@inproceedings{14993, abstract = {Traditional top-down approaches for global health have historically failed to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015). Recently, however, a more holistic, multi-level approach termed One Health (OH) (Osterhaus et al., 2020) is being adopted. Several sets of challenges have been identified for the implementation of OH (dos S. Ribeiro et al., 2019), including policy and funding, education and training, and multi-actor, multi-domain, and multi-level collaborations. These exist despite the increasing accessibility to knowledge and digital collaborative research tools through the internet. To address some of these challenges, we propose a general framework for grassroots community-based means of participatory research. Additionally, we present a specific roadmap to create a Machine Learning for Global Health community in Africa. The proposed framework aims to enable any small group of individuals with scarce resources to build and sustain an online community within approximately two years. We provide a discussion on the potential impact of the proposed framework for global health research collaborations.}, author = {Currin, Christopher and Asiedu , Mercy Nyamewaa and Fourie, Chris and Rosman, Benjamin and Turki, Houcemeddine and Lambebo Tonja, Atnafu and Abbott, Jade and Ajala, Marvellous and Adedayo, Sadiq Adewale and Emezue, Chris Chinenye and Machangara, Daphne}, booktitle = {1st Workshop on Machine Learning & Global Health}, location = {Kigali, Rwanda}, publisher = {OpenReview}, title = {{A framework for grassroots research collaboration in machine learning and global health}}, year = {2023}, } @misc{14994, abstract = {This resource contains the artifacts for reproducing the experimental results presented in the paper titled "A Flexible Toolchain for Symbolic Rabin Games under Fair and Stochastic Uncertainties" that has been submitted in CAV 2023.}, author = {Majumdar, Rupak and Mallik, Kaushik and Rychlicki, Mateusz and Schmuck, Anne-Kathrin and Soudjani, Sadegh}, publisher = {Zenodo}, title = {{A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties}}, doi = {10.5281/ZENODO.7877790}, year = {2023}, } @misc{14995, abstract = {Lincheck is a new practical and user-friendly framework for testing concurrent data structures on the Java Virtual Machine (JVM). It provides a simple and declarative way to write concurrent tests. Instead of describing how to perform the test, users specify what to test by declaring all the operations to examine; the framework automatically handles the rest. As a result, tests written with Lincheck are concise and easy to understand. The artifact presents a collection of Lincheck tests that discover new bugs in popular libraries and implementations from the concurrency literature -- they are listed in Table 1, Section 3. To evaluate the performance of Lincheck analysis, the collection of tests also includes those which check correct data structures and, thus, always succeed. Similarly to Table 2, Section 3, the experiments demonstrate the reasonable time to perform a test. Finally, Lincheck provides user-friendly output with an easy-to-follow trace to reproduce a detected error, significantly simplifying further investigation.}, author = {Koval, Nikita and Fedorov, Alexander and Sokolova, Maria and Tsitelov, Dmitry and Alistarh, Dan-Adrian}, publisher = {Zenodo}, title = {{Lincheck: A practical framework for testing concurrent data structures on JVM}}, doi = {10.5281/ZENODO.7877757}, year = {2023}, } @inproceedings{15023, abstract = {Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment. We propose a novel method for learning a composition of neural network policies in stochastic environments, along with a formal certificate which guarantees that a specification over the policy's behavior is satisfied with the desired probability. Unlike prior work on verifiable RL, our approach leverages the compositional nature of logical specifications provided in SpectRL, to learn over graphs of probabilistic reach-avoid specifications. The formal guarantees are provided by learning neural network policies together with reach-avoid supermartingales (RASM) for the graph’s sub-tasks and then composing them into a global policy. We also derive a tighter lower bound compared to previous work on the probability of reach-avoidance implied by a RASM, which is required to find a compositional policy with an acceptable probabilistic threshold for complex tasks with multiple edge policies. We implement a prototype of our approach and evaluate it on a Stochastic Nine Rooms environment.