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Nanomagnetism concerns the engineering of magnetic interactions in heterostructures that consist of layers of magnetic and non-magnetic materials. Mostly, these interactions are dominated by the minimization of energy. Here, we propose an effective magnetic interlayer coupling that is dominated by the maximization of entropy. As an example, we consider the system that mediates the effective interactions to be square spin ice, in which case we find purely entropic interactions that are long-ranged. We argue that in the thermodynamic limit the entropic interlayer coupling gives rise to entropic torques on the magnetization direction. For small systems, the physical properties are well characterized by the mutual information between the two magnets that are coupled. Because entropic interactions become stronger for higher temperatures, our findings may benefit the development of nanomagnetic devices that require thermal stability., Comment: 9 pages, 9 figures New version: Added references in sections: "Example: square spin ice" and "Conclusion"

Many living systems, such as birds and fish, exhibit collective behaviors like flocking and swarming. Recently, an experimental system of active colloidal particles has been developed, where the motility of each particle is adjusted based on its visual detection of surrounding particles. These particles with visual-perception-dependent motility exhibit group formation and cohesion. Inspired by these behaviors, we investigate intelligent active Brownian particles (iABPs) equipped with visual perception and velocity alignment in three dimensions using computer simulations. The visual-perception-based self-steering describes the tendency of iABPs to move toward the center of mass of particles within their visual cones, while velocity alignment encourages alignment with neighboring particles. We examine how the behavior varies with the visual cone angle theta, self-propulsion speed (Peclet number Pe), and the interaction strengths of velocity alignment (Omega_a) and visual-based self-steering (Omega_v). Our findings show that spherical iABPs form clusters, worm-like clusters, milling behaviors, and dilute-gas phases, consistent with 2D studies. By reducing the simulation box size, we observe additional structures like band-like clusters and dense baitball formations. Additionally, rod-like iABPs form band-like, worm-like, radiating, and helical structures, while iABP worms exhibit streamlined and blob-like structures. Many of these patterns resemble collective behaviors in nature, such as ant milling, fish baitballs, and worm clusters. Advances in synthetic techniques could enable nanorobots with similar capabilities, offering insights into multicellular systems through active matter.

Improving connectivity and completeness are the most challenging aspects of small liver vessel segmentation. It is difficult for existing methods to obtain segmented liver vessel trees simultaneously with continuous geometry and detail in small vessels. We proposed a diffusion model-based method with a multi-scale graph attention guidance to break through the bottleneck to segment the liver vessels. Experiments show that the proposed method outperforms the other state-of-the-art methods used in this study on two public datasets of 3D-ircadb-01 and LiVS. Dice coefficient and Sensitivity are improved by at least 11.67% and 24.21% on 3D-ircadb-01 dataset, and are improved by at least 3.21% and 9.11% on LiVS dataset. Connectivity is also quantitatively evaluated in this study and our method performs best. The proposed method is reliable for small liver vessel segmentation., Comment: This work has been submitted to the IEEE for possible publication

Optimizing the synthesis of zeolites and exploring novel frameworks offer pivotal opportunities and challenges in materials design. While inverse design proves highly effective for simpler crystals, its application to intricate structures like zeolites poses severe challenges. Here, we introduce an innovative inverse design workflow tailored to efficiently reproduce target zeolite frameworks in a binary coarse-grained model using enhanced sampling molecular dynamics simulations. This workflow integrates an evolutionary parameter optimization strategy with a variant of the seeding approach. Using this method, we successfully reproduce Z1 and SGT zeolites, and Type-I clathrates, find new optimal parameters for known phases, such as the SOD and CFI, and even discover novel frameworks, such as Z5. This is done within a simple coarse-grained model for a tetrahedra-forming component and a structure-directing agent. Our methodology not only enables the screening of synthesis protocols but also facilitates the discovery of hypothetical zeolites., Comment: 17 pages, 13 figures

