonlinear response”. Nonlinearity, already enhanced extremely due to localization, can be controlled by the QEs’ level-spacing. Nonlinearity can either be suppressed or be further enhanced by several orders. Fano resonance takes place in...
arXiv (Cornell University), Aug 14, 2017
We demonstrate complete quantum state transfer of an atomic Bose-Einstein condensate to molecular... more We demonstrate complete quantum state transfer of an atomic Bose-Einstein condensate to molecular condensate, mediated by solitonic excitations in a cigar shaped mean-field geometry. Starting with a localized solitonic atomic condensate, we show compatible gray solitonic configuration in the molecular condensate, which results in complete atom-molecule conversion. The effect of inter and intra-species interactions on the formation of molecular condensate is explicated in the presence of Raman Photoassociation. It is found that photoassociation plays a crucial role in the coherent atom-molecule conversion as well as in the soliton dynamics. The gray soliton dispersion reveals bistable behaviour, showing a re-entrant phase in a physically accessible parametric domain.
arXiv (Cornell University), Sep 6, 2019
In the context of a binary classification problem, the optimal linear combination of continuous p... more In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing an empirical estimate of the area under the receiver operating characteristic (ROC) curve (AUC). For multi-category responses, the optimal predictor combination can similarly be obtained by maximization of the empirical hypervolume under the manifold (HUM). This problem is particularly relevant to medical research, where it may be of interest to diagnose a disease with various subtypes or predict a multicategory outcome. Since the empirical HUM is discontinuous, non-differentiable, and possibly multi-modal, solving this maximization problem requires a global optimization technique. Estimation of the optimal coefficient vector using existing global optimization techniques is computationally expensive, becoming prohibitive as the number of predictors and the number of outcome categories increases. We propose an efficient derivative-free black-box optimization technique based on pattern search to solve this problem. Through extensive simulation studies, we demonstrate that the proposed method achieves better performance compared to existing methods including the stepdown algorithm. Finally, we illustrate the proposed method to predict swallowing difficulty after radiation therapy for oropharyngeal cancer based on radiation dose to various structures in the head and neck.
npj Digital Medicine
Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we eva... more Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size ...
The European Physical Journal C
Wormholes are tunnels or short-cuts in space-time, and their existence is very important for huma... more Wormholes are tunnels or short-cuts in space-time, and their existence is very important for human civilization to express the vastness of space and time. So, it is necessary to analyze our own Milky Way galaxy if it can harbour any wormhole. This work is dedicated to the existence of wormhole geometry(at least theoretically) in the bulge and halo of the Milky Way Galaxy. The structure and existence of wormholes are verified in both the bulge and the halo region of the Milky Way galaxy (MWG). Different dark matter profiles like pseudo-isothermal, NFW and Universal Rotational Curve (URC) are analyzed to harbour these cosmic tunnels. Three kinds of redshift functions are used for each dark matter profile with the global monopole charge to cover all the possibilities of MWG supporting wormhole geometry.
Nature Human Behaviour
Making good decisions requires thinking ahead, but the huge number of actions and outcomes one co... more Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near-optimal under some circumstances, but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.
We have used the concept of De Broglie's matter wave associated with particles to derive a invers... more We have used the concept of De Broglie's matter wave associated with particles to derive a inverse square law of gravitation like the newton's law of gravitation at the plank's length with a slight modification. Obtaining the Newtonian form we have further extrapolated it to formulate Einstein's field equation, which generally is embedded with a slight modification.The Schwarzschild solution is calculated from the modified field equation, which gives us the Schwarzschild radius of a miniature black hole. Using the solution, the entropy and the hawking temperature is also modeled theoretically for the miniature black hole at the plank's order.