}, author = {Zikelic, Dorde and Lechner, Mathias and Verma, Abhinav and Chatterjee, Krishnendu and Henzinger, Thomas A}, booktitle = {37th Conference on Neural Information Processing Systems}, location = {New Orleans, LO, United States}, title = {{Compositional policy learning in stochastic control systems with formal guarantees}}, year = {2023}, } @misc{15027, abstract = {This data repository underpins the paper, published in PNAS (doi pending) and bioarxiv (doi: https://doi.org/10.1101/2023.07.05.547777).}, author = {Curk, Samo}, publisher = {Figshare}, title = {{aggregation_data}}, year = {2023}, } @misc{15035, abstract = {This artifact aims to reproduce experiments from the paper Monitoring Hyperproperties With Prefix Transducers accepted at RV'23, and give further pointers to implementation of prefix transducers. It has two parts: a pre-compiled docker image and sources that one can use to compile (locally or in docker) the software and run the experiments.}, author = {Chalupa, Marek and Henzinger, Thomas A}, publisher = {Zenodo}, title = {{Monitoring hyperproperties with prefix transducers}}, doi = {10.5281/ZENODO.8191723}, year = {2023}, } @article{15173, abstract = {We show that the number of linear spaces on a set of n points and the number of rank-3 matroids on a ground set of size n are both of the form (cn+o(n))n2/6, where c=e3√/2−3(1+3–√)/2. This is the final piece of the puzzle for enumerating fixed-rank matroids at this level of accuracy: the numbers of rank-1 and rank-2 matroids on a ground set of size n have exact representations in terms of well-known combinatorial functions, and it was recently proved by van der Hofstad, Pendavingh, and van der Pol that for constant r≥4 there are (e1−rn+o(n))nr−1/r! rank-r matroids on a ground set of size n. In our proof, we introduce a new approach for bounding the number of clique decompositions of a complete graph, using quasirandomness instead of the so-called entropy method that is common in this area.}, author = {Kwan, Matthew Alan and Sah, Ashwin and Sawhney, Mehtaab}, issn = {1778-3569}, journal = {Comptes Rendus Mathematique}, number = {G2}, pages = {565--575}, publisher = {Academie des Sciences}, title = {{Enumerating matroids and linear spaces}}, doi = {10.5802/crmath.423}, volume = {361}, year = {2023}, } @misc{15292, abstract = {We present a rigid body animation technique which prevents solids from interpenetrating, dissipates energy through friction, and propagates shocks through contacts. We employ the Alternating Direction Method of Multipliers (ADMM) to couple non-smooth Coulomb friction with impact propagation, allowing efficient and accurate non-smooth dynamics along with a correct transmission of impacts through assemblies of rigid bodies. We further extend our method to model adhesion, dynamic friction and lubricated contact.}, author = {Chen, Yi-Lu and Ly, Mickaël and Wojtan, Christopher J}, booktitle = {Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation}, location = {Los Angeles, CA, United States}, publisher = {ACM}, title = {{Unified treatment of contact, friction and shock-propagation in rigid body animation}}, doi = {10.1145/3606037.3606836}, year = {2023}, } @inproceedings{15363, abstract = {Knowledge distillation is a popular approach for enhancing the performance of "student" models, with lower representational capacity, by taking advantage of more powerful "teacher" models. Despite its apparent simplicity, the underlying mechanics behind knowledge distillation (KD) are not yet fully understood. In this work, we shed new light on the inner workings of this method, by examining it from an optimization perspective. Specifically, we show that, in the context of linear and deep linear models, KD can be interpreted as a novel type of stochastic variance reduction mechanism. We provide a detailed convergence analysis of the resulting dynamics, which hold under standard assumptions for both strongly-convex and non-convex losses, showing that KD acts as a form of \emph{partial variance reduction}, which can reduce the stochastic gradient noise, but may not eliminate it completely, depending on the properties of the teacher'' model. Our analysis puts further emphasis on the need for careful parametrization of KD, in particular w.r.t. the weighting of the distillation loss, and is validated empirically on both linear models and deep neural networks.