@misc{22868, author = {Pal, Arka and Mihir, Joshi and Thaker, Maria}, publisher = {Repository}, title = {{Raw data for: Too much information? Males convey parasite levels using more signal modalities than females utilise.}}, doi = {10.5281/zenodo.10423539}, year = {2023}, } @misc{22905, abstract = {This repository contains the code and VCF files needed to conduct the analyses in our MS. Each folder contains a readMe document explaining the nature of each file and dataset and the results and analyses that they relate to. The same anlaysis code (but not VCF files) is also available at https://github.com/seanstankowski/Littorina_reproductive_mode}, author = {Stankowski, Sean and Zagrodzka, Zuzanna B. and Garlovsky, Martin D. and Pal, Arka and Shipilina, Daria and Garcia Castillo, Diego and Lifchitz, Hila and Le Moan, Alan and Leder, Erica and Reeve, James and Johannesson, Kerstin and Westram, Anja M. and Butlin, Roger K.}, publisher = {Repository}, title = {{Data and code for: The genetic architecture of a recent transition to live-bearing in marine snails}}, doi = {10.5281/zenodo.8318994}, year = {2023}, } @article{12787, abstract = {Populations evolve in spatially heterogeneous environments. While a certain trait might bring a fitness advantage in some patch of the environment, a different trait might be advantageous in another patch. Here, we study the Moran birth–death process with two types of individuals in a population stretched across two patches of size N, each patch favouring one of the two types. We show that the long-term fate of such populations crucially depends on the migration rate μ between the patches. To classify the possible fates, we use the distinction between polynomial (short) and exponential (long) timescales. We show that when μ is high then one of the two types fixates on the whole population after a number of steps that is only polynomial in N. By contrast, when μ is low then each type holds majority in the patch where it is favoured for a number of steps that is at least exponential in N. Moreover, we precisely identify the threshold migration rate μ⋆ that separates those two scenarios, thereby exactly delineating the situations that support long-term coexistence of the two types. We also discuss the case of various cycle graphs and we present computer simulations that perfectly match our analytical results.}, author = {Svoboda, Jakub and Tkadlec, Josef and Kaveh, Kamran and Chatterjee, Krishnendu}, issn = {1471-2946}, journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences}, number = {2271}, publisher = {The Royal Society}, title = {{Coexistence times in the Moran process with environmental heterogeneity}}, doi = {10.1098/rspa.2022.0685}, volume = {479}, year = {2023}, } @inproceedings{14954, abstract = {When domain knowledge is limited and experimentation is restricted by ethical, financial, or time constraints, practitioners turn to observational causal discovery methods to recover the causal structure, exploiting the statistical properties of their data. Because causal discovery without further assumptions is an ill-posed problem, each algorithm comes with its own set of usually untestable assumptions, some of which are hard to meet in real datasets. Motivated by these considerations, this paper extensively benchmarks the empirical performance of recent causal discovery methods on observational iid data generated under different background conditions, allowing for violations of the critical assumptions required by each selected approach. Our experimental findings show that score matching-based methods demonstrate surprising performance in the false positive and false negative rate of the inferred graph in these challenging scenarios, and we provide theoretical insights into their performance. This work is also the first effort to benchmark the stability of causal discovery algorithms with respect to the values of their hyperparameters. Finally, we hope this paper will set a new standard for the evaluation of causal discovery methods and can serve as an accessible entry point for practitioners interested in the field, highlighting the empirical implications of different algorithm choices.