Chemical Science, 2022
Self-inhibitory feedback regulated transient assembly of a CB[8] based nanozyme is reported whose... more Self-inhibitory feedback regulated transient assembly of a CB[8] based nanozyme is reported whose lifetime can be manipulated in multiple ways, ranging from minutes to hours.
arXiv: Optimization and Control, 2016
Black-box optimization of objective function of parameters belonging to simplex arises in many in... more Black-box optimization of objective function of parameters belonging to simplex arises in many inference and predictive models. Das (2016) introduced Greedy Co-ordinate Descent of Varying Step-sizes on Simplex (GCDVSS) which efficiently optimizes any black-box function whose parameters belong to a simplex. In this paper, that method has been modified and extended for the case where the set of parameters may belong to multiple simplex block of different sizes. The main principle of this algorithm is to make jumps of varying step-sizes within each simplexes simultaneously and searching for the best direction for movement. Since this algorithm is designed specially for multiple simplex blocks parameter space, the proportion of movements made within the parameter space during the update step of a iteration is relatively higher for the proposed algorithm. Starting from a single initial guess, unlike genetic algorithm or simulated annealing, requirement of parallelization for this algorit...
arXiv: Quantum Gases, 2018
The effect of light-matter interaction is investigated for a situation where counter propagating ... more The effect of light-matter interaction is investigated for a situation where counter propagating laser pulses of localized nature are incident on the atomic condensate. In contrast to the earlier investigations on the similar systems, it's assumed that the laser beams are ultra-fast and they have a mathrmsech2\mathrm{sech}^2mathrmsech2 profile. Specifically, we consider a quasi-homogeneous, later extended to inhomogeneous, Bose-Einstein condensate (BEC), which is exposed to two counter propagating orthogonally polarized ultra-fast laser beams of equal intensity. The electromagnetic field creates an optical potential for the Bose-Einstein condensate, which in turn modifies the optical field. Hence, light and matter are found to contentiously exchange energy and thus to modify themselves dynamically. In the inhomogenous case, a self-similar method is used here to treat a cigar-shaped BEC exposed to light. Our theoretical analysis in a hither to unexplored regime of BEC-light interaction hints at the ...
Making good decisions requires thinking ahead, but the huge number of actions and outcomes one co... more Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near-optimal under some circumstances, but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.
Sankhya B, 2020
In this paper, we develop a novel derivative-free deterministic greedy algorithm for global optim... more In this paper, we develop a novel derivative-free deterministic greedy algorithm for global optimization of any objective function of parameters belonging to a unit-simplex. Main principle of the proposed algorithm is making jumps of varying step-sizes within the simplex parameter space and searching for the best direction to move in a greedy manner. Unlike most of the other existing methods of constraint optimization, here the objective function is evaluated at independent directions within an iteration. Thus incorporation of parallel computing makes it even faster. Requirement of parallelization grows only in the order of the dimension of the parameter space, which makes it more convenient for solving high-dimensional optimization problems in simplex parameter space using parallel computing. A comparative study of the performances of this algorithm and other existing algorithms have been shown for some moderate and high-dimensional optimization problems along with some transformed benchmark test-functions on simplex. Around 20-300 folds improvement in computation time has been achieved using the proposed algorithm over Genetic algorithm with more accurate solution.
Journal of Water Resources Planning and Management, 2020
AbstractThe role of large-scale drivers—climate, population, and adaption of efficient irrigation... more AbstractThe role of large-scale drivers—climate, population, and adaption of efficient irrigation practices—in controlling irrigation water use efficiency has rarely been addressed. The primary obj...
Environmetrics, 2017
Ground‐level ozone is 1 of the 6 common air pollutants on which the Environmental Protection Agen... more Ground‐level ozone is 1 of the 6 common air pollutants on which the Environmental Protection Agency has set national air quality standards. In order to capture the spatio‐temporal trend of 1‐ and 8‐hr average ozone concentration in the United States, we develop a method for spatio‐temporal simultaneous quantile regression. Unlike existing procedures, in the proposed method, smoothing across different sites is incorporated within modeling assumptions. This allows borrowing of information across locations, which is an essential step when the number of samples in each location is low. The quantile function has been assumed to be linear in time and smooth over space, and at any given site is given by a convex combination of 2 monotone increasing functions ξ1 and ξ2 not depending on time. A B‐spline basis expansion with increasing coefficients varying smoothly over the space is used to put a prior and a Bayesian analysis is performed. We analyze the average daily 1‐hr maximum and 8‐hr ma...