}, author = {Safaryan, Mher and Peste, Elena-Alexandra and Alistarh, Dan-Adrian}, booktitle = {36th Conference on Neural Information Processing Systems}, issn = {1049-5258}, location = {New Orleans, LA, United States}, title = {{Knowledge distillation performs partial variance reduction}}, volume = {36}, year = {2023}, } @inproceedings{15364, abstract = {Clustering is a fundamental problem in unsupervised machine learning with many applications in data analysis. Popular clustering algorithms such as Lloyd's algorithm and k-means++ can make Ω(ndk) time when clustering n points in a d-dimensional space (represented by an n×d matrix X) into k clusters. On massive datasets with moderate to large k, the multiplicative k factor can become very expensive. We introduce a simple randomized clustering algorithm that provably runs in expected time O(nnz(X)+nlogn) for arbitrary k. Here nnz(X) is the total number of non-zero entries in the input dataset X, which is upper bounded by nd and can be significantly smaller for sparse datasets. We prove that our algorithm achieves approximation ratio ˜O(k4) on any input dataset for the k-means objective, and our experiments show that the quality of the clusters found by our algorithm is usually much better than this worst-case bound. We use our algorithm for k-means clustering and for coreset construction; our experiments show that it gives a new tradeoff between running time and cluster quality compared to previous state-of-the-art methods for these tasks. Our theoretical analysis is based on novel results of independent interest. We show that the approximation ratio achieved after a random one-dimensional projection can be lifted to the original points and that k-means++ seeding can be implemented in expected time O(nlogn) in one dimension.}, author = {Charikar, Moses and Hu, Lunjia and Henzinger, Monika H and Vötsch, Maximilian and Waingarten, Erik}, booktitle = {37th Conference on Neural Information Processing Systems}, issn = {1049-5258}, location = {New Orleans, LA, United States}, title = {{Simple, scalable and effective clustering via one-dimensional projections}}, volume = {36}, year = {2023}, } @article{10770, abstract = {Mathematical models often aim to describe a complicated mechanism in a cohesive and simple manner. However, reaching perfect balance between being simple enough or overly simplistic is a challenging task. Frequently, game-theoretic models have an underlying assumption that players, whenever they choose to execute a specific action, do so perfectly. In fact, it is rare that action execution perfectly coincides with intentions of individuals, giving rise to behavioural mistakes. The concept of incompetence of players was suggested to address this issue in game-theoretic settings. Under the assumption of incompetence, players have non-zero probabilities of executing a different strategy from the one they chose, leading to stochastic outcomes of the interactions. In this article, we survey results related to the concept of incompetence in classic as well as evolutionary game theory and provide several new results. We also suggest future extensions of the model and argue why it is important to take into account behavioural mistakes when analysing interactions among players in both economic and biological settings.}, author = {Graham, Thomas and Kleshnina, Maria and Filar, Jerzy A.}, issn = {2153-0793}, journal = {Dynamic Games and Applications}, pages = {231--264}, publisher = {Springer Nature}, title = {{Where do mistakes lead? A survey of games with incompetent players}}, doi = {10.1007/s13235-022-00425-3}, volume = {13}, year = {2023}, } @article{11434, abstract = {The Indian summer monsoon rainfall (ISMR) has been declining since the 1950s. However, since 2002 it is reported to have revived. For these observed changes in the ISMR, several explanations have been reported. Among these explanations, however, the role of the eastern equatorial Indian Ocean (EEIO) is missing despite being one of the warmest regions in the Indian Ocean, and monotonously warming. A recent study reported that EEIO warming impacts the rainfall over northern India. Here we report that warming in the EEIO weakens the low-level Indian summer monsoon circulation and reduces ISMR. A warm EEIO drives easterly winds in the Indo–Pacific sector as a Gill response. The warm EEIO also enhances nocturnal convection offshore the western coast of Sumatra. The latent heating associated with the increased convection augments the Gill response and the resultant circulation opposes the monsoon low-level circulation and weakens the seasonal rainfall.}, author = {Goswami, Bidyut B}, issn = {1432-0894}, journal = {Climate Dynamics}, pages = {427--442}, publisher = {Springer Nature}, title = {{Role of the eastern equatorial Indian Ocean warming in the Indian summer monsoon rainfall trend}}, doi = {10.1007/s00382-022-06337-7}, volume = {60}, year = {2023}, } @article{10145, abstract = {We study direct integrals of quadratic and Dirichlet forms. We show that each quasi-regular Dirichlet space over a probability space admits a unique representation as a direct integral of irreducible Dirichlet spaces, quasi-regular for the same underlying topology. The same holds for each quasi-regular strongly local Dirichlet space over a metrizable Luzin σ-finite Radon measure space, and admitting carré du champ operator. In this case, the representation is only projectively unique.