Research over the past decade has shown that multiple elements of the climate system, such as the Arctic Winter sea ice or the Amazon rainforest, could undergo abrupt shifts, but there are large uncertainties regarding their timing and spatial extent. In this study we investigated when and where abrupt shifts occur in the latest generation of climate models (CMIP6) under a scenario of a 1% annual increase in CO$_2$. We considered 82 ocean, atmosphere, and land variables across 57 models. We used a Canny edge detection method -- adapted for spatiotemporal dimensions -- to detect abrupt shifts occurring on time scales from years to decades. Then, we performed a connected component analysis to quantify the spatial extent of these shifts. The systems analyzed include the North Atlantic subpolar gyre, Tibetan Plateau, land permafrost, Amazon rainforest, Antarctic sea ice, monsoon systems, Arctic summer sea ice, Arctic winter sea ice, and Barents sea ice. With the exception of the monsoons systems, we found abrupt shifts in multiple models per system. We observed a considerable variation across the different CMIP6 models in terms of timing and spatial extent. Higher levels of global warming increased the risk of abrupt shifts in CMIP6 models. Even at a global warming level of 1.5 $^\circ \text{C}$, six out of ten studied climate subsystems showed abrupt shifts that cover a substantial area in multiple models.

Quantum gases consisting of species with distinct quantum statistics, such as Bose-Fermi mixtures, can behave in a fundamentally different way than their unmixed constituents. This makes them an essential platform for studying emergent quantum many-body phenomena such as mediated interactions and unconventional pairing. Here, we realize an equilibrium Bose-Fermi mixture in a bilayer electron system implemented in a WS$_{2}$/WSe$_{2}$ moir\'e heterobilayer with strong Coulomb coupling to a nearby moir\'e-free WSe$_{2}$ monolayer. Absent the fermionic component, the underlying bosonic phase manifests as a dipolar excitonic insulator. By injecting excess charges into it, we show that the bosonic phase forms a stable mixture with added electrons but abruptly collapses upon hole doping. We develop a microscopic model to explain the unusual asymmetric stability with respect to electron and hole doping. By studying the Bose-Fermi mixture via monitoring excitonic resonances from both layers, we demonstrate gate-tunability over a wide range in the boson/fermion density phase space, in excellent agreement with theoretical calculations. Our results further the understanding of phases stabilized in moir\'e bilayer electron systems and demonstrate their potential for exploring the exotic properties of equilibrium Bose-Fermi mixtures., Comment: 13 pages, 4 figures. Extended Data: 6 figures. We welcome your feedback!

The Atlantic Meridional Overturning Circulation (AMOC) is recognized as a tipping element within the global climate system. Central to its tipping behavior is the salt-advection feedback mechanism, which has been extensively studied in box models and models of intermediate complexity. However, in contemporary, highly complex climate models, the importance and functioning of this feedback mechanism is less clear due to the intricate interplay of numerous ocean-atmosphere-sea ice feedbacks. In this study, we conduct a detailed mechanistic analysis of an AMOC collapse under quasi-equilibrium forcing conditions using the Community Earth System Model (CESM). By reconstructing the AMOC strength from the meridional density contrast across the Atlantic Ocean, we demonstrate that AMOC stability can be related to the Atlantic freshwater budget, revealing several important feedbacks. The dominant contribution is the destabilising salt-advection feedback, which is quantified through a negative sign of the overturning freshwater transport at 34$^{\circ}$S, indicated by $F_{\mathrm{ovS}}$. Other feedbacks are related to changes in North Atlantic sea-ice melt (destabilising), ocean-atmosphere freshwater fluxes (destabilising) and gyre transports (stabilising). Our study clarifies the role of $F_{\mathrm{ovS}}$ as an indicator of the background state stability of the AMOC. As many modern climate models have a positive $F_{\mathrm{ovS}}$ bias this implies that their AMOC is too stable which leads to an underestimation of the risk of an AMOC collapse under climate change., Comment: Article Submitted to 'Journal of Climate (JCLI)' (ISSN: 0894-8755; eISSN: 1520-0442)

