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This study aims to develop a deep learning model for predicting the binding affinity of ligands targeting the Peroxisome Proliferator-Activated Receptor (PPAR) family, using 2D molecular descriptors. A dataset of 3,764 small molecules with known binding affinities, sourced from the ChEMBL database, was preprocessed by eliminating duplicates and incomplete data. Molecular docking simulations using AutoDock Vina were performed to predict binding affinities for the PPAR receptor family. 2D molecular descriptors were computed from the SMILES notation of each ligand, capturing essential structural and physicochemical features. These descriptors, along with the predicted binding affinities, were used to train a deep learning model to predict binding affinity as a regression task. The model was evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. Results indicated strong performance with an R squared value of 0.861 for the training set and 0.655 for the test set, suggesting good model generalization. The model shows promise for predicting ligand-receptor interactions and can be applied in drug discovery efforts targeting PPAR-related diseases., Comment: Contain graphical abstract, 1 figure, and 3 tables

Layer potentials represent solutions to partial differential equations in an integral equation formulation. When numerically evaluating layer potentials at evaluation points close to the domain boundary, specialized quadrature techniques are required for accuracy because of rapid variations in the integrand. To efficiently achieve a specified error tolerance, we introduce an adaptive quadrature method with automatic parameter adjustment for axisymmetric surfaces, facilitated by error estimation. Notably, while each surface must be axisymmetric, the integrand itself need not be, allowing for applications with complex geometries featuring multiple axisymmetric bodies. The proposed quadrature method utilizes so-called interpolatory semi-analytical quadrature in conjunction with a singularity swap technique in the azimuthal angle. In the polar angle, such a technique is used as needed, depending on the integral kernel, combined with an adaptive subdivision of the integration interval. The method is tied to a regular quadrature method that employs a trapezoidal rule in the azimuthal angle and a Gauss-Legendre quadrature rule in the polar angle, which will be used whenever deemed sufficiently accurate, as determined by a quadrature error estimate [C. Sorgentone and A.-K. Tornberg, Advances in Computational Mathematics, 49 (2023), p. 87]. Error estimates for both numerical integration and interpolation are derived using complex analysis, and are used to determine the adaptive panel subdivision given the evaluation point and desired accuracy. Numerical examples are presented to demonstrate the method's efficacy., Comment: 36 pages, 11 figures

We introduce a formal model of transportation in an open-pit mine for the purpose of optimising the mine's operations. The model is a network of Markov automata (MA); the optimisation goal corresponds to maximising a time-bounded expected reward property. Today's model checking algorithms exacerbate the state space explosion problem by applying a discretisation approach to such properties on MA. We show that model checking is infeasible even for small mine instances. Instead, we propose statistical model checking with lightweight strategy sampling or table-based Q-learning over untimed strategies as an alternative to approach the optimisation task, using the Modest Toolset's modes tool. We add support for partial observability to modes so that strategies can be based on carefully selected model features, and we implement a connection from modes to the dtControl tool to convert sampled or learned strategies into decision trees. We experimentally evaluate the adequacy of our new tooling on the open-pit mine case study. Our experiments demonstrate the limitations of Q-learning, the impact of feature selection, and the usefulness of decision trees as an explainable representation.

We study rare transitions in Markovian open quantum systems driven with Gaussian noise, applying transition path and interface sampling methods to trajectories generated by stochastic Schr\"odinger dynamics. Interface and path sampling offer insights into rare event transition mechanisms while simultaneously establishing a quantitative measure of the associated rate constant. Here, we extend their domain to systems described by stochastic Schr\"odinger equations. As a specific example, we explore a model of quantum Brownian motion in a quartic double well, consisting of a particle coupled to a Caldeira-Leggett oscillator bath, where we note significant departures from the Arrhenius law at low temperatures due to the presence of an anti-Zeno effect., Comment: 13 Pages, 12 Figures

This study presents two models to optimize pressure management in water distribution networks. The first model forecasts pressure at distribution points and compares predictions with actual data to detect anomalies such as leaks and blockages. Early detection allows for timely interventions, minimizing economic losses and ensuring system sustainability. The second model estimates the necessary inlet pressure based on the influence of various distribution points, ensuring consistent water supply while reducing waste and optimizing resource management. Both models utilize modern machine learning algorithms to enhance the prediction process. The methodology includes the CNN-EMD model, which analyzes historical data collected every 15 minutes over two months to predict future pressures. The Empirical Mode Decomposition (EMD) method identifies fluctuations and anomalies, improving prediction accuracy. The second model combines CNN, EMD, and LSTM techniques to forecast required inlet pressure, emphasizing the impact of distribution points. Results show that the CNN-EMD and CNN-EMD-LSTM models enhance pressure management capabilities, with the first model achieving an anomaly detection accuracy of 85% to 95% and the second model predicting inlet pressure with an average accuracy of 93%. This enables flexible system adjustments and identifies critical factors affecting inlet pressure. In conclusion, advanced machine learning models like CNN-EMD and LSTM significantly improve pressure management in water distribution networks, facilitating early issue identification, ensuring efficient water supply, and optimizing resource management for future generations., Comment: 13 pages, 14 figures

Detections of gravitational waves since GW150914 has gained renewed interest in a quantum-classical correspondence between GWs and the hypothetical graviton. While a complete quantum theory of gravity remains elusive, massless gravitons in a 3+1 spacetime are often modeled as the quantum noise underlying GWs and between two gravitational masses. This study treats a coalescing binary heuristically as a rotating and contracting Gaussian volume, modeling the gravitons composing the gravitational bulk as an effectively thermal gas. Applying the Einstein-Langevin equation to the coalescing binary system, a relation is established where quanta dissipation scales directly with the third power of osculating eccentricity and inversely with the contracting volume. Numerical analysis employs an Euler iteration scheme to simulate gravitonic Brownian motion throughout binary coalescence. The resulting noise signals exhibit characteristics resembling macroscopic gravitational waveforms. Reproducible coding implementations in Wolfram Mathematica are provided, with equivalent Python commands included in the appendix., Comment: (Version 2: revised as PRD preprint): 10 pages (with 1 appendix), 5 figures; revision includes clarity in introduction and discussion, as well as updated figures

Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in real-time. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers. The framework utilizes CasADi for efficient real-time optimization, enabling the UAV to maintain robust operation even under tight computational constraints. Simulation and real-world indoor and outdoor experiments demonstrated the NMPC ability to adapt to disturbances, resulting in smooth, collision-free navigation., Comment: This manuscript has 7 pages and 8 figures, detailing NMPC for UAV obstacle avoidance using DJI UAVs. It features simulations, experimental results, and uses CasADi for optimization with ROS integration. Code and media at https://github.com/larasupernovae/nmpc_flash_multi_obstacle

We show differences between a language-and-vision model CLIP and two text-only models - FastText and SBERT - when it comes to the encoding of individuation information. We study latent representations that CLIP provides for substrates, granular aggregates, and various numbers of objects. We demonstrate that CLIP embeddings capture quantitative differences in individuation better than models trained on text-only data. Moreover, the individuation hierarchy we deduce from the CLIP embeddings agrees with the hierarchies proposed in linguistics and cognitive science.

This paper introduces Adaptive Mixture Importance Sampling (AMIS) as a novel approach for optimizing key performance indicators (KPIs) in large-scale recommender systems, such as online ad auctions. Traditional importance sampling (IS) methods face challenges in dynamic environments, particularly in navigating through complexities of multi-modal landscapes and avoiding entrapment in local optima for the optimization task. Instead of updating importance weights and mixing samples across iterations, as in canonical adaptive IS and multiple IS, our AMIS framework leverages a mixture distribution as the proposal distribution and dynamically adjusts both the mixture parameters and their mixing rates at each iteration, thereby enhancing search diversity and efficiency. Through extensive offline simulations, we demonstrate that AMIS significantly outperforms simple Gaussian Importance Sampling (GIS), particularly in noisy environments. Moreover, our approach is validated in real-world scenarios through online A/B experiments on a major search engine, where AMIS consistently identifies optimal tuning points that are more likely to be adopted as mainstream configurations. These findings indicate that AMIS enhances convergence in noisy environments, leading to more accurate and reliable decision-making in the context of importance sampling off-policy estimators., Comment: Accepted at the CONSEQUENCES '24 workshop, co-located with ACM RecSys '24

This paper presents an initial exploration on the use of 2D cellular automata (CA) for generating 3D terrains through a simple yet effective additive approach. By experimenting with multiple CA transition rules, this preliminary investigation yielded aesthetically interesting landscapes, hinting at the technique's potential applicability for real-time terrain generation in games.

Assume interest is in sampling from a probability distribution $\mu$ defined on $(\mathsf{Z},\mathscr{Z})$. We develop a framework to construct sampling algorithms taking full advantage of numerical integrators of ODEs, say $\psi\colon\mathsf{Z}\rightarrow\mathsf{Z}$ for one integration step, to explore $\mu$ efficiently and robustly. The popular Hybrid/Hamiltonian Monte Carlo (HMC) algorithm [Duane, 1987], [Neal, 2011] and its derivatives are example of such a use of numerical integrators. However we show how the potential of integrators can be exploited beyond current ideas and HMC sampling in order to take into account aspects of the geometry of the target distribution. A key idea is the notion of integrator snippet, a fragment of the orbit of an ODE numerical integrator $\psi$, and its associate probability distribution $\bar{\mu}$, which takes the form of a mixture of distributions derived from $\mu$ and $\psi$. Exploiting properties of mixtures we show how samples from $\bar{\mu}$ can be used to estimate expectations with respect to $\mu$. We focus here primarily on Sequential Monte Carlo (SMC) algorithms, but the approach can be used in the context of Markov chain Monte Carlo algorithms as discussed at the end of the manuscript. We illustrate performance of these new algorithms through numerical experimentation and provide preliminary theoretical results supporting observed performance.

Robot object manipulation in real-world environments is challenging because robot operation must be robust to a range of sensing, estimation, and actuation uncertainties to avoid potentially unsafe and costly mistakes that are a barrier to their adoption. In this paper, we propose a flexible and generalisable physics-informed causal Bayesian network (CBN) based framework for a robot to probabilistically reason about candidate manipulation actions, to enable robot decision-making robust to arbitrary robot system uncertainties -- the first of its kind to use a probabilistic programming language implementation. Using experiments in high-fidelity Gazebo simulation of an exemplar block stacking task, we demonstrate our framework's ability to: (1) predict manipulation outcomes with high accuracy (Pred Acc: 88.6%); and, (2) perform greedy next-best action selection with 94.2% task success rate. We also demonstrate our framework's suitability for real-world robot systems with a domestic robot. Thus, we show that by combining probabilistic causal modelling with physics simulations, we can make robot manipulation more robust to system uncertainties and hence more feasible for real-world applications. Further, our generalised reasoning framework can be used and extended for future robotics and causality research., Comment: 7 pages, 7 figures, submitted to the 2025 IEEE Conference on Robotics and Automation (ICRA 2025)

We propose a new method for cloth digitalization. Deviating from existing methods which learn from data captured under relatively casual settings, we propose to learn from data captured in strictly tested measuring protocols, and find plausible physical parameters of the cloths. However, such data is currently absent, so we first propose a new dataset with accurate cloth measurements. Further, the data size is considerably smaller than the ones in current deep learning, due to the nature of the data capture process. To learn from small data, we propose a new Bayesian differentiable cloth model to estimate the complex material heterogeneity of real cloths. It can provide highly accurate digitalization from very limited data samples. Through exhaustive evaluation and comparison, we show our method is accurate in cloth digitalization, efficient in learning from limited data samples, and general in capturing material variations. Code and data are available https://github.com/realcrane/Bayesian-Differentiable-Physics-for-Cloth-Digitalization, Comment: 9 pages, 8 figures, to be published in CVPR

Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional high-fidelity simulators for these inverse problems are computationally demanding, restricting the number of simulations possible. Additionally, their non-differentiable nature makes gradient-based optimization methods, known for their efficiency in high-dimensional problems, inapplicable. While machine learning-based surrogate models offer computational efficiency and differentiability, they often struggle to generalize beyond their training data due to their reliance on low-dimensional input-output mappings that fail to capture the complete physics of granular flows. We propose a novel differentiable graph neural network simulator (GNS) by combining reverse mode automatic differentiation of graph neural networks with gradient-based optimization for solving inverse problems. GNS learns the dynamics of granular flow by representing the system as a graph and predicts the evolution of the graph at the next time step, given the current state. The differentiable GNS shows optimization capabilities beyond the training data. We demonstrate the effectiveness of our method for inverse estimation across single and multi-parameter optimization problems, including evaluating material properties and boundary conditions for a target runout distance and designing baffle locations to limit a landslide runout. Our proposed differentiable GNS framework offers an orders of magnitude faster solution to these inverse problems than the conventional finite difference approach to gradient-based optimization.

Our understanding of the Universe breaks down for very small spacetime intervals, corresponding to an extremely high level of granularity (and energy), commonly referred to as the ``Planck scale''. At this fundamental level, there are attempts of describing physics in terms of interacting automata that perform classical, deterministic computation. On one hand, various mathematical arguments have already illustrated how quantum laws (which describe elementary particles and interactions) could in principle arise as low-granularity approximations of automata-based systems. On the other hand, understanding how such systems might give rise to relativistic laws (which describe spacetime and gravity) remains a major problem. I explain here a few ideas that seem crucial for overcoming this problem, along with related algorithmic challenges that need to be addressed. Giving emphasis to meaningful computational counterparts of locality and general covariance, I outline basic ingredients of a distributed communication-rewiring protocol that would allow us to construct multi-automaton models that are viable from a relativistic perspective. I also explain how viable models can be evaluated using a variety of criteria, and discuss related aspects pertaining to the falsifiability and plausibility of the automata paradigm., Comment: 7 pages, 1 figure

Reliable evaluations of geotechnical hazards like landslides and debris flow require accurate simulation of granular flow dynamics. Traditional numerical methods can simulate the complex behaviors of such flows that involve solid-like to fluid-like transitions, but they are computationally intractable when simulating large-scale systems. Surrogate models based on statistical or machine learning methods are a viable alternative, but they are typically empirical and rely on a confined set of parameters in evaluating associated risks. Due to their permutation-dependent learning, conventional machine learning models require an unreasonably large amount of training data for building generalizable surrogate models. We employ a graph neural network (GNN), a novel deep learning technique, to develop a GNN-based simulator (GNS) for granular flows to address these issues. Graphs represent the state of granular flows and interactions, like the exchange of energy and momentum between grains, and GNN learns the local interaction law. GNS takes the current state of the granular flow and estimates the next state using Euler explicit integration. We train GNS on a limited set of granular flow trajectories and evaluate its performance in a three-dimensional granular column collapse domain. GNS successfully reproduces the overall behaviors of column collapses with various aspect ratios that were not encountered during training. The computation speed of GNS outperforms high-fidelity numerical simulators by 300 times.

This paper introduces an efficient approach to reduce the computational cost of simulating collective behaviors, such as fish schooling, using Individual-Based Models (IBMs). The proposed technique employs adaptive and dynamic load-balancing domain partitioning, which utilizes unsupervised machine-learning models to cluster a large number of simulated individuals into sub-schools based on their spatial-temporal locations. It also utilizes Voronoi tessellations to construct non-overlapping simulation subdomains. This approach minimizes agent-to-agent communication and balances the load both spatially and temporally, ultimately resulting in reduced computational complexity. Experimental simulations demonstrate that this partitioning approach outperforms the standard regular grid-based domain decomposition, achieving a reduction in computational cost while maintaining spatial and temporal load balance. The approach presented in this paper has the potential to be applied to other collective behavior simulations requiring large-scale simulations with a substantial number of individuals., Comment: 11 pages

Students' migration from public to private schools, due to lack of school performance of public schools, is one of the major issues faced by the Government of Punjab to provide compulsory and quality education at low cost. Due to complex adaptive nature of educational system, interdependencies with society, constant feedback loops conventional linear regression methods, for evaluation of effective performance, are ineffective or costly to solve the issue. Linear regression techniques present the static view of the system, which are not enough to understand the complex dynamic nature of educational paradigm. We have presented a Cognitive Agent Computing-Based Model for the School Student Migration Problem Using a Descriptive Agent-Based Modeling approach to understand the causes-effects relationship of student migration. We have presented the primary school students' migration model using descriptive modeling approach along with exploratory modeling. Our research, in the context of Software Engineering of Simulation & Modeling, and exploring the Complex Adaptive nature of school system, is two folds. Firstly, the cause-effect relationship of students' migration is being investigated using Cognitive Descriptive Agent-Based Modeling. Secondly, the formalization extent of Cognitive Agent-Based Computing framework is analyzed by performing its comparative analysis with exploratory modeling protocol 'Overview, Design, and Detail'., Comment: 117 pages, MS thesis

High-performance graphical processing units (GPU) are used for the repeated parallelised propagation of non-linear partial differential equations on large spatio-temporal grids. The main challenge results as a combination of the requirement of large grids for exploring scaling over several orders of magnitude, both in space and time, and the need for high statistics in averaging over many runs, in computing correlation functions for highly fluctuating quantum many-body states. With our simulations, we explore the dynamics of complex quantum systems far from equilibrium, with the aim of classifying their universal characteristics such as scaling exponents near non-thermal fixed points. Our results are strongly relevant for the development of synthetic quantum systems when exploring the respective physics in the laboratory., Comment: 6 pages, 2 figures, Proceedings of the 8th bwHPC-Symposium, organized by the High-Performance Computing Center Stuttgart (HLRS) (2022)