}, author = {Dello Schiavo, Lorenzo}, issn = {1572-929X}, journal = {Potential Analysis}, pages = {573--615}, publisher = {Springer Nature}, title = {{Ergodic decomposition of Dirichlet forms via direct integrals and applications}}, doi = {10.1007/s11118-021-09951-y}, volume = {58}, year = {2023}, } @article{10173, abstract = {We study the large scale behavior of elliptic systems with stationary random coefficient that have only slowly decaying correlations. To this aim we analyze the so-called corrector equation, a degenerate elliptic equation posed in the probability space. In this contribution, we use a parabolic approach and optimally quantify the time decay of the semigroup. For the theoretical point of view, we prove an optimal decay estimate of the gradient and flux of the corrector when spatially averaged over a scale R larger than 1. For the numerical point of view, our results provide convenient tools for the analysis of various numerical methods.}, author = {Clozeau, Nicolas}, issn = {2194-0401}, journal = {Stochastics and Partial Differential Equations: Analysis and Computations}, pages = {1254–1378}, publisher = {Springer Nature}, title = {{Optimal decay of the parabolic semigroup in stochastic homogenization for correlated coefficient fields}}, doi = {10.1007/s40072-022-00254-w}, volume = {11}, year = {2023}, } @article{10405, abstract = {We consider large non-Hermitian random matrices X with complex, independent, identically distributed centred entries and show that the linear statistics of their eigenvalues are asymptotically Gaussian for test functions having 2+ϵ derivatives. Previously this result was known only for a few special cases; either the test functions were required to be analytic [72], or the distribution of the matrix elements needed to be Gaussian [73], or at least match the Gaussian up to the first four moments [82, 56]. We find the exact dependence of the limiting variance on the fourth cumulant that was not known before. The proof relies on two novel ingredients: (i) a local law for a product of two resolvents of the Hermitisation of X with different spectral parameters and (ii) a coupling of several weakly dependent Dyson Brownian motions. These methods are also the key inputs for our analogous results on the linear eigenvalue statistics of real matrices X that are presented in the companion paper [32]. }, author = {Cipolloni, Giorgio and Erdös, László and Schröder, Dominik J}, issn = {1097-0312}, journal = {Communications on Pure and Applied Mathematics}, number = {5}, pages = {946--1034}, publisher = {Wiley}, title = {{Central limit theorem for linear eigenvalue statistics of non-Hermitian random matrices}}, doi = {10.1002/cpa.22028}, volume = {76}, year = {2023}, } @article{10550, abstract = {The global existence of renormalised solutions and convergence to equilibrium for reaction-diffusion systems with non-linear diffusion are investigated. The system is assumed to have quasi-positive non-linearities and to satisfy an entropy inequality. The difficulties in establishing global renormalised solutions caused by possibly degenerate diffusion are overcome by introducing a new class of weighted truncation functions. By means of the obtained global renormalised solutions, we study the large-time behaviour of complex balanced systems arising from chemical reaction network theory with non-linear diffusion. When the reaction network does not admit boundary equilibria, the complex balanced equilibrium is shown, by using the entropy method, to exponentially attract all renormalised solutions in the same compatibility class. This convergence extends even to a range of non-linear diffusion, where global existence is an open problem, yet we are able to show that solutions to approximate systems converge exponentially to equilibrium uniformly in the regularisation parameter.}, author = {Fellner, Klemens and Fischer, Julian L and Kniely, Michael and Tang, Bao Quoc}, issn = {1432-1467}, journal = {Journal of Nonlinear Science}, publisher = {Springer Nature}, title = {{Global renormalised solutions and equilibration of reaction-diffusion systems with non-linear diffusion}}, doi = {10.1007/s00332-023-09926-w}, volume = {33}, year = {2023}, } @article{10551, abstract = {The Dean–Kawasaki equation—a strongly singular SPDE—is a basic equation of fluctuating hydrodynamics; it has been proposed in the physics literature to describe the fluctuations of the density of N independent diffusing particles in the regime of large particle numbers N≫1. The singular nature of the Dean–Kawasaki equation presents a substantial challenge for both its analysis and its rigorous mathematical justification. Besides being non-renormalisable by the theory of regularity structures by Hairer et al., it has recently been shown to not even admit nontrivial martingale solutions. In the present work, we give a rigorous and fully quantitative justification of the Dean–Kawasaki equation by considering the natural regularisation provided by standard numerical discretisations: We show that structure-preserving discretisations of the Dean–Kawasaki equation may approximate the density fluctuations of N non-interacting diffusing particles to arbitrary order in N−1 (in suitable weak metrics). In other words, the Dean–Kawasaki equation may be interpreted as a “recipe” for accurate and efficient numerical simulations of the density fluctuations of independent diffusing particles.