On Association Cells in Random Heterogeneous Networks

Characterizing user to access point (AP) association strategies in heterogeneous cellular networks (HetNets) is critical for their performance analysis, as it directly influences the load across the network. In this letter, we introduce and analyze a class of association strategies, which we term stationary association, and the resulting association cells. For random HetNets, where APs are distributed according to a stationary point process, the area of the resulting association cells are shown to be the marks of the corresponding point process. Addressing the need of quantifying the load experienced by a typical user, a "Feller-paradox" like relationship is established between the area of the association cell containing origin and that of a typical association cell. For the specific case of Poisson point process and max power/SINR association, the mean association area of each tier is derived and shown to increase with channel gain variance and decrease in the path loss exponents of the corresponding tier

Maximal fluctuations of confined actomyosin gels: dynamics of the cell nucleus

We investigate the effect of stress fluctuations on the stochastic dynamics of an inclusion embedded in a viscous gel. We show that, in non-equilibrium systems, stress fluctuations give rise to an effective attraction towards the boundaries of the confining domain, which is reminiscent of an active Casimir effect. We apply this generic result to the dynamics of deformations of the cell nucleus and we demonstrate the appearance of a fluctuation maximum at a critical level of activity, in agreement with recent experiments [E. Makhija, D. S. Jokhun, and G. V. Shivashankar, Proc. Natl. Acad. Sci. U.S.A. 113, E32 (2016)].Comment: 12 pages, 5 figure

Soft inclusion in a confined fluctuating active gel

We study stochastic dynamics of a point and extended inclusion within a one dimensional confined active viscoelastic gel. We show that the dynamics of a point inclusion can be described by a Langevin equation with a confining potential and multiplicative noise. Using a systematic adiabatic elimination over the fast variables, we arrive at an overdamped equation with a proper definition of the multiplicative noise. To highlight various features and to appeal to different biological contexts, we treat the inclusion in turn as a rigid extended element, an elastic element and a viscoelastic (Kelvin-Voigt) element. The dynamics for the shape and position of the extended inclusion can be described by coupled Langevin equations. Deriving exact expressions for the corresponding steady state probability distributions, we find that the active noise induces an attraction to the edges of the confining domain. In the presence of a competing centering force, we find that the shape of the probability distribution exhibits a sharp transition upon varying the amplitude of the active noise. Our results could help understanding the positioning and deformability of biological inclusions, eg. organelles in cells, or nucleus and cells within tissues.Comment: 16 pages, 9 figure

Coherent Excitonic Coupling in an Asymmetric Double InGaAs Quantum Well Arises from Many-Body Effects

We study an asymmetric double InGaAs quantum well using optical two-dimensional coherent spectroscopy. The collection of zero-quantum, one-quantum, and two-quantum two-dimensional spectra provides a unique and comprehensive picture of the double well coherent optical response. Coherent and incoherent contributions to the coupling between the two quantum well excitons are clearly separated. An excellent agreement with density matrix calculations reveals that coherent interwell coupling originates from many-body interactions

Development of a protocol for maintaining viability while shipping organoid-derived retinal tissue.

Retinal organoid technology enables generation of an inexhaustible supply of three-dimensional retinal tissue from human pluripotent stem cells (hPSCs) for regenerative medicine applications. The high similarity of organoid-derived retinal tissue and transplantable human fetal retina provides an opportunity for evaluating and modeling retinal tissue replacement strategies in relevant animal models in the effort to develop a functional retinal patch to restore vision in patients with profound blindness caused by retinal degeneration. Because of the complexity of this very promising approach requiring specialized stem cell and grafting techniques, the tasks of retinal tissue derivation and transplantation are frequently split between geographically distant teams. Delivery of delicate and perishable neural tissue such as retina to the surgical sites requires a reliable shipping protocol and also controlled temperature conditions with damage-reporting mechanisms in place to prevent transplantation of tissue damaged in transit into expensive animal models. We have developed a robust overnight tissue shipping protocol providing reliable temperature control, live monitoring of the shipment conditions and physical location of the package, and damage reporting at the time of delivery. This allows for shipping of viable (transplantation-competent) hPSC-derived retinal tissue over large distances, thus enabling stem cell and surgical teams from different parts of the country to work together and maximize successful engraftment of organoid-derived retinal tissue. Although this protocol was developed for preclinical in vivo studies in animal models, it is potentially translatable for clinical transplantation in the future and will contribute to developing clinical protocols for restoring vision in patients with retinal degeneration

Galaxies on graph neural networks: towards robust synthetic galaxy catalogs with deep generative models

The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, production of synthetic data for these surveys, to test and validate analysis methods, suffers from a very high computational cost. In particular, generating mock galaxy catalogs at sufficiently large volume and high resolution will soon become computationally unreachable. In this paper, we address this problem with a Deep Generative Model to create robust mock galaxy catalogs that may be used to test and develop the analysis pipelines of future weak lensing surveys. We build our model on a custom built Graph Convolutional Networks, by placing each galaxy on a graph node and then connecting the graphs within each gravitationally bound system. We train our model on a cosmological simulation with realistic galaxy populations to capture the 2D and 3D orientations of galaxies. The samples from the model exhibit comparable statistical properties to those in the simulations. To the best of our knowledge, this is the first instance of a generative model on graphs in an astrophysical/cosmological context.Comment: Accepted as extended abstract at ICML 2022 Workshop on Machine Learning for Astrophysics. Condensed version of arXiv:2204.0707