Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease

Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer's disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network's classification decisions by highlighting relevant areas in the input image. We compare the methods qualitatively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer's disease, such as inferior and middle temporal gyrus. While the occlusion-based methods focus more on specific regions, the gradient-based methods pick up distributed relevance patterns. Additionally, we find that the distribution of relevance varies across patients, with some having a stronger focus on the temporal lobe, whereas for others more cortical areas are relevant. In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.Comment: MLCN 201

QR-RLS algorithm for error diffusion of color images

Printing color images on color printers and displaying them on computer monitors requires a significant reduction of physically distinct colors, which causes degradation in image quality. An efficient method to improve the display quality of a quantized image is error diffusion, which works by distributing the previous quantization errors to neighboring pixels, exploiting the eye's averaging of colors in the neighborhood of the point of interest. This creates the illusion of more colors. A new error diffusion method is presented in which the adaptive recursive least-squares (RLS) algorithm is used. This algorithm provides local optimization of the error diffusion filter along with smoothing of the filter coefficients in a neighborhood. To improve the performance, a diagonal scan is used in processing the image, (C) 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)00611-5]

Identification of Nonlinear State-Space Systems from Heterogeneous Datasets

This paper proposes a new method to identify nonlinear state-space systems from heterogeneous datasets. The method is described in the context of identifying biochemical/gene networks (i.e., identifying both reaction dynamics and kinetic parameters) from experimental data. Simultaneous integration of various datasets has the potential to yield better performance for system identification. Data collected experimentally typically vary depending on the specific experimental setup and conditions. Typically, heterogeneous data are obtained experimentally through (a) replicate measurements from the same biological system or (b) application of different experimental conditions such as changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. We formulate here the identification problem using a Bayesian learning framework that makes use of “sparse group” priors to allow inference of the sparsest model that can explain the whole set of observed, heterogeneous data. To enable scale up to large number of features, the resulting non-convex optimisation problem is relaxed to a re-weighted Group Lasso problem using a convex-concave procedure. As an illustrative example of the effectiveness of our method, we use it to identify a genetic oscillator (generalised eight species repressilator). Through this example we show that our algorithm outperforms Group Lasso when the number of experiments is increased, even when each single time-series dataset is short. We additionally assess the robustness of our algorithm against noise by varying the intensity of process noise and measurement noise

Interlayer coupling in commensurate and incommensurate bilayer structures of transition-metal dichalcogenides

published_or_final_versio

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

The Evershed Effect with SOT/Hinode

The Solar Optical Telescope onboard Hinode revealed the fine-scale structure of the Evershed flow and its relation to the filamentary structures of the sunspot penumbra. The Evershed flow is confined in narrow channels with nearly horizontal magnetic fields, embedded in a deep layer of the penumbral atmosphere. It is a dynamic phenomenon with flow velocity close to the photospheric sound speed. Individual flow channels are associated with tiny upflows of hot gas (sources) at the inner end and downflows (sinks) at the outer end. SOT/Hinode also discovered ``twisting'' motions of penumbral filaments, which may be attributed to the convective nature of the Evershed flow. The Evershed effect may be understood as a natural consequence of thermal convection under a strong, inclined magnetic field. Current penumbral models are discussed in the lights of these new Hinode observations.Comment: To appear in "Magnetic Coupling between the Interior and the Atmosphere of the Sun", eds. S.S. Hasan and R.J. Rutten, Astrophysics and Space Science Proceedings, Springer-Verlag, Heidelberg, Berlin, 200

Genetic variation at Exon2 of TLR4 gene and its association with resistant traits in chicken