We leverage physics-embedded differentiable graph network simulators (GNS) to accelerate particulate and fluid simulations to solve forward and inverse problems. GNS represents the domain as a graph with particles as nodes and learned interactions as edges. Compared to modeling global dynamics, GNS enables learning local interaction laws through edge messages, improving its generalization to new environments. GNS achieves over 165x speedup for granular flow prediction compared to parallel CPU numerical simulations. We propose a novel hybrid GNS/Material Point Method (MPM) to accelerate forward simulations by minimizing error on a pure surrogate model by interleaving MPM in GNS rollouts to satisfy conservation laws and minimize errors achieving 24x speedup compared to pure numerical simulations. The differentiable GNS enables solving inverse problems through automatic differentiation, identifying material parameters that result in target runout distances. We demonstrate the ability of GNS to solve inverse problems by iteratively updating the friction angle (a material property) by computing the gradient of a loss function based on the final and target runouts, thereby identifying the friction angle that best matches the observed runout. The physics-embedded and differentiable simulators open an exciting new paradigm for AI-accelerated design, control, and optimization., Comment: 6 pages, The 4th workshop on Artificial Intelligence and Machine Learning for Scientific Applications, Super Computing '23

We present a technical enhancement within the p4est software for parallel adaptive mesh refinement. In p4est primitives are stored as octants in three and quadrants in two dimensions. While, classically, they are encoded by the native approach using its spatial and refinement level, any other mathematically equivalent encoding might be used instead. Recognizing this, we add two alternative representations to the classical, explicit version, based on a long monotonic index and 128-bit AVX quad integers, respectively. The first one requires changes in logic for low-level quadrant manipulating algorithms, while the other exploits data level parallelism and requires algorithms to be adapted to SIMD instructions. The resultant algorithms and data structures lead to higher performance and lesser memory usage in comparison with the standard baseline. We benchmark selected algorithms on a cluster with two Intel(R) Xeon(R) Gold 6130 Skylake family CPUs per node, which provides support for AVX2 extensions, 192 GB RAM per node, and up to 512 computational cores in total., Comment: 10 pages, 7 figures and 12 algorithms, submitted to Proceedings of the 2024 SIAM Conference on Parallel Processing for Scientific Computing (PP). For reprodusing results use branches https://github.com/cburstedde/p4est/tree/p4est-virtual and https://github.com/cburstedde/libsc/tree/feature-sc3 and executable build_dir>/example/timings/p8est3_p3time

We show that any application of the technique of unbiased simulation becomes perfect simulation when coalescence of the two coupled Markov chains can be practically assured in advance. This happens when a fixed number of iterations is high enough that the probability of needing any more to achieve coalescence is negligible; we suggest a value of $10^{-20}$. This finding enormously increases the range of problems for which perfect simulation, which exactly follows the target distribution, can be implemented. We design a new algorithm to make practical use of the high number of iterations by producing extra perfect sample points with little extra computational effort, at a cost of a small, controllable amount of serial correlation within sample sets of about 20 points. Different sample sets remain completely independent. The algorithm includes maximal coupling for continuous processes, to bring together chains that are already close. We illustrate the methodology on a simple, two-state Markov chain and on standard normal distributions up to 20 dimensions. Our technical formulation involves a nonzero probability, which can be made arbitrarily small, that a single perfect sample point may have its place taken by a "string" of many points which are assigned weights, each equal to $\pm 1$, that sum to~$1$. A point with a weight of $-1$ is a "hole", which is an object that can be cancelled by an equivalent point that has the same value but opposite weight $+1$., Comment: 17 pages, 4 figures; for associated R scripts, see https://github.com/George-Leigh/PerfectSimulation

Uncertainties in the real world mean that is impossible for system designers to anticipate and explicitly design for all scenarios that a robot might encounter. Thus, robots designed like this are fragile and fail outside of highly-controlled environments. Causal models provide a principled framework to encode formal knowledge of the causal relationships that govern the robot's interaction with its environment, in addition to probabilistic representations of noise and uncertainty typically encountered by real-world robots. Combined with causal inference, these models permit an autonomous agent to understand, reason about, and explain its environment. In this work, we focus on the problem of a robot block-stacking task due to the fundamental perception and manipulation capabilities it demonstrates, required by many applications including warehouse logistics and domestic human support robotics. We propose a novel causal probabilistic framework to embed a physics simulation capability into a structural causal model to permit robots to perceive and assess the current state of a block-stacking task, reason about the next-best action from placement candidates, and generate post-hoc counterfactual explanations. We provide exemplar next-best action selection results and outline planned experimentation in simulated and real-world robot block-stacking tasks., Comment: 4 pages, 4 figures, camera-ready manuscript, accepted to the "Causality for Robotics: Answering the Question of Why" workshop at the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Continued model-based decision support is associated with particular challenges, especially in long-term projects. Due to the regularly changing questions and the often changing understanding of the underlying system, the models used must be regularly re-evaluated, -modelled and -implemented with respect to changing modelling purpose, system boundaries and mapped causalities. Usually, this leads to models with continuously growing complexity and volume. In this work we aim to reevaluate the idea of the model family, dating back to the 1990s, and use it to promote this as a mindset in the creation of decision support frameworks in large research projects. The idea is to generally not develop and enhance a single standalone model, but to divide the research tasks into interacting smaller models which specifically correspond to the research question. This strategy comes with many advantages, which we explain using the example of a family of models for decision support in the COVID-19 crisis and corresponding success stories. We describe the individual models, explain their role within the family, and how they are used - individually and with each other., Comment: 20 pages + 8 pages appendix, 9 figures im main text, 1 figure and 4 tables in appendix, source code to simulations is found on GitHub https://github.com/dwhGmbH/covid19_model_family

