arXiv subjects

Ivo F. Sbalzarini

Publications and source records attributed to Ivo F. Sbalzarini.

At least 55 records · Page 3Linked to original sources

arXiv2020

Bionic Tracking: Using Eye Tracking to Track Biological Cells in Virtual Reality

We present Bionic Tracking, a novel method for solving biological cell tracking problems with eye tracking in virtual reality using commodity hardware. Using gaze data, and especially smooth pursuit eye movements, we are able to track cells in time series of 3D volumetric datasets. The problem of tracking cells is ubiquitous in developmental biology, where large volumetric microscopy datasets are acquired on a daily basis, often comprising hundreds or thousands of time points that span hours or days. The image data, however, is only a means to an end, and scientists are often interested in the reconstruction of cell trajectories and cell lineage trees. Reliably tracking cells in crowded three-dimensional space over many timepoints remains an open problem, and many current approaches rely on tedious manual annotation and curation. In our Bionic Tracking approach, we substitute the usual 2D point-and-click annotation to track cells with eye tracking in a virtual reality headset, where users simply have to follow a cell with their eyes in 3D space in order to track it. We detail the interaction design of our approach and explain the graph-based algorithm used to connect different time points, also taking occlusion and user distraction into account. We demonstrate our cell tracking method using the example of two different biological datasets. Finally, we report on a user study with seven cell tracking experts, demonstrating the benefits of our approach over manual point-and-click tracking.

cs.HC↗

arXiv2020

scenery: Flexible Virtual Reality Visualization on the Java VM

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. Visualization is often the first step in making sense of data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualizations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we introduce scenery, a flexible VR/AR visualization framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features and example applications, such as its use in VR for microscopy, in the biomedical image analysis software Fiji, or for visualizing agent-based simulations.

cs.GR↗

arXiv2020

Multivariate Newton Interpolation

For $m,n \in \mathbb{N}$, $m\geq 1$ and a given function $f : \mathbb{R}^m\longrightarrow \mathbb{R}$, the polynomial interpolation problem (PIP) is to determine a unisolvent node set $P_{m,n} \subseteq \mathbb{R}^m$ of $N(m,n):=|P_{m,n}|=\binom{m+n}{n}$ points and the uniquely defined polynomial $Q_{m,n,f}\in Π_{m,n}$ in $m$ variables of degree $\mathrm{deg}(Q_{m,n,f})\leq n \in \mathbb{N}$ that fits $f$ on $P_{m,n}$, i.e., $Q_{m,n,f}(p) = f(p)$, $\forall\, p \in P_{m,n}$. For $m=1$ the solution to the PIP is well known. In higher dimensions, however, no closed framework was available. We here present a generalization of the classic Newton interpolation from one-dimensional to arbitrary-dimensional spaces. Further we formulate an algorithm, termed PIP-SOLVER, based on a multivariate divided difference scheme that computes the solution $Q_{m,n,f}$ in $\mathcal{O}\big(N(m,n)^2\big)$ time using $\mathcal{O}\big(mN(m,n)\big)$ memory. Further, we introduce unisolvent Newton-Chebyshev nodes and show that these nodes avoid Runge's phenomenon in the sense that arbitrary periodic Sobolev functions $f \in H^k(Ω,\mathbb{R}) \subsetneq C^0(Ω,\mathbb{R})$, $Ω=[-1,1]^m$ of regularity $k >m/2$ can be uniformly approximated, i.e., $ \lim_{n\rightarrow \infty}||\,f -Q_{m,n,f} \,||_{C^0(Ω)}= 0$. Numerical experiments demonstrate the computational performance and approximation accuracy of the PIP-SOLVER in practice. We expect the presented results to be relevant for many applications, including numerical solvers, quadrature, non-linear optimization, polynomial regression, adaptive sampling, Bayesian inference, and spectral analysis.

math.NA↗

arXiv2019

Minimal model of cellular symmetry breaking

The cell cortex, a thin film of active material assembled below the cell membrane, plays a key role in cellular symmetry breaking processes such as cell polarity establishment and cell division. Here, we present a minimal model of the self-organization of the cell cortex that is based on a hydrodynamic theory of curved active surfaces. Active stresses on this surface are regulated by a diffusing molecular species. We show that coupling of the active surface to a passive bulk fluid enables spontaneous polarization and the formation of a contractile ring on the surface via mechano-chemical instabilities. We discuss the role of external fields in guiding such pattern formation. Our work reveals that key features of cellular symmetry breaking and cell division can emerge in a minimal model via general dynamic instabilities.