}, author = {Montagna, Francesco and Mastakouri, Atalanti A. and Eulig, Elias and Noceti, Nicoletta and Rosasco, Lorenzo and Janzing, Dominik and Aragam, Bryon and Locatello, Francesco}, booktitle = {37th Conference on Neural Information Processing Systems}, issn = {1049-5258}, location = {New Orleans, LA, United States}, publisher = {Neural Information Processing Systems Foundation}, title = {{Assumption violations in causal discovery and the robustness of score matching}}, doi = {10.52202/075280-2050}, volume = {36}, 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}, issn = {1049-5258}, location = {New Orleans, LA, United States}, publisher = {Neural Information Processing Systems Foundation}, title = {{Compositional policy learning in stochastic control systems with formal guarantees}}, year = {2023}, } @inproceedings{14953, abstract = {This paper provides statistical sample complexity bounds for score-matching and its applications in causal discovery. We demonstrate that accurate estimation of the score function is achievable by training a standard deep ReLU neural network using stochastic gradient descent. We establish bounds on the error rate of recovering causal relationships using the score-matching-based causal discovery method of Rolland et al. [2022], assuming a sufficiently good estimation of the score function. Finally, we analyze the upper bound of score-matching estimation within the scorebased generative modeling, which has been applied for causal discovery but is also of independent interest within the domain of generative models.y}, author = {Zhu, Zhenyu and Locatello, Francesco and Cevher, Volkan}, booktitle = {37th Conference on Neural Information Processing Systems}, isbn = {9781713899921}, issn = {1049-5258}, location = {New Orleans, LA, United States}, pages = {3325--3337}, publisher = {Neural Information Processing Systems Foundation}, title = {{Sample complexity bounds for score-matching: Causal discovery and generative modeling}}, doi = {10.52202/075280-0147}, volume = {36}, year = {2023}, } @inproceedings{13310, abstract = {Machine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive attributes. We present runtime verification of algorithmic fairness for systems whose models are unknown, but are assumed to have a Markov chain structure. We introduce a specification language that can model many common algorithmic fairness properties, such as demographic parity, equal opportunity, and social burden. We build monitors that observe a long sequence of events as generated by a given system, and output, after each observation, a quantitative estimate of how fair or biased the system was on that run until that point in time. The estimate is proven to be correct modulo a variable error bound and a given confidence level, where the error bound gets tighter as the observed sequence gets longer. Our monitors are of two types, and use, respectively, frequentist and Bayesian statistical inference techniques. While the frequentist monitors compute estimates that are objectively correct with respect to the ground truth, the Bayesian monitors compute estimates that are correct subject to a given prior belief about the system’s model. Using a prototype implementation, we show how we can monitor if a bank is fair in giving loans to applicants from different social backgrounds, and if a college is fair in admitting students while maintaining a reasonable financial burden on the society. Although they exhibit different theoretical complexities in certain cases, in our experiments, both frequentist and Bayesian monitors took less than a millisecond to update their verdicts after each observation.}, author = {Henzinger, Thomas A and Karimi, Mahyar and Kueffner, Konstantin and Mallik, Kaushik}, booktitle = {Computer Aided Verification}, isbn = {9783031377020}, issn = {1611-3349}, location = {Paris, France}, pages = {358–382}, publisher = {Springer Nature}, title = {{Monitoring algorithmic fairness}}, doi = {10.1007/978-3-031-37703-7_17}, volume = {13965}, year = {2023}, } @inproceedings{13228, abstract = {A machine-learned system that is fair in static decision-making tasks may have biased societal impacts in the long-run. This may happen when the system interacts with humans and feedback patterns emerge, reinforcing old biases in the system and creating new biases. While existing works try to identify and mitigate long-run biases through smart system design, we introduce techniques for monitoring fairness in real time. Our goal is to build and deploy a monitor that will continuously observe a long sequence of events generated by the system in the wild, and will output, with each event, a verdict on how fair the system is at the current point in time. The advantages of monitoring are two-fold. Firstly, fairness is evaluated at run-time, which is important because unfair behaviors may not be eliminated a priori, at design-time, due to partial knowledge about the system and the environment, as well as uncertainties and dynamic changes in the system and the environment, such as the unpredictability of human behavior. Secondly, monitors are by design oblivious to how the monitored system is constructed, which makes them suitable to be used as trusted third-party fairness watchdogs. They function as computationally lightweight statistical estimators, and their correctness proofs rely on the rigorous analysis of the stochastic process that models the assumptions about the underlying dynamics of the system. We show, both in theory and experiments, how monitors can warn us (1) if a bank’s credit policy over time has created an unfair distribution of credit scores among the population, and (2) if a resource allocator’s allocation policy over time has made unfair allocations. Our experiments demonstrate that the monitors introduce very low overhead. We believe that runtime monitoring is an important and mathematically rigorous new addition to the fairness toolbox.