Development and testing of multi-robot systems employing wireless signal-based sensing requires access to suitable hardware, such as channel monitoring WiFi transceivers, which can pose significant limitations. The WiFi Sensor for Robotics (WSR) toolbox, introduced by Jadhav et al. in 2022, provides a novel solution by using WiFi Channel State Information (CSI) to compute relative bearing between robots. The toolbox leverages the amplitude and phase of WiFi signals and creates virtual antenna arrays by exploiting the motion of mobile robots, eliminating the need for physical antenna arrays. However, the WSR toolbox's reliance on an obsoleting WiFi transceiver hardware has limited its operability and accessibility, hindering broader application and development of relevant tools. We present an open-source simulation framework that replicates the WSR toolbox's capabilities using Gazebo and Matlab. By simulating WiFi-CSI data collection, our framework emulates the behavior of mobile robots equipped with the WSR toolbox, enabling precise bearing estimation without physical hardware. We validate the framework through experiments with both simulated and real Turtlebot3 robots, showing a close match between the obtained CSI data and the resulting bearing estimates. This work provides a virtual environment for developing and testing WiFi-CSI-based multi-robot localization without relying on physical hardware. All code and experimental setup information are publicly available at https://github.com/BrendanxP/CSI-Simulation-Framework, Comment: 6+1 pages (text + references), 6 figures

Directed graphs provide more subtle and precise modelling tools for optimization in road networks than simple graphs. In particular, they are more suitable in the context of alternative fuel vehicles and new automotive technologies, like electric vehicles. In this paper, we introduce the new general concept of a reachability digraph associated with a road network to model the placement of refuelling facilities in road networks as k-dominating sets in the reachability digraph. Two new greedy heuristics are designed and experimentally tested to search for small k-dominating sets in two types of digraphs, including the reachability digraphs. Refined greedy strategies are shown to be efficient, capable of finding good quality solutions, and suitable for application in very large digraphs and road networks. Also, a probabilistic method is used to prove a new upper bound on the k-domination number of a digraph, which informs the development of a new randomized heuristic to search for k-dominating sets in the digraph. Generalizing the randomized heuristic ideas, making the heuristic more flexible, tuning and combining it with the greedy strategies allows us to obtain even better results for the reachability digraphs. Computational experiments are conducted for a case study of road networks in the West Midlands (UK)., Comment: 27 pages, 1 figure, 7 tables; preliminary results are presented in a short conference paper and at the International Network Optimization Conference INOC 2024, University College Dublin (Ireland) https://inoc2024.sciencesconf.org/

The correctness of complex software depends on the correctness of both the source code and the compilers that generate corresponding binary code. Compilers must do more than preserve the semantics of a single source file: they must ensure that generated binaries can be composed with other binaries to form a final executable. The compatibility of composition is ensured using an Application Binary Interface (ABI), which specifies details of calling conventions, exception handling, and so on. Unfortunately, there are no official ABIs for concurrent programs, so different atomics mappings, although correct in isolation, may induce bugs when composed. Indeed, today, mixing binaries generated by different compilers can lead to an erroneous resulting binary. We present mix testing: a new technique designed to find compiler bugs when the instructions of a C/C++ test are separately compiled for multiple compatible architectures and then mixed together. We define a class of compiler bugs, coined mixing bugs, that arise when parts of a program are compiled separately using different mappings from C/C++ atomic operations to assembly sequences. To demonstrate the generality of mix testing, we have designed and implemented a tool, atomic-mixer, which we have used: (a) to reproduce one existing non-mixing bug that state-of-the-art concurrency testing tools are limited to being able to find (showing that atomic-mixer at least meets the capabilities of these tools), and (b) to find four previously-unknown mixing bugs in LLVM and GCC, and one prospective mixing bug in mappings proposed for the Java Virtual Machine. Lastly, we have worked with engineers at Arm to specify, for the first time, an atomics ABI for Armv8, and have used atomic-mixer to validate the LLVM and GCC compilers against it., Comment: 26 pages, Accepted to OOPSLA (Object-oriented Programming, Systems, Languages, and Applications) 2024