Accurate simulation of granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Granular flows involve a dynamic rearrangement of particles exhibiting complex transitions from solid-like to fluid-like responses. Traditional continuum and discrete numerical methods are limited by their computational cost in simulating large-scale systems. Statistical or machine learning-based models offer an alternative. Still, they are largely empirical, based on a limited set of parameters. Due to their permutation-dependent learning, traditional machine learning-based models require huge training data to generalize. To resolve these problems, we use a graph neural network, a state-of-the-art machine learning architecture that learns local interactions. Graphs represent the state of dynamically changing granular flows and the interaction laws, such as energy and momentum exchange between grains. We develop a graph neural network-based simulator (GNS) that takes the current state of granular flow and predicts the next state using Euler explicit integration by learning the local interaction laws. We train GNS on different granular trajectories. We then assess the performance of GNS by predicting granular column collapse. GNS accurately predicts flow dynamics for column collapses with different aspect ratios unseen during training. GNS is hundreds of times faster than high-fidelity numerical simulators. The model also generalizes to domains much larger than the training data, handling more than twice the number of particles than it was trained on.

Robots operating in real-world environments must reason about possible outcomes of stochastic actions and make decisions based on partial observations of the true world state. A major challenge for making accurate and robust action predictions is the problem of confounding, which if left untreated can lead to prediction errors. The partially observable Markov decision process (POMDP) is a widely-used framework to model these stochastic and partially-observable decision-making problems. However, due to a lack of explicit causal semantics, POMDP planning methods are prone to confounding bias and thus in the presence of unobserved confounders may produce underperforming policies. This paper presents a novel causally-informed extension of "anytime regularized determinized sparse partially observable tree" (AR-DESPOT), a modern anytime online POMDP planner, using causal modelling and inference to eliminate errors caused by unmeasured confounder variables. We further propose a method to learn offline the partial parameterisation of the causal model for planning, from ground truth model data. We evaluate our methods on a toy problem with an unobserved confounder and show that the learned causal model is highly accurate, while our planning method is more robust to confounding and produces overall higher performing policies than AR-DESPOT., Comment: 8 pages, 3 figures, accepted to 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Agent-based modeling is a computational dynamic modeling technique that may be less familiar to some readers. Agent-based modeling seeks to understand the behaviour of complex systems by situating agents in an environment and studying the emergent outcomes of agent-agent and agent-environment interactions. In comparison with compartmental models, agent-based models offer simpler, more scalable and flexible representation of heterogeneity, the ability to capture dynamic and static network and spatial context, and the ability to consider history of individuals within the model. In contrast, compartmental models offer faster development time with less programming required, lower computational requirements that do not scale with population, and the option for concise mathematical formulation with ordinary, delay or stochastic differential equations supporting derivation of properties of the system behaviour. In this chapter, basic characteristics of agent-based models are introduced, advantages and disadvantages of agent-based models, as compared with compartmental models, are discussed, and two example agent-based infectious disease models are reviewed.

The matrix element (ME) calculation in any Monte Carlo physics event generator is an ideal fit for implementing data parallelism with lockstep processing on GPUs and vector CPUs. For complex physics processes where the ME calculation is the computational bottleneck of event generation workflows, this can lead to large overall speedups by efficiently exploiting these hardware architectures, which are now largely underutilized in HEP. In this paper, we present the status of our work on the reengineering of the Madgraph5_aMC@NLO event generator at the time of the ACAT2022 conference. The progress achieved since our previous publication in the ICHEP2022 proceedings is discussed, for our implementations of the ME calculations in vectorized C++, in CUDA and in the SYCL framework, as well as in their integration into the existing MadEvent framework. The outlook towards a first alpha release of the software supporting QCD LO processes usable by the LHC experiments is also discussed., Comment: 8 pages, 4 figures, 4 tables; submitted to ACAT 2022 proceedings in IOP; version 2 includes additional references and figure caption fixes as suggested by journal referee

We have developed a simulation technique that uses non-linear finite element analysis and elastic fracture mechanics to compute physically plausible motion for three-dimensional, solid objects as they break, crack, or tear. When these objects deform beyond their mechanical limits, the system automatically determines where fractures should begin and in what directions they should propagate. The system allows fractures to propagate in arbitrary directions by dynamically restructuring the elements of a tetrahedral mesh. Because cracks are not limited to the original element boundaries, the objects can form irregularly shaped shards and edges as they shatter. The result is realistic fracture patterns such as the ones shown in our examples. This paper presents an overview of the fracture algorithm, the details are presented in our ACM SIGGRAPH 1999 and 2002 papers., Comment: 11 pages, 5 figures, 5 anc videos. Also available at: http://graphics.berkeley.edu/papers/Obrien-AFX-2000-12. arXiv admin note: substantial text overlap with arXiv:2303.02934

In this paper, we introduce techniques for animating explosions and their effects. The primary effect of an explosion is a disturbance that causes a shock wave to propagate through the surrounding medium. This disturbance determines the behavior of nearly all other secondary effects seen in explosions. We simulate the propagation of an explosion through the surrounding air using a computational fluid dynamics model based on the equations for compressible, viscous flow. To model the numerically stable formulation of shocks along blast wave fronts, we employ an integration method that can handle steep gradients without introducing inappropriate damping. The system includes two-way coupling between solid objects and surrounding fluid. Using this technique, we can generate a variety of effects including shaped explosive charges, a projectile propelled from a chamber by an explosion, and objects damaged by a blast. With appropriate rendering techniques, our explosion model can be used to create such visual effects such as fireballs, dust clouds, and the refraction of light caused by a blast wave., Comment: 7 pages, 12 figures, anc videos, 22 citations. Alternative online location: http://graphics.berkeley.edu/papers/Yngve-AEX-2000-07