}, author = {Henzinger, Thomas A and Karimi, Mahyar and Kueffner, Konstantin and Mallik, Kaushik}, booktitle = {FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency}, isbn = {9781450372527}, location = {Chicago, IL, United States}, pages = {604--614}, publisher = {Association for Computing Machinery}, title = {{Runtime monitoring of dynamic fairness properties}}, doi = {10.1145/3593013.3594028}, year = {2023}, } @inproceedings{14454, abstract = {As AI and machine-learned software are used increasingly for making decisions that affect humans, it is imperative that they remain fair and unbiased in their decisions. To complement design-time bias mitigation measures, runtime verification techniques have been introduced recently to monitor the algorithmic fairness of deployed systems. Previous monitoring techniques assume full observability of the states of the (unknown) monitored system. Moreover, they can monitor only fairness properties that are specified as arithmetic expressions over the probabilities of different events. In this work, we extend fairness monitoring to systems modeled as partially observed Markov chains (POMC), and to specifications containing arithmetic expressions over the expected values of numerical functions on event sequences. The only assumptions we make are that the underlying POMC is aperiodic and starts in the stationary distribution, with a bound on its mixing time being known. These assumptions enable us to estimate a given property for the entire distribution of possible executions of the monitored POMC, by observing only a single execution. Our monitors observe a long run of the system and, after each new observation, output updated PAC-estimates of how fair or biased the system is. The monitors are computationally lightweight and, using a prototype implementation, we demonstrate their effectiveness on several real-world examples.}, author = {Henzinger, Thomas A and Kueffner, Konstantin and Mallik, Kaushik}, booktitle = {23rd International Conference on Runtime Verification}, isbn = {9783031442667}, issn = {1611-3349}, location = {Thessaloniki, Greece}, pages = {291--311}, publisher = {Springer Nature}, title = {{Monitoring algorithmic fairness under partial observations}}, doi = {10.1007/978-3-031-44267-4_15}, volume = {14245}, year = {2023}, } @inproceedings{12859, abstract = {Machine learning models are vulnerable to adversarial perturbations, and a thought-provoking paper by Bubeck and Sellke has analyzed this phenomenon through the lens of over-parameterization: interpolating smoothly the data requires significantly more parameters than simply memorizing it. However, this "universal" law provides only a necessary condition for robustness, and it is unable to discriminate between models. In this paper, we address these gaps by focusing on empirical risk minimization in two prototypical settings, namely, random features and the neural tangent kernel (NTK). We prove that, for random features, the model is not robust for any degree of over-parameterization, even when the necessary condition coming from the universal law of robustness is satisfied. In contrast, for even activations, the NTK model meets the universal lower bound, and it is robust as soon as the necessary condition on over-parameterization is fulfilled. This also addresses a conjecture in prior work by Bubeck, Li and Nagaraj. Our analysis decouples the effect of the kernel of the model from an "interaction matrix", which describes the interaction with the test data and captures the effect of the activation. Our theoretical results are corroborated by numerical evidence on both synthetic and standard datasets (MNIST, CIFAR-10).}, author = {Bombari, Simone and Kiyani, Shayan and Mondelli, Marco}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, location = {Honolulu, HI, United States}, pages = {2738--2776}, publisher = {ML Research Press}, title = {{Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels}}, volume = {202}, 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{23012, abstract = {The onset of turbulence in pipe flow has defied detailed understanding ever since Reynolds' first observations revealed the spatially-heterogeneous nature of the transition. While recent theoretical studies and experiments in simpler, shear-driven flows suggest that the onset of turbulence is a directed percolation non-equilibrium phase transition, whether these findings are generic and apply also to open or pressure-driven flows is unknown. In pipe flow, the extremely long time scales near the transition make direct observations of critical behavior