Decoding Hidden Markov Models Faster Than Viterbi Via Online Matrix-Vector (max, +)-Multiplication

In this paper, we present a novel algorithm for the maximum a posteriori decoding (MAPD) of time-homogeneous Hidden Markov Models (HMM), improving the worst-case running time of the classical Viterbi algorithm by a logarithmic factor. In our approach, we interpret the Viterbi algorithm as a repeated computation of matrix-vector $(\max, +)$-multiplications. On time-homogeneous HMMs, this computation is online: a matrix, known in advance, has to be multiplied with several vectors revealed one at a time. Our main contribution is an algorithm solving this version of matrix-vector $(\max,+)$-multiplication in subquadratic time, by performing a polynomial preprocessing of the matrix. Employing this fast multiplication algorithm, we solve the MAPD problem in $O(mn^2/ \log n)$ time for any time-homogeneous HMM of size $n$ and observation sequence of length $m$, with an extra polynomial preprocessing cost negligible for $m > n$. To the best of our knowledge, this is the first algorithm for the MAPD problem requiring subquadratic time per observation, under the only assumption -- usually verified in practice -- that the transition probability matrix does not change with time.Comment: AAAI 2016, to appea

Role of selected aspects of inflammation in age-dependent seizure susceptibility and neuronal injury in rats

Besides systemic infection, proinflammatory signals in the CNS are also recruited by epileptic activity. Chronic stimulation of proinflammatory signals by seizures, or a persistent proinflammatory situation in brain may contribute to the establishment of a pathological substrate (i.e. neurodegeneration, neuronal hyperexcitability, blood-brain barrier damage, etc) playing a role in epileptogenesis and in the acute manifestation or reinforcement of seizures. Interestingly, in humans and in experimental models of epilepsy, seizure susceptibility and associated neuronal damage are age-dependent. Therefore, we wondered whether a hypothetical age-dependent relationship between proinflammatory molecules and seizure-susceptibility and related neurodegeneration there might exist and to what extent. A first group of experiments investigated the hypothesis that activation of glia and subsequent production of proinflammatory molecules such as interleukin-1(3 (IL-1(3) and tumor necrosis factor-a (TNF-a), as well as the naturally occurring anti-inflammatory molecule interleukin-1 receptor antagonist (IL-1Ra), are age-dependently involved in seizure-induced neuronal damage. A second group of experiments investigated whether a pre-existing LPS-induced inflammatory state in the CNS may enhance the predisposition of rat pups to develop seizures. We concluded that the induction of proinflammatory cytokines, per se, cannot be regarded as a general mechanism which underlies the appearance of neurodegeneration. In addition, there were no clear indications emerging from our data concerning the role of proinflammatory cytokines in modulating seizure-susceptibility in rat pups. Nonetheless, we showed that prostaglandin E2 (PGE2) production and the activation of the Hypothalamic-Pituitary-Adrenal axis play a relevant role in the modulation of seizure-susceptibility in rat pups. Interestingly, our experiments seem to support the hypothesis that production of proinflammatory molecules such as IL-1 (3 and TNF-a in rat pups might be down-regulated upon elevation of parenchymal PGE2 levels

Dynamic Controllability Made Simple

Simple Temporal Networks with Uncertainty (STNUs) are a well-studied model for representing temporal constraints, where some intervals (contingent links) have an unknown but bounded duration, discovered only during execution. An STNU is dynamically controllable (DC) if there exists a strategy to execute its time-points satisfying all the constraints, regardless of the actual duration of contingent links revealed during execution. In this work we present a new system of constraint propagation rules for STNUs, which is sound-and-complete for DC checking. Our system comprises just three rules which, differently from the ones proposed in all previous works, only generate unconditioned constraints. In particular, after applying our sound rules, the network remains an STNU in all respects. Moreover, our completeness proof is short and non-algorithmic, based on the explicit construction of a valid execution strategy. This is a substantial simplification of the theory which underlies all the polynomial-time algorithms for DC-checking. Our analysis also shows: (1) the existence of late execution strategies for STNUs, (2) the equivalence of several variants of the notion of DC, (3) the existence of a fast algorithm for real-time execution of STNUs, which runs in O(KN) total time in a network with K contingent links and N time points, considerably improving the previous O(N^3)-time bound