MICADO is a first light instrument for the Extremely Large Telescope (ELT), set to start operating later this decade. It will provide diffraction limited imaging, astrometry, high contrast imaging, and long slit spectroscopy at near-infrared wavelengths. During the initial phase operations, adaptive optics (AO) correction will be provided by its own natural guide star wavefront sensor. In its final configuration, that AO system will be retained and complemented by the laser guide star multi-conjugate adaptive optics module MORFEO (formerly known as MAORY). Among many other things, MICADO will study exoplanets, distant galaxies and stars, and investigate black holes, such as Sagittarius A* at the centre of the Milky Way. After their final design phase, most components of MICADO have moved on to the manufacturing and assembly phase. Here we summarize the final design of the instrument and provide an overview about its current manufacturing status and the timeline. Some lessons learned from the final design review process will be presented in order to help future instrumentation projects to cope with the challenges arising from the substantial differences between projects for 8-10m class telescopes (e.g. ESO-VLT) and the next generation Extremely Large Telescopes (e.g. ESO-ELT). Finally, the expected performance will be discussed in the context of the current landscape of astronomical observatories and instruments. For instance, MICADO will have similar sensitivity as the James Webb Space Telescope (JWST), but with six times the spatial resolution., Comment: Proceedings of the SPIE, Volume 13096, id. 1309611 11 pp. (2024)

There is a reasonable possibility that the present-day Atlantic Meridional Overturning Circulation is in a bi-stable regime and hence it is relevant to compute probabilities and pathways of noise-induced transitions between the stable equilibrium states. Here, the most probable transition pathway of a noise-induced collapse of the northern overturning circulation in a spatially-continuous two-dimensional model with surface temperature and stochastic salinity forcings is directly computed using Large Deviation Theory (LDT). This pathway reveals the fluid dynamical mechanisms of such a collapse. Paradoxically it starts off with a strengthening of the northern overturning circulation before a short but strong salinity pulse induces a second overturning cell. The increased atmospheric energy input of this two-cell configuration cannot be mixed away quickly enough, leading to the collapse of the northern overturning cell and finally resulting in a southern overturning circulation. Additionally, the approach allows us to compare the probability of this collapse under different parameters in the deterministic part of the salinity surface forcing, which quantifies the increase in collapse probability as the bifurcation point of the system is approached.

An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit, are revolutionizing climate science. Big data and associated algorithms, coalesced under the field of Machine Learning (ML), offer the opportunity to study the physics of the climate system in ways, and with an amount of detail, infeasible few years ago. The inference provided by ML has allowed to ask causal questions and improve prediction skills beyond classical barriers. Furthermore, when paired with modeling experiments or robust research in model parameterizations, ML is accelerating computations, increasing accuracy and allowing for generating very large ensembles at a fraction of the cost. In light of the urgency imposed by climate change and the rapidly growing role of ML, we review its broader accomplishments in climate physics. Decades long standing problems in observational data reconstruction, representation of sub-grid scale phenomena and climate (and weather) prediction are being tackled with new and justified optimism. Ultimately, this review aims at providing a perspective on the benefits and major challenges of exploiting ML in studying complex systems., Comment: 25 pages, 4 figures

To understand mechanisms of salt intrusion in estuaries, we develop a semi-analytical model, that explicitly accounts for salt transport by both exchange flow and tidal flow. This model, after calibration, successfully hindcasts hydrodynamics and salinity dynamics in three estuaries that have strongly different characteristics. We find, from analyzing the model results for these three estuaries, that salt transport processes by exchange flow and tidal flow interact through the subtidal stratification. Transport by exchange flow creates stratification, thereby generating a phase shift of tidal salinity with respect to the tidal flow, which is important for the magnitude of the tidal salt transport. Conversely, the strength of tidal currents determines the vertical mixing that breaks down stratification. A new analytical formulation is presented for the component of the salt transport driven by the depth-averaged tidal flow. This salt transport is larger than the component associated with the vertical shear of the tidal current. Finally, a method that yields analytical equations that quantify the importance of different contributions to the salt transport using only primary information is developed using approximate solutions for the subtidal stratification. This method performs well for the estuaries considered.

