Long-term potentiation through calcium-mediated N-Cadherin interaction is tightly controlled by the three-dimensional architecture of the synapse

Poster presentation: Twenty Second Annual Computational Neuroscience Meeting: CNS*2013. Paris, France. 13-18 July 2013. The synaptic cleft is an extracellular domain that is capable of relaying a presynaptically received electrical signal by diffusive neurotransmitters to the postsynaptic membrane. The cleft is trans-synaptically bridged by ring-like shaped clusters of pre- and postsynaptically localized calcium-dependent adhesion proteins of the N-Cadherin type and is possibly the smallest intercircuit in nervous systems [1]. The strength of association between the pre- and postsynaptic membranes can account for synaptic plasticity such as long-term potentiation [2]. Through neuronal activity the intra- and extracellular calcium levels are modulated through calcium exchangers embedded in the pre- and postsynaptic membrane. Variations of the concentration of cleft calcium induces changes in the N-Cadherin-zipper, that in synaptic resting states is rigid and tightly connects the pre- and postsynaptic domain. During synaptic activity calcium concentrations are hypothesized to drop below critical thresholds which leads to loosening of the N-Cadherin connections and subsequently "unzips" the Cadherin-mediated connection. These processes may result in changes in synaptic strength [2]. In order to investigate the calcium-mediated N-Cadherin dynamics at the synaptic cleft, we developed a three-dimensional model including the cleft morphology and all prominent calcium exchangers and corresponding density distributions [3-6]. The necessity for a fully three-dimensional model becomes apparent, when investigating the effects of the spatial architecture of the synapse [7], [8]. Our data show, that the localization of calcium channels with respect to the N-Cadherin ring has substantial effects on the time-scales on which the Cadherin-zipper switches between states, ranging from seconds to minutes. This will have significant effects on synaptic signaling. Furthermore we see, that high-frequency action potential firing can only be relayed to the Calcium/N-Cadherin-system at a synapse under precise spatial synaptic reorganization

Quaternion Singular Value Decomposition based on Bidiagonalization to a Real Matrix using Quaternion Householder Transformations

We present a practical and efficient means to compute the singular value decomposition (svd) of a quaternion matrix A based on bidiagonalization of A to a real bidiagonal matrix B using quaternionic Householder transformations. Computation of the svd of B using an existing subroutine library such as lapack provides the singular values of A. The singular vectors of A are obtained trivially from the product of the Householder transformations and the real singular vectors of B. We show in the paper that left and right quaternionic Householder transformations are different because of the noncommutative multiplication of quaternions and we present formulae for computing the Householder vector and matrix in each case

Computing a logarithm of a unitary matrix with general spectrum

We analyze an algorithm for computing a skew-Hermitian logarithm of a unitary matrix. This algorithm is very easy to implement using standard software and it works well even for unitary matrices with no spectral conditions assumed. Certain examples, with many eigenvalues near -1, lead to very non-Hermitian output for other basic methods of calculating matrix logarithms. Altering the output of these algorithms to force an Hermitian output creates accuracy issues which are avoided in the considered algorithm. A modification is introduced to deal properly with the $J$-skew symmetric unitary matrices. Applications to numerical studies of topological insulators in two symmetry classes are discussed.Comment: Added discussion of Floquet Hamiltonian

Toda Hierarchy with Indefinite Metric

We consider a generalization of the full symmetric Toda hierarchy where the matrix $\tilde {L}$ of the Lax pair is given by $\tilde {L}=LS$, with a full symmetric matrix $L$ and a nondegenerate diagonal matrix $S$. The key feature of the hierarchy is that the inverse scattering data includes a class of noncompact groups of matrices, such as $O(p,q)$. We give an explicit formula for the solution to the initial value problem of this hierarchy. The formula is obtained by generalizing the orthogonalization procedure of Szeg\"{o}, or the QR factorization method of Symes. The behaviors of the solutions are also studied. Generically, there are two types of solutions, having either sorting property or blowing up to infinity in finite time. The $\tau$-function structure for the tridiagonal hierarchy is also studied.Comment: 26 pages, LaTe

Implications of Shallower Memory Controller Transaction Queues in Scalable Memory Systems