In this paper, we augment existing techniques for simulating flexible objects to include models for crack initiation and propagation in three-dimensional volumes. By analyzing the stress tensors computed over a finite element model, the simulation determines where cracks should initiate and in what directions they should propagate. We demonstrate our results with animations of breaking bowls, cracking walls, and objects that fracture when they collide. By varying the shape of the objects, the material properties, and the initial conditions of the simulations, we can create strikingly different effects ranging from a wall that shatters when it is hit by a wrecking ball to a bowl that breaks in two when it is dropped on edge. This paper received the SIGGRAPH 99 Impact Award., Comment: 10 pages, 14 figures. This paper received the SIGGRAPH 99 Impact Award. Alternate location: http://graphics.berkeley.edu/papers/Obrien-GMA-1999-08

Much effort has been put into developing samplers with specific properties, such as producing blue noise, low-discrepancy, lattice or Poisson disk samples. These samplers can be slow if they rely on optimization processes, may rely on a wide range of numerical methods, are not always differentiable. The success of recent diffusion models for image generation suggests that these models could be appropriate for learning how to generate point sets from examples. However, their convolutional nature makes these methods impractical for dealing with scattered data such as point sets. We propose a generic way to produce 2-d point sets imitating existing samplers from observed point sets using a diffusion model. We address the problem of convolutional layers by leveraging neighborhood information from an optimal transport matching to a uniform grid, that allows us to benefit from fast convolutions on grids, and to support the example-based learning of non-uniform sampling patterns. We demonstrate how the differentiability of our approach can be used to optimize point sets to enforce properties.

A novel approach called Moate Simulation is presented to provide an accurate numerical evolution of probability distribution functions represented on grids arising from stochastic differential processes where initial conditions are specified. Where the variables of stochastic differential equations may be transformed via It\^o-Doeblin calculus into stochastic differentials with a constant diffusion term, the probability distribution function for these variables can be simulated in discrete time steps. The drift is applied directly to a volume element of the distribution while the stochastic diffusion term is applied through the use of convolution techniques such as Fast or Discrete Fourier Transforms. This allows for highly accurate distributions to be efficiently simulated to a given time horizon and may be employed in one, two or higher dimensional expectation integrals, e.g. for pricing of financial derivatives. The Moate Simulation approach forms a more accurate and considerably faster alternative to Monte Carlo Simulation for many applications while retaining the opportunity to alter the distribution in mid-simulation., Comment: 17 pages, 1 pdf figure plus internal tikz figures. JEL Classifications: C15, C63, G12, G13, G17

Event generators simulate particle interactions using Monte Carlo techniques, providing the primary connection between experiment and theory in experimental high energy physics. These software packages, which are the first step in the simulation worflow of collider experiments, represent approximately 5 to 20% of the annual WLCG usage for the ATLAS and CMS experiments. With computing architectures becoming more heterogeneous, it is important to ensure that these key software frameworks can be run on future systems, large and small. In this contribution, recent progress on porting and speeding up the Madgraph5_aMC@NLO event generator on hybrid architectures, i.e. CPU with GPU accelerators, is discussed. The main focus of this work has been in the calculation of scattering amplitudes and "matrix elements", which is the computational bottleneck of an event generation application. For physics processes limited to QCD leading order, the code generation toolkit has been expanded to produce matrix element calculations using C++ vector instructions on CPUs and using CUDA for NVidia GPUs, as well as using Alpaka, Kokkos and SYCL for multiple CPU and GPU architectures. Performance is reported in terms of matrix element calculations per time on NVidia, Intel, and AMD devices. The status and outlook for the integration of this work into a production release usable by the LHC experiments, with the same functionalities and very similar user interfaces as the current Fortran version, is also described., Comment: 6 pages, 2 figures, 2 tables; submitted to ICHEP2022 proceedings in PoS

Making general particle transport simulation for high-energy physics (HEP) single-instruction-multiple-thread (SIMT) friendly, to take advantage of accelerator hardware, is an important alternative for boosting the throughput of simulation applications. To date, this challenge is not yet resolved, due to difficulties in mapping the complexity of Geant4 components and workflow to the massive parallelism features exposed by graphics processing units (GPU). The AdePT project is one of the R\&D initiatives tackling this limitation and exploring GPUs as potential accelerators for offloading some part of the CPU simulation workload. Our main target is to implement a complete electromagnetic shower demonstrator working on the GPU. The project is the first to create a full prototype of a realistic electron, positron, and gamma electromagnetic shower simulation on GPU, implemented as either a standalone application or as an extension of the standard Geant4 CPU workflow. Our prototype currently provides a platform to explore many optimisations and different approaches. We present the most recent results and initial conclusions of our work, using both a standalone GPU performance analysis and a first implementation of a hybrid workflow based on Geant4 on the CPU and AdePT on the GPU., Comment: 8 pages, 4 figures, 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), to be published in Journal of Physics: Conference Series, editor Andrei Gheata

In order to solve the problem of opinion polarization and distortion caused by echo chamber effect in the evolution process of online public opinion,a three-dimensional Deffuant-Weisbuch model is proposed to study the formation and elimination of echo chamber effect in this paper. Firstly, the original pairwise interaction model is generalized to three-point interaction model. Secondly, we consider individual psychological mechanism and introduce individual emotional factor into the trust threshold of original model. Finally, the natural evolution coefficient of opinion is introduced to modify the model. The improved model is used to conduct simulation experiments on social networks with different structures, and opinion leaders and active agents are introduced into the network, so as to study the corresponding generation and breaking mechanism of echo chamber. The experimental results show that the change of network structure cannot eliminate the echo chamber effect, and the increase of network stability and connectivity can only slow down the echo chamber effect. Opinion leaders can aggregate opinions within their scope of influence and have a guiding effect on opinions. Therefore, if opinion leaders can change their opinions over time, they can well guide opinions to converge to neutral opinions, thus achieving the purpose of breaking the echo chamber. Active agents can lead the opinions in the network to converge to the neutral, and active agents with high stubbornness can lead the free views to converge to the neutral, thus achieving the purpose of breaking the echo chamber effect., Comment: 34pages 57figures

