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Can topography be used to control bacteria accumulation? We address this question in the model system of smooth-swimming and run-and-tumble \textit{Escherichia coli} swimming near a sinusoidal surface, and show that the accumulation of bacteria is determined by the characteristic curvature of the surface. For low curvatures, cells swim along the surface due to steric alignment and are ejected from the surface when they reach the peak of the sinusoid. Increasing curvature enhances this effect and reduces the density of bacteria in the curved surface. However, for curvatures larger than $\kappa^*\approx 0.3 \mu m^{-1}$, bacteria become trapped in the valleys, where they can remain for long periods of time. Minimal simulations considering only steric interactions with the surface reproduce these results and give insights into the physical mechanisms defining the critical curvature, which is found to scale with the inverse of the bacterial length. We show that for curvatures larger than $\kappa^*$, the otherwise stable alignment with the wall becomes unstable while the stable orientation is now perpendicular to the wall, thus predicting accurately the onset of trapping at the valleys.

Energy systems planning models identify least-cost strategies for expansion and operation of energy systems and provide decision support for investment, planning, regulation, and policy. Most are formulated as linear programming (LP) or mixed integer linear programming (MILP) problems. Despite the relative efficiency and maturity of LP and MILP solvers, large scale problems are often intractable without abstractions that impact quality of results and generalizability of findings. We consider a macro-energy systems planning problem with detailed operations and policy constraints and formulate a computationally efficient Benders decomposition separating investments from operations and decoupling operational timesteps using budgeting variables in the master model. This novel approach enables parallelization of operational subproblems and permits modeling of relevant constraints coupling decisions across time periods (e.g. policy constraints) within a decomposed framework. Runtime scales linearly with temporal resolution; tests demonstrate substantial runtime improvement for all MILP formulations and for some LP formulations depending on problem size relative to analagous monolithic models solved with state-of-the-art commercial solvers. Our algorithm is applicable to planning problems in other domains (e.g. water, transportation networks, production processes) and can solve large-scale problems otherwise intractable. We show that the increased resolution enabled by this algorithm mitigates structural uncertainty, improving recommendation accuracy., Comment: Published in: Anna Jacobson, Filippo Pecci, Nestor Sepulveda, Qingyu Xu, Jesse Jenkins (2023) A Computationally Efficient Benders Decomposition for Energy Systems Planning Problems with Detailed Operations and Time-Coupling Constraints. INFORMS Journal on Optimization 0(0)

Magnetotactic bacteria (MTB) are endowed with an exquisite orientation mechanism allowing them to swim along the geomagnetic field lines [1-3]. This mechanism consists of a chain of bio-synthesized magnetic nano-crystals [1,4,5]. Although the physics behind the minimum size of this biological compass is well understood [6], it is yet unclear what sets its maximum size [6-8]. Here, by using a macroscopic experiment inspired in these microorganisms, we show that larger magnetic moments will drive the collective behavior of MTB into a phase where bacteria are unable to swim freely, in detriment of their evolutive fitness [3,9]. Simple macroscopic experiments, numerical simulations, and analytic estimates are used to explain the upper limit for the size of the chain of nano-crystals in MTB. Our macroscopic bio-inspired experiment, and physical model provide new opportunities to explore and understand the phases of magnetic active matter at all scales.

Machine Learning (ML) applications on healthcare can have a great impact on people's lives helping deliver better and timely treatment to those in need. At the same time, medical data is usually big and sparse requiring important computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations. This can prevent the less favored people from benefiting of the advancement in ML applications for healthcare. In this project we explored methods to increase computational efficiency of ML algorithms, in particular Artificial Neural Nets (NN), while not compromising the accuracy of the predicted results. We used in-hospital mortality prediction as our case analysis based on the MIMIC III publicly available dataset. We explored three methods on two different NN architectures. We reduced the size of recurrent neural net (RNN) and dense neural net (DNN) by applying pruning of "unused" neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the LSTM cell allowing to use less recurrent layers for the model. Finally, we implemented quantization on DNN forcing the weights to be 8-bits instead of 32-bits. We found that all our methods increased computational efficiency without compromising accuracy and some of them even achieved higher accuracy than the pre-condensed baseline models.