virtually impossible. Here, we circumvent these limitations by experimentally characterizing all pairwise interactions between localized patches of turbulence ("puffs"), and using these interactions as input to renormalization group and computer simulations of minimal models that extrapolate to long length and time scales. We show that the universality class of the transition is directed percolation, from which emerges a jammed phase of puffs above the critical point. The stronger interactions in the jamming regime enable us to explicitly measure the turbulent fraction and confirm model predictions. Our work shows that directed percolation scaling applies beyond simple closed shear flows, and underscores how statistical mechanics can lead to profound, quantitative and predictive insights on turbulent flows and their phases.}, author = {Lemoult, Grégoire}, publisher = {Zenodo}, title = {{Directed percolation and puff jamming near the transition to pipe turbulence}}, doi = {10.5281/zenodo.10308791}, year = {2023}, } @misc{22961, abstract = {## Rationale: Differentiation trajectories to generate specialized cellular features rely on sets of transcription factors (TFs) whose temporal dynamics and coexistence underlie morphological and neurochemical divergence among neurons and neuroendocrine cells. Even though transcriptional programs were extensively studied in the developing nervous system, the coupling of TFs to effector gene networks that time and shape the morphogenesis of these polarized cells is less known. ## Results: Here, we make the curious observation that evolutionarily conserved Onecut3, a subordinate in the Onecut TF family known to control fate selection in progenitors, is instead expressed in fate-restricted neuroblasts in the vertebrate hypothalamus. In particular, a pool of Ascl1-containing progenitors in the midgestational mouse hypothalamus gives rise to both GABA and glutamate neurons destined to the periventricular and lateral hypothalamus, respectively, which uniformly upregulate Onecut3 only when exiting the proliferative zone of the 3rd ventricle. By combining single-cell RNA-seq, genetic Onecut3 reporters, gain-of-function models in vitro, and loss-of-function analysis in early-developing mice and C. elegans, we identify that Onecut3 instructs neuronal differentiation and maturation, through a Navigator-2 pathway to modulate neuritogenesis and leading process motility. ## Conclusion: Onecut3 executes an unexpected function by inducing cytoskeletal modifications of key importance for neuronal maturation and network integration during the morphogenesis of many neuronal phenotypes in the vertebrate hypothalamus.}, author = {Zupancic, Maja and Keimpema, Erik and Tretiakov, Evgenii and Bhandari, Pradeep and Eder, Stephanie and Lev, Itamar and Härtig, Wolfgang and Zimmer, Manuel and Clotman, Frederic and Harkany, Tibor}, publisher = {Repository}, title = {{Glutamate and GABA neurons share cascading Onecut3/NAV2-driven differentiation trajectories in the developing hypothalamus}}, doi = {10.6084/m9.figshare.22680433}, year = {2023}, } @article{14514, abstract = {The elastic Leidenfrost effect occurs when a vaporizable soft solid is lowered onto a hot surface. Evaporative flow couples to elastic deformation, giving spontaneous bouncing or steady-state floating. The effect embodies an unexplored interplay between thermodynamics, elasticity, and lubrication: despite being observed, its basic theoretical description remains a challenge. Here, we provide a theory of elastic Leidenfrost floating. As weight increases, a rigid solid sits closer to the hot surface. By contrast, we discover an elasticity-dominated regime where the heavier the solid, the higher it floats. This geometry-governed behavior is reminiscent of the dynamics of large liquid Leidenfrost drops. We show that this elastic regime is characterized by Hertzian behavior of the solid’s underbelly and derive how the float height scales with materials parameters. Introducing a dimensionless elastic Leidenfrost number, we capture the crossover between rigid and Hertzian behavior. Our results provide theoretical underpinning for recent experiments, and point to the design of novel soft machines.