physics.bio-ph↗

arXiv2019

A Proposed Framework for Interactive Virtual Reality In Situ Visualization of Parallel Numerical Simulations

As computer simulations progress to increasingly complex, non-linear, and three-dimensional systems and phenomena, intuitive and immediate visualization of their results is becoming crucial. While Virtual Reality (VR) and Natural User Interfaces (NUIs) have been shown to improve understanding of complex 3D data, their application to live in situ visualization and computational steering is hampered by performance requirements. Here, we present the design of a software framework for interactive VR in situ visualization of parallel numerical simulations, as well as a working prototype implementation. Our design is targeted towards meeting the performance requirements for VR, and our work is packaged in a framework that allows for easy instrumentation of simulations. Our preliminary results inform about the technical feasibility of the architecture, as well as the challenges that remain.

cs.DC↗

arXiv2019

Stability selection enables robust learning of partial differential equations from limited noisy data

We present a statistical learning framework for robust identification of partial differential equations from noisy spatiotemporal data. Extending previous sparse regression approaches for inferring PDE models from simulated data, we address key issues that have thus far limited the application of these methods to noisy experimental data, namely their robustness against noise and the need for manual parameter tuning. We address both points by proposing a stability-based model selection scheme to determine the level of regularization required for reproducible recovery of the underlying PDE. This avoids manual parameter tuning and provides a principled way to improve the method's robustness against noise in the data. Our stability selection approach, termed PDE-STRIDE, can be combined with any sparsity-promoting penalized regression model and provides an interpretable criterion for model component importance. We show that in particular the combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast, parameter-free, and robust computational framework for PDE inference that outperforms previous algorithmic approaches with respect to recovery accuracy, amount of data required, and robustness to noise. We illustrate the performance of our approach on a wide range of noise-corrupted simulated benchmark problems, including 1D Burgers, 2D vorticity-transport, and 3D reaction-diffusion problems. We demonstrate the practical applicability of our method on real-world data by considering a purely data-driven re-evaluation of the advective triggering hypothesis for an embryonic polarization system in C.~elegans. Using fluorescence microscopy images of C.~elegans zygotes as input data, our framework is able to recover the PDE model for the regulatory reaction-diffusion-flow network of the associated proteins.

math.NA↗

arXiv2018

OpenFPM: A scalable open framework for particle and particle-mesh codes on parallel computers

Scalable and efficient numerical simulations continue to gain importance, as computation is firmly established as the third pillar of discovery, alongside theory and experiment. Meanwhile, the performance of computing hardware grows through increasing heterogeneous parallelism, enabling simulations of ever more complex models. However, efficiently implementing scalable codes on heterogeneous, distributed hardware systems becomes the bottleneck. This bottleneck can be alleviated by intermediate software layers that provide higher-level abstractions closer to the problem domain, hence allowing the computational scientist to focus on the simulation. Here, we present OpenFPM, an open and scalable framework that provides an abstraction layer for numerical simulations using particles and/or meshes. OpenFPM provides transparent and scalable infrastructure for shared-memory and distributed-memory implementations of particles-only and hybrid particle-mesh simulations of both discrete and continuous models, as well as non-simulation codes. This infrastructure is complemented with portable implementations of frequently used numerical routines, as well as interfaces to third-party libraries. We present the architecture and design of OpenFPM, detail the underlying abstractions, and benchmark the framework in applications ranging from Smoothed-Particle Hydrodynamics (SPH) to Molecular Dynamics (MD), Discrete Element Methods (DEM), Vortex Methods, stencil codes, high-dimensional Monte Carlo sampling (CMA-ES), and Reaction-Diffusion solvers, comparing it to the current state of the art and existing software frameworks.

cs.DC↗

arXiv2017

A Domain-Specific Language and Editor for Parallel Particle Methods

Domain-specific languages (DSLs) are of increasing importance in scientific high-performance computing to reduce development costs, raise the level of abstraction and, thus, ease scientific programming. However, designing and implementing DSLs is not an easy task, as it requires knowledge of the application domain and experience in language engineering and compilers. Consequently, many DSLs follow a weak approach using macros or text generators, which lack many of the features that make a DSL a comfortable for programmers. Some of these features---e.g., syntax highlighting, type inference, error reporting, and code completion---are easily provided by language workbenches, which combine language engineering techniques and tools in a common ecosystem. In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL and development environment for numerical simulations based on particle methods and hybrid particle-mesh methods. PPME uses the meta programming system (MPS), a projectional language workbench. PPME is the successor of the Parallel Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional implementation strategies. We analyze and compare both languages and demonstrate how the programmer's experience can be improved using static analyses and projectional editing. Furthermore, we present an explicit domain model for particle abstractions and the first formal type system for particle methods.