}, author = {Cornalba, Federico and Fischer, Julian L}, issn = {1432-0673}, journal = {Archive for Rational Mechanics and Analysis}, number = {5}, publisher = {Springer Nature}, title = {{The Dean-Kawasaki equation and the structure of density fluctuations in systems of diffusing particles}}, doi = {10.1007/s00205-023-01903-7}, volume = {247}, year = {2023}, } @article{17074, abstract = {We verify Bogoliubov's approximation for translation invariant Bose gases in the mean field regime, i.e. we prove that the ground state energy EN is given by EN=NeH+infσ(H)+oN→∞(1), where N is the number of particles, eH is the minimal Hartree energy and H is the Bogoliubov Hamiltonian. As an intermediate result we show the existence of approximate ground states ΨN, i.e. states satisfying ⟨HN⟩ΨN=EN+oN→∞(1), exhibiting complete Bose--Einstein condensation with respect to one of the Hartree minimizers.}, author = {Brooks, Morris and Seiringer, Robert}, issn = {2690-1005}, journal = {Probability and Mathematical Physics}, number = {4}, pages = {939--1000}, publisher = {Mathematical Sciences Publishers}, title = {{Validity of Bogoliubov’s approximation fortranslation-invariant Bose gases}}, doi = {10.2140/pmp.2022.3.939}, volume = {3}, year = {2023}, } @article{17079, abstract = {We study moments of characteristic polynomials of truncated Haar distributed matrices from the three classical compact groups O(N), U(N) and Sp(2N). For finite matrix size we calculate the moments in terms of hypergeometric functions of matrix argument and give explicit integral representations highlighting the duality between the moment and the matrix size as well as the duality between the orthogonal and symplectic cases. Asymptotic expansions in strong and weak non-unitarity regimes are obtained. Using the connection to matrix hypergeometric functions, we establish limit theorems for the log-modulus of the characteristic polynomial evaluated on the unit circle.}, author = {Serebryakov, Alexander and Simm, Nick and Dubach, Guillaume}, issn = {2010-3271}, journal = {Random Matrices: Theory and Applications}, number = {01}, publisher = {World Scientific Publishing}, title = {{Characteristic polynomials of random truncations: Moments, duality and asymptotics}}, doi = {10.1142/s2010326322500496}, volume = {12}, year = {2023}, } @unpublished{17100, abstract = {Prophet inequalities are a central object of study in optimal stopping theory. A gambler is sent values online, sampled from an instance of independent distributions, in an adversarial, random or selected order, depending on the model. When observing each value, the gambler either accepts it as a reward or irrevocably rejects it and proceeds to observe the next value. The goal of the gambler, who cannot see the future, is maximising the expected value of the reward while competing against the expectation of a prophet (the offline maximum). In other words, one seeks to maximise the gambler-to-prophet ratio of the expectations. The model, in which the gambler selects the arrival order first, and then observes the values, is known as Order Selection. In this model a ratio of 0.7251 has been proved to be attainable for any instance. In very recent work, this has been improved up to 0.7258. If the gambler chooses the arrival order (uniformly) at random, we obtain the Random Order model. The worst case ratio over all possible instances has been extensively studied for at least 40 years. In the recent work aforementioned, through simulations, this ratio has been shown to be at most 0.7254 for the Random Order model, thus establishing for the first time that carefully choosing the order, instead of simply taking it at random, benefits the gambler. We give an alternative, more rigorous proof of this fact, by showing mathematically that in the Random Order model, no algorithm can achieve a ratio larger than 0.7235. This sets a new state-of-the-art hardness for this model, and establishes more formally that there is a real benefit in choosing the order.}, author = {Giambartolomei, Giordano and Frederik Mallmann-Trenn, Frederik Mallmann-Trenn and Saona Urmeneta, Raimundo J}, booktitle = {arXiv}, title = {{Prophet inequalities: Separating random order from order selection}}, doi = {10.48550/arXiv.2304.04024}, year = {2023}, }