Bandit algorithms are often used in the e-commerce industry to train Machine Learning (ML) systems when pre-labeled data is unavailable. However, the industry setting poses various challenges that make implementing bandit algorithms in practice non-trivial. In this paper, we elaborate on the challenges of off-policy optimisation, delayed reward, concept drift, reward design, and business rules constraints that practitioners at Booking.com encounter when applying bandit algorithms. Our main contributions is an extension to the Open Bandit Pipeline (OBP) framework. We provide simulation components for some of the above-mentioned challenges to provide future practitioners, researchers, and educators with a resource to address challenges encountered in the e-commerce industry., Comment: Published at the CONSEQUENCES+REVEAL '22 workshop @ Recsys 2022, Seattle, WA, USA, Sept. 22nd-23rd 2022

Food image classification serves as the foundation of image-based dietary assessment to predict food categories. Since there are many different food classes in real life, conventional models cannot achieve sufficiently high accuracy. Personalized classifiers aim to largely improve the accuracy of food image classification for each individual. However, a lack of public personal food consumption data proves to be a challenge for training such models. To address this issue, we propose a novel framework to simulate personal food consumption data patterns, leveraging the use of a modified Markov chain model and self-supervised learning. Our method is capable of creating an accurate future data pattern from a limited amount of initial data, and our simulated data patterns can be closely correlated with the initial data pattern. Furthermore, we use Dynamic Time Warping distance and Kullback-Leibler divergence as metrics to evaluate the effectiveness of our method on the public Food-101 dataset. Our experimental results demonstrate promising performance compared with random simulation and the original Markov chain method., Comment: 9 pages, 6 figures,In Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management (MADiMa 22)

A very common way to estimate the parameters of a hidden Markov model (HMM) is the relatively straightforward computation of maximum likelihood (ML) estimates. For this task, most users rely on user-friendly implementation of the estimation routines via an interpreted programming language such as the statistical software environment R (R Core Team, 2021). Such an approach can easily require time-consuming computations, in particular for longer sequences of observations. In addition, selecting a suitable approach for deriving confidence intervals for the estimated parameters is not entirely obvious (see, e.g., Zucchini et al., 2016; Lystig and Hughes, 2002; Visser et al., 2000), and often the computationally intensive bootstrap methods have to be applied. In this tutorial, we illustrate how to speed up the computation of ML estimates significantly via the R package TMB. Moreover, this approach permits simple retrieval of standard errors at the same time. We illustrate the performance of our routines using different data sets. First, two smaller samples from a mobile application for tinnitus patients and a well-known data set of fetal lamb movements with 87 and 240 data points, respectively. Second, we rely on larger data sets of simulated data of sizes 2000 and 5000 for further analysis. This tutorial is accompanied by a collection of scripts which are all available on GitHub. These scripts allow any user with moderate programming experience to benefit quickly from the computational advantages of TMB., Comment: For supporting information, see https://timothee-bacri.github.io/HMM_with_TMB/

We present Monte Carlo Physarum Machine: a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's 2010 agent-based model for simulating the growth of Physarum polycephalum slime mold. We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the Cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies -- called "polyphorms" -- that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological datasets, we then evaluate its ability to produce consistent 3D density maps of the Cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.

Learning the physical dynamics of deformable objects with particle-based representation has been the objective of many computational models in machine learning. While several state-of-the-art models have achieved this objective in simulated environments, most existing models impose a precondition, such that the input is a sequence of ordered point sets. That is, the order of the points in each point set must be the same across the entire input sequence. This precondition restrains the model from generalizing to real-world data, which is considered to be a sequence of unordered point sets. In this paper, we propose a model named time-wise PointNet (TP-Net) that solves this problem by directly consuming a sequence of unordered point sets to infer the future state of a deformable object with particle-based representation. Our model consists of a shared feature extractor that extracts global features from each input point set in parallel and a prediction network that aggregates and reasons on these features for future prediction. The key concept of our approach is that we use global features rather than local features to achieve invariance to input permutations and ensure the stability and scalability of our model. Experiments demonstrate that our model achieves state-of-the-art performance with real-time prediction speed in both synthetic dataset and real-world dataset. In addition, we provide quantitative and qualitative analysis on why our approach is more effective and efficient than existing approaches.

We develop a classical Monte Carlo algorithm based on a quasi-classical approximation for a pseudospin S=1 Hamiltonian in real space to construct a phase diagram of a model cuprate with a high Tc. A model description takes into account both local and nonlocal correlations, Heisenberg spin-exchange interaction, correlated single-particle, and two-particle transport. We formulate a state selection algorithm for a given parameterization of the wave function in order to ensure a uniform distribution of states in the phase space. The simulation results show a qualitative agreement with the experimental phase diagrams., Comment: 9 pages, 3 figures

Thin fiber networks are widely represented in nature and can be found in man-made materials such as paper and packaging. The strength of such materials is an intricate subject due to inherited randomness and size-dependencies. Direct fiber-level numerical simulations can provide insights into the role of the constitutive components of such networks, their morphology, and arrangements on the strength of the products made of them. However, direct mechanical simulation of randomly generated large and thin fiber networks is characterized by overwhelming computational costs. Herein, a stochastic constitutive model for predicting the random mechanical response of isotropic thin fiber networks of arbitrary size is presented. The model is based on stochastic volume elements (SVEs) with SVE size-specific deterministic and stochastic constitutive law parameters. The randomness in the network is described by the spatial fields of the uniaxial strain and strength to failure, formulated using multivariate kernel functions and approximate univariate probability density functions. The proposed stochastic continuum approach shows good agreement when compared to direct numerical simulation with respect to mechanical response. Furthermore, strain localization patterns matched the one observed in direct simulations, which suggests an accurate prediction of the failure location. This work demonstrates that the proposed stochastic constitutive model can be used to predict the response of random isotropic fiber networks of arbitrary size., Comment: 28 pages, 19 figures