Scalable memory systems provide scalable bandwidth to the core growth demands in multicores and embedded systems processors. In these systems, as memory controllers (MCs) are scaled, memory traffic per MC is reduced, so transaction queues become shallower. As a consequence, there is an opportunity to explore transaction queue utilization and its impact on energy utilization. In this paper, we propose to evaluate the performance and energy-per-bit impact when reducing transaction queue sizes along with the MCs of these systems. Experimental results show that reducing 50 % on the number of entries, bandwidth and energy-per-bit levels are not affected, whilst reducing aggressively of about 90 %, bandwidth is similarly reduced while causing significantly higher energy-per-bit utilization

Unital Quantum Channels - Convex Structure and Revivals of Birkhoff's Theorem

The set of doubly-stochastic quantum channels and its subset of mixtures of unitaries are investigated. We provide a detailed analysis of their structure together with computable criteria for the separation of the two sets. When applied to O(d)-covariant channels this leads to a complete characterization and reveals a remarkable feature: instances of channels which are not in the convex hull of unitaries can return to it when either taking finitely many copies of them or supplementing with a completely depolarizing channel. In these scenarios this implies that a channel whose noise initially resists any environment-assisted attempt of correction can become perfectly correctable.Comment: 31 page

A systematic approach of process planning and scheduling optimization for sustainable machining

The lack of effective process planning and scheduling solutions for the sustainable management of machining shop floors, whose manufacturing activities are usually characterized by high variety and low volume, has been crippling the implementation of sustainability in companies. To address the issue, an innovative and systematic approach for milling process planning and scheduling optimization has been developed and presented in this paper. This approach consists of a process stage and a system stage, augmented with intelligent mechanisms for enhancing the adaptability and responsiveness to job dynamics in machining shop floors. In the process stage, key operational parameters for milling a part are optimized adaptively to meet multiple objectives/constraints, i.e., energy efficiency of the milling process and productivity as objectives and surface quality as a constraint. In the consecutive system stage, to achieve higher energy efficiency and shorter makespan in the entire shop floor, sequencing/set-up planning of machining features/operations and scheduling for producing multiple parts on different machines are optimized. Artificial Neural Networks are used for establishing the complex nonlinear relationships between the key process parameters and measured datasets of energy consumption and surface quality. Several intelligent algorithms, including Pattern Search, Genetic Algorithm and Simulated Annealing, are applied and benchmarked to identify optimal solutions. Experimental tests indicate that the approach is effective and configurable to meet multiple objectives and technical constraints for sustainable process planning and scheduling. The approach, validated through industrial case studies provided by a European machining company, demonstrates significant potential of applicability in practice

Machine learning for acquiring knowledge in astro-particle physics

This thesis explores the fundamental aspects of machine learning, which are involved with acquiring knowledge in the research field of astro-particle physics. This research field substantially relies on machine learning methods, which reconstruct the properties of astro-particles from the raw data that specialized telescopes record. These methods are typically trained from resource-intensive simulations, which reflect the existing knowledge about the particles—knowledge that physicists strive to expand. We study three fundamental machine learning tasks, which emerge from this goal. First, we address ordinal quantification, the task of estimating the prevalences of ordered classes in sets of unlabeled data. This task emerges from the need for testing the agreement of astro-physical theories with the class prevalences that a telescope observes. To this end, we unify existing methods on quantification, propose an alternative optimization process, and develop regularization techniques to address ordinality in quantification problems, both in and outside of astro-particle physics. These advancements provide more accurate reconstructions of the energy spectra of cosmic gamma ray sources and, hence, support physicists in drawing conclusions from their telescope data. Second, we address learning under class-conditional label noise. More particularly, we focus on a novel setting, in which one of the class-wise noise rates is known and one is not. This setting emerges from a data acquisition protocol, through which astro-particle telescopes simultaneously observe a region of interest and several background regions. We enable learning under this type of label noise with algorithms for consistent, noise-aware decision thresholding. These algorithms yield binary classifiers, which outperform the existing state-of-the-art in gamma hadron classification with the FACT telescope. Moreover, unlike the state-of-the-art, our classifiers are entirely trained from the real telescope data and thus do not require any resource-intensive simulation. Third, we address active class selection, the task of actively finding those proportions of classes which optimize the classification performance. In astro-particle physics, this task emerges from the simulation, which produces training data in any desired class proportions. We clarify the implications of this setting from two theoretical perspectives, one of which provides us with bounds of the resulting classification performance. We employ these bounds in a certificate of model robustness, which declares a set of class proportions for which the model is accurate with a high probability. We also employ these bounds in an active strategy for class-conditional data acquisition. Our strategy uniquely considers existing uncertainties about those class proportions that have to be handled during the deployment of the classifier, while being theoretically well-justified