High-dimensional networks play a key role in understanding complex relationships. These relationships are often dynamic in nature and can change with multiple external factors (e.g., time and groups). Methods for estimating graphical models are often restricted to static graphs or graphs that can change with a single covariate (e.g., time). We propose a novel class of graphical models, the covariate-varying network (CVN), that can change with multiple external covariates. In order to introduce sparsity, we apply a $L_1$-penalty to the precision matrices of $m \geq 2$ graphs we want to estimate. These graphs often show a level of similarity. In order to model this 'smoothness', we introduce the concept of a 'meta-graph' where each node in the meta-graph corresponds to an individual graph in the CVN. The (weighted) adjacency matrix of the meta-graph represents the strength with which similarity is enforced between the $m$ graphs. The resulting optimization problem is solved by employing an alternating direction method of multipliers. We test our method using a simulation study and we show its applicability by applying it to a real-world data set, the gene expression networks from the study 'German Cancer in childhood and molecular-epidemiology' (KiKme). An implementation of the algorithm in R is publicly available under https://github.com/bips-hb/cvn

The Atlantic Meridional Overturning Circulation (AMOC) is a key component of the climate system and considered to be a tipping element. There is still a large uncertainty on the critical global warming level at which the AMOC will start to collapse. Here we analyse targeted climate model simulations, together with observations, reanalysis products and a suite of state-of-the-art climate model results to reassess this critical global warming level. We find a critical threshold of +3C global mean surface temperature increase compared to pre-industrial with a lower bound of +2.2C (10%-Cl). Such global mean surface temperature anomalies are expected to be reached after 2050. This means that the AMOC is more likely than not (> 50%) to tip within the 21st century under a middle-of-the-road climate change scenario and very likely (> 90%) to tip under a high emissions scenario. The AMOC collapse induced cooling is shown to be offset by the regional warming over Northwestern Europe during the 21st century, but will still induce severe impacts on society.

We present an Alternating Direction Method of Multipliers (ADMM) algorithm designed to solve the Weighted Generalized Fused LASSO Signal Approximator (wFLSA). First, we show that wFLSAs can always be reformulated as a Generalized LASSO problem. With the availability of algorithms tailored to the Generalized LASSO, the issue appears to be, in principle, resolved. However, the computational complexity of these algorithms is high, with a time complexity of $O(p^4)$ for a single iteration, where $p$ represents the number of coefficients. To overcome this limitation, we propose an ADMM algorithm specifically tailored for wFLSA-equivalent problems, significantly reducing the complexity to $O(p^2)$. Our algorithm is publicly accessible through the R package wflsa.

We employ molecular dynamics simulations to investigate ion and water transport driven by an electric field through quasi-two-dimensional nanoslits with a tapered geometry. Despite the absence of surface charge on the channel walls and the associated electric double layer, we do observe a robust ion selectivity. This selectivity favors the transport of cations from base to tip when the electric field is directed from base to tip, and anions from base to tip when the field direction is reversed. Additionally, we observe a corresponding electro-osmotic water flow from base to tip, regardless of the electric field direction. Intriguingly, ion selectivity and electro-osmotic flow are conventionally associated with surface charge and electric double layers. However, in uncharged tapered nanoslits, we uncover a novel mechanism for these phenomena, where ion selectivity arises from the divergence of the heterogeneous water polarization., Comment: 5 pages, 4 figures + supplementary material (8 pages, 11 figures)

We address the issue of resilience of the Atlantic Meridional Overturning Circulation (AMOC) given the many indications that this dynamical system is in a multi-stable regime. A novel approach to resilience based on rare event techniques is presented which leads to a measure capturing `resistance to change` and `ability to return' aspects in a probabilistic way. The application of this measure to a conceptual model demonstrates its suitability for assessing AMOC resilience but also shows its potential use in many other non-autonomous dynamical systems. This framework is then extended to compute the probability that the AMOC undergoes a transition conditioned on an external forcing. Such conditional probability can be estimated by exploiting the information available when computing the resilience of this system. This allows us to provide a probabilistic view on safe operating spaces by defining a conditional safe operating space as a subset of the parameter space of the (possibly transient) imposed forcing., Comment: 35 pages, 8 figures, 1 table