The stochastic variations in the structural properties of thin fiber networks govern to a great extent their mechanical performance. To assess the influence of local structural variability on the local strain and mechanical response of the network, we propose a multiscale approach combining modeling, numerical simulation and experimental measurements. Based on micro-mechanical fiber network simulations, a continuum model describing the response at the mesoscale level is first developed. Experimentally measured spatial fields of thickness, density, fiber orientation and anisotropy are thereafter used as input to a macroscale finite-element model. The latter is used to simulate the impact of spatial variability of each of the studied structural properties. In addition, this work brings novelty by including the influence of the drying condition during the production process on the fiber properties. The proposed approach is experimentally validated by comparison to measured strain fields and uniaxial responses. The results suggest that the spatial variability in density presents the highest impact on the local strain field followed by thickness and fiber orientation. Meanwhile, for the mechanical response, the fiber orientation angle with respect to the drying restraints is the key influencer and its contribution to the anisotropy of the mechanical properties is greater than the contribution of the fiber anisotropy developed during the fiber sheet-making., Comment: 12 pages, 14 figures

Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed "Internet of People" paradigm, accessing relevant data dynamically generated in the environment nearby is one of the key services. Moreover, personal mobile devices become proxies of their human users while exchanging data in the cyber world and, thus, largely use ONs and D2D communications for exchanging data directly. Recently, researchers have successfully demonstrated the viability of embedding human cognitive schemes in data dissemination algorithms for ONs. In this paper, we consider one such scheme based on the recognition heuristic, a human decision-making scheme used to efficiently assess the relevance of data. While initial evidence about its effectiveness is available, the evaluation of its behaviour in large-scale settings is still unsatisfactory. To overcome these limitations, we have developed a novel hybrid modelling methodology, which combines an analytical model of data dissemination within small-scale communities of mobile users, with detailed simulations of interactions between different communities. This methodology allows us to evaluate the algorithm in large-scale city- and country-wide scenarios. Results confirm the effectiveness of cognitive data dissemination schemes, even when content popularity is very heterogenous.

Chemical reaction networks (CRNs) are fundamental computational models used to study the behavior of chemical reactions in well-mixed solutions. They have been used extensively to model a broad range of biological systems, and are primarily used when the more traditional model of deterministic continuous mass action kinetics is invalid due to small molecular counts. We present a perfect sampling algorithm to draw error-free samples from the stationary distributions of stochastic models for coupled, linear chemical reaction networks. The state spaces of such networks are given by all permissible combinations of molecular counts for each chemical species, and thereby grow exponentially with the numbers of species in the network. To avoid simulations involving large numbers of states, we propose a subset of chemical species such that coupling of paths started from these states guarantee coupling of paths started from all states in the state space and we show for the well-known Reversible Michaelis-Menten model that the subset does in fact guarantee perfect draws from the stationary distribution of interest. We compare solutions computed in two ways with this algorithm to those found analytically using the chemical master equation and we compare the distribution of coupling times for the two simulation approaches., Comment: 27 pages, 30 figures

We introduce a new isometric strain model for the study of the dynamics of cloth garments in a moderate stress environment, such as robotic manipulation in the neighborhood of humans. This model treats textiles as surfaces which are inextensible, admitting only isometric motions. Inextensibility is imposed in a continuous setting, prior to any discretization, which gives consistency with respect to re-meshing and prevents the problem of locking even with coarse meshes. The simulations of robotic manipulation using the model are compared to the actual manipulation in the real world, finding that the error between the simulated and real position of each point in the garment is lower than 1cm in average, even when a coarse mesh is used. Aerodynamic contributions to motion are incorporated to the model through the virtual uncoupling of the inertial and gravitational mass of the garment. This approach results in an accurate, as compared to reality, description of cloth motion incorporating aerodynamic effects by using only two parameters., Comment: 29 pages, 18 figures, 3 tables, 1 algorithm

Recently we introduced a model of symbiosis, Model-S, based on the evolution of seed patterns in Conway's Game of Life. In the model, the fitness of a seed pattern is measured by one-on-one competitions in the Immigration Game, a two-player variation of the Game of Life. Our previous article showed that Model-S can serve as a highly abstract, simplified model of biological life: (1) The initial seed pattern is analogous to a genome. (2) The changes as the game runs are analogous to the development of the phenome. (3) Tournament selection in Model-S is analogous to natural selection in biology. (4) The Immigration Game in Model-S is analogous to competition in biology. (5) The first three layers in Model-S are analogous to biological reproduction. (6) The fusion of seed patterns in Model-S is analogous to symbiosis. The current article takes this analogy two steps further: (7) Autopoietic structures in the Game of Life (still lifes, oscillators, and spaceships -- collectively known as ashes) are analogous to cells in biology. (8) The seed patterns in the Game of Life give rise to multiple, diverse, cooperating autopoietic structures, analogous to multicellular biological life. We use the apgsearch software (Ash Pattern Generator Search), developed by Adam Goucher for the study of ashes, to analyze autopoiesis and multicellularity in Model-S. We find that the fitness of evolved seed patterns in Model-S is highly correlated with the diversity and quantity of multicellular autopoietic structures.

This article explores the use of machine learning models to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets. In particular, these synthetic data must preserve the probability distribution of asset returns, the stochastic dependence between the different assets and the autocorrelation across time. The article proposes then a new approach for estimating the probability distribution of backtest statistics. The final objective is to develop a framework for improving the risk management of quantitative investment strategies, in particular in the space of smart beta, factor investing and alternative risk premia., Comment: 72 pages, 30 figures

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility., Comment: 31 pages (10 in previous version), not including references and glossary, 51 in total. Inclusion of a large number of recent publications and expansion of the discussion accordingly