Orbit determination and control for the European Student Moon Orbiter

This paper presents the preliminary navigation and orbit determination analyses for the European Student Moon Orbiter. The severe constraint on the total mission Delta nu and the all-day piggy-back launch requirement imposed by the limited available budget, led to the choice of using a low-energy transfer, more specifically a Weak Stability Boundary one, with a capture into an elliptic orbit around the Moon. A particular navigation strategy was devised to ensure capture and fulfil the requirement for the uncontrolled orbit stability at the Moon. This paper presents a simulation of the orbit determination process, based on an extended Kalman filter, and the navigation strategy applied to the baseline transfer of the 2011-2012 window. The navigation strategy optimally allocates multiple Trajectory Correction Manoeuvres to target a so-called capture corridor. The capture corridor is defined, at each point along the transfer, by back-propagating the set of perturbed states at the Moon that provides an acceptable lifetime of the lunar orbit. (C) 2012 Elsevier Ltd. All rights reserved

A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF

The future evolution of technological systems dedicated to improve energy efficiency will strongly depend on effective and reliable Energy Storage Systems, as key components for Smart Grids, microgrids and electric mobility. Besides possible improvements in chemical materials and cells design, the Battery Management System is the most important electronic device that improves the reliability of a battery pack. In fact, a precise State of Charge (SoC) estimation allows the energy flows controller to exploit better the full capacity of each cell. In this paper, we propose an alternative definition for the SoC, explaining the rationales by a mechanical analogy. We introduce a novel cell model, conceived as a series of three electric dipoles, together with a procedure for parameters estimation relying only on voltage measures and a given current profile. The three dipoles represent the quasi-stationary, the dynamics and the istantaneous components of voltage measures. An Extended Kalman Filer (EKF) is adopted as a nonlinear state estimator. Moreover, we propose a multi-cell EKF system based on a round-robin approach to allow the same processing block to keep track of many cells at the same time. Performance tests with a prototype battery pack composed by 18 A123 cells connected in series show encouraging results.Comment: 8 page, 12 figures, 1 tabl

A Streamlined Model of Conditional Simple Temporal Networks - Semantics and Equivalence Results

A Conditional Simple Temporal Network (CSTN) augments a Simple Temporal Network to include a new kind of time-points, called observation time-points. The execution of an observation time-point generates information in real time, specifically, the truth value of a propositional letter. In addition, time-points and temporal constraints may be labeled by conjunctions of (positive or negative) propositional letters. A CSTN is called dynamically consistent (DC) if there exists a dynamic strategy for executing its time-points such that no matter how the observations turn out during execution, the time-points whose labels are consistent with those observations have all been executed, and the constraints whose labels are consistent with those observations have all been satisfied. The strategy is dynamic in that its execution decisions may react to observations. The original formulation of CSTNs included propositional labels only on time-points, but the DC-checking algorithm was impractical because it was based on a conversion of the semantic constraints into an exponentially-sized Disjunctive Temporal Network. Later work added propositional labels to temporal constraints, and yielded a sound-and-complete propagation-based DC-checking algorithm, empirically demonstrated to be practical across a variety of CSTNs. This paper introduces a streamlined version of a CSTN in which propositional labels may appear on constraints, but not on time-points. This change simplifies the definition of the DC property, as well as the propagation rules for the DC-checking algorithm. It also simplifies the proofs of the soundness and completeness of those rules. This paper provides two translations from traditional CSTNs to streamlined CSTNs. Each translation preserves the DC property and, for any DC network, ensures that any dynamic execution strategy for that network can be extended to a strategy for its streamlined counterpart. Finally, this paper presents an empirical comparison of two versions of the DC-checking algorithm: the original version and a simplified version for streamlined CSTNs. The comparison is based on CSTN benchmarks from earlier work. For small-sized CSTNs, the original version shows the best performance, but the performance difference between the two versions decreases as the number of time-points in the CSTN increases. We conclude that the simplified algorithm is a practical alternative for checking the dynamic consistency of CSTNs

Diverse reductive dehalogenases are associated with Clostridiales-enriched microcosms dechlorinating 1,2-dichloroethane

The achievement of successful biostimulation of active microbiomes for the cleanup of a polluted site is strictly dependent on the knowledge of the key microorganisms equipped with the relevant catabolic genes responsible for the degradation process. In this work, we present the characterization of the bacterial community developed in anaerobic microcosms after biostimulation with the electron donor lactate of groundwater polluted with 1,2-dichloroethane (1,2-DCA). Through a multilevel analysis, we have assessed (i) the structural analysis of the bacterial community; (ii) the identification of putative dehalorespiring bacteria; (iii) the characterization of functional genes encoding for putative 1,2-DCA reductive dehalogenases (RDs). Following the biostimulation treatment, the structure of the bacterial community underwent a notable change of the main phylotypes, with the enrichment of representatives of the order Clostridiales. Through PCR targeting conserved regions within known RD genes, four novel variants of RDs previously associated with the reductive dechlorination of 1,2-DCA were identified in the metagenome of the Clostridiales-dominated bacterial community

Genome Assembly, from Practice to Theory: Safe, Complete and Linear-Time

Peer reviewe