Four on-lattice and six off-lattice models for active matter are studied numerically, showing that in contact with a wall, they display universal wetting transitions between three distinctive phases. The particles, which interact via exclusion volume only, move persistently and, depending on the model, change their direction either via tumble processes or rotational diffusion. When increasing the turning rate $\nu_T$, the systems transit from total wetting, to partial wetting and dewetted phases. In the first phase, a wetting film covers the wall, with increasing heights while decreasing $\nu_T$. The second phase is characterized by wetting droplets on the walls. And, finally, the walls dries with few particles in contact with it. These phases present two continuous non-equilibrium transitions. For the first transition, from partial to total wetting, the fraction of dry sites vanishes continuously when decreasing $\nu_T$, with a power law of exponent 1. And, for the second transition, an order parameter proportional to the excess mass in droplets decreases continuously with a power law of exponent 3 when approaching a critical value of $\nu_T$. The critical exponents are the same for all the models studied.

Hyperuniform states are an efficient way to fill up space for disordered systems. In these states the particle distribution is disordered at the short scale but becomes increasingly uniform when looked at large scales. Hyperuniformity appears in several systems, in static or quasistatic regimes as well as close to transitions to absorbing states. Here, we show that a vibrated granular layer, at the critical point of the liquid-to-solid transition, displays dynamic hyperuniformity. Prior to the transition, patches of the solid phase form, with length scales and mean lifetimes that diverge critically at the transition point. When reducing the wavenumber, density fluctuations encounter increasingly more patches that block their propagation, resulting in a static structure factor that tends to zero for small wavenumbers at the critical point, which is a signature of hyperuniformity. A simple model demonstrates that this coupling of a density field to a highly fluctuating scalar friction field gives rise to dynamic hyperuniform states. Finally, we show that the structure factor detects better the emergence of hyperuniformity, compared to the particle number variance.

A lattice model for active matter is studied numerically, showing that it displays wettings transitions between three distinctive phases when in contact with an impenetrable wall. The particles in the model move persistently, tumbling with a small rate $\alpha$, and interact via exclusion volume only. When increasing the tumbling rates $\alpha$, the system transits from total wetting to partial wetting and unwetting phases. In the first phase, a wetting film covers the wall, with increasing heights when $\alpha$ is reduced. The second phase is characterized by wetting droplets on the wall with a periodic spacing between them. Finally, the wall dries with few particles in contact with it. These phases present nonequilibrium transitions. The first transition, from partial to total wetting, is continuous and the fraction of dry sites vanishes continuously when decreasing the tumbling rate $\alpha$. For the second transition, from partial wetting to dry, the mean droplet distance diverges logarithmically when approaching the critical tumbling rate, with saturation due to finite-size effects.

The emergence of clustering and coarsening in crowded ensembles of self-propelled agents is studied using a lattice model in one-dimension. The persistent exclusion process, where particles move at directions that change randomly at a low tumble rate $\alpha$, is extended allowing sites to be occupied by more than one particle, with a maximum $n_\text{max}$ per site. Three phases are distinguished. For $n_\text{max}=1$ a gas of clusters form, with sizes distributed exponentially and no coarsening takes place. For $n_\text{max}\geq 3$ and small values of $\alpha$, coarsening takes place and few large clusters appear, with a large fraction of the total number of particles in them. In the same range of $n_\text{max}$ but for larger values of $\alpha$, a gas phase where a negligible fraction of particles takes part of clusters. Finally, $n_\text{max}=2$ corresponds to a crossover phase. The character of the transitions between phases is studied extending the model to allow $n_\text{max}$ to take real values and jumps to an occupied site are probabilistic. The transition from the gas of clusters to the coarsening phase is continuous and the mass of the large clusters grows continuously when varying the maximum occupancy, and the crossover found corresponds to values close to the transition. The second transition, from the coarsening to the gaseous phase can be either continuous or discontinuous depending on the parameters, with a critical point separating both cases.

We study the rotational inertia of a model of supersolid in the frame of the mean field Gross-Pitaevskii theory in one space dimension. We discuss the ground state of the model and the existence of a non classical inertia (NCRI) under rotation that models an annular geometry. An explicit formula for the NCRI is deduced. It depends on the density profil of the ground state, in full agreement with former theories. We compare the NCRI computed through this theory with direct numerical simulations of rotating 1D systems.