From self-assembly and protein folding to combinatorial metamaterials, a key challenge in material design is finding the right combination of interacting building blocks that yield targeted properties. Such structures are fiendishly difficult to find; not only are they rare, but often the design space is so rough that gradients are useless and direct optimization is hopeless. Here, we design ultra rare combinatorial metamaterials capable of multiple desired deformations by introducing a two-fold strategy that avoids the drawbacks of direct optimization. We first combine convolutional neural networks with genetic algorithms to prospect for metamaterial designs with a potential for high performance. In our case, these metamaterials have a high number of spatially extended modes; they are pluripotent. Second, we exploit this library of pluripotent designs to generate metamaterials with multiple target deformations, which we finally refine by strategically placing defects. Our pluripotent, multishape metamaterials would be impossible to design through trial-and-error or standard optimization. Instead, our data-driven approach is systematic and ideally suited to tackling the large and intractable combinatorial problems that are pervasive in material science., Comment: 18 pages, 12 figures

There is increasing concern that the Atlantic Meridional Overturning Circulation (AMOC) may collapse this century with a disrupting societal impact on large parts of the world. Preliminary estimates of the probability of such an AMOC collapse have so far been based on conceptual models and statistical analyses of proxy data. Here, we provide observationally based estimates of such probabilities from reanalysis data. We first identify optimal observation regions of an AMOC collapse from a recent global climate model simulation. Salinity data near the southern boundary of the Atlantic turn out to be optimal to provide estimates of the time of the AMOC collapse in this model. Based on the reanalysis products, we next determine probability density functions of the AMOC collapse time. The collapse time is estimated between 2037-2064 (10-90% CI) with a mean of 2050 and the probability of an AMOC collapse before the year 2050 is estimated to be 59 +/- 17%.

The variability of the strength of the Atlantic Meridional Overturning Circulation is influenced substantially by the formation of deep water in the North Atlantic. In many ocean models, so-called deep-decoupling oscillations have been found, whose timescale depends on the characteristics of convective vertical mixing processes. Their precise origin and sensitivity to the representation of mixing have remained unclear so far. To study this problem, we revisit a conceptual Welander model for the evolution of temperature and salinity in two vertically stacked boxes for surface and deep water, which interact through diffusion and/or convective adjustment. The model is known to exhibit several types of deep-decoupling oscillations, with phases of weak diffusive mixing interspersed with strong convective mixing, when the switching between them is assumed to be instantaneous. We present a comprehensive study of oscillations in Welander's model with non-instantaneous switching between mixing phases, as described by a smooth switching function. A dynamical systems approach allows us to distinguish four types of oscillations, in terms of their phases of diffusive versus convective mixing, and to identify the regions in the relevant parameter plane where they exist. The characteristic deep-decoupling oscillations still exist for non-instantaneous switching, but they require switching that is considerably faster than needed for sustaining oscillatory behaviour. Furthermore, we demonstrate how a gradual freshwater influx can lead to transitions between different vertical mixing oscillations. Notably, the convective mixing phase becomes shorter and even disappears, resulting in long periods of much reduced deep water formation. The results are relevant for the interpretation of ocean-climate variability in models and (proxy) observations.

The main science aim of the BlackGEM array is to detect optical counterparts to gravitational wave mergers. Additionally, the array will perform a set of synoptic surveys to detect Local Universe transients and short time-scale variability in stars and binaries, as well as a six-filter all-sky survey down to ~22nd mag. The BlackGEM Phase-I array consists of three optical wide-field unit telescopes. Each unit uses an f/5.5 modified Dall-Kirkham (Harmer-Wynne) design with a triplet corrector lens, and a 65cm primary mirror, coupled with a 110Mpix CCD detector, that provides an instantaneous field-of-view of 2.7~square degrees, sampled at 0.564\arcsec/pixel. The total field-of-view for the array is 8.2 square degrees. Each telescope is equipped with a six-slot filter wheel containing an optimised Sloan set (BG-u, BG-g, BG-r, BG-i, BG-z) and a wider-band 440-720 nm (BG-q) filter. Each unit telescope is independent from the others. Cloud-based data processing is done in real time, and includes a transient-detection routine as well as a full-source optimal-photometry module. BlackGEM has been installed at the ESO La Silla observatory as of October 2019. After a prolonged COVID-19 hiatus, science operations started on April 1, 2023 and will run for five years. Aside from its core scientific program, BlackGEM will give rise to a multitude of additional science cases in multi-colour time-domain astronomy, to the benefit of a variety of topics in astrophysics, such as infant supernovae, luminous red novae, asteroseismology of post-main-sequence objects, (ultracompact) binary stars, and the relation between gravitational wave counterparts and other classes of transients, Comment: Published in PASP