cs.MS↗

arXiv2016

A minimal model for spontaneous cell polarization and edge activity in oscillating, rotating and migrating cells

How the cells break symmetry and organize their edge activity to move directionally is a fun- damental question in cell biology. Physical models of cell motility commonly rely on gradients of regulatory factors and/or feedback from the motion itself to describe polarization of edge activity. Theses approaches, however, fail to explain cell behavior prior to the onset of polarization. Our analysis using the model system of polarizing and moving fish epidermal keratocytes suggests a novel and simple principle of self-organization of cell activity in which local cell-edge dynamics depends on the distance from the cell center, but not on the orientation with respect to the front-back axis. We validate this principle with a stochastic model that faithfully reproduces a range of cell-migration behaviors. Our findings indicate that spontaneous polarization, persistent motion, and cell shape are emergent properties of the local cell-edge dynamics controlled by the distance from the cell center.

physics.bio-ph↗

arXiv2015

A Method for Modeling Growth of Organs and Transplants Based on the General Growth Law: Application to the Liver in Dogs and Humans

Understanding biological phenomena requires a systemic approach that incorporates different mechanisms acting on different spatial and temporal scales, since in organisms the workings of all components, such as organelles, cells, and organs interrelate. This inherent interdependency between diverse biological mechanisms, both on the same and on different scales, provides the functioning of an organism capable of maintaining homeostasis and physiological stability through numerous feedback loops. Thus, developing models of organisms and their constituents should be done within the overall systemic context of the studied phenomena. We introduce such a method for modeling growth and regeneration of livers at the organ scale, considering it a part of the overall multi-scale biochemical and biophysical processes of an organism. Our method is based on the earlier discovered general growth law, postulating that any biological growth process comprises a uniquely defined distribution of nutritional resources between maintenance needs and biomass production. Based on this law, we introduce a liver growth model that allows to accurately predicting the growth of liver transplants in dogs and liver grafts in humans. Using this model, we find quantitative growth characteristics, such as the time point when the transition period after surgery is over and the liver resumes normal growth, rates at which hepatocytes are involved in proliferation, etc. We then use the model to determine and quantify otherwise unobservable metabolic properties of livers.

q-bio.TO↗

arXiv2014

Gradient Distribution Priors for Biomedical Image Processing

Ill-posed inverse problems are commonplace in biomedical image processing. Their solution typically requires imposing prior knowledge about the latent ground truth. While this regularizes the problem to an extent where it can be solved, it also biases the result toward the expected. With inappropriate priors harming more than they use, it remains unclear what prior to use for a given practical problem. Priors are hence mostly chosen in an {\em ad hoc} or empirical fashion. We argue here that the gradient distribution of natural-scene images may provide a versatile and well-founded prior for biomedical images. We provide motivation for this choice from different points of view, and we fully validate the resulting prior for use on biomedical images by showing its stability and correlation with image quality. We then provide a set of simple parametric models for the resulting prior, leading to straightforward (quasi-)convex optimization problems for which we provide efficient solver algorithms. We illustrate the use of the present models and solvers in a variety of common image-processing tasks, including contrast enhancement, noise level estimation, denoising, blind deconvolution, zooming/up-sampling, and dehazing. In all cases we show that the present method leads to results that are comparable to or better than the state of the art; always using the same, simple prior. We conclude by discussing the limitations and possible interpretations of the prior.

cs.CV↗

arXiv2014

PPF - A Parallel Particle Filtering Library

We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributed-memory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with the necessary tools for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency. To the best of our knowledge, the PPF library is the first open-source software that offers a parallel framework for PF applications.

cs.DC↗

arXiv2014

Particle methods enable fast and simple approximation of Sobolev gradients in image segmentation