This study was conducted to analyze the polymorphisms of chicken Toll-like receptors 4(TLR4) gene and aimed to provide a theoretical foundation for a further research on correlation between chicken TLR4 gene and disease resistance. Genetic variations at exon 2 of TLR4 gene in 14 chicken breeds and the red jungle fowl were detected by PCR-SSCP method and two alleles and three genotypes were found, Tibetan chicken and red jungle fowl only had BB genotype, while the others presented three genotypes of AA, BB and AB. Sequencing results showed two mutations, G114A and G142A, located at exon 2 of TLR4 gene. The results of Chi square test showed that all populations, except Xianju chicken, were in accordance with Hardy-Weinberg equilibrium at this locus (P > 0.05). According to analysis of population genetic variation, all the populations were at moderate polymorphism (0.25 < PIC < 0.5) except red jungle fowl and Tibetan chicken (PIC = 0). The study demonstrated that there were differences of normal anti-disease ability in Chinese indigenous chicken breeds and appeared no significant correlation with body size, product type and geographical location. The associated analysis of results showed that the SNPs of TLR4 gene in the study were not linked with potential major loci or genes affecting some resistant traits.Key words: Chicken, TLR4 gene, polymorphism, resistant traits

The Complexity of Routing with Few Collisions

We study the computational complexity of routing multiple objects through a network in such a way that only few collisions occur: Given a graph $G$ with two distinct terminal vertices and two positive integers $p$ and $k$, the question is whether one can connect the terminals by at least $p$ routes (e.g. paths) such that at most $k$ edges are time-wise shared among them. We study three types of routes: traverse each vertex at most once (paths), each edge at most once (trails), or no such restrictions (walks). We prove that for paths and trails the problem is NP-complete on undirected and directed graphs even if $k$ is constant or the maximum vertex degree in the input graph is constant. For walks, however, it is solvable in polynomial time on undirected graphs for arbitrary $k$ and on directed graphs if $k$ is constant. We additionally study for all route types a variant of the problem where the maximum length of a route is restricted by some given upper bound. We prove that this length-restricted variant has the same complexity classification with respect to paths and trails, but for walks it becomes NP-complete on undirected graphs

Modeling Molecular-Line Emission from Circumstellar Disks

Molecular lines hold valuable information on the physical and chemical composition of disks around young stars, the likely progenitors of planetary systems. This invited contribution discusses techniques to calculate the molecular emission (and absorption) line spectrum based on models for the physical and chemical structure of protoplanetary disks. Four examples of recent research illutrate these techniques in practice: matching resolved molecular-line emission from the disk around LkCa15 with theoertical models for the chemistry; evaluating the two-dimensional transfer of ultraviolet radiation into the disk, and the effect on the HCN/CN ratio; far-infrared CO line emission from a superheated disk surface layer; and inward motions in the disk around L1489 IRS.Comment: 6 pages, no figures. To appear in "The Dense Interstellar Medium in Galaxies", Procs. Fourth Cologne-Bonn-Zermatt-Symposiu

On Convergence and Threshold Properties of Discrete Lotka-Volterra Population Protocols

In this work we focus on a natural class of population protocols whose dynamics are modelled by the discrete version of Lotka-Volterra equations. In such protocols, when an agent $a$ of type (species) $i$ interacts with an agent $b$ of type (species) $j$ with $a$ as the initiator, then $b$'s type becomes $i$ with probability $P\_{ij}$. In such an interaction, we think of $a$ as the predator, $b$ as the prey, and the type of the prey is either converted to that of the predator or stays as is. Such protocols capture the dynamics of some opinion spreading models and generalize the well-known Rock-Paper-Scissors discrete dynamics. We consider the pairwise interactions among agents that are scheduled uniformly at random. We start by considering the convergence time and show that any Lotka-Volterra-type protocol on an $n$-agent population converges to some absorbing state in time polynomial in $n$, w.h.p., when any pair of agents is allowed to interact. By contrast, when the interaction graph is a star, even the Rock-Paper-Scissors protocol requires exponential time to converge. We then study threshold effects exhibited by Lotka-Volterra-type protocols with 3 and more species under interactions between any pair of agents. We start by presenting a simple 4-type protocol in which the probability difference of reaching the two possible absorbing states is strongly amplified by the ratio of the initial populations of the two other types, which are transient, but "control" convergence. We then prove that the Rock-Paper-Scissors protocol reaches each of its three possible absorbing states with almost equal probability, starting from any configuration satisfying some sub-linear lower bound on the initial size of each species. That is, Rock-Paper-Scissors is a realization of a "coin-flip consensus" in a distributed system. Some of our techniques may be of independent value