In this paper, we construct and discuss early-warning signs of the approach of a parameter to a deterministic bifurcation on a stochastic partial differential equation (SPDE) model with Gaussian white-noise on the boundary. We specifically focus on the qualitative behaviour of the time-asymptotic autocovariance and autocorrelation of the solutions of the linearized system. We also discuss the reliability of the tools from an analytic perspective and through various examples. Among those, the application of the early-warning signs for an ocean Boussinesq model is explored through numerical simulations. The analytic results obtained expand on previous work and present valuable early-warning signs for various applications., Comment: 24 pages, 40 figures

Despite extensive safety assessments of drugs prior to their introduction to the market, certain adverse drug reactions (ADRs) remain undetected. The primary objective of pharmacovigilance is to identify these ADRs (i.e., signals). In addition to traditional spontaneous reporting systems (SRSs), electronic health (EHC) data is being used for signal detection as well. Unlike SRS, EHC data is longitudinal and thus requires assumptions about the patient's drug exposure history and its impact on ADR occurrences over time, which many current methods do implicitly. We propose an exposure model framework that explicitly models the longitudinal relationship between the drug and the ADR. By considering multiple such models simultaneously, we can detect signals that might be missed by other approaches. The parameters of these models are estimated using maximum likelihood, and the Bayesian Information Criterion (BIC) is employed to select the most suitable model. Since BIC is connected to the posterior distribution, it servers the dual purpose of identifying the best-fitting model and determining the presence of a signal by evaluating the posterior probability of the null model. We evaluate the effectiveness of this framework through a simulation study, for which we develop an EHC data simulator. Additionally, we conduct a case study applying our approach to four drug-ADR pairs using an EHC dataset comprising over 1.2 million insured individuals. Both the method and the EHC data simulator code are publicly accessible as part of the R package https://github.com/bips-hb/expard., Comment: 40 pages, 7 figures

The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory (cDFT), is at best only known approximately for 3D systems. Here we introduce a method for learning a quasi-exact neural-network approximation of this functional by exclusively training on a dataset of radial distribution functions, circumventing the need to sample costly heterogeneous density profiles in a wide variety of external potentials. For a supercritical 3D Lennard-Jones system, we demonstrate that the learned neural free-energy functional accurately predicts planar inhomogeneous density profiles under various complex external potentials obtained from simulations., Comment: 5 pages, 2 figures + supplementary material (7 pages, 4 figures)

The development of new materials typically involves a process of trial and error, guided by insights from past experimental and theoretical findings. The inverse design approach for soft-matter systems has the potential to optimize specific physical parameters such as particle interactions, particle shape, or composition and packing fraction. This optimization aims to facilitate the spontaneous formation of specific target structures through self-assembly. In this study, we expand upon a recently introduced inverse design protocol for monodisperse systems to identify the required conditions and interactions for assembling crystal and quasicrystal phases within a binary mixture of two distinct species. This method utilizes an evolutionary algorithm to identify the optimal state point and interaction parameters, enabling the self-assembly of the desired structure. Additionally, we employ a convolutional neural network (CNN) that classifies different phases based on their diffraction patterns, serving as a fitness function for the desired structure. Using our protocol, we successfully inverse design two-dimensional crystalline structures, including a hexagonal lattice, and a dodecagonal quasicrystal, within a non-additive binary mixture of hard disks. Finally, we introduce a symmetry-based order parameter that leverages the encoded symmetry within the diffraction pattern. This order parameter circumvents the need for training a CNN, and is used as a fitness function to inverse design an octagonal quasicrystal., Comment: 12 pages, 5 figures