}, author = {Binysh, Jack and Chakraborty, Indrajit and Chubynsky, Mykyta V. and Diaz Melian, Vicente L and Waitukaitis, Scott R and Sprittles, James E. and Souslov, Anton}, issn = {1079-7114}, journal = {Physical Review Letters}, number = {16}, publisher = {American Physical Society}, title = {{Modeling Leidenfrost levitation of soft elastic solids}}, doi = {10.1103/PhysRevLett.131.168201}, volume = {131}, year = {2023}, } @misc{14919, abstract = {GLACIER METEOROLOGICAL DATA SWISS ALPS -2022 }, author = {Shaw, Thomas and Buri, Pascal and McCarthy, Michael and Miles, Evan and Pellicciotti, Francesca}, publisher = {Zenodo}, title = {{Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer}}, doi = {10.5281/ZENODO.8277285}, year = {2023}, } @misc{23030, abstract = {%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% GLACIER METEOROLOGICAL DATA SWISS ALPS -2022 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Data gathered and structured by Thomas Shaw (WSL, Switzerland (until Oct 2022)). On and off-glacier meteorological data were gathered and analysed as part of a Marie-Curie project 'TEMPEST' (tempestglacier.com). The dataset consists of hourly low-cost AWS (Davis Vantage Pro2) and simple temperature ('T-')logger (Onset TidBitv2) sensor records on three glaciers in the Swiss Alps (Canton Valais). The glaciers are: Haut Glacier d'Arolla (45.967°N, 7.526°E) Glacier d'Otemma (45.956°N, 7.454°E) Glacier du Corbassière (45.975°N, 7.303°E) Data are provided in individual Excel files per glacier that contain all hourly data for the sub-period of comparison (11 August-18 September, 2022). Data are quality controlled and checked for obvious errors. Any uncertain values are set to NaN. Air temperature data at 'T-Logger' stations were corrected for heating errors using the comparison of measurements in artificially (AWS) and naturally ventilated (T-Logger) radiation shields on Arolla and Corbassiere glaciers. A multiple linear regression model was applied to estimate these differences at all T-Loggers on all glaciers as a function of incoming shortwave radiation (MeteoSwiss station-derived) and wind speed (measured at AWS). Each Excel file contains a 'META' tab for simple metadata related to station locations (latitude 'LAT' (°), longitude 'LON' (°), elevation 'ELE' (m a.s.l.) and flowpath length 'FPL' (m)) and a 'DATA' tab for the hourly data. Suffixes to the station names in each column provide the variable measured at that site: 'TA' - 2m air temperature (°C) 'TA_Hi' - Maximum air temperature for timestep (°C) 'TA_Lo' - Minimum air temperature for timestep (°C) 'RH' - 2m relative humiditiy (%) 'FF' - Wind speed (m s^-1) 'FF_Hi' - Maximum wind speed for timestep (m s^-1) 'FF_Lo' - Minimum wind speed for timestep (m s^-1) 'DIR' - Wind direction (°) 'DEW' - Dewpoint temperature (°C) 'PRESS' - Air pressure (mbar) 'CHILL' - Calculated wind chill temperature (°C) 'Heat_idx' - Calculated heat index (°C) 'THSW' - A calculated index that uses humidity and temperature like for the Heat Index, but also includes the heating effects of sunshine and the cooling effects of wind (like Wind Chill) to calculate an apparent temperature of what it "feels" like out in the shade Wind speeds and direction measured at off-glacier sites 'OG' are for the lower off-glacier station ('OG_Low'). }, author = {Shaw, Thomas E. and Buri, Pascal and McCarthy, Michael and Miles, Evan S. and Pelliciotti, Francesca}, publisher = {Zenodo}, title = {{Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer}}, doi = {10.5281/zenodo.8277284}, year = {2023}, } @misc{23031, abstract = {There are 4 tar.xz files with the result of the model for the paper: A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen, Svalbard. These archives can be unzipped on Linux using the tar command, or on other systems using a specific software. FrontPositions includes data files with the coordinates of the nodes of the front positons. CalvingStats includes txt files with some characteristics of the calving events. HydrologyOutput includes csv files with the main results of the hydrological model. ModelOutput include output and visualization files of the model results. The .vtu and .pvtu files are best viewed in the software Paraview.}, author = {Muñoz Hermosilla, José M}, publisher = {Zenodo}, title = {{A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen}}, doi = {10.5281/zenodo.8005257}, year = {2023}, } @misc{18634, abstract = {There are 4 tar.xz files with the result of the model for the paper: A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen, Svalbard. }, author = {Muñoz Hermosilla, José M}, publisher = {Zenodo}, title = {{A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen}}, doi = {10.5281/ZENODO.8005257}, year = {2023}, } @misc{23032, abstract = {Datasets in .csv or .xlsx format sorted into folders by figures. In some cases additional files with roi/well-to-sample mapping were provided. For more details contact authors.}, author = {Sarkisyan, Karen}, publisher = {Figshare}, title = {{A hybrid pathway for self-sustained luminescence - Raw data}}, doi = {10.6084/m9.figshare.24772872}, year = {2023}, } @inproceedings{14862, author = {Rella, Simon and Kulikova, Y and Minnegalieva, Aygul and Kondrashov, Fyodor}, booktitle = {European Journal of Public Health}, issn = {1464-360X}, keywords = {Public Health, Environmental and Occupational Health}, number = {Supplement_2}, publisher = {Oxford University Press}, title = {{Complex vaccination strategies prevent the emergence of vaccine resistance}}, doi = {10.1093/eurpub/ckad160.597}, volume = {33}, year = {2023}, }