Bio-image analysis is challenging due to inhomogeneous intensity distributions and high levels of noise in the images. Bayesian inference provides a principled way for regularizing the problem using prior knowledge. A fundamental choice is how one measures "distances" between shapes in an image. It has been shown that the straightforward geometric L2 distance is degenerate and leads to pathological situations. This is avoided when using Sobolev gradients, rendering the segmentation problem less ill-posed. The high computational cost and implementation overhead of Sobolev gradients, however, have hampered practical applications. We show how particle methods as applied to image segmentation allow for a simple and computationally efficient implementation of Sobolev gradients. We show that the evaluation of Sobolev gradients amounts to particle-particle interactions along the contour in an image. We extend an existing particle-based segmentation algorithm to using Sobolev gradients. Using synthetic and real-world images, we benchmark the results for both 2D and 3D images using piecewise smooth and piecewise constant region models. The present particle approximation of Sobolev gradients is 2.8 to 10 times faster than the previous reference implementation, but retains the known favorable properties of Sobolev gradients. This speedup is achieved by using local particle-particle interactions instead of solving a global Poisson equation at each iteration. The computational time per iteration is higher for Sobolev gradients than for L2 gradients. Since Sobolev gradients precondition the optimization problem, however, a smaller number of overall iterations may be necessary for the algorithm to converge, which can in some cases amortize the higher per-iteration cost.

cs.CV↗

arXiv2014

Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Particle filters are key algorithms for object tracking under non-linear, non-Gaussian dynamics. The high computational cost of particle filters, however, hampers their applicability in cases where the likelihood model is costly to evaluate, or where large numbers of particles are required to represent the posterior. We introduce the approximate sequential importance sampling/resampling (ASIR) algorithm, which aims at reducing the cost of traditional particle filters by approximating the likelihood with a mixture of uniform distributions over pre-defined cells or bins. The particles in each bin are represented by a dummy particle at the center of mass of the original particle distribution and with a state vector that is the average of the states of all particles in the same bin. The likelihood is only evaluated for the dummy particles, and the resulting weight is identically assigned to all particles in the bin. We derive upper bounds on the approximation error of the so-obtained piecewise constant function representation, and analyze how bin size affects tracking accuracy and runtime. Further, we show numerically that the ASIR approximation error converges to that of sequential importance sampling/resampling (SIR) as the bin size is decreased. We present a set of numerical experiments from the field of biological image processing and tracking that demonstrate ASIR's capabilities. Overall, we consider ASIR a promising candidate for simple, fast particle filtering in generic applications.

stat.CO↗

arXiv2013

Adaptive Distributed Resampling Algorithm with Non-Proportional Allocation

The distributed resampling algorithm with proportional allocation (RNA) is key to implementing particle filtering applications on parallel computer systems. We extend the original work by Bolic et al. by introducing an adaptive RNA (ARNA) algorithm, improving RNA by dynamically adjusting the particle-exchange ratio and randomizing the process ring topology. This improves the runtime performance of ARNA by about 9% over RNA with 10% particle exchange. ARNA also significantly improves the speed at which information is shared between processing elements, leading to about 20-fold faster convergence. The ARNA algorithm requires only a few modifications to the original RNA, and is hence easy to implement.

stat.CO↗

arXiv2013

Balancing indivisible real-valued loads in arbitrary networks

In parallel computing, a problem is divided into a set of smaller tasks that are distributed across multiple processing elements. Balancing the load of the processing elements is key to achieving good performance and scalability. If the computational costs of the individual tasks vary over time in an unpredictable way, dynamic load balancing aims at migrating them between processing elements so as to maintain load balance. During dynamic load balancing, the tasks amount to indivisible work packets with a real-valued cost. For this case of indivisible, real- valued loads, we analyze the balancing circuit model, a local dynamic load-balancing scheme that does not require global communication. We extend previous analyses to the present case and provide a probabilistic bound for the achievable load balance. Based on an analogy with the offline balls-into-bins problem, we further propose a novel algorithm for dynamic balancing of indivisible, real-valued loads. We benchmark the proposed algorithm in numerical experiments and compare it with the classical greedy algorithm, both in terms of solution quality and communication cost. We find that the increased communication cost of the proposed algorithm is compensated by a higher solution quality, leading on average to about an order of magnitude gain in overall performance.

cs.DC↗

arXiv2013

Balanced offline allocation of weighted balls into bins

We propose a sorting-based greedy algorithm called SortedGreedy[m] for approximately solving the offline version of the d-choice weighted balls-into-bins problem where the number of choices for each ball is equal to the number of bins. We assume the ball weights to be non-negative. We compare the performance of the sorting-based algorithm with a naive algorithm called Greedy[m]. We show that by sorting the input data according to the weights we are able to achieve an order of magnitude smaller gap (the weight difference between the heaviest and the lightest bin) for small problems (<= 4000 balls), and at least two orders of magnitude smaller gap for larger problems. In practice, SortedGreedy[m] runs almost as fast as Greedy[m]. This makes sorting-based algorithms favorable for solving offline weighted balls-into-bins problems.

cs.DM↗

arXiv2011

Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.

q-bio.MN↗