In this work, we introduce Variational Umbrella Seeding, a novel technique for computing nucleation barriers. This new method, a refinement of the original seeding approach, is far less sensitive to the choice of order parameter for measuring the size of a nucleus. Consequently, it surpasses seeding in accuracy, and Umbrella Sampling in computational speed. We test the method extensively and demonstrate excellent accuracy for crystal nucleation of nearly hard spheres and of two distinct models of water: mW and TIP4P/ICE. This method can easily be extended to calculate nucleation barriers for homogeneous melting, condensation, and cavitation.

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data, a task that poses significant challenges to traditional processors. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as "point estimates" that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantification. Since both the chaotic light source and the photonic crossbar support multiple independent computational wavelength channels, we sample from the output distributions in parallel at a sampling rate of 70.4 GS/s, limited only by the electronic interface. We exploit the photonic probabilistic architecture to simultaneously perform image classification and uncertainty prediction via a Bayesian neural network. Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling.

Lattice field theory (LFT) is the standard non-perturbative method to perform numerical calculations of quantum field theory. However, the typical bottleneck of fermionic lattice calculations is the inversion of the Dirac matrix. This inversion is solved by iterative methods, like the conjugate gradient algorithm, where matrix-vector multiplications (MVMs) are the main operation. Photonic integrated circuits excel in performing quick and energy-efficient MVMs, but at the same time, they are known to have low accuracy. This can be overcome by using mixed precision methods. In this paper, we explore the idea of using photonic technology to fulfil the demand for computational power of fermionic lattice calculations. These methods have the potential to reduce computation costs by one order of magnitude. Because of the hybrid nature of these methods, we call these 'photonic accelerated inverters (PAIs)'., Comment: 10 pages, 8 figures

Recent simulations performed with the Community Earth System Model (CESM) have suggested a crucial role of sea-ice processes in AMOC hysteresis behaviour under varying surface freshwater forcing. Here, we further investigate this issue using additional CESM simulations and a novel conceptual ocean-sea-ice box model. The CESM simulations show that the presence of sea ice gives rise to the existence of statistical equilibrium states with a weak AMOC strength. This is confirmed in the conceptual model, which captures the same AMOC hysteresis behaviour as in the CESM simulation and where steady states are computed versus forcing parameters. In the conceptual model, transition probabilities between the different equilibrium states are determined using rare event techniques. The transition probabilities from a strong AMOC state to a weak AMOC state increase when considering sea-ice processes and indicate that sea ice promotes these transitions. On the other hand, sea ice strongly reduces the probabilities of the reverse transition from a weak AMOC state to a strong AMOC state and this implies that sea ice also limits AMOC recovery. The results here indicate that sea-ice processes play a dominant role in AMOC hysteresis width and influence transition probabilities between the different equilibrium states.

The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called Trajectory-Adaptive Multilevel Splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called ``committor function" is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a Next-Generation Reservoir Computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called F(ast)-transitions and S(low)-transitions. Results for the F-transtions compare favorably with those in the literature where a physically-informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S-transitions in the same model. In both cases of F-transitions and S-transitions, we also show how the Next-Generation Reservoir Computing technique can be interpreted to retrieve an analytical estimate of the committor function., Comment: 15 pages, 9 figures

By employing brute-force molecular dynamics, umbrella sampling, and seeding simulations, we investigate homogeneous nucleation during melting and freezing of hard spheres. We provide insights into these opposing phase transitions from the standpoint of classical nucleation theory. We observe that melting has both a lower driving force and a lower interfacial tension than freezing. The lower driving force arises from the vicinity of a spinodal instability in the solid and from a strain energy. The lower interfacial tension implies that the Tolman lengths associated with melting and freezing have opposite signs, a phenomenon that we interpret with Turnbull's rule. Despite these asymmetries, the nucleation rates for freezing and melting are found to be comparable.